Reviewerly/exHarmony huggingface dataset:
New Data Transmission Method Based On Book Cipher Enhanced With ASCII Mapping

There are several ways to achieve protection of information follow between user and client from attack, misuse or unauthorized access straightly, one of these ways is steganography, steganography is a science of secretly data transfer using different multimedia like audio, text, video or image as a carrier. In the current paper, the author proposes another steganography procedure that it is easy to find and hard to break by an outsider . It based on combine between ASCII Mapping Technology (AMT) with book cipher encryption method using the image as an encryption key for the book cipher. The (AMT) technique is used to create an encoded table by mapping the text message and match some bits with that of the image, The proposed procedure was tried and the outcome shows its robustness and low computational cost beside it maintain image quality.
Building Health Application Recommender System Using Partially Penalized Regression

Behavioral intervention technologies through mobile applications have a great potential to enhance patient care by improving efficacy. One approach to optimize the utility of mobile health applications is through an individualized health application recommender system. We formalize such a recommender system as a policy that maps individual subject information to a recommended app. We propose to estimate the optimal policy which maximizes the expected utility by partial regularization via orthogonality using the adaptive Lasso (PRO-aLasso). We also derive the convergence rate of the expected outcome of the estimated policy to that of the true optimal policy. The PRO-aLasso estimators are shown to enjoy the same oracle properties as the adaptive Lasso. Simulations studies show that the PRO-aLasso yields simple, more stable policies with better results as compared to the adaptive Lasso and other competing methods. The performance of our method is demonstrated through an illustrative example using IntelliCare mobile health applications.
An Overview on Eco-Friendly Polymer Composites for Heavy Metal Ion Remediation

Background: Water contamination by noxious heavy metals due to urbanization is a global environmental problem. Heavy metal ions pollution make the water unsuitable for drinking and are also highly toxic to human beings and eco-system. Remediation of heavy metals is therefore very crucial. Method: Adsorbents based on biopolymer and eco-friendly polymer composites have been developed and fabricated to remediate and remove heavy metals from the ecosystem. Result: In recent years, biocomposites have been successful as cost effective adsorbents for the remediation of various contaminants with their eco-friendly nature and sustainability. Conclusion: This review article gives an overview on the remediation of heavy metals using green biocomposites.
Economic Versus Statistical Clustering in Multi-Asset Multi-Factor Strategies

Maximizing for diversification in the multi-asset multi-factor universe, the literature advances diversified risk parity strategies across economic clusters. For handling overly complex correlation matrices, hierarchical clustering techniques have recently been put forward to guide risk parity allocations. Indeed, such statistical clusters might be considered natural portfolio building blocks given that they automatically pick up the dependence structure and thus form meaningful ingredients to aid portfolio diversification. We explain the intuition and nature of hierarchical clustering techniques in the context of multi-asset multi-factor investing vis-a-vis the use of economic factors in diversified risk-based allocation paradigms such as 1/N, minimum-variance and diversified risk parity.
ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: CHALLENGES AND OPPORTUNITIES

Artificial Intelligence (AI) is already making a huge impact on society. One might not know that his/her smartphone most probably has an AI engine in it. For example, these engines are optimized for doing calculations used to recognize the face so it lets the user into the phone or they might be to detect the scene that someone is trying to shoot with the camera so it can get a better exposure. The AI is also being applied in all kinds of areas: business, healthcare, gaming, sentencing in the courts to decide the likelihood of somebody reoffending based on their profile, in developing self-drive vehicles or even fully autonomous weapons which would be able to seek and destroy without any human intervention. The higher education realm is surely not exempted from the trend of AI penetration and the buzz seems to be louder than ever. New e-learning platforms that use AI are launched every year and the AI market is receiving amazing financial investments and are gathering many scientific contributors.Every new technology assures in some good benefits and some grave risks. It usually takes time until the legislation responds to the challenges brought by the technology.This paper aims at defining an ethical framework for discerning between the AI capabilities so it is used for the benefit of the education system.
Image Gradient-based Fast Superpixel Segmentation Algorithm for PolSAR Images

