Reviewerly/exHarmony huggingface dataset:
A Double-Edged Sword? Software Reuse and Potential Security Vulnerabilities

Reuse is a common and often-advocated software development practice. Significant efforts have been invested into facilitating it, leading to advancements such as software forges, package managers, and the widespread integration of open source components into proprietary software systems. Reused software can make a system more secure through its maturity and extended vetting, or increase its vulnerabilities through a larger attack surface or insecure coding practices. To shed more light on this issue, we investigate the relationship between software reuse and potential security vulnerabilities, as assessed through static analysis. We empirically investigated 301 open source projects in a holistic multiple-case methods study. In particular, we examined the distribution of potential vulnerabilities between the native code created by a project’s development team and external code reused through dependencies, as well as the correlation between the ratio of reuse and the density of vulnerabilities. The results suggest that the amount of potential vulnerabilities in both native and reused code increases with larger project sizes. We also found a weak-to-moderate correlation between a higher reuse ratio and a lower density of vulnerabilities. Based on these findings it appears that code reuse is neither a frightening werewolf introducing an excessive number of vulnerabilities nor a silver bullet for avoiding them.
An Efficient System for Color Image Retrieval Representing Semantic Information to Enhance Performance by Optimizing Feature Extraction

Image retrieval is a well discussed field in digital image processing. Images can be sort out from a big collection and database of images on the basis of text, color and shape of objects in images. In CBIR systems, using combined features get many most similar and relevant images. In a typical CBIR system, the ocular content of the images from the large collection is extracted and displayed by a storehouse like m-dimensional feature vectors framed out from images. This vector of the images in the collection of images in database is named as a database of features. Most sought out systems represent images with color feature most related to user and shape feature to relate image to exact object from collection and test dataset. In this paper we present introduction, research work on color scheme with HSV colour space and its combination with shape using coiflet wavelet methods. States of art design of overall system is delineated with results evaluated for mean average precision and mean average recall for overall system.
First-Person Action Recognition With Temporal Pooling and Hilbert–Huang Transform

This paper presents a convolutional neural network (CNN)-based approach for first-person action recognition with a combination of temporal pooling and the Hilbert–Huang transform (HHT). The new approach first adaptively performs temporal sub-action localization, treats each channel of the extracted trajectory pooled CNN features as a time series, and summarizes the temporal dynamic information in each sub-action by temporal pooling. The temporal evolution across sub-actions is then modeled by rank pooling. Thereafter, to account for the highly dynamic scene changes in first-person videos, the HHT is employed to decompose the ranked pooling features into finite and often few data-dependent functions, called intrinsic mode functions (IMFs), through empirical mode decomposition. Hilbert spectral analysis is then applied to each IMF component, and four salient descriptors are scrutinized and aggregated into the final video descriptor. Such a framework cannot only precisely acquire both long- and short-term tendencies, but also address the cumbersome significant camera motion in first-person videos to render better accuracy. Furthermore, it works well for complex actions for limited training samples. Simulations show that the proposed approach outperforms the main state-of-the-art methods when applied to four publicly available first-person video datasets.
Blockchain Privacy Protection Scheme Based on Aggregate Signature

With the continuous development and wide application of blockchain technology, the problem of privacy leakage is becoming more and more prominent and must be fully taken seriously. This paper proposes a new blockchain signature scheme based on the aggregation signature scheme for the privacy protection of transaction addresses in blockchain. Compared with related schemes, the privacy protection scheme reduces the computational overhead of the signature and verification process, reduces the storage overhead of the blockchain, and improves the communication efficiency. The theoretical analysis results show that the scheme is safe and effective, and can protect the identity privacy of the receiver in the blockchain transaction.
Constraint projections for semi‐supervised spectral clustering ensemble

Summary Cluster ensemble combines multiple base clustering results in a suitable way to improve the accuracy of the clustering result. In the conventional cluster ensemble frameworks, pairwise constraints and constraint projections have not been used together, and spectral clustering algorithm is rarely adopted to serve as the consensus function. In this paper, we design a constraint projections for semi‐supervised spectral clustering ensemble (CPSSSCE) model. It takes advantages of spectral clustering algorithm and executes semi‐supervised learning twice. Compared to traditional cluster ensemble approaches, CPSSSCE is characterized by several properties. First, the original data are transformed to lower‐dimensional representations by constraint projection before base clustering. Second, a similarity matrix is constructed using the base clustering results and modified using pairwise constraints. Third, the spectral clustering algorithm is applied to process the similarity matrix to obtain a consensus cluster result. Extensive experiments on standard University of California Irvine Machine Learning Repository (UCI) and Microsoft datasets demonstrated that the CPSSSCE is superior to other cluster ensemble algorithms including a semi‐supervised spectral clustering ensemble.
Action Units recognition based on Deep Spatial-Convolutional and Multi-label Residual network

