Reviewerly/exHarmony huggingface dataset:
Locality-aware group sparse coding on Grassmann manifolds for image set classification

Riemannian sparse coding methods are attracting increasing interest in many computer vision applications, relying on its non-Euclidean structure. One such recently successful task is image set classification by the aid of Grassmann Manifolds, where an image set can be seen as a point. However, due to irrelevant information and outliers, the probe set may be represented by misleading sets with large sparse coefficients. Meanwhile, it is difficult for a single subspace to cover changes within an image set and the hidden structure among samples is relaxed. In this paper, we propose a novel Grassmann Locality-Aware Group Sparse Coding model (GLGSC) that attempts to preserve locality information and take advantage of the relationship among image sets to capture the inter and intra-set variations simultaneously. Since the contributions of different gallery subspaces to the probe subspace should vary in importance, we then introduce a novel representation adaption term. In addition, a kernelised version of GLGSC is proposed to handle non-linearity in data. To reveal the effectiveness of our algorithm over state-of-the-art, several classification tasks are conducted, including face recognition, object recognition and gesture recognition.
Privacy-Protected Certificateless Aggregate Signature Scheme in VANET

We propose a certificateless aggregate signature (CLAS) scheme for privacy protection based on the certificateless public key cryptosystem and aggregate signature aiming at the common problems of communication security and efficiency in vehicular ad hoc network (VANET). The scheme can resist two different types of adversary attacks under the random oracle model and satisfy the unforgeability of communication messages as well as traceability and anonymity of vehicles. The scheme avoids the key escrow problem and certificate management. Based on the bilinear pairing operations, the aggregation verification of multiple messages is realized through aggregation signature. We quantitatively analyze the computational overhead of the scheme and define a new variable of computational efficiency. Our scheme is proved to be more efficient when V2I communication is carried out in urban roads compared with other schemes.
Echo-State Networks for Network Traffic Prediction

The ability to forecast future levels of network traffic is very important to network management and planning in enterprise, cloud, data center and WAN networks. A good prediction of future traffic levels makes it possible for network operators to plan ahead on how to achieve service delivery targets in the areas of resource allocation, congestion control, capacity planning, anomaly detection and quality of service. As a result, there has been significant research interest in finding good methods that can be used for traffic prediction.Various neural network based methods have been explored in research for prediction of network traffic. However, many of these approaches often require long processing times to create a model from the network data and predict traffic. In this paper, we present the results of our ongoing study on the use of echo state networks (ESN) for the prediction of network traffic. ESNs are a type of reservoir learning algorithm that have an internal state that is made up of a 'reservoir-like' pool of randomly connected neurons. In our experiments, we compare results obtained by using ESNs with the results obtained by using other techniques for network prediction on two datasets obtained from a well known public network trace repository. We show that ESNs achieve significantly faster training and prediction times for similar levels of accuracy when compared to the other methods.
Large-scale optimisation via cooperatively coevolving competition swarm optimiser

ABSTRACT This paper presents a two-stage approach, named Cooperatively Coevolving Competition Swarm Optimiser (C3SO), for large-scale optimisation. C3SO first detects interactions among the original variables by a differential grouping algorithm, therein decomposing a large-scale problem into several sub-components. In the next stage, a new approach is developed using the competition mechanism to independently optimise each sub-component obtained in the first stage. Hence, C3SO takes advantages of both divide-and-conquer and competition strategies, achieving the ability to address large-scale and complex optimisation problems. The proposed method is evaluated by comparing with several state-of-the-art algorithms on different benchmark functions, and the comparative results demonstrated its effectiveness.
Decision Support System in Determining Structural Position Mutations Using Simple Additive Weighting (SAW) Method

Abstract Mutations are needed in a company to improve the quality of workers. mutations are based on the capabilities of individual workers in the company. HRD assesses the abilities of workers from various aspects. manually the accuracy of mutations based on the value of the criteria is only 38%. with a low value of accuracy resulting in mutations of workers not as expected. the criteria given are FFB Production (C1), Core CPO Production (C2) and Field Care (C3). Range of mutation decision weights are 10-30% Giving First Warning Letter, 31 - 70% Position setting and 71 - 100% Giving promotion. with the SAW method the analysis is done by computerization. after testing the criteria obtained. HRD assesses that the accuracy of workers reaches 85%. and better and in accordance with what the company wants.
Interactive Refactoring Documentation Bot

