The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign transportation tasks to certain vehicles. This article proposes a new deep reinforcement learning approach with a self-attention mechanism to dynamically dispatch the tasks to AGV. The AGV dispatching system is modeled as a less complicated Markov decision process (MDP) using vehicle-initiated rules to dispatch a workcenter to an idle AGV. In order to deal with the highly dynamical environment, the self-attention mechanism is introduced to calculate the importance of different information. The invalid action masking technique is performed to alleviate false actions. A multimodal structure is employed to mix the features of various sources. Comparative experiments are performed to show the effectiveness of the proposed method. The properties of the learned policies are also investigated under different environment settings. It is discovered that the policies explore and learn the properties of different systems, and also smooth the traffic congestion. Under certain environment settings, the policy converges to a heuristic rule that assigns the idle AGV to the workcenter with the shortest queue length, which shows the adaptiveness of the proposed method.

Today, more than 3 million websites rely on online advertising revenue. Despite the monetary incentives, ads often frustrate users by disrupting their experience, interrupting content, and slowing browsing. To improve ad experiences, leading media associations define Better Ads Standards for ads that are below user expectations. However, little is known about how well websites comply with these standards and whether existing approaches are sufficient for developers to quickly resolve such issues. In this paper, we propose Adhere, a technique that can detect intrusive ads that do not comply with Better Ads Standards and suggest repair proposals. Adhere works by first parsing the initial web page to a DOM tree to search for potential static ads, and then using mutation observers to monitor and detect intrusive (dynamic/static) ads on the fly. To handle ads' volatile nature, Adhere includes two detection algorithms for desktop and mobile ads to identify different ad violations during three phases of page load events. It recursively applies the detection algorithms to resolve nested layers of DOM elements inserted by ad delegations. We evaluate Adhere on Alexa Top 1 Million Websites. The results show that Adhere is effective in detecting violating ads and suggesting repair proposals. Comparing to the current available alternative, Adhere detected intrusive ads on 4,656 more mobile websites and 3,911 more desktop websites, and improved recall by 16.6% and accuracy by 4.2%.

WebAssembly is the newest language to arrive on the web. It features a compact binary format, making it fast to be loaded and decoded. While WebAssembly is generally expected to be faster than JavaScript, there have been mixed results in proving which code is faster. Little research has been done to comprehend WebAssembly's performance benefit. In this paper, we conduct a systematic study to understand the performance of WebAssembly applications and compare it with JavaScript. Our measurements were performed on three sets of subject programs with diverse settings. Among others, our findings include: (1) WebAssembly compilers are commonly built atop LLVM, where their optimizations are not tailored for WebAssembly. We show that these optimizations often become ineffective for WebAssembly, leading to counter-intuitive results. (2) JIT optimization has a significant impact on JavaScript performance. However, no substantial performance increase was observed for WebAssembly with JIT. (3) The performance of WebAssembly and JavaScript varies substantially depending on the execution environment. (4) WebAssembly uses significantly more memory than its JavaScript counterparts. We hope that our findings can help WebAssembly tooling developers identify optimization opportunities. We also report the challenges encountered when compiling C benchmarks to WebAssembly and discuss our solutions.

In the social Internet of Things, social networks can be built among smart objects or between smart objects and people just like human beings. One of the factors that determines the effect and efficiency of service matching in SIoT is the structure of social networks. In this paper, we exploit the theory of structural balance in signed networks to optimize SIoT network structures to provide a friendly and stable environment as well as a solid foundation for service matching of social Internet of Things. Next, besides being friends or enemies, which are traditional relationships of structural balance or structural changes in signed networks, in our research, we introduce the ambiguous relationship which is the certain state between the hostile and the friendly status and we discuss the meaning and significance of ambiguous relationship in dynamical changes of structural balance in SIoT networks. Based on previous studies, we apply an enhanced objective function and a modified approach concerning the ambiguous relationship towards the dynamical change process. Experiments show that our approach is more effective and efficient than former studies in optimizing dynamical evolution of structural balance in signed networks of SIoT.