Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Sistem Informasi Pengolahan Data Modul Dan Jadwal Praktikum Berbasis Website

The development of information technology provides great benefits for society, especially in terms of information. STIMIK Sepuluh Nopember Jayapura is one of the private campuses in the city of Jayapura which has a computer laboratory, the STIMIK Sepuluh Nopember Jayapura computer laboratory provides facilities for students to develop skills in information and communication technology. However, the system used today still has deficiencies in displaying schedule information and practicum modules. Therefore, it is necessary to build a website-based information system that can be useful in displaying schedule and module information clearly and easily understood. The development method uses the prototype method, the data collection method uses the interview method, observation and literature study the design method uses a structured method with context diagrams, and the database description uses ERD and this system is built using the PHP programming language and the database used is MySQL. This information system is based on an online website where the output results are in the form of operators being able to access the information system and input module data and schedules so that students can view or download practicum module data along with the particum schedule. Based on the results of the Black Box testing, all system functions can run properly or 100% valid, so that the module data processing information system and practicum schedule are in accordance with what is expected by the user and the user can access information about the module along with the practicum schedule in real time and it is easier to used.
IRISE - A Blockchain-Based Reputation System for Building Individual Professional Reputation

The growth of online interactive services has underscored the significance of individual reputation, particularly in the online labor market. Professional reputation serves as a decisive factor in assessing an individual's employability and potential for career success. An individual professional reputation is passively accumulated through the assessments of other individuals within a particular working organization. If the organization has a good reputation, then the professional reputation of the individual in that organization is considered trustworthy. Therefore, for individuals can proactively build a professional reputation, it is necessary to solve 3 problems: (1) the participating community must be honest and responsible; (2) data that is meaningful in terms of accumulating professional reputation is systematically and reliably collected; (3) data must be stored transparently and verifiable by the community. To address these challenges, reputation systems have been used that are still vulnerable to malicious attacks that compromise data transparency and reliability. The integration of Blockchain technology has emerged as a promising solution to address these existing reputation system challenges. However, the design and implementation of Blockchain-based reputation systems are still in their early stages and present diverse obstacles. This paper presents a Blockchain-based reputation system designed to establish and enhance individual professional reputations. The proposed system offers a fair and objective approach to assessing professional competence, facilitating transparent review and selection processes for organizations seeking to recruit professionals. The results demonstrate the potential of Blockchain technology in providing a trustworthy and decentralized reputation system for building a professional reputation.
Extending the Heatmap Matrix: Pairwise Analysis of Multivariate Categorical Data

Analysts are often interested in understanding the association between variables within a dataset. This paper describes a set of techniques for augmenting the Heatmap Matrix, which represents pairwise intersections of categorical variables. The proposed extensions include adapting the design and layout of the matrix to enhance its readability, expanding the number of metrics that can be presented, displaying matching records in a coordinated table view, and embedding the Chi-square test of independence. These features are demonstrated on two datasets using the empirical prototype that has been developed.
The Application of a Synonyms Dictionary Using Bi-LSTM in Bangla for Better Approximation

With the increasing internet usage, the volume of computerized data in Bangla is also growing exponentially. This vast repository of unstructured web data has various potential applications in Natural Language Processing (NLP). However, research in Bangla NLP faces significant challenges, including language complexity and limited availability of tools and datasets. These factors result in a lower accuracy rate when traditional methods used for English NLP are applied to Bangla. Specifically, the Bangla language has a rich lexicon of root words and characters, leading to an increase in out-of-vocabulary words when dealing with unseen texts. This paper proposes a novel method that employs a synonym dictionary to mitigate this problem. By converting unseen words into their synonyms that are recognized by the model, the method significantly reduces out-of-vocabulary errors. Our approach achieved an accuracy rate of 97.4%, compared to 95.3% without using the synonym dictionary, and had a loss value of 0.96. This methodology holds promise as an effective tool for solving classification problems in Bangla NLP.
Analysis of Preventions of Attacks on IOT Using Block Chain: Review

The increasing acceptance and integration of the Internet of Things (IoT) has made it a prominent element in our everyday existence. Regrettably, a significant level of vulnerability is present in Internet of Things (IoT) devices, which might potentially be abused by malicious actors. The predominant source of security vulnerabilities in IoT systems originates from their centralized architecture. The lack of adequate authentication and access control systems for managing access to information generated by Internet of Things (IoT) devices is a significant concern. Consequently, the issue of verifying the identification of the equipment or communication node arises. The decentralized nature of Blockchain serves as a viable alternative for ensuring secure operations inside a trustless environment. Extensive research has been conducted in the domain of the convergence of Internet of Things (IoT) and Blockchain, yielding notable progress in addressing several significant challenges encountered in the IoT realm. This study investigates the challenges and vulnerabilities associated with the Internet of Things (IoT), as well as explores the potential benefits of integrating Blockchain technology.
THE YOUTH AND THEIR PERCEPTION OF ONLINE HATE SPEECH

