Reviewerly/exHarmony huggingface dataset:
The extent of the use of social networking sites by the students of the Faculty of education

The current study aimed to identify the extent to which students of the Faculty of Education at Tishreen University use social media in the educational process related to their research seminars and graduation projects, according to the variables of gender and academic specialization. The researcher designed for this purpose a questionnaire consisting of (20) items distributed on four main axes, distributed on a random sample, consisting of (339) male and female students in the College of Education from different academic years, and from various specializations. The results of the study showed that the extent of the use of the students of the College of Education for social networking sites in the educational process related to their research seminars and graduation projects is large, as the relative weight of use (80%). It was also found that there was no difference between the average scores of students of the College of Education with regard to the use of social networking sites in the educational process, according to the variables of sex and academic specialization. In addition to the presence of a set of proposals submitted by students to facilitate the use of social networking sites in the educational process, namely: Providing internet service at the university, and facilitating communication with members of the educational body. هدفت الدراسة الراهنة التعرف إلى مدى استخدام طلاب كلية التربية بجامعة تشرين لمواقع التواصل الاجتماعية في العملية التعليمية المتعلقة بحلقاتهم البحث ومشاريع التخرج، وفقا لمتغيري الجنس والتخصص الدراسي. وصممت الباحثة لهذا الهدف استبانة مؤلفة من (20) بندا موزعا على أربعة محاور رئيسة، وزعت على عينة عشوائية، مكونة من (339) طالبا وطالبة في كلية التربية من مختلف السنوات الدراسية، ومن مختلف الاختصاصات. وقد أظهرت نتائج الدراسة، أن مدى استخدام طلاب كلية التربية لمواقع التواصل الاجتماعية في العملية التعليمية المتعلقة بحلقاتهم البحث ومشاريع التخرج كبيرا؛ إذ بلغ الوزن النسبي للاستخدام (80%). وتبين أيضا عدم وجود فرق بين متوسطي درجات طلبة كلية التربية فيما يتعلق باستخدام مواقع التواصل الاجتماعية في العملية التعليمية، تبعا لمتغيري الجنس والتخصص الدراسي. إضافة إلى وجود مجموعة من المقترحات قدمها الطلبة لتسهيل استخدام مواقع التواصل الاجتماعية في العملية التعليمية، وهي: توفير خدمة الانترنت في الجامعة، وتسهيل التواصل مع أعضاء الهيئة التعليمية.
Technology watch of airborne mineral exploration methods

Summary This study presents a bibliometric study on patents and scientific publications related to a selection of airborne mineral exploration methods: airborne electromagnetic methods, airborne gravity gradiometry, airborne magnetometry and drone-borne hyperspectral imaging. A statistical analysis of the documents reveals the main players, technology trends and collaboration patterns via bibliometric techniques.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH PICTURE AND PICTURE

The purpose of the study is to improve students’ reading comprehension of narrative text using Picture and Picture. This study is delimited on using picture and picture in teaching reading comprehension in Narrative Text to the first year student of SMK Santa Familia Tomohon. In this research, the researchers use quantitative research. Where quantitative research is used to obtain and to collect the data in the form of numbers, through Pre-experimental design with one group pre-test and post-test. Based on the explanation of previous chapters, it can be concluded that; picture and picture technique is effective to improve students’ reading comprehension, teaching reading comprehension by using picture and picture can help student improving their comprehension in reading and increase their cooperation to develop the other language skills. Then, the result of this research shows that the students’ score in Post-test is higher than their score in Pre-test. It means that Picture and Picture technique in teaching reading comprehension is effective. Therefore, it is suggested to apply Picture and picture technique in class related to the situation and material that is going to be taught in school to improve more about students’ skill not only in group but also personally. The teacher is suggested to pay more attention to increase students’ comprehension in reading. Keywords: Reading, Picture and Picture Technique, Pre-experimental
DEVELOPING THE STUDENT’S CREATIVITY WITH THE HELP OF PORTFOLIO

This article addresses issues related to updating the educational process, changing its direction towards a creative attitude, directing the learning process to the formation of students' creative ability, continuously evaluating student achievements, analyzing the results obtained and taking necessary measures as a result of analysis, the use of new teaching technologies in schools, as well as improving the quality of school education. The article also analyzes the research of a group of scientists regarding the technology of “portfolio”, which is one of the new technologies used in the educational process, and also emphasizes the role of portfolio technology in the formation of students' creative abilities. It is also stated that at present, the issue of using portfolio technology in the school system of Kyrgyzstan is interconnected with the issue of formation of key competencies of the educational process, and it also said that portfolio technology can be considered as a means of creating students' creative abilities. The capabilities of portfolio technology for a student, teacher and parent are specified.
Learning sums of powers of low-degree polynomials in the non-degenerate case.

