This paper studies the Time-Critical Cooperative Delivery with Unknown Demands (TCDUD) problem, developed from the well-studied Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD), that corresponds to emergency situations requiring 1) time-critical delivery; 2) unknown demands; and 3) cooperative delivery. We formulate the problem as a multi-agent sequential decision problem with a Collaborative Semi-Markov Decision Process (CSMDP) model in which timecritical delivery is urged by the reward function that takes both the amount and the time of fulfilled demands into account. Unlike traditional CVRPSD, we do not include a priori information about a customer's demand in problem definition, and require vehicles to visit a customer before its demand is revealed. A Transformer-based multi-agent reinforcement learning approach, namely MAODN, is devised to learn online policies that direct the vehicles to visit the customers, and perform timely delivery in a cooperative manner. MAODN utilizes a demand updater to accommodate online updates about customers' demands from the vehicles during delivery. Vehicles then make cooperative decisions via individual policy networks, leveraging the fleet state provided by the state aggregator. Experiment results suggest that our approach outperforms the baseline by at least 27.8% and demonstrates better robustness.

Resolving issues is a key activity in open-source software (OSS) development, but the ad hoc nature of issue handling on platforms like GitHub makes it challenging to understand the underlying processes of issue resolution, how well such processes are organized, and how to effectively improve issue resolution efficiency. In this work, we quantitatively examine the relationship between issues' organization levels, modeled as process uncertainties, and resolution efficiency measured by issue lifetime and event transition time. We propose an information-theoretical approach to assess the uncertainty lies in issue processing by calculating the entropy of Direct Follow Graphs (DFGs) that represent issue processes. Instead of performing analysis on a project's all issues as a whole, which may lead to over complex models, we find concise yet representative DFG-based models for a project's issues with an entropy-guided KMeans++ clustering algorithm. Findings from real-world OSS projects' issues suggest that higher process uncertainty of issues is associated with longer issue lifetime and extended event transition time. The result also indicates that enhancing issue process organization can potentially improve issue resolution efficiency.

This study addresses the Min-Max Multiple Traveling Salesmen Problem (m3-TSP), which aims to coordinate tours for multiple salesmen such that the length of the longest tour is minimized. Due to its NP-hard nature, exact solvers become impractical under the assumption that P ≠ NP. As a result, learning-based approaches have gained traction for their ability to rapidly generate high-quality approximate solutions. Among these, two-stage methods combine learning-based components with classical solvers, simplifying the learning objective. However, this decoupling often disrupts consistent optimization, potentially degrading solution quality. To address this issue, we propose a novel two-stage framework named Generate-and-Split (GaS), which integrates reinforcement learning (RL) with an optimal splitting algorithm in a joint training process. The splitting algorithm offers near-linear scalability with respect to the number of cities and guarantees optimal splitting in Euclidean space for any given path. To facilitate the joint optimization of the RL component with the algorithm, we adopt an LSTM-enhanced model architecture to address partial observability. Extensive experiments show that the proposed GaS framework significantly outperforms existing learning-based approaches in both solution quality and transferability.

Semi-supervised medical image segmentation (SS-MIS) shows promise in reducing reliance on scarce labeled medical data. However, SSMIS field confronts challenges such as distribution mismatches between labeled and unlabeled data, artificial perturbations causing training biases, and inadequate use of raw image information, especially low-frequency (LF) and high-frequency (HF) components. To address these challenges, we propose a Wavelet Transform based Bidirectional Copy-Paste SSMIS framework, named WT-BCP, which improves upon the Mean Teacher approach. Our method enhances unlabeled data understanding by copying random crops between labeled and unlabeled images and employs WT to extract LF and HF details. We propose a multi-input and multi-output model named XNet-Plus, to receive the fused information after WT. Moreover, consistency training among multiple outputs helps to mitigate learning biases introduced by artificial perturbations. During consistency training, the mixed images resulting from WT are fed into both models, with the student model’s output being supervised by pseudo-labels and ground-truth. Extensive experiments conducted on 2D and 3D datasets confirm the effectiveness of our model. Code: https://github.com/simzhangbest/WT-BCP.

Resolving issue reports is essential to the development of opensource software, which helps developers collaborate, communicate, and fix bugs, thereby ensuring the quality and functionality of the software.While previous studies focus on the static factors that impact the issue resolution time, few of them investigate the issue resolution patterns.In this paper, we propose an approach based on discriminative sequential pattern mining to discover the patterns in issue resolution processes with different efficiency.We devise an issue resolution process modeling method, which adds events' time interval values to the activity sequences, thus providing a richer understanding of the issue resolution.And we find that there are several typical patterns in the issue resolution process with different lifetimes.To guide developers in prioritizing issues and allocating resources, we propose a prediction model and a recommendation approach based on the mined patterns.We extract features from patterns and construct random forest classifier models to predict the resolution speed of issues (fast, normal or slow).The accuracy of our models reaches 70% or higher in most repositories and mixed repositories' data, indicating a great performance.Finally, we recommend actions to users participating in ongoing issues at an early stage based on the mined patterns, thus improving the efficiency of issue resolution process.

Third-party libraries (TPLs) play a critical role in modern software development by providing reusable code that accelerates project development. However, the vast number of TPLs available makes selecting the appropriate library for a given task or finding replacements for deprecated libraries a challenging task. Existing methods are limited, relying only on miningbased approaches or feature-based solutions. In this study, we propose an innovative attributed multiplex learning approach that combines both textual and relational data across multiple layers to perform effective analogical library recommendation and retrieval. By representing libraries as nodes with attributes and modeling cross-library relationships as graph edges, our method constructs an attributed multiplex network for TPL representation embeddings. Our approach uses a unified, concise model to include different aspects of information. The proposed inductive model can also address cold-start issues. Moreover, our model is scalable and can adapt to a large number of libraries. To validate our approach, we conduct experiments including an ablation study within the NPM ecosystem. By using a ground-truth data set of 8,308 libraries, the results demonstrate a recommendation precision of 89.8 % at Hit@10. Additionally, we contribute a new data set extracted from deprecation messages containing 4,070 migration rules, enriching the relatively small existing data sets in the NPM ecosystem. In summary, our approach is efficient and promising for supporting real-world, large-scale TPL recommendation and retrieval.

