Reviewerly/exHarmony huggingface dataset:
Diversity Measures for Niching Algorithms

Multimodal problems are single objective optimisation problems with multiple local and global optima. The objective of multimodal optimisation is to locate all or most of the optima. Niching algorithms are the techniques utilised to locate these optima. A critical factor in determining the success of niching algorithms is how well the search space is covered by the candidate solutions. For niching algorithms, high diversity during the exploration phase will facilitate location and identification of many solutions while a low diversity means that the candidate solutions are clustered at optima. This paper provides a review of measures used to quantify diversity, and how they can be utilised to quantify the dispersion of both the candidate solutions and the solutions of niching algorithms (i.e., found optima). The investigated diversity measures are then used to evaluate the distribution of candidate solutions and solutions when the enhanced species-based particle swarm optimisation (ESPSO) algorithm is utilised to optimise a selected set of multimodal problems.
Exception Handling Method Based on Event from Look-Up Table Applying Stream-Based Lossless Data Compression

It is getting popular to implement an environment where communications are performed remotely among IoT edge devices, such as sensory devices and the cloud servers due to applying, for example, artificial intelligence algorithms to the system. In such situations that handle big data, lossless data compression is one of the solutions to reduce the big data. In particular, the stream-based data compression technology is focused on such systems to compress infinitely continuous data stream with very small delay. However, during the continuous data compression process, it is not able to insert an exception code among the compressed data without any additional mechanisms, such as data framing and the packeting technique, as used in networking technologies. The exception code indicates configurations for the compressor/decompressor and/or its peripheral logics. Then, it is used in real time for the configuration of parameters against those components. To implement the exception code, data compression algorithm must include a mechanism to distinguish original data before compression and the exception code clearly. However, the conventional algorithms do not include such mechanism. This paper proposes novel methods to implement the exception code in data compression that uses look-up table, called the exception symbol. Additionally, we describe implementation details of the method by applying it to algorithms of stream-based data compression. Because some of the proposed mechanisms need to reserve entries in the table, we also discuss the effect against data compression performance according to experimental evaluations.
Single Image Dehazing via Reinforcement Learning

Single image dehazing aims at restoring the clean image from single hazy one. A novel Reinforcement Learning (RL) based image dehazing method is proposed in this paper to handle the dehazing task with interpretable ability and extensibility. We model the dehazing problem as a Markov Decision Process (MDP) with several existing simple traditional image processing operations and prior knowledge-based dehazing methods as actions. Furthermore, a deep Q-learning network is established to learn the value function for image dehazing. Finally the learned network can iteratively choose the correct action during the processing sequence to produce dehazing results. Extensive results on real hazy images have been conducted to verify the proposed method. Also the learned sequence of image dehazing can provide considerable guidance for human.
Privacy Issues in Medical Image Analysis

The development of new medical imaging systems has resulted in huge amounts of medical image data generation. This chapter aims to introduce briefly the techniques to maintain the privacy of medical images. Medical images collected via different modalities are stored as Digital Imaging and Communications in Medicine (DICOM) images, which is a worldwide accepted format. The chapter focuses on privacy protection methods for medical images, which include two major methods: Encryption-based and image anonymization. Ming Yang et al. proposed a technique for the privacy of medical images using encryption and encoding methods. Thus, the security and privacy capabilities of the DICOM Standard imply only a small piece of the total protection of privacy and protection against intrusion and the hijacking of medical data. Machine learning-based OCR mechanisms can be used to identify sensitive text data in medical image pixel data and anonymize it.
Recommender System: A bibliometric analysis

Abstract The exponential growth in the online share of businesses has to lead to a gigantic wave of options available to the active user. Recommender systems, therefore assist the users to go through the tailored list of products to match their preferences. A range of recommender systems is available to serve the purpose. This article will navigate through the basic of recommender systems, and its classifications types viz. collaborative filtering, content-based filtering, demographic, hybrid, and knowledge-based recommender system. It aims to analyze publications of the Scopus database using biblioshiny tool of RStudio software. A bibliometric analysis is conducted on 556 papers to analyze the recent research trends in recommendation systems. Further, challenges have also been discussed that need to be dealt with the recommender system.
Managing the Complexity of Dialogues in Context: A Data-Driven Discovery Method for Dialectical Reply Structures