Superpixel segmentation has been widely used in the field of image segmentation because of its excellent performance. Simple linear iterative clustering (SLIC) shows excellent performance in optical images and has also been widely used in polarimetric synthetic aperture radar (PolSAR) images. However, the initialization method in SLIC cannot accurately locate class centers, which requires many iterations to correct the errors. This paper proposes a new preprocessing method for SLIC. The preprocessing method uses gradient information of the PolSAR images which is calculated by the CFAR edge detector. Experiments using AirSAR PolSAR data show that the preprocessing algorithm can reduce the number of iterations and improve the segmentation accuracy. The segmentation result is more consistent with the image boundaries in comparison with several other classic SLIC superpixel-based segmentation algorithms.
The development of an Android Applications Model for the Smart Micro-Grid Power Pool System Monitoring and Control

This paper presents the conceptual Android Applications Model for the Smart Micro-Grid Power Pool Monitoring and Control Scheme. The Rational for Energy Sustainability is the focus of this work. Several hybrid formations are utilized on either standalone or off grid basis without formidable measures to monitor and control the system against energy wastage remotely. This research work proposed smart micro-grid integrated scheme with android enable operated softtouch human machine interface for the remote monitoring and control of the hybrid power pool system with its load shedding capability for Energy Sustainability. The optimize hybridized renewable energy resources harvested from the abundant wind, sun, water and bio-resources with the grid and Generator sources from Afikpo Local Government Area of Ebonyi State was considered as a research focal point. Proteus was used in designing system circuitries for the control and monitoring of the power pool system to ascertain its functionability. Arduino IDE was used in developing, monitoring and control algorithm for the system operation. The sublime text enables HTML, JAVA and CSS program for the android application implementation. The design provides a remote operated touch screen Human Machine interface for the pool resources to be centrally manage or control thus, eliminating energy wastage. Five communities where selected for this demonstration. The results reveal that the android enable remote soft touch human machine interface facilitates optimal energy operation. Further research work should be tailored towards developing a similar scheme using same approach for up to 10 communities in the similar local government areas to face out energy sabotage.
The main approaches to making the structure and content of the school course of Computer Science more advanced

The article substantiates the theoretical foundations of building the structure and content of an in-depth school Computer Science course in the context of global integration processes in education. The analysis is made taking into account the main provisions of the state program “Digital Economy of the Russian Federation” and the proposed cross-cutting digital technologies. Approaches to the selection of content and the identification of the basic concepts of the course based on the fundamental directions of science of information science are proposed. At the same time, the hierarchy of competences formed as a result of mastering in-depth informatics in school is identified and described.
SafeLife 1.0: Exploring Side Effects in Complex Environments.

We present SafeLife, a publicly available reinforcement learning environment that tests the safety of reinforcement learning agents. It contains complex, dynamic, tunable, procedurally generated levels with many opportunities for unsafe behavior. Agents are graded both on their ability to maximize their explicit reward and on their ability to operate safely without unnecessary side effects. We train agents to maximize rewards using proximal policy optimization and score them on a suite of benchmark levels. The resulting agents are performant but not safe -- they tend to cause large side effects in their environments -- but they form a baseline against which future safety research can be measured.
Developing an Integrated Framework for Knowledge Management Practices in Organisations

In this knowledge-based economy, the core competence that determines the success of every organisation is knowledge. This calls for organisations to develop and implement systems, strategies and framework to facilitate the effective management of knowledge as an organisational asset. This study sought to assess literature and frameworks of knowledge management (KM) and to develop a framework for KM implementation in organisations. This study reviewed literature on knowledge, KM processes, KM enablers, and KM frameworks to determine and propose a set of guidelines for implementing KM practices in organisations. Arising from the review, the researcher proposed an integrated KM framework that could be used by academics (researchers) and KM practitioners to implement and facilitate KM practices in organisations. The framework identifies the key elements of developing and facilitating KM practices: knowledge (tacit and explicit), KM processes (creation, retention, sharing and utilisation), and KM enablers (technology, leadership, culture and strategy). From organisations’ perspectives, for a successful implementation of KM practices, the focus should be on identifying and linking the knowledge types with the KM processes and the KM enablers. This forms the basis for the proposed framework to guide the successful implementation of KM practices in organisations. By utilising these guidelines to develop an integrated KM implementation framework, it is hoped that a stronger theoretical foundation can be constructed, thus facilitating the accomplishment of KM.
Multi-Granularity Power Prediction for Data Center Operations via Long Short-Term Memory Network

