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The release of millions of financial documents, which has been known as the 'WikiLeaks' of the financial world (a.k.a. 'Panama Papers'), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East and North Africa (MENA) region. This study aims to use social network analytics to study the information contained in these documents. We are checking the major players in the MENA's trends and patterns to determine if it matches the known economic powers. The analysis reveals that while the constructed network enjoys some typical characteristics, many interesting observations and properties are worth discussing. Specifically, using the extracted network consisting of 62 987 nodes and 84 692 edges, our social network analysis finding shows that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlated with perceived economic influence. [ABSTRACT FROM AUTHOR]

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

Intrusion detection has become a research focus in internet information security, with deep learning algorithms playing a crucial role in its development. Typically, intrusion detection data are transformed into a two-dimensional matrix by segmenting, stacking and padding them with zeros for input into deep learning models. However, this method consumes computational resources and fails to consider the correlation between features. In this paper, we transform the data into images through visualization operations and propose an information entropy weighted scheme to optimize the collision element problem during the transformation process. This method enhances the correlation between pixel frame features, leading to approximately 2% improvement in accuracy of the classification model when using the generated image samples for detection in experiments. To address the issues of insensitivity to target feature locations and incomplete feature extraction in traditional neural networks, this paper introduces a new network model called CBAM-CapsNet, which combines the advantages of the lightweight Convolutional Block Attention Module and capsule networks. Experimental results on the UNSW-NB15 and IDS-2017 datasets demonstrate that the proposed model achieves accuracies of 92.94% and 99.72%, respectively. The F1 scores obtained are 91.83% and 99.56%, indicating a high level of detection. [ABSTRACT FROM AUTHOR]

The accurate representation of objects holds pivotal significance in the realm of panoptic segmentation. Presently, prevalent object representation methodologies, including box-based, keypoint-based and query-based techniques, encounter a challenge known as the 'representation confusion' issue in specific scenarios, often resulting in the mislabeling of instances. In response, this paper introduces Convex Object Representation (COR), a straightforward yet highly effective approach to address this problem. COR leverages a CNN-based Euclidean Distance Transform to convert the target instance into a convex heatmap. Simultaneously, it offers a parallel embedding method for encoding the object. Subsequently, COR characterizes objects based on the distinctive embedding vectors of their convex vertices. This paper seamlessly integrates COR into a state-of-the-art query-based panoptic segmentation framework. Experimental findings validate that COR successfully mitigates the representation confusion predicament, enhancing segmentation accuracy. The COR-augmented methods exhibit notable improvements of +1.3 and +0.7 points in PQ on the Cityscapes validation and MS COCO panoptic 2017 validation datasets, respectively. [ABSTRACT FROM AUTHOR]

We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection , the proposed algorithm solves the problem within a finite time period. The algorithm runs in |$\Theta (dn)$| moves and |$O(dn)$| epochs, where |$d$| is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and |$n$| is the total number of robots in the system. [ABSTRACT FROM AUTHOR]

Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers. [ABSTRACT FROM AUTHOR]

Part-of-Speech (POS) tagging is one of the popular Natural Language Processing (NLP) tasks. It is also considered to be a preliminary task for other NLP applications such as speech recognition, machine translation, and sentiment analysis. A few works have been published on POS tagging for the Tamil language. However, the performance of the POS tagger with unknown words is not explored in the literature. The appearance of unknown words is a frequently occurring problem in POS tagging and makes it a challenging task. In this paper, we propose a deep learning-based POS tagger for Tamil using Bi-directional Long Short Term Memory (BLSTM). The performance of the POS tagger was evaluated using known and unknown words. The POS tagger with regular word-level embeddings produces 99.83 and 92.46% accuracies for all known and 63.21% unknown words. It clearly shows that the accuracy decreases when the number of unknown words increases. To improve the performance of the POS tagger with unknown words, the proposed BLSTM model that uses word-level, character-level and pre-trained word embeddings. Test results of this model show a 2.57% improvement for 63.21% of unknown words, with an accuracy of 95.03%. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

