ALBERT

Description: Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

Description: DPA Contest is a world-famous side-channel competition aiming at analyzing and evaluating the implementing security of some latest countermeasures. Improved Rotating S-box Masking Scheme (RSM2.0) is one of the most popular countermeasures designed during DPA Contest V4.2, which arms with both Low Entropy Masking Schemes and shuffling strategy to ensure the software security of AES-128, particularly the non-profiled security. Up to now, conducting high efficient non-profiled attacking scheme with low resource costs is still a challenge. In this paper, we first propose general and non-profiled leakage fingerprint attacks (named NP-LFA) for secret cracking and make use of it to crack RSM2.0 random masks with almost 100% accuracy. Further, we analyze the hidden vulnerabilities embedded in RSM2.0 implementation, and utilize them to bypass the shuffling defense and perform the master key recovery. Official evaluation results show that NP-LFA is capable of compromising RSM2.0 within 14 traces, each of which only costs 60 ms processing time. Such result validates the high efficiency and light-weighted characteristics of our attacking scheme, which has ranked the first in the official website till now. In addition, we discuss and put forward some possible strategies to mitigate our NP-LFA threats.

Description: Secret key leakage has become a security threat in computer systems, and it is crucial that cryptographic schemes should resist various leakage attacks, including the continuous leakage attacks. In the literature, some research progresses have been made in designing leakage resistant cryptographic primitives, but there are still some remaining issues unsolved, e.g. the upper bound of the permitted leakage is fixed. In actual applications, the leakage requirements may vary; thus, the leakage parameter with fixed size is not sufficient against various leakage attacks. In this paper, we introduce some novel idea of designing a continuous leakage-amplified public-key encryption scheme with security against chosen-ciphertext attacks. In our construction, the leakage parameter can have an arbitrary length, i.e. the length of the permitted leakage can be flexibly adjusted according to the specific leakage requirements. The security of our proposed scheme is formally proved based on the classic decisional Diffie–Hellman assumption.

Description: Collaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning strategy for CF to recognize user preferences. Here, black hole entropic fuzzy clustering (BHEFC) is devised for clustering item sequences to form groups with similar item sequences. Moreover, cluster centroids are optimized using the tunicate swarm magnetic optimization algorithm (TSMOA), which is devised by combining tunicate swarm algorithm and magnetic optimization algorithm. After grouping similar items together, the group matching is performed based on a deep convolutional neural network (Deep CNN). Subsequently, the visitor sequence and query sequence are compared using Jaro–Winkler distance, which contributes to the best visitor sequence. From this best visitor sequence, the recommended product is acquired. The proposed TSMOA–BHEFC and Deep CNN outperformed other methods with minimal mean absolute error of 0.200, mean absolute percentage error of 0.198 and root mean square error of 0.447, respectively.

Description: Data owners often encrypt their bulk data and upload it to cloud in order to save storage while protecting privacy of their data at the same time. A data owner can allow a third-party entity to decrypt and access her data. However, if that entity wants to modify the data and publish the same in an authenticated way, she has to ask the owner for a signature on the modified data. This incurs substantial communication overhead if the data is modified often. In this work, we introduce the notion of policy-based editing-enabled signatures, where the data owner specifies a policy for her data such that only an entity satisfying this policy can decrypt the data. Moreover, the entity is permitted to produce a valid signature for the modified data (on behalf of the owner) without interacting with the owner every time the data is modified. On the other hand, a policy-based editing-enabled signature (PB-EES) scheme allows the data owner to choose any set of modification operations applicable to her data and still restricts a (possibly untrusted) entity to authenticate the data modified using operations from that set only. We provide two PB-EES constructions, a generic construction and a concrete instantiation. We formalize the security model for PB-EESs and analyze the security of our constructions. Finally, we evaluate the performance of the concrete PB-EES instantiation.

Description: For the multiprocessor systems modeled by interconnection networks, one of the important properties is the characterization of fault tolerability. Connectivity, as an important parameter to evaluate fault tolerability, has witnessed research achievements. To make the evaluation more practical, conditional connectivity has been promisingly proposed. As one kind of conditional connectivity, $h$-restricted connectivity of a connected graph $G$, denoted by $kappa ^h (G)$, is defined as the cardinality of the minimum vertex cut set $F$ such that $delta (G-F)geq h$. In this paper, we establish a universally $h$-restricted connectivity for a class of hypercube-based compound networks, in which the well-known networks, such as hierarchical cubic network $HCN(n, n)$ and its generalization complete cubic network $CCN(n)$, are involved.

Description: Since its first publication at ASIACRYPT 2014, higher order optimal distinguisher (HOOD) has been the most efficient style of higher order side channel attacks that can be used to evaluate the physical security of a masking device. In practice, the efficiency of HOOD can be empirically evaluated with the success rate (SR) metric. In the empirical evaluation, a large number of power traces are needed, and HOOD should be repeated thousands of times under the values of different parameters, which can make the evaluation process cumbersome and the evaluation price high. In light of this, the exact relationship between the SR of the asymptotic HOOD and the values of different parameters is theoretically built, and the soundness of the theoretical analysis is empirically verified in both the simulated scenario and the real scenario. Then, by setting the values of different parameters, the SR of the asymptotic HOOD can be theoretically estimated. Here, as the signal-to-noise ratio of a masking device approaches to zero, the SR of the asymptotic HOOD approaches to the SR of HOOD. Overall, this contribution may help evaluators to efficiently evaluate the physical security of a masking device with HOOD.

Description: In this paper, we introduce a new construction for unlinkable secret handshake that allows a group of users to perform handshakes anonymously. We define formal security models for the proposed construction and prove that it can achieve session key security, anonymity and affiliation hiding. In particular, the proposed construction ensures that (i) anonymity against protocol participants (including group authority) is achieved since a hierarchical identity-based signature is used in generating group user’s pseudonym-credential pairs and (ii) revocation is achieved using a secret sharing-based revocation mechanism.

Description: Password-based authenticated key exchange (PAKE) allows two parties to compute a common secret key. PAKE offers the advantage of allowing two parties to pre-share only a password. However, when it is executed in a client–server environment, server corruption can expose the clients’ passwords. To be resilient against server compromises, verifier-based authenticated key exchange (VPAKE) is proposed, as an augmented version of PAKE. Thus far, there are two known major VPAKE constructions formally proven secure. However, both involve strong assumptions, such as random oracles. In this paper, we propose a simple and efficient VPAKE using tamper-proof hardware without random oracles to support resilient infrastructures. In particular, we transform Katz–Vaikuntanathan one-round PAKE into two-round VPAKE so as to instill resilience to server compromises. We provide a formal definition of VPAKE using tamper-proof hardware and security proof without random oracles. Finally, we provide a performance analysis and comparisons to previous VPAKE and PAKE protocols. Our transformation supports an efficient VPAKE protocol with six group element communications when the underlying Katz–Vaikuntanathan PAKE is instantiated by Cramer–Shoup ciphertext following the proposal by Benhamouda et al.

Description: Edge centrality has found wide applications in various aspects. Many edge centrality metrics have been proposed, but the crucial issue that how good the discriminating power of a metric is, with respect to other measures, is still open. In this paper, we address the question about the benchmark of the discriminating power of edge centrality metrics. We first use the automorphism concept to define equivalent edges, based on which we introduce a benchmark for the discriminating power of edge centrality measures and develop a fast approach to compare the discriminating power of different measures. According to the benchmark, for a desirable measure, equivalent edges have identical metric scores, while inequivalent edges possess different scores. However, we show that even in a toy graph, inequivalent edges cannot be discriminated by three existing edge centrality metrics. We then present a novel edge centrality metric called forest centrality (FC). Extensive experiments on real-world networks and model networks indicate that FC has better discriminating power than three existing edge centrality metrics.

Description: The Bayesian network (BN) is an important technique to represent and infer knowledge in an Intelligent Tutoring System (ITS); however, ITSs are complex to build. Diverse authors have built BNs based on ontologies to accelerate the building process; nonetheless, they did not fully automate the process, and did not follow the ontologies standard Web Ontology Language, or simplified the final domain. This work proposes a method to build BNs based on ontologies and Wikipedia information to be employed on ITSs. The proposed method constructs the qualitative part of the BN through classes and relations of ontologies; the quantitative part is created based on frequencies, hops, and a measure of similarity between concepts of the ontology represented by Wikipedia articles. This study carried out an experiment to determine the correlation of our method against domain experts opinions; the method obtained a positive correlation of $0.647$ according to the Spearman test. The method constructs a BN to represent the knowledge in ITSs, in a similar way as experts would, supporting the automatic build of these systems.

