As reliable distributed systems, Blockchains have been widely applied in diverse domains, such as the Internet of Things (IoT). Recent studies have explored Deep Reinforcement Learning (DRL) to enhance blockchain performance. However, existing DRL-based blockchain performance optimization methods rely on implicit and idealized assumptions about node behaviors and transactional workloads, limiting their effectiveness and efficiency on the blockchain with dynamic work-loads and heterogeneous nodes. To alleviate this, we propose CHAINOPT, a novel blockchain performance optimization framework devised for optimal parameter configuration to handle dynamic workloads and heterogeneous nodes. Specifically, we first propose an interaction-aware state representation learning module to model both global system-level and local heterogeneous node feature interactions to generate better state representations. Then, a contrastive learning-enhanced workload identification module is designed to extract discriminative workload-specific state representations to improve workload identification accuracy. Finally, we design a workload-similarity guided policy reuse module to produce effective reuse weights to transfer knowledge from history policies based on workload relevance, thereby improving optimization speed and stability. Extensive experiments show the effectiveness of CHAINOPT in improving blockchain performance, achieving 185.87% higher scalability and 1492.16% stronger security with only a marginal 6.26% latency increase. Moreover, it outperforms baselines in static scenarios while maintaining considerable superiority under varying workloads.

Multiple Aircraft Tracking (MAT) is the foundation of many traffic safety applications in airports. However, experiments show that the state-of-the-art algorithms in Multiple Object Tracking (MOT) deteriorate significantly in the airport scene, and the performance drop can even reach 40%. This is because aircraft and airport scenes possess unique characteristics. For instance, aircraft’s low-compact design causes significant changes in appearance from different angles, while the expansive nature of airports presents challenges in multi-scale issues, particularly at smaller scales. In this paper, we introduce a new dataset Airport Ground Video Surveillance-Tracking24 (AGVS-T24), which could servers as a benchmark to study the challenges in MAT. AGVS-T24 includes 53 airport videos, totaling more than 150,000 frames, and precise manual annotations. AGVS-T24 comprehensively presents various motion patterns of the aircraft, such as takeoff, landing, docking, undocking, taxiing, turning, acceleration, deceleration, etc. AGVS-T24 also contains a variety of MAT challenges, such as appearance change, simultaneous multi-scales, weather and illumination changes, similar appearance between aircraft, as well as tracking in infrared and panoramic modes, and so on. Furthermore, we conduct a simple review of current MOT algorithms, and 20 classic algorithms are selected and tested on AGVS-T24. Finally, we also summarize some principles for designing MAT algorithms. This dataset can be downloaded fromwww.agvs-caac.com/AGVS-T/agvst24.html

Sparse mobile crowdsensing is a cost-effective sensing paradigm that infers global data by sensing data from partial areas in a city. With the rapid development of diverse autonomous mobile agents such as unmanned aerial vehicles (UAVs) and ground vehicles, they have been widely applied in sparse mobile crowdsensing. However, existing works often predefine the role structures and behavioral preferences of these agents in tasks, which significantly limits their flexibility and adaptability, and making it difficult to fully exploit the collaborative potential of crowdsensing agents to efficiently achieve high-quality data sensing. In this paper, we propose an adaptive role learning framework for sparse mobile crowdsensing (ARL-SMCS), which focuses on role recognition for heterogeneous agents and role refinement among homogeneous agents. This framework, based on a multi-agent reinforcement learning model, introduces a variational autoencoder to learn the latent role representations of agents and uses maximum mean discrepancy to distinguish the functionalities of different types of agents. Additionally, ARL-SMCS incorporates an evolutionary algorithm to further refine task preferences among homogeneous agents. This framework overcomes the limitations of static role assignment in adapting to dynamic environments and task conflicts during task execution, significantly improving sensing quality and resource utilization efficiency. Extensive experiments on two real-world datasets demonstrate that ARL-SMCS consistently outperforms other baseline methods under various conditions, including different numbers, endurance, and decision interval lengths.