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.
Parallel Algorithm for Solving the Graph Isomorphism Problem

In this paper, we offer an efficient parallel algorithm for solving the Graph Isomorphism Problem. Our goal is to construct a suitable vertex substitution or to prove the absence of such. The problem is solved for undirected graphs without loops and multiple edges, it is assumed that the graphs can be disconnected. e question of the existence or absence of an algorithm for solving this problem with polynomial complexity is currently open. Therefore, as for any time-consuming task, the question arises of accelerating its solution by parallelizing the algorithm. We used the RPM ParLib library developed by the author as the main tool to program the algorithm. This library allows us to develop effective applications for parallel computing on a local network in the .NET Framework. Such applications have the ability to generate parallel branches of computation directly during program execution and dynamically redistribute work between computing modules. Any language with support for the .NET Framework can be used as a programming language in conjunction with this library. For our experiments, we developed some C# applications using this library. The main purpose of these experiments was to study the acceleration achieved by recursive-parallel computing. Specially generated random regular graphs with varying degrees of vertices were used as initial data. A detailed description of the algorithm and its testing, as well as the results obtained, are also given in the paper.
Building the Content of Teacher Training in the Context of Education for Sustainable Development

The article presents and justifies a technology for building the content of teacher training through sustainable development. Considerable attention is paid to environmental issues of society, which urge to change the philosophy of life and the mindset of people. The designed technology for building training content is primarily relevant owing to the need to implement the main concepts of sustainable development and introduce some novel approaches to teacher training. It can be implemented at three levels: degree programmes, curricula, syllabi. The level of building training content consists of such stages: defining expected learning outcomes for courses; outlining the information field of courses; compiling the basic educational material; determining didactic units of the main educational material; designing the structural and semantic model of the basic educational material; dividing the basic educational material into subjects. The article also presents the results obtained from the experiment conducted from 2015 to 2018. The research sample comprised 457 respondents (231 respondents in the control group (CG) and 226 respondents in the experimental group (EG)). The analysis of the obtained results shows that during the formative experiment the number of respondents with advanced (creative) level has increased by 5.82% in the EG compared with the CG; the number of respondents with intermediate (sufficient) level – by 5.18. The number of respondents with basic (reproductive) level has decreased by 11%. The differences between the CG and the EG are statistically significant (χ2 = 8,276), do not exceed the established limits and are at the level of 0.05≤ρ≤0.01 (5.991≤8.276≤9.21).
Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

Sorting is a huge demand research area in computer science.Sorting is a process of arranging the elements in an order.In practical application computing requires things to be in order.A comparative study is done in this study and performed the comparison for both positive and negative numbers by taking the random numbers as input.In this study, different algorithms like UNH Sort, Selection Sort, Bubble Sort, Insertion Sort, Merge Sort, Quick Sort are considered for the experimentation.From the obtained results we can conclude that, Initially when the input size is less UNH Sort is giving best when it is compared with bubble sort.When the input size increases bubble sort takes long time to perform sorting.And Quick sort takes less time to perform sorting.
On the adversarial robustness of DNNs based on error correcting output codes.

