Reviewerly/exHarmony huggingface dataset:
Straightforward Clustering and Identification of Morphological-Level Pancreatic Carcinoma

Invisibility is the main challenge of diagnosis that there are usually no symptoms of Pancreatic carcinoma (Pc) at the early stage. Therefore, the early and accurate identification of Pc is crucial which is expected to capture the imperceptible region of interest of pathological images (PIs) to support diagnosis and further treatment. This paper presents an attempt at a straightforward clustering autoencoder framework for Pc identification alternative. With merging the Marginal Entropy and Conditional Entropy into the framework, the proposed method shows promise of clustering (optimal cluster is 20) experimenting with an authoritative PIs dataset of Pc. Meanwhile, we design a tool used for tracking the attention of clustering decisions, to provide a certain interpretability for the clinical diagnosis. The proposed method is a plain but potential framework that can be used in further development of a complex deep architecture for more accurate cancer-related research.
Image Generation from Brainwaves using Dual Generative Adversarial Training

Representing content of brainwaves captured through a non-invasive EEG device is of practical importance to various diagnostic applications. Although existing generative adversarial networks can obtain decent results in certain fields of neuroscience, performance declines significantly when representing content of brainwaves due to the insufficiency of real brainwaves. This lets us introduce a dual generative adversarial training paradigm to learn a smooth transition between brainwave and target image distributions. Our experiments demonstrate that our training approach outperforms the state-of-the-art training strategy on three benchmark datasets.
Real-Time Monitoring System for Detecting Abnormal Operation Procedures

In this paper, we propose a task monitoring system that exemplifies man-machine collaboration in a small workshop to make real-time judgments and monitor whether the operator’s operation procedures comply with the pre-defined rules. The proposed system comprises an image target detection module, a hand action recognition module, and a procedure comparison module, where YOLOv4 is used to detect relevant objects in the scene, and SlowFast is used to recognize hand actions so as to create an action base to describe the corresponding relationship between the object and action. A Standard Operating Procedures (SOP) with pre-established rules is then used to compare against the action base established by the proposed system to check if the operation procedure flow of the human operator complies with the rules.
Time Domain Analysis for Extracting Spatial Distribution of Visible Light IDs

Extraction of spatial distribution of visible light IDs is useful to recognize spatially multiplexed ID signals which can be used to support automated rescue after a disaster. In this study, we investigate a time domain analysis to extract spatial distribution of visible light IDs. This method is based on an auto-correlation of temporal signals detected by using an image sensor and expected to be useful for real-time operation although both ID signal length and ID signal rate are required to be predefined. Our findings will be an important step in implementing a rescue support system with visible light IDs.
An Extended Visualization Model for Silent Cyber Risks Considering Non-cyber Aspects

The development of technologies such as the Internet of Things that enable various devices to be connected to a network has led to the emergence of a new type of cyber-attack risk, silent cyber risks. Such risks are difficult to recognize, and it is difficult to implement security measures in advance other than purchasing cyber insurance. We propose using a state transition diagram to proactively implement measures against silent cyber risks. In this paper, we newly present an extended visualization model based on a state transition diagram that takes into account not only cyber risks but also non-cyber risks such as physical and psychological risks. Its effectiveness was qualitatively clarified on the basis of case studies.
Metamorphic Testing for Edge Real-Time Face Recognition and Intrusion Detection Solution

Smart city applications are using extensively artificial intelligence for decision-making. Among the fields of application are facial recognition and intrusion detection. The subject is old, but processing techniques and hardware are constantly evolving. This paper will review the most widely known practices and apply them to a smart parking and intrusion detection system using the “JetsonNano” board. Nowadays, quality assurance for machine learning systems is becoming increasingly important. This article focuses on detecting bugs in implementing two classical face recognition algorithms: Eigenface (EF) and Local binary pattern histogram (LBPH). We tested the efficiency of our system using metamorphic testing depending on many factors: weather conditions, pixel noise, and distortion.
Precise Gymnastic Scoring from TV Playback

This paper proposes a method to rate the quality and completion of movements through the television playback of gymnastic competitions. For Action Quality Assessment (AQA) of gymnastics competitions, the position, height, and angle of the athlete’s joints and bones relative to the ground are generally extracted and evaluated by professional referees. This method requires sophisticated movement tracking devices and special labs. Therefore, this paper propose a system that accurately assesses difficulty, execution, and penalty scores directly from TV playback in gymnastics competitions. We designed multiple models and loss functions based on the envisioned task and verified their effectiveness in score evaluation.
Cross-platform Recommendation Considering Common Users’ Preferences Based on Preference Propagation GraphNet

