Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE ROUND TABLE UNTUK MENINGKATKAN PARTISIPASI DAN PRESTASI BELAJAR SISWA

This study aims to describe the application of the round table cooperative learning model to increase participation and learning achievement of class XI MIPA students of SMA Negeri 1 Kota Agung, Lahat Regency, South Sumatra. The design of this study used a classroom action research (PTK) model of Kemmis and Mc Taggart and a quasi experiment. The subjects of this study were students of class XI MIPA 1 SMA Negeri 1 Kota Agung in the second semester of the 2020/2021 academic year. The number of students for the PTK class is 30 people, the experimental class is 30 people, and the control class is 30 people. The data collection technique used the observation sheet and the learning result test, while the data analysis technique used the average score and the t-test. The results showed that the application of the round table cooperative learning model can increase participation, learning achievement, and effectively improve student learning outcomes in the subject of English class XI MIPA SMA Negeri 1 Kota Agung. The implication of the results of this study is if teachers want to increase participation. and student achievement in English subjects, a round table cooperative learning model can be used
Influence of E-Business Strategies on the Productivity of human Capital within Telecommunication Sector in Jordanian Telecommunication Sector

The current study aimed at shedding the light on the role of employing e-business (e-marketing, m-marketing, ecommerce, and m-commerce) in supporting and increasing productivity of human capital within Jordanian telecommunication organizations.Researcher depended on quantitative approach to achieve aim of study, for that sake, a questionnaire was utilized and distributed on ( 121) employees within telecommunication organizations in Jordan.Depending on SPSS, results of study indicated that the main hypothesis was accepted and "e-business strategies have statistically significant influence of human capital productivity" scoring an R value = 0.773 and 59.7% as a variation.In addition to that, all sub-variables were also influential, and it appeared that m-marketing was the most influential with an r= 0.707 and variation of 50%.From that result, current study recommended monitoring prohibited products in E-business as an approach to control this business field and avoid negative ramifications, in addition to that, study recommended focusing more on safety and credibility issues in e-business strategies given that some customers still find it difficult to trust e-business sources due to the lack of security, reliability and integrity issues.Further recommendations were presented later in the study.However, it is suggested to conduct a study that aims to shed the light on the impact of e-business on the dimensions of organizational performance (market share, profitability, customer service).
Stochastic and Dual Adversarial GAN-Boosted Zero-Shot Knowledge Graph

Zero-shot knowledge graph (KG) has gained much research attention in recent years. Due to its excellent performance in approximating data distribution, generative adversarial network (GAN) has been used in zero-shot learning for KG completion. However, existing works on GAN-based zero-shot KG completion all use traditional simple architecture without randomness in generator, which greatly limits the ability of GAN mining knowledge on complex datasets. Moreover, the discriminator not only needs to distinguish true data from generated data but also needs to classify generated data correctly at the same time, which affects the optimization process of the generator. In this work, we propose a novel zero-shot KG framework based on stochastic and dual adversarial GAN (SDA) to better mine the association between semantic information and extracted features. Specifically, we introduce a stochastic generator and an additional classifier to improve the model’s ability of approximating features and classifying unseen tasks. The experiments on NELL-ZS and Wiki-ZS datasets show that the proposed SDA outperforms the classic methods in zero-shot KG completion task. In particular, the proposed SDA receives a 0.6 $$\%$$ and 0.7 $$\%$$ increase on mean reciprocal ranking (MRR) for NELL-ZS and Wiki-ZS datasets, respectively.
Penerapan Algoritma Apriori Pada Analisa Data Penjualan Ecommerce

Already all sectors are now using the role of this technology. The role of technology in the business sector is to carry out online transactions that can be done anytime and anywhere or commonly referred to as E-commerce. Now E-commerce has grown very rapidly, this is because there are many benefits that can be obtained from the use of E-commerce. But that does not mean that the use of e-commerce is not experiencing losses, the use of e-commerce that is not on target will also create losses for business people. Of course this is a problem that must be solved wisely by being able to read the pattern of goods requested by consumers or products that are not in demand by consumers. Where to find out the pattern can be seen from the e-commerce sales data that is already available. Data mining is a technique used to perform data processing. Data processing is carried out in data mining to obtain new information that can be implemented. A priori algorithm is a method used to find a pattern of relationships between one or more dataset items. In the results of the study, it was found that there are 3 items of E-commerce sales data that can be prioritized for sales. Where of the three items there are 3 combinations of items A1èA2 with a support value of 40% and a confidence value of 67%. A combination of items A1èA5 with a support value of 50% and a confidence value of 83%. Combination of items A2èA5 with a minimum support value of 40% and a confidence value of 50%
THE STUDENTS’ FREE WRITING TOWARD ICT AS MEDIA IN ENGLISH LEARNING ON MIDWIFERY STUDENTS OF STIKES PEMBINA

The objective of this research was to find out the description of The students' free writing toward ICT as media in English learning on midwifery students of STIKES Pembina.The sample of this research was taken by using totaling sampling technique with 36 midwifery students from STIKES Pembina, that consist of 13 students from 2nd semester students and 23 students from 4th semester.The data was obtained based on every meeting during online learning, that was from the third meeting to the seventh meeting that consists of 5 days for obtaining the data.Then from the data analysis, it's obtained that there was the good improvement response by writing English related to the material since the frist day to the fifith day that was 63% of students response to the 95% students response in the last day.During the implementation of online learning by using ICT (whatssapp group and edlink) the obstacle raised on students.Many factor that made the students can't join the class and response.One of the factor was the access on internet in their hometown.Even had the obstacle the use of ICT can be good choice in conducting the teaching and learning EFL proces of midwifery students of STIKES Pembina Palembang
Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming

We investigate modeling cost optimal delete-free STRIPS Planning by Integer/Linear Programming (IP/LP). We introduce two IP models and their LP relaxations based on a recently formulated representation of relaxed plans, named causal relaxed plan representation. The new models are produced by enforcing acyclicity in so-called causal relation graphs using vertex elimination and time labeling methods. We empirically show that while the vertex elimination based method outperforms the time labeling based method and all previously introduced domain independent methods for computing the exact values of h+, the time labeling based LP model is faster to solve compared to its vertex elimination based alternative, making it more suitable for using as heuristic function for optimal planning. We also theoretically analyze the admissible heuristic functions obtained by solving our LP models, and prove that the vertex elimination based heuristic is at least as informative as the time labeling based heuristic. Moreover, our empirical analysis shows that our vertex elimination based heuristic, which is a novel admissible estimation of h+, often has information complementary to that of the LM-cut heuristic.
CRIM’s Speech Recognition System for OpenASR21 Evaluation with Conformer and Voice Activity Detector Embeddings

CRIM participated in all the 15 low resource languages and the three languages with case sensitive scoring in OpenASR21 for the constrained condition. For acoustic modeling, we developed both hybrid DNN-HMM systems and a conformer based system. We trained three different multi-stream acoustic models for decoding: with MFCC + i-vector features, with combined MFCC, i-vector and conformer embeddings, and with combined MFCC, i-vector and VAD (voice activity detector) embeddings. For final submission, we used two different VADs for segmenting the evaluation audio: GMM-HMM based and TDNN based. For language model text, we used the training text from LDC corpora when available. We also found significant amount of text over the internet. In the past, using this downloaded text for language modeling increased the word error rate significantly for the development set containing conversational speech. So we used sentence selection to filter this text in order to use it effectively to reduce word error rates (WER). For most languages, we were able to reduce WER with this strongly filtered text. Our best results combined six decodes: two different VAD based segments, and three different acoustic models. In the final evaluation, we ranked second in Tamil, third in Farsi and Javanese, and fourth in seven other languages. Since then, we have reduced the WER for all the languages significantly. Major contributing factors for this additional WER reduction were the intelligent use of MUSAN noise for data augmentation, and further tuning of acoustic models.
SNAC: Speaker-Normalized Affine Coupling Layer in Flow-Based Architecture for Zero-Shot Multi-Speaker Text-to-Speech

Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker.The main challenge of ZSM-TTS is to increase the overall speaker similarity for unseen speakers.One of the most successful speaker conditioning methods for flowbased multi-speaker text-to-speech (TTS) models is to utilize the functions which predict the scale and bias parameters of the affine coupling layers according to the given speaker embedding vector.In this letter, we improve on the previous speaker conditioning method by introducing a speaker-normalized affine coupling (SNAC) layer which allows for unseen speaker speech synthesis in a zero-shot manner leveraging a normalizationbased conditioning technique.The newly designed coupling layer explicitly normalizes the input by the parameters predicted from a speaker embedding vector while training, enabling an inverse process of denormalizing for a new speaker embedding at inference.The proposed conditioning scheme yields the stateof-the-art performance in terms of the speech quality and speaker similarity in a ZSM-TTS setting.
Towards Improved Visualization and Optimization of Aquaculture Production Process

Aquaculture is one of the largest, and fastest growing industries in Norway. Recently, the industry has experienced significant development in the daily operations acquiring new technologies and systems that capture data and automate the different processes. These emerging technologies enable the generation of enormous amounts of data from sensors in the fish cages, cameras, boats, and feeding control rooms. Additional information relevant to the aquaculture industry is based on e-mails, manual notes, or intrinsic experiences and knowledge exchanges. One of the critical aspects of successful fish farming operation management, which is yet not achieved, is to allow domain experts to gain insight into the interconnection between the broad spectrum of heterogeneous data currently realized. This paper describes a framework for storing and retrieving critical information connected to fish farming based on a graph database approach. The overall architecture is presented with detailed illustrations of how data is visualized and interpreted through a user-friendly interface. Accordingly, this work demonstrates how aquaculture users can benefit from the system to identify possible connections in the data and reveal previously undiscovered causalities and correlations that suggest optimal actions. Further, studies and evaluations of the querying system are conducted, evaluating the capability of the proposed design to process complex relationships. This work showcases that the system helps fish farmers and aquaculture users gain knowledge, reveal hidden links in the data, and improve aquaculture operations.
Artefact, Participant and Interaction in Auditory Experiences

The act of sound perception and its subjective dimensions, from physical to psychoacoustics, from semantic to affective, carry an inherent challenge for the conception and evaluation of every audio-based artefact. Starting from a previous framework of evaluation approaches, we seek to deconstruct the confguring elements of these processes, searching for theoretical foundations informing Sound Design and possible applications for Auditory Displays. This work is a first step into identifying a body of knowledge on the listener’s experience, how the act of listening takes place and how the sequence of listening actions can evolve as forms of dialogue, creating dialogical spaces for making sense of auditory information. With this work, practitioners can gain new insights into how existing techniques for creating auditory artefacts can be confgured and transformed into new, alternative approaches.
Virtual and Augmented Reality Applications for Promoting Safety and Security: A Systematic Review

Virtual reality (VR) and augmented reality (AR) are known to offer an immersive environment that serves as a valuable tool in various domains such as education, health and wellness, and gaming. Due to its relevance in many areas, it is a technology worth exploring for promoting safety and security. This paper presents the results of a systematic review of the past 6 years of research (2016–2021) in the field of VR and AR for promoting safety and security. The goal is to (1) uncover the success and effectiveness of VR/AR technology in promoting safety and security, (2) identify the persuasive strategies employed in VR/AR for promoting safety and security, (3) uncover the limitations of existing AR/VR technology for promoting safety and security, and (4) discuss the prospects of VR/AR in promoting safety and security and recommend opportunities for future. To identify relevant papers, we searched 4 popular databases and screened them using a set of eligibility criteria included in the final review. The results showed that 26 of the papers indicated a successful outcome while 5 papers indicated a partially successful outcome with respect to promoting safety and security. Rehearsal and Self-monitoring strategies emerged as the most frequently used persuasive strategy in AR and VR applications for designs promoting safety and security.
VoxCeleb1: Speaker Age-Group Classification using Probabilistic Neural Network

The human voice speech includes essentially paralinguistic information used in many applications for voice ‎recognition. Classifying speakers according to their age-group has been considered as a valuable tool in ‎various applications, as issuing different levels of permission for different age-groups. In the presented ‎research, an automatic system to classify speaker age-group without depending on the text is proposed. The ‎Fundamental Frequency (F0), Jitter, Shimmer, and Spectral Sub-Band Centroids (SSCs) are used as a ‎feature, while the Probabilistic Neural Network (PNN) is utilized as a classifier for the purpose of ‎classifying the speaker utterances into eight age-groups. Experiments are carried out on VoxCeleb1 dataset ‎to demonstrate the proposed system's performance, which is considered as the first effort of its kind. The ‎suggested system has an overall accuracy of roughly 90.25%, and the findings reveal that it is clearly ‎superior to a variety of base-classifiers in terms of overall accuracy.‎
FORMATION OF SCIENTIFIC SPEECH COMPETENCES AMONG STUDENTS OF PHILOLOGY

The article characterizes the main problems of the formation of written scientific speech competences among students- philologists. Also, the article describes ways to overcome the difficulties in the process of studying such disciplines: «Methodology and organization of scientific research» and «The theory and practice of philological research». The major task of the work programs of the disciplines is to master scientific speech skills among students, which is an existing competences representative that allows sharing own achievements and gaining experiences in approbation (printing articles, presentations on seminars and conferences, writing a thesis). In modern higher educational establishments, one of the tasks is to develop scientific – research competences among students. Therefore, teachers have an additional task: not only to train to write and to put works into shape, but first of all they have to motivate to work themselves, to offer relevant, interesting for students research topic without duplication any titles of already existing articles and websites, etc. The article describes the specific of preparation philologists ukrainists and polonists of Khmelnytskyi National University to write scientific articles, term papers, and diploma works. It was suggested algorithms for work with students, which teachers follow during forming scientific speech competences; the peculiarities of the research results presentation was highlighted through speeches at conferences and the publications of articles in student assemblies. It is these types of work that allow to test the results of research and teach students to public speaking, develop skills to lead a discussion. Acquisition of scientific speech competencies will help certified philologists, who can later work in educational institutions, together with students to participate in various scientific activities, including competitions, olympiads, conferences, etc. All participants of the scientific-educational process must follow academic decency. It is obligatory and emphasized. Because this is one of the conditions for Ukraine's educational European integration. In the longer term, we have tentative plans for other issues to be considered, issues related to organizing scientific work in higher educational establishments, including the application preparation, project activities, participation in competitions and contests, etc.
#Emoji: A Study on the Association between Emojis and Hashtags on Twitter

Prevalent on modern social media, both hashtags and emojis are text elements that function beyond plain text. While hashtags utilize free-formed strings and are highlighted by the platform, emojis are bonded by Unicode Standard and rendered by the platforms. Yet both are used to mark discussion topics, express sentiment, show identity, and highlight keywords. This paper analyzes and highlights the strong association between hashtags and emojis, not only in their usage frequency, but also in their semantics. We show that the association is strong enough for improving downstream tasks. To this end, we design a representation learning model that can learn emoji-based representations to improve hashtag prediction.
The application of Internet of things and Oracle database in the research of intelligent data management system

Aiming at the needs of general large-scale monitoring systems, this paper designs an intelligent data management system that can provide fast data query and relieve sudden data congestion through in-depth research on Oracle database and data division. To the data access request from the front desk, the system can respond quickly through the real-time data monitoring module and the online analysis software OLAP mode database, which has far-reaching significance for the development of the Internet of Things and related systems. The experimental results show that, compared with the traditional system, the same bitmap index only occupies about 1/30 of the original table, and the data size is reduced by more than 10 times. The experimental data verifies the characteristics of the system in this paper to strengthen the background data receiving and processing capabilities, and alleviate the problems such as the reduction of the system running rate and even the system paralysis caused by the sudden mass data.
Modeling Human Behavior in Human-Robot Interactions

This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and interactive discussions will focus on the questions such as: How can modeling humans help us understand and design human-robot interactions? What kinds of models are useful for which HRI contexts (physical/cognitive interactions) and purposes (behavior prediction/personalization/theory -of- mind/etc.)? What common lessons can be learned from human behavior modeling in HRI across different application domains? How can modeling humans in HRI tasks help us to better understand human cognition/behavior? By stimulating an interdisciplinary conver-sation around these questions, we aim to raise awareness of the benefits of modeling and expose the wider HRI community to a variety of different modeling approaches, and facilitate the HRI researchers who already engage in modeling to exchange views on methodology of modeling and best nractices from diverse fields.