We develop algorithms for writing a polynomial as sums of powers of low degree polynomials in the non-degenerate case. This problem generalizes symmetric tensor decomposition which is widely studied, having many applications in machine learning. Our algorithm for this more general problem allows us to solve the moment problem for mixtures of zero-mean Gaussians in the nondegenerate case. Our algorithm is based on a scheme for obtaining a learning algorithm for an arithmetic circuit model from lower bound for the same model, provided certain non-degeneracy conditions hold. The scheme reduces the learning problem to the problem of decomposing two vector spaces under the action of a set of linear operators, where the spaces and the operators are derived from the input circuit and the complexity measure used in a typical lower bound proof. The non-degeneracy conditions are certain restrictions on how the spaces decompose. Such a scheme is present in a rudimentary form in an earlier work of Kayal and Saha. Here, we make it more general and detailed, and potentially applicable to learning other circuit models. An exponential lower bound on the representation above is known using the shifted partials measure. However, the number of linear operators in shifted partials is exponential and also the non-degeneracy condition emerging out of this measure is unlikely to be satisfied by a random such circuit when the number of variables is large with respect to the degree. We bypass this hurdle by proving a lower bound (which is nearly as strong as the previous bound) using a novel variant of the partial derivatives measure, namely affine projections of partials (APP). The non-degeneracy conditions appearing from this new measure are satisfied by a random circuit of the above kind. The APP measure could be of independent interest for proving other lower bounds.
Assess Teachers and Students’ Interaction in Campus

There are 3 combinations of teacher-student interaction in school, for the purpose to understand what Interactional deficiency happened in campus, we examined these 3 combinations, 358 college students joined the investigated to find out what they want and what bother them most. We found that there really problems between teachers and students. Besides, from co- authorship network analysis, we found there are also quite a few gaps among college teachers. We collected co-authorship data from 85 journal papers in the field of industrial and management science (IMS) with a time span of twenty years (1997-2016). A bibliometric network is constructed and UCINET 6 applied to calculate three centrality measures (out-degree, in-degree, and betweenness) for individual authors in this network. Also discussed the two subjects I taught to find out students’ learning effectiveness. The results confirm our argument and the mechanism is discussed.
Chapter 10. Random Satisfiabiliy

In the last twenty years a significant amount of effort has been devoted to the study of randomly generated satisfiability instances. While a number of generative models have been proposed, uniformly random k-CNF formulas are by now the dominant and most studied model. One reason for this is that such formulas enjoy a number of intriguing mathematical properties, including the following: for each k≥3, there is a critical value, rk, of the clauses-to-variables ratio, r, such that for r&lt;rk a random k-CNF formula is satisfiable with probability that tends to 1 as n→∞, while for r&gt;rk it is unsatisfiable with probability that tends to 1 as n→∞. Algorithmically, even at densities much below rk, no polynomial-time algorithm is known that can find any solution even with constant probability, while for all densities greater than rk, the length of every resolution proof of unsatisfiability is exponential (and, thus, so is the running time of every DPLL-type algorithm). By now, the study of random k-CNF formulas has also attracted attention in areas such as mathematics and statistical physics and is at the center of an area of intense research activity. At the same time, random k-SAT instances are a popular benchmark for testing and tuning satisfiability algorithms. Indeed, some of the better practical ideas in use today come from insights gained by studying the performance of algorithms on them. We review old and recent mathematical results about random k-CNF formulas, demonstrating that the connection between computational complexity and phase transitions is both deep and highly nuanced.
Stay Alive with Many Options: A Reinforcement Learning Approach for Autonomous Navigation.

Hierarchical reinforcement learning approaches learn policies based on hierarchical decision structures. However, training such methods in practice may lead to poor generalization, with either sub-policies executing actions for too few time steps or devolving into a single policy altogether. In our work, we introduce an alternative approach to sequentially learn such skills without using an overarching hierarchical policy, in the context of environments in which an objective of the agent is to prolong the episode for as long as possible, or in other words `stay alive'. We demonstrate the utility of our approach in a simulated 3D navigation environment which we have built. We show that our method outperforms prior methods such as Soft Actor Critic and Soft Option Critic on our environment, as well as the Atari River Raid environment.
Real-time detection and analysis of facial action Units to identify patients at risk in critical care (abstract only)

The emotional expressions of humans and animals have been investigated ever sinceCharles Darwin.It is generally accepted that facial expressions convey what we are feeling, although interpretations may vary among cultural groups. Previous collaborative research between NorthMiddlesex University Hospital (London) and the GMPR Research Group at SheffieldHallam University[1], [2], [3], [4]have demonstrated for the first time that patterns of facial actionunits identified in deteriorating patients can predict admission to intensive care. Our aims are toextend the current data set and compare automatic predictions with standard methods. We arecollectingfacialdatafrompatientsincriticalcareandinvestigatingmethodsforautomaticrecognitionoffacial action units as predictors of patient deterioration. The system was implemented on aMacBook Pro 2.5GHZ Intel Core i7, 16GB memory 1600 MHz DDR3 running on macOSMojave 10.14.5. The algorithms operate on live video comparing current measurements with abaseline. The baseline is estimated from a patient’s face image from a few hours back or froma previous day. Upon detecting all regions and data points of interest on the face, the followingare the parameters for AU evaluation: • AU15: lip corner depressor. From the detected datapoints on the lip, evaluate the corner depression using both trigonometric relationships andcurvature measures. Compare those measurements with the baseline and determine whether themeasures indicate deterioration or improvement or indifferent. • AU25: lips relaxation.Determine how much the lips are relaxed(open)by evaluating the area enclosed by the detected datapoints. Determine whether the area is increasing, decreasing, or stable as an indication ofdeterioration/improvement/indifferent. • AU43: eyes relaxation. Similar to lips, estimate thearea enclosed by the data points. It constantly compares previous to current image and makes aprediction based on measurements in real time. The green banner means no significant changefrom baseline measurements. To calculate the lip depressor we use the angle between twostraight lines. The lines are defined between the end of the lips intercepting at the point at thecentre of the top lip – note that these points are automatically detected by our algorithms. Letm1 and m2 be the slopes of the two lines, then the angle θ is estimated by: θ = tan−1± m2 −m11 + m1m2 (1) 2 Our approach to automatic recognition of facial action units can overcomehuman factors associated with the lack of recognition of deteriorating patients in the ward. Weare at TRL1-basic principles observed as we have demonstrated that patterns of action unitscan beused as a predictor of admission to critical care, and at TRL2technology conceptformulated for a non-contact device to assist admission to critical care. The advantages of oursolution are independent of human factors (safety) and can decrease the number of nursesneeded to assure monitoring patients in the ward (cost-effective).
Federated Learning in Smart Cities: A Comprehensive Survey.

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities.
THE effect of the different learning scaffold pattern in computer simulation programs on the development of smart phone application programming skills and motivation FOR achievement among secondary students

Key Words: Scaffolding, Learning Scaffolding, Learning Support, Simulation, Learning Scaffolding Based on text, Learning Scaffolding Based on video, Motivation for achievement.The current research aimed at detecting the effect of the different learning scaffold patterns in computer simulation programs on the development of skills of smart phones applications and the motivation for achievement in secondary school students. The semi-experimental method was used to suit the nature of the research. The sample consisted of (60) students from the 2nd grade in secondary school who was randomly distributed and divided into two groups: the first group used the experimental processing using text-based learning scaffolding mode, and the second group used experimental processing using scaffold pattern based on video, and to test the validity of the hypothesis, the T-test was used using the SPSS statistical processing program. The results showed that there were statistically significant differences at (0.05) for the second experimental group which used video pattern in the development of smart phone applications programming skills and motivation for achievement.
The Dialogue Between Forensic Scientists, Statisticians and Lawyers about Complex Scientific Issues for Court [E-book]

Since DNA analysis became part of forensic science in the mid-1980s, its impact on investigation of crime and at court has been immense. In a few years the technique became the gold standard for evaluative evidence, overtaking some other evidence types, and replacing others completely. Part of this impact was due to formal statistical calculations, replacing subjective opinions, on the weight of evidence provided for the prosecution and defence views. The technology has improved quickly with ever more sensitive tests being introduced; the statistical interpretation of increasingly complex DNA results has not been as swift. The absence of a forum for inter-disciplinary discussion between developers and end users has led to methods being developed by statisticians, with little input from the working forensic scientists. It is forensic scientists who will be using the software, and typically have little opportunity to discuss with lawyers the data impact and presentation for non-scientific audiences such as Judges, magistrates and juries. There is a danger for courts in these interpretations — which are produced by a black box — where the reporting forensic scientist has little input and less understanding. It is time for a dialog between the scientists producing the DNA results, the statisticians developing the calculation methods and software and, the lawyers who present the findings to the court.
Theory of Communication Efficient Quantum Secret Sharing.

