This overview presents one of the cup challenges of IEEE BigData 2024, with the topic of suicide risk level detection on social media posts. Given a training set of N = 2000 posts (N = 500 labelled and N = 1500 unlabelled posts) from r/SuicideWatch subreddits, the task of this challenge is to develop a predictive model capable of classifying the suicidal posts into four levels (i.e., indicator, ideation, behaviour, and attempt). The dataset provided simulated the obstacles existed in relevant fields (e.g., model overfitting, data scarcity and class imbalance), participating teams are supposed to tackle these issues while exploring the effectiveness of various model architectures. We received submissions from 21 teams and works of 13 teams underwent final evaluation. Teams addressed key challenges in suicide risk detection including limited suicidal data and suicidal risk imbalance. They employed novel approaches to overcome these obstacles, leveraging a diverse range of models from foundational base language models (BLMs) to state-of-the-art large language models (LLMs). In the competition, the highest weighted F1-score achieved under the final evaluation was 0.7605. The findings of this challenge can provide technical implications to social media suicide detection and contribute the clinical effectiveness to the applications of machine learning in digital suicide or mental healthcare management.

Object detection can localize and identify objects in images, and it is extensively employed in critical multimedia applications such as security surveillance and autonomous driving. Despite the success of existing object detection models, they are often evaluated in ideal scenarios where captured images guarantee the accurate and complete representation of the detecting scenes. However, images captured by image sensors may be affected by different factors in real applications, including cyber-physical attacks. In particular, attackers can exploit hardware properties within the systems to inject electromagnetic interference so as to manipulate the images. Such attacks can cause noisy or incomplete information about the captured scene, leading to incorrect detection results, potentially granting attackers malicious control over critical functions of the systems. This paper presents a research work that comprehensively quantifies and analyzes the impacts of such attacks on state-of-the-art object detection models in practice. It also sheds light on the underlying reasons for the incorrect detection outcomes.

Locomotion has a marked impact on user experience in VR, but currently, common to-go techniques such as steering and teleportation have their limitations. Particularly, steering is prone to cybersickness, while teleportation trades presence for mitigating cybersickness. Inspired by how we manipulate a picture on a mobile phone, we propose illumotion, an optical-illusion-based method that, we believe, can provide an alternative to these two typical techniques. Instead of zooming in a picture by pinching two fingers, we can move forward by “zooming” toward part of the 3D virtual scene with pinched hands. Not only is the proposed technique easy to use, it also seems to minimize cybersickness to some degree.illumotion relies on the manipulation of optics; as such, it requires solving motion parameters in screen space and a model of how we perceive depth. To evaluate it, a comprehensive user study with 66 users was conducted. Results show that, compared with either teleportation, steering or both, illumotion has better performance, presence, usability, user experience and cybersickness alleviation. We believe the result is a clear indication that our novel optically-driven method is a promising candidate for generalized locomotion.

Welcome to COMPSAC 2022, the 46th IEEE Computer Society International Conference on Computers, Software & Applications. Our theme this year is “Computers, Software, and Applications in an Uncertain World.” Consequences of the COVID-19 pandemic, ongoing political disputes, and climate change have led to increasing uncertainty in many aspects of our daily life. To thrive in this increasingly uncertain world, innovations in computer hardware, software, and applications have emerged as pressing needs. Safety, security, and resilience of computer hardware and software systems manifest themselves as ongoing concerns, and unique challenges arise in demonstrating and guaranteeing the upholding of these quality attributes despite imperfect knowledge of the environment in which they will operate. The authors whose work is included in these proceedings, as well as all of those who submitted papers to COMPSAC 2022, have seized the opportunity to meet these challenges.

As an impactful experiential learning pedagogy in higher education, service-learning (SL) can enhance students' academic learning and their sense of community and social responsibility by involving them in comprehensive community services. Much extant literature has justified the positive impacts of SL. However, the lack of quantitative analysis on identifying significant learning and course factors that strongly impact students' SL outcomes limits SL's further enhancement and adaptive development. This paper proposes to use machine learning approaches for modeling and identifying key learning factors in SL. We collect and study a large-scale dataset, including students' feedback on learning factors related to the different student experiences, course elements, and self-perceived learning outcomes. Machine learning algorithms are applied to model the various learning factors, contributing to effective classification models that predict students' learning outcomes using their evaluation on the learning factors. The most predictive model is then selected to identify a key set of important variables most indicative to students' SL outcomes. Our experiment results show that learning factors related to study challenges and interactions have significant positive impacts on students' learning gains. We believe that this paper will benefit future studies in this field.

Writing is one of the most common activities undertaken on a computer, and the activity of writing has been widely studied. Given that writing is an intensively cognitive process, it makes sense that the type of writing that is being produced would have an effect on the writer’s gaze and typing behaviors. However, only a few studies have explored this relationship. In this paper, we study the gaze-typing behaviors, specifically, the coordination between eye gaze and typing dynamics, of writers who are producing original articles in different genres: reminiscent, logical and creative. Our study focuses on Chinese typing, particularly via the Pinyin input method, which generates text via a two step method, and requires additional cognitive processes compared to typing in phonographic languages such as English. Our study involves 46 native Chinese speakers of varying ages from children to elderly. Our method deploys statistics- and sequence-based features to infer the mental state of the author during the writing process. The statistics-based features focus on modeling the overall gaze-typing behaviors during the process and the sequence-based features focus on the transition of the gaze-typing behaviors as the piece of writing progresses. Using a linear support-vector machine, we achieve an overall accuracy over 88% for the article-genre detection by using a leave-one-subject-out cross-validation evaluation.

Have you ever picked up a stuffed toy and pretended to play with it in your childhood? We are motivated by the novel use of stuffed toys in enhancing extended reality interaction. A key goal of extended reality is to induce the feeling of presence in its users. Naturally mapped control interface has been shown to enhance presence. The literature also indicates that a high degree of freedom tracking is important to extended reality. Based on these observations, we show that a free-form naturally mapped control interface is well-motivated via a theoretical contextualization. We explore the possibility of building such a controller in the form of stuffed toys. To realize stuffed toys as controllers, a novel soft-pose estimator empowered by cage-based deformation is proposed. It is shown to be effective in tracking the poses and deformations of real soft objects even by training with synthetic data only. Three gameplay prototypes are developed to demonstrate that interactive play can be enabled by the soft-pose estimator. They also form the basis for two user studies that validate the success of tracking stuffed toys with the soft-pose estimator for interactive play.

Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of a flashover prediction model which can be used to warn fire fighters before flashover occurs. Specifically, we consider the use of a fire simulation program to generate a set of synthetic data and an attention-based bidirectional long short-term memory to learn the complex relationships between temperature signals and flashover conditions. We first validate the fire simulation program with temperature measurements obtained from full-scale fire experiments. Then, we generate a set of synthetic temperature data which account for the realis-tic fire and vent opening conditions in a multi-compartment structure. Results show that our proposed method achieves promising performance for prediction of flashover even when temperature data is completely lost in the room of fire origin. It is believed that the flashover prediction model can facilitate the transformation of fire fighting tactics from traditional experience-based decision marking to data-driven decision marking and reduce fire fighter deaths and injuries.

In recent years, there is a growing phenomenon with children playing mobile devices. Overplaying mobile devices, however, may not lead to a balanced childhood. Lack of physical touch-and-feel stimulus is detrimental to children. Can touch-and-feel stuffed toys be made more appealing to children for a wider variety on their playtime diet? We explore a stuffed toy controller which integrates elements from both digital and make-believe games in an attempt to enrich children interaction experience. A user study was conducted to evaluate the effectiveness of the proposed controller while another children-oriented user study shows that make-believe augmented reality games played by the toy controller can enhance children’s interest in stuffed toys.

The task of summarizing a document is a complex task that requires a person to multitask between reading and writing processes. Since a person's cognitive load during reading or writing is known to be dependent upon the level of comprehension or difficulty of the article, this suggests that it should be possible to analyze the cognitive process of the user when carrying out the task, as evidenced through their eye gaze and typing features, to obtain an insight into the different difficulty levels. In this paper, we categorize the summary writing process into different phases and extract different gaze and typing features from each phase according to characteristics of eye-gaze behaviors and typing dynamics. Combining these multimodal features, we build a classifier that achieves an accuracy of 91.0% for difficulty level detection, which is around 55% performance improvement above the baseline and at least 15% improvement above models built on a single modality. We also investigate the possible reasons for the superior performance of our multimodal features.

Refractive errors, such as myopia and astigmatism, can lead to severe visual impairment if not detected and corrected in time. Traditional methods of refractive error diagnosis rely on well-trained optometrists operating expensive and importable devices, constraining the vision screening process. Advance in smartphone camera has enabled novel low-cost ubiquitous vision screening to detect refractive error or ametropia through eye image processing, based on the principle of photorefraction. However, contemporary smartphone-based methods rely heavily on hand-crafted features and sufficiency of well-labeled data. To address these challenges, this paper exploits active learning methods with a set of Convolutional Neural Network features encoding information of human eyes from pre-trained gaze estimation model. This enables more effective training on refractive error detection models with less labeled data. Our experimental results demonstrate the encouraging effectiveness of our active learning approach. The new set of features is able to attain screening accuracy of more than 80% with mean absolute error less than 0.66, meeting the expectation of optometrists for 0.5 to 1. The proposed active learning also requires significantly fewer training samples of 18% in achieving satisfactory performance.

Uncorrected refractive errors can lead to permanent debilitating eye conditions if not corrected in a timely manner. Contemporary diagnostic methods rely on the professional acumen of optometrists and the use of expensive devices, which may not be easily accessible to all. According to the optical principle of photorefraction, refractive error can be estimated based on a relative pupil and crescent size of an eye image taken by a camera from a specified working distance. A low-cost approach would be to leverage smartphones with cameras for this purpose. However, the poor image quality generated from basic smartphones poses a challenge for the current approach as they often fail to accurately distinguish the crescent from the iris. We propose a novel method to detect and accurately measure the iris and crescent from smartphone photos. Based on this method, we further propose a set of features for machine learning to build our refractive error estimation model. The performance of our models are evaluated in an in-depth experiment.

This paper presents a general framework of an innovative TOUR model with four interrelated elements: Transformation, Outreach, Unification and Reinforcement, enhancing the learning experiences of computer science/computing students via a study tour. We brought our students on an overseas trip as an integral part of an academic course. The immediate goal of the study tour was to gear the students toward the program learning outcomes. Another goal was to take a first step in supporting the TOUR model, to immerse students within these four interrelated important learning and self-improvement elements, and to support each student in becoming a better person. The study tour comprises six core learning activities aligned with the TOUR elements. Evaluations were conducted based on a student survey and self-reflection. The results provide valuable insights into the design of the computer science student study tour, as well as the potential value of the TOUR model.

Typing is one of the most common activities that are undertaken on a computer. It would therefore be interesting to investigate whether it is possible to deduce characteristics of the user, such as their age or the type of the document that they are writing, just simply from typing dynamics. In this paper, we study the coordination between eye gaze and typing dynamics, or the gaze-typing behavior, of subjects who are producing original text. We focus upon the differences between different age groups (children vs elderly seniors) and different genres of writing (reminiscent, logical and creative). Using machine-learning, we achieve an accuracy of 93.5% for age detection and 61.1% for the article-category detection, using a leave-one-subject-out cross-validation evaluation, which is 44% and 28% higher than baselines.

Fall is a common cause of severe injuries that may lead to irreversible body damage and even death. A real-time fall monitoring system can reveal a fall in time for timely medical aid to a victim. This is particularly important in the context of mobile healthcare. Fall detection with most contemporary wearable devices relied solely on acceleration signals, often not flexible and robust enough. In this paper, we propose to deploy body signals in a multi-modality approach. Besides the common acceleration signals, we also make use of physiological signals returned by wearable devices for multiple modalities. Fall detection would not fail easily even if some acceleration signals become ineffective. Our experiment results indicate that we are able to attain an accuracy of more than 96%. An in-depth evaluation demonstrates that physiological signals can contribute in distinguishing falls from actions generating similar acceleration signals, such as jumps, sit-downs and walking-downstairs.

Internal thought refers to the process of directing attention away from a primary visual task to internal cognitive processing. It is pervasive and closely related to primary task performance. As such, automatic detection of internal thought has significant potential for user modeling in human-computer interaction and multimedia applications. Despite the close link between the eyes and the human mind, only few studies have investigated vergence behavior during internal thought and none has studied moment-to-moment detection of internal thought from gaze. While prior studies relied on long-term data analysis and required a large number of gaze characteristics, we describe a novel method that is user-independent, computationally light-weight and only requires eye vergence information readily available from binocular eye trackers. We further propose a novel paradigm to obtain ground truth internal thought annotations by exploiting human blur perception. We evaluated our method during natural viewing of lecture videos and achieved a 12.1% improvement over the state of the art. These results demonstrate the effectiveness and robustness of vergence-based detection of internal thought and, as such, open new research directions for attention-aware interfaces.

Advancement of micro-electromechanical systems enables easy daily activity and physiological data collection with a smart wristband and smartphone. Making use of those signals in various intelligent algorithm can contribute much to trending m-health applications. The ability of continuously monitoring physical activities and stress level can help users to better track their health condition. In this study, we propose to recognize different physical activities and detect long lasting stress level based on the 3-axis acceleration signals and physiological signals. We are able to achieve accuracy of around 97% for physical activities recognition and more than 80% for stress detection. We also discover that physiological signals alone cannot distinguish well between the high intensity activities and the stress condition.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

User experience could be enhanced if the computer could understand human interaction intention. For instance, it could react to intercept and prevent interaction errors. This paper presents an approach to predicting users intention in interaction tasks based on past mouse movements. We adopt a long short-term memory (LSTM) model to predict the users» intention via their next mouse click interaction, upon being trained with past mouse interaction behaviors. To evaluate, we consider two scenarios in daily computer usage: a more structured crowdsourcing annotation task and a more free-form, open-ended web search task. Our results indicate that we could predict the next interaction event with reasonable accuracy. We also conducted a pilot study to investigate the possibility of applying our model for non-intentional mouse click detection. We believe that our findings would be beneficial towards the development of better intelligent agents.

