Reviewerly/exHarmony huggingface dataset:
Discovering Generalizable Skills via Automated Generation of Diverse Tasks

The learning efficiency and generalization ability of an intelligent agent can be greatly improved by utilizing a useful set of skills.However, the design of robot skills can often be intractable in real-world applications due to the prohibitive amount of effort and expertise that it requires.In this work, we introduce Skill Learning In Diversified Environments (SLIDE), a method to discover generalizable skills via automated generation of a diverse set of tasks.As opposed to prior work on unsupervised discovery of skills which incentivizes the skills to produce different outcomes in the same environment, our method pairs each skill with a unique task produced by a trainable task generator.To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks.A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective.The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.We demonstrate that the proposed method can effectively learn a variety of robot skills in two tabletop manipulation domains.Our results suggest that the learned skills can effectively improve the robot's performance in various unseen target tasks compared to existing reinforcement learning and skill learning methods.
Automated short-answer scoring using latent semantic indexing

Short answers are brief descriptions consisting of one to three sentences. They are evaluated by comparing the Student Answers with a Reference Answer. But scoring based on the term similarity alone can be inefficient given the fact that each student can answer in their own words. This approach doesn't take account of the contextual meaning of sentences. As a result, the performance of the system will be unsatisfactory. The proposed model focuses on the semantic relation between the student answer and reference answer by using Latent Semantic Indexing. Latent Semantic Indexing helps in representing the semantic similarity between documents in a reduced dimensional space using TF–IDF Document Matrix and Singular Value Decomposition (SVD). TF–IDF matrix reflects term importance instead of term frequency which is an improvement over previous research. The experimental result shows that both the Mean Squared Error and Mean Absolute Error have considerably decreased, compared to the term-based approach.
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services.However, language generation models' democratization also makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech.Thus, it is essential to understand how people interact with bots and develop methods to detect bot-generated text.This paper shows that bot-generated text detection methods are more robust across datasets and models if we use information about how people respond to it rather than using the bot's text directly.We also analyze linguistic alignment, providing insight into differences between humanhuman and human-bot conversations.
An algorithm for analyzing cloze test results

We present an algorithm for the automatic processing of cloze test results that are traditionally analyzed and adjusted by hand. The algorithm accurately includes inflected and derived forms of the target word as correct responses, as well as minor spelling mistakes and typographical errors. We provide a test of the algorithm against the coding of one expert human observer and find that the algorithm performs with equal accuracy. We further examine the utility of the algorithm for the analysis of data involving word identification with typed responses. The routines are implemented in R-software and are freely available.
Smart Glove for Augmented and Virtual Reality

This research work is carried out on designing and prototyping a smart glove, which can conduct 3D interaction with computer MATLAB model in real time. The smart glove is constructed with only inertial measurement units for gathering and achieving human hand movement position data. This application will support the accuracy of the device and provide additional flexibilities for human interaction with other objects. The purpose of our design is to provide a smart glove with low price (less than 100€) for researchers in different institutions to develop their research projects with virtual and augmented reality. The design of hardware and software, as well as prototyping experiments is also presented.
Advanced Far Field EM Side-Channel Attack on AES

Several attacks on AES using far field electromagnetic (EM) emission as a side channel have been recently presented. Unlike power analysis or near filed EM analysis, far field EM attacks do not require a close physical proximity to the device under attack. However, in all previous attacks traces for the profiling stage are also captured at a distance (fixed or variable) from the profiling devices. This degenerates the quality of profiling traces due to noise and interference. In this paper, we train deep learning models on clean traces, captured through a coaxial cable. Our experiments show that the resulting models can extract the AES key from less than 500 traces on average captured at 15 m from the victim device without repeating each encryption more than once. This is a 20-fold improvement over the previous attacks which require about 10K traces for the same conditions.
Information visualization in context of modern education megatrends

