Reviewerly/exHarmony huggingface dataset:
Observer models utilizing compressed textures

We have previously presented a method for sorting textures based on whether they obscure a signal, and thus hinder the ability of an observer to perform a signal-detection task, or whether the presence of certain textures can be easily ignored by the observer, and thus do little to impede performance. This analysis has led to a surrogate figure of merit that was demonstrated to correlate with human-observer performance as measured by the channelized Hotelling observer. In this work, we generalize our previous results to include more tasks including estimation and combined detection/estimation tasks. We demonstrate the ability of this method to determine the textures present in a set of images that are the most detrimental to the specified task. We further devise alternative surrogate figures of merit can utilize this texture-compression method as a mechanism for generating channels for observer-performance computations.
Finding atrophy patterns of grey matter through orthonormal non-negative factorization

We used orthonormal projective non-negative matrix factorization (OPNMF) to identify distinct spatial components of voxel-wise volume loss in the brains of patients with Alzheimer's disease relative to age-matched normal controls. Non-negative matrix factorization (NMF) is a dimension reduction approach which can be used to extract spatially localized components of volume loss from 3D T1-weighted MR images. OPNMF is a variant of NMF with a potential for providing more biologically meaningful components to help better assess the patterns of brain atrophy. Once components have been defined, non-negative coefficients representing each component's contribution to volume loss can be defined in subjects not included in the group used for component discovery. These coefficients are subject-specific quantitative attributes of atrophy, and we studied their partial dependence network using a Gaussian copula graphical model. We also verified their ability to distinguish diagnosis groups. We performed a clustering approach to investigate possible imaging subtypes of Alzheimer's Disease patients. Our results are the first attempt to apply OPNMF to Alzheimer's Disease and we detect different subtypes based on components of gray matter atrophy which correlate with previous work.
Rancang Bangun Aplikasi Monitoring dan Evaluasi Penjualan Berbasis Web Pada CV Royalty Natural Indonesia

CV Royalty Natural Indonesia is a company engaged in cosmetic products and is a subsidiary of PT. Green Angelica, which was founded in 2011. CV Royalty Natural Indonesia provides several beauty products including royalty day cream, royalty night cream, royalty bust-up cream, royalty cleansing soap, royalty hair tonic, royalty hair shampoo, royalty hair serum, royalty hair removal, royalty mustache cream and royalty beard serum. The marketing has constantly been done through e-commerce and existing social media. However, some obstacles emerge that may cause difficulties in managing data and information, and analyzing which products that are not optimal or the most favorite. Hence, it is not possible to identify the highest and lowest demand of their products which can adversely affect the company's sales target. In view of these problems, the objective of this study is to monitor and evaluate product sales performance in order to reveal which products have the highest and lowest demand and to be able to manage product data, sales data, and customer data. Based on the results of the application testing, it can be utilized by marketing managers in managing sales and customer data. Marketing managers are also able to specify which products have the highest or lowest demand and generate the product sales reports. In addition, this platform can be used to identify further pertaining to sales reports, reports per product, product lists, and sales performance reports. The results of this study showed that Tactical Dashboard can assist the marketing managers in monitoring and evaluating product sales and the target.
Lateral Wedge Insole Uptake in Reducing Pain in Patients with Knee Osteoarthritis: A Meta-Analysis Study

ABSTRACT Background: The increase in rates of knee replacement for osteoarthritis has made the identification of effective nonsurgical treatments a high priority. One type of treatment for medial knee osteoarthritis involves reducing medial loading to ease the physical stress applied to that compartment of the joint. The wedge is placed under the sole of the foot and angulated so that it is thicker over the lateral than the medial edge, transferring loading during weight bearing from the medial to the lateral knee compartment. This study aimed to investigate the effect of lateral wedge insole uptake in reducing pain in patients with knee osteoarthritis. Subjects and Method: A meta-analysis and systematic review conducted by search published articles from PubMed, Science Direct, Google Scholar, Mendeley, and Clinical key databases. Keywords used “Lateral Wedge Insole” OR “Insole” AND “Foot Orthosis “AND “osteoarthritis” AND “patellofemoral osteoarthritis” AND “knee pain” AND “medial knee osteoarthritis “AND” effect lateral wedge insole for osteoarthritis” AND “randomized controlled trial”. The inclusion criteria were full text, using randomized controlled trial (RCT) study design, and reporting effect size (mean and standard deviation). The intervention was lateral wedge insole with comparison non lateral wedge insole. The study outcome was pain. The data were analyzed by Revman 5.3. Results: 6 studies were met the inclusion criteria. This study showed that the use of lateral wedge insole reduced pain in patients with knee osteoarthritis (Mean Difference= -0.17; 95% CI= -0.51 to 0.18; p= 0.340) with heterogeneity I2= 77%. Conclusion: Lateral wedge insole use reduced pain in patients with knee osteoarthritis. Keywords: knee osteoarthritis, lateral wedge insole, randomized control trial Correspondence: Muhibbah Fatati, Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: muhibbahfatati@gmail.com. Mobile: 089633466598. DOI: https://doi.org/10.26911/the7thicph.05.45
Analysis of Data Ownership Rights in the Big Data Era

