Reviewerly/exHarmony huggingface dataset:
Predict Individual Retweet Behavior Based on Multi-feature

Abstract With the rapid development of social media, more and more people get information from social media. The retweet prediction task helps to study the process of information dissemination on social media. Previous methods take into account text features and ignore user’s sentiments and interests. The history tweets tweeted by the user not only reflect the user’s sentiments that the user tends to express on social media, but also shows the user’s interests. In this work, we used a deep learning method to extract features from user profiles, user history, user network, target tweet, then we used these features to predict whether a tweet will be retweeted by a user. Experimental results on dataset show that the proposed model can effectively predict the retweet behavior of users.
Smart Data Management and Privacy System Using Web Based Online ERP Software Solution

University Management System (UMS) is a complete education ERP software solution for universities, colleges and schools to streamline and automate their activities. This system provides a user-friendly interface that covers universities workflow like attendance, courses, results, exams and notifications. University Management System can be used as an information management system for the educational institutions like universities, colleges and schools. The main objective of developing an online University Management System (UMS) is to provide a simple and easy user interface for maintenance of university, student and employee information. The system can be used by educational institutes like universities, colleges and schools to maintain the records of staff, students and institutions easily. The system can maintain records like attendance, marks, notifications, assignments, results and examination dates. The system can provide vast information about students, staff, courses, results, exams, semesters and departments.
A Study on Broadcasting Platforms Using Data Technology: Focusing on Blockchain

이 연구는 미디어 환경의 변화를 주도하는 핵심 기술인 데이터 테크놀로지(DT) 중 블록체인에 초점을 맞추고, 블록체인 기술의 활용이 방송플랫폼의 어떠한 속성과 접목되며, 방송플랫폼의 어떠한 가치를 실현하는 데 부합하는지에 대해 사례연구를 통해 살펴보았다. 이를 위해 우선 방송플랫폼의 미래 변화를 4가지 차원으로 구분하고, 각 차원에 대응하는 블록체인 프로젝트를 분석함으로써 블록체인 기술이 구현할 수 있는 방송플랫폼의 미래 변화를 살펴보았다. 구체적으로 블록체인 기술을 활용하면 첫째, 방송플랫폼의 공적 책무를 이용자 중심의 미디어 서비스로의 전환을 촉진할 수 있으며, 둘째 기존 미디어 산업의 불공정을 해소하고 효율성을 증진함으로써 대안적인 수익모델을 창출할 수 있다. 셋째, 미디어 조직의 일방적, 수직적 의사결정구조를 혁신하고 미디어 수용자들이 광범하게 참여하는 분권화된 협력적 거버넌스를 구축할 수 있으며, 넷째, 블록체인의 기록 투명성을 활용하여 콘텐츠 불법 복제를 방지하면서 새로운 상품화를 타진해볼 수 있다. 이러한 분석을 종합하여 블록체인 기술의 도입이 방송플랫폼의 미래 변화에 미치는 함의에 대해 논의하였다.This study focuses on blockchain among the core technologies that drive changes in the media environment; particularly, the attributes of the broadcasting platform incorporated using this technology, and the value of the broadcasting platform that is well-matched to this technology. To this end, this study divided future changes of broadcasting platforms into four dimensions and examined the value of these changes, which the blockchain technology can help implement, by analyzing media blockchain projects corresponding to each dimension. The results can be summarized as follows. First, the public responsibility of broadcasts can facilitate the transition to a user-centered media service. Second, blockchain can help broadcasting platforms create an alternative business model by addressing the unfairness of the existing media industry and promoting efficiency. Third, blockchain provides broadcasting organizations opportunities to alter the one-way, vertical decision-making structure and build decentralized, collaborative governance with extensive audience participation. Fourth, blockchain’s transparency contributes to preventing illegal copying of contents while exploring new commercialization. Finally, this study discusses the implications of introducing the blockchain technology on future changes in broadcasting platforms.
Incremental Approach for Automatic Generation of Domain-Specific Sentiment Lexicon

