A Neutral Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition

Enhancing attention in autism spectrum disorder: comparative analysis of virtual reality-based training programs using physiological data

BackgroundThe ability to maintain attention is crucial for achieving success in various aspects of life, including academic pursuits, career advancement, and social interactions. Attention deficit disorder (ADD) is a common symptom associated with autism spectrum disorder (ASD), which can pose challenges for individuals affected by it, impacting their social interactions and learning abilities. To address this issue, virtual reality (VR) has emerged as a promising tool for attention training with the ability to create personalized virtual worlds, providing a conducive platform for attention-focused interventions. Furthermore, leveraging physiological data can be instrumental in the development and enhancement of attention-training techniques for individuals.MethodsIn our preliminary study, a functional prototype for attention therapy systems was developed. In the current phase, the objective is to create a framework called VR-PDA (Virtual Reality Physiological Data Analysis) that utilizes physiological data for tracking and improving attention in individuals. Four distinct training strategies such as noise, score, object opacity, and red vignette are implemented in this framework. The primary goal is to leverage virtual reality technology and physiological data analysis to enhance attentional capabilities.ResultsOur data analysis results revealed that reinforcement training strategies are crucial for improving attention in individuals with ASD, while they are not significant for non-autistic individuals. Among all the different strategies employed, the noise strategy demonstrates superior efficacy in training attention among individuals with ASD. On the other hand, for Non-ASD individuals, no specific training proves to be effective in enhancing attention. The total gazing time feature exhibited benefits for participants with and without ASD.DiscussionThe results consistently demonstrated favorable outcomes for both groups, indicating an enhanced level of attentiveness. These findings provide valuable insights into the effectiveness of different strategies for attention training and emphasize the potential of virtual reality (VR) and physiological data in attention training programs for individuals with ASD. The results of this study open up new avenues for further research and inspire future developments

Distributed and asynchronous data collection in cognitive radio networks with fairness consideration

Abstract-As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay

Profile Modeling in Hierarchical Deep Architecture by Mutual Support

Despite significant advances in the field of face analysis over last decade, the current studies are still limited to specific face computation tasks using deep learning approaches. In this paper, we propose an end-to-end hierarchical deep learning structure, called Multi-Features Convolutional Neural Networks (MFCNN), which can comprehensively implement face analysis including age, gender, race and emotion. Moreover, we take the advantages of the mutual support among different facial features from individual tasks to improve the performance of our model. We also contribute one all-labeling dataset called Multiple Facial Features Computation (MFFC) based on Apparent-age-V2 dataset. Firstly, we train four different VGG-based classifiers to analyze facial features independently like age, gender, etc. using MFFC. Then, we systematically introduced cross-task verification approaches to demonstrate features extracted from different pre-trained models have mutual support to each other. After that, multi-task features extracted from pre-trained models are integrated to train MFCNN. Furthermore, feature fusion strategies also have been implemented to enhance our framework from accuracy and time complexity. Finally, the experimental results demonstrate that MFCNN outperforms state-of-the-art methods of face analysis by 10.3% in average and the best result improves up to 21.3% margin for emotion estimation

A Survey on Decentralized Flocking Schemes for a Set of Autonomous Mobile Robots

Recently, control and coordination of a set of autonomous mobile robots has been paid a lot of attentions, because the cooperation of simple robots offers several advantages, such as redundancy and flexibility, and allows performing hard tasks that could be impossible for one single robot. There are a lot of interesting applications of multiple robots, such as satellite exploration and surveillance missions. The characteristic of simplicity of mobile robots brings potential wide applications; however this characteristic also lead to crash with higher probability during cooperation, especially in harsh environment. Surprisingly, only few researches consider the fault tolerance of mobile robots, especially for dynamic coordination application---robot flocking. In this paper, we summarize the existed flocking algorithms and discuss their characteristics. Then we briefly described our fault tolerant flocking algorithms in different models. Finally we proposed the potential future research directions for dynamic flocking of a group of mobile robots. In all, this work can provide a good reference for the researchers working on dynamic cooperation of robots in distributed system

Fine-grained sentiment analysis for customer review

Natural Language Processing (NLP) is one of the most attractive technologies in many applications in real-life. Sentiment analysis, which has devoted to know others\u27 think or feel about an experience or an item and hence take an action, is one of the most developed area in both academia and industry. Among sentiment analysis, fine-grained aspect sentiment analysis attempts to analyze emotional attitude categorized into different aspects or features of an(a) experience/service/product. Although aspect level sentiment analysis could provide more useful information, the proposed models\u27 performance were relative poor compared with document-level or sentence-level sentiment analysis due to the lack of well-labeled aspect-level dataset. This thesis propose a semi-supervised approach using pre-trained BERT model to conduct the fine-grained aspect sentiment analysis, and tests it on the benchmark dataset SemEval2014. Our proposed Sentiment Mask Enhanced BERT pre-training and Multi-Token classification (SME-BERT-MT) model consists of two parts, a masked sentiment pre-trained model and a multi-label classification main model. In the first step, We first leverage the sentiment dictionary SentiWordNet to identify the sentiment words in the reviews of the dataset and mask them, then use the masked sentiment pre-trained model to predict the masked words. In the second step, we use the multi-label classification main model to detect aspect topics and aspect-level sentiment. This multi-label classification main model incorporates our pre-trained model and fine-tuned with addition of classification layers. The testing result on SME-BERT-MT shows an accuracy of 96.6 and 87.6 for aspect detection and aspect-level sentiment classification sub-tasks, respectively, on SemEval2014 dataset. Our model outperforms the baseline BERT model by 28.1% and 7.4%, and the accuracy is also more than 2.9% and 1.9% higher than previous works for the aspect detection and sentiment classification, respectively

Greedy Construction of Load-Balanced Virtual Backbones in Wireless Sensor Networks

Inspired by the backbone concept in wired networks, a virtual backbone is expected to bring substantial benefits to routing in wireless sensor networks (WSNs). A connected dominating set (CDS) is used as a virtual backbone for efficient routing and broadcasting in WSNs. Most existing works focus on constructing a minimum CDS, a k-connect m-dominating CDS, a minimum routing cost CDS, or a bounded-diameter CDS. However, the load-balance factor is not considered for CDSs in WSNs. In this paper, a greedy-based approximation algorithm is proposed to construct load-balanced CDS in a WSN. More importantly, we propose a new problem: the Load-balanced Allocate Dominatee problem. Consequently, we propose an optimal centralized algorithm and an efficient probability-based distributed algorithm to solve the Load-balanced Allocate Dominatee problem. For a given CDS, the upper and lower bounds of the performance ratio of the distributed algorithm are analyzed in the paper. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by up to 80% compared with the most recently published CDS construction algorithm

Data-driven Approaches in FinTech: A Survey

Purpose: This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach: This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings: This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value: To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants

A Knowledge Graph based Method on Language Understanding for Customer Service

Understanding on customer service with comprehensive information has become attracted in recent years due to its importance to business and consumers. Traditional method, which collect questionnaires in paper-format from consumers, is considered to inefficient and time-consuming. As Natural Language Processing (NLP) technologies developing, sentiment analysis and emotion detection has been demonstrated to understand customers’ satisfaction effectively. However, these popular methods only devote the polarity or emotional expression of products or service, they have limitations on exploring relevant knowledge as side information in specific domain. Therefore, a specific knowledge graph can be utilized to construct a question and answering system on customer service. In this thesis, we propose a knowledge graph based method named Custom Understanding and Responding Knowledge Graph (KG) Question and Answering system (CurKG-QA) on language understanding for customer service. Our method utilize two-way trigger including simple similarity match and hierarchical multi-label classification on hierarchical knowledge to effective answer user’s input question in human language. In addition, we explore a new model named Hierar-BERT-RCNN to recognize and classify vague question in hierarchical multi-label classification step. This model outperforms over hierarchical baseline models (BERT, BERT-CNN, BERT-DPCNN) on DuEE dataset on average 0.83% higher in main level and 9.49% higher in sub-level, and it achieves 96.51% accuracy in main level classification and 95.58% accuracy in sub-level classification. Also, the results show that simple similarity match of our CurKG-QA performs well on hierarchical air-service dataset even input question has jump-level or poor format

Security in Fog Computing through Encryption

Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog