Reviewerly/exHarmony huggingface dataset:
A Method for Similarity Detection in Vector Space by Summarizing News Articles

With the spread of internet journalism, traditional media organs are undergoing a huge transformation. News articles on the internet are generally published by different sources with similar content and it is difficult to reach the content of the news quickly for the visitors. Clickbait headlines are used to be visited by more users. It becomes harder for visitors to reach the basic elements of journalism, such as what, when, where, how and who. In this study, 28.000 news which were published in January 2022 from 5 different news sources in Turkish were collected through a web scraper developed by us and summarized with the TextRank algorithm. A method has been developed to detect similar news articles from different sources by creating news vectors using raw and summarized news texts, and approximate nearest neighbors searching among news vectors. To measure the success of the system, a web-based voting system has been developed and evaluated by experts.
A Criticism on Popular Sketch Datasets

Sketching is a tool that people can use without any training and benefit from when communicating, thinking or keeping records. The wide range of uses of sketching has made it a high-potential, promising research topic for human-computer interaction researchers. The first step for the researchers who were working for this purpose was developing sketch recognition models. However, in order to continue these studies, they needed a large amount of sketch data. Creating these datasets is a costly task. For this reason, the cheapest methods that enable to produce a large number of sketches quickly were preferred in the research. Although the required amount of sketching data has been collected thanks to these methods, it is necessary to question their quality and similarity to the sketches created during daily life interactions. In this article, a critical comparison of the most widely used sketch datasets in the literature with the sketches we create during daily life interactions is made. In addition, a new dataset which consists of sketches that are created during human-human interactions is introduced. The study showed that popular sketch datasets do not reflect the quality of sketches we create in our daily life.
Real-Time Construction of Stereoscopic Video Frames using Three Omnidirectional Cameras

There are many studies about acquisition of monoscopic and stereoscopic panorama in the literature. Since obtaining motion parallax accurately without having distortions is a very challenging problem, especially 360-degree stereoscopic image and video capturing has become a prevalent research topic. However, studies in this topic have focused on costly systems with many cameras and high processing power demand. In this study, which presents an efficient solution in terms of processing power and cost, stereoscopic frames are processed in real time using three consumer grade 360-degree cameras whose outputs are sampled according to view orientation. Besides, a method is developed to eliminate the distortions around the borders of the field of view with the help of blending with selected auxiliary camera frames.
Çizge Uygulamalarına Özel İşlemci Tasarımı

Many widely used big data applications make use of irregular and iterative graph processing algorithms like PageRank, collaborative filtering and betweenness centrality. In a graph structure, each vertex corresponds to a person or object, while each edge corresponds to the relationship between a pair of persons or objects. In executing algorithms that process such large datasets, general-purpose processors may be inefficient. In particular, due to power constraints, only a portion of the processor can be used simultaneously. This, in turn, prevents the efficient use of the processor, which leads to inactive areas termed as "dark silicone". The aim of this paper is to design a processor architecture that will run large datasets on irregular graphs quickly, efficiently and in an easily programmable fashion. Most of the studies previously presented for graph applications are only at the software or accelerator level, and the processor-level designs are different from the processor to be introduced for this paper in terms of hardware cost, required architectural support, and instruction set modifications.
Network Traffic Classification with Flow Based Approach

Traffic classification has a crucial place in today&#x2019;s technology in terms of detecting events on the network. With the developing encryption and evasion techniques, the toughness of traffic classification is increasing. It is seen that traditional methods have low success rate on current datasets. In this paper, a classification algorithm based on statistical and payload based features with the use of machine learning techniques is proposed. The proposed algorithm has been tested on a dataset containing up-to-date network traffic. The obtained results show that the algorithm has high accuracy.
A Novel Visual Attribute Disentanglement Approach using Self-Supervision

