Reviewerly/exHarmony huggingface dataset:
Women’s Enlightenment shown in the Magazine “Madam” during the Liberation Period

This study examines women’s enlightenment awareness by investigating the issues, systems, and writings of the women’s magazine, “Madam.” During the liberation period, when “Madam” was first issued, various movements took place in the Japanese colonial era to rebuild a society devastated by exploitation, and “Madam” emphasized women’s active role in society at the time. “Madam” provided information through articles in various fields, such as editorials, novels, poems, essays, films, performances, history, medicine, lyrics, and news. In particular, the magazine included articles on women’s lives at home and abroad, the status of the international community, and works by famous writers in those days. Articles related to families included various topics such as relationships with husbands, relationships with parents-in-law, parenting, houseworks, marriage, and love; they also provided Western recipes and the latest scientific information. In the early days of the publication of “Madam,” the articles were focused on women’s role in re-establishing their country from a political perspective. However, it gradually changed to encourage women to participate in society.
Impact Assessment of Memetic Operators on Communication Topologies for the PSO

Swarm intelligence has emerged as a promising research area due to its potential to solve complex problems. The Particle Swarm Optimisation (PSO) algorithm is widely used in this area due to its efficiency and easy implementation. However, PSO performance can be affected by the choice of the communication topology, which defines the interaction between particles. In this work, we propose a comparative analysis of the impact of memetic operators on three distinct PSO topologies. We explore Interaction Networks’ use to analyse individuals’ social interaction in PSO-based algorithms with memetic operators. Furthermore, we employ the Network Portrait Divergence metric to compare the structural similarity between networks of the algorithms and the Interaction Diversity to measure the diversity of information flow within the swarm. The results obtained in this study provide insights into the topology selection for the PSO and the effect of memetic operators on them.
Biometric Fingerprint Identification for Bank Locker Security System

The implementation of the security system for all devices is through passive communication technology. People from different origins and different regions need a security system for their regions for all their platforms. The convincing type of security system is that it makes sure that it works under proper guidance and natural care of the system. The leverage for the security devices is to make them work under proper conditions and the defined process. The One-time password-based technology is also used for the security system and other surveillance of the system. The process of communication and the surveillance system is wholly monitored by the one-time password-based security theme. The major surveillance is done by the process of superior convenience through the successive surveillance of the devices. The metropolitan areas which are under surveillance will be having the same banking systems through the adhesive nature of the locking system for security purposes and other amenities. The user who accesses the locker will be given a username and password for his access and then they have imprinted their fingerprint for extra care and security. People with extra care and security will be endowed with one-time password access through their registered mobile. The mechanized introduction to the locking system will be enhanced through the proposed system. Thus it will help making the system to work on its way to maintain security and safety of the devices which has been endowed and developed through it.
Using String Data to Estimate the Shading Loss of Solar Power Plants

Aside from a greater emphasis on energy rationalization and efficiency, decentralized electricity supply methods are becoming increasingly significant for the world's energy supply, and their role in power consumption is growing. Examples of these sources include solar and wind energy. While installing photovoltaic (PV) systems, it is imperative to be cognizant of the great nation regulatory backdrop because energy projects typically require significant long-term financial investments. In the recent years, there has been a tendency towards the construction of new photovoltaic power plants mostly employing conventional crystalline PV to pinpoint the system's faulty components. This article uses a PV system in Hungary to illustrate the energy correlations of shading. This study's objective is to present a method for classifying the operational features of strings and PV power plants based on monitoring data, allowing for the estimation of the yearly energy loss. Since the solution is simple to adapt to real-time management and monitoring platforms but it makes the internationalization of Photovoltaic system easier, the model's creative novelty is that its application can provide practical assistance for the operator of Photovoltaic systems across the world potentially troublesome strings, enabling a more targeted examination of PV power installations. The model's new practical advantage is that it makes it easy to identify any power losses due to improper operation (e.g., incorrect inverter operation, adverse effects of trees as well as other objects, or improper spacing among both chords of PV power plants) through a more straightforward calculation mechanism. For the safety of the Photovoltaic panels, the later reconstruction of a strands, or even the solution to issues under warranty, early fault detection is crucial. Also, it is possible to identify any unfavorable changes in the fund's economic characteristics by calculating the annual wasted energy from shading.
THE EFFECT OF USING REPEATED READING STRATEGY TOWARD STUDENTS’ READING COMPREHENSION

This study was intended to find out the effectiveness of repeated reading strategy toward students’ reading comprehension. This quantitative research done due to some problems found during reading class. First, the teacher used the conventional strategy since the teacher acted as the center of the class. Then, the students showed a little comprehension in reading, such as finding the main idea, reference, or inference of the text. Last but not least, the teacher admitted that the students often doubt about finding out the moral value of the text, because it was not written on the text. After doing treatment by implementing repeated reading strategy, researcher found that students’ reading comprehension was improved. It was showed that t-obtained was higher than t-table (6,872&gt;2,064). Hence, there is a significant effect of repeated reading strategy toward student reading comprehension.
Fuzzy Controller from Experts’ Rules for Middle Axis Ball and Beam

The present study examined the nonlinear system of Ball and Beam, to design an intelligent controller for this dynamic system. The authors formulated a mathematical model for the system and performed simulation testing using MATLAB to control it. It is noteworthy that the mathematical model was exclusively used for simulation purposes and not for building the controller. The author's team then proceeded to develop a Fuzzy Controller for the simulation model of the Ball and Beam system. Subsequently, the team tested the Fuzzy controller on the actual Ball and Beam model. The primary objective of this study was to assess the feasibility of building and controlling an intelligent controller for a nonlinear object, without relying on its mathematical model. The findings of the study can be useful in designing and controlling complex systems that are difficult to model mathematically.
Use of Vacuum to Increase the Effectiveness of X-Ray Tube Maintenance

Each device requires redundancy, including the X-ray machine. In an X-ray machine, there is an important component as the location for the creation of X-rays, namely the X-ray tube. This tube needs routine maintenance to increase the tube's lifetime. In addition, maintenance that was originally done manually, with this device can be done automatically and more quickly. In addition, maintenance that was originally done manually can be done automatically and faster with this device. X-ray tube oil vacuum is used as an effective solution for the maintenance of an X-ray tube machine that lasts for days. This device comes with easy use, equipped with two buttons and a display. This device can increase the productivity of both the device and the technician itself. The application of the use of X-ray tube oil vacuum is intended for hospitals and health facilities that have active X-ray machines. This device has been able to be built. Based on the results, it has a 2.5% error for the measurement at minimum and maximum pressure and a 3% error at -15 KPa pressure. The volume of the X-ray tube fully filled is always the same for each measurement. It can be concluded that this device can be effective in time because it only takes minutes to fill the X-ray tube up to full. Still, many parts can be improved and developed, such as the size and the weight.
Increasing the Capacity of Mushroom Production and Their Processing in the “Argodadi Pinilih” Disabled Group

The disabled group "Argodadi Pinilih" has 40 active members and already has a complete board. The Covid 19 pandemic caused some members to lose their jobs. This situation has encouraged members of the disabled group to take up other businesses, including oyster mushroom cultivation. The oyster mushroom business is carried out in a mushroom house which is placed inside the house or yard and the maintenance activities are carried out by the members of the disabled group themselves. The results of oyster mushrooms are all sold to traders around the house because the production of oyster mushrooms is still small. The problems faced by partners are (a) the production capacity of oyster mushrooms is still low, (b). limited skills in processing food from mushrooms, (c). marketing of oyster mushrooms and their processing is still limited. The solutions and output targets to be achieved are (a) increasing the production capacity of oyster mushrooms (b) increasing skills in the manufacture of processed food made from oyster mushrooms, and (c) expanding the marketing of oyster mushrooms. The activities carried out were (a) counseling and technical training on oyster mushroom cultivation as well as providing 2,200 baglogs, (b) training on processing food made from oyster mushrooms, namely in the form of crispy mushrooms and mushroom satay, and providing oil slicer (spinner), and (c) counseling on strategies online marketing and promotion. The results of the dedication showed that there was an increase in the production of oyster mushrooms produced, group members also became skilled in processing oyster mushrooms into crispy mushrooms and mushroom satay. The mushroom market share was getting wider, for starting to promote mushrooms through WhatsApp online media. Group members' knowledge of mushroom cultivation and marketing strategies increased, as evidenced by the pre-test results of 78.46 and 88.46 post-test with an increase of 12.75%.
Development of Applied Science and English Learning Media at Kembangsari Elementary School