The documentation of code changes is significantly important but developers ignore it, most of the time, due to the pressure of the deadlines. While developers may document the most important features modification or bugs fixing, recent empirical studies show that the documentation of quality improvements and/or refactoring is often omitted or not accurately described. However, the automated or semi-automated documentation of refactorings has not been yet explored despite the extensive work on the remaining steps of refactoring including the detection, prioritization and recommendation. In this paper, we propose a semi-automated refactoring documentation bot that helps developers to interactively check and validate the documentation of the refactorings and/or quality improvements at the file level for each opened pull-request before being reviewed or merged to the master. The bot starts by checking the pullrequest if there are significant quality changes and refactorings at the file level and whether they are documented by the developer. Then, it checks the validity of the developers description of the refactorings, if any. Based on that analysis, the documentation bot will recommend a message to document the refactorings, their locations and the quality improvement for that pull-request when missing information is found. Then, the developer can modify his pull request description by interacting with the bot to accept/modify/reject part of the proposed documentation. Since refactoring do not happen in isolation most of the time, the bot is documenting the impact of a sequence of refactorings, in a pull-request, on quality and not each refactoring in isolation. We conducted a human survey with 14 active developers to manually evaluate the relevance and the correctness of our tool on different pull requests of 5 open source projects and one industrial system. The results show that the participants found that our bot facilitates the documentation of their quality-related changes and refactorings.
Incorporating Label Co-Occurrence Into Neural Network-Based Models for Multi-Label Text Classification

Multi-label text classification (MLTC) addresses a fundamental problem in natural language processing, which assigns multiple relevant labels to each document. In recent years, Neural Network-based models (NN models) for MLTC have attracted much attention. In addition, NN models achieve favorable performances because they can exploit label correlations in the penultimate layer. To further capture and explore label correlations, we propose a novel initialization to incorporate label co-occurrence into NN models. First, we represent each class as a column vector of the weight matrix in the penultimate layer, which we name the class embedding matrix. Second, we deduce an equation for correlating the class embedding matrix with the label co-occurrence matrix, ensuring that relevant classes are denoted by vectors with large correlations. Finally, we provide a theoretical analysis of the equation, and propose an algorithm to calculate the initial values of the class embedding matrix from the label co-occurrence matrix. We evaluate our approach with various text extractors, such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Transformer on four public datasets. The experimental results demonstrate that our approach markedly improves the performance of existing NN models.
A Practical Side-Channel Attack of a LoRaWAN Module Using Deep Learning

With the development of Internet-of-Things technology, the electronic devices nowadays are embedded with “smart” functionalities, which enable them to upload data to the cloud for the easy access of the users. Such functionalities have been popular among commercial products. However, as we shall show in this paper, these functionalities, if not protected properly, can be vulnerable to attacks. An unprotected implementation leaks significant level of information related to the sensitive operations, which could pose threats to the security of the device. In this paper, we demonstrate a practical attack against a LoRaWAN communication module. This attack uses the deep-learning-based side-channel attack to recover the key for encrypting the payload data. By capturing less than 100 communication packets, the trained convolutional neural network is capable of recovering the full key for AES encryption.
Quantum compression of tensor network states

We design quantum compression algorithms for parametric families of tensor network states. We first establish an upper bound on the amount of memory needed to store an arbitrary state from a given state family. The bound is determined by the minimum cut of a suitable flow network, and is related to the flow of information from the manifold of parameters that specify the states to the physical systems in which the states are embodied. For given network topology and given edge dimensions, our upper bound is tight when all edge dimensions are powers of the same integer. When this condition is not met, the bound is optimal up to a multiplicative factor smaller than 1.585. We then provide a compression algorithm for general state families, and show that the algorithm runs in polynomial time for matrix product states.
An Efficient Vulnerability Detection Model for Ethereum Smart Contracts

Smart contracts are decentralized applications running on the blockchain to meet various practical scenario demands. The increasing number of security events regarding smart contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on the blockchain. Faced with the increasing quantity of contracts, it is an emerging issue to effectively and efficiently detect vulnerabilities in smart contracts. Existing methods of detecting vulnerabilities in smart contracts like Oyente mainly employ symbolic execution. This method is very time-consuming, as the symbolic execution requires the exploration of all executable paths in a contract. In this work, we propose an efficient model for the detection of vulnerabilities in Ethereum smart contracts with machine learning techniques. The model is able to effectively and fast detect vulnerabilities based on the patterns learned from training samples. Our model is evaluated on 49502 real-world smart contracts and the results verify its effectiveness and efficiency.
Keep the books on the shelves: Library space as intrinsic facilitator of the reading experience

