Reviewerly/exHarmony huggingface dataset:
Improving Person Re-Identification Based on Human Height Information

Over the last few decades, threats to urban security have increased worldwide. Surveillance solutions aim to detect, track, and identify people of interest using a large number of surveillance camera videos. In particular, in Japan, the police force expects surveillance solutions to help criminal tracking. As the number of surveillance cameras has increased in recent years, manual analysis has reached its limit, resulting in a high expectation for automatic analysis using artificial intelligence (AI). The AI study on person re-identification has a high potential for human verification in academia; however, it is still challenging to apply. This is because practical tracking accuracy cannot be obtained when a person changes clothes on the assumption that the criminal is on the run. Therefore, we have developed and evaluated an AI technology that identifies people in a scene by focusing on human height, a body characteristic that does not change. To estimate the height from the video, it is necessary to obtain the camera specific parameters. It is difficult to estimate the parameters of many existing cameras because well-known and other conventional camera calibration methods require tedious manual procedures and preliminary information such as objects of known length to estimate the camera parameters. Therefore, we developed a high-precision height estimation method that does not require prior knowledge or the complicated manual procedure by estimating camera parameters using the skeleton of a person captured by a surveillance camera. Moreover, we developed a method that can determine whether a person is the same person even if the person&#x0027;s clothes change using the human height, representing one of the characteristics unique to the person, together with the image features. When evaluating images to identify similarity in people in different clothing captured at size of &#x003E;200 pixels, the mean average precision improved by 64.8&#x0025; compared with the conventional method, thus suggesting practicability. In future, we plan to confirm the accomplishment of this study using videos closer to the actual environment and use the results to support crime prevention efforts.
Empirical Evaluation of Blockchain Based Remote Data Communication System over Wireless Sensor Network (WSN)

The Internet of Things (IoT) concept enables the connectivity and sending and receiving of data across millions of intelligent objects. This paradigm continues to evolve and increase tremendously, just as the dangers and assaults on IoT infrastructures do. Wireless sensor networks are viewed as excellent prospects for a variety of applications, including industrial monitoring, data gathering in hazardous settings, and military operations. The technology of Blockchain has emerged in recent years as among the most revolutionary technologies. This technology was developed to facilitate digital transactions and to enable safe and trustworthy access to the distributed ledger. Blockchain technology combined with smart contracts enables users to conduct safe transactions without trusting external parties or intermediaries. Attacks and dangers are increased in centralized IoT systems, particularly when data is transferred and shared with other companies between devices. To avoid such circumstances, this study proposes a decentralized approach that ensures an IoT system’s autonomy and security. This paper addresses the recent approaches that are based on blockchain, we will review the present models and discuss their advantages and drawbacks to find out the solutions and the way to other studies.
Attention based Subregion Aggregation Graph Convolution Networks with Extended Neighborhood

Graph Convolution Network (GCN) is a powerful model for graph representation learning. But affected by limited scope of neighborhood and average aggregation. GCNs unable to capture the feature representations from distant nodes, and lose the structural correlation and attribute correlation between nodes. Mapping nodes to a latent-space and building extended neighborhood can improve the problem, in theory. But the irrelevant messages of latent-space neighborhood can adversely affect the performance, and the experiments of predecessors showed that the disadvantages of latent-space neighborhood outweigh the advantages. In this paper, we propose an attention based subregion aggregation graph convolution networks, termed AG-GCN, an efficient representation learning model for node classification to overcome the weaknesses. AG-GCN utilize graph embedding to map the nodes that have the same label with the target node to its neighborhood in the vector space to build extended neighborhood, so the long-range dependencies can be captured. What’s more, we overcome the adverse effects of irrelevant messages from extended neighborhood, and catch the structural correlation as well as attribute correlation by multi region attention mechanism. Our experimental results show that combining network geometry and attention computation yields a general improvement of accuracy on most range of open datasets of graphs.
Virtual body anthropomorphism increases drift in self‐location: Further support for the humanoid shape rule