The exploration of electroencephalogram (EEG) microstates and functional connectivity shows promising potential for both predicting and investigating the neural mechanisms of depression. However, the performance of depression evaluation based on physiological metrics remains unsatisfactory. In this study, we propose a Specific-General Functional Graph Convolutional Network (SGFGCN) to explore biomarkers related to the functional connectivity properties of different EEG microstates. Five microstate topographies, labeled as microstate class A to E, are obtained to describe depressive EEG dynamics, which is highly consistent with the findings of previous studies. Then, incorporating microstate class A to E, our SGFGCN model constructs specific adaptive functional connectivity and general dynamic functional connectivity for each microstate class sequence. It also extracts specific features and general features for each sample using graph convolutional network (GCN). The experimental results, which fuse specific and general depression-related features from resting-state EEG data in the MODMA dataset, demonstrate the superiority of the SGFGCN in identifying depression across various EEG microstate classes compared to previous models. Additionally, based on the recognition results and statistical analysis of the microstates, we confirm that there is a strong correlation between microstate C and depression. These findings suggest the viability of assessing depressive disorders using functional connectivity under certain microstates, offering fresh insights for exploring depression biomarkers.

Empathy is a crucial field of social science research. The current research on the dialog systems with empathy has two main limitations: 1) less adequate integration of the cognition and affection aspects of empathy for enhancing the perceptual and emotional expression abilities and 2) lack of the evaluation of the generated response at the sentence level in the training process for reducing the problem of exposure bias. Therefore, we proposed the reinforcement learning model integrating cognition and affection (RLCA) model that utilizes the RL framework integrating cognition and affection to evoke greater empathetic expression in the model. In particular, the cognitive response generator can reason commonsense information based on the user’s situation to improve the perceptual capabilities of the proposed model. Moreover, the emotional regulator can mitigate the exposure bias problem by distilling multiple emotion signals from predicted responses and imparting higher emotional intelligence to the proposed model. Furthermore, the interaction between the cognition and affection aspects helps the model to learn the features of empathic expressions in human conversation. Extensive experimental findings on a benchmark dataset indicate that the RLCA outperforms the popular baseline models of automatic metrics and human evaluations while generating more interpretable empathetic responses.

Occupational stress has emerged as an undeniable concern. Fortunately, leveraging IoT and AI technologies allows us to gather vital sign data and assess cardiovascular health, individual stress levels, physiological resilience, and emotional states. This study highlights the potential of Heart Rate Variability (HRV) analysis in constructing stress prediction models, with a specific focus on developing an efficient model with minimal data requirements. Convolutional Neural Networks (CNN) have been employed to process raw waveform data for feature extraction. Simultaneously, R-R Interval (RRI) analysis was conducted to derive a set of statistical features. Various dimension reduction algorithms Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Random Projection (RP) were tested, and PCA demonstrated its significance in reducing dataset complexity, enabling swift model training without compromising accuracy excessively. This research aims to explore the feasibility of predicting an individual’s stress levels using minimal and simpler data. The objective is to pave the way for predictive models to be integrated onto lightweight platforms such as millimeter-wave chips. Our approach emphasizes non-contact monitoring of heartbeat variations, particularly beat-to-beat intervals (BBI), offering a novel method for non-invasive stress detection suitable for real-time applications on compact devices.

Spatial-temporal data modeling has attracted attention due to the massive spatial-temporal data acquired by sensors, as well as its importance in the real world. Most existing methods require transferring a huge volume of data from different parties to a central server, which is impractical due to conflicts of benefit and privacy concerns. A party only possesses a part of the entire spatial-temporal data (i.e., a subgraph), and subgraphs are isolated among parties. Federated Learning (FL) is an emerging framework for training models without sharing data, but it still has a high vulnerability when the central server fails. Besides, naively fusing models in most FL may have a negative impact on performance because of insufficient spatial relations among subgraphs and discrepant spatial-temporal patterns among subgraphs. To this end, we propose a Decentralized Subgraph Learning framework for Spatial-Temporal data modeling, namely DeSL-ST, which can efficiently handle the distributed subgraphs without the need of the central server. Specifically, DeSL-ST uses a cross-subgraph spatial relation learning module to tackle the issue of missing spatial relations between subgraphs. Then, a sparse transfer structure learning module is proposed to produce better-personalized models that are beneficial for each subgraph. Experiments on two traffic forecasting tasks demonstrate that DeSL-ST achieves state-of-the-art performance with lower peer-to-peer communication cost.

Human presence detection and water-level detection are two essential functions of smart toilets, making smart toilets more intelligent and hygienic. Traditional solutions require different sensors for each of these functions. Furthermore, existing detection methods in smart toilets have some limitations, such as sensor size, the undesirable effect of environment on detection results, etc. Millimeter-wave (mmWave) radars offer better performance in terms of ranging accuracy and environmental stability. If the mmWave radar is used to achieve the above two functions, the radar system is required to radiate toward the Region of Interest (RoI). The RoI for presence detection is the front of the toilet, while RoI for water-level detection is the toilet bowl. Therefore, the radar needs to radiate signals forward and downward radiation simultaneously, which requires the radar to achieve an orthogonal bidirectional radiation. In this article, we innovatively propose a low-cost radar system with orthogonal bidirectional radiation with good antenna gain and isolation performance by using Vivaldi antennas and wideband high-efficiency electromagnetic structure (WHEMS) antennas. Additionally, appropriate antenna selection gives the antenna system the characteristics of wide bandwidth to match different toilet installation environments with higher reliability. The system can achieve presence detection and water-level detection well, and has the advantages of low cost, small size, and practical application value.

Formal specifications can provide a solid foundation for software development and support for techniques of software quality assurance, such as specification-based inspection and testing. To ensure that these techniques can be applied effectively in practice, efficiently and accurately understanding specifications becomes extremely important. While this may be relatively easy for well-trained developers in formal methods, it can be rather difficult for computer since computer does not easily understand specifications. This difficulty poses a challenge for realizing automatic specification-based verification techniques that are in high demand for reducing development cost and improving software reliability. In this paper, we address this problem by discussing how the formal specification can be transformed into a knowledge graph to provide comprehensible, well-organized details of the specification for developers and computers. The transformation is done by extracting and storing information about attributes of each component and by establishing relationships between components in a formal specification. We elaborate on a top-down approach of constructing a knowledge graph from a specification, including creating an ontology, designing the Entity–Relationship (ER) diagram of the relational database based on the created ontology, extracting and storing attribute and relationship information in the relational database, mapping ontology to its instances and relational data to RDF triples, and displaying knowledge graph. Further, we present a case study to show how our approach works on the formal specification of an ATM system. Finally, we describe three experiments to evaluate its performance in improving specification readability, effectively guiding inspectors to establish traceability links between specifications and programs, and detecting defects through program inspection, respectively.

Linking the components in a formal specification to those in the corresponding program is a prerequisite for formal specification-based program fault detection. Existing traceability link techniques for reducing manpower and time cost suffer from the limitation in effectiveness due to over dependency of textual similarity. Unlike the existing work, this paper presents an automatic method for constructing traceability links between SOFL formal specifications and Java codes, taking semantical, structural, functional, and relational similarities measures into account. It operates at multiple levels of a formal specification, such as data flows, processes, and modules, to establish finegrained link relationships between artifacts. Further, a comparative evaluation of the proposed method, using two selected modules of the SOFL formal specification of a critical ATM system and its Java implementation with 951 code of lines, demonstrates an improvement in precision and more generality than existing latent semantic indexing that is an information retrieval-based method.

