Reviewerly/exHarmony huggingface dataset:
Role of Analytics in IoT

Today, many people live a sedentary life, due to lower rates of activity at home and in the workplace. This coupled with poor nutrition leads to many suffering from obesity or conversely being underweight due to improper nutrition. These problems can cause various health issues. In this chapter, we present a solution for this problem—an automated adult anthropometry station (AAAS)—that can measure a person's body mass index (BMI) as well as waist-hip ratio (WHR) and categorize them based on the range of values provided by the World Health Organization. This helps patients to be aware of their health conditions and motivates them to work on rectifying their health problems. The system contains sensors for measuring every parameter needed for the calculation such as height, weight, and circumference of the hips and waist. Then, these values are processed by the microcontrollers and computer. The user will get feedback through the monitor. Finally, on this system, users can check their readings and values frequently, which helps to control their health. This system can prevent various other illnesses due to patient awareness of abnormal parameters, or patients working to bring their values back to within WHO recommended values.
Combining and Merging Deep Neural Networks for Arabic Text Categorization

Arabic text categorization is a challenging assignment due to the Arabic language’s complexity and richness. Deep neural networks have shown promising performance in different text mining applications such as Information Retrieval, Text summarization, Document Clustering, etc. In this paper, we propose an efficient method for Arabic text categorization by combining and merging deep neural networks. First, we integrate the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) on the top of Word Embedding representations to build the combined CNN-LSTM and LSTM-CNN models. Afterward, we align the output layers of the combined models and merge them into a unified model to generate an enriched text representation. Finally, these resultant representation vectors are passed to a fully connected layer then to an output one to predict texts categories. The proposed method effectively produces a compact model, taking advantages from the local feature extraction, contextual information, and implicit semantics within texts. We also benefit from the two networks’ potentials to improve text categorization. To evaluate our method, we carry out several experiments on the OSAC dataset. Experimental results demonstrate the effectiveness of the proposed method compared to state-of-the-art.
GPTuneBand: Multi-task and Multi-fidelity Autotuning for Large-scale High Performance Computing Applications

This work proposes a novel multi-task and multi-fidelity autotuning framework, GPTuneBand, for tuning large-scale expensive high performance computing (HPC) applications. GPTuneBand combines a multi-task Bayesian optimization algorithm with a multi-armed bandit strategy, well-suited for tuning expensive HPC applications such as numerical libraries, scientific simulation codes and machine learning (ML) models, particularly with a very limited tuning budget. Our numerical results show that compared to other state-of-the-art autotuners, which only allows single-task or single-fidelity tuning, GPTuneBand obtains significantly better performance for numerical libraries and simulation codes, and competitive validation accuracy for training ML models. When tuning the Hypre library with 12 parameters, GPTuneBand wins over its single-fidelity predecessor GPTune on 62.5% tasks, with a maximum speedup of 1.2x, and wins over a single-task, multi-fidelity tuner BOHB on 72.5% tasks. When tuning the MFEM library on large numbers of CPU cores, GPTuneBand obtains a 1.7x speedup when compared with the default code parameters.
SOCIO-PEDAGOGICAL SUPPORT OF FOREIGN STUDENTS IN RUSSIAN UNIVERSITIES ON THE EXAMPLE OF RUSSIAN STATE VOCATIONAL PEDAGOGICAL UNIVERSITY

The article considers the relevant issue of the social and pedagogical support of foreign students in Russian universities (on the example of Russian State Vocational Pedagogical University). The article describes the activities of university departments that support foreign students and that are actively involved in their adaptation to the learning environment of the Russian university. The study defines the initial data for the subsequent study of the teacher's readiness to ensure adaptation and social assimilation of a foreign student in conditions of the education system of Russia. The article presents the first stage of the study, aimed at determining the existing characteristics of the organization of activities for the adaptation of foreign students in Russian State Vocational Pedagogical University (RGPPU) as well as the features of establishing conditions for their mastering higher education programs.
Simulation of Automatic Conveying System for Steel Frame of Bridge Box Girder

Abstract Aiming at the inefficiency of processing steel frame of the bridge box girder, to improve the automation degree of the production process, the intelligent manufacturing is applied to the automatic conveying system. The configuration control technology is used to simulate the automatic conveying system for the steel frame of bridge box girder. The control layer based on the PLC and the monitoring layer based on the WinCC communicates through TCP-IP protocol. The monitoring layer includes screen configuration, data monitoring, alarm designing, and other functions. Through the system coordination, the simulation of the automatic conveying system for the steel frame of the bridge box girder is carried out.
Parallel Quantum Chemistry on Noisy Intermediate-Scale Quantum Computers

A novel parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The new approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.
Ethnicity Factors in Language Learning : Interest in Learning English Among Papuan Students at Jayapura

This research discused the phenomenon of the interest in learning English among Papuan students, especially in the Jayapura university. The purpose of this research was to find out and examine emergence of interest in learning and ethnicity factors that influence among Papuan students. The researchers used a quantitative approach with a descriptive method. The data collection technique used observation, interviews, and documentation study. The data data collection tools in the form of observation, questioner, interview, and documentation. The research findings showed that the interest in learning English among Papuan students still low, it can be seen from the score of students learning in the class, this was due to the ethnicity inherent in the people of the Papua. Based on the observations, interviews and documentations that the researcher carried out proved that Papuan students and students of other ethnic group has not been well involved in English learning activities in the class&#x0D; Keywords: Ethnicity Factor Language, Ethnicity Papuan Students, English Learning Interest
Human Behavior Detection using Image Processing and IoT

Digital image processing and computer vision technology have both advanced significantly in recent years. The human posture is comprehended based on visual information, and improper behavior can be detected in real time using detection, extraction, and identification processes. From the given training set, our proposed system would recognize behavior as normal or aberrant. A computer system is taught using a variety of photos, and after comparing the input image with the previously recorded database, the machine can identify the human being to be tested. Using image processing, this research proposes a method for detecting diverse human shapes. This technique is mostly focused on shape-based detection. An operator is used to extract the shape of the input image. The system is trained using a variety of photos. When a test image is presented for detection, the test image is compared with the database after the system has been trained with the input image. The test image is regarded as the specific human if a certain threshold value is found.
Resisting Silence towards Women

The research provided a descriptive analysis of six articles published by Magdalene, an Indonesian publication that resonated with feminists, pluralists, and progressive voices, to identify the subtle tactics men used to silence women. The idea of neutrality in language endured a long-standing debate between scholars in the communication field. In the perspective of feminist thinkers, language served a function of sexual division, placing men in domination and women in oppression. Women were constrained by a social system governed and communicated through a language that did not represent their experience and perspective. Men’s language denied and negated women’s authenticity. Women were only authentic when their voice was narrated by the language unfamiliar to the scream of their anguish and struggles. The language was not only a medium of expression but also a method of silence. In order to resist oppression, one must be critical of the technique men use in silencing women. Using silence methods from Muted Group Theory provided by Cheris Kramare, the research finds that silence dominantly occurs in the form of violence. The overlap of silence methods is mostly followed by censorship towards women. In one case of sexual harassment towards a female university student, the educational institution is considered the embodiment of patriarchal social construction for coercing silence to victims and doubling its function as sources of knowledge and sexual harassment perpetrators. Lastly, another finding suggests that men who strongly associate with feminist traits and whose sexual identification is against the dominant masculine norm are also disempowered.
Knowledge Representation for Driving Context Cognition in a Smartphone-based ADAS

In the near future, autonomous vehicles will drive on behalf of the passengers. The vehicle shall be equipped with accessible knowledge whenever and wherever it is needed. Such knowledge includes driving and traffic rules. The idea is simple: the vehicle will access its knowledge-base in order to perceive its environment and recognize its driving situation; it will react accordingly, behaving exactly like a human driver. More particularly, it is capable of detecting collision before it actually happens, and maneuvers to avoid it. The vehicle of the future has its ways of collecting parameters from different sources (sensors, smart city, Internet, etc.) and combine them to perceive the driving situation. An autonomous vehicle should be able to reason out and decide what actions to be implemented. Such action is implemented by sending signals to the vehicle&#x0027;s actuators. In the same manner, a semi-autonomous vehicle would behave the same, except that the driving assistance (notification, alert or danger messages) are sent to a human driver, and the action signals to the actuators. In this work, knowledge representation related to the vehicle is implemented using ontology. SWRL (semantic web rule language) is used to formulate knowledge related to driving and traffic rules. The driving assistance mechanism ADAS (advanced driving assistance system) is deployed in a smartphone. A use case is used to validate the knowledge representation and formulation.
Exploring Semantic Similarity Measure Based on Word Embedding Representation for Arabic Passages Retrieval

Passages Retrieval is a challenging task that directly affects the performance of the whole Arabic Question Answering Systems. Text representation is a fundamental process in many text mining application methods. Due to their ability to capture semantic meanings of vocabulary terms, Word Embeddings (WE) vector representations have shown their effectiveness for Natural Language Processing (NLP) tasks, especially for information Retrieval and Question Answering Systems. In this paper, we explore Arabic passages Retrieval based on both words embedding vector representation and Semantic Similarity Measures. First, we extract candidate passages from the obtained text document (Search Engine). Then, we compute vector representations for the question text of all generated passages using pre-trained models including Word2vec, GloVe and FastText. Eventually, we compute the semantic similarity between the question and the retrieved passage representations, in order to get the N top-ranked passages that are going to serve as answer candidates. We conduct several experiments using TREC and CLEF questions and retrieved passages from Wikipedia documents; we evaluate the performance using the ranking metrics: recall, precision, and F1-measure. The obtained results show that the cosine similarity gives the best results with Word2vec and GloVe. In contrast, with FastText, where Euclidean and Word Mover’s distances give the highest similarity ranks.
Fine-grained identification of camera devices based on inherent features

Camera devices are being deployed everywhere. Cities, enterprises, and more and more smart homes are using camera devices. Fine-grained identification of devices brings an in-depth understanding of the characteristics of these devices. Identifying the device type helps secure the device safe. But, existing device identification methods have difficulty in distinguishing fine-grained types of devices. To address this challenge, we propose a fine-grained identification method based on the camera deviceso inherent features. First, feature selection is based on the coverage and differences of the inherent features type. Second, the features are classified according to their representation. A design feature similarity calculation strategy (FSCS) for each type of feature is established. Then the feature weights are determined based on feature entropy. Finally, we present a device similarity model based on the FSCS and feature weights. And we use this model to identify the fine-grained type of a target device. We have evaluated our method on Dahua and Hikvision camera devices. The experimental results show that we can identify the deviceos fine-grained type when some inherent feature values are missing. Even when the inherent feature pmissing rateq is 50%, the average accuracy still exceeds 80%.
GRSS Accomplishments in 2021: Success and Unexpected Turns [President’s Message]

In this last message of the year, I think it is important to summarize accomplishments of the Society in 2021 and describe our preparations for success in 2022. By the numbers, our Society continues to grow. We added two student branch Chapters in September with another one expected in November. When this one receives its final approval, the IEEE Geoscience and Remote Sensing Society (GRSS) community will total 70 Chapters, 28 student branch Chapters, and 10 ambassadors engaged with communities in locations where we hope GRSS Chapters will form in the near future. This means that the GRSS community now consists of 98 combined Chapters with membership in the Society surpassing 5,300 members and submissions to our journals surpassing expectations. The numbers confirm continued success for GRSS in 2021, but this year also brought some unexpected twists and turns with high expectations at the beginning to immediately resume in-person meetings.
Hedging at-the-money digital options near maturity

Hedging at-the-money digital options near maturity, remains a challenge in quantitative finance. In the present work, we carry out a hedging strategy by means of a bull spread. We study the probability of super- and sub-hedge the digital option and minimize the probability of a sub-hedge considering the cost of hedging and illiquidity issues. We perform a wide variety of numerical experiments under different models for the underlying asset dynamics. A calibration to market data is provided and used to get the optimal composition of the bull spread satisfying the cost of hedging restriction.
Conversational Chatbot for College Management Using LSTM

The Chatbot is a software application developed to have meaningful conversations with human users, especially over the internet. It presents us with a new way of getting assisted by the software around us. A chatbot allows humans to simply ask questions as if they would ask fellow humans for any kind of assistance. These Chatbots are well developed and set together using new world software programs that make us feel like we are interacting with a peer. The main task of a chatbot is to provide the information asked by the user via text-to-text or text-to-speech. The use of chatbots has seen a rapid increase in almost all the industries all over the world. Chatbots are especially used widely in the IT service sector where the chatbots play a huge role in assisting customers 24/7. Chatbots amuse people by its gratifying User Interface. There are wide categories of chatbots, and they are classified based on their characteristics and also the software used for developing them. Our Chatbot which is used for giving information based on the queries asked by the student or parent. This is a trained chatbot that consists of keywords which will be used by the bot for getting to the context of the question asked by the user. Here, when the user enters a query, it captures the keywords present in the text, and particulates the question that is being asked. The next is to give away the banswer that is already written by the developer, based on the question. In this work, we use cloud deployment which makes it easy for the user to get his queries answered anytime with a stable internet connection.
Wearable Technology Design and Accessibility Considerations Studio

The studio proposed here will focus on design and accessibility considerations for wearable technology. In this studio we will explore how to develop a robust set of design and accessibility considerations (guidelines) for wearable technology. The course will begin a presentation on wearability and accessibility then participants will engage in an activity using a new Wearable Technology Designer’s Web Tool. The tool can be accessed again after the course and shared with students and colleagues. The course will end with a discussion about what design considerations might need to be added to the tool and what human factors information or other research should be updated.
ANALISIS KESALAHAN SISWA KELAS IV SD MENYELESAIKAN SOAL KPK DAN FPB BERDASARKAN PROSEDUR NEWMAN

AbstractThis study aims to analyze the types of errors and the factors that cause students to make mistakes in solving the LCM (Least Common Multiple) and GCD (Greatest Common Divisor) questions based on Newman's procedures in grade IV State Elementary School 01 Balai Karangan. The research method used in this research is descriptive qualitative. The research subjects in this study were the fourth grade students of State Elementary School 01 Balai Karangan, totaling 28 students. Data collection techniques in this study were tests and interviews. Based on the results of the study, the errors made by students were (1) misunderstood the question as much 55,36%, (2) transformation error as much 58,93%, (3) process skill error as much 83,04%, and (4) errors in writing the final answer as much 88,39%. While the factors that cause students to make mistakes are internal factors, namely from the lack of ability of the students themselves and external factors, namely from lack of attention, guidance, and motivation from teachers and families. Keyword: Error Analysis, Prosedur Newman, Error Type, Causative Factor, LCM and GCD.
Analysis of Strategies in Praise Articles on the Website of Local Governments

이 연구에서는 지자체 홈페이지의 칭찬하는 글에서 어떠한 전략들이 사용되고 있는지를 기본전략과 강화전략두 가지로 분류하여 살펴보았다. 칭찬의 대상은 공무원과 운전종사자에 한정되었으며 이들은 불특정 다수를 대상으로 하는 감정노동 직업군으로 분류된다는 공통점을 지니고 있었다. 이는 여기에서 분류한 칭찬 전략이 감정노동종사자의 정서적 안정과 만족감에 도움이 될 수 있는 표현들이며 서비스사용자들을 대상으로 하는 교육이 필요함을 말해준다. 분석 결과 74.5%의 텍스트에서 직접적인 칭찬 대신 감사와 감동을 표현하고 있었다. 이 두 가지 긍정정서의 원인은 대부분 친절함에 있으며 칭찬하는 사람의 대부분은 의도나 목적을 가지고 칭찬한다는 기존의 주장과는 달리 분석한 칭찬하는 글은 친절함으로 인해 감사와 감동을 표현하는 글이므로 의도나 목적이 존재하지않는 것으로 해석할 수 있었다. 이것을 칭찬의 기본전략으로 보았다. 또한 만족감 표현하기, 반성하기, 감정이입하기, 사실에 개입하기, 칭찬범위 확대하기 등과 같은 여러 가지 장치를 사용하고 있었는데 감사와 감동을 표현하는글에서 이러한 장치의 사용은 상대방에 대한 칭찬을 더욱 돋보이게 하고 비대면 상황에서 글쓴이의 마음을 더욱강하게 표현하여 주는 강화전략으로 보았다.In this study, what strategies are being used in praise articles on the local government’s website were classified into two categories: basic strategies and reinforcement strategies. The subjects of praise were limited to public officials and drivers, and they had in common that they were classified as emotional labor occupations targeting an unspecified number of people. This suggests that the praise strategies classified here are expressions that can help emotional stability and satisfaction of emotional workers, and that education for service users is necessary. As a result of the analysis, 74.5% of the text expressed gratitude and emotion instead of direct praise. Most of these two positive emotions are due to kindness, and unlike the existing argument that most of the complimenters praise with intention or purpose, the analyzed compliment could be interpreted as having no intention or purpose because it expresses gratitude and emotion due to kindness. This was viewed as the basic strategy of praise. In addition, various devices such as expressing satisfaction, reflecting, empathizing, intervening in facts, and expanding the scope of praise were used, and in articles expressing gratitude and emotion, the use of these devices was seen as a reinforcement strategy to enhance praise for the writer in non-face-to-face situations.