In this technological era, human activity recognition (HAR) plays a significant role in several applications like surveillance, health services, Internet of Things, etc. Recent advancements in deep learning and video summarization have motivated us to integrate these techniques for HAR. This paper introduces a computationally efficient HAR technique based on a deep learning framework, which works well in realistic and multi-view environments. Deep convolutional neural networks (DCNNs) normally suffer from different constraints, including data size dependencies, computational complexity, overfitting, training challenges and vanishing gradients. Additionally, with the use of advanced mobile vision devices, the demand for computationally efficient HAR algorithms with the requirement of limited computational resources is high. To address these issues, we used integration of DCNN with video summarization using keyframes. The proposed technique offers a solution that enhances performance with efficient resource utilization. For this, first, we designed a lightweight and computationally efficient deep learning architecture based on the concept of identity skip connections (features reusability), which preserves the gradient loss attenuation and can handle the enormous complexity of activity classes. Subsequently, we employed an efficient keyframe extraction technique to minimize redundancy and succinctly encapsulate the entire video content in a lesser number of frames. To evaluate the efficacy of the proposed method, we performed the experimentation on several publicly available datasets. The performance of the proposed method is measured in terms of evaluation parameters Precision, Recall, F-Measure and Classification Accuracy. The experimental results demonstrated the superiority of the presented algorithm over other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Evaluating program reliability against software faults involves the work of analyzing program failure against software faults as behavior on failures is fundamental for assessing reliability. Similar failure behavior reflects similar reliability assessment against software faults of same time–space distribution. Since program failure is determined by fault manifestations, working on similarities on fault manifestations helps figuring out failures in similarity as well. In this paper, we propose a novel method to characterize program behavior by defining fine-grained runtime states at assembly-level of code, aiming to capture the very first abnormal manifestation—first-error—after software fault being activated during program run. Failure prediction model and measurement is presented based on similarities reflected by the runtime behaviors obtained. Fault injection experiments are conducted to verify the prediction measurement by utilizing software faults of Orthogonal Defect Classification to inject and MiBench programs to perform. Over 15 000+ times of fault injection by considering type and location of fault were conducted into each program in order to quantify and analyze the sensitivity on first-error and similarity on failure accordingly. The results show that programs having particular structural features (e.g. massive operations with regards to calculations, nested function-calls, case structures and loop structures, etc.) are well characterized toward first-error behaviors by extracting fine-grained states. Similarities on first-error sensitivity can be represented better for these programs as well. Same trend is seen on failure behavior and its prediction measurement. [ABSTRACT FROM AUTHOR]

An important open problem in supersingular isogeny-based cryptography is to produce, without a trusted authority, concrete examples of 'hard supersingular curves' that is equations for supersingular curves for which computing the endomorphism ring is as difficult as it is for random supersingular curves. A related open problem is to produce a hash function to the vertices of the supersingular |$\ell $| -isogeny graph, which does not reveal the endomorphism ring, or a path to a curve of known endomorphism ring. Such a hash function would open up interesting cryptographic applications. In this paper, we document a number of (thus far) failed attempts to solve this problem, in the hope that we may spur further research, and shed light on the challenges and obstacles to this endeavour. The mathematical approaches contained in this article include: (i) iterative root-finding for the supersingular polynomial; (ii) gcd's of specialized modular polynomials; (iii) using division polynomials to create small systems of equations; (iv) taking random walks in the isogeny graph of abelian surfaces, and applying Kummer surfaces and (v) using quantum random walks. [ABSTRACT FROM AUTHOR]

Website fingerprinting (WF) attacks on Tor enable a passive adversary to predict the encrypted web browsing activity of a victim by matching the eavesdropped traffic with pretrained classifiers. Nowadays, deep learning-based methods have led to significant achievements in the single-tab Tor WF attack. However, the practical implementation of these single-tab methods is challenging due to most real-world Tor traffic involving multiple tabs. Existing single-tab methods hardly identify multi-tab WF due to the overlapping areas of the traffic trace confusing the original features. In this paper, we propose a Trace Image-based Object Detection model named TIOD as a novel multi-tab attacking model. Specifically, we model the traffic overlap in Tor as the object overlap in object detection tasks from the computer vision field. Besides, we propose a special S-matrix scheme to convert a trace into an image: (i) Retaining the original features by keeping the direction and order of the cells and (ii) Bringing cells of the same page closer together in space. We then utilize a specially designed object detection model for trace images, WF R-CNN, to extract features and identify potential destination websites within multi-tab traces. Comparative analysis with other multi-tab attacks is conducted, and the empirical results consistently underscore the superior performance of the proposed trace image model across diverse datasets. In the two-tab setting, TIOD achieves the best accuracy with more than 85% on both the first and second tabs. [ABSTRACT FROM AUTHOR]

Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model's interpretability diminishes as the ensemble's complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches. [ABSTRACT FROM AUTHOR]

With the increasing strain on today's healthcare resources, there is a growing demand for pre-diagnosis testing. In response, researchers have suggested diverse machine learning models for disease prediction, among which logistic regression stands out as one of the most effective models. Its objective is to enhance the accuracy and efficiency of pre-diagnosis testing, thereby alleviating the burden on healthcare resources. However, when multiple medical institutions collaborate to train models, the untrusted cloud server may pose a risk of private data leakage, enabling participants to steal data from one another. Existing privacy-preserving methods often suffer from drawbacks such as high communication costs, long training times and lack of security proofs. Therefore, it is imperative to jointly train an excellent model collaboratively and uphold data privacy. In this paper, we develop a highly optimized two-party logistic regression algorithm based on CKKS scheme. The algorithm optimizes ciphertext operations by employing ciphertext segmentation and minimizing the multiplication depth, resulting in time savings. Furthermore, it utilizes least squares to approximate sigmoid functions within specific intervals that cannot be handled by homomorphic encryption. Finally, the proposed algorithm is evaluated on a breast cancer dataset, and simulation experiments demonstrate that the model's prediction accuracy, after machine learning training, exceeds 96% for two-sided encrypted data. [ABSTRACT FROM AUTHOR]

