Reviewerly/exHarmony huggingface dataset:
Enhancing Agricultural Supply Chain Management using Blockchain Technology

In recent years we have seen many incidents of farmers committing suicide due to debt and low agricultural yields. In the context of escalating farmer suicides and agricultural challenges, particularly in densely populated countries like India, this study underscores the need for innovation to mitigate distressing issues. The reduction in arable land due to urbanization and industrialization has intensified the necessity for increased agricultural productivity. Yet, farmers grapple with obstacles including high bank interest rates for initial investments, market intermediaries affecting air pricing, and inadequate market trend analysis. Inefficient supply chains compound these woes, impacting crop quality. Correspondingly, consumers confront inflated prices and compromised quality due to middleman practices. In response to this complex landscape, the proposed framework envisions as a multi-faceted solution that integrates block chain technology in to the agricultural supply chain. The system allows farmers to list their crop plans and expected yields on a tamper-proof distributed ledger, fostering transparency and instilling consumer confidence. Consumers, armed with reliable data, can make well-informed decisions about their investments. Smart contracts play a pivotal role in this ecosystem. They automate agreements between farmers and consumers, ensuring that the terms are upheld without the need for intermediaries. This not only expedites transactions but also minimizes the potential for disputes.
RVAIC: Refined visual attention for improved image captioning

Visual attention has emerged as a prominent approach for improving the effectiveness of image captioning, as it enables the decoder network to focus selectively on the most salient regions in the image content, thereby facilitating the generation of precise and informative captions. Although visual attention achieves the improvement, the small numerical values of its input have a negative impact on its softmax, decreasing its effectiveness. To address this limitation, we propose a refined visual attention (RVA) framework that internally reweights visual attention by leveraging the language context of previously generated words. We first feed the language context into a fully connected layer to obtain appropriate dimensions for the visual features. Then, we use a sigmoid function to obtain a probability distribution to reweight the softmax’s input by applying the multiplication process. Experiments conducted on the MS COCO dataset demonstrate that RVA outperforms traditional visual attention and other existing image captioning methods, highlighting its effectiveness in enhancing the accuracy and informativeness of image captions.
Quality assessment of mobile phone applications aimed at dental education

Purpose: evaluate mobile applications aimed at dental education. Methods: Three searches were conducted in each online store, on the leading platforms (iOS and Android), using the keywords: “Dental Teaching”; “Dental Education” and “Learning in Dentistry”. The search was carried out in Portuguese. Results: 44 applications were found on the iOS operating system using the three keywords, but only three were included. In the Android operating system, 743 applications were found (388 after removing triplicates), and only three were included in the research. In total, of the six applications, four were evaluated, as there were two duplicates. The application that obtained the highest score in the “Design” category was “iDent”, which also received the best performance in the “Instructions” and “Security” aspects. Regarding usability, the application that stood out the most was “Odontologia Concursos”. The application ranked first, according to the evaluation criteria, was RadioXtudy (13.4 points), followed by iDent (12.4 points), Odontologia Concursos (10 points) and Odonto Questões (7.1 points). Conclusions: With this study, it was possible to establish an updated overview of the applications available in the central operating systems' virtual stores related to dental education. Furthermore, it was observed that the collection of mobile applications available for this purpose on the leading search platforms is insufficient, requiring developers to provide tools adapted to the needs of students.
Experiential Marketing Tourism and Hospitality Tours Generation Hybrid Model

The emergence of immersive technologies presents unprecedented opportunities for both users and organizations. This paper explores the future of digital marketing as a new ecosystem wherein innovative marketing strategies enable organizations to communicate with their customer base in ways previously unattainable, reshaping traditional marketing concepts into novel and unimaginable actions. This study proposes an experiential marketing tourism and hospitality tours generation hybrid model. The model focuses on generating virtual tours based on 360° VR videos, specifically designed for hotel environments, their surroundings, and tourist zones. The immersive environment proposal includes the design of user interface prototypes and incorporates the automated division of 360° videos using convolutional neural networks. Subsequently, personalized tours are composed based on user profiles, utilizing a Case-Based Reasoning (CBR). Functionality tests for the video division and labeling component, as well as the composition of tours according to user profiles recommended by the CBR, yielded satisfactory results. The application of this system has the potential to positively influence reservation intentions and enhance brand image. Immersive experiences have the capability to trigger effects in affective, attitudinal/behavioral, and cognitive dimensions.
Blended Learning as a Means of Forming Mathematical Competence of Future Teachers of the new Ukrainian School: Neuropedagogical Aspects