Abstract Previous studies on bodily self‐consciousness (BCS) have shown that self‐location and body ownership are prone to changes based on the perceptual appearances of the fake virtual body. In the current study with 36 participants, we assessed the influence of virtual avatar anthropomorphism and the synchronicity of the visuo‐tactile stimulation on self‐location using a virtual reality full‐body illusion experiment. During the experiment, half of the participants observed a gender‐matched full‐body humanoid avatar from a first‐person perspective (1PP) and the other half observed a less anthropomorphic full‐body cubical avatar from 1PP while they were receiving synchronous and asynchronous visuo‐tactile stimulation. Results showed a significant main effect of the synchronicity of the visuo‐tactile stimulation and avatar body type on self‐location but no significant interaction was found between them. Moreover, the results of the self‐report questionnaire provide additional evidence showing that participants who received synchronous visuo‐tactile stimulation, experienced not only greater changes in the feeling of self‐location, but also, increased ownership, and referral of touch. Our results provided further support for the previous findings that showed evidence for the effect of virtual avatar appearance on BCS.
Chinese Short Text Classification Based On Deep Learning

With the development of Internet technology, more and more Chinese platforms are creating massive Chinese texts. At present, obtaining data samples for training is no longer a problem, and more and more researchers are beginning to devote themselves to obtaining great value from mining text information. Chinese text classification is mainly used for user sentiment analysis, personalized recommendation, topic tracking, and public opinion monitoring. However, Chinese texts naturally have many difficulties, such as many ambiguities, difficult word segmentation, fewer words, and sparse features. Traditional machine learning has a poor realization effect on Chinese texts. Deep neural networks are gradually becoming a new trend in Chinese text classification.
Blockchain and its Application in the Airline Industry - A Call for Action

Airlines are operating in an environment of stiff competition and volatility, with their revenues tightly linked to global economic cycles. The airline industry has been going through many challenges with epidemics, financial crises, and a recent pandemic since 2020. The changes in political, economic, social, and technological fronts are dynamic and directly impact the industry profitability. Cybersecurity threats are also on the rise. Under these circumstances, disruptive technologies like Blockchain and Artificial Intelligence play a vital role in providing a differential advantage by increasing productivity and efficiency. In this research, an extensive literature review and Delphi study technique was used to review the benefits and challenges of implementing blockchain and to explore the deterrents to accelerated adoption of Blockchain by airlines in the UAE. The findings revealed that the topmost barriers to adoption are related to people, followed by process, cost/ funding, and then technology. It then recommended a roadmap for blockchain implementation to transition from the current state to the desired state within the airline industry/organization for accelerated adoption of Blockchain. The roadmap in terms of people, process and technology will benefit the cybersecurity/ blockchain practitioners, and decision makers in the airline industry.
Automatic selection of routers using YANG tool

Network protocol plays a major role in data transfer between routers and switches. There exist several approaches to manage routers for configuration and operation of data using Command Line Interface (CLI). The SNMP is used to mange the network devices. The problem faced by SNMP is that the configuration is done manually. In order to overcome the manual process the proposed system known as YANG/NETCONF came into existence. The YANG is modelling language along with NETCONF that configures the network devices automatically. The proposed work is using YANG to design automatic identification system. The automatic identification system is used to select the router in the network. The designed system is experimented using the YANG tool and the accuracy and the time taken to identify the appropriate router are recorded. The current proposed system is designed as new app that allowed all users to choose their desirable testbed/routers under the series NCS 500 and NCS 5500. The automatic identification system yielded 98.23% accuracy and 150ms as time taken to locate the appropriate routers take into identify the router.
E-Certi: Design and Implementation of MVT Architecture Based E-Certificate Generation System

This paper discusses a Model-View-Template (MVT) architecture based system called “E-Certi”. This system facilitates event organizers to generate the participation certificate automatically. In addition to this, it also has a provision to mail it to the participants on their registered E-mail ID. This does not only reduce the overhead of writing the names of participants on certificate, but also relieves the organizers from the additional efforts required for handling and distributing the certificate to the appropriate participant. In addition to this “E-Certi” contributes to the “Go Paperless” movement and also saves the money spent in printing the certificate. While developing the system, we have taken care of the security concerns as well. Every e-certificate generated using “E-Certi” has a verification link containing some unique hash value called certificate identifier, which is associated with the information of participant stored in the database. So when anyone accesses this link, all the details related to the certificate identifier get displayed. Thus one can easily verify whether the certificate details are genuine or not. To measure the system performance, we have tested it for different batches having different number of participants. For measuring system usability, feedback is taken and analysis is carried out.
Rare Traffic Signs Recognition