Sentiment lexicon plays a vital role in lexicon-based sentiment analysis. The lexicon-based method is often preferred because it leads to more explainable answers in comparison with many machine learning-based methods. But, semantic orientation of a word depends on its domain. Hence, a general-purpose sentiment lexicon may gives sub-optimal performance compare with a domain-specific lexicon. However, it is challenging to manually generate a domain-specific sentiment lexicon for each domain. Still, it is impractical to generate complete sentiment lexicon for a domain from a single corpus. To this end, we propose an approach to automatically generate a domain-specific sentiment lexicon using a vector model enriched by weights. Importantly, we propose an incremental approach for updating an existing lexicon to either the same domain or different domain (domain-adaptation). Finally, we discuss how to incorporate sentiment lexicons information in neural models (word embedding) for better performance.
How Much Self-Attention Do We Needƒ Trading Attention for Feed-Forward Layers

We propose simple architectural modifications in the standard Transformer with the goal to reduce its total state size (defined as the number of self-attention layers times the sum of the key and value dimensions, times position) without loss of performance. Large scale Transformer language models have been empirically proved to give very good performance. However, scaling up results in a model that needs to store large states at evaluation time. This can increase the memory requirement dramatically for search e.g., in speech recognition (first pass decoding, lattice rescoring, or shallow fusion). In order to efficiently increase the model capacity without increasing the state size, we replace the single-layer feed-forward module in the Transformer layer by a deeper network, and decrease the total number of layers. In addition, we also evaluate the effect of key-value tying which directly divides the state size in half. On TED-LIUM 2, we obtain a model of state size 4 times smaller than the standard Transformer, with only 2% relative loss in terms of perplexity, which makes the deployment of Transformer language models more convenient.
A Cooperative Coevolutionary Algorithm For KNN Training Set Optimization

The traditional evolutionary instance selection algorithm has the risk of redundant and noise training samples in the training set selection, which affects the classification effect. In this paper, instance selection, instance weighting and feature weighting are integrated into the cooperative coevolution framework, and a cooperative coevolutionary algorithm for KNN training set optimization selection is proposed. The CHC algorithm based on multi-point crossover strategy is used to further improve the accuracy of instance selection. The SSGA algorithm based on fast mutation strategy of instance weighting and feature weighting is synergistic with the instance selection, and it helps to remove noise and irrelevant data in the process of instance selection. The proposed method can speed up the convergence of the population, it can also improve the efficiency of the algorithm, and improve the KNN classification performance. The experimental results show that this method has advantages in classification accuracy and efficiency compared with some current evolutionary instance selection algorithms.
Objective Evaluation of Tonal Fitness for Chord Progressions Using the Tonal Interval Space

Chord progressions are core elements of Western tonal harmony regulated by multiple theoretical and perceptual principles. Ideally, objective measures to evaluate chord progressions should reflect their tonal fitness. In this work, we propose an objective measure of the fitness of a chord progression within the Western tonal context computed in the Tonal Interval Space, where distances capture tonal music principles. The measure considers four parameters, namely tonal pitch distance, consonance, hierarchical tension and voice leading between the chords in the progression. We performed a listening test to perceptually assess the proposed tonal fitness measure across different chord progressions, and compared the results with existing related models. The perceptual rating results show that our objective measure improves the estimation of a chord progression’s tonal fitness in comparison with existing models.
A Proposed Audiovisual Quality Assessment Model Associated with Multiplayer Online Battle Arena Game: A Pilot Study with ROV

This study proposes an audiovisual quality assessment model for a popular gaming scenario, called the Multiplayer Online Battle Arena (MOBA) gaming. The subjective data about voice quality during conversation of the game players, the video quality, the overall quality, and the weighted percentage of voice and video have been gathered from 40 participants who competed in a small competition event of the Garena ROV (Arena of Valor) in Thailand. Then, three quarters of data have been used to create the model, while the last quarter of data has been used for model evaluation. After creating the model, it has been evaluated using Mean Absolute Percentage Error (MAPE) with three audiovisual quality evaluation models that provide good performances. It has been found that the proposed audiovisual quality assessment model provides better performance than previous works with MAPE of 6.16 %, while other models provide MAPE of 7.67 - 8.54%. Therefore, this evidence supports that the proposed model could be applied for evaluation of audiovisual quality based on the MOBA gaming scenario.
A Study of Curriculum Development to Improve Speaking Ability in Liberal Arts Education

이 연구는 최근 대학 교양교육과정에서 비중이 증대되고 있는 ‘말하기’ 관련 교과목의 개발 과정과 운영 사례를 통해 바람직한 말하기 교육의 방향에 대해 모색하기 위한 것이다. 건국대학교 글로컬캠퍼스에서 말하기 교과목으로 <발표와토론>을 개발 및 운영하게 된 배경과 그로 인해 나타나게 된 교과목 운영의 특징을 사례로 살펴보았다. 건국대 글로컬캠퍼스에서 <발표와토론> 교과목이 개설된 배경으로는 첫째, 최근 대학에서 ‘말하기’ 관련 교과목의 중요성이 점차 부각되기 시작한 점, 둘째, 핵심역량 교육이 강조되면서 말하기 능력이 중요한 역량진단 요소이자 의사소통능력의 핵심요소로 인정되기 시작한 점을 꼽을 수 있다. 기업이 인재선발을 위해 면접의 비중을 강화하고, 대학 교육의 초점이 지식 중심에서 역량 중심으로 변화하면서 대학 교육에서 말하기의 중요성이 부각되게 되었다. 건국대 글로컬캠퍼스 <발표와토론> 교과목 개설의 계기는 K-CESA 핵심역량 진단 결과였다. 말하기 역량이 하위권으로 드러남에 따라 교육과정에서 시급하게 말하기 관련 교육의 강화가 필요했다. 그 결과로 말하기 교과목으로 <발표와토론>이 3학점 교양필수 교과목으로 개설되었다. 이러한 교과목 개설의 취지에 부합하도록 <발표와토론>은 이론(30%)보다는 실습(70%)의 비중을 가지도록 수업이 설계되었다. 실습은 개인발표, 토의, 토론을 균형 있게 연습할 수 있도록 하였다.In this article, I will explore the direction of speaking education through the development process of speaking-related courses, which are increasing in number in the current college liberal arts curriculum, and provide examples of the curriculum’s composition and operation. I will examine the background and the characteristics of the “Presentation and Debate” course as a speaking subject at Konkuk University s Glocal Campus. The following issues existed as background to the establishment of the “Presentation and Debate” course at our university. First, “speaking” as an academic subject gained in the importance at the university, and second, with the emphasis on core competency education, speaking skills started to be recognized as an important competency assessment factor and a key element of communication skills. As companies strengthen the importance of interviews in talent selection, and university education goes from knowledge to a competency focus, the importance of speaking in university education has become apparent. The K-CESA “core competency” diagnosis led to the launch of the “Presentation and Debate” course. Because the K-CESA core competency diagnosis revealed that students’ speaking skills were sub-level, it was necessary to strengthen speaking-related education in the curriculum. As a result, the “Presentation and Debate” as a speaking subject was offered as a three-credit liberal arts compulsory course. To meet the purpose of the course’s establishment, the “Presentation and Debate” class was designed with a larger proportion of practice (70%) than theory (30%). Hands-on exercises in class provided a balanced practical program of personal presentations, discussions, and debates.
Evaluation of 9-Year Compulsory Basic Education Learning Program in Serang Banten

Abstract The purpose of this study was to evaluate the nine-year compulsory education program in Serang, Banten. This research used qualitative research with an evaluation approach. The evaluation model used the CIPP model (context, input, process, and product). The process of collecting data used observation, documents and interviews. Data analysis techniques were data reduction, data presentation, discussion and conclusions. The results show that 1) the context in the nine-year compulsory education program has been determined in accordance with the objectives stated in the applicable policy and regulatory documents; 2) inputs relating to human resources, facilities and infrastructure and financing are systematically determined and owned students who refer to the policies and regulations in force; 3) the process of the nine-year compulsory education program has been carried out sequentially in accordance with the activities specified to achieve the program’s objectives; and 4) the product produced shows that the gross enrollment rate, school participation rate, continuing rate and the results of passing the national exams in both elementary and secondary schools have achieved the goal of the nine-year study program. Thus, the results of this study recommend that the education office in Serang is more clearly map the need for program implementation and the involvement of schools or the community as a community to be further improved.
A forward approach for solving linear Diophantine equation

A simple forward approach for solving linear Diophantine equations is presented, which does not involve using backward substitutions. It is suitable for teaching undergraduate students as an alternative to the backward substitution method commonly described in mathematics textbooks.
Abnormality classification in the kidney ultrasound images using singular value decomposition features

Kidney diseases are evolving as a common chronic disease like hypertension, diabetes, and cardiovascular disease. They do not show any significant symptoms at an earlier stage. Therefore, monitoring of kidney diseases at regular interval of time is required to prevent kidney failure. This paper deals with the automatic abnormality classification in the kidney ultrasound images. The singular value decomposition (SVD) algorithm is used to extract features from ultrasound images and these features are given to the support vector machine (SVM) classifier for classification. The performance comparison of SVM is done with different classifiers along with the extracted SVD features to detect the abnormalities. The kidney classes are classified into normal and abnormal kidney with a total of 100 ultrasound images. The efficiency of the classifier is measured in terms of recall, selectivity and accuracy.
How Many Playwrights Wrote Arden of Faversham? The Samuel Rowley Connection

Thomas Kyd, Christopher Marlowe, and William Shakespeare have long been the main rival candidates for the authorship of Arden of Faversham, published anonymously in 1592.1 The leading specialist in attribution during the early twentieth century, E. H. C. Oliphant, accepted the case made for Kyd by a series of scholars but was convinced that Arden contained the writing of more than one man. ‘The play’, he declared, ‘is not all in a single style’. This, he thought, was only to be expected: ‘In the case of an anonymous play, the chances are always greater of its being a joint work’ than a sole-authored one.2 He was confident that ‘there are passages in the play that are quite beyond the reach of Kyd, as we know him—beyond, in fact, the reach of every dramatist of the time, save Marlowe and the young Shakespeare’. He instanced ‘the magnificent quarrel...
New Boundary Constraint Loss to Facilitate Glands Segmentation

Histological assessment of glandular formation plays a crucial role in cancer grading, and automated glandular structures segmentation is the first step. To achieve an accurate boundary representation to separate packed glands, an extra branch is normally introduced to predict binary boundary mask in deep network. In this paper, we only use simple single stream networks, facilitated by our proposed boundary constraint loss, to locate the gland boundary and separate clustered glands, making our model efficient in terms of memory usage. To achieve this, we define a new boundary potential map from ground truth region mask. During training, edge representation is calculated from predicted region segmentation, from which a new regularization term is generated to force edge align well with the boundary potential. In addition, we also present a new differentiable version of argmax function to allow the model to be trained in an end-to-end manner. In evaluation, we employed three base model structures (suggestive annotation, U-net and FCN-32s) and two public dataset (GlaS15 and CRAG). Extensive experiments consistently demonstrated that it improves the boundaries segmentation performance over its counterparts (3% in object F1-score and 2% in object Dice).
ALeader-following Method Based on Binocular Stereo Vision For Quadruped Robots

This paper presents a leader-following framework by stereo-vision, which allows quadruped robots to adapt to challenging outdoor environments. Our framework relies on the deeply supervised object detector (DSOD) detection framework and the kernelized correlation filter (KCF) tracker. The algorithm can enhance the cognitive ability of robots and lay a foundation for the autonomy of robots in outdoor environments. Without the use of pre-trained models, DSOD is a framework for training object detectors from scratch, with greater accuracy than state-of-the-art detectors. We propose an improved tracker based on traditional KCF with multi-feature fusion and multi-scale estimation. Pedestrian re-identification and relocation are introduced to mitigate effects of strong vibration, scale variation, occlusion, and illumination variation. To compensate for the possible transient balance loss caused by short tracking drift, the heading angle of the robot is smoothed and filtered. Outdoor experiments with the quadruped robot show the effectiveness and robustness of this framework.
Error Detection and Correction Code Using Density-based Clustering Algorithm

