ALBERT

Description: Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.

Description: In the field of satellite data broadcasting, the management quality of data broadcasting bandwidth is directly related to the throughput of the broadcasting system and plays an important role in the performance of satellites. In this paper, for the sun-synchronous orbit meteorological satellite broadcasting which has the conventional product files and emergency information, a broadcast bandwidth statistical multiplexing and control method is designed for bandwidth management. It can be used for the management of broadcasting between regular products and emergency information, as well as internal broadcasting among regular products. This paper is the first to apply common multiplexing of PID and channel mode (CMPCM) to satellite broadcasting. The test verified that the broadcast channel of the parameters and the broadcast schedule management channel resources achieved statistical multiplexing of bandwidth, ratio of channel management functions, and data broadcast control. Broadcasting occupation ratio (BOR) and broadcasting file error ratio (BER) improved significantly. This is significant for improving the efficiency of satellite uplink broadcasting.

Description: Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

Description: In order to investigate the intrinsic relationship between residence choice and urban rail transit, this paper establishes a housing valuation model, explores the interface link between the rail transit and other transport modes by the establishment of a model, and also obtains the family transportation impedance. According to the balanced housing price, the various districts’ hedonic cost, and the generalized transportation impedance, the attractiveness of the various districts with respect to each mobile home is obtained. Satisfaction of any resident is received by establishing a close degree model. Due to the satisfaction and the price, we construct a largest consumer surplus model and then obtain the residence of the greatest consumer surplus for mobile home. Numerical example’s result indicates that all high-income mobile homes will chose the residence for the commute destination district, especially the one in the suburbs. Furthermore, the low-income families chose the residence for the commute destination district, which has the rail transit if the income is allowed, or the nearest district to the destination with rail transportation if not. This illustrates that whether a road having urban rail transit plays a significant impact only on the low-income family residence choice when the commuter routes pass through the road and almost has no influence for other families. Hence, it is shown that the reasonable urban planning is important and that urban rail transit should form a network that will play a key role.

Description: In a distributed system, cross-domain access control is an important mechanism to realize secure data sharing among multiple domains. Most of the existing cross-domain access control mechanisms are generally based on a single-server architecture, which has limitations in terms of security and reliability (the access decision may be incorrect) and completeness and confidentiality (the access records can be modified). Blockchain technology with decentralization, verifiability, and immutability properties can solve these problems. Motivated by these facts, in this article, we construct a trusted and efficient cross-domain access control system based on blockchain. Consequently, we integrate blockchain and role mapping technology to provide reliable and verifiable cross-domain access process. We use blockchain to record user roles, role mapping rules, access policies, and audit records, realizing user self-validation, and access nonreputation. Considering the low throughput of the blockchain, we design an efficient smart contract to make the access decision based on the access history of users. Finally, a performance evaluation of the system is presented to demonstrate the feasibility of the proposed system.

Description: 5-Hydroxymethylfurfural as a versatile organic compound is considered as a promising biomass-derived product via hydrolysis followed by dehydration of lignocellulosic biomass using solid catalysts. In this study, lignocellulosic materials (corncob) were utilized to synthesize 5-hydroxymethylfurfural via solid acid catalytic conversion. The precursor of the catalyst material was chemically impregnated with ZnCl2 prior to carbonization. The solid catalyst was prepared with three different acid concentrations of 98%, 96%, and 94% of sulfuric acid. The prepared catalyst was characterized by acid density elemental analysis, FTIR, XRD, and SEM. The maximum result of the total acid density and amount of SO3H group was recorded as 3.5 mmol/g and 0.61 mmol/g, respectively, with high sulfur content of 1.87%. The result from FTIR spectra of BC-SO3H−1 confirms the incorporation of -SO3H groups into the carbon material. BC-SO3H−1 was selected based on the acid density and elemental analysis of the catalyst. The activity of the selected catalyst (BC-SO3H−1) was studied on the transformation of corncob to 5-hydroxymethylfurfural using biphasic solvent (water: ethyl acetate) and NaCl in the reaction medium. The intermediate result in the hydrolysisdehydration reaction was analyzed using FTIR and the functional groups observed confirm the occurrence of 5-HMF in the intermediate reaction result.

