ACM Transactions on Internet Technology最新文献

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.

The voice-based Internet of Multimedia Things (IoMT) is the combination of IoT interfaces and protocols with associated voice-related information, which enables advanced applications based on human-to-device interactions. An example is Automatic Speech Recognition (ASR) for live captioning and voice translation. Three major issues of ASR for IoMT are IoT development cost, speech recognition accuracy, and execution time complexity. For the first issue, most non-voice IoT applications are upgraded with the ASR feature through hard coding, which are error prone. For the second issue, recognition accuracy must be improved for ASR. For the third issue, many multimedia IoT services are real-time applications and, therefore, the ASR delay must be short.

This article elaborates on the above issues based on an IoT platform called VoiceTalk. We built the largest Taiwanese spoken corpus to train VoiceTalk ASR (VT-ASR) and show how the VT-ASR mechanism can be transparently integrated with existing IoT applications. We consider two performance measures for VoiceTalk: speech recognition accuracy and VT-ASR delay. For the acoustic tests of PAL-Labs, VT-ASR's accuracy is 96.47%, while Google's accuracy is 94.28%. We are the first to develop an analytic model to investigate the probability that the VT-ASR delay for the first speaker is complete before the second speaker starts talking. From the measurements and analytic modeling, we show that the VT-ASR delay is short enough to result in a very good user experience. Our solution has won several important government and commercial TV contracts in Taiwan. VT-ASR has demonstrated better Taiwanese Mandarin speech recognition accuracy than famous commercial products (including Google and Iflytek) in Formosa Speech Recognition Challenge 2018 (FSR-2018) and was the best among all participating ASR systems for Taiwanese recognition accuracy in FSR-2020.

Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol MWPoW+, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to f < n/2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.