Qun Jin

Waseda University | Sōdai · Department of Human Informatics and Cognitive Sciences

In our previous study, we used Naive Bayes to discriminate and quantify approval desire in tweets based on words. To correctly understand a sentence, it is important to consider not only words but also word relationships and grammar. In this study, we investigate the possibility of improving the accuracy of discrimination and quantification of the...

Recently, machine/deep learning techniques are achieving remarkable success in a variety of intelligent control and management systems, promising to change the future of artificial intelligence (AI) scenarios. However, they still suffer from some intractable difficulty or limitations for model training, such as the out-of-distribution (OOD) issue,...

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing mach...

Knowledge graph completion (KGC) aims at complementing missing entities and relations in a knowledge graph (KG). Popular KGC approaches based on KG embedding are typically limited to the transductive setting, i.e., all entities must be seen during training, which is impractical for real-world KGs where new entities are emerging daily. Recent induct...

Personal health data collected via wearable devices can be used for sharing and utilization to provide smart healthcare services. Since personal health data involves sensitive information, it is necessary to require a secure way to manage and use data with the consent of each individual. To integrate and share health data securely, many frameworks...

With the development of the Internet of Things (IoT) and sensors, personal health data can be collected by wearable devices. However, one of the biggest concerns about the storage and use of sensitive personal health data is privacy. To address this issue, we propose a new Multi-Ledger Coordinating Mechanism (MLCM) with blockchain for trustworthy d...

Group behavior modeling is an important research topic in the field of social network analysis. Existing methods regarding this topic can only learn the static group preference, ignoring the multiple characteristics of the group behavior, so they cannot model the group behavior in a complete way. In this paper, we propose a Dynamic Multi-view Group...

It is important to study the evolution mechanism of group behavior for the prevention and control of harmful group behavior on social networks. Most researchers focus on the study of group behavior evolution in a deterministic environment, ignoring the influence of the uncertain environment formed by the randomness of behavioral decision-making, th...

Swarms of mobile robots are being widely applied for complex tasks in various practical scenarios toward modern smart industry. Federated learning (FL) has been developed as a promising privacy-preserving paradigm to tackle distributed machine learning tasks for mobile robotic systems in 5G and beyond networks. However, unstable wireless network co...

The location technology of information sources in social networks is a major factor in exploring the means of information propagation. In this context, most existing methods ignore the direction of infected nodes and fail to make full use of the diffusion information, resulting in poor identification of source localization. To address such problems...

Most of previous research works of sentiment analysis on SNS mainly focused on polarity analysis to probe into user tendencies. However, human emotions are complex and changeable. It is difficult to use the results of traditional polarity analysis in the real-time application services. Although finer-grained sentiment analysis may provide more deta...

In the mountainous areas of Japan, the weeds on the slopes of terraced rice paddies still need to be cut by the elderly manually. Therefore, more attention should be given to maintain proper postures while performing mowing actions (especially the pre-cutting actions) to reduce the risk of accidents. Given that complex mowing actions can be decompo...

The impact of Internet of Things (IoT) has become increasingly significant in smart manufacturing, while deep generative model (DGM) is viewed as a promising learning technique to work with large amount of continuously generated industrial Big Data in facilitating modern industrial applications. However, it is still challenging to handle the imbala...

As one of fundamental tasks in online learning platform, Interactive Course Recommendation (ICR) aims to maximize the long-term learning efficiency of each student, through actively exploring and exploiting the student's feedbacks, and accordingly conducting personalized course recommendation. Recently, Deep Reinforcement Learning (DRL) has witness...

With the explosion of information, personalized recommender plays a vital role in almost all economic platforms. Usually, the recommender exploits user–item (UI) interactive data to learn users’ latent interests, and then correspondingly conducts recommendations. To address the problem of sparse interactions, graph neural networks (GNNs) have been...

This paper explores the cause-and-effect relationships among a set of health indices using causal discovery. The data we used to analyze was obtained from wearable devices, Traditional Chinese Medicine (TCM) diagnosis, and self-assessment of subjects in an experiment. Firstly, three machine learning algorithms were employed to address the issue of...

Previous studies have shown that about 90% of traffic accidents are due to human error, which means that human factors may affect a driver's braking behaviors and thus their driving safety, especially when the driver makes a braking motion. However, most studies have mounted sensors on the brake pad, ignoring to some extent an analysis of the drive...

As the specific incarnation of cyber-physical-social systems, in deregulated electricity market, the market gaming behaviors may have significantly affected the costs of electricity delivered to the market. Especially, from the supply side, the primary goal of power generating companies (PGCs) is to develop strategic biddings to maximize their prof...

