ALBERT

Beschreibung: Festivals are experiential products heavily depending on the recommendations of previous visitors. With the power of social media growing, understanding the antecedents of positive electronic word-of-mouth (eWOM) intentions of festival attendees is immensely beneficial for festival organizers to better promote their festivals and control negative publicity. However, there is still limited research regarding eWOM intentions in the festival context. Thus, this study aims to fill such a gap by investigating the relationships among festival attendees’ enjoyment seeking motivation, perceived value, visitor satisfaction, and eWOM intention in a local festival setting. Additionally, the moderating role of gender was tested as it is one of the most important demographic variables to show individual differences in behavioral intentions. The results of structural equation modeling showed a positive effect of enjoyment seeking motivation on perceived value, visitor satisfaction, and eWOM intention. Moreover, gender differences in eWOM intention and a full mediating effect of visitor satisfaction between perceived value and eWOM intention for female respondents were revealed. The findings of this study extend the existing festival literature and provide insights for strategically organizing and promoting festivals to generate more positive eWOM which can be utilized as an effective marketing tool and a feedback channel.

Beschreibung: The emergence and outbreak of the novel coronavirus (COVID-19) had a devasting effect on global health, the economy, and individuals’ daily lives. Timely diagnosis of COVID-19 is a crucial task, as it reduces the risk of pandemic spread, and early treatment will save patients’ life. Due to the time-consuming, complex nature, and high false-negative rate of the gold-standard RT-PCR test used for the diagnosis of COVID-19, the need for an additional diagnosis method has increased. Studies have proved the significance of X-ray images for the diagnosis of COVID-19. The dissemination of deep-learning techniques on X-ray images can automate the diagnosis process and serve as an assistive tool for radiologists. In this study, we used four deep-learning models—DenseNet121, ResNet50, VGG16, and VGG19—using the transfer-learning concept for the diagnosis of X-ray images as COVID-19 or normal. In the proposed study, VGG16 and VGG19 outperformed the other two deep-learning models. The study achieved an overall classification accuracy of 99.3%.

Beschreibung: Background: Health benefits from physical activity (PA) can be achieved by following the WHO recommendation for PA. To increase PA in inactive individuals, digital interventions can provide cost-effective and low-threshold access. Moreover, gamification elements can raise the motivation for PA. This study analyzed which factors (personality traits, app features, gamification) are relevant to increasing PA within this target group. Methods: N = 808 inactive participants (f = 480; m = 321; age = 48 ± 6) were integrated into the analysis of the desire for PA, the appearance of personality traits and resulting interest in app features and gamification. The statistical analysis included chi-squared tests, one-way ANOVA and regression analysis. Results: The main interests in PA were fitness (97%) and outdoor activities (75%). No significant interaction between personality traits, interest in PA goals, app features and gamification were found. The interest in gamification was determined by the PA goal. Participants’ requirements for features included feedback and suggestions for activities. Monetary incentives were reported as relevant gamification aspects. Conclusion: Inactive people can be reached by outdoor activities, interventions to increase an active lifestyle, fitness and health sports. The study highlighted the interest in specific app features and gamification to increase PA in inactive people through an app.

Beschreibung: The recent literature concerning globalizing regional development has placed significant emphasis on the Global Production Network (GPN 2.0). GPN 2.0 in economic geography emphasizes that regional growth is caused by a shift in the strategic coupling mode from a low to high level. In addition, GPN 2.0 regards firm-level value capture trajectories as key analytical object, rather than the interactive relationships among scalar and divergent actors in GPN 1.0. To provide a better understanding of causal linkages between the GPNs and uneven regional development in the background of globalization and to test the applicability of GPN 2.0 analysis framework, the paper analyzed 62 Korean-invested automotive firms in Jiangsu Province, China. In order to explore the value capture trajectories of lead firms in the GPNs, the authors applied K-means clustering method to quantitatively analyze the local supply networks of lead firms from organizational and spatial dimensions. Then, comparisons were made between strategic coupling modes of GPNs and regional development in North and South Jiangsu. This study found obvious similarities within these two regions but obvious differences between them in terms of value capture trajectories. We observed that North Jiangsu is currently in the stage of “structural coupling”, whereas South Jiangsu is in the stage of “functional coupling.” Thus, this article argues that spatial settings such as regional assets and autonomy are key factors influencing uneven economic development. This research may provide a crucial reference for the regional development of Jiangsu, China.

Beschreibung: It is critical for organizations to self-assess their Industry 4.0 readiness to survive and thrive in the age of the Fourth Industrial Revolution. Thereon, conceptualization or development of an Industry 4.0 readiness model with the fundamental model dimensions is needed. This paper used a systematic literature review (SLR) methodology with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and content analysis strategy to review 97 papers in peer-reviewed academic journals and industry reports published from 2000 to 2019. The review identifies 30 Industry 4.0 readiness models with 158 unique model dimensions. Based on this review, there are two theoretical contributions. First, this paper proposes six dimensions (Technology, People, Strategy, Leadership, Process and Innovation) that can be considered as the most important dimensions for organizations. Second, this review reveals that 70 (44%) out of total 158 total unique dimensions on Industry 4.0 pertain to the assessment of technology alone. This establishes that organizations need to largely improve on their technology readiness, to strengthen their Industry 4.0 readiness. In summary, these six most common dimensions, and in particular, the dominance of the technology dimension provides a research agenda for future research on Industry 4.0 readiness.

Beschreibung: This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

Beschreibung: Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.

Beschreibung: Fifth generation (5G) is a new generation mobile communication system developed for the growing demand for mobile communication. Channel coding is an indispensable part of most modern digital communication systems, for it can improve the transmission reliability and anti-interference. In order to meet the requirements of 5G communication, a dual threshold self-corrected minimum sum (DT-SCMS) algorithm for low-density parity-check (LDPC) decoders is proposed in this paper. Besides, an architecture of LDPC decoders is designed. By setting thresholds to judge the reliability of messages, the DT-SCMS algorithm erases unreliable messages, improving the decoding performance and efficiency. Simulation results show that the performance of DT-SCMS is better than that of SCMS. When the code rate is 1/3, the performance of DT-SCMS has been improved by 0.2 dB at the bit error rate of 10 − 4 compared with SCMS. In terms of the convergence, when the code rate is 2/3, the number of iterations of DT-SCMS can be reduced by up to 20.46% compared with SCMS, and the average proportion of reduction is 18.68%.

Beschreibung: The collection and processing of personal data offers great opportunities for technological advances, but the accumulation of vast amounts of personal data also increases the risk of misuse for malicious intentions, especially in health care. Therefore, personal data are legally protected, e.g., by the European General Data Protection Regulation (GDPR), which states that individuals must be transparently informed and have the right to take control over the processing of their personal data. In real applications privacy policies are used to fulfill these requirements which can be negotiated via user interfaces. The literature proposes privacy languages as an electronic format for privacy policies while the users privacy preferences are represented by preference languages. However, this is only the beginning of the personal data life-cycle, which also includes the processing of personal data and its transfer to various stakeholders. In this work we define a personal privacy workflow, considering the negotiation of privacy policies, privacy-preserving processing and secondary use of personal data, in context of health care data processing to survey applicable Privacy Enhancing Technologies (PETs) to ensure the individuals’ privacy. Based on a broad literature review we identify open research questions for each step of the workflow.

Beschreibung: Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

Beschreibung: When highly automated driving is realized, the role of the driver will change dramatically. Drivers will even be able to sleep during the drive. However, when awaking from sleep, drivers often experience sleep inertia, meaning they are feeling groggy and are impaired in their driving performance―which can be an issue with the concept of dual-mode vehicles that allow both manual and automated driving. Proactive methods to avoid sleep inertia like the widely applied ‘NASA nap’ are not immediately practicable in automated driving. Therefore, a reactive countermeasure, the sleep inertia counter-procedure for drivers (SICD), has been developed with the aim to activate and motivate the driver as well as to measure the driver’s alertness level. The SICD is evaluated in a study with N = 21 drivers in a level highly automation driving simulator. The SICD was able to activate the driver after sleep and was perceived as “assisting” by the drivers. It was not capable of measuring the driver’s alertness level. The interpretation of the findings is limited due to a lack of a comparative baseline condition. Future research is needed on direct comparisons of different countermeasures to sleep inertia that are effective and accepted by drivers.

Beschreibung: Fitness and physical exercise are preferred in the pursuit of healthier and active lifestyles. The number of mobile applications aiming to replace or complement a personal trainer is increasing. However, this also raises questions about the reliability, integrity, and even safety of the information provided by such applications. In this study, we review mobile applications that serve as virtual personal trainers. We present a systematic review of 36 related mobile applications, updated between 2017 and 2020, classifying them according to their characteristics. The selection criteria considers the following combination of keywords: “workout”, “personal trainer”, “physical activity”, “fitness”, “gymnasium”, and “daily plan”. Based on the analysis of the identified mobile applications, we propose a new taxonomy and present detailed guidelines on creating mobile applications for personalised workouts. Finally, we investigated how can mobile applications promote health and well-being of users and whether the identified applications are used in any scientific studies.

Beschreibung: Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the text similarity measurement method is described by two aspects: text distance and text representation. The text distance can be divided into length distance, distribution distance, and semantic distance; text representation is divided into string-based, corpus-based, single-semantic text, multi-semantic text, and graph-structure-based representation. Finally, the development of text similarity is also summarized in the discussion section.

Beschreibung: Partially automated driving (PAD, Society of Automotive Engineers (SAE) level 2) features provide steering and brake/acceleration support, while the driver must constantly supervise the support feature and intervene if needed to maintain safety. PAD could potentially increase comfort, road safety, and traffic efficiency. As during manual driving, users might engage in non-driving related tasks (NDRTs). However, studies systematically examining NDRT execution during PAD are rare and most importantly, no established methodologies to systematically evaluate driver distraction during PAD currently exist. The current project’s goal was to take the initial steps towards developing a test protocol for systematically evaluating NDRT’s effects during PAD. The methodologies used for manual driving were extended to PAD. Two generic take-over situations addressing system limits of a given PAD regarding longitudinal and lateral control were implemented to evaluate drivers’ supervisory and take-over capabilities while engaging in different NDRTs (e.g., manual radio tuning task). The test protocol was evaluated and refined across the three studies (two simulator and one test track). The results indicate that the methodology could sensitively detect differences between the NDRTs’ influences on drivers’ take-over and especially supervisory capabilities. Recommendations were formulated regarding the test protocol’s use in future studies examining the effects of NDRTs during PAD.

Beschreibung: The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.

Beschreibung: Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples. This challenging scenario is commonly known as few-shot learning. Few-shot learning has garnered increased attention in recent years due to its significance for many real-world problems. Recently, new methods relying on meta-learning paradigms combined with graph-based structures, which model the relationship between examples, have shown promising results on a variety of few-shot classification tasks. However, existing work on few-shot learning is only focused on the feature embeddings produced by the last layer of the neural network. The novel contribution of this paper is the utilization of lower-level information to improve the meta-learner performance in few-shot learning. In particular, we propose the Looking-Back method, which could use lower-level information to construct additional graphs for label propagation in limited data settings. Our experiments on two popular few-shot learning datasets, miniImageNet and tieredImageNet, show that our method can utilize the lower-level information in the network to improve state-of-the-art classification performance.

Beschreibung: With the rise of partially automated cars, drivers are more and more required to judge the degree of responsibility that can be delegated to vehicle assistant systems. This can be supported by utilizing interfaces that intuitively convey real-time reliabilities of system functions such as environment sensing. We designed a vibrotactile interface that communicates spatiotemporal information about surrounding vehicles and encodes a representation of spatial uncertainty in a novel way. We evaluated this interface in a driving simulator experiment with high and low levels of human and machine confidence respectively caused by simulated degraded vehicle sensor precision and limited human visibility range. Thereby we were interested in whether drivers (i) could perceive and understand the vibrotactile encoding of spatial uncertainty, (ii) would subjectively benefit from the encoded information, (iii) would be disturbed in cases of information redundancy, and (iv) would gain objective safety benefits from the encoded information. To measure subjective understanding and benefit, a custom questionnaire, Van der Laan acceptance ratings and NASA TLX scores were used. To measure the objective benefit, we computed the minimum time-to-contact as a measure of safety and gaze distributions as an indicator for attention guidance. Results indicate that participants were able to understand the encoded uncertainty and spatiotemporal information and purposefully utilized it when needed. The tactile interface provided meaningful support despite sensory restrictions. By encoding spatial uncertainties, it successfully extended the operating range of the assistance system.

Beschreibung: The COVID-19 pandemic exploded at the beginning of 2020, with over four million cases in five months, overwhelming the healthcare sector. Several national governments decided to adopt containment measures, such as lockdowns, social distancing, and quarantine. Among these measures, contact tracing can contribute in bringing under control the outbreak, as quickly identifying contacts to isolate suspected cases can limit the number of infected people. In this paper we present BubbleBox, a system relying on a dedicated device to perform contact tracing. BubbleBox integrates Internet of Things and software technologies into different components to achieve its goal—providing a tool to quickly react to further outbreaks, by allowing health operators to rapidly reach and test possible infected people. This paper describes the BubbleBox architecture, presents its prototype implementation, and discusses its pros and cons, also dealing with privacy concerns.

Beschreibung: Variation, adaptation, heredity and fitness, constraints and affordances, speciation, and extinction form the building blocks of the (Neo-)Darwinian research program, and several of these have been called “Darwinian principles”. Here, we suggest that caution should be taken in calling these principles Darwinian because of the important role played by reticulate evolutionary mechanisms and processes in also bringing about these phenomena. Reticulate mechanisms and processes include symbiosis, symbiogenesis, lateral gene transfer, infective heredity mediated by genetic and organismal mobility, and hybridization. Because the “Darwinian principles” are brought about by both vertical and reticulate evolutionary mechanisms and processes, they should be understood as foundational for a more pluralistic theory of evolution, one that surpasses the classic scope of the Modern and the Neo-Darwinian Synthesis. Reticulate evolution moreover demonstrates that what conventional (Neo-)Darwinian theories treat as intra-species features of evolution frequently involve reticulate interactions between organisms from very different taxonomic categories. Variation, adaptation, heredity and fitness, constraints and affordances, speciation, and extinction therefore cannot be understood as “traits” or “properties” of genes, organisms, species, or ecosystems because the phenomena are irreducible to specific units and levels of an evolutionary hierarchy. Instead, these general principles of evolution need to be understood as common goods that come about through interactions between different units and levels of evolutionary hierarchies, and they are exherent rather than inherent properties of individuals.

Beschreibung: This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.

Beschreibung: Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.

Beschreibung: In the existing attribute-based encryption (ABE) scheme, the authority (i.e., private key generator (PKG)) is able to calculate and issue any user’s private key, which makes it completely trusted, which severely influences the applications of the ABE scheme. To mitigate this problem, we propose the black box traceable ciphertext policy attribute-based encryption (T-CP-ABE) scheme in which if the PKG re-distributes the users’ private keys for malicious uses, it might be caught and sued. We provide a construction to realize the T-CP-ABE scheme in a black box model. Our scheme is based on the decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model. In our scheme, we employ a pair (ID, S) to identify a user, where ID denotes the identity of a user and S denotes the attribute set associated with her.

Beschreibung: Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources. Firstly, concepts are extracted from encyclopedias. Information in digital publishing resources is then reorganized by concepts. Secondly, concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors. As a result, the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading. History data or users’ preferences data are not needed for recommendation in a specific domain. The technique may not be language-specific. The method shows potential usability for e-learning in a specific domain.

Beschreibung: This article discusses the problem of user identification and psychophysiological state assessment while writing a signature using a graphics tablet. The solution of the problem includes the creation of templates containing handwriting signature features simultaneously with the hidden registration of physiological parameters of a person being tested. Heart rate variability description in the different time points is used as a physiological parameter. As a result, a signature template is automatically generated for psychophysiological states of an identified person. The problem of user identification and psychophysiological state assessment is solved depending on the registered value of a physiological parameter.

Beschreibung: Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier) is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

Beschreibung: This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno’s fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach.

Beschreibung: This paper considers the relationship between information and meaning through an analysis of a set of measures of information. Since meaning is expressed using information the analysis of the measures illuminates the relationship. The conventions that govern the production and exchange of information are the outcome of selection processes, so the quality of information (as expressed in the measures) is subject to limitations caused by selection. These limitations are analysed using a technique called viewpoint analysis that enables the measures and their relationship with meaning to be examined. Viewpoint analysis is applied to several logical paradoxes and resolves them in a manner consistent with common sense. Finally, the approach is used to address Floridi’s questions about the philosophy of information associated with meaning.

Beschreibung: More and more functions are performed automatically with the use of various electrical appliances and sophisticated control systems in all spheres of human life. In this regard, the demand for reliable and quality power supply is increasing. To date, low power quality, in particular unacceptable voltage levels, is an important deterrent for introducing technologies of smart electricity consumers (smart homes, smart companies, smart cities). This paper presents a comprehensive solution of this problem with the use of a voltage control system in the distribution grids, which is oriented on grids with a large number of heterogeneous loads and low level of Information Technology (IT) penetration. It is proposed to be installed on distribution substation special devices that perform continuous measurements of voltage levels, produce short-term forecasts and transmit the permissible ranges of voltage control to the power supply centers. The computing unit at the primary substation analyzes the data received from all distribution substations, and determines the optimum control actions to meet the requirements of all consumers. The proposed system was compared with conventional voltage control methods. The results have proved the effectiveness of the proposed approach.

Beschreibung: The physical security strategy in the wireless network with a single-antenna eavesdropper is studied. The information transmits from a single-antenna source to a single-antenna destination, and an energy-limited multi-antenna relay is employed to forward information. The antennas of the relay are divided into two groups. One group receives and forwards information, and the other converts the received signal into energy. Beamforming is used by the relay to prevent the eavesdropper from intercepting confidential information. For the purpose of maximizing the secrecy rate, antenna grouping and beamforming vectors are designed. A low complexity scheme of antenna grouping is presented. The simulation results show that the secrecy rate can be significantly improved by arranging part of the antennas for energy harvesting, and part for forwarding and optimizing the beamforming vector at the relay. The antenna grouping scheme significantly reduces the computational complexity at the cost of acceptable performance loss.

Beschreibung: The actual bus headway often deviates from the planned departure frequency because of external factors, such as traffic conditions and public transport demand, leading to transit resource waste and reducing the quality of service. In view of the existing shortcomings of the current dispatching strategy, a mixed integer programming model, integrating a bus-holding and stop-skipping strategy, is constructed to improve transit service with a minimum cost. The real-time optimal holding and stop-skipping strategies can be obtained by solving the proposed model using the Lagrangian relaxation algorithm. A numerical example is conducted using real transit GPS (Global Position System) and IC (Intelligent Card) data in Harbin. The results show that compared to a single control strategy, the proposed hybrid model is a better trade-off between the quality of the transit service and the operation cost. Notably, such a strategy would produce a minimal passengers’ average travel time coefficient. It is a great help for promoting the transit service level and increasing competitiveness.

Beschreibung: Recent security breaches show the need to secure large, distributed, complex systems. A fundamental, but little discussed aspect of security is how to evaluate when a complete system is secure. Purely formal methods cannot handle this level of complexity. Code checking does not consider the interaction of separate modules working together and is hard to scale. Model-based approaches, such as patterns and problem frames, can be effective for handling large systems. Their use in evaluating security appears promising. A few works in this direction exist, but there is a need for more ideas. This Special Issue focuses on global, model-based, architectural, and systems-oriented evaluation methods.

Beschreibung: The assessment of mobility and functional impairments in the elderly is important for early detection and prevention of fall conditions. Falls create serious threats to health by causing disabling fractures that reduce independence in the elderly. Moreover, they exert heavy economic burdens on society due to high treatment costs. Modern smartphones enable the development of innovative mobile health (mHealth) applications by integrating a growing number of inertial and environmental sensors along with the ever-increasing data processing and communication capabilities. Mobility assessment is one of the promising mHealth application domains. In this paper, we introduce a suite of smartphone applications for assessing mobility in the elderly population. The suite currently includes smartphone applications that automate and quantify the following standardized medical tests for assessing mobility: Timed Up and Go (TUG), 30-Second Chair Stand Test (30SCS), and 4-Stage Balance Test (4SBT). For each application, we describe its functionality and a list of parameters extracted by processing signals from smartphone’s inertial sensors. The paper shows the results from studies conducted on geriatric patients for TUG tests and from experiments conducted in the laboratory on healthy subjects for 30SCS and 4SBT tests.

Beschreibung: Is it possible to quantify the information content of accounting reports? If possible, then how? This study examines accounting as a classical communication system with the purpose of providing a framework with which to approach these fundamentally important questions. Information theory was established in the early-mid 20th century to describe the properties of classical communication systems. Applying concepts from this theory to an accounting context provides insight into the questions asked above. Specifically, a measure of the information content of financial statement numbers is developed from these information theory concepts. The measure is also applied to several large companies’ earnings numbers and aids in predicting their price movements.