A $((k,n))$ quantum threshold secret sharing (QTS) scheme is a quantum cryptographic protocol for sharing a quantum secret among $n$ parties such that the secret can be recovered by any $k$ or more parties while $k-1$ or fewer parties have no information about the secret. Despite extensive research on these schemes, there has been very little study on optimizing the quantum communication cost during recovery. Recently, we initiated the study of communication efficient quantum threshold secret sharing (CE-QTS) schemes. These schemes reduce the communication complexity in QTS schemes by accessing $d\geq k$ parties for recovery; here $d$ is fixed ahead of encoding the secret. In contrast to the standard QTS schemes which require $k$ qudits for recovering each qudit in the secret, these schemes have a lower communication cost of $\frac{d}{d-k+1}$ for $d>k$. In this paper, we further develop the theory of communication efficient quantum threshold schemes. Here, we propose universal CE-QTS schemes which reduce the communication cost for all $d\geq k$ simultaneously. We provide a framework based on ramp quantum secret sharing to construct CE-QTS and universal CE-QTS schemes. We give another construction for universal CE-QTS schemes based on Staircase codes. We derived a lower bound on communication complexity and show that our constructions are optimal. Finally, an information theoretic model is developed to analyse CE-QTS schemes and the lower bound on communication complexity is proved again using this model.
Persistent Rule-based Interactive Reinforcement Learning.

Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time. Current interactive reinforcement learning research has been limited to interactions that offer relevant advice to the current state only. Additionally, the information provided by each interaction is not retained and instead discarded by the agent after a single-use. In this work, we propose a persistent rule-based interactive reinforcement learning approach, i.e., a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Our experimental results show persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer. Moreover, rule-based advice shows similar performance impact as state-based advice, but with a substantially reduced interaction count.
Exploration and Practice of the Dual-System Talent Cultivation Mode of “Teaching for Cultivation, Cultivation for Spirits” in Guangdong University of Petrochemical Technology

Based on the OBE education concept and the implementation of the College Students' Quality Expansion Credits Program in Guangdong University of Petrochemical Technology since 2009, the dual-system talent cultivation mode of “Teaching for Cultivation, Cultivation for Spirits” has been innovatively proposed. This article introduces in detail the content, basic principles, organization, implementation, effects and plans for the reform of the dual-system talent cultivation training mode in Guangdong University of Petrochemical Technology.
Prediction of Breast Cancer of Women Based on Support Vector Machines

In recent years, the incidence of female breast cancer in China has increased rapidly, and breast cancer has become the most seriously threaten of Chinese women. Therefore, it is of great significance to make accurate prediction and judgment female breast tumors being benign or malignant. This study is based on Support Vector Machines (SVM) to classify and predict the female breast tumors benign or malignant. First, breast cancer data has been dealing with, then will be classified. We adopt SVM algorithm and three models including SVC (kernel = 'linear'), SVC (kernel = 'RBF) and linear SVC () and Nu SVC (). The classify algorithms are linear, nonlinear, nuclear. We combine the three algorithms with the previous models to forecast breast tumor benign or malignant. The classifier model is evaluated by integrating the indexes such as precision rate, recall rate and running time, and then the super parameters of the classifier are analyzed and optimized. Through trial-and-error, it can be found that model SVC (kernel= 'RBF') has the best prediction effect. The optimized classifier model can be achieved 98.86% accuracy, 95.29% recall and 95.46% harmonic average (F1-score).
Determinants Of Service Trainers' Skills Of Selected Training Centers, Eastern Province, Kingdom Of Saudi Arabia

Investments in further development of skills are the essential assets that an individual carries to a workplace and can be used in various jobs. Employing multi-skilled trainers is an effective strategy in bringing the training centers and other service firms to the dash, thereby contributes to greater height and staying competitive in the marketplace. Keeping in this perspective, this study was perusing the personal attributes, job requirements, and organizational landscape factors as determinants of service trainers' skills. A structured survey questionnaire was constructed and systematically distributed during September and October 2020 base on purposive sampling. The sampling frame consists of thirty-six (36) employed service trainers as respondents. Five-point Likert was used. The results found significant relationship with motivation. The researcher concluded that motivation significantly predicts trainers' skills. This means that highly motivated trainers are likely to have higher job performance as trainers. The findings suggest that harnessing trainers' motivation will spur their skills as trainers.