Reviewerly/exHarmony huggingface dataset:
Surprise: Result List Truncation via Extreme Value Theory

Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of result list truncation, or where to truncate the ranked list of results, however, has received less attention despite being crucial in a variety of applications. Such truncation is a balancing act between the overall relevance, or usefulness of the results, with the user cost of processing more results. Result list truncation can be challenging because relevance scores are often not well-calibrated. This is particularly true in large-scale IR systems where documents and queries are embedded in the same metric space and a query's nearest document neighbors are returned during inference. Here, relevance is inversely proportional to the distance between the query and candidate document, but what distance constitutes relevance varies from query to query and changes dynamically as more documents are added to the index. In this work, we propose Surprise scoring, a statistical method that leverages the Generalized Pareto Distribution that arises in extreme value theory to produce interpretable and calibrated relevance scores at query time using nothing more than the ranked scores. We demonstrate its effectiveness on the result list truncation task across image, text, and IR datasets and compare it to both classical and recent baselines. We draw connections to hypothesis testing and p-values.
Civil Society Project Initiative In Formation Of University 3.0

The article is devoted to the problems of the development of Russian higher education according to the model of University 3.0. A comparison of the development models of leading Russian and foreign universities is given. Russian project initiatives aimed at the development of civil society are considered as priority areas to ensure the implementation of the third mission by Russian education aimed at creating a highly intellectual environment motivated by social changes. The authors presented one example of social constructions, the creation of which makes it possible in Russian conditions to build elements of the University 3.0. These are the project initiatives of the Presidential Grants Fund for the Development of Civil Society. It should be noted that the fund has been the single operator of grants by the President of the Russian Federation for the development of civil society since April 3, 2017. It is shown that the social structure presented in the framework of the project initiative forms a separate element of the infrastructure at the stage of the establishment of University 3.0 for involving schoolchildren and students in initial productive activities. These schoolchildren and students, being participants in this activity, become individuals with identified specialized creativity, scientific research skills that can become carriers of complex high-level competencies. The type of such projects includes the creation of a mini-museum of inventions in the House of Science and Technology.
The Problem of Mind and Reason and Its Philosophical and Educational Solution by V.V. Davydov

The article considers the philosophical and educational solution to the problem of mind and reason in the work of V.V. Davydov. The authors’ attention is focused on the dialectic of the correlation of the categories of ‘reason’ and ‘mind’. It is argued that reason and mind appear not just as levels, but also as ways of thinking, ways of human world-relation. The basis for distinguishing between mind and reason is the presence in human activity of two levels: adaptive-using and creative-transformative. Reason works in opposition to the subject-object, while the mind has an attitude towards reality as a ‘subject’. External reason applies its scheme to the object, while mind transforms the form of its movement so that it corresponds to the essence of the matter. Reason is a formal culture of thinking, mind is entirely substantial. Reason as a way of thinking is generated by a practice that does not need to master its substantial (primarily social-historical, human) content — the practice of adaptation and use. It flourishes most magnificently in the conditions of reification of social relations, when they are separated from individuals and confront them as an external, alien, formal reality. V.V. Davydov’s achievment is the allocation of two types of generalization in educational activity: rational-empirical and reasonablе-theoretical, and the justification of the possibility and necessity of such a transformation of all didactics so that the teaching would contribute to the development of theoretical thinking. Only in this case can one seriously talk about developmental education.
An Empirical Study on the Course of “Database Principle and Application” Based on the Mixed Teaching Mode of “Massive Course”

Database Principles and Applications" is a compulsory basic course for undergraduates majoring in information science and computer science.This course is rich in teaching content, strong in theory, and closely related to actual operation.Therefore, this course has always been regarded as "difficult for teachers to teach and difficult for students Learning".The author tried to use the mixed teaching mode of combining classroom and "Massive Course" to construct a teaching measurement evaluation scale and measure the results of teaching reform.After analysis and research, it reveals that the "massive course" mixed teaching model has a significant positive impact on the teaching course of "Database Principles and Applications", which help solve the problems of less class hours and more content in the course.This article adopts an empirical research paradigm, conducts teaching reform experiments in the three grades of two schools, and analyses the results from both qualitative and quantitative aspects.Finally, it concludes that the mixed teaching model of "massive course" is beneficial to improve teaching effective.The full article firstly introduces the characteristics of the "Database Principles and Applications" course and the characteristics of the "massive course", and then introduces the basic situation and result analysis of the course reform, and finally draws a conclusion.
HRFA: High-Resolution Feature-based Attack