In this paper, we propose a cross-platform recommendation (CPR) considering common users’ preferences. Conventional CPR approaches focus on a situation where users utilize only one platform. However, with the growth of subscription services in real-world applications, more users utilize multiple platforms in search of platform-specific contents. Therefore, we construct a CPR method assuming that users utilize multiple platforms. Inspired by the concept of the cross-domain recommendation that leverages other domains’ information via common users, the proposed method enables propagation of information from another platform through interactions of common users. As a result, the proposed method can estimate users’ preferences and improve the recommendation performance even if they have a few interactions. Experiments on an open dataset show the effectiveness of the proposed method.
Compact Clean Unit System Platform (CUSP) for Quality-of-life Improvement

New closed-air-system enabled by clean unit system platform (CUSP) and Gas-Exchange-Membrane/Gas–Exchange-Unit (GEM/GEU) has been demonstrated to be versatile for improving health by reducing air-borne particle/microbe density. Also, CUSP can help us check sleep quality and analyze metabolism, movement-consciously, with GEU. Based upon two scaling rules, CUSP with GEU can be used for homes, hospitals, schools, and vehicles for good health and Quality-of-Life improvement.
Support for Hand Writing Practice with Emphasized Visual Instructions

We propose practice support for hand written characters with a pen-based tablet. The difference between the user’s input and the model character is emphasized to better understand parts which need to be improved. Our system is based on bounding boxes to match the area and location of written characters. We also normalize the writing velocity of each stroke to stably find several maxima in the distance between character strokes. 24 people participated in our series of experiments where participants were asked to practice characters with either of 1) papers, 2) the proposed system, and 3) ours but without emphasized difference. Our system shows successful results in 2) and 3), while emphasizing difference has little effects with characters studied in our experiments.
Semantic Segmentation of Equirectangular Images with UniFuse

UniFuse is a neural network that has achieved high accuracy in generating depth maps for equirectangular images. In this paper, we modified UniFuse to semantic segmentation task of equirectangular images and carried out comparative experiments with the popular UNet neural network. The experimental results showed the effectiveness of our proposed method.
Analysis of People’s Dissatisfaction on COVID-19 using Discontent Questionnaire Data

The COVID-19 outbreak and accompanying policies for prevention and control, such as lockdown and movement constraints, have influenced many areas of society and every aspect of everyone’s life and work. To find out factors that have the biggest influences on people’s lives, we use the BERT model to classify “Discontent Questionnaire Data on COVID-19” and apply machine learning methods to evaluate the accuracy of the results. The results show the top three influencing factors are work, stress, and worry about the future, and the classification results show a high degree of consistency and correlation.
Automatic normality estimation of 3D printers using peeling sounds

Liquid crystal display type of three-dimensional (3D) printers generates a small peeling sound when creating 3D objects. The sound is generated when the 3D printer peels off the curable resin from a fluorinated ethylene propylene film. Moreover, this peeling sound occurs continuously when the 3D objects are properly created. Thus, if we can properly distinguish this peeling sound from the mechanical noise of the 3D printer, we can prevent the loss of time and increase in material cost due to modeling failure. Thus, in this paper, we propose a method to automatically identify the normal operation of a 3D printer using the peeling sound in the operating noise. To identify the normality of the 3D printer, we convert the obtained machine noise from the 3D printer into short-time spectrogram images and build a machine learning model using the created spectrogram images. Moreover, we evaluate the normality of the 3D printer from its machine noises using the learning model.
A Self-Supervised Learning-Based Computer Recognition Platform for Orchidaceae Classification

This paper proposes an orchid classification platform based on a self-supervised learning model. Users can view orchid information through the computer application platform and upload unknown orchid images to the platform. The orchid information is displayed on the visualization window. In terms of the recognition of orchids, fine-grained classification is used to classify orchids. The fine-grained classification model is usually applied to plants. This model is more suitable for the classification of hundreds of orchid species. The fine-grained classification of orchids is suitable for this task, and the classification system is built on the server side. The user only needs to install the client-side application and operate it directly.
Cross-modal Image Retrieval Considering Semantic Relationships with Object Information

Cross-modal image retrieval methods enable users to find desired images from a text query via the embedding space. However, most existing methods do not consider the semantically similar texts and images in the embedding space. In this paper, we propose a novel cross-modal image retrieval method that can consider the relationships between semantically similar texts and images. Our method constructs an embedding space consistent with the semantic similarity by using the object information in images. Experimental results verify that our method is effective for keeping the semantically similar texts and images close in the embedding space compared to the existing methods.
A Robust Learning Framework Using Self-supervised Learning for Learning with Noisy Labels