Abstract Current formal dialectical models postulate normative rules that enable discussants to conduct dialogical interactions without committing fallacies. Though the rules for conducting a dialogue are supposed to apply to interactions between actual arguers, they are without exception theoretically motivated. This creates a gap between model and reality, because dialogue participants typically leave important content-related elements implicit. Therefore, analysts cannot readily relate normative rules to actual debates in ways that will be empirically confirmable. This paper details a new, data-driven method for describing discussants’ actual reply structures, wherein corpus studies serve to acknowledge the complexity of natural argumentation (itself understood as a function of context). Rather than refer exclusively to propositional content as an indicator of arguing pro/contra a given claim, the proposed approach to dialogue structure tracks the sequence of dialogical moves itself. This arguably improves the applicability of theoretical dialectical models to empirical data, and thus advances the study of dialogue systems.
FORTUNES OF PERSONAL LIBRARIES IN THE ERA OF PAPER BOOKS AND DIGITAL TECHNOLOGIES

The article is intended to draw attention to problems common to all existing libraries: forming and picking out personal collections from ones that are more common, their history of existence study, identification, assigning in a single collection, preservation, popularization and accessibility to modern readers. The work is devoted to the theme of personal libraries in the era of paper books and digital technologies and includes materials presented in the series of webinars “Bibliosphere: New Formats” held by the State Public Scientific Technological Library of SB RAS in the summer of 2020. By a personal library the authors mean an ordered collection of books and other documents (electronic, handwritten, etc.), belonging to an individual or family, formed by him / them for personal use. The problems presented in the article concern personal libraries formation in the late XIX – early XXI century: composition and design peculiarities of bibliophile collections; practice of using stamps as a sign of ownership on books; variety of marking personal book collections (super ex-librises, ex-librises, owner’s inscriptions, owner’s stamps, owner’s and book signs); approaches and preferences of personal libraries’ owners; fortunes of books and bibliophiles; problems of book collections preservation in the stocks all over the world; demand for personal collections by contemporaries: researchers, librarians, bibliophiles, readers/users.
A Hand Gesture Recognition based Communication System for Mute people

Communication between deaf-mute people becomes more difficult in everyday life. Muteness creates a speechaffliction where the patient is unable to have the normal capacity to speak resulting in the complete absence or at least a significant loss of verbal communication under neurological diseases. Sometimes the major cause of muteness is deafness. Around 466 million people worldwide are deaf and mute, and 34 million of these are children. The communication among a deaf and listening to person poses to be an excessive hassle compared to communication among blind and regular visible humans. Gestural type of communication called Sign language which is observed amongst deaf groups in the world. We proposed a Deaf-Mute verbal exchange device that translates the hand gestures to audio massage as an interpreter. The purpose to develop this plan, is to facilitate humans with the help of a deaf-mute communication interpreter device. In the designed model, we used the KINECT sensor-based system to interpret hand gestures. The KINECT sensor captures the motions of mute people like images and then after segmentation, it identifies the gestures which are then decoded into meaningful audio messages that enable the communication more effective. The proposed system is user friendly, as it is easy to use and capable to build efficient and effective human computer interaction.
Inertia weight strategies in Multiswarm Particle swarm Optimization

The Particle Swarm Optimization (PSO) algorithm is widely applied in several areas of activity, namely image processing, modeling and system identification. To improve the search performance of this algorithm, several strategies have been used at this level. Among these are the MSPSO (Multiswarm Particle Swarm Optimization) algorithm. On the other hand, like the majority of metaheuristic algorithms, its performances are strongly correlated with the settings parameters, namely, component of inertia (w), cognitive component (c1) and social component (c2)). Since the introduction of this parameter, there have been a number of proposals of different strategies for determining the best value of inertia weight. This paper presents the first comprehensive review of the various inertia weight strategies. In MSPSO algorithm, these approaches are classified and discussed in three main groups. Studies on 12 static test problems show that the weight of adaptive inertia w6 in the third class is the best strategy for better accuracy, which is efficient enough to adapt the value of w in the search space.
EduChain: A Blockchain-Based Education Data Management System

The current centralized educational data management system results in problems like malicious tampering, low cost of diploma fabrication, and high cost of certificate verification. The start-of-the-art decentralized blockchain technology can be applied to solve the problems. In this paper, we reveal the shortages of current centralized systems and propose EduChain, a heterogeneous blockchain education data management system, which leverages the advantages of both private blockchains and consortium blockchains. We also propose an effective mechanism to conduct database consistency check and error traceback based on the second consensus via pt-table-checksum tool to solve the database mismatching problem. This system shows good performance in information verification, error traceback and data security.
Enhanced Confix Stripping Stemmer And Cosine Similarity For Search Engine in The Holy Qur’an Translation

The Holy Qur'an is a Muslim guidelines book. The Holy Qur'an has been translated into many languages to make it easier to understand the meaning of every verse. The lack of understanding of the meaning of the verse on the Holy Qur'an translation needs a misinterpretation or a detailed explanation. Along with technology development, many interpretations of the Holy Qur'an digital version have been equipped with the searching system. However, there are still difficulties in finding an interpretation of The Holy Qur'an in accordance with the discussion or topic because each surah has similarities and differences in a particular topic. This research is purposed a search engine system using Information Retrieval that allows users to find relevant information in the Holy Quran translation based on the information (query) entered by the user. The text processing in this research used Enhanced Confix Stripping Stemmer to find accurate basic words and Cosine Similarity to calculate the similarity of queries and database. Based on the system testing results using Enhanced Confix Stripping Stemmer stemming, the precision is 71.96%, and the ability to find all the relevant verses (recall) is 98.64%, whereas without using stemming, the precision is 82.95% and the recall of 76.70%.
Learning from the Negativity: Deep Negative Correlation Meta-Learning for Adversarial Image Classification

Adversarial images are commonly viewed negatively for neural network training. Here we present an opposite perspective: adversarial images can be used to investigate the problem of classifying adversarial images themselves. To this end, we propose a novel framework termed as Deep Negative Correlation Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. We further formulate a deep negative correlation learning, and design a novel meta-learning-based classifier to prevent overfitting and learn discriminative features. The final classification is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts.
Grade estimation by a machine learning model using coordinate rotations

Machine learning (ML) models provide useful tools to generate spatial estimations of geological features, but they do not consider the spatial dependence among the observations and they primarily use coordinates as predictors. Thus, many ML models produce visible artifacts in the resulting estimates along the coordinate directions. To overcome this significant problem, this paper presents an ensemble super learner (ESL) model which uses the super learner (SL) model as the ML model. In the ESL model, numerous training sets are created from the original dataset by a coordinate rotation strategy and then the estimates obtained from the fitted SL models are ensembled to produce a final estimate. A dataset from a high-grade gold deposit demonstrates the approach and compares the results to kriging and the SL model. The results demonstrate that the ESL model manages artifacts in ML spatial estimation. It also provides better results than the kriging and SL model in terms of estimation accuracy.
Overview of the PAN@FIRE 2020 Task on the Authorship Identification of SOurce COde

Authorship identification is essential to the detection of undesirable deception of others’ content misuse or exposing the owners of some anonymous malicious content. While it is widely studied for natural languages, it is rarely considered for programming languages. Accordingly, a [email protected] task, named Authorship Identification of SOurce COde (AI-SOCO), is proposed with the focus on the identification of source code authors. The dataset consists of crawled source codes submitted by the top 1,000 human users with 100 correct C++ submissions or more from the CodeForces online judge platform. The participating systems are asked to predict the author of a given source code from the predefined list of code authors. In total, 60 teams registered on the task’s CodaLab page. Out of them, 14 teams submitted 94 runs. The results are surprisingly high with many teams and baselines breaking the 90% accuracy barrier. These systems used a wide range of models and techniques from pretrained word embeddings (especially, those that are tweaked to handle source code) to stylometric features.
SMOTE-based Category Imbalance for Radar Radiation Source Sorting and Identification

