Reviewerly/exHarmony huggingface dataset:
Peningkatan Keterampilan Menulis Teks Biografi Melalui Penerapan Metode Quantum (Tandur) pada Siswa Kelas X SMA Yayasan Purwa Bhakti Ciawi Bogor

The aim of the research is to know the improvement of writing biography text through method of quantum (TANDUR) and to improve the effectiveness of the students study in writing biography text for grade 10 students of Yayasan Purwa Bhakti Ciawi Bogor. The hypothesis of this research is the there is improvement and the effect of student’s ability to write the biography text through method of quantum (TANDUR). The hypothesis can be accepted and be showed with the score average from the students and also can be showed with the result of observation that be done by two collaborators to know the effectiveness of students study. The writing skill in writing biography text can be improved after the researcher used the quantum (TANDUR) as the method in learning process. That was showed with increasing of the score average in each and every cycle. The score average of pre-test is 59,8 or 60% in percentage. In cycle I obtained the increasing of the score average become 72 or 72% and in cycle II obtained the significant score 81,6 or 82%. The success of writing ability in writing biography text after be applied of quantum (TANDUR) method in classroom action research showed the improvement with the score average 82%. In addition, the difference significantly between the results of test in cycle I obtained 72% and 82% in cycle II. It showed successfulness in applying the method of quantum (TANDUR) through improve the writing ability in writing biography text.
A CNN Based Approach for Crowd Anomaly Detection

Automatic Anomaly detection in a crowd scene is very significant because of more apprehension with people's safety in a public place. Because of usefulness and complexity, currently, it is an open research area. In this work, a new Convolutional Neural Network (CNN) model is proposed to detect crowd anomaly. Experiments are carried out on two publicly available datasets. The performance is measured by Accuracy and Area Under the ROC Curve (AUC). The experimental results determine the efficacy of the proposed model.
MT-lib: A Topology-aware Message Transfer Library for Graph500 on Supercomputers

We present MT-lib, an efficient message transfer library for messages gather and scatter in benchmarks like Graph500 for Supercomputers. Our library includes MST version as well as new-MST version. The MT-lib is deliberately kept light-weight, efficient and friendly interfaces for massive graph traverse. MST provides (1) a novel non-blocking communication scheme with sending and receiving messages asynchronously to overlap calculation and communication;(2) merging messages according to the target process for reducing communication overhead;(3) a new communication mode of gathering intra-group messages before forwarding between groups for reducing communication traffic. In MT-lib, there are (1) one-sided message; (2) two-sided messages; and (3) two-sided messages with buffer, in which dynamic buffer expansion is built for messages delivery. We experimented with MST and then testing Graph500 with MST on Tianhe supercomputers. Experimental results show high communication efficiency and high throughputs for both BFS and SSSP communication operations.
New Physics with LHCb to explain loss of lepton universality, or just gravity?

In a multi-fold universe, gravity emerges from Entanglement through the multi-fold mechanisms. As a result, gravity-like effects appear in between entangled particles that they be real or virtual. Long range, massless gravity results from entanglement of massless virtual particles. Entanglement of massive virtual particles leads to massive gravity contributions at very smalls scales. Multi-folds mechanisms also result into a spacetime that is discrete, with a random walk fractal structure and non-commutative geometry that is Lorentz invariant and where spacetime nodes and particles can be modeled with microscopic black holes. All these recover General Relativity (GR) at large scales and semi-classical model remain valid till smaller scale than usually expected. Gravity can therefore be added to the Standard Model resulting into what we defined as SMG. This can contribute to resolving several open issues with the Standard Model without new Physics other than gravity. These considerations hints at a even stronger relationship between gravity and the Standard Model.Recently, there has been a flurry of enthusiasm following announcement of 3.1-sigma violation of lepton universality at LHCb. Many articles, papers and pundits see it as hints of New Physics.In this paper we hypothesize that gravity could explain these observation, instead of New Physics understood as new particles or forces. The proposal can be strongly justified in a multi-fold universe with SMG. It could also make sense, in general, whenever SMG can be motivated.
Low-code/no-code software development platforms and their uses in computer science and information technology education

Low Code/No-Code (LCNC) software development has gained its share and popularity in today's software development market, especially in cloud-based application development. In addition to companies ...
The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL

In this work, we analyze the reinstatement mechanism introduced by Ritter et al. (2018) to reveal two classes of neurons that emerge in the agent's working memory (an epLSTM cell) when trained using episodic meta-RL on an episodic variant of the Harlow visual fixation task. Specifically, Abstract neurons encode knowledge shared across tasks, while Episodic neurons carry information relevant for a specific episode's task.
Knowledge Graph Management on the Edge.

