The Vehicle Routing Problem with Time Windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid swarm intelligence algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed algorithm.

Fusion technologies have rapidly evolved. These technologies are normally customized according to the needs of domains. Despite a large number of publications on intelligence fusion applications for various domains, they are scattered. The aim of this review is to present the state of the art for intelligence fusion applications within a specific domain. We identified three major domains for the purpose, namely robotics, military, and healthcare, during the initial process of the systematic review. These three domains are always in need of superior intelligence. Articles were searched mainly in IEEE Xplore. We limit the range of publications to the year 2014 to 2019, to focus on the most recent publications. We adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to screen, filter and evaluate qualities of each retrieved article. As a result, we retrieved 675 articles at the initial stage of the search, we conducted screening and filtering process and reviewed 153 articles potential articles, and finally, we excluded 36 articles as they do not comply with our quality assessment criteria. Only 117 articles are included. The results of this study are a list of classified applications within the domains and a number of relevant techniques or approaches used in each classified application. The finding of this review showed that the most published works for the use of intelligence fusion are mainly applications in the robotics domain, where mostly used techniques are Kalman Filter and its variants. Outcomes of this study can be a guideline or an insight for researchers to further develop and implement in this field.

A new convex optimization framework for approximately solving timetabling problems that can be described as integer linear programs is proposed. The method is based on converting the timetabling problem into a cardinality constrained problem while viewing the operational constraints of the timetable as noisy measurements with an unknown average slack. The problem is then iteratively solved using weighted Lasso with weights that are updated using a simple linear program to satisfy the cardinality constraint while respecting the operational constraints in the least squares sense. Compared with previous convex relaxations for solving timetabling problems, our solution technique will converge to a binary solution without the need for subsequent randomized rounding. Moreover, the number of Lasso iterations required are in the order of the number of events to be scheduled and hence the method can handle very large timetabling problems using efficient Lasso solvers. We provide the assumptions required on the linear timetabling model and establish the associated error bound of the technique upon convergence assuming restricted strong convexity and uniqueness. We also study the effect of the Lasso regularization parameter and the effect of relaxing the objective on the extent of constraint satisfaction through several simulation experiments. Our experiment on one of the benchmark datasets for university examination timetabling improved the best documented result by more than 30% with 99.9% of all constraints satisfied.

Gaze behaviors contain rich information regarding a person's emotions and engagements. Reciprocal eye contact can invoke feelings of liking between two strangers. But blind people cannot perceive and establish the eye contact with sighted counterparts, causing their feelings of social isolation and low confidence in conversations. Thus, our research purpose is to let blind people perceive and react gaze behaviors in social interactions. A Social Glasses system has been implemented iteratively to deliver the multisensory feedback channels of the “eye contact”, integrating both visual and tactile feedback. Specifically, the system consists of a Social Glasses device and a tactile wristband, which are worn by a blind person. The Social Glasses simulates the natural gaze for the blind person, aiming at establishing the “eye contact” between blind and sighted people. The tactile wristband enables the blind person to perceive the corresponding tactile feedback when an “eye contact” happens. To test the system, we conducted a user experiment with 40 participants, including 10 blind-sighted pairs (N = 20) and 10 blindfolded-sighted pairs (N = 20), to see how it could help increase the communication quality between blind and sighted people, as well as to suggest implications for its design. Our main findings demonstrated that both the simulated gaze and the tactile feedback were significantly effective to enhance the communication quality in blind-sighted conversations. Overall, we contribute (1) empirical research findings on how a Social Glasses system enhances the communication quality in blind-sighted conversations; and (2) design principles to inform future assistive wearable device for augmenting social interactions.

In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia. Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks. However, the existing detection tools are far from satisfactory. In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs. Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies. Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods. In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.

In the Web 2.0 age, mass media disseminates the disinformation of companies and exerts considerable influence. How to manage this trend in a timely and effective fashion in this big data era has become difficult. In this study, we delve into this issue by trying to identify the core disseminators in the dissemination process. We propose the concept of a disinformation channel and quantitatively analyse these company-related disinformation channels among media outlets. By empirically analysing 4,689 disinformation news values and 330 channels in 2018, we reveal that the disinformation values and negative news values are characteristics. We also build automatic identification models to identify these channels from the media combined with machine learning algorithms. Our study sheds light on disinformation, thus providing managers with an empirical basis upon which to analyse the media and help them address the disinformation problem.