Reviewerly/exHarmony huggingface dataset:
Intelligent Behavior Recognition based on Gyroscope and Accelerometer

In an aging society, the rate of dementia would be dramatically increased. It is urgent to provide intelligent facilitative devices and appropriate medical care for those who with dementia symptoms, especially for the elders. Accordingly, the primary research target of this study is to automatically detect and monitor behavior of the elderly suffered from dementia. Although previous research has been conducted to identify various human movement patterns, the experimental data were all received from cameras. Furthermore, these related applications are not specifically designed to identify the behavior of the elderly with dementia. In order to improve previous research methods, our study adopted wearable multi-axis sensors to collect corresponding motion data with protected privacy. In addition to collecting raw data containing time series signals, frequency and spatial domain features were applied to detect human behaviors, and Random Forest algorithm was applied to construct a motion prediction model. The classification system for real-time motion recognition contained two main actions of users including holding/putting hand gestures and walking model. Finally, an additional camera on the chest was designed to be triggered and to take a shot according to previous settings. It could enhance practical applicability of the proposed intelligent system to assist the elderly with dementia to find her/his lost objects. This study developed an app for recording individual behaviors, checking the location and time of lost objects, and even recording activity information to provide healthcare professionals with decision-making suggestions for subsequent precision medical care.
TCB Convolution: Ternary-Coded Binarized Convolutions with Fixed-Point Filters

Winograd convolution has almost become a standard for reducing computation complexity for convolutional neural network. However, its efficiency is limited due to the surge in the number of additions. In this paper, we analyze the upper bound of addition count of Winograd convolution with fixed-point filters and an estimated arithmetic weight density, and then proposed a ternary-coded binarized (TCB) convolution that can eliminate all multiplications and reduce more than 99% of addition operations.
Automatic Deep Compression Based on Simplified Swarm Optimization

In recent years, convolutional neural networks (CNNs) have been proven and widely applied in the field of image recognition, including anomaly detection in manufacturing sites, and object detection in autonomous driving. However, the parameters obtained from the CNN increase exponentially with the depth of the network. Therefore, it is difficult to deploy the model in environments with limited computing resources. This study proposes a compression method for CNN by combining Simplified Swarm Optimization(SSO) with structured pruning. Our method can compress VGG16 to approximately 8.3 times smaller without sacrificing accuracy. The more important is, our method uses a heuristic approach to find the optimal pruning scheme without the need for repeated experimental verification.
A Blockchain-Based Transactive Energy Management Scheme for Nano-Grids using Power Flow Coloring

The proliferation of distributed energy resources (DER) and the widespread adoption of the Internet of Things (IoT) in Prosumer Nano-Grids (P-NGs) pose severe challenges in existing energy management schemes, thus necessitating the need for the development of novel solutions which can guarantee system efficiency and security. To address such challenges, we propose a novel scheme leveraging the benefits of distributed power flow coloring, blockchain technology, and Transactive Energy Management (TEM). The proposed system model offers P-NGs a secure, transparent, trustworthy, and efficient energy management and transactions platform that can: maintain the overall voltage stability and specified power ratios against power fluctuations within the P-NG, offer an increase in energy security, provide real-time monitoring, and reduce energy waste.
The Effect of Bitrate Control Methods of MPEG-DASH Streaming on Multi-View Video and Audio QoE

This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the MVV-A system with MPEG-DASH. We conduct a subjective experiment changing available network bandwidth and investigate the effect of the methods on QoE.
AES Encryption Method Based on Chaotic Cryptography

This paper presents a novel chaos-based Advanced Encryption Standard technique for image and data protection that can be applied to industrial controller for the Internet of Things (IoT). The proposed Chaos-based Advanced Encryption Standard (Chaos-AES) has two main features: (1) The rolling chaos key provides the characteristics of not being easy to crack and rolling correction. (2) The encryption rounds are flexible, and the protection level of encrypted data can be adjusted as required. Experimental results show that the proposed Chaos-AES structure is effective and feasible for data protection during transmission.
Multi-Task Deep Learning Model for Autonomous Driving: Object Detection, Semantic Segmentation, and Depth Estimation

In the field of autonomous driving, many models based on deep learning methods have been constructed to solve computer vision tasks related to this domain, such as object detection, semantic segmentation, and depth estimation. Each model can provide different information about the surrounding environment of a vehicle to assist in driving. However, for applying in the real world, more detailed information about the surrounding environment is essentially required. In this study, we propose a model based on the concept of multi-task learning. This model is an encoder-decoder architecture mainly consisting of an encoder using the hard-parameter sharing technique and three decoders for individual tasks. Therefore, this model can handle object detection, semantic segmentation, and depth estimation at the same time. Our proposed multi-task model has been verified to perform well on the public dataset Cityscapes and has higher generalizability than other models.
Named Entity Recognition for Chinese Healthcare Applications

