Reviewerly/exHarmony huggingface dataset:
A Practical Model Driven Approach for Designing Security Aware RESTful Web APIs Using SOFL

RESTful web APIs have become ubiquitous with most modern web applications embracing the micro-service architecture. A RESTful API provides data over the network using HTTP probably interacting with databases and other services and must preserve its security properties. However, REST is not a protocol but rather a set of guidelines on how to design resources accessed over HTTP endpoints. There are guidelines on how related resources should be structured with hierarchical URIs as well as how the different HTTP verbs should be used to represent well-defined actions on those resources. Whereas security has always been critical in the design of RESTful APIs, there are few or no clear model driven engineering techniques utilizing a secure-by-design approach that interweaves both the functional and security requirements. We therefore propose an approach to specifying APIs functional and security requirements with the practical Structured-Object-oriented Formal Language (SOFL). Our proposed approach provides a generic methodology for designing security aware APIs by utilizing concepts of domain models, domain primitives, Ecore metamodel and SOFL. We also describe a case study to evaluate the effectiveness of our approach and discuss important issues in relation to the practical applicability of our method.
Enhanced Full Attention Generative Adversarial Networks

In this paper, we propose improved Generative Adversarial Networks with attention module in Generator, which can enhance the effectiveness of Generator. Furthermore, recent work has shown that Generator conditioning affects GAN performance. Leveraging this insight, we explored the effect of different normalization (spectral normalization, instance normalization) on Generator and Discriminator. Moreover, an enhanced loss function called Wasserstein Divergence distance, can alleviate the problem of difficult to train module in practice.
Numerical method for analyzing steady-state oscillation in trumpets

Interactions between the airflow, elastic body of the lips, and acoustic resonator of the instrument cause self-sustained oscillation of the lips when generating sound using brass instruments, and the steady-state oscillation of the instrument can be expected to be periodic. However, quasi-periodic oscillation or period doubling can also occur, and a cascade of period doublings may further introduce chaos. Therefore, given a set of dynamic equations representing the acoustic behaviors of the airflow, lips, and instrument, a method for detecting and obtaining the periodic solution by adopting a shooting method that relies on the match between the initial and terminal states after the time corresponding to the oscillation period has passed is presented in this paper. Experiments were performed for a trumpet model, where the resonance frequency of the lips and the blowing pressure were used as the main control parameters. The minimum blowing pressure was estimated using a linear stability analysis. The method could capture the corresponding changes in the periodic solution very finely when a small perturbation was successively applied to the control parameters; however, it was less effective when the acoustic load of the instrument was capacitive at the oscillation frequency.
Physical modeling of wind instruments involving three-dimensional radiated sound fields

For the Schumacher model and Adachi–Sato model, which are effective physical models of wind instruments, the present study introduces a more precise sound field expression using a three-dimensional wave equation, instead of one-dimensional analytical solutions of a wind instrument bore. The sound field around a bell section is expressed by the normally differentiated Kirchhoff–Huygens formula, then the boundary element method is applied to numerically analyze the formula. The remaining sound field inside the tube is analyzed with a conventional transfer matrix approach, and the two sound fields are coupled correctly, thereby modeling the total acoustic behavior of the wind instrument. The improved acoustic analysis method is combined with the Schumacher model and Adachi–Sato model to accomplish the physical modeling sound synthesis. Trial numerical calculations demonstrate that the developed method is effective in the sound synthesis of a clarinet and trumpet, involving three-dimensional characteristics of their radiated sound.
藤井厚二の欧米視察について（その２）：視察の内容と設計活動への影響

This paper examines the contents of Koji Fujii’s visit to Europe and the United States and its impact on his later design activities. As a result, the following points were clarified.
Learning Local Similarity with Spatial Interrelations on Content-Based Image Retrieval

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.
Regression CNN Based Fast Fading Channel Tracking Using Decision Feedback Channel Estimation

In a high-speed moving mobile environment, the channel state information (CSI) in the last part of the packet is different from the actual channel in the beginning part. Therefore, the channel estimation accuracy is degraded, especially when a small number of pilot symbols are used to ensure transmission efficiency. For the above reasons, it is necessary to compensate for CSIs to achieve reliable communication. Decision feedback channel estimation (DFCE) has been widely considered to be one of the channel tracking methods. However, the presence of time and frequency selective fading environments still causes estimation errors due to the decision-making process. We focused on the time-frequency domain response of the CSIs, which can be represented as a two-dimensional image. This paper newly proposes a regression convolutional neural network (CNN) based channel tracking scheme using the time-frequency domain response of the CSIs by DFCE for training and prediction to solve these problems. Computer simulation results demonstrate that the proposed scheme can achieve higher BER performance than the conventional schemes.
Modality-Fused Graph Network for Cross-Modal Retrieval

Cross-modal hashing technology has attracted much attention for its favorable retrieval performance and low storage cost. However, for existing cross-modal hashing methods, the heterogeneity of data across modalities is still a challenge and how to fully explore and utilize the intra-modality features has not been well studied. In this paper, we propose a novel cross-modal hashing approach called Modality-fused Graph Network (MFGN). The network architecture consists of a text channel and an image channel that are used to learn modality-specific features, and a modality fusion channel that uses the graph network to learn the modality-shared representations to reduce the heterogeneity across modalities. In addition, an integration module is introduced for the image and text channels to fully explore intra-modality features. Experiments on two widely used datasets show that our approach achieves better results than the state-of-the-art cross-modal hashing methods.
Reversible Data Hiding in Encrypted Image via Joint Encoding of Multiple MSB and Pixel Difference

Reversible data hiding in encrypted image (RDHEI) has become a research hotspot, which can effectively protect image content privacy. An RDHEI scheme based on the joint encoding of multiple MSBs (most significant bits) and pixel difference is proposed in this paper. A block-based image encryption method is adopted on the content owner side, which can securely protect the image contents while retaining the spatial correlation within each block. By the joint encoding strategy of multiple MSB and pixel difference, the redundancy within the bit plane is sufficiently compressed to accommodate more additional data; thus, a high embedding rate can be achieved. According to different kinds of available keys, image decryption and data extraction can be separably conducted on the receiver side. Experimental results show that our scheme can achieve a higher embedding rate than some state-of-the-art schemes.
対話中における聞き手の頭部運動と相槌の相乗機能の解析

A novel machine learning framework is proposed to automatically recognize the synergetic functions of the Aizuchi and the head movements of listeners in conversations. The listeners’ head movements, such as nodding, and Aizuchi, i.e., listeners’ short back-channel utterances, play a variety of functions, such as expressing the sign of listening, agreement, and emotions. This paper presents a functional Aizuchi corpus and analyzes it with the functional head-movement corpus that the authors have previously created. The analysis reveals the synergetic relationship between Aizuchi and head movements including reinforcement, multiplexing, and complementary. Then, this paper defines a functional category system called synergetic functions, which classifies reinforcement and multiplexing as product functions and complementary as sum functions. Next, several models using convolutional neural networks (CNNs) are designed to recognize such synergetic functions from the time series of the prosodic features and the head pose of the listeners. More specifically, we compare some different architectures, which employ early/late feature fusions and single/two-stage decision-making. The experimental results shows the proposed models achieved the maximum F1-score of 0.71 for the product function of Aizuchi’s continuer and head movement back-channel and that of 0.88 for a sum function called back-channel acknowledgment that was complementarily expressed by head movements and Aizuchi. These results confirms the potential of the proposed framework.
Attenuation characteristics of tones and vibrations in guitars with nylon, fluorocarbon, and phosphor bronze strings pressed down against fret

We report on the effects of internal losses in strings on the attenuation characteristics of tones and vibrations in guitars with nylon, fluorocarbon, and phosphor bronze strings. Radiated sounds and vibrations of bridges generated by individually plucking the 1st, 2nd, 3rd strings pressed down against the 1st or 13th fret were studied. Attenuation coefficients of sound and vibration acceleration from the 1st frequency mode to the 10th frequency mode were determined on the basis of time variations of sound pressure and vibration acceleration levels. Storage and loss elastic moduli and elastic loss factors of nylon, fluorocarbon, and phosphor bronze strings were measured using a dynamic mechanical analyzer. The motion equation, which includes the complex elastic modulus, provided the mode frequency relating the inharmonicity and attenuation coefficient depending on the loss elastic modulus. Nylon and fluorocarbon strings demonstrated further increases in attenuation coefficient when they were pressed down against the 13th fret. The motion equation suggests that the results are caused by the loss elastic modulus of the string material. Attenuation coefficients of fluorocarbon strings were smaller than those of nylon strings because of their higher tension and density and smaller diameter, even though their loss elastic moduli were the same.
Investigating the Effect of Gender on 85 ESL DELL UOB Students’ Self-Confidence When Speaking English

There are many psychological factors negatively affecting English language learners. This research aims to investigate the psychological factors, negatively affecting 85 ESL, UOB, DELL students’ lack of self-confidence when speaking English with their classmates, especially classmates of the opposite gender, through conducting 2 classroom observations, administering a self-assessed questionnaire, &amp; analyzing data using IMB SPSS. Moreover, the findings of the 2 classroom observations suggest that students lack self-confidence when speaking English with their classmates is negatively affected regardless of their classmates’ gender, whereas, the questionnaire suggest that the students’ self-confidence is negatively affected when speaking English with classmates of the opposite gender which is principally due to psychological factors. Furthermore, the Bivariate Correlation test conducted via IMB SPSS indicateS that the relationship between the student's gender and their self-confidence when speaking English with classmates of the same and/or opposite gender is negative.
Comparative Analysis of Deep Learning based Vehicle Detection Approaches

Numerous traffic-related problems arise as a result of the exponential growth in the number of vehicles on the road. Vehicle detection is important in many smart transportation applications, including transportation planning, transportation management, traffic signal automation, and autonomous driving. Many researchers have spent a lot of time and effort on it over the last few decades, and they have achieved a lot. In this paper, we compared the performances of major deep learning models: Faster RCNN, YOLOv3, YOLOv4, YOLOv5, and SSD for vehicle detection with variable image size using two different vehicle detection datasets: Highway dataset and MIOTCD. The datasets that are most commonly used in this domain are also analyzed and reviewed. Additionally, we haveemphasized the opportunities and challenges in this domain for the future.
Text Simplification to Specific Readability Levels

The ability to read a document depends on the reader’s skills and the text’s readability level. In this paper, we propose a system that uses deep learning techniques to simplify texts in order to match a reader’s level. We use a novel approach with a reinforcement learning loop that contains a readability classifier. The classifier’s output is used to decide if more simplification is needed, until the desired readability level is reached. The simplification models are trained on data annotated with readability levels from the Newsela corpus. Our simplification models perform at sentence level, to simplify each sentence to meet the specified readability level. We use a version of the Newsela corpus aligned at the sentence level. We also produce an augmented dataset by automatically annotating more pairs of sentences using a readability-level classifier. Our text simplification models achieve better performance than state-of-the-art techniques for this task.
Comparing the Analytical Algorithms for Unsupervised e-News Summarization Using Machine Learning Tactics

The Text mining domain is acquiring importance and is used everywhere as the electronic documents are flooding the internet now a days.One of the significant e-content used by the students giving competitive exams is Marathi e-news articles which can be summarized in an extractive way without changing its meaning.We are trying to develop a system which will help students to extract current information from e-news in a reduced amount of amount of time.The comparison of the two recent and advanced methods is achieved for accentuating on the better one.The extractive summarization is performed using 3 different methods of TextRank algorithm i.e., by using summarize function, by using ratio and by using wordcount.This paper emphases on the analysis of results we got from TextRank algorithm using Gensim Framework with ROUGE method.
Measuring Validity of Interactive Presentation Media Using Content Validity Index (CVI)

Interactive video presentation media is a hybrid of presentation material slides and the teacher's direct interaction in the media via media lectures that are integrated into an application.The purpose of this research is to collect information on the feasibility or validation of digital interactive presentation media geared toward problem solving.The data processed includes both qualitative and quantitative information.To use quantitative descriptive analysis techniques to analyze the research data.Content Validity Index (CVI) is one of the most suitable formulas for testing the content validity of interactive learning media.The media aspect scored 0.93, the material aspect scored 0.95, and the education aspect scored 0.91 based on the validity test results.The test results can be translated as digital interactive learning video media oriented to the problem-solving model, which is very important to develop because it has been tested in terms of media material and testing by education experts.
Practicality Project-Based Light Vehicle Engine Maintenance Module

This study aims to analyze the level of practicality of developing teaching materials in the form of project-based light vehicle engine maintenance modules.This development was carried out due to the unavailability of teaching materials that are in accordance with the characteristics of students in the light vehicle engine maintenance subject at Vocational High School 1 Lahat and are not studentoriented.Therefore, a product in the form of a project-based learning module was developed to solve these problems.This study uses a Research and Development (R&D) approach with the development model being the ADDIE model.The subjects in this study were students of Vocational High School 1 Lahat majoring in light vehicle engineering class II.The instruments used in this study were interview sheets, questionnaire validation sheets, and response questionnaires.The module is validated by 5 experts from expert lecturers and productive teachers.In the practical process, teacher response questionnaires were distributed to 5 teachers who were users of the teaching materials that had been developed.The data were analyzed using percentage analysis and then interpreted using a data interpretation table.The results showed that the level of practicality of teaching materials in the form of a light vehicle engine maintenance module on average based on the teacher's response was 88.75%.The percentage results after being interpreted showed that the product was in the very practical category.It can be concluded that the modules developed are very practical to use and useful as teaching materials to support the learning process, especially the subject of light vehicle engine maintenance optimally.
Minimum Competency Assessment (MCA) Using Android-Based Try Out Application

The main problem in training students' abilities to work on the Minimum Competency Assessment (MCA) try-out questions is that each uses a textbook, where each student sometimes forgets to bring it to school, resulting in some students using two to three MCA books in working on these questions.So it is necessary to develop an Android-based MCA Try Out application.The purpose of this research is to develop an Android-based try-out application to solve student problems so that no more students work on try our questions in two or three.The development of the MCA Try Out application uses the Multimedia Development Life Cycle (MDLC) method based on 6 (six) stages consisting of Concept, Design, Material collecting, Assembly, Testing, and Distribution.The results of this study produced an Android-based MCA try-out application with the initial stages of analyzing application needs, secondly conducting application development display designs, thirdly collecting data and materials, fourthly carrying out material preparation and making applications, fifthly conducting application testing, and finally deploying and deploying the application to users.This MCA application can be used as a medium for practicing MCA try-out questions which are packaged in digital form.The MCA application also carried out a practicality test analysis, where the resulting application scored 80.02% in the practical category.The conclusion of this study is that the Android-based MCA try-out application can answer the problems that occur to students in working try-out questions and are more practical in their use.
The Implementation of Computer Adaptive Test (CAT) Using Expert System to Test Student Competencies in Higher Education

This research is based on a need analysis which finds the phenomenon that conventional learning models are considered not effective enough in developing students' learning potential to the fullest and various kinds of problems related to problems made by lecturers, most of which only come to measuring low-level thinking and problems cognitive mastery skills with the tools and test instruments used.This study aims to create a Computer Adaptive Test (CAT) test model using an expert system as a test measuring tool for students' ability to think, namely Higher Order Thinking/HOT which consists of 3 levels, namely low, middle and high.This research is a Research and Development research with ADDIE method.The data that is processed after the application of this model came from lecturers and students as respondents.The results of the practicality test of this study were 87.5 which were categorized as very practical.It can be concluded that the CAT test model is more effective than the conventional model in testing student competencies in higher education.The result of this study is the application of the CAT test model based on the Expert System as a tool for measuring the ability of Higher Order Thinking/HOT in supporting a practical partial assessment of learning outcomes (Assessment of Learning/AoL).
Attendance Monitoring and Management Using Facial Recognition

When it comes to recognizing a specific person, nothing beats seeing their face. Face recognition technology is useful because it can use a person’s unique facial characteristics as biometrics. Tracking who was present when is often the most challenging part of the job. Teachers traditionally have taken attendance by calling out students and recording their physical presence or absence. These conventional approaches, however, are laborious and time-consuming. In this research, a fully fetched system is being designed, that will take into account student/course creation as well as enrollment. Together with this, attendance will be taken using facial recognition, recorded and sent to the teacher via email. The system uses FisherFace Algorithm with EmguCV Library and WinForms C# for development. In addition, the projection method is based on Fisher’s Linear Discriminant. This method provides well-separated classes in a low-dimensional subspace, despite the fact that the lighting and facial expressions can change substantially. The computing requirements for the Eigenface technique, which is another method, are quite similar to those for the previous methodology. This method involves projecting the picture space in a linear fashion onto a subspace that has fewer dimensions. However, a vast number of studies have demonstrated that the “FisherFace” approach, which was proposed, has error rates that are lower than those of the Eigenface methodology, which was utilized for tests. This was found to be the case when comparing error rates between the two methods.