Reviewerly/exHarmony huggingface dataset:
事例から入る利活用者向けAI技術学習法とUXデザインのペルソナ手法による実習評価

AI, Artificial Intelligence, is being used in many products and services while still being developed. A policy of human resources development that responds to the times and sustainably applying AI to all industries, regions, and governments was issued in the Cabinet Office’s AI Strategy 2019. In this paper, we suggest an AI education method for AI users to expand the base of AI human resources. This education method teaches AI users about various AI cases, data analysis functions, and their relationships. Then, the method creates ideas that can be solved by AI using UX, User Experience, persona and they improve AI understanding. AI users create ideas that can be realized with AI on familiar themes that everyone has experienced. After that they learn what functions the idea is used. In addition, they improve AI understanding by repeating training in the AI users’ specialized fields. Also, we describe the practical evaluation and result of their understanding for AI users, humanities, science university students.
Deep-Learning-Based Speech Emotion Recognition Using Synthetic Bone-Conducted Speech

Speech emotion recognition has drawn extensive attention in recent years. We propose deep learning (DL)-based speech emotion recognition using synthetic bone-conducted (BC) speech. In our proposed model, air-conducted(AC) speech is transformed to BC speech using an infinite impulse response (IIR) filter. Data augmentation techniques are utilized and the parameters of convolutional neural network (CNN) models are modified to enhance the accuracy of the proposed model. Simulation results demonstrate that the proposed model outperforms the existing models in terms of recognition accuracy for BC speech. The accuracy of the proposed model is 72.50% for BC speech, whereas that of the existing model is 69.83% for AC speech. This is because BC speech can enhance low-frequency components, which is important for recognizing emotions.
The Analysis of Writing Format and Abstract Structure of Dissertation Written by Ph.D Students of State Universities, Surabaya

As one of the most important part in one research, an abstract should be written as well as possible to make the readers be capable of comprehending and assessing whether its quality is good or not. Based on Koopman (1997: 1) and Owen D Williamson (2007: 3) that one of the criteria of good abstract is that it should have the complete abstract structure, that is Introduction, Objective or Aim, Methodology, Findings/Results and Conclusion written within 5 (five) paragraphs. In fact, not all abstract written by Ph.D students have those 5 (five) aspects and format. Referring to the problem stated above, this research is aimed at finding and describing some variations of writing format and abstract structure written by Ph.D students of State Universities in Surabaya. Meanwhile, the methodology employed in this research was descriptive qualitative. The data were taken from 15 dissertation abstracts written by Ph.D Students: 8 abstracts taken from the Faculty of Civil Engineering and Planning/FTSP-ITS and the 7 others were from Medical Science of Airlangga University Surabaya. The data collected were the writing format dealing with the number of paragraphs of each abstract and its abstract structure. Furthermore, having been analyzed and discussed, among those 15 abstract texts, it was found that the writing format and its variations of abstract structure of dissertation was: [a] in accordance with the number of paragraphs: 6 texts (40%) written in 3 paragraphs, 4 texts (26,66%) written in 5 paragraphs, 3 abstracts (20%) written in 4 paragraphs, 2 texts (13,33%) written in 1 paragraph; [b] Meanwhile in accordance with the number of abstract structures: most of the abstracts: 9 abstract texts (60%) were not completed with ‘conclusion‘, 5 texts (33,33%) with no ‘objective‘, only 4 texts (26,66%) were completed with 5 abstract structure, 4 texts (26,66%) were not supported with the 2 abstract structure: ‘objective and conclusion‘ and 1 text (6,66%) did not have an ‘introduction‘. Finally, it was found that there were several variations dealing with the writing format of dissertation abstracts both in line with the number of paragraphs and and its abstract stucture. You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 words.
A Multi-FPGA Implementation of FM-Index Based Genomic Pattern Search

FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.
Development and Practice of Learning Materials for an Engineering Oriented Circuit Design Workshop for Elementary School Student

This study aimed to develop teaching materials for circuit design and fabrication for elementary school students, and conduct workshops oriented to engineering. First, the content of the circuit design workshop was discussed and it was determined that a connection-focused design would be appropriate. Second, we examined the issue of existing teaching materials. Many of the existing kit materials were not suitable for learning circuit design as it was already completed. In addition, many of them required soldering work, which was too difficult for elementary school students. Hands-on materials that allow students to construct circuits through trial and error were suitable for learning circuit design. Therefore, we developed design and fabrication support materials using a 3D printer and conductive tape. Next, we provided workshop opportunities using developed material, with the theme of “making light-up cards,” wherein elementary school students worked on designing the layout of the components and fabrication. Consequently, students' conceptual understanding of the connection was improved. All students were able to complete the circuit, with high self-evaluations of satisfaction and understanding suggesting that the activity was engineering-oriented.
Inverse Heat Dissipation Model for Medical Image Segmentation

The diffusion model has achieved success in generating and editing high-quality images because of its ability to produce fine details. Its superior generation ability has the potential to facilitate more detailed segmentation. This study presents a novel approach to segmentation tasks using an inverse heat dissipation model, a kind of diffusion-based models. The proposed method involves generating a mask that gradually shrinks to fit the shape of the desired segmentation region. We comprehensively evaluated the proposed method using multiple datasets under varying conditions. The results show that the proposed method outperforms existing methods and provides a more detailed segmentation.
Enhancing Cup-Stacking Method for Collective Communication

An interconnection network is an inevitable component for constructing parallel computers. It connects computation nodes so that the nodes can communicate with each other. As a parallel computation essentially requires inter-node communication according to a parallel algorithm, the interconnection network plays an important role in terms of communication performance. This paper focuses on the collective communication that is frequently performed in parallel computation and this paper addresses the Cup-Stacking method that is proposed in our preceding work. The key issues of the method are splitting a large packet into slices, re-shaping the slice, and stacking the slices, in a genetic algorithm (GA) manner. This paper discusses extending the Cup-Stacking method by introducing additional items (genes) and proposes the extended Cup-Stacking method. Furthermore, this paper places comprehensive discussions on the drawbacks and further optimization of the method. Evaluation results reveal the effectiveness of the extended method, where the proposed method achieves at most seven percent improvement in duration time over the former Cup-Stacking method.
Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits

The Variational Quantum Eigensolver (VQE) algorithm is gaining interest for its potential use in near-term quantum devices. In the VQE algorithm, parameterized quantum circuits (PQCs) are employed to prepare quantum states, which are then utilized to compute the expectation value of a given Hamiltonian. Designing efficient PQCs is crucial for improving convergence speed. In this study, we introduce problem-specific PQCs tailored for optimization problems by dynamically generating PQCs that incorporate problem constraints. This approach reduces a search space by focusing on unitary transformations that benefit the VQE algorithm, and accelerate convergence. Our experimental results demonstrate that the convergence speed of our proposed PQCs outperforms state-of-the-art PQCs, highlighting the potential of problem-specific PQCs in optimization problems.
Desafíos de la enseñanza de Química en pandemia covid-19: ventajas y limitaciones

The covid-19 pandemic has significantly affected education worldwide, including chemistry education. The abrupt transition to online learning and the need to adapt to new ways of teaching have posed significant challenges. Therefore, it is important to conduct a literature review with the aim of analyzing the main challenges and measures adopted in Chemistry teaching in the context of the covid-19 pandemic, to identify the most effective practices to address these challenges and to develop strategies to support learning. The methodology maintained a qualitative approach, of descriptive type, constituting a bibliographic review, based on the hermeneutic and analytical-synthetic research methods, as well as on the content analysis technique, for which a total of 57 publications from databases such as Scopus, Dialnet, Latindex and Scielo were taken as a sample, compiling high impact articles in Spanish, English and Portuguese. The results show that the pandemic has boosted the implementation of innovative measures and strategies in chemistry teaching, although it has also revealed the limitations and challenges that must be overcome to ensure quality education in the future. These new approaches need to be further evaluated and improved to ensure their effectiveness and accessibility for all students.
Recent Advancement and Challenges of Deep Learning for Breast Mass Classification from Mammogram Images

Deep learning (DL) has become a critical component of medical image processing. Over time, DL methods have changed. Advances in the field of DL have resulted in a computer-aided diagnosis system (CADs) that is more sophisticated and self-reliant. In medical image analysis, convolutional neural networks (CNN) are becoming increasingly extensively employed as a DL approach. This study aims to survey state-of-the-art approaches of breast mass classification using CNNs. The breast cancer mammography repositories have also been examined. Various limitations that demand further examination are also discussed. We looked at articles published on well-known publishing platforms like Google Scholar, PubMed, Science Direct, and IEEE Xplore to conduct the literature study. These papers are all SCOPUS/SCI/SCIE indexed and focus on using CNN algorithms in mammogram images. We also present the advancements and challenges of CNNs for breast cancer diagnosis. When it comes to medical image processing, using CNNs has proved to be more beneficial to researchers than using a traditional approach. However, better architectures, larger datasets that address class imbalance issues and improved optimization methods are still required.
Adder Design Using Reversible Logic

Reversible logic is a prominent area of research due to its interesting characteristics. Reversible logic is also useful in low power computing. The quantum computing technique is also reversible, and so another attraction is attached with this logic. In computing, adders are very important circuit. Adders play a major role in the design of various computing devices. And so, adders are extensively studied. There are adders proposed in many literatures, even though there is always an ample scope to improve the designs. In this manuscript, two reversible gates also called microblocks are presented. Then using these gates, two new adders are designed. One of the designs is a ripple carry adder, and the other design is an improved design in terms of quantum cost and delay. These proposed circuits are constructed using reversible logic. The presented adders are compared with some available literatures on full adders, and it is found that the performance of the proposed adders is of comparable standard. Two designs of 2-bit adders are also presented, and using these deigns, n-bit realization of adders can be achieved.
Improved Detection of Large-Sized Pedestrians Using Non-linear Scale Space and Combination of HOG and Dense LDB Features

Multi-scale detection in pedestrian detection plays a vital step due to its use in detecting pedestrians in different scales. However, multi-scale detection is a challenging task due to various reasons like selection of appropriate scale factor, low resolution and loss of sharp edges at deeper pyramidal levels, etc. In this paper, new pedestrian detectors are presented which uses non-linear scale space for multi-scale detection. The proposed detector uses histogram of oriented gradient features in combination with dense local difference binary features. Different classifiers like linear SVM and cascade of boosted classifiers are used to train the detector. INRIA pedestrian dataset is used to train and test the proposed detectors. The proposed system is evaluated in terms of precision versus recall and miss-rate versus FPPW/ FPPI as well as computational speed. The performance of the proposed detectors is also compared with some similar existing pedestrian detectors.
Sentiment Analysis on COVID-19 Tweets: Machine Learning Approach

Analysis of tweets accompanying a catastrophic situation is a crucial chore. Sentiment analysis is the field of study to analyze the varied opinions shared by diverse users on social networking platforms on various social phenomena. In this paper, an analysis of the sentiments on thousands of tweets collected from Kaggle on the ongoing pandemic of COVID-19 is carried out. Data preprocessing technique followed by TF-IDF approach for uni-gram and bi-gram features is extracted. Three different supervised machine learning classifiers such as Bernoulli’s Naïve Bayes (BNB), Gaussian Naïve Bayes (GNB), and Random Forest (RF) models are applied. Experimental results suggest that on both the feature extraction models, i.e., uni-gram and bi-gram feature extraction techniques, RF classifier has performed better than the other two models. With 70%-30% train–test set, RF has achieved an accuracy of 90.06% to classify the tweets into negative, neutral, and positive classes.
Kiite Cafe: A Web Service Enabling Users to Listen to the Same Song at the Same Moment While Reacting to the Song

This paper describes a public web service called Kiite Cafe that lets users get together virtually to listen to music. When users listen to music on Kiite Cafe, their experiences are enhanced by two architectures: (i) visualization of each user's reactions, and (ii) selection of songs from users' favorite songs. These architectures enable users to feel social connection with others and the joy of introducing others to their favorite songs as if they were together listening to music in person. In addition, the architectures provide three user experiences: (1) motivation to react to played songs, (2) the opportunity to listen to a diverse range of songs, and (3) the opportunity to contribute as a curator. By analyzing the behavior logs of 2,399 Kiite Cafe users over a year, we quantitatively show that these user experiences can generate various effects (e.g., users react to a more diverse range of songs on Kiite Cafe than when listening alone). We also discuss how our proposed architectures can enrich music listening experiences with others.
Line Segment Detection Based on False Peak Suppression and Local Hough Transform and Application to Nuclear Emulsion

This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the "Difference of Gaussian" method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.
THE AUTHOR'S ANSWER TO DISCUSSION BY SHIGEKI NAKAYAMA

We are grateful for Prof. Nakayama’s discussion to our paper. In this answer, we will give our opinions on questions about the comments on results, scrutiny of descriptions and use of terminology.
A Deep Reinforcement Learning Method for Service Composition in Cyber-Physical-Social Systems

Cyber-Physical-Social Systems(CPSS), oriented towards services, view various resources from physical, network, and social domains as service components. Through orchestrated composition techniques, these resources from the three domains are integrated to achieve personalized system integration. Despite the effective support of existing service-oriented architectures for service composition, accomplishing large-scale service composition within the highly dynamic CPSS environment remains a considerable challenge. To address these challenges more effectively, this paper proposes a CPPS service composition method based on Proximal Policy Optimization (PPO). This approach involves iteratively training and learning the optimal composition strategy through Deep Reinforcement Learning (DRL). The introduced learning algorithm designs the action space of PPO as a variable action set for each decision state. Moreover, it enhances and refines the reward function based on CPSS scenarios, aiming to attain service compositions that meet constraints while optimizing the Quality of Service (QoS). Experimental comparisons with other DRL methods and heuristic algorithms demonstrate that our proposed approach exhibits superior reliability, adaptability, and scalability. It outperforms alternative methods in addressing large-scale dynamic service composition challenges within the CPSS context.
Secure Authentication Scheme for Large-scale Video Surveillance System Based on Quantum Key

With the continuous improvement of computing power, especially the emergence of quantum computers, the key data with long-term value encrypted by traditional encryption techniques in video surveillance systems have been exposed to the risk of quantum attacks. Quantum key distribution is the most mature quantum cryptography technique, which can guarantee the high security of quantum keys. In this paper, we design a secure identity authentication scheme based on quantum key for large-scale video surveillance systems. The experimental results show that the time of the authentication scheme proposed in this paper grows linearly with the increase of the number of users, and the key matching degree can be improved by about 70.16%, which can achieve the performance matching of quantum key distribution with the demand rate of large-scale authentication keys in the video surveillance network.
A Study on Academic Literature Resource Aggregation and Decision Support Service in Vertical Domain

Due to an exponential increase in the number of academic literature resources, difficulty in resource integration and utilization becomes even greater. Especially, aggregation of academic literature resources of different sources, in different formats and at diverse granularities is now confronted with new challenges. At present, industrial users have put forward a requirement of rapidly acquiring high quality information resources. To meet this requirement, we explored a vertical domain oriented information aggregation model. By virtue of this model, it is expected to solve relevant integration and sharing problems such as “Data resources + Instrumental methods + Industrial wisdom + Service decision-making” on one hand, and realize rapid acquisition and effective utilization of information resources in specific domains on the other hand. Now, the model has been successfully applied in China's “Foreign Agriculture Cooperation Public Information Service Platform”; and good results have been achieved.
OrdinalNet - Dynamic and Robust Backdoor Attacks

DNN are vulnerable to backdoor attacks that can manipulate their outputs, leading to incorrect results. We point out the limitations of current backdoor attack methods, which rely on static and fixed triggers, making them easily detectable by existing backdoor defenses. In response, we propose a novel backdoor attack approach based on image features. This method leverages image to create an ordinal network that captures the precise image structure. Triggers are then generated using the information from this ordinal network. Additionally, we introduce a regularization method to enhance the robustness of our backdoor attack method against model diagnose-based defences. Our experimental results demonstrate the effectiveness of our approach. When compared to traditional backdoor attack methods such as BadNet and Blend, our method achieves attack success rate exceeding 99% on datasets like CIFAR-10, Tiny-ImageNet, and CelebA. Notably, the accuracy on clean samples only experiences a marginal decrease of less than 1%. Furthermore, our approach showcases its generalizability across different neural network architectures.