Reviewerly/exHarmony huggingface dataset:
Convolution Neural Network Approach for Facial Mask Detection

The utilization of Convolutional Neural Network (CNN) in facial mask detection serves as a method to enforce health protocols. In response to the COVID-19 pandemic and the significance of adhering to safety measures, this study employed a You Only Look Once (YOLO) v8 to extract essential characteristics from facial images and categorize into three classes: images with face masks, images without face masks, and images showing improper use of face masks. The vitality of this approach lies in its potential to contribute significantly to public health and safety. By automating the process of mask detection YOLO v8, the model can be deployed in various settings, such as airports, hospitals, workplaces, and public spaces, to identify compliance with mask-wearing guidelines. With a mean average precision (mAP) of 88.2% at 300 epochs, the object detection model demonstrates a positive and excellent performance. The evaluation metrics reveal the model's exceptional precision rate of 96.2%, indicating its ability to confidently and accurately identify positive instances of face masks with minimal false positives. However, the study also acknowledges a recall rate of 73.3%, suggesting that there is room for improvement in capturing all positive instances, and the model may miss some objects during detection.
Face Reenactment with Diffusion Model and Its Application to Video Compression

In this study, we present a face video compression scheme featuring extremely low bit rates. In our proposed method, a face image is reconstructed from the previous frame by recursively using a diffusion model, thereby reducing the trade-off between person identification and facial expression generation while achieving smoothness between frames. Experimental results show that our proposed method outperforms baseline methods in terms of image quality.
Marker-Less Athletic Posture Detection System Based on Human Pose Estimation via Multi Cameras for Bowling

This paper presents a sensorless athletic posture detection system for bowling by utilizing multi-view human pose estimation with YOLO v7. Firstly, audio synchronization is used to ensure the accurate alignment of the captured data. Afterward, the human joint locations from the image being taken by multiple cameras are detected. And then, a three-dimensional (3D) posture is generated by synthesizing both joint locations. This approach eliminates the need for markers, addressing the challenge of acquiring accurate posture data without impeding gameplay. Experimental results show that the system demonstrates performance in accurately estimating body joint positions and angles, enabling precise analysis of key aspects such as balance and arm swing.
Feature Transfer Block for Feature Fusion in Lightweight Object Detectors

Modern object detectors often use multi-scale detection to improve their performance. To enrich the semantic information from the original feature map generated by backbone, various pyramid-liked architectures such as FPN and PANet are used as the neck of an object detector. However, most researches focus on the macro design of the topology connection of the pyramid structure while putting less effort into the micro design of feature fusion strategy. To address this, we propose a novel fusion strategy that adopts a cross-attention fashion. We named it Feature Transfer Block (FTB) as it explicitly selects semantic information from different scales of feature maps for fusion. This design is cost-efficient and easy to integrate with current designs. Experimental results demonstrate a moderate improvement of YOLOX series on COCO and CrowdHuman when implemented with FTB.
Analysis of Relationship Between Non-Identifiable TV Viewing History Data and Web Search Trends

Non-identifiable viewing history data can be collected from internet-connected TVs, and it is possible to grasp information such as IP addresses and viewing times without identifying individuals. Yomiuri Telecasting Corporation has collected data from approximately 3.5 million TV units, but it has been considered challenging to find new value from these data alone. Therefore, in this study, we combined non-identifiable viewing history data with internet search data, such as Google Trends, to analyze the impact of TV commercials on viewers’ internet search behavior. As a result, we found that as the number of commercial broadcasts increased, search behavior also increased, and we were able to identify the broadcast times of commercials that efficiently led to search behavior.
Three-Dimensional Baseball Strike Judgement by Monocular Video from Umpire’s Viewpoint