Library literature frequently reports projects to remove print collections and replace them with other amenities for patrons. This project challenges the untested assumption that the physical library itself serves no useful function to its users unless they are actively consulting books from the shelves. The alternative hypothesis is that readers benefit from the mere act of studying while in a book-filled environment. To test this possibility, ten subjects completed SAT-style reading comprehension tests in both a traditional library environment, and a renovated chapel that strongly resembles library space except for lacking books. Results provide a reasonable basis to support an expectation that readers perform better on reading comprehension tasks performed in book-rich environments.
LaT-Voting: Traceable Anonymous E-Voting on Blockchain

In order to achieve anonymous voting, various cryptographic techniques are usually leveraged to avoid privacy leakage. However, traditional e-voting systems are excessively dependent on a centralized platform for publishing voting activity and storing ballots, which cannot ensure the security against tampering. Recently, numerous solutions based on blockchain are proposed to eliminate this threat. Nevertheless, these schemes are not applicable for wide adoption owing to the inefficiency in detecting double-voting. Meanwhile, there are no novel manners can both trace back to a malicious voter who voted twice, and discover his real identity simultaneously. Therefore, to settle the two aforementioned issues effectively, the first blockchain-based anonymous and traceable decentralized voting scheme called LaT-Voting is proposed in this paper. To better coordinate the conflict relationship between anonymity and accountability, we propose a new notion called prefix-based linkable-and-traceable anonymous authentication, which (i) achieves the authentication process without disclosing user privacy; (ii) provides a practical linkability to rapidly link two messages originated from a user; (iii) realizes a subtle traceability to track the user who authenticated twice.
Newest collaborative and hybrid network intrusion detection framework based on suricata and isolation forest algorithm

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Indeed, intrusions or attacks can lead to irreparable damages, information leakage and significant financial losses. Hence, there is a great need for an effective Network Intrusion Detection System (NIDS). In the current study, we propose a hybrid NIDS to detect network attacks in the network environment by monitoring network traffic, thereby achieving a solid line of protection against inside and outside intruders and maintaining performance and service quality. In our NIDS framework, we use Suricata as a signature based detection to uncover known attacks, while for detecting network anomaly, we use Isolation Forest Algorithm (IFA). By applying Suricata prior to the IFA classifier, IFA has to detect only unknown attacks. Therefore, detection time is reduced and computational power is saved. Suricata is an open source IDS, which has been advanced as a multi-threaded alternative to popular Snort IDS. The major benefits of a multi-threaded design is that it offers increased speed and efficiency in network traffic analysis and can also help divide up the IDS workload based on where the processing needs are. Consequently, Suricata shows an increase in accuracy and system performance over the de facto standard, single threaded NIDS Snort. While, IFA is one of the newest approaches to detect anomalies/outliers, which introduces the use of isolation as a more effective and efficient means to recognize anomalies than the popularly used basic distance and density measures. In fact, IFA uses no distance or density measures to identify outliers, this eliminates major computational cost of distance calculation in all distance-based and density-based algorithms. Additionally, IFA has a low constant in its computational complexity. Moreover, in this framework, the NIDSs operate in collaborative way to oppose attacks by sharing alerts stored in central log. In this way, unknown attacks that were detected by any NIDS can easily be detected by others IDSs. This also helps to reduce computational cost for detecting intrusions at others NIDSs, and improve detection rate in overall the network environment.
Learning Generative Models Using Denoising Density Estimators

Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised machine learning. We introduce a new generative model based on denoising density estimators (DDEs), which are scalar functions parametrized by neural networks, that are efficiently trained to represent kernel density estimators of the data. Leveraging DDEs, our main contribution is a novel technique to obtain generative models by minimizing the Kullback–Leibler (KL)-divergence directly. We prove that our algorithm for obtaining generative models is guaranteed to converge consistently to the correct solution. Our approach does not require specific network architecture as in normalizing flows (NFs), nor use ordinary differential equation (ODE) solvers as in continuous NFs. Experimental results demonstrate substantial improvement in density estimation and competitive performance in generative model training.
Parametric randomization, complex symplectic factorizations, and quadratic-exponential functionals for Gaussian quantum states