Young people, as the most sensitive category of society, are daily exposed to hate speech through numerous communication channels. Media content that promotes violence, content that clearly expresses intolerance towards differences, content that promotes wrong value systems and allows daily disparagement, insults and belittling, contribute to the creation of an inadequate society for growing up and educating young people. Precisely with the aim of determining the attitude of youth towards hate speech to which they are exposed in the online space, a survey was conducted on a sample of 108 respondents. The results of the survey showed that more than 90% of young people witnessed some form of hate speech, which was most often directed at political dissenters or members of the LGTB+ community. Also, in the largest percentage, they condemn every form of violence, including verbal violence, believing that hate speech on the Internet can affect mental health. However, young people are not sufficiently informed about hate speech and do not know how to react in situations where they are exposed to it. Additional education is necessary, as well as the reduction of content that propagates this type of communication.
CHALLENGES AND OPPORTUNITIES OF BLOCKCHAIN TECHNOLOGY REGULATION

Accelerated technological development has brought many novelties, among which is distributed ledger technology (DLT), often called blockchain (BC). BC is perceived as a peer-to-peer distributed immutable ledger that could revolutionize economies, societies and even our daily lives. All protocols for dealing with data and transactions are coded with an algorithm, so there is no need to trust the other contracting party or the intermediary. With the concept of decentralization and the absence of hierarchy, BC wants to avoid all traditional intermediaries and any regulation. The question arises, are BCtechnologies really decentralized and who controls them? What are the consequences if decision-making in BC is influenced by small groups of people or corporations? This article, in an attempt to answer these questions, explores technological scandals in which there have been significant deviations from the basic principles of BC (The DAO Hack, Parity's Smart Contract Bug on Ethereum and Facebook's Libra). Analysis of the above scandals suggests that decentralization is threatened and the current regulatory status of BC is substandard. It was shown that BC technology, due to its deterministic nature, cannot provide solutions for all life situations and that human judgment is irreplaceable.
Natural image restoration based on multi-scale group sparsity residual constraints

The Group Sparse Representation (GSR) model shows excellent potential in various image restoration tasks. In this study, we propose a novel Multi-Scale Group Sparse Residual Constraint Model (MS-GSRC) which can be applied to various inverse problems, including denoising, inpainting, and compressed sensing (CS). Our new method involves the following three steps: (1) finding similar patches with an overlapping scheme for the input degraded image using a multi-scale strategy, (2) performing a group sparse coding on these patches with low-rank constraints to get an initial representation vector, and (3) under the Bayesian maximum a posteriori (MAP) restoration framework, we adopt an alternating minimization scheme to solve the corresponding equation and reconstruct the target image finally. Simulation experiments demonstrate that our proposed model outperforms in terms of both objective image quality and subjective visual quality compared to several state-of-the-art methods.
GetFace: A Real-time Face Detection Solution for Surveillance Footage

Recently, facial recognition technology has advanced significantly and become widely used. However, detecting faces from surveillance camera footage in real time can be a difficult task due to a variety of factors. One major challenge is that the quality of the footage may be poor, with low resolution or high levels of noise. Additionally, lighting conditions can also make it difficult to detect faces, especially in low light environments. Furthermore, faces may be partially obscured by objects such as hats or sunglasses, or by changes in pose or expression. Finally, the number of faces in a given scene can also make it difficult to detect and track individuals in real-time. time is the presence of occlusions. Occlusions occur when an object or person blocks the view of a face, making it difficult or impossible to detect. Additionally, faces may be captured at different angles and scales, which can further complicate the detection process. Another factor that makes face detection difficult is that faces can appear differently due to various factors such as age, gender, race, and facial expressions. This can make it hard for the algorithm to generalize and detect all types of faces. In this article, we propose GetFace, an intelligent system for enhancing surveillance footage. GetFace uses CNNs, super-resolution methods, human shape and face detection techniques, Wiener filter, and face clustering to improve the image quality.
Practical Insights on Incremental Learning of New Human Physical Activity on the Edge

Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance. While these advantages are compelling, they introduce unique challenges absent in traditional cloudbased approaches. In this paper, we delve into the intricacies of Edge-based learning, examining the interdependencies among: (i) constrained data storage on Edge devices, (ii) limited computational power for training, and (iii) the number of learning classes. Through experiments conducted using our MAGNETO system, that focused on learning human activities via data collected from mobile sensors, we highlight these challenges and offer valuable perspectives on Edge ML.
V2V-Assisted Timely Hierarchical Federated Learning

To meet the timeliness and data privacy requirements of vehicular applications, federated learning (FL) has been applied in edge training for connected vehicles. However, FL in vehicular networks needs to meet stringent latency requirements due to the high mobility of vehicles. This paper proposes a vehicle-to-vehicle (V2V)-assisted hierarchical federated learning (VAHFL) architecture to reduce the communication latency for model uploading. We formulate the communication latency minimization problem as a sequential decision-making problem. This problem is too complex and we divide it into three sub-problems: V2V link scheduling, bandwidth allocation, and sequential node classification. The first two problems can be solved using optimization methods, while the third problem is still hard to solve due to unavailable noncausal information. We develop a graph neural network (GNN)-reinforcement learning (RL) based framework to solve the third problem in an online manner. Experimental results show that our proposed scheme can reduce the latency by at least 41.99% compared with the benchmarks. Additionally, the convergence speed and the testing accuracy of our proposed scheme exceed that of the benchmarks under both i.i.d. and non-i.i.d. settings.
Prototype Traceability Blockchain for Security-Regulated Industries

By recording the transactions and events of all relevant parties on the blockchain, the industrial blockchain can realize the data traceability and tracking of the whole process, thus helping enterprises to realize the supply chain management, production process management, and quality control of the whole chain. Nevertheless, with the widespread deployment of industrial blockchain provenance, many security issues have emerged, such as centralized registration authority and potential distributed denial-of-service (DDoS) attacks. These existing issues threaten the usability of industrial blockchain traceability(IBT). This paper mainly studies some identity authentication problems that still exist in industrial blockchain traceability. Combining blockchain, elliptic curve cryptography, dynamic link-out mechanism, and batch verification, a reliable and efficient logistics information and utility center authentication protocol is proposed. Experimental results show that the proposed protocol achieves improvements in security and performance compared to other related schemes.
Base Form and Other Forms of the Estonian Verb

The article discusses the implicational patterns present in the Estonian verb paradigm: which paradigm slot acts as the base form, which slots act as other principal parts, and what does their dependency hierarchy look like. The argument relies on data from three different types of source: acquisition of Estonian as the first language by children; verbal inflectional classes that are reconstructed from the 17th century Tallinn variety of Estonian; and statistics from different contemporary corpora. The article arrives at a different implicational schema than that which is generally accepted in the Estonian grammar tradition. The article suggests that the base form is the bare stem (which is used as the 2nd person imperative and prohibitive, as well as the negation of present indicative), and that the other three principal parts are: the infinitive; the wordform representing simultaneously the past participle impersonal and past indicative negative impersonal; and third-person singular past indicative. The supine, which is traditionally regarded as the base form, is relegated to being dependent on the third-person singular past indicative. The article acknowledges that the proposed schema causes difficulties with the algorithm of generating paradigm slots for words that now exhibit strengthening gradation pattern, traditionally considered to be unproductive for Estonian words, and even completely missing for verbs.
Layers of Folkloric Variation: Computational Explorations of Poetic and Narrative Text Corpora

Variation is a core feature of folklore that plays a part in configuring the processes of folkloric communication, transmission and creativity. Computational analysis of large folklore collections offers new outlooks on the study of variation. The article explores the nature of folkloric variation on the basis of folk song and fairy tale text corpora from the Estonian Folklore Archives. Our enquiry into regional variation in Estonian folk song showed different patterns of geographic variation for metre, repertoire and language. To investigate further the possibilities to disassemble the components of variation we turned to a smaller corpus of fairy tale texts from the distinct Seto language community. The results of stylometric analysis of fairy tales collected within the close language community as a rule found that word usage patterns in stories told by the same storytellers were closer to each other than stories with the same content (i.e. tale types). However, other factors, such as collectors’ individual styles, recording time, length of text, and storyteller’s place of origin contributed notably to similarity as per stylometric scores. The study has shown that the individual features and aspects of such a complex phenomenon as folklore can follow its own variation patterns. Computational analysis of the variation in large text corpora helps us get a better understanding of the functioning of variation, and the processes of folkloric creation. However, the layers of folkloric variation are not that easy to disassemble, one must be aware of the biases in text corpora and keep in mind the effect of the linguistic variation on the results.
Project AKAY Approach: A Reading Intervention for Non-Readers