Adversarial attacks have long been developed for revealing the vulnerability of Deep Neural Networks (DNNs) by adding imperceptible perturbations to the input. Most methods generate perturbations like normal noise, which is not interpretable and without semantic meaning. In this paper, we propose High-Resolution Feature-based Attack (HRFA), yielding authentic adversarial examples with up to $1024 \times 1024$ resolution. HRFA exerts attack by modifying the latent feature representation of the image, i.e., the gradients back propagate not only through the victim DNN, but also through the generative model that maps the feature space to the image space. In this way, HRFA generates adversarial examples that are in high-resolution, realistic, noise-free, and hence is able to evade several denoising-based defenses. In the experiment, the effectiveness of HRFA is validated by attacking the object classification and face verification tasks with BigGAN and StyleGAN, respectively. The advantages of HRFA are verified from the high quality, high authenticity, and high attack success rate faced with defenses.
Diagnosa Tingkat Depresi Mahasiswa Akhir Terhadap Penelitian Ilmiah Menggunakan Algoritma K-Nearest Neighbor

The achievement of a success is considered not an easy thing, such as reversing between a leaf and another, success must be achieved with sincerity, even risking everything that is in a person to achieve success and a lot of success can be obtained through higher education, higher education is a way Reaching goals, companies, industry and government prioritize higher education as trusted human resources, many final students experience depression due to many demands and several factors, excessive levels of depression result in all efforts and efforts will cause all chaos and can make someone making the wrong decision to result in death, a system is needed in diagnosing the level of depression in final students to reduce the risk of continuous depression using the k-Nearest Neighbor algorithm approach, with this algorithm it is il in the form of a decision on the level of depression experienced by final students
Exploring search space trees using an adapted version of Monte Carlo tree search for a combinatorial optimization problem.

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics.
상과 양태의 관계 탐색과 문법교육 내용화에 관한 시론

The purpose of this study is to explore the relationship between aspect and modality and to lay the foundation for constructing grammar education content. In the meantime, aspects and modality have been learned naturally for native speakers, and the category itself has a fairly complicated system and has been somewhat ignored by grammar education contents. However, aspect and modality are both selected by speaker / author, so it is worth educating. This is because the grammatical education should take into consideration the demands of the education field that the learner should construct the contents of education so as to have the power to select or interpret the grammatical expressions based on the linguistic sensitivity. In this paper, we Focused on the commonality of ''subjectivity of choice'' and ''polysemy and fluidity of semantic components'' of aspect and modality, and investigated the relationship between them and explored the possibility of describing it as a method of component analysis. Particularly noteworthy grammatical forms in this study was ''-ass/ess-'', ''-gess-'', ''-deo-'' and ‘-(eu)l geos-’. In order to analyze how these forms are handled in grammar education, we examined the technical aspects of the category of ''language and medium'' textbooks based on the 2015 curriculum. Then, we propose a methodology to educate the diversity of aspect and modality discussed in previous research.
Incremental Policy Gradients for Online Reinforcement Learning Control

Policy gradient methods are built on the policy gradient theorem, which involves a term representing the complete sum of rewards into the future: the return. Due to this, one usually either waits until the end of an episode before performing updates, or learns an estimate of this return--a so-called critic. Our emphasis is on the first approach in this work, detailing an incremental policy gradient update which neither waits until the end of the episode, nor relies on learning estimates of the return. We provide on-policy and off-policy variants of our algorithm, for both the discounted return and average reward settings. Theoretically, we draw a connection between the traces our methods use and the stationary distributions of the discounted and average reward settings. We conclude with an experimental evaluation of our methods on both simple-to-understand and complex domains.
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking

This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, firstly, our method leverages the Siamese network applied on RGB images to produce 2D region proposals which are then extruded into 3D viewing frustums. Besides, we perform an online accuracy validation on the 3D frustum to generate refined point cloud searching space, which can be embedded directly into the existing 3D tracking backbone. For efficiency, our approach gains better performance with fewer candidates by reducing search space. In addition, benefited from introducing the online accuracy validation, for occasional cases with strong occlusions or very sparse points, our approach can still achieve high precision, even when the 2D Siamese tracker loses the target. This approach allows us to set a new state-of-the-art in 3D single object tracking by a significant margin on a sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single object tracking show that our framework boosts 2D tracking performance as well.
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior

Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evidence supporting Basque as a related language, concurring with the favored position by the current scholarship.
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training

A class of recent semi-supervised learning (SSL) methods heavily rely on domain-specific data augmentations. In contrast, generative SSL methods involve unsupervised learning based on generative models by either joint-training or pre-training, and are more appealing from the perspective of being domain-agnostic, since they do not inherently require data augmentations. Joint-training estimates the joint distribution of observations and labels, while pre-training is taken over observations only. Recently, energy-based models (EBMs) have achieved promising results for generative modeling. Joint-training via EBMs for SSL has been explored with encouraging results across different data modalities. In this paper, we make two contributions. First, we explore pre-training via EBMs for SSL and compare it to joint-training. Second, a suite of experiments are conducted over domains of image classification and natural language labeling to give a realistic whole picture of the performances of EBM based SSL methods. It is found that joint-training EBMs outperform pre-training EBMs marginally but nearly consistently.
귀화자 병역의무화 정책의 타당성에 관한 연구

