Reviewerly/exHarmony huggingface dataset:
Heuristic Approach for PRI Modulation Recognition Based on Symbolic Radar Pulse Trains Analysis

We present a novel heuristic approach for pulse repetition interval (PRI) modulation recognition by identifying the temporal pattern based on a symbolic radar pulse train analysis. The analysis of the symbolization of radar pulse trains is presented as a metric for the ability to identify the temporal PRI modulation characteristic. The recognition approach developed based on a time series analysis technique has to transform the radar pulse trains into a corresponding sequence of symbols. We retain temporal information from transforming the time series of pulse trains through numerical computations. The PRI pattern is obtained for real-time monitoring, and then the modulation types are identified based on characteristics. The simulation results show that the proposed algorithm can effectively recognize the PRI modulation type of radar pulse trains.
Hybrid Granularity-Based Medical Event Extraction in Chinese Electronic Medical Records

Chinese medical event extraction (CMEE) has risen the attention of a large amount of researchers. The event extraction in Chinese electronic medical records (CEMRs) has been an important aspect of EMRs information extraction. Most recent work solves the CMEE task, either by a pre-training language model and ensemble, or performing data augmentation through generating dummy data. In this work, we present a hybrid-granularity approach for CMEE task, and obtain a 3.46% absolute improvement in the F1-score over previous models with the same condition of low resource in trainingdata. Our approach essentially builds on a hybrid granularity-based extracter and a multi-rule ensembler based on medical feature word according to the characteristics of the data itself. Shown by the results, our extracter can extract more rightful tumor-related attributes from different granularities of data by reducing the harmful noise from useless sentences of original clinical text. Moreover, the results show that the multi-rule ensembler based medical feature word efficiently solves the problem of Chinese medical entities de-duplication and boundary ambiguity.
Digital Transformation of Public Administration in the Higher Education System

The article examines the problems of digital transformation of public administration in the field of science and higher education, its regulatory aspects, and suggests new approaches to management for the effective implementation of the goals of digital transformation of public administration. Using the example of educational institutions of higher education, in particular Tomsk Polytechnic University, the strategy of digital transformation and its effectiveness are analyzed.
BG-INT: An Entity Alignment Interaction Model Based on BERT and GCN

Knowledge fusion can complementarily integrate knowledge in multi-source knowledge graphs, and is a key step in building knowledge graphs, where multi-source data includes monolingual and multilingual data. Entity alignment is a key technology for knowledge fusion, while existing entity alignment models only use entity part information to learn vector representations, which limits the performance of the models. This paper proposes an entity alignment interaction model based on BERT and GCN - BG-INT. The model learns entity embeddings from entity names/descriptions, relationships, attributes, and graph structures, and makes full use of all aspects of entity information for entity alignment. Experiments on the public multilingual dataset DBP15K show that BG-INT outperforms existing models in entity alignment. In addition, on the multi-source monolingual cardiovascular and cerebrovascular dataset, the HitRatio@1 of BG-INT also reaches 96.5%, and 2840 pieces of cardiovascular and cerebrovascular data redundancy are eliminated after knowledge fusion. We propose an entity interaction model based on BERT and GCN, and demonstrate the effectiveness of the proposed model on the multilingual dataset DBP15K and the monolingual cardiovascular and cerebrovascular dataset.
A Phenomenological Study on the Perception of Specialized High School by Middle School Teachers in Busan

본 연구의 목적은 부산지역 중학교 교사들이 인식하는 특성화고등학교의 인식을 연구하여, 여러 가지 어려운 상황에 있는 특성화고등학교에 미래 발전 방향과 빠르게 변화하고 있는 사회에서 미래지향적인 인재를 육성하고자 함에 보탬이 되고자 한다. 이에 본 연구에서는 부산지역 중학교 교사 7명을 대상으로 질적연구를 시행하였다. 본 연구에서는 인터뷰 참여 교사들의 일반적인 영역, 교사 시각에서의 특성화고등학교의 이미지, 교사가 바라보는 학생 시각에서의 특성화고등학교의 이미지, 특성화고등학교 진학 학생의 특성, 특성화고등학교 교육정책에 대한 의견, 전문가가 생각하는 특성화고등학교의 장점과 단점, 전문가가 생각하는 특성화고등학교에 필요한 개선점의 7가지 영역으로 인터뷰를 실시하였다. 인터뷰 내용을 전사하여 이를 네트워크 분석을 실시하였고, 빈도수와 중심성을 상위 단어를 바탕으로 주제별･하위요인별 분석을 실시하였다. 이를 통해 첫째, 특성화고등학교에 대한 인식개선이 필요성, 둘째, 기초학력 부족 학생을 위한 지원과 대책의 필요성을 제안하였다.The purpose of this study is to study the perception of specialized high schools recognized by middle school teachers in Busan and to help foster future-oriented talents in the direction of future development and rapidly changing society in various difficult situations. Therefore, in this study, a qualitative study was conducted on 7 middle school teachers in Busan. In this study, the interview was conducted in seven areas: general areas of interview participants, image of specialized high school from teacher's perspective, image of specialized high school from teacher's perspective, student's opinion, opinion on specialized high school education policy, and improvement needed for specialized high school. The interview contents were transcribed and network analysis was conducted, and the frequency and centrality were analyzed by subject and sub-factor based on the upper words. Through this, first, the necessity of improving awareness of specialized high schools, and second, the necessity of support and countermeasures for students with insufficient basic education was proposed.
Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations

Systems performing large data-parallel computations, including online analytical processing (OLAP) systems like Druid and search engines like Elasticsearch, are increasingly being used for business-critical real-time applications where providing low query latency is paramount. In this paper, we investigate an underexplored factor in the performance of data-parallel queries: their parallelism. We find that to minimize the tail latency of data-parallel queries, it is critical to place data such that the data items accessed by each individual query are spread across as many machines as possible so that each query can leverage the computational resources of as many machines as possible. To optimize parallelism and minimize tail latency in real systems, we develop a novel parallelism-optimizing data placement algorithm that defines a linearly-computable measure of query parallelism, uses it to frame data placement as an optimization problem, and leverages a new optimization problem partitioning technique to scale to large cluster sizes. We apply this algorithm to popular systems such as Solr and MongoDB and show that it reduces p99 latency by 7-64% on data-parallel workloads.
Pembuatan Website Sekolah Sebagai Media Informasi dan Promosi

Website is an information technology that can be used to exchange information between users quickly by using the internet network. The website has been used by various institutions as a medium of information and promotion. One of them is school. Currently, SMKN 1 Jonggat does not have a website that is used as a medium of information and promotion. This causes problems for both the community and the school. People to get the information they need from schools, they have to come to school. The school also does not have a media that can be used to provide information and school promotion. Therefore, the service team held service activities that aimed to assist the school in creating a website that can be used as a medium of information and promotion. This activity is divided into three stages, namely planning, implementation and evaluation. The result of the planning stage is cooperation related to activities and data collection needed in making the website. At the implementation stage, the service team created a school website according to the results of the planning stage. The evaluation stage is the inspection stage related to the school website that has been created. The service team succeeded in creating a school website according to the needs of the school that can be used as a medium of information and promotion. With the school website, the problems of SMKN 1 Jonggat related to the distribution and delivery of information and promotions can be minimized
WSDM 2023 Workshop on Interactive Recommender Systems

Interactive recommender systems have attracted increasingly research attentions from both academia and industry. This workshop is a half-day event, which provides a forum for researchers and practitioners to discuss recent research progress and novel research directions about interactive recommender systems. The program will include two keynotes and 6 to 8 research paper presentations. The objective of this workshop is to consolidate the recent technical progresses about interactive recommendation, which will be a promising research and development direction for future recommendation technologies. This workshop will attract the attention of researchers from both academia and industry. It aligns with WSDM's spirit of promoting the collaborations between academia and industry.
Integrating IMS data in the analysis of the Nord Stream underwater blasts in the Baltic Sea

On September 26, 2022 the International Monitoring System (IMS) of the CTBTO detected two seismic events near the Danish island of Bornholm as reported in the Reviewed Event Bulletin (REB). In the REB phases include both seismic and infrasound data up to 1600 km away and the events are located near the positions where large gas leaks from the Nord Stream pipelines were also observed. The events were recorded clearly by the Danish seismograph network with the closest two seismographs located on Bornholm at distances of 40 &amp;#8211; and 70 km. Furthermore, seismographs in the surrounding countries picked up the signals and data were analysed at the national data centers. The waveforms exhibited clear properties of underwater blasts with significant P-energy and much smaller S-energy, as well as other characteristics not associated with natural earthquakes. Further analysis revealed that the second event may consist of multiple blasts close in time and space. Analysis of the events included integration between IMS data and regional data from Denmark, Sweden, Germany and Norway, communication with CTBTO experts, and served as an exercise in collaboration during an international crisis.
Considering the Impact of Technology on Society

Moshe Vardi on having the difficult conversation about technology and social responsibility.
Level of Online Game Addiction on Adolescents