Adversarial examples represent a great security threat for deep learning systems, pushing researchers to develop suitable defense mechanisms. The use of networks adopting error-correcting output codes (ECOC) has recently been proposed to deal with white-box attacks. In this paper, we carry out an in-depth investigation of the security achieved by the ECOC approach. In contrast to previous findings, our analysis reveals that, when the attack in the white-box framework is carried out properly, the ECOC scheme can be attacked by introducing a rather small perturbation. We do so by considering both the popular adversarial attack proposed by Carlini and Wagner (C&W) and a new variant of C&W attack specifically designed for multi-label classification architectures, like the ECOC-based structure. Experimental results regarding different classification tasks demonstrate that ECOC networks can be successfully attacked by both the original C&W attack and the new attack.
Assessing, Explaining, and Conveying Robot Proficiency for Human-Robot Teaming

This workshop focuses on issues surrounding human-robot interaction for robot self-assessment of system proficiency. For example, how should a robot convey predicted ability on a new task? How should it report performance on a task that was just completed? Communities in both computer science and robotics have addressed questions of introspection to monitor system performance and adjust behavior to guarantee or improve performance. Self-assessment can range from simple detection of proficiency up through evaluation, explanation, and prediction. Robots need the ability to make assessments and communicate them a priori, in situ, and post hoc in order to support effective autonomy and utilization by human partners and supervisors. This is a pressing challenge for human-robot interaction for a variety of reasons. Prior work has shown that robot expression of performance can alter human perception of the robot and decisions on control allocation. There is also significant evidence in robotics that accurately setting human expectations is critical, especially when proficiency is below human expectations. Therefore, more knowledge is needed on how systems should communicate specifics about current and future task competence.
Improving Multi-class Co-Clustering-Based Collaborative Recommendation Using Item Tags

Multi-class Co-Clustering (MCoC)-based recommendation system is a method for recommending favorite items to users.It, firstly, groups items and users in a way that users have common interests and their favored items are put in the same group.Then, to estimate unrated items of each group, for each group, an independent collaborative filtering approach is implemented.MCoC-based recommendation system uses rating vectors to determine similar items and to group them together.We expect that the ratings of two items with the same tag be closer than two items with different tags.Therefore, tags can also be used to determine similar items for co-clustering.The purpose of this study is to enhance the performance of co-clustering to increase the accuracy of MCoC-based recommendation system by using item tags in addition to item rating vectors.Our experiments show that mean absolute error of our proposed method is much less than that of MCoC-based recommendation system.
Graph Dilated Network with Rejection Mechanism

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.
Jaw-Operated Human Computer Interface Based on EEG Signals via Artificial Neural Networks

Man and machine interfaces help paralyzed peoples to communicate and control their environment.This work purpose to introduce and shows a novel kind of machine interface using the horizontal signs of conscious jaw motions on brain signals stored in electroencephalogram (EEG).Electrical functions of the brain are extricated and transformed to control commands.Jaw-Machine Interface (JMI) serve a new functionality for tetraparesis to run peripheral devices with the help of a computer using only horizontal jaw motions.In this study, mean absolute deviation (MAD) and entropy (S) values are derived of EEG and hemispherical designs are valued and examined as offline analysis approach.Principle component analysis (PCA) is used to reduce redundant information from data and two types of artificial neural networks which are Multilayer Neural Network with Levenberg Marquardt training algorithm (MLNN+LM) and Probabilistic Neural Network (PNN) via k-fold method are run to find out horizontal jaw patterns on brain waves.
Developing a memory representation: Do we visualize or do we “verbalize” objects?

According to encoding specificity, participants perform better when testing conditions match learning conditions. It is interesting that recent findings in visuo-haptic object identification violate this principle: Participants who learned to recognise objects haptically performed just as well when asked to identify objects by sight and by touch. One possible explanation is that participants who explore objects haptically visualize the objects they explore, creating a multisensory memory trace equally accessible to vision and touch. We evaluated this possibility by asking undergraduate participants to learn to recognise novel objects either by sight or by touch. Participants completed sequences of learning trials where they explored each object and test trials where they recalled the name of each object. During learning trials, some participants were presented with a visual distractor (either verbal or nonverbal characters) that they had to recognise later, whereas other participants completed a distractorless control condition. Consistent with past findings, our results violated encoding specificity for participants who learned to recognise objects haptically-this was not modified by the addition of a secondary task. It is interesting, however, that only the verbal distractors interfered with learning. These results suggest that the creation of memory representations for novel objects involves a verbal code rather than visualization, independently of how objects are initially explored. (PsycInfo Database Record (c) 2021 APA, all rights reserved).