Reviewerly/exHarmony huggingface dataset:
Distributed Representation of Entity Mentions Within and Across Multiple Text Documents

Regarding to the importance of entities as a base of information for several NLP applications, Cross- Document Coreference Entity Resolution (CDCR) provides techniques for the identification of textual mentions of entities and clustering co-referent mentions across multiple documents. In such context, while prior works employ Knowledge Bases (KB) as a structured information resource to enrich the context of mentions, however these methods have limitations with KB’s unknown entities, with effects on the accuracy and performance of the task. Accordingly, this paper presents a new approach to improve the state-of-the-art by concentration on the knowledge provided by the input text of the mentions, regardless of any external knowledge resource. For this purpose, we first construct the context of mentions using the sequence of informative words around the mention (known as content-words). Furthermore, by abstraction of the mention vector representation to a limited size using an artificial neural network technique of continuous representation of words (i.e. Word2Vec), we reduce the computational cost of the co-referring mentions sub-task. By analyzing the results of experiments with two datasets, significant gains in the accuracy of CDCR as well as run-time efficiency are achieved, compared to the best prior methods.
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training

Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to a label-scarce or even unlabeled target domain. Recently domain-adversarial training (DAT) has shown promising capacity to learn a domain-invariant feature space by reversing the gradient propagation of a domain classifier. However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversarial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space. In this paper, we propose a novel Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial Reconstruction Network (ARN). The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures. Furthermore, ARN demonstrates strong robustness to a wide range of hyper-parameters settings, greatly alleviating the task of model selection. Extensive empirical results validate that our approach outperforms other state-of-the-art domain alignment methods. Moreover, reconstructing adapted features reveals the domain-invariant feature space which conforms with our intuition.
The Bibliographical Metadata Collection. A Guide for Contributors

This Hoe Working Paper offers an overview of the functionalities of the websites of the HOE project, especially of the meta-data collection itself. It also provides a user guide for those who what to contribute data to the meta-data collection.This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The Types of Task in Teaching English at SD AlamMuhammadiyah Banjarbaru

In ELT (English Language Teaching) for young learners, the use of techniques,methods, or approaches is the most important thing that the teacher has tounderstand and use in teaching the English Language in the classroom. Besidesusing teaching methods and approaches, teacher also have to do assignment tostudent by giving them task. A task is an assignment or work given by a teacher tothe students in the classroom. It can be formed such us interaction, discussion,giving information, personal expression, etc. It must be finished within a specifictime. According to Prabhu, there are various tasks that can be used to help theteacher in evaluating students' understanding of learning English which areInformation-gaps activity, Reasoning-gaps Activity, and Opinion-gaps Activity.The researcher interested to do a research about the types of task and how theimplementation of the task at SD Alam Muhammadiyah Banjarbaru.This study uses descriptive qualitative find the the types of task and how theimplementation of the task at SD Alam Muhammadiyah Banjarbaru. To collectdata, the researcher used classroom observations 4 times in first and second grade.The research subjects were an English teacher of SD Alam MuhammadiyahBanjarbaru. In analyzing the data that the researcher got, he used four steps ofdata analysis, namely data collection, data reduction, data display, and drawingconclusions. Then, all data are analyzed and concluded descriptively.Based on the results of the study show that the teacher used information-gapactivity as task in teaching English at the classroom and she implemented it bydividing students into groups and asking them to help other group that cannotanswer the question that given by the teacher
Multi-Task Bidirectional Transformer Representations for Irony Detection

Supervised deep learning requires large amounts of training data. In the context of the FIRE2019 Arabic irony detection shared task (IDAT@FIRE2019), we show how we mitigate this need by fine-tuning the pre-trained bidirectional encoders from transformers (BERT) on gold data in a multi-task setting. We further improve our models by by further pre-training BERT on `in-domain' data, thus alleviating an issue of dialect mismatch in the Google-released BERT model. Our best model acquires 82.4 macro F1 score, and has the unique advantage of being feature-engineering free (i.e., based exclusively on deep learning).
A Novel CBIR System Based on Combination of HSI - Color moment and Gabor Filter