In light of technological advancement and formation of new visual culture, there arises a vital problem of visual presentation of information for solving new problems and performing a wider range of functions in education. In this regard, visualization should be considered in context of such megatrends as the Prosumer concept, knowledge management, the use of platform services to present information as a single unity. Cognitive visualization as the basis for visual presentation of educational information is represented by five main tendencies which students should take into consideration creating infographics: concentration and generalization of knowledge, expansion of the orientation and presentation functions of visual aids, algorithmization of educational and cognitive actions by visual means, and multicode presentation of information. Experimental training resulted in establishing connections between these megatrends and the cognitive visualization tendencies; besides it revealed that concentration of knowledge and multicode presentation of information were the most difficult information visualization operations for the students when using platform services for creating visual content.
Optimizing the Power Station Project Management Using Knowledge Graphs

In “The Belt and Road”, there are more and more investment projectsof hydropower stations abroad in China. This paper proposes to use thesemantic interconnection ability of knowledge map to manage the big dataof hydropower using knowledge graphs. It solves the problem that a lot ofproject information is in the state of dispersion. To further understand thedevelopment space and potential of hydropower construction investment indifferent countries.
Video Source Identification from MP4 Data Based on Field Values in Atom/Box Attributes

Identifying the source of a video recording created by a camera or smartphone has been a common and challenging task in media forensics for many years. We present an approach for source identification on the very common MP4 file format. In extension to related works, we propose to consider the suitability of attribute field values and their respective order in the atom/box tree in a specific manner. The significance of a field attribute and its particular value for source identification will be reflected by means of up and down weighting during the training and the matching process. Experimental result indicate that our approach allows distinguishing major brands. Even device identification is possible for a subset of our training data.
Machinery Fault Detection Using Autoencoder and Online Sequential Extreme Learning Machine

Fault detection is one of the most challenging tasks in industrial applications, which aims at identifying the faulty condition deviating from the normal condition of the machine. In this work, a fault detection method is proposed based on autoencoders and online sequential extreme learning machines (OS-ELM). The autoencoder is employed for high-level feature extraction from the monitoring signal and the OS-ELM is developed based on features extracted from signals of normal condition. The fault detection is performed based on i) the updating of OS-ELM using the newly collected data; ii) the quantification of the model modification. The data collected under the faulty condition is expected to significantly modify the OS-ELM model. The proposed fault detection method is validated considering a benchmark bearing case study.
Tracking Child Language Development With Neural Network Language Models

Recent work on the application of neural networks to language modeling has shown that models based on certain neural architectures can capture syntactic information from utterances and sentences even when not given an explicitly syntactic objective. We examine whether a fully data-driven model of language development that uses a recurrent neural network encoder for utterances can track how child language utterances change over the course of language development in a way that is comparable to what is achieved using established language assessment metrics that use language-specific information carefully designed by experts. Given only transcripts of child language utterances from the CHILDES Database and no pre-specified information about language, our model captures not just the structural characteristics of child language utterances, but how these structures reflect language development over time. We establish an evaluation methodology with which we can examine how well our model tracks language development compared to three known approaches: Mean Length of Utterance, the Developmental Sentence Score, and the Index of Productive Syntax. We discuss the applicability of our model to data-driven assessment of child language development, including how a fully data-driven approach supports the possibility of increased research in multilingual and cross-lingual issues.
Impact analysis of agile method based on risk management for developing technology management in (SMEs) Small and Medium- Enterprises

Nowadays, information systems such as the Agile model can adapt to the structures and processes of smart organizations, particularly SMEs. The faster an information technology company adopts risk management-based Agile in a work environment, the higher its performance and energy efficiency. To maximize the value generated by agile decision-makers or managers, SMEs must have the capability and willingness to adopt new risk management-based technology models. Additionally, the study identifies a gap in the use of the Agile Model to influence the success of SME projects in the Middle East region's SMEs (, UAE, KSA, Oman, Egypt, Iraq, and Qatar). The study is aimed at SMEs in the electronics and energy sectors, as well as IT businesses and big data projects, with the goal of encouraging them to adopt an Agile model. This study was characterized positively in numerous ways, including the adoption of technology in the workplace to accommodate abrupt changes in energy management schemes and various government policies promoting energy conservation. Additionally, employee training and adaptability in dealing with programs that can affect the process help accomplish the objectives. Restructuring medium and small businesses to improve their energy management and overall performance.
Decentralizing Finance via Cryptocurrencies and Tokenization of Assets and Peer-to-Peer Platforms