For working out the problem of ownership rights of data in the Big Data era, this paper proposes an establishment method of data ownership rights based on data classification. By summarizing characteristics of big data and analyzing current main views of the data ownership rights, this proposed method is following the principles of protecting data confidentiality and acknowledging the greatest contributors. First, according to the different involvement degree of participants in data generation processes, the data is divided into two categories: participatory data and non-participatory data. The participatory data is subdivided into equal participatory data and non-equal participatory data based on different contributions of participants. Since the non-participatory data generally involves the private and confidential information of the recorded parties, it is proposed that the ownership rights of this kind of data should belong to the recorded parties. Following the principle of acknowledging the greatest contributors, this paper proposes that the ownership rights of the equal participatory data belongs to all participants and that of non-equal participatory data belongs to the active participants.
Innovation and Entrepreneurship Education Model for Computer Major

The advent of the information age has prompted the computer major to become one of the most popular majors. The concepts of innovative education and entrepreneurship education have a leading position in the teaching of colleges and universities. Based on the necessity of innovation and entrepreneurship education in computer science, this article focuses on the research and discussion of the developing path of innovation and entrepreneurship education model in computer science.
RANCANG BANGUN PERINGATAN DINI KEBAKARAN RUMAH BERBASIS INTERNET OF THINGS

Urban areas have densely populated housing, which can cause problems in the event of a fire. The problem that often occurs is that when a fire occurs, the fire department often arrives late, so that the losses due to the fire are even greater. Based on these problems, the authors want to design a House Fire Early Warning System Design Based on the Internet of Things, which is a prototype of a fire early warning tool as a medium of information. In this tool, the system designed is a fire detection system that works in real time and also knows the location of the fire. This system uses a fire sensor (KY-026), a smoke sensor (MQ-2) and a temperature and humidity sensor (DHT 11), based on the NodeMCU microcontroller to detect the presence of fire, smoke and fire temperature. The data received from the three sensors will be sent to the server using NodeMCU. This system utilizes the Telegram Bot and Website as an interface to provide location information to the nearest fire department by displaying it on the Mapbox and the LED lights up as an indication of a fire and a buzzer sounds to alert the home owner.
피뢰시스템의 고장모드 영향 분석

The purpose of this paper is to carry out failure modes and effects analysis in order to determine risk priority number of the components of lightning protection system. Failure modes for components is referred of IEEE Gold book and FMD 2016. In risk priority number, more then 60 had 4 failure modes; more specifically, down conductor had 1 mode, earth-termination system(2 modes), intermal lighting protection system(1 mode) and thus 4 components had failure modes. The components which had high risk priority number were connector of down conductor, GND and under connector of earth-termination system, SPD of intermal lighting protection system was chosen as an intensive management component.
ПОДГОТОВКА ВИДЕОСЮЖЕТА КАК ФОРМА ПРЕДСТАВЛЕНИЯ ИНДИВИДУАЛЬНОГО ЗАДАНИЯ ПО УЧЕБНОЙ ПРАКТИКЕ ПО ФАРМАКОГНОЗИИ

Based on existing experience, interactive forms of fulfilling an individual task in educational practice in pharmacognosy are considered.
To Mine or to Trade? An Empirical Study of Bitcoin Exchange and Mining Markets

Problem Definition: The recent upsurge in Bitcoin prices has raised interest in the investment of Bitcoin. However, faced with the investment options of mining and trading, the behavior of investors is not clear and requires a closer scrutiny of both the Bitcoin exchange market and the mining market. Specifically, little is known about the pattern of the interaction of the two markets. As mining significantly determines the reliability and security of the operation of the Bitcoin system, it’s important to understand whether the trading market can influence the investment in mining.Academic/Practical Relevance: Our research bridges the gap between operation management, finance and information technology, and makes contribution to studies in the operations and economics of cryptocurrency systems.Methodology: Using a variety of datasets at a large scale, our empirical study uncovers the causal impact of the exchange market on the mining market. Specifically, we formulate an empirical framework that encompasses key dimensions of the Bitcoin exchange market, exchange rate and liquidity, and also jointly models the entry and exit of the mining market characterized by supply and demand of Bitcoin mining rigs. The framework also accounts for a holistic set of demand and supply shifters that exogenously alter the investment behavior in the Bitcoin mining market.Results: We empirically show people tend to associate a higher valuation of Bitcoin with a higher Bitcoin exchange rate, and such exchange rate has significant impacts on both the supply and demand of the mining rigs. Furthermore, we demonstrate that the Bitcoin exchange market serves as a substitute for the Bitcoin mining market for investment. This substitution effect is particularly pronounced to those investors as potential entrants to the Bitcoin mining market.Managerial Implications: Our study sheds light on the impact of the Bitcoin exchanges on the Bitcoin mining activities which contribute to the operation of the Bitcoin system.
Political Messaging through Language Patterns and Sentiment Analysis in Twitter Messages - Focusing on the US President Twitter

This paper makes a text mining analysis of the US President Donald Trump and the former President Barack Obama’s remarks on their respective Twitter accounts to examine what words are mainly used by these powerful politicians, and through that what political messages they attempt to convey. The study analyzes Twitter messages from 2017 to 2019 in terms of language patterns and sentiment analysis. The analysis of the language patterns is focused on the use of parts of speech, the use of capital letters, and exclamation points. The results show that Trump uses pronouns most and verbs and nouns are followed, while Obama uses nouns most and pronouns and verbs are next. Trump uses capital letters much more than Obama and the most frequently used word is GREAT while Obama hardly uses capital letters. In the case of Trump, he uses exclamation points with the word, you most while the word everybody is the case for Obama. As there is no nonverbal language elements such as intonation in text messages, Trump tends to use many capital letters and exclamation points to express his feelings as nonverbal elements of language. Through sentiment analysis it was found that both politicians use more positive words than negative words. However, Trump uses more negative words than Obama. As the messages on Twitter have become a primary means for political communication, the two politicians tend to use more positive words to create the positive images.
Usable Information and Evolution of Optimal Representations During Training

We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training, and how they adapt to different tasks. We use this to characterize the transient dynamics of deep neural networks on perceptual decision-making tasks inspired by neuroscience literature, as well as on standard image classification tasks. We show that both the random initialization and the implicit regularization from Stochastic Gradient Descent play an important role in learning minimal sufficient representations for the task. In addition, we evaluate how perturbing the initial part of training impacts the learning dynamics and resulting representations.
Quasi-Newton Adversarial Attacks on Speaker Verification Systems

This paper proposes a framework for generating adversarial utterances for speaker verification systems. Our main idea is to formulate an optimization problem to generate adversarial utterances that fool speaker verification models and solve it by a second-order optimization method. We first present our algorithm, which uses the first-order Gauss-Newton method, and then extend it to second-order Quasi-Newton methods. Our experiments on the VoxCeleb 1 dataset show that the proposed method can fool a speaker verification system with a smaller degree of perturbations than those of conventional methods. We also show that second-order optimization methods are effective for finding small perturbations.
A Digital 'Yellow Card' for Securely Recording Vaccinations Using Community PKI Certificates

The Yellow Card or carte jaune is a paper booklet in a standard format set by the World Health Organisation in which a person’s vaccinations are recorded by healthcare officials. Numerous initiatives are striving to create both digital vaccination records and new digital identities for people with little or no official documentation; i.e. “low doc” persons. Yet there is no globally agreed model for identity, nor any standardized way to establish identity. Nevertheless, field workers today are able by and large to establish the bona fides of Yellow Card holders with adequate certainty for the paper-based system to function most of the time. This paper contends that vaccinations should be digitized without introducing new identity systems, since a lack of formal identification is obviously not preventing Yellow Cards today. This paper describes a new digital Yellow Card, deployable on most regular mobile phones, in which public key certificates represent vaccinations and other credentials, vouched for by officials or field workers. The design has practical benefits for the digital engagement and privacy of low doc persons. It also shows how traditionally hierarchical public key infrastructure can be deployed without dictating identification protocols to communities, thus avoiding some of the controversies that plague this technology. The PKI security function can remain centralized while certificate issuance is decentralized, which leaves community organizations free to carry on their business as usual.
Semantic applications for ontological information theory

Abstract Information theory is a field that has applications in several scientific domains. This field, which was initially proposed by Claude Shannon, is interdisciplinary; it combines probability, electrical engineering, information engineering, computer science, and statistics. Representing knowledge in information theory provides scientific scholars a resourceful reference for the filed. By learning the precise terminology used in information theory, researchers can discover documents in the domain. In addition, they can communicate knowledge and ideas by using the accurate jargon. A scientific ontology representing knowledge corpus can be implemented digitally by articulating the concepts in the domain of information theory using semantic graphs. These scientific semantic artifacts are valuable with Web 3 technologies. They facilitate searching documents online by providing precise related results. In addition, it is expected that the semantic knowledge to be applicable in Web service applications based on information theory as Web services are software components that communicate information. This work presents an ontology for the Information theory. The ontology conceptualizes the knowledge of information domain by identifying the main concepts, principles, equations, metrics, and history of information theory. The ontology composed of classes, instances, and properties is built using protege, an ontology editor, and is stored in OWL format. Educational applications for the built ontology are indicated.
On the methods and organizational forms of music education of students in the special piano class

In the article, the author analyzes a synthesis of the most important methods and organizational forms of music education of students in the special piano class. Verbal, visual, and illustrative methods, which are successfully used in the process of pianists’ training, are described. Among the organizational forms of teaching process, a special emphasis is made on the teacher’s class recital. This creative pedagogical activity is presented basing on the personal experience of the author who studied piano in the class of L. Oxinoit, one of the leading Moldavian piano pedagogues. A conclusion is drawn that the efficacy of educational process depends on the teacher–student creative contact, as well as on the pedagogue’s personality, human and professional qualities. Key words: music education methods, organizational forms of teaching process, creative contact, teacher, pupil
Vocational Education Curriculum Evaluation Model Based on Big Data

The emergence of big data technology makes it possible to make formative evaluation of teachers' teaching effect and students' learning effect. Big data technology will change the teaching thinking of vocational education and affect the mainstream values of students. This will change the traditional mode of vocational education and carry out accurate personalized vocational education. Big data technology is also helpful for teaching quality evaluation to shift from rule-based to data-based, from small sample data to full data-based, so as to dig out the hidden value and connection of data. According to the characteristics of vocational education, this paper constructs a diversified evaluation system of vocational education teaching quality based on big data technology. This paper studies the algorithm of the diversified evaluation system of teaching quality, and puts forward the data visualization scheme of the evaluation results. The experimental results show that this method can improve the efficiency of vocational education and the accuracy of diversified evaluation data of teaching quality.
From AI to Chatbots, Tech Shines in Agencies' COVID-19 Response

As federal agencies have worked to engage more with citizens in the face of coronavirus pandemic, they've looked to artificial intelligence to help with the spike in people seeking information and ...
Measurement Error Mitigation for Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are a promising application for near-term quantum processors, however the quality of their results is greatly limited by noise. For this reason, various error mitigation techniques have emerged to deal with noise that can be applied to these algorithms. Recent work introduced a technique for mitigating expectation values against correlated measurement errors that can be applied to measurements of 10s of qubits. We apply these techniques to VQAs and demonstrate its effectiveness in improving estimates to the cost function. Moreover, we use the data resulting from this technique to experimentally characterize measurement errors in terms of the device connectivity on devices of up to 20 qubits. These results should be useful for better understanding the near-term potential of VQAs as well as understanding the correlations in measurement errors on large, near-term devices.
Oxford Intellectual Property Research Centre Submission: IPO Artificial Intelligence and Intellectual Property: Call for Views - Trade Marks

The Oxford Intellectual Property Research Centre’s Submission for the UK Intellectual Property Office’s call for views on Artificial Intelligence and Intellectual Property – Trade Marks.
The Meanings and Usages of Tokorohe

This study describes the derivation paths leading to the development of the conjunctive particle tokorohe. The main findings are as follows.BR First, as a noun, tokorohe functions as a complement that represents the spatial direction or destination of the motion represented by the predicate—that is, “place.”BR Second, the meaning of tokoro can be abstract, so tokorohe can present the temporal situation in which the event occurs. As the meaning of tokoro changes, its function changes from that of a complement in a sentence to a conjunctive particle that fulfills the grammatical function of connecting clauses.BR Third, in some contexts, tokorohe can acquire the meaning of an “addition or accumulation of a new event.” The events connected by tokorohe (the events of the subordinate and the main clause) are not usually coincidental situations but situations that share similarities. Tokorohe can express not just an event’s current temporal phase, but its situation before new circumstances are added (something just happened or is about to happen). Syntactically, it typically takes the form of “X tokorohe Y {node / kara / non-finite verb form} Z.” Tokorohe’s meaning is similar to noni and ueni, which represent additions or accumulations.BR To summarize, tokorohe can represent a spatial direction (place), and as the meaning of tokoro changes, its function changes from that of a complement in a sentence to a conjunctive particle expressing the temporal situation of an event and implying the addition or accumulation of a new event (something just happened or is about to happen).