Reviewerly/exHarmony huggingface dataset:
Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments

In Internet-of-Media-Things (IoMT) environments, users can access and view high-quality Over-the-Top (OTT) media services anytime and anywhere. As the number of OTT platform users has increased, the original content offered by such OTT platforms has become very popular, further increasing the number of users. Therefore, effective resource-management technology is an essential aspect for reducing service-operation costs by minimizing unused resources while securing the resources necessary to provide media services in a timely manner when the user’s resource-demand rates change rapidly. However, previous studies have investigated efficient cloud-resource allocation without considering the number of users after the release of popular content. This paper proposes a technology for predicting and allocating cloud resources in the form of a Long-Short-Term-Memory (LSTM)-based reinforcement-learning method that provides information for OTT service providers about whether users are willing to watch popular content using the Korean Bidirectional Encoder Representation from Transformer (KoBERT). Results of simulating the proposed technology verified that efficient resource allocation can be achieved by maintaining service quality while reducing cloud-resource waste depending on whether content popularity is disclosed.
Computer Vision System for Speed Limit Traffic Sign Recognition

The paper is devoted to the development of a portable speed limit traffic sign recognition system widely available for general use. A shape-based approach has been applied to detect a speed limit sign. The sign recognition has been carried out with a convolutional neural network. A single board computer was used as a prototype of a hardware part of the proposed system. The hardware and software parts of the proposed system have been developed and tested. The traffic sign detection range is 25 meters in case of good lighting, the average recognition time is 5 seconds. The methods and techniques to increase system performance have been proposed.
Design of ARQ: An Augmented Reality System for Assembly Training Enhanced with QR-Tagging and 3D Engineering Asset Model

The object assembly process is frequently carried out throughout the lifespan of an asset. To prevent asset damage, it is necessary to perform adequate training to ensure process efficiency and appropriate asset handling. Thus, the industry constantly seeks better training solutions to reduce costs and maximize efficiency in the assembly task. Here, we present an augmented reality system to aid these efforts by using QR-tagging and 3D models. Moreover, we describe the system design using an engineering design methodology.
Pricing GAN-based data generators under Rényi differential privacy

As smart devices are becoming increasingly common in people’s daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under rényI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners’ privacy. After that, the broker uses rényi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, Fashion-MNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers’ demand for quantity and quality but also protect the owners’ privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker’s revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing.
Exploiting Sparse Self-Representation and Particle Swarm Optimization for CNN Compression

Structured pruning has received ever-increasing attention as a method for compressing convolutional neural networks. However, most existing methods directly prune the network structure according to the statistical information of the parameters. Besides, these methods differentiate the pruning rates only in each pruning stage or even use the same pruning rate across all layers, rather than using learnable parameters. In this article, we propose a network redundancy elimination approach guided by the pruned model. Our proposed method can easily tackle multiple architectures and is scalable to the deeper neural networks because of the use of joint optimization during the pruning procedure. More specifically, we first construct a sparse self-representation for the filters or neurons of the well-trained model, which is useful for analyzing the relationship among filters. Then, we employ particle swarm optimization to learn pruning rates in a layerwise manner according to the performance of the pruned model, which can determine optimal pruning rates with the best performance of the pruned model. Under this criterion, the proposed pruning approach can remove more parameters without undermining the performance of the model. Experimental results demonstrate the effectiveness of our proposed method on different datasets and different architectures. For example, it can reduce 58.1% FLOPs for ResNet50 on ImageNet with only a 1.6% top-five error increase and 44.1% FLOPs for FCN_ResNet50 on COCO2017 with a 3% error increase, outperforming most state-of-the-art methods.
Audience Experiences of a Volumetric Virtual Reality Music Video

Music videos are short films that integrate songs and imagery and are produced for artistic and promotional purposes. Modern music videos apply various media capture techniques and creative postproduction technologies to provide a myriad of stimulating and artistic approaches to audience entertainment and engagement for viewing across multiple devices. Within this domain, volumetric technologies are becoming a popular means of recording and reproducing musical performances for new audiences to access via traditional 2D screens and emergent virtual reality platforms. However, the precise impact of volumetric video in virtual reality music video entertainment has yet to be fully explored from a user&#x2019;s perspective. Here we show how users responded to volumetric representations of music performance in virtual reality. Our results preliminarily demonstrate how audiences are likely to respond to music videos and offer insight into how future music videos may be developed for different user types. We anticipate our essay as a formative starting point for more sophisticated, interactive music videos that can be accessed and presented via extended-reality technologies.
Virtual Try-On: How to Enhance Consumer Experience?

