Reviewerly/exHarmony huggingface dataset:
Phishing URL Classification Using ARM Based Association Rules

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Revising Attention with Position for Aspect-Level Sentiment Classification

As a fine-grained classification task, aspect-level sentiment classification aims at determining the sentiment polarity given a particular target in a sentence. The key point of this task is to distinguish target-related words and target-unrelated words. To this end, attention mechanism is introduced into this task, which assigns high attention weights to target-related words and ignores target-unrelated words according to the semantic relationships between context words and target. However, existing work not explicitly take into account the position information of context words when calculating the attention weights. Actually, position information is very important for detecting the relevance of the word to target, where words that are closer to the target usually make a greater contribution for determining the sentiment polarity. In this work, we propose a novel approach to combine position information and attention mechanism. We get the position distribution according to the distances between context words and target, then leverage the position distribution to modify the attention weight distribution. In addition, considering that sentiment polarity is usually represented by a phrase, we use CNN for sentiment classification which can capture local n-gram features. We test our model on two public benchmark datasets from SemEval 2014, and the experimental results demonstrate the effectiveness of our approach.
Improving Search Engines by Demoting Non-Relevant Documents

A good search engine aims to have more relevant documents on the top of the list.This paper describes a new technique called "Improving search engines by demoting non-relevant documents" (DNR) that improves the precision by detecting and demoting non-relevant documents.DNR generates a new set of queries that are composed of the terms of the original query combined in different ways.The documents retrieved from those new queries are evaluated using a heuristic algorithm to detect the non-relevant ones.These non-relevant documents are moved down the list which will consequently improve the precision.The new technique is tested on WT2g test collection.The testing of the new technique is done using variant retrieval models, which are the vector model based on the TFIDF weighing measure, the probabilistic models based on the BM25, and DFR-BM25 weighing measures.The recall and precision ratios are used to compare the performance of the new technique against the performance of the original query.
Learning to Explain Chinese Slang Words

The explosive development of social media has generated a large number of slang words in Chinese social network. The appearance of Chinese slang words has affected the accuracy of reading comprehension and word segmentation tasks. In this paper, we propose explaining Chinese slang word automatically for the first time. Unlike matching words in dictionary, we use a novel neural network called DCEAnn (a Dual Character-level Encoder using Attention-based neural network) for this specific task. One encodes slang word and its phonetics to learn the word representation, the other encodes example sentence containing slang word to enrich the semantic information of the slang word. Besides, we propose a public dataset for the first time to deal with the absence of parallel corpus for training model. Manual evaluation of experimental results shows that our model can generate reasonable explanations. Furthermore, we find that our model has a better performance on the network digital language which only contains numbers. To be specific, we get the state-of-the-art result on Chinese slang words interpretation whose BLEU score is 23.64, 3.59 higher than our baseline, and the state-of-the-art result on network digital language interpretation whose BLEU score is 54.23, 3.18 higher than our baseline.
THE ROLE OF MUSIC ART IN SOCIALISATION OF SCHOOLS

The problem of music education for children and youth becomes an important social problem. In the article, music art is considered as an important means of socializing students, because music is the basis for the musical culture of the people, its moral and spiritual content is the main means of influencing the personality, and this inevitably influences the important aspects of musical culture as a whole.The article deals with the theoretical analysis of the peculiarities of socialization of schoolchildren, which allowed to assert that one of the means of optimizing this process is music art as artistic and creative activity, since it reveals the spiritual and creative potential of a child and helps to adapt him to society.Curricula in Music Education were analyzed for all grades of general secondary schools, where the guidance was analyzed in terms of the presence of incentives for each of the components of social and musical activity: emotional, cognitive, axiological and praxeological. Attention was paid to the analysis of the content of the activities offered to pupils by the curriculum in Music Education, which requires correction and addition, as well as the process of perceiving musical compositions by schoolchildren. The article outlines the pedagogical conditions for schoolchildren socialization by means of music.The basic areas of work for phased involving schoolchildren to spiritual values at Music Lessons are defined: stimulating schoolchildren’s cognitive activity at Music Lessons, enriching emotional and sensory experience of schoolchildren, activating their creative self-expression.The article analyzes the process of perceiving music by schoolchildren. Attention is paid to the culture of artistic perception as a means of influencing the development of students’ aesthetic ideals, the development of their artistic tastes, understanding the artistic image and language of music art.
Frozen Sections of Sentinel Lymph Nodes From Breast Cancer Patients Who Undergo Neoadjuvant Therapy Are Accurately Diagnosed Using Telepathology

