Reviewerly/exHarmony huggingface dataset:
The Effect of GeoGebra-Based Learning on Students Spatial Ability and Motivation

The aims of this study are identifying differences between the application of GeoGebra-based learning and Non-GeoGebra on the spatial abilities of students in Geometry Transformation, identifying difference between learning motivation categories and students' spatial ability and identifying differences the effect of interaction between GeoGebra based learning and motivation on students' spatial abilities.The subject in this research was 60 students from 20 classes.Data collection was carried out by taking a questionnaire for learning motivation data and spatial abilities test.The results of the study show the spatial abilities of students with GeoGebra-based learning has a better value than students given Non-GeoGebra learning, students with high motivation and medium motivation have better spatial abilities than students with low motivation.Students with high motivation and medium motivation categories have the same influence on their spatial abilities.Furthermore, there is no interaction between GeoGebra based learning and motivation on students' spatial abilities.
IMPACT OF THE INCLUSION OF ONLINE PRACTICAL ACTIVITIES IN THE SUBJECT PSYCHOLOGICAL ASSESSMENT

Appears in: EDULEARN20 Proceedings Publication year: 2020Page: 817ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0293Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Musikvideor inom konstvetenskaplig tolkning II : Med fokus på jämförande analyser

This essay starts out in the world of art science looking out through the glasses of Anne D’Allevas analysis of performance and videoart, digital art and Roland Bathes Semiotics. By using Meta narr ...
Perancangan Dan Pengujian Perangkat Lunak Kriptografi Gabungan Playfair Cipher Dan Electronic Code Book (ECB)

Information technology is a set of tools to assist the processing or arrangement of the data which has the value of knowledge for its users. In assuring the confidentiality and security of the data required kriptogafi method, which is the study of encryption to maintain the confidentiality the information. It requires software to support specific encoding method so the sent message has become more secure. In this study the authors used a form of research literature and pure experimentation. While the software design methods using RAD (Rapid Application Development) for the software development process emphasize the short development cycles and utilization in the previous function.The data collection techniques using Study Literature, the author studied the books, periodicals, theses, journals, searching the Internet with a trusted source as well as the existing references related to theories of cryptography, algorithms Playfair Cipher with Eletronic Code Book (EBC) and software design. Designing software using Microsoft Visual Basic 6.0. , Combined Playfair Cipher software and Electronic Code Book (ECB) has been run as expected.
O núcleo familiar como coletividade hipervulnerável e a necessidade de sua proteção contra os abusos da publicidade dirigida ao público infantil.

The present work focuses its atten­ tion in the position of hyper-vulnerability of the family core concerning the advertisement of products and services to children. An intense and sophisticated advertising business, serving a billionaire market of products and services directed to children, is able to make vulnerable in an aggravated manner, not only the child, but the parents as well and other members of the family,complicating the adequate exercise of fa­ mily power. ln this sense, the family core must be understood as a collectivity compareci to the hyper-vulnerable child, as it is similarly exposed to the abuses of the consumer market (CDC, art. 29), notably concerning products and services destined to ehildren.
Lightweight Ciphers on a 65 nm ASIC - A Comparative Study on Energy Consumption.

Low energy consumption is an important factor in today's technologies as many devices run on a battery and there are new applications which require long runtimes with very small batteries. As many of these devices are connected to some kind of network, they require encryption/decryption to securely transmit data. Hence, the energy consumption of the cipher is an important factor for the battery life. We evaluate the energy consumption of lightweight ciphers implemented on a custom 65 nm ASIC. Since the energies to measure are very small, we first introduce, compare and evaluate two techniques to precisely measure the energy consumption of a real cryptographic core. In our comparative investigations, using the PRINCE block cipher we examine the effect of the design architecture (round-based versus unrolled) on the amount of energy consumption. In addition to considering other effects (like fixed key versus random key), we compare round-based implementations of different block ciphers (PRINCE, MIDORI and SKINNY) under similar settings providing first such practical investigations.
Criteria for Bracket Abstractions Design

A bracket abstraction is a syntactic operator to abstract variables in combinatory logic. There are different algorithms for different combinator systems. In this paper we present a recursive algorithm scheme, which generates a family of brackets abstractions, in which all the bracket abstractions referenced here are found. In addition, theorems with certain hypotheses are enunciated about the scheme, which state that the resulting abstraction operators, has one property or another. Thus forming a criteria for designing bracket abstractions that comply with a given property.
Text Detection and Text Recogonition in MultiScript

Text is one of the amazing inventions of human. It acts as a bridge between knowledge gainer and knowledge giver. Text can give us the life for what we see. In a field of Computer Vision, text detection and recogoniton plays a vital role in achieving the intelligence for machine. As a human text detection is an easy task. But an achieving that machine is an crucial task. The main objective is to integrate text detection and recognition as one thing. The System consists of 4 modules. They are 1)Text Detection 2)Preprocess the Detected part 3)Text Recogonition 4)Exporting as Documents. The first part is text detection by using Efficiency Accuracy Scene Text Detector and obtaining region of interest. The efficiency accuracy scene text detector contains of two steps only. A first step is to apply Fully Convolution Network to produce text regions. A second step is to apply non-maxima suppression to avoid multiple region of interest. The second part is preprocess the region of interest for increasing the efficiency of recogoniton. This is done by applying threshold to easily separate text from background and apply Gaussian blur for it. The third part is text recogonition by using tesseract OCR. The fourth part is exporting text into documents format by using docx, pypdf package.
Energy Consumption Modeling in Activated Sludge Process Using Coupling PCA-ANFIS Approach

The main challenge in Wastewater Treatment Plants (WWTP) by activated sludge process is the reduction of the energy consumption that varies according to the pollutant load of influent. However, this energy is fundamentally used for aerators in a biological process. The modeling of energy consumption according to the decision parameters deemed necessary for good control of the active sludge process namely the removal yields of parameters pollutant such as Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended solids (SS) and Ammoniac (NH4+) that must meet the required standards. To achieve this objective, a coupling of two approaches, the principal components analysis (PCA) method and the Adaptatif Neural  Fuzzy Inference System (ANFIS) model was envisaged, to improve the performance of fuzzy reasoning. Indeed, PCA as a factorization tool allowing the reducing of the variable that allows the reduction of the complexity of the studied phenomenon. The neuro-fuzzy learning from the data projected on the principal axes allows the improvement of the model, both in learning and validation periods. The comparative study between ANFIS model, the regression PCA model, and the coupling PCA-ANFIS method applied to the raw data was effected. The results indicate a significant improvement in the validation criteria obtained in the coupling PCA-ANFIS model compared to the other models for the learning and validation periods. The result shows that the coupling PCA-ANFIS can be used to extract information from data and to describe the nonlinearity of complex wastewater treatment processes.
FEMALE AND MALES’ BRAIN TENDENCIES IN LEARNING ENGLISH AS A SECOND LANGUAGE

This research is about the female and males’ brain tendencies in learning English as a second language, and it was done at Universitas Kristen Indonesia. The purposes of conducting this research is to find out female and males’ brain tendencies in learning English as a second language. This research was a library research, where researchers as "key instruments" of the research that find any information deals with the topic discussed from books, journals and proceedings sourced from “Pubmed, Google Scholar, Research Gate and other online sources” in order to answer the question. The result is that learning English as a second language achievement of students is not influenced by the competence of the right brain or left brain of students, but the left and right brain provides an understanding of the structure and function of the brain. The division of brain function based on the brain hemisphere allows students to gain a deeper understanding of how the brain works to help them improve students' mastery of subjects.
Examining the Part-of-speech Features in Assessing the Readability of Vietnamese Texts

The readability of the text plays a very important role in selecting appropriate materials for the level of the reader. Text readability in Vietnamese language has received a lot of attention in recent years, however, studies have mainly been limited to simple statistics at the level of a sentence length, word length, etc. In this article, we investigate the role of word-level grammatical characteristics in assessing the difficulty of texts in Vietnamese textbooks. We have used machine learning models (for instance, Decision Tree, K-nearest neighbor, Support Vector Machines, etc.) to evaluate the accuracy of classifying texts according to readability, using grammatical features in word level along with other statistical characteristics. Empirical results show that the presence of POS-level characteristics increases the accuracy of the classification by 2-4%.
Designing Neural Speaker Embeddings with Meta Learning