The strategy of education modernization at the present stage, the creation of a new information enlightening space, the use of global network technologies at all levels of education have led to the requirement to increase the level of methodological training of teachers who are ready to use new information technology in their professional activities. The necessity to involve advanced pedagogical technologies that form the student's network culture in the training of specialists in the educational institutions becomes topical. One of such technologies is the technology of blended learning, which combines online learning with face-to-face learning, integrating traditional forms with electronic technologies as an achpekt of neuro-pedagogical education. In particular, the concept of blended learning in the context of formation of mathematical competence is clarified and considered as an optimal environment for the development of competence of cooperation and interaction of future teachers in group projects in the conditions of neuropedagogical aspect.
DEVELOPMENT OF POWERPOINT-BASED INTERACTIVE LEARNING MEDIA ON CLASS VIII EXCRETORY SYSTEM MATERIAL SMP

Learning media had an important role in the learning process. The selection of appropriate learning media can increased students motivation and understanding in the teaching and learning process. One of the media that often used was PowerPoint, which can presented text, images, and animated videos. However, in one SMPN Riau, still used whiteboard as a learning. The aims of this research was to produced PowerPoint-based interactive learning media on the excretory system material that was feasible to used. This research followed the ADDIE development procedure through analysis, design, development, implementation, and evaluation. The validator declared the results of this study were very valid with an average score of 3.72. The practical test results obtained from the implementation of the media usage sheet and questionnaire responses of students achieved an average score of 92.16%, indicating very practical criteria. The effectiveness of the media was then evaluated based on the results of the post-test, which showed an average value of 87.5, which indicated very effective criteria. Thus the PowerPoint-based interactive learning media that has been developed is deemed fit to be used as a learning media.
Sistem Pakar Untuk Mendiagnosa Penyakit Degeneratif Pada Lanjut Usia Menggunakan Metode Certainty Factor Berbasis Web

Elderly is a natural process that undergoes organ changes that ultimately affect the state of the function and ability of the body as a whole. Degenerative diseases that occur in the elderly accompany the aging process in a person. This study aims to diagnose degenerative diseases in the elderly with an expert system.The Expert System which was built to diagnose degenerative in the elderly using the CF method aims to trace the symptoms displayed in the form of questions in order to diagnose diseases with web-based software.Expert system software can recognize degenerative diseases in the elderly experienced by users after answering some of the questions displayed by the application.The expert system designed can diagnose degenerative causes in the elderly because in this expert system knowledge is given in the form of symptoms that are used as input to the user.
Penerapan Metode Rabin-Karp untuk Mengukur Kemiripan Kata Dua Dokumen Berbasis Web

Scientific literature plays a significant role in the academic requirements of colleges, encompassing various types such as papers, reports, journals, and scripts. However, the issue of plagiarism, including the copying and plagiarizing of others' work, remains prevalent in the creation of scientific papers. In particular, digital content plagiarism often involves copy-pasting and quoting from original documents. To address this, measuring the similarity of words between documents becomes essential. In Dhamayanti's research, the recommendation is to enhance the Rabin-Karp algorithm by utilizing a distinct method [1]. This study builds upon previous research employing a string-matching method. Instead of the conventional cosine method, the substitution method employed string-Karp techniques within the Rabin-Karp algorithm, resulting in improved similarity percentages. The manufacturing of the application adopts the string-matching method using the Rabin-Karp algorithm. The algorithm matches 5-gram word sequences converted into hash values, and the similarity percentage is determined based on matching hash values. The presence of identical words indicates similarity. The application is tested using six scientific writing documents from diverse sources with related titles. Through 15 test runs, the accuracy level reached 90%.
Towards Fine-Grained Task Allocation With Bilateral Access Control for Intelligent Transportation Systems

