Reviewerly/exHarmony huggingface dataset:
Do you trade with your friends or become friends with your trading partners? A case study in the $\breve {G}1$ cryptocurrency

Abstract We study the interplay between social ties and financial transactions made through a recent cryptocurrency called $\breve {G}1$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Ğ</mml:mi><mml:mn>1</mml:mn></mml:math> . It has the particularity of combining the usual transaction record with a reliable network of identified users. This gives the opportunity to observe exactly who sent money to whom over a social network. This social network is a key piece of this cryptocurrency, which therefore puts much effort in ensuring that nodes correspond to unique, well identified, real living human users, linked together only if they met at least once in real world. Using this data, we study how social ties impact the structure of transactions and conversely. We show that users make transactions almost exclusively with people they are connected with in the social network. Instead, they tend to build social connections with people they will never make transactions with.
Real and Fake Label Image Classification Algorithm Based on HOG and SVM

As the market for second-hand luxury goods continues to expand, authenticity must be verified when trading. In order to solve the problems of low efficiency and high cost of manually identifying luxury goods, this paper proposes a luxury identification method based on HOG feature extraction and SVM. This method uses a black label image of Gucci bags with anti-counterfeiting as an example. First, Collect true and fake sample photos, group and label the photos, make positive and negative samples, and perform normalization processing, and then extract the true and fake label HOG features, and then train the SVM as a true and fake label image classifier, using 200 test experiments were performed on the test samples, and the experimental results show that the accuracy rate of true and fake label image classification of this method reaches 90.25%, which can well identify the true and false labels under different lighting conditions.
Breast Cancer Detection Improvement by Grasshopper Optimization Algorithm and Classification SVM

Given that breast cancer is one of the most difficult and dangerous cancers, the use of diagnostic methods in the early stages of its development can be very effective and important in the process of treating patients.This early diagnosis can help doctors treat patients, thus greatly reducing mortality.Many different features have been collected to diagnose and predict breast cancer, and it is very difficult for specialists to use all of these features for a large number of cancers.The aim of this study is to provide a new method for minimizing the process of breast cancer diagnosis through the Grasshopper optimization algorithm.The steps of the proposed method consist of three main parts: The first step after receiving the data is to normalize the pre-processed data.The second step is to reduce the features using the GOA.The final step is to select the optimal features and improve the parameters using the SVM Classifier.The experiments in this study were performed on three datasets, namely WBC (Wisconsin Breast Cancer), WDBC (Wisconsin Diagnosis Breast Cancer) and WPBC (Wisconsin Prognosis Breast Cancer).The results show that the accuracy of the proposed method is 99.51, 98.83 and 91.38 for the WBC, WDBC and WPBC datasets, respectively.In comparison with other methods, the results show that the proposed method has better performance.
Values of a Successful Person: a Postmodern Education in a Post-Industrial Society

This article is devoted to the role of education in forming a successful personal development trajectory through a value system. Study shows that modern era can be described by categories of two concepts that are similar in essence and contradictory in certain manifestations: postmodernism and postindustrialism. Article hypothesizes that a postmodern value system is necessary to overcome digital destructive influence on education that is imposed by the information and digital priorities of postindustrialism. It shows that, with the universal digitization of knowledge, there is an urgent need to form three primary competencies of individual that can be provided only by education: values, communications, and knowledge, that are not subject to digital replication. It argues that growing trend of turning universities into digital corporations contains threats to the future development of the emerging personality and its value system. Article concludes that it is possible to overcome the identified threats in implementation of global educational policy, at national and global levels, which will require a critical understanding of emerging trends in digital world.
Perbandingan Seleksi Fitur Term Frequency &amp; Tri-Gram Character Menggunakan Algoritma Naïve Bayes Classifier (Nbc) Pada Tweet Hashtag #2019gantipresiden

Towards an election year (elections) in 2019 to come, many mass campaign conducted through social media networks one of them on twitter. One online campaign is very popular among the people of the current campaign with the hashtag #2019GantiPresiden. In studies sentiment analysis required hashtag 2019GantiPresiden classifier and the selection of robust functionality that mendaptkan high accuracy values. One of the classifier and feature selection algorithms are Naive Bayes classifier (NBC) with Tri-Gram feature selection Character &amp; Term-Frequency which previous research has resulted in a fairly high accuracy. The purpose of this study was to determine the implementation of Algorithm Naive Bayes classifier (NBC) with each selection and compare features and get accurate results from Algorithm Naive Bayes classifier (NBC) with both the selection of the feature. The author uses the method of observation to collect data and do the simulation. By using the data of 1,000 tweets originating from hashtag # 2019GantiPresiden taken on 15 September 2018, the author divides into two categories: 950 tweets as training data and 50 tweets as test data where the labeling process using methods Lexicon Based sentiment. From this study showed Naïve Bayes classifier algorithm accuracy (NBC) with feature selection Character Tri-Gram by 76% and Term-Frequency by 74%,the result show that the feature selection Character Tri-Gram better than Term-Frequency.
Scalable Gaussian Process Regression Networks

Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods use a fully factorized structure (or a mixture of such structures) over all the outputs and latent functions for posterior approximation, which, however, can miss the strong posterior dependencies among the latent variables and hurt the inference quality. In addition, the updates of the variational parameters are inefficient and can be prohibitively expensive for a large number of outputs. To overcome these limitations, we propose a scalable variational inference algorithm for GPRN, which not only captures the abundant posterior dependencies but also is much more efficient for massive outputs. We tensorize the output space and introduce tensor/matrix-normal variational posteriors to capture the posterior correlations and to reduce the parameters. We jointly optimize all the parameters and exploit the inherent Kronecker product structure in the variational model evidence lower bound to accelerate the computation. We demonstrate the advantages of our method in several real-world applications.
Digital didactics: technologies &amp; practices

On April 25, 2020, MGIMO University held the inter-institutional scientific and practical seminar (with international participation) “Digital Didactics: Technologies and Practices” organized by the Department of Pedagogy and Psychology. Educators and researchers from Russia, Belarus and Moldova met in the wake of the COVID-19 pandemic, which sent students and professors online, to discuss the problems related to teaching and learning from home and share the immediate experience of the first month in quarantine conditions. MGIMO welcomed colleagues from Moscow Pedagogical State University, Financial University under the Government of the Russian Federation, Moscow Polytechnic University, Academy of Labour and Social Relations, Esenin Ryazan State University, Pushkin State Russian Language Institute, Belarusian State University, Komrat State University (Moldova), Federal Research Centre Computer Science and Control of the Russian Academy of Sciences. The participants of the seminar discussed the place and role of online courses in university education, ways of presenting teaching material and teaching methods as well as pedagogical and psychological problems in online education.
Facial Spots Detection Using Convolution Neural Network

Nowadays security became a major global issue. To manage the security issue and its risk, different kinds of biometric authentication are available. Face recognition is one of the most significant processes in this system. Since the face is the most important part of the body so the face recognition system is the most important in the biometric authentication. Sometimes a human face affected due to different kinds of skin problems, such as mole, scars, freckles, etc. Sometimes some parts of the face are missing due to some injuries. In this paper, the main aim is to detect a facial spots present in the face. The total work divided into three parts first, face and facial components are detected. The validation of checking facial parts is detected using the Convolution Neural Network (CNN). The second part is to find out the spot on the face based on Normalized Cross-Correlation and the third part is to check the kind of spot based on CNN. This process can detect a face under different lighting conditions very efficiently. In cosmetology, this work helps to detect the spots on the human face and its type which is very helpful in different surgical processes on the face.
Two-Modified Emperor Penguins Colony Optimization Algorithms

Meta heuristic algorithms are very important methods, they used mainly in solving combinatorial optimization problems.They are stochastic in manner and simulate the behavior of any population of particles.Meta heuristics algorithms try to find optimal or near to optimal solution when solving complex optimization problem.In this paper two modified emperor penguins colony optimization algorithms MEPC1 and MEPC2 were developed.Original Emperor Penguins Colony (EPC) algorithm simulates the behavior of emperor of penguins, two modifications are done to the original EPC, Archimedes and hyperbolic spiral like movement are used instead of logarithmic spiral like movement.The two modified algorithms are compared with the original algorithm through ten test functions; the results show that the modified algorithms are achieved better than the original one.
The Fourth Industrial Revolution of Healthcare Information Technology: Key Business Components to Unlock the Value of a Blockchain-Enabled Solution

Editor's note: This article is one of an ongoing series covering topics published in conjunction with the Health Information Management and Systems Society (HIMSS) describing the development of blockchain technology and its applicability to healthcare.
Teaching Reform and Innovation of Embedded System Development in the Context of Artificial Intelligence

Embedded system is a special computer application system embedded in the object system. It is a combination of software and hardware. After decades of development, embedded systems have gradually entered many important industries such as industrial production, military, aerospace, transportation, wireless communications, and consumer electronics products, and their applications are extensive. The diversity of embedded systems has driven the rapid development of the embedded product market. In this paper, we analyze the teaching reform of embedded system development from the perspective of artificial intelligence. The sample models are integrated to help understand the proposed methodology. The numerical analysis proves the novelty.
SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search

Bayesian methods have improved the interpretability and stability of neural architecture search (NAS). In this paper, we propose a novel probabilistic approach, namely Semi-Implicit Variational Dropout one-shot Neural Architecture Search (SI-VDNAS), that leverages semi-implicit variational dropout to support architecture search with variable operations and edges. SI-VDNAS achieves stable training that would not be affected by the over-selection of skip-connect operation. Experimental results demonstrate that SI-VDNAS finds a convergent architecture with only 2.7 MB parameters within 0.8 GPU-days and can achieve 2.60% top-1 error rate on CIFAR-10. The convergent architecture can obtain a top-1 error rate of 16.20% and 25.6% when transferred to CIFAR-100 and ImageNet (mobile setting).
Investigating Reading Behavior in Fine-grained Relevance Judgment

A better understanding of users' reading behavior helps improve many information retrieval (IR) tasks, such as relevance estimation and document ranking. Existing research has already leveraged eye movement information to investigate user's reading process during document-level relevance judgments and the findings were adopted to build more effective ranking models. Recently, fine-grained (e.g., passage or sentence level) relevance judgments have been paid much attention to with the requirements in conversational search and QA systems. However, there is still a lack of thorough investigation on user's reading behavior during these kinds of interaction processes. To shed light on this research question, we investigate how users allocate their attention to passages of a document during the relevance judgment process. With the eye-tracking data collected in a laboratory study, we show that users pay more attention to the "key" passages which contain key useful information. Users tend to revisit these key passages several times to accumulate and verify the gathered information. With both content and user behavior features, we find that key passages can be predicted with supervised learning. We believe that this work contributes to better understanding users' reading behavior and may provide more explainability for relevance estimation.
Convolutional Neural Networks Based on Residual Block for No-Reference Image Quality Assessment of Smartphone Camera Images

The quality of image captured by smartphone camera is one of the most important factors influencing consumers’ choice of mobile phones. Since the objective evaluation methods specifically designed for the quality assessment of smartphone camera image are relatively rare, it is meaningful to design an effective model for this challenge. In this paper, we propose a carefully-designed Convolutional Neural Network (CNN) with residual block to predict image quality without a reference image. Within the network structure, the feature extraction and regression are integrated into one optimization process. The input of network is selected using the saliency map generated by SalGAN. Experimental results show that the model proposed can obtain a better performance for quality assessment of smartphone images on all four aspects viz. color, exposure, noise and texture than the traditional noreference image quality assessment (NR IQA) methods.
Query Answering for Existential Rules via Efficient Datalog Rewriting

Existential rules are an expressive ontology formalism for ontology-mediated query answering and thus query answering is of high complexity, while several tractable fragments have been identified. Existing systems based on first-order rewriting methods can lead to queries too large for DBMS to handle. It is shown that datalog rewriting can result in more compact queries, yet previously proposed datalog rewriting methods are mostly inefficient for implementation. In this paper, we fill the gap by proposing an efficient datalog rewriting approach for answering conjunctive queries over existential rules, and identify and combine existing fragments of existential rules for which our rewriting method terminates. We implemented a prototype system Drewer, and experiments show that it is able to handle a wide range of benchmarks in the literature. Moreover, Drewer shows superior or comparable performance over state-of-the-art systems on both the compactness of rewriting and the efficiency of query answering.
Improving High School and Vocational School Teachers in Writing Skill

Sekolah Tinggi Teknologi Muhammadiyah Cileungsi has the duty of the College Dharma Chess which includes education and teaching activities, research and community service, and AIKA and Al-Islam. The purpose of this research is to teach writing methods to teachers, to facilitate teaching and learning. The method used in this research are identification of the problem, conduct a field survey, finding material, and direct training to representatives of each high school / vocational school teachers. In this training participants were given the easiest writing method. From telling great writers in life in the world, where they can exist in writing. Then the participants were asked to write an article to get used to writing. After the end of the activity, participants are asked to share in the group or discuss directly with the speaker, so that the writing that has been made can be immediately reviewed. Solutions in writing training, taking the time to write, and frequent discussions with people who often write. The result of this research is the teachers know writing techniques to develop teaching activity in the classroom.
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation

Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think.It is challenging to specify the level of education, experience, relevant skills per the company information and job description.To this end, we propose a novel task of Job Posting Generation (JPG) that is cast as a conditional text generation problem to generate job requirements according to the job descriptions.To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA.Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels.At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results.The proposed approach is evaluated on real-world job posting data.Experimental results clearly demonstrate the effectiveness of the proposed method 1 .
Soft Threshold Ternary Networks

Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. In previous ternarized neural networks, a hard threshold Δ is introduced to determine quantization intervals. Although the selection of Δ greatly affects the training results, previous works estimate Δ via an approximation or treat it as a hyper-parameter, which is suboptimal. In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold. Concretely, we replace the original ternary kernel with the addition of two binary kernels at training time, where ternary values are determined by the combination of two corresponding binary values. At inference time, we add up the two binary kernels to obtain a single ternary kernel. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and extreme low bit networks. Experiments on ImageNet with AlexNet (Top-1 55.6%), ResNet-18 (Top-1 66.2%) achieves new state-of-the-art.
Potential Driven Reinforcement Learning for Hard Exploration Tasks

Experience replay plays a crucial role in Reinforcement Learning (RL), enabling the agent to remember and reuse experience from the past. Most previous methods sample experience transitions using simple heuristics like uniformly sampling or prioritizing those good ones. Since humans can learn from both good and bad experiences, more sophisticated experience replay algorithms need to be developed. Inspired by the potential energy in physics, this work introduces the artificial potential field into experience replay and develops Potentialized Experience Replay (PotER) as a new and effective sampling algorithm for RL in hard exploration tasks with sparse rewards. PotER defines a potential energy function for each state in experience replay and helps the agent to learn from both good and bad experiences using intrinsic state supervision. PotER can be combined with different RL algorithms as well as the self-imitation learning algorithm. Experimental analyses and comparisons on multiple challenging hard exploration environments have verified its effectiveness and efficiency.
QUANTIFYING THE SINGLE LEG BALANCE USING MULTIPLE DYNAMIC POSTURES ON STABLE SURFACE

Introduction: Singe leg balance (SLB) is a key neuro-biomechanical asset of several fundamental and advanced closed kinetic chain activities of lower limbs.From the perspectives of healthy ageing and fall prevention strategies, there is a compelling need to explore and devise a safe and effective SLB assessment tool to incorporate in multifactorial health evaluations and gerontological education.Methodology: About twenty dynamic SLB exercise techniques were opted and used as assessment techniques to build a dynamic SLB assessment scale.Results: As a preliminary testing, when this assessment scale was applied on non-athlete individuals in the age group of 35 to 50 years old, nine men (Mean body weight = 79 Kg, Mean fat percentage = 32) and five women (Mean body weight = 65 Kg, Mean fat percentage = 36), who have been exercising two to three days per week under the supervision of exercise professional, they displayed excellent SLB in almost all the techniques. Conclusion:It is prudent to utilize this dynamic SLB assessment scale not only to diagnose and quantify SLB performance but also to motivate the individuals by giving the highest rating even if the individuals display precise SLB only after multiple trials and errors.Numerous such simple, inexpensive and non-invasive diagnostic tools are needed in the health education and health care system.
Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem.