Reviewerly/exHarmony huggingface dataset:
Recent Advances in End-to-End Learned Image and Video Compression

The DCT-based transform coding technique was adopted by the international standards (ISO JPEG, ITU H.261/264/265, ISO MPEG-2/4/H, and many others) for nearly 30 years. Although researchers are still trying to improve its efficiency by fine-tuning its components and parameters, the basic structure has not changed in the past two decades.The deep learning technology recently developed may provide a new direction for constructing a high-compression image/video coding system. Recent results, particularly from the Challenge on Learned Image Compression (CLIC) at CVPR, indicate that this new type of schemes (often trained end-to-end) may have good potential for further improving compression efficiency.In the first part of this tutorial, we shall (1) summarize briefly the progress of this topic in the past 3 or so years, including an overview of CLIC results and JPEG AI Call-for-Evidence Challenge on Learning-based Image Coding (issued in early 2020). Because Deep Neural Network (DNN)-based image compression is a new area, several techniques and structures have been tested. The recently published autoencoder-based schemes can achieve similar PSNR to BPG (Better Portable Graphics, H.265 still image standard) and has superior subject quality (e.g., MSSSIM), especially at the very low bit rates. In the second part, we shall (2) address the detailed design concepts of image compression algorithms using the autoencoder structure. In the third part, we shall switch gears to (3) explore the emerging area of DNN-based video compression. Recent publications in this area have indicated that end-to-end trained video compression can achieve comparable or superior rate-distortion performance to HEVC/H.265. The CLIC at CVPR 2020 also created for the first time a new track dedicated to P-frame coding.
Multi-level Qualitative Reasoning Logic

Abstract Qualitative reasoning is a very efficient method that people often use to solve problems. Recently, the literature about qualitative reasoning as a causal analysis and decision-making tool has been emerging. However, the existing qualitative reasoning methods are mainly used for the modeling of imprecise problems. There are not many studies on formalization tools of qualitative “inference”. This paper proposes a logical system for qualitative reasoning (QRL). The main research contents include: the grammar and semantic structure of QRL are given; the meaning of formula logic truth value in QRL and its assignment rules are discussed; and the reliability and completeness of QRL are proved. Compared with fuzzy logic, probability logic and other uncertain reasoning methods, the advantage of QRL method is that it can use axiomatic reasoning method, and does not need to construct membership functions and collect a large number of samples.
Sliding Window Filtering for Ground Moving Targets with Cross-Correlated Sensor Noises

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.
ELECTRONIC SYSTEM OF BIBLIOGRAPHIC CONSULTATION AS AN INTEGRAL PART OF THE EXPERT SYSTEM

The purpose of the study was to develop a subsystem for bibliographic consultation when creating expert information and analytical systems for scientific research on biomedical orientation.
Privacy issues in blockchain

Blockchain technology is a new innovation of secure computing without centralized authority in an open -networked system as explained by Nakamoto. Blockchain stores the data in a distributed database that maintains a table of transaction record by arranging them into a hierarchical order of blocks. As far as security is concerned, blockchain is a decentralized peer -to -peer (P2P) network that is secured by an intelligent cryptography algorithm. Each node that is involved in the transaction is connected to the same blockchain data transaction itself since this has a distributed database. For example, in a digital currency, the blockchain is available to all such that entire transaction process can be monitored again to the first block. Bitcoin contains the false name in which the data nodes are not directly connected with the original data, but the repeated occurrences are associated with other. Blockchain is an innovation that is created by utilizing a mix of different strategies, for example, arithmetic, calculations, cryptography, and monetary models.
ЦИФРОВЫЕ ТЕХНОЛОГИИ КАК СРЕДСТВО ПРОФЕССИОНАЛЬНОГО ВЫБОРА: БУДУЩЕЕ И НАСТОЯЩЕЕ

