ALBERT

Description: Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.

Description: Cloud adoption has significantly increased using the infrastructure-as-a-service (IaaS) paradigm, in order to meet the growing demands of computing, storage and networking, in small as well as large enterprises. Different vendors provide their customized solutions for OpenStack deployment on bare metal or virtual infrastructure. Among these many available IaaS solutions, OpenStack stands out as being an agile and open-source platform. However, its deployment procedure is a time-taking and complex process with a learning curve. This paper addresses the lack of basic infrastructure automation in almost all of the OpenStack deployment projects. We propose a flexible framework to automate the process of infrastructure bring up for deployment of several OpenStack distributions, as well as resolving dependencies for a successful deployment. Our experimental results demonstrate the effectiveness of the proposed framework in terms of automation status and deployment time, that is, reducing the time spent in preparing a basic virtual infrastructure by four times, on average.

Description: Subdivision, triangulation, Kronecker product, corona product and many other graph operations or products play an important role in complex networks. In this paper, we study the properties of $q$-subdivision graphs, which have been applied to model complex networks. For a simple connected graph $G$, its $q$-subdivision graph $S_q(G)$ is obtained from $G$ through replacing every edge $uv$ in $G$ by $q$ disjoint paths of length 2, with each path having $u$ and $v$ as its ends. We derive explicit formulas for many quantities of $S_q(G)$ in terms of those corresponding to $G$, including the eigenvalues and eigenvectors of normalized adjacency matrix, two-node hitting time, Kemeny constant, two-node resistance distance, Kirchhoff index, additive degree-Kirchhoff index and multiplicative degree-Kirchhoff index. We also study the properties of the iterated $q$-subdivision graphs, based on which we obtain the closed-form expressions for a family of hierarchical lattices, which has been used to describe scale-free fractal networks.

Description: Subversion of cryptography has received wide attentions especially after the Snowden Revelations in 2013. Most of the currently proposed subversion attacks essentially rely on the freedom of randomness choosing in the cryptographic protocol to hide backdoors embedded in the cryptosystems. Despite the fact that significant progresses in this line of research have been made, most of them mainly considered the classical setting, while the research gap regarding subversion attacks against post-quantum cryptography remains tremendous. Inspired by this observation, we investigate a subversion attack against existing protocol that is proved post-quantum secure. Particularly, we show an efficient way to undetectably subvert the well-known lattice-based encryption scheme proposed by Regev (STOC 2005). Our subversion enables the subverted algorithm to stealthily leak arbitrary messages to the outsider who knows the backdoor. Through theoretical analysis and experimental observations, we demonstrate that the subversion attack against the LWE encryption scheme is feasible and practical.

Description: This paper describes a roadmap for the development of the SP Machine, based on the SP Theory of Intelligence and its realization in the SP Computer Model. The SP Machine will be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer. The system should help users visualize knowledge structures and processing. Research is needed into how the system may discover low-level features in speech and in images. Strengths of the SP System in the processing of natural language may be augmented, in conjunction with the further development of the SP System’s strengths in unsupervised learning. Strengths of the SP System in pattern recognition may be developed for computer vision. Work is needed on the representation of numbers and the performance of arithmetic processes. A computer model is needed of SP-Neural, the version of the SP Theory expressed in terms of neurons and their interconnections. The SP Machine has potential in many areas of application, several of which may be realized on short-to-medium timescales.

Description: Efficient key revocation in Identity-based Encryption (IBE) has been a both fundamental and critical problem when deploying an IBE system in practice. Boneh and Franklin proposed the first revocable IBE (RIBE) scheme where the size of key updates is linear in the number of users. Then, Boldyreva, Goyal and Kumar proposed the first scalable RIBE by using the tree-based approach where the size of key updates is $O(rlog (N/r))$ and the size of every user’s long-term secret key is $O(log N)$ with $N$ being the number of users and $r$ the number of revoked users. Recently, Qin et al. presented the notion of server-aided RIBE where the size of every user’s long-term secret key is $O(1),$ and users do not need to communicate with Key Generator Center (KGC) during every key updates. However, users must change their identities once their secret keys are revoked as they cannot decrypt ciphertexts by using their revoked secret keys. To address the above problem, we formalize the notion of RIBE with identity reuse. In our system model, users can obtain a new secret key called the reuse secret key from KGC when their secret keys are revoked. The decryption key can be derived from the reuse secret key and new key updates while it cannot be derived from the revoked secret key and the new key updates. We present a concrete construction that is secure against adaptive-ID chosen plaintext attacks and decryption key exposure attacks under the $mathsf{ADDH}1$ and $mathsf{DDH}2$ assumptions in the standard model. Furthermore, we extend it to server-aided RIBE scheme with identity reuse property that is more suitable for lightweight devices.

Description: Diagnosability of a multiprocessor system is an important research topic. The system and an interconnection network have an underlying topology, which is usually presented by a graph. Under the Maeng and Malek's (MM) model, to diagnose the system, a node sends the same task to two of its neighbors, and then compares their responses. The MM$^{*}$ is a special case of the MM model and each node must test all pairs of its adjacent nodes. In 2009, Chiang and Tan (Using node diagnosability to determine $t$-diagnosability under the comparison diagnosis (cd) model. IEEE Trans. Comput., 58, 251–259) proposed a new viewpoint for fault diagnosis of the system, namely, the node diagnosability. As a new topology structure of interconnection networks, the nest graph $CK_{n}$ has many good properties. In this paper, we study the local diagnosability of $CK_{n}$ and show it has the strong local diagnosability property even if there exist $(frac{n(n-1)}{2}-2)$ missing edges in it under the MM$^{*}$ model, and the result is optimal with respect to the number of missing edges.

Description: The crossing resolution of a non-planar drawing of a graph is the value of the minimum angle formed by any pair of crossing edges. Recent experiments suggest that the larger the crossing resolution is, the easier it is to read and interpret a drawing of a graph. However, maximizing the crossing resolution turns out to be an NP-hard problem in general, and only heuristic algorithms are known that are mainly based on appropriately adjusting force-directed algorithms. In this paper, we propose a new heuristic algorithm for the crossing resolution maximization problem and we experimentally compare it against the known approaches from the literature. Our experimental evaluation indicates that the new heuristic produces drawings with better crossing resolution, but this comes at the cost of slightly higher edge-length ratio, especially when the input graph is large.

Description: Due to the positive impact of ride sharing on urban traffic and environment, it has attracted a lot of research attention recently. However, most existing researches focused on the profit maximization or the itinerary minimization of drivers, only rare work has covered on adjustable price function and matching algorithm for the batch requests. In this paper, we propose a request matching algorithm and an adjustable price function that benefits drivers as well as passengers. Our request-matching algorithm consists of an exact search algorithm and a group search algorithm. The exact search algorithm consists of three steps. The first step is to prune some invalid groups according to the total number of passengers and the capacity of vehicles. The second step is to filter out all candidate groups according to the compatibility of requests in same group. The third step is to obtain the most profitable group by the adjustable price function, and recommend the most profitable group to drivers. In order to enhance the efficiency of the exact search algorithm, we further design an improved group search algorithm based on the idea of original simulated annealing. Extensive experimental results show that our method can improve the income of drivers, and reduce the expense of passengers. Meanwhile, ride sharing can also keep the utilization rate of seats 80%, driving distance is reduced by 30%.

Description: Cloud Service Providers supply services to clients in terms of their demands. They need to be constantly under monitoring for their services with respect to consensus agreements between clients and service providers. A Third Party Auditor or TPA as a trusted organization appears to be necessary to monitor executing agreements of cloud services. Using a third party as an extra component creates cost overheads for clients in a cloud environment. Thus, introducing a cost efficient framework for a cloud environment which includes a third party is an eminent achievement to make a TPA feasible and practical in cloud environments. In this paper, we propose a TPA framework for monitoring service level agreements between cloud service providers and cloud clients using several cloud resources. This framework employs different types of service deployments from various cloud service providers excluding the cloud service provider which is being monitored. Then, we demonstrate that the framework can mitigate costs of a third party auditor in a cloud environment. Simulations of trends for costs exhibits cost efficiency of at least forty percent over ten years when a TPA follows our proposed framework in comparison to other frameworks. Finally, we provide an analysis to compare characteristics of our framework with other frameworks and discuss the advantages of our proposed framework. Our results indicate that TPA as a component of the framework not only reduces overall costs of its presentation in a cloud environment but additionally improves management efficiency and security.

Description: Most graphs have this property: after removing a linear number of vertices from a graph, the surviving graph is either connected or consists of a large connected component and small components containing a small number of vertices. This property can be applied to derive fault-tolerance related network parameters: extra edge connectivity and component edge connectivity. Using this general property, we obtained the $h$-extra edge connectivity and $(h+2)$-component edge connectivity of augmented cubes, Cayley graphs generated by transposition trees, complete cubic networks (including hierarchical cubic networks), generalized exchanged hypercubes (including exchanged hypercubes) and dual-cube-like graphs (including dual cubes).