Named Entity Recognition is a fundamental task in information extraction, which locates and classifies defined named entities in unstructured text. Chinese NER is more difficult than English NER. Since there are no separators between Chinese characters, incorrectly segmented entity boundaries will cause error propagation in NER. In this study, named entity recognition is constructed and applied in the Chinese medical domain, where Chinese medical datasets are labeled in BIO format. The Chinese HealthNER Corpus contains 33,89 sentences, of which 2531 sentences are divided into the validation set and 3204 sentences are divided into the test set. This study uses PyTorch Embedding + BiLSTM + CRF, RoBERTa + BiLSTM + CRF, BERT Classifier, and BERT + BiLSTM + CRF for training and compares their model performance. Finally, the BERT + BiLSTM + CRF achieves the best prediction performance with a precision of 91.30%, recall of 89.46%, and F1 score of 90.53%
An Expressive Chat Room with Personalized Fonts Mimicking Actual Inconsistencies in Human Handwriting

Handwritten messages can often impart personal touch to the text. However, this personal touch cannot be achieved in computer-mediated communication. Although features such as emojis, emoticons, or stickers can help enhance the expressiveness in the computer-mediated messages, they cannot lend the messages a unique individuality. In this study, we propose a chatroom website utilizing a person’s handwritten font that can automatically select different glyphs for the same character, thereby creating the perception of human handwriting and providing a personal touch to digital communication.
Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market

Blockchain technology has gained widespread use. However, it faces several challenges including throughput, transaction delay, security, and decentralization. This paper presents the Avalon protocol based on a novel Proof-of-Market (PoM) consensus mechanism to address these issues. PoM is a type of Proof-of-Work (PoW) consensus that incorporates market-driven leader election and shifts PoW from mining pools to consumers based on transactions. The matching incentive mechanism makes PoM incentive compatible. PoM decouples the scalability and security of Bitcoin, which means that Avalon can optimize the capacity and interval of blocks without compromising other performance goals. Our analysis shows that Avalon can tolerate malicious nodes possessing up to <inline-formula><tex-math notation="LaTeX">$\bf{1/3}$</tex-math></inline-formula> of the network's total computational power. Furthermore, the implementation of Avalon is similar to Bitcoin and is highly concise. We evaluate the performance of Avalon through a simulated network of over <inline-formula><tex-math notation="LaTeX">$\bf{1,000}$</tex-math></inline-formula> nodes. Experimental results demonstrate that Avalon can achieve a throughput of <inline-formula><tex-math notation="LaTeX">$\bf{4,000}$</tex-math></inline-formula> TPS (transactions per second), which is significantly better than state-of-the-art schemes ( <inline-formula><tex-math notation="LaTeX">$\bf{10\boldsymbol{\times}}$</tex-math></inline-formula> Bitcoin-NG, <inline-formula><tex-math notation="LaTeX">$\bf{5\boldsymbol{\times}}$</tex-math></inline-formula> ByzCoin, and <inline-formula><tex-math notation="LaTeX">$\bf{4\boldsymbol{\times}}$</tex-math></inline-formula> Algorand). Additionally, it has a transaction confirmation delay of up to <inline-formula><tex-math notation="LaTeX">$\bf{40}$</tex-math></inline-formula> s, which is twice better than Bitcoin-NG and ByzCoin while experiencing only minimal blockchain splits and maintaining excellent decentralization.
PRAT: Accurate object tracking based on progressive attention

Object tracking aims to estimate the position of a given object in subsequent video sequences. One of the research focuses in tracking is feature fusion as the similar response maps generated by feature fusion can significantly affect tracking accuracy. However, traditional naive correlation and depthwise correlation blur the spatial information and do not perform well in low resolution, similar objects, partial occlusion and other scenes. In this paper, we propose a progressive attention tracker called PRAT. It performs sufficient similarity learning between the template and search region to achieve more accurate object tracking. Specifically, PRAT performs self-enhancement on template features, and uses unidirectional cross enhancement and progressive enhancement to fuse template features into search features. Therefore, the search region features have the ability of target perception. In addition, we also design a convolution-based network to replace the FFN in the original Transformer to enhance local semantics. Experiments on six challenging benchmarks show that our PRAT achieves state-of-the-art performance. Particularly, on the challenging UAV123, PRAT sets a new record with 0.703 SUC score. PRAT runs at 63 fps on GPU.
Fast fractional-order polar linear canonical transform: Theory and application