Content-Based Image Recovery (CBIR) is a Digital Image Processing (DIP) platform for search and recovery of query photos from various databases. CBIR process includes various steps in which highlights are extracted, as well as images, are taken from large image databases based on a visual substance like color, form and structure in line with the interest of the user. CBIR does some research to provide a reasonable answer to your client question. A new way to index colors, through the implementation of a simple SVM system is planned in this article. This paper introduces a new technique for indexing color photographs through manipulation of the elegance of the equalization process of Histogram. We also have a combination of color, texture in this algorithm. The work of the past has been strengthened in this method to achieve greater precision. We suggest histogram equalization in this paper to increase the image quality or distance matrix in order to obtain better performance than basic work.The three channels are then autonomously encrypted. The element extraction was seen here as a paired order issue but SVM was used to organize this issue & the whole image, extracted after the feature extraction process, is grouped accordingly.
Clothing mark index system with image saliency in surveillance videos

In order to strengthen the management and control of special crowd and increase the application effectiveness of video as a typical unstructured data, this paper focuses on how to effectively extract and utilize the rich content contained in it to transform into intelligence information and build an effective personnel-specific tracking system. The experimental results show that the combination of video motion region extraction and personnel image features can more accurately complete person detection, and the introduction of multi-feature fusion to extract special person regions, such as clothing logo identification and other areas, for specific person to carry out feature labeling to index, improve the accuracy of personnel tracking. From the perspective of police technology innovation, this paper introduces the method of personnel characteristics analysis, comparison, retrieval, data mining and intelligent early warning to improve the application level of valuable personnel characteristics data in video images, and establish a feature analysis system to achieve continuous tracking of targets.
Ruth Crawford’s String Quartet, Mvt. 3: An Analysis of Dynamic Counterpoint, Contour Similarity, and Musical Form

In the third movement of her String Quartet, locally Ruth Crawford crafts a unique dynamic contrapuntal fabric, where each instrument plays a different alternation of crescendo and diminuendo, thus the same dynamics never synchronize. Globally, this dynamic contrapuntal texture projects an extensively long gradual sound expansion from ppp to fff. In addition, Crawford stresses two temporary dynamics f and fff, subtly dividing the form into three parts. To support Crawford’s subtle formal division, I translate Crawford’s dynamic counterpoint into a chronological sequence of verticalized contours and measure their similarity. I find the essential changes in contour similarity always coincide with the structural boundary, sharpening the formal division in Crawford’s schematic design.
Effect of Arduino Uno Based Microcontroller Media TrainerAgainst Student Learning Outcomes Psychomotor in the Basic Competencies Controlling the Input and Output Ports in Surabaya KAL-1 Vocational School Judging from the Level of Creative Thinking

The objectives of the study were: (1) to analyze the differences in learning outcomes between students who studied using Arduino Uno media trainers and students who studied using AVR media trainers at Surabaya KAL-1 Vocational School; (2) analyze the differences in learning outcomes between students who have a high level of creative thinking and students who have a low level of creative thinking at Surabaya KAL-1 Vocational School; (3) analyze the interaction between the use of media trainers and creative thinking on the learning outcomes of students of Surabaya KAL-1 Vocational School.The type of this research is quasy experiment using factorial 2x2 design, and data is analyzed using two-way Anava.The study found: (1) the average score of psychomotor domain learning outcomes (85.11) for students learning by using Arduino media trainers, significantly higher than the average score of psychomotor domains (74.80) for students learning using AVR media trainer; (2) the average score of psychomotor domains for students who have a high level of creative thinking (91.47), significantly higher than psychomotor domain learning outcomes for students who have a low level of creative thinking (78.09); (3) there is a significant interaction between the application of the media trainer and the level of creative thinking on the learning outcomes of the psychomotor domain.
How Does BN Increase Collapsed Neural Network Filters?

Improving sparsity of deep neural networks (DNNs) is essential for network compression and has drawn much attention. In this work, we disclose a harmful sparsifying process called filter collapse, which is common in DNNs with batch normalization (BN) and rectified linear activation functions (e.g. ReLU, Leaky ReLU). It occurs even without explicit sparsity-inducing regularizations such as $L_1$. This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result. This phenomenon becomes more prominent when the network is trained with large learning rates (LR) or adaptive LR schedulers, and when the network is finetuned. We analytically prove that the parameters of BN tend to become sparser during SGD updates with high gradient noise and that the sparsifying probability is proportional to the square of learning rate and inversely proportional to the square of the scale parameter of BN. To prevent the undesirable collapsed filters, we propose a simple yet effective approach named post-shifted BN (psBN), which has the same representation ability as BN while being able to automatically make BN parameters trainable again as they saturate during training. With psBN, we can recover collapsed filters and increase the model performance in various tasks such as classification on CIFAR-10 and object detection on MS-COCO2017.
Differentially Private Group-by Data Releasing Algorithm

