Reviewerly/exHarmony huggingface dataset:
The nature of property in cryptoassets

Abstract Disputes relating to cryptoassets have proliferated in recent years, along with the rise of the cryptoasset market. Some of these can be resolved using traditional principles of contract, tort or trust law, but proprietary issues raise particular conundrums. While cryptoassets have generally been accepted to be property, that is merely the starting point. To properly resolve proprietary disputes, it is necessary to provide a reasoned and robust explanation for why particular rules of title originally developed in the context of tangible property should apply. In turn, two foundational questions must be answered. First, what is the subject-matter of the property right in cryptoassets? And secondly, what is the proprietary effect of a blockchain transaction? These issues have received relatively little attention in the literature, which has focused on whether cryptoassets are property at all, and existing contributions (including the Law Commission's recent Consultation Paper on Digital Assets) which do engage these issues are far from reaching consensus. This paper critically examines those views and puts forward its own reasoned approach for the application of traditional rules of title to cryptoassets.
Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data

Brain degeneration is commonly caused by some chronic diseases, such as Alzheimer's disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data).Several deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results.As different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively.The prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods.
Super Resolution for QR Code Images

In this paper, we propose an image denoising and a super resolution method for converting unreadable low resolution QR code images into readable high resolution ones. We propose an image denoising and a super resolution method using a simple CNN-based model. Image denoising using CNN-based image generation models can be applied to various noises by training model with different levels of noise. On the other hand, denoising using the conventional image processing method can only be applied to the specific type of noise. Therefore, image denoising using CNN is considered to be superior to image processing methods in terms of generalization performance. We further implement super resolution method to convert into high resolution images. We propose QRCNN and QRGAN, simple image denoising and super resolution models for QR code images. QRCNN and QRGAN are based on SRResNet and SRGAN, respectively. However, they both have simpler structure like SRCNN. Given QR code images for the dataset, we can reduce the computational complexity and memory usage by modifying model structure.
Writer Alternation Detection for Online Exam by Exponential Moving PCA

Many online handwriting authentications have been developed to prevent proxy-test taking in unsynchronized online exams. Here, we propose an unsupervised anomaly detection method based on principal component analysis calculated from incremental statistics to develop an examinee alternation detection system without template information or pre-training. The evaluation results show that the accuracy of this system is over 90%.
A Real-Time AR Visualization System of Snow-Covered Situations

It is important to know the expected amount of snowfalls to ensure a smooth and safe life. Knowing the snow-covered situation makes it possible to plan and act in advance. This paper proposes a real-time AR (Augmented Reality) snow visualization system to visually display the superimposed snow-covered situations according to the information obtained from a snowfall forecast service. Our system offloads the spatial information process, which is a CPU-bound process, to the edge server to achieve the real-time AR visualization on the mobile device.
Implementation of a Smart Checkout System Based on Face Recognition Using YOLO v3-tiny

With the application of biometrics such as voice, fingerprint, face, and iris recognition, electronic systems are becoming more and more automated and intelligent. The reduction of paper use accordingly results in the effect of environmental protection, by protecting the forest resources as well as reducing the environmental pollution. Based on these two goals, face recognition technology and Line communication software, being inseparable in life, are combined in this paper to complete an intelligent checkout system. A face recognition system based on YOLOv3-tiny is then built in this paper. In this paper for face recognition, the WIDER FACE database is used for training and the GoogLeNet is used for improving the recognition rate. The result shows that the accuracy rate for this improved model is 7.57% higher than without the GoogLeNet one. The combination of recognition result and LINE Notify function can then automatically transmit the consumption details, at a mode of paperless environmentally friendly shopping checkout.
Floor Projection with QR code-based Interactions and Visitor Drawings