In recent years, automatic judging is being introduced in many sports. In professional baseball, in particular, advanced equipment such as laser scanner and multiple cameras have been introduced. However, amateur and youth baseball teams cannot afford such expensive systems. Therefore, we propose a method to judge strike in three dimensions using a monocular video. The method consists of Camera Calibration block, Strike Zone Estimation block and Ball Trajectory Estimation block. Experiment results confirmed that automatic strike judgement in 3D space is possible by using the location information of the home base. Our method uses only one camera, reducing equipment cost and user effort.
Improve User Experience of Augmented Reality Tour Guide Application by Using ChatGPT

Augmented Reality (AR) tour guide applications can effectively integrate digital content with the environment and objects in the real world. However, they are limited by the presentation of pre-arranged information and the length of content. Additionally, these applications lack an efficient pathway to acquire relevant information and are restricted by the limited screen space of the device itself. As a result, there is still room for further improvement in user experience. In this study, the use of a Natural User Interface (NUI) with interactive content generation can provide a more convenient way to gather information while freeing up screen space and allowing information to flow. In this study, we proposed to integrate the ChatGPT within OpenAI with AR tour guide applications to enhance the user experience by providing generated informative content with dialogue interaction. A pilot study results show that the proposed integration of this study improves the accessibility of information that users are interested in, thereby enhancing the overall user experience.
A Study of Speech Recognition, Speech Translation, and Speech Summarization of TED English Lectures

Our research focuses on developing an automatic speech recognition system for English lectures, which involves summarizing the content and providing Japanese subtitles. Subtitling the entire audio of an English lecture could hinder comprehension and readability, so a summarization system is desired. By employing the DNN-HMM based speech recognition system, we achieved an 88% word accuracy for recognizing TED lecture speeches. Speech translation results showed a lower BLEU score of approximately 14% compared to text translation. Conversely, speech summarization proved its robustness to speech recognition errors, as the extracted important sentences were almost the same as those in the text summarization process.
Time Related Information Description of Business Hours of Clinics for Regional RDF Data

This report aims to efficiently create RDF data to promote regional open data and summarizes the results of a study of data from Sendagaya, Shibuya-ku, Tokyo, where Tsuda University is located, as a case study. However, it was complex to describe the time schedule of the medical institutions because their hours of operation and days off. In this study, we extended the provisions of the IMI Common Vocabulary Infrastructure to introduce the concept of days of the week, making it easier to specify the start and end times of medical services at medical institutions, whose hours of operation often differ for each day of the week, and enabling more efficient searching and other operations. Comparing this with the Schema.org vocabulary, we were able to construct RDF data with almost the same scale of triples and confirmed the functionality and operation of the newly created medical institution RDF data.
Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras

Vision-based applications have been gradually applied to city management demands in recent years. This study proposes an enhanced speed estimation method that uses video data from publicly accessible government surveillance cameras. By incorporating YOLO and DeepSORT, the system deduced each vehicle's position and estimated their speed accurately in a regression manner. According to the preliminary experimental result, the proposed method can produce data regarding vehicle counting and speed estimation; it showed that the proposed method could gain comparable content to the ground truth of the transportation web service platform.
Speed-Up Method for Magic Square Generation Using Multi-Stage Evolutionary Strategy Cascaded by Satisfiable Individuals for Magic Sum

In this paper, we proposes a multi-stage evolutionary strategy that hierarchically classifies the constraints of magic squares into three stages of rows, columns, and diagonals, and uses the individuals satisfying each layer’s constraints as the initial individuals for the next layer. The proposed method’s efficiency is evidenced through experimental comparison of execution time and generations with a previous study, demonstrating substantial reductions in execution time.
Improving Accuracy for Cat Detection Utilizing Learning Methods

To make it easier to identify the number of cats, we propose a cat detection system using YOLOv7. To demonstrate the effectiveness of our proposed method, a comparison experiment is conducted with a system using YOLOv3. As a result, our proposed method show better results in mAP and FPS.
Visitor Counting in a Museum with a 2D LiDAR Sensor

We propose a visitor counting method with a low-cost two-dimensional LiDAR sensor. Our visitor tracking box also uses two detection planes reflected by a mirror, vertical and one for counting visitors and estimating visitor heights, and horizontal one for counting and tracking visitor positions. We conducted a series of evaluation experiments at a science museum, and had an accuracy of approximately 81.0% in estimating the total number of visitors, while with less accuracy in counting children. Our vertical sensing plane allows us to estimate visitor heights at a very high accuracy rate of 96.1% for people taller than 120 (cm).
Construction of an Information Security Learning Environment for Beginners toward Wide-Area Deployment and Proposal for Utilization of Logs

In recent years, the rapid growth of a wide variety of ICT content has created a need to optimize the provision, development, and configuration of information security learning environments and content. Therefore, this study aims to develop a learning environment for information security for beginners who aim for continuous development through the management of a technical community. Currently, there are many examples of teacher-led workshops. However, more is needed to build an environment where participants learn from each other spontaneously. In this report, we present the construction and results of a trial of a place to learn together for a group of people beginning to be interested in information security, mainly in online technical events. The nature of the gameplay and the use of pseudo-teachers will also be mentioned for individual optimization. Furthermore, elements that can be utilized for wide-area development will be clarified, and the possibility of log data analysis using simple machine learning and recommendation functions will be discussed. This effort verifies the usefulness of cooperative learning and playing together in a sustainable technical learning community.
Analysis of Embedding Space of Speech Synthesis System as High-Dimension, Low-Sample-Size Data

Principal component analysis (PCA) is often used to visualize and cluster embedding vectors of deep-learning models; however large noise can degrade the results of PCA when the dimension of the embedding vectors is much larger than the data size. These types of data are referred to as high-dimension, low-sample-size (HDSS) data, where the number of data dimension is much larger than the sample size, and they must be treated differently than typical data, because the eigenvalues of a HDSS data covariance matrix are impacted by large noise. Previous studies on HDSS data proposed the noise reduction methodology (NRM) and cross-data matrix methodology (CDM) to reduce such effects. In this study, we apply NRM and CDM to visualize the embedding vectors of an end-to-end speech synthesis system. Experimental results show that the NRM can estimate reduced eigenvalues by eliminating the large noise and that the CDM can provide a more reasonable visualization, although the results are dependent on the initial CDM clusters. In addition, we propose a method for estimating the power exponent of a general spiked model to assess whether the estimated eigenvalues are consistent.
Camera Motion Generation Method Based on Performer's Position for Performance Filming

The role of camera techniques in video quality, a crucial component of visual expression, cannot be overstated. Unlike film or drama productions, live performances offer limited opportunities for reshoots, underscoring the need for meticulous preplanning of camera movements. The proposed method leverages deep neural networks to learn and replicate the camera's positions and postures in response to performers' placements and orientations on stage, thereby mimicking the tacit knowledge of professional camerawork. The method unfolds in two phases: Initially, a network is used to determine camera placements and postures based on the performers' positions and orientations as indicated in the stage script. Subsequently, a second network generates the live camera movements during the performance, factoring in both the performers' placements and orientations and the preliminary camera placements and postures determined by the first network. The architecture of the network incorporates a Transformer that infuses a relative position representation into the input data, proving its ability to more accurately learn camera motion features compared to the standard Transformer.
Is the Blockchain Useful for Sharing Sensitive Data?

Leakage of sensitive data frequently occurs in network systems. If the secret key is leaked, the original data can be also leaked even if sensitive data are encrypted. In this study, we propose a secure data-sharing framework in which the authorization and authentication for accessing sensitive data are automatically combined together by smart contracts. Through the above combination, the framework prevents access to sensitive data even if the private key is leaked. Therefore, our blockchain-based framework is effective for securely sharing sensitive data.
Robust Tokenizer for Vision Transformer