Description: In the past few years, much importance and attention have been attached to completely independent spanning trees (CISTs). Many results, such as edge-disjoint Hamilton cycles, traceability, number of spanning trees, structural properties, topological indices, etc., have been obtained on line graphs, and researchers have applied the line graphs of some interconnection networks such as generalized hypercubes, augmented cubes, crossed cubes, etc., into data center networks, such as SWCube, AQLCube, BCDC, etc. At the meanwhile, few results of CISTs are reported on the line graphs. In this paper, we establish the relation of edge-disjoint spanning trees in an interconnection network $G$’ with its line graph $G$ by proposing a general algorithm for the first time. By this method, more CISTs can be obtained comparing with results in the literature. Then, the decrease of diameter is discussed and simulation experiments are shown on the line graphs of hypercubes.

Description: The security of most lattice-based cryptography schemes are based on two computational hard problems which are the short integer solution (SIS) and learning with errors (LWE) problems. The computational complexity of SIS and LWE problems are related to approximating shortest vector problem and bounded distance decoding (BDD) problem. Approximating BDD is a special case of approximating closest vector problem (CVP). In this paper, we revisit the study for approximating CVP. We give a proof that approximating the CVP over $ell _infty $-norm (CVP$_infty $) within any constant factor is NP-hard. The result is obtained by the gap-preserving reduction from Min Total Label Cover problem in $ell _1$-norm to to CVP$_infty $. This proof is simpler than known proofs [ 10].

Description: According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising.

Description: The stream ciphers RCR-64 and RCR-32 designed by Sekar et al. are the most recent additions to the Py-family of stream ciphers, originally designed by Biham et al. The ciphers are among the fastest stream ciphers on software. To the best of our knowledge, the only reported attacks on the ciphers are due to Ding et al., published in the Journal of Universal Computer Science. In this paper, we review these alleged attacks on the RCR ciphers and show that they are based on non-existent keystream biases stemming from flawed probability calculations.

Description: Scanning images and converting the scanned information into digital format is an active research area. Scanning is an automated, fast and efficient process as compared to the traditional data entry, and the resultant converted data is more accurate. Recognizing digits from the scanned images is a challenging task. To address this issue, most of the existing techniques perform multiple individual steps that are localization, segmentation and recognition. Some researchers also focused on adopting a unified approach that combined these three steps for multi-digit recognition of up to five digits. To cope with the modern requirements, a unified multi-digit recognition technique capable of recognizing more than five digits is the need of the hour. Considering this necessity, a unified multi-digit recognition approach is presented in the current study that can recognize sequences up to 18 digits long. The proposed technique is based on a deep convolutional neural network algorithm that performs two basic functions. First, it localizes and extracts the region of interest in the image, and then it performs multi-digit recognition. The proposed algorithm recognizes sequences of up to 18 characters that makes it one of the preferred recognition techniques among the existing algorithms. The proposed technique is compared with state-of-the-art techniques and is proved to be superior and robust. The experiments are performed on two datasets, and overall accuracy up to 98% is achieved.

Description: This paper proposes an approach to the specification and model checking of a large, important class of distributed algorithms called control algorithms (CAs), which are superimposed on underlying distributed systems (UDSs). The approach is based on rewriting logic by moving from its object level to the meta-level. We introduce the idea of specifying CAs as meta-programs that take the specifications of UDSs and automatically generate the specifications of the UDSs on which the CAs are superimposed (UDS-CAs). Due to many options, such as network topologies, even fixing the number of each kind of entities, such as mobile support stations (MSSs) and mobile hosts (MHs) in a mobile checkpointing algorithm, there are many instances of a UDS. To address the problem, we generate all possible initial states of a UDS for a fixed number of each kind of entities such that some constraints, such as MSSs strongly connected with a wired network, are fulfilled and conduct model checking for each of the initial states. We demonstrate the usefulness by reporting on a case study where a counterexample is found for some specific initial states but not for the other initial states, detecting a subtle flaw lurking in a mobile checkpointing algorithm.

Description: A $k$-query locally decodable code (LDC) $C$ allows one to encode any $n$-symbol message $x$ as a codeword $C(x)$ of $N$ symbols such that each symbol of $x$ can be recovered by looking at $k$ symbols of $C(x)$, even if a constant fraction of $C(x)$ has been corrupted. Currently, the best known LDCs are matching vector codes (MVCs). A modulus $m=p_1^{alpha _1}p_2^{alpha _2}cdots p_r^{alpha _r}$ may result in an MVC with $kleq 2^r$ and $N=exp (exp (O((log n)^{1-1/r} (log log n)^{1/r})))$. The $m$ is good if it is possible to have $k

Description: Rotational-XOR cryptanalysis is a very recent technique for ARX ciphers. In this paper, the probability propagation formula of RX-cryptanalysis in modular addition is extended, and the calculation of RX-difference probability for any rotation parameter ($0

Description: The traditional relay deployment problem typically assumes that the locations of users are known and stationary, which is not realistic in practice. The prevalence of mobile devices has made it possible to collect user trajectory and account for user movement while deploying relays. Under this background, a novel problem trajectory-based relay deployment (TBRD) is put forward. This problem considers communication-related metrics and is aimed at maximizing user connection time as users roam through the target area under relay resource constraints, which is more reasonable than the goal of expanding the relay coverage. To figure out the TBRD, we first propose the concept demand nodes (DNs), which are virtual weighted nodes representing the locations where users frequently pass or stay for a long period. Next, we design a Demand Node Generation algorithm that can transform the continuous historical user trajectory into a number of discrete DNs. By generating DNs, we convert the TBRD problem into a demand node coverage (DNC) problem, which is proved to be NP-complete. Followed by that, we introduce an approximation algorithm, named Submodular Iterative Deployment Algorithm, which solves the DNC problem with the approximation factor $1-frac{1}{sqrt{ecdot (1-1/k)}}$, where $e$ is the mathematical constant, and $k$ is the relay number constraint. Finally, five real trajectory datasets are used to evaluate our proposed algorithm, and the simulation results demonstrate that our algorithm can obtain high coverage for users in motion, which can lead to better user experience. In addition, we also analyze the impact of different parameters on the coverage performance, and under this circumstance, we may safely come to the conclusion that our work is at the leading edge to utilize user trajectories for relay deployment in wireless networks.

Description: Certificateless public key cryptography (CL-PKC) overcomes the difficulties of the certificate managements in traditional public key infrastructure (PKI) and the key escrow problem in ID-Based public key cryptography (ID-PKC), concurrently. In 2018, Tseng et al. proposed a certificateless signature (CLS) scheme and claimed that their proposal is the first scheme which satisfies the security against the level-3 KGC (according to Girault’s three categorizations of the honesty level of a trusted third party (TTP) which is proposed in 1991), in the standard model. However, we will show that unfortunately their scheme is even vulnerable against a malicious KGC. Afterwards, we will improve their scheme to be robust against the proposed attack. Finally, we will propose a CLS scheme secure against the level-3 KGC in the standard model, based on Yuan and Wang’s CLS scheme. We will show that our proposal not only satisfies the level-3 security as well as the basic security requirements of a CLS scheme in the standard model, but also is more efficient than the previous works in the sense of computation and communication costs.

Description: Existing association-based outlier detection approaches were proposed to seek for potential outliers from huge full set of uncertain data streams ($UDS$), but could not effectively process the small scale of $UDS$ that satisfies preset constraints; thus, they were time consuming. To solve this problem, this paper proposes a novel minimal rare pattern-based outlier detection approach, namely Constrained Minimal Rare Pattern-based Outlier Detection (CMRP-OD), to discover outliers from small sets of $UDS$ that satisfy the user-preset succinct or convertible monotonic constraints. First, two concepts of ‘maximal probability’ and ‘support cap’ are proposed to compress the scale of extensible patterns, and then the matrix is designed to store the information of each valid pattern to reduce the scanning times of $UDS$, thus decreasing the time consumption. Second, more factors that can influence the determination of outlier are considered in the design of deviation indices, thus increasing the detection accuracy. Extensive experiments show that compared with the state-of-the-art approaches, CMRP-OD approach has at least 10% improvement on detection accuracy, and its time cost is also almost reduced half.