Neural speaker embeddings trained using classification objectives have demonstrated state-of-the-art performance in multiple applications. Typically, such embeddings are trained on an out-of-domain corpus on a single task e.g., speaker classification, albeit with a large number of classes (speakers). In this work, we reformulate embedding training under the meta-learning paradigm. We redistribute the training corpus as an ensemble of multiple related speaker classification tasks, and learn a representation that generalizes better to unseen speakers. First, we develop an open source toolkit to train x-vectors that is matched in performance with pre-trained Kaldi models for speaker diarization and speaker verification applications. We find that different bottleneck layers in the architecture variedly favor different applications. Next, we use two meta-learning strategies, namely prototypical networks and relation networks, to improve over the x-vector embeddings. Our best performing model achieves a relative improvement of 12.37% and 7.11% in speaker error on the DIHARD II development corpus and the AMI meeting corpus, respectively. We analyze improvements across different domains in the DIHARD corpus. Notably, on the challenging child speech domain, we study the relation between child age and the diarization performance. Further, we show reductions in equal error rate for speaker verification on the SITW corpus (7.68%) and the VOiCES challenge corpus (8.78%). We observe that meta-learning particularly offers benefits in challenging acoustic conditions and recording setups encountered in these corpora. Our experiments illustrate the applicability of meta-learning as a generalized learning paradigm for training deep neural speaker embeddings.
Personal Semantic Variations in Word Meanings: Induction, Application, and Analysis

When people verbalize what they have felt with different sensory functions, they often represent different meanings such as with temperature range using the same word cold or the same meaning by using different words (e.g., hazy and cloudy). These interpersonal variations in word meanings have the effects of not only preventing people from communicating efficiently with each other but also causing troubles in natural language processing (NLP). Accordingly, to highlight interpersonal semantic variations in word meanings, a method for inducing personalized word embeddings is proposed. This method learns word embeddings from an NLP task, distinguishing each word used by different individuals. Review-target identification was adopted as a task to prevent irrelevant biases from contaminating word embeddings. The scalability and stability of inducing personalized word embeddings were improved using a residual network and independent fine-tuning for each individual through multi-task learning along with target-attribute predictions. The results of the experiments using two large scale review datasets confirmed that the proposed method was effective for estimating the target items, and the resulting word embeddings were also effective in solving sentiment analysis. By using the acquired personalized word embeddings, it was possible to reveal tendencies in semantic variations of the word meanings.
SOM Library: Find It @ UofSC Libraries Project: Tutorials

Describes the changes to searching for resources at the SOM LIbrary and the statewide library systems.
In-GPU Cache for Acceleration of Anomaly Detection in Blockchain

Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.
Security Recommendatios And Security Issues Associated With Big Data In Cloud Computing

In this paper, we discuss security surveillance for Big data, cloud computing, Map Reduce and Hadoop environment. The focal point is on security surveillance in cloud computing that are corresponding with big data. Big data applications are a admirable beneficent to organizations, business, companies and many large scale and small scale industries.We also talk about various attainable solutions for the issues in Hadoop and cloud computing security. Cloud computing security is being blossom at a rapid pace which incorporate with computer security, network security, information security, and data privacy.Cloud computing plays a very crucial role in keep safe data, applications and the related infrastructure with the help of technologies, policies,controls , and big data tools. Moreover, Cloud computing, big data and its applications, advantages are likely to illustrated the most hopeful new boundaries in science.
Evaluation information within the culture of social and humanities research