Although Augmented reality (AR) implication has increased in the cosmetic industry, the technology seems to not be always accepted and used by consumers. This is a challenging issue calling further investigation. Present paper addresses this challenge in an attempt to provide the needed understanding on the key drivers of AR experience and how these might enhance the consumer purchase experience itself. After trying a product via virtual try-on, participants in the current study had to provide evaluation in terms of both AR and purchase experience. The results are clear in showing that interactivity, realism, ease of use and immersion modulate AR experience evaluation and thus, the user satisfaction. Purchase experience correlated with utilitarian and hedonic values, predetermined by aesthetic and information quality. Utilitarian and hedonic values also affected the product knowledge. These relationships were well pronounced for participants reporting high satisfaction with the experience. Current outcomes are discussed further in a single framework combining various factors to provide the much-needed understanding on AR environments and their effect on consumer experience evaluation. Results could find a direct application in practices to design AR environments to augment consumer satisfaction and thus, enhancing the purchase journey.
Comparison of KNN, ANN, CNN and YOLO algorithms for detecting the accurate traffic flow and build an Intelligent Transportation System

This main objective of this System is to perform object detection which serves as the foundation for controlling critical traffic data such as vehicle count, vehicle movements. This study compares state-of-the-art object detectors used to incorporate traffic state estimates. To distinguish vehicles, three different object detectors are compared. Here four algorithms are compared to show which algorithm is the best to detect the vehicle. The algorithms used are CNN, KNN, HAARCASCADE and YOLO. Algorithms which are considered as group 1 and 2 respectively. The sample size considered for implementing this work was <tex>$N=10$</tex> for each of the groups considered. The sample size calculation was done using clincalc. The pretest analysis was kept at 80&#x0025;. During the process of testing five different videos were taken and the accuracy rate was calculated and the accuracy rate appears to be high for YOLO with 93&#x0025; which is compared with three algorithms such as KNN, Haarcascade and CNN algorithm with accuracy of 59&#x0025;, 89&#x0025; and 59&#x0025; respectively and proved that YOLO performance better in novel vehicle detection in real time road traffic. The statistical significance value achieved for calculation of accuracy was found to be 0.187The effectiveness of the YOLO object detection looks to be superior than CNN, KNN, and HAARCASCADE based on the accuracy value.
Entangled Qubit States and Linear Entropy in the Probability Representation of Quantum Mechanics

The superposition states of two qubits including entangled Bell states are considered in the probability representation of quantum mechanics. The superposition principle formulated in terms of the nonlinear addition rule of the state density matrices is formulated as a nonlinear addition rule of the probability distributions describing the qubit states. The generalization of the entanglement properties to the case of superposition of two-mode oscillator states is discussed using the probability representation of quantum states.
Asymmetric Lateral Field-of-View Restriction to Mitigate Cybersickness During Virtual Turns

Field-of-view (FOV) restriction is a common technique to reduce cybersickness in commercial virtual reality (VR) applications. However, the majority of existing FOV restriction techniques are implemented as symmetric imagery, which occludes users&#x2019; views during virtual rotation. In this paper, we proposed and evaluated a novel variant of FOV restriction, referred to as a side restrictor. Side restriction uses an asymmetric mask to obscure only one side region of the periphery during virtual rotation and laterally shifts the center of restriction towards the direction of the turn. We conducted a study using a between-subjects design that compared the side restrictor, a traditional symmetric restrictor, and a control condition without FOV restriction. Participants were required to navigate through a complex maze-like environment using a controller using one of three restrictors. Compared to the control condition, the side restrictor was effective in mitigating cybersickness, reducing discomfort, improving subjective visibility, and enabling users to remain immersed for a longer period of time. Additionally, we found no empirical evidence of negative drawbacks when compared to the symmetric restrictor, which suggests that side restriction is an effective cybersickness mitigation technique for virtual environments with frequent turns.
MR-RIEW: An MR Toolkit for Designing Remote Immersive Experiment Workflows

We present MR-RIEW, a toolkit for virtual and mixed reality that provides researchers with a dynamic way to design an immersive experiment workflow including instructions, environments, sessions, trials and questionnaires. It is implemented in Unity via scriptable objects, allowing simple customisation. The graphic elements, the scenes and the questionnaires can be selected and associated without code. MR-RIEW can save locally into the headset and remotely the questionnaire&#x0027;s answers. MR-RIEW is connected to Google Firebase service for the remote solution requiring a minimal configuration.
Temporal Effects in Motion Behavior for Virtual Reality (VR) Biometrics

Using the motion behavior of users in virtual reality (VR) as a biometric signature has the potential to enable continuous identification and authentication of users without compromising VR applications if traditional passwords are acquired by malicious agents. Users exhibit natural variabilities in behavior over time that influence their body motions and can alter the trajectories of VR devices such as the headset and the controllers. Behavior variabilities may negatively impact the success rate of VR biometrics. In this work, we evaluate how deep learning approaches to match input and enrollment trajectories are influenced by user behavior variation over varying time scales. We demonstrate that over short timescales on the order of seconds to minutes, no statistically significant relationship is found in the temporal placement of enrollment trajectories and their matches to input trajectories. We find that on medium-scale separation between enrollment and input trajectories, on the order of days to weeks, median accuracy is similar within users who provide input close and distant to enrollment data. Over long timescales on the order of 7 to 18 months, we obtain optimal performance for short and long enrollment/input separations by using training sets from users providing long-timescale data, as these sets encompass coarse and fine-scale changes in behavior.
Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.
Rage against the empathy machine revisited: The ethics of empathy-related affordances of virtual reality

Virtual reality (VR) has been designated as the ‘ultimate empathy machine’ due to its alleged ability to powerfully immerse users in another’s perspective. As VR has attracted growing attention, criticism of its alleged ‘empathic superpowers' has also gained strength. Critics have recently argued that the empathic VR vision is ethically flawed since it is misleading and denies non-communicable aspects of the Other. Moreover, several scholars argue that VR empathy rhetoric in fact exploits the marginalised targets of empathy, turning them to objects ‘identity tourism’ for the privileged. The paper revisits these claims, arguing that they rely on empathy notions that are dominant in traditional art-media, while overlooking VR’s unique experiential affordances. Drawing on psychophysiological evidence, it argues that the ethical significance of VR lies in the unique ways in which it manipulates the user’s body scheme via multisensory stimulation. These manipulations result in unprecedented empathy-related perceptual and conceptual transformations whose ethical implications require new ethical framing.
A Multi-channel Hierarchical Graph Attention Network for Open Event Extraction

Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient consideration of the node level and type information in Graph Attention Network (GAT), and (3) insufficient joint exploitation of the node dependency type and part-of-speech (POS) encoding on the graph structure. To address these issues, we propose a novel framework for open event extraction in documents. Specifically, to obtain an enhanced dependency structure with powerful encoding ability, our model is capable of handling an enriched parallel structure with connected ellipsis nodes. Moreover, through a bidirectional dependency parsing graph, it considers the sequence of order structure and associates the ancestor and descendant nodes. Subsequently, we further exploit node information, such as the node level and type, to strengthen the aggregation of node features in our GAT. Finally, based on the coordination of triple-channel features (i.e., semantic, syntactic dependency and POS), the performance of event extraction is significantly improved. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Furthermore, in-depth analyses are provided to explore the essential factors determining the extraction performance.
Optimization of AES-128 Encryption Algorithm for Security Layer in ZigBee Networking of Internet of Things

With the rapid development of network and communication technology, the interaction of various information data is more and more frequent, and people pay more and more attention to information security. The information encryption algorithm is a research hotspot in the field of information security. The Advanced Encryption Standard (AES) algorithm has been widely used in the field of information security with its high security and encryption efficiency. This paper mainly introduces the optimization of the AES-128 encryption algorithm of the security layer in ZigBee networking of the Internet of Things. By analyzing the principles of ZigBee networking and the AES encryption algorithm, the changes are optimized. In this paper, the new S-box cryptographic properties are used after analysis and calculation. The affine transformation period, the number of iteration cycles, and the algebraic expression of the S-box are improved. Its cryptographic properties are better than the S-box of the original algorithm, and the security of the algorithm is improved. In the theory of column hybrid algorithm, the computational complexity is reduced by changing the fixed polynomial, and the efficiency of the column hybrid algorithm is improved. In this paper, the performance of the improved AES algorithm is tested. The results show that, in the power consumption curve experiment, the recovery success rate of the original algorithm is about 42%, and the recovery success rate of the improved algorithm is nearly 60%. The improved algorithm is faster than the original algorithm in achieving a recovery success rate of 100%. Experimental results show that the design can accurately complete the encryption and decryption of the AES algorithm, and the performance is better than the original algorithm, which proves the overall superiority of the algorithm.
Unleashing the power of compiler intermediate representation to enhance neural program embeddings

Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings directly from the program source codes, by learning from features such as tokens, abstract syntax trees, and control flow graphs.
Multilingual fine-tuning for Grammatical Error Correction

Finding a single model capable of comprehending multiple languages is an area of active research in Natural Language Processing (NLP). Recently developed models such as mBART, mT5 or xProphetNet can solve problems connected with, for instance, machine translation and summarization for many languages. However, good multilingual solutions to the problem of Grammatical Error Correction (GEC) are still missing — this paper aims at filling this gap. We first review current annotated GEC datasets and then apply existing pre-trained multilingual models to correct grammatical errors in multiple languages. In our experiments, we compare how different pre-training approaches impact the final GEC quality. Our result is a single model that can correct seven different languages and is the best (in terms of F-score) currently reported multilingual GEC model. Additionally, our multilingual model achieves better results than the SOTA monolingual model for Romanian. • Single model is capable of solving GEC for multiple languages. • Pre-trained denoising autoencoder is effective in solving multilingual GEC. • Fine-tuning is sufficient for developing multilingual GEC system.