Reviewerly/exHarmony huggingface dataset:
Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance

Summary The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.
Effective ROI extraction methods for hybrid medical image compression

Medical images do contain important and unimportant spatial regions.Compression methods which are capable of reconstructing the image with high quality are required to compress the medical images.For these images, only a portion of it is useful for diagnosis hence a region based coding techniques are significant for compressing and transmission.Extracting a significant region is of great demand since a slighter mistake may leads to wrong diagnosis.This paper is focused on investigating multiple image processing algorithms for medical images.All the images may not contain the same region of interest, so different approaches are supposed to apply for different images.In these three types of medical images were considered like magnetic resonance (MR) brain images, computer tomography (CT) abdomen images and X-ray lung images.In this paper three automatic region of interest extraction algorithms were proposed for different types of images.
Discriminative Multiview Nonnegative Matrix Factorization for Classification

Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination. In this paper, we introduce a novel discriminative multiview nonnegative matrix (DMultiNMF) algorithm to learn discriminative and consistent representations for facilitating classification. In this algorithm, we apply discriminative patch alignment to enhance the local discrimination in each view and utilize the large margin principle to improve global discrimination. At the same time, we use a shared representation to propagate information among the multiple views to ensure consistency. Apart from that, we measure the reconstruction errors utilizing the correntropy-induced metric to improve the robustness. The experiments on face recognition, handwritten digit recognition, Xmedia, and Wikipedia multiview data sets demonstrate the advantages of the proposed method compared with other algorithms like single view using concatenated views and substantially better than other multiview nonnegative matrix factorization algorithms.
Arabic Text Classification using Feature-Reduction Techniques for Detecting Violence on Social Media

With the current increase in the number of online users, there has been a concomitant increase in the amount of data shared online.Techniques for discovering knowledge from these data can provide us with valuable information when it comes to detecting different problems, including violence.Violence is one of the significant problems humanity has faced in recent years all over the world, and this is especially a problem in Arabic countries.To address this issue, this research focuses on detecting violence-related tweets to help in solving this problem.Text mining is an important technique that can be used to find and predict information from text.In this study, a text classification model is built for detecting violence in Arabic dialects on Twitter using different feature-reduction approaches.The experiment comprises bagging, K-nearest neighbors (KNN), and Bayesian boosting using different extraction features, namely, root-based stemming, light stemming, and n-grams.In addition, the study used the following feature-reduction techniques: support vector machine (SVM), Chi-squared (CHI), the Gini index, correlation, rules, information gain (IG), deviation, symmetrical uncertainty, and the IG ratio.The experiment showed that the bagging with tri-gram approach has the highest accuracy at 86.61%, and a combination of IG with SVM from reduction features registers an accuracy of 90.59%.
A Structural Attack on Type-I Generalized Feistel Networks

This paper presents generic structural cryptanalysis against type-I generalized Feistel networks (GFN), in which all the inner transformations are unknown. The target of our attack is to retrieve all the unknown round functions. We provide an improved yoyo game distinguisher, in which one rejects a large group of start guesses by a single wrong guess, hence is quite advantageous for reducing the complexity. Next, we exploit this distinguisher to develop a recovery attack of such structure and find the look-up tables of the first, eighth, and ninth round functions. Then by the encryption and decryption similarity, we recover the LUTs of the second, third, and tenth round functions from the decrypt direction. Finally, we retrieve the rest rounds by using the analytic relationships between the plaintexts and their four-round encryption results. Our complete recovery requires time complexity O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.36n</sup> ) and memory O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), where n is the branch size. For 64-bit block cipher, our result will approximate a real-life attack. This paper is the first recovery attack against ten-round type-I GFN.
A Survey of Object Co-Segmentation

It is widely acknowledged that object segmentation is a significant research field for computer vision and a key process for many other visual tasks.In the past unsupervised single-image segmentation, there are often cases where the segmentation result is not good.In the current supervised single-image segmentation, it is necessary to rely on a large number of data annotations and long-term training of the model.Then, people attempted to segment simultaneously the common regions from multiple images.On the one hand, it does not need to use a large amount of labeled data to train in advance.On the other hand, it utilizes the consistency constraint between images to better obtain the object information.This idea can generate better performance than the traditional one did, resulting in many methods related to object cosegmentation.This paper reviews some classic and effective object co-segmentation methods, including saliency-based approaches, joint-processing-based approaches, graph-based approaches, and others.For different methods, we select two or three related models to elaborate, such as a model based on random walks.Moreover, in order to exhibit and evaluate these methods objectively and comprehensively, we not only summarize them in the form of flowcharts and algorithm summaries, but also compare their performance with visualization methods and evaluation metrics, such as intersection-over-union, consistency error, and precision-recall rate.From the experiment, we also attempt to clarify and analyze the existing problems.Finally, we point out the challenges and directions and open new venues for future researchers in the field.
The Blockchain Random Neural Network in Cybersecurity and the Internet of Things