Detecting human fight behavior from videos is important in social signal processing, especially in the context of surveillance. However, the uncommon occurrence of real human fight events generally restricts the data collection for fight detection in machine learning, and thus hampers the performance of contemporary data-driven approaches. To address this challenge, we present a novel cross-species learning method with a set of low-computational cost motion features for fight detection. It effectively circumvents the problem of limited human fight data for data-demaining approaches. Our method exploits the intrinsic commonality between human and animal fights, such as the physical acceleration of moving body parts. It also leverages an ensemble learning mechanism to adapt useful knowledge from similar source subsets across species. Our evaluation results demonstrate the effectiveness of the proposed feature representation for cross-species adaptation. We believe that cross-species learning is not only a promising solution to the data constraint issue, but it also sheds lights on the studies of other human mental and social behaviors in cross-disciplinary research.

Understanding human visual attention is essential for understanding human cognition, which in turn benefits human--computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously “learns” its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.

A user-specific model generally performs better in facial affect recognition. Existing solutions, however, have usability issues since the annotation can be long and tedious for the end users (e.g., consumers). We address this critical issue by presenting a more user-friendly user-adaptive model to make the personalized approach more practical. This paper proposes a novel user-adaptive model, which we have called fast-Personal Affect Detection with Minimal Annotation (Fast-PADMA). Fast-PADMA integrates data from multiple source subjects with a small amount of data from the target subject. Collecting this target subject data is feasible since fast-PADMA requires only one self-reported affect annotation per facial video segment. To alleviate overfitting in this context of limited individual training data, we propose an efficient bootstrapping technique, which strengthens the contribution of multiple similar source subjects. Specifically, we employ an ensemble classifier to construct pretrained weak generic classifiers from data of multiple source subjects, which is weighted according to the available data from the target user. The result is a model that does not require expensive computation, such as distribution dissimilarity calculation or model retraining. We evaluate our method with in-depth experimental evaluations on five publicly available facial datasets, with results that compare favorably with the state-of-the-art performance on classifying pain, arousal, and valence. Our findings show that fast-PADMA is effective at rapidly constructing a user-adaptive model that outperforms both its generic and user-specific counterparts. This efficient technique has the potential to significantly improve user-adaptive facial affect recognition for personal use and, therefore, enable comprehensive affect-aware applications.

Stress is a fact of daily life. Stress can also deteriorate human's attention and memory, which, when a user is engaged in interactive applications, will negatively affect the user experience and downgrade the delivered performance. Traditional stress inference is mainly based on user physical features like Blood Volume Pulse, Galvanic Skin Response, often captured via devices that intrude on the user space. In contrast, this paper proposes a non-intrusive approach that exploits the consistency of users' behavioral patterns when interacting with a user interface, specifically, in terms of eye gaze and mouse movement. The relationship between the stress experienced by the user and his/her eye gaze and gaze-mouse coordination patterns are investigated. We show that both eye gaze and gaze-mouse coordination patterns can be exploited to distinguish whether a user is under stress. We also discover that a user's eye gaze behavior patterns are more consistent when he/she is under stress. This understanding of how a user's behavior differs under stress could be useful in the development of effective adaptive systems that can maximize user potential.

Gaze estimation has widespread applications. However, little work has explored gaze estimation on smartphones, even though they are fast becoming ubiquitous. This paper presents ScreenGlint, a novel approach which exploits the glint (reflection) of the screen on the user's cornea for gaze estimation, using only the image captured by the front-facing camera. We first conduct a user study on common postures during smartphone use. We then design an experiment to evaluate the accuracy of ScreenGlint under varying face-to-screen distances. An in-depth evaluation involving multiple users is conducted and the impact of head pose variations is investigated. ScreenGlint achieves an overall angular error of 2.44º without head pose variations, and 2.94º with head pose variations. Our technique compares favorably to state-of-the-art research works, indicating that the glint of the screen is an effective and practical cue to gaze estimation on the smartphone platform. We believe that this work can open up new possibilities for practical and ubiquitous gaze-aware applications.

This paper presents an investigation into user intention prediction in two common web-based tasks: crowdsourcing annotation and web search, based on human-mouse interaction information. User experience is gaining importance within the research area of human-centered computing, and is particularly useful for complex, multi-step tasks. To enhance user experience, the computer should be intelligent enough to be able to predict the user intention. For instance, an intelligent agent might be able to anticipate when the user is about to press a button, and helpfully enlarge or highlight it in advance. In this paper, we propose two prediction models on user intention: a classical model that considers only historical mouse activity sequence, and a multimodal model that utilizes mouse interaction signals as well as features extracted from mouse trajectory and clicking events. We evaluate our models and find that they achieve reasonable accuracy. Our preliminary results indicate that we can dynamically learn a multimodal model that can effectively predict a user's next activity from historical activity sequence and mouse interaction signals.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

We present PACE, a Personalized, Automatically Calibrating Eye-tracking system that identifies and collects data unobtrusively from user interaction events on standard computing systems without the need for specialized equipment. PACE relies on eye/facial analysis of webcam data based on a set of robust geometric gaze features and a two-layer data validation mechanism to identify good training samples from daily interaction data. The design of the system is founded on an in-depth investigation of the relationship between gaze patterns and interaction cues, and takes into consideration user preferences and habits. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Quantitative evaluation on 31 subjects across different interaction behaviors shows that training instances identified by the PACE data collection have higher gaze point-interaction cue consistency than those identified by conventional approaches. An in-situ study using real-life tasks on a diverse set of interactive applications demonstrates that the PACE gaze estimation achieves an average error of 2.56º, which is comparable to state-of-the-art, but without the need for explicit training or calibration. This demonstrates the effectiveness of both the gaze estimation method and the corresponding data collection mechanism.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Stress sensing is valuable in many applications, including online learning crowdsourcing and other daily human-computer interactions. Traditional affective computing techniques investigate affect inference based on different individual modalities, such as facial expression, vocal tones, and physiological signals or the aggregation of signals of these independent modalities, without explicitly exploiting their inter-connections. In contrast, this paper focuses on exploring the impact of mental stress on the coordination between two human nervous systems, the somatic and autonomic nervous systems. Specifically, we present the analysis of the subtle but indicative pattern of human gaze behaviors surrounding a mouse-click event, i.e. the gaze-click pattern. Our evaluation shows that mental stress affects the gaze-click pattern, and this influence has largely been ignored in previous work. This paper, therefore, further proposes a non-intrusive approach to inferring human stress level based on the gaze-click pattern, using only data collected from the common computer webcam and mouse. We conducted a human study on solving math questions under different stress levels to explore the validity of stress recognition based on this coordination pattern. Experimental results show the effectiveness of our technique and the generalizability of the proposed features for user-independent modeling. Our results suggest that it may be possible to detect stress non-intrusively in the wild, without the need for specialized equipment.

