Reviewerly/exHarmony huggingface dataset:
The concept of distance learning web environment

In modern conditions it is necessary to master existing and new learning technologies. Among such technologies, distance learning is important. Participants in the distance learning process need to rethink the learning process in a new format. This involves identifying the main components of the process and new forms of its organization. The main problem, in our opinion, is the lack of many participants in the educational process of the concept of its system. Namely, they have a lack of understanding of the interaction of the constituent elements of distance learning. Therefore, our goal is to provide participants in the educational process with the concept of a single system of distance learning environment. The distance learning web environment is a specific set of conditions, methods and forms of learning. It is also a set of tools for joint distance learning, education and development of students. In addition, the web environment contains the means of interaction of distance learning entities and the means of managing distance learning. At the same time, the interaction between the participants of the educational process provides access to various educational web resources. Educational web resources are information resources that contain a set of specific educational information. The classification of network technologies for working with educational web resources is given. Distance learning tools, which are one of the components of the web environment, are considered. The scheme of the general structure of the web distance learning environment is given. An important place in this structure is occupied by the conditions of distance learning. The list of basic conditions is given. The concept of web-based distance learning as a single system of its components is formulated. It provides for the following conditions: – the educational process must be clearly organized. – the relationship between learning conditions and means should be established. – there must be technical means and possibilities of their use. Therefore, the web-based distance learning environment will be the only system that will enable applicants to receive quality education. Key words: web environment, web resources, distance learning, network learning technologies, web resource format
English-language communication as a means of professional competence development of aviation specialists

The article concerns the need for an interdisciplinary approach in teaching English in higher aviation educational establishments, where knowledge of the English language becomes not the purpose in itself of the educational process, and a means of training, forming and developing the professional competence of a future aviation specialist. Key words: competence-based approach, English-language communication, cross-disciplinary communication, professional activity, standard task of professional training, specialized course of English
Application of autonomous pathfinding system to kinematics and dynamics  problems by implementing network constraints

A neural network system in an animal brain contains many modules and generates adaptive behavior by integrating the outputs from the modules. The mathematical modeling of such large systems to elucidate the mechanism of rapidly finding solutions is vital to develop control methods for robotics and distributed computation algorithms. In this article, we present a network model to solve kinematics and dynamics problems for robot arm manipulation. This model represents the solution as an attractor in the phase space and also finds a new solution automatically when perturbations such as variations in the end position of the arm or obstacles occur. In the proposed model, the physical constraints, target position, and the existence of obstacles are represented by network connections. Therefore, the theoretical framework of the model remains almost the same when the number of constraints increases. In addition, as the model is regarded as a distributed system, it can be applied toward the development of parallel computation algorithms.
Siamese Natural Language Tracker: Tracking by Natural Language  Descriptions with Siamese Trackers

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU.
Effects of Different Superpixel Algorithms on Interactive Segmentations

Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm.
Using RCRR Strategy to Improve Students’ Reading Comprehension Skill

The aim of this study was to find out the significant difference of the skill in reading comprehension of narrative text between the students by using RCRR Strategy and those who are not. The subject of this study were the students of eighth grade students of a state high school in Kelapa district, Bangka Belitung province. In this study, the researcher used quantitative approach with quasi-experimental design.the findings showed that the result of mean score of post-test in the experimental group was higher (71.11) than the mean score in control group (60.29). Meanwhile, the result of independent samples test of students’ post–test between experimental and control group, the t-otained in equal variances assumed was 3.40 and the significant (2- tailed) was.001. Since the t-obtained was higher than t-table ((3.40&gt;2.05)) and the significant (2-tailed) was lower than computation with level significant (.001&lt;0.05). It could be concluded that there was a significant difference between the samples significantly, and RCRR Strategy improved the students’ skill in reading comprehension of narrative text. Therefore, the result of this study is expected to contribute to the development of the process of English teaching and learning, epecially for teaching and learning reading comprehension.
Learning with Hierarchical Complement Objective

Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation. Nevertheless, state-of-the-art methods often deploy cross-entropy loss that implicitly assumes class labels to be exclusive and thus independence from each other. Motivated by the fact that classes from the same parental category usually share certain similarity, we design a new training diagram called Hierarchical Complement Objective Training (HCOT) that leverages the information from label hierarchy. HCOT maximizes the probability of the ground truth class, and at the same time, neutralizes the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly. The proposed HCOT is evaluated on both image classification and semantic segmentation tasks. Experimental results confirm that HCOT outperforms state-of-the-art models in CIFAR-100, ImageNet-2012, and PASCAL-Context. The study further demonstrates that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.
Implementation of a Digital Payment Processing System Using Spring Boot Interceptor Patterns via Swift Messages