Two algorithms, both based around multiplication, one defined by Andrew Donald Booth in 1950 and the other defined by Anatoly Alexeevitch Karatsuba in 1960 can be applied to other types of operations. We know from recent results that to perform some algebraic transformations it is more efficient to calculate an inverse effect of a smaller magnitude together with an original action with a higher rank, than the initial operation, reaching the final element with less transitions. In this paper, we present an addition algorithm on |$\mathbb {Z}/m\mathbb {Z}$| of big-integer numbers based on these concepts, using an alternative to traditional hardware implementation for binary addition based on FULL-ADDER cells, allowing the reduction of space complexity compared with other techniques, such as carry-lookahead, letting us calculate a modular addition in an optimal order complexity of |$\mathcal {O}(n)$| without adding more complexity due to reduction operations. [ABSTRACT FROM AUTHOR]

Byzantine fault-tolerant (BFT) consensus protocols are essential in distributed computing. Most partially synchronous BFT protocols proceed in views and rely on a view synchronizer module to guarantee liveness by synchronizing honest replicas to the same view. HotStuff is a leading BFT consensus protocol known for achieving linear view change and optimistic responsiveness. To achieve these desirable properties, HotStuff relies on a candidate solution for the view synchronizer based on a recomposed timer doubling mechanism. However, a formal analysis of this mechanism is currently lacking. This paper delves into HotStuff with the recomposed timer doubling mechanism. To facilitate accurate analysis, we introduce a new specification for the view synchronizer, incorporating two paths for view switching as in HotStuff's setting. Surprisingly, we observe that the adversary can disrupt the view synchronization and launch a liveness attack, stalling the confirmation process. Besides, the adversary can further recover or control the confirmation process at will. A repairment that retains the desirable feature of HotStuff is also presented. We simulate the liveness attack and the repairment, demonstrating their effectiveness. Specifically, the liveness attack can cause HotStuff's throughput to drop and remain at 0. When equipped with our repairment, HotStuff can resist the attack and retain the throughput performance. [ABSTRACT FROM AUTHOR]

Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an |$l$| -level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable. [ABSTRACT FROM AUTHOR]

The synchronous stream ciphers RCR-64 and RCR-32 designed by Sekar, Paul and Preneel are strengthened variants of the ciphers TPy and TPypy (designed by Biham and Seberry), respectively. The RCR ciphers have remained unbroken since they were published in 2007. In this paper, we present arguments that not only support the designers' security claims but suggest, in general, that the ciphers are secure against several classes of cryptanalytic attacks. We find that the ciphers are best used with 256-bit keys and 384-bit IVs. We also suggest ways to protect software implementations of the RCR ciphers against (cache-)timing and processor flag attacks. Our performance evaluation suggests that the protected implementation of the RCR-64 encrypts long messages at speeds comparable to some of the fastest stream ciphers available today. Consequently, we find that the RCR ciphers may be well suited for PC-based applications in general and streaming audio / video applications in particular. This is the first paper to present a detailed study on the security and performance of the RCR ciphers. [ABSTRACT FROM AUTHOR]

As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics. If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code. Currently, scientists rarely assure the quality of the code they rely on, and rarely make it accessible for scrutiny. Even when available, scientists rarely provide adequate documentation to understand or use it reliably. This paper proposes and justifies ways to improve science using code: 1.   Professional Software Engineers can help, particularly in critical fields such as public health, climate change and energy. 2.   'Software Engineering Boards,' analogous to Ethics or Institutional Review Boards, should be instigated and used. 3.   The Reproducible Analytic Pipeline (RAP) methodology can be generalized to cover code and Software Engineering methodologies,        in a generalization this paper introduces called RAP +. RAP + (or comparable interventions) could be supported and or even required       in journal, conference and funding body policies. The paper's Supplemental Material provides a summary of Software Engineering best practice relevant to scientific research, including further suggestions for RAP + workflows. 'Science is what we understand well enough to explain to a computer.' Donald E. Knuth in |$A=B$| [ 1 ] 'I have to write to discover what I am doing.' Flannery O'Connor, quoted in Write for your life [ 2 ] 'Criticism is the mother of methodology.' Robert P. Abelson in Statistics as Principled Argument [ 3 ] 'From its earliest times, science has operated by being open and transparent about methods and evidence, regardless of which technology has been in vogue.' Editorial in Nature [ 4 ] [ABSTRACT FROM AUTHOR]

With the development of NAND flash, the storage density has become larger and larger, but the reliability has become lower and lower. To address this problem, stronger error correction codes, low-density parity-check (LDPC) codes, are widely used in flash memory. However, the direct use of LDPC codes for error correction has a great impact on the read and write performance of SSDs. Given this, this paper proposes a new mentality that combines replica and LDPC decoding methods to improve the read performance of solid state drives (SSDs). Considering the characteristics of the quantization level of LDPC soft-decision decoding, this paper proposes the IDSD (LDPC soft-decision decoding and duplication method) and IDHSD (a method combining duplication and LDPC hybrid decoding)schemes. These schemes are mainly for hot data. When reading the data, the IDSD scheme needs to first judge whether it is soft-decision decoding. Then, according to the number of quantization levels recorded, it needs to select whether to use duplication or soft-decision decoding to correct the error data. The IDHSD scheme does not need to judge the soft-decision decoding but directly uses the duplication after the hard-decision decoding fails. Both schemes reduce the average response time to a certain extent and optimize the read performance of the SSD. The experimental results show that compared with the Native Scheme, the average response delay time of the IDSD scheme decreases by 5–30% and the IDHSD scheme slashes by 5–38%. Compared with the Traditional Refresh Scheme, the average response delay time of the IDSD scheme cuts down by 1–30% and the IDHSD scheme lowers by 1–29%. [ABSTRACT FROM AUTHOR]

Cloud data center serves tremendous workload demand due to the ever-increasing usage of internet services. Scheduling of these workloads over the physical servers is the combinatorial problem that resembles to an NP-complete problem. Furthermore, the workload is dynamic and changes at each scheduling interval result in high power consumption and Service Level Agreement (SLA) violation. Virtual Machine (VM) migrations provide the opportunity to balance this dynamic workload. However, it results in additional power consumption and performance loss. Therefore, this paper aims to find the optimal VM scheduling with minimum VM migration to minimize power consumption and ensure the SLA. This paper proposes a Residual Optimum Power Efficiency (ROPE) aware Improved Clonal Selection Algorithm (ICSA) for dynamic VM scheduling. ICSA-ROPE algorithm finds optimal VM schedules at each scheduling interval guided by two optimization functions Total Datacenter Residual Optimum Power-Efficiency (TDCROPE) and VM Migration Cost (VMC). TDCROPE ensures that servers operate at optimum power efficiency as they consume less power and are less prone to SLA violation, while VMC ensures to find optimal VM schedule with minimum VM migrations. The proposed approach is implemented on a CloudSim simulator, and results show that the ICSA-ROPE is 95.54 %,90.34% and 88.49% more significant in terms of performance efficiency than the LrMmt, DthMf and VMS-MCSA. [ABSTRACT FROM AUTHOR]

The brain is regarded as the central part of the human body and has a very complicated structure. The abnormal growth of tissue inside the brain is called a brain tumour. Tumour detection at an early stage is the most difficult task in the discipline of health. In this review article, the authors have deeply analysed and reviewed the brain tumour detection mechanisms which include manual, semi- and fully automated techniques. Today, fully automated mechanisms apply deep learning (DL) methods for tumour detection in brain magnetic resonance images (MRIs). This paper deals with previously published research articles relevant to various brain tumour detection techniques. Review of various types of tumours, MRI modalities, datasets, filters, segmentation methods and DL techniques like long short-term memory, gated recurrent unit network, convolution neural network, auto encoder, deep belief network, recurrent neural network, generative adverse network and deep stacking networks have been included in this paper. It has been observed from the analysis that the use of DL techniques in the detection of brain tumours improves accuracy. Finally, this paper reveals research gaps, limitations of existing methods, challenges in tumour detection and contributions of the proposed article. [ABSTRACT FROM AUTHOR]

Whirlpool is a hash function that has been standardized by ISO/IEC. In this paper, we develop a new type of distinguishing property for its underlying permutation |$ W $|⁠. Division property proposed by Todo at EUROCRYPT 2015 was initially used in the integral cryptanalysis of symmetric-key algorithms. This work for the first time utilizes the MILP method to search for the integral distinguishers of |$ W $| in both the forward and backward directions while concentrating on word-based division property. Under the known-key model, the fact that the permutation used in the hash function does not depend on any secret parameters allows the previous properties to be exploited from the middle, i.e. from an intermediate internal state. Therefore, we apply the inside-out strategy which is the essential step in the zero-sum property to connect the trails in opposite directions. Consequently, we obtain new distinguishers up to full rounds for the |$ W $|⁠. To further reduce the complexity of the integral distinguishers, we add one round in the middle with the help of subspace trails. Finally, we succeed in extending the length and improving the complexity of the integral distinguishers. To the best of our knowledge, all the results in this paper are competitive with the previous work in both computational cost and memory complexity. It is worth mentioning that the methods presented in this paper are applicable to a broad class of hash functions. [ABSTRACT FROM AUTHOR]

