Reviewerly/exHarmony huggingface dataset:
Dynamic quantitative visualization of transient shear stresses in solids

To aid in shear strength analysis, a technique combined with a time-resolved imaging scheme to achieve direct dynamic quantitative visualization of the distribution and evolution of transient shear stresses in solids through one measurement is studied. By modulating the polarization state of the elliptically polarized light transmitted or reflected from the sample, this method requires only one measurement to achieve visualization. Mathematical models of the correlation between the variation of modulated light field and the shear stresses in the plane perpendicular to the optical axis are established based on the theory of mechanics and photoelasticity. The maximum shear stresses of any section are further derived. As an example, the distribution and evolution of shear stresses induced by an ultrasonic field have been quantitatively visualized by the technique. The visualization results are in fairly good agreement with the finite element simulation results. The simplicity and efficiency of this technique are embodied in the fact that one single measurement can directly obtain the distribution of shear stresses in solids, and the measurements of dynamic shear stresses can be conveniently realized through continuous monitoring.
The Impact of Sample Size on Exchangeability in the Bayesian Synthesis Approach to Data Fusion

Data fusion approaches have been adopted to facilitate more complex analyses and produce more accurate results. Bayesian Synthesis is a relatively new approach to data fusion where results from the analysis of one dataset are used as prior information for the analysis of the next dataset. Datasets of interest are sequentially analyzed until a final posterior distribution is created, incorporating information from all candidate datasets, rather than simply combining the datasets into one large dataset and analyzing them simultaneously. One concern with this approach lies in the sequence of datasets being fused. This study examines whether the order of datasets matters when the datasets being fused each have substantially different sample sizes. The performance of Bayesian Synthesis with varied sample sizes is evaluated by examining results from simulated data with known population values under a variety of conditions. Results suggest that the order in which the dataset are fused can have a significant impact on the obtained estimates.
Persistence of metric biases in body representation during the body ownership illusion

Our perception of the body’s metric is influenced by bias according to the axis, called the systematic metric bias in body representation. Systematic metric bias was first reported as Weber’s illusion and observed in several parts of the body in various patterns. However, the systematic metric bias was not observed with a fake hand under the influence of the body ownership illusion during the line length judgment task. The lack of metric bias observed during the line length judgment task with a fake hand implies that the tactile modality occupies a relatively less dominant position than perception occurring through the real body. The change in weight between visual and tactile modalities during the body ownership illusion has not been adequately investigated yet, despite being a factor that influences the perception through body ownership illusion. Therefore, this study aimed to investigate whether the dominance of vision over tactile modality is prominent, regardless of the task type. To investigate whether visual dominance persists during the process of inducing body ownership illusion regardless of task type, we introduced spatial visuotactile incongruence (2 cm, 3 cm) in the longitudinal and transverse axes during the visuotactile localization tasks and measured the intensity of the body ownership illusion using a questionnaire. The results indicated that participants perceived smaller visuotactile incongruence when the discrepancy occurred in the transverse axis rather than in the longitudinal axis. The anisotropy in the tolerance of visuotactile incongruence implies the persistence of metric biases in body representation. The results suggest the need for further research regarding the factors influencing the weight of visual and tactile modalities.
A bibliometric analysis of the International Journal of Advances in Soft Computing and its Applications: Research influence and Contributions

The International Journal of Advances in Soft Computing and its Applications (IJASCA) is a rapidly growing academic journal published by Al-Zaythoonah University of Jordan (ZUJ). IJASCA publishes original contributions on soft computing, machine learning and artificial intelligence, cloud computing, big data and other current science and technological trends. This study uses different bibliometric analysis tools to analyze the IJASCA published research papers between 2009 and 2021. The analysis includes annual publication growth, citation patterns, most prolific authors, institutions and countries, co-citation and co-occurrence networks analysis. A total of 317 published papers have been studied. The results show that IJASCA has grown in research contributions from 12 papers in 2009 to 40 papers in 2021, and citations have grown drastically to 2253. Universiti Teknologi Malaysia, Johor, is the institution that contributed the most to IJASCA publications with 109 papers. Malaysia is the country that was cited the most with 794 citations. Thematic analysis shows that the most important author keywords are soft computing, optimization, machine learning, big data and cloud computing. Overall, the findings are beneficial to the IJASCA editorial board. Its retrospective review will most likely encourage journal readers and assist the editorial team in developing research strategies that will allow research scientists to contribute high-quality research papers to the IJASCA. Keywords: IJASCA, Bibliometric analysis, co-citation analysis, co-occurrence analysis, thematic analysis, Scopus
A Minimal Deductive System for RDFS with Negative Statements