This paper presents Personalized Affect Detection with Minimal Annotation (PADMA), a user-dependent approach for identifying affective states from spontaneous facial expressions without the need for expert annotation. The conventional approach relies on the use of key frames in recorded affect sequences and requires an expert observer to identify and annotate the frames. It is susceptible to user variability and accommodating individual differences is difficult. The alternative is a user-dependent approach, but it would be prohibitively expensive to collect and annotate data for each user. PADMA uses a novel Association-based Multiple Instance Learning (AMIL) method, which learns a personal facial affect model through expression frequency analysis, and does not need expert input or frame-based annotation. PADMA involves a training/calibration phase in which the user watches short video segments and reports the affect that best describes his/her overall feeling throughout the segment. The most indicative facial gestures are identified and extracted from the facial response video, and the association between gesture and affect labels is determined by the distribution of the gesture over all reported affects. Hence both the geometric deformation and distribution of key facial gestures are specially adapted for each user. We show results that demonstrate the feasibility, effectiveness and extensibility of our approach.

Presents a listing of the Symposium organizing committee.

The amount of data stored in databases is increasing at a tremendous rate. They are oftentimes stored over the web to be accessed by various clients. One useful and interesting query to a collection of databases is to capture a consistent snapshot of a set of interested attributes across the databases. While traditional snapshot algorithms for a distributed database could be adopted, they are mainly designed for database recovery and are costly to execute. We need efficient algorithms to return good-enough snapshots for querying purposes, in the absence of a consistent but costly snapshot as defined in distributed systems. In this paper, we propose the notion of an approximate consistent snapshot by considering and minimizing the deviation of the collected snapshot covering the interested attributes from a reference absolute snapshot. There are several variations of this kind of approximate snapshots. We propose effective algorithms to capture such snapshots in a collection of databases and then conduct performance evaluation on their efficiency. We believe that this notion of approximate consistency would be useful in practical situations.

Social signal processing is becoming an important topic in affective computing. In this paper, we focus on an important social interaction in real life, namely, fighting. Fight detection will be useful in public transportation, prisons, bars, or even sport. A robust mechanism in detecting fights from a video will be extremely useful, especially in applications relevant to surveillance systems. Recent research works focus on extracting visual features from high resolution video, leading to computationally expensive systems. In this paper, we propose an approach to detect fights in a natural and robust way based on motion analysis, which is not only intuitive, but also robust. Experimental results show that we can accurately detect fight activities in different video surveillance settings.

We present a vision-based, data-driven approach to identifying and measuring refractive errors in human subjects with low-cost, easily available equipment and no specialist training. Vision problems, such as refractive error (e.g. nearsightedness, astigmatism, etc) are common ocular problems, which, if uncorrected, may lead to serious visual impairment. The diagnosis of such defects conventionally requires expensive specialist equipment and trained personnel, which is a barrier in many parts of the developing world. Our approach aims to democratize optometric care by utilizing the computational power inherent in consumer-grade devices and the advances made possible by multimedia computing. We present results that show our system is able to match and outperform state-of-the-art medical devices under certain conditions.

This paper presents KID, an interactive app on a smart device, designed to facilitate and encourage young children to learn and practice Chinese characters. It relies on pen dynamics to extract the strokes and map the written character to the proper one. The stroke orientation is also analyzed for ordering and spatial alignment features that pinpoint common errors. A visual pictorial feedback is then provided to motivate children and to arouse their interest. We iterate the prototype design and implementation upon collecting feedback from focus group interviews, from where the system is greeted with positive comments.

Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06º, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.

As computer and interaction technologies mature, a much broader range of media is now used for input and output, each of which has its own rich repertoire of techniques, instruments, and cultural heritage. The combination of multiple media can produce novel multimedia human-computer interaction approaches which are more efficient and interesting than traditional single media methods. This paper presents CalliPaint, a system for cross-modal art generation that links together Chinese ink brush calligraphy writing and Chinese landscape painting. We investigate the mapping between the two modalities based on concepts of metaphoric congruence, and implement our findings into a prototype system. A multi-step evaluation experiment with real users suggests that CalliPaint provides a realistic and intuitive experience that allows even novice users to create attractive landscape paintings from writing. Comparison with a general-purpose digital painting software suggests that CalliPaint provides users with a more enjoyable experience. Finally, exhibiting CalliPaint in an open-access location for use by casual users without any training shows that the system is easy to learn.

This article presents the design and development of i*CATch, a construction kit for physical and wearable computing that was designed to be scalable, plug-and-play, and to provide support for iterative and exploratory learning. It consists of a standardized construction interface that can be adapted for a wide range of soft textiles or electronic boards, a set of functional components, and an easy-to-use hybrid text-graphical integrated development environment. The objective was to design an easily usable, manufacturable and extensible construction kit that can be used in a wide range of teaching tasks for a wide variety of student demographic profiles. We present detailed specifications of our construction kit and explain some of the major design decisions. Experiences in using the kit in multiple teaching environments, ranging from elementary school to postgraduate, demonstrate that the design objectives have been achieved.

In this paper, we propose an efficient and scalable query processing framework for continuous spatial queries (range and k-nearest-neighbor queries) in mobile peer-to-peer (P2P) environments, where no fixed communication infrastructure or centralized/distributed servers are available. Due to the limitations in mobile P2P environments, for example, user mobility, limited battery power, limited communication range, and scarce communication bandwidth, it is costly to maintain the exact answer of continuous spatial queries. To this end, our framework enables the user to find an approximate answer with quality guarantees. In particular, we design two key features to adapt continuous spatial query processing to mobile P2P environments. 1) Each mobile user can specify his or her desired quality of services (QoS) for a query answer in a personalized QoS profile. The QoS profile consists of two parameters, namely, coverage and accuracy. The coverage parameter indicates the desired level of completeness of the available information for computing an approximate answer, and the accuracy parameter indicates the desired level of accuracy of the approximate answer. 2) We design a continuous answer maintenance scheme to enable the user to collaborate with other peers to continuously maintain a query answer. With these two features in our framework, the user can obtain a query answer from a local cache if the answer satisfies his or her QoS requirements. Otherwise, the user enlists neighbors for help to share their cached information to refine the answer. If the refined answer still cannot satisfy the QoS requirements, the user broadcasts the query to the peers residing within the required search area of the query to find the most accurate answer. Experiment results show that our framework is efficient and scalable and provides an effective trade-off between the communication overhead and the quality of query answers.

In this paper, we present a new type of spatial queries called Nearest Surrounder (NS) queries. An NS query determines the nearest polygon-shaped spatial objects (referred to as nearest surrounder objects) and their orientations with respect to a query point from an object set. Besides, we derive two NS query variants, namely, multitier NS (m-NS) queries and angle-constrained NS (ANS) queries. An m-NS query searches multiple layers of NS objects for the same range of angles from a query point. An ANS query searches for NS objects within a specified range of angles. To evaluate NS queries and their variants, we explore angle-based and distance-based bound properties of polygons, and devise two efficient algorithms, namely, Sweep and Ripple, based on R-tree. The algorithms access objects in an order according to their orientations and distances with respect to a given query point, respectively. They are efficient as they can finish a search with one index lookup. Besides, they can progressively deliver a query result. Through empirical studies, we evaluate the proposed algorithms and report their performance for both synthetic and real object sets.

Navigational path query, one of the most popular location-based services (LBSs), determines a route from a source to a destination on a road network. However, issuing path queries to some non-trustworthy service providers may pose privacy threats to the users. For instance, given a query requesting for a path from a residential address to a psychiatrist, some adversaries may deduce "who is related to what disease". In this paper, we present an obfuscator framework that reduces the likelihood of path queries being revealed, while supporting different user privacy protection needs and retaining query evaluation efficiency. The framework consists of two major components, namely, an obfuscator and an obfuscated path query processor. The former formulates obfuscated path queries by intermixing true and fake sources and destinations and the latter facilitates efficient evaluation of the obfuscated path queries in an LBS server. The framework supports three types of obfuscated path queries, namely, independent obfuscated path query, shared obfuscated path query, and anti-collusion obfuscated path query. Our proposal strikes a balance between privacy protection strength and query processing overheads, while enhancing privacy protection against collusion attacks. Finally, we validate the proposed ideas and evaluate the performance of our framework based on an extensive set of empirical experiments.

Directions search returns the shortest path from a source to a destination on a road network. However, the search interests of users may be exposed to the service providers, thus raising privacy concerns. For instance, a path query that finds a path from a resident address to a clinic may lead to a deduction about "who is related to what disease". To protect user privacy from accessing directions search services, we introduce the OPAQUE system, which consists of two major components: (1) an obfuscator that formulates obfuscated path queries by mixing true and fake sources/destinations; and (2) an obfuscated path query processor installed in the server for obfuscated path query processing. OPAQUE reduces the likelihood of path queries being revealed and allows retrieval of requested paths. We propose two types of obfuscated path queries, namely, independently obfuscated path query and shared obfuscated path query to strike a balance between privacy protection strength and query processing overhead, and to enhance privacy protection against collusion attacks.

Abstract A dialogue‐based interface for information systems is considered a potentially very useful approach to information access. A key step in computer processing of natural‐language dialogues is dialogue‐act (DA) recognition. In this paper, we apply a feature‐based classification approach for DA recognition, by using the maximum entropy (ME) method to build a classifier for labeling utterances with DA tags. The ME method has the advantage that a large number of heterogeneous features can be flexibly combined in one classifier, which can facilitate feature selection. A unique characteristic of our approach is that it does not need to model the prior probability of DAs directly, and thus avoids the use of a discourse grammar. This simplifies the implementation of the classifier and improves the efficiency of DA recognition, without sacrificing the classification accuracy. We evaluate the classifier using a large data set based on the Switchboard corpus. Encouraging performance is observed; the highest classification accuracy achieved is 75.03%. We also propose a heuristic to address the problem of sparseness of the data set. This problem has resulted in poor classification accuracies of some DA types that have very low occurrence frequencies in the data set. Preliminary evaluation shows that the method is effective in improving the macroaverage classification accuracy of the ME classifier.

One way to improve the throughput of a wireless ad hoc network at the media access (MAC) layer is to allow as much as possible concurrent transmissions among neighboring nodes. In this paper, we present a novel high-throughput MAC protocol, called Concurrent Transmission MAC(CTMAC), which supports concurrent transmission while allowing the network to have a simple design with a single channel, single transceiver, and single transmission power architecture. CTMAC inserts additional control gap between the transmission of control packets (RTS/CTS) and data packets (DATA/ACK), which allows a series of RTS/CTS exchanges to take place between the nodes in the vicinity of the transmitting or receiving node to schedule possible multiple, concurrent data transmissions. To safeguard the concurrent data transmission, collision avoidance information is included in the control packets and used by the neighboring nodes to determine whether they should begin their transmissions. Also, to isolate the possible interference between DATA packets and ACK packets, a new ACK sequence mechanism is proposed. Simulation results show that a significant gain in throughput can be obtained by the CTMAC protocol compared with the existing work including the IEEE 802.11 MAC protocol.

In distance-based location update schemes with a predefined distance threshold d, an object reports its location to the location server, whenever it is located more than a distance of d away from the location expected of by the server. Adopting a small threshold can keep locations maintained in the location server close to exact object locations, but that incurs high location update costs. In this paper, we address the important issue of finding an optimal distance threshold. Our approach exploits a costfunction that takes into account location update and query processing costs, the two key performance costs, based on which an optimal threshold that minimizes the overall cost is derived. In dynamic environments, costs may vary over time, so a threshold good at one moment could become bad at another. To determine an optimal threshold adaptively, we propose two optimization algorithms, namely, conjectural algorithm and progressive algorithm. Conjectural optimization algorithm " guesses" the current system conditions, based on which it directly determines the most probable optimal value. Progressive optimization algorithm starts with a certain threshold value and adjusts it gradually towards the optimal point. To evaluate our proposed algorithms, various simulation studies are conducted and significant performance gain is observed with our algorithms.

In a mobile cooperative caching environment, we observe the need for cooperating peers to cache useful data items together, so as to improve cache hit from peers. This could be achieved by capturing the data requirement of individual peers in conjunction with their mobility pattern, for which we realized via a GROup-based COoperative CAching scheme (GroCoca). In GroCoca, we define a tightly-coupled group (TCG) as a collection of peers that possess similar mobility pattern and display similar data affinity. A family of algorithms is proposed to discover and maintain all TCGs dynamically. Furthermore, two cooperative cache management protocols, namely, cooperative cache admission control and replacement, are designed to control data replicas and improve data accessibility in TCGs. A cache signature scheme is also adopted in GroCoca in order to provide information for the mobile clients to determine whether their TCG members are likely caching their desired data items and to perform cooperative cache replacement Experimental results show that GroCoca outperforms the conventional caching scheme and standard COoperative CAching scheme (COCA) in terms of access latency and global cache hit ratio. However, GroCoca generally incurs higher power consumption.

Many general range query schemes for DHT-based peer-to-peer (P2P) systems have been proposed, which do not need to modify the underlying DHTs. However, most existing works have the query delay depending on both the scale of the system and the size of the query space or the specific query, and thus cannot guarantee to return the query results in a bounded delay. In this paper, we propose Armada, an efficient general range query scheme to support single-attribute and multipleattribute range queries. Armada is the first delaybounded range query scheme over constant-degree DHTs, and can return the results for any range query within 2logN hops in a P2P system with N peers. Results of analysis and simulations show that the average delay of Armada is less than logN, and the average message cost of single-attribute range queries is about logN+2n..2 (n is the number of peers that intersect with the query). These results are very close to the lower bounds on delay and message cost of range queries over constant-degree DHTs.

In this paper, we study a new type of spatial query, Nearest Surrounder (NS), which searches the nearest surrounding spatial objects around a query point. NS query can be more useful than conventional nearest neighbor (NN) query as NS query takes the object orientation into consideration. To address this new type of query, we identify angle-based bounding properties and distance-bound properties of Rtree index. The former has not been explored for conventional spatial queries. With these identified properties, we propose two algorithms, namely, Sweep and Ripple. Sweep searches surrounders according to their orientation, while Ripple searches surrounders ordered by their distances to the query point. Both algorithms can deliver result incrementally with a single dataset lookup. We also consider the multiple-tier NS (mNS) query that searches multiple layers of NSs. We evaluate the algorithms and report their performance on both synthetic and real datasets.