&amp;nbsp; The modern social and humanities research use evaluation information, therefore the key task of the scientific-methodological culture of a researchers is to philosophically substantiate the veracity of research results of the object (conducted by the subject), which represents evaluation information on properties of the objects. This determines the undeniable relevance of this work. The subject of philosophical study is the evaluation-value problematic. The goal consists in formation of socio-philosophical representation on the problems of application of evaluation information in the culture of social and humanities research. The scientific novelty consists in philosophical analysis of the evaluation-value problems within the culture of social and humanities cognition. The objectivity of cognition is to subjectively (this is the only way) reveal all properties of the object. Moreover, in not only actual science, but also social life, objectivity is defined subjectively by the means of evaluation. The ideas contained in this article may become the conceptual framework for social and humanities theories, as well as practical activity of the institutions of government regulation of social relations. &amp;nbsp;
Research Guides: Creative Commons Licenses: Compilations, Mashups and Other Tricky Situations

This guide provides information on licensing your materials using Creative Commons Licenses and using items that are so licensed.
Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous challenge in the watermark field. Although a lot of geometric distortions resilient watermarking schemes have been proposed, few of them perform well against local geometric distortion like random bending attack (RBA). To address this problem, this paper proposes a novel watermark synchronization process and the corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to get a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then the symmetrical watermark is embedded into the spatial domain of the host image in an additive way. In watermark extraction, we first get the theoretically mean-square error minimized estimation of the watermark. Then the auto-convolution function is applied to this estimation to detect the symmetry and get a watermark units map. According to this map, the watermark can be accurately synchronized, and then the extraction can be done. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks.
Pelatihan Penggunaan Strategi Membaca bagi Siswa Kelas IX SMP Negeri 4 Kota Solok dalam Menjawab Soal Ujian Nasional Bahasa Inggris

The readiness of grade IX students of SMP in facing the national exam is still not optimal. It can be seen from the obstacles encountered by the students related to the amount of material being studied and the limited time available at school. Furthermore, the students are also still unfamiliar with the UN questions tested due to the lack of practice in discussing UN questions. This problem can be overcomed by providing training to students on the use of reading strategies to be applied in answering English UN questions. The subjects of this service program are grade IX students of SMP Negeri 4 Solok City who will take the national exam. The use of reading strategies is an approach to understand English texts that can be used in solving English UN questions. The method of implementing this service program is carried out through several stages, namely (1) giving try out to assess students' ability to answer UN questions, (2) giving guidance and training in using reading strategies in answering UN questions, and (3) giving final try out as evaluation of the implementation of community service activities. The results of this community service activity showed that students have broad insights on strategies to solve English National Examination questions. Students also become more enthusiastic and active in answering English UN questions. Besides that, students also get information that is very useful in preparing themselves as early as possible to face the national exam. Given the large benefits of community service activities, it is necessary to provide intensive and structured training for grade IX students of SMP on tips and tricks for answering English UN questions easily.
The Effect of Automatic Feedback System on Students’ Comments to Improve their Performance

This research deeply focuses on understanding student performance by giving automatic feedback after writing freestyle comment data in each lesson. Writing comments express students’ learning activities, tendencies, attitudes, and situations involved with the lesson. Random Forest and Support Vector Machine were applied to analyze the students’ prediction results. Also, a Majority Vote (MV) method is employed in consecutive lessons during the semester to the predicted results. The proposed system tracks student's learning activities and attitudes with different courses and provides valuable feedback to improve the educational performance. Keywords— Automatic feedback, Comment data, SVM, RF, MV
Integrating Design Thinking into Scrum Framework in the Context of Requirements Engineering Management.

Scrum is one of the Agile software development processes broadly adopted by industry. Scrum promotes frequent customer involvement and incremental short releases. Despite its popular use, Scrum's requirements engineering stage is inadequately defined which can lead to increase development time and cost, along with low quality or failure for the end products. This paper proposes an integrated framework, where the Design Thinking model is integrated with the Scrum framework in the context of requirements engineering management. Design Thinking is an iterative process, human-centric, and structured problem-solving approach. This integration aims to improve the existing Scrum requirements engineering by overcoming its challenges in cases, such as providing a comprehensive understanding of the customer's needs, and addressing the effects of the challenges in other cases, such as frequent changes of requirements.