Description: We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

Description: This paper presents SGAC (Solution de Gestion Automatisée du Consentement / automated consent management solution), a new healthcare access control model and its support tool, which manages patient wishes regarding access to their electronic health records (EHR). This paper also presents the verification of access control policies for SGAC using two first-order-logic model checkers based on distinct technologies, Alloy and ProB. The development of SGAC has been achieved within the scope of a project with the University of Sherbrooke Hospital (CHUS), and thus has been adapted to take into account regional laws and regulations applicable in Québec and Canada, as they set bounds to patient wishes: for safety reasons, under strictly defined contexts, patient consent can be overriden to protect his/her life (break-the-glass rules). Since patient wishes and those regulations can be in conflict, SGAC provides a mechanism to address this problem based on priority, specificity and modality. In order to protect patient privacy while ensuring effective caregiving in safety-critical situations, we check four types of properties: accessibility, availability, contextuality and rule effectivity. We conducted performance tests comparison: implementation of SGAC versus an implementation of another access control model, XACML, and property verification with Alloy versus ProB. The performance results show that SGAC performs better than XACML and that ProB outperforms Alloy by two order of magnitude thanks to its programmable approach to constraint solving.

Description: Finding global optima for functions is a very important problem. Although a large number of methods have been proposed for solving this problem, more effective and efficient methods are greatly required. This paper proposes an innovative method that combines different effective techniques for speeding up the convergence to the solution and greatly improving its precision. In particular, the method uses feedback-guided random search technique to identify the promising regions of the domains and uses the biased mapping technique to focus the search on these promising regions, without ignoring the other regions of the domains. Therefore, at any point of time, the domain of each variable is entirely covered with much more emphasis on the promising regions. Experiments with our prototype implementation showed that our method is efficient, effective, and outperformed the state-of-art techniques.

Description: Spectrum-sensing as a service has been proposed and studied by many researchers over the past decade as a promising approach to support the viability of cognitive radio networks (CRNs). A spectrum-sensing service provider (SSP) provides information about spectrum occupancy to its clients that is generally more accurate than what clients can learn on their own. Two approaches are used by SSPs in their operation, the dedicated sensing infrastructure approach (sensor-aided CRN) and the crowdsensing approach. In this work, we assume a hybrid model where a dedicated sensing infrastructure is used along with crowdsensing. We study the tradeoff between sensing time paid by cognitive users to the SSP and their achievable transmission time. Our objective is to maximize the minimum achievable transmission time for any cognitive user in the network by carefully selecting the channels to be used. Two algorithms are proposed, one is based on the hill-climbing search algorithm (abbreviated HCA) and the other is a less optimal but faster greedy selection algorithm (abbreviated GSA). Results show that both HCA and GSA are within 3% of the optimal solution. Results also confirm that GSA is faster than HCA, while HCA outperforms GSA.

Description: Area detection and measuring is one of the most important problems in wireless sensor network because it mainly relates to the continuity and functionality of most routing protocols applied to the region of interest (ROI). Electronics failure, random deployment of nodes, software errors or some phenomena such as fire spreading or water flood could lead to wide death of sensor nodes. The damage on ROI can be controlled by detecting and calculating the area of the holes, resulting from the damaged sensor networks. In this paper, a new mathematical algorithm, wireless sensor hole detection algorithm (WHD), is developed to detect and calculate the holes area in ROI where the sensor nodes are spread randomly. WHD is developed for achieving quality of service in terms of power consumption and average hole detection time. The dynamic behavior of the proposed WHD depends on executing the following steps. Firstly, WHD algorithm divides down the ROI into many cells using the advantage of the grid construction to physically partition the ROI into many small individual cells. Secondly, WHD algorithm works on each cell individually by allocating the nearest three sensor nodes to each of the cell’s coordinates by comparing their positions, WHD connects each cell’s coordinate points with the selected sensor nodes by lines that construct a group of triangles, then WHD calculates the area of upcoming triangles. Repeating the previous step on all the cells, WHD can calculate and locate each hole in the ROI. The performance evaluation depends on the NS-2 simulator as a simulation technique to study and analyze the performance of WHD algorithm. Results show that WHD outperforms, in terms of average energy consumption and average hole discovery time, path density algorithm, novel coverage hole discovery algorithm and distriputed coverage hole Detection.

Description: Due to the strong security and high performance of the AES block cipher, many hash functions take AES-like structures as building blocks. To evaluate the security of these AES-like structures against differential cryptanalysis, giving the lower bounds on the number of active S-boxes in a differential trail, is an important perspective. However, the original ‘wide-trail strategy’ for AES becomes less effective to get tight bounds for these AES-like structures, because of the different state dimensions (M×M2, instead of M×M) and different round functions from AES. In this paper, we focus on a kind of AES-like structure with state dimensions M×M2, diffusion-optimal permutations and MixColumns transformations using MDS matrices. Inspired by the ‘wide-trail strategy’, we propose a theoretical method to count active S-boxes, by which we prove that there are at least rBd(Bd−1) active S-boxes in any 2r(r≥3) rounds of such an AES-like structure, where Bd is the differential branch number of the MixColumns transformation and equals to M+1. What’s more, this lower bound can be achieved by some diffusion layers. As examples, we apply our method to the LANE hash function and 3D block cipher, optimal lower bounds are both got.

Description: Presently, software industry is severely suffering from inaccurate effort estimation and inadequate unstructured or semi-structured project history management. In fact, both are difficult to accomplish and hence badly impact the software projects. We proposed improvements in the effort estimation and the project history management of e-commerce projects focusing on Extreme Programing (XP) and Scrum methodologies using ontology models in our software effort estimation system. Proposed system infers suitable estimate in the form of time, resources and lessons learnt as per the project leader’s requirements by using description logic and HermiT reasoner. To validate our approach, we have performed a case study comprising 20 Business-to-Consumer (B2C) web projects and performed comparative analysis on the collected efforts in both XP and Scrum contexts by applying (Mean Magnitude of Relative Error) MMRE and PRED(25) prediction accuracy measures. Likewise, software functional size of understudy e-commerce projects was measured using COSMIC functional size measurement methodology. Regression analysis of relations among actual COSMIC function points, estimated effort, and actual effort spent for the projects show better significance-F and R2 values for our approach. The comparative results show that overall proposed approach provides accurate estimates and significantly improves over planning poker and delphi methods by 10% and 30%, respectively.

Description: Web Browser Fingerprinting is a process in which the users are, with high likelihood, uniquely identified by the extracted features from their devices, generating an identifier key (fingerprint). Although it can be used for malicious purposes, especially regarding privacy invasion, Web Browser Fingerprinting can also be used to enhance security (e.g. as a factor in two-factor authentication). This paper investigates the use of Web Audio API as a Web Browser Fingerprinting method capable of identifying the devices. The idea is to prove or not if audio can provide features capable to identify users and devices. Our initial results show that the proposed method is capable of identifying the device’s class, based on features like device’s type, web browser’s version and rendering engine.

Description: Although analyzing and mining user’s trajectory data can provide outstanding benefit, data owners may not be willing to upload their trajectory data because of privacy concerns. Recently, differential privacy technology has achieved a good trade-off between data utility and privacy preserving by publishing noisy outputs, and relevant schemes have been proposed for trajectory release. However, we experimentally find that a relatively accurate estimate of the true data value can still be obtained from the noisy outputs by means of a posterior estimation. But there are no practical mechanisms against current schemes to verify their effectiveness and resistance. To fill this gap, we propose a solution to evaluate the resistance performance of differential privacy on trajectory data release, including a notion of correlation-distinguishability filtering (CDF) and a privacy quantification measurement. Specifically, taking advantage of the principle of filtering that independent noise can be filtered out from correlated sequence, CDF is proposed to sanitize the noise added into the trajectory. To conduct this notion in practice, we attempt to apply a Kalman/particle filter to filter out the corresponding Gaussian/Laplace noise added by differential privacy schemes. Furthermore, to quantify the distortion of privacy strength before and after filtering, an entropy-based privacy quantification metric is proposed, which is used to measure the lost uncertainty of the true locations for an adversary. Experimental results show that the resistance performance of current approaches has a degradation to varying degrees under the filtering attack model in our solution. Moreover, the privacy quantification metric can be regarded as a unified criterion to measure the privacy strength introduced by the noise that does not conform to the form required by differential privacy.

