Reviewerly/exHarmony huggingface dataset:
Unknown Oriented Programming: Mathematical Continuation

Natural language semantic engineering problems are faced with unknown input and intensive knowledge challenges. In order to adapt to the featuresof natural language semantic engineering, the AI programinglanguage needs to be expanded mathematically: 1) Using many ways to improve the spatial distribution and coverage of instances; 2) Keeping different abstract function versions running at the same time; 3) Providing a large numberof knowledge configuration files and supporting functions to deal with intensive knowledge problems; 4) Using the most possibilitypriority call to solve the problem of multiple running branchestraversal. This paper introduces the unknown oriented programming ideas, basic strategy formulation,language design and simulation running examples. It provides a new method for the incremental research and development of large-scale natural language semantic engineeringapplication. Finally, this paper summarizes the full text and puts forward the further research direction.
Framework for tasks suggestion on web search based on unsupervised learning techniques

Search systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user’s search task can be complex and may not be expressed using a single search query, requiring the user to write several queries to fulfill all the aspects of his or her needs. In such scenarios, an intelligent search system would be beneficial to identify and understand the original search task issued by a user and then suggest several search tasks (in a form of key-phrases or short topics) related to the original search task. Aiming to tackle this limitation, this paper proposes a framework for applying several unsupervised learning approaches, including topic modeling and log mining. The results of applying these approaches to large user session data show that, indeed, these approaches would be applicable in search suggestion and task recommendation, reaching a significant improvement over a strong baseline.
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs

Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. To tackle these problems, we propose a graph-text joint representation learning model called JointGT. During encoding, we devise a structure-aware semantic aggregation module which is plugged into each Transformer layer to preserve the graph structure. Furthermore, we propose three new pre-training tasks to explicitly enhance the graph-text alignment including respective text / graph reconstruction, and graph-text alignment in the embedding space via Optimal Transport. Experiments show that JointGT obtains new state-of-the-art performance on various KG-to-text datasets.
한국의 자유학기제 연구동향 분석

The purpose of this study is to analyze the research trend related to Free Semester System more accurately using the content analysis method. To achieve the research purpose, 172 articles were selected for analysis. The results of this study are as follows: First, the studies for free semester system are steadily increasing from 2013 to 2018. Second, studies related to the free semester system have been published in various journals, but there is a lack of continuous research. Third, there was a high proportion of subjects that could be handled in free semester activities and gymnasium subjects but the studies related to subjects such as Korean, English, and Mathematics are very insufficient in general. Forth, activity programs related career exploration were large, while research on field experience and club programs was hardly found. Fifth, The research on program R&D and application was most actively conducted, and it is steadily researched in the order of quantitative research, literature research, and qualitative research. Mixed studies, on the other hand, were not carried out much. Sixth, compared to student-related studies, fewer articles were published for teachers and parents. Seventh, although the researches on the curriculum and teaching methods are the most conducted, there is a lack of research on performance regarding student evaluation, and research on actual condition analysis related to student evaluation showed the least. The results of this study will provide implications for future research direction about free semester System or free school year.
Machine Learning approach for Content Mining System

Text Classification (TC), also known as Text Categorization, is the mission of robotically classifying a set of text documents into dissimilar categories from a predefined set. If a manuscript belongs to exactly one of the categories, it is a single-label categorization task; otherwise, it is a multi-label categorization task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much consideration in the last years from both researchers in academia and manufacturing developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most appropriate documents.
Performance analysis and system optimization of an energy-saving mechanism in cloud computing with correlated traffic

&lt;p style='text-indent:20px;'&gt;Energy consumption is becoming a significant part of overall operational cost in cloud data centers. For the purpose of satisfying the Service Level Agreement (SLA) of cloud users while enhancing the energy efficiency in cloud computing systems, in this paper we propose an energy-saving mechanism with a sleep mode. Taking into consideration the traffic's correlation and the stochastical behavior of data arrival requests in a random cloud environment with the proposed energy-saving mechanism, we model the system as a MAP/M/&lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ N $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;/&lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ N $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;+&lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ K $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; queue with a synchronous multi-vacation. Then, we present a theoretical basis for analyzing and evaluating the system performance by taking a state transition rate matrix in the steady state. Next, we investigate the change trends for the energy saving rate of the system and the average latency of tasks by carrying out numerical experiments. Moreover, we give a&lt;/p&gt;
The Use of Cartoon Media for Junior High Students of Their Writing Ability