The Internet of Things (IoT) enables increased connectivity between devices; however, this benefit also intrinsically increases cybersecurity risks as cyber attackers are provided with expanded network access and additional digital targets. To address this issue, this paper presents a holistic digital and physical cybersecurity user authentication method based on the Blockchain Random Neural Network. The Blockchain Neural Network connects increasing neurons in a chain configuration providing an additional layer of resilience against Cybersecurity attacks in the IoT. The proposed user access authentication covers holistically its digital access through the seven OSI layers and its physical user identity such as passport before the user is accepted in the IoT network. The user’s identity is kept secret codified in the neural weights, although in case of cybersecurity breach, its physical identity can be mined and the attacker identified, therefore enabling a safe decentralized confidentiality. The validation results show that the addition of the Blockchain Neural Network provides a user access control algorithm with increased cybersecurity resilience and decentralized user access and connectivity.
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar

Wenchao Du, Alan W Black. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Framework for a Systematic Parametric Analysis to Maximize Energy Output of PV Modules Using an Experimental Design

Use of photovoltaic modules in buildings has been reported to be an effective tool in managing energy consumption. The novelty in the research herein is in a framework that integrates different performance parameters through the use of an experimental design to expect all variables via linear regression analysis. An emphasis is placed on making the method readily available to practitioners and experts in the area of renewable energy, using standard procedure and easily accessible software. This work empowers the decision-making process and sustainability through a parametric analysis of the installation of photovoltaic modules to increase their energy output towards nearly zero energy buildings. A case study of a group of photovoltaic modules is examined in four cities with different locations and climate data to validate the proposed framework. Results demonstrate that the installation of photovoltaic modules on the mounted roof is better than elevations, and the vertical installation of modules is the worst possible inclination to maximize the yielded energy. The impact of inclination is higher than orientation in influencing the energy productivity of photovoltaic modules. This work specifies integrating such modules mounted on roofs and elevations towards the equator line, by a proportion of inclination/latitude equal to 85 ± 3%, to maximize the energy output.
Researching into a Course of Information Technologies and Software in The Context of Digital Citizenship through Student Opinions

Information technologies literate is an individual who produces information, can use it functionally in life, can solve problems, think critically, has entrepreneurial, stable, communication skills, empathizes, contributes to society and culture etc.The purpose of this research is to examine the effectiveness of "Information Technologies and Software Course (ITS)" in gaining knowledge, skills and values of digital citizenship with student views.Mixed research method has been adopted in the research.The sample of the study consisted of 150 students attending the 5th and 6th grade students in a public secondary school in Aksaray province in the 2018-2019 academic year.50 students (33.3%) were in the fifth grade and 100 (66.7%) were in the sixth grade and 86 (57.3%) were female and 64 (42.7%) were male."Digital Citizenship Scale (DCS) in ITS Scale" developed by Elci and Sarı (2016) was used as a measurement tool.The obtained data were interpreted by using independent sample t-test and one-way analysis of variance in the analysis programs.As a result of the research; in the ITS course of the students, it has been observed that students have differentiated according to the variables of gaining knowledge, skills and values of digital citizenship, class, gender, socioeconomic status and having Internet access.However, it was found that there were no differences in terms of computer ownership status, mothers' education level and fathers' education level.
Multiple Criteria Fake Reviews Detection Using Belief Function Theory

Checking online reviews before making a purchase becomes a permanent habit. Hence, online consumer reviews, product and services play an increasingly spreading role in consumer purchasing decisions. Unfortunately, the importance of advertising and the attraction of profit have led to the appearance of fake reviews in order to mislead readers. Considering that the reviews are generally imperfect, the spam reviews detection becomes one of the most important problems. To tackle this problem, we propose a new method of multi-criteria fake reviews under belief function theory. This approach treats the uncertainty in the rating reviewers’ given to multiple evaluation criteria, takes into account the similarity between all provided reviews and deals with missing data. We evaluate our method through artificial datasets. Then, we use a real dataset to validate it. The results prove that the proposed approach is a useful solution for the fake reviews detection problem.
ResGAN: A Low-Level Image Processing Network to Restore Original Quality of JPEG Compressed Images

Low-level image processing is mainly concerned with extracting descriptions (that are usually represented as images themselves) from images. With the rapid development of neural networks, many deep learning-based low-level image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning based approach for low-level image processing, in particular, image denoising, image deblurring, and compressed image restoration. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a multi-objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on three low-level image processing tasks on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against all current state-of-the-art approaches.
Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant

Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems.In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system.The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process.A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map.The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities.To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments.Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.
Soft Decoding Based on Ordered Subsets of Verification Equations of Turbo-Productive Codes

Methods of soft decoding of cascade code constructions based on the schemes-products of linear block codes (Turbo Product Codes) are considered.An approach is being developed based on the iterative exchange of soft solutions between block codes constituting a cascade design.It is shown that a sequential execution of procedures for the formation of ordered subsets of test equations and the logarithms estimation of a likelihood ratio allows decoding of turbo-productive codes according to the criterion of minimizing the erroneous reception of code symbols.
Rift: a high-performance consensus algorithm for Consortium Blockchain

The emergence of Blockchain have revolutionize the decentralization in distributed architecture. The advances in the consensus mechanism techniques and the development of different variants of consensus algorithms gives a huge impact on its progress. These technologies allow to have a distributed peer-to-peer network in which each external entity can be able to interact with other entities without any trusted intermediary in a verifiable manner. The existing consensus algorithms are mostly concerned with public blockchain having focused on public ledgers in general. The consortium blockchain is least focused as compared with other variants of blockchain (public and private) showing the need to address this vacuum. In this paper, we proposed a consensus algorithm named Rift for consortium blockchain which works on the principle of trust mechanism for achieving consensus in a blockchain. The consensus is achieved by distributed nodes in a consortium blockchain which were controlled by consortium members to decentralize the arbitration by voting and trust metrics. In this paper, we elaborate the comprehensive idea of Rift and discuss the working model for this algorithm. We also perform simulation on the proposed algorithm and determine the performance variables to evaluate the effectiveness of Rift. The evaluated results show the improvement in the performance which is the objective requirement for the evaluation.
A Comparative Study of Various Intelligent Algorithms based Path Planning for Mobile Robots

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot. Simulation results, which carried out using MATLAB 2014 environment, show the validity of the kinematic model for Nonholonomic mobile robot and demonstration that the proposed algorithm perform better than original PSO and FF algorithms under the same environmental constraints by providing the smoothness velocity and shortest path for each mobile robot. &#x0D;
Finite-Time Analysis of Q-Learning with Linear Function Approximation.

Motivated by applications in Reinforcement Learning (RL), in this paper, we study a nonlinear Stochastic Approximation (SA) algorithm under Markovian noise, and derive its finite-sample convergence bounds. Our proof is based on the Lyapunov drift arguments, and to handle the Markovian noise, we exploit the fast mixing of the underlying Markov chain. Our result is used to show the finite-sample bounds of the popular Q-learning with linear function approximation algorithm for solving the RL problem. Since Q-learning with linear function approximation may diverge in general, we study it under a condition on the behavior policy that ensures the stability of the algorithm. Due to the generality of our SA results, we do not need to make the unnatural assumption that the samples are i.i.d. (since they are Markovian), and do not require an additional projection step in the algorithm to maintain the boundedness of the iterates.
IoT for Playful Intergenerational Learning about Cultural Heritage: The LOCUS Approach

LOCUS is a three-year multidisciplinary project with the goal of co-design, develop and evaluate an IoT system and understand its potential to support playful intergenerational engagement in creating and exploring cultural contents and learning about cultural heritage of rural territories from the Centre Region of Portugal, namely Amiais village, in Sever do Vouga.By implementing a playful and immersive cultural heritage tourism approach to foster Amiais' cultural and socioeconomic development, LOCUS will allow visitors to have immersive gamified experiences, by using a wearable device (bracelet) and their smartphones to interact with augmented everyday things around the village and to collaboratively learn about Amiais' culture and produce and share multimedia georeferenced contents.
Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment

Cloud Computing (CC) is a recent technology in the Information and Communication Technology (ICT) field.It provides an on-demand access to the shared pool of resources via virtualization.Large enterprises move toward CC due to its flexibility and scalability driven from its elastic pay-per-use model.To provide ensured efficient performance to users, tasks should be efficiently mapped to available resources.Therefore, Task Scheduling (TS) is significant issue in the CC technology.TS is a NP-complete optimization problem, so a deep investigation of different metaheuristic and heuristic TS algorithms is presented here.Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as metaheuristic algorithms are implemented and their performance have been compared to heuristic techniques (First Come First Serve (FCFS) and Shortest Job First (SJF)) on symmetric and asymmetric environment.The cloud service providers and users have different performance requirements.Six performance metrics including makespan, flow time, response time, resource utilization, throughput time and degree of imbalance have been measured.For asymmetric environment, real environment, metaheuristic TS algorithms surpassed the heuristic methods.