In this paper, we propose a secure fine-grained task allocation scheme with bilateral access control (FTA-BAC) for intelligent transportation systems. To enhance the security, we formulate bilateral access control in task allocation, by adopting the matchmaking encryption (ME) to encrypt the task requirements/interests for secure task matching. In this way, both task requesters and workers can specify their match policies simultaneously, without revealing their sensitive information (i.e. attributes, geographical location). To realize fine-grained task allocation, we use linear integer secret sharing scheme (LISS) to represent task requirements/interests, supporting the AND/OR operation on match policies. To further improve the efficiency, we design a delegation mechanism to reduce the computation burden on resource-limited end devices, by diverting the high-frequency matching operations to edge nodes. Then, we prove the security of FTA-BAC under formally defined security model. Finally, we analyze the performance of FTA-BAC through theoretical analysis and experimental evaluation, demonstrating that FTA-BAC can provide practical task allocation for intelligent transportation systems compared with the-state-of-art works.
Shedding Light on Blind Spot of Backward Privacy in Dynamic Searchable Symmetric Encryption

Dynamic searchable symmetric encryption (DSSE) enables users to outsource their data while retaining the capability to search and update on the encrypted database. Although various DSSE schemes have been proposed to achieve higher efficiency and stronger security, many of them incurred information leakages due to the linkability between ciphertexts and queries as side information. The notions of forward and backward privacy are defined to capture such information leakage in DSSE formally. In particular, backward privacy guarantees that queries do not reveal their relationship with the deleted database, which is further classified into four types (Type-I, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> , II, and III) based on the types of information leakage. In this study, we provide a backward privacy attack that exploits the information leakages and apply it to Type-I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> backward private schemes to lower their security level to Type-III. We then propose a new DSSE framework, which is robust against the proposed attack. We apply our framework to the previous DSSE scheme (Zuo et al., ESORICS 2019) to build the first forward and backward Type-I private DSSE scheme under the backward privacy attack, and demonstrate its efficacy.
Efficient Task-specific Feature Re-fusion for More Accurate Object Detection and Instance Segmentation

Feature pyramid representations have been widely adopted in the object detection literature for better handling of variations in scale, which provide abundant information from various spatial levels for classification and localization sub-tasks. We find that inter sub-task feature disentanglement and intra sub-task feature re-fusion are crucial for final prediction performance, but are hard to be achieved simultaneously considering the computational efficiency. We find this issue can be addressed by delicate module design. In this paper, we propose an Efficient Task-specific Feature Re-fusion (ETFR) module to mitigate the dilemma. ETFR disentangles inter sub-task features, reduces the output channels of multi-scale features based on their importance and re-fuses intra sub-task features via concatenation operation. As a plug-and-play module, ETFR can remarkably and consistently improve the well-established and highly-optimized object detection and instance segmentation methods, such as RetinaNet, FCOS, BlendMask and CondInst, with neglectable extra computation cost. Extensive experiments demonstrate that ETFR has good generalization ability on various changeling datasets, including COCO, LVIS and Cityscapes.
Fall Detection Using Time-Series Data with Obrid-Sensor

The elderly dying alone has become a significant concern. Monitoring systems using cameras have been widely used conventionally, although it has the possibility of invading privacy. Therefore, a system based on WiFi sensing has recently been proposed, but data on various states of the target is necessary for fall detection. We proposed a fall detection system in an ideal environment using Obrid-Sensor. This sensor consists of a cylindrical lens and a line sensor. The sensor compresses the brightness feature of the subject space into one dimension for capturing, thus enabling privacy-conscious monitoring. In this study, we proposed positioning sensors at high place to detect falling in practical environments. A waveform approximating a Gaussian function was created from the brightness distribution waveform obtained by the background subtraction method. The difference between the approximate waveform and the measured waveform was calculated by Pearson's correlation coefficient and used as the degree of approximation. LSTM predicts the degree of approximation after a time interval from the current time, and the error from the actual measurement is used for fall detection. The results of the validation experiment showed that the true positive rate was 90.9%. Furthermore, we calculated the true positive rate for each fall position. It was 100% at specific fall positions. These results confirm the effectiveness of highly accurate fall detection from a small feature.
Improving a Faster R-CNN Model for Vehicle Detection and Human Action Recognition at Night via Infrared Thermal Imaging using Transfer Learning

