Reviewerly/exHarmony huggingface dataset:
Correction: Ren et al. Research on an Intelligent Agricultural Machinery Unmanned Driving System. Agriculture 2023, 13, 1907

In the original publication [...]
10.47435/jtmt.v4i02.1916 Technological Pedagogical Content Knowledge (TPACK) Teacher in Teaching Geometry in Grade V Elementary School

This study aimed to determine the level of Technological Pedagogical Content Knowledge (TPACK) of fifth-grade teachers at Ma’rang District, Pangkep Regency, particularly on geometry. This research uses the descriptive qualitative type of research. The participants were three fifth-grade teachersfrom SD Negeri 6 Pitue, SD Negeri 13 Padang Lampe, and SD Negeri 20 Alesipitto. These schools were chosen because they were once the targets of the Kampus Mengajar Program, while one of the program’s activities was to assist in adapting technology in schools. The data collection used observation and interview techniques. The data analysis consists of data condensation, data presentation, and conclusion. The research results show that the teacher occasionally delivered geometry subject matter using technological devices such as a laptop and projector, as well as the Microsoft PowerPoint application. The use of technological devices and applications in learning was based on teachers’ consideration to make the lessons enjoyable and easy to understand by the students. The teachers also used WhatsApp social media to facilitate communication with students and parents. The obstacles to using technology in learning were the internet network which was not good enough and technological tools that were broken or even non-existent. The teachers’ consideration of the proper use of media in learning shows that teachers had good pedagogical and content knowledge, even though they had not optimally utilized technology. The TPACK of the teachers, especially in geometry, is still at the recognizing level.
Web-Based Student Payment Administration Information System Using The Waterfall Method

This research aims to develop an Information System that can assist the school admin in providing the validation process for tuition payments, speed of access, and completeness in providing information. This research aims to overcome administrative challenges that often occur in schools, such as errors in recording payments, late payments, and the difficulty of tracking payment history to save time and effort previously spent processing payments manually. The research method used is the Waterfall method. This application will make it easier for the finance department to record student tuition payments method was chosen because of its deep capabilities and describes a flow or stages that run like a waterfall from one stage to the next. This approach relies heavily on good initial planning because changes made in the early stages may be difficult or expensive to implement in later stages. Based on the results of system design and black box testing, it can run well, and user responses also provide very good responses, with 94% of respondents giving very good responses and 0% saying good and quite good. Based on these findings is hoped that it can be used more efficiently and also ensure data security and can store the results of the data that has been processed.
PROMOSI PERPUSTAKAAN MELALUI KEGIATAN PERLOMBAAN DI PERPUSTAKAAN UMUM KOTA PAYAKUMBUH

Abstract The main problem in this thesis is to find out the procedures for implementing library promotion and what results the library gets after carrying out library promotion through media competitions at the Payakumbuh Public Library. The purpose of this study is to find out the competition activities held by the Payakumbuh City Public Library and how the Payakumbuh City Public Library seeks existing constraints to carry out competition activitie This type of research is descriptive qualitative. Data collection techniques through observation, interviews with librarians, employees and users at the Payakumbuh City Public Library, and documentation. Data analysis techniques start from data reduction, data presentation and drawing conclusions. Guaranteeing the validity of the data in this study uses source triangulation.Based on the results of the research, (1) the Public Library of Payakumbuh City conducts competition activities to support library promotion activities and attract the public to come to the library, routine activities are held every year, namely story telling competitions and poetry reading competitions. (2) after carrying out the competition activities the results obtained by the Payakumbuh City Public Library were that the community knew about the existence of the library so that it increased interest in visiting the community to come to the library, increasing the literacy of the community around the library. (3) the constraints faced by the library, namely in terms of inadequate human resources, funds (budget) that are not in accordance with the expected plan to carry out activities so that the Payakumbuh City Public Library reduces the quota of participants who take part in competitions, and inadequate library facilities so that efforts carried out by the library involving all employees of the Payakumbuh Public Library, as well as in terms of facilities the Payakumbuh Public Library rented tents and stages for performances
Open Set Recognition of Radar Signals Based on Time-Frequency Fusion and OVA Network

For open electromagnetic environment, most of the radar signal modulation recognition methods available cannot handle the open set recognition well. To overcome this problem, a time frequency fusion and One-vs-All (OVA) network based radar signal open set recognition algorithm is proposed. Firstly, combining the advantages of Wigner-Ville distribution (WVD) with high time-frequency resolution and Choi Williams distribution (CWD) with a good cross terms suppression ability, a multi time-frequency image feature fusion algorithm based on wavelet transform is proposed, where fusion criteria for low-frequency and high-frequency components of the image are designed to improve the quality of time-frequency images. Then, a radar signal open set recognition method based on OVA network is proposed, where a closed set classifier is used for pre-classification, and the OVA network is trained with the most similar negative samples to maximize the classification boundary of similar categories, and its classification confidence is improved via open set entropy minimization strategy and threshold judgment. For closed set scenario simulations, it can achieve 92.6% accuracy with signal to noise ratio (SNR) of -6 dB and have the ability to handle small sample cases. What's more, for open set scenarios, both known and unknown modulation types of radar signals can be recognized well.
Sentiment Analysis of Restaurant Reviews Using Machine Learning Algorithms

Customer input is crucial for organizations, and since social media is such a strong platform, it can be utilized to grow and improve company chances. However, this requires prompt analysis of the comments posted on social media. Consequently, this paper's primary objective is to analyze customer feedback. Data was gathered from Jerran, a social media platform for Arabic reviews. The collected data was initially analyzed to determine the emotions expressed in each comment, categorizing them as positive or negative. Subsequently, comments were automatically sorted using text classification algorithms based on assessments related to meal quality, surroundings, service, and price. A meticulously annotated dataset comprising approximately 4000 entries was used for training and testing purposes. Classification techniques such as Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) were employed, and a comparison of their performance was conducted. The Support Vector Machine algorithm exhibited the most favorable outcomes.
A Framework for Improving Software Maintenance Using World Class Manufacturing Methodology

The software that is being developed is becoming more complex and huge. The software industry has a huge problem with software maintenance. During the initial stages of software development, it becomes necessary to identify the ease with which a software system or component can be modified to correct faults, increase performance or adapt to a changing environment, i.e. software maintainability. This aids in the reduction of maintenance costs. A software is developed for some particular purpose, which may or may not change depending on the needs of the customer or the technological process. Hence, it leads to the evolution of the software to fit with the changing requirements. This non-trivial problem requires significant attention. According to some surveys, software maintenance accounts for about 80% of total software development costs. Therefore, this paper proposed a framework to integrate various World Class Manufacturing (WCM)-based technologies to create a model that can be utilized for improving maintenance, reduce time and cost of software maintenance
Visual practices of human creation in postmodern culture

The relevance of the study is due to the transformation of views on the processes of human creative activity in visual practices in postmodern culture. Changing views on the key elements of the individual characteristics of representatives of a particular type of culture allows us to talk about a new character of a person, passing through numerous stages of formation in the process of socialization. The purpose of the article is to form the basic principles of postmodern human creativity in the context of demonstrating this process within the framework of visual cultural and artistic practices and to identify the main directions of the transformation of a postmodern person under the influence of external circumstances. During the study, scientific methods of analysis, synthesis, induction, deduction, analogy, abstraction, system analysis, comparative method, methods of systematization and classification were used. The article gives a generalized description of the main processes of human creativity as the formation of the personality of postmodern culture, identifies the main problems of the transformation of the consciousness of a postmodern personality, positive trends in changes in human characteristics, and confirms the main approaches to the interpretation of personality in culture and art.
COMPARING VARIED EFFECTS OF WORK AND PARTICIPATION ON DEVELOPMENT OF OLDER JAPANESE ACROSS GENDERS

Abstract Recently, the physical and cognitive status of older Japanese adults has improved on previous generations. Half of 60-year-old Japanese remain in the labor force. With extended life expectancy, improved health, and ability to remain productive into older age, some gerontologists now regard generativity as a developmental task for older adults before becoming frail, rather than for middle-aged people, as previously thought. These changes assume that older adults continue to develop, and this study examines the effects of work and active participation on development of older adults as a potential human resource for society. A 2019 random sampling survey was conducted with Tokyo and Hyogo Prefecture residents; the response rate was 43.0% (N = 1,063, aged 50-74). ANCOVA tests were conducted to gauge sense of community, interest in politics and generativity (all were positively affected by social and civic participation). Older people exhibited a higher sense of community than younger people. Females showed higher generativity and sense of community than males, while males scored higher than females for interest in politics. We observed interaction effects of work and civic participation. Working males in engaging civic activity exhibited greater positive change compared to non-working males with civic activity. Non-working females with active participation demonstrated greater positive change in generativity and interest in politics than working females with activity. This suggests older males enriched their experience by participating in civic activity while older females developed it through work. Both cases suggested that older adults can develop when they experience an unfamiliar diverse world.
IMMERSIVE VIRTUAL REALITY INTERVENTIONS FOR OLDER ADULTS: A SERIES OF META-ANALYSES

Abstract Many older adults desire to age in place, a term used to describe living independently versus transitioning to a residential care facility. Virtual reality (VR) has the potential to positively impact the functioning of older adults as an assessment or intervention to help compensate for lost or declining functions. VR captures a wide variety of technologies ranging from non-immersive to fully immersive, designed to give the user a feeling of presence. To date, VR interventions have been used with older adults including improving health outcomes, improving functional abilities, and for neuropsychological purposes. A handful of meta-analyses (M-As) attempt to summarize the overall effectiveness of VR interventions with older adults, but many contain methodological issues and cover the gamut of VR technology. The present M-A fills a gap in the literature by assessing the literature on immersive VR interventions for older adults. Sixteen articles met inclusion for meta-analysis, and 41 effect sizes (ES) were calculated. ES were grouped by category, neuropsychological, health/mental health, functional, and eight meta-analyses were performed with one exploratory analysis. Results revealed small, significant ES for VR interventions on measures of global cognition, simple attention tasks, executive functioning tasks, anxiety ratings, and pain ratings. Nonsignificant ES were found for stress ratings, gait speed, depression ratings, and TUG task (exploratory M-A). Continuing to develop affordable, safe interventions to assist with aging in place is important as the number of older adults continues to increase. Immersive VR is a promising avenue to continue exploring to better assist the aging population.
Concept Drift Challenges in the Internet of Things (IoT) Era of Smart Cities: A Preliminary Investigation

Recently, smart cities and their enabling technologies, such as the Internet of Things (IoT), have grown significantly. This trend has led to an exponential increase in smart devices and, as a result, facing the phenomenon of streaming big data continuously being produced with high volume, speed, and variety. This issue has created an unprecedented opportunity for businesses to exploit this data through a new generation of data-driven applications. However, the real-time, dynamic, non-stationary nature and the changing statistical patterns of big data streams in the IoT present data analysis with concept drift challenges as the leading cause of the gradual decline in efficiency and unreliability of static machine learning models. Dealing with this issue and making real-time decisions in dynamic situations is one of this research field's challenges. Although concept drift and IoT have each been independently researched in the literature, the confluence of these subjects has not yet received any attention. Consequently, this paper marks the initial stride towards tackling the issue in this domain. After discussing the significance and various notions of the concept drift, this paper will highlight some of the latest research works in this field. The paper will present this field's challenges and research opportunities.
Research on Oral English Learning Platform Based on Intelligent Recognition

Intelligent recognition is a technology that allows machines to transform signals into appropriate text through recognition and understanding. Nowadays, economic globalization has become the mainstream situation, and English has become an important tool for people to communicate with each other. However, due to the limitations of various methods and environments, the spoken English level in China is generally low. This paper designs and implements a spoken English learning system based on intelligent recognition technology. This paper mainly introduces the theories of related technologies, and designs an effective oral English learning system based on these theories. Through the test of recognition rate and real-time performance of the system, it is proved that the system meets the requirements.
Web prototype of an intelligent system for learning panamanian sign language using artificial vision.

Sign language is important for people with some communication limitations, and a greater knowledge of the population is necessary to achieve better inclusion. The project consists of developing a system that will be able to translate the Panamanian sign language. This with the support of a software tool that uses Deep Learning and Artificial Vision techniques that learns and interprets the sign language through images that will later translate this information in real time to the end user with the help of natural language processing to text. This project would be aimed at the general population, with the purpose of reducing the communication gap. This article presents the necessary hardware and software tools, a conceptual design, the prototype construction process, and the results obtained from the tests carried out. Test results demonstrated an 80% accuracy rate of the prototype at this early stage. The development of the prototype will continue to increase the vocabulary of the tool and carry out new tests.
An Effective and Balanced Storage Extension Approach for Sharding Blockchain Systems