Beschreibung: This paper deals with the problem of securing the configuration phase of an Internet of Things (IoT) system. The main drawbacks of current approaches are the focus on specific techniques and methods, and the lack of a cross layer vision of the problem. In a smart environment, each IoT device has limited resources and is often battery operated with limited capabilities (e.g., no keyboard). As a consequence, network security must be carefully analyzed in order to prevent security and privacy issues. In this paper, we will analyze the IoT threats, we will propose a security framework for the device initialization and we will show how physical layer security can effectively boost the security of IoT systems.

Beschreibung: Passive positioning systems with a small aperture array exhibit poor accuracy of target estimation under strong interference in near-field environments. To improve this accuracy, we propose a novel cross localization algorithm for direction-finding using the orientation angle. Improved geometric and numerical target-positioning models are constructed after analyzing the mechanism of the conventional positioning algorithm. The target prediction equation is then derived using the constructed models, and the equation for nonlinear estimation is linearized using the Taylor series. An unbiased estimation of the target is obtained by optimizing the control of the iteration process, thus achieving an accurate positioning of the target. The performance of the proposed algorithm was evaluated in terms of its effectiveness and positioning accuracy under varying signal-to-noise conditions and orientation angle-measurement errors. Simulation results show that the proposed algorithm is capable of positioning the target effectively, and offers better positioning accuracy than traditional algorithms under the conditions of large orientation angle measurement errors or high-level background noise.

Beschreibung: The Teaching-Learning-Based Optimization (TLBO) algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO) algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.

Beschreibung: The automotive industry has successfully collaborated to release the ISO 26262 standard for developing safe software for cars. The standard describes in detail how to conduct hazard analysis and risk assessments to determine the necessary safety measures for each feature. However, the standard does not concern threat analysis for malicious attackers or how to select appropriate security countermeasures. We propose the application of ISO 27001 for this purpose and show how it can be applied together with ISO 26262. We show how ISO 26262 documentation can be re-used and enhanced to satisfy the analysis and documentation demands of the ISO 27001 standard. We illustrate our approach based on an electronic steering column lock system.

Beschreibung: As an important part of knowledge management (KM), the KM performance evaluation tries to find out the key factors restraining the enhancement of the enterprises' performance. This paper investigates the feasibility of the balanced scorecard (BSC) method in enterprise knowledge management and then proposes a simplified and applicable performance evaluation model based on the BSC approach. Finally, fuzzy comprehensive evaluation (FCE) is used to evaluate the effectiveness and applicability of the proposed model. The result shows that the model is useful for evaluating the performance of KM in enterprises.

Beschreibung: Privacy is a software quality that is closely related to security. The main difference is that security properties aim at the protection of assets that are crucial for the considered system, and privacy aims at the protection of personal data that are processed by the system. The identification of privacy protection needs in complex systems is a hard and error prone task. Stakeholders whose personal data are processed might be overlooked, or the sensitivity and the need of protection of the personal data might be underestimated. The later personal data and the needs to protect them are identified during the development process, the more expensive it is to fix these issues, because the needed changes of the system-to-be often affect many functionalities. In this paper, we present a systematic method to identify the privacy needs of a software system based on a set of functional requirements by extending the problem-based privacy analysis (ProPAn) method. Our method is tool-supported and automated where possible to reduce the effort that has to be spent for the privacy analysis, which is especially important when considering complex systems. The contribution of this paper is a semi-automatic method to identify the relevant privacy requirements for a software-to-be based on its functional requirements. The considered privacy requirements address all dimensions of privacy that are relevant for software development. As our method is solely based on the functional requirements of the system to be, we enable users of our method to identify the privacy protection needs that have to be addressed by the software-to-be at an early stage of the development. As initial evaluation of our method, we show its applicability on a small electronic health system scenario.

Beschreibung: With rapid development and application of mobile internet, geographic information in the field of business process is now more widely used. There are more and more researches in the field of the relationships between geographic information and workflow modeling. According to the workflow with geospatial constraints, this paper first discusses the geospatial constraints theory deeply, proposes a new concept of geospatial constraints unit, and then designs a geospatial constraint net model (GCNet). Secondly, this paper designs a new workflow model with geospatial constraints (GCWF-net) based on GCNet and workflow net (WF-net), and then analyzes some properties of the model. Finally, this paper discusses how to put GCWF-net into application practice from three aspects: extending PNML (Petri Net Markup Language) labels for GCWF-net, converting PNML to BPEL (Business Process Execution Language) and implementing BPEL.

Beschreibung: Routing Protocol for Low power and Lossy network (RPL) topology attacks can downgrade the network performance significantly by disrupting the optimal protocol structure. To detect such threats, we propose a RPL-specification, obtained by a semi-auto profiling technique that constructs a high-level abstract of operations through network simulation traces, to use as reference for verifying the node behaviors. This specification, including all the legitimate protocol states and transitions with corresponding statistics, will be implemented as a set of rules in the intrusion detection agents, in the form of the cluster heads propagated to monitor the whole network. In order to save resources, we set the cluster members to report related information about itself and other neighbors to the cluster head instead of making the head overhearing all the communication. As a result, information about a cluster member will be reported by different neighbors, which allow the cluster head to do cross-check. We propose to record the sequence in RPL Information Object (DIO) and Information Solicitation (DIS) messages to eliminate the synchronized issue created by the delay in transmitting the report, in which the cluster head only does cross-check on information that come from sources with the same sequence. Simulation results show that the proposed Intrusion Detection System (IDS) has a high accuracy rate in detecting RPL topology attacks, while only creating insignificant overhead (about 6.3%) that enable its scalability in large-scale network.

Beschreibung: The wiretap channel models secure communication between two users in the presence of an eavesdropper who must be kept ignorant of transmitted messages. This communication scenario is studied for arbitrarily varying channels (AVCs), in which the legitimate users know only that the true channel realization comes from a pre-specified uncertainty set and that it varies from channel use to channel use in an arbitrary and unknown manner. This concept not only captures the case of channel uncertainty, but also models scenarios in which malevolent adversaries influence or jam the transmission of the legitimate users. For secure communication over orthogonal arbitrarily varying wiretap channels (AVWCs) it has been shown that the phenomenon of super-activation occurs; that is, there are orthogonal AVWCs, each having zero secrecy capacity, which allow for transmission with positive rate if they are used together. It is shown that for such orthogonal AVWCs super-activation is generic in the sense that whenever super-activation is possible, it is possible for all AVWCs in a certain neighborhood as well. As a consequence, a super-activated AVWC is robust and continuous in the uncertainty set, although a single AVWC might not be. Moreover, it is shown that the question of super-activation and the continuity of the secrecy capacity solely depends on the legitimate link. Accordingly, the single-user AVC is subsequently studied and it is shown that in this case, super-activation for non-secure message transmission is not possible making it a unique feature of secure communication over AVWCs. However, the capacity for message transmission of the single-user AVC is shown to be super-additive including a complete characterization. Such knowledge is important for medium access control and in particular resource allocation as it determines the overall performance of a system.