Vision Transformer is the State of the Art(SOTA) method in the image processing domain, but its robustness against adversarial examples still needs to be improved. This paper proposes Tokenizer called "ArbViT" (Arbitrary patches for Visual Transformer). To enhance the robustness to adversarial attacks, we introduce our Tokenizer for feature processing before Transformer. Specifically, when converting image patches into embedded features (or tokens), we do not use weights but reorder the features. In experiments, we show the results by introducing ArbViT and by the conventional method, ViT. Although no clear difference in accuracy between the model using ArbViT and the original ViT, we show that the generated model by introducing ArbViT is more robust against the adversarial attacks than the method using the conventional Tokenizer.
Developing a Knowledge Organization System for Ethnic Groups in Lao PDR through Linked Open Data Techniques

The objective of this study is to propose a knowledge organization system (KOS) for the ethnic groups of Laos by utilizing linked open data (LOD) to standardize the metadata. Data of 49 Laos ethnic groups and the techniques of Linked Open Data were used. Linked data of ethnic groups in Lao PDR from other external standardized data sources, including DBpedia and Wikidata, was found. The results indicate that there is limited an information on the ethnic groups of Laos in online databases. This study aims to link and improve data on Laos ethnic groups by utilizing ontology and reconciliation techniques to link incomplete or outdated data, as well as using OpenRefine and RDF extension to link the source database. Although the country has implemented policies to promote the national identity and the tourism economy, much of the related information remains inaccessible to the public as it is only available in official documents. The available information in the online database system would be compared to foreign languages like Vietnamese, Thai, etc., because these areas and ethnic groups were close to each other, and the languages were in the same family.
Learning to summarize and answer questions about a virtual robot’s past actions

Abstract When robots perform long action sequences, users will want to easily and reliably find out what they have done. We therefore demonstrate the task of learning to summarize and answer questions about a robot agent’s past actions using natural language alone. A single system with a large language model at its core is trained to both summarize and answer questions about action sequences given ego-centric video frames of a virtual robot and a question prompt. To enable training of question answering, we develop a method to automatically generate English-language questions and answers about objects, actions, and the temporal order in which actions occurred during episodes of robot action in the virtual environment. Training one model to both summarize and answer questions enables zero-shot transfer of representations of objects learned through question answering to improved action summarization.
Deep learning-based solution for smart contract vulnerabilities detection

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.
On-chip polarization-encoded single-qubit gates with twisted waveguides

Integrated photonics is a remarkable platform for scalable classical and quantum light-based information processing. However, polarization manipulation on a chip despite of its fundamental significance in information processing remains elusive. Polarization manipulation capabilities have been recently demonstrated in femtosecond laser-inscribed twisted waveguides, although the systematic theoretical description of polarization manipulation has not been established for this architecture. In this paper we develop a rigorous theory of a twisted waveguide unveiling its eigenmodes and transmission matrix in the closed form. Utilizing the developed theory, we demonstrate that twisted waveguides can realize virtually arbitrary polarization transformations while satisfying reasonable design constraints. This fact combined with low cost and ease of prototyping of laser inscribed photonic integrated circuits allows us to suggest twisted waveguide as a robust building block for on-chip polarization-encoded information processing.
Analysis of Adaptability Difference of Ecological Migration Based on Environmental Distance

Background and aims: Ecological migration refers to the population who originally lived in an area with a harsh natural environment and basically no living conditions, moved out of their original residence, settled in the new area, and rebuilt their homes. The large settlement formed after the migration is the ecological migration area. Thus, this study aims to analyze the orientation accumulation of emotion, behavior, and cognition. Materials and methods: Takes the ecological migration group in Minning Town, Ningxia as an example, from the three dimensions of emotion, behavior, and cognition, adopts the concept of "environmental distance", and comprehensively uses quantitative research methods such as the questionnaire method, environmental distance model and multiple linear regression model. Results: The results show that to more scientifically explore the degree of change of teachers, students, parents, and administrators due to environmental changes before and after poverty alleviation in basic education in ecological immigrant areas, as well as the direct and indirect effects of factors affecting the adaptability of the basic education environmentConclusions: The author hopes to put forward the development path and strategy of basic education in ecological immigrant areas.