Online game is increasingly popular entertainment for all ages, especially on adolescents. Previous study had shown that the most internet users in Indonesia are adolescents and playing online game is the most activities frequently accessed by adolescents. Previous research also showed that online game addiction among Indonesian students was estimated at 10,15%. This study aimed to identify the level of online game addiction in adolescents. This research was in form of quantitative descriptive. The study population was all students of class X dan XI who obtained students from screening results and have met certain criteria with total of 241 people. The population and sample criteria in this study were adolescents who played online games for at least 6 months starting from the first time playing online game. The sampling technique used was total sampling, that all members of the population were used as sample. Data was collected within a month using The Indonesian Online Game Addiction Questionnaire and data obtained directly through google form. The result of the study was analyzed by univariate analysis then grouped into groups of respondents with a score of 7-13 no addiction, 14-21 on mild level of addiction, and 22-35 on high level of addiction. Base on the data analysis, the result of this study showed that almost all respondents were male (87,1%) with duration of ≤ 3 hours/day atau ≤ 21 hours/week to play online game (69,3%). Nearly half of respondents were addicted at a mild level (49,8%) and partially at a high level (10,8%). The conclusion of this study was that the level of addiction in adolescent was dominated at a mild level. The suggestion for school and school health nurses are to collaborate in prevention and treatment efforts by providing education about time management and the dangers of online game addiction to students.
Iterated Local Search with Genetic Algorithms for the Photo Slideshow Problem

In this extended abstract, we present a two-stage approach for solving the photo slideshow problem as defined in the qualification round of the Google Hash Code 2019. In the first stage, we apply a Genetic Algorithm to produce a good-quality initial solution, whereas, in the second stage, we apply an Iterated Local Search metaheuristic to further optimize the solution. The presented computational study in four challenging test instances shows that our approach produces comparable results to the ones achieved in the competition, where, for two of the instances, new benchmark results are obtained.
Creating Mobile VR Visualisations of 3D Objects from the Area of the Silk Road

The paper presents a process of creating, optimising and testing a virtual reality application for displaying the “Hujra”, a room for students of the 17th century madrassa in the area of the Silk Road, together with interactive objects. Introduction of manual 3D modelling could allow the cooperating teams to work at a distance, aggregating their qualifications in the area of IT and cultural heritage. By applying the procedure of optimisation, the designed 3D objects had sufficiently small file sizes to be introduced into virtual reality, and the fully mobile application worked effectively using smartphones within the price group available to the average user. The combination of the complexity of the project and its high availability via smartphones was considered a successful solution.
Training Engineering University Students to Participate in Academic Mobility Programs

Today, Europe pays much attention to mutual recognition of academic degrees and international cooperation in science and education, which is very important for Russian universities. The paper is relevant due to the importance of academic mobility in integrating foreign students into the educational environment of European universities. The paper is to show that there are special educational programs that prepare students to study in Europe. The paper demonstrates the Professional Success Psychology program implemented at KNRTU. In order to properly prepare students for academic mobility, the program includes the following topics: interpersonal communication, psychology of success, cross-cultural communication, ethics in business communication, general principles for building a successful professional career, leadership, image, academic mobility – to name just a few. The paper also shows the French for Academic Mobility program implemented. This educational program include cultural, methodological, and linguistic aspects necessary for preparing students to study in France. The paper shows that in order to draw up and implement a program for preparing students for academic mobility, a survey was conducted on the reasons why students attend classes in preparation for studies at French universities. The analysis of the reasons listed allowed the teacher to properly draw up a plan and a program of classes in order to use more effective methods, techniques and means of training. The paper also presents the content of the developed program prepared according to the requirements of students.
Accuracy improvement in simple and complex Human Activity Recognition using a CNN-BiLSTM multi-task deep neural network

Human Activity Recognition (HAR) using wearable systems in telerehabilitation and clinical applications has caught the attention of many researchers, especially for Parkinson’s disease (PD) movement therapy. However, the distinction between simple activities and complex ones and how to handle them have not been thoroughly investigated. We propose and compare two variants of a multi-task network with shared parameters to recognize simple activities (SAs) and complex activities (CAs) simultaneously. We do so by introducing a branched deep neural network that uses a shared feature space for both SAs and CAs, and further enriches the features for CAs using a deep recurrent neural network. The variants are CNN-LSTM and CNN-BiLSTM. We trained and evaluated the models with 65 activities; 51 SAs and 14 CAs composed of Lee Silverman Voice Treatment-BIG (LSVTBIG) and functional activities. Our dataset consisted of 43 healthy subjects, seven women and 36 men. The data were recorded using four smart bands with embedded IMUs, placed on both wrists and both thighs. Our results show that the CNN-BiLSTM model with an average accuracy of 84.17% and 78.78% for SAs and CAs, correspondingly, outperforms the CNN-LSTM model with average accuracies of 71.83% and 66.46%.
Research on Obstacle Avoidance Algorithm of Fixed-wing UAV Swarms Based on Improved Artificial Potential Field

In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.
Research on Using Docker Container Technology to Realize Rapid Deployment Environment on Virtual Machine

At present, with the rapid development of IT technology, the traditional computing mode has become more and more difficult to meet the processing and various engineering or scientific computing tasks. In fact, with the gradual popularization of computers and the continuous progress of semiconductor technology, the computing model has experienced several major changes. These changes can be summarized into four stages: character dumb terminal host, client server, cluster computing and cloud computing. Virtual machine services account for a large proportion of cloud computing services. The implementation of virtual machine can enable users to improve use efficiency, reduce use cost, facilitate migration and backup, and reduce maintenance cost. Docker container technology has the above advantages at the same time. How to combine the two to improve the operation and maintenance efficiency and deploy the project environment faster. This is exactly what we discuss in this article.
CenterPNets: A Multi-Task Shared Network for Traffic Perception

The importance of panoramic traffic perception tasks in autonomous driving is increasing, so shared networks with high accuracy are becoming increasingly important. In this paper, we propose a multi-task shared sensing network, called CenterPNets, that can perform the three major detection tasks of target detection, driving area segmentation, and lane detection in traffic sensing in one go and propose several key optimizations to improve the overall detection performance. First, this paper proposes an efficient detection head and segmentation head based on a shared path aggregation network to improve the overall reuse rate of CenterPNets and an efficient multi-task joint training loss function to optimize the model. Secondly, the detection head branch uses an anchor-free frame mechanism to automatically regress target location information to improve the inference speed of the model. Finally, the split-head branch fuses deep multi-scale features with shallow fine-grained features, ensuring that the extracted features are rich in detail. CenterPNets achieves an average detection accuracy of 75.8% on the publicly available large-scale Berkeley DeepDrive dataset, with an intersection ratio of 92.8% and 32.1% for driveableareas and lane areas, respectively. Therefore, CenterPNets is a precise and effective solution to the multi-tasking detection issue.
Enhancing Cloud Forensic Investigation System in Distributed Cloud Computing Using DK-CP-ECC Algorithm and EK-ANFIS

The investigation as well as recovery of data gathered as of digital devices associated with computer crime is involved in Digital Forensics (DF). In a distributed Cloud Server (CS), DF investigation is more complicated (during collecting, preserving, and reporting the evidence) as well as insecure during gathering evidence as of the cloud sources. Centered on the DF investigation system, numerous works were performed. However, lots of challenges still remain that bring about cybercrime. The work developed a robust cloud forensic investigation system centered upon distributed Cloud Computing (CC) for conquering the challenges. It is framed into the ‘3’ phase (i.e.) originally, Group Key Generation (GKG) phase that enables the authorized user to upload or download the evidence for maintaining the evidence’s trustworthiness. Distributed Key Cipher Policy Elliptic Curve Cryptography (DK-CP-ECC) algorithm performed the Secure Data Transfer (SDT) phase. It aids in maintaining the evidence’s privacy together with confidentiality. Exponential Membership Function Adaptive Neuro-Fuzzy Interference System (EK-ANFIS) carries out the CS selection with the aid of a deer hunting genetic algorithm that evades the reporting issues and renders secure evidence storage. 97% Security Level (SL) is obtained by the proposed work that is better analogized to the prevailing frameworks.
Post-suppositions and Uninterpretable Questions

For a sentence like exactly three boys are between 5 ft 10 in. and 6 ft tall, why cannot we abstract the height information out and raise a corresponding degree question like #How tall are exactly three boys? Inspired by the ideas that (i) there is a connection between wh-questions (e.g., who did Mary kiss) and definite descriptions (e.g., the people that Mary kissed) and (ii) definite descriptions and modified numerals (e.g., exactly three boys) bring post-suppositions (i.e., delayed evaluations that lead to relative definiteness, Brasoveanu 2013, Bumford 2017), I propose that when different elements that bring post-suppositions are present, a potential conflict arises in computing relative definiteness, leading to uninterpretability.
AI driven Chatbot and its Evolution

In today's fast-paced world, when humans want everything to be done swiftly and precisely, traditional systems cannot keep up it, and hence the necessity for chatbots arose. When compared to a manual system, a chatbot responds to frequently requested inquiries faster and in less time. A chatbot is a software that interacts with humans. Artificial Intelligence (AI) has enabled human-machine interaction, with Natural Language Processing (NLP) playing a critical role in language learning. NLP facilitates the development of chatbots. It provides us with an answer via pattern matching. Over the years, several varieties of chatbots have been developed, including text-to-text, text-to-speech, and speech-to-speech.