Abstract. The article is devoted to the analysis of the possibilities and the real state of the use of digital technologies in career guidance as a means of implementing the principle of personalization of education. Based on theoretical and empirical research, it is concluded that there is a need for mutual rapprochement, complementing the possibilities of the digital and real social environment when a young person makes a decision in a situation of choosing an individual educational trajectory.The relevance of the research is associated with the task of identifying the psychological and pedagogical conditions for the formation of a new type of learner's future - self-governing student, his conscious, independent and responsible choice of the direction of his education.The article poses the problem of using digital career guidance platforms as technologies for maintaining individuality, activating personal and professional self-determination, synchronizing individual and collective processes in career guidance based on a review of the experience of digitalization in education, the existing techno-humanitarian imbalance is indicated.The task of comprehending the place and role of digital technologies in the situation of real choice by young people of the trajectory of vocational education is posed and solved. In the light of the cultural-historical theory of development of L. S. Vygotsky, the ontological concept of development of B.D. Elkonin, the selection process is considered as a mediated act of personal development, and digital technology as an information-digital shell of this “mediator”.The results of a questionnaire survey of 233 first-year students on the subject of relations and use in a situation of professional choice of digital technologies are presented. It is concluded that despite the fact that the majority of respondents have a positive attitude to the use of digital resources and artificial intelligence technologies when choosing a profession, the real awareness and use of available career guidance digital platforms is very low. The influence of a real social, rather than virtual, digital environment turns out to be a powerful decision-making factor for a young person in a situation of professional choice.The prospects for the use of digital technologies in vocational guidance are seen as psychological and pedagogical technologies for digital synchronization of interpersonal and interinstitutional levels of vocational guidance and digital mediation of the processes of professional self-determination.
Rapid Extraction of Research Areas from Scientific and Technological Literature

Along with the rapid development of Internet Plus, big data, and other technologies, the construction of smart cities is promoting the transformation and upgrading of mapping geographic information models from traditional information services to intelligent services with spatial sensing.At present, however, most of the knowledge needed to provide intelligent services is implicit in the form of unstructured text in various books and journal papers in related fields, which is difficult to capture, use, analyze, and share.In particular, geographical feature knowledge is one of the types of knowledge that needs to be extracted urgently.To solve this problem, in this paper, we propose a method for the rapid extraction of research areas from scientific and technological literature abstracts.Firstly, with the help of a general naming entity identification tool, we propose a method of rapidly annotating place-name entities in administrative divisions.Then, combining the bidirectional long short-term memory conditional random field (BiLSTM-CRF) model with a place-name database covering five levels of administrative divisions in China, the identification, disambiguation, and relationship extraction of place names in different administrative divisions are realized.On this basis, the extraction of research areas is regarded as a two-classification problem, feature vectors such as frequency and location are constructed for the names of the extracted administrative divisions, and the classification model is constructed with the random forest algorithm to rapidly extract research areas.The experimental results show that the recognition accuracy of place names in administrative areas in this study is 92.61% and the recognition accuracy of research areas is 90.31%.The results are superior to those of similar algorithms; thus, the proposed method can accurately and rapidly extract research areas.
End to End Deep Neural Network Classifier Design for Universal Sign Recognition

Self-driving cars and Advanced Driver Assistance Systems rely heavily on Traffic Sign Recognition for safe maneuvering on the roads. But traffic signs can vary from one country to another, thereby necessitating multiple classifiers or a single universal classifier which can handle variations across countries. This paper reports our attempt at building a universal classifier. This classifier has to deal with large intra-class variations in the classes and also similarities among various difficult to distinguish traffic sign classes. This paper is an extension of our previous work in which we proposed a hierarchical classifier for traffic signs of a specific country. In hierarchical classification, dedicated classifiers are trained for classes which are more difficult to distinguish. Such similar classes are grouped together automatically by learning category hierarchy from the confusion matrix of a flat classifier (building block). In this paper, we use attention network for country independent classification. Here, CNN itself pays attention to regions in an image which are more discriminative and thus results in better classification for such problems. The aim here is to design a traffic sign recognition framework which can be used for multiple countries and be able to classify even the hard to distinguish classes by exploiting category hierarchy of traffic signs. The model is evaluated on traffic signs of seven countries namely Belgium, China, Croatia, Russia, Spain, Germany and Italy. The new building block architecture shows significant improvement of classification accuracy that is 97.7% as compared to building block architecture (VGG) used in our previous paper that is 95.1%.
Current research on the use of HCI in decision-making to build digital musical instruments

Over the past four decades, there has been an increase in the spread of digital musical instruments (DMIs), largely due to the ease of access to cheap technologies with great processing and storage power. As such systems are flexible and can be easily reconfigured, there are few restrictions on their creation. In this context, this paper is based on techniques from the field of human-computer interaction (HCI) to propose a list of decisions to be taken into account when creating a DMI, observing its gains and losses, in order to find a common point for the building of this equipment.
Course Recommendation System for Post-graduate (Masters in Science) Aspirants