Facial Action Unit (AU) recognition is an essential step in the facial analysis. A facial image has one or more AU(s). Given an AU, the number of images without the AU is far greater than that of images with the AU. So, AU recognition is not only a sample imbalance problem but also a multi-label learning problem. For the two problems, we proposed a novel Multi-label Slope Rate (MSR) loss function and an Advanced-MSR (Ad-MSR) loss function in deep network architecture to recognize AU. For other characters of AU recognition, a local convolution and residual units are used in the architecture. The experimental results on two expression databases labeled AU show that the proposed loss functions not only address overfitting of the network on the training set and enhancing the generalization ability on the test set. The proposed architecture also gets well performance in the databases.
A Novel Approach to Solve Class Imbalance Problem Using Noise Filter Method

Today's one of the popular pre-processing technique in handling class imbalance problems is over-sampling. It balances the datasets to achieve a high classification rate and also avoids the bias towards majority class samples. Over-sampling technique takes full minority samples in the training data into consideration while performing classification. But, the presence of some noise (in the minority samples and majority samples) may degrade the classification performance. Hence, this work introduces a noise filter over-sampling approach with Adaptive Boosting Algorithm (AdaBoost) for effective classification. This work evaluates the performance with the state of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 14 imbalance binary class datasets with various Imbalance Ratios (IR). The experimental results show that our approach works as promising and effective for dealing with imbalanced datasets using metrics like F-Measure and AUC.
A Novel Swarm Intelligence Based Optimization Method: Harris’ Hawk Optimization

Swarm intelligence is a modern optimization technique, and one of the most promising techniques for solving optimization problems. In this paper, a new swarm intelligence based algorithm namely, Harris’ Hawk Optimizer (HHO) is proposed. The algorithm mimics the cooperative hunting behaviour of Harris’ hawks. The algorithm is analysed for twenty five well known benchmark functions. Performance of HHO is compared with Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO) and The Whale Optimization Algorithm (WOA). HHO is implemented and results present HHO as one of the efficient optimization methods.
Toward evolutionary and developmental intelligence

Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence.
Improvement of Chatbot in Trading System for SMEs by Using Deep Neural Network

This research presents a method for developing chatbots to serve their users. In general, these chatbots are used for answering questions in many businesses, providing customer information, providing train schedules, helping customer reservations, virtual assistants; serve as call centers to serve ten million customers automatically. A deep learning based conversational artificial intelligence technique was used as tools for learning conversation between machine and customer. In addition, the steps required are the technique used in conjunction with the convolution neural network technique by using Tensorflow training to improve the accuracy of these chatbots. From the experimental results, using deep learning for chatbots learning, the accuracy is better than the traditional model.
Detection Process of Ships in Aerial Imagery Using Two Convnets

In this article, we focus on boat real time detection in high resolution aerial images taken by UAVs, using convolutional neural networks. This problem is similar to object detection in images taken from the terrestrial surface at ground level or on elevations, but present certain specificities regarding: the smaller size of objects to be detected and camera orientations that are different. As a result, the methods designed for the general detection purpose are not adapted to our problem. Some of these convolutional network-based methods have been adapted for locating objects on remote sensing images by making changes and enhancements on the networks' models and parameters. In this article, we will take a look at some of these techniques and architectures. We will also draw up a comparison table between the various improvements used in these methods. We will also present the strategy and techniques we will adopt in order to provide practical advantages and a reliable solution to our problem. And finally, we will expose the results obtained using our approach.
Permanental generating functions and sequential importance sampling

We introduce techniques for deriving closed form generating functions for enumerating permutations with restricted positions keeping track of various statistics. The method involves evaluating permanents with variables as entries. These are applied to determine the sample size required for a novel sequential importance sampling algorithm for generating random perfect matchings in classes of bipartite graphs.
Decentralized Information Filter with Noncommon States