Deep neural networks (DNNs) have achieved remarkable performance in various fields; however, a drop in accuracy is inevitable when training on data with noisy labels. In recent years, researchers on learning with noisy labels have focused on sample selection that uses loss values for each sample to divide the training data into clean and noisy samples. However, the performance of noisy label removal in a sample selection approach depends on the learning progress of the initial learning step. Furthermore, as the number of labeled samples used for learning a DNN decreases, the learning efficiency decreases. We propose a learning framework that improves the performance of the initial learning step of a DNN and the learning efficiency until convergence by introducing SimCLR as a pre-training method. The experimental results on the CIFAR-100 dataset containing noisy labels with the reproduced symmetric noise of various noise rates demonstrate the effectiveness of the proposed method.
An Adaptive Partitioning Method for Distributed Stream Processing Systems

In recent years, stream processing systems have attracted attention for their ability to process big data, that is constantly generated, with low latency. However, the performance of stream processing deteriorates due to the bias in the amount of input data for parallel processing. This paper proposes an adaptive partitioning method based on the latency of each parallel process. Experimental results using a prototype system show that the latency bias can be reduced to half.
Simple Adaptive HDR System using Gamma Correction Circuit with Variable Gamma Value

To apply to simple surveillance systems, which are equipped with small camera device such as web camera, we proposed a small-scale adaptive HDR system. Furthermore, we developed new gamma correction circuit to be installed in our system to correct image degradation in the camera image.
Smart Augmented Reality Self-Guiding Application for Museum Visitors of Different Age Groups

At present, museums are attempting to create interactive augmented reality (AR) contents for different target audiences to enhance the self-guided visiting experience. Such AR applications often focus on one group at a time to ensure proper content. However, in many cases, young children share a device with their adult family members, which makes the switching of the AR content a deciding factor for an enjoyable shared AR self-guided tour. This study presents a working prototype that uses a gyroscope to detect the user’s height and automatically switch the AR content to that suitable for the user’s knowledge level. The field test exhibits promising results, suggesting that the implementation of natural user interface in AR applications can ensure a smooth shared self-guided tour.
Improving Accuracy of Position Information in Egocentric Pose Estimation Methods

In Virtual Reality (VR) with Head Mounted Displays (HMDs), detecting how the wearer moves is very important. If these movements are reflected in the VR with the HMD, the reality of the user's experience will be enhanced. The HMD detects the wearer's movements with sensors mounted on it and it’s controller. However, this alone has limitations in reflecting the wearer’s movements. Egocentric pose estimation can be used without hardware modification and is expected to improve the accuracy of wearer motion detection. Even today, there are ways to improve the accuracy of detection of the wearer's movements, but all of them require additional devices or a large space, which also detracts from the ease of use. Therefore, the ability to use the system without hardware changes is very important in that it does not compromise ease of use. However, there is no VR that uses egocentric pose estimation, indicating that the accuracy of existing methods is not suitable for use in VR. Therefore, we propose a method to improve the accuracy of location information, by modifying the reward function of the existing method. Evaluation experiments show that the proposed method improves both quantitatively and qualitatively.
Motion Matching Based on Pose Similarity Using Movement of Body Parts

The use of high-performance equipment to analyze motion has become popular in the field of sports. However, high-performance systems are expensive and difficult to install for amateur players and teams. Therefore, a method of analyzing motion based on video captured by smartphones and cameras is attracting attention as an easy-to-introduce system. One way for amateur athletes to practice efficiently is to compare their motion videos with the expert’s one. However, since different players operate at different speeds, it is difficult to make an accurate comparison by simply playing back the two videos at the same time. Therefore, we propose a method to synchronize the timing of similar motion in two videos with high accuracy. To improve the accuracy of the mapping, we corrected the right and left inversion detection of the skeleton and considered the amount of time variation of the skeleton. Through evaluation experiments, we confirm that the proposed method is effective in improving the performance of motion matching.
Submarines Analysis of Baseball using Smartphone with Athletic Trainers

In sport, 3D information that helps visualize human movement is important. In the field of sport education, such as athletic training, there is a need for easy-to-understand explanations using such 3D-based indicators. This study proposes a calculation method for obtaining the upper arm angle, which have been considered important in analyzing the pitching, based on 3D data obtained from a camera in a slate-type terminal. This method enables the extraction of the upper arm angle from an image grabbed by a popular mobile camera, e.g. smartphone, and will allow trainers to analyze the pitching based on the angle.
Abstracting Teammate With Multi-Agent DDPG Using Dual Encoders