This community service was carried out in Munggur Hamlet, Srimartani, Piyungan, Bantul, which is 22 KM from the UMY campus, with SD Kembangsari partners. Along with the rapid development of technology and the construction of public facilities which are increasing and adopting high technology, it is necessary to develop learning media also up to elementary schools without leaving the basic knowledge that must be mastered by elementary school children. In the context of delivering teaching materials, the preparation that needs to be done is to create learning content that pays attention to pedagogic aspects. Teachers need basic multimedia skills so that teaching materials delivered through various media can be optimized so that they are more compact, communicative and systematically arranged according to the administration of lesson plans.
Semantic-Driven Global-Local Cooperative Contrastive Learning for Medical Report Generation

As the demand for radiology continues to increase, the shortage of specialized professionals becomes a challenging issue. Such shortage highlights the need to utilize advancements in artificial intelligence (AI) to automatically generate draft medical reports from radiology images. Recently, the application of contrastive learning has been leveraged in image-to-text generation because it allows the model to learn meaningful representations in latent space by contrasting similar and dissimilar image-text pairs. However, existing approaches to applying contrastive learning in medical report generation are limited by the following: 1) they are performed as an independent pretraining step, which hinders the cooperation between contrastive learning and the subsequent report generation step; 2) these methods are contingent on pairing images with their corresponding reports, thus establishing similarity based solely on this association. Such contingency inadvertently overlooks the situation where unpaired reports could also be relevant to a given image, thereby failing to accurately capture and understand the semantic relationships within the data; and 3) existing contrastive learning in medical report generation only utilizes the global representation, which cannot capture subtle but crucial local visual information. To address these limitations, we propose a Semantic-Driven Global-Local Cooperative Contrastive Learning Network (SGLCCNet), which integrates contrastive learning into the training process of report generation, enriched with semantic information extracted from reports and enhanced by the inclusion of local feature exploration. Extensive experiments on the IU-Xray dataset demonstrate that our method achieved the state-of-the-art. Further, we demonstrate how each of our proposed steps adds to the overall performance.
Secure and Efficient Group Decision-Making with Blockchain-Based Consensus and Trust Management

Trust-building is of paramount importance for managing and improving consensus in group decision-making (GDM). This mechanism usually involves a trust propagation process for estimating the level of trust among decision-makers (DMs). However, this process is computationally expensive and hinders the speed of consensus reaching. To address this issue, this work proposes a novel trust-building mechanism that does not rely on the trust propagation process to quantify DMs' level of trust. Instead, it makes use of Blockchain technology to facilitate communication between the moderator and the group of DMs. This novel trust-building mechanism does not rely on trust propagation, which makes it computationally efficient for building trust among DMs while also providing a secure and efficient communication protocol to accelerate the consensus-reaching process. The proposed GDM model is illustrated through an example, and the sensitivity of the model to various assumptions is analyzed, demonstrating the practical applicability of this approach.
An Enumeration-Based Deterministic Algorithm for Solving optimization Problem

An enumeration-based deterministic algorithm is proposed for solving optimization problem. Discretization of the feasible region for each decision variable is applied to establish search tree so that all the solutions could be enumerated. A new search region is established from the optimization of previous literation. Meanwhile, adjustment of search step with exponential decay function is conducive to reducing the search scope until the convergence of result is realized. The proposed algorithm is applied for three kinds of typical optimization problem, including Non-linear Programing Problem, Job-Shop Scheduling Problem and Traveling Salesman Problem. The results obtained by the new algorithm are compared with results of swarm intelligent optimization algorithm. Results indicate that for the combinational optimization, high convergence rate could be realized by adjusting the feasible region for each decision variable. For non-linear programming problem, the accuracy of results obtained from deterministic algorithm is better than that from self-organizing migrating algorithm, which infers that enumeration-based deterministic algorithm could be used to solve NP hard problem.
Luma-only Resampling-based Video Coding with CNN-Based Super Resolution

Resampling-based video coding strategy is well-known for compressing high-resolution video with limited bandwidth. To keep the high frequency texture in chroma components, we propose a luma-only resampling-based video coding method, which includes three aspects. Firstly, the luma component is downsampled while the chroma components remain unchanged in the pre-processing module. Secondly, a convolutional neural network-based (CNN) module is adopted to remove coding artifacts and restore the reconstructed frame. Finally, a single channel based super resolution method, which utilizes CNN as basic structure, is proposed to restore the original color format. Extensive experimental results demonstrate the benefits of the proposed method. Compared with VTM-11.0, the proposed method achieves {8.63%, 7.82%, 8.34%} BD-rate reductions on 4K sequences for Y, U, and V components under all-intra configuration, respectively.
Extended Adaptive Loop Filter Beyond VVC

Adaptive Loop Filter (ALF) has been adopted into VVC standard, which significantly improves the video compression performance. In this paper, we propose two extensions for ALF beyond VVC to further increase the coding efficiency. First, a cross filter shape with long filter length is proposed to utilize information from the reference samples far away from current filtering sample. Different from the existing diamond filter shape in VVC, the proposed new filter shape can further explore the correlations between long-distance samples. Second, more extended taps for ALF are introduced into online-trained-filters to involve more texture information provided by the offline-trained-filter-output of ALF. Compared with the existing design in VVC, more diversified information is input into ALF. The proposed extensions for ALF have been implemented on top of ECM software published by JVET. Experimental results show that the proposed extensions can achieve -0.13%, -0.22% and -0.41% coding performance on the luma component in All-Intra, Random-Access and Low-Delay-B configurations respectively, on top of ECM-5.0, with negligible encoding/decoding complexity impact. The proposed extensions have been both adopted into ECM.
A Practical, Robust, Accurate Gaze-Based Intention Inference Method for Everyday Human-Robot Interaction

Gaze estimation is a crucial component of human-robot Interaction (HRI). While previous gaze estimation methods have been widely applied in advertising and gaming with head-mounted devices or complicated camera systems, little research has been conducted in everyday HRI scenarios. During interactions, robots have the potential to infer human intention through static gaze directions and dynamic eye movements, enabling them to behave more intelligently and friendly. This paper combines appearance-based gaze estimation methods with eye movement analysis methods to infer human intentions, particularly in human-robot interaction scenarios. Real interactions were conducted to test the accuracy and robustness of the methods developed. The experiments demonstrate that our methods deliver practical, robust, and accurate results.
Immersive experience via 6DoF adaptive streaming

The highest immersive experience can be enjoyed by enabling 6DoF (6-degree of freedom) content delivery and playback. On the other hand, the ultra-high data rate associated with 6DoF content imposes quite a challenge. To achieve that, highly capable compression of such contents, adaptive and efficient streaming, as well as real-time rendering of such contents are required. In this demo session, we will introduce a 6DoF streaming system and player that can demonstrate true 6DoF immersion experience with smooth streaming and playback capability of high-resolution, dynamic 3D contents.
Variable-rate Learned Image Compression with Adaptive Quantization Step Size

Nowadays, learned image compression has outperformed traditional coding methods like VVC. However, learned image compression methods are often optimized for specific rates and lack support for variable rate compression. Though some efforts have been made to address variable rate compression, striking the right balance between performance and complexity remains a challenge. To address this, we propose the Adaptive Quantize Step Size (AQS) technique, allowing continuous variable rate within a single model. Unlike methods that learn to scale the latent representation, AQS enables learning of different quantization step sizes for different rates, preserving the latent representation’s distribution while only affecting the quantization step size, which is beneficial for the entropy model. As a result, our method achieves superior performance compared to existing methods while maintaining similar complexity. It delivers comparable results across a wide range of rates using a single model with minimal additional parameters.
PUW-Feat: A Progressive and Unified Method for Weakly Supervised Local Feature Learning

Local feature extraction is a fundamental module in computer vision. Weakly supervised learning methods have more convenience of collecting datasets, but they have still not get proper trade-off among training costs, accuracy and speed. In this paper, we propose PUW-Feat, a Progressive and Unified Weakly supervised learnable local Feature extractor. We design a progressive describe-then-detect learning pipeline to save training costs, which partly decouples the training process yet ensures its consistency by sharing the loss function structure. We build a unified keypoint location training framework which can predict keypoint locations by a learnable network branch to avoid slow post-process, thus we increase speed while keep accuracy. Our method achieves the best balance on training costs, accuracy and real-time performance in experiments on different tasks.
Video Coding for Machines using Object Analysis and Standard Video Codecs

The paper presents a brief review of the current standardization activities in Video Coding for Machines (VCM), including the MPEG 2022 Call for Proposals for VCM. These activities and the conditions defined for this call are the background of the proposed technology for VCM. In this technology, a search for Regions of Interest (RoIs) is implemented in the encoder in order to find the areas where the objects are possibly located. The background (the areas outside RoIs) is simplified and retargeting is executed before the video is compressed. For low bitrates, the experimental results demonstrate significant improvements of the results for object detection, instant segmentation and object tracking as compared to the state-of-the-art VVC coding.