In this work, we propose the EarlyPR framework that identifies and predicts potential pull-request (PR) contri-butions from an open source software (OSS) project's forks, which can potentially improve the efficiency of the fork-and-pull based development in OSS projects by supporting early warning of duplicated and rejected contributions, and detection of lost contributions. Unlike traditional, PR-based studies that rely on the descriptions and contents of PRs provided by their creators, which are only available after the PRs are created, EarlyPR makes predictions before the creation of PRs by mining the forks' commit history. EarlyPR's task is challenging because of the explosive number of commit subsets in a fork's commit history that may form PRs, and the absence of resulting, real PR-related information. To tackle the challenges, we adopt the state-of-the-art, Transformer-based architecture to extract rich statistical and content information from the forks and their commits to support the prediction of potential PR contributions. And to make the algorithms scalable, we devise a TemporalFilter to find candidate PRs by mimicking the real-world processes of picking subsets of commits from a fork's commit history when creating PRs. Experimental results on real-world OSS project data suggest that EarlyPR is effective in predicting PRs, which are essentially sets of commits selected from forks to compose these PRs. Experimental results obtained using real-world OSS projects' and their forks' data suggest that EarlyPR is effective by achieving a hitting rate of 0.790 and a missing rate of 0.367 by matching the predicted and real PRs under a stringent criterion of IoU > 0.5. We further demonstrate that we can forecast the merging of PRs based on EarlyPR's predictions with an accuracy of 70.8%. In summary, the proposed approach can potentially improve the efficiency of the fork-and-pull based OSS development by making accurate and early predictions of PR contributions from the distributed, and often independently, developed forks.

This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and expand events. The indices have proven properties like monotonicity, and they have defined maximum and minimum values that signify meaningful scenarios. These indices can be combined to describe complex community evolution events such as emergence and extinction. Expanding upon these indices, this research proposes a novel machine learning approach, leveraging shapelet mining, to unearth representative patterns of community evolution. The results from real-world OSS projects show that these indices effectively capture various community evolution behaviors with a 94.1% accuracy compared to existing work. They also predict OSS team productivity with a 0.718 accuracy. With the shapelet mining and learning framework, the indices can identify patterns of community evolution and predict the survival of OSS projects with 93% accuracy 3 months before the projects’ last observed commits. The findings highlight the potential of these entropy-based indices for understanding OSS project status and predicting future trends, which are valuable for supporting future research on DSNs and OSS communities.

On open source software (OSS) platforms such as GitHub, forking and accepting pull-requests is an important approach for OSS projects to receive contributions, especially from external contributors who cannot directly commit into the source repositories. Having a large number of forks is often considered as an indicator of a project being popular. While extensive studies have been conducted to understand the reasons of forking, communications between forks, features and impacts of forks, there are few quantitative measures that can provide a simple yet informative way to gain insights about an OSS project's forks besides their count. Inspired by studies on biodiversity and OSS team diversity, in this paper, we propose an approach to measure the diversity of an OSS project's forks (i.e., its fork population). We devise a novel fork entropy metric based on Rao's quadratic entropy to measure such diversity according to the forks' modifications to project files. With properties including symmetry, continuity, and monotonicity, the proposed fork entropy metric is effective in quantifying the diversity of a project's fork population. To further examine the usefulness of the proposed metric, we conduct empirical studies with data retrieved from fifty projects on GitHub. We observe significant correlations between a project's fork entropy and different outcome variables including the project's external productivity measured by the number of external contributors' commits, acceptance rate of external contributors' pull-requests, and the number of reported bugs. We also observe significant interactions between fork entropy and other factors such as the number of forks. The results suggest that fork entropy effectively enriches our understanding of OSS projects' forks beyond the simple number of forks, and can potentially support further research and applications.

Parkinsons disease (PD) is a brain disorder that causes unintended or uncontrollable movements. The diagnosis of the disease mainly relies on clinical test rather than a definite medical test, and the diagnostic accuracy is only about 80%. Thus, an efficient and explainable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. We present a novel way to classify Parkinsons disease from RGB videos, which is contactless and accurate. Our new algorithm is SimpleHandFormer, which only requires non-intrusive RGB-video recordings of the candidates as input. Our algorithm is motivated by the observation that tremors (shaking movement) will cause the hand keypoint’s location changes, and focusing on the change of the keypoints would improve the performance. For the first time, we propose to use a novel frame–keypoint different attention to effectively detect tremors in the human hands. This design aids in improving both binary classification performance and algorithm explanation. Experimental results show that our system outperforms state-of-the-arts by achieving a balanced accuracy of 93.9% and an F1-score of 92.6% in classifying Parkinson’s Disease.

Studies on the OSS communities have shown that financial supports are critical to OSS developers and projects to maintain their progress and sustainability. However, there were few developers being paid directly for maintaining OSS projects in the past. The GitHub Sponsors program that brings financial supports to the general OSS developers in GitHub-the world's largest OSS platform may make a difference on this situation in the future. In this paper, we present a data set on GitHub Sponsors and conduct a data-driven study to analyze the participants of the program and the impact of sponsorships to developers' activities and their projects' outcomes and qualities. The results of our survey suggest that most developers state they will contribute more with sponsorships and provide some privilege for their sponsors. And through quantitative study, we find that developers make more contributions on GitHub after they got/offered sponsorships. Moreover, gaining sponsorship also has a weakly positive impact on developers' collaborators that did not get sponsorship. And not only developers, but their own or contributed projects also can be motivate by sponsorships. Our findings are useful to the community by understanding the impact of sponsorships on users' activities and projects' progress and sustainability, and helping the managers to improve the current financial support mechanism.

Existing studies in both psychology and software engineering have shown the importance of emotions in complex learning and programming tasks. For students who are learning to program, rich emotions are experienced which can provide valuable feedback to their teachers. To accurately model stu-dents' emotions, this paper adopts the well-recognized model of emotions during complex learning that involves four states: engaged, confused, frustrated, and bored. To perform continuous estimation of students' emotions in a non-intrusive manner, this paper proposes to track students' programming behavior and estimate their corresponding emotional states. Compare to the existing approaches on acquiring the students' emotional states with self-reports or bio-sensors, the proposed approach is more feasible in conducting real-world, and large-scale studies for not requiring extensive human interventions or additional devices. Evaluated using data collected from a real-world course project, the proposed approach is showed to be promising for achieving an estimation accuracy of 72.06 % for the above four emotional states. As an enabling technology, the proposed is potentially useful in supporting many applications and improve the quality of programming education in computer science.