In this work, we present a self-supervised method to identify the semantic axes in the latent space of a generator in a generative adversarial network (GAN) setting. In this context, a novel Direction-Disentangler (DD) model is proposed. The data that is necessary for the training of this model is generated using the generator in a self-supervised fashion. Using this data, the DD model is trained to identify the direction that encodes the discriminative features between the two reference images in the generator&#x2019;s latent space. The early empirical results using the MNIST dataset show that the identified direction is effective for editing the relevant semantic attributes of different reference images as well.
Semantic Borrowing for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is one of the most realistic but challenging problems due to the partiality of the classifier to supervised classes, especially under the class-inductive instance-inductive (CIII) training setting, where testing data are not available. Instance-borrowing methods and synthesizing methods solve it to some extent with the help of testing semantics, but therefore neither can be used under CIII. Besides, the latter require the training process of a classifier after generating examples. In contrast, a novel non-transductive regularization under CIII called Semantic Borrowing (SB) for improving GZSL methods with compatibility metric learning is proposed in this paper, which not only can be used for training linear models, but also nonlinear ones such as artificial neural networks. This regularization item in the loss function borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unknown classes would not be available for training while this approach does NOT need it. Extensive experiments on GZSL benchmark datasets show that SB can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification, surpassing inductive GZSL state of the arts.
Two measures elaborated for entangled states: Quantum entropy and fidelity using Schmidt coefficients of the reduced density matrix of full TRI

In the present study, we determined quantum entanglement in a full trapped ion (TRI)-coherent system and its dependence on the LambDicke parameter (LDP). We investigated the entanglement in view of two elaborated measurements of the family: entropy and fidelity. We selected three values of the deep LDP to demonstrate the benefits of these two critical measures. The findings obtained in this study showed that the maximum value of fidelity for entangled states is quantified approximately to be 0.35, and the long lifetime is also observed with entropy measurement. The findings suggest that three coupling parameters play a significant role in developing quantum entanglement.
Challenges and Opportunities of Machine Learning and Deep Learning Techniques for the Internet of Drones

The term IoD originates from internet of things (IoT), drones replace things. The term IoD is introduced in the autonomous flying robot or machine to make drones smarter. The internet of drones (IoD) is a layered framework control building organized generally for arranging the passage of unmanned aeronautical vehicles to controlled airspace and giving course organization between zones suggested as center points. Artificial intelligence (AI) is a term used straightforwardly in the drone business: anyway, Now human-made think AI is used in drones to make this flying machine increasingly splendid. In collaboration of drones to IoD, it will make, the existing sectors intelligent and smarter to work. Many studies have been utilized the power of supervised, semi-supervised, unsupervised machine learning (ML) and deep learning (DL) algorithms and the IoD to extract useful information from various sector where we can use application of IoD. ML, DL and IoD are the only two fields which when both applied together are able to give solutions to solve the real-world problems. This study focuses on the significant research in the area of smart drones. We will find the various algorithms of ML, deepfake, GAN, and DL. We also compare the performance of various algorithms in terms of computational performance which will make more smart drones. So, findings of this study are helpful for various application field of drones.
An Analysis of the Characteristics of Victims of Special Fraud

There are various crimes that threaten our daily lives. Among them, special fraud, in which criminals impersonate family members, deceive victims, and demand money from them, has become a problem. Although the number of special frauds has been on the decline since its peak in 1997, the number of victims has not dissipated. The government, police, financial institutions, and others are cooperating to take various measures. However, at present, many people are easily deceived.Therefore, this study aims to clarify the awareness of victims based on a questionnaire survey of victims and those who escaped being victimized.
Estimating the Difficulty of Courses From Syllabuses

Syllabuses of many universities are published on the Web, and in some cases, past average scores are also published for the convenience of student registration. In this study, we investigate the relationship between the content of the syllabuses and the average scores, and clarify whether the difficulty level of the courses can be estimated from the syllabuses. In the proposed method, first, the topics that make up the syllabuses are extracted using topic analysis. Then, we find out how much each syllabus contains these topics. The difficulty of courses is estimated from these features using support vector machine. 10-fold cross-validation was performed for the evaluation, and it became clear that the difficulty level can be estimated with an accuracy of 62.3%.
Changes of Moodle Usage Trends by Academic Year in a Medical School during the COVID-19 Pandemic