Edge connectivity is an important parameter for the reliability of the inter-connection network. A graph |$G$| is strong Menger edge-connected (⁠|$SM$| - |$\lambda $| for short) if there exist min |$\{\deg _{G}(u),\deg _{G}(v)\}$| edge-disjoint paths between any pair of vertices |$u$| and |$v$| of |$G$|⁠. The conditional edge-fault-tolerance strong Menger edge connectivity of |$G$|⁠ , denoted by |$sm_{\lambda }^{r}(G)$|⁠ , is the maximum integer |$m$| such that |$G-F$| remains |$SM$| - |$\lambda $| for any edge set |$F$| with |$|F|\leq m$| and |$\delta (G-F)\geq r$|⁠ , where |$\delta (G-F)\geq r$| is the minimum degree of |$G-F$|⁠. Most of the previous papers discussed |$sm_{\lambda }^{r}(G)$| in the case of |$r\leq 2$|⁠. In this paper, we show that |$sm_{\lambda }^{r}(FQ_{n})=2^{r}(n-r+1)-(n+1)$| for |$1\leq r\leq n-2$|⁠ , where |$n\geq 4$|⁠. [ABSTRACT FROM AUTHOR]

This paper studies adversarial attacks on network intrusion detection systems (IDSs) based on deep or machine learning algorithms. Adversarial attacks on network IDSs must maintain the functional logic of the attack flow. To prevent the produced adversarial examples from violating the attack behavior, most solutions define some limited modification actions. The result limits the production of adversarial examples, and the produced adversarial examples are not guaranteed to find the attack packets. This paper proposes the concept of flow containers to model packets in a flow. Then, we propose a generative adversarial network framework with dual adversarial training to train the generator to produce adversarial flow containers. Flow containers can correlate attack packets and feature vectors of attack flows. We test the evasion rate of the produced adversarial examples using 12 deep and machine learning algorithms. For experiments on the CTU42 data set, the proposed adversarial examples have the highest evasion rates among all 12 classifiers, with the highest evasion rate as high as 1.00. For experiments on the CIC-IDS2017 data set, the proposed adversarial examples have the highest evasion rate among the five classifiers, and the highest evasion rate is also up to 1.00. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence deep learning model for an energy-aware task scheduling algorithm based on learning automata (LA) in the Fog Computing (FC) Applications. FC is a distributed computing model that serves as an intermediate layer between the cloud and Internet of Things (IoT) to improve the quality of service. The IoT is the closest model to the wireless sensor network (WSN). One of its important applications is to create a global approach to health care system infrastructure development that reflects recent advances in WSN. The most influential factor in energy consumption is task scheduling. In this paper, the issue of reducing energy consumption is investigated as an important challenge in the fog environment. Also, an algorithm is presented to solve the task scheduling problem based on LA and measure the makespan (MK) and cost parameters. Then, a new artificial neural network deep model is proposed, based on the presented LA task scheduling fog computing algorithm. The proposed neural model can predict the relation among MK, energy and cost parameters versus VM length for the first time. The proposed model results show that all of the desired parameters can be predicted with high precision. [ABSTRACT FROM AUTHOR]

Security faces huge challenges in Internet of Things (IoT) environments. In particular, conventional access control standards and models tend to be less tailored for IoT due to the constrained nature of smart objects. Usually, a powerful third party is used to handle the access control logic. However, this third party is lacking in transparency and could harm user privacy. Therefore, providing a distributed access control solution, while considering transparency and privacy-preserving awareness in IoT smart systems, is of paramount importance. The described issue can be addressed using the emergent Blockchain technology that provides a promising choice to build a new generation of decentralized and transparent access control solutions. This paper proposes a smart contract-based access control framework for IoT smart healthcare systems, which is based on smart contracts to provide a distributed and trustworthy access control, combined with the GTRBAC model to express fine-grained access control policies while considering temporal authorization constraints. To prove the feasibility and validity of the proposed framework, this paper also provides a detailed technical description and an initial implementation and execution. An experimental evaluation shows that security properties' analyses on smart contracts achieved the best possible evaluation with no vulnerabilities found, and the cost of access control operations increases linearly as the number of policy constraints increases. Besides, a comparative analysis reveals that the proposed approach can achieve good results with low gas costs and latency. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) has been proposed to enable flexible management and deployment of the network service in cloud. In NFV architecture, a network service needs to invoke several service functions (SFs) in a particular order following the service chain function. The placement of SFs has significant impact on the performance of network services. However, stochastic nature of the network service arrivals and departures as well as meeting the end-to-end Quality of Service(QoS) makes the SFs placement problem even more challenging. In this paper, we firstly provide a system architecture for the SFs placement of cloud service with end-to-end QoS deadline. We then formulate the end-to-end service placement as a Markov decision process (MDP) which aims to minimize the placement cost and the end-to-end delay. In our MDP, the end-to-end delay of active services in the network is considered to be the state of the system, and the placement (nonplacement or placement) of SF is considered as the action. Also, we discuss the rationality of our analytical model by analyzing the Markov stochastic property of the end-to-end service placement. To obtain the optimal placement policy, we then propose an algorithm (Algorithm 1) for dynamic SFs placement based on our model and use successive approximations, i.e. |$\epsilon $| -iteration algorithm (Algorithm 2) to obtain action distribution. Finally, we evaluate the proposed MDP by comparing our optimal method with DDQP, DRL-QOR, MinPath and MinDelay for QoS optimization, including acceptance probability, average delay, resource utilization, load-balancing and reliability. [ABSTRACT FROM AUTHOR]

Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its own weaknesses. Because the algorithm calculates local density and allocates points based on the distances between certain points, the algorithm has difficulty in classifying points into proper groups with varying densities or nested structures. This paper proposes an improved density peaks clustering algorithm called Dratio-DPC to overcome this weakness. First, Dratio-DPC adjusts the original local density with a coefficient calculated with the density ratio. Second, Dratio-DPC takes density similarity into consideration to calculate the distances between one point and other points with higher local densities. We design and perform experiments on different benchmark datasets and compare the clustering results of Dratio-DPC, traditional clustering algorithms and three improved DPC algorithms. Comparison results show that Dratio-DPC is effective and applicable to a wider range of scenarios. [ABSTRACT FROM AUTHOR]

Cyberbullying is a significant concern in this digital age due to its harmful effects on individuals and society. Sadly, social media platforms have only exacerbated the problem, making it imperative to find effective ways to identify and prevent offensive content. While previous research has extensively focused on English and explored machine learning techniques to tackle this issue. To address this gap, this paper introduces a new hybrid deep learning model called Gray Wolf Algorithm-convolutional neural network (GWA-CNN), explicitly designed to detect cyberbullying in the Kurdish language on Twitter. The proposed model combines the CNN framework with an optimised GWA version to improve CNN's parameters and reduce training time. We evaluated GWA-CNN thoroughly utilizing the first-ever manually annotated Kurdish dataset of 30k tweets that have been meticulously curated and divided into three categories, namely sexism, racism and neutral expressions, and compared its performance to those of state-of-the-art algorithms such as Naïve Bayes, K-Nearest Neighbors, Recurrent Neural Networks, Gated Recurrent Units and attention-based transformer. The experimental results demonstrate that GWA-CNN exhibited superior performance in all scenarios, outperforming other approaches in detecting cyberbullying on Twitter. [ABSTRACT FROM AUTHOR]

Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class. [ABSTRACT FROM AUTHOR]

Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch timing. The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal attention mechanism is added to the LSTM to enhance the importance of temporal information. Moreover, the particle swarm optimization K-Means algorithm is proposed in order to cluster the traffic prediction results, output the corresponding time points of the lower traffic value and to obtain the optimal switch-off periods of the base station. Extensive experiments across multiple base stations over an extended period of time have validated our approach. The results show that this method offers accurate traffic prediction with minimal average errors in traffic prediction and the on/off timings of the base stations are in line with the "tide effect" of traffic, thereby achieving the goal of energy savings. [ABSTRACT FROM AUTHOR]

In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection. [ABSTRACT FROM AUTHOR]

Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost. [ABSTRACT FROM AUTHOR]

Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel Single-Encoder-Dual-Decoder (SEDD) watermarking architecture to achieve high imperceptibility and strong robustness. Precisely, the single encoder utilizes normalizing flow to realize watermark embedding, which can effectively fuse the watermark and cover image. For watermark extraction, we introduce a parallel dual-decoder to improve the imperceptibility and extracting ability. Extensive experiments demonstrate that better watermark robustness and imperceptibility are obtained by SEDD architecture. Our method achieves a bit error rate less than 0.1% under most attacks such as JPEG compression, Gaussian blur and crop. Besides, the proposed method also obtains strong robustness under combined attacks and social platform processing. [ABSTRACT FROM AUTHOR]

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks , i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks , i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies. [ABSTRACT FROM AUTHOR]

Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin. [ABSTRACT FROM AUTHOR]

Link residual closeness is a newly proposed measure for network vulnerability. In this model, vertices are perfectly reliable and the links fail independently of each other. It measures the vulnerability even when the removal of links does not disconnect the graph. In this paper, we characterize those graphs that maximize the link residual closeness over the connected graphs with fixed order and one additional parameter such as connectivity, edge connectivity, bipartiteness, independence number, matching number, chromatic number, number of cut vertices and number of cut edges. [ABSTRACT FROM AUTHOR]

The present era is at the peak of technological advancement in image and video processing techniques, with user-friendly accessible tools/techniques. This immersive technology development makes video forensics enormously challenging. Specifically, the passive approaches to object-based forgeries in videos are crucial for legal and judicial matters. Hence, to ensure the integrity of the videos, a scientific, statistical and passive investigation of videos is required to maintain the spatial and temporal information content. This paper aims to develop a passive approach for digging out the forgery traces by applying the motion residue windowing technique for object removal forgery in surveillance videos. The novel max averaging windowing techniques improve visual imprints of the object removal forgery in the videos from the existing methods in the literature. A deep learning approach is the next step for achieving forgery detection in surveillance videos. The proposed lightweight depth-separable layer-optimized CNN has fast execution speed, optimized in terms of parameters without compromising the desired accuracy. This network is trained at a frame level with 98.60% testing accuracy, followed by a pipeline architecture of the proposed model for detection of forgery at video level with 99.01% accuracy. The suggested model works better than current models regarding post-processing operations, compression rates, forged video detection accuracy, precision, recall and F1 score. [ABSTRACT FROM AUTHOR]

