Reviewerly/exHarmony huggingface dataset:
BIM Sign Language Translator Using Machine Learning (TensorFlow)

This project is developed to provide an application service to assist the deaf community where the deaf community in Malaysia has been facing difficulties conveying their messages to the non-deaf community. This project aims to design an offline and stand-alone BIM sign language translator, develop a BIM sign language translator system using TensorFlow machine learning, and conduct acceptance and functionality testing of the prototype to the deaf community non-fluent BIM user. The study uses Waterfall Model as a software development guide and uses TensorFlow, Python, OpenCV, and Qt as a core development library. The working application that translates sign language is then tested on the target users. The testing result shows that the target user can easily use the application to translate their sign language and shows positive indications while using the application. Even though there exist some limitations, such as the lack of a sign language dictionary that the application can translate. Still, it is hoped that this application will benefit the users.
Multi-Modal Adversarial Example Detection with Transformer

Although deep neural networks have shown great potential for many tasks, they are vulnerable to adversarial examples, which are generated by adding small perturbations to natural examples. Recently, many studies have proved that making full use of different modalities can effectively enhance the representational ability of deep neural networks. We propose a multi-modal deep fusion Transformer, termed MDFT. First, the audio feature and the rich semantic text features are extracted by audio encoders and text encoders, respectively. Then, multi-modal attention mechanisms are established to capture the high-level interactions between the audio and linguistic domains to obtain joint multi-modal representation. Finally, the representation is propagated to a dense layer to generate the detection result. The accuracy of this model compared with its unimodal variant on WiAd dataset and BlAd dataset are improved by 0.12 % and 0.19 %, respectively. Experimental results on the two datasets show that MDFT outperforms its unimodal variant model.
Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm

The COVID-19 pandemic has such a significant impact and causes difficulties in many aspects that the new normal rules should be implemented to reduce the effects.New normal rules have been implemented by governments worldwide to break the virus chain and stop its transmission among the society.Even if the COVID-19 outbreak is under control, governments still need to know whether society could adapt and adjust to their new daily lifestyles.Many precautions still must be addressed as the transition to endemic status does not mean that COVID-19 will naturally eventually disappear.The World Health Organization also has warned that it is too early to treat COVID-19 as an endemic disease.Since the pandemic, many interactions have been done online, leading to the increasing social media usage to express opinions about COVID-19.The objective of the study is to explore the capability of the Naïve Bayes algorithm in the sentiment classification of the public's acceptance on the new normal in the COVID-19 pandemic.Naïve Bayes has been chosen for its good performance in solving various other classification problems.In this study, Twitter data were used for the analysis and were collected between March and June 2022.The evaluation results have shown that Naïve Bayes could generate excellent and acceptable performance in the classification with an accuracy of 83%.According to the findings of this research, many people have accepted the new normal in their daily lives.The future works would include scrapping more data based on geolocation, improving the feature extraction technique, balancing the dataset and comparing Naïve Bayes performance with other well-known classifiers.The subsequent study could also focus on detecting the emotions of the public and processing non-English tweets.
ANALYZE THE EFFECTIVENESS OF TWITTER AS AN EWOM MEDIA (STUDY ON TENSAYAA'S GROUP ORDER)

Electronic word of mouth is a form of marketing with a high credibility because it comes from consumers who have experience with related products. Electronic word of mouth is also quite effective because one individual with another individual does not need to meet face to face and can be easily found on any media. The purpose of this research is to find out how effective Twitter is as an electronic word of mouth media for Tensayaa group order business and what marketing strategies are suitable to increase Tensayaa group order electronic word of mouth on Twitter. The method in this research is descriptive quantitative, with purposive sampling, where the population of this research is the Twitter followers of Tensayaa group order, and a sample of 227 respondents was found with the minimum determination of respondents is determined using the slovin formula. This research was also tested for validity, reliability, and descriptive statistics. The results of this research show that Twitter is an effective social media for conducting electronic word of mouth processes in the Tensayaa group order business by being reviewed through seven measurement dimensions.
Feature Selection and Composition Using PyOracle

A system is described which uses the Audio Oracle algorithm for music analysis and machine improvisation. Some improvements on previous Factor Oracle-based systems are presented, including automatic model calibration based on measures from Music Information Dynamics, facilities for compositional structuring and automation, and an audio-based query mode which uses the input signal to influence the output of the generative system.
Evaluation of Geophysical and Geochemical Characteristics of Soil and Groundwater Resources in Yenagoa Environs, Niger Delta, Nigeria