The triple language RDFS is designed to represent and reason with positive statements only (e.g., "antipyretics are drugs"). In this paper, we extend RDFS to deal with various forms of negative statements under the Open World Assumption (OWA). To do so, we consider ρdf, a minimal, but significant RDFS fragment that covers all essential features of RDFS, and then extend it to ρdf¬⊥, allowing express also statements such as ``radio therapies are non drug treatments", "Ebola has no treatment", or "opioids and antipyretics are disjoint classes". The main features of our proposal are: (i) ρdf¬⊥ remains syntactically a triple language by extending ρdf with new symbols with specific semantics and there is no need to revert to the reification method to represent negative triples; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra capabilities; (iii) despite negated statements, every ρdf¬⊥ knowledge base is satisfiable; (iv) the ρdf¬⊥ entailment decision procedure is obtained from ρdf via additional inference rules favouring a potential implementation; and (v) deciding entailment in ρdf¬⊥ ranges from P to NP.
On Syntactic Forgetting with Strong Persistence

It is generally agreed upon that so-called strong persistence (SP) captures best the essence of forgetting in logic programming. While classes of operators, such as F_R and F_SP, that satisfy immediate relaxations of (SP), and in the case of F_SP, even (SP) whenever possible, have been characterized semantically, many practical questions have yet to be addressed. This technical paper aims to answer one of them: How can atoms be forgotten from a program without having to calculate its exponential number of models? To this end, we introduce two concrete representatives of F_R and F_SP that forget sets of atoms by syntactical manipulation of a program’s rules. This may in many cases prevent exponential blowups and produce a forgetting result that is close to the original program.
Digital Forensic and Distributed Evidence

Digital Forensics investigation is the science and legal process of investigating computer/cybercrimes and digital media or objects to gather evidence. This new and fast evolving field encompasses computer forensics, network forensics, mobile forensics, cloud computing forensics, and IoT forensics; and for this reason have digital evidence distributed widely when the need arises for crime prosecution. Digital evidence must be authentic, accurate, complete, and convincing to the jury for legal admissibility at the court of law. In many instances due to the distributed nature of digital forensic evidence and the legal procedures to be adhered to in evidence gathering at a digital crime scene, presenting at the law courts have proven to be challenging and in some instances inadmissible. Following legal procedures in evidence gathering at a digital crime scene is critical for admissibility and prosecution. This paper aims to discuss digital forensics investigations jurisprudence in relation to distributed digital evidence. For the study to be relevant to policy and practice, forensic tools and frameworks, legal and ethical obligations, and digital evidence handling and admissibility are highlighted. This paper does not follow any forensic investigations process; but rather discusses the need for development and implementation of unique frameworks that could be utilised to gather distributed digital evidence required for admissibility in court. Keywords - Digital forensics investigations; Digital evidence; Jurisprudence BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Emmanuel Kpakpo Brown (2022): Digital Forensic and Distributed Evidence Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 357-362 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P57
A Graphical User Interface (GUI) Based Speech Recognition System Using Deep Learning Models

Presently, TSPI (Time Space Position Information)/Surveillance/Search and Track systems are more dependent on Artificial Intelligence technologies such as fingerprint, iris-based (eyes scanning), facial recognition-based, voice-based, etc. Such systems often demand remote operation which needs to be secure with authorized operators/users. In order to incorporate this technology, few human features play an important role toward unique identity. In this paper, we have tried to make an intelligent system that will be able to recognize authorized voice input and can convert those to commands in perfect sequence for real-time control and operation. Deep Learning concept is implemented in python to know the accuracy for recognizing voice commands. The same process has also been linked to the existing Graphical User Interface (GUI) of management console in parallel for monitoring. The main emphasis is to build a data-set for reference to correlate for recognition and then to process the voice input (for real-time operation) through an efficient and fast method. Intelligent modules are incorporated for better accuracy and to generate perfect sequencing of commands on basis of weight/relevance factor of voice input through various layers.
Single Channel Image Enhancement (SCIE) of White Blood Cells Based on Virtual Hexagonal Filter (VHF) Designed over Square Trellis