Abstract Objectives Telepathology enables histologic diagnosis to be made from a scanned slide visualized on a computer. Frozen sections (FSs) can be performed at remote locations and read by a pathologist at a central site. At our institution, qualifying breast cancer patients are enrolled in clinical trials that require FS on sentinel lymph nodes (SLNs) after neoadjuvant therapy (NAT), including chemotherapy or endocrine therapy. In this setting, histology is complicated by treatment effect and biopsy site changes. Others have reported good accuracy of FSs on SLNs after NAT. We investigated whether pathologists are accurate in diagnosing SLN FSs for such cases while using telepathology. To our knowledge, this has not been reported previously. Methods SLNs were entirely submitted and serially sectioned (2-mm thickness). At least two levels were cut. All FSs were submitted for formalin-fixed, paraffin-embedded permanent sections. A pathology assistant at the remote location prepared the FSs and scanned slides using the VisionTek M6 digital microscope ecosystem (East Dundee, IL). Cases were interpreted by board-certified pathologists who completed training on the VisionTek system. For this study, diagnoses from FSs and permanents were compared. Results Forty-five SLNs from 19 breast neoadjuvant cases were read by VisionTek from March 2017 to January 2019. Forty-three cases (96%) called negative by FSs were confirmed negative (on permanents). One hundred percent called positive by FSs were positive. Four SLN called atypical on FSs were positive. Three of these were neoadjuvant endocrine cases for invasive lobular carcinoma. Two cases called atypical were negative. One of these, called atypical/suspicious, resulted in axillary dissection. This case was reviewed by three pathologists at the time of surgery. It had abundant treatment effect, mimicking carcinoma. Conclusion While pitfalls exist, overall, the diagnostic accuracy of frozen section analysis by telepathology of SLNs from breast cases after neoadjuvant therapy is high.
Routing Queries to the Right Expert in Online CQA

The popular websites such as Stack Overflow and Yahoo provides the question and answer which is posted by the anonymous user by all over the world.In these sites, the programmers from all over the world will answer for the queries which are posted on the websites.In the existing system, if any user wants to know the exact answer they have to mine the answer between the thousands of answer posted on the website and still it was very complicated to know the exact answer and it was very time consuming. So that user has to apply all the answer to get the exact answer. So to overcome from this situation, we proposing the technique in which user can view the exact answer from the websites as it will be displayed as a first answer for that question through post voting prediction. In the voting process, the voting will be given based on the conditions. If that answer satisfies the conditions, it will be up voted. In this way, our proposed technique provides the effectiveness and efficiency, optimality, correctness and complexity.
Building Virtual Environments for Optimizing Learning Processes inside the Modern Organization

This article presents different ways in which new virtual reality technologies can be used in the development of software applications dedicated to supporting organizational learning / training processes. Thus, virtual reality can offer employees of enterprises in the Romanian economy access to an advanced training system by creating virtual environments that simulate real scenarios in which users have total freedom of movement and interaction with the constituent elements. Virtual Reality addresses to senses and perception. The VR technologies presented in this article address not only basic senses such as vision or hearing, but also senses such as balance or intuition. Thus, the brain is helped by the entire sensory system to receive a rich flow of information that starts from the environment and comes to mind. Therefore, if we present the artificially created information to the senses, the way in which reality is perceived will also be altered. In this context, we will also present the current state of the virtual reality technologies that can be used to implement VR applications as well as the essential principles applicable to the development of virtual reality applications. At the same time, the article shows how the use of virtual reality can support the three important aspects of vocational training: work experience - users having the opportunity to be in unfamiliar workplaces in a 360 degree perspective, learning skills – virtual reality helps to identify the balance between acquiring knowledge and training the experience, allowing the same scenarios to be scanned several times, without additional costs or inconveniences, access to a different perspective - allowing the user to perform actions normally performed by experienced employees.
Efficient and Secure Mechanism for Privacy Preserving and Data Sharing in Online Social Networks

The main aim of our research topic is privacy protection in online social network in particular when a user's confidential data is exposed in contents posted by another's, such as pics, with individuals being tagged. Day to day life people are mostly linked with the online social networking. By using this platform every user can share their data to other user. Online social networking plays an important role for individual. Along with their is a huge risk of sharing their own data with end users. Because there is a link between one users. Multiple users every user has their own opinion on the data share. There is a huge risk in online networking platform. so keeping in view about the upper problem they brought on mechanism it; In this research paper we implement an novel secure mechanism called trust based collaborative privacy management in this mechanism if the user wont to share any data to other user that can be kept only to the said user according to the privacy of the individual with this mechanism securely can be maintained and that would result to the encouragement of an individual for using online networking platform and can make Indian as a digital Indian Be free and live safe.
Facilitating Secure Cloud Based Mobile Healthcare Application using Encryption Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
The Role of Public Law in the Age of the Fourth Industrial Revolution

현재 세계는 인공지능, 빅데이터, 클라우드 컴퓨팅, 사물인터넷 기술 등과 같은 다양한 분야의 지능정보기술 및 과학기술의 발전과 융합으로 초래되는 4차 산업혁명 시대를 맞이하고 있다. 그간 인류가 경험한 혁명적 발전과정은 인간이 선도적으로 핵심적 역할을 수행하여 이루어진 것이었지만, 이제 막 경험하고 있는 4차 산업혁명은 인간이 설계하고 인공지능 등으로 대변되는 기계가 실행을 담당한다는 점에서 본질적인 차이가 있다고 할 것이다. 4차 산업혁명은 정보통신의 발달에 기초한 보편적이고 자유로운 소통의 가능성과 개별적으로 발달한 각종 기술의 융합을 특성으로 한다. 이로 인하여 점진적 확장이 아닌 순간적 일반화가 가능한 시대가 도래 하였고, 이 과정에 등장하는 새로운 기술이나 물건의 전파 속도는 과거의 기준으로 가늠할 수 없는 수준에 이르렀다. 이와 같은 특성으로 인하여 종래의 규제 수준이나 규제 속도로는 이에 능동적으로 대처하지 못하는 문제점을 야기할수 있으므로 공공이익의 실현을 위한 산업혁명을 억제하지 않으면서 기술발전을 유도할수 있는 법제 정비가 필요하다. 이 논문에서는 이와 같은 문제의식에 기초하여 4차 산업혁명기에 공법의 영역에서 검토하여야 할 새로운 개념들, 그리고 그와 관련된 입법상황을 검토하고 연구방향에 관한 시사점을 제기하고자 한다.The world is now facing an era of the fourth industrial revolution, resulting from the development and convergence of intelligence information technology and science and technology in various fields such as artificial intelligence, big data, cloud computing and Internet of Things technology. The revolutionary development process that mankind has experienced has been achieved by playing a leading role, but the fourth industrial revolution that we are beginning to experience is an essential difference in that machines that are designed by humans and represented by artificial intelligence and others are responsible for implementation. The fourth industrial revolution features the possibility of universal and free communication based on the development of information and communication and the convergence of various individually developed technologies. As a result, the time has arrived for momentary generalization rather than gradual expansion, and the rate of propagation of new technologies or objects in this process has reached an unobservable level by the past standards. Such characteristics can cause problems that could not be actively dealt with at the existing regulatory level or the regulatory speed, so legal provisions are needed to induce technological development without suppressing the industrial revolution for the realization of public interest. Based on this sense of problem, this paper aims to review the new concepts that should be reviewed in the area of the public law in the fourth industrial revolution period, and to review the relevant legislative situation and raise the implications of the research direction.
A Classification Method for Small Sample Multi-label Images

This paper studies the classification problem of the small sample multi-label image scene recognition. Combining convolutional neural network (CNN) and multi-label K neighborhood algorithm (MLKNN), the CNN-MLKNN classification method is proposed. The method uses CNN to automatically extract the features of small sample images, and combines transfer learning to optimize the model structure and weight to reduce the risk of over-fitting. MLKNN algorithm is used to replace the sigmoid function of CNN, and the output features of the FC layer are used as input features of MLKNN for image classifier training. Based on the classification experiments of two small sample multi-label image sets, seven multi-label evaluation indicators are used for testing. The experimental results show that the CNN-MLKNN method proposed in this paper has a better classification effect.
Control of magnetization dynamics by spin-Nernst torque (Conference Presentation)

Spin current can be used to control the magnetization dynamics of a nano-magnet. There are various ways to generate spin current. Electrical current or heat current passing through a ferromagnet can generate spin current due to the spin dependent conductivity or spin-dependent Seebeck effect. Electrical current passing through a non-magnetic material with high spin-orbit coupling can generate spin current via the spin-Hall effect. Heat current passing through a non-magnet can also generate spin current due the spin-orbit coupling. This effect known as the spin-Nernst effect (SNE), has been shown recently by measuring thermally driven spin-Hall magneto-resistance [1-3]. We have shown that the spin-Nernst effect can be measured directly via a multi-terminal device with ferromagnetic Ni contacts on Pt [4]. We generated heat current in the Pt and the resultant spin current was then detected by Ni contacts. The same multi-terminal device was used to measure the spin-Hall angle for comparison with spin-Nernst angle. In a further work, we have shown that the spin current generated via spin-Nernst effect can be injected into an adjacent FM layer to exert spin-transfer torque (spin-Nernst torque) [5]. We could change the effective damping of the ferromagnet via spin-Nernst torque. [1] S. Meyer et.al., Nat. Mater. 16, 977 (2017). [2] P. Sheng et.al., Sci. Adv. 3, e1701503 (2017). [3] D. J. Kim, et.al., Nat. Commun. 8, 1400 (2017). [4] A. Bose et.al., Applied Physics Letters, 112, 162401 (2018). [5] A. Bose et.al., Phys. Rev. B 98, 184412 (2018).
Real-time Path Planning for Time-Optimal Helicopter Shipboard Landing via Trajectory Parameterization

A real-time path planning algorithm is developed to generate time-optimal trajectory for helicopter shipboard landing. The trajectory optimization problem is translated to the lower dimensional flat output space by exploiting the differential flatness property of the simplified helicopter model. Then, the flat outputs are parameterized using piecewise spline functions with adjustable coefficients, which are used to shape the trajectory and approximate the optimal solution. Further, by allowing the flexible selection of each spline segment's time-duration and enforcing additional path constraints, the time-optimality of the planned trajectory is largely preserved without violation of state and input bounds. Compared to pure temporal discretization methods, the proposed algorithm employs considerably less decision variables and significantly reduces the computational time by 75%, which only leads to a 0.5% growth in the optimal flight time as the trade-off. The improvement in computational efficiency enables the real-time recalculation of the time-optimal trajectories on-the-fly if there are unforeseen deviations from the planned flight path.
PENERAPAN METODE K-NEAREST NEIGHBOR DAN GINI INDEX PADA KLASIFIKASI KINERJA SISWA

Predicting student academic performance is one of the important applications in data mining in education. However, existing work is not enough to identify which factors will affect student performance. Information on academic values ​​or progress on student learning is not enough to be a factor in predicting student performance and helps students and educators to make improvements in learning and teaching. K-Nearest Neighbor is a simple method for classifying student performance, but K-Nearest Neighbor has problems in terms of high feature dimensions. To solve this problem, we need a method of selecting the Gini Index feature in reducing the high feature dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with values ​​of k (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the highest average accuracy of 74.068 while the K-Nearest Neighbor and Gini Index methods showed the highest average accuracy of 76.516. From the results of these tests it can be concluded that the Gini Index is able to overcome the problem of high feature dimensions in K-Nearest Neighbor, so the application of the K-Nearest Neighbor and Gini Index can improve the accuracy of student performance classification better than using the K-Nearest Neighbor method.