Reviewerly/exHarmony huggingface dataset:
SGD<sup>2</sup>: Secure Group-based Device-to-Device Communications with Fine-grained Access Control for IoT in 5G

The fifth generation (5G) mobile networks provide intensive and low-latency communications, which are applicable to an emerging data sharing/exchange technology called Device-to-device (D2D) communication. For a safety D2D communication, is essential to ensure the legitimacy of devices and the secrecy of communications before it practiced. However, the basic security specification can only support secure one-to-one or one-to-many D2D communications with the assistance of security-related components,i.e., access and mobility management function (AMF), authentication server function (AuSF), and unified data management (UDM), in the 5G core network. It results that 5G core networks always have to handled the discovery of mobile devices. Since the components related to user authentication are involved, the device discovery procedures result in no privacy for users. To overcome the above issues, this work proposes a secure attribute-based access control mechanism to support secure device discovery with fine-grained access control based on edge computing model for D2D communications in 5G, called SGD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . SGD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> guarantees the privacy of D2D communications against the infrastructures of 5G mobile networks. Additionally, this work provides security analysis and empirical performance evaluation for the security and feasibility. To sum up, the proposed SGD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is the practice of D2D communications with the features of fine-grained access control and privacy against mobile network operators in 5G for the applications of IoT.
Segmentation of SAR Images Based on the Optimal Level Sets Using CWOA

In the previous work, the presented multi-texture-based segmentation model based on level sets for synthetic aperture radar (SAR) images generally can attain good results. However, the weighting parameters in this model have non-ignorable influences on segmentation performance, while such parameters usually are determined by the past experiences, which decreases the flexibility of this method. To address this problem, based on this model, we propose a SAR image segmentation method with the optimal level sets using chaotic whale optimization algorithm (CWOA). First, various image segmentation results are attained by the multi-texture-based model with several random sets of weighting parameters, samples are then automatically generated by comparing these segmentation results. Second, search agents (humpback whales) are defined with respect to the weighting parameters that need to be optimized, and the fitness function (prey) is associated with the samples. The optimal level sets are then established by integrating the multi-texture-based model and CWOA. Finally, the experimental result achieved from a SAR image shows the effectiveness of the proposed method.
Local feature extraction of sheepskin based on structure contour shape description

Based on the precise sheepskin contour extracted by computer vision technology in the previous research of the team, this paper proposes the shape description technology based on the structure contour to extract the local features of the sheepskin, such as the head and hooves and the waste edge, which is the basis for the automatic edge removal of the sheepskin in the future. The algorithm uses Angle and position relation to segment the precise contour track of raw sheepskin into graph elements, and then uses geometric parameter shape description operator to describe and extract the edges that need to be removed, so as to obtain the starting point and end point of each local contour that needs to be removed. In this paper, the principle and implementation steps of this method are introduced in detail, and the experimental simulation verification shows that the extraction effect is good, which can meet the requirements of subsequent industrial production of automatic sheepskin cutting.
Improving Perceived QoS of Delay-sensitive Video Against A Weak Last-mile: A Practical Approach

We present a novel approach to improve the perceived QoS for video-feeds in delay-sensitive interactive applications under practical last-mile impairments. Conventional approaches consume additional communication and computation resources in the effort to regain the PSNR of the frames by trying to recover the lost information. Such approaches undermine the strict latency requirements leading to eventual frame-drop and reduction in overall system QoE. Instead, we aim to create an approximation of the corrupted frame by regaining the structural information and minimizing the effect of the lost packet(s). The objective is to minimize the possibility of frame drop under weakened last-mile connection so that the remote user can have a better perceived QoS at the application level and can have better probability of reacting to remote scene despite lossy condition. We prove the efficiency of the system under real-life situations with impairments in real field deployment. The benchmarking is performed in terms of full referential video quality metrics against established state-of-the art like WebRTC and RTP.
Unscientific Mining of Corundum at Gobbagurthi, Khammam, Telangana, India

Abstract Unscientific mining of Industrial grade Corundum and gem variety, (gemstone ruby and semiprecious abrasive corundum) is observed surrounding Lakshmipuram, Gobbagurti, Donabanda and Singaraipalem areas of Khammam District of Andhra Pradesh. Corundum is found occurring in the Khammam Schist Belt., a northern extension of the Nellore Schist Belt of the Indian Peninsular Shield. Local entrepreneurs are involved in the mining of corundum since a long time. Both gem variety and industrial grade occurrences of corundum are observed as in-situ, float, and placer concentrations. Exploration of corundum is manifested in geomorphology, structure and lithology in this area which have proved to be very useful guides for understanding the various controls of corundum mineralization at Gobbagurthi as well as in surrounding areas of corundum occurrences in Khammam district .
A Novel Multi-feature Skeleton Representation for 3D Action Recognition