We propose a floor projection exhibit with visitor interaction based on QR codes at a museum. Our QR code-based identification significantly reduces costs to provide individual visitors with different information, even if they do not have their own smartphones. As proof-of-concept experiments, we conducted a series of user study in a newly-opened local museum where visitors drew their own imaginary ‘future-scape’ of the town. 25.8 % of 528 scanned drawings were recalled in our projection system with their own QR codes given when they submitted their drawings. This QR-code based interaction proved to be a promising way to attract repeat visitors.
Proposal on Password Reminder System for Partial Forgetting

Personal identification technology to confirm user’s legitimacy is an indispensable technology for various information system. Password authentication method, which is one of the knowledge authentication methods, is widely used in general. In order to use password authentication securely, it is necessary to set longer and more complex passwords, but this increases the memory burden on an user, so the user may forget all or part of the password and be unable to use the service. Many web services require users to reset their passwords when they forget them, but this can cause problems such as registering insecure passwords and not being able to use the service until identification is completed. Therefore, it is not desirable to reset the password frequently. In this paper, we propose a password reminder system that aims to remind the forgotten part and fix it in the memory when the password is partially forgotten. The partial hash matching algorithm is presented to enhance the security of the system.
ThermalPose: A Real-Time 2D Human Skeleton Recognition System Using Thermal Imaging

This paper proposes a thermal imaging-based real-time 2D human skeleton recognition system, named ThermalPose, which consists of a thermal imaging camera, an AI edge module, and a thermal imaging dataset. ThermalPose adopts human thermal images capturing by a thermal imaging camera to perform human skeleton recognition functionalities. Experimental results demonstrate that ThermalPose can effectively detect the 2D thermal imaging pose.
An AI Edge Computing-Based Intelligent Hand Painting Teaching System

This paper proposes an intelligent painting hand teaching system based on AI edge computing technology and uses gesture tracking and gesture recognition techniques. The overall operation process only needs to move the hand, and the correctness of the stroke trajectory can be clearly detected during the writing or drawing process. In addition, this system builds a complete data storage system in the teaching host. After the students operate on their personal devices, the files can be uploaded to the teaching host through this system, and the system records the uploaded data of each student in the database.
OTOMO: Electric wheelchair-type remote communication mobility

OTOMO is a mobility system that uses telepresence technology to communicate with a passenger in an electric wheelchair and a driver in remote to experience the sensation that they are moving together in the same space. The driver in remote wears an HMD and operates a controller made of a PVC balloon 1.2 m in diameter. The driver feels as if they are a dwarf by operating the 1.2-meter-diameter controller with their entire body while viewing a magnified video of the wheelchair’s surroundings on the HMD. The passenger in the electric wheelchair gives instructions while watching the dwarf-sized driver projected in the cockpit at the front of the vehicle. Driving experiments using OTOMO confirmed interactive communication between the driver and passenger.
First-person view hand posture estimation and fingerspelling recognition using HoloLens

Sign language users tend to be socially restricted due to the general population’s lack of knowledge of sign language. Some attempts have been made to develop technologies that improve this aspect by translating sign language. However; these approaches generally use a third-person camera for collecting the information, limiting sign users to environments prepared for this purpose.In this study, we develop a first-person view Japanese fingerspelling recognition system using an Optical See-Through Head Mount Display (OSTHMD). The system estimates the hand posture from the camera mounted on the OSTHMD and applies machine learning to the hand posture data to classify the hand gestures and convert them into speech. 37 Japanese sign language fingerspelling were successfully recognized by using a Microsoft pose extractor. Next, using a support vector machine, 37 out of 53 Japanese sign language fingerspelling were successfully identified with more than 70% identification rate. Finally, the specified labels were converted into speech using the speech output module with Azure API.The main purpose of this research is to propose a system that enables sign language users to communicate with verbal people without environmental restrictions.
Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.
Recognizing Confusion in Assembly Work based on a Hidden Markov Model of Gaze Transition

