Reviewerly/exHarmony huggingface dataset:
Nori: Concealing the Concealed Identifier in 5G

IMSI catchers have been a long standing and serious privacy problem in pre-5G mobile networks. To tackle this, 3GPP introduced the Subscription Concealed Identifier (SUCI) and other countermeasures in 5G. In this paper, we analyze the new SUCI mechanism and discover that it provides very poor anonymity when used with the variable length Network Specific Identifiers (NSI), which are part of the 5G standard. When applied to real-world name length data, we see that SUCI only provides 1-anonymity, meaning that individual subscribers can easily be identified and tracked. We strongly recommend 3GPP and GSMA to standardize and recommend the use of a padding mechanism for SUCI before variable length identifiers get more commonly used. We further show that the padding schemes, commonly used for network traffic, are not optimal for padding of identifiers based on real names. We propose a new improved padding scheme that achieves much less message expansion for a given $k$-anonymity.
Impact of the variability of presentation ways of stimuli and their associated sign-language explanation in educational computer programs on academic achievement of the English language among Deaf High school students: أثر اختلاف أسلوب عرض المثيرات والشرح الإشاري المصاحب لها في برامج الحاسوب التعليمية على التحصيل الدراسي في مادة اللغة الإنجليزية لدى التلاميذ الصم بالمرحلة الثانوية

This research aims at exposing the impact of the variability of presentation ways of visual stimuli and their associated sign-language explanation(visual stimuli without sign-language explanation/ visual stimuli followed by sign-language explanation/ visual stimuli simultaneous with the presentation of sign-language explanation) in educational computer programs on academic achievement of some English words among high school students (deaf group) in the integration program At Ain Jaloot Secondary School and the integration program in Dumah Al Jandal Secondary School. The study population included students of the integration program of the two schools, the sample size was determined and it included (36) deaf students who were randomly distributed into three pilot groups. The prior assessment was applied by using the electronic achievement test prepared by the Quiz Creator application, its reliability and validity were then confirmed by checking the coherence of the three groups. The three pilot groups enrolled for an educational computer program, in which the first group studied the impact of the variability of visual stimuli without sign-language explanation, the second group studied the visual stimuli followed by sign-language explanation, then the third group studied the visual stimuli simultaneous with the presentation of sign-language explanation the groups and each group of the three groups included a sample of 12 deaf students. The results of the study showed: Presence of differences which are statically significant (P value= 0.05) between the average degrees of the three groups in favor of the second group who studied the visual stimuli followed by a sign-language explanation.
Analysis on the Path of English Pronunciation Teaching in Colleges and Universities

Pronunciation is the pronunciation of language, the carrier of language symbols and the foundation of learning language. College English pronunciation teaching aims to help students develop their sense of English language and improve their English pronunciation and intonation level, especially important for English majors. Only by learning English pronunciation knowledge, can we improve our English listening, speaking, reading and writing skills and get better English application level. However, there are still some problems in English pronunciation teaching in colleges and universities, and the teaching effect is not significant, which needs further optimization and improvement. This paper first analyzes the current situation of English pronunciation teaching in colleges and universities, and then discusses the implementation path of English pronunciation teaching, with a view to constructing an effective English pronunciation teaching mode, aiming to play an important role in English pronunciation teaching.
Multi Vendor E-commerce Website for Farmers

Abstract As we all know that e-commerce is an emerging field in this digital world, all are familiar and known with this technology. There are no boundaries in this field, urban to rural all are frequently using online buying and selling operations in various aspects of their livelihood. Food is one of the major needs of human beings, which is fulfilled by the farmers. Right now, the farmers are failing to get a proper price for their products. So, now we are bringing new and unique thought into action that is an ecommerce website for farmers. We are trying to do a small help for farmers through this website. E-commerce is opening to have a crucial impact in the farming sector. This is an e-commerce website for farmers to ease their work in agriculture by providing a simple online platform using the latest technologies. Apart from this, we are also providing information about the new techniques and latest technologies in the agricultural sector. This will supervise the farmer’s entrance to the new farming techniques and also helps in comparing the present market rate of different products. This will help the farmers for the utilization of current technology to find out the profitable ways for income. And also it reduces the extra costs like transport and labour to the farmers. The farmer can save time for the sake of searching for their desired seeds in the market as we are providing a single easy platform. A User-friendly interface helps the farmer to make the desired operations easily.
Persepsi Siswa Tentang Kualitas Mengajar Guru Dan Pemberian Tugas Terstruktur Terhadap Prestasi Belajar Akuntansi Siswa Kelas XI SMK Muhammadiyah 2 Klaten Utara