Edge computing emerges as an innovative platform for services requiring low latency decision making. Its success partly depends on the existence of efficient data management systems. We consider that knowledge graph management systems have a key role to play in this context due to their data integration and reasoning features. In this paper, we present SuccinctEdge, a compact, decompression-free, self-index, in-memory RDF store that can answer SPARQL queries, including those requiring reasoning services associated to some ontology. We provide details on its design and implementation before demonstrating its efficiency on real-world and synthetic datasets.
CONDITIONS OF ADAPTATION OF JUNIOR STUDENTS TO THE EDUCATIONAL ENVIRONMENT OF ZVO IN THE PROCESS OF PROFESSIONAL TRAINING

The concept of "adaptation" and related concepts and concepts are analyzed. The essence of the concept "adaptation of junior students to the educational environment of higher education in the process of professional training" is clarified.The social adaptation of first and second year students to the educational environment of a higher education institution is revealed as the ability of an individual to meet the requirements and norms of the educational institution, as the ability to develop, realize their abilities and needs without conflict with this environment. The phenomenon of "adaptation to professional activity" is described as a complex process of gradual entry of a person into a future profession in the educational environment of a higher education institution. It is multifunctional and is a necessary condition and means of identifying a person with a professional activity, optimizing interaction with the professional environment - the necessary components of his professional development and development. At the same time, it is clarified that the essence of the studied phenomenon is that it is a cyclical process of changes in the student's psyche, aimed at his adaptation to the real conditions of professional activity.The organizational and pedagogical conditions of adaptation of first and second year students to the educational environment of a higher educational institution in the process of professional training (environmental, stimulating-activity, preventive) are determined and substantiated.
Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach

Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this work we propose an alternative approach by viewing the training task as a nonlinear optimal control problem. Under this lens, backpropagation amounts to the sequential approach (single shooting) to optimal control, where the states variables have been eliminated. It is well known that single shooting may lead to ill conditioning, and for this reason the simultaneous approach (multiple shooting) is typically preferred. Motivated by this hypothesis, an augmented Lagrangian algorithm is developed that only requires an approximate solution to the Lagrangian subproblems up to a user-defined accuracy. By applying this framework to the training of neural networks, it is shown that the inner Lagrangian subproblems are amenable to be solved using Gauss-Newton iterations. To fully exploit the structure of neural networks, the resulting linear least squares problems are addressed by employing an approach based on forward dynamic programming. Finally, the effectiveness of our method is showcased on regression datasets.
INTEGRATED APPROACH IN THE METHOD OF REPRESENTATION OF THE LECTURE MATERIAL ON HUMANITARIAN CYCLE DISCIPLINES

This article presents the methodological development of lectures, material which may be used in the study course "Philosophy", "Philosophy of science and technology" and "concepts of modern natural Sciences" (cmns) for students of the Humanities in Russian universities. This lecture on "Philosophical understanding of the concept of "life": biocentricity picture of the world" presents the main approaches to the idea of the modern scientific picture of the world. Such a summary of the lecture material, according to the authors, due to the need to change existing approaches to teaching of specific disciplines. In particular those which involve integrated knowledge from different scientific disciplines and the subject of study which are universal categories and phenomena. It is noted that in the conditions of modern technogenic civilization machineoriented, justified is the issue of revision of existing views about the current ideological approach to the basic concepts, components of a comprehensive scientific picture of the world. In this article, the authors examine biocentricity picture of the world in which the author focuses on the leading role of the life. It is concluded that a comprehensive summary of the lecture material various areas of scientific knowledge, contributes to the formation of metacognitive abilities of students in the course of studying the above disciplines.
Proposal of Situation Estimation System Using AI Speaker

We propose a system that uses the sound acquired by voice AI to guess the user's situation and provide services according to the situation. The system analyzes environmental sounds before the user's command, and records and collects the characteristic environmental sounds as symbol sounds, the type and time of occurrence. We thought we could analyze the pattern in which a particular command was executed each time a particular symbol string occurred at the same time interval. In Experiment 1, we investigated the characteristic sounds that were candidates for symbol sounds from the recording of daily environmental sounds. In Experiment 2, we selected three characteristic sounds, converted 120 recorded data into spectrogram images, and obtained a Convolutional Neural Network (CNN) model. We built a model, trained it, identified unidentified data in the trained model, and verified whether the prediction accuracy can identify it.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the prevailing type of GNNs, have been theoretically shown unable to distinguish, detect or count many graph substructures. While efforts have been paid to complement the inability, existing works either rely on pre-defined substructure sets, thus being less flexible, or are lacking in theoretical insights. In this paper, we propose GSKN, a GNN model with a theoretically stronger ability to distinguish graph structures. Specifically, we design GSKN based on anonymous walks (AWs), flexible substructure units, and derive it upon feature mappings of graph kernels (GKs). We theoretically show that GSKN provably extends the 1-WL test, and hence the maximally powerful MPGNNs from both graph-level and node-level viewpoints. Correspondingly, various experiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range of baselines, endorsing the analysis.
Categorical semantics of a simple differential programming language.

With the increased interest in machine learning, and deep learning in particular, the use of automatic differentiation has become more wide-spread in computation. There have been two recent developments to provide the theoretical support for this types of structure. One approach, due to Abadi and Plotkin, provides a simple differential programming language. Another approach is the notion of a reverse differential category. In the present paper we bring these two approaches together. In particular, we show how an extension of reverse derivative categories models Abadi and Plotkin's language, and describe how this categorical model allows one to consider potential improvements to the operational semantics of the language.
Information Retrieval Based on Telugu Cross-Language Transliteration

In the current scenario, information access over cyberspace has increased gradually. A huge amount of information in different languages are available on cyberspace. Most of the people want to retrieve the information in their native languages. This paper portrays regarding an English-Telugu cross-lingual information retrieval (CLIR) system. CLIR fetches Telugu documents to answer a specified English (or) Telugu query. In Indian subcontinent, Telugu is the most well-known spoken dialect in the states of Telangana, Andhra Pradesh and Union Territories of Andaman Nicobar and Yanam. A cross-lingual information retrieval process desires a parallel corpora or bilingual dictionary, which is used to translate texts from one language to another. For in-house translation, the proposed approach uses a bilingual dictionary. This scheme also supports the monolingual information retrieval method. This study also presents the effect of using language dependent and independent stemmers in monolingual information retrieval system.
Сравнение моделей векторного представления текстов в задаче создания чат-бота

Nowadays, a field of dialogue systems and conversational agents is one of the rapidly growing research areas in artificial intelligence applications. Business and industry are showing increasing interest in implementing intelligent conversational agents into their products. There are numerous applications of chatbots in industry, banking, healthcare, and education; and it keeps on growing year-by-year. Many recent studies has tended to focus on possibility of creating intelligent bots helping users not only to accomplish specific tasks (by identifying their intents from text or voice conversations using artificial intelligence), but to capture the user’s identity, attributes, engagement data, and any feedback the user provides - to better handle a wide variety of conversational topics imitating human-like behavior. In this paper, we review the recent progress in developing intelligent conversational agents (or chatbots), its current architecture (rule-based, retrieval based and generative-based models) as well as discuss the main advantages and disadvantages of the approaches. Additionally, we conduct a comparative analysis of state-of-the-art text data vectorization methods (i. e. word/sentence embeddings) which we apply in implementation of a retrieval-based chatbot as an experiment. The results of the experiment are presented as a quality of the chatbot responses selection using various R10@k measures. We also focus on the features of open data sources providing dialogues in Russian. Natural language processing (NLP) techniques for the collected dialogue data are described. Both the final dataset and program code are published. In this paper, the authors also discuss the issues of assessing the quality of chatbots response selection, in particular, emphasizing the importance of choosing the proper evaluation method. We also demonstrate examples of chatbot dialogues implemented using text vectorization models (TF-IDF-weighted Word2Vec embeddings and LASER sentence embeddings) which revealed best performance. Our future work research is also briefly described in this paper.
A simpler encoding of indexed types

In functional programming languages, generalized algebraic data types (GADTs) are very useful as the unnecessary pattern matching over them can be ruled out by the failure of unification of type arguments. In dependent type systems, this is usually called indexed types and it's particularly useful as the identity type is a special case of it. However, pattern matching over indexed types is very complicated as it requires term unification in general. We study a simplified version of indexed types (called simpler indexed types) where we explicitly specify the selection process of constructors, and we discuss its expressiveness, limitations, and properties.
The Impact of Presentation Style on Human-In-The-Loop Detection of Algorithmic Bias

While decision makers have begun to employ machine learning, machine learning models may make predictions that bias against certain demographic groups. Semi-automated bias detection tools often present reports of automatically-detected biases using a recommendation list or visual cues. However, there is a lack of guidance concerning which presentation style to use in what scenarios. We conducted a small lab study with 16 participants to investigate how presentation style might affect user behaviors in reviewing bias reports. Participants used both a prototype with a recommendation list and a prototype with visual cues for bias detection. We found that participants often wanted to investigate the performance measures that were not automatically detected as biases. Yet, when using the prototype with a recommendation list, they tended to give less consideration to such measures. Grounded in the findings, we propose information load and comprehensiveness as two axes for characterizing bias detection tasks and illustrate how the two axes could be adopted to reason about when to use a recommendation list or visual cues.
LINEAR COMPLEXITY OF GENERALIZED CYCLOTOMIC BINARY SEQUENCES OF PERIOD pq

In this paper, a class of generalized cyclotomic binary sequences of period pq is proposed, where p and q are two distinct odd primes. By using Whiteman's generalized cyclotomy of order 4 and classic cyclotomy of order 2, the sequences are almost balanced and the exact value of their linear complexity is calculated, which shows that the proposed sequences are quite good in terms of the linear complexity.
Using DPI and Statistical Analysis in Encrypted Network Traffic Monitoring

The pervasive use of encrypted protocols and new communication paradigms based on mobile and home IoT devices has obsoleted traffic analysis techniques that relied on clear text analysis. This has required new monitoring metrics being able to characterise, identify, and classify traffic not just in terms of network protocols but also behaviour and intended use. This paper reports the lessons learnt while analysing traffic in both home networks and the Internet, and it describes how monitoring metrics used in experiments have been implemented on an open source toolkit for deep packet inspection (DPI)and traffic analysis developed by the authors.The validation process confirmed that combining the proposed metrics with DPI, it is possible to effectively characterise and fingerprint encrypted traffic generated by home IoT and non-IoT devices, paving the way to next generation DPI toolkit development.
TEKNIK MENULIS YANG EFISIEN DAN EFEKTIF DI MEDIA ONLINE

Writing news in online media are still many that do not meet the requirements. News is written too longand follows the writing style in print media. Writing news in online media must be efficient and effectiveto be easily scanned by readers. Efficient and effective is different from print and television media.Online media space is very limited compared to the two media. If the news is made too long and longwinded,the reader will quickly saturate and make them reluctant to read it. In fact, the language ofnews in online media must be pleasing, readable and easy to understand. This is what underlies thewriting training in online media for Inews TV employees in December 2018. The training is conductedby providing material on how to write good news in online media. After that, the news will be analyzedin the Inews TV online media, whether it has fulfilled the provisions. The results of the analysis aremonitored together, is there still a lack of writing in the next edition. This training is expected to makeemployees more adept at writing news in online media. After several times the training began to showbetter writing changes
초등 과학수업의 다면적 분석을 중심으로 한 교사 참여형 교육프로그램이 초보교사의 수업전문성에 미치는 효과

The purpose of this study is to analyze The Effect of Teacher Participation-oriented Education Program (TPEP) centered on Multi-Faceted Analysis of Elementary Science Classes on the Class Expertise of novice teacher. First, in order to develop the TPEP, lectures and exploratory science classes were analyzed using imaging and eye-tracking techniques. In this study, the TPEP was developed in five stages: image analysis, eye analysis, teaching language analysis, gesture analysis, and class development. Participants directly analyzed the classes of experienced and novice teachers at each stage. The TPEP developed in this study is different from the existing teacher education program in that it reflected the human performance technology aspects. The participants analyzed actual elementary science classes in a multi-faceted way and developed better classes based on them. The results of this study are as follows. First, at the teacher training institutions and the school sites, pre-service teachers and novice teachers should be provided with various experiences in class analysis and multi-faceted analysis of their own classes. Second, through this study, we were able to identify the limitations of existing class observations and video analysis. Third, the TPEP should be developed to improve the novice teachers’ class expertise. Finally, we hope that the results of this study are used as basic data in developing programs to improve teachers’ class expertise in teacher training institutions and education policy institutions.
Contextual Text Embeddings for Twi

Transformer-based language models have been changing the modern Natural Language Processing (NLP) landscape for high-resource languages such as English, Chinese, Russian, etc. However, this technology does not yet exist for any Ghanaian language. In this paper, we introduce the first of such models for Twi or Akan, the most widely spoken Ghanaian language. The specific contribution of this research work is the development of several pretrained transformer language models for the Akuapem and Asante dialects of Twi, paving the way for advances in application areas such as Named Entity Recognition (NER), Neural Machine Translation (NMT), Sentiment Analysis (SA) and Part-of-Speech (POS) tagging. Specifically, we introduce four different flavours of ABENA -- A BERT model Now in Akan that is fine-tuned on a set of Akan corpora, and BAKO - BERT with Akan Knowledge only, which is trained from scratch. We open-source the model through the Hugging Face model hub and demonstrate its use via a simple sentiment classification example.