Description: Recently, much attention has been focused on designing provably secure cryptographic primitives in the presence of key leakage, even the continuous leakage attacks. However, several constructions on the (continuous) leakage-resilient certificate-based encryption (CBE) scheme were proposed based on the bilinear pairings, and the corresponding computational efficiency is lower. Also, the leakage on the master secret key is omitted in the previous constructions. In this paper, to further achieve the better performance, a new construction method of continuous leakage-resilient CBE scheme without bilinear pairings is proposed, and the chosen-ciphertext attacks security of designed scheme is proved based on the hardness of the classic decisional Diffie–Hellman assumption. The performance analysis shows that our method not only can obtain higher computational efficiency but also enjoys better security performances, such as the leakage parameter of secret key of user has the constant size, and an adversary cannot obtain any leakage on the secret key of user from the corresponding given ciphertext etc. The advantage is that our proposal allows leakage attacks of multiple keys, i.e. continuous leakage resilience of the secret key of user and bounded leakage resilience of the master secret key. Additionally, to provide the leakage resilience for the cloud computing, a novel data access control scheme for cloud storage service is proposed from our continuous leakage-resilient CBE scheme, which can keep its claimed security in the leakage seting.

Description: Effective protection against cyber-attacks requires constant monitoring and analysis of system data in an IT infrastructure, such as log files and network packets, which may contain private and sensitive information. Security operation centers (SOC), which are established to detect, analyze and respond to cyber-security incidents, often utilize detection models either for known types of attacks or for anomaly and applies them to the system data for detection. SOC are also motivated to keep their models private to capitalize on the models that are their propriety expertise, and to protect their detection strategies against adversarial machine learning. In this paper, we develop a protocol for privately evaluating detection models on the system data, in which privacy of both the system data and detection models is protected and information leakage is either prevented altogether or quantifiably decreased. Our main approach is to provide an end-to-end encryption for the system data and detection models utilizing lattice-based cryptography that allows homomorphic operations over ciphertext. We employ recent data sets in our experiments which demonstrate that the proposed privacy-preserving intrusion detection system is feasible in terms of execution times and bandwidth requirements and reliable in terms of accuracy.

Description: Twitter is an online micro-blogging platform through which one can explore the hidden valuable and delightful information about the current context at any point of time, which also serves as a data source to carry out sentiment analysis. In this paper, the sentiments of large amount of tweets generated from Twitter in the form of big data have been analyzed using machine learning algorithms. A multi-tier architecture for sentiment classification is proposed in this paper, which includes modules such as tokenization, data cleaning, preprocessing, stemming, updated lexicon, stopwords and emoticon dictionaries, feature selection and machine learning classifier. Unigram and bigrams have been used as feature extractors together with χ2 (Chi-squared) and Singular Value Decomposition for dimensionality reduction together with two model types (Binary and Reg), with four types of scaling methods (No scaling, Standard, Signed and Unsigned) and represented them in three different vector formats (TF-IDF, Binary and Int). Accuracy is considered as the evaluation standard for random forest and bagged trees classification methods. Sentiments were analyzed through tokenization and having several stages of pre-processing and several combinations of feature vectors and classification methods. Through which it was possible to achieve an accuracy of 84.14%. Obtained results conclude that, the proposed scheme gives a better accuracy when compared with existing schemes in the literature.

Description: Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.

Description: Emotion recognition is a key work of research area in brain computer interactions. With the increasing concerns about affective computing, emotion recognition has attracted more and more attention in the past decades. Focusing on geometric positions of key parts of the face and well detecting them is the best way to increase accuracy of emotion recognition systems and reach high classification rates. In this paper, we propose a hybrid system based on wavelet networks using 1D Fast Wavelet Transform. This system combines two approaches: the biometric distances approach where we propose a new technique to locate feature points and the wrinkles approach where we propose a new method to locate the wrinkles regions in the face. The classification rates given by experimental results show the effectiveness of our proposed approach compared to other methods.

Description: Microblog is a popular social network in which hot topics propagate online rapidly. Real-time topic detection can not only understand public opinion well but also bring high commercial value. We design a method for real-time microblog data analysis in order to detect popular long lasting events as well as emerging events. Firstly, a mining frequent items algorithm on microblog data stream is proposed to count approximate word frequency. This mining frequent items algorithm can find the frequent words for some time. Secondly, the windows size of the monitored words is adjusted dynamically according to the duration time and the evolution of events. Lastly, new topics and trends of existing topics can be detected by using dynamic clustering algorithm based on vector space model. Experimental results show that the proposed algorithms can improve performance in terms of running time and accuracy.

Description: Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM–KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and to ameliorate the generalization of the SVM–KPCA algorithm. Three databases are used to validate the suggested SVM–KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier’s performance compared with similar algorithms working on the proposed database. Second, the SVM–KPCA algorithm performs well in differentiating between two classes of medical images. Third, the approach is robust and might be utilized for other MRIs. This proposes a significant role for computer aided diagnosis analysis systems used for clinical practice.

Description: Despite the development of two- and three-dimensional (2D&3D) technology, it has attracted the attention of researchers in recent years. This research is done to reveal the detailed effects of 2D in comparison with 3D technology on the human brain waves. The impact of 2D&3D video watching using electroencephalography (EEG) brain signals is studied. A group of eight healthy volunteers with the average age of 31 ± 3.06 years old participated in this three-stage test. EEG signal recording consisted of three stages: After a bit of relaxation (a), a 2D video was displayed (b), the recording of the signal continued for a short period of time as rest (c), and finally the trial ended. Exactly the same steps were repeated for the 3D video. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. After testing all the EEG frequency bands, delta and theta were extracted as the features. Partial least squares regression (PLSR) and Support vector machine (SVM) classification algorithms were considered in order to classify EEG signals obtained as the result of 2D&3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions.

Description: Blind signatures are an important and useful tool in designing digital cash schemes and electronic voting protocols. Ring signatures on the other hand provide the anonymity of the signer within the ring of users. In order to fit to some real-life applications, it is useful to combine both protocols to create a blind ring signature scheme, which utilizes all of their features. In this paper, we propose, for the first time, a post-quantum blind ring signature scheme. Our scheme is constructed based on multivariate public key cryptography, which is one of the main candidates for post-quantum cryptography.

Description: With the development of Lie theory, Lie groups have attained profound significance in several branches of Mathematics and Physics. In Lie theory, the matrix exponential plays a crucial role between Lie groups and Lie algebras. Meanwhile, as the finite analogue of Lie groups, finite groups of Lie type have potential applications in cryptography due to their unique mathematical structures. In this paper, we first put forward a novel idea of designing cryptosystems based on Lie theory. First of all, combing with discrete logarithm problem and group factorization problem, we proposed several new intractable assumptions based on the matrix exponential in finite groups of Lie type. Subsequently, in analog with Boyen’s scheme (Asiacrypt 2007), we designed a public-key encryption scheme based on the non-abelian factorization problem in finite groups of Lie type. Finally, our proposal was proved to be indistinguishable against adaptively chosen-ciphertext attack in the random oracle model. It is encouraging that our scheme also has the potential to resist against Shor’s quantum algorithm attack.

Description: Suicide is a major health issue nowadays and has become one of the highest reason for deaths. There are many negative emotions like anxiety, depression, stress that can lead to suicide. By identifying the individuals having suicidal ideation beforehand, the risk of them completing suicide can be reduced. Social media is increasingly becoming a powerful platform where people around the world are sharing emotions and thoughts. Moreover, this platform in some way is working as a catalyst for invoking and inciting the suicidal ideation. The objective of this proposal is to use social media as a tool that can aid in preventing the same. Data is collected from Twitter, a social networking site using some features that are related to suicidal ideation. The tweets are preprocessed as per the semantics of the identified features and then it is converted into probabilistic values so that it will be suitably used by machine learning and ensemble learning algorithms. Different machine learning algorithms like Bernoulli Naïve Bayes, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine were applied on the data to predict and identify trends of suicidal ideation. Further the proposed work is evaluated with some ensemble approaches like Random Forest, AdaBoost, Voting Ensemble to see the improvement.

Description: In certificateless proxy signature (CLPS), the key generation center is responsible for initializing the system parameters and can obtain the opportunity to adaptively set some trapdoors in them when wanting to launch some attacks. Until now, how to withstand the malicious-but-passive key generation center (MKGC) attacks in CLPS is still an interesting problem. In this paper, we focus on the challenging issue and introduce a CLPS scheme provably secure in the standard model. To the best of our knowledge, we are the first to demonstrate its security under MKGC attacks by adopting the technology of embedding the classic difficulty problems into the target entity public key rather than the system parameters during the security proof process.

Description: Security is a growing concern in developing software systems. It is important to face unknown threats in order to make the system continue operating properly. Threats are vague and attack methods change frequently. Coping with such changes is a major feature of an adaptive software. Therefore, designing an adaptive secure software is an appropriate solution to address software security challenges. Through estimation of maximum amount of system assets security, one can determine whether the system is protecting the assets or not; if not, reconfiguration can be employed. This paper proposes a new requirement model for secure adaptive systems using fuzzy, goal modeling and Description Logic concepts. The model contains three phases of modeling security aspects of the system, identifying formalizations and relations between the requirements and monitoring and adapting, when needed. To illustrate the relations between the requirements, goal modeling is used in the first phase and fuzzy Description Logic in the second phase. For the third phase, four algorithms are proposed to monitor and determine whether reconfiguration is needed or not. Theorems are given to prove concept satisfaction of the requirements. Furthermore, examples and case studies are discussed to evaluate and show applicability of the proposed model.