Recently, the field of Natural Language Processing (NLP) has made significant progress with the evolution of Contextualised Neural Language Models (CNLMs) and the emergence of large LMs. Traditional and static language models exhibit limitations in tasks demanding contextual comprehension due to their reliance on fixed representations. CNLMs such as BERT and Semantic Folding aim to produce feature-rich representations by considering a broader linguistic context. In this paper, Deep Learning-based Aspect Category Detection approaches are introduced to perform text classification. The study extensively assesses classification model performance, emphasising enhanced representativeness and optimised feature extraction resolution using CNLMs and their hybridised variants. The effectiveness of the proposed approaches is evaluated on benchmark datasets of 4500 reviews from the laptop and restaurant domains. The results show that the proposed approaches using hybridised CNLMs outperform state-of-the-art methods with an f-score of 0.85 for the laptop and f-scores higher than 0.90 for the restaurant dataset. This study represents a pioneering work as one of the initial research efforts aiming to jointly evaluate the representation performance of CNLMs with different architectures to determine their classification capabilities. The findings indicate that the proposed approaches can enable the development of more effective classification models in various NLP tasks. [ABSTRACT FROM AUTHOR]

Multi-dimensional packet classification is one of the most important functions to support various services in next generation routers. Both the memory-efficient data structure to support larger rule tables and the hardware architecture to achieve a higher throughput are desired. In this paper, we propose a parallel and pipelined architecture called Set-Pruning Segment Trees with Buckets (SPSTwB) for multi-dimensional packet classification. SPSTwB significantly reduces rule duplication based on a novel partitioning scheme and an efficient bucket merging scheme. The key feature of our proposed architecture is that memory consumption is reduced significantly regardless of the characteristics of various rule tables. In addition, the logic complexity of each pipeline stage is simplified by not storing rule IDs and priorities and thus it can run at a high clock rate. The proposed scheme needs less than 20 bytes per rule for various 100 K rule tables generated by ClassBench. In addition, the proposed scheme supports fast incremental rule update. The proposed pipelined architecture can achieve a throughput of 134 Gbps from the implementation on Xilinx Virtex-7 FPGA device with dual-ported Block RAM. [ABSTRACT FROM AUTHOR]

This research presents a new problem maximum scatter colored traveling salesman problem (MSCTSP), the objective of MSCTSP is to find Hamiltonian cycles with the minimal edge as max as possible, it is used to simulate the real-world applications of network and transport. Since MSCTSP has been proved to be a NP-hard problem, population-based algorithms can be used for solving it. However, the performances are not satisfactory. Thus, it is necessary to develop novel algorithms to obtain high quality feasible solution. Based on the above reason, the paper proposes a novel hybrid ITÖ (HITÖ) algorithm, which integrates the two new strategies: crossover operator and mutation strategy, into the standard ITÖ. In the iteration course of HITÖ, the dual-chromosome coding is used to code a feasible solution of MSCTSP, and the stochastic drift and volatility operators are used to explore and exploit new unknown region. During the process, drift operator is performed by crossover operator, volatility operator is carried out by mutation strategy, and they are both affected by activity intensity of particles which functionally depends on the radius and temperature. Experiments display HITÖ shows an improvement over comparative algorithms on solution quality. [ABSTRACT FROM AUTHOR]

A connected graph |$G$| is called strongly Menger edge connected if |$G$| has min{deg |$_{G}(x)$|⁠ , deg |$_{G}(y)$| } edge-disjoint paths between any two distinct vertices |$x$| and |$y$| in |$G$|⁠. In this paper, we consider two types of the strongly Menger edge connectivity of the line graphs of generalized |$n$| -dimensional hypercubes with faulty edges, namely the |$m$| -edge-fault-tolerant and |$m$| -conditional edge-fault-tolerant strongly Menger edge connectivity. We show that the line graphs of all generalized |$n$| -dimensional hypercubes are |$(2n-4)$| -edge-fault-tolerant strongly Menger edge connected for |$n\geq 3$| and |$(4n-10)$| -conditional edge-fault-tolerant strongly Menger edge connected for |$n\geq 4$|⁠. The two bounds for the maximum numbers of faulty edges are best possible. [ABSTRACT FROM AUTHOR]

Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on |$k$| -cliques, i.e. complete subgraphs on |$k$| nodes. However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called |$k$| -diamonds and defined as cliques of size |$k$| with one missing edge. We first provide a sequential algorithm that, in |$O(nm^{(k-1)/2})$| time, counts and lists all the |$k$| -diamonds in large graphs, for any constant |$k \geq 4$|⁠. A parallel extension of the sequential algorithm is then proposed and analyzed in a MapReduce-style model, achieving the same local and total space usage of the state-of-the-art algorithms for |$k$| -cliques. The running time is optimal on dense graphs and |$O(\sqrt{m})$| larger than |$k$| -clique counting if the graph is sparse. Our algorithms compute induced diamonds by analyzing the structure of directed stars formed by the graph nodes and their neighbors. [ABSTRACT FROM AUTHOR]

State machines are widely used in industry and academia to capture behavioural models of control. They are included in popular notations, such as UML and its variants, and used (sometimes informally) to describe computational artefacts. In this paper, we present laws for state machines that we prove sound with respect to a process algebraic semantics for refinement, and complete, in that they are sufficient to reduce an arbitrary model to a normal form that isolates basic (action and control) elements. We consider two variants of UML-like state machines, both enriched with facilities to deal with time budgets, timeouts and deadlines over triggers and actions. In the first variant, machines are self-contained components, declaring all the variables, events and operations that they require or define. In contrast, in the second variant, machines are open, like in UML for instance. Laws for open state machines do not depend on a specific context of variables, events and operations, and normalization uses a novel operator for open-machine (de)composition. Our laws can be used in behaviour-preservation transformation techniques. Their applications are automated by a model-transformation engine. [ABSTRACT FROM AUTHOR]

According to Menger's theorem, connectivity and edge connectivity are closely related to node-disjoint paths and edge-disjoint paths, respectively. Node- and edge-disjoint paths can keep the effective transmission of information and confidentiality. Therefore, node-disjoint paths and edge-disjoint paths are two important parameters to measure the reliability of a network. For a faulty node (resp. edge) set |$S\subset V$| (resp. |$S\subset E$|⁠), a connected graph |$G=(V,E)$| is |$S$| -strongly Menger-node-connected (resp. Menger-edge-connected) if any two distinct nodes |$x$| and |$y$| in |$G-S$| are connected by |$\min \{\deg _{G-S}(x),\deg _{G-S}(y)\}$| internally node-disjoint (resp. edge-disjoint) paths in |$G-S$|⁠ , where |$\deg _{G-S}(x)$| and |$\deg _{G-S}(y)$| are the degrees of |$x$| and |$y$| in |$G-S$|⁠ , respectively. And most of the previous studies are based on networks that are triangle-free. In this paper, we consider the strongly Menger (edge) connectedness of a class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| with triangles. Moreover, we show that |$G_{r}$| is |$(rl-l-1)$| -strongly Menger-node-connected. And then we show that |$G_{r}$| is |$[k+(r-1)l-2]$| -strongly Menger-edge-connected of order 1 and |$[2k+2(r-1)l-6]$| -strongly Menger-edge-connected of order 2. Since the class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| includes not only data center networks DCell and generalized DCell but also interconnection network dragonfly, etc. all the results are appropriate for these networks. [ABSTRACT FROM AUTHOR]

A confidential reporting system (CRS) allows reporters to report concerns or problems in confidence without the fear of blame or reprisals. Nevertheless, privacy has been the primary concern of reporters. In this paper, we propose a privacy-preserving confidential reporting system with designated reporters (PPCRS-DR) to protect the privacy of reporters and the confidentiality of reports. Our PPCRS-DR provides the following interesting features: (1) for an event, an auditor can designate a reporter to report; (2) an auditor can neither see the report nor know the reporter's identity from an encrypted report if the reporter is not the designated one; (3) when an auditor is unavailable, he/she can temporarily designate a delegatee to collect and review reports on behalf of him/her. We formalize both the definition and security model of our PPCRS-DR, and propose a concrete construction. Furthermore, the security of the proposed PPCRS-DR is formally proven. The implementation shows that it is efficient. The novelty is to implement flexible decryption delegation of CRSs and protect reporters' privacy. [ABSTRACT FROM AUTHOR]

Two vertices |$u$| and |$v$| in a graph |$G=(V,E)$| are in the same orbit if there exists an automorphism |$\phi $| of |$G$| such that |$\phi (u)=v$|⁠. The orbit number of a graph |$G$|⁠ , denoted by |$Orb(G)$|⁠ , is the smallest number of orbits, which form a partition of |$V(G)$|⁠ , in |$G$|⁠. All vertex-transitive graphs |$G$| are with |$Orb(G)=1$|⁠. Since the |$n$| -dimensional hypercube, denoted by |$Q_{n}$|⁠ , is vertex-transitive, it follows that |$Orb(Q_{n})=1$| for |$n\geq 1$|⁠. Pai, Chang, and Yang proved that the |$n$| -dimensional folded crossed cube, denoted by |$FCQ_{n}$|⁠ , is vertex-transitive if and only if |$n\in \{1,2,4\}$|⁠ , namely |$Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$|⁠. In this paper, we prove that |$Orb(FCQ_{n})=2^{\lceil \frac{n}{2}\rceil -2}$| if |$n\geq 6$| is even and |$Orb(FCQ_{n}) = 2^{\lceil \frac{n}{2}\rceil -1}$| if |$n\geq 3$| is odd. [ABSTRACT FROM AUTHOR]

Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners. [ABSTRACT FROM AUTHOR]