ACM TRANSACTIONS ON INTERNET TECHNOLOGY (TOIT) 汇集了许多计算学科，包括计算机软件工程、计算机编程语言、中间件、数据库管理、安全性、知识发现和数据挖掘、网络和分布式系统、通信、性能和可扩展性等。TOIT 将涵盖各个学科的结果和作用以及它们之间的关系。

ACM Transactions on Internet Technology (TOIT) brings together research from various computer science disciplines, including software engineering, programming languages, middleware, database management, security, knowledge discovery and data mining, networking and distributed systems, communication, performance, and scalability. TOIT focuses on the results and interrelations of these disciplines.

《ACM Transactions on Internet Technology (TOIT)》汇集了多个计算机学科的研究成果，包括计算机软件工程、编程语言、中间件、数据库管理、安全性、知识发现与数据挖掘、网络与分布式系统、通信、性能与可扩展性等。TOIT 关注这些学科的研究成果及其相互关系。

ACM Transactions on Internet Technology

Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of &lt;underline&gt;Du&lt;/underline&gt;al-&lt;underline&gt;ro&lt;/underline&gt;bust Enhanced Spatial-temporal Learning &lt;underline&gt;Net&lt;/underline&gt;work (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.

Kaixi Hu

Lin Li

Jianquan Liu

Daniel Sun

DuroNet

The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the MEC-enabled IoV architecture, in which all vehicles and MEC servers act as offloading nodes. Mobile offloading nodes (i.e., vehicles) and fixed offloading nodes (i.e., MEC servers) provide low latency offloading services cooperatively through roadside units. Then we propose the task offloading scheme that considers task classification and offloading nodes selection (TO-TCONS). Our goal is to minimize the total execution time of tasks. In TO-TCONS Scheme, we divide the task offloading into the same region offloading mode and cross-region offloading mode, which is based on the delay constraints of tasks and the travel time of the target vehicle. Moreover, we propose the mobile offloading nodes selection strategy to select offloading nodes for each task, which evaluates offloading candidates for each task based on computing resources and transmission rates. Simulation results demonstrate that TO-TCONS Scheme is indeed capable of reducing total latency of tasks execution under the delay constraints in MEC-enabled IoV.

Rui Zhang

Libing Wu

Shuqin Cao

Xinrong Hu

Shan Xue

Dan Wu

Qingan Li

Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV

基于任务分类与卸载节点选择的MEC支持的IoV任务卸载

We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

Saif M. Mohammad

Parinaz Sobhani

Svetlana Kiritchenko

ACM Trans. Internet Technol.

Stance and Sentiment in Tweets

推文中的立场与情感

The blockchain technology has been gaining an increasing popularity for the last years, and smart contracts are being used for a growing number of applications in several scenarios. The execution of smart contracts on public blockchains can be invoked by any user with a transaction, although in many scenarios there would be the need for restricting the right of executing smart contracts only to a restricted set of users. To help deal with this issue, this article proposes a system based on a popular access control framework called RT, Role-based Trust Management, to regulate smart contracts execution rights. The proposed system, called Layer 2 DecentrAlized Role-based Trust management (L2DART), implements the RT framework on a public blockchain, and it is designed as a layer-2 technology that involves both on-chain and off-chain functionalities to reduce the blockchain costs while keeping blockchain auditability, i.e., immutability and transparency. The on-chain costs of L2DART have been evaluated on Ethereum and compared with a previous solution implementing on-chain all the functionalities. The results show that the on-chain costs of L2DART are relatively low, making the system deployable in real-world scenarios.

Andrea De Salve

Luca Franceschi

Andrea Lisi

Paolo Mori

Laura Ricci

L2DART: A Trust Management System Integrating Blockchain and Off-Chain Computation

L2DART：一种集成区块链与链下计算的信任管理系统

The advent of Deep Neural Networks (DNNs) has empowered numerous computer-vision applications. Due to the high computational intensity of DNN models, as well as the resource constrained nature of Industrial Internet-of-Things (IIoT) devices, it is generally very challenging to deploy and execute DNNs efficiently in the industrial scenarios. Substantial research has focused on model compression or edge-cloud offloading, which trades off accuracy for efficiency or depends on high-quality infrastructure support, respectively. In this article, we present EdgeDI, a framework for executing DNN inference in a partitioned, distributed manner on a cluster of IIoT devices. To improve the inference performance, EdgeDI exploits two key optimization knobs, including: (1) Model compression based on deep architecture design, which transforms the target DNN model into a compact one that reduces the resource requirements for IIoT devices without sacrificing accuracy; (2) Distributed inference based on adaptive workload partitioning, which achieves high parallelism by adaptively balancing the workload distribution among IIoT devices under heterogeneous resource conditions. We have implemented EdgeDI based on PyTorch, and evaluated its performance with the NEU-CLS defect classification task and two typical DNN models (i.e., VGG and ResNet) on a cluster of heterogeneous Raspberry Pi devices. The results indicate that the proposed two optimization approaches significantly outperform the existing solutions in their specific domains. When they are well combined, EdgeDI can provide scalable DNN inference speedups that are very close to or even much higher than the theoretical speedup bounds, while still maintaining the desired accuracy.

Weiwei Fang

Wenyuan Xu

Chongchong Yu

Neal. N. Xiong

Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters

工业物联网集群上DNN推理的联合架构设计与工作负载划分

Spatiotemporal Mobile CrowdSourcing (MCS) is a new intelligent sensing paradigm for large-scale data acquisition where requesters can recruit a crowd of workers to perform data collection tasks. How to recruit suitable workers in a dynamic environment to maximize platform utility is a key issue and has become a research hotspot. Many past studies have made great efforts in this regard, but most of them either assume that the worker quality is known in advance or ignore the limitations of workers’ short-term ability to provide resources. In this paper, we consider a platform-centered online spatiotemporal MCS system where mobile workers have both long-term and short-term constraints for providing resources, and their quality is unknown to the platform, while the platform has a long-term budget constraint for recruiting workers. We aim to find an online worker scheduling scheme to maximize the platform’s long-term utility without violating the constraints of both workers and the platform. To address this problem, we first transform the long-term utility maximization problem into a real-time utility maximization problem by leveraging the Lyapunov optimization, then design algorithms based on the Upper Confidence Bound (UCB) and Markov approximation to solve each real-time utility maximization problem with unknown worker quality. We demonstrate that our UCB-based algorithm has a sublinear regret and prove that our proposed framework has a performance guarantee for the addressed problem. Finally, we evaluate our design through numerical simulation experiments, and the results demonstrate the effectiveness of our algorithm.

Jiajun Wang

Pengfei Lin

Xingjian Ding

Jianxiong Guo

Zhiqing Tang

Deying Li

Weili Wu

Online Worker Scheduling for Maximizing Long-Term Utility in Crowdsourcing with Unknown Quality

众包中基于未知质量的长期效用最大化的在线工作者调度

When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

Peikun Ni

Barbara Guidi

Andrea Michienzi

Jianming Zhu

Relieving Overexposure in Information Diffusion Through a Budget Multi-stage Allocation

通过预算多阶段分配缓解信息扩散中的过度曝光问题

The traditional MapReduce frameworks were originally designed for processing data within a single cluster and are not suitable for handling geo-distributed data. Consequently, alternative approaches such as Hierarchical and Geo-Hadoop have been proposed to address this limitation. However, these approaches still face challenges in efficiently managing inter-cluster data transfer, particularly considering the heterogeneity of clusters and varying bandwidth among them. Moreover, the need to transmit results to a central global reducer for geo-distributed MapReduce operations adds unnecessary complexity. To tackle these issues, we introduce Extended Cross-MapReduce (ECMR), a framework that integrates resource heterogeneity and network links in geo-distributed MapReduce workflows. ECMR optimizes data management and determines the necessary data volume for generating final results. To enhance performance, ECMR leverages the overlap between data transfer and execution time by utilizing multiple global reducers and grouping temporary results that require data transfer over the Internet. In ECMR, we propose a bipartite graph and extend the Gale-Shapley algorithm to determine the optimal number of clusters and select the most suitable locations for global reducers. Through extensive experimental evaluations conducted on a real testbed, we demonstrate the effectiveness of our proposed ECMR method. The results exhibit significant improvements over traditional Hierarchical and Geo-Hadoop approaches, achieving reductions of up to 81% and 85% in overall makespan, respectively.

Saeed Mirpour Marzuni

Adel Toosi

Abdorreza Savadi

Mahmud Naghibzadeh

David Taniar

Optimizing Geo-Distributed Data Processing with Resource Heterogeneity over the Internet

优化互联网上资源异构的地理分布式数据处理

In crowd-sourced data aggregation over the Internet, participants share their data points with curators. However, a lack of strong privacy guarantees may discourage participation, which motivates the need for privacy-preserving aggregation protocols. Moreover, existing solutions remain limited with respect to public auditing without revealing the participants’ data. In realistic applications, however, there is an increasing need for public verifiability (i.e., verifying the protocol correctness) while preserving the privacy of the participants’ inputs, since the participants do not always trust the data curators. At the same time, while publicly distributed ledgers may provide public auditing, these schemes are not designed to protect sensitive information. In this work, we introduce two protocols, dubbed Masquerade and zk-Masquerade, for computing private statistics, such as sum, average, and histograms, without revealing anything about participants’ data. We propose a tailored multiplicative commitment scheme to ensure the integrity of data aggregations and publish all the participants’ commitments on a ledger to provide public verifiability. zk-Masquerade detects malicious participants who attempt to poison the aggregation results by adopting two zero-knowledge proof protocols that ensure the validity of shared data points before being aggregated and enable a broad range of numerical and categorical studies. In our experiments, we use homomorphic ciphertexts and commitments for a variable number of participants and evaluate the runtime and the communication cost of our protocols.

Dimitris Mouris

Nektarios Tsoutsos

Masquerade: Verifiable Multi-Party Aggregation with Secure Multiplicative Commitments

Abbreviation	ACM Trans. Internet Technol.
Journal Impact	3.77
Quartiles(Global)	COMPUTER SCIENCE, SOFTWARE ENGINEERING(Q1)

ISSN	1533-5399, 1557-6051
h-index	65

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2001，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 1.52%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

ACM Transactions on Internet Technology

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