Description: Touristic City Cards are managed by some city councils in order to make a step toward the SmartCity concept. Touristic City Cards enable the integration of different touristic services so as to facilitate the access to transport, cultural sites and touristic attractions to visitors. In this paper, we introduce a secure mobile system called mCityPASS that allows the smart management and secure access to this kind of service. We have implemented the system in a smartphone platform in such a way that the mobile device contains the purchased access element that holds a set of e-tickets that enables the visitors of a city to make use of the specified touristic services. The paper presents the details of the whole implementation of the system and an evaluation of the performance and the effectiveness of it. Also, an analysis of the privacy and the security properties of the presented scheme has been conducted. As a result, we can assure that the system preserves the privacy of the tourists and fulfills the more challenging security properties: fairness, non-overspending, exculpability, unsplittability and selective reusability.

Description: Response and recovery are the two most crucial aspects associated with post disaster management. Both these operations need real-time data and location at which the event occurs. These operations require event-based collection of data at critical times. The events may occur at any place and the data may be needed from either or all of the events for strategic planning and event handling. It is a challenging task to process the events whose locations are uncertain. In this view, there is a need to know the real-time location of events occurring in the surrounding. Moreover, there is a possibility of huge amount of signal processing among the devices deployed in the terrain. In this context, an event localization algorithm is proposed based on the Direction of Arrival estimation technique in the Internet of Things environment. It estimates the location of events by mapping the deployed devices using concentric circular array in the region. Further, the Cramer–Rao bound for the proposed algorithm is derived and compared with the existing schemes for efficacy. The algorithm is implemented on real test bed and presented with comparative evaluation to validate the work.

Description: Certificateless public key cryptography (CL-PKC) promises a practical resolution in establishing practical schemes, since it addresses two fundamental issues, namely the necessity of requiring certificate managements in traditional public key infrastructure (PKI) and the key escrow problem in identity-based (ID-based) setting concurrently. Signcryption is an important primitive that provides the goals of both encryption and signature schemes as it is more efficient than encrypting and signing messages consecutively. Since the concept of certificateless signcryption (CL-SC) scheme was put forth by Barbosa and Farshim in 2008, many schemes have been proposed where most of them are provable in the random oracle model (ROM) and only a few number of them are provable in the standard model. Very recently, Luo and Wan (Wireless Personal Communication, 2018) proposed a very efficient CL-SC scheme in the standard model. Furthermore, they claimed that their scheme is not only more efficient than the previously proposed schemes in the standard model, but also it is the only scheme which benefits from known session-specific temporary information security (KSSTIS). Therefore, this scheme would indeed be very practical. The contributions of this paper are 2-fold. First, in contrast to the claim made by Luo and Wan, we show that unfortunately Luo and Wan made a significant error in the construction of their proposed scheme. While their main intention is indeed interesting and useful, the failure of their construction has indeed left a gap in the research literature. Hence, the second contribution of this paper is to fill this gap by proposing a CL-SC scheme with KSSTIS, which is provably secure in the standard model.

Description: The proliferation of Internet of Things (IoT) devices has led to many applications, including smart homes, smart cities and smart industrial control systems. Attacks like Distributed Denial of Service, event control hijacking, spoofing, event replay and zero day attacks are prevalent in smart environments. Conventional Network Intrusion Detection Systems (NIDSs) are tedious to deploy in the smart environment because of numerous communication architectures, manufacturer policies, technologies, standards and application-specific services. To overcome these challenges, we modeled the operational behavior of IoT network events using timed ACs and proposed a novel hybrid NIDS in this paper. A web server is integrated with IoT devices for remote access, and Constrained Application Protocol is employed in inter- and intra-smart device communication. Experiments are conducted in real time to validate our proposal and achieve 99.17% detection accuracy and 0.01% false positives.

Description: Efficient user revocation is a necessary but challenging problem in many multi-user cryptosystems. Among known approaches, server-aided revocation yields a promising solution, because it allows to outsource the major workloads of system users to a computationally powerful third party, called the server, whose only requirement is to carry out the computations correctly. Such a revocation mechanism was considered in the settings of identity-based encryption and attribute-based encryption by Qin et al. (2015, ESORICS) and Cui et al. (2016, ESORICS ), respectively. In this work, we consider the server-aided revocation mechanism in the more elaborate setting of predicate encryption (PE). The latter, introduced by Katz et al. (2008, EUROCRYPT), provides fine-grained and role-based access to encrypted data and can be viewed as a generalization of identity-based and attribute-based encryption. Our contribution is 2-fold. First, we formalize the model of server-aided revocable PE (SR-PE), with rigorous definitions and security notions. Our model can be seen as a non-trivial adaptation of Cui et al.’s work into the PE context. Second, we put forward a lattice-based instantiation of SR-PE. The scheme employs the PE scheme of Agrawal et al. (2011, ASIACRYPT) and the complete subtree method of Naor et al. (2001, CRYPTO) as the two main ingredients, which work smoothly together thanks to a few additional techniques. Our scheme is proven secure in the standard model (in a selective manner), based on the hardness of the learning with errors problem.

Description: Information in the form of the image conveys more details than any other form of information. Several software packages are available to manipulate the images so that the authenticity of the images is being questioned. Several image processing approaches are available to create fake images without leaving any visual clue about the forging operation. So, proper image forgery detection tools are required to detect such forgery images. Over the past few years, several research papers were published in the digital image forensics domain for detecting fake images, thus escalating the legitimacy of the images. This survey paper attempts to review the recent approaches proposed for detecting image forgery. Accordingly, several research papers related to image forgery detection are reviewed and analyzed. The taxonomy of image forgery detection techniques is presented, and the algorithms related to each technique are discussed. The comprehensive analysis is carried out based on the dataset used, software used for the implementation and the performance achievement. Besides, the research issues associated with every approach were scrutinized together with the recommendation for future work.

Description: Developers use software information sites such as Stack Overflow to get and give information on various subjects. These sites allow developers to label content with tags as a short description. Tags, then, are used to describe, categorize and search the posted content. However, tags might be noisy, and postings may become poorly categorized since people tag a posting based on their knowledge of its content and other existing tags. To keep the content well organized, tag recommendation systems can help users by suggesting appropriate tags for their posted content. In this paper, we propose a tag recommendation scheme that uses the textual content of already tagged postings to recommend suitable tags for newly posted content. Our approach combines multi-label classification and textual similarity techniques to improve the performance of tag recommendation. We evaluate the performance of the proposed scheme on 11 software information sites from the Stack Exchange network. The results show a significant improvement over TagCombine, TagMulRec and FastTagRec, which are well-known tag recommendation systems. On average, the proposed model outperforms TagCombine, TagMulRec and FastTagRec by 26.2, 15.9 and 13.8% in terms of Recall@5 and by 16.9, 12.4 and 9.4% in terms of Recall@10, respectively.

Description: The rapid increase in information and technology has led to the increased amount of web pages, which raises the complexity in sticking to relevant web pages, and the visitor suffers due to wastage of time resulting in lack of satisfaction. This paper proposes a web page prediction method using a weighed support and Bhattacharya distance-based (WS-BD) two-level match. The major aim of the proposed method is to attain customer satisfaction. Initially, interesting sequential patterns are obtained using the weighed support that filters the sequential patterns obtained using a PrefixSpan algorithm based on the frequency, duration and recurrence of the web pages. Interesting sequential patterns are clustered using the proposed dice similarity-based Bayesian fuzzy clustering, and the web page is predicted using the two-level match based on Bhattacharya distance. The experimentation is performed using the CTI and MSNBC data which proves the effectiveness of the proposed method. The proposed method shows 9.59, 21.22 and 10.17% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the CTI dataset. Also, the proposed method shows 2.58, 22.17 and 7.83% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the MSNBC dataset.

Description: This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

Description: The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual’s dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ‘CalBehav’. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

Description: The successful application of deep learning has led to increasing expectations of their use in embedded systems. This, in turn, has created the need to find ways of reducing the size of neural networks. Decreasing the size of a neural network requires deciding which weights should be removed without compromising accuracy, which is analogous to the kind of problems addressed by multi-armed bandits (MABs). Hence, this paper explores the use of MABs for reducing the number of parameters of a neural network. Different MAB algorithms, namely $epsilon $-greedy, win-stay, lose-shift, UCB1, KL-UCB, BayesUCB, UGapEb, successive rejects and Thompson sampling are evaluated and their performance compared to existing approaches. The results show that MAB pruning methods, especially those based on UCB, outperform other pruning methods.

