The Multiple Traveling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, ...transportation, networking, etc. Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.
Blockchain has a range of built-in features, such as distributed ledger, decentralized storage, authentication, security, and immutability, and has moved beyond hype to practical applications in ...industry sectors such as Healthcare. Blockchain applications in the healthcare sector generally require more stringent authentication, interoperability, and record sharing requirements, due to exacting legal requirements, such as Health Insurance Portability and Accountability Act of 1996 (HIPAA). Building on existing blockchain technologies, researchers in both academia and industry have started to explore applications that are geared toward healthcare use. These applications include smart contracts, fraud detection, and identity verification. Even with these improvements, there are still concerns as blockchain technology has its own specific vulnerabilities and issues that need to be addressed, such as mining incentives, mining attacks, and key management. Additionally, many of the healthcare applications have unique requirements that are not addressed by many of the blockchain experiments being explored, as highlighted in this survey paper. A number of potential research opportunities are also discussed in this paper.
Consumer sentiment analysis is a recent fad for social media-related applications such as healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to gain insight into ...the desired objective and reviews is significant. With the advancement of technology, a massive amount of social web data increasing in volume, subjectivity, and heterogeneity becomes challenging to process manually. Machine learning (ML) techniques have been utilized to handle this difficulty in real-life applications. This paper presents a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism. We show a systematic literature review to compare, analyse, explore, and understand the attempts and directions to find research gaps in illustrating the future scope of this pairing. The primary objective is to read and analyse the use of ML techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. This research has significant implications for service providers in terms of developing managerial strategies for consumers in terms of selecting services that meet their needs. Furthermore, there is high impact for researchers in terms of prospective research directions.
The underlying technology of Bitcoin is blockchain, which was initially designed for financial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and ...cryptographic security benefits such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain's viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identified the gaps by mapping the security and performance benefits inferred by the blockchain technologies and some of the blockchain-based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the significant challenges to the blockchain's adoption in IoT.
Fully supervised semantic segmentation has performed well in many computer vision tasks. However, it is time-consuming because training a model requires a large number of pixel-level annotated ...samples. Few-shot segmentation has recently become a popular approach to addressing this problem, as it requires only a handful of annotated samples to generalize to new categories. However, the full utilization of limited samples remains an open problem. Thus, in this article, a mutually supervised few-shot segmentation network is proposed. First, the feature maps from intermediate convolution layers are fused to enrich the capacity of feature representation. Second, the support image and query image are combined into a bipartite graph, and the graph attention network is adopted to avoid losing spatial information and increase the number of pixels in the support image to guide the query image segmentation. Third, the attention map of the query image is used as prior information to enhance the support image segmentation, which forms a mutually supervised regime. Finally, the attention maps of the intermediate layers are fused and sent into the graph reasoning layer to infer the pixel categories. Experiments are conducted on the PASCAL VOC-<inline-formula> <tex-math notation="LaTeX">5^{i} </tex-math></inline-formula> dataset and FSS-1000 dataset, and the results demonstrate the effectiveness and superior performance of our method compared with other baseline methods.
Transformers in Vision: A Survey Khan, Salman; Naseer, Muzammal; Hayat, Munawar ...
ACM computing surveys,
01/2022, Letnik:
54, Številka:
10s
Journal Article
Recenzirano
Odprti dostop
Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, ...Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks, e.g., Long short-term memory. Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text, and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers, i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization), and three-dimensional analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges toward the application of transformer models in computer vision.
A survey of network anomaly detection techniques Ahmed, Mohiuddin; Naser Mahmood, Abdun; Hu, Jiankun
Journal of network and computer applications,
January 2016, 2016-01-00, Letnik:
60
Journal Article
Recenzirano
Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication ...devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection.
•Maps different types of anomalies with network attacks.•Provides an up-to-date taxonomy of network anomaly detection.•Evaluates effectiveness of different categories of techniques.•Explores recent research related to publicly available network intrusion evaluation datasets.
With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such ...systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.
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