Mobile crowdsensing (MCS) is a cost-effective paradigm for gathering real-time and location-related urban sensing data. To complete MCS tasks, MCS platform needs to exploit the trajectory of participants (vehicles or individuals, etc.) for effectively choosing participants. On one hand, the existing works usually assume that platform has possessed...

The development of interface designs can reduce extraneous processing for users and increase the effectiveness of multimedia presentations. In this study, we investigate cognitive load in multimedia presentations. First, we present a quantitative model to measure cognitive load in terms of information comprehension in which the pupil diameter varia...

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (D...

The spread of rumors has a major negative impact on social stability. Traditional rumor spreading models are mostly based on infectious disease models and do not consider the influence of individual differences and the network structure on rumor spreading. In this paper, we propose a rumor Fick‐spreading model that integrates information decay in s...

In recent years, personal health data can be collected via wearable devices and sensors and used for healthcare services improvement through data sharing. To share sensitive personal health data securely, many frameworks and approaches using blockchain-based systems have been proposed. However, the issue of letting individuals control and manage th...

BACKGROUND Background In the post-coronavirus disease (COVID-19) pandemic era, many countries launched apps to trace contacts of COVID-19 infections. Each contact tracing application (CTA) faces a variety of issues owing to different national policies or technologies to trace contacts. OBJECTIVE Objective We aimed to investigate all CTAs used to t...

Background: In the post-coronavirus disease (COVID-19) pandemic era, many countries launched apps to trace contacts of COVID-19 infections. Each contact tracing application (CTA) faces a variety of issues owing to different national policies or technologies to trace contacts. Objective: We aimed to investigate all CTAs used to trace contacts in...

Behavior is autonomous, convergent, and uncertain, which brings challenges to the modeling of social network behavior spread. In this article, we propose a behavior spread model based on group cohesion under uncertain environments. First, for behavioral convergence, we define group cohesion to quantify the convergent effects of group. Second, based...

Internet of Everything (IoE) is playing an increasingly indispensable role in modern intelligent applications. These smart applications are known for their real-time requirements under limited network and computing resources, in which it becomes a high consuming task to transform and compute tremendous amount of raw data in cloud center. The edge-c...

Modern manufacturing process is commonly composed of multiple automated devices working together efficiently. Cloud-based manufacturing aims to achieve better efficiency by allowing the collaborative manufacturing across a group of automated robots. Cooperations between multiple robots can accomplish more complicated tasks that is beyond the capabi...

Safety production surveillance is of great significance to industrial operation management. While augmented intelligence of things is demonstrating tremendous potential in industrial applications, the analyzed information offers lots of benefits to the higher-level planning in the enterprise management systems, to further improve the operational ef...

Location‐related data are an important subset of personal data. An individual may have a positive or negative feeling for a specific place, which is important for personal data analysis. There are many studies on sentiment analysis within text data, such as tweets, but few studies have been conducted specifically on an individual's feelings regardi...

Background and objectives Infection with human papillomavirus (HPV) is the main cause of cervical cancer, and vaccination is an effective method to prevent HPV infection. In Japan, adverse reactions were reported in some HPV-vaccinated people in March 2013, and while Japan’s Ministry of Health, Labor, and Welfare withdrew active recommendation of t...

The smart grid is now increasingly dependent on smart devices to operate, which leaves space for cyber attacks. Especially, the intentionally designed false data injection attack (FDIA) can successfully bypass the traditional measurement residual-based bad data detection scheme. Considering that the smart grid data naturally contain linear and nonl...

COVID-19 has resulted in a public health global crisis. The pandemic control necessitates epidemic models that capture the trends and impacts on infectious individuals. Many exciting models can implement this but they lack practical interpretability. This study combines the epidemiological and network theories and proposes a framework with causal i...

Measuring the space area of obstacles is one of the important problems in obstacle localizing fields. Most of the existing research works on the localization of obstacles focus on where the obstacles are, and few of them measure both the positions and the areas of the obstacles. In this paper, we propose a Minimum convex bounding Polygon localizing...

Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic networ...

Intelligent anomaly detection for identifying cyber/physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this study, we propose a few-shot learning model based on Siamese Convolution Neural Network (FS-SCNN), to alleviate t...

With the surge in popularity of wearable devices, collection of personal health data has become quite easy. Many studies have been conducted using health data to estimate the onset and progression of illness. However, life habits may vary among individuals. By analyzing the life cycle from health-related data, conventional studies may be improved....

A color is a powerful tool used to attract people’s attention and to entice them to purchase a product. However, the way in which a specific color influences people’s color selection and the role of their eye movements and cultural factors in this process remain unknown. In this study, to delve into this problem, we designed an experiment to determ...