Description: Using historical data, a machine learning model is usually built to forecast the future meteorological elements such as temperature, precipitation, etc. However, for numerous small and medium-sized cities, it is a challenging task because the maintained data of these cities are usually very limited due to historical or infrastructural reasons. So it is difficult to build an accurate forecast model in small and medium-sized cities. Aiming at this problem, a forecast method based on transfer learning method is proposed. Using instance-based transfer learning, this method extends the data of the target city by transferring the data from related cities and then builds a forecast model based on the extended dataset, so that the problem of insufficient samples in machine learning is solved. As a case study, the proposed technique is applied in Zhaoqing City, China. In the experiments, the data of temperature sequence and the precipitation sequence of Gaoyao weather station in Zhaoqing district are extended according to the data of related cities. The transferred temperature data and precipitation data are collected from 1884 to 1997 in Hong Kong and 1908 to 2016 in Guangzhou, respectively. Then temperature and precipitation forecasting models are built based on least square method and autoregressive integrated moving average. The experimental results have been verified by the actual situation. The results justify the effectiveness of the proposed method in building accurate meteorological forecasting model with limited data, and the superiority over existing techniques.

Description: The power of the WWW (World Wide Web) lies in the adoption of multiple types of media, which makes effective communication of content. Videos have emerged into a quick and efficient information delivery channel for users. To make a video accessible to persons with disabilities, several approaches have been proposed, which include various accessibility features such as closed captions and video descriptions. Captioning agencies use English as a primary language for creating captions, which cause problems for persons with low literacy skills. Ambiguity in the video affects the persons with low literacy skills and senior citizens and it leads them to consume more time in identifying/capturing the intended meaning. As the closed captions run along with the video frame, the comprehension is an issue. For predicting the readability assessment of closed captions for persons with low literacy skills, we developed a statistical formula based on the lexical and semantical ambiguity of the context. As a case study, caption files of top 50 English movies (as per IMDB top rated movies chart) were chosen and their readability scores were calculated. In addition, a machine learning model was developed using the ambiguity feature set in assessing the readability score of the closed captions. The result shows, our model is predicting the readability score with an accuracy of 92.6%.

Description: In the domain of technology-enhanced competency-based learning and training, there is an increased interest in the integration of competency-related information for supporting competency-driven decision-making purposes. Indeed, since competency development draws upon several related areas, including teaching subjects, instructional design, learning resource annotation, e-Portfolios and motivated by the need for an integrated and semantic-based approach to competency management and development, a series of ontological structures have been formalized and developed for each of these areas. This paper aims to provide a framework specification for lifelong competency management and development, called LCMDF. The main advantage of this framework lies in its ability to provide a unifying semantic foundation in the form of a set of controlled vocabularies for describing competencies and their related details within the contexts of technology-enhanced competency-based learning and training. Moreover, this framework provides a novel integrated model to support a wide range of use cases. The proposed framework results from reusing widespread international standards for competency modeling which helps designing and implementing interoperability architecture of semantically-enhanced competency-based learning/human resource (HR) systems.

Description: Dropout has been proven to be an effective technique for regularizing and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability of p during the training stage of DNN to avoid overfitting. The working mechanism of dropout can be interpreted as approximately and exponentially combining many different neural network architectures efficiently, leading to a powerful ensemble. In this work, we propose a novel diversification strategy for dropout, which aims at generating more different neural network architectures in less numbers of iterations. The dropped units in the last forward propagation will be marked. Then the selected units for dropping in the current forward propagation will be retained if they have been marked in the last forward propagation, i.e., we only mark the units from the last forward propagation. We call this new regularization scheme Tabu dropout, whose significance lies in that it does not have extra parameters compared with the standard dropout strategy and is computationally efficient as well. Experiments conducted on four public datasets show that Tabu dropout improves the performance of the standard dropout, yielding better generalization capability.

Description: The usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is a lot of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve the users’ privacy through the anonymization of social network graphs. In this regard, the utilization of the k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique, the nodes are clustered together to form the super-nodes of size at least k. Our main idea in this paper is, initially, to optimize the clustering process in the k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to minimize the normalized structural information loss (NSIL), which is equal to maximizing 1-NSIL. Although the proposed PSO-based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) method, it did not provide a lower NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from the PSO algorithm, we present hybrid solutions based on the GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships. The simulation results demonstrate the efficiency of the proposed model to balance the maximized 1-NSIL and the algorithm’s convergence rate.

Description: Simple and efficient exploration remains a core challenge in deep reinforcement learning. While many exploration methods can be applied to high-dimensional tasks, these methods manually adjust exploration parameters according to domain knowledge. This paper proposes a novel method that can automatically balance exploration and exploitation, as well as combine on-policy and off-policy update targets through a dynamic weighted way based on value difference. The proposed method does not directly affect the probability of a selected action but utilizes the value difference produced during the learning process to adjust update target for guiding the direction of agent’s learning. We demonstrate the performance of the proposed method on CartPole-v1, MountainCar-v0, and LunarLander-v2 classic control tasks from the OpenAI Gym. Empirical evaluation results show that by integrating on-policy and off-policy update targets dynamically, this method exhibits superior performance and stability than does the exclusive use of the update target.

Description: At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.

Description: The advent of big data era has given rise to the big data trading market because of the potentially enormous economic value. However, designing an effective trading mechanism for the data trading market is still in its infancy. Existing several incentive mechanisms have neglected the important fact that data consumers have both preferences and complex conflicts of interest (CoI) relations among them. In response to the limitations of existing trading mechanisms, we propose DTPCI, a truthful double auction mechanism for a Data Trading market with two unique characteristics of consumers’ Preferences and complex CoI relations among them. However, three major challenges have to be addressed, i.e. diverse market preferences, the complex CoI relations of data consumers and the strategic behaviors of both two sides. To jointly address the three challenges, we propose DTPCI to achieve nonnegative social welfare, which features a group rule and a data trading rule. The group rule generates all conflict-free virtual groups based on the CoI graph. The data trading rule adopts the group buying to share data and expense. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that DTPCI achieves all the desired economic properties.

Description: Information flow control (IFC) regulates where information is permitted to travel within information systems. To enforce IFC, access control encryption (ACE) was proposed to support both the no read-up rule and the no write-down rule. There are some problems in existing schemes. First, the communication cost is linear with the number of receivers. Second, senders are not authenticated, namely an unauthorized sender can send a message to a receiver. To reduce communication cost and implement sender authentication, we propose an attribute-based IFC (ABIFC) scheme by introducing attribute-based systems into IFC. Our ABIFC scheme captures the following features: (i) flexible IFC policies are defined over a universal set of descriptive attributes; (ii) both the no read-up rule and the no write-down rule are supported; (iii) the communication cost is linear with the number of required attributes, instead of receivers; (iv) receivers can outsource heavy computation to a server without compromising data confidentiality; (v) authorized senders can control release their attributes when sending messages to receivers. To the best of our knowledge, it is the first IFC scheme where flexible policies are defined over descriptive attributes and outsourced computation is supported.

Description: Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination.

Description: The ability to remove noise from remote sensing images, while retaining the important features of the images, is becoming increasingly important. In this paper, we introduce the multi-band contourlet transform, a new method for adaptively denoising remote sensing images. We describe existing methods that use multi-resolution analysis transforms for denoising images and discuss their respective advantages and disadvantages. We then introduce our novel denoising method, which exploits the advantages of existing methods. We summarize the results of a comprehensive set of experiments designed to evaluate the performance of our method and compare it with the performance of existing methods. The results demonstrate that our method is superior to existing methods, both in terms of its ability to denoise images and to retain salient features of those images following denoising.

Description: Overtaking maneuvers in roads cause a large number of accidents. To prevent collisions, numerous cooperative overtaking assistance systems, based on Vehicular Ad hoc Networks (VANET), have been proposed. However, these systems may meet problems in case of data congestion or data loss. To improve overtaking safety, this paper studies the use of Vehicle-To-Vehicle (V2V) communications and Real-Time DataBases (RTDB). More precisely, we suggest a new integrated system, called Cooperative Overtaking Assistance Persistent System (COAPS), to better avoid potential collisions during overtaking maneuvers. Each vehicle stores its information in a local RTDB, along with the adjacent vehicles’ information. While detecting an overtaking intention, the vehicle COAPS queries its database and selects the preceding vehicle(s) and the oncoming one(s) from the other direction to make decision about the overtaking. It controls the overtaking in case of positive decision or reduces the vehicle speed to avoid a collision ahead, in the other case. Simulations of different driving scenarios, under the Vehicles In Network Simulation (VEINS) framework, show that COAPS provides a high level of overtaking safety due to the V2V communications, the RTDB and the notion of Quality of Data used to reduce the number of data freshness transactions.

Description: Sentiment analysis has been the main focus of plenty of research efforts, particularly justified by its commercial significance, both for consumers and businesses. Thus, many methods have been proposed so far, and the most prominent have been compared in terms of effectiveness. Nonetheless, the literature is deficient when it comes to assessing the efficiency of these methods for processing large volumes of data. In this study, we performed an experimental assessment of the efficiency of 22 methods in total, whose implementations were available. We also proposed and assessed an environment for distributed processing methods for sentiment analysis, using the Apache Spark platform, named BigFeel. In this environment, the existing methods, outlined to run in a non-distributed way, can be adapted, without altering their source code, to run in a distributed manner. The experimental results reveal that (i) few methods are efficient in their native form, (ii) the methods improve their efficiency after having been integrated into BigFeel, (iii) some of them, which were unfeasible to process a large dataset, became viable when deployed in a computer cluster and (iv) some methods can only handle small datasets, even in a distributed manner.

Description: Software-defined networking (SDN) is an emerging trend where the control plane and the data plane are separated from each other, culminating in effective bandwidth utilization. This separation also allows multi-vendor interoperability. Link failure is a major problem in networking and must be detected as soon as possible because when a link fails the path becomes congested and packet loss occurs, delaying the delivery of packets to the destination. Backup paths must be configured immediately when a failure is detected in the network to speed up packet delivery, avoid congestion and packet loss and provide faster convergence. Various SDN segment protection algorithms that efficiently reduce CPU cycles and flow table entries exist, but each has drawbacks. An independent transient plane technique can be used to reduce packet loss but is not as efficient when multiple flows try to share the same link. The proposed work focuses on reducing congestion, providing faster convergence with minimal packet loss and effectively utilizing link bandwidth using bandwidth-sharing techniques. An analysis and related studies show that this method performs better and offers a more reliable network without loss, while simultaneously ensuring the swift delivery of data packets toward the destination without congestion, compared to the other existing schemes.

Description: In the actual applications, an adversary can break the security of cryptography scheme through various leakage attacks (e.g. side-channel attacks, cold-boot attacks, etc.), even the continuous leakage attacks. That is, a practical cryptography scheme must maintain its claimed security in the continuous leakage setting. However, the previous constructions on the leakage-resilient identity-based encryption (IBE) scheme could tolerate a leakage that is bounded, and cannot resist the continuous leakage attacks. In order to further achieve the better security, a novel method to build the continuous leakage-resilient IBE scheme with tight security is presented in this paper, and the scheme’s security is proved, in the standard model, based on a stronger security assumption that depends on the number of queries made by the adversary. In addition, our proposal has several advantages over previous such constructions, e.g. shorter public parameters, higher communication efficiency, tight security, etc.

Description: Vehicular ad hoc networks (VANETs) have emerged as an appropriate class of information propagation technology promising to link us even while moving at high speeds. In VANETs, a piece of information propagates through consecutive connections. In the most previous vehicular connectivity analysis, the provided probability density function of intervehicle distance throughout the wide variety of steady-state traffic flow conditions is surprisingly invariant. But, using a constant assumption, generates approximate communication results, prevents us from improving the performance of the current solutions and impedes designing the new applications on VANETs. Hence, in this paper, a mesoscopic vehicular mobility model in a multilane highway with a steady-state traffic flow condition is adopted. To model a traffic-centric distribution for the spatial per-hop progress and the expected spatial per-hop progress, different intervehicle distance distributions are utilized. Moreover, the expected number of hops, distribution of the number of successful multihop forwarding, the expected time delay and the expected connectivity distance are mathematically investigated. Finally, to model the distribution of the connectivity distances, a set of simplistic closed-form traffic-centric equations is proposed. The accuracy of the proposed model is confirmed using an event-based network simulator as well as a road traffic simulator.

Description: The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station’s storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.

Description: Routing in the Internet of Things (IoT) renders the protection against various network attacks as any attacker intrudes the routing mechanism for establishing the destructive mechanisms against the network, which insists the essentiality of the security protocols in IoT. Thus, the paper proposes a secure protocol based on an optimization algorithm, Monarch-Earthworm Algorithm (Monarch-EWA), which is the modification of the Monarch Butterfly algorithm using the Earthworm Optimization Algorithm (EWA) in order to render effective security to the network. Initially, the effective nodes are selected using the Deep Convolutional Neural Network (deep CNN) classifier based on the factors, trust and energy of the node, and stochastic gradient descent algorithm trains the deep CNN classifier. The secure nodes are involved in routing for which the secure multipath is chosen optimally using the proposed Monarch-EWA, which chooses the secure multipath based on the factors, energy and trust. The analysis of the proposed method in the presence of attacks, such as black hole, message replicate and distributed denial of service, reveals that the proposed method outperformed the existing methods. The proposed Monarch-EWA protocol acquired the maximal energy, throughput and detection rate of 0.2268 J, 48.2759% and 82.6231%, respectively, with the minimal delay of 0.0959 ms.

Description: Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security.

Description: Wireless-charging technology can utilize a mobile wireless charging vehicle (WCV) to rescue dying nodes by supplementing their remaining energy, and using WCVs in this way forms wireless rechargeable sensor networks (WRSNs). However, a WCV in a WRSN encounters several challenges, collectively called the optimized charging problem. This problem involves a set of sensor nodes randomly distributed on the ground for which the WCV must determine an appropriate travel path to charge the sensor nodes. Because these sensor nodes have different workloads, they exhibit different energy consumption profiles over time. Resolving the above-mentioned problem requires the determination of the priority of charging the sensor nodes based on the order in which they are expected to die and subsequently finding the most efficient path to charge the sensor nodes such that sensor death is avoided for as long as possible. Furthermore, the most efficient placement of the charging point needs to be considered when planning the charging path. To address this, the proposed multinode virtual point-based charging scheme (MNVPCS) considers both the planning of an efficient charging and the best location for the charging point. Experimental results show that MNVPCS can improve the lifetime of the entire WRSN and substantially outperform other methods on this measure.

Description: We present a silent self-stabilizing distributed algorithm computing a maximal $ p$-star decomposition of the underlying communication network. Under the unfair distributed scheduler, the most general scheduler model, the algorithm converges in at most $12Delta m + mathcal{O}(m+n)$ moves, where $m$ is the number of edges, $n$ is the number of nodes and $Delta $ is the maximum node degree. Regarding the time complexity, we obtain the following results: our algorithm outperforms the previously known best algorithm by a factor of $Delta $ with respect to the move complexity. While the round complexity for the previous algorithm was unknown, we show a $5 ig lfloor frac{n}{p+1} ig floor +5$ bound for our algorithm.

Description: The connectivity of a graph is a classic measure for fault tolerance of the network. Restricted connectivity measure is a crucial subject for a multiprocessor system’s ability to tolerate fault processors, and improves the connectivity measurement accuracy. Furthermore, if a network possesses a restricted connectivity property, it is more reliable with a lower vertex failure rate compared with other networks. The $left (n,k ight )$-dimensional enhanced hypercube, denoted by $Q_{n,k}$, a variant of hypercube, which is a well-known interconnection network. In this paper, we analyze the fault tolerant properties for $left (n,k ight )$-enhanced hypercube, and establish the $1$-restricted connectivity of $Q_{n,k} (nge k+1)$ and ${2,3}$-restricted connectivity of $(n,k)$-enhanced hypercube $Q_{n,k} (n=k+1)$. Furthermore, we propose the tight upper bound of ${2,3}$-restricted connectivity of $Q_{n,k} (n〉 k+1)$. Moreover, we show many figures to better illustrate the process of the proofs.

Description: In this paper, we propose a new digragh model for system level fault diagnosis, which is called the $(f_1,f_{2})$-bounded Preparata–Metze–Chien (PMC) model (shortly, $(f_1,f_{2})$-BPMC). The $(f_1,f_{2})$-BPMC model projects a system such that the number of faulty processors that test faulty processors with the test results $0$ does not exceed $f_{2}$$(f_2leq f_{1})$ provided that the upper bound on the number of faulty processors is $f_{1}$. This novel testing model compromisingly generalizes PMC model (Preparata, F.P., Metze, G. and Chien R.T. (1967) On the connection assignment problem of diagnosable systems. IEEE Tran. Electron. Comput.,EC-16, 848–854) and Barsi–Grandoni–Maestrini model (Barsi, F., Grandoni, F. and Maestrini, P. (1976) A theory of diagnosability of digital systems. IEEE Trans. Comput.C-25, 585–593). Then we present some characterizations for one-step diagnosibility under the $(f_1,f_{2})$-bounded PMC model, and determine the diagnosabilities of some special regular networks. Meanwhile, we establish the characterizations of $f_1/(n-1)$-diagnosability and three configurations of $f_1/(n-1)$-diagnosable system under the $(f_1,f_{2})$-BPMC model.

Description: There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.

