Reviewerly/exHarmony huggingface dataset:
Setting Goals on Smart Techniques and Affecting Student Motivation

The article raises the question of how to correctly set students ’goals (primarily on Smart Techniques) and how it affects their motivation. The most common method of setting SMART-goals is a type of standard. According to this standard, each target must meet certain criteria before subordinators. There are 5: clear, measurable, achievable, real, determined in time.
A Content Based Filtering Approach for the Automatic Tuning of Compiler Optimizations

Recently a large number of compiler conversions have been implemented to optimize programs.  A comprehensive exploration of all possible sequences of optimization  is not practical because the search space is huge considering the large number of compiler optimizations passes. In addition, predicting the effectiveness of these optimizations is not an easy task.  In this work, the suggested approach offers automatic tuning of compiler optimization sequences in place of manually tuning by recommended optimization sequences based on program features. Techniques inspired from the Recommendation System (RS) field to provide a solution to the autotuning of compiler optimizations problem. Content Based filtering method is finding a group of programs that are closest to the unseen program based on the similarity of their features. Then the best optimization sequences for these programs are recommended to the unseen one. Two versions of the CBF method, with and without rate value are presented.The approach is evaluated using three benchmark suites PolyBench, Shootout, and Stanford, including 50 different programs and using LLVM (Low Level Virtual Machine) compiler passes down Linux Ubuntu. Results obtained showed that such method is superior to the standard level of optimization -O3 of LLVM compiler in improving the execution time  by an average of 9.3 % for CBF without rate, 13.7% for CBF with rate.
Techniques d’apprentissage automatique pour les données Semistructurées

In a context where most of our data is online, information comes from a great diversity of actors, and is now stored in a great variety of formats. This ranges from highly structured formats such as traditional databases to simple texts, and includes many intermediary formats - such as XML, HTML, JSON or HTML for instance - usually grouped under the term semi-structured data. This diversity in format, but also the wide range of tools that are needed to manipulate those data, coupled with the fact that their structuration is often underspecified is a problem for the end-user as it can be hard for a non-expert to simply extract the data he truly needs. Machine learning can provide solutions to automatically design tools that helps the user to query or transform semistructured data. In this work, we investigate how technics issued from grammatical inference can be adapted in this framework.
An Ontology-Based Virtual Orrery

Tutorials in this technical memorandum explain how to use the Protege ontology editor to import data from Excel and export data from Protege in the JavaScript Object Notation Linked Data (JSON-LD) format then how to transform the JSON-LD file into variables for web apps. These tutorials apply JavaScript and R programming languages and 3D graphics libraries. Ontologies enable standardization, data sharing, and semantic interoperability. An ontology models a domain of knowledge as taxonomies of annotated classes, data properties, relational object properties, and inference rules. This technical memorandum includes a data dictionary for a subset of the Space Situational Awareness Ontology (SSAO).
Improving the Security of Steganography in Video Using Genetic Algorithm

There has been a great development in information technology and methods of sending it via the Internet, but these developments expose the information transferred to violations of its security. To maintain information security, steganography and encryption are used. Hiding technology in multimedia, such as audio, video, text or pictures, provides an ability to protect information. Digital video is one of the most common data embedding media for its high ability to hide sensitive data. Where many methods have been proposed to hide the data in the video to prevent it from being seen by unauthorized persons. This paper proposes a new approach to hiding data in video by using two bitand and bitor processes without using the well-known LSB algorithm in the hiding method. In addition, confidential information is first encrypted using a genetic algorithm by which the encryption key is generated, which increases the strength of the proposed work. The experimental results showed that the used method is effective and safe as it provides a PSNR of 63, which makes the embedding capacity high.
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks

Most of existing statistical theories on deep neural networks have sample complexities cursed by the data dimension and therefore cannot well explain the empirical success of deep learning on high-dimensional data. To bridge this gap, we propose to exploit low-dimensional geometric structures of the real world data sets. We establish theoretical guarantees of convolutional residual networks (ConvResNet) in terms of function approximation and statistical estimation for binary classification. Specifically, given the data lying on a $d$-dimensional manifold isometrically embedded in $\mathbb{R}^D$, we prove that if the network architecture is properly chosen, ConvResNets can (1) approximate Besov functions on manifolds with arbitrary accuracy, and (2) learn a classifier by minimizing the empirical logistic risk, which gives an excess risk in the order of $n^{-\frac{s}{2s+2(s\vee d)}}$, where $s$ is a smoothness parameter. This implies that the sample complexity depends on the intrinsic dimension $d$, instead of the data dimension $D$. Our results demonstrate that ConvResNets are adaptive to low-dimensional structures of data sets.
Research Guides: IDT 4170 and 7170: Mobile Learning: Contact Your Librarian