Description: Innovation is a game process; in particular, the behavior among multiple agents in responsible innovation is susceptible to the influence of benefits, risks, responsibilities, and other factors, resulting in unstable collaborative relationships. Therefore, this paper constructs a tripartite evolutionary game model including the government, enterprises, and the public, combined with system dynamics modeling to simulate and analyze the tripartite behavior strategy and sensitivity to relevant exogenous variables. The study shows that the tripartite game eventually converges to a stable state of the government active supervision, enterprises making responsible innovation, and the public’s positive participation. The positive participation of the public drives rapidly the game to a steady state, while the behavioral strategies of enterprises are more susceptible to the behavior of the government. Supervision cost, penalty amount, and value compensation are the most critical factors influencing the change of the corresponding agents’ behavior strategy, and the final strategic stability of tripartite is affected by multiple exogenous variables.

Description: During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

Description: In this study, mixed oxides of Mn-Cu and Fe-Cu on OMS-2 support having an octahedral structure were synthesized by the refluxing and impregnation methods. The characteristics of the materials were analyzed by XRD, FTIR, SEM, EDX, and H2-TPR. In the CO oxidation test, CuFeOx/OMS-2 had slightly higher catalytic activity but is significantly more stable than CuMnOx/OMS-2 and CuO/OMS-2. Due to its lower reduction temperature in H2-TPR analysis, the Mars-Van-Krevelen mechanism for CuFeOx/OMS-2 (Cu2+–O–Fe3+ ↔ Cu+–□–Fe2+) could take place more energetically than CuO/OMS-2 and CuMnOx/OMS-2 (Cu2+–O2−–Mn4+ ↔ Cu+–□–Mn3+). In addition, the interaction between Fe and Cu in the catalyst could improve the durability of the surface oxides structure in comparison with that between Mn and Cu. With the high specific rate and TOF of 28.6 mmol/h.g and 0.508, respectively, CuFeOx/OMS-2 has a great potential as an effective catalyst for low-temperature oxidation application in CO and possible VOCs removal.

Description: With the development of open source community, the software ecosystem has become a popular perspective in the research on software development process and environment. Software productivity is an important evaluation indicator of the software ecosystem health. A successful software ecosystem relies on long-term and stable production activities by the users, which ensures that the software ecosystem can continuously provide the value needed by users. Therefore, the measurement of software ecosystem productivity can help maintain the user development efficiency and the stability of the software ecosystem. However, there is still little literature on the productivity of open source software ecosystems. By analogy with the natural ecosystem, this paper gives the relevant definitions of software ecosystem productivity and analyzes the factors affecting the productivity of software ecosystem. Based on the factors of the ecosystem productivity and their interrelationships, this paper establishes a software ecosystem productivity model and takes the GitHub platform as an example for detailed analysis and explanation. The results show that the model can better explain the factors affecting the productivity of software ecosystems. It is helpful for the research on the measurement of the software ecosystem health and the software development efficiency.

Description: With the advancement of China's interest rate marketization reform, commercial banks' net interest margin has narrowed. This paper selects 16 representative listed banks as the research object and conducts an empirical analysis from the two dimensions: profit level and profit structure. The study finds that the marketization of interest rates promoted the narrowing of net interest margins caused by the narrowing of net interest margins, and the profitability of commercial banks was suppressed. The narrowing of net interest spreads forced commercial banks to actively expand their intermediate business activities and adjust business structure correspondingly. The narrowing of net interest spreads has different impacts on the profitability of commercial banks of different sizes.

Description: The Internet has revolutionized the patterns of financial development and economic growth. To assess the impacts of internet penetration on the financial industry, this paper analyzed ten-year Chinese provincial panel data and concluded that regional Internet penetration accelerates financial development. Furthermore, the efficiency of Internet investment in underdeveloped provinces is better than that in developed provinces. More meaningfully, Internet penetration promotes the transparency of the securities market and regional financial participation. This indicates that Internet technology facilitates the advancement of the finance industry and the securities market.

Description: This paper carried out the study on removal of ammonium from aqueous solutions by zeolite derived from electrolytic manganese residue (EMR) via a fusion method. The variables of pH, contact time, EMRZ (EMR-based zeolite) dosage, initial ammonium concentration, and competitive cations and anions on the ammonium uptake capacity were systematically investigated in an attempt to illustrate adsorption performance of EMRZ. The results show that these influence factors had a remarkable impact on the ammonium uptake capacity of EMRZ. Maximum ammonium uptake capacity was achieved at pH value 8.0, EMRZ dosage 0.2 g/100 mL, contact time 100 min, initial ammonium concentration 200 mg/L, and temperature 35°C. Under optimized conditions, ammonium uptake capacity onto EMRZ was up to 27.89 mg/g. The competitive degree of cations in ammonium adsorption process follows the sequence of Na+〉K+〉Ca2+〉Mg2+, and the sequence of anion effect on ammonium removal onto EMRZ is CO32− 〉 Cl− 〉 SO42− 〉 PO43−. The adsorption kinetic was explored and best represented by pseudo-second-order kinetic model. And the adsorption isotherm experimental data had best fitness with the Freundlich and Koble–Corrigan model, suggesting that heterogeneous uptake was the principal mechanism adopted in the process of ammonium adsorption. Moreover, calculation of thermodynamic parameters such as change in free energy (ΔG), enthalpy (ΔH), and entropy (ΔS) was carried out and it was determined to be −15.77∼−14.03 kJ·mol−1, +37.66 kJ·mol−1, and +173.38 J·mol−1·K−1, respectively. These parameters confirmed that ammonium uptake onto EMRZ was an endothermic and spontaneous process. Moreover, no obvious deterioration tendency was observed for the regenerated EMRZ compared with fresh EMRZ. These results indicate that EMRZ has wide application prospects in removing ammonium from wastewater.

Description: In recent years, increased attention is being given to software quality assurance and protection. With considerable verification and protection schemes proposed and deployed, today’s software unfortunately still fails to be protected from cyberattacks, especially in the presence of insecure organization of heap metadata. In this paper, we aim to explore whether heap metadata could be corrupted and exploited by cyberattackers, in an attempt to assess the exploitability of vulnerabilities and ensure software quality. To this end, we propose RELAY, a software testing framework to simulate human exploitation behavior for metadata corruption at the machine level. RELAY employs the heap layout serialization method to construct exploit patterns from human expertise and decomposes complex exploit-solving problems into a series of intermediate state-solving subproblems. With the heap layout procedural method, RELAY makes use of the fewer resources consumed to solve a layout problem according to the exploit pattern, activates the intermediate state, and generates the final exploit. Additionally, RELAY can be easily extended and can continuously assimilate human knowledge to enhance its ability for exploitability evaluation. Using 20 CTF&RHG programs, we then demonstrate that RELAY has the ability to evaluate the exploitability of metadata corruption vulnerabilities and works more efficiently compared with other state-of-the-art automated tools.

Description: Access control models are an important tool developed for securing today’s data systems. Institutions use the access control models specifically to define who their employees are, what they can do, which resources they can reach, and which processes they can perform and use them to manage the whole process. This is a very hard and costly process for institutions with distributed database systems. However, access control models cannot be implemented in a qualified way due to the fact that the conditions for defining users’ demands to reach resources distributed on different servers, one of which is consequentially bound to the other, the verification and authorization of those user demands, and being able to monitor the actions of the users cannot be configured in an efficient way all the time. With our model suggested in this study, the aim is to automatically calculate the permissions and access levels of all users defined in the distributed database systems for the objects, and, in this way, we will reach a more efficient decision as to which objects the users can access while preventing their access to the information they do not need. Our proposed model in this study has been applied to real life data clusters from organizations providing health and education services and a public service. With the proposed model, all models have been run on servers sharing resources in a private network. The performance of the proposed model has been compared to that of traditional access models. It was confirmed that the proposed model presented an access control model providing more accurate access level results as well as being scalable to many distributed database systems.

Description: The feature ranking as a subcategory of the feature selection is an essential preprocessing technique that ranks all features of a dataset such that many important features denote a lot of information. The ensemble learning has two advantages. First, it has been based on the assumption that combining different model’s output can lead to a better outcome than the output of any individual models. Second, scalability is an intrinsic characteristic that is so crucial in coping with a large scale dataset. In this paper, a homogeneous ensemble feature ranking algorithm is considered, and the nine rank fusion methods used in this algorithm are analyzed comparatively. The experimental studies are performed on real six medium datasets, and the area under the feature-forward-addition curve criterion is assessed. Finally, the statistical analysis by repeated-measures analysis of variance results reveals that there is no big difference in the performance of the rank fusion methods applied in a homogeneous ensemble feature ranking; however, this difference is a statistical significance, and the B-Min method has a little better performance.