The aim of this study is to realize the problem of sorting and identifying radar radiation sources with category imbalance. In this paper, a method based on the Synthetic Minority Oversampling Technique (SMOTE) is presented. In the study, the SMOTE technique is applied to generate a larger number of minority samples in the data sets. In the study, the SMOTE technique is applied to generate a larger number of minority samples in the database In the study, the SMOTE technique is applied to generate a larger number of minority samples in the data sets, so that the number of majority and minority categories are gradually balanced, and then the parameters of the radar radiation source signal (direction of arrival (DOA), pulse width (PW), pulse repetition frequency (PRF), and radar frequency (RF)) are selected as characteristics for classification, using decision trees, random forest, and XGboost three supervised learning algorithms are used to classify the rebalanced data sets. The experimental results show that the STOME algorithm excels in category-imbalanced radar source sorting identification, improving the problem of low accuracy caused by the presence of a few class samples in traditional methods.
Generalising via Meta-examples for Continual Learning in the Wild

Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur. As a step towards this goal, we present FUSION (Few-shot UnSupervIsed cONtinual learning), a learning strategy that enables a neural network to learn quickly and continually on streams of unlabelled data and unbalanced tasks. The objective is to maximise the knowledge extracted from the unlabelled data stream (unsupervised), favor the forward transfer of previously learnt tasks and features (continual) and exploit as much as possible the supervised information when available (few-shot). The core of FUSION is MEML - Meta-Example Meta-Learning - that consolidates a meta-representation through the use of a self-attention mechanism during a single inner loop in the meta-optimisation stage. To further enhance the capability of MEML to generalise from few data, we extend it by creating various augmented surrogate tasks and by optimising over the hardest. An extensive experimental evaluation on public computer vision benchmarks shows that FUSION outperforms existing state-of-the-art solutions both in the few-shot and continual learning experimental settings. (The code is available at https://github.com/alessiabertugli/FUSION ).
Political language and fake news

This article outlines political symbolism and language in the 2019 election in Indonesia and aims to situate fake news narratives within them. By analysing official election campaign posters (spand...
Deep Learning for Threat Actor Attribution from Threat Reports

Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.
Federated Learning and Control at the Wireless Network Edge

We are at the cusp of two transformational technologies, namely the fifth generation of wireless communication systems, known as 5G, and machine learning (ML). On the one hand, while the evolutionary part of 5G, enhanced mobile broadband (eMBB), focusing mainly on millimeter-wave transmissions has made significant progress, fundamentals of ultra-reliable and low-latency communication (URLLC), one of the major tenets of the 5G revolution, are yet to be fully understood. In essence, URLLC warrants a departure from average-based system design toward a clean slate design centered on tail, risk, and scale [1]. While risk is encountered when dealing with decision making under uncertainty, scale is driven by the sheer amount of devices, antennas, sensors, and actuators, all of which pose unprecedented challenges in network design, optimization, and scalability.
Reverse Image Search Analysis Based on Pre-Trained Convolutional Neural Network Model

Search engine features on the Internet are increasingly getting more sophisticated. Not only by using keywords, searching for information now can also be done by inputting images through an image feature. It returns the results related to the query image, its size, and the sites that load the image as a query. This method is called Reverse Image Search (RIS), one type of Content-Based Image Retrieval (CBIR). Occasionally, the query image provides several other images that are similar and correlated with the query image. Thus, it requires a machine that must distinguish and find correlations between one image and another. In this paper, the author extracted image features from RIS s results by implementing a CNN (Convolutional Neural Network) model trained to extract image features and performed RIS on a database. We compared similar and relevant content to the query image with the performance vector distance extracted by comparing the Pre-Trained CNN Model with the Pre-Trained CNN Model method and conventional methods such as perceptual hashing. In this paper, the Pre-Trained CNN Model method succeeded in handling orientation information. However, the Euclidean distance from the feature vector of several images was quite close, albeit different.
Availability modeling in redundant OpenStack private clouds

Abstract In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state‐based and nonstate‐based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady‐state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose.