With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

In this article, we focus on the robustness of behavior-based malware analysis models, justified by the need to address the high mutation rates of malware executables that debilitate conventional signature-based approaches and even behavior-based AI solutions. In response to these challenges, we propose MAMBA + , an obfuscation-resistant dynamic analysis approach tailored for uncovering malware behavior. We have assembled a comprehensive collection of behavioral obfuscation attacks designed to undermine behavior-based models. The central concept behind MAMBA + involves treating obfuscated calls as perturbed data and introducing a novel loss function to effectively balance ground-truth predictions and the handling of these perturbations. To facilitate this approach, MAMBA + designs adapted embedding mechanisms to transform traces of API calls into high-dimensional vectors for attention calculations. Through a comprehensive empirical study with seven obfuscations and three unseen attacks, we reveal important qualitative properties of MAMBA + , and quantitatively demonstrate its superiority in performance and robustness to all compared methods.

The task of retrieving known Advanced Persistent Threat (APT) campaigns from system activity graphs, where nodes represent MITRE ATT&CK techniques and edges encode temporal or resource-level relationships, requires reasoning over structures. In operational settings, these target graphs are often noisy due to incomplete detection, technique misclassification, and benign-induced structural artifacts. To address this issue, we formulate the task as approximate subgraph matching between a known APT query graph and a noisy, partially observed technique graph. In this poster, we introduce a preliminary embedding-based retrieval method, aiming to promote it as a robust and practical framework for retrieving known APTs in real-world environments.

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. The approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that the logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. After identifying Techniques from the log, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five synthetic real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

This paper presents APILI, an innovative approach to behavior-based malware analysis that utilizes deep learning to locate the API calls corresponding to discovered malware techniques in dynamic execution traces. APILI defines multiple attentions between API calls, resources, and techniques, incorporating MITRE ATT&CK framework, adversary tactics, techniques and procedures, through a neural network. We employ fine-tuned BERT for arguments/resources embedding, SVD for technique representation, and several design enhancements, including layer structure and noise addition, to improve the locating performance. To the best of our knowledge, this is the first attempt to locate low-level API calls that correspond to high-level malicious behaviors (that is, techniques). Our evaluation demonstrates that APILI outperforms other traditional and machine learning techniques in both technique discovery and API locating. These results indicate the promising performance of APILI, thus allowing it to reduce the analysis workload.

Sparse grid imputation (SGI) is a challenging problem, as its goal is to infer the values of the entire grid from a limited number of cells with values. Traditionally, the problem is solved using regression methods such as KNN and kriging, whereas in the real world, there is often extra information—usually imprecise—that can aid inference and yield better performance. In the SGI problem, in addition to the limited number of fixed grid cells with precise target domain values, there are contextual data and imprecise observations over the whole grid. To solve this problem, we propose a distribution estimation theory for the whole grid and realize the theory via the composition architecture of the Target-Embedding and the Contextual CycleGAN trained with contextual information and imprecise observations. Contextual CycleGAN is structured as two generator–discriminator pairs and uses different types of contextual loss to guide the training. We consider the real-world problem of fine-grained PM2.5 inference with realistic settings: a few (less than 1%) grid cells with precise PM2.5 data and all grid cells with contextual information concerning weather and imprecise observations from satellites and microsensors. The task is to infer reasonable values for all grid cells. As there is no ground truth for empty cells, out-of-sample mean squared error and Jensen–Shannon divergence measurements are used in the empirical study. The results show that Contextual CycleGAN supports the proposed theory and outperforms the methods used for comparison.

Cyber Threat Intelligence (CTI) plays a crucial role in understanding and preemptively defending against emerging threats. Typically disseminated through unstructured reports, CTI encompasses detailed insights into threat actors, their actions, and attack patterns. The MITRE ATT&CK framework offers a comprehensive catalog of adversary tactics, techniques, and procedures (TTPs), serving as a valuable resource for deciphering attacker behavior and enhancing defensive measures. Addressing the challenge of time-consuming manual analysis of MITRE TTPs in unstructured CTI reports, this paper presents MITREtrieval, a novel system that leverages deep learning and ontology to efficiently extract MITRE techniques. This approach mitigates issues related to the implicit nature of TTPs, textual semantic dependencies, and the scarcity of adequately labeled datasets, enabling more effective analysis even with limited sample sizes. Our approach combines a sophisticated sentence-level BERT deep learning model with ontology knowledge to address sparse data challenges, using a voting algorithm to merge outcomes. This results in a more accurate classification of MITRE techniques, capturing contextual nuances effectively. Our evaluation confirms MITREtrieval’s effectiveness in identifying techniques, regardless of their representation in training samples. MITREtrieval has surpassed benchmarks, achieving F2 scores of 58%, 62%, and 69% in multi-label technique identification across 113, 46, and 23 CTI reports, respectively, thereby streamlining CTI analysis and improving threat intelligence.

This work investigates the framework and statistical performance guarantee of the composite neural network, which is composed of a collection of pre-trained and non-instantiated neural network models connected as a rooted directed acyclic graph, for solving complicated applications. A pre-trained neural network model is generally well trained, targeted to approximate a specific function. The advantages of adopting a pre-trained model as a component in composing a complicated neural network are two-fold. One is benefiting from the intelligence and diligence of domain experts, and the other is saving effort in data acquisition as well as computing resources and time for model training. Despite a general belief that a composite neural network may perform better than any a single component, the overall performance characteristics are not clear. In this work, we propose the framework of a composite network, and prove that a composite neural network performs better than any of its pre-trained components with a high probability. In the study, we explore a complicated application---PM2.5 prediction---to support the correctness of the proposed composite network theory. In the empirical evaluations of PM2.5 prediction, the constructed composite neural network models perform better than other machine learning models.

Cyber threats have become more prevalent than ever. Cyber Threat Intelligence (CTI) reports and MITRE ATTCK® framework play an imperative role in helping experts and organizations assess current and potential attacks, such as Advanced Persistent Threats (APT). However, the task of extracting valuable information from unstructured texts remains an ongoing challenge. In this work, we present a framework for understanding and reasoning adversary tactics and techniques. We construct an ontology structure and propose an automatic information extraction method that is capable of integrating the parsed information from CTI reports into each instance. The ontology is represented in the Web Ontology Language (OWL) accessible with the SPARQL query language. Our evaluation shows that the proposed information extraction method outperforms other state-of-the-art neural network-based methods in terms of precision. Furthermore, our framework can effectively infer adversary information, which efficiently supports security analysts recognize tactics and techniques.

Cyber threats are one of the most pressing issues in the digital age. There has been a consensus on deploying a proactive defense to effectively detect and respond to adversary threats. The key to success is understanding the characteristics of malware, including their activities and manipulated resources on the target machines. The MITRE ATT&CK framework (ATT&CK), a popular source of open source intelligence (OSINT), provides rich information and knowledge about adversary lifecycles and attack behaviors. The main challenges of this study involve knowledge collection from ATT&CK, malicious behavior identification using deep learning, and the identification of associated API calls. A MITRE ATT&CK based Malicious Behavior Analysis system (MAMBA) for Windows malware is proposed, which incorporates ATT&CK knowledge and considers attentions on manipulated resources and malicious activities in the neural network model. To synchronize ATT&CK updates in a timely manner, knowledge collection can be an automatic and incremental process. Given these features, MAMBA achieves the best performance of malicious behavior discovery among all the compared learning-based methods and rule-based approaches on all datasets; it also yields a highly interpretable mapping from the discovered malicious behaviors to relevant ATT&CK techniques, as well as to the related API calls.

To protect people from hazardous pollution exposure, mainly Particulate Matter of diameter 2.5 microns or less (PM2.5), countries continuously monitors the air quality via air quality monitors and internet of things based sensors. However, these monitors and sensors are deployed at sparse locations that pose the challenge of estimating the location of the PM2.5 pollution sources. To cope with this challenge, we propose a location estimator (LOST) scheme, which locates PM2.5 pollution sources in a sparsely deployed sensors environment. LOST efficiently models the spatio-temporal dispersion of PM2.5 of each pollution source, which enables LOST to compute the strength of the surrounding sensors based on the wind direction and sensors-source proximity. LOST utilizes a robust approach to backtrack the PM2.5 pollution sources via the weighted spatiotemporal strength of sensors' PM2.5 concentration. Compared to the state-of-the-art estimation schemes for source location, LOST shows improved performance. Our experiments show that, on average, LOST, attains higher closeness and lower failure ratio by more than 16.2% and 6.5%, respectively.

With the advance of hardware, network, and virtualization technologies, cloud computing has prevailed and become the target of security threats such as the cross virtual machine (VM) side channel attack, with which malicious users exploit vulnerabilities to gain information or access to other guest virtual machines. Among the many virtualization technologies, the hypervisor manages the shared resource pool to ensure that the guest VMs can be properly served and isolated from each other. However, while managing the shared hardware resources, due to the presence of the virtualization layer and different CPU modes (root and non-root mode), when a CPU is switched to non-root mode and is occupied by a guest machine, a hypervisor cannot intervene with a guest at runtime. Thus, the execution status of a guest is like a black box to a hypervisor, and the hypervisor cannot mediate possible malicious behavior at runtime. To rectify this, we propose a hardware-assisted VMI (virtual machine introspection) based in-guest process monitoring mechanism which supports monitoring and management applications such as process profiling. The mechanism allows hooks placed within a target process (which the security expert selects to monitor and profile) of a guest virtual machine and handles hook invocations via the hypervisor. In order to facilitate the needed monitoring and/or management operations in the guest machine, the mechanism redirects access to in-guest memory space to a controlled, self-defined memory within the hypervisor by modifying the extended page table (EPT) to minimize guest and host machine switches. The advantages of the proposed mechanism include transparency, high performance, and comprehensive semantics. To demonstrate the capability of the proposed mechanism, we develop an API profiling system (APIf) to record the API invocations of the target process. The experimental results show an average performance degradation of about 2.32%, far better than existing similar systems.

This work investigates the efforts of composite neural network in PM2.5 prediction. A composite neural network is a combination of several pre-trained and non-instantiated neural networks and according to [1], under certain condition, it is almost guaranteed that the performance of composite network is better than any of its pre-trained component. In this study, we designed a series of systematic experiments to examine the effect of the theory towards PM2.5 prediction. The empirical results support the theory and identify a reasonably good composite neural network design for PM2.5 prediction.

Many researchers have indicated that the prior knowledge has great effects on students' performance in learning mathematics. The low prior knowledge (LPK) students were expected to have lower learning performance and achievement than those of high prior knowledge (HPK) students. To help the low prior knowledge students, researchers suggested that to provide a self-regulated learning (SRL) support may be effective and helpful. However, the prior knowledge might also affect students' self-regulated learning. Thus, the relationship among SRL, prior knowledge, and learning performance remain unclear. To deal with this issue, this study not only investigates whether an enhanced SRL is helpful for students to improve learning performance but also explored how HPK and LPK students behaved differently when interacting with the SRL environment. The results suggested the SRL environment offers opportunities to remove the gap caused by prior knowledge in long-term. Accordingly, further finding, and implications are discussed.

Malware developers often use various obfuscation techniques to generate polymorphic and metamorphic versions of malicious programs. As a result, variants of a malware family generally exhibit resembling behavior, and most importantly, they possess certain common essential codes so to achieve the same designed purpose. Meantime, keeping up with new variants and generating signatures for each individual in a timely fashion has been costly and inefficient for anti-virus software companies. It motivates us the idea of no more dancing with variants. In this paper, we aim to find a malware family's main characteristic operations or activities directly related to its intent. We propose a novel automatic dynamic Android profiling system and malware family runtime behavior signature generation method called Runtime API sequence Motif Mining Algorithm (RasMMA) based on the analysis of the sensitive and permission-related execution traces of the threads and processes of a set of variant APKs of a malware family. We show the effectiveness of using the generated family signature to detect new variants using real-world dataset. Moreover, current anti-malware tools usually treat detection models as a black box for classification and offer little explanations on how malwares behave and how they proceed step by step to infiltrate targeted system and achieve the goal. We take malware family DroidKungFu as a case study to illustrate that the generated family signature indeed captures key malicious activities of the family.

Abuse of drug cause serious problems around the world by years. Thus, anti-drug materials have been increasingly developed. However, conventional anti-drug materials had limited effects on preventing drugs abuse, especially for drug addictive users. To this end, game-based anti-drug materials have been considered as a promising approach to deal with the issue in this study. As far as the drug-addicted experience is concerned, how drug addictive users and drug non-addictive users interacted with the game-based anti-drug materials is analyzed. The results revealed that the drug-addicted experience influenced users' performance of accessing information during gaming process, preference of completing tasks and the approach to reach goals.

Over the last few decades, there has been a growing interest in technology assisted multimedia learning. The purpose of this study was to develop an online multimedia learning system, TEDQuiz, for English language learners to practice their listening skills. The system is comprised of an easy-to-use browser extension and a personalized online learning management platform. This can help learners to manage listening materials by automatically generating multiple -- choice questions, synchronously connecting to friends in a social network website and personally managing their learning progress and word bank. The use of the system involves six steps: skimming, asking questions, listening, answering questions, linking and reviewing. Taking TED talks as an example, users can skim the website, have guiding questions, listen to talks, answer the questions, link to a social network website, and review their profile on the TEDQuiz website. The experimental results demonstrate that students with the TEDQuiz system spent more time on video watching. In the questionnaire, students had a positive view of the functions of the TEDQuiz system. They thought it is useful and helpful, and they were willing to use it in the future. We also found that the usefulness factor was statistically significant in predicting the future usage of TEDQuiz.

Malicious apps may install unwanted program or gather sensitive information from mobile devices. We notice Android apps fork several threads to accomplish a complex task intrinsically, and so does Android malware, that makes security experts difficult to analyze them without knowing their structure. In this paper, we propose an analysis scheme to group and analyze Android malware based on their dynamic behaviors, and to identify the behaviors of a malware family. In addition, we apply the techniques of phylogenetic tree, significant principal components and dot matrix on different malware families to demonstrate their behavioral correlations. The proposed methods can automatically discover similar behaviors of different malware groups, extract the characteristics of each malware group, and provide visualized information based on runtime behaviors. We anticipate the grouping result and the structure of malware family are important and essential for further malware behavior analysis researches.

Online tests have been identified as a core learning activity. Unlike conventional online tests, which cannot completely reflect students' learning status, two-tier tests not only consider students' answers, but also take into account reasons for their answers. Thus, research into a two-tier test had mushroomed but few studies examined why the two-tier test approach was effective. To this end, we conducted an empirical study, where a lag sequential analysis was used to analyze behavior patterns. The results indicated students with the two-tier test demonstrated different behaviors which develop "breadth to depth" and "depth to breadth" strategies.

The existing handover decision schemes in smallcell networks are mostly based on received signal strength (RSS) to make hangover decision. However, these schemes suffer from ping-pong effect and, as a result, perform unnecessary handovers incurring extra signaling overhead, which is even more exaggeratedly in the dense smallcell environment. Therefore, we propose a navigation-assisted seamless handover (NASH) scheme for reducing unnecessary handovers and ping-pong effect. We use the bicasting scheme with coordination multi-point (CoMP) and carrier aggregation (CA) techniques in handover procedure to avoid packet loss problem and enhance throughput. The simulation results show that the proposed scheme has the better performance in terms of handover latency, packet loss, network throughput, and the number of unnecessary handovers.

In the last few years, researchers in the field of e-learning and Natural Language Processing (NLP) have shown an increased interest in automatic question generation. However, little research has discussed the automatic evaluation of listening comprehension in multimedia learning. In this work, we present an automatic quiz generation for TED Talks video clips, called TED Quiz. TED Quiz generates multiple-choice questions in two question types, gist-content questions and detail questions. We use a graph-based algorithm, Lex Rank, to identify the most important part of a talk, as the main concept of a gist-content question. We also proposed an approach to distractor selection for detail question generation that generates grammatically correct but semantically wrong sentences as distractors. The experimental results demonstrated that the measured results from automatically generated questions are comparable with that from manually generated questions because their scores were significantly correlated. Moreover, most subjects agreed that the generated listening comprehension questions were of quality and usefulness.

High-speed rail systems are becoming increasingly popular among long-distance travelers. With the explosive growth in the number of mobile devices, the provision of high-quality telecommunication and Internet access services on high-speed trains is now a pressing problem. Network mobility (NEMO) has been proposed to enable a large number of mobile devices on a vehicle to access the Internet; however, several issues must be solved before it can be put into practice, e.g., frequent handovers, long handover latency, and poor quality of service. To resolve these problems, we propose an LTE femtocell-based network mobility scheme that uses multiple egress network interfaces to support seamless handover for high-speed rail systems called MEN-NEMO. The results of simulations show that the proposed MEN-NEMO scheme reduces the handover latency and transmission overhead of handover signaling substantially.

Providing end-to-end quality of service (QoS) for delay-sensitive flows with variable-bit-rate (VBR) traffic in wireless mesh networks is a major challenge. There are several reasons for this phenomenon, including time-varied bandwidth requirements, competition for transmission opportunities from flows on the same link, and interference from other wireless links. In this paper, we propose a flexible bandwidth allocation and uncoordinated scheduling scheme, called two-stage link scheduling (2SLS), to support flow delay control in TDMA-based wireless mesh networks. The scheme is implemented in two stages: slot allocation and on-the-go scheduling. The slot allocation mechanism allocates contiguous time slots to each link in each frame based on predefined maximum and minimum bandwidth requirements. Then, each link's on-the-go scheduling mechanism dynamically schedules the transmissions within the allocated time slots. The objective is to maximally satisfy the demand of all flows on the link according to the bandwidth requirements and channel condition. Compared with traditional slot allocation approaches, 2SLS achieves higher channel utilization and provides better end-to-end QoS for delay-sensitive flows with VBR traffic.

A slow-paced attack, such as slow worm or bot, can remain undetectable indefinitely by slowing down the pace of its movement. Detecting slow attacks based on traditional anomaly detection techniques may yield high false alarm rates. Since attacks are usually controlled by pre-programmed computer codes, their behaviors have regularity. In this paper, we track outbound connections of hosts by using a time series. Although the correlation among slow attacks' connections is temporally weak; the regularity of these connections remains preserved in the time series. Accordingly, we focus on time series spectrum analysis, and propose a detection method to identify peculiar spectral patterns which can represent the occurrence of a recurring and persistent activity in the time domain. We use both synthesized traffic and real-world traffic to evaluate our method. The results show that our method is efficient and effective in detecting slow-paced persistent activities even in a noisy environment with legitimate traffic.

With the emergence of Web 2.0, social tagging provides an opportunity to help learners to share, organize, and manage the learning information from reading materials. Moreover, a tag-based learning system can enable them to complete their learning activities in an effective and efficient way through the use of web 2.0 social tagging technologies. However, few studies have directly discussed why social tagging can benefit from user-generated tags in reading learning. Therefore, this paper first explores the use of effective social tagging learning to help students not only improve their understanding of the English material that they read, but also develop their ability to read well. We then investigate how to apply tag-based learning to help learners focus on studying the resources and make sense of the material and remember it more easily. The experimental results showed that tag-based learning can improve users’ efficiency in reading learning.

Recent research shows that in wireless wide area networks (WWANs), users who subscribe to multicast traffic from WWAN can exchange network-coded packets with one another via their secondary radio interfaces such as Wi-Fi in order to efficiently recover lost packets from the WWAN. Different from existing works which assume users are cooperative, in this work, we model the users as selfish players in the network-coding based peer-to-peer packet repairing game. To stimulate the users' cooperation, we introduce a payment-based incentive mechanism in the packet repairing game. The utility function of a user/player is also formulated to reflect both the number of useful packets and the available resource. Through analysis of the packet repairing game, we show that the optimal strategy for a user can be derived only with its local information. The impact of the pricing rules and the convergence conditions of the packet repairing game is also analyzed. We show theoretically as well as by simulation that under proper conditions, the packet repairing game can converge to the best case where each user can acquire all of its missing packets. Via computer simulations, we also show that with the proposed selection criteria, the packet repairing game is both effective and efficient: not only can the utilities of the players be greatly improved, but also the convergence time of the game and the utility gain of the players are comparable to those of the ideal case where every user is always willing to forward packets to others.

With the rapid developments in vehicular communication technology, academics and industry researchers are paying increasing attention to vehicular ad hoc networks (VANETs). In VANETs, dissemination delay and reliability are important criteria for many applications, especially for emergency messages. Existing approaches have difficulty satisfying both requirements simultaneously because they conflict with one another. In this paper, we propose a novel mechanism, called the Density-aware Emergency message Extension Protocol (DEEP) to disseminate emergency messages in VANETs. DEEP resolves the broadcast storm problem, achieves low dissemination delay, and provides high reliability over a realistic multi-lane freeway scenario. The mechanism delivers emergency messages to a specific area (e.g., the area before the exit) in a timely manner and guarantees that all relevant vehicles in that area will receive the messages. Drivers can then change their routes and avoid getting caught in a traffic jam. Performance evaluations via NS-2 simulations demonstrate that DEEP achieves both lower dissemination delay and higher reliability than existing approaches.

In this paper, we develop a new bus travel time prediction framework, called Historical Trajectory based Travel/Arrival Time Prediction (HTTP) for real-time prediction of travel time over future segments (and thus the arrival time at stops) of an on-going bus journey. The basic idea behind HTTP is to use a collection of historical trajectories "similar" to the current bus trajectory to predict the future segments. Specifically, the HTTP framework (1) samples a set of similar trajectories as the basis for travel time estimation instead of relying on only one historical trajectory best matching the on-going bus journey; and (2) explores different prediction schemes, namely, passed segments, temporal features, and hybrid methods, to identify the sample set of similar trajectories. We conduct a comprehensive empirical experimentation using real bus trajectory data collected from Taipei City, Taiwan to validate our ideas and to evaluate the proposed schemes. Experimental result shows that the proposed prediction schemes significantly outperforms the state-of-the-art and baseline techniques.

Prior knowledge in concept acquisition among students is an important issue. Traditional studies on prior knowledge generation during learning have focused on extracting sentences from reading materials that are manually generated by website administrators and educators. From the reports of previous studies, tag-based recommendation and assessment has been recognized as being an effective approach that can assist learners in finding out the clues and concepts of articles, such that it enables learners to be familiar with the learning content. However, sparse noisy data influences the quality of recommendations, especially for tag choices that are not mapped to the features of both the users and content. To cope with these problems, we adopt a hybrid recommendation method to consider tag preference, tag relevance and social networking. The experimental results show that the approach benefits from the additional information embedded in social knowledge, and can be an effective and efficient mechanism for enhancing the quality of prior knowledge recommendation.

Prior knowledge learning for increasing the efficiency of the learning processes is an important issue. Traditional studies on prior knowledge generation during language learning have focused on providing additional supplemental materials from reading materials that are manually generated by educators. This is time-consuming and hence personalized prior knowledge recommendation is difficult to perform and monitor. To cope with these problems, we purpose a novel tag-cloud visualization learning approach to automatically monitor running activities and student status. In addition, we incorporate cloud tags into a prior knowledge learning system (TAK), which provides students an engaging way to reinforce meaningful topics, identifies suitable supplementary materials through tag cloud navigation, and helps students reevaluate their reading process. Our experimental results demonstrate that our approach not only significantly improves the efficiency of prior knowledge learning but also helps teachers assist students in improving their reading ability.

With growing interest in estimating true ability in contemporary learning, the demand for personalized learning and Web-based learning environments has become increasingly important. This paper develops a statistical and interpretable method of estimating ability. This method captures the succession of learning over time and provides an explainable interpretation of a statistical measurement, based on Item Response Theory and the quantiles of acquisition distributions. The results from the simulation and empirical study demonstrate that the estimated abilities can successfully recognize the actual abilities of students. The correlation values between the estimated abilities and the post-test score, which incorporate this testing history, are higher than values that only consider test responses at the time of testing. Furthermore, the pre-test and post-test administered to the experimental group show significant student improvement. These results suggest that this method serves as a successful alternative ability estimation and provides a better understanding of student competence.

Recent years have seen increased attention given to computer-aided question generation for language student testing and evaluation. However, this approach often directly provides examinees with exhaustive questions. This is inappropriate, because these questions are not designed for any specific testing purpose. In this work, we present a personalized automatic quiz generation model that generates multiple-choice questions at various difficulty levels and categories, including grammar, vocabulary, and reading comprehension. We combined this model with a quiz strategy for estimating examinee proficiency and question selection. The proficiency is estimated using Exponential Moving Average, combining the test responses with a student’s past history. The results show that the subjects in the experimental group corrected their mistakes more frequently as well as answered more difficult questions than the control group. The experimental group also demonstrated the most progress between the pre-test and post-test. In addition, most of subjects agree the quality of the generated questions in the questionnaire analysis.

A topic is defined as a seminal event or activity along with all directly related events and activities. It is represented by a chronological sequence of documents published by different authors on the Internet. In this study, we define a task called topic anatomy, which summarizes and associates the core parts of a topic temporally so that readers can understand the content easily. The proposed topic anatomy model, called TSCAN, derives the major themes of a topic from the eigenvectors of a temporal block association matrix. Then, the significant events of the themes and their summaries are extracted by examining the constitution of the eigenvectors. Finally, the extracted events are associated through their temporal closeness and context similarity to form an evolution graph of the topic. Experiments based on the official TDT4 corpus demonstrate that the generated temporal summaries present the storylines of topics in a comprehensible form. Moreover, in terms of content coverage, coherence, and consistency, the summaries are superior to those derived by existing summarization methods based on human-composed reference summaries.

In this paper, we introduce two types of probabilistic aggregation queries, namely, Probabilistic Minimum Value Queries (PMVQ)s and Probabilistic Minimum Node Queries (PMNQ)s. A PMVQ determines possible minimum values among all imprecise sensed data, while a PMNQ identifies sensor nodes that possibly provide minimum values. However, centralized approaches incur a lot of energy from battery-powered sensor nodes and well-studied in-network aggregation techniques that presume precise sensed data are not practical to inherently imprecise sensed data. Thus, to answer PMVQs and PMNQs energy-efficiently, we devised suites of in-network algorithms. For PMVQs, our in-network minimum value screening algorithm (MVS) filters candidate minimum values; and our in-network minimum value aggregation algorithm (MVA) conducts in-network probability calculation. PMNQs requires possible minimum values to be determined a prior, inevitably consuming more energy to evaluate than PMVQs. Accordingly, our one-phase and two-phase in-network algorithms are devised. We also extend the algorithms to answer PMNQ variants. We evaluate all our proposed approaches through cost analysis and simulations.

With the advent of Web 2.0 technology, researchers have attempted to use Web 2.0 tools to develop adaptive and cooperative learning environments. However, in building learning and teaching diagnostic system, one of the major difficulties is the lack of prior knowledge to help learners read and understand what they read in articles. Moreover, because of the lack of a mechanism to assist teachers in monitoring the running activities and student progress, such that constructive suggestions can be given to the students and tutoring strategies can be improved accordingly. Therefore, this paper presents a framework for calculating semantically meaningful prior knowledge and generating spreading energy for discovering student's reading status in semantic networks using a modified version of Semantic Analysis and Social Network techniques. An application to the development of a Tag-based Collaborative reading learning system is very useful for teachers and students.

In many metropolitan areas, traffic congestion is an escalating problem which causes a significant waste of money and time. Nowadays, cars equipped with GPS devices become widespread. The location information of those cars is very useful for estimate traffic condition in the complex city road network. Using the accurate and real time traffic condition, we can provide dynamic route guidance to ease traffic congestion. In this paper, we proposed a speed pattern model, called two phase piecewise linear speed model (2PEED), to estimate traffic condition and represent speed pattern in a road network using GPS data collected vehicles. With the estimated traffic condition and speed pattern, a proposed classification-based route guidance approach using machine learning technique provides dynamic routing for drivers. Using both current traffic data and the experience learned from history data, our route guidance approach is able to accurately predict the future traffic condition and selects a best route. We give simulation results to show that the proposed approach is able to select and dynamically update a route to prove drivers a best (e.g., less traffic and shortest travel time) route to their destination.

The wireless spectrum is a limited resource. The concepts of cognitive radio and dynamic spectrum allocation (DSA) have been considered as a possible mechanism to improve the efficiency of bandwidth usage and solve the bandwidth deficiency problem. In this work, we propose an open interactive dynamic bandwidth trading model to enable periodic, short-term trading of unused bandwidth by licensed spectrum owners to sublet their surplus resources to service providers that need bandwidth. The model comprises of two phases. The goal of the phase one is to find out the demand of the intended buyers through multiple rounds of learning and revising of the quantity-price schedule. Based on the type distribution of the intended potential buyers, the optimal quantity-price schedule is derived which satisfies the incentive compatibility (aka self-selection) and the individually rational constraints that maximizes the MNO's profit and assures that the quantity-price pair designed for MVNO of a specific type will choose the pair that maximizes its utility. We also present an algorithm to convert the continuous optimal quantity-price schedule to a discrete one so as to provide a simple easy-to-read format for MVNOs' selection. If the total number of bandwidth requests exceeds the total capacity constraint, a bounded knapsack problem is used to resolve for final bandwidth allocation. Lastly, an example is provided to illustrate the process.

Traditional spectrum allocation approaches assume that the intended users of BSs are treated equally, without consideration of their distances. Consequently, the efficiency of spectrum usage for downlink transmissions may suffer, because a BS may interfere with other BS transmissions; this would not happen if a proper transmission power is used. In this paper, we propose a user-centric spectrum allocation algorithm based on a physical interference model for downlink transmissions. In our approach, BSs set the power of an allocated spectrum band to ensure an effective transmission range for covering a group of intended users. A spectrum band is allocated to a BS if it can satisfy a group of users with the least amount of power possible (relative to other spectrum bands). Thus, after spectrum bands and transmission ranges are assigned, each BS owns several spectrum bands that can cover a group of users within varying transmission ranges. This approach leads to a higher rate of spectral reuse and improves system performance. In addition, the approach properly considers fairness issues among both users and BSs. Overall, simulation results show that our approach not only outperforms traditional BS-centric approaches by offering a 26% higher throughput in dense networks, but also provides fairer spectrum allocation.

Reading exercises are critical for developing strong reading comprehension. With the advent of Web 2.0 technology, researchers have attempted to use Web 2.0 tools to develop adaptive and cooperative learning environments. Moreover, previous research has demonstrated that the use of social tagging technologies allows teachers to empower learners and create exciting new learning opportunities. However, in building a teaching support system, one of the major difficulties is the lack of a mechanism to assist teachers in monitoring running activities and student progress, such that constructive suggestions can be given to the students, and tutoring strategies can be improved accordingly. To cope with this problem, this paper proposes an automatic tag-based early-alert mechanism (TEA). Moreover, an application to the development of a tag-based collaborative reading learning system has depicted the superiority of our approach.

Reading difficulty is a measurement for estimating the appropriate reading level of a document. Almost all prior studies are designed for first language learners, but not for second language learners. In this study, we propose a robust estimation scheme, including features such as word frequency, official word grade and grammar patterns, to train a linear model to estimate the difficulty of the document for second language learners. The experiment results show that the proposed estimation scheme outperforms other reading difficulty estimations.

Reading is an important skill for gaining knowledge and discovering new information in a global society. Reading competence also strongly influences a person’s learning ability. However, in building better reading skills, one of the major difficulties is an absence of background knowledge to help learners read and understand material. Background knowledge helps learners make connections to construct clues within the text and determine the meaning of new vocabulary or sentences. Often, learners lack a mechanism to help them construct prior knowledge, preventing them from fully understanding their reading. To cope with these problems, this study adopts one of the most popular Web 2.0 techniques—social tagging—to help learners both read and understand English articles. We test our approach using a tag-based collaborative reading learning system. Our conclusions demonstrate the approach’s effectiveness, and reveal areas for future research.

One of the most popular applications of IEEE 802.16 network is to serve as a backhaul service for IEEE 802.11 networks. However, the traffic of an IEEE 802.16 connection aggregated from IEEE 802.11 networks fluctuates. Thus, efficient bandwidth reservation at the subscriber station (SS) is an importance issue. This study proposes a simple and flexible bandwidth reservation scheme at the SS, called multi-stage self-correction bandwidth reservation (MSBR), to make effective use of the bandwidth without violating the QoS requirements for real-time traffic under the proposed cost model. The MSBR scheme introduces the concept of Decision Period for bandwidth reservation to reduce the control message overheads. The proposed method also adopts the RLS algorithm to predict the traffic arrival and applies the MSBR method to capture the traffic dynamics for bandwidth reservation. Simulation results demonstrate that the proposed MSBR scheme utilizes the bandwidth efficiently without violating the QoS requirements of real-time services.

With the birth of Web 2.0, the development of the Internet has entered a new age. In addition to constant changes in information technology, Internet user behavior is radically different than in the past. However, as an educational tool, the application of Web 2.0 is still in its infancy. In this paper, we develop IWiLL 2.0, an online learning community based around the core concepts of Web 2.0. According to our research results, we find that learners experience more interaction from IWiLL 2.0 than IWiLL 1.0, with significant differences. We also present the future work for further study.

Reading exercises are critical for developing strong reading comprehension. However, due to resource constraints and a lack of accurate evaluation methods, English instructors can hardly assess student reading ability effectively. In past decades, learning by reading is known to be challenging for both teachers and students involved, especially for students learning English as a Foreign Language (EFL). To cope with this problem, in this paper, we proposed a Tag-based assessment approach to both elicit reading behaviors from EFL learners and assist the teachers in tracing and evaluating the student reading ability effectively. The experimental results showed that the novel approach can not only find out the relations between learners’ tags and their comprehension, but also help teachers to evaluate students’ reading ability.

This paper presents DISQO, a DIStributed Framework for Spatial Queries over Moving Objects. Distinguished from existing work, DISQO aims at achieving high scalability and system performance in support of both snapshot and continuous spatial queries over moving objects. The design of DISQO is based on our observation that exchanging object location information and query information between the location server and moving objects can reduce communication cost and facilitate scalable query processing. Thus, DISQO is built upon the notions of roaming regions and query maps in correspondence with object location information and query information. A comprehensive performance evaluation has been conducted to demonstrate the superiority of DISQO design, compared with existing state-of-the-art frameworks for monitoring moving objects.

We anticipate future attacks would evolve to become more sophisticated to outwit existing intrusion detection techniques. Existing anomaly analysis techniques and signature-based detection practices can no longer effective. We believe intrusion detection systems (IDSs) of the future will need to be capable to detect or infer attacks based on more valuable information from the network-related properties and characteristics. We observed that even though the signatures or traffic patterns of future stealthy attacks can be modified to outwit current IDSs, certain behavioral aspects of an attack are invariant. We propose a novel approach that jointly monitors network activities at three different levels: transport layer protocols, (vulnerable) network services, and invariant anomaly behaviors (called attack symptoms). Our system, SecMon, captures the network behaviors by simultaneously performing cross-level state correlation for effective detection of anomaly behaviors. For the most part, the invariant anomaly behavior has not been fully exploited in the past. A probabilistic attack inference model is also proposed for attack assessment by correlating the observed attack symptoms to achieve the low false alarm rate. The evaluations demonstrate our prototype system is efficient and effective for sophisticated attacks, including polymorphism, stealthy, and unknown attack.

Employing multiple radio access technologies such as WiMAX, HSDPA and Wi-Fi in a mobile network to provide users with more cost-effective communication services has long been a vision of many service network providers. In this paper, we propose a novel user-decided service model for a cooperative wireless network in which the radio resources of multiple wireless networks are collectively managed. Under the model, users are provided with multiple service options with different levels of service quality and charges. It is up to users to choose the most suitable service option (and access network) based on their personal preference and the amount of money they are willing to pay. We use a video streaming service in a WiMAX/HSDPA network as an example to illustrate the key concepts and resource management of our approach. The results of simulation show that, under the model, the service network can accommodate more users and provide higher user satisfaction than the traditional network-decided service model. It can also achieve higher resource utilization and revenues. This demonstrates the importance of defining the concept of user-decided service model in a cooperative heterogeneous networking environment.

Wireless channel contention/interference problems combined with the constraints imposed by the TCP congestion control scheme have a severe impact on TCP throughput performance and fairness in a wireless mesh network. In this paper, we propose a TCP-sleek spatial TDMA scheduling algorithm to construct a sleek data and ACK packet flowing environment for TCP flows along the wireless paths. The proposed scheme considers the TCP demands of individual directional links during channel access scheduling and time slot assignment to enable the TCP congestion control mechanisms to perform as effectively as they do in wired networks, irrespective of their spatial locations and the number of hops traversed. In addition, employing link-layer retransmission can effectively hide local transmission errors from the end-to-end TCP congestion control mechanisms. The simulation results demonstrate that, under the proposed scheme, the problem of unfair bandwidth sharing to long-hop TCP flows caused by wireless communication is successfully eliminated, and the throughput disparity between long- and short-hop TCP flows is also substantially reduced. The scheme also achieves high overall network throughput.

When an event occurs, it attracts attention of information sources to publish related documents along its lifespan. The task of event detection is to automatically identify events and their related documents from a document stream, which is a set of chronologically ordered documents collected from various information sources. Generally, each event has a distinct activeness development so that its status changes continuously during its lifespan. When an event is active, there are a lot of related documents from various information sources. In contrast when it is inactive, there are very few documents, but they are focused. Previous works on event detection did not consider the characteristics of the event's activeness, and used rigid thresholds for event detection. We propose a concept called life profile, modeled by a hidden Markov model, to model the activeness trends of events. In addition, a general event detection framework, LIPED, which utilizes the learned life profiles and the burst-and-diverse characteristic to adjust the event detection thresholds adaptively, can be incorporated into existing event detection methods. Based on the official TDT corpus and contest rules, the evaluation results show that existing detection methods that incorporate LIPED achieve better performance in the cost and F1 metrics, than without.

Online learning communities are gradually altering the traditional learning style of people because of the pervasiveness of the Internet. The environment of the online learning community has been formed gradually as more and more people join Web sites and learn from each other. A total of 436 senior high school students in Taiwan participated in this research. To test the hypotheses of this research, we use structural equation modeling (SEM) method for validation. All hypotheses we proposed were supported. Finally, we list several implications of this research results as guidelines for developing an online learning community for future study.

A topic is defined as a seminal event or activity along with all directly related events and activities. It is represented as a chronological sequence of documents by different authors published on the Internet. In this paper, we define a task called topic anatomy, which summarizes and associates core parts of a topic graphically so that readers can understand the content easily. The proposed topic anatomy model, called TSCAN, derives the major themes of a topic from the eigenvectors of a temporal block association matrix. Then, the significant events of the themes and their summaries are extracted by examining the constitution of the eigenvectors. Finally, the extracted events are associated through their temporal closeness and context similarity to form the evolution graph of the topic. Experiments based on the official TDT4 corpus demonstrate that the generated evolution graphs comprehensibly describe the storylines of topics. Moreover, in terms of content coverage and consistency, the produced summaries are superior to those of other summarization methods based on human composed reference summaries.

Network mobility introduces a new communication paradigm that provides sets of mobile hosts (MH) moved collectively as a unit with high-mobility. Efficient network mobility handover design is essential to meet QoS requirements for real-time VoIP-like applications. IETF MIPv6-NEMO design has some problems and introduces extra overheads on packet header and tunnel processing, and added transmission delays due to additional routing legs. Inflexible execution sequence between tasks in MIPv6-NEMO causes negative effect on handover latency performance. In this work, we combine the designs of routing and resource allocation with network mobility management, and introduce the notion of foreign mobility agent in a hierarchical backhaul packet forwarding architecture, referred to as HiMIP-NEMO, to facilitate QoS-handovers and reduce latency and packet loss during handover. Under the architecture, a QoS-incorporated registration protocol and handover protocol with several new layers 2 and 3 message types are also proposed. Both the analytical and simulation results are presented. The results show the effectiveness of the proposed integrated designs in the support of QoS-handover and significant improvements in latency and packet loss performances.

The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a threshold-resilient online algorithm, called the incremental probabilistic latent semantic indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the topic detection and tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent ~ 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.

In this paper, we study the fairness problem in multi-hop wireless backhaul networks in the presence of selfish transit access points (TAPs). We design an incentive-based mechanism which encourages TAPs to forward data for other TAPs, and thus eliminates the location-dependent unfairness problem in the backhaul network. We prove the correctness and truthfulness of the proposed mechanism, and evaluate its performance via ns-2 simulations. The results show that the proposed mechanism achieves fairness even when there are idle TAPs in the network.

In this study, we design and prototype an automatic quiz generation system (auto-quiz for short) for a given English text to test learner comprehension of text content and English skills. The auto-quiz process parses an English text into a semantic network representation and enhances the semantic network iteratively with intrinsic knowledge, such as English grammar and writing styles, and extrinsic knowledge, such as the word relationship in WordNet and statistics from corpus or search engines. Then, the quiz generation process generates quiz from the text based on learner comprehension skills and according to the learner learning status and needs, such as English proficiency and frequent errors.

In this paper, we study the behavior of voice over IP (VoIP) traffic over IEEE 802.11 wireless networks. Specifically, we propose a QoS provisioning mechanism for VoIP traffic, and suggest a practical solution to configuring the 802.lie enhanced distributed control access (EDCA) parameter sets for different types of traffic. With our mechanism, the EDCA parameter sets can be easily configured via software interface in off-the-shelf WiFi phone products and it is not required to modify the operations of access points or 802.11 MAC layer protocols. The performance of our mechanism is evaluated via ns-2 simulations and via laboratory experiments over Quanta's 02 dual-mode handsets. The results show our mechanism can provide effective and efficient QoS provisioning for VoIP traffic over IEEE 802.11 WLANs.

Recent trend in the development of mobile devices, wireless communications, sensor technologies, weblogs, and peer-to-peer communications have prompted a new design opportunity for enhancing social interactions. This paper introduces our preliminary experiences in designing a prototype utilizing the aforementioned technologies to share life experience. Users equipped with camera phones coupled with short-range communication technology, such as RFID, can capture life experience and share it as weblogs to other people. However, in reality, this is easier said than done. The success of weblogs relies on the active participation and willingness of people to contribute. To encourage active participations, a ranking system, AgreeRank, is specifically developed to get them motivated.

An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further

Newly popular Internet applications such as WebTV and Internet streaming requires network to support end-to-end delay bound. In this paper, we propose a novel network scheduling scheme, called the bulk scheduling scheme (BSS), built on top of existing schedulers of intermediate nodes (routers) without modifying transmission protocols on both sender and receiver. By inserting TED packets into packet flows at the ingress router periodically, the BSS schedulers of the intermediate nodes can dynamically allocate the necessary bandwidth to each flow to enforce the end-to-end delay. The introduction of TED packets incurs a lower overhead than the per-packet marking approaches, while achieves similar performance. Three flow bandwidth estimation methods are presented and a dropping policy is introduced to discard late packets. We also propose a feedback mechanism to discover and resolve the bottlenecks for the BSS. The simulation results show that BSS performs efficiently as expected.

This research mainly investigates how to utilize Internet technology to assist the teaching of "Supply Chain Management". It uses the related web-based learning theories as the foundation to develop a webbase learning system. Researchers use observations, questionnaires, and learner portfolio records to collect data for quantitative and qualitative analyses. The major findings of this research are as follow: 1. Through the use of this web-based learning system, students show obvious improvement in learning, and it also serve as supplement for the insufficiency of traditional classroom learning. 2. Among peer students, there shows a high rate of interaction. 3. From verification, the design of this web-based learning system meets the related learning theories.

In this paper, we propose the chi2-classifier employing the chi2-test to test the homogeneity of two random samples of term vectors for text categorization decision. First, the properties of chi2-test for text categorization are studied. One of the advantages of chi2-test is that its significance level a is the same as the miss rate that provides a foundation for theoretical performance guarantee. The chi2-classifier also considers term aggregation and selection methods to improve the categorization performance. Generally cosine similarity with TF*IDF weighting function performs reasonably well in text categorization. However, the performance of cosine similarity depends on the given threshold value, and its categorization performance may fluctuate even near the optimal threshold value. To alleviate the problems, the chi2-classifier proposes a combination of chi2-test and cosine similarity. Extensive experiment results have verified the properties of chi2-test and performance of the combined classifier

In this paper, we study fair queuing in the MAC layer of IEEE 802.11 with the distributed coordination function (DCF). In particular, we propose an IFS-based distributed fair queuing (IDFQ) mechanism to provide differentiated service in conformance with the 802.11 standard. IDFQ is designed to emulate self-clocked fair queuing (SCFQ) in a distributed manner. There is no backoff in IDFQ. Thus, it provides better stability and improved aggregate throughput, as compared to existing work. Simulation results show that IDFQ supports differentiated service for different flows in proportion to their weights. More importantly, it outperforms existing solutions in terms of fairness and stability, rendering IDFQ an excellent candidate to provide weighted fairness for IEEE 802.11 WLANs.

In this paper, we provide a differentiated service model in IEEE 802.11e wireless local area networks (WLANs). In our service model, a new mechanism called differentiated service - EDCA (DS-EDCA) is proposed to provide both strict priority and proportional fair service for IEEE 802.11 WLANs. The EDCA parameter set of lower priority traffic is carefully designed, and the backoff intervals are determined according to the distributed scheduling discipline DFS. Furthermore, a hierarchical link sharing model is proposed for IEEE 802.11e WEANs, which allocates different amount of link resource for AP and mobile stations. The performance of DS-EDCA and EDCA is compared via ns-2 simulations. The results show that DS-EDCA outperforms the original EDCA in terms of the support for both strict priority and weighted fair service. Most importantly, DS-EDCA can be easily implemented, and is compatible to the IEEE 802.11 standard.