Description: The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representations with the prior semantic knowledge on large-scale unlabelled corpora, have achieved state-of-the-art results for a wide variety of Natural Language Processing (NLP) tasks. However, the capabilities of pretraining models have not been fully exploited, and applications of other pretraining models except BERT in specific domains, such as NER in Chinese medical literature, are also of interest. In this paper, we enhance the performance of NER in Chinese medical literature using pretraining models. First, we propose a method of data augmentation by replacing the words in the training set with synonyms through the Mask Language Model (MLM), which is a pretraining task. Then, we consider NER as the downstream task of the pretraining model and transfer the prior semantic knowledge obtained during pretraining to it. Finally, we conduct experiments to compare the performances of six pretraining models (BERT, BERT-WWM, BERT-WWM-EXT, ERNIE, ERNIE-tiny, and RoBERTa) in recognizing named entities from Chinese medical literature. The effects of feature extraction and fine-tuning, as well as different downstream model structures, are also explored. Experimental results demonstrate that the method of data augmentation we proposed can obtain meaningful improvements in the performance of recognition. Besides, RoBERTa-CRF achieves the highest F1-score compared with the previous methods and other pretraining models.

Description: In the petroleum industry, the researchers have developed a new technique called enhanced oil recovery to recover the remaining oil in reservoirs. Some reservoirs are very complex and require advanced enhanced oil recovery (EOR) techniques containing new materials and additives in order to produce maximum oil in economic and environmental friendly manners. In this work, the effects of nanosuspensions (KY-200) and polymer gel HPAM (854) on oil recovery and water cut were studied in the view of EOR techniques and their results were compared. The mechanism of nanosuspensions transportation through the sand pack was also discussed. The adopted methodology involved the preparation of gel, viscosity test, and core flooding experiments. The optimum concentration of nanosuspensions after viscosity tests was used for displacement experiments and 3 wt % concentration of nanosuspensions amplified the oil recovery. In addition, high concentration leads to more agglomeration; thus, high core plugging takes place and diverts the fluid flow towards unswept zones to push more oil to produce and decrease the water cut. Experimental results indicate that nanosuspensions have the ability to plug the thief zones of water channeling and can divert the fluid flow towards unswept zones to recover the remaining oil from the reservoir excessively rather than the normal polymer gel flooding. The injection pressure was observed higher during nanosuspension injection than polymer gel injection. The oil recovery was achieved by about 41.04% from nanosuspensions, that is, 14.09% higher than polymer gel. Further investigations are required in the field of nanoparticles applications in enhanced oil recovery to meet the world's energy demands.

Description: Speech intelligibility is affected by various interfering factors in a speech transmission system. Noise is one of the most common affecting factors. Subjective listening experiments were, respectively, carried out in pink noise, speech noise, and white noise-interfering environment. The perceptual characteristics of the initials, finals, tones, and syllable intelligibility were analyzed, and the function relationships between Chinese speech intelligibility and SNR in noise environment were concluded, which could be used to evaluate or predict the Chinese speech intelligibility under noise transmission conditions.

Description: High temperature is the main factor responsible for degrading the lubrication and antiwear properties of aero-lubricating oils. Accordingly, this study assessed the effects of thermal treatment of diester aviation lubricating oil and the associated mechanism. Fourier-transform infrared spectroscopy and gas chromatography/mass spectrometry analyses showed that low-molecular-weight compounds, such as monoesters, diesters, alcohols, and olefins, were the primary degradation products. An assessment of the degradation mechanism of bis(2-ethylhexyl)decanedioate showed that pyrolysis, resulting in the cleavage of β-C–H and C–C bonds, was the main process involved. Additional investigation using advanced polymer chromatography showed that the molecular weights of oil samples changed slightly at high temperatures, while the viscosity and viscosity-temperature index values were relatively stable. High-pressure differential scanning calorimetry established that the thermal oxidation stability of these oils decreased above 250°C. Finally, variations in the chemical compositions of the oil samples were found to be highly correlated with changes in physicochemical properties during thermal processing, with the formation of low-molecular-weight polar compounds greatly increasing the acid numbers of the oils.

Description: Since the 1990s, the increasing development of digital-driven technologies such as the Internet, cloud computing, big data, and the Internet of Things and the popularization of computers and mobile electronic devices have accelerated the evolution of global business organizations, thus making a new form of business organization, platform economy. As the most important form of industrial organization in the new economic era, the development of the platform has received extensive attention from the academia. Through literature analysis and inductive deduction, this paper reviews the connotation of platform economy, the historical context of development, the competition and monopoly (differentiation) of multilateral platforms, the evaluation mechanism of platform, antimonopoly governance, and research methods, and provides theoretical references and new ideas for future research directions.

Description: Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

Description: Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters’ network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters’ network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians’ social network(s), (3) investigates the supporters’ reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter’s community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters’ network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation.

Description: In the design of hydrostatic thrust bearings, power loss that occurs during operation is an important parameter that affects the design, and due to such features, it falls within the interest of design optimisation studies. The fact that the decimal places of the constraints and design variables used for minimum power loss optimisation of hydrostatic thrust bearings are highly effective on the result is a challenge for the design optimisation studies carried out on the problem and has yet made it rather attractive for the researchers. In this study, it is this feature of the problem that makes it the most important motivator in researching the performance of different metaheuristic optimisers in solving the minimum power loss problem. To this end, 7 different optimisers, four of them for the first time, were applied under equal conditions with various pop sizes and a number of iterations, and their performances were addressed under this challenging benchmark problem. The performances of these methods were compared to each other. In addition to the success of optimisers in reaching a solution, their performance in different populations and iterations is also discussed in the study. Considering the results, it is seen that MVO is the most effective optimiser in solving the problem and is followed by the WOA, PSO, and GWO. The application of WOA, MVO, CS, and SSA, for the first time, on the problem has exhibited that these methods could be used in optimisation of such delicate engineering problems.

Description: Clustering is one of the most important unsupervised machine learning tasks, which is widely used in information retrieval, social network analysis, image processing, and other fields. With the explosive growth of data, the classical clustering algorithms cannot meet the requirements of clustering for big data. Spark is one of the most popular parallel processing platforms for big data, and many researchers have proposed many parallel clustering algorithms based on Spark. In this paper, the existing parallel clustering algorithms based on Spark are classified and summarized, the parallel design framework of each kind of algorithms is discussed, and after comparing different kinds of algorithms, the direction of the future research is discussed.

Description: This paper studies a coordinated system for multidrone single-truck distribution, where a truck delivers goods to a group of customers along a closed ground path with the help of a number of drones. For each delivery, the truck departs from the distribution centre with drones and all goods needed and returns back to the centre after fulfilling the delivery tasks. That is, the truck assigns these delivery tasks to several of its drones, each of which is responsible for sending goods to a different subgroup of customers in the empty air space. This study provides a new mixed-integer programming model of the routing problem with this distribution system based on urban road network. Meanwhile, a hybrid genetic algorithm and a hybrid particle swarm algorithm are designed. Experimental results show that the performance of the hybrid algorithms is better than that of the corresponding basic algorithms.

Description: Considering the existing medical image watermarking algorithms, a single function often has limitations, and a multipurpose watermarking algorithm for medical images is proposed. First, medical images are divided into regions of interest (ROIs) and regions of noninterest (RONIs). Then, the authentication watermark produced for each subblock of the ROI is embedded into the corresponding mapping subblock. The visible watermark is embedded into the RONI, and, finally, the watermark information and constructed authentication information in each subblock of the ROI are embedded into the corresponding RONI subblock. Simulation results show that the embedded visible watermark can protect and facilitate medical image management. In addition, the proposed algorithm has strong robustness and very good visual quality. It can simultaneously realize copyright protection and content authentication and also has high tamper localization capability.

Description: Aiming at the problem of network congestion and unbalanced load caused by a large amount of data capacity carried in elephant flow in the data center network, an elephant flow detection method based on SDN is proposed. This method adopts the autodetect upload (ADU) mechanism. ADU is divided into two parts: ADU-Client and ADU-Server, in which ADU-Client runs in the host computer and ADU-Server runs in the SDN controller. When the host sends the elephant flow, the ADU-Client generates a packet with forged source IP address and triggers the Packet_in message of the edge switch to report the information of the elephant flow to the SDN controller and the ADU-Server completes the elephant flow identification. Experimental results show that the ADU elephant flow detection mechanism can effectively detect elephant flow in the data center network, reduce detection time, and improve network performance.

Description: The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

Description: Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. The algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. The experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

Description: Insect Intelligent Building (I2B) is a novel platform of intelligent buildings. The outstanding feature of I2B is the decentralized network structure connected by smart nodes. I2B can employ APPs (applications) developed by various practitioners or programming fans to manage and control buildings. However, due to the unique parallel operation of I2B platform and the popularization of APP developers, there still exists no effective approach to supporting I2B APP development. To deal with the challenges and provide meaningful guidance for describing and developing I2B APP and motivating the prospective programming language design, we propose INR, a programming model for I2B APP development. Three submodels in INR, namely, Individual, Neighborhood, and Region, are defined and implemented, respectively, for describing different task requirements. Moreover, new mechanisms of Tag-based programming and Clustering operation are established to support the plug-and-play and parallel abilities of APPs in I2B. Finally, we apply the programming model into an application case to illustrate the developing pattern of the I2B APP and verify the effectiveness of our approach.

Description: In this paper, green and facile synthesis of sulfur- and nitrogen-codoped carbon nanospheres (CNs) was prepared from the extract of Hibiscus sabdariffa L by a direct hydrothermal method. Finally, sulfur-carbon nanospheres (CNs) were used as the adsorbent to remove Pb+2 ions from aqueous solutions because of the high surface area of S-CNs from CNs and N-CNs. The synthesized nanospheres were examined by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy, transmission electron microscopy (TEM), and nitrogen adsorption-desorption isotherms. The results show spherical shapes have a particle size of up to 65 nm with a high surface area capable of absorbing lead ions efficiently. Additionally, the factors affecting the process of adsorption that include equilibrium time, temperature, pH solution, ionic intensity, and adsorbent dose were studied. The equilibrium removal efficiency was studied employing Langmuir, Freundlich, and Temkin isotherm forms. The kinetic data were analyzed with two different kinetic models, and both apply to the adsorption process depending on the values of correlation coefficients. The thermodynamic parameters including Gibbs free energy (ΔG°), standard enthalpy change (ΔH°), and standard entropy change (ΔS°) were calculated for the adsorption process.

Description: This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.

Description: The standard method used in the Weather Research and Forecasting (WRF) model for distributing MPI processes across the processors is not always optimal. This circumstance affects performance, i.e., execution times, but also energy consumption, especially if the application is to be extended to exascale. The authors found that the reason why the standard method for process distribution is not always optimal was an imbalance between the orthogonality of the communication and the proper cache usage, and this affects energy consumption. We present an improved MPI process distribution algorithm that increases the performance. Furthermore, scalability analyses for the new algorithm are presented and the energy use of the system is evaluated. A solution for balancing energy use with performance is also proposed for cases where the former is a concern.

Description: Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

Description: Nearest neighbour search (NNS) is the core of large data retrieval. Learning to hash is an effective way to solve the problems by representing high-dimensional data into a compact binary code. However, existing learning to hash methods needs long bit encoding to ensure the accuracy of query, and long bit encoding brings large cost of storage, which severely restricts the long bit encoding in the application of big data. An asymmetric learning to hash with variable bit encoding algorithm (AVBH) is proposed to solve the problem. The AVBH hash algorithm uses two types of hash mapping functions to encode the dataset and the query set into different length bits. For datasets, the hash code frequencies of datasets after random Fourier feature encoding are statistically analysed. The hash code with high frequency is compressed into a longer coding representation, and the hash code with low frequency is compressed into a shorter coding representation. The query point is quantized to a long bit hash code and compared with the same length cascade concatenated data point. Experiments on public datasets show that the proposed algorithm effectively reduces the cost of storage and improves the accuracy of query.

Description: Nowadays, Android applications declare as many permissions as possible to provide more function for the users, which also poses severe security threat to them. Although many Android malware detection methods based on permissions have been developed, they are ineffective when malicious applications declare few dangerous permissions or when the dangerous permissions declared by malicious applications are similar with those declared by benign applications. This limitation is attributed to the use of too few information for classification. We propose a new method named fine-grained dangerous permission (FDP) method for detecting Android malicious applications, which gathers features that better represent the difference between malicious applications and benign applications. Among these features, the fine-grained feature of dangerous permissions applied in components is proposed for the first time. We evaluate 1700 benign applications and 1600 malicious applications and demonstrate that FDP achieves a TP rate of 94.5%. Furthermore, compared with other related detection approaches, FDP can detect more malware families and only requires 15.205 s to analyze one application on average, which demonstrates its applicability for practical implementation.

Description: With the growing popularity of unmanned aerial vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation cannot be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static, and moving obstacles to predict and avoid the following: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and complex event processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.

Description: Cluster analysis, which is to partition a dataset into groups so that similar elements are assigned to the same group and dissimilar elements are assigned to different ones, has been widely studied and applied in various fields. The two challenging tasks in clustering are determining the suitable number of clusters and generating clusters of arbitrary shapes. This paper proposes a new concept of “epsilon radius neighbors” which plays an essential role in the cluster-forming process, thereby determining both the number of clusters and the shape of clusters, automatically. Based on “epsilon radius neighbors,” a new clustering algorithm in which the epsilon radius value is adapted to the characteristics of each cluster in the current partition is proposed. Recently, clustering has been widely applied in environmental applications, including underground water quality monitoring. However, the existing studies have simply applied conventional clustering techniques, in which the abovementioned two challenging tasks have not been solved already. Therefore, in this paper, the proposed clustering algorithm is applied in assessing the underground water quality in Phu My Town, Ba Ria-Vung Tau Province, Vietnam. The experimental results on benchmark datasets demonstrate the effectiveness of the proposed algorithm. For the quality of underground water, the new algorithm results in four clusters with different characteristics. Through this application, we found that the new algorithm might provide valuable reference information for underground water management.

Description: This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.

Description: It has been observed that the structure of whale optimization algorithm (WOA) is good at exploiting capability, but it easily suffers from premature convergence. Hybrid metaheuristics are of the most interesting recent trends for improving the performance of WOA. In this paper, a hybrid algorithm framework with learning and complementary fusion features for WOA is designed, called hWOAlf. First, WOA is integrated with complementary feature operators to enhance exploration capability. Second, the proposed algorithm framework adopts a learning parameter lp according to adaptive adjustment operator to replace the random parameter p. To further verify the efficiency of the hWOAlf, the DE/rand/1 operator of differential evolution (DE) and the mutate operator of backtracking search optimization algorithm (BSA) are embedded into WOA, respectively, to form two new algorithms called WOA-DE and WOA-BSA under the proposed framework. Twenty-three benchmark functions and six engineering design problems are employed to test the performance of WOA-DE and WOA-BSA. Experimental results show that WOA-DE and WOA-BSA are competitive compared with some state-of-the-art algorithms.

Description: The most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.

Description: Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. According to the sharing of the intermediate data in the Spark SQL workflow, this paper proposes a cost-based correlation merging algorithm, which can effectively reduce the cost of reading and writing redundant data. This paper develops the SSO (Spark SQL Optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.

Description: Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.

Description: Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.

Description: Subflow prediction is required in resource active elastic scaling, but the existing single flow prediction methods cannot accurately predict the peak variation of subflow in hybrid data flow. These do not consider the correlation between subflows. The difficulty is that it is hard to calculate the correlation between different data flows in hybrid data flow. In order to solve this problem, this paper proposes a new method DCCSPP (subflow peak prediction of hybrid data flow based on delay correlation coefficients) to predict the peak value of hybrid data flow. Firstly, we establish a delay correlation coefficient model based on the sliding time window to determine the delay time and delay correlation coefficient. Next, based on the model, a hybrid data flow subflow peak prediction model and algorithm are established to achieve accurate peak prediction of subflow. Experiments show that our prediction model has achieved better results. Compared with LSTM, our method has decreased the MAE about 18.36% and RMSE 13.50%. Compared with linear regression, MAE and RMSE are decreased by 27.12% and 25.58%, respectively.

Description: Fly ash, a waste product from thermal power plants, is one of the good alternatives for use as a filler in polymers, especially in flame retardants. Fly ash is an environmentally friendly fire retardant additive for composites, used in place of conventional flame retardant additives such as halogenated organic compounds, thus promoting environmental safety. In this study, fly ash was modified with stearic acid to improve adhesion at the polymers interface and increase compatibility. Fly ash was studied at various volumes (5, 10, and 20 wt.% fly ash) used in this study to synthesize fly ash-epoxy composites. The results show that the tensile strength, flexural strength, compressive strength, and impact strength of these synthetic materials increase when fly ash is modified to the surface, compressive strength: 197.87 MPa, flexural strength: 75.20 MPa, impact resistance: 5.77 KJ/m2, and tensile strength: 47.89 MPa. Especially, the fire retardant properties are improved at a high level, with a modified 20% fly ash content: the burning rate of 16.78 mm/min, minimum oxygen index of 23.2%, and meet the fire protection standard according to UL 94HB with a burning rate of 8.09 mm/min. Scanning electron microscopy (SEM) and infrared spectroscopy were used to analyze the morphological structure of fly ash after being modified and chemically bonded with epoxy resin background.

