Reviewerly/exHarmony huggingface dataset:
Repercussions of the Continuous Instability for the Quality of Education in the State of Libya: An Investigational Study within the Faculty of Education, University of Benghazi (UoB)

Many confirmed negative signs and facts point to the general deterioration of the quality of Libyaâs entire education system, with serious consequences for the future of the state of Libya. This deterioration is due to countless factors, such as ongoing political and militant conflicts, semi-paralyzed educational administrations, a lack of continuing professional development (CPD), and insufficient awareness within the Ministry of Education (MoE) of the state of the education system. Other contributing factors are the inefficient educational legislations from the parliament and the poor desire of policy makers to empower profound research, teamwork, reform ideas, or any suggested transformational strategies. Furthermore, the Centralized Management System (CMS) has detrimental effects, along with official reports on corruption issues in the MoE. Parallel to these causes, other circumstances impact the quality of the input and output of the Libyan education system (LES), such as the prevailing culture, beliefs, and attitudes on how education should be, whose most significant features are memorization and indoctrination. Such challenges have produced unqualified leadership and a huge number of decision makers who have harmed the LES instead of supporting it. Moreover, as a result of the COVID-19 pandemic and various suspensions (strikes/wars) of basic, secondary, and even vocational university education, several classes were postponed, canceled, or disrupted, which led to a great loss of knowledge among both teachers and learners. Correspondingly, Elabbarâs ([1], p. 121; [2], p. 19) studies have raised the warning indications to the highest level for the government, parliament, and all education authorities in the state of Libya, stating that the whole LES âmay collapse very soonâ if they do not adopt serious steps and fundamental measures âbefore it is too late.â Thus, this research study attempts to investigate such impacts on the quality of the education process and knowledge management (QEPKM) among students, teaching assistants, lecturers, and administrators at the Faculty of Education, University of Benghazi (UoB), with a special focus on the English Department. To closely classify the scale of impacts on the LES, this study uses four qualitative tools to collect and analyze data gathered from a wide range of participants: official documents, authentic academic reports, and experimental face-to-face interviews. The study recommends adding bridge programs for present and new students at the Faculty of Education, besides returning to the annual system (for at least 8 years) instead of the existing semester system, in addition to endorsing Elabbar's [2] proposed seven years of connected gradual reform phases for the reform strategy of the entire LES; otherwise, if all leading authorities do not seriously act toward reform, the whole education system in Libya will face major failings.
MChCNN: a deep learning approach to detect text-based hate speech

Meaningful combinations of words have been used for the representation of diverse ideologies and thoughts associated with various demographics and culture around the world since ancient times. The exponential development of client-created content via web-based media circumscribing hate speech is progressively disturbing. Since the degree of spread of hate speech through the Internet is expanding, strategies that naturally recognize hate speech require immediate attention. The answer to the issue comes into the picture with artificial intelligence. In this chapter, for the detection of hate speech, we are using a benchmark dataset from the Kaggle website. We have deployed a novel approach for the detection of hate speech from non-hate speech, and we have integrated the GloVe word embeddings in our proposed model. The multichannel convolutional neural network (MChCNN) is used as a base model for binary classification that has performed well by delivering a very high testing accuracy. Using our proposed model, we have reached the state-of-the-art result compared to the existing methods. The results obtained from our experiments showcase that our model can be used by researchers for further exploration.
Development of Village Open Data using DevOps Approach

Open data is one of the important e-government implementations as part of digital transformation. This research aims to survey Open Data applications in various cities, bodies, and ministries of Indonesia and overseas. The survey results are important for developing an open data application in Alang-Alang village, Bangkalan, Madura, Indonesia. The research contributes by presenting survey results for preparing the Open data implementation for the digital village in Indonesia. Moreover, the novelty of this research is the survey results from various institutions and countries, including Indonesia. This is a research in progress paper in the early stages; The research is still ongoing now. Many institutions in numerous countries have been implementing the Open Data application, including Indonesian institutions. There are also various menus, data formats, and published data in the Open Data applications. The various menus are from basic to advance. We also have designed a Use Case and Activity diagrams of the Open Data Application for Alang-Alang village. Also, the Open data application for the village is still limited. This research has some implications, such as for theory, it extends the body of knowledge of Open Data, Digital Village, and Digital Government fields. In practice, this research supports the village government publishing public data, improving transparency, and enhancing public trust. Hopefully, it also helps citizens to get access to public data easily.
Understandable Automatic Generated Unit Tests using Semantic and Format Improvement

