Reviewerly/exHarmony huggingface dataset:
A feature-centric spam email detection model using diverse supervised machine learning algorithms

Purpose This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection. Design/methodology/approach Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam). Findings Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%. Originality/value This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.
A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.
Exploiting the Human Factor: Social Engineering Attacks on Cryptocurrency Users

Social engineering is one of the preferred methods used by criminals to gain unauthorized access to information and information systems. Social engineering targets especially the users of a system. It is increasingly being applied to cryptocurrency users. The paper looks at five cases of cryptocurrency frauds that left a lasting impression in the cryptocurrency community. The cases are systematically investigated using an ontological model for social engineering attacks. The paper analyses which psychological tricks or compliance principles have been used by the social engineers in these cases. With the exploitation of principles such as "Distraction", "Authority", and "Commitment, Reciprocation & Consistency" the attackers gained access to users' financial values, stored in cryptocurrencies, without undermining the security features of the blockchain itself. One reason for the attackers' success is a lack of knowledge about risks and security among cryptocurrency users. Efforts to increase the information security awareness of cryptocurrency and blockchain users is recommended to protect them.
A Fast Deep AutoEncoder for high-dimensional and sparse matrices in recommender systems

A latent factor analysis (LFA)-based model has outstanding performance in extracting desired patterns from High-dimensional and Sparse (HiDS) data for building a recommender systems. However, they mostly fail in acquiring non-linear features from an HiDS matrix. An AutoEncoder (AE)-based model can address this issue efficiently, but it requires filling unknown data of an HiDS matrix with pre-assumptions that leads to the following limitations: a) prefilling unknown data of an HiDS matrix might skew its known data distribution to generate ridiculous recommendations; and b) incorporating a deep AE-style structure to improve its representative learning ability. Experimental results on three HiDS matrices from real recommender systems show that an FDAE-based model significantly outperforms state-of-the-art recommenders in terms of recommendation accuracy. Meanwhile, its computational efficiency is comparable with the most efficient recommenders with the help of parallelization.
Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm

Data visualization is a powerful tool and widely adopted by organizations for its effectiveness to abstract the right information, understand, and interpret results clearly and easily. The real challenge in any data science exploration is to visualize it. Visualizing a discrete, categorical data attribute using bar plots, pie charts are a few of the effective ways for data exploration. Most of the datasets have a large number of features. In other words, data is distributed across a high number of dimensions. Visually exploring such high-dimensional data can then become challenging and even practically impossible to do manually. Hence it is essential to understand how to visualize high-dimensional datasets. t-Distributed stochastic neighbor embedding (t-SNE) is a technique for dimensionality reduction and explicitly applicable to the visualization of high-dimensional datasets.
Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

The exponential growth of documents in various languages throughout the web, along with the availability of several editing and translation tools have made the cross-language plagiarism detection a challenging issue. Regarding its high importance, the present study focuses on the task of cross-language text alignment also known as detailed analysis which works on the outputs of the source retrieval step of cross-language plagiarism detection systems. The paper proposes a two-level matching approach with the aim of considering both syntactic and semantic information to align plagiarism fragments from the source and suspicious documents, accurately. At the first level, a vector space model which employs a multilingual word embeddings based dictionary and a local weighting technique is used in order to extract a minimal set of highly potential candidate fragment pairs rather than considering all possible pairs of fragments. This step also contains a dynamic expansion technique to cover more candidate pairs aiming at improving the system's recall. It is followed by a more precise algorithm that examines the candidate pairs at the sentence level using a graph-of-words representation of text. As a result, by modelling both the words and their relationships, an acceptable increase in the system's precision which is the goal of the second level is also observed. To identify evidence of plagiarism, i.e. potential cases of unauthorized text reuse, the algorithm tries to find maximum cliques from the match graph of source and suspicious texts. With this two-level investigation, the approach is capable to discriminate true plagiarism cases from the original text. The experimental results on different datasets such as PAN-PC-11, PAN-PC-12, and SemEval-2017 show that the proposed cross-language text alignment approach significantly outperforms the state-of-the-art models and can be fed into an expert system for further improvement of cross-language plagiarism detection. The source codes are publicly available on GitHub1, for the purposes of reproducible research.
Protecting Privacy of Location-Based Services in Road Networks

Location-Based Services (LBS), which answer users’ location-dependent queries to Points of Interest (POI), have become popular along with mobile devices’ widespread use. While benefiting from convenience, users may suffer privacy leak risk, due to publishing sensitive information, i.e., locations, to servers. Previous studies have proposed a number of methods for protecting LBS. Through providing provable privacy protection, Private Information Retrieval (PIR) becomes well-known for its high effectiveness in protecting privacy. However, the PIR-protected LBS must cautiously handle the consequent transmission/computation cost and service error brought by adopting PIR, as both of them impact user experience. Then an important question arises: Can the user experience of PIR-protected LBS be greatly improved? We answer it by presenting a novel interface from PIR to LBS, gaining benefits from road networks. The proposed interface is comprised of two road partition methods fully considering POI’s distribution along roads, and two response-calculating methods to satisfy users’ requirements on driving distance. Since Vehicular Location Based Services contain adequate road network knowledge, we accomplish our work on them. The experimental results on a real dataset validate that our interface can improve PIR-protected LBS greatly in user experience while protecting privacy.
PAVED: Pareto Front Visualization for Engineering Design

Abstract Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED , an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains.
Separable Reversible Data Hiding for Encrypted Three-Dimensional Models Based on Spatial Subdivision and Space Encoding

Reversible data hiding for encrypted media not only preserves the privacy of the media content but also can convey additional information during message transmission. Some studies advocate separability, that is, the message can be correctly extracted irrespective of whether the encrypted media has been decrypted. However, current research has focused on encrypted images. Urgent research is required on encrypted three-dimensional (3D) models. This paper proposes a separable reversible data hiding method based on spatial subdivision and space encoding for encrypted 3D models. A bounding volume is first constructed using the vertices with boundary values in the processing model. Each vertex coordinate value is then converted into a ratio (between 0 and 1) of the distance between the vertex and minimum boundary point to the side length of the bounding volume. The owner of the 3D model then uses a secret key to encrypt all ratios except those of the boundary vertices to obtain an encrypted 3D model of the same size as the original model. The spatial subdivision technique and a subdivision threshold are subsequently used to divide the bounding volume into a series of blocks and simultaneously control the vertex distortion. The secret message is embedded in the encrypted vertex by using the space encoding method with an embedding threshold. Experimental results indicate that the proposed algorithm enables high privacy, performs separable reversible data hiding, and has low computational complexity, high embedding capacity, and controllable distortion.
Deep Reinforcement Learning based Path Planning for Mobile Robot in Unknown Environment

Abstract It is a trend for robots to replace human in industrial fields with the increment of labor cost. Mobile robots are widely used for executing tasks in harsh industrial environment. It is an important problem for mobile robots to plan their path in unknown environment. The ordinary deep Q-network (DQN) which is an efficient method of reinforcement learning has been used for mobile robot path planning in unknown environment, but the DQN generally has low convergence speed. This paper presents a method based on Double DQN (DDQN) with prioritized experience replay (PER) for mobile robot path planning in unknown environment. With sensing its surrounding local information, the mobile robot plans its path with this method in unknown environment. The experiment results show that the proposed method has higher convergence speed and success rate than the normal DQN method at the same experimental environment.
Research on Blockchain for Sustainable E-Agriculture

The following topics are dealt with: innovation management; organisational aspects; Internet of Things; supply chains; social networking (online); cryptography; strategic planning; financial management; sustainable development; and supply chain management.
Amoeba: QoS-Awareness and Reduced Resource Usage of Microservices with Serverless Computing