Abstract: A financial transaction is an agreement, or conversation, entered between a purchaser and a dealer for the trade of an asset for virtual processing of payments. E-cash (digital money) is the crucial fee way in digital exchange, this is seen as the same as coin in the real world. A financial transaction is one wherein there is some kind of pastime that modifies the rate of the property, liabilities, or owner's fairness of a business enterprise. The ones styles of transactions are - component transactions which includes a buyer and a seller, and that they usually include cash in some way. The aim of this work was to develop a systematic ordering, comprehensive and classification of issues in the area of financial services. Spring Boot is used for the implementation of the product and it is developed with the help of swift messaging standard for easier transitions and efficient transactions. A customer will subscribe for the service which is provided by the customer service provider. The customer will have to pay the monthly fees for that service. It will be automatically debited from the bank account. The work of this project is for the customer to review the billing amount and also request for billing date extension and request for bill cancellation if needed. The complete process is divided into two parts which are inbound and outbound. There will be a queueing process which picks the customer the query and processes it one by one. The customer will initiate a query and after all the verification and validation the response will be sent to the customer.
Automation identifier approach for library management information system

Rapid technological developments provide benefits for library management. One of them is the application of automatic identifiers in library management, through RFID and QR Code implementation. Some problems that are faced by libraries at the moment, include the lack of guarantees for the safety management of books in the library, lack of services human resources library management, and book availability information has not been accurate. This research conducts the development of a library management information system to solve the problems by using automatic identifiers utilizing Near Field Communication and QR Code technology. The implementation scenario is each library member has the NFC Card and their personal information stored in the NFC card, also each book in the library is put with a QR Code label containing book code information. Transaction of book loan and book return are done by initially tapping NFC Card to record visitor returned or borrow the book and then continue to scanning QR Code label of each book borrowed or returned. In the final stage, the application tested by tapping the NFC card (RFID) on the reader/writer device, and it found the NFC Card could be read or record data from the device at a maximum of 7 cm, while testing phase conducted QR Code label could be read at a maximum distance of 40 centimeters from the device. Implementing automatic identifier help to manage transaction in library more accurate, improve services and provide added value for the library.
SELF-ORGANIZING RESERVOIR NETWORK FOR ACTION RECOGNITION

Current research in human action recognition (HAR) focuses on efficient and effective modelling of the temporal features of human actions in 3-dimensional space. Echo State Networks (ESNs) are one suitable method for encoding the temporal context due to its short-term memory property. However, the random initialization of the ESN's input and reservoir weights may increase instability and variance in generalization. Inspired by the notion that input-dependent self-organization is decisive for the cortex to adjust the neurons according to the distribution of the inputs, a Self-Organizing Reservoir Network (SORN) is developed based on Adaptive Resonance Theory (ART) and Instantaneous Topological Mapping (ITM) as the clustering process to cater deterministic initialization of the ESN reservoirs in a Convolutional Echo State Network (ConvESN) and yield a Self-Organizing Convolutional Echo State Network (SO-ConvESN). SORN ensures that the activation of ESN’s internal echo state representations reflects similar topological qualities of the input signal which should yield a self-organizing reservoir. In the context of HAR task, human actions encoded as a multivariate time series signals are clustered into clustered node centroids and interconnectivity matrices by SORN for initializing the SO-ConvESN reservoirs. By using several publicly available 3D-skeleton-based action recognition datasets, the impact of vigilance threshold and reservoir perturbation of SORN in performing clustering, the SORN reservoir dynamics and the capability of SO-ConvESN on HAR task have been empirically evaluated and analyzed to produce competitive experimental results.
Exploring Lack of Due Diligence as a Threat to Forensic Analysis Preparation and Readiness

The usage of digital technology in the digital forensic investigation has grown in tandem with the rising importance of technology today. Too many incidences of digital and physical crime which is the focus of the world nowadays. To gather the finest evidence and investigative outcomes, a digital forensic model must be established. This study included a review of the literature on digital forensics and models established in digital forensics. According to the findings, the majority of research involves broad inquiries and procedures that overlap. Furthermore, no model has been developed to design a systemic inquiry. In this study, we propose a methodology for digital forensic examination to address this issue. This model combines several of the previous models and adds some new variables that are relevant to the study. Keywords: Due Diligence, Threats, Forensic Analysis, Preparation, Readiness, Cyber Security, BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Jonas Takyi Asamoah (2022): Exploring Lack of Due Diligence as a Threat to Forensic Analysis Preparation and Readiness Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 307-314 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P49
[Fostering the chemistry and biology dual-disciplinary core literacy].