In recent years, multi-agent reinforcement learning has been applied in various fields, and learning models have been proposed using a wide variety of approaches. One of the most common is to estimate Q-values using the observations and actions of all agents, such as centralized critic, as a countermeasure against non-stationarity. Although this type of Q-value estimation is effective, it has a scalability problem, which is increasing the amount of information to be considered, as the number of agents increases. The problem makes learning difficult. In this paper, we propose a learning model that facilitates learning by inputting the values of teammates’ observations and actions into dual encoders and extracting features that select which agent’s information is effective for learning. We confirm the effectiveness of the proposed model by comparing with MADDPG, which is a well-known model in multi-agent reinforcement learning.
Does Blockchain Qualify as Secret-free Immutable Storage?

In this study, we demonstrate that blockchain is an efficient secret-free immutable storage system that guarantees not only the integrity of data but also the integrity of the smart contract and its execution results (i.e., transaction receipt) without a secret key. In short, blockchains eliminate the need for secret key management by storage managers. For example, the secure storage of non-fungible token (NFT) history on a blockchain can also be achieved without the secret key of the NFT marketplace
Learning Self-Expressive Representations via Nearest Subspace Model for Motion Segmentation

Subspace clustering methods based on self-expressive model aim to represent each data point as a linear combination of data points belonging to the same subspace. However, conventional methods based on a self-expressive model are known to suffer from a connectivity problem in which data points belonging to the same subspace are not sufficiently connected. To address this problem, we propose a subspace clustering method by learning self-representations using the nearest subspace model. The adoption of the nearest subspace model leads to the dense connection between data lying on the same subspace. In addition, we incorporate the update of the subspace model into the optimization process to refine the model. Evaluation experiments were conducted using real-world data, showing the effectiveness of clustering in the self-expressive model based on the nearest subspace model.
Performance Optimization of Energy Efficient Semantic Communications over Wireless Networks

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks is investigated. In the considered model, each user first extracts the semantic information from its large-scale data, and then transmits the small-sized semantic information to the base station (BS) which recovers the original data. Due to the limited energy budget of wireless users, both local computational energy and transmission energy must be considered. This joint computation and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the network under a latency constraint. To solve this problem, an iterative algorithm is proposed where the optimal solution for joint bandwidth allocation, power control, and computation frequency optimization problem can be obtained. Numerical results show the effectiveness of the proposed algorithm.
Interference Identification Based on China Mobile Current Network Data

Since the 4G standard TD-LTE commercialization, TD-LTE network has entered a stage of steady development, with huge network scale and complex system deployment. Therefore, the network interference problem faced by the system becomes more and more serious, which seriously affects the objective quality of the network and the quality of service to users. The common inter-network interference includes stray interference, blocking interference, jammer interference and intermodulation interference. In addition, the communication base station and terminal in the system may cause serious intra-network interference problems including ultra-distant interference, clock out-of-synchronization interference, signal amplifier/repeater interference and physical uplink control channel interference and physical uplink shared channel. Interference type identification is an important task in network maintenance and optimization of mobile operators. In this paper, an interference identification algorithm based on a deep neural network is proposed and verified on the 4G current network data of some cities in Guangxi province provided by China Mobile Communication Corporation. Experimental results show that compared with the interference analysis tool platform inside the operation, the algorithm has a higher recognition rate for 10 manual calibration categories, which can improve the efficiency of interference identification.
UAV Path Planning in Urban Environments with Dynamic Risk-Map Generation by Vehicle and Pedestrian Perception

In this paper, we propose the UAV path planning method in urban environments with dynamic risk-map generation by vehicle and pedestrian perception. In urban environments, it is the challenging task for Unmanned Aerial Vehicles (UAVs) to generate the safety flight path such that they will not collide with the ground vehicles and pedestrians in case of accidental fall. To mitigate the collision risk, the proposed method dynamically generates the risk-map, where collision risks within specific areas are mapped based on mobility of ground vehicles and pedestrians. To perceive the mobility of them, the proposed method utilizes Vehicle-to-Everything (V2X) messages that include position, velocity, and acceleration of the ground vehicles and pedestrians. Based on the proposed risk-map, UAVs seek their flight paths that minimize the collision risk by Rapidly-exploring Random Tree Star (RR$\mathrm{T}^{*}$) algorithm. Simulation result shows that the flight path of the proposed method decreases the collision risk compared with the shortest path and previous safety path in the situation that there are 200 vehicles and 400 pedestrians in 1250 m×1050m city.