Reviewerly/exHarmony huggingface dataset:
Dual-Process Theory as a Theory of the Classification of Information Processing Acts

The article proposes a consideration of the dual-process theory of higher cognition as a theory of the classification of acts of information processing. One of the reasons why the dual-process approach has been criticized is the fact that the information processing process can sometimes have characteristics that undermine a clear-cut attribution to one of the two traditionally defined opposite types. To avoid this criticism, it is proposed that the object of classification should not be the processes of information processing, but separate acts of combining two units of information. Unlike a process, a particular act of information processing at a particular moment in time cannot simultaneously have opposite characteristics, nor can it simultaneously have and not have some characteristic. In order to show the qualitative difference between various information processing acts as falling individually into either Type 1 or Type 2 processing, it is proposed to classify them by a feature that is present in one type and absent in another. It is suggested to take conscious control as such a feature. As a result, in the information processing acts corresponding to Type 2 category, units of information are combined in a consciously controlled way, whereas in the acts to be considered as Type 1, those units either already are combined or combine autonomously due to the existence of indirect associative connections.
Combining Hindsight with Goal-enhanced Prediction for Multi-goal Reinforcement Learning

In multi-goal reinforcement learning (RL), efficient learning from sparse rewards remains a major challenge. One of the most successful solutions to the challenge is Hindsight Experience Replay (HER), a model-free algorithm that relabels desired goals with achieved goals. However, HER and its previous variants are still limited in efficiency and require millions of samples for training. In this paper, leveraging the power of a learned dynamics model, we propose Hindsight Experience Replay with Model-based Prediction (HERO) to further improve the sample efficiency of HER. The core technique of HERO is a two-stage value estimation algorithm combining hindsight relabeled rewards and model-based predictive rewards. To effectively model complex dynamics of robot manipulation tasks, we introduce the goal-enhanced predictive model (GPM) and the achieved-goal variance prioritization (AVP). GPM pays more attention to predicting the achieved goal in the next state, while AVP prioritizes trajectories based on the variance of achieved goals in each trajectory. In our experiments, we evaluate HERO on a set of challenging robot manipulation tasks. Empirical results demonstrate that HERO achieves significantly higher sample efficiency than previous multi-goal RL algorithms.
Development of a text analysis agent for a logistics company's Q&amp;A system

Abstract The "Twin" system is an omnichannel communication platform for building voice and chatbots, capable, in particular, of receiving data in one language and transmitting in another. "Twin" can record voice and text, display detailed statistics and analytics for each call or dialog. In this article, using the Twin system, a chatbot was created for the field of cargo transportation, it describes its advantages and disadvantages and the principle of creating of such chatbots.
EPTS: Energy-saving pre-emptive task scheduling for homogeneous cloud systems

The importance of cloud computing grows staggeringly by the day as newer technology is introduced resulting in greater connectivity across systems worldwide inculcating higher demand for resources supplied by cloud providers. Every datacentre responsible for providing these resources consumes a hefty amount of power and cloud service providers have to bear the monetary costs that come with it. Recent studies show that energy consumption of resources at utilizations over 70% grows non-linearly and hence it is best to avoid resources reaching high levels of utilization. In this paper, we introduce EPTS (Energy-saving Pre-emptive Task Scheduling) that utilizes the 0-70% interval of utilization of resources and evenly distributes the energy consumption of tasks by pre-empting tasks consuming high amounts of power by less power-demanding tasks. These pre-empted tasks are rescheduled into the virtual machines where they may result in lower utilization rates, hence increasing energy conservation. We compare EPTS with MaxUtil and FCFS (First Come First Serve) via thorough experimentation on different synthetic datasets and show that EPTS outperforms MaxUtil and FCFS by approximately 50% in terms of energy consumption.
Model Agnostic Explainability Techniques in Ultrasound Image Analysis

The current adoption of Medical Artificial Intelligence (AI) solutions in clinical practice suggest that despite its undeniable potential AI is not achieving this potential. A major barrier to its adoption is the lack of transparency and interpretability, and the inability of the system to explain its results. Explainable AI (XAI) is an emerging field in AI that aims to address these barriers, with the development of new or modified algorithms to enable transparency, provide explanations in a way that humans can understand and foster trust. Numerous XAI techniques have been proposed in the literature, commonly classified as model-agnostic or model-specific. In this study, we examine the application of four model-agnostic XAI techniques (LIME, SHAP, ANCHORS, inTrees) to an XGBoost classifier trained on real-life medical data for the prediction of high-risk asymptomatic carotid plaques based on ultrasound image analysis. We present and compare local explanations for selected observations in the test set. We also present global explanations generated from these techniques that explain the behavior of the entire model. Additionally, we assess the quality of the explanations, using suggested properties in the literature. Finally, we discuss the results of this comparative study and suggest directions for future work.
BLE Based Exposure Notification System for Contact Tracing