Description: Increase in the world energy demand also increases the concentration of CO2 in the atmosphere, which contributes to global warming and ocean acidification. This study proposed the simulation process to utilize CO2 released from the acid gas removal unit in one of gas processing plants in Indonesia to enhance the production of dimethyl ether (DME) through unreacted gas recycle that can be beneficial in reducing CO2 emission to the atmosphere. Simulation was developed in Unisim R390.1 using Peng–Robinson–Stryjek–Vera (PRSV) as a fluid package. Simulation was validated by several studies conducted by many researchers and giving satisfactory results especially in terms of productivity, conversion, and selectivity as a function of reactor temperatures in the indirect and the direct DME synthesis processes. Simulation results show that the DME production was enhanced by around 49.6% and 65.1% for indirect and direct processes, respectively, at a recycling rate of 7 MMSCFD. Compressor is required to increase the unreacted gas pressure to the desired pressure in the methanol reactor or dual methanol-DME reactor in both processes. Specific power consumption (SPC) was used as a tested parameter for the effectiveness of recycling unreacted gas. Based on the simulation, the direct DME synthesis process is superior over the indirect process in terms of DME and methanol productions, SPCs, and system energy efficiencies.

Description: In this work, geopolymer synthesized with perlite and an alkaline activator medium was evaluated as a new adsorbent and photocatalyst for degradation of methylene blue (MB) dye from an aqueous medium. The functional group, the structure, and the morphology of the raw and the synthesized materials were characterized using FT-IR, XRD, and SEM analysis. The degradation of MB in the contaminated solution was examined using the spectrophotometric technique. Several analysis methods revealed the formation of the aluminosilicate gel after the geopolymerization reaction. The kinetics data with UV and without UV irradiations were well fitted with the pseudo-second-order equation. The results indicated that the degradation efficiency of cationic dye by perlite-based geopolymer without and with UV was up to 88.94% and 97.87% in 4 hours, respectively. The degradation efficiencies of methylene blue are in the following order: perlite-based geopolymer under UV irradiations is greater than perlite-based geopolymer without UV irradiations that is larger than UV irradiations. The overall experimental results suggested that the new elaborated material with synergetic adsorption and photocatalytic activities has a great potential for the treatment of water contaminated by hazardous substances.

Description: This study investigated the potential of the bacterium Stenotrophomonas maltophilia UCP 1601 to produce a new biomolecule with emulsifying properties by determining the hemolytic activity, obtaining a halo of 9 mm in blood agar. Fermentations were carried out in saline mineral medium supplemented with 10% waste soybean oil (WSO) and different concentrations of glucose, peptone, ZnCl2, and MgSO4, according to a 24 full-factorial design. The results showed that the best results were obtained in condition 6 (medium composed of 4% glucose, 1% peptone, 2.72% ZnCl2, and 2.46% MgSO4), with excellent high emulsification index of 82.74%, using burned motor oil. The emulsifying property of the biomolecule produced was confirmed by the emulsification index of 78.57, 54.07, and 58.62%, using soybean, corn, and diesel oils, respectively, and the stability at different values of pH, temperature, and NaCl concentrations. The yield of the produced bioemulsifier was 2.8 g/L, presenting an anionic character and polymeric nature (37.6% lipids, 28.2% proteins, and 14.7% carbohydrates), confirmed by FTIR. The new bioemulsifier demonstrated promising potential for bioremediation of hydrophobic contaminants in the environment, since it had the ability to reduce the viscosity of WSO and burned motor oil, as well as excellent dispersion capacity of the burned motor oil in water (69.94 cm2 of oil displacement area), and removing 71.7% of this petroleum derivative from sandy soil.

Description: In this paper, glucosamine was produced by acid hydrolysis of five mushrooms. The glucosamine yields were investigated, and the optimum conditions were obtained as follows: acid type, sulfuric acid; acid concentration, 6 M; ratio of raw material to acid volume, 1 : 10; hydrolysis temperature, 100°C; and time, 6 h. Under these conditions, the glucosamine conversion from chitin content reached up to 92%. The results of hydrolysis kinetics indicated that hydrolysis of five mushrooms to glucosamine followed zero-order kinetics. Moreover, the relatively low activation energy for hydrolysis of straw mushroom (18.31 kJ/mol) and the highest glucosamine yield (56.8132 ± 3.5748 mg/g DM, 0.9824 g/g chitin) indicated that hydrolysis of straw mushroom was energy-saving. Thus, sulfuric acid hydrolysis of straw mushroom for glucosamine production should be considered as an efficient process for the future industrial application. However, further study is needed for glucosamine purification.