Reviewerly/exHarmony huggingface dataset:
ASSESSMENT OF THE USE OF MOBILE PHONE TECHNOLOGY: EXTENSION SIGNIFICANCE IN ORASHI REGION OF RIVERS STATE, NIGERIA

The study assessed the use of Mobile Phone Technology: Extension Significance in Orashi Region of Rivers State, Nigeria. A total number of four hundred (400) respondents were randomly selected from farmers, traders, civil servants, community leaders and students from the four Local Government headquarters in the region. Four specific objectives were framed to guide the study. Primary data used was collected through a structured questionnaire. Both descriptive (Percentage and weighted mean scores) and inferential statistics (ANOVA) were employed for data analysis. The result showed that mobile phones were massively used by all and sundry in the region. It showed that the most used networks were MTN (GM = 3.48), Airtel (GM =3.44), 9-Mobile (GM =3.17) and GLO (GM =3.02) in the area. It showed that mobile phones were used to make and receive calls (GM=3.71), send messages (GM=3.70), listen to music (GM =3.67), e-mail (GM =3.63), research purpose (GM =3.62), play games (GM =3.54), access the internet (GM=3.12), watch video/film (GM =3.06), snap pictures (GM =3.05) and facilitates crimes like kidnapping (GM =3.04), among others. It shows that registration of mobile phone (GM =3.59), not using mobile phone along the road (GM =3.57), not using mobile phone while driving (GM =3.47), punishing offenders publicly (GM =3.43), not using mobile phone in public places (GM =3.38), not using mobile phone in the classroom, (GM =3.14) and periodic verification of phone numbers (GM = 3.40) are ways of conditioning the use of mobile phone in the area. The study therefore recommends that: 1)Post – impact assessment of the use of mobile phone be carried out in the area, 2) All users of mobile phones should be subjected to register their phones to march their Biodata Verification Number (BVN), 3) A task force should be set up by the state government to check and arrest those making use of mobile phones along the streets, roads and while driving in the area, and 4) Network providers like the popular MTN should develop effective mechanisms that will work in all mobile phones for tracking crimes involving mobile phones within and beyond Orashi region in Nigeria.
Deep Learning Based Near-Orthogonal Superposition Code for Short Message Transmission

Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning based near-orthogonal superposition (NOS) coding scheme is proposed for reliable transmission of short messages in the additive white Gaussian noise (AWGN) channel for mMTC applications. Similar to recent hyper-dimensional modulation (HDM), the NOS encoder spreads the information bits to multiple near-orthogonal high dimensional vectors to be combined (superimposed) into a single vector for transmission. The NOS decoder first estimates the information vectors and then performs a cyclic redundancy check (CRC)-assisted K-best tree-search algorithm to further reduce the packet error rate. The proposed NOS encoder and decoder are deep neural networks (DNNs) jointly trained as an auto encoder and decoder pair to learn a new NOS coding scheme with near-orthogonal codewords. Simulation results show the proposed deep learning-based NOS scheme outperforms HDM and Polar code with CRC-aided list decoding for short(32-bit) message transmission.
Effectiveness of Human Resources Managements in Riyadh’s Private Universities: فاعلية إدارة الموارد البشرية في الجامعات الأهلية بمدينة الرياض

The Study aims at discovering how effective the Human Resources Managements (HRMs) in some of Riyadh’s Private Universities are. To achieve this main objective, the researchers used the descriptive approach and applied a scale that consists of 70 questions covering 9 issues, and it was applied to all managers of human resources departments and their employees, the total number of participants is 36 which represents the total of the HRMs’ heads and employees of the privet universities: (Prince Sultan University, Dar Al Uloom University, AlMaarefah University, and Arab Open University) in Riyadh.The study found several significant results. One of them is that the participants agreed to a certain extent that HRMs are effective in their universities. The results also show that the HRMs are highly effective in devices and software used in HRMs’ issue as its score was Average, followed by policies, strategies and objectives of the HRMs’ issue as its score was Average as well. The remaining issues came, according to their scores, in the following sequence: HRMs’ planning’s issue, headhunting, recruiting, and supporting HRs' issue, legislations of the HRMs' issue, performance evaluation issue, work design and analysis’s issue, HRMs’ training's issue, and finally salaries and both material and nonmaterial incentives' issue.Based on the findings and conclusions, the researchers’ recommendations include encouraging HRMs in Private Universities in Riyadh to provide flexible and effective incentives programs to encourage their employees and increase their affiliation to the organization and designing clear-cut performance evaluation criteria and measurements.
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs

Deep neural networks (DNNs) in the infinite width/channel limit have received much attention recently, as they provide a clear analytical window to deep learning via mappings to Gaussian Processes (GPs). Despite its theoretical appeal, this viewpoint lacks a crucial ingredient of deep learning in finite DNNs, laying at the heart of their success -- feature learning. Here we consider DNNs trained with noisy gradient descent on a large training set and derive a self consistent Gaussian Process theory accounting for strong finite-DNN and feature learning effects. Applying this to a toy model of a two-layer linear convolutional neural network (CNN) shows good agreement with experiments. We further identify, both analytical and numerically, a sharp transition between a feature learning regime and a lazy learning regime in this model. Strong finite-DNN effects are also derived for a non-linear two-layer fully connected network. Our self consistent theory provides a rich and versatile analytical framework for studying feature learning and other non-lazy effects in finite DNNs.
Tech You Will Need for Your Program

This chapter presents an optimal equipment list for establishing a creative music technology lab. While preservice music teachers are taught how to purchase and maintain instruments, they are rarely given equivalent advice for music production tools. This chapter provides practical recommendations for purchasing and maintaining hardware, software, and furniture, including computers, digital audio workstation software, headphones, MIDI controllers, microphones, tables, podiums, display screens, and soundproofing. Suggestions are given for arrangement and design of the overall space as well as design of individual workstations. The chapter also includes suggestions for managing wear and tear on equipment, for maintenance and cleaning routines, and for sustainable budgeting. Finally, for situations where the optimal setup is not immediately attainable, the various items described here are ranked in terms of priority.
An Architecture for Blockchain-based Cloud Banking.

Blockchain has been practiced in crypto-currencies and cross-border banking settlement. However, no clear evidence that a distributed ledger network (or Blockchain) is built within domestic payment systems, although many experts believe that Blockchain has wide applicability in various industries and disciplines. As the author’s best knowledge, no one has published a clear architecture and a feasible framework for a Blockchain-based banking network. Thus, “how Blockchain can be implemented in domestic banking systems” is a big challenge. The most important contribution of this work is to give a feasible and viable framework resolving that problem. The author investigates a Blockchain-based payment framework, more explicitly, a decentralized banking architecture running on the top of existing banking cores. The Blockchain network has two tiers: master nodes (block generators) and normal nodes (validators). The consensus mechanism is introduced as a composition of Proof of Stake, Proof of Reputation and/or practical Byzantine Fault Tolerance. In addition, nomination and approval mechanisms are added to the governance to enhance legal compliance and compatibility with real Fintech space. Some qualitative analysis is provided to show that the proposed Blockchain banking framework offers better security, scalability and decentralization, while easily adapt with different national regulation environments, among other Blockchains. In the application aspects, the framework is implementable and deployable for decentralized payment network and smartcontract infrastructure for domestic markets, then enable a complete and unified digitized space for cloud banking and financial services.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted high-confident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT and FHDecoder, our model achieves state-of-the-art performance.
HUBUNGAN PEMANFAATAN SUMBER BELAJAR MELALUI INTERNET DENGAN MOTIVASI BELAJAR SISWA KELAS V DI SDN KERAMAT MINA KABUPATEN BANJAR

The purpose of this study was to determine whether there is a relationship between the use of learning resources via the internet and the learning motivation of fifth grade students at SDN Keramat Mina, Banjar Regency. The object of this research is the relationship between the use of learning resources via the internet and the learning motivation of fifth grade students at SDN Keramat Mina, Banjar Regency. The subjects in this study were all fifth grade students at SDN Keramat Mina, Banjar Regency. The sample in this study using purposive sampling amounted to 30 students. The data mining tool used was a questionnaire. The data analysis used was correlation technique. Based on data analysis, it is concluded that there is a relationship between the use of learning resources via the internet and the learning motivation of grade V students at SDN Keramat Mina, Banjar Regency based on the analysis of rxy = 0,451 > rtabel= 0,361 at a significance level of 5% of N = 30. Based on the research conducted, the suggestions that the researcher wants to put forward are as follows: study harder and help students learn more actively in order to improve learning outcomes after utilizing learning resources via the internet, b) from the research results it is proven that there is a relationship between the use of learning resources via the internet and student learning motivation. For this reason, parents are advised to provide support and supervision in using the internet as a source of student learning. So that it can increase learning motivation, and provide positive information for students. c) from the research results it is suggested that teachers be motivated and creative in using the internet as a source of material for teaching so that they do not only provide material from books, and by using the internet so as to improve the quality of education at SDN Keramat Mina, Banjar Regency, d) the results of the research are suggested schools can make a good positive contribution to teaching and learning activities after utilizing learning resources via the internet, as well as being a reference for increasing student motivation at SDN Keramat Mina Banjar Regency in the use of learning resources via the internet, e) from the research results it is suggested to researchers who conducting similar research, so that it can be used as an addition to insight and knowledge in children's education, especially for student motivation.Keywords: utilization of learning resources, learning
STUDENTS’ MOTIVATION FOR DISTANCE LEARNING IN A PANDEMIC SITUATION

Social tendency to distance various forms of social interaction, including learning, is intensifying in the times of highly developed information and communication technologies. Online communication of teachers and students — in chats, online forums and conferences — becmes an alternative to traditional learning. During the quarantine related to the COVID-19 pandemic, distance learning (DL) was introduced in higher education institutions of Ukraine. The aim of the present study is to reveal the characteristics of students’ motivation for distance learning within the pandemic. Following methods were used for this purpose: the author’s questionnaire «Attitude to Distance Learning in Higher Education»; the method of «Eductional Motviation in Higher Education» by T. I. Ilyina, «Method of Diagnosing Personality for Motivation to Avoid failure» (by T. Ehlers), «Method of Diagnosing Personality for Motivation to Succeed» (by T. Ehlers). The empirical study continued throughout July-August 2020. The survey of students’ attitudes to DL showed three types of such attitudes: 48 % are positive about distance learning, 30 % are neutral, and 22 % are negative. Diagnosis of motivation to get higher education in students with different attitudes to distance learning revealed following trends: the highest rate of motivation to acquire knowledge is observed in students with a positive attitude to DL; the motive for obtaining a diploma dominates among students with a negative attitude to DL. High levels of motivation to succeed do not depend on the type of attitude to DL. The dominance of high levels of motivation to succeed compared to low levels of motivation to avoid failure is inherent in students with a positive attitude to DL. Significant differences were found between the parameters of «motivation to succeed» and «motivation to avoid failure» (p&gt; 0.05) among students with a neutral attitude to distance learning. Thus, based on our empirical observation, we noted the correlation between the type of attitude of students to DL and their motivation to avoid failure, and the lack of such correlation between the type of attitude of students to DL and their motivation to succeed.
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications

Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQL intermediate representation (IR) called Natural SQL (NatSQL). Specifically, NatSQL preserves the core functionalities of SQL, while it simplifies the queries as follows: (1) dispensing with operators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which are usually hard to find counterparts for in the text descriptions; (2) removing the need for nested subqueries and set operators; and (3) making schema linking easier by reducing the required number of schema items. On Spider, a challenging text-to-SQL benchmark that contains complex and nested SQL queries, we demonstrate that NatSQL outperforms other IRs, and significantly improves the performance of several previous SOTA models. Furthermore, for existing models that do not support executable SQL generation, NatSQL easily enables them to generate executable SQL queries, and achieves the new state-of-the-art execution accuracy.
Image Classification with Consistent Supporting Evidence

Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible and sufficient with respect to model predictions. We propose measures of model inconsistency and regularizers that promote more consistent evidence. We demonstrate our ideas in the context of edema severity grading from chest radiographs. We demonstrate empirically that consistent models provide competitive performance while supporting interpretation.
Quantum Heaviside Eigen Solver

Solving Hamiltonian matrix is a central task in quantum many-body physics and quantum chemistry. Here we propose a novel quantum algorithm named as a quantum Heaviside eigen solver to calculate both the eigen values and eigen states of the general Hamiltonian for quantum computers. A quantum judge is suggested to determine whether all the eigen values of a given Hamiltonian is larger than a certain threshold, and the lowest eigen value with an error smaller than $\varepsilon $ can be obtained by dichotomy in $O\left( {{{\log }}{1 \over \varepsilon }} \right)$ iterations of shifting Hamiltonian and performing quantum judge. A quantum selector is proposed to calculate the corresponding eigen states. Both quantum judge and quantum selector achieve quadratic speedup from amplitude amplification over classical diagonalization methods. The present algorithm is a universal quantum eigen solver for Hamiltonian in quantum many-body systems and quantum chemistry. We test this algorithm on the quantum simulator for a physical model to show its good feasibility.
Defining and Quantifying the Emergence of Sparse Concepts in DNNs

This paper aims to illustrate the concept-emerging phenomenon in a trained DNN. Specifically, we find that the inference score of a DNN can be disentangled into the effects of a few interactive concepts. These concepts can be understood as causal patterns in a sparse, symbolic causal graph, which explains the DNN. The faithfulness of using such a causal graph to explain the DNN is theoretically guaranteed, because we prove that the causal graph can well mimic the DNN's outputs on an exponential number of different masked samples. Besides, such a causal graph can be further simplified and re-written as an And-Or graph (AOG), without losing much explanation accuracy.
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering

Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks.
Article Review: Text Similarity Techniques Used In Automated Short Answer Scoring (ASAS)

In Automated short answer scoring systems, text similarity measurement plays an important role. it compares student's responses with reference response to indicate a final score based on the degree of similarity between the two answers. There have been many studies of text similarity and resulting in various approaches and algorithms. This paper investigates two majors of text similarity measurements, namely lexical and semantic similarity. The results of the investigation showed that the semantic similarity approach is more rational in subjective answers. While lexical similarity performs better and faster in objective answers.
Adoption of Open Learning Systems and MOOCS during COVID-19 by Academic Libraries

The entire world is going through COVID-19 pandemic as a result every institution, industry has badly affected. In these challenging times, libraries have been gateways for accurate information &source of Knowledge for conducting research. This paper emphasizes the need for online learning platforms during COVID-19. It also identifies the advocacy role taken up by libraries and library professionals. It traces the various digital platforms made available in India. This paper highlights the important projects carries out by Government of India for online learning. The paper also explores the challenges experienced during COVID-19 and are transformed into opportunities.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders

Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the quality and efficiency of healthcare. In contrast, health systems value deeply patient privacy and data security. De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited. Synthetic EHRs offer a potential solution. In this paper, we propose EHR Variational Autoencoder (EVA) for synthesizing sequences of discrete EHR encounters (e.g., clinical visits) and encounter features (e.g., diagnoses, medications, procedures). We illustrate that EVA can produce realistic EHR sequences, account for individual differences among patients, and can be conditioned on specific disease conditions, thus enabling disease-specific studies. We design efficient, accurate inference algorithms by combining stochastic gradient Markov Chain Monte Carlo with amortized variational inference. We assess the utility of the methods on large real-world EHR repositories containing over 250, 000 patients. Our experiments, which include user studies with knowledgeable clinicians, indicate the generated EHR sequences are realistic. We confirmed the performance of predictive models trained on the synthetic data are similar with those trained on real EHRs. Additionally, our findings indicate that augmenting real data with synthetic EHRs results in the best predictive performance - improving the best baseline by as much as 8% in top-20 recall.
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

Knowledge graph embedding models learn the representations of entities and relations in the knowledge graphs for predicting missing links (relations) between entities. Their effectiveness are deeply affected by the ability of modeling and inferring different relation patterns such as symmetry, asymmetry, inversion, composition and transitivity. Although existing models are already able to model many of these relations patterns, transitivity, a very common relation pattern, is still not been fully supported. In this paper, we first theoretically show that the transitive relations can be modeled with projections. We then propose the Rot-Pro model which combines the projection and relational rotation together. We prove that Rot-Pro can infer all the above relation patterns. Experimental results show that the proposed Rot-Pro model effectively learns the transitivity pattern and achieves the state-of-the-art results on the link prediction task in the datasets containing transitive relations.