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

A recommendation system is an integral part of any modern online shopping or social network platform. The product recommendation system as a typical example of the legacy recommendation systems suffers from two major drawbacks: recommendation redundancy and unpredictability concerning new items (cold start). These limitations take place because the legacy recommendation systems rely only on the user's previous buying behavior to recommend new items. Incorporating the user's social features, such as personality traits and topical interest, might help alleviate the cold start and remove recommendation redundancy. Therefore, in this article, we propose Meta-Interest, a personality-aware product recommendation system based on user interest mining and metapath discovery. Meta-Interest predicts the user's interest and the items associated with these interests, even if the user's history does not contain these items or similar ones. This is done by analyzing the user's topical interests and, eventually, recommending the items associated with the user's interest. The proposed system is personality-aware from two aspects; it incorporates the user's personality traits to predict his/her topics of interest and to match the user's personality facets with the associated items. The proposed system was compared against recent recommendation methods, such as deep-learning-based recommendation system and session-based recommendation systems. Experimental results show that the proposed method can increase the precision and recall of the recommendation system, especially in cold-start settings.

With the alarming increase in breast cancer cases, researchers have considered it a challenging research problem to propose dependable solutions. It is quite essential for early detection, prevention, and control against breast cancer. Existing schemes still does not utilize recent information technology support, and hence preventive measures and factors are also not appropriate. This paper adopts cloud computing to present association rule-based breast cancer prevention and control system. We have categorized our work into two phases. In phase 1 titled prevention and control, we propose item association rule (IAR) algorithm and N-IAR algorithm for n-item associations. It can be used to discover risk factors for breast cancer. Our algorithm discovers more risk factors than the traditional logistics method. Some factors which can be modified are used for breast cancer prevention and control. In addition, existing risk assessment models are not applicable to Chinese women as well. In phase 2, we manage this by introducing a new model based on machine learning. It utilizes real data from Chinese women and more risk factors for breast cancer. Moreover, we have identified and evaluated a number of new common risk factors. Results prove that our system achieves higher assessment values as compared to preliminaries.

With the progressive development of information and communication technologies, we are now forming a new world called hyperworld that is composed by the cyber world and the physical world with various digital explosions including data, connectivity, service and intelligence. Therefore, Cyber-I has been proposed, which is a real individual's counterpart in cyberspace, and is to create a unique, digital, comprehensive description for every individual person. As similar to our human, a Cyber-I once born should be able to grow. Therefore, a Cyber-I's model must be a dynamic one, and can be built successively by utilizing an increasing amount of personal data with adaptive methods. Namely, a growable Cyber-I model is necessary to achieve the adaptation for successive approximations to its corresponding real individual (Real-I). This paper presents our research and development of an adaptable system, called Cyber-I growth modeling system (CGMS). This research is mainly to (1) schedule a Cyber-I's growth according to data and time; (2) manage the quantity of raw data that is involved in a specific growth process; (3) generate the Cyber-I model data with appropriate growth forms, and (4) keep the update records of a Cyber-I model's growth process into a log file in personal database.

Transport infrastructure generates a huge amount of city transportation data due to the significant increasing of advanced devices, such as sensing devices, mobile devices and real-time monitors. However, transportation big data cannot be fully analyzed and utilized by urban traffic data services currently. This paper proposes a novel City Traffic Data-as-a-Service (CTDaaS), which fuses data from distributed providers. Initially, we build an Internet of Traffic Data Service (IoTDS) model to identify associations and relationships among data resources. Then a CTDaaS agent is developed under Transparent Computing paradigm and service oriented architecture. It receives user requests, fuses knowledge from a variety of data sources according to different computing models, and responses differentiated Quality of Data (QoD). Finally, an application scenario, named Park and Ride (P+R), is implemented and evaluated to demonstrate how the service works using existing dynamic city traffic data.

Cooperative data delivery among mobile nodes can improve the performance of data delivery in mobile social networks. However, data routing in the presence of socially selfish (SS) nodes is challenging, where they mitigate the degree of their cooperation level based on their social features and ties to achieve their social objectives. This issue becomes more challenging when they prevent revealing their reactions about incoming messages, which leads data forwarding under uncertain behavior. In this paper, we propose a signaling game approach, namely, Sig4UDD, to study the impact of uncertain cooperation among well-behaved and SS nodes on the performance of data forwarding. In Sig4UDD, we employ Bayesian Nash equilibrium to analyze one-stage interactions among nodes. Then, perfect Bayesian equilibrium is applied to analyze their multistage interactions. In this stage, we establish a belief system to help SS nodes predict the type of their opponents and take appropriate actions to maximize their utilities. To update the beliefs of SS nodes, we devised the weighted social distance metric to measure the global social distance among nodes. Finally, we compare the performance of Sig4UDD to some benchmark cooperative and noncooperative data forwarding protocols using Reality Mining and Social Evolution data sets.

By leveraging online social networks as an underlying infrastructure, Social Participatory Network (SPN) has been becoming a new paradigm of participatory sensing systems. However, a significant barrier to the widespread use of SPN applications is their vulnerability to various forms of malicious attacks. Such threats inhibit human participation and thus the viability of SPN systems in everyday use. To solve this problem, this paper proposes a trust evaluation framework for participants to encourage wider human participation in SPN. The proposal is based on the Tianjin University's own existing SPN system, named CRCS (ClassRoom Cloud System), which enables participants to use the cloud resources for online lessons or library study. It derives the trust value of participants by using entropy-weight method and data mining algorithms to deal with the behaviors data of participants. Our proposed solution can detect malicious participants easily, and more importantly, it outperforms other work for its low cost and simple deployment. For now, though our solution is based on a specified SPN system, we are confident that this solution is highly applicable to most other SPN systems.

Global darknet monitoring provides an effective way to observe cyber-attacks that are significantly threatening network security and management. In this paper, we present a study on characterization of cyberattacks in the big stream data collected in a large scale distributed darknet using association rule learning. The experiment shows that association rule learning in the darknet stream data can support strategic cyberattack countermeasure in the following ways. First, statistics computed from malware-specific rules can lead to better understanding of the global trend of cyberattacks in the Internet. Second, strong association rules can lead to further insights into the nature of the attacking tools and hence expedite the diagnosis. Then, the discovery of emerging new attacks may lead to early detection and prompt prevention of pandemic incidents, preventing damage to the IT infrastructure and extensive financial loss. Finally, exploring the knowledge in the frequent attacking patterns can enable accurate prediction of future attacks from analyzed hosts, which could improve the performance of honeypot systems to collect more pertinent malware information using limited system and network resources.

Participatory sensing [Burke 2006] is an emerging computing paradigm that tasks everyday mobile devices to form participatory sensor networks.It allows the increasing number of mobile phone users to share local knowledge acquired by their sensorenhanced devices, such as monitoring of pollution or noise levels and traffic conditions.The sensing data from volunteer contributors can be further analyzed and processed to form crowd intelligence [Zhang et al. 2011], which can be elaborated into three dimensions: personal awareness, social awareness, and urban awareness.Layered on these concepts, we have raised the new term mobile crowd sensing and computing (MCSC) to characterize crowd intelligence extraction from large-scale and heterogeneous usercontributed data [Guo et al. 2014].A formal definition of MCSC is as follows: a new sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices, then aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery.It has the following three features compared to participatory sensing:-MCSC leverages both sensed data from mobile devices (from the physical space) and user-contributed data from mobile social network services (from the cyber space).In other words, MCSC counts both explicit and implicit user participation for data collection.-Having both online and offline user-contributed data, MCSC highlights the usage of heterogeneous crowdsourced data for crowd intelligence extraction.

Social media facilitates the process of information sharing, and meanwhile, prompts the generation of a considerable amount of data. Although the data, or user-generated contents, enrich the results for the process of information seeking, it causes the complexity to identify the value of data. Thus, an approach that achieves efficient management of user-generated data was proposed. It especially concentrates on the correlations among data and interactions with users. A state machine is designed to identify the user-generated data, and corresponding usage scenarios. The performance and feasibility can be revealed by the experiments sourced by the data collected from open social networks.

This paper proposes an agent based approach to deal with the world wide concerned eco problems, i.e., saving energy consumption and reducing CO2emission. This paper demonstrates a simulated home with the facilities of calculation the average energy consumption and CO2emmision of possible devices or appliances. Various agents for the support of reducing energy consumption and CO2emission at home are designed and deployed in a multi-agent framework. They are working in a collaboration way in the terms of sharing their knowledge resources and working together toward a same goal. It is challenge to make influence on people towards energy saving and CO2reducing life habit and style. This issue is to be discussed in this paper as well.

Radio frequency identification (RFID) is a kind of electronic identification technology that is becoming widely deployed. Compared to traditional RFID system, tags in the proposed smart RFID system would store not only the fixed ID information but also some information which is “active” and encoded in the form of mobile codes indicating the up-to-date situation and associated services' directives. In the proposed system, the service that the RFID tag bearer needs can be explained in a context-aware decision making system to provide a situation-aware system response and offer a good quality of service (QoS).

Grid computing has made rapid strides from their first serving the scientific computing domain to having great impact on the life science area and their use in the daily activities of users from the resource constrained ubiquitous devices such as PDA and mobile phone. To allow ubiquitous devices to use grid services, there is a necessity to having a platform or middleware, a bridge linking the devices to grid services. This paper presents such bridge named BtoG (bridge to grid). The design idea and system architecture are described, a sample application of skin checking, accessing to a skin-expert service from a mobile phone via the proposed bridge, is explained, and evaluation and comparisons with other related platforms are given in the paper.

Sim proposed a market-driven negotiation agent model that makes adjustable amounts of concession by reacting to different market situations and trading constraints, and it was improved with an enhanced market-driven strategy by taking opponent eagerness into consideration. In both Sim’s original model and improved model, however, it was implied that a negotiation agent has same behaviors and actions to all trading partners referring to a same trading issue. It is not quite true in a real world trading negotiation. Based on both models, this paper proposes a real trading model based price negotiation agents that take each trading partner as an individual with different strategies and actions. Moreover, negotiation actions between a negotiation agent and a trading partner are kept in secret and unknown to others.

Sim proposed a market-driven negotiation model [I] for designing negotiation agents. Although agent itself eagerness was taken into consideration as a fixed value in Sim's proposed model, opponent's eagerness was not considered. This paper proposes an improved market-driven negotiation model in which Bayesian updating rule is applied to learn opponent's eagerness since opponent's eagerness is unknown to an agent and may vary with dynamic changing market situation.

To automate code generation, grid services are represented in class models of unified modeling language (UML). The UML output in XMI (XML metadata interchange) format derived from a CASE (computer aided software engineering) tool is used as input of AndroMDA to generate a suite of source code files and related settings. In order to achieve this, a new cartridge of AndroMDA for GT3 (Globus Toolkit 3) was developed, named "andromda-gt3". Stereotypes for expressing the specific services of GT3 are also newly defined, and corresponding templates for generating files are included in the new cartridge. An example of design of a grid service instance is given and, design of a more complicated grid system that consists of more than one service instance is also discussed in this paper.

This paper presents a virtual real-time 3D objects sharing system. There are many potential applications that include housing design, car design, and computer art design. Moreover, it can be used for supporting distance education and training of young/junior designers. This paper describes functionality and implementation of the system with underlying considerations: avoiding the bottleneck network problem and avoiding collisions of operations shared objects.