Description: Malware brings a big security threat on the Internet today. With the great increasing malware attacks. Behavior-based detection approaches are one of the major method to detect zero-day malware. Such approaches often use API calls to represent the behavior of malware. Unfortunately, behavior-based approaches suffer from behavior obfuscation attacks. In this paper, we propose a novel malware detection approach that is both effective and efficient. First, we abstract the API call to object operation. And then we generate the object operation dependency graph based on these object operations. Finally, we construct the family dependency graph for a malware family. Our approach use family dependency graph to represent the behavior of malware family. The evaluation results show that our approach can provide a complete resistance to all types of behavior obfuscation attacks, and outperforms existing behavior-based approaches in terms of better effectiveness and efficiency.

Description: Traffic and air pollution caused by the increasing number of cars have become important issues in nowadays cities. A possible solution is to employ recommender systems for efficient ridesharing among users. These systems, however, typically do not allow specifying ordered stops, thus preventing a large amount of possible users from exploiting ridesharing, e.g. parents leaving kids at school while going to work. Indeed, if a parent desired to share a ride, he/she would need to indicate the following constraint in the path: the stop at school should precede the stop at work. In this paper, we propose a ridesharing recommender, which allows each user to specify an ordered list of stops and suggests efficient ride matches. The ride-matching criterion is based on a dissimilarity between the driver’s path and the shared path, computed as the shortest path on a directed acyclic graph with ordering constraints between the stops defined in the single paths. The dissimilarity value is the detour requested to the driver to visit also the stops of the paths involved in the ride-share, respecting the visiting order of the stops within each path. Results are presented on a case study involving the city of Pisa.

Description: Hardware/software (HW/SW) partitioning, that decides which components of an application are implemented in hardware and which ones in software, is a crucial step in embedded system design. On modern heterogeneous embedded system platform, each component of application can typically have multiple feasible configurations/implementations, trading off quality aspects (e.g. energy consumption, completion time) with usage for various types of resources. This provides new opportunities for further improving the overall system performance, but few works explore the potential opportunity by incorporating the multiple choices of hardware implementation in the partitioning process. This paper proposes three algorithms for multiple-choice HW/SW partitioning of tree-shape task graph on multiple processors system on chip (MPSoC) with the objective of minimizing execution time, while meeting area constraint. Firstly, an efficient heuristic algorithm is proposed to rapidly generate an approximate solution. The obtained solution produced by the first algorithm is then further refined by a customized Tabu search algorithm. We also propose a dynamic programming algorithm to calculate the exact solutions for relatively smaller scale instances. Simulation results show that the proposed heuristic algorithm is able to quickly generate good approximate solutions, and the solutions become very close to the exact solutions after refined by the proposed Tabu search algorithm, in comparison to the exact solutions produced by the dynamic programming algorithm.

Description: The merging problem consists of forming an ordered sequence with n elements from two ordered sequences with n1 and n2 elements (where n=n1+n2). One recent development in in-place merging is a minimum storage algorithm (DuelMerge) that requires half the number of moves in average, when compared to other partition-based algorithms. The reduction is achieved by limiting the exchange to blocks with equal sizes, and using a particularly suited exchange method. The algorithm proposed in this paper (UnfairDuelMerge) reduces the number of moves even further, by limiting the exchange to blocks where the left side is one element larger than the right side, and using the floating hole technique to perform the exchange. As its predecessor, the algorithm is stable, in-place and the merge is asymptotically performed with a O(nlognn) runtime. A formal description of the method is provided, as well as an analysis of stability, memory cost and time complexity. Experimental results show that the proposed algorithm outperforms other partition-based solutions when applied directly to merge ordered sequences and when used within MergeSort.

Description: For an integer $ell geqslant 2$, the $ell $-component connectivity (resp. $ell $-component edge connectivity) of a graph $G$, denoted by $kappa _{ell }(G)$ (resp. $lambda _{ell }(G)$), is the minimum number of vertices (resp. edges) whose removal from $G$ results in a disconnected graph with at least $ell $ components. The two parameters naturally generalize the classical connectivity and edge connectivity of graphs defined in term of the minimum vertex-cut and the minimum edge-cut, respectively. The two kinds of connectivities can help us to measure the robustness of the graph corresponding to a network. In this paper, by exploring algebraic and combinatorial properties of $n$-dimensional balanced hypercubes $BH_n$, we obtain the $ell $-component (edge) connectivity $kappa _{ell }(BH_n)$ ($lambda _{ell }(BH_n)$). For $ell $-component connectivity, we prove that $kappa _2(BH_n)=kappa _3(BH_n)=2n$ for $ngeq 2$, $kappa _4(BH_n)=kappa _5(BH_n)=4n-2$ for $ngeq 4$, $kappa _6(BH_n)=kappa _7(BH_n)=6n-6$ for $ngeq 5$. For $ell $-component edge connectivity, we prove that $lambda _3(BH_n)=4n-1$, $lambda _4(BH_n)=6n-2$ for $ngeq 2$ and $lambda _5(BH_n)=8n-4$ for $ngeq 3$. Moreover, we also prove $lambda _ell (BH_n)leq 2n(ell -1)-2ell +6$ for $4leq ell leq 2n+3$ and the upper bound of $lambda _ell (BH_n)$ we obtained is tight for $ell =4,5$.

Description: In PKC 2000, Pointcheval presented a generic technique to make a highly secure cryptosystem from any partially trapdoor one-way function in the random oracle model. More precisely, any suitable problem providing a one-way cryptosystem can be efficiently derived into a chosen-ciphertext attack (CCA) secure public key encryption (PKE) scheme. In fact, the overhead only consists of two hashing and a XOR. In this paper, we consider the key-dependent message (KDM) security of the Pointcheval’s transformation. Unfortunately, we do not know how to directly prove its KDM-CCA security because there are some details in the proof that we can not bypass. However, a slight modification of the original transformation (we call twisted Pointcheval’s scheme) makes it possible to obtain the KDM-CCA security. As a result, we prove that the twisted Pointcheval’s scheme achieves the KDM-CCA security without introducing any new assumption. That is, we can construct a KDM-CCA secure PKE scheme from partial trapdoor one-way injective family in the random oracle model.

Description: Lossy trapdoor functions (LTDFs) were introduced by Peikert and Waters (STOC 2008) and have a number of applications in cryptography. All-but-many lossy trapdoor functions (ABM-LTDFs) are generalizations of LTDFs studied by Hofheinz (Eurocrypt 2012). Specially, using ABM-LTDFs to construct public key encryption (PKE) scheme with selective opening security has been proven feasible. Existing ABM-LTDFs were built on pairings, lattices and decisional composite residuosity (DCR) assumption. However, pairing-based ABM-LTDFs and DCR-based ABM-LTDFs rely on non-standard assumptions. In this paper, we construct an ABM-LTDF under the decisional RSA subgroup (DRSA) assumption, and we employ it to construct PKE scheme with selective opening security. We also propose a construction of DCR-based ABM-LTDF relying on standard assumption in Appendix.

Description: In this paper, we introduce a new construction for linkable secret handshake that allows authenticated users to perform handshake anonymously within allowable times. We define formal security models for the new construction, and prove that it can achieve session key security, anonymity, untraceability and linkable affiliation-hiding. In particular, the proposed construction ensures that (i) anyone can trace the real identities of dishonest users who perform handshakes for more than k times; and (ii) an optimal communication cost between authorized users is achieved by exploiting the proof of knowledges.

Description: Identity-based revocation system (IBRS) generates the ciphertext with a revoked identity list such that only the non-revoked identities can use their private keys to decrypt this ciphertext. IBRS can be efficiently applied in some practical applications, such as the pay-TV systems when the number of revoked identities are much less than the non-revoked ones. However, since IBRS is based on identity-based cryptography, it also suffers from the inherent key escrow problem where the private key generator (PKG) has full control of each user’s private key. As a consequence, it is hard to judge whether a pirated private key is generated by the PKG or the suspected user. There is no study on IBRS fulfilling accountability in literature to date. In this paper, we introduce the notion of accountable authority IBRS (A-IBRS), which provides accountability in IBRS schemes. In an A-IBRS, the aforementioned problem can be alleviated and resolved. Furthermore, a full black-box A-IBRS can distinguish the creator of a black box between the PKG and the associated user and the dishonest PKG is allowed to access the decryption results of the user private key. We formalize the definition and security models of the full black-box A-IBRS schemes. Then, we present a concrete full black-box A-IBRS scheme with constant-size master public key and private key. Finally, we prove the security of our scheme under the defined security models without random oracle.

Description: Vector commitment (VC) schemes allow committing to an ordered sequence of ${q}$ values ${(m_1,cdots ,m_q)}$ in such a way that one can later open the commitment at specific positions. However, the existing VC schemes suffer from two substantial shortcomings that limit their use: (i) the commitments cannot be opened except at some specific positions, and (ii) their security only captures position-binding but offers no privacy: the client may learn additional information about the committed sequence through the proofs and the commitments. To resolve these problems, we first extend VC to a more expressive primitive called VC with sum binding (VCS), in which the commitment can also be opened to the sum of all elements in the committed sequence. VCS additionally satisfies the security of sum binding, which guarantees that the commitment cannot be opened to different sums. To enhance its privacy, we extend VCS to zero-knowledge VCS (ZKVCS), in which commitments and proofs constructed during the protocol execution leak nothing about the committed sequence. We formalize this new property by a standard real/ideal experiment. Meanwhile, the detailed performance analyses and simulations show that our proposed schemes are more practical. Finally, we introduce a novel notion of (zero-knowledge) verifiable database supporting sum and show how to construct it from our (ZK)VCS scheme.

Description: Lossy trapdoor functions (LTFs), introduced by Peikert and Waters (STOC’08), have already been found to be a very useful tool in constructing complex cryptographic primitives in a black-box manner, such as one-way trapdoor functions, deterministic public-key encryption, CCA-secure public-key encryption, etc. Due to the existence of the side-channel attack, the leakage of trapdoor information in lossy trapdoor function systems can lead to the impossibility of provable security. Recently, Zhang et al. introduced a model of consecutive and continual leakage-resilient and updatable lossy trapdoor functions (ULTFs) and provided a concrete construction to achieve the security. Meanwhile, they proposed a consecutive and continual leakage-resilient public-key encryption scheme. However, in this paper, we demonstrate that the correctness of injective function can not be satisfied. Furthermore, the attacker can easily distinguish the evaluation key of ULTFs generated by the challenger according to the security model. Finally, we show two new constructions based on the continual leakage-resilient public-key encryption scheme of Brakerski et al. (FOCS 2010) and demonstrate the security of our scheme in the consecutive and continual leakage model.

Description: Nowadays, mobile applications are the devices’ core, so their security is essential for the platform on which are installed. Therefore, to make this security strong, Android implements permission system and sandboxing mechanism to reduce the attacks contingency. Also, antivirus software is used to detect the already known malware based on their signature, but unfortunately, this kind of program cannot detect cloned or repackaged malware. Thus, using authorizations to estimate the security vulnerability will surely be very useful for avoiding applications that are more susceptible to be dangerous according to the risk values obtained. Consequently, this will provide systematic support that will make it easier for users to make appropriate decisions and therefore greatly improve the Android devices’ security. In this article, additionally to the risk measures that have been already proposed in previous studies based on functionalities such as permissions and function calls, we add a set of mathematical metrics describing the case of susceptible and innocent applications already known. Our risk assessment exploits applications already known as malware and safe samples without any threat.

Description: Goal-oriented requirements engineering frameworks are used to model stakeholders’ objectives and requirements using goals. In a real-time environment, stakeholders’ requirements may have opposing objectives. Hence, a novel framework is needed that captures the real issues in order to achieve multi-objective optimization of inter-dependent actors. For obtaining an optimum strategy for inter-dependent actors in the i* goal model, a multi-objective two-person zero-sum game theory-based approach is applied in this paper, by balancing the opposing goals reciprocally. The proposed approach involves the generation of each objective function based on the inter-dependency relationships, the creation of decision pay-off matrices based on the objective function values and their variation to a final decision pay-off matrix. A Maxmin solution is formulated for the multi-objective game model, in which the optimization problem for each player is a linear programming problem. Finally, the most desirable strategies and their proportion values are found. By integrating Java with the IBM CPLEX optimization tool, a simulation model based on the proposed method was developed. A successful evaluation was conducted on various case studies from the existing literature. Evaluation results indicate that the developed simulation model helps users to choose an optimal alternative design option feasible in real-time competitive environments that have goals with opposing objectives.

Description: In this paper, we proposed an idea to construct a general multivariate public key cryptographic (MPKC) scheme based on a user’s identity. In our construction, each user is distributed a unique identity by the key distribution center (KDC) and we use this key to generate user’s private keys. Thereafter, we use these private keys to produce the corresponding public key. This method can make key generating process easier so that the public key will reduce from dozens of Kilobyte to several bits. We then use our general scheme to construct practical identity-based signature schemes named ID-UOV and ID-Rainbow based on two well-known and promising MPKC signature schemes, respectively. Finally, we present the security analysis and give experiments for all of our proposed schemes and the baseline schemes. Comparison shows that our schemes are both efficient and practical.

Description: There are a number of issues related to the development of biometric authentication systems, such as privacy breach, consequential security and biometric template storage. Thus, the current paper aims to address these issues through the hybrid approach of watermarking with biometric encryption. A multimodal biometric template protection approach with fusion at score level using fingerprint and face templates is proposed. The proposed approach includes two basic stages, enrollment stage and verification stage. During the enrollment stage, discrete wavelet transform (DWT) is applied on the face images to embed the fingerprint features into different directional sub-bands. Watermark embedding and extraction are done by quantizing the mean values of the wavelet coefficients. Subsequently, the inverse DWT is applied to obtain the watermarked image. Following this, a unique token is assigned for each genuine user and a hyper-chaotic map is used to produce a key stream in order to encrypt a watermarked image using block-cipher. The experimentation results indicate the efficiency of the proposed approach in term of achieving a reasonable error rate of 3.87%.

Description: In recent years, sentiment analysis based on aspects has become one of the research hotspots in the field of natural language processing. Aiming at the fact that the existing network model cannot fully obtain the interrelationship between sentences in the same comment and the long-distance dependence of specific aspects in the whole comment, a multilingual deep hierarchical model combining regional convolutional neural network and bidirectional LSTM network is proposed. The model obtains the time series relationship of different sentences in the comments through the regional CNN, and obtains the local features of the specific aspects in the sentence and the long-distance dependence in the whole comment through the hierarchical attention network. In addition, the model improves the word vector representation based on the gate mechanism to make the model completely independent of the language. Experimental results for different domain datasets in multi-language show that the proposed model achieves better classification results than the traditional deep network model, the network model combining with the attention mechanism and considering the relationship between sentences.

Description: Object tracking is a very important step in building an intelligent video monitoring system that can protect people’s lives and property. In recent years, although visual tracking has made great progress in terms of speed and accuracy, there are still few real-time high-precision tracking algorithms. Although discriminative correlation filters have excellent performance in tracking speed, there are deficiencies in handling fast motion. This leads to the inability to achieve long-term stable tracking results. The long-time tracking with discriminative correlation filter (LT-DCF) was proposed to solve these deficiencies. We use larger size detection image blocks and smaller size filters to increase the proportion of real samples to solve the boundary effects of fast motion. And we combine the histogram of oriented gradient (HOG) feature detection and scale-invariant feature transform (SIFT) key point detection to solve the obstacles caused by scale variations. The detector with deep feature flow is then incorporated into the tracker to detect key frames to improve tracking accuracy. This method has achieved more than 75% of the distance accuracy and 70% of the overlapping success rate on the VOT2015 and VOT2016 datasets, and the stable tracking video length can reach 6895 frames.

Description: The images involved in the content-based image retrieval (CBIR) applications are collectively represented by features such as color, texture and shape. The precision of the CBIR application relies on the key features used in image representation and its similarity measure. In CBIR, dominant color feature extraction is affected by the predefined intervals used in color quantization. The proposed work mainly concentrates on extracting the dominant color information of the image using the clustering process. The clustering process is initiated by the proposed seed point’s selection approach. This approach derives the number of seed points using the first order statistical measure and maximum range of the distributed pixel values. Moreover, this work gives equal priority to dominant color and its occurrence information in calculating the similarity between query and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OT-scene, Oxford flower and GHIM are taken to investigate the performance of the proposed dominant color based image retrieval application.

Description: In the last decades, various methodologies have been proposed by the researchers for developing effective disease diagnosis support systems (DDSSs). The present research proposes a two-step framework in which an entropy-based feature-selection approach is introduced in the first step and a rule-base hybrid model using Perfect Rule Induction by Sequential Method (PRISM) is explored in the subsequent step for effective diagnosis of diseases. The suggested feature-selection technique is validated using five state-of-the-art classifiers namely C4.5 (a decision tree-based classifier), naïve Bayes (NB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), neural network (NN) and support vector machine (SVM) over fourteen benchmark diseases that are very common and the leading causes of deaths. Next, on the basis of top three performance metrics, viz., prediction accuracy, sensitivity and false positive rate, the performance of the hybrid model over the datasets is compared with its base learner: PRISM, two other competent learners namely C4.5 and NN, and some specialized models. The empirical outcomes positively demonstrate that the hybrid model with application of feature-selection method is a generic model and effective in diagnosing diseases. More importantly, the model not only is able to produce good results but also to elucidate its knowledge in understandable: IF-THEN form (convenient for medical practitioners).