The problem of potable water has increased in recent time due to increase in population density, petroleum exploration activities and spillages along the creeks. Thus, three (3) Vertical Electrical Soundings (VES) were made using Schlumberger array in the study location to obtain geophysical data, using the ABEM SAS 1000 terrameter, in order to investigate the occurrence of groundwater in relation to the depth and thickness of viable aquifer. Six (6) surface water samples and six (6) subsoil samples were obtained from the study area. Both subsoil and surface water samples were analyzed to determine the concentration of contaminant. Analysis was performed using the Atomic Absorption Spectroscope (AAS) method which was compared with the World Health Organization Standard. Result and interpretation of analyses revealed high contaminant contents of lead (Pb) with values of subsoil samples at S2 (18 mg/l) and S6 (8.1302 mg/l), while Mn show very high concentration of contaminant values for all subsoil samples which exceeded the WHO permissible standard which indicates that aquifer may likely be polluted by intrusion. Cr for subsoil samples and Mn of surface water samples are likely within or slightly higher concentration value when compared with WHO limit. It is advisable to carry out proper water treatment from source and soil degradation, for potable drinking water and cultivation of agricultural product respectively.
Comparison of Connectedness in Online, Blended, and Face-to-Face Research Methods Courses among Hispanic and Low-Income Students

&#x0D; &#x0D; &#x0D; When students feel connected to the instructor, they are more likely to remain motivated, engaged, and persist toward completing an online course. Rarely have studies compared connectedness in three modalities: online only, blended, and face-to-face. This study compared perceptions of connectedness among students (N = 27) from an Hispanic Serving Institution with their instructor and peers in a research methods course. The sample of students took the same course in three different sections- each taught in a different modality by the same white instructor. Connectedness and students’ grades were lower for students who took the course fully online. However, student ratings of teachings were highest for those who took the online-only section. Latinx students reported less connectedness in the online-only section than others. The results inform decisions about teaching modalities during the pandemic and in the future; synchronous learning is critical to obtain equitable connectedness among Latinx students.&#x0D; &#x0D; &#x0D;
GEOLOGICAL POSITION, MINERALS OF RARE AND NOBLE METALS IN THE ORES OF THE KUN-MANIE COPPER-NICKEL DEPOSIT (SOUTHEASTERN FRINGE OF THE SIBERIAN PLATFORM)

In this paper we present new data on geology of the Kun-Manie copper-nickel-PGE sulfide deposit of the Kun-Manie ore field located on the eastern periphery of the Pristanovoy orogen in the southeast of the Siberian Platform, as well as a detailed description of ores and basic minerals and the distribution of PGE mineralization in ores of the deposit. The ore bodies are spatially associated with sills of the Late Paleoproterozoic Kun-Manie gabbronorite-websterite-lherzolite complex of 1.76–1.69 Ga. It has been established that the outlines of disseminated Cu-Ni ores and ore-bearing sills of mafic-ultramafic rocks match almost exactly. Breccia and massive ores are confined to schists of the upper and lower parts of the ore shoots. Patchy disseminated and vein-disseminated mineralization is concentrated in zones of ductile deformation and along contacts between rock layers of different competence. The main ore minerals are pyrrhotite, pentlandite and chalcopyrite; less common are pyrite, marcasite, cubanite, bornite, chalcocite, sulfoarsenides (gersdorfite, glaucodot), arsenides (nicolite) and tellurides (melonite) of nickel. Magnetite and ilmenite occur in small amount. PGE mineralization is represented by arsenides (sperrylite), sulfoarsenides (irarsite, holingworthite), tellurides (Pd-melonite, merenskite, kotulskite), and tellurobismuthides (moncheite, michenerite). Arsenides, sulfoarsenides and tellurides of PGE, Ni, Ag and Bi, native gold and silver were found in vein-disseminated, patchy disseminated and breccia Cu-Ni ores. The concentration of a considerable part of the main sulfide minerals and the productivity of PGE mineralization are assumed to be due not only to complex processes of magmatic differentiation, but also to sulfurization of magma differentiates in zones of ductile (tectonic) deformation under the influence of recrystallized fluids. The productivity of PGE mineralization characteristically increases with increasing fluid concentrations of S, As, Te, Bi, and Sb.
Immersiveness in modern culture

In the XX century such a phenomenon as immersiveness appeared. This is since various computer technologies using virtual reality (VR), augmented reality (AR), mixed reality (MR) is intensively developing. All of them have been widely used in modern art culture. However, at the first stages they were associated with developments in the military-strategic sphere. Gradually, they became widespread in medicine, which improved not only the diagnosis, but also the treatment processes. Methods of immersiveness are widely used in modern psychology. It turned out that immersiveness contributes to the effectiveness of journalism and education. Today thanks to immersiveness are common quests – games with the use of new techniques that have a large number of fans of this kind of leisure have become widespread. Immersiveness is a sign of modern culture.
Recommendations for Handling Image Integrity Issues

Background: Inaccurate, false or incomplete research publications may mislead readers including researchers and decision-makers.It is therefore important that such problems are identified and rectified promptly.This usually involves collaboration between the research institutions and academic journals involved, but these interactions can be problematic.Methods: These recommendations were developed following discussions at World Conferences on Research Integrity in 2013 and 2017, and at a specially convened 3-day workshop in 2016 involving participants from 7 countries with expertise in publication ethics and research integrity.The recommendations aim to address issues surrounding cooperation and liaison between institutions (e.g.universities) and journals about possible and actual problems with the integrity of reported research arising before and after publication. Results:The main recommendations are that research institutions should: 1) develop mechanisms for assessing the integrity of reported research (if concerns are raised) that are distinct from processes to determine whether individual researchers have committed misconduct; 2) release relevant sections of reports of research integrity or misconduct investigations to all journals that have published research that was investigated; 3) take responsibility for research performed under their auspices regardless of whether the researcher still works at that institution or how long ago the work was done; 4) work with funders to ensure essential research data is retained for at least 10 years.
Robust active learning

Active learning is an established technique to reduce the labeling cost for building high-quality machine learning models. However, state-of-the-art approaches focus on maximizing the clean performance (e.g. accuracy) but disregarding robustness. In this work, we propose Robust Active Learning, an active learning process that integrates adversarial training, the most established method to produce robust models. First, we conduct an empirical study to evaluate the effectiveness of existing approaches and uncover the characteristics of data. Then, we propose a novel approach, density-based robust sampling with entropy (DRE), to target both clean performance and robustness. Our experiments are conducted on 11 acquisition functions, 4 datasets, 6 DNN architectures, and 15105 trained DNNs.
The Concept of Interaction and Augmented Reality in Artworks

This research explores the way in which artworks with augmented reality (AR) interact with the recipient. The expanded capabilities of new technologies allow the stimulation of the human senses at a higher level, which opens wider perspectives for visual artists for new creative pursuits. Such artists with their works try to include the observer in them, challenging his senses, both on a physical and psychological level. Thus, through the interaction work-observer is given a new role for the viewer as an active participant and this makes his emotional experience more complete. Historical connections between the concept of interactivity, augmented reality and the visual arts are also sought. Last but not least, personal experience is shared in the creative search in the field of interactive art, the realization of one with augmented reality and ultimately the feedback of the viewer-participant after immersion in this different from the conventional, extended experience.
Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

The demand for cloud services has increased drastically in recent times. To fulfil the high demand for cloud services requires a higher number of servers of datacentres, and the datacentres consume high energy. Virtual Machine Consolidation (VMC) is a technique for lowering energy usage in datacentres by enhancing the utilisation of servers and shutting down underutilised server with maintain Service Level Agreement (SLA). Further, the VMC process is divided into four sub-policies: host overload detection, host underload detection, VM selection and VM placement. This article proposes the Hybrid VM Allocation and Placement (HVMAP) strategy, which uses migration control to detect the overload host and effectively locate the destination host. We utilised the CloudSim simulator with PlanetLab and Bitbrains data sets, to evaluate the performance. The evaluation results show that our strategy decreases Energy consumption and SLA Violation (ESLAV) by 101%, 54%, and 34% for the Planetlab data set, and 170%, 166% and 31% for the Bitbrain data set for three existing policies, respectively.
ERGATIVITY AND SUBJECT DELETION IN STYLISTIC-GRAMMATICAL CONSTRUCTIONS OF MINANGKABAUNESE