As human beings take actions with a goal in mind, we could predict the following action of a person depending on his previous actions. Inspired by this, after collecting and analyzing more than 13,000 repositories with 441,290 Python source code files from the Internet, we find the actions expressed in code are in the developers’ high-level programming language statements.

Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand, emerge, and extinct. However, existing pattern-based approaches are limited in supporting quantitative analysis, and are potentially problematic for using the patterns in a mutually exclusive manner when describing community evolution. In this work, we propose that different patterns can occur simultaneously between every pair of communities during the evolution, just in different degrees. Four entropy-based indices are devised to measure the degree of community split, shrink, merge, and expand, respectively, which can provide a comprehensive and quantitative measure of community evolution in DSNs. The indices have properties desirable to quantify community evolution including monotonicity, and bounded maximum and minimum values that correspond to meaningful cases. They can also be combined to describe more patterns such as community emerge and extinct. We conduct studies with real-world OSS projects to evaluate the validity of the proposed indices. The results suggest the proposed indices can effectively capture community evolution, and are consistent with existing approaches in detecting evolution patterns in DSNs with an accuracy of 94.1%. The results also show that the indices are useful in predicting OSS team productivity with an accuracy of 0.718. In summary, the proposed approach is among the first to quantify the degree of community evolution with respect to different patterns, which is promising in supporting future research and applications about DSNs and OSS development.

Bowel sounds are the sounds produced by intestinal peristalsis and can reflect intestinal activities. There is a long history of using bowel sounds as indicators for intestinal health in clinical practices, making bowel sound detection potentially useful as a key enabling technology for health care applications. Traditionally, bowel sounds are detected manually by medical staff. However, manual inspections are time consuming and prone to personal differences. In modern hospitals, advanced medical devices are used to detect the patients’ intestinal activities with rich sensing modalities. However, these devices are often invasive, expensive and large in size, making them only used in limited, critical scenarios. To facilitate bowel sound detection and further intestinal activity analysis in daily living, this paper presents a wearable system to capture and recognize users’ bowel sounds and other types of body sounds without restricting their daily activities. We propose to use dual-channel, stethoscope augmented microphones to capture users’ body sounds. With the captured audio stream, we extract both time- and frequency-domain features to represent signals in each frame. Beyond traditional audio features such as energy and MFCC, we further extract dual-channel features to augment the information obtained from a frame. We evaluate the system’s performance with four subjects in real-world settings, the results suggest our system is effective for achieving a detection accuracy of 85.7%.

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent issues due to its devastating impact on service quality, and is challenging because of the absence of coevolving data sequences for reference. As a result, many existing approaches that rely on data from other coevolving sequences for missing value recovery are infeasible in handling blackouts. To address the issue, this work proposes a novel Hankel matrix factorization approach, HKMF-T, to recover missing values during blackouts for tagged time series, where a tagged time series consists of a data sequence and a corresponding tag sequence. Motivated by real-world observations, HKMF-T decomposes the data sequence into two components: 1) an internal, slowly-varying smooth trend, and 2) external impacts indicated by the tag sequence. By transforming a partially observed data sequence into a corresponding Hankel matrix, we learn the above two components and estimate the missing values under a unified framework of Hankel matrix factorization. Extensive experiments are conducted to evaluate the practical performance of HKMF-T on real-world data sets. And the results suggest HKMF-T outperforms the baseline approaches for blackouts with long durations.

The quality of software products is closely related to software developers. A good psychological state can improve the quality of software products to a certain extent. More and more researchers are beginning to study how to effectively identify the psychological state of developers to help their work. However, many of these works have a significant intrusion into developers and are not suitable for long-term deployment in real enterprises. In this paper, we present a tool for IDE-based programming activity data collection for software developers. A conceptual model is presented for raw data collection and hierarchical concept encapsulation. We implement this tool in the form of an IDE plug-in for popular IDEs including IntelliJ IDEA and Android Studio, which can collect and process developers' in-IDE activity data in a non-intrusive and privacy-preserving manner. To demonstrate its usefulness, we deploy the proposed tool in a real enterprise environment. Based on this tool and the data collected in the real enterprise environment, we build applications including programming activity statistics and flow state recognition. The experiment results suggest the proposed approach can effectively capture the detailed programming activities and perform flow state recognition. The recognition accuracy is 80.14%.

Flow is a holistic description of people's optimal experiences during creative activities that can be characterized as being totally concentrated on, and actively involved in the task, enjoying the process of creation, and achieving a balance between one's skill and the task's challenge. Understanding software developers' flow states has attracted an increasing attention in both research and practice because of the strong link between being in flow and achieving good performance. In this paper, we study the problem of tracking and recognizing developers' flow states by tracing their computer interactions including activities of using the keyboard, mouse, IDE functions, and switching application windows. Compared to the traditional approaches that rely on self-reports or wearable sensors, a major advantage of the proposed approach is being non-invasive for not requiring any additional efforts from the developers after the training phase is completed, which is important because the developers' flow states can easily be interrupted by external interferences. Based on the captured interaction traces, we represent the developers' activities with extensive features, and propose to address the flow state recognition problem using machine learning technologies. And a hierarchical recognition model is built following the multi-dimensional construct of the flow concept, which is interpretable and effective. We develop a prototype system and conduct a 17-day field study in a medium-sized IT company in China to collect real-world data. The results show that our approach is effective by achieving the highest recognition accuracy of 92.6%, and efficient for performing real-time recognition.

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent and challenging issues. While the existing approaches for missing value recovery in time series could not handle this issue properly, in this work, we proposes a Hankel matrix factorization-based approach for tagged time series called HKMF-T, following the idea of decomposing a data sequence into the smooth trend and the external impact components. By transforming the data sequence into its Hankel matrix form, HKMF-T models the smooth trend implied by high-order temporal correlations as the product of two low-rank matrices, and learns the external impacts indicated by a corresponding tag sequence. Through extensive experiments conducted on three real-world data sets, HKMF-T shows its effectiveness by outperforming all baseline methods for blackouts with durations longer than nine sampling intervals.

This paper presents the CARMUS framework for continuous activity recognition with missing values on smartphones. Besides the power and resource constraints discussed in existing work, our framework is proposed to further tackle the critical issue of missing values during data collection. We demonstrate the issue's impact on continuous recognition through a motivating example, and specify two challenges-blackouts and resource constraints-with respect to smartphone-based sensing and processing platforms. To address the challenges, CARMUS provides a novel framework which involves a light-weight admission control unit and a data imputation unit intuited by the daily repeated pattern and temporal smoothness of human activity data. Based on extensive experiments conducted on a real-world data set with 37% of the data missing, we show that the CARMUS framework is effective for achieving an 85.5% recognition accuracy by adopting the state-of-the-art imputation algorithms.

With the rapid development of mobile devices, Android applications (apps) are universally used. However, attackers repackage Android apps and release them to the markets for illegal purposes, which brings great threats to the Android ecosystem. To leverage the popularity of original apps, they keep similar software behaviors to confuse app users. Furthermore, repackaged apps can be obfuscated or encrypted to avoid being detected. Besides, hybrid mobile apps, built by combining web technology and native elements, are becoming a preferred choice for developers. The structure of hybrid apps differs a lot from that of native apps which would raise great challenges to repackaging detection. Existing works still have some limitations in detecting repackaging from obfuscated and encrypted apps. Besides, few of them can deal with hybrid apps. In this paper, we proposed an approach based on the app UI regions extracted from app's runtime UI traces. We also implement a tool named RegionDroid based on the approach. We apply RegionDroid to tree datasets with totally 369 apps. It successfully finds all the 98 obfuscated or encrypted repackaged pairs in dataset S1. It also shows good credibility in distinguishing another 114 commercial apps in dataset S2. We also test our approach in dataset S3with 157 hybrid apps by comparing them pairwisely and the false positive rate is 0.016%.

Understanding the relevance between the Free/Libra Open Source Software projects is important for developers to perform code and design reuse, discover and develop new features, keep their projects up-to-date, and etc. However, it is challenging to perform relevance ratings between the FLOSS projects mainly because: 1) beyond simple code similarity, there are complex aspects considered when measuring the relevance; and 2) the prohibitive large amount of FLOSS projects available. To address the problem, in this paper, we propose a method to measure and further predict the relevance ratings between FLOSS projects. Our method uses topic features extracted by the LDA topic model to describe the characteristics of a project. By using the topic features, multiple aspects of FLOSS projects such as the application domain, technology used, and programming language are extracted and further used to measure and predict their relevance ratings. Based on the topic features, our method uses matrix factorization to leverage the partially known relevance ratings between the projects to learn the mapping between different topic features to the relevance ratings. Finally, our method combines the topic modeling and matrix factorization technologies to predict the relevance ratings between software projects without human intervention, which is scalable to a large amount of projects. We evaluate the performance of the proposed method by applying our topic extraction and relevance modeling methods using 300 projects from GitHub. The result of topic extraction experiment shows that, for topic modeling, our LDA-based approach achieves the highest hit rate of 98.3% and the highest average accuracy of 29.8%. And the relevance modeling experiment shows that our relevance modeling approach achieves the minimum average predict error of 0.093, suggesting the effectiveness of applying the proposed method on real-world data sets.

Services are widely used in Android apps. However, services may leak such that they are no longer used but cannot be recycled by the Garbage Collector. Service leaks may cause an app to misbehave, and are vulnerable to malicious external apps when the service is exported or it is accessible through other exported services. In this paper, we present LESDroid for exported service leaks detection. LESDroid automatically generates service instances and workloads (start/stop or bind/unbind of exported services) of the app under test, and applies a designated oracle to the heap snapshot for service leak detection. We evaluated LESDroid using 375 commercial apps, and found 97 leaked services and 98 distinct leak entries in 70 apps.

Memory leak, one of the most common problems threatening android apps, might drain the limited memory of mobile devices, cause unexpected delays, no-responses or even crashes to apps. Activity/Fragment Leak is one of the most common and serious causes of memory leaks and has a vast influence on the Android app market. Existing work to identify leaked activities/fragments either depend highly on the experience of developers, or require app's source code and manual interactions. In this paper, we propose an automatic tool named LeakDAF for detecting leaked activities/fragments automatically without manual intervention. LeakDAF makes use of UI testing technique to execute automatically the app under test, and applies memory analysis technique to inspect dumped heap files to identify leaked activities and fragments based on Android's mechanisms for managing them. To evaluate the effectiveness of LeakDAF, we successfully applied it to 35 open source and 64 commercial apps, and we detected at least one leaked activity or fragment for 10 open source apps and 35 commercial apps.

In recent years, with the explosive growth of mobile smart phonesnes, the number of Android applications (apps) increases rapidly. Attackers usually leverage the popumobile smart phoneslarity of Android apps by inserting malwares, modifying the original apps, repackaging and releasing them for their own illegal purposes. To avoid repackaged apps from being detected, they usually use sorts of obfuscation and encryption tools. As a result, it's important to detect which apps are repackaged. People often intuitively judge whether two apps are a repackaged pair by executing them and observing their runtime user interface (UI) traces. Hence, we propose layout group graph (LGG) built from UI trances to model those UI behaviors and use LGG as the birthmark of Android apps for identification. Based on LGG, we also implement a dynamic repackaging detection tool, RepDroid. Since our method does not require the apps' source code, it is resilient to app obfuscation and encryption. We conducted an experiment with two data sets. The first set contains 98 pairs of repackaged apps. The original apps and repackaged ones are compared and we can detect all of these repackaged pairs. The second set contains 125 commercial apps. We compared them pair-wisely and the false positive rate was 0.08%.

Latent factor models have become a prevalent method in recommender systems, to predict users' preference on items based on the historical user feedback. Most of the existing methods, explicitly or implicitly, are built upon the first-order rating distance principle, which aims to minimize the difference between the estimated and real ratings. In this paper, we generalize such first-order rating distance principle and propose a new latent factor model (HoORaYs) for recommender systems. The core idea of the proposed method is to explore high-order rating distance, which aims to minimize not only (i) the difference between the estimated and real ratings of the same (user, item) pair (i.e., the first-order rating distance), but also (ii) the difference between the estimated and real rating difference of the same user across different items (i.e., the second-order rating distance). We formulate it as a regularized optimization problem, and propose an effective and scalable algorithm to solve it. Our analysis from the geometry and Bayesian perspectives indicate that by exploring the high-order rating distance, it helps to reduce the variance of the estimator, which in turns leads to better generalization performance (e.g., smaller prediction error). We evaluate the proposed method on four real-world data sets, two with explicit user feedback and the other two with implicit user feedback. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods in terms of the prediction accuracy.