Unit testing is the important yet the most laborious testing activity because the developer must create and execute unit tests for each class that is created. Unit tests can be created manually by the developer or automatically by using code-based test case generation techniques, such as random, search-based, or symbolic execution techniques. The automatic generated testcases reducing developer effort in writing the unit test for every method and class. However, this automated unit test case more difficult to understand compared to manual unit test. After the unit tests are executed, the results must be validated or maintained by the developer team and they must understand the content of unit tests. This applies not only to the development phase but also to the software maintenance phase. Therefore, understandability is an important aspect that test cases need to have. To explore what kind of test cases are easy for developers to understand, a requirement gathering activities related to understandability improvement in unit test cases is conducted. This research involved the developers and experts in software development by exploring their opinion in semantic, format and function of automatic unit test cases. Based on expert's opinion and developer's interview, requirement list is mapped to the Evosuite and Randoop generated unit test case
New Advances in Development of Clinical Trial Management Systems

The electronic systems for medical data take bigger and bigger place and importance in our life. One important part of them are Medical trial systems. Those systems give possibility to doctors, patients and hospitals to elaborate and make the treatment more precise. The continuous process of development of new functionalities makes those systems much more useful. A lot of solutions are presented on the market. In the presented research is made an attempt to present the process of development of such a type of software. All the data entered can be easily via them processed. The importance is also in this that CTS save working time and can reduce the prices for hospitals and patients.
Reducing False-Positive Detections Using the Distance Between Activation Distributions in Individual Channels

We propose a novel method to estimate the confidence of outputted predictions of a convolutional neural network. We show that different channels in one layer can be treated as an ensemble and extract the confidence of a prediction from a single channel. To achieve this, we compute statistical distances between activation distributions located at the predicted mask and its surrounding area and aggregate it across all channels in a deep layer of a network. Research on a segmentation network of lung cancer nodules from 3d computer tomography images has shown growth of precision compared to the thresholding output network values. The more layers used to compute confidence, the better performance obtained, allowing for up to 18% fewer false-positives detections on the source Cancer dataset and up to 54% fewer false-positives detections on an unseen Covid dataset. Analyzing channel activations doesn’t require any changes in the training procedure with a negligible amount of additional computations at the inference time.
An Improved Algorithm for Defogging Based on Fused Underwater Images

This paper describes an improved algorithm for de-fogging based on fusion underwater images. Based on the fusion principle, our algorithm only needs to obtain its input map and weight map through the original degraded image. To overcome the limitations of underwater media, we define two inputs, representing color correction and contrast enhancement of the original underwater image, and four weights, which aim to enhance distant objects degraded by medium scattering and absorption visibility. Our method is a single-image method and does not require specialized hardware or knowledge about underwater conditions or scene structure. Our fusion framework also supports temporal correlation between adjacent images by applying an efficient edge denoising strategy. The enhanced image features reduced noise levels, improved exposure in dark areas, and increased overall contrast, while significantly enhancing the finest details and edges.
Method of Integration of Formal Forecasting Methods into Data Assimilation on the Example of Autoregressive Time Series

The article describes the method developed for integration of formal methods of time series (TS) forecasting (such as autoregressive integrated moving average (ARIMA), singular spectrum analysis (SSA), group method of data handling (GMDH), artificial recurrent neural network with long short-term memory (LSTM)) into the Data Assimilation (DA) technique. The method can be used in cases where mathematical model of the dynamic system generating the TS is not known (for example, TS consisting of economic indicators). The performance of the integration method is confirmed by a forecasting of a TS generated using AR(p) process of order p, where $p = \overline {1,10} $. The comparative analysis of the forecasting accuracy of the method against ARIMA method was carried out. The developed technique showed high accuracy, except for a small set of parameters that lead to an increase in the forecasting error. This, from our point of view, is due to the local features of the predicted TS. The analysis shows that the developed method shows higher forecasting accuracy in comparison with formal methods.
Trust Me, I’m a Liar

Cyberattacks will continue to thrive as long as their benefits exceed their cost. A successful cyberattack requires a vulnerability, but also and foremost the attacker’s willingness to exploit. Can we reduce this willingness, can we even the attack/defense asymmetry?
A new approach to loanword orthography of Russian