When an uninfected person is within 6 feet of an infected person for more than 15 minutes, the probability of COVID-19 transmission increases. Contact Tracing Apps aid people in COVID-19 infection prevention. Our proposed work focuses on developing a Contact Tracing App named CT-19 using Bluetooth Low Energy (BLE). When users run CT-19 App on their smartphone, it broadcasts and records unique private key along with private keys broadcasted from nearby smartphones. When a patient is informed that he/she has COVID-19 infection, the health authority uploads private key from their phone to a central server (only with the consent of patient). Periodically central server broadcasts these keys to all the users who has installed CT-19 App. Once broadcasted keys are received, CT-19 App checks whether any already stored keys match with this received broadcasted key. If it matches, then it implies that user was in physical proximity to an infected individual. Based on this information user can take some precautionary steps like undergoing COVID-19 tests/quarantine. When a user runs an app on his/her smartphone, user privacy is extremely crucial. Our proposed app CT-19 protects user privacy by neither capturing nor keeping the user's GPS or phone number.
Information technology platform for the selection and analytical processing of information on COVID-19

Currently, the most effective way to counteract COVID-19 is to slow its spread through personal distancing, hand washing and the use of personal protective equipment. Vaccination processes of citizens are becoming widespread. At the same time, information technology will be able to help slow down the spread of COVID-19 by early detection, prediction and monitoring of new cases. This paper provides an overview of current research on the selection and processing of COVID-19 data. The role and location of IoT devices, communication networks and cloud infrastructure for the selection and processing of COVID-19 data are described. Based on the analysis of IoT -platforms for detection and monitoring of COVID-19, the structure of the information technology platform was formed. There are included data collection tools, primary networks, Internet, cloud infrastructure, data presentation tools. The architecture of the information technology platform for the selection and processing of COVID-19 data is proposed. A description of the process of collecting and analytical processing of COVID-19 information using machine learning algorithms is given. The model of the information technology platform classes structure for the selection and processing of COVID-19 data is considered. That contains more than 50 classes to describe more than 120 characteristics of information entities. The processes of selection and aggregation of COVID-19 data and integration of analytical processing tools based on machine learning algorithms into the information technology platform are described.
The emerging complexity of Open Science: assessing Intelligent Data Openness in Genomic Anthropology and Human Genomics.

In recent decades, the scientific community has become aware of the importance of science being effectively open in order to speed up scientific and technological progress. In this context, the achievement of a robust, effective and responsible form of data sharing is now widely acknowledged as a fundamental part of the research process. The production and resolution of human genomic data has steadily increased in recent years, mainly due to technological advances and decreasing costs of DNA genotyping and sequencing. There is, however, a downside to this process due to the huge increase in the complexity of the data and related metadata. This means it is advisable to go beyond traditional forms of sharing analysis, which have focused on data availability only. Here we present a pilot study that aims to complement a survey on the availability of data related to peer-reviewed publications with an analysis of their findability, accessibility, useability and assessability (according to the "intelligent data openness" scheme). Sharing rates in genomic anthropology (73.0%) were found to be higher than human genomics (32.4%), but lower than closely related research fields (from 96.8% to 79.2% for paleogenetics and evolutionary genetics, respectively). We discuss the privacy and methodological issues that could be linked to this finding. Comparisons of sharing rates across a wide range of disciplines has suggested that the idea of human genomics as a forerunner for the open data movement should be questioned. Finally, both in genomic anthropology and human genomics, findability and useability were found to be compliant with the expectations of an intelligent data openness, whereas only a minor part of studies met the need to make the data completely assessable.
An Efficient Data Hiding Method Using Correlation Coefficients among Index Colors for Palette-Based Images

In this paper, we propose an extended data hiding method for palette-based images to enhance the marked image quality. Before an embedding process, index colors are typically preprocessed in order to minimize differences between original and marked pixels. The proposed method introduces correlation coefficients among index colors for preprocessing. In the embedding process, we provide a large number of candidate sets of alternative index colors for original ones, and replace the original index colors with the optimal set of candidates, which causes the minimal color difference. Experimental results show the effectiveness of the proposed method in terms of marked image quality using PSNR and SSIM.
Application of the Isolation Forest Algorithm to Substantiate the Uniqueness of Water Bodies in the Group of Karst Lakes

Improving the scientific foundations for the development and expansion of the network of specially protected natural areas requires the search for algorithms that could be used to identify unique ecosystems. Algorithmization of the anomaly identification process provides an opportunity not only to process large amounts of data but also leads to obtaining objective and comparable estimates. The purpose of this research is to identify the most optimal mechanisms for identifying anomalous values for the morphometric characteristics of karst lakes, which may indicate the uniqueness of the entire lake ecosystem. Within the framework of this article, the study was carried out based on a mathematical analysis of samples built for various characteristics based on the WORLDLAKE database. Statistical methods and the Isolation Forest (iForest) machine learning algorithm were used as methods of analysis. As a result of applying the iForest algorithm to a sample of morphometric parameters of karst lakes, consisting of 738 objects, 43 anomalous water bodies were identified. An expert assessment of the final set of lakes for the uniqueness of their ecosystems showed that the chosen method for identifying anomalous values is well suited for the task at hand. Many lakes with an anomaly index above 60% can be recognized as unique due to the unusualness of their abiotic characteristics; a number of them also have a peculiar biota. The anomalous objects included such well-known lakes as Tserik-Kol’, Crveno, Salda Lake, Trihonida, Vegoritida, Petron, etc. Moreover, for most of them, anomalies were detected for several parameters at once. Thus, the applied algorithm for identifying anomalous morphometric characteristics of lakes made it possible to obtain interesting samples for further expert analysis of the entire lake ecosystem for its uniqueness.
Influence of Load on Reliability of Storage Area Networks

During the coronavirus pandemic, telecommuting is widely required, making remote data access grow significantly. This requires highly reliable data storage solutions. Storage area networks (SANs) are one of such solutions. To guarantee that SANs can deliver the desired quality of service, cascading failures must be prevented, which occur when a single initial incident triggers a cascade of unexpected failures of other devices. One such incident is the data loading/overloading, causing the malfunction of one device and further cascading failures. Thus, it is crucial to address influence of data loading on the SAN reliability modeling and analysis. In this work, we make contributions by modeling the effects of data loading on the reliability of an individual switch device in SANs though the proportional-hazards model and accelerated failure-time model. Effects of loading on the reliability of the entire SAN are further investigated through dynamic fault trees and binary decision diagrams-based analysis of a mesh SAN system.
Blockchain and Copyright

Advances in the field of digital technology are constantly introducing new levels of controversy into copyright policy. Blockchain is the most recent technology with significative impact in digital copyright. Combined with smart contracts, blockchain enables new efficient forms of distribution of copyrighted works and also a new model of private ordering regarding the control of uses of works on the Internet. The chapter aims to examine the relationship and the most relevant intersections between blockchain, digital exploitation of copyrighted works, copyright law, and privacy law.
Age and Gender Prediction using Face Recognition

Automatic age and gender prediction from face images has lately attracted much attention due to its wide range of applications in numerous facial analyses. We show in this study that utilizing the Caffe Model Architecture of Deep Learning Frame Work; we were able to greatly enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a much simpler convolutional net architecture that can be employed even if no learning data is available. In a recent study presenting a potential benchmark for age and gender estimation, we show that our strategy greatly outperforms existing state-of-the-art methods.
Tutorial: A Lightweight Web Application for Software Vulnerability Demonstration

In cybersecurity education, it is critical to introduce students to security concepts and keep them aware of common software security weaknesses. However, the effectiveness of delivering such knowledge is complicated by the lack of practical security content and a case study that embeds contemporary security vulnerabilities for education.In this tutorial, we propose to introduce the participants to common web application vulnerabilities using a novel lightweight application case study that demonstrates software security weaknesses in a practical manner. With this tutorial, we will facilitate discussion about ways to expose students to security weaknesses, how to mitigate software vulnerabilities through secure software design and coding practices, as well as share ideas on how to improve the case study application.This three-part tutorial will first introduce the National Vulnerability Database. In addition, we will discuss the Common Weakness Enumeration and the relationship between vulnerabilities and weaknesses. We will then illustrate how the database is used to derive the common web application weaknesses. The second part of the tutorial will demonstrate the lightweight web application as a case study to illustrate the most common web application weaknesses. The participants will be guided on how to download and use the web application. This will include practical exercises of how to activate the built-in vulnerabilities that expose the common security weaknesses. Lastly, we will facilitate a discussion on the efficacy of the case study as a means for practical software vulnerability demonstration for education with a view on ways to enhance the case study.
Machining Trouble Prediction System Using Auto-Encoders

In the current cutting process, wear always occurs in cutting tools, and the detection of wear is an important issue. The current wear detection relies on the skills of skilled workers. On the other hand, in recent years, many examples of attempts to detect anomalies using machine learning have been reported, and their effectiveness has been demonstrated. Therefore, in this research, we focused on the effectiveness of anomaly detection in autoencoders among machine learning, and thought that if anomalies in cutting tools could be detected by introducing autoencoders, it would lead to prediction of cutting troubles. The purpose of this paper is to show that the detection of abnormality by autoencoder using audio data of cutting sound and acceleration data is effective in predicting cutting trouble.
Analysis of Type-2 Fuzzy Recurrent Wavelet Neural Network to Identify Non-linear dynamic System

This piece of work proposes a novel Type-2Fuzzy Recurrent Wavelet Neural Network (T2FWNN) to estimate the nonlinear systems. Such analysis facilitates to solve several realworld issues such as control and power system design and development, industrial sectors, broadband networks, etc. An extensive investigation of the nonlinear systems has been carried out to test the performance of the proposed T2FWNN model in estimating non-linear systems. The networks have been compared in terms of the error parameters, rate of convergence, and computational complexity. The novel structure has been shown to outperform other conventional techniques in modelling non-linear systems. It has witnessed a better convergence, low error, and faster response as revealed from our results.
Design and Implementation of Quantum Key Distribution Network Control and Management

Quantum Key Distribution (QKD) technology enables the capabilities to distribute secure keys consisting of symmetric random bit strings that can be proven to be secure even against a cyberattack with an unbounded computational ability. QKD, however, has critical limitations in terms of scalability, availability and expense. QKD network (QKDN) is a technology which addresses these shortcomings by control and management capabilities to extend the reachability and availability. This paper proposes an architecture for QKDN control and management to address the required functionality and its proof of concept implementation.
Sign Language Recognition using Hand Gestures

Sign language is one form of language for communication, but since most people do not know, this research work has come up with a method using neural networks for fingerspelling based on American Sign Language. In our method, a snapshot of a hand gesture of a real time video is taken and is passed through a filter known as a Gaussian blur filter, which is then passed through CNN to predict the class of the hand gestures. The proposed method provides an accuracy of 95.8% for the 26 letters of the alphabet, thus it acts as an assistant for speech impaired people.
Future Frame Prediction Using Deep Learning

In today’s era, the usage of videos has led to major contributions in the technological world. Be it CCTV footage captured in apartments and at traffic intersections or real-time recordings in the medical industry, videos help in discerning various patterns and uncovering hidden details, accounting for its extensive usage. Future frame prediction involves using the analysis of videos to predict what could happen next in the video. This is an emerging field of deep learning and computer vision whose advancement would provide large potential to enable faster decision making processes in areas like autonomous driving cars, anomaly detection, and aggression in people and falls in the elderly. Due to the proven effectiveness of deep learning in image processing, our goal is to develop a generative model capable of producing a future frame given a set of sequential frames as input. Generative adversarial networks (GANs) and autoencoders are the models used in implementing and achieving the target results. This paper presents the analysis and results of different proposed architectures that take multiple frames as input and aim to produce accurate future frames with the use of multiple efficiency measures. A structural similarity index (SSIM) score of 0.875 was obtained for the predicted frame.
An overview of popular website platforms and mobile apps for language learning

This paper aims at providing a survey of the most popular online educational platforms and mobile apps for language learning. It gives an overview of the development and current trends in what is generally labelled computer-assisted language learning (CALL) with reference to select pedagogical theories; in particular, those embedded in the theoretical framework of broadly defined constructivism, as well as select learning processes, including self-regulation, self-directed learning, personal knowledge management and the concept of learner autonomy. Following this theoretical backdrop, the analysis of 30 educational platforms for language learning is presented, which were evaluated against several criteria, such as accessibility, revised Bloom et al.’s taxonomy of learning outcomes, language levels, typology of exercises, etc.