Sha Zhao

• PhD
• Professor (Assistant) at Zhejiang University

Prevalence of smartphones is changing people's lifestyle. Mobile applications (abbr. APPs) on a smartphone serve as entries for users to access a wide range of services. What APPs installed on one's smartphone, i.e., APP list, convey lots of information regarding his/her personal attributes, such as gender, occupation, income, and preferences. This...

Understanding smartphone users is fundamental for creating better smartphones, and improving the smartphone usage experience and generating generalizable and reproducible research. However, smartphone manufacturers and most of the mobile computing research community make a simplifying assumption that all smartphone users are similar or, at best, co...

App usage prediction, i.e. which apps will be used next, is very useful for smartphone system optimization, such as operating system resource management, battery energy consumption optimization, and user experience improvement as well. However, it is still challenging to achieve usage prediction of high accuracy. In this paper, we propose a novel f...

The number and popularity of smartphone applications is rising dramatically. Users install and use applications depending on their needs and interests. Applications on smartphones convey lots of personal information, providing us a new lens to well profile users. In this paper, we first describe application information for user profiling. Second, w...

The integration of 5G networks and AI benefits to create a more holistic and better connected ecosystem for industries. User profiling has become an important issue for industries to improve company profit. In the 5G era, smartphone applications have become an indispensable part in our everyday lives. Users determine what apps to install based on t...

We present the Large PSG Model (LPSGM), a unified and flexible framework for sleep staging and disease diagnosis using polysomnography (PSG) data. LPSGM is designed to address the challenges of cross-center generalization in sleep staging and to enable fine-tuning for downstream disease diagnosis tasks. LPSGM introduces a unified training framework...

Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer from poor generalization to new subjects because they are trained and tested on the same labeled datasets, overlooking individual differences. To tackle this issue, we prop...

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models,...

Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1)...

Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signals, e.g. electroencephalogram (EEG) and electrooculogram (EOG), is a gold standard for sleep staging....

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG...

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and...

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some cha...

Massively multiplayer online role-playing game (MMORPG) has been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the bal...

Earthquake early warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake risk. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary resea...

Crime prediction has attracted increasing attention due to its significance in public safety and growing availability of heterogeneous relevant data. Existing works on crime prediction usually failed to capture its dynamics and inherent non-linear relationships. To address these issues, in this paper, we propose an attentional recurrent neural netw...

Crime risk prediction is helpful for urban safety and citizens’ life quality. However, existing crime studies focused on coarse-grained prediction, and usually failed to capture the dynamics of urban crimes. The key challenge is data sparsity, since that 1) not all crimes have been recorded, and 2) crimes usually occur with low frequency. In this p...

Online gaming is a multi-billion dollar industry that entertains a large, global population. Empowering online games with AI has made a great success, however, ignores the explainability of black-box model makes AI less responsible and hinders its further development. In this paper, we introduce and discuss the audience and the concept of XAI (eXpl...

Crime risk prediction is crucial for city safety and residents’ life quality. However, without labeled data, it is challenging to predict crime risk in cities. Due to municipal regulations and maintenance costs, it is not trivial for many cities to collect high-quality labeled crime data. In particular, some cities have lots of labeled data while o...

Aftershocks can cause disasters again after mainshocks, which result in threat to life and economic loss. In order to avoid secondary disasters, it is necessary to predict whether aftershocks would happen in a given region. There have been studies using different features and methods to predict aftershocks spatial distribution. However, it is still...

Crime has been a complex social problem worldwide, impacting numerous individuals in both property and psychology, and affecting public safety as well. To prevent and avoid crime is of great importance for urban authorities and citizens. Crime prediction using various urban sensing data provides a promising paradigm to cope with this challenging pr...

Role-playing games (RPGs) are one of the most exciting and most rapidly expanding genres of online games. Virtual characters that are not controlled by players, have become an integral part, which helps to advance narratives of RPGs. Believable characters can enhance game engagement and further improve player retention. However, game players easily...

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more use...

Earthquake Early Warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake damage. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary res...

To discover the condition of roads, a large number of detection algorithms have been proposed, most of which apply machine learning methods by time and frequency processing in acceleration and velocity data. However, few of them pay attention to the similarity of the data itself when the vehicle passes over the road anomalies. In this article, we p...

Graph classification problem is becoming one of research hotspots in the realm of graph mining, which has been widely used in cheminformatics, bioinformatics and social network analytics. Existing approaches, such as graph kernel methods and graph Convolutional Neural Network, are facing interpretable ability challenge and the high dimensionality p...

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term pred...

Smartphones are changing humans' life style. Mobile applications (abbr. apps) on smartphones serve as entries for users to access a wide range of services in our daily lives. What apps installed on one's smartphone, i.e. app list, conveys lots of personal information, such as demographics, interests, and needs. This has the potential to provide us...

Multi-social-temporal (MST) data, which represent multi-attributed time series corresponding to the entities in multi-relational social network series, are ubiquitous in real-world and virtual-world dynamic systems, such as online games. Predictions over MST data such as social time series prediction and temporal link weight prediction are of great...

Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users...

Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users' behaviors and activities , providing a lens for understanding users, which is an important point in the community of ubiquitous computing. In UbiComp 2018, we successfully held the first International workshop AppLens 2018: mining and learning from smartp...

Online gaming is a multi-billion dollar industry that entertains a large, global population. However, one unfortunate phenomenon known as real money trading harms the competition and the fun. Real money trading is an interesting economic activity used to exchange assets in a virtual world with real world currencies, leading to imbalance of game eco...

The prevalence of smartphones equipped with various sensors enables pervasive capture of users’ location data. WiFi scan lists on one smartphone, i.e., scan results of network in a range, can roughly indicate the physical location of the phone in a time period. Considering the close relationship between location and daily life, users’ life style ca...

Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. App usage behaviors reveal rich clues regarding one's personal attributes. It is possible to predict smartphone users' demographic attributes through their app usage behavior...

Smartphone applications (abbr. apps) are becoming ubiquitous in our everyday life. Apps on smartphones can sense users' behaviors and activities, providing a lens for understanding users, which is an important point in the community of ubiquitous computing. In UbiComp 2018, we would like to run a workshop on mining and learning from smartphone apps...

As social media users are increasingly going mobile, various location based services (LBS) have been deployed on social media like Twitter. The success of them heavily depends on the availability and accuracy of users' location information. However, only a small fraction of tweets are geo-tagged. Thus, it is necessary to infer locations for tweets...

Device analyzer can provide a large-scale dataset that captures real-world usage of smart phones [1]. Detailed usage records in smart phones, conveying a partial life log, are important for a deep scientific understanding of human characteristics. In this study, we proposed a feature-based labeling method to characterize users. Eight features from...

Understanding smartphone users is fundamental for creating better smartphones, improving the smartphone usage experience, and generating generalizable and reproducible research. However, smartphone manufacturers and most of the mobile computing research community make a simplifying assumption that all smartphone users are similar or, at best, const...

Accepted by IEEE Systems Journal

The prevalence of smart phones equipped with various sensors enables pervasive capturing users' mobility data (GPS, GSM network, WiFi, etc.), which contains approximate whereabouts of users. In order to protect users' privacy some mobility data is anonymized, which is challenging for discovering individual information implicated in the data. In thi...

An increasing number of connectable devices have been used in our everyday living environments. However, their working status usually cannot be remotely monitored in a ubiquitous way, leading to potential inefficiency in energy consumption. In this paper, we conducted a case study to investigate the effectiveness of connecting devices into social n...