Beschreibung: Often the Internet of Things (IoT) is considered as a single problem domain, with proposed solutions intended to be applied across a wide range of applications. However, the privacy and security needs of critical engineering infrastructure or sensitive commercial operations are very different to the needs of a domestic Smart Home environment. Additionally, the financial and human resources available to implement security and privacy vary greatly between application domains. In domestic environments, human issues may be as important as technical issues. After surveying existing solutions for enhancing IoT security, the paper identifies key future requirements for trusted Smart Home systems. A gateway architecture is selected as the most appropriate for resource-constrained devices, and for high system availability. Two key technologies to assist system auto-management are identified. Firstly, support for system auto-configuration will enhance system security. Secondly, the automatic update of system software and firmware is needed to maintain ongoing secure system operation.

Beschreibung: Image sizes have increased exponentially in recent years. The resulting high-resolution images are often viewed via remote image browsing. Zooming and panning are desirable features in this context, which result in disparate spatial regions of an image being displayed at a variety of (spatial) resolutions. When an image is displayed at a reduced resolution, the quantization step sizes needed for visually lossless quality generally increase. This paper investigates the quantization step sizes needed for visually lossless display as a function of resolution, and proposes a method that effectively incorporates the resulting (multiple) quantization step sizes into a single JPEG 2000 codestream. This codestream is JPEG 2000 Part 1 compliant and allows for visually lossless decoding at all resolutions natively supported by the wavelet transform as well as arbitrary intermediate resolutions, using only a fraction of the full-resolution codestream. When images are browsed remotely using the JPEG 2000 Interactive Protocol (JPIP), the required bandwidth is significantly reduced, as demonstrated by extensive experimental results.

Beschreibung: There is little knowledge regarding the exchange of academic information on religious contexts. The objective of this informational study was to perform an overall analysis of all Buddhism-related communications collected in the Web of Science (WoS) from 1993 to 2011. The studied informational parameters include the growth in number of the scholarly communications, as well as the language-, document-, subject category-, source-, country-, and organization-wise distribution of the communications. A total of 5407 scholarly communications in this field of study were published in the selected time range. The most preferred WoS subject category was Asian Studies with 1773 communications (22.81%), followed by Religion with 1425 communications (18.33%) and Philosophy with 680 communications (8.75%). The journal with the highest mean number of citations is Numen: International Review for the History of Religions—with 2.09 citations in average per communication. The United States was the top productive country with 2159 communications (50%), where Harvard University topped the list of organization with 85 communications (12%).

Beschreibung: In this study, we combine the fuzzy customer information problem with the multicommodity multimodal routing with schedule-based services which was explored in our previous study [1]. The fuzzy characteristics of the customer information are embodied in the demanded volumes of the multiple commodities and the time windows of their due dates. When the schedule-based services are considered in the routing, schedule constraints emerge because the operations of block container trains should follow their predetermined schedules. This will restrict the routes selection from space-time feasibility. To solve this combinatorial optimization problem, we first build a fuzzy chance-constrained nonlinear programming model based on fuzzy possibility theory. We then use a crisp equivalent method and a linearization method to transform the proposed model into the classical linear programming model that can be effectively solved by the standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. The sensitivity of the best solution with respect to the values of the confidence levels is also examined.

Beschreibung: This paper discusses robust link adaptation for a downlink precoded multiple input single output system, for guaranteeing ultra-reliable (99.999%) transmissions to mobile users (e.g., slowly moving machines in a factory) served by a small cell network. The proposed technique compensates the effect of inaccurate channel state information (CSI) caused by user mobility, as well as the variation of precoders in the interfering cells. Both of these impairments translate into instability of the received signal-to-noise plus interference ratios (SINRs), and may lead to CSI mispredictions and potentially erroneous transmissions. We show that, by knowing the statistics of the propagation channels and the precoders variations, it is possible to compute a backoff that guarantees robust link adaptation. The backoff value is based on the statistics of realized SINR, and is consequently used to adapt the transmissions according to current channel state. Theoretical analysis accompanied by simulation results show that the proposed approach is suitable for attaining 5G ultra-reliability targets in realistic settings.

Beschreibung: Spam reviews are increasingly appearing on the Internet to promote sales or defame competitors by misleading consumers with deceptive opinions. This paper proposes a co-training approach called CoSpa (Co-training for Spam review identification) to identify spam reviews by two views: one is the lexical terms derived from the textual content of the reviews and the other is the PCFG (Probabilistic Context-Free Grammars) rules derived from a deep syntax analysis of the reviews. Using SVM (Support Vector Machine) as the base classifier, we develop two strategies, CoSpa-C and CoSpa-U, embedded within the CoSpa approach. The CoSpa-C strategy selects unlabeled reviews classified with the largest confidence to augment the training dataset to retrain the classifier. The CoSpa-U strategy randomly selects unlabeled reviews with a uniform distribution of confidence. Experiments on the spam dataset and the deception dataset demonstrate that both the proposed CoSpa algorithms outperform the traditional SVM with lexical terms and PCFG rules in spam review identification. Moreover, the CoSpa-U strategy outperforms the CoSpa-C strategy when we use the absolute value of decision function of SVM as the confidence.

Beschreibung: Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been widely used to improve the performance of various natural language processing tasks. However, they have not been used in DNR systems. Compared to the semantic features based on drug dictionaries, the advantage of word embeddings lies in that learning them is unsupervised. In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries. We propose a conditional random fields (CRF)-based system for DNR. The skip-gram model, an unsupervised algorithm, is used to induce word embeddings on about 17.3 GigaByte (GB) unlabeled biomedical texts collected from MEDLINE (National Library of Medicine, Bethesda, MD, USA). The system is evaluated on the drug-drug interaction extraction (DDIExtraction) 2013 corpus. Experimental results show that word embeddings significantly improve the performance of the DNR system and they are competitive with semantic features based on drug dictionaries. F-score is improved by 2.92 percentage points when word embeddings are added into the baseline system. It is comparative with the improvements from semantic features based on drug dictionaries. Furthermore, word embeddings are complementary to the semantic features based on drug dictionaries. When both word embeddings and semantic features based on drug dictionaries are added, the system achieves the best performance with an F-score of 78.37%, which outperforms the best system of the DDIExtraction 2013 challenge by 6.87 percentage points.

Beschreibung: A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device’s functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.

Beschreibung: Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

Beschreibung: We explore the class of positive integers n that admit idempotent factorizations n = p ¯ q ¯ such that λ ( n ) ∣ ( p ¯ − 1 ) ( q ¯ − 1 ) , where λ is the Carmichael lambda function. Idempotent factorizations with p ¯ and q ¯ prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p ¯ and/or q ¯ . Idempotent factorizations are exactly those p ¯ and q ¯ that generate correctly functioning keys in the Rivest–Shamir–Adleman (RSA) 2-prime protocol with n as the modulus. While the resulting p ¯ and q ¯ have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.

Beschreibung: Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities [...]

Beschreibung: Finite element data form an important basis for engineers to undertake analysis and research. In most cases, it is difficult to generate the internal sections of finite element data and professional operations are required. To display the internal data of entities, a method for generating the arbitrary sections of finite element data based on radial basis function (RBF) interpolation is proposed in this paper. The RBF interpolation function is used to realize arbitrary surface cutting of the entity, and the section can be generated by the triangulation of discrete tangent points. Experimental studies have proved that the method is very convenient for allowing users to obtain visualization results for an arbitrary section through simple and intuitive interactions.

Beschreibung: The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

Beschreibung: This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

Beschreibung: The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

Beschreibung: Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.

Beschreibung: Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

Beschreibung: Appropriate business processes management (BPM) within an organization can help attain organizational goals. It is particularly important to effectively manage the lifecycle of these processes for organizational effectiveness in improving ever-growing performance and competitivity-building across the company. This paper presents a process discovery and how we can use it in a broader framework supporting self-organization in BPM. Process discovery is intrinsically associated with the process lifecycle. We have made a pre-evaluation of the usefulness of our facts using a generated log file. We also compared visualizations of the outcomes of our approach with different cases and showed performance characteristics of the cash loan sales process.

Beschreibung: Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.

Beschreibung: The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.

Beschreibung: The advent of utility computing has revolutionized almost every sector of traditional software development. Especially commercial cloud computing services, pioneered by the likes of Amazon, Google and Microsoft, have provided an unprecedented opportunity for the fast and sustainable development of complex distributed systems. Nevertheless, existing models and tools aim primarily for systems where resource usage—by humans and bots alike—is logically and physically quite disperse resulting in a low likelihood of conflicting resource access. However, a number of resource-intensive applications, such as Massively Multiplayer Online Games (MMOGs) and large-scale simulations introduce a requirement for a very large common state with many actors accessing it simultaneously and thus a high likelihood of conflicting resource access. This paper presents a systematic mapping study of the state-of-the-art in software technology aiming explicitly to support the development of MMOGs, a class of large-scale, resource-intensive software systems. By examining the main focus of a diverse set of related publications, we identify a list of criteria that are important for MMOG development. Then, we categorize the selected studies based on the inferred criteria in order to compare their approach, unveil the challenges faced in each of them and reveal research trends that might be present. Finally we attempt to identify research directions which appear promising for enabling the use of standardized technology for this class of systems.

Beschreibung: This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

Beschreibung: An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this problem, semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms are combined to form a systematic solution. A semantic channel in G theory consists of a group of truth functions or membership functions. In comparison with the likelihood functions, Bayesian posteriors, and Logistic functions that are typically used in popular methods, membership functions are more convenient to use, providing learning functions that do not suffer the above problem. In Logical Bayesian Inference (LBI), every label is independently learned. For multilabel learning, we can directly obtain a group of optimized membership functions from a large enough sample with labels, without preparing different samples for different labels. Furthermore, a group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions in a two-dimensional feature space, only 2–3 iterations are required for the mutual information between three classes and three labels to surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved to form the CM-EM algorithm, which can outperform the EM algorithm when the mixture ratios are imbalanced, or when local convergence exists. The CM iteration algorithm needs to combine with neural networks for MMI classification in high-dimensional feature spaces. LBI needs further investigation for the unification of statistics and logic.

Beschreibung: Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

Beschreibung: In this paper, I first review signal detection theory (SDT) approaches to perception, and then discuss why it is thought that SDT theory implies that increasing attention improves performance. Our experiments have shown, however, that this is not necessarily true. Subjects had either focused attention on two of four possible locations in the visual field, or diffused attention to all four locations. The stimuli (offset letters), locations, conditions, and tasks were all known in advance, responses were forced-choice, subjects were properly instructed and motivated, and instructions were always valid—conditions which should optimize signal detection. Relative to diffusing attention, focusing attention indeed benefitted discrimination of forward from backward pointing Es. However, focusing made it harder to identify a randomly chosen one of 20 letters. That focusing can either aid or disrupt performance, even when cues are valid and conditions are idealized, is surprising, but it can also be explained by SDT, as shown here. These results warn the experimental researcher not to confuse focusing attention with enhancing performance, and warn the modeler not to assume that SDT is unequivocal.

Beschreibung: This paper deals with the Arabic translation taṣawwur in Averroes’ Great Commentary of the term τῶν ἀδιαιρέτων νόησις (“ton adiaireton noesis”, thinking of the indivisibles) in Aristotle’s De anima and the Latin translation from Arabic with (in-)formatio, as quoted by Albertus Magnus [...]

Beschreibung: Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies.

Beschreibung: With digital media, not only are media extensions of their human users, as McLuhan posited, but there is a flip or reversal in which the human users of digital media become an extension of those digital media as these media scoop up their data and use them to the advantage of those that control these media. The implications of this loss of privacy as we become “an item in a data bank” are explored and the field of captology is described. The feedback of the users of digital media become the feedforward for those media.

Beschreibung: Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.

Beschreibung: The current trend in the European Union (EU) is to support the development of online dispute resolution (ODR) that saves financial and human resources. Therefore, research articles mainly deal with the design of new ODR solutions, without researching the social aspects of using different kinds of ODR solutions. For this reason, the main aim of the article is an empirical evaluation of two kinds of ODR solutions in business-to-business (B2B) relationships from the perspective of a selected social category. The article focuses on: (1) comparing unassisted and smart assisted negotiation while using the artificial intelligence approach; (2) the satisfaction and attitudes of Generation Y members from the Czech and Slovak Republic towards different ways of negotiating. The conclusions of this study can help researchers to design or improve existing ODR solutions, and companies to choose the most suitable managers from Generation Y for B2B negotiation. The results show that Generation Y members prefer computer-mediated communication as compared to face to face negotiation; the participants were more satisfied with the negotiation process when using smart assisted negotiation. Through a computer-mediated negotiation, even sellers with lower emotional stability can maintain an advantageous position. Similarly, buyers with lower agreeableness or higher extraversion can negotiate more favorable terms and offset their loss.

Beschreibung: Unmanned and unwomaned aerial vehicles (UAV), or drones, are breaking and creating new boundaries of image-based communication. Using social network analysis and critical discourse analysis, we examine the 60 most popular question threads about drones on Zhihu, China’s largest social question answering platform. We trace how controversial issues around these supposedly novel tech products are mediated, domesticated, visualized, or marginalized via digital representational technology. Supported by Zhihu’s topic categorization algorithm, drone-related discussions form topic clusters. These topic clusters gain currency in the government-regulated cyberspace, where their meanings remain open to widely divergent interpretations and mediation by various agents. We find that the largest drone company DJI occupies a central and strongly interconnected position in the discussions. Drones are, moreover, represented as objects of consumption, technological advancement, national future, and uncertainty. At the same time, the sense-making process of drone-related discussions evokes emerging sets of narrative user identities with potential political effects. Users engage in digital representational technologies publicly and collectively to raise questions and represent their views on new technologies. Therefore, we argue that platforms like Zhihu are essential when studying views of the Chinese citizenry towards technological developments.

Beschreibung: The ageing population has become an increasing phenomenon world-wide, leading to a growing need for specialised help. Improving the quality of life of older people can lower the risk of depression and social isolation, but it requires a multi-dimensional approach through continuous monitoring and training of the main health domains (e.g., cognitive, motor, nutritional and behavioural). To this end, the use of mobile and e-health services tailored to the user’s needs can help stabilise their health conditions, in terms of physical, mental, and social capabilities. In this context, the INTESA project proposes a set of personalised monitoring and rehabilitation services for older people, based on mobile and wearable technologies ready to be used either at home or in residential long-term care facilities. We evaluated the proposed solution by deploying a suite of services in a nursing home and defining customised protocols to involve both guests (primary users) and nursing care personnel (secondary users). In this paper, we present the extended results obtained after the one-year period of experimentation in terms of technical reliability of the system, Quality of Experience, and user acceptance for both the user categories.