In data area, to achieve good information for decision making, suitable processing of data is needed. Data need to be transferred. They are transferred as a vector which contains features of data. During data transferring, errors may occur. Errors change the features of data vector (instance). In this case, error detection and correction techniques are needed to tackle this issue. If data transferred as groups based on its features, any change in the features of any vector will change the group (cluster) of this vector. So, to cluster an incomprehensible data for operating any method of data mining, an influential technique is needed, and this technique should ensure the correctness of the cluster using error detection and correction codes like Hamming and Golay. This paper presents a technique to detect and correct clustered data after the transfer process to reduce the misclustered instances. The main concept is the reemploying the error-correction Golay code with splitting of the data word and code word to symbols. DENCLUE clustering algorithm is used in the step of clustering as density-based clustering algorithm. Comparison with other related works is performed and the simulation results stated that the proposed technique achieved better performance.
Designing a secure positioning system using blockchain technology

Achieving a secure positioning system is one of the most prominent issues in the field of wireless security. The central task of this paper is introducing a system that is provably secure for sharing the most sensitive data among the users in a peer-to-peer network. In such systems, the geographical position of the users or the nodes in the network plays a vital role in maintaining the security of the entire network especially for defence purpose. Apart from the existing bounded storage models, the system gives a positive result for 'secure positioning problem'. The designing of the proposed system is based on the network, where the public ledger is maintained by all the nodes independently without any central authority. This zero knowledge protocol proposed in this paper makes the search space size to grow exponentially, which is a major challenge for the intruder to prove their position.
Recurrent Attention Networks for Medical Concept Prediction.

This paper presents the working notes for the CRADLE group's participation in the ImageCLEF2019 medical competition. Our group focused on the concept detection task which challenged participants to approximate the mapping from radiology images to concept labels. Traditionally, such a task is often mod-elled as an image tagging or image retrieval problem. However, we empirically discovered that many concept labels had weak visual connotations; hence, image features alone are insufficient for this task. To this end, we utilize a recurrent neural network architecture which enables our model to capture the relational dependencies among concepts in a label set to supplement visual grounding when their association to image features is weak or unclear. We also exploit soft attention and visual gating mechanisms to enable our network to dynamically regulate “where” and “when” to extract visual data for concept generation.
Exploration of Hidden Fraudulent Website Dataset with Different Perspective

The lucrative nature of fraud cannot be denied especially, its existence in the form of electronic fraud. The electronic fraud is on rise nowadays. A dataset is investigated for hidden fraudulent websites that can be helpful in assessing a classifier’s efficiency in discriminating against hidden fraudulent URLs. The dataset includes URLs marked with 185180 and some associated characteristics. Through different features, it has been shown here in the paper that how the dataset can be further utilized. Further, these features can be categorized and helpful for classifying the best features for fraudulent website detection.
Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia

Mammal type can be classified based on the face. Every mammal’s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal’s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.
Structuring a Folksonomy in a Community of Questions and Answers

Knowledge communities are composed of individuals who share the same interests and voluntarily work together to expand knowledge and understanding of a domain through learning and sharing. When a user asks a question in a knowledge community, he or she may usually add tags to categorize the question. These tags make it easy for experts to find the question and then answer it. One of the problems with this tagging system is that there is no hierarchy to aid in the search for broader themes. This study proposes a way of hierarchizing the folksonomy of an online knowledge community. In order to verify the applicability of the proposed approach, we used the Stack Exchange's biology community. Through a crowdsourcing evaluation it was possible to show that the developed approach performed better than other heuristics and it was also possible to identify quality issues on the description of WordNet synsets.