Reviewerly/exHarmony huggingface dataset:
Interact-Pose Datasets for 2D Human Pose Estimation in Multi-person Interaction Scene

In recent years, several excellent works dealing with human pose estimation in complex multi-person scenes have focus on the problems of complex backgrounds and the multiplayer scene. However, when facing the scene of multi-person interaction, the results of current mainstream algorithms are still unsatisfactory and some common datasets are not suitable for coping with interaction problems. Therefore, we propose a new dataset named Interact-Pose for solving multi-person interactions problems. Firstly, We use the MSCOCO format to annotate Interact-Pose. Except that, we adopt the corresponding data augmentation scheme to exchange the background of the Interact-Pose Dataset to make it more complex and have better generalization performance. Then it is trained after being fused with the MSCOCO dataset. After training on HigherHRNet, the average AP value of our test results is 67.3% on the Validation set of COCO2017, which is higher than that of the test only being trained by MSCOCO.
Optimal Liquidation Strategy for Cryptocurrency Marketplaces Using Stochastic Control

The cryptocurrency marketplace provides liquidity receiving orders from investors. This requires managing the appropriate inventory risk. An optimal liquidation strategy based on stochastic control has been proposed for such inventory risk management problems of FX dealers. However, the cryptocurrency market has different characteristics than the FX market. Therefore, we propose an optimal liquidation strategy based on stochastic control that considers the characteristics of the cryptocurrency marketplace. We also perform an empirical study using the trading data of a cryptocurrency marketplace. We show that order bias affects the optimal liquidation strategy, and our proposed method is effective for simple liquidation strategies.
Analyzing field audio of parent-child book reading activities across various audio capture devices

Language ENvironment Analysis (LENA) is a light weight audio capture device commonly used to monitor language including frequency of parent-child interactions for research purposes. However, various factors including price and technical limitations often limit the use of LENA for low-income families. Using a smartphone can be a feasible alternative when LENA is not available to practitioners/families. Over the past three decades, significant advancements have resulted in smartphone platforms/microphones/technology, allowing for high quality recorded audio which are available in most households. In this study we compare audio quality and measure performance of several Automatic Speech recognition (ASR) engines on audio captured from iPhone and Android relative to LENA devices. Families who consented in this study recorded reading activities with their children at home using both personal smartphones and LENA. Some challenges we found include recording synchronization, unnecessary background noises, and uneven room acoustics. Audio quality comparison is measured using Speech-Signal-to-noise ratio (SSNR) metric. Both open-source and fine-tuned ASR models are explored, with results reported using overall Word Error Rate (WER), as well as separated by speaker–Child versus Adult. Results show that smartphone platforms can be used versus LENA for child-adult language assessment. [Work sponsored by NSF through Grant Nos. 1918032 and 1918012.]
Supporting Sustainability Through Minimalist Interaction Design Aesthetics

The frequent consumption and discarding of clothing are unsustainable. Some minimalist practices in fashion can reduce fashion waste and the cost of carbon, one of them is the practice of capsule wardrobes. To support the sustainable practice of capsule wardrobe, we designed Minnie, the Musical Clothes Hanger —Minnie for short. Minnie is designed to help people who want to build a capsule wardrobe or other form of minimalist practice in their wardrobes. Applying the method of Research through Design (RtD), we investigate how people perceive minimalist lifestyles and reflect on Marie Kondo’s minimalist wardrobe practices. Based on the design research, we formed the appearance design, system design, and sound interaction design of Minnie. We propose that our study provides insights for designing artefacts to support minimalism in fashion.
BUILDING DIGITAL AND NATURE COMPETENCY IN EDUCATION OF CIVIL PROTECTION

We live in a time that is based on technology and information, a time that forms a digital generation of students. For this reason, Mr. Dinev and Ms. Dineva, teachers at The First Primary School, decided to make and implement an innovative practice – a binary civil protection lesson on the topic: „Behavior and actions of students in an earthquake“, combined with programming. This article presents a traditional lesson, combined with something innovative, forming digital competencies. The good practice we present is realized in the 2th grade and 7th grade in the First Primary School „St. Constantine and Helena“. St. „Cyril and Methodius“, gotse Delchev.
Lexical Expressions with Temporal Semantics: the Corpus Study

The field of study that can be named “temporal semantics” is marked by the domination of grammatical studies. The researchers’ interest is focused on the tense and the aspect, lexical expressions with temporal meaning stay in the background. Obviously, it is grammatical means that for the most part express temporal meanings: they are present in most languages and in every sentence in these languages. However, lexical means can convey the same information. We argue that the cataloging of lexical means and comparing the systems of these means in different languages allow us to outline the boundaries of human perception of time and to better understand what is universal and what is specific in it. Our goal is to conduct a corpus study of temporal lexical expressions in the Russian language. To do this, we developed an annotation scheme and performed a pilot markup on some fragments of the SynTagRus. The paper describes our annotation scheme and provides linguistic grounds for the decisions made during its development. We also compare our annotation scheme with the TimeML scheme, the most used annotation scheme for temporal information in the world. We show that despite the elaborated structure and a large set of elements, TimeML fails to grasp some important fragments of the expressions’ semantics. The proposed annotation scheme is more expressive and flexible and suits for the annotation of the complex examples: sekretarša na minutu vyšla (‘the secretary left for a minute’), delatʹ čto-libo tretij čas podrjad (‘do something more than two hours in succession’ – literally ‘for the third hour in a row’).
A Human-Following Platooning System for Rover Groups Used in Disasters

This research proposed to develop a rover system that follows a human and runs in a platoon for the disaster response. In the case of this system, we need non-collision route planning avoidance actions to prevent collisions and know the route accurately. In this paper, we developed a system that uses a flocking algorithm to prevent collisions and ROS Topic from sending the front rover or human’s position, velocity, and angle information. At the same time, we search the appropriate frequency for following and platooning in the simulation test. As a result, we realize a rover-following and running in a platoon with four or more rovers and automatically return to the starting point with three or more rovers in the simulation test. In an actual machine test, we realize a human following and running in a platoon with two types of rovers and return to the starting point automatically with a rover. In addition, we find the appropriate frequency. As a result, the frequency is about 5Hz.
KGChain: A Blockchain-Based Approach to Secure the Knowledge Graph Completion

In the last recent decade, the knowledge graph become essential in many artificial intelligent applications such as link prediction, recommendations, entity resolution…etc. Knowledge graph completion aims at predicting missing triples where the embedding methods take the lion’s share. These methods embed entities and relations into continuous vector spaces and use scoring functions to compute the plausibility of triples. The knowledge graph privacy and security take an important role to protect the data and the prediction model. We propose KGChain, a new framework which combines an off-chain storage with the blockchain in both the embedding and the completion tasks. Our work has two steps: protecting the knowledge graph and the model privacy using Fabric Hyperledger and securing the completion tasks by smart contracts. The proposed framework is evaluated with several datasets using translation-based embedding.
Some problems when registering publication date in Crossref

Currently, the vast majority of scientific publishers register the metadata of their publications in the Crossref bibliographic database. One of the meta-attributes of any article is the publication date. The completeness and correctness of metadata directly or indirectly affects the number of views and links to the publication, and, accordingly, the ratings of scientific publications, authors and organizations. Some of the problems associated with setting this entry attribute are the subject of this work.
Bayesian Reinforcement Learning

In this chapter, we return to the setting of subjective probability and utility by formalizing the reinforcement learning problem as a Bayesian decision problem and solving it directly. In the Bayesian setting, we are acting in an unknown environment, and we represent our subjective belief about the environment in the form of a probability distribution. We shall first consider the case of acting in unknown MDPs, which is the focus of the reinforcement learning problem. We will examine a few different heuristics for maximizing expected utility in the Bayesian setting and contrast them with tractable approximations to the Bayes-optimal solution. We then present extensions of these ideas to continuous domains. Finally, we draw connections of this setting to partially observable MDPs.
Use cases of attack graph for SOC optimization purpose

The development and deployment of dynamic and adaptive cyber defense solutions are essential to face the extremely fast evolving and increasingly common cyberattacks. Security Operation Centers (SOC) are widely used recently to correctly identify, analyze, defend, and investigate security incidents. In order to defend our networks, it is also essential to identify vulnerabilities and weaknesses in the system. Nowadays, attack graphs are the most frequently used graphical representations of a system's vulnerabilities as well as different attack paths an attacker may carry out. This paper investigates the possible use cases of attack graphs in a Security Operation Center. It also identifies different areas and point out potential research directions.
Implementasi Metode SQ3R pada Teks Eksposisi sebagai Upaya Peningkatan Keterampilan Membaca Pemahaman Siswa

Reading is one of the four language skills that are very important in educational activities. . The low reading ability of students is one of the reasons researchers study or identify the cause. This research is a classroom action research, with the model of Kemmis and Mc. Taggart. The results showed that the use of the SQ3R method could improve the quality of the learning process and the results of students' reading comprehension skills. Improving the quality of the learning process is shown by students being more active and enthusiastic about the learning process of reading comprehension using the SQ3R method. The improvement in the results of reading comprehension skills using the SQ3R method was shown by the increase in the average score of the students, at the average pre-action score of 55.75.
Path Planning and Control for Omni-directional Mobile Robot using Q-Learning and CLIK Algorithm for Home Environment

Localization, mapping, and planning are the three crucial steps to accomplishing the autonomous navigation of mobile robots in an unfamiliar environment. Since implementation of reinforcement learning (RL) algorithms for autonomous navigation in the case of omni-directional robots is a less explored research area, and also such robots have a unique feature over differential drive robots that they can also produce sideways movement. Therefore, in this paper, an RL algorithm called Q-learning is used to get the safe and shortest path from a start point (SP) to a goal point (GP) in a home environment. The path trajectories are obtained by using polynomial curve fitting. The closed-loop inverse kinematics (CLIK) algorithm is used to control a three-wheeled omni-directional mobile robot to follow the desired path. The simulation and plotting are done using MATLAB. The simulation results show that the suggested algorithm can effectively recognize and avoid static obstacles of different shapes and dimensions in an indoor home environment.
A Novel Integrating Approach Between Graph Neural Network and Complex Representation for Link Prediction in Knowledge Graph

Deep learning brings high results in many problems, including Link Prediction on Knowledge Graphs (KGs). Although there are many techniques to implement deep learning into KGs, Graph Neural Networks (GNNs) have recently emerged as a promising direction for representing the structure of KGs as input for a decoder. With this structural information, GNNs can help to retain more information from the original graph than conventional embeddings like TransE, TransH, RESCAL. As a result, the learning model achieves higher accuracy in predicting missing links between entities in the KG. Meanwhile, several studies have successfully demonstrated the intrinsic properties of the embedding process in complex space while keeping many binary relations (symmetric and asymmetric). Thus, this paper proposes deploying GNNs into complex space to increase the model’s predictive capability. Another issue with GNNs is that they are susceptible to over-squashing when a large amount of information propagating between nodes is compressed down to a fixed representation space. As a result, we utilize a dynamic attention mechanism to minimize the adverse effects of these factors, and experiments on benchmark datasets have indicated that our proposal achieves a significant improvement compared to baseline models on almost all standard metrics.
Chronometric Dating of Monuments and Iron Smelting Site at the Sungai Batu Archaeological Complex, Bujang Valley, Kedah, Malaysia to Fulfill Requirement of Urbanization and Land Use Development Since 788 Bc

Archaeological research at the Sungai Batu Archeological Complex, which has been carried out until 2022, aims to complete the chronological data and land use of the Kedah Tua, especially in the matter of the urbanization development process in this complex. This research allows important data related to evidence of the existence and continuity of Kedah Tua kingdom in the world iron trade to be reconstructed based on archaeological studies. This is because archaeological research in this complex has found river jetty, port management and ritual structures built entirely of bricks, which allow chronometric dating to be carried out on brick samples using the OSL method. In addition, the iron-smelting workshop that has also been found in this complex also undergoes radiocarbon dating using in-situ charcoal samples in the furnace area. The chronometric dating is also strengthened with data from geophysical mapping and core drilling which allows information on the land use of the complex to be recorded through scientific studies. The results of the dating have proven that the Kedah Tua kingdom existed since 788 BC, oriented towards the trade of iron resources, which allowed the use of the area to grow rapidly. The process of urbanization and land use clearly conforms to Lynch's principle, which emphasizes the principles of 1) path, 2) edges, 3) districts, 4) nodes and 5) landmarks in the development process of the land use in the complex.
Wushu movement evaluation method based on Kinect

In the field of computer vision and image processing, the recognition of human behavior and posture has become an important topic. However, there are still many problems in the recognition and evaluation of martial arts. Based on Kinect, this paper studies the evaluation method of martial arts action, discusses the acquisition of Kinect bone data and human action recognition, and verifies the martial arts action evaluation system through DTW algorithm. The test results show that the evaluation method proposed in this paper can score martial arts actions in a more standard and timely manner, and improve the understanding and recognition accuracy of human behavior; The system can also be used in the fields of somatosensory games, sports or military training.
Extractive Summarization Utilizing Keyphrases by Finetuning BERT-Based Model

Summarization is a natural language processing (NLP) task of producing a brief text, which provide a compressed text that contains the main content and key information of the source document. Both extractive summarization and keyphrase extraction are the tasks that extract shorter texts keeping salient information and main points from the source document. Compared with keyphrases, summaries composed of sentences are larger granular texts that have high probability of being related to the keyphrases of the document. On one hand, previous work lacks research on whether keyphrases are beneficial for extracting important sentences. On the other hand, with the development of deep neural network, pretrained language models, especially BERT-based models which can adapt to various natural language processing (NLP) tasks by finetuning, have attracted extensive attention. For these reasons, we propose KeyBERTSUM, in which we try to leverage keyphrases in the extractive summarization task based on a BERT encoder, guiding the model focusing on the important contents instead of the entire document. In addition, we also introduce the confidence of guiding phrases in sentence updating. Experimental evaluations of our methods on CNN/Daily Mail New York Times 50 and DUC2001 datasets have shown improvement on ROUGE scores over baselines.
Informational technologies use experience in the educational process in Voronezh State University