Moving object databases (MODs), the core component of location server to support location-related applications, keep track of the locations of moving objects which submit location update reports to the centralized server. In resource-limited wireless environments, the frequency and conditions for generating location update messages exert a strong impact on system performance in terms of update message cost and object location accuracy, hence the query result precision. Conceptually, moving objects are the sources of the location data while the MOD caches recently reported object locations for query processing. Owing to the inherent imprecision of the cached values, we impose a bounded level of inconsistency for the cached values, realized in the form of a "safe range" for a moving object. The cached value needs not be invalidated so long as the deviation of the object's current location from its reported location is within the safe range. A smaller safe range results in a higher accuracy of the cached value and hence more accurate query result at the expense of higher update cost, and vice versa. Since the size of the safe range is the key to system performance, we derive a system cost model to determine its appropriate value. Furthermore, to cater for highly dynamic environments in which object movement, query access pattern and system workload always change, we propose two adaptive safe range adjustment algorithms. Through extensive simulation experiments, the benefits brought about by our algorithms are evidenced

In wireless sensor networks, energy is the most crucial resource. In-network data processing is a common technique in which an intermediate proxy node is chosen to house a possibly complicated data transformation function to consolidate the sensor data streams from the source nodes, en route to the sink node. We investigate into the placement problem of the proxy. We formulate and solve the energy minimization problem analytically, based on an ENergy- Efficient Rate-Governed Yardstick (ENERGY). An optimal solution is derived based on complete network topology information. Taking into account realistic sensor network constraints that only neighboring network connectivity is known to a node, we develop an approximate but effective solution, ENERGY . We evaluate the performance of ENERGY, which performs well even in low-density networks and for queries requesting from data sources at a distance.

Caching is a key technique for improving data retrieval performance of mobile clients. The emergence of state-of-the-art peer-to-peer communication technologies now brings to reality what we call "cooperative caching" in which mobile clients not only can retrieve data items from mobile support stations, but also from the cache in their peers, thereby inducing a new dimension for mobile data caching. In this paper, we propose a distributed group-based cooperative caching scheme, in which we define the concept of a tightly-coupled group (TCG) by capturing the data affinity of individual peers and their mobility patterns, in a mobile broadcast environment. A distributed stable peer discovery protocol is proposed for discovering all TCGs dynamically. In addition, a cache signature scheme is adopted to provide hints for the mobile clients to determine whether their required data items are cached by their neighboring peers, and to perform cooperative cache replacement to increase overall data availability. Simulation studies are conducted to evaluate the effectiveness of our distributed group-based cooperative caching scheme.

Predictive continuous nearest neighbor queries are concerned with finding the nearest neighbor objects for some future time period according to the current object and query locations and their motion information. Existing continuous query processing algorithms are not efficient enough, requiring multiple dataset lookups to evaluate the query results throughout the duration of a continuous query. More importantly, the complete result for the whole query time interval is only available at the moment when all object motion updates have been examined, based on which adjustment of the query result is made. In this paper, we propose an algorithm which requires only one dataset lookup to deliver a complete predictive result. We then apply a differential update technique to maintain the query results incrementally in the presence of object location and motion updates.

Caching is a key technique for improving data retrieval performance of mobile clients in mobile environments. The emergence of robust and reliable peer-to-peer (P2P) technologies now brings to reality what we call "cooperative caching" in which mobile clients can access data items from the cache in their neighboring peers. This paper considers a COoperative CAching scheme for mobile systems, called COCA. A cache signature scheme is devised for COCA that provides hints for the mobile clients to determine whether a required data item is cached by their neighboring peers based on their local state. The trade-off between the improvement in system performance and the overheads of the cache signature scheme in COCA is discussed. The performance of COCA with and without the cache signature scheme is evaluated through a number of simulated experiments. COCA is shown to be capable of effectively reducing the number of server requests and power consumption, as well as shortening the access latency as the number of neighboring peers increases. The inclusion of cache signature scheme further improves on the access latency.

A load balancing scheme is presented which allows HTTP requests to be dynamically migrated between clustered back-end Web servers based on the loading condition of the system. We adopt a nearest neighborhood clustering algorithm whereby an adaptive number of requests are migrated as determined by the real-time distribution of load among the servers. Experiment results demonstrate that our proposed algorithm yields the best performance when compared with several other common approaches.

Mobility of hosts increases the system flexibility, but also costs some problems in management. In our model, we consider an (ad hoc) arbitrary wireless backbone with some mobile hosts, which we call mobile agents. A mobile agent can visit every node in the backbone. It can store data in any nodes, invoke processes for computation and waiting for messages. The output of computation, messages, and the stored data is sent back to the home of the mobile agent upon request or whenever the node needs the space for other purpose. We give protocols (1) for the mobile agents to store its data to a foreign node, and (2) for a node to collect some buffer space occupied by other mobile agent(s), and (3) for the a node to collect the data stored outside by its mobile agent.

Caching is a key technique for improving data retrieval performance of mobile clients. The emergence of robust and reliable peer-to-peer (P2P) communication technologies now brings to reality what we call "cooperating caching" in which mobile clients not only can retrieve data items from mobile support stations, but also can access them from the cache in their neighboring peers, thereby inducing a new dimension for mobile data caching. This work extends a cooperative caching scheme, called COCA, in a pull-based mobile environment. Built upon the COCA framework, we propose a group-based cooperative caching scheme, called GroCoca, in which we define a tightly-coupled group (TCG) as a set of peers that possess similar movement pattern and exhibit similar data affinity. In GroCoca, a centralized incremental clustering algorithm is used to discover all TCGs dynamically, and the MHs in same TCG manage their cached data items cooperatively. In the simulated experiments, GroCoca is shown to reduce the access latency and server request ratio effectively.

The formulation of a context-aware middleware requires researchers to devise suitable control mechanisms that allow applications to directly participate in resource adaptation in response to dynamic operating environments. This paper describes the design and implementation of an event model for a highly adaptive mobile middleware, Web Proxy for Actively Deployable Services (WebPADS) platform. The event model provides a highly composable event notification framework that uses multiple levels of environment monitors to provide a complex setup of composite events. Based on the event model and the dynamic reconfiguration feature WebPADS supports context awareness and a high level of adaptation to contextual changes through reconfiguration and migration of services.

XML (extensible Markup Language) has been developed and deployed by domain-specific standardization bodies and commercial companies. Studies have been conducted on a wide variety of issues encompassing XML. In the use of XML for wireless computing, the focus has been on investigating ways to efficiently represent XML data for transmission over a wireless environment. We propose a middleware, Xstream (XML Streaming), for efficiently streaming XML contents over a wireless environment by leveraging the rich semantics and structural characteristics of XML documents and by flexibly managing units containing fragments of data into autonomous units, known as XDU (Xstream Data Unit) fragments. The concept of an XDU is fundamental to the operation of Xstream. It provides for the efficient transfer of documents across a wireless link and allows other issues and challenges pertaining to wireless transmission to be addressed. By fragmenting and organizing an XML document into XDU fragments, we are able to incrementally send fragments across a wireless link, while the receiver is able to perform look-ahead processing of the document without having to wait for the entire document to be downloaded. We propose a fragmenting strategy based on the value of the wireless link's Maximum Transfer Units (MTUs). In addition, we present and evaluate several packetizing strategies, i.e., strategies wherein a collection of XDUs are grouped into a packet to optimize packet delivery and processing. At the receiving end of this process, a reassembly strategy incrementally reconstructs the XML document as XDU fragments are being received, thereby facilitating client application implementation of look-ahead processing.

XML (eXtensible Markup Language) has been developed and deployed by domain-specific standardization bodies and commercial companies. We investigate the possibilities and issues encompassing the use of generalized XML in a wireless computing environment. Current approaches of fragmenting data do not take into account of the semantics and structure of the data, therefore ignoring the specific needs of individual application. We propose a middleware, Xstream (XML Streaming) for augmenting XML contents by leveraging on the rich semantics and structural characteristics of the XML document into autonomous units, which are known as XDU (Xstream Data Unit). In this paper we describe the framework and the techniques involved and study the performance of the techniques.

A distributed virtual walkthrough environment allows users connected to the geometry server to walk through a specific place of interest, without having to travel physically. This place of interest may be a virtual museum, virtual library or virtual university. There are two basic approaches to distribute the virtual environment from the geometry server to the clients, complete replication and on-demand transmission. Although the on-demand transmission approach saves waiting time and optimizes network usage, many technical issues need to be addressed in order for the system to be interactive. CyberWalk is a web-based distributed virtual walkthrough system developed based on the on-demand transmission approach. It achieves the necessary performance with a multiresolution caching mechanism. First, it reduces the model transmission and rendering times by employing a progressive multiresolution modeling technique. Second, it reduces the Internet response time by providing a caching and prefetching mechanism. Third, it allows a client to continue to operate, at least partially, when the Internet is disconnected. The caching mechanism of CyberWalk tries to maintain at least a minimum resolution of the object models in order to provide at least a coarse view of the objects to the viewer. All these features allow CyberWalk to provide sufficient interactivity to the user for virtual walkthrough over the Internet environment. In this paper, we demonstrate the design and implementation of CyberWalk. We investigate the effectiveness of the multiresolution caching mechanism of CyberWalk in supporting virtual walkthrough applications in the Internet environment through numerous experiments, both on the simulation system and on the prototype system.

The mobile environment is inherently asymmetric. To utilize the downstream bandwidth effectively, hot data items should be disseminated over the broadcast channel to the mobile clients. To equip clients with the ability to identify the nature of the broadcast and to determine the answerability of their queries, semantic descriptions are associated with information units in the broadcast, organized into data chunks. Based on the nature of data items received over the broadcast, clients can initiate appropriate requests to make up for the remaining items over the back channels in an on-demand basis. We investigation into several semantic-based algorithms to organize data chunks and compare their performance with traditional data item-based organization in the asymmetric environment.

Abstract XML holds the promise to yield (1) a more precise search by providing additional information in the elements, (2) a better integrated search of documents from heterogeneous sources, (3) a powerful search paradigm using structural as well as content specifications, and (4) data and information exchange to share resources and to support cooperative search. We survey several indexing techniques for XML documents, grouping them into flat‐file, semistructured, and structured indexing paradigms. Searching techniques and supporting techniques for searching are reviewed, including full text search and multistage search. Because searching XML documents can be very flexible, various search result presentations are discussed, as well as database and information retrieval system integration and XML query languages. We also survey various retrieval models, examining how they would be used or extended for retrieving XML documents. To conclude the article, we discuss various open issues that XML poses with respect to information retrieval and database research.

Abstract The event service of the Common Object Request Broker Architecture (CORBA) is useful in supporting decoupled and asynchronous communication between distributed object components. However, the specification of the event service standard does not require implementation to provide facilities to guarantee efficient event/data delivery. Consequently, applications in which a large number of objects need to communicate via an event service channel may suffer from poor performance. In this paper, a generic CORBA‐based framework is proposed to tackle this scalability problem. Two techniques are applied; namely, event channel federation and load balancing. The solution is transparent in the sense that it exports the same IDL interface as the original event service. We explore three critical dimensions underlying the design of the load‐balancing algorithm and conduct experiments to evaluate their impact on the overall performance of the framework. The results provide some useful insights into the improvement of the scalability of the event service. Copyright © 2002 John Wiley & Sons, Ltd.

Database broadcast is an effective and scalable approach to disseminate information of high affinity to a large collection of mobile clients. A common problem of existing broadcast approaches is the lack of knowledge for a client to determine if all data items satisfying its query could be obtained from the broadcast. We therefore propose a semantic-based broadcast approach. A semantic descriptor is attached to each broadcast unit, called a data chunk. This semantic descriptor allows a client to determine if a query can be answered entirely based on broadcast items and, if needed, identify the precise definition of the remaining items in the form of a "supplementary" query. Data chunks can be of static or dynamic sizes and organized hierarchically. Their boundary can be determined on-the-fly, adaptive to the nature of client queries. We investigate different ways of organizing the data chunks over a broadcast channel to improve access performance. We introduce the data affinity index metric, which more accurately reflects client-perceived performance. A simulation model is built to evaluate our semantic-based broadcast schemes.

We consider the view maintenance problem in a web-based environment, in which c l i e n ts query information from databases, stored in the form of materialized data warehouses, without accessing the original data sources.In addition to base data, data w arehouses also con tain highly aggregated and summarized information suitable for decision support.As changes are made to the data sources, the warehouse views must be updated to re ect a consistent state of the data sources.Recomputation is often too expensive.We describe in this paper an incremental view maintenance method based on the net c hanges on base table, to maintain the views eciently in the presence of aggregation and summary information.An architecture of a web-based data w arehousing system is described to minimize the workload of the data warehouse.Finally, w e d e v elop a prototype application utilizing the new maintenance algorithm and conduct a performance study based on the prototype, comparing it with existing maintenance algorithm.

Data broadcast is an effective approach to disseminate information from a database server to numerous mobile clients in a mobile environment. Since a broadcast session contains only a subset of the database items, a client might not be able to obtain all its items from the broadcast and is forced to request additional ones from the server on demand. We describe a semantic-based broadcast approach which attaches a semantic description to each broadcast unit, called a chunk, which is a cluster of data items. This allows a client to determine if a query can be answered entirely using a broadcast as well as defining the precise nature of the remaining items in the form of a "supplementary" query. Chunks could be of different sizes and are hierarchically organized. We propose a heuristic to schedule the broadcast order of the chunks to improve the tuning time, access time, and a new metric called a data affinity index. The performances are evaluated via experiments based on a simulation model.

A mobile environment is weakly-connected, characterized by low communication bandwidth and poor connectivity. The conventional paradigm for surfing mobile Web documents is ineffective since portions of a document could be corrupted during transmission and it is expensive to re-transmit the whole document. It is important that the high content-bearing portions should be transmitted successfully so that a mobile client could at least obtain a high level content and determine if the corrupted portions need to be retransmitted. We have proposed a multi-resolution transmission paradigm which allows higher content-bearing portions of a Web document to be transmitted, by partitioning it into multiple organizational units and associating an information content with each unit. The client can explore the higher content-bearing portion earlier and terminate browsing an irrelevant document sooner. We extend our previous work and propose a fault-tolerant multi-resolution transmission scheme which allows units of higher information content to be recovered from transmission error. The client can obtain an overall content of a Web document and either terminate the transmission of the remaining portions or decide if the corrupted portions need to be retransmitted. We demonstrate its feasibility with a prototype and with simulation results.

Advanced Traveler Information Systems (ATIS) require efficient information retrieval and updating in a dynamic environment at different geographical scales. ATIS applications are useful in yielding a better utilization of the limited costly transportation arteries and providing value-added traveler information. Many ATIS applications are built on the functionalities provided by Geographical Information Systems (GIS), which often cannot meet extra requirements like real-time response. We investigate GIS-based systems in ATIS and propose a system architecture based on GIS and distributed database technology. Issues on data modeling, data representation, storage and retrieval, data aggregation, and parallel processing of queries are discussed. This paper introduces a distributed system architecture for ATIS based on recent technology. It presents new data models for information representation and proposes data shipping for efficient query processing and function shipping for reducing communication overhead. The paper also examines the use of a network of computers for solving complex problems more timely and privacy protection for sensitive data.

Article Free Access Share on Incremental maintenance for dynamic database-derived HTML pages in digital libraries Authors: Ken C. K. Lee Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong kong Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong kongView Profile , Hong V. Leong Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong kong Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong kongView Profile , Antonio Si Sun Microsystems Inc., Palo Alto, CA Sun Microsystems Inc., Palo Alto, CAView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 20–29https://doi.org/10.1145/288627.288637Online:01 November 1998Publication History 2citation619DownloadsMetricsTotal Citations2Total Downloads619Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Communication between mobile clients and database servers in a mobile computing environment is via wireless channels with low bandwidth and low reliability. A mobile client could cache its frequently accessed database items into its local storage in order to improve performance of database queries and availability of database items for query processing during disconnection. We describe a mobile caching mechanism for a mobile environment utilizing point to point communication paradigm. In particular, we investigate issues on caching granularity, coherence strategy, and replacement policy of mobile caching. Via a detailed simulation model, we compare our proposed caching mechanism with conventional ones and discover that our mobile caching mechanism outperforms conventional ones in most situations.

Article Free Access Share on On caching and prefetching of virtual objects in distributed virtual environments Authors: Jimmy H. P. Chim Department of Computing, The Hong Kong Polytechnic University, Hong Kong Department of Computing, The Hong Kong Polytechnic University, Hong KongView Profile , Mark Green Department of Computer Science, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada Department of Computer Science, University of Alberta, Edmonton, Alberta, T6G 2H1, CanadaView Profile , Rynson W. H. Lau Department of Computer Science, City University of Hong Kong, Hong Kong Department of Computer Science, City University of Hong Kong, Hong KongView Profile , Hong Va Leong Department of Computer Science, City University of Hong Kong, Hong Kong Department of Computer Science, City University of Hong Kong, Hong KongView Profile , Antonio Si Sun Microsystems, 901 San Antonio Road, Palo Alto, CA Sun Microsystems, 901 San Antonio Road, Palo Alto, CAView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 171–180https://doi.org/10.1145/290747.290769Published:01 September 1998Publication History 51citation594DownloadsMetricsTotal Citations51Total Downloads594Last 12 Months27Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Distributed virtual environments allow users at different geographical locations to share and interact within a common virtual environment via a local network or through the Internet. To deliver a good performance for such applications, we need to address several issues in different research disciplines. First, we must be able to model virtual objects effectively. The recently developed multi-resolution techniques for object modeling are of great value here, since they are capable of simplifying the object models and therefore reducing the time to render them. This may greatly reduce the demand for rendering performance on the client machines. Second, with the constraint of the limited bandwidth of the Internet, we need to reduce the response time by reducing the amount of data requested over the network. Caching of suitable object models of high affinity will reduce the amount of data requested over the network for a faster response time. Prefetching object models by predicting those ...

Previous research on broadcast databases in a mobile computing environment utilizing wireless channels has been focused on mechanisms for a mobile client to selectively pick database items in which the client is interested from a broadcast channel. The fundamental issue of identifying the appropriate database items for broadcast or refrained from being broadcast have largely been ignored. In this paper, we consider the concept of ‘air-storage’, by treating the wireless broadcast media as a layer of cache storage. Broadcasting database items over the air-storage becomes similar in spirit to the caching of database items from the database server. Similarly, determining which database items need to be broadcast or refrained from being broadcast becomes similar in nature to cache management. Existing caching mechanisms are reviewed and management issues specific to the new air-storage are raised and discussed. In view of new issues in air-storage management, we propose and investigate several mechanisms in selecting the proper database items to be placed over this new layer of air-storage under a variety of data access patterns. Finally, the effectiveness of the mechanisms is evaluated by means of simulated experiments and the results are discussed.

Article Free Access Share on Data broadcasting strategies over multiple unreliable wireless channels Authors: Hong V. Leong Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong KongView Profile , Antonio Si Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong KongView Profile Authors Info & Claims CIKM '95: Proceedings of the fourth international conference on Information and knowledge managementDecember 1995 Pages 96–104https://doi.org/10.1145/221270.221339Published:02 December 1995Publication History 30citation412DownloadsMetricsTotal Citations30Total Downloads412Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Recoveryby checkpointing on distributed shared memory systems is investigated in this paper.The no-

Maya is a platform for investigating the impact of different memory coherence protocols on parallel architectures. We present the implementations of several weak memory protocols, together with some new primitives dedicated to weak memories using Maya. The results of some user applications are summarized and the impact of weak memories on the efficiency of these parallel programs is discussed.>

Recovery from failures can be achieved through asynchronous checkpointing and optimistic message logging. These schemes have low overheads during failure-free operations. Central to these protocols is the determination of a maximal consistent global state, which is recoverable. Message semantics is not exploited in most existing recovery protocols to determine the recoverable state. We propose to identify messages that are not influential in a computation through message semantics. These messages can be logically removed from the computation without changing its meaning or result. We show that considering these messages in the recoverable state computation gives rise to recoverable states that dominate the recoverable state defined under conventional model. We then develop an algorithm for identifying these messages. This technique can also be applied to ensure a more timely commitment for output in a distributed computation.>

The concept of a structured distributed shared memory in which memory units are objects is introduced. The coherence of object replicas is maintained by type-specific coherence protocols that are based on the semantics of operations on objects. The aim is to reduce message traffic and operation latency in many common situations. The protocols subsume traditional distributed shared memory protocols based on the read/write model.>