White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early stage can help fast and precise diagnosis, as compared to manual inspection. Sometimes, acquired blood cell smear images from an L2-type microscope are of very low quality. The manual handling, haziness, and dark areas of the image become problematic for an efficient and accurate diagnosis. Therefore, WBC image enhancement needs attention for an effective diagnosis of the disease. This paper proposed a novel virtual hexagonal trellis (VHT)-based image filtering method for WBC image enhancement and contrast adjustment. In this method, a filter named the virtual hexagonal filter (VHF), of size 3 × 3, and based on a hexagonal structure, is formulated by using the concept of the interpolation of real and square grid pixels. This filter is convolved with WBC ALL-IBD images for enhancement and contrast adjustment. The proposed filter improves the results both visually and statically. A comparison with existing image enhancement approaches proves the validity of the proposed work.
Online Problem-Based Learning: Possibilities for Engineering Vocabulary Acquisition in ESP Course at Technical University

The article aims to examine the effect of the online problem-based learning (PBL) method on engineering-related vocabulary acquisition by ESP students and their satisfaction with the learning experience. The study involved 70 third-year undergraduate students at the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (Kyiv, Ukraine). They were taught with the use of traditional methods (control groups) and PBL method employing different online platforms, such as, Quizlet, LearningApps, Wordwall, and Quizizz (experimental groups). The learning outcomes were assessed using a vocabulary test and compared by means of the Fisher Criterion using online statistical software. The data about the students’ attitudes towards online problem-based vocabulary learning was collected with the use of a questionnaire in Google forms application which was automatically processed and analyzed by the authors. The findings of our study showed that the students who were taught with the PBL method online had statistically higher results in vocabulary acquisition than those taught with traditional methods. Students’ attitude to problem-based vocabulary learning was positive. Based on our findings we can conclude that the factor of the authenticity of the problems plays a vital role in vocabulary learning, makes it more enjoyable and meaningful for students, promotes their motivation and self-direction. The use of online vocabulary learning tools and videoconferencing technologies provides the students with a considerable degree of flexibility and autonomy and creates an opportunity to study at various locations. The online mode of instruction is appropriate for the context of distance learning, which is especially valuable during the pandemic.
Brain-computer interface as a symbol of the co-evolution of man and technology

The article deals with one of the key problems of the digital transformation of society – the problem of human adaptation to the increasingly complex information and technological environment of the digital society, the increased information load. One of the promising di­rections for solving this problem is the development of neurotechnologies, and, in particular, brain-computer neural interfaces. The possibilities and prospects of using the brain-com­puter neural interface and neurotechnologies are considered. It is shown that the develop­ment of neurotechnologies and, in particular, the brain-computer neural interface should be accompanied by the study and early warning of the risks associated with their development, as well as the identification and discussion of social, existential and ethical problems in the development of neurotechnologies, and the development of ways to solve them. It is im­portant to form a public discourse on various aspects of the use of neurotechnologies by humans.
Dangerous Action Recognition for Spatial-Temporal Graph Convolutional Networks

Human behavior recognition is important to research in intelligent surveillance systems. For the criminal act of dangerous accident prevention and identification, dangerous human behavior of recognition is more than important. Aiming at the identification of dangerous human behavior, a human risk behavior detection algorithm based on a time-space graph convolutional network is proposed. The algorithm first extracts the skeleton sequence of human behavior by monitoring the video in the system. Then based on the obtained skeleton features, combined with the time vector in the skeleton sequence, a spatial time map model is established to identify dangerous behaviors. Finally, the hazard level of the behavior is determined according to the setting of the corresponding threshold. Experimental results on the Kinetics and RGB+D datasets show that the algorithm can identify a variety of dangerous behaviors and can effectively identify dangerous behaviors in different scenarios.
Quantum channel measurement with local quantum Bernoulli noises

As an important stochastic process, quantum Bernoulli noises has a very important physical background and is an important research object in the field of quantum information. In this paper, we review local quantum Bernoulli noises and local quantum mutual entropy, then introduce quantum channel measurement with local quantum Bernoulli noises. On this basis, we give the channel structure between two systems, and prove the completely positivity of this quantum channel. We also give a channel application on the local quantum mutual entropy.
Smart Contract Blockchain Application Design Based on The Distribution of Product Return Transaction Data