Improving students' comprehensive quality and developing their core literacy are the training objectives of high school subject curriculums, which puts forward new requirements for teachers' professionalism and core literacy. In order to adapt to the high school curriculum reform and the new college entrance examination reform, the Ministry of Education approved the development of high-quality, integrated and master-level high school teachers training pilot program. The aim of this program was to foster a group of dual-disciplinary integrated high school teachers who are suitable for teaching, enjoying teaching and skillful in teaching. How to foster the dual-disciplinary core literacy of normal students through subject curriculum is one of the challenges faced by the pilot program. Following the training objectives of the pilot program "Chemistry and Biological Sciences", we proposed to integrate the dual-disciplinary literacy into four aspects: material concept, scientific thinking, inquiry practice, attitude and responsibility. This was proposed based on analyzing the core literacy of the disciplines of chemistry and biology, and aimed to promote teachers and normal students' understanding and practice of dual-discipline core literacy. With biochemistry course as an example, we further explored and practiced on how to foster the dual-disciplinary core literacy of normal students, aiming to provide reference for the reform of other courses included in the dual-disciplinary integrated programs.
FUZZY VECTOR AUTOREGRESSION FOR FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE

Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.
Evidence Based Reconstruction for Digital Forensics

Evidence based reconstruction may often be even more illuminating than other traditional evidence gathering strategies, but it is also extremely delicate and unpredictable due to the fact that evidence may not always be conclusive. The integrity of digital evidence is therefore extremely important, particularly when it comes from allegedly unlawful, illegitimate, or harmful activity. Regardless of the good or bad consequences of the acts and activities that created the evidence, the capture and reconstruction of events are critical to the operation of the digital world. Owing to the lack of skill and knowledge of digital forensics in Ghana, it creates a susceptible environment for criminals to continue their operations while avoiding prosecution due to a lack of evidence to prosecute them. The reconstruction of occurrences based on evidence is considered an eminent need for Africa and for that matter Ghana. Hence, the need for well-defined and advanced knowledge in evidence based reconstruction in digital forensics investigation to bridge the gap currently existing. This paper reviews literature on the concept of evidence based reconstruction as a means to advance knowledge on its relevance to the Africa region and Ghana for that matter. This would help forensics investigators to better understand the need for focus on using digital tools for reconstruction and focusing on evidence driven activities in case of crime and investigations. Finally, this paper presents an elaborated view from a literature point of view over the evidence based reconstruction and also helps other fellow colleagues in their quest to further understand the concept. Keywords: Evidence, Reconstruction, Digital evidence, Digital forensics, Investigation.
Projection of Belief in the Presence of Nondeterministic Actions and Fallible Sensing

In a recent paper, we presented a Situation Calculus-based framework for modelling an agent that has incomplete or inaccurate knowledge about its environments, whose actions are non-deterministic, and whose sensor might give incorrect results. Generalizing earlier proposals, the presented approach represented the agent's epistemic state by a set of situations ranked by their respective plausibility, which would then be updated by modifying the plausibility ranks accordingly. In this short paper, we extend our earlier work by considering the problem of projection in this framework, i.e. the question whether a certain (epistemic) formula will hold after a given sequence of actions. We present results on both regression, where the query is transformed into an equivalent one about the initial situation, as well as progression, where the knowledge base is updated to reflect the situation after executing the action sequence in question.
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement

Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models’ outputs. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds, which are means for characterising the relations in the causal model argumentatively. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement as an explanation mould to forge bipolar AFs as explanations for the outputs of causal models. We perform a theoretical evaluation of these argumentative explanations, examining whether they satisfy a range of desirable explanatory and argumentative properties.
Normalisations of Existential Rules: Not so Innocuous!

Existential rules are an expressive knowledge representation language mainly developed to query data. In the literature, they are often supposed to be in some normal form that simplifies technical developments. For instance, a common assumption is that rule heads are atomic, i.e., restricted to a single atom. Such assumptions are considered to be made without loss of generality as long as all sets of rules can be normalised while preserving entailment. However, an important question is whether the properties that ensure the decidability of reasoning are preserved as well. We provide a systematic study of the impact of these procedures on the different chase variants with respect to chase (non-)termination and FO-rewritability. This also leads us to solve open problems related to chase termination of independent interest.
A General Framework for Modelling Conditional Reasoning - Preliminary Report

We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.