Privacy concerns are growing fast because of data protection regulations around the world. Many works have built private algorithms avoiding sensitive information leakage through data publication. Differential privacy, based on formal definitions, is a strong guarantee for individual privacy and the cutting edge for designing private algorithms. This work proposes a differentially private group-by algorithm for data publication under the exponential mechanism. Our method publishes data groups according to a specified attribute while maintaining the desired privacy level and trustworthy utility results.
Directions for Developing the Efficiency of Scientific Research

The article surprises elements of novelty and needs in the activity of scientific research and communication, identifies the needs and their practical settlement. We identified the needs and behavior of the academic staff involved in research and we analyzed trends in contemporary research. The universities have the opportunity to improve their services for research support in today’s information society using new technologies and different avenue for this move into the future. The effect of libraries using the same, shared hardware, services and data, enhances the both library user’s experience and library staff workflows.Connected data, collaboration, digitization and cloud computing will strongly increase the role of library support for academic research.
Narrow-Profile Standards: Pros and Cons

Currently, in Russia, within the system of standards on information, librarianship and publishing (SIBID) there are developing the documents that reflect specific aspects of technological processes for libraries of separate range. Such trends are observed in the group of national standards for scientific libraries. Along with the positive aspects of establishing narrow-profile standards aimed at methodological support of library-information activities of scientific libraries, there arises the need for similar justification of the specifics of all existing libraries (public, municipal, children’s) and the development of similar documents on standardization. In the practice of the International Organization for Standardization (ISO) there is no area for the development of standards for different types of libraries; standardization is focused not only on the unification of terminological directions in the library community as a whole, but also on the interaction with the related branches. In the draft of the developed national standard GOST R “SIBID. Library and information activities. Terms and definitions” there are allocated more than ten types of libraries that have their own specifics, which must be taken into account when organizing the work. Narrow-profile standards were approved and positively evaluated by the target audience — scientific libraries; and the departmental interest of the founders of group of scientific libraries was taken into account. The debatable issue of expediency to develop this direction is the need to elaborate a series of similar standards for different types of libraries. Considering the wide range of cultural and educational activities of different types of libraries, there will be required the substantial revision of classification of services that will actually generate a new document. The author notes the limited nature of action of the draft standard GOST R 7.0... — “SIBID. State registration number of legal deposit copy of printed publication. Structure, execution and use” developed in accordance with the Program of national standardization for 2019. The draft document presents the developed technological process of one organization and does not provide recommendations on the procedure for assigning and using the state registration numbers of legal deposit copies of printed publications for all recipient organizations established by legislation. This draft can be considered as a standard of the organization. The emerging trend of creating narrow-profile standards, on the one hand, contributes to the development of thorough documents, on the other hand is aimed at supporting the activities of specific area. The efficiency of such approach to standardization requires discussion in the library community and, in case of further development, actions to strengthen methodological support for the maintenance and implementation of narrow-profile standards.
DESIGN OF EXPERT SYSTEM TO DIAGNOSE SEXUALLY INFECTED DISEASES IN HUMAN USING THE BACKWARD CHAINING METHOD

Expert systems (expert systems) in general is trying to adopt a system of human knowledge into a computer, so the computer can resolve the problem as was done by the experts. Or in other words, the expert system is a system designed and implemented with the help of a specific programming language to be able to resolve the problem as done by experts. In this case I tried to implement an expert system to diagnose sexually transmitted disease from the symptoms and the causes of Sexually Transmitted Disease. The purpose of this paper is to build a knowledge-based system on Sexually Transmitted Disease using backward chaining method that will be displayed in the form of a website using PHP programming and MySQL database
A study on the role of subsidiary information in replay attack spoofing detection

In this study, we analyze the role of various categories of subsidiary information in conducting replay attack spoofing detection: `Room Size', `Reverberation', `Speaker-to-ASV distance, `Attacker-to-Speaker distance', and `Replay Device Quality'. As a means of analyzing subsidiary information, we use two frameworks to either subtract or include a category of subsidiary information to the code extracted from a deep neural network. For subtraction, we utilize an adversarial process framework which makes the code orthogonal to the basis vectors of the subsidiary information. For addition, we utilize the multi-task learning framework to include subsidiary information to the code. All experiments are conducted using the ASVspoof 2019 physical access scenario with the provided meta data. Through the analysis of the result of the two approaches, we conclude that various categories of subsidiary information does not reside enough in the code when the deep neural network is trained for binary classification. Explicitly including various categories of subsidiary information through the multi-task learning framework can help improve performance in closed set condition.
2020 Announcement of Big Data Analysis and Data Mining, July 17-18, 2020 | Vienna, Austria

Data mining is the process of identifying valid, novel, useful and understandable patterns in data and Extraction or discovery of useful summaries of data patterns or knowledge from huge amount of data from massive amount of data and also called as KDD Knowledge Discovery in Databases. A KDD process includes data cleaning, data mining, data selection, transformation, data integration, pattern evaluation, and knowledge presentation. It’s an explosive Growth of Data from terabytes to petabytes. Confluence of Multiple Disciplines includes Machine Learning, Statistics, Artificial Intelligence, visualization, High Performance Computing, Database Technology, Algorithm, Pattern Recognition its applications includes Biological and medical data analysis: classification, cluster analysis and microarray data analysis, web mining
The Italian Competition Authority publishes guidelines and policy recommendations for big data aimed to understand the implications that developments in digital economy have for antitrust, regulation and consumer protection

BIG DATA: ITALIAN AUTHORITIES PUBLISHED GUIDELINES AND POLICY RECOMMENDATION ON COMPETITION, CONSUMER PROTECTION AND DATA PRIVACY ISSUES* Introduction On July 10, 2019, the Italian Competition…
A Novel Structure-Based Data Sharing Scheme in Cloud Computing

Cloud computing is a unlimited computing resource and storing resource, which provides a lot of convenient services, for example, Internet and education, intelligent transportation system. With the rapid development of cloud computing, more and more people pay attention to reducing the cost of data management. Data sharing is a effective model to decrease the cost of individuals or companies in dealing with data. However, the existing data sharing scheme cannot reduce communication cost under ensuring the security of users. In this paper, an anonymous and traceable data sharing scheme is presented. The proposed scheme can protect the privacy of the user. In addition, the proposed scheme also can trace the user uploading irrelevant information. Security and performance analyses show that the data sharing scheme is secure and effective.
Treating for-Loops as First-Class Citizens in Proofs

Indexed loop scopes have been shown to be a helpful tool in creating sound loop invariant rules in dynamic logic for programming languages with abrupt completion, such as Java. These rules do not require program transformation of the loop body, as other approaches to dealing with abrupt completion do. However, indexed loop scopes were designed specifically to provide a loop invariant rule for while loops and work rather opaquely. Here we propose replacing indexed loop scopes with a more transparent solution, which also lets us extend this idea from while loops to for loops. We further present sound loop unrolling rules for while, do and for loops, which require neither program transformation of the loop body, nor the use of nested modalities. This approach allows for loops to be treated as first-class citizens in proofs -- rather than the usual approach of transforming for loops into while loops -- which makes semi-automated proofs more transparent and easier to follow for the user, whose interactions may be required in order to close the proofs.
Semi-supervised Batch Mode Active Learning for Multi-class Classification

Most of classification tasks typically require a large number of labelled samples to learn an accurate classifier, while labelled samples are usually limited in practice, and obtaining a lot of labelled samples is a time consuming and laborious work. Active learning is a paradigm to reduce the expensive human action by selecting the most valuable unlabeled samples iteratively. Existing works on active learning evaluate an unlabeled sample by one or two criteria, such as uncertainty, diversity, representativeness and so on. In this paper, three criteria, i.e., uncertainty based on a semi-supervised classifier, diversity based on the distance between samples and representativeness based on the distribution of data, are combined to design a batch mode sample selection strategy for multi-class active learning. By minimizing the expected error with respect to the optimal classifier, we can select the most valuable unlabeled samples for labeling. Experimental results on three public image datasets have demonstrated the effectiveness of our proposed method.
On the possibility of a diachronic speech corpus of Japanese

This book presents empirical methodologies and insights into the field of spoken and written discourse, in syntax, lexis, in corpus-based research and its applications to Japanese language education, and an exploration of the differences across time and register in diachronic Japanese language corpora. As such, it is divided into four parts, which are presented below with a brief overview of each chapter.