The introduction of informational technologies in all spheres of life inevitably affected the development of electronic technologies in the higher education system. The spread of Covid-19 to a pandemic has accelerated the education system transition to the in-depth use of informational educational technologies and the development of its own distance education systems. The purpose of this work is to study the experience of using information technologies in the educational process of the pharmaceutical faculty, based on the implementation of e-learning and distance learning technologies (Voronezh State University). The research methods were the analysis of the information environment Moodle (Modular Object Oriented Dynamic Learning Environment) of Voronezh State University and the correspondence anonymous survey of students of the pharmaceutical faculty using Google forms. The article presents the results of generalizing the experience of using information technologies of the electronic information educational environment of Voronezh State University, technical and instrumental capabilities of the electronic educational environment, highlights the advantages, disadvantages and limitations, presents statistics on the use of the capabilities of the electronic environment by students and teachers. Reserves of improving the quality of e-learning and the use of distance learning technologies at the pharmaceutical faculty of Voronezh State University are revealed.
Physical Aspects of Computing

Considering physical systems as information processors has proven enormously fruitful and illuminating: the computer as a metaphor. However, computers do not constitute an independent separate world. They are natural systems themselves, subject to the laws of physics and the limitations they entail for their functioning. This last part of the book is intended to integrate computing systems into their broader context in nature, to explain why on the one hand, they are “nothing but” physical dynamical systems, and on the other, to understand what makes them special—if anything. In the same sense that the first part, inspired by Wheeler’s “It from bit” [Whe90a, Whe90b], interpreted natural systems as computers, the following sections complement it with the “Bit from it” [AFM15]. They provide an overview from a new perspective; they are not intended as an outline, even rudimentary, of the theory of computation. For comprehensive monographs and anthologies on this subject, see, e.g., Refs. [BEZ00, CT06]. Analyzing computers as dynamical physical systems can shed light on both sides, on physics and on computing.
An Efficient Clustering-Based Privacy-Preserving Recommender System

Recommender systems help online services deliver personalised content and facilitate information overload. Collaborative Filtering is a commonly used technique for building recommender systems. It analyses data collected from users in order to predict their preferences, this causes concerns over the privacy and security of the data as conventional techniques do not prioritise data privacy. Incorporating cryptography into recommender systems resolves the privacy issues, and yet it is impractical in many cases due to the computational overhead imposed by the cryptography and the sheer amount of data needed for computations. In this paper, we propose an efficient clustering-based privacy-preserving recommender system. The system employs homomorphic encryption for protecting user data while retaining the ability to generate recommendations using Collaborative Filtering. To facilitate information overload, a secure clustering technique is adopted to partition data prior to generating recommendations. The proposed system is secure under the semi-honest adversary model. Experiments show that the proposed system is efficient, scalable and produces accurate recommendations.
Pengembangan Multimedia Edugames Berbasis Autoplay pada Materi Sistem Reproduksi untuk SMP/ MTs Kelas IX

The demands of the era of society 5.0 with the development of information technology in all fields need special attention, including in the world of education. Education in Indonesia currently requires improvement efforts, due to the lack of teacher creativity as a facilitator in the learning process, which of course can lead to low use of learning resources and the development of learning media, so that the needs of students in learning are not met. The learning media that can be developed is by utilizing interactive multimedia as a result of the Autoplay program, in the form of educational games (edugames). This study aims to increase students' motivation to learn the material with a happy atmosphere and encourage students to think critically and effectively. This type of research is development research, with the ADDIE model (Analysis, Design, Develop, Implement, Evaluate). However, this research stopped at the Develop stage, while the Implement and Evaluate stages could not be carried out due to time constraints. The results of the research that have been carried out have found that autoplay-based multimedia education on reproductive system material for SMP/MTs class IX meets the eligibility requirements of 96.92% from the material aspect with very good criteria, 73.75% feasible from the media aspect with good criteria and and get a response from teachers with very good criteria with a feasibility level of 96.67% and responses from students with very good criteria with a feasibility level of 97.75%. Based on these data, it can be concluded that the multimedia learning media based on autoplay on the reproductive system material for SMP/MTs class IX is very feasible to use in learning.