Recognition of road signs is an important part of the control systems of autonomous vehicles and driver assistance systems. Modern recognition methods based on neural networks require large well-labeled datasets. Marking up data is quite expensive, but it is even more difficult to mark up rare classes of objects. To solve this problem in this article, we use synthetic data. We improve the marking of the Russian traffic signs dataset (RTSD) in semi-automatic mode adding 9 thousand new road signs. We perform an experimental evaluation of the currently best classifiers and detectors in the task of recognizing road signs. To improve the performance of classification, we use stochastic weight averaging (SWA) and contrastive loss. The use of modern methods allows us to train a high-quality neural network on synthetic data, which was previously impossible, and significantly improves the metrics of recognition of both rare and frequent road signs.
Multiple Granularities with Gradual Transition Network for Person Re-identification

Person re-identification (Re-ID) is a challenging task in computer vision, which aims at retrieving a target pedestrian from a gallery of person images captured from various cameras. Recent part-based methods, which employ horizontal splitting to integrate global and local information as final person representation, are not efficient enough in cases where the discriminative information near the splitting boundary is missing or incomplete due to partition. To address this issue, we proposed a novel method called Multiple Granularities with Gradual Transition Network (MGGTN) to fully mine fine-grained features at each part level and make the person representation more discriminative and robust. Our model introduces multi-branch network architecture to extract features with multiple granularities and uses a gradual transition strategy to obtain partial regions instead of easily partitioning the feature map into several stripes. Experimental results demonstrate the effectiveness of our method for Re-ID task. Especially, we achieve the new state-of-the-art results on both DukeMTMC-ReID and CUHK03 datasets and obtain the top rank1 result on Market1501 dataset.
Declarative Programming with Intensional Sets in Java Using JSetL

Abstract Intensional sets are sets given by a property rather than by enumerating their elements. In a previous work, we have proposed a decision procedure for a first-order logic language which provides restricted intensional sets (RISs), i.e. a sub-class of intensional sets that are guaranteed to denote finite—though unbounded—sets. In this paper, we show how RIS can be exploited as a convenient programming tool also in a conventional setting, namely the imperative O-O language Java. We do this by considering a Java library, called JSetL, that integrates the notions of logical variable, (set) unification and constraints that are typical of constraint logic programming languages into the Java language. We show how JSetL is naturally extended to accommodate for RIS and RIS constraints and how this extension can be exploited; on the one hand, to support a more declarative style of programming and, on the other hand, to effectively enhance the expressive power of the constraint language provided by the library.
Planar Pushing Manipulation with a Group of Mobile Robots

This paper addresses the problem of collaborative manipulation of an object performed by a group of robots. In particular, the objective is achieved by means of pushing, implemented by a group of small wheeled mobile robots. The proposed method exploits the well known Voronoi-based coverage control method to deploy the robots in the environment according to a given probability density function. The shape of such a density function is defined, in a time varying manner, by a pushing policy function to enable planar manipulations, considering only the footprint of the manipulated object. The proposed method is validated by means of extensive simulations and real world scenario experiments with different sized robot groups and different manipulated objects.
Revisit Gaussian Embedding: An Effective Method for Scalable Knowledge Graph

Knowledge Graph with Gaussian Embedding (KG2E) is designed to capture the uncertainties of knowledge and hence shows great potential on various applications such as information retrieval, crisis management and so on. However, it is computational expensive, and limited to a Mahalanobis distance which essentially views relations as translation operator. This paper describes the theory and implementation of a highly scalable and effective solver to simplify KG2E, through decomposition of the objective of into two parts: an adaptive margin and a distance metric. For the first part, we provide a sound theoretical guarantee for the equivalence between uncertainties and the adaptive margin. We show that, under certain assumptions, it enjoys a linear time complexity with the help of SVD. For the second part, we extend the distance metric to capture the complicated effect of relations, enhancing the model's flexibility. Compared to original KG2E, our methods boost the convergence as well as the performance (over 10% improvement on Hits@10). On four large-scale knowledge graph benchmarks, our methods also achieve better or comparable performance among the state-of-the-art models.
A Semantic Framework for Secure and Efficient Contact Tracing of Infectious Diseases