The purpose of this study discusses problems in loanword orthography of Russian and proposes an alternative to it. To this end, the differences between the national norm of foreign language in Russian and previous studies were reviewed. The national norm and the norms of previous studies were made based on the principle of one phoneme - one notation and the principle of sound similarity. As in previous studies, this study also proposed a norm of loanword orthography of Russian according to these principles. However, what this study differs from previous studies is that it compares Russian norm with loanword orthographies of other languages. The results discussed are as follows. Considering the phonetic characteristics of Russian voiceless plosives and fricative /s/, it was proposed to correspond them to fortis consonants of Korean. And the soft consonants /д, т/ correspond to 'ㄷ, ㄸ’ like the hard consonant /д, т/. In all languages except Russian, fricatives are not used as a coda within a syllable. Russian fricatives do not have phonetic differences from those in other languages. Therefore, it was proposed that Russian fricatives, like those of other languages, are not used as a coda within a syllable.
Data and AI based Model Development to Solve the Ambiguity Problem: focusing on Bulgarian and Korean application cases

In this paper, I briefly review the examples of ambiguity problem in Korean and Bulgarian, and examine methods for solving the ambiguity problem, focusing on previous studies. Resolving the ambiguity of the word stage has progressed to some extent through the dictionary work (off-line work) so far, and it is also true that the importance of understanding the meaning of the ambiguity of a word alone in the actual dialogue act process is relatively low. Therefore, solving the problem of ambiguity in the units of phrases, sentences, and texts is the central part of this thesis. Since there are quantitative and temporal limitations in the database that researchers collect and create, the method of computerizing this and the method of solving(processing) when a phrase or sentence to be identified appears in the main study from the point of view of artificial intelligence processing. The first step in the research for resolving ambiguity in Bulgarian is to continuously discover ambiguous expressions that appear in words, vocabulary, sentences, and texts, and create a data base. Theoretical research on such matters should proceed. Collection of examples (DB) at individual stages can be continuously performed off-line and on-line. Off-line, it will be individual dictionaries such as homonyms and Bulgarian dictionary, and on-line, it will be a web crawling collection of data from Bulgarian Wikipedia, Wordnet, and online portals. WordNet, an English-centered thesaurus, is not just a digitized dictionary, but is organized into a Synset in which nouns, verbs, adjectives, and adverbs are a set of analogous words. WordNet also provides antonyms, metonymy, pertainymy, homonymy, and entailment. It can also be an efficient and good way to secure additional data in Bulgarian by using an artificial intelligence automatic translation technique for WordNet, which is mainly provided in English. In particular, considering that the linguistic characteristics of Bulgarian are syntactically similar to English and the role of function words including prepositions is strong due to the analytic language structure, this method is very meaningful. To add one more thing, this method is very meaningful because English is the language resource that currently holds the most ambiguity data in the world. This paper is a theoretical focus on how to solve the ambiguity problem through data and model development. Accuracy analysis and parameter adjustment through actual data processing remain as future tasks to be attempted by introducing an engineering methodology.
Signal Processing at the Epicenter of Ground-Shaking Research: Researchers turn to signal processing to minimize earthquake damage, rescue victims, and perhaps even provide advance warnings [Special Reports]

Earthquakes have afflicted people throughout history. Today, thanks to advanced technology, more is known about earthquakes, and more can be done to protect people against them. Signal processing is playing a key role as investigators examine ways to combat one of humanity’s most deadly foes.
Proposal of a Hand Motion and Control Method Matching System for Interaction Optimized for Mixed Reality Control: Focusing on Meta Interface and Augmented Behavior

In an era where non-face-to-face meetings become common, eXtended Reality(XR) is rapidly developing and filling areas that are not satisfied in online meetings based on existing photos/video method. In particular, general users are also able to easily access and use HMD-type mixed reality devices. However, the basic operations applied in HMD-type in Mixed Reality(MR) with hands as the main input tool do not have a standardized system, and each manufacturer operates in a separate response to each other's hand movements. Therefore, this study considered that a systematic hand motion matching system considering the usability and efficiency of operations performed in mixed reality was necessary, and conducted a study to clarify this. First, the basic operation performed in the MR environment and its attributes were investigated, and at the same time, the structure of the hand and the attributes of the possible hand movements were identified. Based on the identified properties, it is intended to present a system that can intuitively and efficiently match basic operation properties in the MR environment with subtle operation properties according to the structure/context of the hand.
BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual architecture for hardware-efficient video inference

Long Short-Term Memory (LSTM) and 3D convolution (Conv3D) show impressive results for many video-based applications but require large memory and intensive computing. Motivated by recent works on hardware-algorithmic co-design towards efficient inference, we propose a compact binarized Conv3D-LSTM model architecture called BILLNET, compatible with a highly resource-constrained hardware. Firstly, BILLNET proposes to factorize the costly standard Conv3D by two pointwise convolutions with a grouped convolution in-between. Secondly, BILLNET enables binarized weights and activations via a MUX-OR-gated residual architecture. Finally, to efficiently train BILLNET, we propose a multi-stage training strategy enabling to fully quantize LSTM layers. Results on Jester dataset show that our method can obtain high accuracy with extremely low memory and computational budgets compared to existing Conv3D resource-efficient models.
Anomaly detection using a class of supervised and unsupervised learning algorithms

Anomaly detection is a concept that has been studied for many decades now, and many frameworks and algorithms are proposed after that. Intrusion can occur in any scenario, so identifying one becomes a vital task. The reverberation of not detecting these outliers can be severe and can result in a huge loss. In this chapter, we concentrate on identifying an anomaly using supervised and unsupervised learning techniques, for instance support vector machine (SVM), artificial neural network (ANN), long short-term memory recurrent neural network (LSTM-RNN), isolation forest, and K-means clustering algorithm. Four real-world open-source datasets are studied for this purpose, and a statistical method known as the adaptive threshold is utilized for the detection of anomalous data points in these datasets. Several regression metrics are obtained for the datasets, and anomalies corresponding to these datasets are compared with unsupervised learning algorithms.
Multi-layer encryption algorithm for data integrity in cloud computing

The Internet revolution has changed all aspects of our life, accessing and surfing the Internet is becoming as necessity in our life to obtain any kind of information needed, and that produces a huge quantity of data available anytime, anywhere. The greater the facilities, less is our data privacy. For that maintaining data confidentiality and integrity in the Internet and cloud world is a primary requirement to continue working and using these facilities. In this paper, we propose a security mechanism that makes accessing the data from unauthorized persons more complex. Our mechanism easy to implement and understand, it works in three levels in each level is used a new encryption key generated randomly and in each level is used a different encryption algorithm. Our results show that the encryption/decryption process time is increased linearly with the text size for the authorized persons and is increased exponentially for unauthorized persons.
Improving support vector machine accuracy with Shogun's multiple kernel learning

A traditional kernel-based classifier only uses a single kernel, but the real-world implementations have stressed the need to acknowledge a combination of different kernels, widely known as multiple kernels. To improve the accuracy of the classification, multiple kernel learning should be applied. In this paper, we apply an experimental combination of Gaussian and polynomial kernels on a simple dataset in the form of an XOR gate that uses the logical or function from NumPy and observe how much the combination tunes the prediction accuracy. To achieve this purpose, the Shogun machine learning toolbox’s help with a Python interface and SVMLight as an SVM solver is taken. The experimental SVM-MK shows that the proposed combination of kernels provides a better accuracy for the prediction of a binary output.
Implementing the FAIR Principles Through FAIR-Enabling Artifacts and Services

How does a Research Software Engineer (RSE) ??? often responsible for developing infrastructure to manage and share digital research objects (data, models, code, notebooks, workflows, etc.
Research on Visual Analysis of Network Topology Based on Geometric Constraints

Large-scale networks have the characteristics of complex topological structure, difficult to display internal characteristics clearly, and different temporal and spatial connection patterns, which makes analysis tasks extremely complicated. Therefore, detecting the structural characteristics of large-scale networks is an important means of cyberspace security situation awareness. However, current related research mainly studies specific analysis tasks from a micro perspective, and it is difficult to provide an overall cyberspace security situation awareness from a macro perspective.. In this paper, driven by the demand for situational awareness in the field of cyberspace security, with traffic monitoring data in network connection activities as the research object, this paper studies rendering technology of large-scale network topology, and gives the analysis methods of the macroscopic and microscopic perspectives of the cyberspace situation.
Architecture as a Backbone for Safe DevOps in Automotive Systems

Today <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> automotive systems are huge and complex-with thousands of software-controlled functions implemented by a rich number of reasonably complex subsystems. Therefore, in a distributed agile development of such functions and subsystems, architecture must serve as a backbone. It must be abstract, easily deployable, modular, and supporting information hiding and encapsulation. In this paper, we present our long-term vision of a triad of three different architectural facets: (1) data, (2) services, and (3) technical aspects. We introduce a framework for such an architectural description. By placing architecture at the centre of development, we give it the necessary importance. Based on this, we discuss its role as the backbone of an agile and DevOps-oriented future development methodology.