A method to detect the states of confusion during an assembly work for a user-adaptive work support system is proposed in this study. The eye-gaze data from an eye tracker are discretized into predefined areas of interest, and a hidden Markov model is used to classify the cognitive states associated with confusion and types of work steps involved. An offline experiment using data collected from 16 participants showed low task dependency (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -scores of 0.638 and 0.639 for dependent and independent evaluations, respectively) and relatively high person dependency (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 0.621 for independent evaluation). The high discriminability between the two assembly steps and tendency of misclassification in confused states were confirmed.
Improve object detection performance with efficient task-alignment module

Dense object detector has received many improvements over a short period. Mainstream object detectors are commonly constituted of two sub-tasks, including classification and localization tasks, implemented by two parallel heads. However, recent object detectors still suffer from misalignment between two tasks, classification, and localization. In this paper, we conduct a set of experiments based on one existing detection head and propose a lightweight attention mechanism that enhances the learning capability of the detection head handling task misalignment. Our proposed method boosts the performance of available T-Head [1] by 0.4 mAP on COCO at single-model single-scale settings. Code available at: https://github.com/sun-asterisk-research/E TOOD
ECHONET Lite as an Interface for Z-Wave Devices in a Home Automation System

Although Z-Wave and ECHONET Lite are both popular networking solutions with overlapping use cases in the domain of smart homes and automation, they are inherently incompatible with each other. With the rising popularity and open standards approach of ECHONET Lite and with the introduction of newer alternatives to Z-Wave such as Thread and Matter, we consider the feasibility of using ECHONET Lite as an interface for Z-Wave devices. We evaluate our proposed solution in terms of response times and compare it to the Z-Ware web API. Both approaches show similar response times and are suitable for use in home automation systems.
A Multi-Task Semantic Communication System for Natural Language Processing

Recently, task-oriented semantic communication has received increasing attention due to its potential to transform the communication landscape by going beyond the Shannon paradigm. While most existing researches focus on a single task, we propose a multi-task semantic communication system for text tasks, inspired by the impressive results of multitask learning and bidirectional encoder representations from transformers (BERT). Based on BERT, the proposed system extracts semantic information from the text at the transmitter and then transmits it to the receiver, which aims to accomplish a series of different tasks. The transmitter and receiver would be trained jointly in an end-to-end manner. Compared with the traditional communication system, the proposed model is more robust to distortion caused by physical channels and exhibits better performance, especially in the low signal-to-noise ratio regime. Moreover, we investigate the relationship between the number of tasks and the required length of transmitted symbols in a multi-task setting.
Skinfold Recognition for Positioning Quality Evaluation on Mammography

Skinfold is one of the positioning issues in mammography quality evaluation. As it overlays breast tissues in mammograms, it implies that splints over-compresses the breast or the patient moves slightly. Skinfold affects radiologists in screening mammography. Therefore, radiographers adjust their operations during taking mammograms. In this paper, we propose a skinfold recognition method based on a convolutional neural network and extract features for analyzing the skinfold recognizer. The dataset consists of patches annotated with positive-class or negative-class, which is the consistent decision-making by four radiographers. Our method performs DenseNet-121 to train a skinfold recognizer and compares Vgg-16 and ResNet-101. Furthermore, extracting the class activation map is for interpreting what features the skinfold recognizer detects. The experiment results demonstrate that the proposed scheme outperforms the compared approaches.
Friendly Chatbot with IBM Watson Assistant Support Elderly Delightful in Smart Mirror

This paper used IBM Watson Assistant to create dialog for a “friendly” chatbot for supporting elderly people who have to live alone. The system invites the elderly to have a continued conversation for the purpose of decreasing problems related to depressions and slowing the progression of dementia. We created dialogues in Japanese for three characters, a benign nurse, a lovely girl, and a kind senior, and seven events. The accuracy was 93.44% in the case of talking with the different words in dialogues having the same meaning. This chatbot can act as a communication function in our previously proposed smart mirror to support the elderly by enabling them to talk with a bot in long-term health-care programs for monitoring the elderly in the future.
Learning Support by Visualizing Game Strategies From Imitation Learning Agents

If it is possible to machine-learn how a human learner behaves in a certain situation, it can be used as an agent that behaves like a human. Such imitation learning agents can be used for various educational purposes. This paper proposes a support function to visualize strategies by applying XAI (Explainable AI) technology.
Detection of Spam Transactions in Blockchain by Graph Analysis

In the current study, we propose a new technique to detect spam attacks against the blockchain network. We show the results after applying our method to the bitcoin network.
Accurate Hand Gesture Recognition Using Color and Depth Images with Modality-invariant Fusion

Gesture recognition is actively used, and has been applied in various fields, including games and medicine. For accurate gesture recognition, multi-modal information with color and depth images has recently been used. In multimodal gesture recognition, the fusion of color and depth images is crucial. To date, early and late fusion approaches have been widely used for the fusion of color and depth images. However, the enhancement of performance is limited due to the gap between the modalities. In this study, we proposed a modality-invariant fusion approach to overcome the modality gap issue. We applied the proposed approach to a public and our private data set and verified its effectiveness.
A Dialogue System Making Humor by Nori-Tsukkomi

For the formation of friendly relationships between users and dialogue systems, this study develops the related research and proposes the method to generate mishearing words and nori-tsukkomi text considering the context of the user utterances.
Real-time Implementation and Evaluation of SDR-based Deep Joint Source-Channel Coding

Benefiting from the advantage of joint source-channel coding in the finite block length regime and the advancement of AI technologies, deep joint source channel coding (DJSCC) has been extensively investigated for various sources (e.g., text source, image source, and video source) and achieved remarkable performance in limited bandwidth and low signal-to-noise ratios (SNRs). However, the performance gain brought by DJSCC methods is all observed via simulations in literature, where synchronization, channel estimation and the power amplifier are assumed to be perfect. In this paper, we design a software-defined radio (SDR) based platform to validate the DJSCC method for image transmission in real wireless scenarios.
Cross-Person Activity Recognition Method Using Snapshot Ensemble Learning

Human activity recognition (HAR) is one of the most promising technologies in the smart home, especially radio frequency (RF-based) method, which has the advantages of low cost, few privacy concerns and wide coverage. In recent years, deep learning (DL) has been introduced into HAR and these DL-based HAR methods usually have outstanding performance. However, as the recognition scenarios and target change, the model performance drops sharply. To solve this problem, we propose a generalized method for cross-person activity recognition (CPAR), which is called snapshot ensemble learning based an attention with bidirectional long short-term memory (SE-ABLSTM). Specifically, by defining the cosine annealing learning rate, the models with diversity are saved and integrated in the same training process. In addition, we provide a dataset for CPAR and simulation results show that our method improves generalization performance by 5% compared to the original method. The source code and dataset for all the experiments can be available at https://github.com/NJUPT-Sivan/Cross-person-HAR.
An Autonomous Valet Parking Algorithm for Path Planning and Tracking

Autonomous valet parking (AVP) is a popular application scenario for autonomous driving in the future. For AVP path planning, the original hybrid A-star ($\mathrm{A}^{*}$) algorithm has problems of large search costs, searching towards wrong directions and generating unreasonable parking paths. To solve these problems and generate a better path, a path planning method is proposed for typical AVP scenarios. The method divides path planning into global part and local part. The global path is planned based on graph search and state lattice algorithm. Then the hybrid $\mathrm{A}^{*}$ algorithm and Reeds-Shepp curve are modified to complete the local path planning, and finally a complete path that can be executed by the vehicle is generated. Then, a controller for path tracking based on model predictive control (MPC) is designed to overcome the shortcomings of traditional proportional integral derivative (PID) control such as overshoot and difficulty in precise control. Finally, the feasibility of the path planning and tracking method is verified by simulation using MATLAB and the vehicle simulation software CarSim. The results show that the planning efficiency and rationality are improved by implementing the proposed method, and the parking process can be done well with a small tracking error.