In addition to grammatical-formal constructions, Minangkabaunese has another type of grammatical constructions in which the stylistic-cultural meanings are capsulated. Such stylistic-grammatical constructions bring about language politeness values, as well. This paper, which is derived from and developed based on a part of the result of a research conducted in 2017and in 2019, particularly discusses the phenomena of ergativity and subject deletion of the stylistic-grammatical constructions in Minangkabaunese in conveying language politeness value. Two questions are respectively answered in this paper: (i) what is the degree of ergativity of stylistic-grammatical constructions in Minangkabaunese?; and (ii) how do the ergativity and subject deletion convey the language politeness value and language education? The studies were descriptive-qualitative researches operationally conducted in the form of a field linguistic research. The data are in the form of syntactic constructions which are categorized into the stylistic-grammatical constructions. The result of data analysis reveals that: (i) the stylistic-grammatical constructions of Minangkabaunese have high degree of ergativity; and (ii) they bring about the languge politeness value by means of ergative construction, subject deletion, and using ready-made and fixed constructions. These are all useful for language education, especially in Minangkabaunese and in other languages with the same or similar culture.
Exploration on industrial system-aware dataspace towards smart manufacturing

Thanks to the rapid development of information technology, the concept of smart manufacturing has been widely promoted in recent years, and gave birth to a large number of industrial software. The large amount of data generated and accumulated in industrial software has the characteristics of distributed, multi format and complex relationship. The traditional relational database cannot complete the unified description of these data and respond to their rapid changes. Dataspace is a new data management method that can deal with multi-source heterogeneous data and update dynamically. Therefore, this paper determines the requirements of industrial system data integration. Based on this, a construction and update process of industrial dataspace is proposed. Metadata extraction is used to complete the unified description of data, and then through the construction of data relations and data-service mapping, multi-source heterogeneous data can be effectively organized. Finally, the effectiveness of this method is evaluated through a case study in a nuclear power enterprise.
Planning for Risk-Aversion and Expected Value in MDPs

Planning in Markov decision processes (MDPs) typically optimises the expected cost. However, optimising the expectation does not consider the risk that for any given run of the MDP, the total cost received may be unacceptably high. An alternative approach is to find a policy which optimises a riskaverse objective such as conditional value at risk (CVaR). However, optimising the CVaR alone may result in poor performance in expectation. In this work, we begin by showing that there can be multiple policies which obtain the optimal CVaR. This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal. We present an algorithm for this problem and evaluate our approach on four domains. Our results demonstrate that our lexicographic approach improves the expected cost compared to the state of the art algorithm, while achieving the optimal CVaR.
Peningkatan Kompetensi Membaca Teks Berbahasa Arab melalui Metode Qiraah Mata Pelajaran Bahasa Arab di MTs Negeri 8 Karangmojo

This research aims to describe the implementation of professional method in improving students ' competency in reading Arabic text in class VII-A students at MTs Negeri 8 Karangmojo, in the even semester, school year 2018/2019. The study uses action research methods in two cycles. The results showed increased student reading competence gained through several activities, namely: 1) demonstrating reading, saying letters, words (Mufrodaat), sentences, and phrases aloud and repeatedly; 2) Habituation of reading Al-Quran (Tadarus) with teacher monitoring; and 3) memorization deposits. The ability to read students on Arabic text increases on each cycle with an average of 28 percent of the 26 students; 53.85% in good criteria, 38.46% in very good criteria, and 7.69% in less good criteria. Qiraah method can improve the students ' ability to read Arabic text.
Optimization of Linear Algebra Core Function Framework on Multicore Processors

Abstract Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of high efficiency, low power consumption and scalability. It has applications in several fields, such as industrial environments, military weapons systems, aerospace technology, and biomedicine. The core function of a multicore processor is an algorithm that can realize the expansion of the capacity of the isoparametric center and the lifting space, whose main idea is to optimize the spatial distribution by changing the input vector. Traditional solution methods generally use iterative or variational methods to solve nonlinear programming problems, but linear algebraic function models are widely used because of their simplicity and ease of numerical computation. The discrete and multi-attribute matrix combination algorithm can be used to realize the process of transforming complex problems into easier solutions. This paper introduces the basic concepts of linear programming and conducts simulation experiments with a multicore processor, and then makes improvements to a typical optimization problem. This paper analyzes the mathematical principles and steps of the nonlinear mapping algorithm; then MATLAB is used as a platform to study the design process of the multicore set-driven function. Finally it verifies that the method is feasible in solving complex models by arithmetic examples.
Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment

Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance.