Reviewerly/exHarmony huggingface dataset:
Developing and Reviewing a Mobile Application for a 7th Grade Mathematics Course

The Covid-19 epidemic that occurred in 2019 prevented education from continuing face to face. Various searches were conducted as part of this procedure that can be used effectively in online education. As a result, the GeoHepta mobile application was created to help students study the 7th grade mathematics course subjects using the ADDIE design methodology. Activities using dynamic mathematics software that students may use interactively were created as part of the content of the built mobile application. Students were also given the opportunity to complete online evaluation using Web 2.0 tools after learning the lesson's topics. The research was performed by 7th-grade students in a secondary school in the Central Anatolia Region in the second term of the 2020-2021 academic year, utilizing a mobile application in the distance education process. A quasi-experimental design was used to conduct the study. The “Mobile Application Usability Scale” was involved in the analysis to assess the usability of the mobile application after the experimental group had been educated. As a result of the research, it was established that the GeoHepta mobile application is "usable" by the students, and their attitudes about the mobile application are good. Participants' opinions on the mobile application's style, colour, control, typeface and hierarchy are also "useful". The "control" function of the mobile application received the greatest score, while the "aesthetic" aspect of the mobile application received the lowest grade.
"The influence of smartphone addiction, social media addiction and sleep quality on satisfaction with life among health care professionals in Saudi Arabia"

Current increased use of digital technology is not only modifying ways of living and communicating but is also changing social life and psychological well-being.In this study we aimed to assess the influence of smartphone addiction, social media addiction and sleep quality on life satisfaction among healthcare professionals in Saudi Arabia.Participants completed anonymous and self-administered electronic questionnaire encompassing five sections: Demographic data, Smartphone (Application-Based Addiction Scale -SABAS), (Bergen Social Media Addiction Scale -BSMAS), (Sleep Quality Scale -SQS) and (Satisfaction with Life Scale -SWLS).Out of 715 healthcare providers, 40.28% were male and 59.72% were female, with a mean age of 26 (± 8.56) years.Most participants were working as public healthcare providers (37.48%), dentists (25.31%), physicians (22.52%) and pharmacists (14.69%).Overall, 32.6% of participants were satisfied and/or extremely satisfied and 25.3% were dissatisfied and/or extremely dissatisfied.Although the results showed no strong association with smartphone addiction, social media addiction and sleep quality on satisfaction with life, this study highlights the need to raise awareness about the impact of modern lifestyles on psychological health and mental well-being among healthcare professionals.
Mobile Mixer

Mobile application development is an increasingly common and popular elective in computer science departments. This course can be an ideal vessel to introduce, or cement the understanding of multiple concepts covered in other computer science courses, such as UI design, networking, multi-threading and software engineering. The existence of multiple platforms for mobile application development and the absence of standard textbooks on the topic has led to the creation of a great variety of courses taught at many different levels.
Advancing Human-Robot Interaction Research and Benchmarking Through Open-Source Ecosystems

Recent rapid progress in HRI research makes it more crucial than ever to have systematic development and benchmarking methodologies to assess and compare different algorithms and strategies. Indeed, the lack of such methodologies results in inefficiencies and sometimes stagnation, since new methods cannot be effectively compared to prior work and the research gaps become challenging to identify. Moreover, lacking an active and effective mechanism to disseminate and utilize the available datasets and benchmarking protocols significantly reduces their impact and utility. A unified effort in the development, utilization, and dissemination of open-source assets amongst a governed community of users can advance these domains substantially; for HRI, this is particularly needed in the curation and generation of datasets for benchmarking. This workshop will take a step towards removing the roadblocks to the development and assessment of HRI by reviewing, discussing, and laying the groundwork for an open-source ecosystem at the intersection of HRI and robot manipulation. The workshop will play a crucial role for identifying the preconditions and requirements to develop an open-source ecosystem that provides open-source assets for HRI benchmarking and comparison, aiming to determine the needs and wants of HRI researchers. Invited speakers include those who have contributed to the development of open-source assets in HRI and robot manipulation and discussion topics will include issues related to the usage of open-source assets and the benefits of forming of an open-source ecosystem.
Factors Influencing the Adoption of Cloud Computing and Information Management

Purpose: Over the years, advances in science and technology have resulted in several modern educational methods that have improved learners' expertise. These new methods have been distinguished by their reliance on technological innovations to improve educational effectiveness and efficiency. E-learning and distance learning are two of the most significant technological innovations that have emerged in recent years, with the goal of making the learner the focus of the educational process rather than the teacher . The appropriate application of technological innovations such as cloud computing in the educational process will undoubtedly lead to the discovery of novel solutions to educational problems. This study, therefore, sought to identify the factors influencing the adoption of cloud computing and information management systems in academic institutions.&#x0D; Methodology: A desktop literature review was used for this purpose in identifying relevant seminal references and journal articles for the study using Google Scholar. The inclusion criteria entailed papers that have already been done since the introduction of cloud computing in businesses and educational facilities.&#x0D; Findings: The study found that organizational factors such as top management commitment, staff attitudes, and appropriate skills have a significant influence on the adoption of cloud computing and information management technology within academic institutions. Environmental related factors including industry competition as well as technological factors such as perceived benefits, complexity, and compatibility influenced academic institutions' adoption of cloud computing technology.&#x0D; Unique Contribution to Theory, Practice and Policy: To promote cloud computing, service providers should provide successful case studies and statistics to assist companies, particularly academic institutions, in realizing the potential benefits.
Research on Evaluation Method of College Students’ Innovation and Entrepreneurship Training Mode Based on Deep Neural Network

China adheres to the core of “cultivating college students’ innovation and entrepreneurship ability” and attaches great importance to innovation and entrepreneurship education. On the basis of establishing the evaluation index system of classroom teaching quality, this paper establishes the evaluation model of classroom teaching quality based on BP neural network. The results were validated using the Matlab Neural Network Toolkit, and the collected data was used for network training, optimization, and testing. It shows that the model can be used to evaluate the quality of classroom teaching, and can make scientific and reasonable decisions according to the evaluation indicators.
Analysis of Trends in Private Education of General Subjects using Latent Profile Analysis: Comparing data from 2011, 2016, and 2021

보다 효과적인 사교육 경감 대책을 마련하기 위해서는 현재 사교육 시장의 동향을 면밀하게 분석할 필요가 있다. 이 연구에서는 초·중·고 사교육비 조사 자료에 잠재 프로파일 분석 기법을 적용하여 2011년, 2016년, 2021년 우리나라 초·중·고 학생들의 일반교과 사교육 참여 유형을 도출하고 그 유형의 시기별 변화를 살펴보고자 하였다. 연구의 결과는 다음과 같다. 첫째, 2011년 초등학생들은 ‘영어집중’, ‘영수집중’ ‘종합집중’, ‘영수논술집중’, ‘소극참여’ 유형으로 분류되었다. 2016년에는 ‘종합집중’ 유형과 ‘영수논술집중’ 유형이 사라지고 ‘국영수집중’ 유형과 ‘기타집중’ 유형이 새로 등장하였으며, 2021년에 들어서는 ‘영수집중’과 ‘소극참여’ 유형만 유효하고 다른 유형들 대신 ‘영수논술집중’ 유형이 다시 등장하였다. 둘째, 2011년 중학생들은 ‘영수집중’, ‘영수논술집중’, ‘국영수집중’, ‘종합집중’, ‘소극참여’ 유형으로 분류되었는데, 2016년에는 ‘영수집중’ 유형이 ‘영수집중고지출’ 유형과 ‘영수집중저지출’ 유형으로 세분화되었으며, ‘종합집중’이 사라졌다. 2021년에는 ‘영수집중고지출’ 유형과 ‘영수집중저지출’ 유형이 다시 ‘영수집중’ 유형으로 통합되었으며, ‘영수탐구집중’ 유형이 새롭게 등장하였다. 셋째, 2011년과 2016년의 고등학생들은 ‘영수집중고지출’, ‘영수집중저지출’, ‘국영수집중’, ‘수학집중’, ‘소극참여’ 유형으로 분류되었다. 반면, 2021년에 들어서는 ‘영수집중고지출’ 유형과 ‘영수집중저지출’ 유형이 ‘영수집중’ 유형으로 통합되었으며, ‘수학집중’ 유형이 사라지고, 대신 ‘종합집중’ 유형이 새롭게 등장하였다. 이와 같은 연구의 결과는 학생들의 사교육 참여 유형이 시간과 정책기조에 따라 변화하며 이러한 변화에 대응할 수 있는 유연한 사교육 경감 대책이 필요함을 시사한다.This study attempted to derive and compare the trend of Korean school students' participation in private education of general subject by applying latent profile analysis to the private education survey data of 2011, 2016, and 2021. The results are as follows. First, in 2011, elementary school students were classified into profiles of ‘English Focus’, ‘English & Math Focus’, ‘Comprehensive Participation’, ‘English, Math, & Essay Focus’, and ‘Passive Participation’. In 2016, ‘Comprehensive Participation’ and ‘English & Essay Focus’ profiles disappeared but instead, ‘Korean, English, & Math Focus’ and ‘Other focus’ profiles appeared. In 2021, profiles of ‘English & Math Focus', ‘English, Math, & Essay Focus’ and 'Passive Participation' were derived. Second, in 2011, middle school students were classified into ‘English & Math Focus’, ‘English, Math, & Essay Focus’, ‘Korean, English, & Math Focus’, ‘Comprehensive Participation’, and ‘Passive Participation’ profiles. In 2016, ‘English & Math Focus’ profile was subdivided into ‘English & Math High Expenditure’ and ‘English & Math Low Expenditure’ profiles, whereas ‘Comprehensive Participation’ disappeared. In 2021, ‘English & Math High Expenditure’ and ‘English & Math Low Expenditure’ profiles were integrated into ‘English & Math Focus’, and ‘English, Math, Humanities & Sciences focus’ profile emerged. Third, High school students in 2011 and 2016 were classified into ‘English & Math High Expenditure’, ‘English & Math Low Expenditure’, ‘Korean, Math & English Focus’, ‘Math Focus’, and ‘Passive Participation’. On the other hand, in 2021, the ‘English & Math High Expenditure’, ‘English & Math Low Expenditure’ profiles were integrated, ‘Math Focus’ profile disappeared, and 'Comprehensive Participation’ profile emerged. The results of these studies imply that the trends in private education change according to time and changes in policy, and flexible measures are needed to respond to these changes.
A Proof Tree Builder for Sequent Calculus and Hoare Logic

We have developed a web-based pedagogical proof assistant, the Proof Tree Builder, that lets you apply rules upwards from the initial goal in sequent calculus and Hoare logic for a simple imperative language.We equipped our tool with a basic proof automation feature and Z3 integration.Students in the automated reasoning course at Princeton University used our tool and found it intuitive.
Evaluation of Autonomous Capability of Ground Attack UAV Based on Hierarchical Analysis Method

In view of the lack of an autonomous capability evaluation system for ground-attack UAVs in China, this paper establishes an evaluation method for the autonomous ground-attack capability of UAVs based on OODA-loop theory and carries out research on the evaluation of autonomous capability based on hierarchical analysis, which is well solved and verified in the process of calculation, test, and simulation. The autonomous capability evaluation system and the calculation method based on the hierarchical analysis method proposed in this paper are confirmed to have the advantages of intuition, simplicity, science, high efficiency, and easy modifiability in the solution process, which can reflect the level of autonomous evaluation capability of ground-attack UAV intuitively and comprehensively and can provide theoretical support for the quantitative evaluation of the autonomous capability of medium and large UAVs.
A Secured Land Registration System Using Smart Contracts

A secure land registration system using smart contracts is method to store land information which involves managing transactions of land titles, location, and ownership details. The blockchain technology is a distributed database ledger which stores all records of transactions available in the network. The important feature of the blockchain is establishing communication between trusted and untrusted third parties. On top of the blockchain, different distributed applications beyond cryptocurrencies can be deployed. One such application is a smart contract that executes codes between trusted parties. The secured land registration system is a powerful use case for blockchain technology which provides security for land documents. The system and method provide a solution to the problems related to the land registration and also develop a user friendly framework of smart contracts for the limitations of the people’s knowledge about the blockchain and its applications.
An Unsupervised Sentiment Classification Method Based on Multi-level Sentiment Information Extraction Using CRbSA Algorithm