The rationale behind this research is to demonstrate the tokenization effects of the blockchain on all types of assets, and how blockchain technology might improve the way we view digital exchange and use digital money. The finance industry is progressing towards innovative solutions, capable of dealing with traditional problems and of increasing efficiency, sustainability and accountability. This progression may also address market failures within the economy.&#x0D; In our conceptual chapter, we provide a short discussion of the decentralization of finance and where we are today. We begin with the fundamentals of money and the evolution of cryptocurrency issuance from ICOs to STOs then to stablecoins like CBDCs. We then examine the concept of tokenizing various types of assets and deliberate on a conceptualization of a tokenized capital markets trading platform. To clarify tokenization and its benefits, we provide an example on the tokenization concept for agriculture and livestock in raising capital for small farms. Finally, we conclude discussions on concerns for privacy and security which emphasizes on self-governance, self-regulation and cybersecurity measures.
Building robust neural networks using different loss functions

The paper considers the problem of building robust neural networks using different robust loss functions. Applying such neural networks is reasonably when working with noisy data, and it can serve as an alternative to data preprocessing and to making neural network architecture more complex. In order to work adequately, the error back-propagation algorithm requires a loss function to be continuously or two-times differentiable. According to this requirement, two five robust loss functions were chosen (Andrews, Welsch, Huber, Ramsey and Fair). Using the above-mentioned functions in the error back-propagation algorithm instead of the quadratic one allows obtaining an entirely new class of neural networks. For investigating the properties of the built networks a number of computational experiments were carried out. Different values of outliers’ fraction and various numbers of epochs were considered. The first step included adjusting the obtained neural networks, which lead to choosing such values of internal loss function parameters that resulted in achieving the highest accuracy of a neural network. To determine the ranges of parameter values, a preliminary study was pursued. The results of the first stage allowed giving recommendations on choosing the best parameter values for each of the loss functions under study. The second stage dealt with comparing the investigated robust networks with each other and with the classical one. The analysis of the results shows that using the robust technique leads to a significant increase in neural network accuracy and in a learning rate.
Understandings the Reading Habit and Reading Attitudes Among Students in Research University Library in Malaysia

Reading is one of the activities known to give a lot of benefits to readers. In the context of university students, reading can improve their academic performance. However, literature indicated that students read-only to pass their examination and not for pleasure and acquiring knowledge. The study reported in this paper investigated the reading habit and reading attitudes among students in the research university library in Malaysia. It is believed that positive reading attitudes towards education materials or non-education materials lead to positive reading experiences, which also lead to higher academic performance. A descriptive study used questionnaires as an instrument of a quantitative method. Convenience sampling was employed and 400 respondents from four research university status libraries were selected. Moderate levels of reading attitude were obtained from the result of the study, in which several variables produced a significant relationship in the reading attitude (education background and favorite activities during leisure time). The findings of this study have shown the behavior of academic library users in terms of reading habits, reading attitudes, and selection of reading materials. Several suggestions such as providing a variety of reading material (sense of humor and humanities, public figures and tv personalities) and reading encouragement activities have been suggested. This study provides useful inputs to the Ministry of Higher Education (MOHE) in constructing effective strategies to cultivate reading habits among reluctant readers in public universities.
Experimental Implementation of Tracking Error Elimination for Omnidirectional Wheelchair Using PD-Fuzzy-P Controller