Description: Presently, facial image recognition via a thermal camera is a critical phase in numerous fields. Systems using thermal facial images suffer from numerous problems in face identification. In this paper, a model Edge-Aided Generative Adversarial Network (EA-GAN) is introduced to overcome the difficulties of thermal face identification by synthesizing a visible faces image from the thermal version. To enhance the performance of the Conditional Generative Adversarial Network (CGAN) model for the create realistic face images, the edge information extracted from the thermal image has been used as input, thus lead to improving overall the system's achievement. Moreover, a new model is presented in the present work for face identification by integrating two Convolutional Neural Networks (CNN) to achieve high and rapid accuracy rates. Based on the experiments on the Carl dataset for faces, it is indicated that EA-GAN can synthesize visually comfortable and identity-preserving faces; thus, better performance is achieved in comparison with the state-of-the-art approaches for thermal facial identification.

Description: The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge base to reach a preliminary diagnosis of diseases based on patients’ symptoms. However, instead of sending every response to the cloud and getting data from cloud server, it may often be desirable to prune a portion of the knowledge base that is stored in a graph form and download in to the mobile devices. Downloading data from cloud depends on the storage, battery power, processor of a mobile device, wireless network bandwidth and cloud processor capacity. In this paper, we focus on developing mathematical expressions involving the above mentioned criteria and show how these parameters are dependent on each other. The expressions built in this paper can be used in real-life scenarios to take decisions regarding the amount of data to be pruned in order to save energy as well as time.

Description: The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.

Description: Identifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The DC is an efficient method but not effective. The BC and CC are effective but not efficient. They have high computational complexity. To balance the effectiveness and efficiency, this paper proposes the neighborhood entropy centrality to rank the influential nodes. The proposed method uses the notion of entropy to improve the DC. For evaluating the performance, the susceptible-infected-recovered model is used to simulate the information spreading process of messages on nine real-world networks. The experimental results reveal the accuracy and efficiency of the proposed method.

Description: Recently, unmanned aerial vehicles (UAVs) have emerged to enhance data processing, network monitoring, disaster management and other useful applications in many different networks. Due to their flexibility, cost efficiency and powerful capabilities, combining these UAVs with the existing wireless sensor networks (WSNs) could improve network performance and enhance the network lifetime in such networks. In this research, we propose a task offloading mechanism in UAV-aided WSN by implementing a utility-based learning collaborative algorithm that will enhance the service satisfaction rate, taking into account the delay requirements of the submitted tasks. The proposed learning algorithm predicts the queuing delays of all UAVs instead of having a global overview of the system, which reduces the communication overhead in the network. The simulation results showed the effectiveness of our proposed work in terms of service satisfaction ratio compared with the non-collaborative algorithm that only processes the task locally in the WSN cluster.

Description: Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.

Description: Modern 5G networks promise more bandwidth, less delay and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization and Network Slicing as key enablers. Within that context, efficiently provisioning the network and cloud resources of a wide variety of applications with dynamic user demand is a real challenge. We study here the network slice reconfiguration problem. Reconfiguring network slices from time to time reduces network operational costs and increases the number of slices that can be managed within the network. However, this affect the Quality of Service of users during the reconfiguration step. To solve this issue, we study solutions implementing a make-before-break scheme. We propose new models and scalable algorithms (relying on column generation techniques) that solve large data instances in few seconds.

Description: There are several Contact Tracing solutions since the outbreak of SARS COVID-19. All these solutions are localized—specific to a country. The Apps supported by these solutions do not interwork with each other. There are no standards to the proximity data collected by these Apps. Once the international travel restrictions are relaxed, this will become an issue. This paper explores this issue, by addressing one of the key requirements of Contact Tracing solutions. All the current solutions use an Identifier, Proximity Identifier (PID), that anonymously represents the user in the proximity data exchanged. The PID used in these applications varies in their structure, management and properties. This paper first identifies the common desirable properties of PID, including some non-obvious ones for its global application. This identification is essential for the design and development of the Contact Tracing solution that can work across boundaries seamlessly. The paper also evaluates representative solutions from two different design classes against these properties.

Description: Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.

Description: The advancements in the area of object localization are in great progress for analyzing the spatial relations of different objects from the set of images. Several object localization techniques rely on classification, which decides, if the object exist or not, but does not provide the object information using pixel-wise segmentation. This work introduces an object detection and localization technique using semantic segmentation network (SSN) and deep convolutional neural network (Deep CNN). Here, the proposed technique consists of the following steps: Initially, the image is denoised using the filtering to eliminate the noise present in the image. Then, pre-processed image undergoes sparking process for making the image suitable for the segmentation using SSN for object segmentation. The obtained segments are subjected as the input to the proposed Stochastic-Cat Crow optimization (Stochastic-CCO)-based Deep CNN for the object classification. Here, the proposed Stochastic-CCO, obtained by integrating stochastic gradient descent and the CCO, is used for training the Deep CNN. The CCO is designed by the integration of cat swarm optimization (CSO) and crow search algorithm and takes advantages of both optimization algorithms. The experimentation proves that the proposed Stochastic-CCO-based Deep CNN-based technique acquired maximal accuracy of 98.7.

Description: The innovative trends of cloud computing acquired the interest of several individuals or enterprises that started outsourcing data to the cloud servers. Recently, numerous techniques are introduced for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and incur huge information loss. This paper introduces the efficient technique, named the chronological sailfish optimizer (CSFO) algorithm for privacy preservation in cloud computing. The proposed CSFO is devised by integrating the chronological concept in SailFish optimizer. The input data are fed to a privacy-preservation process wherein hamming weight-based RSA and Khatri-Rao products are utilized for data privacy. Here, the hamming weighted-based RSA is determined by combining the sha256 algorithm with the hamming weight with Rivest–Shamir–Adleman (HRSA) system. Hence, an optimization-driven algorithm is utilized to evaluate optimal matrix generation to handle both the utility and the sensitive information. Here, the fitness function is newly devised considering, realism, privacy and fitness. The experimentation is performed using four datasets, like Pathway Interaction Database, Hungarian, Cleveland and Switzerland. The proposed CSFO provided superior performance with maximal privacy of 0.2173, maximal realism 0.9456 and maximal fitness of 0.5416.

Description: Multi-keyword ranked searchable encryption (MRSE) supports multi-keyword contained in one query and returns the top-k search results related to the query keyword set. It realized effective search on encrypted data. Most previous works about MRSE can only make the complete keyword search and rank on the server-side. However, with more practice, users may not be able to express some keywords completely when searching. Server-side ranking increases the possibilities of the server inferring some keywords queried, leading to the leakage of the user’s sensitive information. In this paper, we propose a new MRSE system named ‘multi-keyword ranked searchable encryption with the wildcard keyword (MRSW)’. It allows the query keyword set to contain a wildcard keyword by using Bloom filter (BF). Using hierarchical clustering algorithm, a clustering Bloom filter tree (CBF-Tree) is constructed, which improves the efficiency of wildcard search. By constructing a modified inverted index (MII) table on the basis of the term frequency-inverse document frequency (TF-IDF) rule, the ranking function of MRSW is performed by the user. MRSW is proved secure under adaptive chosen-keyword attack (CKA2) model, and experiments on a real data set from the web of science indicate that MRSW is efficient and practical.

Description: Thanks to excellent reliability, availability, flexibility and scalability, redundant arrays of independent (or inexpensive) disks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces $au $ to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling.

Description: Brain tumor classification is highly effective in identifying and diagnosing the exact location of the tumor in the brain. The medical imaging system reported that early diagnosis and classification of the tumor increases the life of the human. Among various imaging modalities, magnetic resonance imaging (MRI) is highly used by clinical experts, as it offers contrast information of brain tumors. An effective classification method named fractional-chicken swarm optimization (fractional-CSO) is introduced to perform the severity-level tumor classification. Here, the chicken swarm behavior is merged with the derivative factor to enhance the accuracy of severity level classification. The optimal solution is obtained by updating the position of the rooster, which updates their location based on better fitness value. The brain images are pre-processed and the features are effectively extracted, and the cancer classification is carried out. Moreover, the severity level of tumor classification is performed using the deep recurrent neural network, which is trained by the proposed fractional-CSO algorithm. Moreover, the performance of the proposed fractional-CSO attained better performance in terms of the evaluation metrics, such as accuracy, specificity and sensitivity with the values of 93.35, 96 and 95% using simulated BRATS dataset, respectively.

Description: The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach.

Description: Unmanned aerial vehicle (UAV) co-operative systems are complex cyber-physical systems that integrate a high-level control algorithm with pre-existing closed implementations of lower-level vehicle kinematics. In model-driven development, simulation is one of the techniques that are usually applied, together with testing, in the analysis of system behaviours. This work proposes a method and tools to validate the design of UAV co-operative systems based on co-simulation and formal verification. The method uses the Prototype Verification System, an interactive theorem prover based on a higher-order logic language, and the Functional Mock-up Interface, a widely accepted standard for co-simulation. In this paper, results on the co-simulation and proofs of safety requirements of a representative co-ordination algorithm are shown and discussed in a scenario where quadcopters are deployed and perform space-coverage operations.

Description: We present the first constant round, multicast, authenticated tree-based R-LWE group key exchange protocol with logarithmic communication and memory complexity. Our protocol achieves post-quantum security through a reduction to a Diffie–Hellman-like analogue to the decisional R-LWE problem. We also present a sequential version with constant memory complexity but a logarithmic number of rounds and communication complexity.

Description: The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. Wireless heterogeneous networks are created by combining several radio access technologies, each with its own potentials, capabilities and limitations. In these networks, providing real-time services with quality assurance is essential. For effective use of radio resources, the Radio Resource Management method was introduced which its performance and efficiency is better than the control of independent radio resources in any radio access technology. In this paper, we introduced a novel approach to select the most effective radio access technologies by taking into account some performance parameters like the type of service, users’ distribution pattern and the cost of the services. It also optimizes the handover relations between macrolayer and small cells. The proposed approach is a self-optimizing model can be employed to control resources and improve performance indices associated with mobile networks without human interference by only relying on network intelligence. In order to maximize the network performance, we applied the dynamic backhauling technique to analyze the uplink signaling data which increased the validity level of the decision-making process. Based on the extracted semantic information, the network decision-making engine is able to adjust the network parameters and efficiently allocate the resources. The numerical results exhibit considerable power saving for different traffic models in addition to reduce the rate of vertical handovers. The results also show increase the network throughput by up to 30%.

Description: As the scale of the system expands, processor failures are inevitable. Fault diagnosis has great significance in analyzing the reliability of multiprocessing systems. Probabilistic fault diagnosis is a method that attempts to diagnose nodes correctly with high probability. In this paper, we extend the threshold $t leq 2$ to threshold $t=3$ for regular networks based on probabilistic diagnosis algorithm and determine the status of a cluster of nodes by analyzing the local performance. Moreover, we evaluate the global performance based on the Poisson distribution and the Binomial distribution and show that the achievement in terms of correctness demonstrates a good performance. Finally, we employ the probabilistic diagnosis scheme to explore some well-known networks, including complete cubic networks, dual cubes and hierarchical hypercubes as well.

Description: Fully homomorphic encryption (FHE) allows direct computations over the encrypted data without access to the decryption. Hence multi-key FHE is well suitable for secure multiparty computation. Recently, Brakerski et al. (TCC 2019 and EUROCRYPT 2020) utilized additively homomorphic encryption to construct FHE schemes with different properties. Motivated by their work, we are attempting to construct multi-key FHE schemes via additively homomorphic encryption. In this paper, we propose a general framework of constructing multi-key FHE, combining the additively homomorphic encryption with specific multiparty computation protocols constructed from encryption switching protocol. Concretely, every involved party encrypts his plaintexts with an additively homomorphic encryption under his own public key. Then the ciphertexts are evaluated by suitable multiparty computation protocols performed by two cooperative servers without collusion. Furthermore, an instantiation with an ElGamal variant scheme is presented. Performance comparisons show that our multi-key FHE from additively homomorphic encryption is more efficient and practical.

Description: Mobile edge computing (MEC) is a key feature of next-generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research, we investigated on connection management approaches in multi-access edge computing systems. This paper presents joint radio resource allocation and MEC optimization in a multi-layer NOMA HetNet in order to maximize system’s energy efficiency. The continues carrier allocation and handoff decision variables, in addition to the interference incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming. Network selection is done statically based on the Analytic Hierarchy Process, and station selection is done dynamically based on the Data Envelope Analysis method. Also, an effective feedback mechanism has been designed in collaboration with the server resource manager to solve a global optimization problem in order to load balancing and meet the users quality of service constraints simultaneously. To reduce the computational complexity and to achieve a locally optimal solution, we applied variable relaxation and majorization minimization approach in which offloading decision and multi-part Markov noise analysis have been developed to model users’ preferences without the need for explicit information from the users. Based on the simulations, the proposed approach not only results in a significant increase of system’s energy efficiency but also enhances the system throughput in multiple-source scenarios.

Description: This paper plans to develop the optimal brain tumor classification model with diverse intelligent methods. The main phases of the proposed model are ‘(a) image pre-processing, (b) skull stripping, (c) tumor segmentation, (d) feature extraction and (e) classification’. At first, pre-processing of the image is performed by converting the image from red green blue to gray followed by median filtering. Further, skull stripping is done for removing the extra-meningeal tissue from the head image, which is done by Otsu thresholding. As the main contribution, the tumor segmentation is done by the optimized threshold-based tumor segmentation using multi-objective randomly updated beetle swarm and multi-verse optimization (RBS-MVO). The objective constraints considered for the segmentation of the tumor is entropy and variance. Next, the feature extraction techniques like gray level co-occurrence matrix, local binary pattern and gray-level run length matrix is accomplished to extract the set of features. The classification side uses the combination of neural network (NN) and deep learning model called convolutional neural network (CNN) for tumor classification. The extracted features are subjected to NN, and the segmented image is taken as input to CNN. In addition, the weight function of NN and hidden neurons of CNN is optimized by the RBS-MVO.

Description: The task of question generation (QG) aims to create valid questions and correlated answers from the given text. Despite the neural QG approaches have achieved promising results, they are typically developed for languages with rich annotated training data. Because of the high annotation cost, it is difficult to deploy to other low-resource languages. Besides, different samples have their own characteristics on the aspects of text contextual structure, question type and correlations. Without capturing these diversified characteristics, the traditional one-size-fits-all model is hard to generate the best results. To address this problem, we study the task of cross-lingual QG from an adaptive learning perspective. Concretely, we first build a basic QG model on a multilingual space using the labelled data. In this way, we can transfer the supervision from the high-resource language to the language lacking labelled data. We then design a task-specific meta-learner to optimize the basic QG model. Each sample and its similar instances are viewed as a pseudo-QG task. The asking patterns and logical forms contained in the similar samples can be used as a guide to fine-tune the model fitly and produce the optimal results accordingly. Considering that each sample contains the text, question and answer, with unknown semantic correlations among them, we propose a context-dependent retriever to measure the similarity of such structured inputs. Experimental results on three languages of three typical data sets show the effectiveness of our approach.

Description: Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.

Description: Laplacian support vector machine (LapSVM) is an extremely popular classification method and relies on a small number of labels and a Laplacian regularization to complete the training of the support vector machine (SVM). However, the training of SVM model and Laplacian matrix construction are usually two independent process. Therefore, In this paper, we propose a new adaptive LapSVM method to realize semi-supervised learning with a primal solution. Specifically, the hinge loss of unlabelled data is considered to maximize the distance between unlabelled samples from different classes and the process of dealing with labelled data are similar to other LapSVM methods. Besides, the proposed method embeds the Laplacian matrix acquisition into the SVM training process to improve the effectiveness of Laplacian matrix and the accuracy of new SVM model. Moreover, a novel optimization algorithm considering primal solver is proposed to our adaptive LapSVM model. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics on both real datasets and synthetic datasets.

Description: The amount of data generated is increasing day by day due to the development in remote sensors, and thus it needs concern to increase the accuracy in the classification of the big data. Many classification methods are in practice; however, they limit due to many reasons like its nature for data loss, time complexity, efficiency and accuracy. This paper proposes an effective and optimal data classification approach using the proposed Ant Cat Swarm Optimization-enabled Deep Recurrent Neural Network (ACSO-enabled Deep RNN) by Map Reduce framework, which is the incorporation of Ant Lion Optimization approach and the Cat Swarm Optimization technique. To process feature selection and big data classification, Map Reduce framework is used. The feature selection is performed using Pearson correlation-based Black hole entropy fuzzy clustering. The classification in reducer part is performed using Deep RNN that is trained using a developed ACSO scheme. It classifies the big data based on the reduced dimension features to produce a satisfactory result. The proposed ACSO-based Deep RNN showed improved results with maximal specificity of 0.884, highest accuracy of 0.893, maximal sensitivity of 0.900 and the maximum threat score of 0.827 based on the Cleveland dataset.