Deep-learning-based methods have been used for 3D action recognition in recent years. Methods based on recurrent neural networks (RNNs) have the advantage of modeling long-term context, but they focus mainly on temporal information and ignore the spatial relationships in each skeleton frame. In addition, it is difficult to handle a very long skeleton sequence using an RNN. Compared with an RNN, a convolutional neural network (CNN) is better able to extract spatial information. To model the temporal information of skeleton sequences and incorporate the spatial relationship in each frame efficiently using a CNN, this paper proposes a multi-feature skeleton representation for encoding features from original skeleton sequences. The relative distances between joints in each skeleton frame are computed from the original skeleton sequence, and several relative angles between the skeleton structures are computed. This useful information from the original skeleton sequence is encoded as pixels in grayscale images. To preserve more spatial relationships between input skeleton joints in these images, the skeleton joints are divided into five groups: one for the trunk and one for each arm and each leg. Relationships between joints in the same group are more relevant than those between joints in different groups. By rearranging pixels in encoded images, the joints that are mutually related in the spatial structure are adjacent in the images. The skeleton representations, composed of several grayscale images, are input to CNNs for action recognition. Experimental results demonstrate the effectiveness of the proposed method on three public 3D skeleton-based action datasets.
MagnaSense: The Flying Mouse

This paper presents MagnaSense, the first of its kind wireless computer `mouse' that uses two magnetometers, and principles from magnetism and electromagnetic induction to achieve performance comparable to a traditional computer `mouse', but with three degrees of freedom instead of two. We demonstrate MagnaSense to have the ability to draw various shapes as well as write text on the screen. Our prototype of MagnaSense is affordable, non-intrusive, handy and easy to use, with the user just requiring a magnet in his hand.
An Application of Deep Neural Network for Robbery Evidence Using Face Recognition Approach

Video vigilance has been implemented at many public places such as streets or buildings to prevent robberies and to obtain proofs from crimes. This information is not always publicly available since has a direct relation with people’s privacy. Another commonly used approach to monitor the security of a city, used by smart cities is the Volunteered Personal Information (VPI) where people provide information through different applications and computational solutions focused in solving urban problems. How to use the above mentioned tools for increase the smart cities security? One solution could be the use of video and volunteered personal information of their citizens. The present approach arises from the people's necessity to have proofs when unfortunately a robbery occurs. In the present approach a deep neural network is implemented in order to identify people and faces inside images that could be analyzed when a robbery occurs, the deep neural network is trained and proved with volunteered personal information (VPI) open source datasets and compared with some other recognition algorithms in order to determine its precision and usability. The deep neural network makes use of bounding boxes to identify people and faces from images. This project is part of a bigger one where the deep neural network would be implemented on a wearable device able to take images in real time when a robbery is happening to its user, hence the need for developing a deep neural network dedicated to the people and faces recognition.
The Sociology of Ransomware

The cybersecurity industry has been quick to adopt existing risk mitigation strategies and new cybersecurity products to defend against ransomware attacks. However, limited research has analysed whether there are any causal factors associated with ransomware attacks. Causal factors may range from why an individual develops or launches a ransomware attack to why ransomware attacks continue to be effective against large corporations. Are ransomware attacks so technically advanced that large corporations cannot stop them, or are enterprises failing to mitigate their risk effectively? Society understands that ransomware poses a serious threat, but we do not have a clear understanding of what factors may be contributing to its further development or exacerbating the problem.
Reducing the Price of Protection: Identifying and Migrating Non-sensitive Code in TEE

As the trusted computing base (TCB) unnecessarily increases its size, the performance and security of Trusted Execution Environments (TEE) can deteriorate rapidly. Existing solutions focus on placing only the necessary program parts in TEE, but neglect the numerous cases of legacy software with misplaced TEE-based non-sensitive code. In this paper, we introduce a new type of software refactoring-TEE Insourcing-that identifies and migrates non-sensitive code out of TEE. We present TEE-DRUP, the first semi-automated TEE Insourcing framework whose process comprises two phases: (1) a variable sensitivity analysis designates each variable as sensitive or non-sensitive; (2) a compiler-assisted program transformation automatically moves the functions that never operate on the sensitive variables out of TEE. Developers can participate to verify and confirm sensitive variables, and specify additional non-sensitive functions to migrate. The evaluation results of TEE-DRUP on real-world programs are encouraging. TEE-DRUP distinguishes between sensitive and non-sensitive variables with satisfactory accuracy, precision, and recall - all of their actual values are greater than 80% in the majority of evaluation scenarios. Further, moving non-sensitive code out of TEE improves system performance, with the speedup ranging between 1.35 and 10K. Finally, TEE-DRUP's automated program transformation requires only a small programming effort.
Vehicle Detection with Partial Anchors in Remote Sensing Images

Vehicle detection in remote sensing(RS) images has been an active topic with the development of computer vision in recent years. However, directly applying conventional horizontal anchor-based detection methods in oriented vehicle detection often acquires poor performance. Although rotated anchors have been used to tackle this problem, this design leads to heavy computational cost because of thousands of rotated anchors generated in each level feature map. In this paper, we propose to detect vehicles with partial anchors, which greatly accelerates detection process. The novel Partial Anchors based Detection Network(PADeN) filter out redundant anchors with semantic information. To boost the performance of PADeN, the centerness mask branch is added into the network. The results demonstrate that PADeN significantly outperforms previous approaches in vehicle detection and achieves the mAP of 76.9%.
Fine-Tuning for One-Look Regression Vehicle Counting in Low-Shot Aerial Datasets

We investigate the task of entity counting in overhead imagery from the perspective of re-purposing representations learned from ground imagery, e.g., ImageNet, via feature adaptation. We explore two directions of feature adaptation and analyze their performances using two popular aerial datasets for vehicle counting: PUCPR+ and CARPK. First, we explore proxy self-supervision tasks such as RotNet, jigsaw, and image inpainting to re-fine the pretrained representation. Second, we insert additional network layers to adaptively select suitable features (e.g., squeeze and excitation blocks) or impose desired properties (e.g., using active rotating filters for rotation invariance). Our experimental results show that different adaptations produce different amounts of performance improvements depending on data characteristics. Overall, we achieve a mean absolute error (MAE) of 3.71 and 5.93 on the PUCPR+ and CARPK datasets, respectively, outperforming the previous state of the art: MAEs of 5.24 for PUCPR+ and 7.48 for CARPK.
Designing client-server and service oriented architecture of the distance learning system

Advancement of information and communication technologies crossed the boundaries of different fields and converged them for making traditional services easier for consumption and delivery. Considering pandemic period in entire world education sector offered online and distance learning systems for keeping educational processes going on. Regarding the abovementioned motivational factors, this paper is devoted to the designing of distance learning information system which is very crucial for the development and timely delivery of educational services to the learners. Three layered client-server architecture and service oriented architecture of the e-LMS distance learning systems have been suggested considering the processes and aspects of education system of Uzbekistan.
Introducing Region Pooling Learning

In recent years, the advancement of convolutional neural network (CNN) topologies have been constantly evolving at an increasingly fast pace, with current novel proposals like Inception, ResNet, MobileNet, etc., pushing the performance results on available benchmarks, and optimizing towards smaller models with lower number of trainable parameters capable of achieving comparable results to the known state of the art. To this day, most of these novel approaches either rely on the use of pooling layers or the use of strided convolutions as one of the main building blocks of a CNN, i.e., the traditional max pooling and average pooling layers, as a way to reduce spatial dimensionality of features progressively through the network and focusing on obtain a value that not necessarily contains valuable information of the position of the object of interest. The selection between these two layers is typically based on the experience of the Neural Network architect, were several training procedures have to be evaluated to guarantee the best accuracy. In this work, we introduce the concept of the region pooling learning, in which an optimal pooling behavior is learned through training. Additionally, the knowledge of the region pooling layers can be leveraged by deeper layers. The region pooling layer could learn to behave as a max-pooling or an average-pooling, or it might learn to pool the most convenient value based on training. The experimental results presented in this work over two available image datasets suggest that the use of the region pooling layer improves the performance of a Resnet18 CNN network, outperforming in some cases a typical ResNet110 CNN.
Simulate and sense force exertions during virtual patient transfer tasks

Virtual reality (VR) is commonplace for training, yet simulated physical activities in VR do not require trainees to engage and contract the muscle groups normally engaged in physical lifting. This paper presents a muscle activity-driven interface to elicit the sensation of forceful, physical exertions when lifting virtual objects. Users contracted and attained predefined muscle activity levels that were calibrated to user-specific muscle activity when lifting the physical counterpart. The overarching goal is to engage the appropriate muscles, and thereby encourage and elicit behaviors normally seen in the physical environment. Activities of 12 key muscles were monitored using electromyography (EMG) sensors while they performed a three-part patient lifting task in a Cave Automatic Virtual Environment. Participants reported higher task mental loads and less physical loads for the virtual lift than the physical lift. Findings suggest the potential to elicit sensation of forceful exertion via EMG feedback but needed fine-tuning to offset perceived workload.
Edge Reasoning on Context-Aware Environments: Application for the indoor displacement proble

Nowadays, populations in buildings are heterogeneous. They cohabit with robots and machines that move inside buildings. The capacity of each individual to process and perceive its environment is as miscellaneous as the populations themselves. These individuals deal with simple and quotidian problems of indoor displacement, which reduce their productivity, comfort and even the performance of buildings. The need of a context-aware environment that provides quick, safe and direct solutions to this issue is evident. We present in this paper a Context-Aware Edge Layer named CAEL, made with Internet Of Things-based cyber-physical system and ontologies. CAEL takes reasoning skills on a context-aware system close to the source of data to create interoperability. This latter intends to improve mobility in indoor environments. Our approach addresses the displacement problem from two different perspectives: the occupants and the building. CAEL improves occupants' perception and processing by enriching their knowledge and reducing processing load respectively, while the building re-configures its topology to improve mobility. To validate our prototype, we constructed a physical scale model of a building. Inside, we placed small autonomous mobile robots to represent populations. We evaluate CAEL performance to improve indoor mobility by running multiple use cases. Results have shown evidence of cooperation between the environment and its occupants in terms of mobility. This study paves the work for a complete reasoning distributed architecture.
Promoting libraries using social media videos

Social media videos have emerged as a new mode of public communication in recent years. They are widely used, require only basic equipment, are easy to share, and are interactive. Libraries should try to provide social media video services, such as live stream communication and short video clips, to help people become acquainted with and visit libraries.
Text Localization in Scene Images Using Faster R-CNN with Double Region Proposal Networks

The problem of text extraction is an interesting area of research in computer vision domain. In the recent years, emergence of various applications on smart hand-held devices such as translation of text from one language to another in real time, computerized aid for visually impaired, user navigation & track monitoring and driving assistance systems, has stimulated the renewed research interest in this domain. Although various Convolutional Neural Network (CNN) based methods have been explored for text localization in scene images, method using Faster R-CNN with double region proposal network (RPN) has not been explored yet. The conventional Faster R-CNN produces regions-of-interest (ROIs) through a single RPN utilizing the feature matrix of the last convolutional layer, whereas the present investigation proposes an end-to-end method of scene text localization where ROIs are generated by double RPNs using the feature matrices of thirteen different convolutional layers and four poolings. Both of these RPNs have then been merged, which enables the system to locate the text regions in the scene images. The performance of the present system has been assessed using ICDAR 2013/2015 RRC test dataset and it has outperformed all existing studies on scene text detection.
Nonlinear Temporal Correlation Based Network for Action Recognition

Action recognition, a trending topic in current research, is important for human behavior analysis, virtual reality, and human computer interaction. Recently, Some of the latest works have achieved impressive results in action recognition by decomposing 3D convolutions into temporal and spatial convolutions, respsctively. Modelling the temporal features is important for action recognition. In this paper, we reconsider the decomposing of convolution operations. In the previous temporal convolution operations, the temporal features are extracted by simple linear transformation, and the temporal relations among adjacent frames are not fully considered. Therefore, we propose a novel temporal structure, namely, Nonlinear Temporal Extractors, to replace the existing 1D temporal convolutions. On the one hand, this operation can extract temporal features by considering the relation along the time dimension. On the other hand, this enhances network’s representation ability by increasing the nonlinearity of the network. Finally, we perform experiments on the common action classification datasets, including UCF-101, HMDB-51, and mini-Kinetics-200. Experimental results show the effectiveness of our proposed structure.
Probabilistic Text Entry—Case Study 3

chapter Share on Probabilistic Text Entry—Case Study 3 Author: Keith Vertanen Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 277–320https://doi.org/10.1145/3447404.3447420Online:23 February 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access