Social big data is a feedback data or text obtained from the users on social media like Twitter, YouTube, etc. This is large in size and unstructured data format. So there is a need of collecting the information from the massive social big data to the marketers and investors. In order to do this operation, an automatic process is required. There is an enormous change in the social big data every day, so unsupervised techniques are not acquiring the label training data, and it gives the focus on the natural language processing and sentiment classification. Hence, the contrast rule-based sentiment analysis (CRbSA) algorithm is introduced in order to find required information from the massive data by using an unsupervised sentiment classification method. This method works based on “multi-level sentiment” data collecting. This is a starting level of computing the sentiment intensity of users or reviewers. Then after a multi-level union, action based on the sentiment category is introduced. Contrast rule-based sentiment analysis algorithm is developed as a new sentiment algorithm, and it is used to collect or gather the required information automatically. At last, the extraction of required data from the complex social big data can be achieved by the classifier. This is the best method over the baseline in sentiment classification as general counting and SentiStrength algorithms to get the more accuracy. Marketing system uses this type of algorithms to extract the user’s feedbacks.
Closely related Indonesian language identification using deep learning

Twitter is a well-known social media platform with over 500 million users worldwide. More than 100 languages were identified from among a million tweets. However, only 34 formal languages are supported by Twitter. It becomes challenging to do regional language identification. In Indonesia, Twitter does not recognize the Javanese and Sundanese languages. In this paper, a mechanism to automatically identify Javanese and Sundanese languages is proposed. It implements six deep learning methods: RNN, LSTM, Bi-LSTM, GRU, CNN, and Multichannel CNN. Twitter data is used in the experiment. Generally, those methods performed well in discriminating the languages. A comparison of the results shows that the LSTM technique performed better than the other techniques with an F1-score of 0.9985.
A Hybrid Cryptosystem of Vigenère and Hill Cipher Using Enhanced Vigenère Table

Data security is a huge challenge in this new technical era where everything is handled online. With the increase in population, the size of data is also increasing sharply on a regular basis. Now to protect our data, cryptography plays a major role, Vigenère cipher, which has been used in various platforms for encrypting purposes. Traditional Vigenère cipher uses a table of 26 * 26; however, in this paper, an enhanced and hybrid cryptosystem is proposed, where in place of 26, 62 * 62 table is used, and Hill cipher is combined with it to make it more secure.
A DIGITAL PRINTING APPLICATION AS AN EXPRESSION IDENTIFICATION SYSTEM.

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.
Intelligent Technology of the Unmanned System Based on Internet Big Data

In view of the problems faced by the intelligent technology of unmanned system, such as few samples, strong game confrontation, incomplete information, etc. To break through the traditional AI processing methods of the military, this paper puts forward the intelligent technical solutions of unmanned system based on Internet big data, and activates the military application value in massive Internet data by studying its key technologies and algorithms, in order to empower the intelligent technology of the unmanned system.
HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

To date, facial recognition has been one of the most intriguing, interesting research topics over years. It requires some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms derive from heavy deep learning architectures that cause limitations for development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also calls for large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese celebrity dataset and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. Comparison with another popular algorithm on the dataset, our proposed pipeline not only outweighs the accuracy counterpart, but it also achieves a very speedy time inference for a real-time face recognition application.
Correction for Cueva et al., Low-dimensional dynamics for working memory and time encoding

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Special Issue on “Logistics Optimization for Social Good”

International Transactions in Operational ResearchVolume 30, Issue 4 p. 2122-2123 Call for Papers Special Issue on “Logistics Optimization for Social Good” Jesica de Armas, Jesica de Armas Universitat Pompeu Fabra, SpainSearch for more papers by this authorHelena Ramalhinho, Helena Ramalhinho Universitat Pompeu Fabra, SpainSearch for more papers by this authorStefan Voß, Stefan Voß University of Hamburg, GermanySearch for more papers by this author Jesica de Armas, Jesica de Armas Universitat Pompeu Fabra, SpainSearch for more papers by this authorHelena Ramalhinho, Helena Ramalhinho Universitat Pompeu Fabra, SpainSearch for more papers by this authorStefan Voß, Stefan Voß University of Hamburg, GermanySearch for more papers by this author First published: 10 March 2023 https://doi.org/10.1111/itor.13005Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume30, Issue4Special Issue: OR and Big Data in AgricultureJuly 2023Pages 2122-2123 RelatedInformation
Compressed Vision for Efficient Video Understanding

Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. This is because handling longer videos require more scalable approaches even to process them. In this work, we propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos. We replace standard video compression, e.g. JPEG, with neural compression and show that we can directly feed compressed videos as inputs to regular video networks. Operating on compressed videos improves efficiency at all pipeline levels – data transfer, speed and memory – making it possible to train models faster and on much longer videos. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. We also perform proof-of-concept experiments with new tasks defined over hour-long videos at standard frame rates. Processing such long videos is impossible without using compressed representation.