Contact tracing is the process of identifying people who came into contact with an infected person (“case”) and collecting information about these contacts. Contact tracing is an essential part of public health infrastructure and slows down the spread of infectious diseases. Existing contact tracing methods are extremely time and labor intensive due to their reliance on manually interviewing cases, contacts, and locations visited by cases. Additionally, complex privacy regulations mean that contact tracers must be extensively trained to avoid improper data sharing. App-based contact tracing, a proposed solution to these problems, has not been widely adopted by the general public due to privacy and security concerns. We develop a secure, semantically rich framework for automating the contact tracing process. This framework includes a novel, flexible ontology for contact tracing and is based on a semi-federated data-as-a-service architecture that automates contact tracing operations. Our framework supports security and privacy through situation-aware access control, where distributed query rewriting and semantic reasoning are used to automatically add situation based constraints to protect data. In this paper, we present our framework along with the validation of our system via common use cases extracted from CDC guidelines on COVID-19 contact tracing.
A Critical Analysis of Biomedical Image Classification on Deep Learning

In computer-aided diagnostic technologies, deep convolutional neural image compression classifications are a crucial method. Conventional methods rely primarily on form, colouring, or feature descriptors, and also their configurations, the majority of which would be problem-specific that has been depicted to be supplementary in image data, resulting in a framework that cannot symbolize high problem entities and has poor prototype generalization capability. Emerging Deep Learning (DL) techniques have made it possible to build an end-to-end model, which could potentially general the last detection framework from the raw clinical image dataset. DL methods, on the other hand, suffer from the high computing constraints and costs in analytical modelling and streams owing to the increased mode of accuracy of clinical images and minimal sizes of data. To effectively mitigate these concerns, we provide a techniques and paradigm for DL that blends high-level characteristics generated from a deep network with some classical features in this research. The following stages are involved in constructing the suggested model: Firstly, we supervisedly train a DL model as a coding system, and as a consequence, it could convert raw pixels of medical images into feature extraction, which possibly reflect high-level ideologies for image categorization. Secondly, using image data background information, we derive a collection of conventional characteristics. Lastly, to combine the multiple feature groups produced during the first and second phases, we develop an appropriate method based on deep neural networks. Reference medical imaging datasets are used to assess the suggested method. We get total categorization reliability of 90.1 percent and 90.2 percent, which is greater than existing effective approaches.
Reinforced CNN Forensic Discriminator to Detect Document Forgery by DCGAN

Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous. Nonetheless, few studies have been conducted on document forgery and new forgery-related attacks have emerged daily. Therefore, in this study, we propose a novel convolutional neural network (CNN) forensic discriminator that can detect forged text or numeric images by GANs using CNNs, which have been widely used in image classification for many years. To strengthen the detection performance of the proposed CNN forensic discriminator, CNN was trained after image preprocessing, including salt and pepper as well as Gaussian noises. Moreover, we performed CNN optimization to make existing CNN more suitable for forged text or numeric image detection, which have mainly focused on the discrimination of forged faces to date. The test evaluation results using Hangul texts and numbers showed that the accuracy of forgery discrimination of the proposed method was significantly improved by 20% in Hangul texts and 5% in numbers compared with that of existing state-of-the-art methods, which proved the proposed model performance superiority and verified that it could be a useful tool in reducing crime potential.
Comparison of Containerization and Virtualization in Cloud Architectures

Virtualization and Containerization both come with several advantages and disadvantages, mostly over each other. As these are both used for a varied range of applications and are used for different purposes, choosing one over another can be of significant importance. While there are several literary works that provide the broad differences between virtualization and containerization, this work focuses on the performance differences in the 3 cloud architectures- IaaS, PaaS, and SaaS. The results obtained in this work are a step towards gauging the differences between containerization and virtualization and to allow users of these services to appropriately decide their method of operation, by providing clear, quantifiable points of difference between these 2 methods, across the cloud architectures. This work found that while containers can be more efficient in their performance for SaaS and PaaS offerings, the parameters measured are nearly identical for IaaS offerings
Regional Attention Reinforcement Learning for Rapid Object Detection

When people observe a picture, they first pay attention to local areas of the picture, rather than the whole areas, then combine them with previous experience in the brain, and finally make judgments through thinking. This is human visual logic. In this paper, we propose a regional attention reinforcement learning model for object detection. The proposed model uses human visual logical to solve the detection problem of small and complex targets in the picture. The model uses a recurrent network structure as the main framework to extract historical information, and fuse the historical information with the current concerned information. At each recurrent time step, it can pay attention to the fused information, especially pay more attention to the information that may have objects. Experimental results show that the proposed method has more than 5% improved in recognition accuracy to the conventional methods. In terms of FLOPs, the conventional methods normally require 170M, while the proposed method only needs 25.4M. This means that the proposed method has higher detection efficiency.