Reviewerly/exHarmony huggingface dataset:
Cyber Security Application in ERP Implementation

Commercial procedure re-engineering is the fundamental reform of corporate procedures to complete affected enhancements in perilous parts like value, production, price, provision, and speediness. Commercial procedure reengineering methods lie at critical downcast venture prices and procedure repetition on a high vast measure. Commercial method re-developing is essential l to the design of commercial procedures to achieve affected enhancements in vital features are as excellence, productivity, price, provision, and speediness. Commercial method re-developing goals at scaling down venture prices also value repetition on an awfully large measure. BPR was a vital management idea from the mid-1980s to the mid-1990s. The idea is usually attributable to Massachusetts Institute of Technology faculty member Michael Hammer and Babson faculty member Thomas Davenport. Hammer and Davenport started as colleagues, performing on an exploration program referred to as PRISM (Partnership for analysis in info Systems Management) Most businesses believe they won't experience a cyber security breach. But if you were being hacked right now, how would you ever know? The fact is that more than a million records were exposed in 12 distinct breaches in 2019 that affected ERP systems in the financial services, telecommunications, retail, education and even medical research industries. Their analysis efforts of security measures, which were sponsored by a number of the most important companies at the time, concerned developing an associate of nursing study model that may facilitate massive corporations’ benefit from recent advances in technology, as well as personal computers and therefore the web.
AN OVERVIEW ON APPLICATIONS AND DEVELOPMENTS IN EDUCATION ON AGENT BASED ONTOLOGICAL COOPERATIVE LEARNING

In this paper, we have defined various types of concepts which are applied in the real time classroom environments such as Ontology, Cooperative Learning through an I-MINDS infrastructure which could enable the students to participate actively in virtual mode rather than passive listening to the lectures in a traditional way of teaching and learning environment. In teaching and learning process, I-MINDS plays a major role where it monitors’ the students activities and help the teacher manage and adopt to the class where as in learning environment, it has its applications which helps to measure the knowledge and understanding capability of the students which helps them to interact with other agents to support Cooperative Learning Environment.Mainly,we discuss about the agents involved in the development of learning process of education ,we also discuss about the methodologies and architecture involved in the development of the learning process through ontological Cooperative Environment.
Problem-based learning and its relationship to neuroscience in undergraduate university students

Problem-based learning and its relationship to neuroscience in undergraduate university students aim to describe the level of knowledge and the ways in which university students transmit the information obtained through neurodidactics in the educational process. To do this, the research is based on a quantitative approach, to establish, including the use of documentary and bibliographic methods, that students in university research centers focus on the use of Problem-Based Learning (PBL) to improve their academic performance. The relationship between PBL and neurodidactics is that this methodology ensures that academic preparation is in accordance with the real interests and needs of students, works on relevant situations of daily life in an interdisciplinary approach and promotes the creation of experiences, and useful and appropriate teaching methods to the context. Research shows that neuroscience-based data can help teachers better choose the most beneficial methods to stimulate students to actively learn, leading to sustained attention and focus.
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference

Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will increase that number by 1-2 orders of magnitude. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. To leverage the increased statistical power of the lenses that will be discovered through upcoming surveys, automated lens analysis techniques are necessary. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate the successful application of Neural Posterior Estimation (NPE) to automate the inference of a 12-parameter lens mass model for DES-like ground-based imaging data. We compare our NPE constraints to a Bayesian Neural Network (BNN) and find that it outperforms the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.
To Discriminate General Election system in Thailand by using K-Means Clustering

Thailand uses the ballot paper in the general election since 1933. In the present day, technology has been involved in daily basics. This study aims to explore the hypothesis of the election system in Thailand which can use the technology in the election process name as electronic voting (e-voting) or still need to use the traditional method. Before implementation, it should study in terms of the area that is ready to implement the new method and where still need to use the current one. This study takes the relevant factors to analyze with the data of each area collected from various sources. The clustering method used in this study is k-means. Then to find the acceptable k cluster the silhouette method is used. The result is 2 cluster is a perfect fit with the 11 factors that used in this study. The first cluster is Bangkok which is the capital city to be matched with the e-voting method and the second cluster is the remainder province in Thailand (76 provinces). This can be used for the next study in terms of supply chain design for e-voting and developing the ballot paper logistics.
INNOVATIVE TECHNOLOGIES IN TEACHING STUDENTS OF TECHNICAL HIGHER EDUCATION

The article analyzes the development of students' direct imaginative thinking, the formation of their initial knowledge and creative skills, as well as the possibility that students can theoretically analyze what they are studying and create a new theory using the Keys-stadi method in teaching general sciences.
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation

The black-box nature of end-to-end speech translation (E2E ST) systems makes it difficult to understand how source language inputs are being mapped to the target language. To solve this problem, we would like to simultaneously generate automatic speech recognition (ASR) and ST predictions such that each source language word is explicitly mapped to a target language word. A major challenge arises from the fact that translation is a non-monotonic sequence transduction task due to word ordering differences between languages -- this clashes with the monotonic nature of ASR. Therefore, we propose to generate ST tokens out-of-order while remembering how to re-order them later. We achieve this by predicting a sequence of tuples consisting of a source word, the corresponding target words, and post-editing operations dictating the correct insertion points for the target word. We examine two variants of such operation sequences which enable generation of monotonic transcriptions and non-monotonic translations from the same speech input simultaneously. We apply our approach to offline and real-time streaming models, demonstrating that we can provide explainable translations without sacrificing quality or latency. In fact, the delayed re-ordering ability of our approach improves performance during streaming. As an added benefit, our method performs ASR and ST simultaneously, making it faster than using two separate systems to perform these tasks.
MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection

Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED include how well existing ED models perform on different languages, how challenging ED is in other languages, and how well ED knowledge and annotation can be transferred across languages. To answer those questions, it is crucial to obtain multilingual ED datasets that provide consistent event annotation for multiple languages. There exist some multilingual ED datasets; however, they tend to cover a handful of languages and mainly focus on popular ones. Many languages are not covered in existing multilingual ED datasets. In addition, the current datasets are often small and not accessible to the public. To overcome those shortcomings, we introduce a new large-scale multilingual dataset for ED (called MINION) that consistently annotates events for 8 different languages; 5 of them have not been supported by existing multilingual datasets. We also perform extensive experiments and analysis to demonstrate the challenges and transferability of ED across languages in MINION that in all call for more research effort in this area.
THE DELPHI TECHNIQUE IN DIGITAL TRANSFORMATION TOWARD INNOVATIVE LEARNING READINESS

COVID-19 turned many organizations’ long-term wish lists into essential needs overnight. Therefore, innovative learning factors became an integral part of digital transformation that includes learning, learning to learn, which helps students develop a growth mindset belief system about their abilities. Therefore, the successful application of innovative learning depends on the students’ readiness toward learning in a new norm. The aim is to collect expert-based judgments and often use them to identify consensus. The application of Delphi techniques in the digital transformation is to discuss the processes of the results. The findings of this research are summarized using systematic reviews of Delphi techniques and examined from a methodological perspective. Five systematic reviews of Delphi techniques from higher learning institutions were conducted and thoroughly analysed. Data were gathered from five experts. They are all associated with the sphere of education, with professors in statistical and research design relating to digital and innovative services, as well as academic associated fields. Much research on innovative or digital transformations have been published, but this study aims to develop consensus by using a series of questionnaires distributed in multiple iterations to collect data from a panel of selected experts. The Delphi results have confirmed indicators that can be successfully used as a benchmarking for digital transformation integration toward innovative education. Hence, several revisions to the Delphi technique have now been established. This method for evaluating instruments relies on human judgment to provide proof of the reliability of the content being evaluated. Ultimately, after three rounds of Delphi methodologies, the consensus was reached. Apart from that, it is also necessary to explain what specific criteria are used to evaluate the quality of their implementation and reporting. This model is believed to be a future reference for institutions of higher learning education to help understand and appreciate the design thinking approach and the ICT aspects when assessing the readiness for innovative learning. Based on the results, it is obvious that further research is needed to shed light on the methodological approaches and modifications of Delphi techniques.
A Libertarian Money

After VERY briefly reviewing the last 350 years of money and central bank monetary policy, the author reviews whether recently developed and developing cryptocurrencies provide more stable money. The answer is no. The author proposes that central banks adopt currency board rules that eliminate monetary policy in place of a market determined money supply. Moreover, he proposes that such currencies’ value be anchored to the market value of a small basket of globally traded commodities. By adopting the IMF’s SDR as the currency anchor and replacing SDR allocations with issuing them under currency board rules and eventually replacing the SDR valuation basket of currencies with the above-mentioned commodity basket, the SDR is likely to replace the dollar or any other currency for international payments and reserves.
Claims, Debt and Equity in REA With FIBO Extensions

This paper develops a method to identify and formally model how the REA concept of Claims fits into our understanding and extension of the core REA concepts in work derived from early FIBO (Financial Industry Business Ontology) and now characterized as the Semantic Shed’s Business Core Concept Ontology (BCO). In this exploration we determine that the notion of Claim relates to two important concepts, those of some imbalance between commitments and of the perspective on such imbalances. This paper sets out the basic concepts of REA and its later extensions by the FIBO team and the Semantic Shed community, in set theoretic concept intensions. The paper goes on to define the key concepts of imbalances and of perspectives on commitments and on imbalances. We then consider the definition of ‘Claim’ in REA as a terminological challenge. Some conclusions are given about where Claim fits in with the ontological terms given here and how Equity and Debt are framed with reference to these.
CoRAL: a Context-aware Croatian Abusive Language Dataset

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL -- a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.
Speech Recognition via CTC-CNN Model

Abstract In modern society, human communication is increasingly frequent, and speech plays an extremely important role in social activities. Words can be used to express emotions and thoughts. However, numerous individuals are troubled by "language barriers", due to which their communication with others is limited. This study proposes a method to address the needs of speech-impaired and deaf-mute individuals. A basic deep neural network (DNN) acoustic model was established through a voice database combined with a deep neural network. A sound sensor was used to convert the collected voice signals and process them into text or corresponding voice signals to improve communication. This method can be extended to modern artificial intelligence technology, with diversified applications, such as verbatim transcripts of meeting minutes, medical reports, automotive, sales, etc. The results obtained in this study demonstrate the efficiency of the proposed method and discuss its significance.
Enhanced digital learning strategies: A case of a rural senior secondary school

As qualitative research, this paper aims to identify the impact of the necessary significance of enhanced digital learning strategies in rural senior secondary schooling. The main findings about improved digital learning strategies in rural schools are (i) limited availability of digital resources coupled with (i) motivation as a factor for teachers and learners as recipients of the schooling process. As a result, this paper concludes by outlining the necessary significance for teachers and learners to have upgraded skills for viability in the digital world. Therefore, this paper recommends that teachers require more professional support and development to transform teaching strategies.
ASSESSING THE E-LEARNERS SATISFACTION AMID THE COVID-19 PANDEMIC OF SAUDI ARABIA'S HIGHER EDUCATION INSTITUTIONS

Electronic learning commonly known as e-learning is an interactive learning method that involves the use of information and communication technology to create a learning experience with sufficient freedom.E-learning has gained significant attention in education sectors around the world during the COVID-19 pandemic since it provides several benefits to learners including flexible learning facilities, distance learning opportunities, consistency in learning, time-saving, tracking capabilities, minimal cost facilities, quick response, interactive learning facilities, etc. From this point of view, this study assessed the e-learners' satisfaction amid the COVID-19 pandemic in Saudi Arabian higher education institutions.The study was conducted using mixed methods, including quantitative and qualitative approaches.The findings of the study demonstrated that higher education institutions in Saudi Arabia were well-prepared to ensure quality education for e-learners during the pandemic.In particular, it was found that the majority of students in higher education in Saudi Arabia were very pleased with the e-learning classroom facilities, teacher contact, accessibility, assessment, course content, course design, and course activities.Conversely, the findings of the study reveal several existing challenges of e-learning in higher education institutions, including high internet costs, technological issues, time management issues, less motivation to access e-classrooms, the quality of online classes, etc.Therefore, this paper recommends that to address the issues that might influence to e-learners' satisfaction of e-learning in higher education institutions in Saudi Arabia, concerned authorities should provide special attention to ensure technological development, professional development, communication development, and updated policies and standards.
MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts.
Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks

Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such skills, the key challenge is to obtain training data that covers diverse and feasible variations of the task, which often requires non-trivial manual labor and domain knowledge. In this work, we introduce Active Task Randomization (ATR), an approach that learns robust skills through the unsupervised generation of training tasks. ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks. We propose to predict task diversity and feasibility by jointly learning a compact task representation. The selected tasks are then procedurally generated in simulation using graph-based parameterization. The active selection of these training tasks enables skill policies trained with our framework to robustly handle a diverse range of objects and arrangements at test time. We demonstrate that the learned skills can be composed by a task planner to solve unseen sequential manipulation problems based on visual inputs. Compared to baseline methods, ATR can achieve superior success rates in single-step and sequential manipulation tasks.
English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data. In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS. The performance can be further enhanced when cross-lingual NLI data is available. Our code is publicly available at https://github.com/yaushian/mSimCSE.
Understanding Approximation for Bayesian Inference in Neural Networks

Bayesian inference has theoretical attractions as a principled framework for reasoning about beliefs. However, the motivations of Bayesian inference which claim it to be the only 'rational' kind of reasoning do not apply in practice. They create a binary split in which all approximate inference is equally 'irrational'. Instead, we should ask ourselves how to define a spectrum of more- and less-rational reasoning that explains why we might prefer one Bayesian approximation to another. I explore approximate inference in Bayesian neural networks and consider the unintended interactions between the probabilistic model, approximating distribution, optimization algorithm, and dataset. The complexity of these interactions highlights the difficulty of any strategy for evaluating Bayesian approximations which focuses entirely on the method, outside the context of specific datasets and decision-problems. For given applications, the expected utility of the approximate posterior can measure inference quality. To assess a model's ability to incorporate different parts of the Bayesian framework we can identify desirable characteristic behaviours of Bayesian reasoning and pick decision-problems that make heavy use of those behaviours. Here, we use continual learning (testing the ability to update sequentially) and active learning (testing the ability to represent credence). But existing continual and active learning set-ups pose challenges that have nothing to do with posterior quality which can distort their ability to evaluate Bayesian approximations. These unrelated challenges can be removed or reduced, allowing better evaluation of approximate inference methods.