Beschreibung: Public key encryption with disjunctive keyword search (PEDK) is a public key encryption scheme that allows disjunctive keyword search over encrypted data without decryption. This kind of scheme is crucial to cloud storage and has received a lot of attention in recent years. However, the efficiency of the previous scheme is limited due to the selection of a less efficient converting method which is used to change query and index keywords into a vector space model. To address this issue, we design a novel converting approach with better performance, and give two adaptively secure PEDK schemes based on this method. The first one is built on an efficient inner product encryption scheme with less searching time, and the second one is constructed over composite order bilinear groups with higher efficiency on index and trapdoor construction. The theoretical analysis and experiment results verify that our schemes are more efficient in time and space complexity as well as more suitable for the mobile cloud setting compared with the state-of-art schemes.

Beschreibung: Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.

Beschreibung: Recent high-profile cyber-attacks exemplify why organizations need better cyber-defenses. Cyber-threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into the groups’ collective malicious activity. We propose a novel approach to predict cyber-events using sentiment analysis. We test our approach using cyber-attack data from two major business organizations. We consider three types of events: malicious software installation, malicious-destination visits, and malicious emails that surmounted the target organizations’ defenses. We construct predictive signals by applying sentiment analysis to hacker forum posts to better understand hacker behavior. We analyze over 400 K posts written between January 2016 and January 2018 on over 100 hacking forums both on the surface and dark web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can complement state-of-the-art time-series models on forecasting cyber-attacks weeks ahead of the events.

Beschreibung: The evaluation and selection of a sustainable modular supplier is a strategic decision towards sustainability of manufacturing. However, few related studies have been conducted, particularly in the modular production field. In this paper, a fuzzy evaluation method is used to evaluate sustainable modular suppliers. Different from previous studies, in designing the index system of the fuzzy evaluation method, this study introduces an organizational identity perspective. What is more, an empirical study based on a formative model is conducted to design the index system. Both measures ensure the appropriateness of the index system. The stability of the fuzzy evaluation method is also discussed. By introducing a dispersion degree and discussing the different circumstances of subjective judgment errors, the stability analysis helps us to better understand the reliability of the results. Lastly, this study applies this method and the index system to a practical case. The results show that the fuzzy evaluation method is effective and can be used to evaluate sustainable modular suppliers.

Beschreibung: The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of gyroscopic signals acquired offline through a smartphone. A specific free mobile application is used to acquire the gyroscopic signals and to transmit them to a personal computer through a wireless network. The personal computer elaborates the acquired data and returns the kinematic jerk index associated with a motor task. A comparison of the kinematic jerk index value on a number of data sets confirms that such index can be used to evaluate the fluency of hips orientation during motion. The present research confirms that the proposed gyroscopic data acquisition/processing setup constitutes an inexpensive and portable solution to motion fluency analysis. The proposed data-acquisition and data-processing setup may serve as a supporting eHealth technology in clinical bio-mechanics as well as in sports science.

Beschreibung: An important function of next-generation (5G) and beyond mobile communication systems is aim to provide thousand-fold capacity growth and to support high-speed data transmission up to several megabits per second. However, the research community and industries have to face a dilemma of power consumption and hardware design to satisfy the increasing communication requirements. For the purpose of improving the system cost, power consumption, and implementation complexity, a novel scheme of symbol timing and frequency offset estimation with low-resolution analog-to-digital converters (ADCs) based on an orthogonal frequency division multiplexing ultra-wideband (OFDM-UWB) system is proposed in this paper. In our work, we first verified the principle that the autocorrelation of the pseudo-noise (PN) sequences was not affected by low-resolution quantization. With the help of this property, the timing synchronization could be strongly implemented against the influence of low-resolution quantization. Then, the transmitted signal structure and low-resolution quantization scheme under the synchronization scheme were designed. Finally, a frequency offset estimation model with one-bit timing synchronization was established. Theoretical analysis and simulation results corroborate that the performance of the proposed scheme not only approximates to that of the full-resolution synchronization scheme, but also has lower power consumption and computational complexity.

Beschreibung: With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

Beschreibung: Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

Beschreibung: Robot technology at small- and medium-sized enterprises has become a crucial part of current business operations. Beginning with the manufacturing industry, more industries than ever before have recently begun making use of robot technology to increase operational efficiency and productivity. However, prior studies regarding innovation related to intelligent robot use have been limited to developing strategies for describing robot technologies in general. Therefore, we developed a research model for investigating process innovation as it relates to intelligent robots. Based on the literature, two variables of technology benefits (direct usefulness and indirect usefulness) and two constructs of environmental pressure (industry and government) were incorporated into the research model as key determinants of a firm’s process innovation. Furthermore, organizational supports as moderating variables were added to the relationship between technology benefits and process innovation. We collected 257 responses in managerial position at various firms in order to test the proposed hypotheses using structural equation modeling in the statistical software (AMOS 22.0). The results revealed that all variables have a significant impact on process innovation, as well as the moderator. The findings of this study provide theoretical and practical implications for process innovation based on intelligent robot technology.

Beschreibung: The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level.

Beschreibung: Social customer relationship management (SCRM) is a new philosophy influencing the relationship between customer and organization where the customer gets the opportunity to control the relationship through social media. This paper aims to identify (a) the current level of SCRM and (b) the influence of basic organizational characteristics on the SCRM level. The data were gathered through a questionnaire distributed to 362 organizations headquartered in the Czech Republic. The questionnaire comprised 54 questions focusing on the significance of marketing and CRM practices, establishing a relationship with the customer, online communities, the use of social media in marketing, and acquiring and managing information. Scalable questions with a typical five-level Likert scale were applied in the questionnaire. The results show that larger firms more often set up their own online communities and manage them strategically; moreover, they are able to manage information better. Contrariwise, small-sized organizations use social networks as a way to establish communication with the customer more than large-sized entities. The use of social media for marketing purposes is significantly higher in organizations oriented to consumer markets than in those oriented to business markets.

Beschreibung: OpenCourseWare (OCW) systems have been established to provide open educational resources that are accessible by anyone, including learners with special accessibility needs and preferences. We need to find a formal and interoperable way to describe these preferences in order to use them in OCW systems and retrieve relevant educational resources. This formal representation should use standard accessibility definitions of OCW that can be reused by other OCW systems to represent accessibility concepts. In this article, we present an ontology to represent the accessibility needs of learners with respect to the IMS AfA specifications. The ontology definitions together with rule-based queries are used to retrieve relevant educational resources. Related to this, we developed a user interface component that enables users to create accessibility profiles representing their individual needs and preferences based on our ontology. We evaluated the approach with five examples profiles.