Network based groupware systems are for supporting collaborations among a group of people who are engaged in a common task or goal using computers connected by a variety of networks including the Internet. A synchronous groupware system supports group members' collaborative activities at the same time. Due to the almost all of current synchronous collaborative systems have been implemented either using a centralized topology or a hybrid topology, the research presented in this paper has been devoted to investigation, design and implementation of a peer-to-peer (P2P) groupware system, called DSC, using a decentralized topology. As it does not use any server at all, peers in a group need to coordinately manage their group by themselves, and each peer has to fully handle the correct message passing by itself. The DSC system is implemented using the JXTA technology and platform that enable peers to find each other, form groups and exchange messages across firewalls and NATs. It currently offers three shared objects of Web browser, file viewer and drawing pad as well as a text chat tool. The synchronous controls of a shared space, the objects, telepointer and so on are also provided. Its evaluations with a practical test environment are given in detail.

This paper proposes an agent based e-shopping system that uses different negotiation models to match different users and generates different negotiation model objects for adaptation of different user preferences. Five different levels from simple to sophisticated of negotiation models are described. How different models are matched to different users is explained and how adaptation of different user preferences is shown in this paper. Finally, an e-shopping system architecture is presented and how the e-shopping system works is demonstrated.

Objects in this article refer to sharable applications, such as a whiteboard and a video player, used by multi-users who are in different sites and have computers connected to networks. The objects are important elements in our Internet-based desktop collaborative system, called virtual collaboration room (VCR). We argue that a natural collaborative environment should be developed in a framework of using both a room metaphor and an object metaphor, i.e., emulating the fundamental characteristics of real rooms and real objects, respectively. This article gives the first systematic specifications of object physical and social characteristics, and discusses how to exploit and implement the object characteristics in VCR. A preliminary prototype of platform independent real-time audio/video communications among multiple users is also described. It can be used together with VCR.

Distance learning is one of the interesting research topics of distributed computing. This paper discusses a joint research project between the University of Aizu in Japan and Tamkang University in Taiwan. A software system supporting virtual university operations is proposed. The software architecture is designed based on three criteria of virtual university operations: administration, awareness and assessment. Specifications of each tools in the system are proposed, with some implementation details and solutions being discussed. We also point out some interesting research directions in the realization of a virtual university.

Web based distance learning is a trend of instruction delivery. One of the most difficult challenges of such a learning mechanism is the assessment of students' learning criteria. It is hard to judge the behavior of a student since the instructor is separated spatially and temporally from the students. However, it is possible to rely on some Web based tools to keep track of a student's course attendance, as well as the navigation behavior of that student. In addition, the navigation behavior of an individual can be compared to those of others. Analysis can be conducted, and an interactive tutorial can be generated to assist the student with a poor score. The paper proposes such a mechanism, as well as its supporting system run on Windows browsers.

The general purpose virtual collaboration room (VCR) is an Internet based desktop groupware system that enables a group of remote individuals to flexibly and naturally conduct their collaborative teaching/learning/working without constraints on collaboration types, working styles, group scales, and system platforms. To cope with the complexity, a room metaphor, i.e., emulating a physical room and objects in it, is used as a framework of the system. System implementation becomes no more complex than the case of using one object by identifying associated objects in communication messages between a room server and clients. The VCR provides rich and effective support of awareness of the user, object, space, and their mutual relations. With the use of Java applets for system implementations, users can enter and use a VCR from any standard Java enabled Web browser.

We introduce a new concept of ridges, ravines and related structures (skeletons) associated with surfaces in three-dimensional space that generalizes the medial axis transformation approach. The concept is based on singularity theory and involves both local and global geometric properties of the surface; it is invariant with respect to translations and rotations of the surface. It leads to a method of hierarchic description of surfaces that yields new approaches to shape coding, rendering and design. The extraction of the features is based on differential geometry of surfaces with consequent segregation via multiscale analysis. Terrain feature recognition, dental shape reconstruction and medical imagery are a partial list of applications.>