Currently, Japan has more people aged 65 years and older as a percentage of the total population than anywhere else in the world. Consequently, older people are the fastest‑growing segment of the driving population, which had led to higher rates of traffic accidents. Data on the number of casualties in accidents involving pedestrians and motor vehicles during the day and at night show that the proportion of fatalities is significantly higher at night. In this study, we propose a system to support the safety and security of pedestrians and drivers by using infrared thermal imaging data to reduce traffic accidents at night. In previous studies, we have provided methods to detect vehicles and recognize human actions in real time at night using machine learning techniques and infrared thermal imaging. However, we only considered scenarios with a single person. To the best of our knowledge, no methods have been provided in the literature to recognize human actions in scenes with multiple people. Furthermore, the accuracy of our previous methods was relatively low for target objects far from the camera. Therefore, in this study, we acquired new video data showing multiple human actions using an infrared thermal camera to investigate vehicle detection and action recognition in scenes with multiple humans. Moreover, we propose improvements to an existing detection method using transfer learning and a Faster R-CNN model. We experimentally evaluated the performance of our method in terms of detection accuracy and speed, and the results show that our proposed approach was able to detect actions in scenes with multiple people. It also exhibited better speed and accuracy compared to conventional methods.
Kontribusi Mahasiswa Kampus Mengajar Angkatan 6 Dalam Meningkatkan Kemampuan Literasi dan Numerasi Siswa di SDN 8 Cakranegara

Literacy and numeracy skills are basic competencies that every student must have in order to learn well and serve as a foundation of knowledge for learning in higher classes. Literacy skills are not limited to just the ability to read, but also include the ability to analyze a reading text and understand its meaning and concepts. Meanwhile, numeracy abilities include skills in applying mathematical concepts to solve practical problems in various situations in everyday life. The campus teaching program at elementary school 8 Cakranegara aims to help the school in the learning process, especially in the areas of literacy and numeracy. The method used in this service starts from stage (1) Pre-activity, namely debriefing; (2) Observation of placement schools; (3) Prepare an activity program design; (4) Conducting communication forums and school coordination; (5) Carry out the agreed work program and (6) Evaluate the implementation of activities. The target of this service activity is all students in elementary school 8 Cakranegara. To improve students' literacy and numeracy skills, researchers implemented several work programs, including Saturday storytelling programs, calistung tutoring, using learning media during the teaching and learning process, and implementing contextual learning. These programs have a positive impact on the school environment, especially in terms of improving the literacy and numeracy skills of students at elementary school 8 Cakranegara
Personality Development in the Paradigm of Current Neuropedagogy

The importance of the article is underscored by the fact that the development of a child's personality in the paradigm of today's education requires special study, since the lack of generally accepted methods for forming key competences does not allow the competence-based approach to learning to develop fully. This article is based on the idea that the approaches, methods, systems and technologies widely known in pedagogy can be applied for their formation. The aim of the article is to show that the implementation of the problem-based approach contributes to the formation of key competences and development of a child's personality in the paradigm of today's education. The evolution of the educational paradigm, the whole education at different historical stages was formulated differently. Depending on the level of development of society, changes in economic formation and other reasons, different requirements were imposed on the result of the educational process. Currently, a new educational paradigm is being formed. This formation process is quite complicated. It is characterized by the fact that the previous paradigm based on the system of KSA (knowledge, skills, ability) no longer meets the requirements of modern education. The need, first and foremost, to organize and apply in practice an ever-growing volume of knowledge does not correspond to the orientation in learning to their reproduction. The article presents the essence of the competence approach, investigates the essence of the problem approach; presents the effectiveness of the problem approach within the competence approach; outlines the limits of the problem approach, outlines the development of personality in the paradigm of current neuropedagogy.
Dynamic Path Planning for Multiple Robots Transporting Objects in a Deformable Sheet

This paper presents the design of a path-planning algorithm for a multi-robot transportation system that uses a deformable sheet to transport and manipulate objects. The algorithm aims to develop a path that navigates around obstacles and considers the size of the transportation system in the planning process. The algorithm employs a polynomial function to represent the path coordinates between the system's current position and the desired destination. The coefficients of the polynomial function are adjusted to satisfy two competing objectives. These objectives are 1) minimizing the path's length and 2) maintaining a minimum safe distance away from obstacles while the system moves along the path. The paper represents the problem as an optimization task whose solution achieves an equilibrium between these two competing objectives. By iteratively solving for the optimal coefficients of the polynomial function, the algorithm dynamically adapts the path to achieve the shortest path length and safe obstacle avoidance. We present simulation results that explain how the path is determined as the robots move toward the destination. Experimental results show the algorithm's use during the transportation and manipulation of a spherical object.