The choicePal, Shivani ofShahi, Anjali higherRai, Shubham educationGulwani, Reshma program plays a major role in shaping up one’s career. Hence, it is worth investing time in gathering the right information to make an informed decision. For this cause, to the extent of our knowledge there are no dedicated tools. Students tend to make decision either based on the peer review without giving much thought on it. The system aims to consider student’s interest as sole parameter to recommend the course which will be best to pursue. The interest of the student will be gauged on subjects which the student has already undertaken as undergraduate courses. The proposed system provides the user with a Web application where the user can make its profile and input his/her interest based on the parameters. K-Nearest Neighbours Classification Algorithm is used to select the best-suited courses based on Euclidean distance. The proposed recommendation system is a content-based Recommendation System since it recommends items (courses in this case) based on content of items (the undergraduate courses) and user profile (the rating of courses based on interest). Our proposed system implements a recommendation prototype with a focus on Computer Science and Information Technology disciplines as the chosen field of knowledge.
The First Chatbot of a Tourism/Hospitality Journal: Editor’s Impressions

The first chatbot of an academic journal in the field of tourism and hospitality was launched in 2017. This editorial presents the structure of the chatbot of the European Journal of Tourism Research, reflects on Editor’s experience in developing and using the chatbot, and provides practical recommendations to editors and publishers who may wish to adopt chatbots for social media communications.
WORKSHOP ON WRITING OF SCIENTIFIC JOURNAL ARTICLES FOR TEACHERS OF SMA NEGERI 1 PALANGKA RAYA

Nowadays, in order to obtain promotion from the rank of Group IVa to IVb, a teacher is required to provide scientific works. However, according to Ministerial Decree No. 16/2009, scientific work has become a promotion requirement from Group IIIb to IIIc; thus, the teacher must have the ability to produce scientific papers. This community service was aimed to provide knowledge and skills for teachers of SMAN 1 Palangka Raya to produce and publish scientific papers in the ISSN local journals. Participants in the activity generally consisted of junior teachers and by using training methods in the form of lectures and questions and answers, demonstrations and practice.
Pre-IdentifyNet: An Improved Neural Network for Image Recognition

With the rise and development of artificial intelligence, image recognition and classification technology has received more and more attention as an important branch of its research field. Among them, the introduction of deep learning networks and the construction of neural network structures not only avoid a lot of the tedious work of manual extraction, but also improve the accuracy of image recognition. Convolutional neural networks have many advantages that conventional neural networks do not have. Therefore, image classification systems based on convolutional neural networks emerge in endlessly, but there is still much room for improvement in terms of recognition accuracy and recognition speed. Based on this, this paper proposes an improved deep convolutional neural network to improve the accuracy of the network by changing a series of parameters such as the number of channels of the convolution layer, the size of the convolution kernel, the learning rate, the number of iterations, and the size of the small batch with speed. In this paper, three data sets were selected, namely sewage, animals and the Simpson Family. Comparing the improved convolutional neural network network with the existing SqueezeNet and GoogleNet. It is found that the accuracy of the network is maintained while maintaining a similar speed. Both F1-score and F1-score have been improved with a higher recognition rate and better recognition effect in image recognition classification.
High-performance single-photon sources from solid-state quantum emitters

We develop high-efficiency single- and entangled-photons sources that simultaneously combine high purity, efficiency, and indistinguishability. We develop elliptical microcavities to boost the polarized single-photon extraction efficiency over 50%. We coherently excite a quantum dot with a dichromatic pulsed laser to circumvent the 50% loss issue in resonance fluorescence due to the polarization-extinction configuration. We directly observe intensity squeezing of 0.59 dB in resonance fluorescence, and a corrected squeezing of 3.29 dB at the first lens. Quantum interference between quantum-dot single photons and sunlight is also demonstrated in our experiments.
Segmentasi Citra Digital Dengan Menggunakan Algoritma Khutlang

The segmentation process in digital images is done with edge detection to provide a target path along the edge (edge) of the image object. The edge in the image is the area on the edge of the image object with strong contrast intensity, so that the intensity jumps from one pixel to another. Edge detects images by significantly reducing the amount of data and filtering out useless filtering, and also preserving the important properties needed in the image in this case which supports the appearance of the edges of the image. Edge Detection in an image of a process that results in the edges of image objects, the purpose is to mark the details of the image and to improve the details of the blurred image, which occurs due to errors or the effects of the acquisition process image. There are many methods for detecting this edge, but the method for locating this edge is characteristic of the gradient filter category and is included for searching image segmentation using the Khutlang method.
Use of Deep Learning Algorithm to Detect Road Damage and Closures After the 2016 Kumamoto Japan Earthquake

This study tries to detect the earthquake-induced damage to the roadways and road closures due to collapsed buildings using the images captured by a vehicle-mounted camera. The images employed by this study were sequentially taken after the 2016 Kumamoto earthquake with a camera installed in an automobile. To achieve the objective, the authors try to develop an image classifier to detect the earthquake-induced damage to the roadways and road closures based on deep learning algorithm. The 31,951 images captured on April 17 and 20, 2016 were employed to develop the image classifier. The images were visually classified into the three classes: damage to roadway, road closure, and no damage, and they were divided into training and test sets. The overall accuracy of the image classifier was calculated using the test set, and it reached 81%. The image classifier properly recognized the images with no damage on roadway with the accuracy of 90%. It was difficult to detect the damage to roadway because some of the images include only minor cracks.
COMPARATIVE STUDY BETWEEN CLUSTERING TECHNIQUE AND MIND MAPPING TECHNIQUE TO IMPROVE STUDENTS’ WRITING ABILITY AT THE EIGHT GRADE OF SMPN 2 TOLITOLI

The objective of this research is to find out the significant difference between Clustering technique and Mind Mapping technique in improving students’ writing ability at the eight grade of SMPN 2 Tolitoli. It is a true-experimental research. It was conducted at eight grade of SMP Negeri 2 Tolitoli. The researcher gave a pre-test to measure the students’ previous ability in writing. Then, a post-test was to find out the students’ writing ability after given treatment which is the application of clustering and mind mapping technique. The researcher used SPSS 21 program to analyze the data collection. The result of data analysis showed that clustering technique and mind mapping technique gave a significance effect to the students in writing ability. The mean score of post test in mind mapping technique (71,00) was higher than the mean score of pre-test (32,33). And the mean score of post-test in clustering technique (68,10) was higher than the mean score of pre-test (34,76). Although, the improvement in mind mapping technique was more effective than clustering technique  but the testing of hypothesis showed that the value of t count was lower than t table (0,717≤2,021). Hence, there was no a significance difference in writing achievement score between students were taught to using clustering technique and mind mapping technique at the eight grade of SMPN 2 Tolitoli. Key words: Comparative, mind mapping technique, clustering technique, writing ability.
A Systematic Comparative Analysis of Clustering Techniques

Abstract Clustering has now become a very important tool to manage the data in many areas such as pattern recognition, machine learning, information retrieval etc. The database is increasing day by day and thus it is required to maintain the data in such a manner that useful information can easily be extracted and used accordingly. In this process, clustering plays an important role as it forms clusters of the data on the basis of similarity in data. There are more than hundred clustering methods and algorithms that can be used for mining the data but all these algorithms do not provide models for their clusters and thus it becomes difficult to categorise all of them. This paper describes the most commonly used and popular clustering techniques and also compares them on the basis of their merits, demerits and time complexity.
Entanglement in Classical Light

Within previous chapters the ability to customize complex singular light fields in its various properties has been demonstrated. Intriguing light fields structured in 2d, 3d or even 4d have been presented giving new insights into the fundamental properties of singular light and paving the way to pathbreaking applications, e.g., in optical micromanipulation or to the implementation of functional 4d materials. However, there is another property enlarging the already rich phenomenology of structured light and opening up new perspectives for applied optics: so-called classical entanglement. Entanglement is today considered as a property typically associated to quantum mechanics. This property is essential for prominent quantum observations and studies as the Gedankenexperiment of Schrödinger’s cat [1, 2], the Einstein–Podolsky–Rosen paradox [3], Bell’s inequality [4], quantum cryptography [5], or quantum computing [6, 7]. However, it became clear that the algebraic concept underlying entanglement can indeed be created in classical optics, facilitating the classical analogon of quantum entanglement, thus, bridging a gap between classical and quantum optics. The analogon displays essential quantum mechanical features of entanglement as the key feature of non-separability. For example structured light, namely, vectorial beams can be described as classically entangled, being non-separable in its polarization and spatial shape, as it will be shown in the following section.
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction

Definition Extraction systems are a valuable knowledge source for both humans and algorithms.In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus.We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them.Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.