The people of South Korea are very sensitive when it comes to the subject of mandatory military service, and the discussion of the conscription of naturalized citizens may become a social issue. Therefore, this paper seeks to analyze the feasibility of conscripting naturalized citizens by identifying issues and conflicting views. The feasibility analysis of conscription of naturalized citizens is conducted through three criteria: military adaptability, social equity, and policy effectiveness. The analysis results show that the conscription of naturalized citizens is appropriate as Korean society faces inevitable flow toward diversified society. Further, this new policy will achieve greater social integration through the creation of the social capital and expansion of shared national sentiments.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, we introduce two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible, but while Kinyarwanda has been studied in Natural Language Processing (NLP) to some extent, this work constitutes the first study on Kirundi. Along with the datasets, we provide statistics, guidelines for preprocessing, and monolingual and cross-lingual baseline models. Our experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. In addition, the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER. The datasets, stopwords, and pre-trained embeddings are publicly available at https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus .
Token Drop mechanism for Neural Machine Translation

Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs. We propose Token Drop to improve generalization and avoid overfitting for the NMT model. Similar to word dropout, whereas we replace dropped token with a special token instead of setting zero to words. We further introduce two self-supervised objectives: Replaced Token Detection and Dropped Token Prediction. Our method aims to force model generating target translation with less information, in this way the model can learn textual representation better. Experiments on Chinese-English and English-Romanian benchmark demonstrate the effectiveness of our approach and our model achieves significant improvements over a strong Transformer baseline.
Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry

Data analysis of social media posting continues to offer a huge variety of information about the health situation faced by an individual. Social networking or social media websites provide us a wealth of information generated by users in a variety of domains, that generated information are unstructured and unlabeled and are not captured in an exceedingly systematic manner, as info generated is not humanly possible to process due to its size. One traditional way of collecting patients experience is by conducting surveys and questionnaires, as these methods ask fixed questions and are expensive to administer. In this paper, a patient feedback platform (PFP) using free text sentiment analysis is developed to computationally identify and categorize the polarity expressed in a piece of text. Six machine learning latest algorithms have been used as key evaluation for evaluating accuracy of the developed (PFP) model. Results achieved have shown 88 % accuracy on the basis of which it is recommended that developed (PFP) patient feedback platform could be used to improve E-health care services indeed.
“Hate Speech” Phenomenon in Troll Discourse (by the Material of the French Language)

The article considers the currently relevant phenomenon of Internet trolling and “hate speech” as an integral element of modern Internet communication. The research objectives are as follows: to identify “hate speech” elements in troll discourse and to reveal their role in enhancing efficiency of the trolling strategy. Scientific originality of the paper lies in the fact that the author for the first time examines the “hate speech” phenomenon in the French-language discourse of network trolling and concludes that “hate speech” themes coincide with the most frequent troll targets (race, religion, sexual preferences, appearance, etc.). The research findings are as follows: the author proves that “hate speech” elements are observable in troll discourse; they raise efficiency of the trolling strategy.
Linguistics Devices’ Appreciation in Semarang’s Online Cuisine Advertisement Text

Persuasive communication can be found in advertisement language. Creating advertisement that attractive, right on target, and effective in attracting potential customers, is a must to win the competition. An advertisement must be able to present everything that is smart, innovative, and must also be different in a unique sense compared to the others. This article specifically explores the appreciation devices in Semarang’s cuisine advertisement text phasing on the internet. Appreciation is one of the choices from the attitude aspects that part of appraisal system. Data in the form of attitudinal lexis were collected from online advertising texts which promote foods in Semarang. The method of providing data using the observation method. The basic technique used is the tapping technique. Meanwhile, the advance techniques used are the free, active, proficient listening technique and the note – taking technique. The data analysis in this study used a separate method. The basic technique used is the technique for direct elements, while the advance technique uses the lesion technique and the dressing technique. The results of the discussion show that the appreciation used in the Semarang’s cuisine advertisement texts on the internet is positive appreciation.
Development of Critical Thinking in Gifted Human by Supply Chain Strategies

Abstract this article discusses the development of supply chain for the gifted humans in education system. The article describes the experience of identifying potentially gifted students at the initial stage of their education, as well as the organization of work with gifted children in primary school. The article notes that special attention should be paid to the self-realization of a gifted student during the lesson and during extracurricular hours. Finally it confirmed by the reviewing the literature review the effectiveness of supply chin strategy in critical thinking.