In this paper, we study the problem of pedestrian relative positioning with respect to their walking direction. Existing approaches are mainly based on trajectory information or device proximity detection, and they highly rely on infrastructure or specialized device support. Importantly, most work does not provide relative position information with respect to people's walking direction. To address the above issues, we propose a direction-aware, audio-based solution that only uses daily wearable devices. Based on the fact that pedestrian's arms often swing back and forth during walking, we develop the wrist-body model that formally models the distance change between a user's wrist and his/her walking mate's body when walking together. Based on this model, we design our system by attaching the audio sources to a user's wrists and an audio receiver to the other user's body. We develop key indicators that characterize the received audio signal's Doppler shift induced by arm swing motions and the differences in signal strength. We further propose methods such as cycle segmentation and aggregation to deal with several real-world challenges. The performance of our approach is studied through extensive experiments. Evaluation conducted using real-world data suggests the prototype system achieves 85.9% positioning accuracy, demonstrating its effectiveness.

In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The centerpiece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.

Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent.

Smoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by detecting a smoker's content of breathing, lighter usage, breathing, and gesture patterns. However, the existing systems are complex, and they are usually vulnerable to confounding activities and diversity of smoking behavior. To address these limitations, this paper proposes the design and implementation of a simple and compact smart neckband device for smoking detection. The device is equipped with both passive and active acoustic sensors to detect smoking sessions and puffs. We propose a hierarchical processing framework in which the lower-layer detects the sub-movements, i.e., lighter usage, hand-to-mouth gesture and deep breathing, from perceived audio data; and the higher-layer, based on the lower-layerąŕs detection results, detects smoking puffs and sessions using temporal sequence analysis techniques. Real-world experiments suggest our system can accurately detect smoking puffs and sessions with F1 score of respectively 93.59% and 92.96% in complex environments with the presence of confounding activities and diverse ways of smoking.

Using magnetic field data as fingerprints for smartphone indoor positioning has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which result in unreliable systems, or impose strong restrictions on smartphone such as fixed phone orientation, which are not practical for real-life use. In this paper, we present a novel indoor positioning system for smartphones, which is built on our proposed reliability-augmented particle filter. We create several innovations on the motion model, the measurement model, and the resampling model to enhance the basic particle filter. To minimize errors in motion estimation and improve the robustness of the basic particle filter, we propose a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model, combining a new magnetic fingerprinting model and the existing magnitude fingerprinting model, to improve system performance, and importantly avoid calibrating magnetometers for different smartphones. In addition, we propose an adaptive sampling algorithm to reduce computation overhead, which in turn improves overall usability tremendously. Finally, we also analyze the “Kidnapped Robot Problem” and present a practical solution. We conduct comprehensive experimental studies, and the results show that our system achieves an accuracy of 1~2 m on average in a large building.

Abstract Traditional fingerprint‐based localization techniques mainly rely on infrastructure support such as GSM and Wi‐Fi. They require war‐driving, which is both time‐consuming and labor‐intensive. With recent advances of smartphone sensors, sensor‐assisted localization techniques are emerging. However, they often need user‐specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present Barometer‐based floor Localization system (B‐Loc), a novel floor localization system to identify the floor level in a multi‐floor building on which a mobile user is located. It makes use of the barometer on smartphone. B‐Loc does not rely on any Wi‐Fi infrastructure and requires neither war‐driving nor prior knowledge of the buildings. Leveraging on crowdsourcing, B‐Loc builds the barometer fingerprint map, which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B‐Loc. Our simulation shows that B‐Loc can locate the user fast and the field study in a 10‐floor building shows that B‐Loc achieves an accuracy of over 98%. Copyright © 2016 John Wiley & Sons, Ltd.

With user experience becoming richer and richer on Android smartphones, limited battery capacity has become a major concern for users. It is a good practice for Android to tell users where the battery power has gone. Android achieves this by a power profile provided by OEMs that specifies the power usage rate for each system component staying at each specific state. According to a recent study, screen is one of the most dominant power-hungry system components on Android smartphones. However, the accuracy of Android's screen power usage model is to be improved, taking not only the brightness, but also the displayed content into account. We modified Android source code to improve its screen power usage model and verified this improvement with experiments. The results show that our new screen power usage model is more accurate to a significant extent and introduces little overhead.

Hot deployment is a typical feature in mainstream application servers. But current application servers treat each module as a standalone application and may fail if a module with dependencies against other ones is partially updated with hot deploying. The reason lies in that those module dependencies are not respected in current application servers. Direct countermeasures that manage dependencies in application servers are actually inefficient or even infeasible. So in this paper, we propose an approach that automatically constructs the module dependencies with class loading mechanism which further helps to reconstruct the modular application respecting the dependencies upon hot deploying. Experiments show that our technology of hot deployment can ensure partial update of the modular applications correctly and efficiently.

As applications deployed in a smart space share the same physical environment, they may interfere (or even conflict) with one another. To guarantee the performance and user experience of the entire smart space, mechanisms for handling such interferes (or conflicts) have to be introduced. We believe that conflicts among multiple applications are caused by their different requirements and impacts on the shared environment, and we model the conflict resolution problem as a Constraint Satisfaction Problem(CSP). We further propose a framework for managing and coordinating context-aware applications in a smart space, and provide an effective and efficient strategy to solve the corresponding CSP. Exhausted simulations are carried out to show the effectiveness of the proposed resolution strategy.

Software update in wireless sensor networks requires the ability of disseminating bulk data to specified sensors of a network with low latency in an energy efficient manner. This paper proposes a target-aware, transmission power-adaptive, and collision-free data dissemination protocol to fulfill these requirements. This protocol disseminates data to sensors of a network by first constructing a connected dominating set (CDS) in the network. We propose a target-aware CDS construction to exclude many unnecessary non-target sensors from the data dissemination process. By allowing some dominators of the CDS to increase their transmission power to disseminate data to more dominatees, the protocol efficiently reduces the total energy consumption. In addition, we propose a collision-based channel assignment strategy to eliminate communication collisions among dominators so as to reduce latency. We have implemented the protocol and evaluated it through simulations and a real application scenario. Our experimental results show that the proposed protocol at most reduces non-target sensors by 75.3%, total energy consumption by 57.1%, and latency by 39.8% compared to existing dissemination protocols.