Description: Nowadays, Apache Hadoop and Apache Spark are two of the most prominent distributed solutions for processing big data applications on the market. Since in many cases these frameworks are adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted applications, for instance when service-level agreements are established with end-users. In this work, we propose and validate a hybrid approach for the performance prediction of big data applications running on clouds, which exploits both analytical modeling and machine learning (ML) techniques and it is able to achieve a good accuracy without too many time consuming and costly experiments on a real setup. The experimental results show how the proposed approach attains improvement in accuracy, number of experiments to be run on the operational system and cost over applying ML techniques without any support from analytical models. Moreover, we compare our approach with Ernest, an ML-based technique proposed in the literature by the Spark inventors. Experiments show that Ernest can accurately estimate the performance in interpolating scenarios while it fails to predict the performance when configurations with increasing number of cores are considered. Finally, a comparison with a similar hybrid approach proposed in the literature demonstrates how our approach significantly reduce prediction errors especially when few experiments on the real system are performed.

Description: Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.

Description: This paper proposed a novel precise point set registration method based on feature fusion for three-dimensional data. Firstly, for the prominent foreground with dense and continuous cluster structure, we propose an automatic extraction method combining the principal component analysis projection and density-based clustering method. Secondly, for point sets containing noises, we introduce correntropy measurement into registration to weaken their influence. Thirdly, for the precise registration of uneven distribution of points in the same point set, we propose a feature fusion based algorithm which is distribution specific, using point-to-point measurement for densely distributed foreground and point-to-plane measurement for sparsely distributed background, in case that only one measurement method is used for the whole point set the registration gets trapped into local extremum. Finally, we give the optimization algorithm of the proposed method. We conduct experiments on real orthodontics scenes to verify the effectiveness of our proposed feature extraction method and registration algorithm, and experimental results demonstrate that both the proposed solutions are proper for their respective tasks than other existing methods.

Description: Recently, significant breakthroughs have been achieved in the field of object detection. However, existing methods mostly focus on the generic object detection task. Performance degradation can be unavoidable when applying the existing methods to some specific situations directly, e.g. a low-light environment. To address this issue, we propose a single-shot real-time object Detector based on Low-light image Enhancement, namely LEDet. LEDet adapts itself to the low-light detection task in three aspects. First, a low-light enhancement module is introduced as the image preprocessor, producing the augmented inputs from the low-light images. Second, two modules, i.e. low-light and enhanced features fusion module and the scale-aware channel attention dilated convolution module are designed. These two modules aim at learning robust and discriminative features from objects of various sizes hidden in the darkness. In experiments, we validate the effectiveness of each part of our LEDet model via several ablation studies. We also compare LEDet with various methods on the Exclusively Dark dataset, showing that our model achieves the state-of-the-art performance on the balance between speed and accuracy.

Description: Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task.

Description: Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.

Description: Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.

Description: Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance.

Description: Events represent a tipping point that affects users’ opinions and vary depending upon their popularity from local to international. Indeed, social media offer users platforms to express their opinions and commitments to events that attract them. However, owing to the volume of data, users are encountering a difficulty to accede to the preferred events according to their features that are stored in their social network profiles. To surmount this limitation, multiple event recommendation systems appeared. Nevertheless, these systems use a limited number of event dimensions and user’s features. Besides, they consider users’ features stored in a single user’s profile and disregard the semantic concept. In this research, an approach for multi-dimensional event recommendation is set forward to recommend events to users resting on several event dimensions (engagement, location, topic, time and popularity) and some user’s features (demographic data, position and user’s/friend’s interests) stored in multi-user’s profiles by considering the semantic relationships between user’s features, specifically user’s interests. The performance of our approach was assessed using error rate measurements (mean absolute error, root mean squared error and cross-validation). Experiment that results on real-world event data sets confirmed that our approach recommends events that fit the user more than the previous approaches with the lowest error rate values.

Description: Reliability evaluation of interconnection networks is of significant importance to the design and maintenance of interconnection networks. The component connectivity is an important parameter for the reliability evaluation of interconnection networks and is a generalization of the traditional connectivity. The $g$-component connectivity $ckappa _g (G)$ of a non-complete connected graph $G$ is the minimum number of vertices whose deletion results in a graph with at least $g$ components. Determining the $g$-component connectivity is still an unsolved problem in many interconnection networks. Let $Q_{n,k}$ ($1leq kleq n-1$) denote the $(n, k)$-enhanced hypercube. In this paper, let $ngeq 7$ and $1leq k leq n-5$, we determine $ckappa _{g}(Q_{n,k}) = g(n + 1) - frac{1}{2}g(g + 1) + 1$ for $2 leq g leq n$. The previous result in Zhao and Yang (2019, Conditional connectivity of folded hypercubes. Discret. Appl. Math., 257, 388–392) is extended.

Description: A general framework to investigate the interference and coverage probability is proposed in this paper for indoor terahertz (THz) communications with beamforming antennas. Due to the multipath effects of THz band (0.1–10 THz), the line of sight and non-line of sight interference from users and access points (APs) (both equipped with beamforming antennas) are separately analyzed based on distance-dependent probability functions. Moreover, to evaluate the effects of obstacles in real applications, a Poisson distribution blockage model is implemented. Moreover, the coverage probability is derived by means of signal to interference plus noise ratio (SINR). Numerical results are conducted to present the interference and coverage probability with different parameters, including the indoor area size, SINR threshold, numbers of interfering users and APs and half-power bandwidth of beamforming antenna.

Description: We consider the problem of real-time scheduling in uniprocessor devices powered by energy harvesters. In particular, we focus on mixed sets of tasks with time and energy constraints: hard deadline periodic tasks and soft aperiodic tasks without deadlines. We present an optimal aperiodic servicing algorithm that minimizes the response times of aperiodic tasks without compromising the schedulability of hard deadline periodic tasks. The server, called Slack Stealing with energy Preserving (SSP), is designed based on a slack stealing mechanism that profits whenever possible from available spare processing time and energy. We analytically establish the optimality of SSP. Our simulation results validate our theoretical analysis.

Description: Reversible data hiding (RDH) with contrast enhancement (RDH-CE) is a special type of RDH in improving the subjective visual perception by enhancing the image contrast during the process of data embedding. In RDH-CE, data hiding is achieved via pairwise histogram expansion, and the embedding rate can be increased by performing multiple cycles of histogram expansions. However, when embedding rate gets high, human visible image degradation is observed. Previous work designed an upper bound of the embedding level for RDH-CE, which effectively avoids image over-sharping but offers limited embedding capacity. In this paper, a better tunable bound is designed to enhance the embedding capacity of RDH-CE by exploiting the characteristics of histogram distribution. Furthermore, the objective distortion introduced by histogram pre-shifting is minimized when the embedding level is no more than the upper bound, and the human visible degradation is minimized when the embedding level exceeds the limitation of the proposed upper bound. Experimental results validate that the proposed method provides appropriate upper bound of the embedding level, increases the effective embedding capacity and offers better image contrast.

Description: The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge service. We focus on two types of physical device (PD)-allocation policies that define how to select a PD from a CDC/EDC for service provision. The first is randomly selecting a PD, denoted as RandAvail. The other is denoted as SEQ, in which an available idle PD is selected to serve client requests only after the waiting queues of all busy PDs are full. We first present the models in the case of an On–Off request arrival process and verify the approximate accuracy of the proposed models through simulations. Then, we apply analytical models for comparing RandAvail and SEQ policies, in terms of request rejection probability and mean response time, under various system parameter settings.

Description: The amount of online video content is exponentially increasing, which spurs its access demands. Providing optimal quality of service (QoS) for this ever-increasing video data is a challenging task due to the number of QoS constraints. The system resources, the distributed system platform and the transport protocol thus all need to collaborate to guarantee an acceptable level of QoS for the optimal video streaming process. In this paper, we present a comprehensive survey on QoS management for the video-on-demand systems. First, we focus on load management and replication algorithms in content delivery networks and peer-to-peer (P2P) networks for their shortcomings. We also address the problem of admission control and resource allocation with the objectives of congestion avoidance and frame-loss reduction. Besides, we introduce and discuss various replication schemes. For both the client–server architecture and P2P networks, we highlight the need for a specific storage management policy to preserve system reliability and content availability. We also focus on content distribution and streaming protocols scaling. We deduce that content availability is linked to the characteristics and the performance of the streaming protocols. Finally, we create a comparison table that presents the different contributions of the discussed approaches as well as their limitations. We believe that such a comprehensive survey provides useful insights and contributes to the related domains.

Description: The inability to scale is one of the most concerning problems looming in blockchain systems, where every node has to store all contents of the ledger database locally, leading to centralization and higher operation costs. In this paper, we propose a model named virtual block group (VBG), which aims at addressing the node storage scalability problem. Adopting the VBG model, each node only needs to store part of block data and saves the VBG storage index to distributed hash table by taking block data as a resource, thus improving the query efficiency of block data. With the incentive mechanism of block data storage, and the storage verification and audit mechanism of block data, the security and reliability of block data storage can be ensured. The analysis and calculation show that this model saves hard drive storage space of the node to a greater extent with a shorter time of requesting block data, in the premise of ensuring secure and reliable block data. Compared to other technologies such as sharding, our model does not change the consensus mechanism or the network topology and retains the reliability and security of the original blockchain system.

Description: Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol.

Description: The exponential growth in mobile broadband data traffic with demand for faster data connectivity has become the most engaging challenges for mobile operators. They are facing an enormous data load in the core network and are finding new solutions to offload data to other complementary technologies. Mobile data offloading using device-to-device (D2D) communication stands out as the promising and the low-cost solution to reduce the burden on cellular network. Data offloading is the process of reducing the load in the cellular medium by using alternative wireless technologies for bearing data using opportunistic assignment of nodes. In this paper, iNHeRENT, a Novel HybRid user equipment (UE) selection scheme using D2D communication in next generation wireless networks that provides better offloading efficiency and throughput than the existing schemes, is proposed. Here, a small set of Wi-Fi-enabled hybrid user equipment ($UE_H$*) is chosen to offload cellular data in an efficient way. The objective of the work is to use minimum number of $UE_H$* to cover maximum number of UE in the serving area of an evolved Node B and to offload maximum amount of data. A $UE_H$* is a special UE with both cellular and Wi-Fi interfaces enabled to offload data. The coverage, throughput, packet delivery ratio and offloading efficiency metrics for the selected number of $UE_H$* are considered, and it is found that an offloading efficiency of 95.45% was achieved for a minimum number of 7% $UE_H$* using iNHeRENT.

Description: In the modern era, Internet usage has become a basic necessity in the lives of people. Nowadays, people can perform online shopping and check the customer’s views about products that purchased online. Social networking services enable users to post opinions on public platforms. Analyzing people’s opinions helps corporations to improve the quality of products and provide better customer service. However, analyzing this content manually is a daunting task. Therefore, we implemented sentiment analysis to make the process automatically. The entire process includes data collection, pre-processing, word embedding, sentiment detection and classification using deep learning techniques. Twitter was chosen as the source of data collection and tweets collected automatically by using Tweepy. In this paper, three deep learning techniques were implemented, which are CNN, Bi-LSTM and CNN-Bi-LSTM. Each of the models trained on three datasets consists of 50K, 100K and 200K tweets. The experimental result revealed that, with the increasing amount of training data size, the performance of the models improved, especially the performance of the Bi-LSTM model. When the model trained on the 200K dataset, it achieved about 3% higher accuracy than the 100K dataset and achieved about 7% higher accuracy than the 50K dataset. Finally, the Bi-LSTM model scored the highest performance in all metrics and achieved an accuracy of 95.35%.

Description: In this research, an automated analysis is performed on students’ chat and text data generated by social media platforms over the course of one semester and thoroughly analyzed for potential feedback about teaching, exams, and course contents. A data crawler is developed that performs horizontal and vertical samplings of the data. After data crawling, a few preprocessing steps are performed including text extraction, noise removal, stop-word removal, word stemming, text classification, and feature extraction. The intensity of a review is determined using four measures containing knowledge and understanding, course contents, teaching style, and assessment procedures for a specific course. The proposed system contains features from text mining and web mining to automatically identify a review whenever a user writes comments on their studies. This system aims to provide curriculum development committees with valuable online student feedback and assist in curriculum improvements. By comparing these automated reviews to results obtained from manual student survey forms, we found that the automated system yields the same output but at a fraction of the cost and time typically spent on collecting and analyzing manual student surveys.

Description: As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

Description: The prices in the stock market are dynamic in nature, thereby pretend as a hectic challenge to the sellers and buyers in predicting the trending stocks for the future. To ensure effective prediction of the stock market, the chronological penguin Levenberg–Marquardt-based nonlinear autoregressive network (CPLM-based NARX) is employed, and the prediction is devised on the basis of past and the recent rank of market. Initially, input data are subjected to the features extraction that is based on the technical indicators, such as WILLR, ROCR, MOM, RSI, CCI, ADX, TRIX, MACD, OBV, TSF, ATR and MFI. The technical indicator is adapted for predicting the stock market. The wrapper-enabled feature selection is employed for selecting the highly significant features that are generated using the technical indicators. The highly significant features of the data are fed to the prediction module, which is developed using the NARX model. The NARX model uses the CPLM algorithm that is formed using the integration of the chronological-based penguin search optimization algorithm and the Levenberg–Marquardt algorithm. The prediction using the proposed CPLM-based NARX shows the superior performance in terms of mean absolute percentage error and root mean square error with values of 0.96 and 0.805, respectively.

Description: Location-based services have attracted much attention in both academia and industry. However, protecting user’s privacy while providing accurate service for users remains challenging. In most of the existing research works, a semi-trusted proxy is employed to act on behalf of a user to minimize the computation and communication costs of the user. However, user privacy, e.g. location privacy, cannot be protected against the proxy. In this paper, we design a new blind filter protocol where a user can employ a semi-trusted proxy to determine whether a point of interest is within a circular area centered at the user’s location. During the protocol, neither the proxy nor the location-based service provider can obtain the location of the user and the query results. Moreover, each type of query is controlled by an access tree and only the users whose attributes satisfy this access tree can complete the specific type of query. Security analysis and efficiency experiments validate that the proposed protocol is secure and efficient in terms of the computation and communication overhead.

Description: In this paper, we present a full-reference quality assessment metric based on the information of visual saliency. The saliency information is provided under the form of degrees associated to each vertex of the surface mesh. From these degrees, statistical attributes reflecting the structures of the reference and distorted meshes are computed. These are used by four comparisons functions genetically optimized that quantify the structure differences between a reference and a distorted mesh. We also present a statistical comparison study of six full-reference quality assessment metrics for 3D meshes. We compare the objective metrics results with humans subjective scores of quality considering the 3D meshes in one hand and the distorsion types in the other hand. Also, we show which metrics are statistically superior to their counterparts. For these comparisons we use the Spearman Rank Ordered Correlation Coefficient and the hypothetic test of Student (ttest). To attest the pertinence of the proposed approach, a comparison with a ground truth saliency and an application associated to the assessment of the visual rendering of smoothing algorithms are presented. Experimental results show that the proposed metric is very competitive with the state-of-the-art.

Description: Diagnosability and connectivity are important metrics for the reliability and fault diagnosis capability of interconnection networks, respectively. The g-extra connectivity of a graph G, denoted by $kappa _g(G)$, is the minimum number of vertices whose deletion will disconnect the network and every remaining component has more than $g$ vertices. The g-extra conditional diagnosability of graph G, denoted by $t_g(G)$, is the maximum number of faulty vertices that the graph G can guarantee to identify under the condition that every fault-free component contains at least g+1 vertices. In this paper, we first determine that g-extra connectivity of DQcube is $kappa _g(G)=(g+1)(n+1)-frac{g(g+3)}{2}$ for $0leq gleq n-3$ and then show that the g-extra conditional diagnosability of DQcube under the PMC model $(ngeq 4, 1leq gleq n-3)$ and the MM$^ast$ model $(ngeq 7, 1leq gleq frac{n-3}{4})$ is $t_g(G)=(g+1)(n+1)-frac{g(g+3)}{2}+g$, respectively.

Description: In this paper, using Mixed-Integer Linear Programming, a new automatic search tool for truncated differential characteristic is presented. Our method models the problem of finding a maximal probability truncated differential characteristic, being able to distinguish the cipher from a pseudo-random permutation. Using this method, we analyze Midori64, SKINNY64/X and CRAFT block ciphers, for all of which the existing results are improved. In all cases, the truncated differential characteristic is much more efficient than the (upper bound of) bit-wise differential characteristic proven by the designers, for any number of rounds. More specifically, the highest possible rounds, for which an efficient differential characteristic can exist for Midori64, SKINNY64/X and CRAFT are 6, 7 and 10 rounds, respectively, for which differential characteristics with maximum probabilities of $2^{-60}$, $2^{-52}$ and $2^{-62.61}$ (may) exist. Using our new method, we introduce new truncated differential characteristics for these ciphers with respective probabilities $2^{-54}$, $2^{-4}$ and $2^{-24}$ at the same number of rounds. Moreover, the longest truncated differential characteristics found for SKINNY64/X and CRAFT have 10 and 12 rounds, respectively. This method can be used as a new tool for differential analysis of SPN block ciphers.

Description: Action recognition is a challenging task. Deep learning models have been investigated to solve this problem. Setting up a new neural network model is a crucial and time-consuming process. Alternatively, pre-trained convolutional neural network (CNN) models offer rapid modeling. The selection of the hyperparameters of CNNs is a challenging issue that heavily depends on user experience. The parameters of CNNs should be carefully selected to get effective results. For this purpose, the artificial bee colony (ABC) algorithm is used for tuning the parameters to get optimum results. The proposed method includes three main stages: the image preprocessing stage involves automatic cropping of the meaningful area within the images in the data set, the transfer learning stage includes experiments with six different pre-trained CNN models and the hyperparameter tuning stage using the ABC algorithm. Performance comparison of the pre-trained CNN models involving the use and nonuse of the ABC algorithm for the Stanford 40 data set is presented. The experiments show that the pre-trained CNN models with ABC are more successful than pre-trained CNN models without ABC. Additionally, to the best of our knowledge, the improved NASNet-Large CNN model with the ABC algorithm gives the best accuracy of 87.78% for the overall success rate-based performance metric.

Description: At present, the tactile perception of 3D geometric bumps (such as sinusoidal bumps, Gaussian bumps, triangular bumps, etc.) on touchscreens is mainly realized by mapping the local gradients of rendered virtual surfaces to lateral electrostatic friction, while maintaining the constant normal feedback force. The latest study has shown that the recognition rate of 3D visual objects with electrovibration is lower by 27$\%$ than that using force-feedback devices. Based on the custom-designed tactile display coupling with electrovibration and mechanical vibration stimuli, this paper proposes a novel tactile rendering algorithm of 3D geometric bumps, which simultaneously generates the lateral and the normal perceptual dimensions. Specifically, a mapping relationship with the electrostatic friction proportional to the gradient of 3D geometric bumps is firstly established. Then, resorting to the angle between the lateral friction force and the normal feedback force, a rendering model of the normal feedback force using mechanical vibration is further determined. Compared to the previous works with electrovibration, objective evaluations with 12 participants showed that the novel version significantly improved recognition rates of 3D bumps on touchscreens.

Description: In context-aware recommendation systems, most existing methods encode users’ preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users’ preferences for a candidate item are influenced by the changes in temporal and historical behaviours, it is unreasonable to predict correlations between users and candidates by using users’ fixed features. A fine-grained and coarse-grained information based framework proposed in our paper which considers multi-granularity information of users’ historical behaviours. First, a parallel structure is provided to mine users’ preference information under different granularities. Then, self-attention and attention mechanisms are used to capture the dynamic preferences. Experiment results on two publicly available datasets show that our framework outperforms state-of-the-art methods across the calculated evaluation metrics.

Description: In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

Description: For a high level of data availability and reliability, a common strategy for cloud service providers is to rely on replication, i.e. storing several replicas onto different servers. To provide cloud users with a strong guarantee that all replicas required by them are actually stored, many multi-replica integrity auditing schemes were proposed. However, most existing solutions are not resource economical since users need to create and upload replicas of their files by themselves. A multi-replica solution called Mirror is presented to overcome the problems, but we find that it is vulnerable to storage saving attack, by which a dishonest provider can considerably save storage costs compared to the costs of storing all the replicas honestly—while still can pass any challenge successfully. In addition, we also find that Mirror is easily subject to substitution attack and forgery attack, which pose new security risks for cloud users. To address the problems, we propose some simple yet effective countermeasures and an improved proofs of retrievability and replication scheme, which can resist the aforesaid attacks and maintain the advantages of Mirror, such as economical bandwidth and efficient verification. Experimental results show that our scheme exhibits comparable performance with Mirror while achieving high security.

Description: A fuzzy extractor derives uniformly random strings from noisy sources that are neither reliably reproducible nor uniformly random. The basic definition of fuzzy extractor was first formally introduced by Dodis et al. and has achieved various applications in cryptographic systems. However, it has been proved that a fuzzy extractor could become totally insecure when the same noisy random source is extracted multiple times. To solve this problem, the reusable fuzzy extractor is proposed. In this paper, we propose the first reusable fuzzy extractor based on the LPN assumption, which is efficient and resilient to linear fraction of errors. Furthermore, our construction serves as an alternative post-quantum reusable fuzzy extractor.

Description: A microarray dataset contains thousands of DNA spots covering almost every gene in the genome. Microarray-based gene expression helps with the diagnosis, prognosis and treatment of cancer. The nature of diseases frequently changes, which in turn generates a considerable volume of data. The main drawback of microarray data is the curse of dimensionality. It hinders useful information and leads to computational instability. The main objective of feature selection is to extract and remove insignificant and irrelevant features to determine the informative genes that cause cancer. Random forest is a well-suited classification algorithm for microarray data. To enhance the importance of the variables, we proposed out-of-bag (OOB) cases in every tree of the forest to count the number of votes for the exact class. The incorporation of random permutation in the variables of these OOB cases enables us to select the crucial features from high-dimensional microarray data. In this study, we analyze the effects of various random forest parameters on the selection procedure. ‘Variable drop fraction’ regulates the forest construction. The higher variable drop fraction value efficiently decreases the dimensionality of the microarray data. Forest built with 800 trees chooses fewer important features under any variable drop fraction value that reduces microarray data dimensionality.

Description: Tactile feedback added to touchscreens provides users with a high-quality interactive experience. The effect of tactile feedback on typical interaction gestures requires to be evaluated. With a custom-designed electrostatic tactile feedback device, we explore the effects of tactile feedback on zoom-in/out gestures and determine the issues satisfied by the relationship between completion time (CT) and index of difficulty (ID). Specifically, we compare the effect of electrostatic tactile feedback on the efficiency and accuracy of zoom-in/out gestures in three conditions, that is, no tactile feedback, linearly increasing tactile feedback force over operation process, and tactile feedback only in a target area. Then, we study the relationship between CT and ID with tactile feedback added to the target area. Results of experimental data from 12 participants show that tactile feedback added only to a target area can significantly increase operational efficiency and accuracy of zoom-in/out gestures. Furthermore, the relationship between CT and ID agrees well with Fitts’ law, and the correlation coefficient is larger than 0.9.

Description: Artificial intelligence is one of the most trending topics in the field of Computer Science which aims to make machines and computers ‘smart’. There are multiple diverse technical and specialized research associated with it. Due to the accelerating rate of technological changes, artificial intelligence has taken over a lot of human jobs and is giving excellent results that are more efficient and effective, than humans. However, a lot of time there has been a concern about the following: will artificial intelligence surpass human intelligence in the near future? Are computers’ ever accelerating abilities to outpace human jobs and skills a matter of concern? The different views and myths on the subject have made it even a more than just a topic of discussion. In this research paper, we will study the existing facts and literature to understand the true definitions of artificial intelligence (AI) and human intelligence (HI) by classifying each of its types separately and analyzing the extent of their full capabilities. Later, we will discuss the possibilities if AI eventually can replace human jobs in the market. Finally, we will synthesize and summarize results and findings of why artificial intelligence cannot surpass human intelligence completely in the future.

Description: The 5G mobile communication system is coming with a main objective, known also as IMT-2020, that intends to increase the current data rates up to several gigabits per second. To meet an accompanying demand of the super high-speed encryption, EIA and EEA algorithms face some challenges. The 3GPP standardization organization expects to increase the security level to 256-bit key length, and the international cryptographic field responds actively in cipher designs and standard applications. SNOW-V is such a proposal offered by the SNOW family design team, with a revision of the SNOW 3G architecture in terms of linear feedback shift register (LFSR) and finite state machine (FSM), where the LFSR part is new and operates eight times the speed of the FSM, consisting of two shift registers and each feeding into the other, and the FSM increases to three 128-bit registers and employs two instances of full AES encryption round function for update. It takes a 128-bit IV, employs 896-bit internal state and produces 128-bit keystream blocks. The result is competitive in pure software environment, making use of both AES-NI and AVX acceleration instructions. Thus, the security evaluation of SNOW-V is essential and urgent, since there is scarcely any definite security bound for it. In this paper, we propose a byte-based guess-and-determine attack on SNOW-V with complexity $2^{406}$ using only seven keystream blocks. We first improve the heuristic guessing-path auto-searching algorithm based on dynamic programming by adding initial guessing set, which is iteratively modified by sieving out the unnecessary guessing variables, in order to correct the guessing path according to the cipher structure and finally launch smaller guessing basis. For the specific design, we split all the computing units into bytes and rewrite all the internal operations correspondingly. We establish a backward-clock linear equation system according to the circular construction of the LFSR part. Then we further simplify the equations to adapt to the input requirements of the heuristic guessing-path auto-searching algorithm. Finally, the derived guessing path needs modification for the pre-simplification and post-reduction. This is the first complete guess-and-determine attack on SNOW-V as well as the first specific security evaluation to the full cipher.

Description: Perceptible visual tracking acts as an important module for distinct perception tasks of autonomous robots. Better features help in easier decision-making process. The evaluation of tracking objects, dynamic positions and their visual information in results are quite difficult tasks. Until now, most real-time visual tracking algorithms suffer from poor robustness and low occurrence as they deal with complex real-world data. In this paper, we have proposed more robust and faster visual tracking framework using scale invariant feature transform (SIFT) and the optical flow in belief propagation (BF) algorithm for efficient processing in real scenarios. Here, a new feature-based optical flow along with BF algorithm is utilized to compute the affine matrix of a regional center on SIFT key points in frames. Experimental results depict that the proposed approach is more efficient and more robust in comparison with the state-of-the-art tracking algorithms with more complex scenarios.

Description: In order to comprehensively evaluate the achievements of the 'Belt and Road' in integrated transportation, researchers need to optimize the method of generating evaluation indices and construct the framework structure of the 'Belt and Road' transportation index system. This paper used GDELT database as data source and obtained full text data of English news in 25 countries along ‘the Belt and Road’. The paper also introduced the topic model, combined with the unsupervised method (latent Dirichlet allocation, LDA) and the supervision method (labeled LDA) to mine the topics contained in the news data. It constructed the transportation development model and analyzed the development trend of transportation in various countries. The study found that the development trend of transportation in the countries along the line is unbalanced, which can be divided into four types: rapid development type, stable development type, slow development type and lagging development type. The method of this paper can effectively extract temporal and spatial variation of news events, discover potential risks in various countries, support real-time and dynamic monitoring of the social development situation of the countries along the border and provide auxiliary decision support for implementation of the ‘the Belt and Road’ initiative, which has important application value.

Description: Social media is believed to have played a central role in the mobilization of Algerian citizens to peaceful protest against their country’s corrupt regime. Since no one foresaw these protests (called ‘The Revolution of Smiles’ or ‘The Hirak Movement’), this research conducted social media analysis to elicit vital insights about both the intensity of sentiment and the influence of social media on this unexpected instigation of political protest. This work built a deep learning model and analysed the influence of content, sentiment and user features on information spread. The model used the learning capability of a long short-term memory network to predict ‘retweetability’. Experiments were conducted on two real-world datasets (Hirak and Brexit) collected from Twitter. User features were found to be a key element in the diffusion of information. The strongest feelings about event context actively influenced the spread of tweets. The Twitter emotion corpus was found to improve the predictive ability of the model developed in this study.

Description: This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature as second stage. After that, the learning process is carried out by LDCRFs. Finally, simple protocol and RDF (resource description framework) query language (i.e. SPARQL) on input text or image query is to retrieve semantic image based on sequential processes of query engine, matching module and ontology manager. Experimental findings show that our approach can be successful to retrieve images against the mammal’s benchmark with retrieving rate of 98.11%. Such outcomes are likely to compare very positively with those accessible in the literature from other researchers.

Description: Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

Description: The concept of smart systems blessed with different technologies can enable many algorithms used in Machine Learning (ML) and the world of the Internet of Things (IoT). In a modern city many different sensors can be used for information collection. Algorithms that are cast-off in Machine Learning improves the capabilities and intelligence of a system when the amount of data collectedincreases. In this research, we propose a TCC-SVM system model to analyse traffic congestion in the environment of a smart city. The proposed model comprises an ML-enabled IoT-based road traffic congestion control system whereby the occurrence of congestion at a specific point is notified.

Description: In view of the fact that the existing public opinion propagation aspects are mostly based on single-layer propagation network, these works rarely consider the double-layer network structure and the negative opinion evolution. This paper proposes a new susceptible-infected-vaccinated-susceptible negative opinion information propagation model with preventive vaccination by constructing double-layer network topology. Firstly, the continuous-time Markov chain is used to simulate the negative public opinion information propagation process and the nonlinear dynamic equation of the model is derived; secondly, the steady state condition of the virus propagation in the model is proposed and mathematically proved; finally, Monte Carlo method is applied in the proposed model. The parameters of simulation model have an effect on negative public opinion information propagation, the derivation results are verified by computer simulation. The simulation results show that the proposed model has a larger threshold of public opinion information propagation and has more effective control of the scale of negative public opinion; it also can reduce the density of negative public opinion information propagation and suppress negative public opinion information compared with the traditional susceptible infected susceptible model. It also can provide the scientific method and research approach based on probability statistics for the study of negative public opinion information propagation in complex networks.

Description: Twitter is an extensively used micro-blogging site for publishing user’s views on recent happenings. This wide reachability of messages over large audience poses a threat, as the degree of personally identifiable information disclosed might lead to user regrets. The Tweet-Scan-Post system scans the tweets contextually for sensitive messages. The tweet repository was generated using cyber-keywords for personal, professional and health tweets. The Rules of Sensitivity and Contextuality was defined based on standards established by various national regulatory bodies. The naive sensitivity regression function uses the Bag-of-Words model built from short text messages. The imbalanced classes in dataset result in misclassification with 25% of sensitive and 75% of insensitive tweets. The system opted stacked classification to combat the problem of imbalanced classes. The system initially applied various state-of-art algorithms and predicted 26% of the tweets to be sensitive. The proposed stacked classification approach increased the overall proportion of sensitive tweets to 35%. The system contributes a vocabulary set of 201 Sensitive Privacy Keyword using the boosting approach for three tweet categories. Finally, the system formulates a sensitivity scaling called TSP’s Tweet Sensitivity Scale based on Senti-Cyber features composed of Sensitive Privacy Keywords, Cyber-keywords with Non-Sensitive Privacy Keywords and Non-Cyber-keywords to detect the degree of disclosed sensitive information.

Description: Fog computing has become an emerging environment that provides data storage, computing and some other services on the edge of network. It not only can acquire data from terminal devices, but also can provide computing services to users by opening computing resources. Compared with cloud computing, fog devices can collaborate to provide users with powerful computing services through resource allocation. However, as many of fog devices are not monitored, there are some security problems. For example, since fog server processes and maintains user information, device information, task parameters and so on, fog server is easy to perform illegal resource allocation for extra benefits. In this paper, we propose a secure computing resource allocation framework for open fog computing. In our scheme, the fog server is responsible for processing computing requests and resource allocations, and the cloud audit center is responsible for auditing the behaviors of the fog servers and fog nodes. Based on the proposed security framework, our proposed scheme can resist the attack of single malicious node and the collusion attack of fog server and computing devices. Furthermore, the experiments show our proposed scheme is efficient. For example, when the number of initial idle service devices is 40, the rejection rate of allocated tasks is 10% and the total number of sub-tasks is changed from 150 to 200, the total allocation time of our scheme is only changed from 15 ms to 25 ms; additionally, when the task of 5000 order matrix multiplication is tested on 10 service devices, the total computing time of our scheme is $sim$250 s, which is better than that of single computer (where single computer needs more than 1500 s). Therefore, our proposed scheme has obvious advantages when it faces some tasks that require more computational cost, such as complex scientific computing, distributed massive data query, distributed image processing and so on.