While microservices that have stringent Quality-of-Service constraints are deployed in the Clouds, the long-term rented infrastructures that host the microservices are under-utilized except peak hours due to the diurnal load pattern. It is resource efficient for Cloud vendors and cost efficient for service maintainers to deploy the microservices in the long-term infrastructure at high load and in the serverless computing platform at low load. However, prior work fails to take advantage of the opportunity, because the contention between microservices on the serverless platform seriously affects their response latencies.Our investigation shows that the load of a microservice, the shared resource contentions on the serverless platform, and its sensitivities to the contention together affect the response latency of the microservice on the platform. To this end, we propose Amoeba, a runtime system that dynamically switches the deployment of a microservice. Amoeba is comprised of a contention-aware deployment controller, a hybrid execution engine, and a multi-resource contention monitor. The deployment controller predicts the tail latency of a microservice based on its load and the contention on the serverless platform, and determines the appropriate deployment of the microservice. The hybrid execution engine enables the quick switch of the two deploy modes. The contention monitor periodically quantifies the contention on multiple types of shared resources. Experimental results show that Amoeba is able to significantly reduce up to 72.9% of CPU usage and up to 84.9% of memory usage compared with the traditional pure IaaS-based deployment, while ensuring the required latency target.
What does Power Consumption Behavior of HPC Jobs Reveal? : Demystifying, Quantifying, and Predicting Power Consumption Characteristics

As we approach exascale computing, large-scale HPC systems are becoming increasingly power-constrained, requiring them to run HPC workloads in an energy-efficient manner. The first step toward achieving this goal is to better understand, analyze, and quantify the power consumption characteristics of HPC jobs. However, there is a lack of understanding of the power consumption characteristics of HPC jobs which run on production HPC systems. Such characterization is required to guide the design of the next generation of power-aware resource management. To the best of our knowledge, we are the first study to open-source the data and analysis of power-consumption characteristics of HPC jobs and users from two medium-scale production HPC clusters.
A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method

Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. Crow Search Algorithm (CSA) is a novel, and efficient swarm-based metaheuristic algorithm that inspired from the way crows storing and retrieving food. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/improved results both in time and uniformity.
Performance Comparison of Terraform and Cloudify as Multicloud Orchestrators

Studies indicate that by 2022 multicloud models will reach 75% of the cloud computing market. To handle the high-featured set of computing capability on this paradigm, often called "Sky Computing", infrastructure tools such as cloud orchestrators has emerged. This paper analyzes the most referenced tools in the literature such as Cloudify, Heat, CloudFormation, Terraform and Cloud Assembly, as well as the TOSCA standard. The literature review, complemented by a practical experiment, revealed that Terraform and Cloudify presents great affinity with Sky Computing scenarios. In the experiment Terraform outperformed Cloudify in several aspects.
A modified Lanczos Algorithm for fast regularization of extreme learning machines

This paper presents a new regularization for Extreme Learning Machines (ELMs). ELMs are Randomized Neural Networks (RNNs) that are known for their fast training speed and good accuracy. Nevertheless the complexity of ELMs has to be selected, and regularization has to be performed in order to avoid underfitting or overfitting. Therefore, a novel Regularization is proposed using a modified Lanczos Algorithm: Iterative Lanczos Extreme Learning Machine (Lan-ELM). As summarized in the experimental Section, the computational time is on average divided by 4 and the Normalized MSE is on average reduced by 11%. In addition, the proposed method can be intuitively parallelized, which makes it a very valuable tool to analyze huge data sets in real-time.
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

Abstract We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co‐correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain‐ability in the field of visual analytics.
Agent Categorization With Group Role Assignment With Constraints and Simulated Annealing

Agent categorization (AC) is a common, complex, and critical activity of collaboration and social systems. Specifying this problem through formalization and then determining an effective solution are an important and valuable task. This article formalizes the problem of AC by using the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. An innovative solution is proposed using the Group Role Assignment with Constraints (GRA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) algorithm and the Simulated Annealing (SA) algorithm. The proposed solution is verified by a real-world case study. Its practicability is confirmed through simulations using SA and the IBM ILOG CPLEX Optimization Platform (CPLEX). The contributions of this article include a novel formalization of the AC problem and an innovative and practical solution established by combining the GRA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> and SA algorithms. The generalized form of this solution provides a solid foundation for decision-making over a broad range of applications.
CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.