This paper combines probabilistic and algebraic techniques for computing quantum expectations of operator exponentials (and their products) of quadratic forms of quantum variables in Gaussian states. Such quadratic-exponential functionals (QEFs) resemble quantum statistical mechanical partition functions with quadratic Hamiltonians and are also used as performance criteria in quantum risk-sensitive filtering and control problems for linear quantum stochastic systems. We employ a Lie-algebraic correspondence between complex symplectic matrices and quadratic-exponential functions of system variables of a quantum harmonic oscillator. The complex symplectic factorizations are used together with a parametric randomization of the quasi-characteristic or moment-generating functions according to an auxiliary classical Gaussian distribution. This reduces the QEF to an exponential moment of a quadratic form of classical Gaussian random variables with a complex symmetric matrix and is applicable to recursive computation of such moments.
Breast cancer image segmentation and classification

Breast cancer is the most common malignancy that affects women all over the world, especially in morocco with 35.8% [1]. The effective way to diagnose and treat breast cancer is the earlier detection of symptoms and signs which can arise the chances of preconizing an earlier treatment, probably specific one. Thus, it can extremely affect the chances to survive by delaying the cancer or the total elimination. Mammography is a highly accurate radiography technique that represents a basic and crucial phase in preventing and detecting early this disease. Using the mammography images, we propose in this paper to develop a computer aided diagnosis (CAD) system named Earlier Breast Cancer Computer Aided Detection (EBCCAD) which aim is to detect and classify breast cancer images. The obtained results are promising and encouraging to evaluate the proposed system for real cases.
HLT*: Real-time and Any-angle Path Planning in 3D Environment

Even though path planning is a well-studied problem in 2D environment, finding an optimal or near-optimal path in a complex and unknown 3D environment has great prospect, but it is hard to find the optimal path quickly. In this paper, we propose a new algorithm called Hierarchical Lazy Theta* (HLT*), which can plan the near-optimal path efficiently for real-time operation based on the heuristic-based path-finding algorithm Lazy Theta* with a hierarchical path planning approach. Path refinement, smoothing, and polishing are used to refine the path to ensure the feasibility of computed path. Its computation time and path quality are dependent on parameters, such as map size, environment complexity, sensor detection range, refinement range, computation time limits, and any restriction on planning time. Simulation experiments are used to assess the performance, and the simulation results show that HLT* algorithm is capable of planning a high-quality path in a shorter time.
A Transfer Learning based Approach for Aspect Based Sentiment Analysis

Understanding sentiments embeded in online consumer reviews can assist a variety of decision making processes. Recent studies have extended sentiment analysis to a fine-grained level, such as toward different features or characteristics of products/services, which is known as Aspect Based Sentiment Analysis (ABSA). In this study, we propose a transfer learning based multi-label classification approach, building on a pre-trained advanced deep learning model. We evaluate the proposed approach in an experiment by comparing it with mainstream multi-label classification approaches in the context of ABSA on Yelp restaurant reviews. To the best of our knowledge, this is the first study conducting multi-label classification of ABSA on online consumer reviews. The experiment results confirm that our approach outperforms other multi-classification approaches.
State transfer in strongly regular graphs with an edge perturbation

Quantum walks, an important tool in quantum computing, have been very successfully investigated using techniques in algebraic graph theory. We are motivated by the study of state transfer in continuous-time quantum walks, which is understood to be a rare and interesting phenomenon. We consider a perturbation on an edge uv of a graph where we add a weight β to the edge and a loop of weight γ to each of u and v. We characterize when this perturbation results in strongly cospectral vertices u and v. Applying this to strongly regular graphs, we give infinite families of strongly regular graphs where some perturbation results in perfect state transfer. Further, we show that, for every strongly regular graph, there is some perturbation which results in pretty good state transfer. We also show for any strongly regular graph X and edge e∈E(X), that ϕ(X\e) does not depend on the choice of e.
SysML Modeling Mistakes and Their Impacts on Requirements

The Systems Modeling Language (SysML) represents a significant and increasing segment of industrial support for building critical systems. Because modeling is a human-centric activity, mistakes are unavoidable. Although there exist several software defect classifications, little is known about the mistakes pertaining to SysML modeling and the implications of those mistakes in model-driven requirements engineering. In this paper, we report a systematic mapping through which 42 SysML modeling mistakes are identified from 19 primary studies. With an emphasis on the evidence of industrial relevance, we further uncover that, despite some mistakes hurt requirements satisfaction, others help make the requirements more complete and the specifications more precise. Our work sheds light on understanding the scope of the SysML mistakes and checking requirements fulfillment in the face of the mistakes.