The Project Adequate Knowledge Acquisition for Young Learners (AKAY) approach serves as an intervention designed to support Grade 4 students who have experienced significant cognitive challenges due to the COVID-19 pandemic. This approach places a strong emphasis on the advantages of homogenous grouping in the teaching process. Its main objective is to assist students in acquiring fundamental skills, including knowledge of the alphabet, letter sounds, reading of consonant-vowel-consonant (CVC) pattern words, numerical comprehension, handwriting of names, and following simple directions. This study delved into the implementation of the Project AKAY approach for Grade 4 students attending Marawoy Elementary School in Lipa City during the 2022–2023 academic year. The researcher aimed to assess the reading proficiency of these students using the Philippine Informal Reading Inventory (PHIL-IRI) assessment both before and after the implementation of the approach. The study also examined the extent to which the Project AKAY approach was utilized to guide students in acquiring these essential skills. Additionally, the research explored the challenges faced by teachers in implementing the Project AKAY approach and their strategies for overcoming these challenges. The study employed a descriptive quantitative research design and employed purposive sampling as the chosen sampling technique. Data collection relied on the PHIL-IRI assessment tool and interviews as research instruments. The collected data underwent thorough analysis, including statistical treatments such as frequency and percentage calculations. Ultimately, the research findings indicated that the Project AKAY approach effectively served as an intervention for non-readers.
What the editors are reading: Social media

Wang S, Malik R. Social Media Apps in Urology. Curr Surg Rep 2023 May 20:1–8. doi: 10.1007/s40137-023-00366-9.
From Compliance to Play: Enhancement of Phonemic Awareness Through Play-Based Learning Activities in Kindergarten

Phonemic awareness is a skill that begins developing from birth and continues to develop throughout a child’s early years. In the context of 21st-century learning, there is a growing demand for students to establish a solid foundation in this skill during their early developmental stages. Despite the recognized importance of phonemic awareness, there have been instances of low achievement in this area. This study aimed to assess the impact of incorporating play-based activities on improving phonemic awareness in kindergarten learners. The variables examined within phonemic awareness include letter sound fluency, blending, and segmenting. The study employed a quasi-experimental research design and utilized purposive sampling. A total of 20 kindergarten learners from Tangway Loob Elementary School participated in the study. The primary instruments for data collection were pre-tests and post-tests. The findings revealed that, before the implementation of play-based activities, kindergarten learners had a mean phonemic awareness score of 42.58, interpreting a moderate level of awareness. After the intervention, the learners attained a mean of 56.53, also interpreted as a moderate level of awareness. The analysis revealed a significant improvement in letter sound fluency and blending skills between the pre-test and post-test assessments. However, no significant difference was observed in terms of segmenting. In conclusion, this study demonstrated the effectiveness of incorporating play-based activities in enhancing the phonemic awareness of kindergarten learners. It is recommended that these activities be integrated into instructional practices to further develop phonemic skills, which serve as a crucial foundation for more advanced literacy skills.
Effect Of UV-Assisted Titanium Dioxide (TiO2) Photocatalysis on Viable Airborne Bacteria and Fungi in The Seasoning Powder Industry

Titanium dioxide (TiO2) has been known to have anti-microbial activity against fungi, bacteria, and viruses. The application of TiO2 in the membrane attached to the air conditioner was expected to minimize airborne microorganisms. This study contribution to investigate the efficacy of UV-assisted TiO2 photocatalysis in controlling airborne microorganisms in the seasoning powder industry. TiO2 was coated to the non-woven membrane and attached with a UV lamp inside the air conditioner. When the AC was on, the Ti3+ was released into the air. Enumeration of airborne microorganisms was conducted by opening the agar plates for 15 min at several food processing facilities, followed by incubating agar plates for 72 hours at 30 °C. The result shows dry processing facility had the highest number of initial airborne bacteria while the wet production facility had the highest number of initial airborne fungi. Interestingly, the number of airborne microorganisms fluctuated in the packaging facility related to the number of people going inside and outside. On the contrary, the number of airborne microorganisms was deficient in empty rooms, which indicated that the number of airborne microorganisms might be related to people and products. Overall, airborne microorganisms were reduced after 60 min of irradiation. Therefore, it is recommended to irradiate the facilities for 60 min before production start. SEM images and EDAX analysis results show TiO2 was detected after 160 h. However, it was recommended to change the TiO2 membrane after one month. This research was expected to contribute to controlling airborne microorganisms during food production in food processing facilities.