Content-based communication is a novel communication paradigm that enables users to communicate with others based on message's content, instead of message's address. Groupware is a kind of software that supports coordination between individual users. An important feature of groupware is that the communication between the specified users is at a high frequency, which is determined by the applied coordination mechanism. Efficient communication in a groupware can support effective coordination between the users. This paper presents a topology-enhanced content-based network, which combines content-based communication with the topology between groupware users, to support content-based communication in groupware. We give a predicate-based method to define cooperation topology, which can effectively describe the topology between groupware users. We also propose a multi-level index structure to support efficient matching of cooperation topology in the forwarding mechanism of the proposed network. We implement the basic feature of our network and evaluate the prototype in a motivating scenario consisting of several coordination tasks. The results show that our method improves the message forwarding efficiency of 1 to 2 magnitude orders.

Using magnetic field data as fingerprints for localization in indoor environment has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which makes the system unreliable, or impose strong restrictions on smartphone such as fixed phone orientation, which is not practical for real-life use. In this paper, we present an indoor localization system named MaLoc, built on our proposed augmented particle filter. We create several innovations on the motion model, the measurement model and the resampling model to enhance the traditional particle filter. To minimize errors in motion estimation and improve the robustness of particle filter, we augment the particle filter with a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model which combines a new magnetic fingerprinting model and the existing magnitude fingerprinting model to improve the system performance and avoid calibrating different smartphone magnetometers. In addition, we present a novel localization quality estimation method and a localization failure detection method to address the "Kidnapped Robot Problem" and improve the overall usability. Our experimental studies show that MaLoc achieves a localization accuracy of 1~2.8m on average in a large building.

Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM, Wi-Fi or GPS. They work by war-driving the entire indoor spaces which is both time-consuming and labor-intensive. With recent advances of smartphone and sensing technologies, sensor-assisted localization techniques leveraging on mobile phone sensing are emerging. However, sensors are inherently noisy, making this technique challenging for real deployment. In this paper, we present F-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It does not need to war-drive the entire building. Leveraging on crowdsourcing and mobile phone sensing, we collect users' Wi-Fi traces and accelerometer readings. Through advanced clustering and cluster manipulating techniques, we are able to build the Wi-Fi map of the entire building, which can then be used for floor localization. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of F-Loc. Our field study in a 10-floor building shows that F-Loc achieves an accuracy of over 98%.

Handling uncertainty in software self-adaptation has become an important and challenging issue. In our previous work, we proposed a fuzzy control based approach named Software Fuzzy Self-Adaptation (SFSA) to address fuzziness, a kind of uncertainty in software self-adaptation. However, our SFSA approach still lacks a tool to efficiently support the implementation process of SFSA. Existing tools for realizing self-adaptive applications does not directly deal with fuzziness in self-adaption loops. In this paper, we present the FuAET, a tool designed for building fuzzy self-adaptive software systems. The novelty of the tool is that it can not only provide a friendly GUI for editing and testing fuzzy self-adaptation strategies in intelligible domain specific language (DSL), but also automatically convert DSL-based fuzzy selfadaptation strategies into aspect-based programming code in native-language (NL), e.g., C++. This paper describes the design framework and implementation principles of FuAET, and then proposes a general development process using FuAET for reference to developers. Finally, we conduct an empirical study for evaluation of FuAET using an industrial control application. The results show that FuAET can automate the development of SFSA and ease the burden of the software engineers.

Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone only. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowd sourcing, B-Loc builds the barometer fingerprint map which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%.

We have seen increasingly popularity in embedding barometer into smartphone today. A barometer measures the barometric pressure, and it can be used for a variety of applications. For example, in localization techniques, it is used to detect the altitude or altitude change of a user. Unfortunately, t

Traditional fingerprint based localization techniques mainly rely on infrastructure support such as RFID, Wi-Fi or GPS. They operate by war-driving the entire space which is both time-consuming and labor-intensive. In this paper, we present M-Loc, a novel infrastructure-free localization system to locate mobile users in a metro line. It does not rely on any Wi-Fi infrastructure, and does not need to war-drive the metro line. Leveraging crowdsourcing, we collect accelerometer, magnetometer and barometer readings on smartphones, and analyze these sensor data to extract patterns. Through advanced data manipulating techniques, we build the pattern map for the entire metro line, which can then be used for localization. We conduct field studies to demonstrate the accuracy, scalability, and robustness of M-Loc. The results of our field studies in 3 metro lines with 55 stations show that M-Loc achieves an accuracy of 93% when travelling 3 stations, 98% when travelling 5 stations.

Service composition provides supports for automatic construction of required services on the fly from component services provided by different providers. In a smart space, as new providers may come in and existing ones may leave from time to time, the collection of available component services may change dynamically, resulting in broken compositions. Automatic reselection or recomposition mechanisms can be applied to fix broken compositions. However, they may introduce extra efforts and time and there are applications requiring (composed)services to be continuously available (at least) during a period of time, otherwise the applications would break down. Both the situations affect users' experience. If we could know how long each component service would be continuously available, we could optimize the composition process by reducing the frequency of broken compositions. In this paper we propose a scheme to achieve the goal. It utilizes presence-patterns of providers to estimate how long a component service may continuously available and it always selects the most continuously available composition that matches requirements. Simulations are carried out to show how and how well the presence-patterns based scheme work.

Using mobile devices, people can easily obtain their location information, and access a wide range of location based services (LBSs). Many existing LBSs rely in accurate, continuous, and real-time streams of location information to provide quality of service guarantees. In this case, even if an user accesses LBSs anonymously, the identity of the user can still be revealed by analyzing the mobility trace. To protect user privacy, existing work sacrifice the quality of LBSs by degrading spatial and temporal accuracy. To achieve a better tradeoff between user privacy and the quality of service, we present a novel approach, Complete Bipartite Anonymity (CBA), to confuse the paths of nearby users by connecting different users' real traces with fake ones. CBA protects user privacy as users become indistinguishable after their paths are confused, the quality of service of LBSs is also guaranteed since users are able to report their accurate locations. We evaluate CBA by comparing the system and privacy performance with existing techniques such as Path Confusion or Query Obfuscation using a real-world data set, the results show that our scheme increases the chance for a user joining an anonymity group by 10 times in low user density areas, and reduces the resources consumed by about 90% for achieving the same anonymity degree.