This paper addresses the problem of state estimation using a decentralized estimator in the presence of sensor or node-specific local state variables that arise from heterogeneous sensor dynamics, time-varying biases, or both. The state vector at each node is divided into two sets: states common to all nodes and states describing the local sensor dynamics and bias. Each node works as an independent Kalman filter to estimate the common as well as the local states associated with it, whereas communication between the notes is restricted to just the common states. The novelty of the proposed algorithm lies in the assimilation process of the decentralized estimator, wherein the local state vectors are fused without increasing the computational or the communication-related costs. Additionally, the proposed algorithm is extended by incorporating covariance intersection to address the problem of unknown correlation between the local estimators in decentralized estimation. The algorithm is validated using numerical simulations. The error characteristics of the proposed algorithm are comparable to those of a centralized Kalman filter, and the proposed algorithm is seen to offer a substantial reduction in the costs associated with estimation.
Access Control for Binary Integrity Protection using Ethereum

The integrity of executable binaries is essential to the security of any device that runs them. At best, a manipulated binary can leave the system in question open to attack, and at worst, it can compromise the entire system by itself. In recent years, supply-chain attacks have demonstrated that binaries can even be compromised unbeknownst to their creators. This, in turn, leads to the dissemination of supposedly valid binaries that need to be revoked later.
Ontologies for the classification of cultural heritage data

A large amount of data is published every day on the Web, making search and discovery tasks increasingly difficult. The cultural heritage data are data held by public bodies in general (museums, archive organizations, libraries, cultural centers, etc.). The sources of heritage data are therefore numerous, which justifies the need for a system for collecting data from heterogeneous sources. The use of semantic Web techniques to describe, process and exploit legends of heritage will be very useful. This work is a comparative study of the main ontologies of cultural heritage. The objective is to select one among them to propose a complete system of management, processing, and publication of data on the tangible and intangible cultural heritage in Morocco.
Anomaly Detection in Network Traffic Security Assurance

The paper focuses on a selected element of network security assurance, which is anomaly detection in network traffic monitoring. The anomaly detection component is developed as part of Regional Security Operation Center (developed in the RegSOC project) – a local instance of the Security Operational Center (SOC) – to detect incidents or their symptoms in terms of outlier observations in data. The objective of the research is to assess and select for implementation methods and tools satisfying the requirements of the performed RegSOC project. The paper discusses the role and placement of such tools in the general SOC architecture and requirements to be satisfied by these tools in a view of the specific RegSOC project needs. Next, a review of available methods and tools is performed to select the most useful ones. Using the selected tool, a general concept of security analysis component is presented and assessed against the project requirements.
A fast implementation of interactive-model generalized labeled multi-bernoulli filter for interval measurements

Aiming at tracking multi-maneuvering targets for interval measurements, we introduce the multi-model concept into the Generalized Labeled Multi-Bernoulli (GLMB) algorithm and propose an Interactive Multi-Model GLMB (IMM-GLMB) tracking algorithm for interval measurements. The sampled particles are predicted by the Interactive Multi-Model (IMM) algorithm. Afterwards, we update the predicted particles by introducing a generalized likelihood function in conjunction with the GLMB filter update strategy. With the combination of the characteristics of GLMB filtering and the IMM method, this algorithm can effectively improve the target state prediction accuracy and avoid tracking failure caused by model mismatch in the maneuvering process of targets, but it requires different truncation step for each component in the prediction and update. In order to improve the efficiency of the algorithm, we adopt the idea of a fast algorithm proposed in the literature and propose the IMM Generalized Labeled Multi-Bernoulli Fast (IMM-GLMBF) algorithm. This IMM-GLMBF algorithm integrates the prediction and the update steps of the IMM-GLMB algorithm, so that it requires only one truncation. Compared to the IMM-GLMB algorithm, the proposed IMM-GLMBF algorithm is more accurate in estimating the number of targets and their states, and it greatly reduces the computation cost. In addition, we present the Sequential Monte Carlo (SMC) implementation of the IMM-GLMB and IMM-GLMBF algorithms.
Spark Framework for Real-Time Analytic of Multiple Heterogeneous Data Streams