Owing to the COVID-19 pandemic, many universities have had to use online based education in 2020 which has continued into 2021. However, many universities restarted face-to-face classes in 2021. In this study, the differences in and usage trends of the Learning Management System (LMS) in a medical school were examined. The analysis was conducted using the log counts of eleven activities including assignment submission and viewing on-demand lecture videos. Although 2021 showcased less usage of the LMS than 2020 owing to the restarting of face-to-face classes, some specific usages were higher than in 2020, such as HTML 5 Package (H5P) usage. Additionally, the usages of resources such as PDFs and lecture videos did not differ greatly between 2020 and 2021 when compared to other activities such as quizzes. Although more detailed analysis, including a statistical approach, are needed to clarify the usage trends, these differences would be important in discussing the trends and changes of teaching and learning styles.
Feature Selection by Thematic and Temporal Distinction in Research Grant Applications

We propose an effective method for selecting feature documents from a research grant database. The goal is to build a useful corpus for analytical tasks. While grant applications adopted in the past contain abundant information for institutional research, older applications are not assigned newer category labels for research areas. It is often difficult to apply unlabeled data to established techniques for data science and text analysis. To deal with this issue, our method automatically categorizes unlabeled grant applications into existing research categories. Using a document-by-document search technique, our method selects the best feature documents that are effective for improving the classification accuracy. To confirm the effectiveness of our proposed method, we conducted experiments using actual grant applications. The useful findings obtained in this study are as follows. (i) Using labeled grant applications, unlabeled grant applications are assigned labels to build a well-assorted corpus that includes the same number of grant applications from each research category of each year. (ii) By selecting a certain number of best feature documents from each research category of each year, the classification accuracy can be improved compared to that obtained using the initial dataset of labeled documents.
Impact of Missing Data on the Processing of Educational Questionnaires and the Reported Results

Recent developments in Information and Communication Technology have made it possible to utilize various types of data to improve methods and outcomes in the field of education. However, data verification requires proper handling of that data. Therefore, in this study, we examined missing data and the handling of information from an educational questionnaire of student perceptions about blended learning at a graduate school in Tokyo, Japan. The results suggest that the missing data in the studied questionnaire occurred in a specific cluster. However, in this case, the missing data were not significant enough to alter conclusions based on data analysis of the questionnaire results.
Exploring Relations in Untrimmed Videos for Self-Supervised Learning

Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this sense, these methods are not really self-supervised. In this paper, we propose a novel self-supervised method, referred to as Exploring Relations in Untrimmed Videos (ERUV), which can be straightforwardly applied to untrimmed videos (real unlabeled) to learn spatio-temporal features. ERUV first generates single-shot videos by shot change detection. Then a designed sampling strategy is used to model relations for video clips. The strategy is saved as our self-supervision signals. Finally, the network learns representations by predicting the category of relations between the video clips. ERUV is able to compare the differences and similarities of videos, which is also an essential procedure for action and video related tasks. We validate our learned models with action recognition and video retrieval tasks with three kinds of 3D CNNs. Experimental results show that ERUV is able to learn richer representations and it outperforms state-of-the-art self-supervised methods with significant margins.
DEVELOPING ELECTRONIC STUDENT-BASED PROBLEM WORKSHEET FOR PRIMARY SCHOOL STUDENT

The shift from face-to-face learning to online learning during the COVID-19 pandemic has made printed teaching materials irrelevant. Teaching materials in online learning need to be designed to be more interactive in the form of electronic teaching materials in order to create two-way communication between teaching materials and students because online learning requires maximum learning autonomy from students. The irrelevance of existing teaching materials causes various learning problems in primary schools such as the teaching materials used do not encourage students' willingness to learn, some students experience learning backwards, and teachers need extra time to correct students' assignments. This study aims to develop electronic student worksheets based on Problem Based Learning (PBL) in primary schools. This development research refers to the ADDIE model as the model consists of systematic stages in an effort to solve learning problems according to the characteristics of the learner. The subjects of this study were 40 grade 3 students of Madrasah Ibtidaiyah (Islamic Primary School) Pembangunan Lamongan. The results showed 1) the validity of the electronic student worksheets in the very valid category based on the assessments of linguists, material experts, design experts, and learning experts, 2) The attractiveness of electronic student worksheets in the very interesting category. Based on the results of this study, the use of electronic student worksheets is appropriate to be used to support the thematic learning process.
A Framework for Developing XR Applications Including Multiple Sensorial Media