In 2020, there will be 1% bulk sugar product returns. Direct return to warehouse; it is not known how much and what kind of sugar was returned. Changes in the number of uncontrolled product availability occur in the logistics sector. We designed a sugar volume return mechanism to verify the identity of the buyer, the amount and time of the transaction, using the steps of investigation, analysis, and system design that can implement. The application is based on the truffle test framework and smart contracts on the Ropsten test network on the Ethereum Metamask platform wallet, localhost memory, and a decentralized web-based dashboard. Input data on the smart contract so that during the Ropsten net test process, it will generate blocks, hash codes, and contract hashes as transaction details. It also displays a summary report and a blockchain transaction dashboard. How much volume will increase or decrease due to returns, buyers, type of sugar commodity, time, and volume of sugar during data transactions is known. The features developed for smart contracts are private, semi-public transactions with consensus proof of work as validation and verification of the success of transaction data records.
The Influence of Mid-East Sundanese Dialect (L1) in The Pronounciation of English Among English Department Students at Universitas Majalengka

In a local society, it is still rare to find students who are able to speak English because Indonesian loves to use local language or Bahasa Indonesia. It causes different dialects that makes harder to speak English naturally. This research is to describe and explain the articulatory phonetics that is influenced by Mid-East Sundanese dialect.This research was a qualitative case study. The subjects of this study were 18 English department students of Universitas Majalengka academic year 2017/2018. The data were collected by using questionnaires, studentsâ€™ recording, observation checklist, and an interview.Based on the research data, it can be concluded that the negative transfer of L1 through vowel sounds was sound [Ã¦]. The students tended to replace the sound [Ã¦] with [ÊŒ], and [e] as well as sounds [É™ÊŠ] and [eÉ™] that replaced with sounds [ÊŒ], and [ÉœË]. While, the consonant sounds which negatively affected by L1 were [Î¸], [Ã°], [Ê§]. The students tended to replace the sound with [c], [d], [t], or [s]. Otherwise, L1 positively affected the cluster sounds [pr], [kw], [bl], and [str]. All of the students could pronounce them properly.Based on the findings, the mispronounced words mostly are caused by the lack of pronunciation practice. It helped them to improve and to evaluate their pronunciation ability. Since the students were able to recognize those words, then teacher facilitate them to practicing pronunciation.
Event Extraction from Telugu-English Code Mixed Social Media Text

Natural Language Processing Event Extraction work is crucial. Social media has become increasingly significant in today's world. Using natural language, people can express their views on a wide range of topics on social media. Social Media tools popularized the devices among the masses making the information distribution faster and easier. The exchange of text is the most popular means of communication across social media users. It had become necessary to understand the semantics of messages communicated as the messages had a wide effect across the users. Event extraction means extracting the events across streams of the social media messages. Event extraction helps in taking corrective actions in case of natural calamities and hence possibly save the lives of many people. The major objective of the task is to draw specific knowledge to predict the events(incidents) specified in the code-mixed digital text. This paper proposes a two-step procedure for the extraction of the events. The first phase is by applying a binary classifier to identify the messages containing the event. The second phase is by applying a sequence labeling technique, conditional random fields (CRF), to extract the event from the message. As social media text is a bit noisy, it is a major challenge to develop learning algorithms for these tasks. Parts of Speech (POS) tags and Named Entity Recognition (NER) are used for the words to address some issues in this challenge.
Link Prediction in Continuous-Time Dynamic Heterogeneous Graphs with Causality of Event Types

Dynamic heterogeneous graphs comprise different types of events with temporal labels.In many real-world scenarios, the temporal order of different types of events possibly implies causal relationships between these event types.However, existing methods designed to model dynamic heterogeneous graphs neglect the underlying causal relationships between event types.For instance, the determination of the occurrence of a new event is misled by irrelevant historical events considering the type and could lead to performance degradation.First, this paper explicitly defines the causality of event types by the heterogeneous causality graph to utilize such causality from the perspective of the graph structure to tackle the aforementioned issue.Second, this paper proposes the event type causality based continuous-time heterogeneous attention network (ECHN) to model dynamic heterogeneous graphs.ECHN aggregates features based on the strength of different causal relationships between event types in the prediction process to utilize the causality of event types from the perspective of the modeling algorithm.The utilities of event type causality weaken the negative effect of irrelevant events.Experimental results demonstrate that ECHN outperforms state-ofthe-arts in the link prediction task.The authors believe that this paper is the first study to model the causality of event types in dynamic heterogeneous graphs explicitly.