Mobile phone localization plays a key role in the fast-growing Location Based Applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, WiFi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone's accelerometer only without any infrastructure support. It does not require any prior knowledge of the building such as floor height. By capturing user encounters and analyzing user trails, FTrack finds the mapping from the traveling time (when taking the elevator) or the step counts (when walking on the stairs) between any two floors to the number of floor levels. The mapping can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the effectiveness of FTrack. Our field trial in a 10-floor building shows that FTrack achieves an accuracy of over 90% after two hours in our experiment.

Runtime detection of contextual properties is one of the primary approaches to enabling context-awareness in pervasive computing scenarios. Among various properties the applications may specify, the concurrency property, i.e., property delineating concurrency among contextual activities, is of great importance. It is because the concurrency property is one of the most frequently specified properties by context-aware applications. Moreover, the concurrency property serves as the basis for specification of many other properties. Existing schemes implicitly assume that context collecting devices share the same notion of time. Thus, the concurrency property can be easily detected. However, this assumption does not necessarily hold in pervasive computing environments, which are characterized by the asynchronous coordination among heterogeneous computing entities. To cope with this challenge, we identify and address three essential issues. First, we introduce logical time to model behavior of the asynchronous pervasive computing environment. Second, we propose the logic for specification of the concurrency property. Third, we propose the Concurrent contextual Activity Detection in Asynchronous environments (CADA) algorithm, which achieves runtime detection of the concurrency property. Performance analysis and experimental evaluation show that CADA effectively detects the concurrency property in asynchronous pervasive computing scenarios.

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of single user. In this work, we focus on the fundamental problem of recognizing activities of multiple users using a wireless body sensor network, and propose a scalable pattern mining approach to recognize both single- and multiuser activities in a unified framework. We exploit Emerging Pattern-a discriminative knowledge pattern which describes significant changes among activity classes of data-for building activity models and design a scalable, noise-resistant, Emerging Pattern-based Multiuser Activity Recognizer (epMAR) to recognize both single- and multiuser activities. We develop a multimodal, wireless body sensor network for collecting real-world traces in a smart home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability, and robustness.

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. We argue that in asynchronous environments, the concept of time needs to be reexamined. Instead of assuming the availability of global time or synchronous interaction, we should rely on logical time. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context-awareness based on logical time. Design and operation of MIPA are explained in detail. We also evaluate MIPA with a comprehensive case study. The evaluation results show the cost-effectiveness and scalability of MIPA.

The emerging participatory sensing applications have brought a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy by generalizing a precise user location to a coarse-grained location, and hence they cannot be applied in those applications requiring fine-grained location information. To address this issue, in this paper we propose a novel method to preserve location privacy by anonymizing coarse-grained locations and retaining fine-grained locations using Attribute Based Encryption (ABE). In addition, we do not assume the service provider is an trustworthy entity, making our solution more feasible to practical applications. We present and analyze our security model, and evaluate the performance and scalability of our system.

Detection of contextual properties is one of the primary approaches to enabling context-awareness. In order to adapt to temporal evolution of the pervasive computing environment, context-aware applications often need to detect behavioral properties specified over the contexts. This problem is challenging mainly due to the intrinsic asynchrony of pervasive computing environments. However, existing schemes implicitly assume the availability of a global clock or synchronous coordination, thus not working in asynchronous environments. We argue that in pervasive computing environments, the concept of time needs to be reexamined. Toward this objective, we propose the Ordering Global Activity (OGA) algorithm, which detects behavioral contextual properties in asynchronous environments. The essence of our approach is to utilize the message causality and its on-the-fly coding as logical vector clocks. The OGA algorithm is implemented and evaluated based on the open-source context-aware middleware MIPA. The evaluation results show the impact of asynchrony on the detection of contextual properties, which justifies the primary motivation of our work. They also show that OGA can achieve accurate detection of contextual properties in dynamic pervasive computing environments.

The open, dynamic and uncertain internet platform requires software systems running on it should be able to probe the changes of their running environments and adapt themselves accordingly. The Internetware paradigm and the environment driven application model open a promising way to cope with these requirements. A uniform middleware for environmental context information handling is essential to achieve the environment driven application model. In this paper, we argue that mobile agent technology is competent for building such a middleware and propose the Probe system, implemented based on mobile agent technology, with respects to the environment driven application model. The Probe system provides a uniform, hierarchical, and dynamic configurable platform for environmental context information collection, dissemination and processing for Internetware applications.

With the rapid advances of wearable sensors and wireless networks. There is a growing research interest in building Body Area Sensor Network (BSN) systems to support applications such as human activity recognition and daily health monitoring. The main challenges of building a BSN system include: First, the wearable sensor nodes are highly constrained in resources; Second, the sensor nodes are prong to failures; Third, the applications requires the sensors to work with high sampling rate which results in the heavy load on each sensor node. Beside the technical challenges, the usability of the system is also a critical issue when deployed into the the real-world environment.

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a real time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%.

One of the major applications of wireless ad hoc networks is to extend the Internet coverage and support pervasive and efficient data dissemination and sharing. To reduce data access cost and delay, caching has been widely used as an important technique. The efficiency of data access in caching systems largely depends on the cost for maintaining cache consistency, which can be high in wireless ad hoc networks due to network dynamism. Therefore, to make better trade-off between cache consistency and the cost incurred, it would be highly desirable to provide users the flexibility in specifying consistency requirements for their applications. In this paper, we propose a general consistency model called Probabilistic Delta Consistency (PDC), which integrates the flexibility granted by existing consistency models, covering them as special cases. We also propose the Flexible Combination of Push and Pull (FCPP) algorithm which satisfies user-specified consistency requirements under the PDC model. The analytical model of FCPP is used to derive the balance of minimizing the consistency maintenance cost and ensuring the specified consistency requirement. Extensive simulations are conducted to evaluate whether FCPP can satisfy arbitrarily specified consistency requirements, and whether FCPP works cost-effectively in dynamic wireless ad hoc networks. The evaluation results show that FCPP can adaptively tune itself to satisfy various user-specified consistency requirements. Moreover, it can save the traffic cost by up to 50 percent and reduce the query delay by up to 40 percent, compared with the widely used Pull with TTR algorithm.