In this study, to alleviate the inconsistency between dimensionality reduction and feature retention in anomaly detection, we propose a Variational Long Short-Term Memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. A compressed network associated with a variational reparameterization schem...

With the recent development of machine learning (ML), artificial intelligence (AI) and cyber technologies in the field of industrial informatics, it is important to migrate the traditional businesses and services in the physical world to the digital cyber-enabled world [item 1) of the Appendix]. Cyber intelligence technologies, such as the fifth-ge...

Underlying latent factors may cause a person to feel unwell. As the influence of the latent factors increases, the person will become sick. It is difficult to directly measure the influence of latent factors on risk degrees. However, early symptoms of a disease may affect vital signs such as body temperature and blood pressure, which may be a resul...

The crowdsourcing system is a distributed problem-solving platform, in which tasks are delivered to the crowd (i.e., crowdworkers) in the form of an open call. Usually, large-scale crowdsourcing systems contain abundant microtasks, and the overhead of a crowdworker spending on searching the appropriate task may be comparable to the cost of completi...

Hyper-parameters in deep learning are sensitive to prediction results. Non-maximum suppression (NMS) is an indispensable method for the object detection pipelines. NMS uses a pre-defined threshold algorithm to suppress the bounding boxes while their overlaps are not significant. We found that the pre-defined threshold is a hyper-parameter determine...

Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in Human Act...

In the study of influence maximization in social networks, the speed of information dissemination decreases with increasing time and distance. The investigation of the characteristics of information dissemination is of great significance to the management and control of public opinion. A three-hop velocity decay propagation model is proposed to det...

The mobile revolution is changing the way we interact with the people and things around us. Proximity awareness, the ability to actively/passively and continuously search for relevant value in one’s physical/virtual proximity, is at the core of this phenomenon.

The prebraking-related actions typically studied are the main maneuvers carried out to avoid collision. Especially for those braking actions taken when turning or parking, accidents often occur because of human errors such as the incorrect choice of pedal. However, regarding these daily braking-related driving behaviors, the effects of the driver c...

Background: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optim...

In recent years, deep learning has made great progress in image classification and detection. Popular deep learning algorithms rely on deep networks and multiple rounds of back-propagations. In this paper, we propose two approaches to accelerate deep networks. One is expanding the width of every layer. We reference to the Extreme Learning Machine,...

Deep neural network (DNN) learning has witnessed significant applications in various fields, especially for prediction and classification. Frequently, the data used for training are provided by crowdsourcing workers, and the training process may violate their privacy. A qualified prediction model should protect the data privacy in training and clas...

Pulse diagnosis is a typical diagnosis of Traditional Chinese Medicine (TCM). However, it is not clear if there is any relationship between the result of pulse diagnosis and other health related data. In this study, we investigate this and analyze pulse diagnosis data from a TCM doctor and a pulse diagnostic instrument (PDI) by Random Forest. Subje...

Process mining is a technology to gain knowledge of the business process by using the event logs and achieve a model of the process, which contributes to the detection and improvement of the business process. However, most existing process mining algorithms have drawbacks associated with managing uncertain data, and the method of using the frequenc...

With the development of eye-tracking technology, existing studies verified the effect of culture on eye movements. However, the detail of how the different cultures, especially the different Asian cultures affect people’s color preferences about products by visual attention remains in black box. In this paper, we focus on the difference between Chi...

Twitter, as a popular social networking service, is used all over the world, with which users post tweets for various purposes. When users post tweets, an emotion may be behind the messages. As the emotion changes over time, we should better consider their emotional changes and states when analyzing the tweets. In this study, we improve polarity cl...

For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the el...

Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous stu...

In trustworthy service discovery for Mobile Social Networking in Proximity (MSNP), conventional trust computation faces a big challenging issue—relatively high latency. To cope with it, trustworthiness determination strategies were proposed in our previous study, aiming at avoiding trust computation under certain conditions, so as to reduce the lat...

Feature selection (FS) is one of fundamental data processing techniques in machine learning algorithms, especially for classification of healthcare data. It is a challenging issue due to the large search space. Binary Particle Swarm Optimization (BPSO) is an efficient evolutionary computation technique, and has been widely used in FS. However, in t...

Introduction The utilization of information and communications technology (ICT) devices is a new way to record and analyze health data of the elderly. This time-series study aimed to analyze health changes, and the correlation between pulse manifestation and health indicators, in the elderly. Methods We conducted continuous 93-day monitoring of he...

Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains as a key concern during the sharing service development process. While significant efforts have been undertaken...