Sharding technology has become crucial for enhancing the scalability of blockchains owing to the rapid extension of blockchain data. However, data migration and state reconstruction may cause a high overhead when a new shard is added. Existing solutions have high latency when expanding, and the balance of the state data between shards is poor after extension. To this end, this paper proposes an Effective and Balanced Storage Extension (EBSE) approach for sharding blockchain systems. EBSE can reduce the overhead and latency of the system extension, while ensuring a balance between shards after extension. When implementing the EBSE, we address the following three challenges. 1) To design a data structure that incorporates allocation principles, we designed a Jump Merkle Tree (JMT) based on the Merkle Tree prototype, incorporating node migration and orderliness. 2) To design additional rules that ensure the integrity of the state tree during shard addition, we designed a shard addition protocol to coordinate and standardize the behavior of each shard during the extension process. 3) To ensure the sustainability of the system after extension, we designed state tree addition and cleaning algorithms to remove the invalid information after the system extension. Extensive experiments are conducted to evaluate the performance of the proposed approach. The experimental results show that the EBSE outperforms the existing solutions in terms of balance and latency. Compared with the state-of-the-art sharding storage, EBSE effectively reduces the shard addition latency by 60% and achieves 4× superior shard data balance.
Enabling Encrypted Delta Compression for Outsourced Storage Systems via Preserving Similarity

Outsourcing storage to the cloud can relieve pressure on storage capacity, wherein encrypted deduplication is often used to perform deduplication on encrypted data, for ensuring both storage efficiency and security. However, encrypted deduplication only removes duplicate encrypted data without considering non-duplicate but very similar ones. In this paper, we propose EDelta, which is the first to enable encrypted delta compression so as to further reduce redundancy of similar encrypted data atop encrypted deduplication, by means of preserving similarity after encryption. We also propose two methods to speed up the encryption process of encrypted delta compression. We prototype EDelta in cloud environments and our experimental results based on real-world datasets show that EDelta can achieve an additional data reduction ratio by up to 45.0% on top of encrypted deduplication.
Hardware-Software Co-Design for Content-Based Sparse Attention

Attention-based pre-trained large models have demonstrated impressive performance in many domains such as natural language processing and computer vision. Unfortunately, due to the quadratic complexity incurred by calculating pairwise correlations across the entire input sequence, processing the attention mechanism becomes the arguably major bottleneck of the whole inference execution. To accelerate the attention mechanism with no loss of accuracy, we present a novel algorithm-architecture co-design that can substantially save runtime as well as energy spent on the attention mechanism. Inspired by the observation that only a small subset of content highly correlates with the others under attention, we devise a hardware-friendly content-based sparsity scheme to eliminate unnecessary relations, thus reducing computation complexity effectively. Furthermore, we develop a tailored hardware for this content-based sparse attention mechanism to best utilize this algorithm innovation. Experiments show that, compared with the implementation based on an Nvidia V100-SXM2 GPU, on average, our design achieves 63× speedup and 505× energy saving with no accuracy loss.
IEA-Plot: Conducting Wafer-Based Data Analytics Through Chat

This paper presents key ideas behind IEA-Plot, a software framework designed to conduct test data analytics through chat. We use wafer-based data analytics as an application example to discuss the ideas. IEA-plot interacts with a user through a dialog and produces plots according to user instructions. At the core of IEA-Plot is a knowledge graph connecting a frontend natural language parser to a backend API. This knowledge graph captures our analytics knowledge in the specific context. Usage examples are presented based on test data collected from a recent production line.
A Hardware Validation Framework for a Networked Dynamic Multi-factor Security Protocol

As the use of low-power Internet of Things (IoT) devices becomes widespread, the volume of data generated and transmitted is increasing rapidly. However, transmitting sensitive data over unreliable wireless networks gives rise to an increased attack surface due to the lack of secure communication. Accordingly, a lightweight and secure communication protocol is required for transmitting data between IoT devices. In this paper, we present results of experiments to conduct the hardware validation of a novel secure communication protocol which was introduced in [1]. This previously developed protocol utilizes multiple factors for authentication, which are updated after every protocol iteration. Ensuring that the same factors are not reused for authentication reduces the risk of potential attacks. Our implementation of this protocol utilizes a True Random Number Generator (TRNG) for factor updates to ensure that the factors are not based on a deterministic algorithm. In addition, our experiments validate the unicast and multicast communication features of the protocol. Our hardware platform is based on a network of Raspberry Pi boards and is designed to be both fast and computationally lightweight. We present results of experiments we conducted to assess the ability of our implementation to withstand network errors and delays. We also present an approach to choose the timeout value for a hardware implementation of the protocol. Our results demonstrate that our unicast and multicast implementations are scalable, while exhibiting good power, energy, and delay characteristics, thereby making our approach suitable for secure communication for smart IoT applications.