Virtual private networks (VPNs) provide customers with a secure and manageable communication environment. A VPN resource provisioning model, called hose model, provides customers with the flexibility to specify the bandwidth requirement of a VPN. For the network service provider (NSP), traffic engineering is an essential requirement for better utilization of the network backbone, while meeting the requirements specified by customers. In this paper, two vital traffic engineering problems for hose-model VPNs are investigated: (1) how to maximize the number of VPNs successfully established on the network backbone, and (2) how to find a set of resource-efficient backup paths for a VPN under a single link failure model. To address these problems, we propose two novel algorithms for hose-model VPN provisioning. We use rejection ratio for the first problem and protected bandwidth allocation for the second as the main performance metrics to evaluate different algorithms, respectively. Simulation results show that our algorithms outperform previous proposed approaches.

Rapid deployment of IEEE 802.11 based wireless access networks in hot spots and the integration of the networks to the existing wide-area communication infrastructure have become a major driving force to speed up the design and development of necessary security and quality of service (QoS)-guaranteed mechanisms with roaming capacity to mobile users. Three issues are raised in such a communication environment: a) service users would like to have IP-based roaming capability as they move rather than being constrained to a single spot or being forced to disconnect because his/her service provider does not have entire coverage of the city/region; b) the need on security and accounting management for mobile Internet; and c) the execution of AAA however would incur extra delay to handoff latency. For applications like VoIP, video streaming and TCP connections, it may disrupt the on-going communications if such latency becomes too large. In this paper, we propose an AAA-enabled roaming alliance architecture that provides fast and secure universal roaming service across multiple service domains. The associated protocols and the supporting security mechanisms are also proposed. Our design provides continuing communications service to mobile user belonging to different service operators to quickly and securely access service when roaming across multiple service domains. Mobile users only need to carry a U-mobile token to receive the service. The schemes proposed only incur minimal latency in security check. This is particularly important to the support of real-time mobile applications.

In this paper, the proposed LIPED (LIfe Profile based Event Detection) employs the concept of life profiles to predict the activeness of event for effective event detection. A group of events with similar activeness patterns shares a life profile, modeled by a hidden Markov model. Considering the burst-and-diverse property of events, LIPED identifies the activeness status of event. As a result, LIPED balances the clustering precision and recall to achieve better F1 scores than other well known approaches evaluated on the official TDT1 corpus.

In this paper, we study the proportional fairness problem in IEEE 802.11e wireless local area networks (WLANs). With 802.11e EDCA, only priority-based service is supported. Such priority-based service, while allowing differentiated service for flows of different priorities, cannot ensure service amount in proportion to their demands. This calls for weighted fair service to be supported by EDCA. In this paper, we propose a mechanism called weighted fair-EDCA (WF-EDCA) to provide proportional fairness for IEEE 802.11 WLANs. With WF-EDCA, weighted fair service among different access categories (ACs) is provided, and strict priority service can also be implemented. We then conduct simulations based on ns-2 to compare the performance of WF-EDCA and EDCA. The results show that WF-EDCA outperforms EDCA in terms of providing proportional fairness and strict priority service for IEEE 802.11 WLANs while retaining comparable total throughput.

In this paper, we study the provision of per-class QoS for IEEE 802.11e enhanced distributed channel access (EDCA) WLANs. We propose two mechanisms, called BIWF-SP and IDFQ-SP, based on backoff interval (BI) and inter-frame space (IFS), respectively. In our mechanisms, both strict priority and proportional fair service are supported. We describe the operations of the proposed mechanisms in details, and compare their performance with the original EDCA mechanism via simulations. The results show that both BIWF-SP and IDFQ-SP outperform the original EDCA in terms of the support for both strict priority and weighted fair service. Compared to IDFQ-SP, BIWF-SP is easier to be implemented in real systems; compared to BIWF-SP, IDFQ-SP has better aggregate throughput and is more stable. More importantly, both mechanisms conform to the IEEE 802.11e EDCA standard, rendering both good candidates to provide per-class QoS service for IEEE 802.11 WLANs.

In this paper, a Web document classification scheme, progressive analysis scheme (PAS) is proposed to efficiently and effectively classify HTML Web documents. When an author writes a Web document, HTML tags are used to visually emphasize the texts related to main concepts. The design of PAS is to catch the authoring convention in terms of the contributions of nested HTML tags to document classification. During the learning phase, PAS provides an enhanced tag sequence model to resolve the sample lacking problem in learning the classification contributions of HTML tag sequences. While in classification phase, PAS decomposes a Web document into regions based on the DOM tag-tree, and analyzes the regions in the descending order of their classification contributions. PAS also provides a mechanism called emphasis degree adjustment to defer the processing of noisy region during classification. The simulation results shows that PAS has better performance than full-text (e.g. SVM) and sequential classifier.

Although there are various wireless access network technologies with different characteristics and performance level have been developed, no single network that can satisfy the anytime, anywhere, and any service wireless access needs of mobile users. A truly seamless mobile environment can only be realized by considering vertical and horizontal handoffs together. With the advantages of Mobile IPv6, a more comprehensive and integrated framework of heterogeneous networks can be developed. In this paper, we discuss the issues related to handoffs including horizontal and vertical handoffs. We present a scheme for integrating wireless local area network and wide area access networks, and propose a micromobility management method called HiMIPv6+. We also propose a QoS-based (quality-of-service-based) vertical handoff scheme and algorithm that consider wireless network transport capacity and user service requirement. Our prototype evaluations and the simulations show that our framework performs as expected.

We present a Web-based authoring tool to create interactive multimedia contents for English learning. Target users of this authoring tool are English teachers and content designer, therefore, ease of use and simplicity are the fundamental issues. Furthermore, the authoring tool and multimedia database are integrated in the context of intelligent Web-based interactive language learning (IWiLL) system. Special language tools to access corpus, manipulate multimedia elements, and create collaborative learning sessions are designed in the system. These resources and tools on hand yield the potential to create rich and deep interactive multimedia contents.

While existing weighted fair scheduling schemes guarantee minimum service rates of a shared server (such as a computer or a communication channel), maximum rate control was generally enforced by employing policing mechanisms. The previous approaches use either a concatenation of rate controller and scheduler, or a policer in front of scheduler. The concatenation method uses two sets of queues and management apparatus, and thus incurs overhead. The other method allows bursty job requests that may violate maximum rate constraint. In this paper, we present a new weighted fair scheduling scheme, WF/sup 2/Q-M, to simultaneously support maximum rate control by distributing the excess bandwidths of maximum rate constrained sessions to other sessions without recalculating the virtual starting and finishing times of regular sessions. In terms of performance metrics, we prove that WF/sup 2/Q-M is theoretically bounded by a fluid reference model. A procedural scheduling implementation of WF/sup 2/Q-M is proposed and proof of correctness is given. Finally, we conduct extensive experiments to show the performance of WF/sup 2/Q-M is just as we claim.

Maximum rate control in a shared channel is important to service providers and carriers for various reasons. Previous approaches either use a concatenation of regulator and scheduler, which employs two set of queues and two management systems, or a policer in front of scheduler. The former requires extra management overhead and inaccuracy, and the latter causes bursty traffic as well as inaccuracy. In this paper, we propose a new scheduling algorithm, called WF/sup 2/Q-M (worst-case fair weighted fair queueing with maximum rate control), to simultaneously support maximum rate control and provide minimum service rate guarantee. WF/sup 2/Q-M has similar worst case time complexity with WF/sup 2/Q designed to provide accurate scheduling. WF/sup 2/Q-M employs virtual clock adjustment to distribute the excess bandwidth of saturated sessions to other sessions without recalculating their virtual starting and finishing times. WF/sup 2/Q-M performance is theoretically bounded by a fluid reference mode, and simulations show WF/sup 2/Q-M performs just as claimed.

This paper presents an intelligent Internet information system, Automatic Classifier for the Internet Resource Discovery (ACIRD), which uses machine learning techniques to organize and retrieve Internet documents. ACIRD consists of a knowledge acquisition process, document classifier, and two-phase search engine. The knowledge acquisition process of ACIRD automatically learns classification knowledge from classified Internet documents. The document classifier applies learned classification knowledge to classify newly collected Internet documents into one or more classes. Experimental results indicate that ACIRD performs as well or better than human experts in both knowledge acquisition and document classification. By using the learned classification knowledge and the given class lattice, the ACIRD two-phase search engine responds to user queries with hierarchically structured navigable results (instead of a conventional flat ranked document list), which greatly aids users in locating information from numerous, diversified Internet documents.

The design and implementation of an Intelligent Web-based Interactive Language Learning (IWiLL) system to support English learning on the Internet is described. We designed two kinds of learning environments: (1) interactive English writing environment and (2) mining movies for real English. These are intended to improve learners’ basic language skills such as listening, reading, and writing. In addition, the system also offers authoring tools that facilitate teachers’ content preparation. The system not only provides multimedia learning environments for users, but also builds a learner corpus, an archive of annotated English texts written by learners for whom English is a second language. Further analysis of the learner corpus creates the potential to detect the users’ persistent errors and then provide adequate help to the users.

In this paper, we present PVA, an adaptive personal view information agent system to track, learn and manage, user's interests in Internet documents. When user's interests change, PVA, in not only the contents, but also in the structure of user profile, is modified to adapt to the changes. Experimental results show that modulating the structure of user profile does increase the accuracy of personalization systems.

Differentiation service (Diffserv) is regarded as one of the practical architectures to realize quality of service (QoS) on the Internet. Relative differentiated service, which achieves relative QoS differentiation between traffic classes, is a simple and easily-deployed service model. Based on the concept of relative differentiated service, Dovrolis et al. (ACM SIGMETRICS Performance Evaluation Review vol.27, no.1, pp.204-5, 1999; IEEE Network, September 1999; ACM SIGCOMM-99, September 1999) proposed a proportional differentiation service model which guarantees the ratios of service differences between classes. They claimed that weighted fair queuing (WFQ) is not suitable for implementing relative differentiation service and employed priority-based scheduling algorithms in their model. In this paper, we extend WFQ and apply it to proportional delay differentiation service. The extended WFQ algorithm adjusts the weighting of each class dynamically so that the delay differences between classes can be well controlled. Simulations show that the proposed methods can realize proportional delay differentiation service effectively and efficiently.

Traffic smoothing for delivery of online VBR media streams is one of the most important problems in designing multimedia systems. Given available client buffer and a window-sliding size, conventional approaches try to reduce bandwidth allocated in each window. However, they can not lead to the minimization of bandwidth allocated for transmitting the entire stream. Although a window-sliding approach was introduced previously to further reduce the bandwidth allocated, it was computational costly. In this paper, an effective and efficient online traffic-smoothing scheme is proposed. Different from the conventional static window-sliding approaches, this approach dynamically decides the suitable window-sliding size to online smooth the bursty traffic. Then, an aggressive workahead scheme is applied in transmitting the entire stream. By examining different media streams, the approach has a small bandwidth, high bandwidth utilization and small computation cost. Considering the online transmission of a Star War movie, our approach result is 13% less for the bandwidth and 4% less for the network idle rate than SLWIN(1). Comparing the number of window sliding, our approach is 75% less than SLWIN(1). The relations between the characteristic of the input traffic and the behavior of obtained scheduling results are discussed. Finally, an extension of the proposed approach to resolve the latency and quality tolerance applications is also introduced.

Given a pre-recorded VBR video, we have proposed an O(n) algorithm to smooth the transmission schedule with the minimum required resources. N is the number of video frames. As n is usually very large and varying for different videos, it is not suitable for online computation. To facilitate resource management and admission control for QoS (quality-of-service) guarantees, we need to explore the relations among the required resources. Thus, whenever a new request is presented, the admission control procedure can easily check the required resources against the available resources and decides to admit this new request or not. To compute these relations (such as rate-buffer and rate-delay), a native algorithm takes O(n/sup 3/) time complexity. An O(n log n) algorithm is proposed to characterize the low-bounds of resources allocated for transmitting a pre-recorded VBR video. Having these pre-computed functions, the admission control procedure is as simple as a chart look-up with O(1) time complexity to allocate the required resources.

In this paper, we present a system that extracts and generalizes terms from Internet documents to represent classification knowledge of a given class hierarchy.We propose a measurement to evaluate the importance of a term with respect to a class in the class hierarchy, and denote it as support.With a given threshold, terms with high supports are sifted as keywords of a class, and terms with low supports are filtered out.To further enhance the recall of this approach, Mining Association Rules technique is applied to mine the association between terms.An inference model is composed of these association relations and the previously computed supports of the terms in the class.To increase the recall rate of the keyword selection process.we then present a polynomialtime inference algorithm to promote a term, strongly associated to a known keyword, to a keyword.According to our experiment results on the collected Internet documents from Yam search engine, we show that the proposed methods In the paper contribute to refine the classification knowledge and increase the recall of keyword selection. 'In the paper, we use "term" as word or phrase, which is extracted from the Internet documents, rather than use "keyword"."Keyword" is representative to the concept, while "term" is probably nonrepresentatlve.

Conventional operating systems employ a kernel-controlled caching strategy that cannot properly serve all access-pattern types used by applications. When running under these systems, many memory-intensive applications with mis-matching access patterns cause excessive page faults and page replacements that reduce the application's performance. This paper presents the hipec system, which allows applications to have their own caching strategies for managing page frames with negligible overhead. Since application designers know the access patterns of their applications, the specific caching strategies can be tuned to meet the needs of each application. Empirical results show that the hipec system significantly improves application performance and system throughput. © 1997 John Wiley & Sons, Ltd.

A new approach is presented for organizing a large collection of multidimensional data with an unknown distribution by partitioning the data such that the data are relatively homogeneously distributed in each block. A multidimensional tree is generated according to this partition. After the tree is generated, summary data estimation such as selectively estimation can be performed via a tree search. This approach is applicable to both ordered and categorial attributes. The merits of this method are verified theoretically and by simulation.>

A data model and an access method for summary data management are proposed. Summary data, represented as a trinary tuple, consist of metaknowledge summarized by a statistical function of a category of individual information typically stored in a conventional database. The concept of category (type or class) and the additivity property of statistical functions form a basis for the model that allows for the derivation of summary data. The complexity of deriving summary data has been found computationally intractable in general, and the proposed summary data model, with disjointness constraint, solves the problem without the loss of information. The proposed access method, called the summary data tree, or SD-tree, which handles an orthogonal category as a hyperrectangle, realizes the proposed summary data model. The structure of the SD-tree provides for efficient operations including summary data search, derivation, and insertion on the stored summary data.>

A data model and an access method for summary data management are presented. Summary data, represented as a trinary tuple (statistical function, category, summary), are metaknowledge summarized by a statistical function of a category of individual information typically stored in a conventional database. For instance, (average-income, female engineer with 10 years' experience and master's degree, $45000) is a summary datum. The computational complexity of the derivability problem has been found intractable in general, and the proposed summary data model, enforcing the disjointness constraint, alleviates the intractable problem without loss of information. In order to store, manage, and access summary data, a multidimensional access method called summary data (SD) tree is proposed. By preserving the category hierarchy, the SD tree provides for efficient operations, including summary data search, derivation, insertion, and deletion.>