Real-time streaming applications with multiple heterogeneous data streams have become increasingly popular especially in IoT applications; however, many issues still exist, especially in deploying and maintaining these large amounts of data streams. Using Spark Structured Streaming, this paper introduces a Spark Streaming framework for multiple heterogeneous data streams which allows the deployment of multiple heterogeneous data stream processing in a single Spark application; reducing deployment difficulty, coding redundancy, monitoring difficulties, and solving the problem of inefficient job queueing in multi-stream applications.
AES encryption and decryption standards

Abstract Over the last years there has been massive changes that lead to the growth of information in technology, that brought significant changes in the part of cryptography and its applications like confidentiality of data and many such. Privacy and secrecy is what everyone desires for their data or accounts. Encryption is one such method to achieve it. Advanced Encryption Standard (AES), can be used to protect the information. The primary preferred standpoint of AESis, it very well may be can be reproduced or worked with unadulterated equipment. In this paper, Xilinx 9.2i is utilized for recreation and improvement of VHDL code. Integrating and execution of the code is completed on Xilinx - Project Navigator ISE 9.2i suite. Xilinx XC3S500 gadget of Spartan Family and is utilized for equipment usage. This undertaking proposes a strategy to coordinate the AES encrypter and the AES descrypter.
Effect of an EAP unit on EFL student effective and appropriate source-use skills

It is widely believed source-use skills are among the most important academic literacy skills that students need to develop throughout their college years. Although there has been extensive discussion of the difficulties experienced by ESL/EFL students developing these skills, few studies have focused on instructional interventions designed specifically to build source-use skills. To the best of our knowledge, no intervention recorded in the literature has focused on both essential aspects of source use — effective as well as appropriate source use at the same time. In this classroom-based study, we investigated whether a source-use oriented EAP unit can markedly improve student source-use skills. Results showed that the EAP unit led to significant improvement in three out of the four operations under source-use effectiveness, that is, using sources for giving support to an argument, establishing links among sources, and integrating source texts into one's own writing; the EAP unit led to significant improvement in all three operations under source-use appropriateness, namely, adequate attribution to source authors, accurate representation of source ideas, and substantial transformation of source language. A number of pedagogical implications were drawn from this study.
Investigating the Effectiveness of Mutation Testing Tools in the Context of Deep Neural Networks

Verifying the correctness of the implementation of machine learning algorithms like neural networks has become a major topic because – for example – its increasing use in the context of safety critical systems like automated or autonomous vehicles. In contrast to evaluating the learning capabilities of such machine learning algorithms, in verification, and particularly in testing we are interested in finding critical scenarios and in giving some sort of guarantees with respect to the underlying used tests. In this paper, we contribute to the area of testing machine learning algorithms and investigate the effectiveness of traditional mutation tools in the context of Deep Neural Networks testing. In particular, we try to answer the question whether mutated neural networks can be identified considering their learning capabilities when compared to the original network. To answer this question, we performed an empirical study using Java code implementations of such networks and a mutation tool to create mutated neural networks models. As an outcome, we are able to identify some mutations to be more likely to be detected than others.
Generalized Variant Support Vector Machine

With the advancement in information technology, datasets with an enormous amount of data are available. The classification task on these datasets is more time- and memory-consuming as the number of data increases. The support vector machine (SVM), which is arguably the most popular classification technique, has disappointing performance in dealing with large datasets due to its constrained optimization problem. To deal with this challenge, the variant SVM (VSVM) has been utilized which has the fraction (1/2)b <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in its primal objective function, where b is the bias of the desired hyperplane. The VSVM has been solved with different optimization techniques in more time- and memory-efficient fashion. However, there is no guarantee that its optimal solution is the same as the standard SVM. In this paper, we introduce the generalized VSVM (GVSVM) which has the fraction (1/2t)b <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in its primal objective function, for a fixed positive scalar t. Further, we present the thorough theoretical insights that indicate the optimal solution of the GVSVM tends to the optimal solution of the standard SVM as t → ∞. One vital corollary is to derive a closed-form formula to obtain the bias term in the standard SVM. Such a formula obviates the need of approximating it, which is the modus operandi to date. An efficient neural network is then proposed to solve the GVSVM dual problem, which is asymptotically stable in the sense of Lyapunov and converges globally exponentially to the exact solution of the GVSVM. The proposed neural network has less complexity in architecture and needs fewer computations in each iteration in comparison to the existing neural solutions. Experiments confirm the efficacy of the proposed recurrent neural network and the proximity of the GVSVM and the standard SVM solutions with more significant values of t.