Zero-watermarking is a promising technology for image copyright protection due to its excellent imperceptibility and robustness. Compared with deep learning (DL)-based zero-watermarking schemes which are time-consuming and unable to respond in real-time, the moment-based zero-watermarking schemes do not have the aforementioned drawbacks and have drawn great attention with their strong description ability and geometric invariance. However, most existing moment-based zero-watermarking methods bear a number of drawbacks, in particular: (i) Direct computation of image moments is inefficient and inaccurate; (ii) They have difficulty obtaining the favorable trade-off between robustness and discriminability; (iii) Generating pseudo-random numbers based on traditional logistic map results in their lower security performance. To deal with those issues, this paper introduces a new image zero-watermarking method that relies on fast fractional-order polar linear canonical transform (FFrPLCT) features and asymmetric tent map. Firstly, we propose a novel computation strategy called fast polar linear Canonical transform (FPLCT), to save the calculation time of moments. Secondly, we define the fast fractional-order polar linear Canonical transform (FFrPLCT), which is characterized by the generic nature and time-frequency analysis capability. Thirdly, we present the FFrPLCT mixed low-order features by fully exploiting the time-frequency property of FFrPLCT, to improve both the robustness and discriminability of image representation. Finally, we develop a new image zero-watermarking method using FFrPLCT domain mixed low-order features and asymmetric tent map. Experimental results and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed image zero-watermarking scheme.
A Proposal for Efficient Multiplication and Inverse Calculation in Streamlined NTRU Prime

With the development of quantum computers, widely used cryptosystems will be able broken in the near future. Therefore, researches on Post-Quantum Cryptography (PQC) has been actively conducted. In this paper, a method to improve the calculation of Streamlined NTRU Prime, which is one of the PQC. The authors propose to employ the Cyclic Vector Multiplication Algorithm (CVMA), which uses a nomal basis called Type-II Optimal Normal Basis, with the parameters of Streamlined NTRU Prime. The processing speed of multiplication and inversion are compared with those of previous studies. As a result, the multiplication by the CVMA was 58% faster than the previous method with a certain condition. On the other hand, the inverse calculation was 50 times slower than the previous method due to the lack of optimizations.
A Medical Domain Visual Question Generation Model via Large Language Model

This paper proposes a medical visual question generation model for generating higher-quality questions from medical images. The visual question generation model can guide the diagnostic process and improve the utilization of medical resources by reducing the dependence on physician involvement. Our model uses cross-attention and the large language model to preserve inherent information and addresses the issue of inferior generation performance in the medical domain due to a lack of data. We also control the category of generated questions by setting guidance sentences that include interrogative words. The experimental results demonstrate that our method generates higher-quality questions than previous approaches.
Wearing Mask Detection Based on T-shaped Face

This study proposes an intelligent mask detection system. During the continuous epidemic situation, it is necessary to detect whether visitors are wearing masks. When the body temperature sensor sounds, if the face detection system detects a face, it indicates that the visitor is not wearing a mask as required; otherwise, it means that the face is wearing a mask. After many live simulation tests by the students of our laboratory, it was found that this research can indeed achieve the expected goal with a 98.6 % recognition rate and effectively remind visitors to wear their masks.
Process-Product-Resource based Requirement Model for Discrete Manufacturing Systems

In recent years, the development of the Industrial Internet accelerates the product time to market and boosts mass customization production. These lead to system production quick requirement changes. Faced with these changes, the existing requirement modeling methods will take a long development time and cannot adapt to rapid requirement changes. In this paper, a Process-Product-Resource based requirement modeling method for discrete manufacturing systems is proposed, which includes the structured requirements elicitation technology and the requirements supplement method. The requirement modeling method is verified with a discrete manufacturing case. The proposed requirement modeling method offers an efficient and flexible solution for rapid requirement modeling and maximizing resource utilization to reduce the time-to-market of products significantly.
Positioned Quality Evaluation of Posterior Breast by Consensus Decision-making from Multi-candidate Classification Results

This paper presents a positioned quality evaluation method for posterior breasts in Craniocaudal-view mammograms. The proposed method employs a convolutional neural architecture (CNN) to recognize multiple candidates extracted from the posterior-inner and posterior-outer breasts and determines whether visualize fat posterior to glandular tissues. To find the proper classifier, we experiment with the two-category classifications with various CNN models, including, Squeezenet, Densenet-121, Alexnet, Vgg-19, and Resnet-101. Our method determines the evaluation result of posterior breasts by consensus decision-making from the classification results of multiple candidates. The experiment results demonstrate that Densenet-121 is the best choice for this work.
A Consideration of Averaging the Calculation Cost of CVMA for A Secure Session based Data Transmission

The authors focus on a Cyclic Vector Multiplication Algorithm that can randomize a basis parameter for each encryption. On the other hand, the calculation cost of vector multiplication depends on the basis parameter. The calculation cost should be constant for strict security such as a countermeasure against side-channel attack. The authors propose a method for averaging the calculation cost for vector multiplication.
Intelligent Lighting Control under AI Architecture

This study utilized an optimized YOLOv3 training model to improve the accuracy of image recognition technology to over 90% through the data results obtained from the grayscale image and personnel recognition technology of the surveillance system at the experimental field - Baoshan Library. In addition, the system's control screen, combined with the verification field's system architecture, achieved the function of detecting and lighting fixtures within 1.4 to 1.6 seconds when personnel appeared within the detection system range, and the system could turn off the lighting fixtures within 1.4 to 1.9 seconds when personnel disappeared from the detection system.
Design and Development of a Music Game on Smartphone with Three-axis Gyroscope

In recent years, mobile music games have become a popular form of entertainment, but gameplay is limited to the operation on the screen. The use of three-axis gyroscope sensing technology in mobile games is very common, but not in music games. This study proposes a motion-sensing music rhythm game, using three-axis gyroscope sensing to increase the judgement of operation methods and thereby increase the game play. Players must hold the mobile phone with both hands, and as specific beat notes appear in the game interface from the center to the circle edge, they are required to rotate the body of the mobile phone to correspond to the correct area of judgement. The judgement area is responsible for detecting the type of note and determining the direction in which the user should rotate. Through this game, it is expected to bring new gaming experiences to players.
Proficiency-level Estimation Using Heterogeneous Features via Label Dequantized CCA

A proficiency-level estimation method using heterogeneous features by supervised fractional-order embedding multi-view canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) is presented in this paper. The proposed method constructs a common latent space by SFEMCCA-OLD, and estimates proficiency-level using projected features to the common latent space. Then we can utilize new features that have the powerful representation ability since SFEMCCA-OLD enables the construction of the common latent space without dimensionality limitation. Consequently, the performance improvement of the proficiency-level estimation becomes feasible.
Enhancing Academic Writing: A Smart Citation Recommendation System Leveraging BERT and Weighted Bag-of-Words Models

In the current era, there has been a rapid advancement in the field of information technology. When scholars compose their research papers, it can be challenging for them to conduct an exhaustive examination of the existing literature. Therefore, it is essential to create a system that can suggest suitable citations to researchers during the writing process. This study aims to recommend relevant citations to researchers by utilizing weighted bag of words and BERT models. The proposed mechanism provides the following benefits: (1) Clear and transparent representation of document vectors; (2) A range of diverse natural language processing techniques, and (3) Avoidance of redundant citation suggestions.
Multiple Object Detection Architecture-based Comparative Performance for Safe Construction Scenario

Artificial intelligence has access to every field in this era. Currently, you have access to everything, from simple tasks to quick calculations. The construction industry is one of them. Safety work, installation, and construction are also part of the drive. Demonstrating the pipeline to date does not prepare as comprehensive an assessment as it could. To this end, we benchmark performance using several cutting-edge approaches that have recently the best performance from state-of-the-art method studies, such as YOLOv5x, YOLOv6l, YOLOv7x, and YOLOv8x. The result show that the recent YOLOv8x accomplish the most effective at generating region of interest box comprehensively. Whereas some existing approaches, like YOLOv5x and v7x, get the highest capacity at classification instead.
MADnet: a Multiple Attention Decoder Network for Segmentation of Remote Sensing Images

Segmentation of high-resolution remote sensing images is a challenging task. Some recent studies have used complex convolutional neural networks to address this problem, and they can only extract multi-scale features and improve object boundary discrimination. However, these neural networks are difficult to train due to high complexity. In this paper, we propose two new attention modules, Local Channel Spatial Attention and 16-piece Local Channel Spatial Attention, which can focus on improving the feature representation of each local area. In order to effectively utilize multi-scale features, we also propose a Twice Decoder Module, which is integrated into our model to improve the discrimination ability of target boundary. Our proposed method is verified through extensive experiments and experimental results show that our model can better improve the overall accuracy compared with other state-of-the-art methods.