Reviewerly/exHarmony huggingface dataset:
The Implementation Of Conference Video Application Media “Google Meet” As Alternative Media To Improve Student’s Reading Ability Of Pai Program

This article is a report of a study regarding the impact of a drastic change in learning model from face to face in class to become virtual class, due to covid-19 pandemic, in IAI Hamzanwadi NW Pancor. The gap in this current research is using online conference video media, to teach the students in university level, in the middle of virus pandemic period. With the target of ability is reading ability with supported by ‘Google Meet’ conference video online as media which has function to improve the first semester students of PAI Program of Tarbiyah Faculty of IAI Hamzanwadi NWDI Pancor. In this research applied Classroom action research to conduct it. The next applying the two cycles, the researcher set up some actual ways to improve the reading skill and the ability of the first semester students at PAI Program with Google Meet conference video media. For the First, Actions using the Google Meet application are expected to be able or even more able to attract students' attention and also be able to motivate them in the teaching and learning process, especially in the pandemic era. The result will be measured by knowing the increasing reading ability score between students who use the online video media or not. The finding showed that the implementation of Google Meeting conference video online as media was successful in improving students’ reading comprehension. The improvement could be seen from the increase of students’ average reading scores. Besides, the finding also showed that Google Meeting conference video online as media was effective in enhancing the students’ participation, especially in raising their hands competitively to answer the questions during the answer-checking session.
New Interfaces for Spatial Musical Expression

This work is in part supported by the National Science Foundation under Grant No. 1748667 and Virginia Tech’s Institute for Creativity, Arts, and Technology (ICAT).
A Novel Curve-Fitting Algorithm Based on Levenberg-Marquardt Method of Single-Diode Model Photovoltaic Modules

Aiming at matrix singularity and decrease accuracy among the processing of curve-fitting algorithm of singlediodemodel photovoltaic (PV) modules, this paper proposes a novel curve-fitting algorithm based on Levenberg-Marquardt Method. According to the sample data with the same voltage sampling interval of monocrystallinesilicon photovoltaic module and polysilicon photovoltaic modules under standard environment, practical resultsshows that the proposed curve-fitting algorithm has the advantage of convergence effectively and broadapplicability. Besides, comparing with the previous curve-fitting algorithm based on the Modified Newton methodand Reduced-forms, proposed algorithm has a superior advantage of high accuracy.
Developing a Decision Support System for Water Resources Dispatching

Contradiction between water demand and water supply have a huge impact on social and economic development. This paper presents the development of a water resources dispatch decision support system. The system integrates models related to water dispatch such as streamflow forecast model, water allocation model and water dispatch model. Each model runs as an independent service and is registered in the model platform. The model platform interacts with the service layer and data layer through the model adapter. The model adapter is designed for converting the model input data sent by the service layer and the basic data and observation data queried by the data layer into the format required by the model. In case study, we took the Fu River Basin as an example to demonstrate an application of the system. The system realizes the complete process of data collection, streamflow forecast, water demand declaration, water distribution and water dispatch. User can get the recommended operation plan of the reservoir and the corresponding water supply result through the user interface. Process variables can also be viewed through the system, such as streamflow forecast results and water distribution results, etc. The proposed system can provide technical support and assistance for the decision makers, which also provide an effective demonstration for water resources management in other rivers.
ACSAC 2020: Furthering the Quest to Tackle Hard Problems and Find Practical Solutions

The Annual Computer Security Applications Conference (ACSAC) 2020 marked the 36th edition of ACSAC. The vision of Marshall Abrams, a beloved founder and tireless organizer of ACSAC, is to take hard problems and find practical solutions. In September 2020, Marshall died of heart failure at the age of 79. Prior to his passing, he was actively working on signing a virtual meeting vendor for the conference. As Charles Payne, ACSAC local arrangements chair, said during the tribute, Marshall asked us to focus on solving hard problems, hard problems that require the combined effort of government, industry, and academia to address; and Marshall insisted on practical solutions. With rampaging ransomware and zero-day supply-chain attacks tearing through critical national and computing infrastructures, Marshall’s longtime vision seems more relevant today than ever.
Research-Related Pathways

Exploratory research may be ethnographic research, in which the employers watch users during their normal day-to-day activities perhaps with previous versions of the product or perhaps using competing products. Many techniques in a user researcher’s toolbox are qualitative—in-depth sessions with limited numbers of people. The employers may also find opportunities for quantitative research, for example surveys of users or perhaps usability studies that have enough participants to be valid as quantifiable data. Human factors engineering, as a discipline, is rooted in the military, aviation, and transportation industries but has grown and flourished in a wide variety of other industries such as government, healthcare, consumer products, software, and manufacturing. Accessibility-checking algorithms can certainly be very robust, but they cannot replace the employees actually confirming that the meaning of the content that should be conveyed to users, is, in fact, being conveyed properly.
Artificial Intelligence and Big Data in Fraud Detection

Artificial intelligence is used for many purposes nowadays.With the developments in technology, the fraudsters develop their methods.On the other hand, artificial intelligence methods are used in fraud detection for increasing the efficiency of corporations.AI and big data play an important role in real time data enrichment, deep learning integration and decisions.There are ten artificial intelligence methods explained which are used for fraud detection.Each method has its unique bases and it can not be said that there is only one optimal method.In this research, the methods are briefly explained, and a comparison is done for accuracy of methods.Supervised machine learning, unsupervised machine learning or semi-supervised machine learning as well as adaptive machine learning techniques against adaptive attacks with the advantage of big data and artificial intelligence are discussed with effectiveness usage for the future applications.
Управление логическими операциями с изображениями в аккумулированной эхо-голографии внешними пространственно-неоднородными электрическими полями

Ihe realization of logical operations for images using an accumulated echo hologram in the presence of external spatially inhomogeneous electric fields is considered. It is shown that the photon echo locking effect allows controlling the type of logical operations (from uniting the sets to their symmetric difference and superposition) by varying the values of gradients of external spatially inhomogeneous electric fields.
A Rose by Any Other Name: JACR Name Change Policy

Publications represent a tangible representation of one’s academic work and are currency. The discoverability and attributability of publications to individual researchers, for example, through PubMed searches, rely on one’s name. Authors may change their names for many different reasons including marriage or divorce, gender identity change, or religious or other personal reasons. Transgender and gender-diverse individuals, who do not have the same legal protections against discrimination as cisgender people, are particularly vulnerable when name changes in a public forum such as scholarly publications draw attention to their gender. Additionally, it can be personally traumatizing for trans individuals when they are connected to their pretransition names and identities, even inadvertently. Changing one’s name in a scholarly publication currently requires significant effort and time from all parties involved. Typically, authors must persuade the publishers of their work to grant them a name change request, which can be expensive, time-consuming, and complex. Publishers often require a burden of proof in the way of a documented legal name change. However, legal name changes are not universally accessible, affordable, or expedient for all trans people. Many trans authors are faced with abandoning their previous scholarship because of these numerous barriers to a successful name change in scholarly publication. To provide a more inclusive policy for all researchers who change their name, discretion, timeliness, accessibility, and simplicity are paramount. Minimal bureaucracy reduces both the burden and labor on the part of the requesting author and the time it takes to make the necessary changes. Of equal importance is the discretion of the name change process, conducted without a formal announcement or notice of correction unless otherwise permitted by the author. For transgender scholars, discretion is of particular importance given the potential for discrimination and bias against this vulnerable group. Ultimately, name changes are implemented by the publisher; therefore, in partnership with Elsevier, our publisher, we will facilitate linking scholars to Elsevier to initiate the name change process, accessible at https://www.elsevier.com/about/policies/inclusive-author-name-changes. This process minimizes the burden of such requests and enables invisible name changes. We at JACR affirm our commitment to diversity, equity, and inclusion in scholarly publishing, medicine, and the broader world. We support our authors in maintaining their publications record across changes in name and identity. Our policies will align with Committee on Publication Ethics best practices once issued. Until then, we will take the lead in addressing this important topic, allowing our authors to discreetly change their name, in a timely manner. For further information or to request assistance from the JACR, please contact JACR Managing Editor, Kelly Church ( [email protected] ).
Development of Exponentially Weighted Sine Cosine Optimization Algorithm

This paper focuses on developing an exponentially weighted sine cosine algorithm that helps achieve faster convergence and better global optimal solutions. The proposed algorithm has been achieved by incorporating exponential weight functions in the position updation equations that change during each algorithm iteration. Further, a comparison of the proposed algorithm with the traditional technique is made on various benchmark functions-selecting these functions of multiple categories such as unimodal, multimodal, and composite. The optimization results indicated that the proposed exponentially weighted algorithm performed superior to the traditional and other adaptive weighted algorithms. The best performance is proper for most functions concerning mean, best, and standard deviation values. Moreover, the conventional technique has reached the worst global minima values for most of the test functions than the proposed algorithm.
The Latent Investigation

Chapter 17 continues with the investigative process by concentrating on events after the preliminary investigation known as the latent phases. Even when the offender has been identified, there are relatively few cases in which the prosecutor is ready to proceed with an indictment just after the preliminary investigation. There are many critical steps to be taken during this phase, including accounting for all evidence and sending items forward to the crime lab; collection of all documentation from everyone involved in the preliminary investigation; and the initiation of an investigative plan, which is essentially a road map of investigative leads that need to be accomplished. Efforts are to establish as much corroboration of the victim statement as possible as well as checking suspect alibies, interviewing key witnesses, and if they are identified, then conducting a complete background check on the suspect. Efforts to find other potential victims eliminate the “he said, she said” type of situation from developing. For unknown offenders, efforts are focused on their identification through witness interviews, lineups, or forensic means.
Opportunities, Risks, and Breakdowns Brought by AI to Commercial Banks

Since the coining of the term “Artificial Intelligence” (AI), its definition scope has evolved from an application program with pre-set rules of logic, to a technology that simulates human cognitive function. The goal of AI is to allow computers to imitate human intelligence, perception, thinking, and action, so as to achieve automation and gain analytical insights. To imitate human intelligence, AI applications adopt two methods: rule-based and non-rule-based algorithms. Rule-based AI uses pre-set rules to “learn”, while non-rule-based AI uses machine learning algorithms to “learn” and a trained AI model to “think”. The key driving force for the development of AI is the huge increase of available data and the great improvement of computing power in traditional computers and mobile devices, as well as the continuous development of machine learning algorithms. Commercial banks conduct large numbers of customer capital transactions, and hold large amounts of account management information. The application of AI in commercial banks will effectively improve the retail service level, enhance customer experience, and alleviate difficulties such as “financial disintermediation” and fierce market competition, or become the next profit growth point of commercial banks.
Detection of Earnings Manipulation Practices in Bangladesh

This study is conducted to detect earnings manipulation practices in selected textile companies of Bangladesh. It investigates whether the textile companies of Bangladesh are engaged in earnings manipulation of not. For testing this hypothesis, Beneish M-Score is being used collecting data of 13 textile companies from financial year 2012-2018. This study indicates a great number of earnings manipulation practices conducted in textile industries. This paper, based on Beneish M-Score, finds evidences regarding manipulation of earnings through disproportionate rise in receivables, cost deferral, less cash behind reported income, etc. by listed companies. This paper finds that the textile companies are manipulating earnings. While this model can be easily used to detect earnings manipulations using annual reports provided by listed companies, further investigations are needed to identify the reasons behind distortions in reported numbers are actually earnings manipulation or any other organizational origin. Being a cost effective tool in detecting frauds through manipulating earnings, this model can be a great tool for auditors and other respective regulatory authorities.
Seismographs at Eskdalemuir Observatory, 1908&amp;ndash;1925: tools for rethinking the origins of international cooperation in seismology

The British seismologist John Milne (1850–1913) is a dominant and celebrated figure in historical accounts of seismology. In this paper I use evidence from seismographs preserved in the collections of the Science Museum Group to reconsider his contribution, particularly to the progress of international collaboration in seismology. These instruments were installed at Eskdalemuir Observatory in Dumfriesshire, Scotland between 1908 and 1925; not only were they situated far from Milne’s private observatory on the Isle of Wight, but they represented examples of Russian, Japanese and German seismograph design as well as Milne’s own. No other site in Britain boasted such an international suite of earthquake instruments. Together, they reveal an alternative vantage-point from which to view his influence. By attending closely to the instruments’ provenance, I show how the collection can be a powerful tool for decentring the hagiography that surrounds the so-called ‘Father of Modern Seismology’.
Green innovation adoption mediated by legislation on environmental performance: The case of Gambia smes

Green innovation strategy has greatly received attention worldwide due to the growing concern from governments, community and the end users especially with degradation of the natural resources and environmental pollution. Small and Medium Enterprises (SMEs) are the largest business establishment and vital component of the country’s economic development in Gambia. Despite of the uprising demand for a green product worldwide; the SMEs in Gambia faced a different challenge altogether especially when it is at its infancy stage. The paper begins by defining the SMEs and the background of SMEs in Gambia. The discussion then leads to the concept of sustainability and green knowledge and how this affects the adoption of green strategies by SMEs in Gambia. The environmental performance of SME firms in the Gambia has remained largely untested. As such, this conceptual paper provides a basic framework for examination to be carried out in the context of the environmental performance of Gambia SMEs.
Continual learning: linear layer classifier concatenation using image processing transform functions

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, is referred to as lifelong learning. In machine learning, however, algorithms are trained on available data and re-training them on new data is a major challenge due to catastrophic forgetting. Therefore, lifelong learning remains a long-standing challenge for machine learning and neural network models. In this research we aimed to solve the continual learning catastrophic forgetting issue using image processing methods. We proposed multiple methods including alpha blending, histogram equalization and a pruner. We build a universal network, using a previously trained one as a feature extractor. After that, we implemented our proposed methods. In alpha blending method, we used a technique that separate knowledge from unknown knowledge. This method shows better results than normal concatenation technique. The other method was applying histogram equalization. In this method we used two kind of techniques; one is equalizer as pruner and the other is equalizer as enhancer. Our methods got promising results.
Sound production in four mallet marimba performance: The role of limb velocity variability

Sound production in a percussion context like marimba results from using two mallets held in each hand to strike a unique bar/target location. If the mallets contact the bar above the resonator below, optimal sound production occurs. This study examined mallet accuracy to further understand mechanisms underlying motor control in marimba performance. Thirteen percussionists played a four-mallet excerpt in three tempo conditions including slow, intermediate, and fast. Mallets were held proximal to the pinky (mallet 1: left hand, mallet 4: right hand) and thumb (mallet 2: left hand, mallet 3: right hand). Motion tracking measured movements and velocities of the mallets, wrists, and elbows. Instructions were to terminate each mallet's movement onto a visual target located on the marimba's bars above the resonators. Contrasting each mallet's terminal position with its unique target location when all four mallets contacted the marimba simultaneously (i.e., playing chords) assessed mallet accuracy. Velocity analyses examined the variability of upper-limb and mallet movements. It was hypothesized that increases in limb velocity variability would negatively impact mallet accuracy. The results showed that accuracy was lowest in the fast vs. the intermediate and slow conditions within the outer mallets (i.e., 1, 4). In both elbows and wrists, velocity variability was greater in the fast vs. the slow condition. It may be surmised that increasing velocity variability of limb movements reduces mallet accuracy resulting in suboptimal sound production in the outer mallets specifically. Motor skill acquisition in marimba performance may be facilitated by emphasizing temporal control in the upper-limbs.Acknowledgments: The Canada Foundation For Innovation and the Percussion Department in the Faculty of Music at U of T
Design of household gas emergency solution based on earthquake early warning and intelligent gateway

At present, the national earthquake intensity quick report and early warning project is in the full implementation stage. In Fujian, the earthquake early warning information can be provided to the public through mobile app and specific terminals. But there is also a lack of effective solutions to use the early warning information to reduce personal and property losses for the majority of family users. Earthquake emergency disposal is the key technology to use the earthquake early warning information to solve the problem in last 1km. The intelligent gateway receives the earthquake early warning information through the message middleware, and processes to evaluate the safety of earthquake gas, then judges whether to start the gas earthquake emergency disposal system. The intelligent gateway communicates with the server through WiFi. and communicates with gas earthquake emergency disposal system through ZigBee wireless communication technologies, and realizes the automatic shutdown of gas when destructive earthquake occurrence. The system can prevent the occurrence of gas accidents in destructive earthquakes, make full use of earthquake early warning information, realize the home application of earthquake early warning information, and reduce casualties and economic losses caused by earthquakes.
Soft-BAC: Soft Bidirectional Alignment Cost for End-to-End Automatic Speech Recognition

Connectionist temporal classification (CTC) has gained success in both end-to-end ASR model and as an auxiliary task for attention-based sequence-to-sequence (S2S) system. However, the special topological structure of CTC and the modeling form that a redundant blank symbol to be optionally inserted between each modeling units makes the CTC inclined to model blank symbols, resulting in a worse than expected model alignment effect, and frames are usually aligned with redundant symbols. In this paper, we design a new simple topology and propose a novel smooth alignment optimization method named soft bidirectional alignment cost (soft-BAC), which is an alternative to the CTC. We propose a scheme that only inserts identifiers between consecutive repetitive labels and solve the alignment problem between two time series of speech-transcription pair by minimizing all costs of the left-to-right and right-to-left alignment process. Experiments on the LibriSpeech corpus show that the proposed soft-BAC method achieves significant improvement in word error rate and alignment effect over the CTC-based baseline model.