This research was proposed from the problem of students writing ability in the descriptive text of the eight-grade students at MTs N 1 Pekanbaru. They were not able to write in English well. The objective of this research was to find out whether there was or nor the significant effect of using cartoon media. The design of this research was Quasi-Experimental Research. The Population of this research was all of the eighth-grade students. The total number of the population was 350 students. The writer used a cluster sampling technique by taking only two classes as a sample; VIII.1 that co­nsisted of 35 students as experimental class and VIII.2 consisted of 35 students as control class. So the number of samples from two classes was 70 students. To analyze the data, the writer used an independent sample t-test. Based on the analysis of the t-test Formula, the sig. value is 0.000. It could be stated that 0.000&lt;0.05. Then, the effect size is 0.387 categorized into a moderate level. The writer concluded that there is a significant effect of using cartoon media on students’ writing ability in the descriptive text at MTs N 1 Pekanbaru. And the biggest effect occurred on the writing ability where the effect size is 0.14 categorized into large effect.
f-Domain-Adversarial Learning: Theory and Algorithms

Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset. In this paper, we introduce a novel and general domain-adversarial framework. Specifically, we derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences. It recovers the theoretical results from Ben-David et al. (2010a) as a special case and supports divergences used in practice. Based on this bound, we derive a new algorithmic framework that introduces a key correction in the original adversarial training method of Ganin et al. (2016). We show that many regularizers and ad-hoc objectives introduced over the last years in this framework are then not required to achieve performance comparable to (if not better than) state-of-the-art domain-adversarial methods. Experimental analysis conducted on real-world natural language and computer vision datasets show that our framework outperforms existing baselines, and obtains the best results for f-divergences that were not considered previously in domain-adversarial learning.
ARGUMENTATION PATTERNS OF TOULMIN STUDENTS DURING ONLINE LECTURES IN THE PANDEMIC

Student argumentation skills are considered weak during online lectures. The Toulmin Argumentation Model is an excellent model in analyzing argumentation discourse in the Science Classroom. This study aims to describe Toulmin's argumentation patterns during online lectures during the Covid-19 Pandemic. The research method used is a qualitative descriptive method. Data collection techniques used in the form of an open questionnaire. Respondents used in this study were 50 samples. The results showed that the argumentation pattern was divided into 2 categories namely, (1) Category Four Elements and (2) Category Five Elements. The Four Elements category is divided into 2 patterns, namely the C-G-W-B Pattern and the G-C-M-W Pattern. In the CGWB Pattern, the argumentation elements used are a claim (position statement), ground (data or facts), warrant (guarantee), and backing (support), while in the GCMW Pattern the argumentation elements used are ground (data or facts), claim (position statement), Capital Qualifier, and warrant. The Five Elements category forms the G-C-W-B-Pc Pattern. In the G-C-W-B-Pc Pattern, the argumentation elements used are grounds (data or facts), claims (position statements), warrant (s), backing (support), and possible rebuttal (exceptions). In the case of the five-element category, the argumentation pattern does not use a Capital qualifier
Professional mobility of the future officer: to the formulation of the development problem

In modern psychological and pedagogical studies, social and professional mobility is rarely correlated with the performance of military service duties. This circumstance is dictated by the ideas about the uniformity and legal «regulation» of military-service relations. The authors substantiate the need for the development of professional mobility, both on the vertical and on the horizontal layer, since the variety of duties performed by officers in the course of military work and the presence of various military positions to which he can be appointed, requires rapid adaptation to changing conditions of the social environment. The novelty of the research is that: the scientific results of the analysis of the structure of professional mobility of future officers are presented; the concept of «professional mobility of the future officer» is concretized based on the features of solving professional tasks by officers in the military; the conditions and means of developing the professional mobility of future officers are established and proposed.
A systematic review on security metric in secure software development lifecycle

A security metric evaluates the security performance, goals, and objectives of software and provides information that helps in the assessment of software security. With the advancement in technology, software is faced with lots of threats and risks to customers’ data and privacy. Most of these threats are a result of security being considered as a feature of software and not taking it into account during the development of the software. Security metrics obtained during the development process go a long way to improve the core security properties of software, that is: confidentiality, integrity, and availability. Thus, it is of obvious importance to consider security across the software development lifecycle, hence, the use of software security metrics. This paper reviews the various security metrics that are meditated in the copious phases during the progression of the lifecycle with the aim of providing practitioners, managers, and researchers the substantial knowledge for further security assessment.
Mali - Land, climate, energy, agriculture and development: A study in the Sudano-Sahel Initiative for Regional Development, Jobs, and Food Security

Mali is a Sub-Saharan African country with 19.1 million people. Almost half of this population lives in poverty, due to the dysfunction of activity sectors (agriculture, energy, education, employment, services, etc.). Natural resource management especially land and water together with corollaries remain one of the greatest challenges for this dryland region to sustainably face climate change, meet food demand and improve its economy. The social crisis (especially in the northern region) is significantly affecting development and human security as well. This study had been carried out to review the current state of affairs, key trends, problems, solutions and their implications for sustainable development of Mali under changing climate and the impacts of land degradation. Likewise, investment opportunities in the priority areas are highlighted in this report.
Evaluation of Saliency-based Explainability Method

A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working. These methods provide an intuitive way for users to understand predictions made by CNNs. Other than quantitative computational tests, the vast majority of evidence to highlight that the methods are valuable is anecdotal. Given that humans would be the end-users of such methods, we devise three human subject experiments through which we gauge the effectiveness of these saliency-based explainability methods.
Evidences of epithermal mineralization at Bidook gold vein deposit (east of Iran), based on geology, alteration, mineralization, geochemistry and thermometery data.

Evidences of epithermal mineralization at Bidook gold vein deposit (east of Iran), based on geology, alteration, mineralization, geochemistry and thermometery data.
Emergency Infrastructure and Locational Extraction: Problematizing Computer Assisted Dispatch Systems as Public Good

This article analyzes the increasing articulation between third-party software and emergency infrastructures through a focus on the computer-assisted dispatch system RapidDeploy, which purports to help 9-1-1 responders more accurately and efficiently respond to emergency situations. We build from research that focuses on overlaps between surveillance and emergency response to demonstrate how large-scale data mining practices—in particular, location extraction—are repurposed as beneficial, if not life-saving, measures. In focusing on the capacity to extract and analyze location in the name of public good, we discursively analyze how RapidDeploy’s official company blog constructs the benevolence of data collection. We then demonstrate how these viewpoints are reproduced in the news reports in one city—Charleston, South Carolina—where RapidDeploy has formally partnered with emergency response services. This analysis is used to demonstrate how words like “data” and “cloud” continue to be used as vague buzzwords for companies situated at the intersections of surveillance and civic function, and to argue for greater attention to how the trend towards platformization continues to blur the relationships between surveillance and emergency response systems. This case study examines the public face of this company and, as such, analyzes the language used to gain public assent for the software and its function.
A Case Study on silicon chip and Digital signal processor

A silicon chip may be a laptop processor wherever the info process logic and management is enclosed on one microcircuit, or tiny low range of integrated circuits. The silicon chip contains the arithmetic, logic, and management electronic equipment needed to perform the functions of a computer’s central process unit. The microcircuit is capable of deciphering and capital punishment program directions and performing arts arithmetic operations. The silicon chip may be a useful , clock-driven, register-based, digital microcircuit that accepts binary information as input, processes it consistent with directions keep in its memory, and provides results (also in binary form) as output. Microprocessors contain each combinatory logic and successive digital logic
Pretext Tasks selection for multitask self-supervised speech representation learning

Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In various application domains, including computer vision, natural language processing and audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learning to predict such features has proven to be a particularly relevant pretext task leading to building useful self-supervised representations that prove to be effective for downstream tasks. However, methods and common practices for combining such pretext tasks, where each task targets a different group of features for better performance on the downstream task have not been explored and understood properly. In fact, the process relies almost exclusively on a computationally heavy experimental procedure, which becomes intractable with the increase of the number of pretext tasks. This paper introduces a method to select a group of pretext tasks among a set of candidates. The method we propose estimates properly calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process. The experiments conducted on speaker recognition and automatic speech recognition validate our approach, as the groups selected and weighted with our method perform better than classic baselines, thus facilitating the selection and combination of relevant pseudo-labels for self-supervised representation learning.
Recovery-based Data Sharing in Cloud and Recommended by Collaborative Filtering based on Dependable Outsourcing Scheme

Cloud computing is more and more well-liked nowadays. Cloud services like data-outsourcing services offer a growing sort of user’s access to cloud storage for large quantities of data, and enterprise square measure turning to cloud storage for cost-efficient remote backup. In 2011, DEPSKY(Dependable and Secure Storage in Cloud of Clouds) overcomes the restrictions that under the effectiveness of cloud storage: loss of convenience, loss, and corruption of data, loss of privacy, and marketer lock-in. DEPSKY lacks an error detection mechanism and comes with significant computing prices. Therefore, we have got a bent to propose a replacement data-outsourcing theme overcoming not only the four limitations, however conjointly the shortcomings of DEPSKY. We have got a bent to use time server for memory management on cloud, once point in time crossed for the file that files mechanically destroy from the cloud. During this manuscript, we have got a bent to switch Nyberg’s accumulator and apply it to our three projected error-detection strategies. Moreover, we have got a bent to specially style a fast recovery methodology that's quicker than DEPSKY and various strategies.
Opinion Mining for restaurant reviews using Naive Bayes Algorithm

A wealth of unstructured opinion data exists online. Every day, millions of consumers add to this data when they share their opinion on a range of things, including feedback about their experiences with products and services. This feedback is volunteered, it contains the raw, unsolicited views and opinions about a brand, individual or event. Opinion Mining finds out the drivers behind the sentiment. By understanding what is driving the sentiment and how one is performing based on Net Sentiment, opinion data can be used to expose critical areas of strength and weakness. This data allows decision-makers in business, from customer experience and marketing to risk and compliance teams, to make the targeted, strategic overhauls needed to reinvigorate profitability or reclaim slipping market share. It is practically impossible to analyze all this reviews manually, so and automated aspect-based opinion mining approach is used. This paper focuses on aspect level, shows a comparative study amongst existing algorithm and proposes a new syntactic based approach which uses dictionary, aggregate score for opinion words. The dataset used was for restaurant reviews. The proposed method achieved a total accuracy of 87.06%.
Use of Electronic Resources by the Undergraduate Students in Private Engineering Colleges in Coimbatore, Tamil Nadu

The present study investigates the use of electronic resources by the undergraduate students in the private engineering colleges in Coimbatore. The study followed the questionnaire method for collecting the required information from the undergraduate students. Out of the three hundred questionnaires distributed, two hundred and fifty questionnaires were received. The study found thatmajority of the respondents were usedthe Google chrome (72.8%) and Mozilla Firefox (60.8%) web browsers and most preferred search engine is Google (88.4%). The study also found that e- books (72%), e-journals (70%) and e-thesis (64%) are the major e-resources used by respondents.
Research on Theoretical Innovation and Practice of Talents Training Mode in Local Application-oriented Universities Based on Excel and SPSS17.0 Software

With the development of economy and society, it was very important to innovate the training mode of applied talents. Combined with the Internet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> big data platform, this paper investigated the professional cognition data of graduates and teachers and students, used Excel and SPSS17.0 software technology, adopted the principal component analysis and analytic hierarchy process research methods, and combined the expert survey method to study the theoretical innovation and practical path of the talent training mode of local applied colleges. The results showed that the "3+1" talent training innovation model was in line with the development orientation of local application-oriented colleges, and the practice path measures of talent training program were in line with the goals and requirements of talent training. The research results could provide theoretical support for the revision of the talent training program of application-oriented universities.
LibGuides: Introduction to eReading: Video Streaming

Digital collections are an ever-growing part of public library catalogs. This guide will introduce users to options for accessing free eBooks at the public library. This guide is intended to be a basic guide for patrons unfamiliar with eBooks.