Reviewerly/exHarmony huggingface dataset:
Augmenting Human Perception: Mediation of Extrasensory Signals in Head-Worn Augmented Reality

Mediated perception systems are systems in which sensory signals from the user’s environment are mediated to the user’s sensory channels. This type of system has great potential for enhancing the perception of the user via augmenting and/or diminishing incoming sensory signals according to the user’s context, preferences, and perceptual capability. They also allow for extending the perception of the user to enable them to sense signals typically imperceivable to human senses, such as regions of the electromagnetic spectrum beyond visible light.In this paper, we present a prototype mediated perception system that maps extrasensory spatial data into visible light displayed within an augmented reality (AR) optical see-through head-mounted display (OST-HMD). Although the system is generalized such that it could support any spatial sensor data with minor modification, we chose to test the system using thermal infrared sensors. This system improves upon previous extended perception augmented reality prototypes in that it is capable of projecting registered egocentric sensor data in real time onto a 3D mesh generated by the OST-HMD that is representative of the user’s environment. We present the lessons learned through iterative improvements to the system, as well as a performance analysis of the system and recommendations for future work.
A New Hybrid Approach for Encrypting XML Documents

This paper proposes an approach for encrypting XML documents by using the elliptic curve cryptography (ECC) and chaos to overcome the problem of allocating secret keys and to make fast encryption mechanism respectively. The purposes of applying the ECC are to generate and allocate secret keys and the digital signature. The secret keys generated are then used to encrypt the XML documents by using chaos. The applicability of the proposed approach is demonstrated by an experimental example
Comparative Analysis of AES-ECC and AES-ECDH Hybrid Models for a Client-Server System

Data security refers to safeguard or protect the data. Data security is an essential prerequisite of the world today because of internet that provides a medium for communication between different communities of the world. Communication can be through a wireless media or wired, that requires security of data while transmitting through it because such mediums are susceptible to different external threats, so there’s a strong need of data security for those channels. Cryptography is an art to write code for solving such data threats related problems for communications over an unsecure channel. Cryptography provides diverse set of services to the data such as authentication, privacy, integrity and nonrepudiation through a wide range of techniques. It is broadly classified into two categories named as symmetric and asymmetric cryptography; symmetric technique is fast and uses the same key for encrypting the data and converting the cipher back to its original text using decryption process whereas asymmetric requires different key pair for encrypting and decrypting data. This paper presents a comparative analysis of two hybrid models AES-ECC and AES-ECDH implemented for a client server system. AES is a symmetric technique and is first implemented with ECC for a client server system. After first implementation for further enhancement in the security of data communication between client and server, AES is again implemented with another asymmetric technique ECDH commonly known as a key agreement protocol and a variant of Diffie-Hellman combined with elliptic curve cryptography that adds up more security by establishing a shared secret after a successful key agreement between client and server. After implementing, both the models are analyzed on the basis of various parameters. This paper thus presents the comparison between AES-ECC and AES-ECDH on the basis of various metrics that signify the performance, effectiveness, strength and weakness of an algorithm and also the paper will verify which hybrid technique will be more superior in providing the security and effective delivery of confidential information for a client server communication system.
Early Fusion Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted much attention in computer vision. Recently, Graph Convolutional Networks (GCNs) with multi-stream fusion strategies have obtained remarkable performance. Most of these models make decisions of action recognition by merging the prediction scores of multiple streams, while ignoring the complementary properties of different data streams for building representative features. In this paper, we propose a novel Early Fusion Graph Convolutional Network (EF-GCN), which fuses hidden features extracted from multiple skeleton data streams at different levels to enhance the discriminative power of features. Unlike the previous GCN-based models that train networks corresponding to different streams independently, all the subnetworks in the proposed EF-GCN are jointly learned in an end-to-end manner. Experiments conducted on two skeleton datasets (i.e., NTU-RGB+D and NTU-120 RGB+D) show the superior performance of EF-GCN.
Unlocking Data from the Online Footage of the Edenville Dam Breach

This paper presents a computer vision analysis to extract data from the footage of the Edenville Dam breach. The Edenville Dam breached on May 19, 2020, leading to extensive flooding in Central Michigan. Engineers have been eagerly seeking first-hand information to study this catastrophic event. Fortunately, the dam breach was captured by a local resident with a smartphone and was published online. Leveraging computer vision technology, we conducted an in-depth analysis of the video in the following steps: (1) geo-reference the 2D scene of the video with 3D real-world coordinate; (2) stabilize the camera movement and recover the dam motion; (3) track soil body motion via optical flow and interpret geotechnical failure mechanism; and (4) extract data for displacement and velocity and reconstruct 3D scene. The acquired data offer better visualization and insights of the dam failure mechanism. The proposed method has great potential in forensic analyses of geohazards' events.
Indicators of Training Success in Virtual Reality Using Head and Eye Movements

An essential aspect in the evaluation of Virtual Training Environments (VTEs) is the assessment of users&#x2019; training success, preferably in real-time, e.g. to continuously adapt the training or to provide feedback. To achieve this, leveraging users&#x2019; behavioral data has been shown to be a valid option. Behavioral data include sensor data from eye trackers, head-mounted displays, and hand-held controllers, as well as semantic data like a trainee&#x2019;s focus on objects of interest within a VTE. While prior works investigated the relevance of mostly one and in rare cases two behavioral data sources at a time, we investigate the benefits of the combination of three data sources. We conduct a user study with 48 participants in an industrial training task to find correlations between training success and measures extracted from different behavioral data sources. We show that all individual data sources, i.e. eye gaze position and head movement, as well as duration of objects in focus are related to training success. Moreover, we find that simultaneously considering multiple behavioral data sources allows to better explain training success. Further, we show that training outcomes can already be predicted significantly better than chance by only recording trainees for parts of their training. This could be used for dynamically adapting a VTE&#x2019;s difficulty. Finally, our work further contributes to reaching the long-term goal of substituting traditional evaluation of training success (e.g. through pen-and-paper tests) with an automated approach.
Blind Super-Resolution Network for Remote Sensing Image

In the past few years, image super-resolution has achieved remarkable progress, but due to the texture particularity of remote sensing images, the performance in remote sensing images are not very good. We consider that the texture of the same object in remote sensing image is highly similar, so that relevant textures can be transferred from the high resolution remote sensing image to the low resolution remote sensing image. In this paper, we propose a novel super-resolution network for remote sensing image. Specifically, we take a low resolution image and a reference high resolution image as the inputs, then utilize transformer structure to learn the texture transfer which allows the model to enhance the texture details of the low resolution image, to generate the super-resolution image. Experiments on public datasets show that our method achieves visual improvements.
The Relationship of the Components of the "I"-Image with the Mental States of Students in the Process of Studying over Time

The study is devoted to the study of the structure of the interconnections of the components of the "I" -image of students with the various mental states they experience in the context of studying at a university. Students determined the severity of the components of the "I" -image in three time continua - in the past (1st year), in the present (3rd year), in the future (4th year). They also determined the frequency of occurrence of mental states during the first two years of study using a special questionnaire. Then a correlation analysis was carried out, which made it possible to find relationships with a high level of reliability between the components of the "I" -image of students and some mental states experienced in two educational situations, which differ qualitatively depending on the time continuum. Based on the results obtained, conclusions were drawn about the presence of relationships, thanks to which it is possible to exert a certain influence on the experienced mental sta
Keyword-fusion Pointer-Generator Network for Policy Text Title Summarization

In this paper, we study the electricity price policy text title summarization problem. Comparing with conventional summarization tasks, title summarization of policy text has an extra characteristic. Policy texts always contain many professional keywords. In order to retain the main information in title summarization as much as possible, we propose keyword-fusion pointer-generator network with additional consideration of keywords of policy text. We incorporate keywords information from the original policy texts into our model by a new attention mechanism called keyword-fusion attention mechanism so that keywords can be generated in the title. What's more, our keyword-fusion pointer-generator network contains a more useful coverage vector using exponentially weighted averages method in order to solve the problem of repetition. Experimental results show that our model outperforms the other baselines.
Is There Collective Responsibility for Misogyny Perpetrated on Social Media?

Abstract Women, particularly those who work in public positions (such as journalists, politicians, celebrities, activists) are subject to disproportionate amounts of abuse on social media platforms such as Twitter. This abuse occurs in a landscape that those platforms designed and that they maintain. Focusing in particular on Twitter, as typical of the kind of platform we are interested in, this chapter argues that it is the platform, not (usually) the individuals who use it, that bears collective responsibility as a corporate agent for misogyny. Social media platforms, however, should not overstep the prevention of misogyny into interference in open political debates.
Energy Efficiency of Photonic Convolution for Artificial Intelligence Workloads

Energy Efficiency of Artificial Intelligence (AI) workloads increasingly becomes a challenge as i) they are being adopted by a growing community of industries and ii) the AI models used are growing tremendously in complexity. We can identify certain common operations which contribute to the bulk of the computations of these workloads and thus also to the overall energy footprint. Besides data-transport and generic multiply-accumulate operations, convolutions with relatively small kernels constitute a substantial part of today’s AI workloads. In this paper we will investigate potential and limitations of optical convolutional processors for AI workloads to improve their energy efficiency. We underline our findings with a thorough system analysis and with simulation and measurement results of a sequential lattice filter type optical convolutional processor on silicon.
Design and Research of Virtual Reality Course Management System Based on Artificial Intelligence

Abstract Today, artificial intelligence and virtual reality are rapidly developing as a new technology, and major universities and training institutions have opened relevant courses one after another. However, the traditional course management system cannot meet the needs of virtual reality technology. Therefore, the virtual reality course management system based on artificial intelligence has become an important technological breakthrough to solve this problem. The purpose of this article is to study the design of a virtual reality course management system based on artificial intelligence. This article first, on the basis of fully understanding the core concepts of virtual reality, transforms the curriculum management needs into actual functions for design, and designs the three key functional modules of the design and research of the virtual reality curriculum management system based on artificial intelligence in detail. At the same time, the software architecture and database of the system are rationally designed, the system is designed and explained, and the key points in the main functional modules are explained in more detail. Finally, combined with the artificially designed virtual reality curriculum management system based on artificial intelligence, the application technology, education application and application experience analysis are carried out through questionnaire survey. The results of the questionnaire show that 47% of students are “very willing” to join the immersive virtual reality classroom teaching, and 40% of students choose “more willing”, indicating that students are more willing to accept the management system model.
An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources

Abstract In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.
Aggressive Action Recognition Using Pre-trained 3D CNNs with Attention

The article is devoted to the problem of improving the accuracy of aggressive action recognition on video. The application of the attention mechanism to improve the recognition accuracy in pre-trained 3D Convolutional Neural Networks (3D CNN) is considered. Inception 3D neural network (I3D), Residual 3D neural network (R3D), Residual (2+1)D neural network (R(2+1)D) are used as models to compare. The neural attention mechanism that distinguishes the most significant information on the RSL feature maps of video frames is described. The architectural features of the application of the attention mechanism in 3D CNNs are also given. The research was carried out on the RWF-2000 dataset. The considering neural networks were evaluated in terms of accuracy, precision, recall, f1-score metrics. Comparative analysis of the results of models with attention mechanism and models without attention showed an increase in recognition accuracy in neural networks with Attention Mechanism for all the considering 3D CNN architectures.
Arm Motion Capture and Recognition Algorithm Based on MEMS Sensor Networks and KPA

With the development of artificial intelligence, human-computer interaction technology has become more and more popular. Motion capture and recognition technology based on sensor networks and machine learning algorithms gradually drawn wide attention and extensive works have been done. This article introduces the arm motion capture system based on MEMS sensor networks that takes the micro inertial measurement unit as the core, and uses the Kernel Perceptron Algorithm (KPA) to realize motion classification and recognition. This algorithm combines the advantages of Support Vector Machine (SVM) and perceptron algorithm to gain a faster model training speed under the premise of high recognition accuracy. Extensive experiments can prove that arm motions can be accurately captured by MEMS sensor networks, and the KPA has a good performance of motion recognition and faster model training speed than SVM.
Research on Data Analysis and Electronic Forensics Algorithm of Telecom Fraud Activity

This paper aims to provide effective and complete electronic evidence of telecom fraud activity for public safety agency. We proposed telecom fraud activity computer forensics platform architecture based on the characteristics of telecom operator data. The telephone fraud and SMS fraud were taken as the research object. The electronic evidence table of telecom fraud activities was generated. An electronic evidence analysis algorithm for telecom fraud activities was proposed. Dataset was analyzed from two aspects of user characteristics and fraud types. From the analysis results, it is known that there are more types of telecom fraud that require further analysis and research, in addition to telephone fraud and SMS fraud.
Automating Camera Rover for Sports Broadcast

A considerable amount of time and resources are spent on training pilots for a sports broadcast. A typical Camera Rover system requires multiple pilots to capture shots, first to control the rover, second to control the camera, and the third pilot to verify the output feed. Synchronization of these pilots is crucial for capturing the best shots. In today's world where Artificial Intelligence, Robotics, and Automation is helping the world reach new heights, automation in the Broadcast field still depends on human administration. This can be reduced by using algorithms for capturing shots and reducing the synchronization cost of pilots. This paper proposes a system to minimize this problem by automating the need for one pilot and providing a user-friendly interface for the extraction of the footage of the desired object. A user interface is provided to the pilot to select the object to be tracked and the system will try to follow the object based on the trajectory. The system consists of a camera rover, micro-controller unit, an onboard Computer, Gimbal, and a User Interface.
Research on Intelligent Diagnosis of Switch Partial Discharge Based on Power Internet of Things

The power Internet of Things revolves around all aspects of the power system, and makes full use of mobile interconnection, artificial intelligence technology, and advanced communication technology to realize the interconnection of everything and human-computer interaction in all aspects of the power system. Build a smart service system with comprehensive status perception, efficient information processing, and convenient and flexible application. In this paper, aiming at the calculation and analysis requirements of collecting data on the edge computing node side, this paper studies the intelligent diagnosis algorithm on the edge computing node side based on ultrasonic sensors. Convolutional neural network is used to monitor the partial discharge of switchgear deployed on the edge side. Upload abnormal data to the platform layer for diagnosis and analysis, and promptly push early warning information to operation and maintenance personnel, and adjust status monitoring strategies.
Edge Detection technique based on HDR image quality assessment

Abstract Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.
A Novel Bottom-Up Semi-Supervised Learning Framework for Salient Object Detection

Salient object detection, which aims at locating the most important object in a scene (or image), has been extensively studied in various tasks, such as robot vision. In this paper, we present an effective salient object detection framework based on a novel bottom-up semi-supervised learning algorithm, which obviously outperforms the existing works in complex scenes. Given an input image, it is firstly segmented into a fixed number of non-overlapping image patches as basic units. A novel segmentation-based sampling method is developed to select a subset of all image patches as training samples. Then, all samples are divided into labeled and unlabeled groups based on multiple prior cues. The labels of all the unlabeled data are inferred by a novel label propagation mechanism. As a result, a complete training set can be obtained and used to train a classifier to classify all image patches into salient object and background. In addition, we also use neighbor-constraint smoothness function to further boost the saliency map. We compare the proposed method with the state-of-the-art approaches on two datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method.