Your CECH Library course guide for IDT 4170 and 7170. This guide links to course readings by module. For research support, see the Technology and Instructional Design research guide linked from the home section of this course guide.
Designing Lightweight S-Box Using Simplified Finite Field Inversion Mapping

Emerging areas like the IoT, etc., have computing environments that consist of numerous resource-constraint devices that are interconnected and communicated to each other. These devices need to operate in a secured environment; however, conventional cryptography is not suitable as they have low computational and memory resources. Security for such devices can be ensured by using lightweight cryptography instead. In this paper, a technique was proposed to design smaller S-boxes that can be used in lightweight block ciphers, hash functions, etc. The design technique used in the AES S-box was adopted and simplified in order to make these smaller S-boxes. The proposed S-boxes were compared with those used in the PRESENT cipher and the LUFFA hash function in terms of the different cryptographic properties and parameters. In addition, a change in the nonlinearity value of the proposed S-box was also calculated with reference to that of an AES S-box.
Damage of reading Indonesian phrase with dislexic patients in Medan: neuropsycholinguistics analyze:

The objectives of this study were (1) to classify the damage of reading Indonesian phrase in Dyslexic Patients (PDS) (2) Classify the disorder of neuropsychocognitive of  Dyslexic Patients (PDS). Theoretically this research explains there is intervention and ability concerning memories, productions, thoughts, meanings, and emotions that are very influential in someone's reading because of disordering in the brain. Given this research is very complex, so this research apply research and development method (research & development). This research is oriented to a cycle that begins with the collection of information research results in the form of difficulty reading and neuropsychocognitive. Dyslexic patient in research subject are 3 peoples.Method used for data collection is test reading and memories. The basic technique is used by using tapping technique, ie tapping the PDS 1-3 taken from Medan Extraordinary School.  Data analyzing in this research is to classified of difficulty of reading and memories and to classified long instruction in the sama time. The conclusions are (1) dyslexic patient could’nt read and thinking tree words or the complex words (2) dyslexic patient also difficult to understanding the long instruction, long term memories also damage.
A Detail Survey on Deep Learning Based Outfit Recommendation System

The Outfit recommending plays a growing role in the online retail market.1 The aim of the equipment advisory is to inspire the engagement and involvement of people in online shopping by recommending trendy outfits. Propose a neural multi- task system, known as the guideline for neural equipment (NOR). Two main ingredients are composed of NOR: matching and generation of statement. A neural network convolutionary (CNN) with a framework for shared attention to derive visual characteristics of outfits. A multi-modal, gated recurring (RNN) neural network to transform visual functions into a succinct phrase. System need to recommend a short list of bottoms according to top and recommend a short list of tops according to bottom so as to explain why the top and the bottom match, and vice versa.
Framework for Quantifying Cyber Security Risks

In recent years we see that an increasing amount of information becomes available that can be beneficial to the security risk process. Traditionally, security risk management is an asset-based, qualitative process based on expert opinion and information at hand; periodically a group of experts assesses applicable risks and determines correct risk levels, and whether new risks should be added to the list. We propose in this paper a threat-based, traceable quantitative risk management approach, that uses current information to quantify risks. This leads to a near real-time risk process, where available information is processed and the risks are automatically updated. The approach was tested in practice at the main banks in the Netherlands
Analysis of User’s Opinion using Deep Neural Network Techniques

Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network.
A Survey of Various Recommendation Systems Based on Cloud Systems

The world needs serial growth in the sense of business as well as economy. Business is the need which provides employment to a number of people struggling to build their life. Here comes the benevolence of technology that enhances the business strategies to improve in every possible aspect. And that is where recommendation systems emerge as a boon. A recommendation system could profit businesses in every manner possible right from e-commerce to media, banking, and many more. A step closer to this can be predicting a specific activity from the number of events that occurred over time. A fusion of more than two models could work even better to build such a system.
Изучение теоретико-методологической готовности студентов-дефектологов к овладению магистерским уровнем образования

Introduction. The aim of the research was to study the level of studentsʼ readiness to follow master degree programs in the field of special (defectological) education.Materials and Methods. The research constitutes a theoretical analysis of the problem of readiness of defectology students to pursue the master degree level of education. In the course of empirical research, the results of a questionnaire and self-assessment were used. The survey included 68 master degree students of 4 master degree programs. Statistical processing of the results was carried out using the Statistica 10.0 software package for the Windows.Results. The authors found that the target guidelines for the reconstruction of the professional training of master students in the field of special (defectological) education is a set of expected knowledge, skills, and abilities from the standpoint of modern requirements and social need for the activities of a specialist in the changing conditions of education, upbringing and development of children with disabilities; the designing of a master program requires the actualization of theoretical and methodological training, taking into account its extrapolation to the level of implementation of applied professional competencies in accordance with the modern needs of society and the dynamics of the educational labor market.Discussion and Conclusion. The results obtained facilitate the development of professional training of master students in the field of special (defectological) education and allow us to consider theoretical and methodological readiness to pursue a master course in terms of continuity of educational tasks and the continuity of educational levels that make up the fundamental principles for the preparation of a competitive graduate.
EFEKTIVITAS KIT RANGKAIAN LISTRIK SEBAGAI MEDIA PEMBELAJARAN UNTUK MENINGKATAN HASIL BELAJAR SISWA SMP

Abstrak Penelitian ini bertujuan untuk mendeskripsikan efektivitas Kit Rangkaian Listrik sebagai media pembelajaran untuk meningkatkan hasil belajar siswa SMP. Jenis penelitian yang digunakan adalah penelitian pengembangan Research & Development yang terdiri dari 10 tahap namun dibatasi sampai pada tahap uji coba produk saja. Subjek uji coba penelitian ini terdiri dari 32 siswa kelas IX-A MTs Negeri 3 Kediri. Keefektifan dari Kit Rangkaian Listrik yang dikembangkan diperoleh berdasarkan uji N-Gain score dari nilai pretest dan posttest hasil belajar siswa pada aspek pengetahuan. Hasil penelitian didapatkan rata-rata N-Gain pretest dan posttest sebesar 0.6 dengan kategori sedang. Berdasarkan hasil penelitian tersebut dapat disimpulkan bahwa Kit Rangkaian Listrik sebagai media pembelajaran untuk meningkatkan hasil belajar siswa SMP layak digunakan sebagai media pembelajaran. Kata Kunci: Efektivitas, Kit Rangkaian Listrik , Hasil Belajar Abstract The purpose of research is to describe the effectiveness of the Electric Circuit Kit as a learning medium to improve junior high school student learning outcomes. The type of research used is Research & Development which consists of 10 stages but is limited to the product testing phase only. The subjects of this research trial consisted of 32 students of class IX-A MTs Negeri 3 Kediri. The effectiveness of the Electric Circuit Kit that was developed was obtained based on the N-Gain score from the value of the pretest and posttest of student learning outcomes on the knowledge aspect. The results showed that the average N-Gain pretest and posttest was 0.6 with the medium category. Based on the results of these studies it can be concluded that the Electric Circuit Kit as a learning medium to improve junior high school student learning outcomes is worthy of being used as a learning medium. Keywords: Effectiveness, Electric Circuit Kits, Learning Outcomes
Roadway Traffic Analysis Scheme using Unmanned Aerial Vehicle Based on Image Processing and Edge Computing

Traffic congestion is a major problem which is increasing day by day in developing countries. So, traffic analyzing has become a more important issue. For the development of smart cities, traffic management using UAV is one of the most important asset. UAV used for roadway traffic management are more efficient and easier to detect the traffic and send the required data to the receiver station and it performs continuous operations with no human intervention. There has been voluminous research which provided that unmanned aerial vehicles (UAV’s) are a viable and less time-consuming alternative to real time disturbed traffic analysis and management, providing the eye-in the sky solution to the problem. UAVs use image processing algorithms for detecting the roadways and vehicles. We present a simple approach to road traffic analyzing using UAVs by combining the concepts of edge detection in image processing and edge computing representing the road network. The results reflect the value of flexibility and bird-eye view provided by UAV videos or images thereby depicting the overall applicability of the UAV-based traffic analysis system. The further Research On this application might be done in future.
Empathy, Diversity, and Poetry: The Position of Multicultural Literature in Digitalized Network Society

Poetry is more than just a literary expression. It may produce empathy and cultural competence vitally needed to navigate todays digitalized network society when cultivated in the right way. This paper elaborates empathy on diversity through poetry. This is a qualitative research conducted in two layers of analysis: textual analysis of the poems and analysis of students responses on the empathy and diversity issues. The two poems on diversity are an Indonesian poem I See Indonesia by Soekarno and a Taiwanese poem Song of the Island by Chen Li. 75 respondents from English Department of Bina Nusantara University, Jakarta Indonesia and Aletheia University, New Taipei-Taiwan were involved. The results show that the two poems have reflected and produced empathy both in the authors and the readers. It is also concluded that poetry is utilized beyond its literary function in navigating diversity through cultivating empathy in studying poetry and multicultural literature as the research has proven that almost all (96.66%) of Indonesian students agree that multicultural literature helps them develop their CQ (Cultural Quotient/Competence) and 86.66% of Taiwanese students also agree and believe the same. Most of them finally believe that CQ positively contributes to their preparation of their future profession.
The Effectiveness of Intercultural Communication in Comprehending the Short Story - Shooting an Elephant by George Orwell

This study aims at analyzing intercultural communication in shooting an Elephant (Short Story) by George Orwell. The study investigates the intercultural communication from various perspectives: culture, identity, prejudice, ethnicity, nationality, gender and social class. The researchers choose this short story because it encompasses intercultural communication, which is composed of British and Burma in colonization era. The method of the study is based on discursive, close and deconstructive reading in comprehending the text. The researchers use ethnographic approach entailing the following aspects: nationality, ethnicity, social stratification, culture, gender, age, and prejudice. The story shows the intercultural communication from various perspectives in terms of colonizer and being colonized. The study recommends that identity involves collective thinking and mass orientation, feeling of being oppressed can lead to prejudice and otherization and intercultural communication should be considered from negotiate shared meanings, which have three layers of meaning: content, relational, and identity and it should be understood from relational, psychological, and physical contexts aspect.
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact. On the algorithmic side, many NP-hard problems can be expressed as integer programs, in which the constraints play the role of their specification. In this work, we aim to integrate integer programming solvers into neural network architectures as layers capable of learning both the cost terms and the constraints. The resulting end-to-end trainable architectures jointly extract features from raw data and solve a suitable (learned) combinatorial problem with state-of-the-art integer programming solvers. We demonstrate the potential of such layers with an extensive performance analysis on synthetic data and with a demonstration on a competitive computer vision keypoint matching benchmark.
담화 표지 화제 결속 기능 연구 - 화제 시작 기능을 중심으로 -

This study aims to categorize ‘topic coherence function’ of Korean discourse markers and analyze ‘starting a topic function’. First, the study analyzes the functions of discourse markers from the perspective of ‘discourse coherence’ and analyzes their functions to classify them into seven types of coherence layers. The topic coherence function was further divided into ‘macroscopic coherence of topic’ including starting a topic, changing a topic, and returning to a topic and ‘microscopic coherence of topic’ including ‘filling the gap,’ ‘modifying the articulation,’ and ‘listing.’ Then discourse markers provided according to the functions and the usage of each function have been analyzed. A list of discourse markers by function is provided. The significance of the present study comes from systematically classifying the topic coherence functions of discourse markers that have been sporadically suggested and providing a list of discourse markers for Starting a topic function from the perspective of teaching Korean as a foreign language. The results of the present study can be utilized as fundamental data in selecting discourse markers as a lesson and in composing a syllabus.
Effect of PDCA Cycle Management Mode on Drug Loss in Inpatient Pharmacy

Objective: To assess effect of PDCA cycle management mode on drug loss in inpatient pharmacy.Methods: From January 2019 to December 2020, we collected the data from hospital work record of inpatient pharmacy each season and data of total drug loss.The valid data of scrap drugs included item name, specification, packing, quantity, wholesale price, expiry date, and scrap reason.In scrap drugs record of hospital, the inpatient pharmacy managers often record drug data from actual situation of inpatient pharmacy and documents from the drug supplier.In addition, we also collected the change of for each season, and compare the result between 2019 and 2020.Result: The results showed that the number of damaged batches reported in 2019 was significantly higher than the number reported in 2020 (122 vs 77), with a difference of 68% between them.Among the drug loss amount, the loss amount increased with the increase of the number of batches reported to be damaged, and the result of loss amount differed by 54%.In quarter records, we observed that most of the losses occurred in the first quarter and the fourth quarter, with monetary losses of around RMB 2,000 in 2020 and about RMB 3,200 in 2019.Compared with 2019 group, there is a lower amount loss (RMB 10,157.88vs RMB 5515.14) in the amount loss caused by drug loss in 2020, and the annual reported loss in 2020 group is 54% of the annual reported loss in 2019.Further, the dollar loss for each quarter in 2020 group was lower than for each quarter in 2019.Conclusion: PDCA cycle management mode effectively reduced drug broken event, that it provided continuous improvement as the inpatient pharmacy carried out this cycle management.