ACM Transactions on Intelligent Systems and Technology

Includes bibliographical references

A Comprehensive Survey of the key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks -- A Camera Identity-guided Distribution Consistency method for Unsupervised Multi-target Domain Person Re-identification -- Linking Multiple User Identities of Multiple Services From Massive Mobility Traces -- MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification -- VSumVis: Interactive Visual Understanding and Diagnosis of Video Summarization Model -- Modeling Complementarity in behavior Data with Multi-type Itemset Embedding -- StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing -- Multiview Common Subspace Clustering via Coupled Low Rank Representation -- Nova: Value-based Negotiation of Norms -- A Comprehensive Approach to On-board Autonomy Verification and Validation -- Mining Customers' Changeable Electricity Consumption for Effective Load Forecasting -- Modeling Customer Experience in a Contact Center through Process Log Mining -- DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhance Urban Features -- TLDS: A Transfer-Learning-Based Delivery Station Location Selection Pipeline -- Parallel Connected LSTM for Matrix Sequence Prediction with Elusive Correlations.

[Article Title: A Comprehensive Survey of the key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks/ JZhenchang Xia, Jia Wu, Libing Wu, Yanjiao Chen. Jian Yang, and Philip S. Yu, p. 37:1-1:30] Abstract: Vehicular ad hoc networks (VANETs) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.;[Article Title: A Camera Identity-guided Distribution Consistency method for Unsupervised Multi-target Domain Person Re-identification/ Jiajie Tian, Qihao Tang, Rui Li, Zhu Teng, Baopeng Zhang, and Jianping Fan, p. 38:1-1:18] Abstract: Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are generally trained on one labeled source set and adapted on the other unlabeled target set. In this article, we put forward a new issue on person re-ID, namely, unsupervised multi-target domain adaptation (UMDA). It involves one labeled source set and multiple unlabeled target sets, which is more reasonable for practical real-world applications. Enabling UMDA has to learn the consistency for multiple domains, which is significantly different from the UDA problem. To ensure distribution consistency and learn the discriminative embedding, we further propose the Camera Identity-guided Distribution Consistency method that performs an alignment operation for multiple domains. The camera identities are encoded into the image semantic information to facilitate the adaptation of features. According to our knowledge, this is the first attempt on the unsupervised multi-target domain adaptation learning. Extensive experiments are executed on Market-1501, DukeMTMC-reID, MSMT17, PersonX, and CUHK03, and our method has achieved very competitive re-ID accuracy in multi-target domains against numerous state-of-the-art methods.;[Article Title: Linking Multiple User Identities of Multiple Services From Massive Mobility Traces/ Huandong Wang, Yong Li, Gang Wang, and Depeng Jin, p. 39:1-1:28] Abstract: Understanding the linkability of online user identifiers (IDs) is critical to both service providers (for business intelligence) and individual users (for assessing privacy risks). Existing methods are designed to match IDs across two services but face key challenges of matching multiple services in practice, particularly when users have multiple IDs per service. In this article, we propose a novel system to link IDs across multiple services by exploring the spatial-temporal features of user activities, of which the core idea is that the same user's online IDs are more likely to repeatedly appear at the same location. Specifically, we first utilize a contact graph to capture the "co-location" of all IDs across multiple services. Based on this graph, we propose a set-wise matching algorithm to discover candidate ID sets and use Bayesian inference to generate confidence scores for candidate ranking, which is proved to be optimal. We evaluate our system using two real-world ground-truth datasets from an Internet service provider (4 services, 815K IDs) and Twitter-Foursquare (2 services, 770 IDs). Extensive results show that our system significantly outperforms the state-of-the-art algorithms in accuracy (AUC is higher by 0.1-0.2), and it is highly robust against data quality, matching order, and number of services.;[Article Title: MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification / Xiangjun Shen, Kou Lu, Sumet Mehta,Jianming Zhan, Weifeng Liu, Jianping Fan, and Zhengjun Zha, p. 40:1-1:21] Abstract: In this article, a novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss. Different from the previous multiple kernel learning (MKL) methods, which attempt to seek a linear combination of basis kernels as a unified kernel, our MKEL model aims to find multiple solutions in corresponding Reproducing Kernel Hilbert Spaces (RKHSs) simultaneously. To achieve this goal, multiple individual kernel losses are integrated into a unified ensemble loss. Therefore, each model can co-optimize to learn its optimal parameters by minimizing a unified ensemble loss in multiple RKHSs. Furthermore, we apply our proposed ensemble loss into the deep network paradigm and take the sub-network as a kernel mapping from the original input space into a feature space, named Deep-MKEL (D-MKEL). Our D-MKEL model can utilize the diversified deep individual sub-networks into a whole unified network to improve the classification performance. With this unified loss design, our D-MKEL model can make our network much wider than other traditional deep kernel networks and more parameters are learned and optimized. Experimental results on several mediate UCI classification and computer vision datasets demonstrate that our MKEL model can achieve the best classification performance among comparative MKL methods, such as Simple MKL, GMKL, Spicy MKL, and Matrix-Regularized MKL. On the contrary, experimental results on large-scale CIFAR-10 and SVHN datasets concretely show the advantages and potentialities of the proposed D-MKEL approach compared to state-of-the-art deep kernel methods.