Abstract Cooperative caching is an important technique to support pervasive Internet access. In order to ensure valid data access, the cache consistency must be maintained properly. However, this problem has not been sufficiently studied in mobile computing environments, especially those with ad hoc networks. There are two essential issues in cache consistency maintenance: consistency control initiation and data update propagation. Consistency control initiation not only decides the cache consistency provided to the users, but also impacts the consistency maintenance cost. This issue becomes more challenging in asynchronous and fully distributed ad hoc networks. To this end, we propose the predictive consistency control initiation (PCCI) algorithm, which adaptively initiates consistency control based on its online predictions of forthcoming data updates and cache queries. In order to efficiently propagate data updates through multi‐hop wireless connections, the hierarchical data update propagation (HDUP) algorithm is proposed. Theoretical analysis shows that cooperation among the caching nodes facilitates data update propagation. Extensive simulations are conducted to evaluate performance of both PCCI and HDUP. Evaluation results show that PCCI cost‐effectively initiates consistency control even when faced with dynamic changes in data update rate, cache query rate, node speed, and number of caching nodes. The evaluation results also show that HDUP saves cost for data update propagation by up to 66%. Copyright © 2009 John Wiley & Sons, Ltd.

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants l

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved and concurrent) manner in real life. In this paper, we propose a novel emerging patterns based approach to sequential, interleaved and concurrent activity recognition (epSICAR). We exploit emerging patterns as powerful discriminators to differentiate activities. Different from other learning-based models built upon the training dataset for complex activities, we build our activity models by mining a set of emerging patterns from the sequential activity trace only and apply these models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home, and the evaluation results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity.

Contexts, the pieces of information that capture the characteristics of computing environments, are often inconsistent in the dynamic and uncertain pervasive computing environments. Various schemes have been proposed to check context consistency for pervasive applications. However, existing schemes implicitly assume that the contexts being checked belong to the same snapshot of time. This limitation makes existing schemes do not work in pervasive computing environments, which are characterized by the asynchronous coordination among computing devices. The main challenge imposed on context consistency checking by asynchronous environments is how to interpret and detect concurrent events. To this end, we propose in this paper the concurrent events detection for asynchronous consistency checking (CEDA) algorithm. An analytical model, together with corresponding numerical results, is derived to study the performance of CEDA. We also conduct extensive experimental evaluation to investigate whether CEDA is desirable for context-aware applications. Both theoretical analysis and experimental evaluation show that CEDA accurately detects concurrent events in time in asynchronous pervasive computing environments, even with dynamic changes in message delay, duration of events and error rate of context collection.

Context-awareness is a key issue in pervasive computing. Context-aware applications are prone to the context consistency problem, where applications are confronted with conflicting contexts and cannot decide how to adapt themselves. In pervasive computing environments, users are often willing to accept certain degree of context inconsistency, as long as it can reduce the consistency maintenance cost, e.g., query delay and battery power. However, existing consistency maintenance schemes do not enable the users to make such tradeoffs. To this end, we propose the probabilistic consistency checking for pervasive context (PCCPC) algorithm. Detailed performance analysis shows that PCCPC enables the users to check consistency over arbitrarily specified ratio of context. We also conduct experiments to study the cost reduced by probabilistic checking. The analytical and the experimental results show that PCCPC enables the users to efficiently make tradeoffs between context consistency and the associated checking cost.

Location Based Service (LBS) is one kind of ubiquitous applications whose functions are based on the locations of clients. The core of LBS is an effective positioning system. As wireless LAN (WLAN) costs less and is easy to access, using WLAN for indoor positioning has been widely studied recently. K nearest neighbors (KNN) is one of the basic deterministic fingerprint based algorithms and widely used for WLAN-based indoor positioning. However, KNN takes all the nearest K neighbors for calculating the estimated result, which could be improved if some selective work could be done to those neighbors beforehand. In this paper we propose a new scheme called "cluster filtered KNN" (CFK). CFK utilizes clustering technique to partition those neighbors into different clusters and chooses one cluster as the delegate. In the end, the final estimate can be calculated only based on the elements of the delegate. With experiments, we found that CFK does outperform KNN.

Building large-scale smart environments is one of the long-term goals of ubiquitous computing. The widespread of context information in such environments necessitates an effective context retrieval mechanism. This paper proposes a novel context retrieval method based on applications' query patterns. We propose high dimensional vector to model contexts from applications' perspective, and apply the normalized inner product of high dimensional vectors to measure context distance. Contexts with similar query patterns are clustered into the same group. To improve the performance of context retrieval, we build distributed indices on each node to speed up a local search, and create shortcuts based on clustering results to facilitate query routing. We show how our proposed methods can be applied to existing context retrieval mechanisms. Our experimental results show that our method can significantly reduce retrieval cost.

Pervasive computing is to enhance the environment by embedding many computers that are gracefully integrated with human users. To achieve this, the key research thrust is to create a smart context-awareness environment which should enclose various users and satisfy different needs of the users. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains. To address this problem, we propose a context-aware computing infrastructure, called FollowMe. Our infrastructure integrates an ontology based context model and a workflow based application model with the OSGi framework. By plugging different domain contexts and applications, FollowMe can be customized to various domains.

Software coordination is central to the construction of large-scale high-performance distributed applications with software services scattered over the decentralized Internet. In this paper, a new mobile-agent-based architecture is proposed for the utilization and coordination of geographically distributed computing resources. Under this architecture, a user application is built with a set of software agents that can travel across the network autonomously. These agents utilize the distributed resources and coordinate with each other to complete their task. This approach’s advantages include the natural expression and flexible deployment of the coordination logic, the dynamic adaptation to the network environment and the potential of better application performance. This coordination architecture, together with an object-oriented hierarchical parallel application framework and a graphical application construction tool, is implemented in the HOOPE environment, which provides a systematic support for the development and execution of Internet-based distributed and parallel applications in the petroleum exploration industry.