ACM Transactions on Internet Technology最新文献

Dynamic and flexible business relationships are expected to become more important in the future to accommodate specialized change requests or small-batch production. Today, buyers and sellers must disclose sensitive information on products upfront before the actual manufacturing. However, without a trust relation, this situation is precarious for the involved companies as they fear for their competitiveness. Related work overlooks this issue so far: Existing approaches only protect the information of a single party only, hindering dynamic and on-demand business relationships. To account for the corresponding research gap of inadequately privacy-protected information and to deal with companies without an established trust relation, we pursue the direction of innovative privacy-preserving purchase inquiries that seamlessly integrate into today’s established supplier management and procurement processes. Utilizing well-established building blocks from private computing, such as private set intersection and homomorphic encryption, we propose two designs with slightly different privacy and performance implications to securely realize purchase inquiries over the Internet. In particular, we allow buyers to consider more potential sellers without sharing sensitive information and relieve sellers of the burden of repeatedly preparing elaborate yet discarded offers. We demonstrate our approaches’ scalability using two real-world use cases from the domain of production technology. Overall, we present deployable designs that offer two-way privacy for purchase inquiries and, in turn, fill a gap that currently hinders establishing dynamic and flexible business relationships. In the future, we expect significantly increasing research activity in this overlooked area to address the needs of an evolving production landscape.

Gait authentication as a technique that can continuously provide identity recognition on mobile devices for security has been investigated by academics in the community for decades. However, most of the existing work achieves insufficient generalization to complex real-world environments due to the complexity of the noisy real-world gait data. To address this limitation, we propose an intelligent Implicit Real-time Gait Authentication (IRGA) system based on Deep Neural Networks (DNNs) for enhancing the adaptability of gait authentication in practice. In the proposed system, the gait data (whether with complex interference signals) will first be processed sequentially by the imperceptible collection module and data preprocessing module for improving data quality. In order to illustrate and verify the suitability of our proposal, we provide analysis of the impact of individual gait changes on data feature distribution. Finally, a fusion neural network composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is designed to perform feature extraction and user authentication. We evaluate the proposed IRGA system in heterogeneous complex scenarios and present start-of-the-art comparisons on three datasets. Extensive experiments demonstrate that the IRGA system achieves improved performance simultaneously in several different metrics.

In this article, we consider security aspects of online meeting applications based on protocols such as WebRTC that leverage the Information-centric Networking (ICN) architecture to make the system fundamentally more scalable. If the scalability properties provided by ICN have been proved in recent literature, the security challenges and implications for real-time applications have not been reviewed. We show that this class of applications can benefit from strong security and scalability jointly without any major tradeoff and with significant performance improvements over traditional WebRTC systems. To achieve this goal, some modifications to the current ICN architecture must be implemented in the way integrity and authentication are verified. Extensive performance analysis of the architecture based on the open source implementation of Hybrid-ICN proves that real-time applications can greatly benefit from this novel network architecture in terms of strong security and scalable communications.

In the last years, Internet of Things (IoT) platforms have been designed to provide IoT applications with various services such as device discovery, context management, and data filtering. The lack of standardization has led each IoT platform to propose its own abstractions, APIs, and data models. As a consequence, programming interactions between an IoT consuming application and an IoT platform is time-consuming, is error prone, and depends on the developers’ level of knowledge about the IoT platform. To address these issues, this article introduces IoTvar, a middleware library deployed on the IoT consumer application that manages all its interactions with IoT platforms. IoTvar relies on declaring variables automatically mapped to sensors whose values are transparently updated with sensor observations through proxies on the client side. This article presents the IoTvar architecture and shows how it has been integrated into the FIWARE, OM2M, and muDEBS platforms. We also report the results of experiments performed to evaluate IoTvar, showing that it reduces the effort required to declare and manage IoT variables and has no considerable impact on CPU, memory, and energy consumption.

The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user’s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What’s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts (DPSR for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What’s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of DPSR, we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.

When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.

Software Defect Prediction (SDP) is crucial towards software quality assurance in software engineering. SDP analyzes the software metrics data for timely prediction of defect prone software modules. Prediction process is automated by constructing defect prediction classification models using machine learning techniques. These models are trained using metrics data from historical projects of similar types. Based on the learned experience, models are used to predict defect prone modules in currently tested software. These models perform well if the concept is stationary in a dynamic software development environment. But their performance degrades unexpectedly in the presence of change in concept (Concept Drift). Therefore, concept drift (CD) detection is an important activity for improving the overall accuracy of the prediction model. Previous studies on SDP have shown that CD may occur in software defect data and the used defect prediction model may require to be updated to deal with CD. This phenomenon of handling the CD is known as CD adaptation. It is observed that still efforts need to be done in this direction in the SDP domain. In this article, we have proposed a pair of paired learners (PoPL) approach for handling CD in SDP. We combined the drift detection capabilities of two independent paired learners and used the paired learner (PL) with the best performance in recent time for next prediction. We experimented on various publicly available software defect datasets garnered from public data repositories. Experimentation results showed that our proposed approach performed better than the existing similar works and the base PL model based on various performance measures.

Fusion positioning technology requires stable and effective positioning data, but this is often challenging to achieve in complex Non-Line-of-Sight (NLoS) environments. This paper proposes a fusion positioning method that can achieve stable and no hop points by adjusting parameters and predicting trends, even with a one-sided lack of fusion data. The method combines acoustic signal and Inertial Measurement Unit (IMU) data, exploiting their respective advantages. The fusion is achieved using the Kalman filter and Bayesian parameter estimation is performed for tuning IMU parameters and predicting motion trends. The proposed method overcomes the problem of fusion failure caused by long-term unilateral data loss in traditional fusion positioning. The positioning trajectory and error distribution analysis show that the proposed method performs optimally in severe NLoS experiments.