Beschreibung: In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

Beschreibung: Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

Beschreibung: This paper presents a rehabilitation system based on a customizable exergame protocol to prevent falls in the elderly population. The system is based on depth sensors and exergames. The experiments carried out with several seniors, in a day care center, make it possible to evaluate the usability and the efficiency of the system. The outcomes highlight the user-friendliness, the very good usability of the developed system and the significant enhancement of the elderly in maintaining a physical activity. The performance of the postural response is improved by an average of 80%.

Beschreibung: In the present paper, a smart planar electrically steerable passive array radiator (ESPAR) antenna was developed and tested at the frequency of 1.33 GHz with the main goal to control the main radiation lobe direction, ensuring precise communication between the antenna that is implemented in an unmanned aerial vehicle (UAV) and the base station. A control system was also developed and integrated into the communication system: an antenna coupled to the control system. The control system consists of an Arduino, a digital potentiometer, and an improved algorithm that allows defining the radiation-lobe direction as a function of the UAV flight needs. The ESPAR antenna was tested in an anechoic chamber with the control system coupled to it so that all previously established requirements were validated.

Beschreibung: To address the issue of cloud mixed workloads scheduling which might lead to system load imbalance and efficiency degradation in cloud computing, a novel cloud task staggering peak scheduling policy based on the task types and the resource load status is proposed. First, based on different task characteristics, the task sequences submitted by the user are divided into queues of different types by the fuzzy clustering algorithm. Second, the Performance Counters (PMC) mechanism is introduced to dynamically monitor the load status of resource nodes and respectively sort the resources by the metrics of Central Processing Unit (CPU), memory, and input/output (I/O) load size, so as to reduce the candidate resources. Finally, the task sequences of specific type are scheduled for the corresponding light loaded resources, and the resources usage peak is staggered to achieve load balancing. The experimental results show that the proposed policy can balance loads and improve the system efficiency effectively and reduce the resource usage cost when the system is in the presence of mixed workloads.

Beschreibung: With the advancement and diversification of information retrieval technology, such technology has been widely applied in recent years in personalized information recommender systems (RSs) and e-commerce RSs in addition to data-mining applications, especially with respect to mobile phone purchases. By integrating the weights of fuzzy ordered weighted averaging (OWA) and gray relational analysis, this research calculated the recommended F1 indices of three weight calculation methods to be 20.5%, 14.36%, and 16.43% after an examination by 30 experimenters. According to the operational results attained by the 30 experimenters, the recommended products obtained by the fuzzy OWA and gray relational analysis calculation method covered the products recommended by the other two weight calculation methods with a higher recommendation effect.

Beschreibung: Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is required. This work investigates into greater depth a type of model that can produce ideological segregation as a result of polarization depending on the strength of homophily and the ability of users to access similar minded individuals. Whether increased access can induce larger amounts of societal separation is important to investigate, and this work sheds further insight into the phenomenon. Center to the hypothesis of homophilic alignments in friendship generation is that of a discussion group or community. These are modeled and the investigation into their effect on the dynamics of polarization is presented. The social implications demonstrate that initial phases of an ideological exchange can result in increased polarization, although a consensus in the long run is expected and that the separation between groups is amplified when groups are constructed with ideological homophilic preferences.

Beschreibung: In recent years, there has been a rapid evolution of wireless technologies that has led to the challenge of high demand for spectral resources. To overcome this challenge, good spectrum management is required that calls for more efficient use of the spectrum. In this paper, we present a general system, which makes a tradeoff between the spectral efficiency (SE) and energy efficiency (EE) in the cellular cognitive radio networks (CCRN) with their respective limits. We have analyzed the system taking into account the different types of power used in the CCRN, namely the spectrum detection power (Zs) and the relay power (Zr). Optimal policy for emission power allocation formulated in the function of sub-channel activity index (SAI) as an optimization problem in order to maximize spectrum utilization and minimize the energy consumption in the base station of the secondary system energy consumption, is subject to different constraints of the main user system. We also evaluate the collaborative activity index of the sub-channel describing the activity of the primary users in the CCRN. The theoretical analyses and simulation results sufficiently demonstrate that the SE and EE relationship in the CCRN is not contrary and thus the achievement of optimal tradeoff between SE and EE. By making a rapprochement with a cognitive cellular network where SBSs adopts an equal power allocation strategy for sub-channels, the results of our proposed scheme indicate a significant improvement. Therefore, the model proposed in this paper offers a better tradeoff between SE and EE.

Beschreibung: Augmented reality (AR) combines information from databases with information derived directly through the human senses or different sensors. AR is the organic, real-time, and dynamically overlaying virtual images created by computers and other information on a real environment that the observer can see. Virtual information changes according to the movement of the observer, as if that virtual information truly exists in the real world. The convergence of geographic information systems (GIS), web mapping, mobile technology, and augmented reality (AR) implies the emergence of location based systems (LBS) and, in turn, the diminishing use of traditional analog maps by smartphone users. The focus and review of current achievements in this subject on the wider trends of the use of AR also prove that coding of some data and metadata on an image or a map (both in digital and analogue form) permits inter alia the inclusion of, for example, a paper map or analogue image into the chain of digital devices use. Some solutions, remarks, and comments concerning functioning of the digitally augmented (printed) map and their digital counterparts within the information society are presented.

Beschreibung: The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.

Beschreibung: Information management is a common paradigm in modern decision-making. A wide range of decision-making techniques have been proposed in the literature to model complex business processes. In this Special Issue, 16 selected and peer-reviewed original research articles contribute to business information management in various current real-world problems by proposing crisp or uncertain multiple-criteria decision-making (MCDM) models and techniques, mostly including multi-attribute decision-making (MADM) approaches in addition to a single paper proposing an interactive multi-objective decision-making (MODM) approach. The papers are mainly concentrated in three application areas: supplier selection and rational order allocation, the evaluation and selection of goods or facilities, and personnel selection/partner selection. A number of new approaches are proposed that are expected to attract great interest from the research community.

Beschreibung: With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.

Beschreibung: Business innovation is a process that requires creativity, and benefits from extensive collaboration. Currently, computational support in creativity processes is low, but modern techniques would allow these processes to be sped up. In this context, we provide such a computational support with software for business innovation design that uses computational creativity techniques. Furthermore, the software enables a gamified process to increase user engagement and collaboration, which mimics evolutionary methods, relying on a voting mechanism. The software includes a business innovation ontology representing the domain knowledge that is used to generate and select a set of diverse preliminary representations of business ideas. Indeed, the most promising for novelty and potential impact are identified to ignite a business innovation game where team members collaborate to elaborate new innovation ideas based on those inputs until convergence to a shortlist of business model proposals. The main features of the approach are illustrated by means of a running example concerning innovative services for smart cities.

Beschreibung: A triple band-notched ultrawide band (UWB) antenna is presented to avoid the interference of services working in the UWB band, such as WLAN, WiMAX and X-band satellite systems. The arc H-shaped slot on the radiating patch creates a low frequency notched band, while the other two band-notched bands are formed by cutting narrow slots on the ground plane. The presented antenna can operate on the ultrawide band efficiently and inhibit interference from three different kinds of narrow band communication systems. The simulation and measurement results show that the antenna has excellent band-notched function on the rejectband and almost omnidirectional radiation pattern on the passband.