Wheelchairs have been an active-research problem in the maneuverability of mobile robots over the past decade. This study aims to develop the transportability of disabled people by controlling the motion of their smart wheelchairs. It presents a design of omnidirectional mobile robot where its wheels can be equipped to replace a conventional electric wheelchair in order to achieve flexible and reliable maneuverability. The decentralized algorithm is used for motion control of the omnidirectional robot, which deals with the three independent components of robot’s motion in the body coordinate frame: rotational moving, horizontal moving and vertical moving, which can be controlled separately with separated and different sub-controllers. The simulation analysis of the proposed study has been tested in authors’ previous work and two approaches of artificial intelligent-based controllers (PD-Fuzzy-P and GA-PID controllers) have been built in order to control optimally the maneuverability of such system. The results have showed that the PD-fuzzy-P controller has converged faster than the GA-PID controller and the robot has been able to track successfully the sharp curves’ maneuverability, such as a 90° corner in the squared shape and a U-turn in the rose shape with an error approaching zero. In this paper, the experimental tests of the PD-fuzzy-P controller are carried out on a real robot developed using EV3 Lego kit parts and Vicon motion capturing system is used to capture the robot’s motion in various trajectory scenarios in order to test the effectiveness of the proposed control design.
Object Recognition Using Enhanced Particle Swarm Optimization

The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation and sampling iteration operations for feature selection in object recognition. Specifically, five PSO variants integrating different mutation and sampling strategies have been proposed to select the most discriminative feature subsets for the classification of different objects. A mutation strategy is firstly proposed by randomly flipping the particle positions in some dimensions to generate new feature interactions. Moreover, instead of embarking the position updating evolution in PSO, the proposed PSO variants generate offspring solutions through a sampling mechanism during the initial search process. Two offspring generation sampling schemes are investigated, i.e. the employment of the personal and global best solutions obtained using the mutation mechanism, respectively, as the starting positions for the subsequent search process. Subsequently, several machine learning algorithms are used in conjunction with the proposed PSO variants to perform object classification. As evidenced by the empirical results, the proposed PSO variants outperform the original PSO algorithm, significantly, for feature optimization.
USE OF VIRTUAL LABORATORIES IN THE PROCESS OF TEACHING CHEMICAL DISCIPLINES

The introduction of information and communication technologies in the educational process of teaching various disciplines is becoming increasingly important. Accordingly, the issue of application of these technologies in the teaching of chemical disciplines is relevant, namely the study of the possibility of using virtual laboratories in Chemistry as an interactive learning tool to improve the learning process and provide opportunities for professional and research skills. The purpose of the article is to study the possibilities of using virtual laboratories in the process of teaching chemical disciplines and to determine the opinion of students on the effectiveness of this type of activity. The following research methods are used in the article: theoretical – analysis of scientific publications on the research problem; systematization and generalization of information; empirical – questionnaires and analysis of its results.&#x0D; The article considers the advantages and disadvantages of using virtual Chemistry laboratories in the educational process. The article analyzes the concept of «virtual laboratory» and gives the definition of a virtual laboratory of Chemistry as a software environment that allows to form practical skills by conducting laboratory experiments, simulating the sequence of actions of the researcher in real laboratory (or managing chemical plants by robotic means) with use of computer models or contactless (remotely) with real equipment and reagents. We consider the virtual Chemistry laboratories available on the Internet, which provides free access to the educational resources and explores their didactic and methodological opportunities to increase the level of formation of practical skills and abilities during chemical experiments. It is established that the use of a virtual laboratory in Chemistry contributes to the formation of practical skills in conducting interactive laboratory classes with further consolidation in real conditions; provides an individual pace of mastering the skills of chemical experiment; allows students to explore processes that require special conditions; increases the motivation to master new material by working in a virtual environment that is familiar and interesting for the modern student; provides realization of its independent preparation for performance of experiments in real laboratory conditions. It is substantiated the use of virtual laboratories in Chemistry cannot substitute the traditional performance of laboratory work, but is only a supplementary form.
Learning and Well-Being in Educational Practices with Children and Adolescents Undergoing Cancer Treatment

The use of information communication technologies (ICT) in education brings up new possibilities of promoting learning and health experiences. In this sense, education contexts of the 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games in order to improve learning and well-being in children and adolescents undergoing cancer treatment in a non-formal educational setting. The methodology is based on qualitative case studies with content-based data analyses, involving informal interviews and observation methods. The study considers data from five patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being experience of patients during the treatment.