This study aims to determine students' perceptions of the quality of teacher teaching on accounting learning achievement of class XI students of SMK Muhammadiyah 2 North Klaten, to determine students' perceptions of structured assignments on accounting learning achievement of class XI students of SMK Muhammadiyah 2 North Klaten, to determine students' perceptions about the quality of teacher teaching and structured assignments to the learning achievement of students in class XI SMK Muhammadiyah 2 North Klaten. This type of research is quantitative research. The design of this study was a survey. The population of this study were all class XI accounting students of SMK Muhammadiyah 2 North Klaten, totaling 69 students. Data collection techniques with questionnaires and documentation. The data analysis technique is testing prerequisite analysis with normality test, multicollinearity test, heteroscedasticity test, and hypothesis testing with, t test, F test, multiple linear regression analysis, effective and relative contribution. Sampling using saturated sampling technique. The results of this study indicate that the quality of teacher teaching and structured assignments together have a positive effect on the learning achievement of students in class XI SMK Muhammadiyah 2 North Klaten. Obtained R square 0.308 means that student achievement in accounting is influenced by student perceptions of the quality of teacher teaching and structured assignments of 30.8% and 69.2% influenced by other variables.
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning

Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment. The advent of deep learning has been associated with highly notable successes with sequential decision making problems - such as defeating some of the highest-ranked human players at Go. In digital advertising, real-time bidding (RTB) is a common method of allocating advertising inventory through real-time auctions. Bidding strategies need to incorporate logic for dynamically adjusting parameters in order to deliver pre-assigned campaign goals. Here we discuss techniques toward using RL to train bidding agents. As a campaign metric we particularly focused on viewability: the percentage of inventory which goes on to be viewed by an end user. This paper is presented as a survey of techniques and experiments which we developed through the course of this research. We discuss expanding our training data to include edge cases by training on simulated interactions. We discuss the experimental results comparing the performance of several promising RL algorithms, and an approach to hyperparameter optimization of an actor/critic training pipeline through Bayesian optimization. Finally, we present live-traffic tests of some of our RL agents against a rule-based feedback-control approach, demonstrating the potential for this method as well as areas for further improvement. This paper therefore presents an arrangement of our findings in this quickly developing field, and ways that it can be applied to an RTB use case.
Estudio exploratorio de los nativos digitales en el ámbito Universitario de la Región Mixteca

First year university students need to make efficient use of technological tools, but there is a lack of accessibility of these tools to students from rural communities. This is a problem as one of the sustainable development goals is guaranteeing quality education that is both inclusive and equitable for all students. In the case of rural areas, students cannot be considered “Digital Natives” just because they have grown up in the digital age, because they have not had the exposure to technological tools that their urban counterparts have had. Based on the case study methodology and in consideration of the foregoing premises, this article has applied a strategy to investigate the level of technological competence of the so-called Digital Natives entering the Technological University of the Mixteca (UTM). The study identified that first-year UTM students do not consider themselves to be Digital Natives and acknowledge having deficiencies in their knowledge in the use of technological tools. Additionally, incoming students face academic requirements and expectations for which most are not prepared. Despite the deficiencies, students show a disposition and ability to learn to use technological tools that allow them to achieve meaningful learning.
Supplemental Material: A metasedimentary source of gold in Archean orogenic gold deposits

Compositions of metasedimentary samples, and Table S2 (geochemical compositions of Pontiac, Porcupine and Cadillac samples collected in this study including sample locations, grid references and metamorphic facies grouping; black shale database including Au, As, and Sb data and sample name; and Mann-Whitney U-test results).&lt;br&gt;
Hand Gesture Recognition By Deep Convolutional Neural Network

Today the hand gesture is increasingly in the human-computer interactions. In all project, there is a minimum number of the process involved in the hands only and maximum contains the body, background, etc., In this paper, it shows that the location of the hand, hand gesture, and applies it in home and also the interaction between the system and the users. Hand gesture means that it is the form of sign language in the form of passing the signals and expressions. The movement occurred in the hand is called the hand gesture which can be used in hearing loss peoples. The processing of the hand gesture is that it shares the communication to others with the help of hands. The different symbols and numbers are forming with the hands and the signs form a group of words that can be understood by other people. There are two types of hand gestures like a glove based and vision-based.In this paper, a new approach called deep convolutional neural networks, which used in hand gestures to classify the images held in the hand gesture. The deep convolutional hand gesture can be used to find a more number of hand gestures. The hand gestures require more time for processing. The convolutional neural network can be used for saving the timing and reducing the time used in the separation of things or images. Finding the state of hand gesture, which will also, needs more time. The deep CNN provides a faster process in that. Using the deep CNN method, provide the system with more accuracy, specificity, sensitivity compared to the other systems. The proposed method as CNN, which separates the different sections, provides good accuracy, specificity, sensitivity from the dataset. The system process ends until the hand gesture image is leaving from the camera.
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA) at web scale. Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. In this paper, we propose a new framework to exploit more valid facts while obtaining explainability for multi-hop QA by dynamically constructing a semantic graph and reasoning over it. We employ Abstract Meaning Representation (AMR) as semantic graph representation. Our framework contains three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts. (b) A novel path-based fact analytics approach exploiting {\tt AMR-SG} to extract active facts from a large fact pool to answer questions. (c) A fact-level relation modeling leveraging graph convolution network (GCN) to guide the reasoning process. Results on two scientific multi-hop QA datasets show that we can surpass recent approaches including those using additional knowledge graphs while maintaining high explainability on OpenBookQA and achieve a new state-of-the-art result on ARC-Challenge in a computationally practicable setting.
Data Management for Researchers: 8 Principles of Good Data Management

This is a copy of the original blog post "8 Principles of Good Data Management", formatted into a single document and uploaded to OSF/Psyarxiv for the purpose of having persistent DOIs for reference. Feel free to refer to the entire OSF repository for Data Management for Researchers (https://osf.io/sja89/) or individual posts by using their respective Psyarxiv entries.
College Students’ Satisfaction Survey on Online Teaching of a University in Shanghai

Through the questionnaire survey in A public university in Shanghai, this paper revealed the college students' satisfaction with online teaching after the outbreak of Coronavirus epidemic, and found that students were more satisfied with professors' teaching attitude, less satisfied with online lessons' atmosphere and time arrangement.It also explored the factors influencing the overall satisfaction of online teaching, found the factors that affect the overall satisfaction from big to small in turn are assisted instruction before class, tutoring after class, class teaching and technical means.Colleges can take some corresponding measures to improve the overall satisfaction of online teaching from the conclusions, and use the online teaching opportunity to promote the development of online teaching in the future.
ПРЕДИКТОРЫ ИССЛЕДОВАТЕЛЬСКОЙ ПРОДУКТИВНОСТИ: ЭФФЕКТИВНЫ ЛИ ТРЕНИНГИ ДЛЯ УЧЕНЫХ?

Purpose – To study predictors of research productivity, including mediator and moderator variables. Methodology – A model that consists of such variables as research self-efficacy research training and the knowledge sharing climate in an organization was developed and tested. Data was collected using questionnaires. Originality/value – The paper contributes to an understanding of mediator and moderator factors in the relationship between human resource development practices and research productivity. The research is also of practical importance to administrators in the field of science. Findings – The results of the study show that scientific training positively affects the productivity of scientists, but they are not enough to obtain a high positive effect. University administrations need to focus on improving the climate, where scientists freely share their knowledge.
Parallel Quantum Algorithm for Hamiltonian Simulation

We study how parallelism can speed up quantum simulation. A parallel quantum algorithm is proposed for simulating the dynamics of a large class of Hamiltonians with good sparse structures, called uniform-structured Hamiltonians, including various Hamiltonians of practical interest like local Hamiltonians and Pauli sums. Given the oracle access to the target sparse Hamiltonian, in both query and gate complexity, the running time of our parallel quantum simulation algorithm measured by the quantum circuit depth has a doubly (poly-)logarithmic dependence $\operatorname{polylog}\log(1/\epsilon)$ on the simulation precision $\epsilon$. This presents an exponential improvement over the dependence $\operatorname{polylog}(1/\epsilon)$ of previous optimal sparse Hamiltonian simulation algorithm without parallelism. To obtain this result, we introduce a novel notion of parallel quantum walk, based on Childs' quantum walk. The target evolution unitary is approximated by a truncated Taylor series, which is obtained by combining these quantum walks in a parallel way. A lower bound $\Omega(\log \log (1/\epsilon))$ is established, showing that the $\epsilon$-dependence of the gate depth achieved in this work cannot be significantly improved. Our algorithm is applied to simulating three physical models: the Heisenberg model, the Sachdev-Ye-Kitaev model and a quantum chemistry model in second quantization. By explicitly calculating the gate complexity for implementing the oracles, we show that on all these models, the total gate depth of our algorithm has a $\operatorname{polylog}\log(1/\epsilon)$ dependence in the parallel setting.
A Comparative Study Between the Most Usable Object Detection Methods Based on Deep Convolutional Neural Networks

Object detection is a computer vision technique that has been revolutionized by the rapid development of convolutional neural network architectures. These networks consist of powerful tools, able to learn and extract high-level features more complex. They are introduced to deal with the problems existing in traditional architectures, to find and characterize a large number of objects in an image. This technique has two types of detection: a simple detection that aims to identify a single object in an image, it is a classification problem. And multiple detections that aim not only to identify all the objects in the image but also to find the location of the objects. This article describes a simple summary of datasets and deep learning algorithms commonly used in object detection.
ORIENTAL CALLIGRAPHY IN ART PEDAGOGY

Nowadays, the art of calligraphy is being revived. Computer typing, has largely replaced handwritten version, however it cannot develop fine motor skills, memory training, and concentration. Thus, a person does not receive a certain training set necessary for the harmonious development of brain activity. In recent years, the direction of "calligraphy therapy" has been developing, which is precisely aimed at filling the missing skills. In modern pedagogy, inclusive education comes to the fore in importance. Methods of working with people with special needs are being developed, including the direction of "art pedagogy", that is, learning through art. And it is precisely oriental (art) calligraphy (Chinese, Japanese), which is a whole philosophy and art that has developed over many centuries, that can fully meet new pedagogical tasks. The article proposes the concept of a new method of inclusive education - "art calligraphy", which can be used as one of the elements of art pedagogy for working with people with special needs.
THE ASSOCIATION BETWEEN KNOWLEDGE AND THE READINESS OF HEALTH WORKERS IN IMPLEMENTING WIFI TB PROGRAM

Background: Semarang city contributes to reducing the number of missing TB cases by using Wajib Notifikasi TB (WiFi TB) application program, which began to be used simultaneously in 2018. WiFi TB application is an application to record and report every TB case found by health facilities, and it is part of Directly Observed Treatment Short-course (DOTS) strategy in monitoring, recording, and reporting component. Aim: Knowing the association between knowledge and the readiness of health workers in implementing WiFi TB program. Methods: The research design used in this study was analytic observational with a cross sectional approach. The qualitative method was also used in this study to know the evaluation about the implementation of WiFi TB program from the subjects. The sample size of this study were 27 subjects using purposive sampling method from each of the 10 Primary Clinics in Semarang city that included inclusion criteria. Data were collected by recording the results of answers to questionnaires and interviews. Fisher exact was used to test the hypothesis. Results: Most of the health workers from each of the 10 Primary Clinics in Semarang city had good readiness in implementing WiFi TB program. The results of this study showed a significant association between knowledge (p = 0,030) and the readiness of health workers in implementing WiFi TB program. Conclusion: There was an association between knowledge and the readiness of health workers in implementing WiFi TB program.Key Words: Tuberculosis, WiFi TB, readiness, knowledge
DEFT: distilling entangled factors by preventing information diffusion

Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order to provide an insight into the problem, we develop an annealing test to calculate the information freezing point (IFP), which is a transition state to freeze information into the latent variables. We also explore these clues or inductive biases for separating the entangled factors according to the differences in the IFP distributions. We found the existing approaches suffer from the information diffusion problem, according to which the increased information diffuses in all latent variables. Based on this insight, we propose a novel disentanglement framework, termed the distilling entangled factor (DEFT), to address the information diffusion problem by scaling backward information. DEFT applies a multistage training strategy, including multigroup encoders with different learning rates and piecewise disentanglement pressure, to disentangle the factors stage by stage. We evaluate DEFT on three variants of dSprite and SmallNORB, which show low-variance and high-level disentanglement scores. Furthermore, the experiment under the correlative factors shows incapable of TC-based approaches. DEFT also exhibits a competitive performance in the unsupervised setting.
Blockchain for SME Finance: Call for Empirical Testing

For many countries worldwide, small and medium enterprises (SMEs) are the backbones of their economy, more than half of the overall world population is working for SMEs. Historically, research studies on SME lending have emphasised that SMEs are less transparent and have an information advantage in comparison to external lenders as many businesses are owner-managed and privately owned. Distributed Ledger Technology (DLT), also known as a blockchain, opens new opportunities to SMEs. It could be used as a network system to exchange financial and non-financial data for assessing SMEs' creditworthiness. Hence, blockchain could bridge the information/data gap between lenders and SMEs and enhance the overall financing speed. This article provides a theoretical overview of the policy developments for blockchain and DLTs with the final recommendations for empirical testing.