eXtended Reality applications include multiple sensorial media to increase the quality of the User Experience. In addition to traditional media, video, and sound, two other senses are typically integrated: touch and smell. The development of applications that integrate multiple sensorial media requires a framework for properly managing their activation and synchronization. The paper describes a framework for the development of eXtended Reality mulsemedia applications and presents some applications based on the integration of smells developed using the framework.
Scientific and Technological Support: Fundamental Theoretical Issues with Revolutionary Technologies

AbstractGlobal-scale collaborative observation on land and sea requires cutting-edge deep-Earth research. The fundamental prerequisite for obtaining full information in the deep Earth is to conduct high-quality seismic observation in marine areas, given that the oceans and seas cover 70% of the Earth’s surface..
Aspects of Human Capital Management and Employee Job Performance: The Moderation Role of Perceived Organizational Support

Objective: This study examines the aspects of human capital management and its effects on employee job performance and the moderation role of perceived organizational support. Design/Methods/Approach: This study is structured on a quantitative approach, with stratified and simple random sampling techniques. This study reports the responses of 426 respondents from twelve banking institutions in Addis Ababa, Ethiopia. Exploratory and confirmatory factor analyses were applied to test the factors and verify the factor structure of a set of observed variables. The scales were tested for reliability and validity. Structural equation modeling with AMOS was used to test the hypothesized relationships. Findings: The results show that the aspects of human capital management, namely knowledge accessibility, learning capacity, workforce optimization, leadership practice, and career advancement, are positively related to employee job performance. Moreover, the results also reveal that perceived organizational support positively moderates the relationship between knowledge accessibility, learning capacity, leadership practice, career advancement, and employee job performance. Conversely, it is shown that perceived organizational support has an insignificant moderation effect on the relationship between workforce optimization and employee job performance Originality: Our research highlights the importance of focusing on the intangible assets of an organization (i.e., human capital). Besides, this study contributes to creating a new theoretical framework for the relationship between the aspects of HCM, perceived organizational support, and employee job performance by integrating crucial factors that have not been previously connected.
The Relationship Between Home Literacy Environment and Parent Involvement Towards Early Childhood Language Skills in PWP Dama Kumara

The home literacy environment should be used as one of the things that is prioritized in providing home facilities, therefore it will continue to support the process of child development properly, especially language development.The purpose of this study was to determine the relationship between the home literacy environment and parent involvement to early childhood language skills. In this study, the method used is the correlation method and ex post facto. The data were collected using a questionnaire and to test the hypothesis using the product-moment correlation technique. In this study, it was found that the home literacy environment variable had an effective contribution of 30.89% to early childhood language skills. The parent involvement variable has an effective contribution of 40.65% to early childhood language skills.
Context Modeling with Evidence Filter for Multiple Choice Question Answering

Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In the OpenbookQA dataset, the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options collectively and to potentially highlight the evidence sentences and filter out unrelated sentences. In addition to the effective reduction of human efforts of our approach compared, through extensive experiments on OpenbookQA, we show that the proposed approach outperforms the models that use the same backbone and more training data; and our parameter analysis also demonstrates the interpretability of our approach.
Klasterisasi Minat Baca Mahasiswa Menggunakan Metode K-Means

What can do one of the ways to increase student interest in reading by increasing the collection of books in the Kerinci IAIN library. But so far, determining the collection of books to be added is still done manually, so it takes a long time besides that the process of adding books which refers to the statistics of borrowing the most books, makes the collection of books less diverse. For this reason, a book collection clustering method is needed using the k-Means method to obtain optimal results. Book collections are grouped into 3 clusters that are, considerations that need to be added, priorities to be added, and not need to be added can be a reference for librarians in maximizing the process of adding book collections.