Reviewerly/exHarmony huggingface dataset:
Rendering Optimizations for Virtual Reality Using Eye-Tracking

Optimizing rendering in virtual reality is an open problem in computer science. The nature of modern VR display technology (high refresh rate and increasing pixel density), coupled with the relatively slow growth modern compute capability, is leading to a bottleneck in VR performance. As we further research methodologies for improving rendering performance and accuracy for VR, it is important to understand the historical approaches and where they succeeded or failed in their approaches. Some implementations will double computing because of the need of stereoscopy, and thus have higher overhead for rendering. This can be improved with Multi-View Rendering where the GPU hardware can assist in duplicating rasterization for multiple views with differing projections. More recently, perception-based rendering has gained traction, which can be further accelerated using Variable Shading Rate or Multi-Rate Shading technology found on more recent GPUs. There has also been some success in using deep neural networks to assist with transmitting foveated content over a network. The advances in the field leave many open research questions, including sparse pixel rendering, driving user attention, and techniques and methodologies for combining variable shading rate images. This review focuses research associated with rendering optimizations for virtual reality using eye tracking, since it is becoming a feature present in consumer-level head-mounted displays. From our review, affordable off-the-shelf virtual reality and eye tracking are both leading to freeing up rendering resources towards improved performance and visual fidelity, as well as providing new and exciting opportunities for human-computer interaction.
The Promise and Peril of Mobile Phones for Youth in Rural Uganda: Multimethod Study of Implications for Health and HIV

In East Africa, where landlines are used by 1% of the population and access to the internet is limited, owning a cell phone is rapidly becoming essential for acquiring information and resources. Our analysis illuminates the perils and potential promise of mobile phones with implications for future interventions to promote the health of adolescents and young adults (AYAs) and to prevent HIV infection.The aim of this study is to describe the current state of AYAs' phone use in the region and trace out the implications for mobile health interventions.We identified 2 trading centers that were representative of southern Uganda in terms of key demographics, proportion of cell phone ownership, and community HIV prevalence. We stratified the sample of potential informants by age group (15-19 years and 20-24 years), gender, and phone ownership and randomly sampled 31 key informant interview participants within these categories. In addition, we conducted 24 ethnographic participant observations among AYAs in the communities of study.AYA frequently reported barriers to using their phones, such as difficulty accessing electricity. Nearly all AYAs used mobile phones to participate in the local economy and communicate with sexual partners. Phone use was frequently a point of contention between sexual partners, with many AYAs reporting that their sexual partners associated phone use with infidelity. Few AYAs reported using their phones for health-related purposes, with most getting health information in person from health workers. However, most AYAs reported an instance when they used their phone in an emergency, with childbirth-related emergencies being the most common. Finally, most AYAs reported that they would like to use their phones for health purposes and specifically stated that they would like to use their mobile phones to access current HIV prevention information.This study demonstrates how mobile phones are related to income-generating practices in the region and communication with sexual partners but not access to health and HIV information. Our analysis offers some explanation for our previous study, which suggested an association between mobile phone ownership, having multiple sexual partners, and HIV risk. Mobile phones have untapped potential to serve as tools for health promotion and HIV prevention.
Dealing with Label Quality Disparity in Federated Learning

Federated Learning (FL) is highly useful for the applications which suffer silo effect and privacy preserving, such as healthcare, finance, education, etc. Existing FL approaches generally do not account for disparities in the quality of local data labels. However, the participants tend to suffer from label noise due to annotators’ varying skill-levels, biases or malicious tampering. In this chapter, we propose an alternative approach to address this challenge. It maintains a small set of benchmark samples on the FL coordinator and quantifies the credibility of the participants’ local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the participant’s local model on the benchmark dataset. Then, a credit-weighted orchestration is performed to adjust the weight assigned to participants in the FL model based on their credibility values. By experimentally evaluating on both synthetic data and real-world data, the results show that the proposed approach effectively identifies participants with noisy labels and reduces their impact on the FL model performance, thereby significantly outperforming existing FL approaches.
Stability Investigation of Improved Whale Optimization Algorithm in the Process of Feature Selection

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have been widely used in the field of knowledge discovery over the last few years. The whale optimization algorithm (WOA) is a swarm-based metaheuristic technique that is often used in the field of dimensionality reduction. Among the various WOA-based feature selection techniques in the literature, not a single technology illuminates the stability issue of WOA. Stability is often identified as a sensitivity to the disruption of input data during the process of selecting significant features. In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure. In addition, the results of the proposed model are compared with other contemporary meta-heuristics techniques. The proposed iWOA proposes its identification as a well-stable feature selection technique according to the strength of the stability index agreement.
BioMetricNet: Deep Unconstrained Face Verification Through Learning of Metrics Regularized onto Gaussian Distributions

We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features. Differently from popular methods such as FaceNet, the proposed approach does not impose any specific metric on facial features; instead, it shapes the decision space by learning a latent representation in which matching and non-matching pairs are mapped onto clearly separated and well-behaved target distributions. In particular, the network jointly learns the best feature representation, and the best metric that follows the target distributions, to be used to discriminate face images. In this paper we present this general framework, first of its kind for facial verification, and tailor it to Gaussian distributions. This choice enables the use of a simple linear decision boundary that can be tuned to achieve the desired trade-off between false alarm and genuine acceptance rate, and leads to a loss function that can be written in closed form. Extensive analysis and experimentation on publicly available datasets such as Labeled Faces in the wild (LFW), Youtube faces (YTF), Celebrities in Frontal-Profile in the Wild (CFP), and challenging datasets like cross-age LFW (CALFW), cross-pose LFW (CPLFW), In-the-wild Age Dataset (AgeDB) show a significant performance improvement and confirms the effectiveness and superiority of BioMetricNet over existing state-of-the-art methods.
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation

Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipelines, segmentation and label assignment are solved separately since joint optimization is computationally expensive. We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm. It optimizes an objective function closely related to the Asymmetric Multiway Cut objective and empirically shows efficient scaling behavior. Due to the algorithm’s efficiency it can operate directly on pixels without prior over-segmentation of the image into superpixels. We evaluate the performance on the Cityscapes dataset (2D urban scenes) and on a 3D microscopy volume. In urban scenes, the proposed algorithm combined with current deep neural networks outperforms the strong baseline of ‘Panoptic Feature Pyramid Networks’ by Kirillov et al. (2019). In the 3D electron microscopy images, we show explicitly that our joint formulation outperforms a separate optimization of the partitioning and labeling problems.
Combination of temporal‐channels correlation information and bilinear feature for action recognition

In this study, the authors focus on improving the spatio–temporal representation ability of three‐dimensional (3D) convolutional neural networks (CNNs) in the video domain. They observe two unfavourable issues: (i) the convolutional filters only dedicate to learning local representation along input channels. Also they treat channel‐wise features equally, without emphasising the important features; (ii) traditional global average pooling layer only captures first‐order statistics, ignoring finer detail features useful for classification. To mitigate these problems, they proposed two modules to boost 3D CNNs’ performance, which are temporal‐channel correlation (TCC) and bilinear pooling module. The TCC module can capture the information of inter‐channel correlations over the temporal domain. Moreover, the TCC module generates channel‐wise dependencies, which can adaptively re‐weight the channel‐wise features. Therefore, the network can focus on learning important features. With regards to the bilinear pooling module, it can capture more complex second‐order statistics in deep features and generate a second‐order classification vector. We can get more accurate classification results by combining the first‐order and second‐order classification vector. Extensive experiments show that adding our proposed modules to I3D network could consistently improve the performance and outperform the state‐of‐the‐art methods. The code and models are available at https://github.com/caijh33/I3D_TCC_Bilinear .
Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change

In this paper, we propose a vehicle lane change system using model predictive path planning (MPPP) based on the artificial potential field (APF) for speeding vehicles. It is shown that APF has high performance in real-time obstacle avoidance. However, it remains unpractical for self-driving cars because the point model used for the APF ignores the lateral vehicle dynamics for the lane-keeping system. To resolve the problem, this paper introduces a novel curve-fitting method combined with the APF applied to plan a drivable path for autonomous vehicles in the lane change action. The proposed system was validated through MATLAB/Simulink with the empirical kinematic model. The simulation results indicate that the model predictive path planning algorithm is highly effective in high-speed lane change scenarios to avoid dynamic obstacle vehicles.
An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.
Energy Consumption Analysis of Lightweight Cryptographic Algorithms That Can Be Used in the Security of Internet of Things Applications

The Internet of Things (IoT) has begun to acquire place in our lives quietly and gradually thanks to the presence of wireless communication systems. An increasing number of M2M applications, such as smart meters, healthcare monitoring, transportation and packaging, or asset tracking, make a significant contribution to the growth of devices and connections. Within such a large and uncontrollable ecosystem, IoT poses several new problems. Security and privacy are among the most important of these problems. Lightweight cryptography can be used more effectively for small size, low energy, and small footprint such as RFID tags, sensors, and contactless smart cards. Therefore, it can be used to ensure security and privacy in the IoT applications. In this study, PRESENT, CLEFIA, PICCOLO, PRINCE, and LBLOCK lightweight cryptographic algorithms, which can be used to secure data in IoT applications, were analyzed in a test environment. As a result of the tests, the energy consumption of the algorithms, current measurement, active mode working time, and active mode energy consumption were identified and based on this, some inferences have been made.
The influence of hardiness and habit on security behaviour intention

Small and medium-sized enterprises (SMEs) make up a large percentage of businesses; they employ over 50% of all employees, and account for most partnerships in supply chain networks. This makes SME employees a target for cybercriminals. This study examines the factors that influence the security behaviour intention of SME employees to better understand how SMEs can manage cybersecurity risk caused by employee security behaviour. In a survey of 294 employees, the research model developed from the theory of planned behaviour and protection motivation theory, including the habit and hardiness personality trait, is empirically validated to understand employee security behaviour intention. The results of the study show that hardiness and habit have a significant effect on employee security behaviour intention. The findings contribute to the literature on personality traits and habit in the context of information security behaviour. The study's implications for research and practice are also discussed.
Heuristic Evaluation of COVID-19 Chatbots

Chatbots have been adopted in the health domain and their number grew during the current COVID-19 pandemic. As a new kind of interface, chatbots combine visual elements with natural conversation. While conversational capabilities of chatbots improve, little attention has been given to the evaluation of the user experience and chatbot usability. This paper presents the results of a heuristic review of 24 COVID-19 chatbots on different channels (webchat vs messengers), for diverse topics (symptom-checker vs FAQ) and with varying interaction styles (visual-centric vs content-centric vs conversation-centric). It proposes a generic evaluation framework with 12 heuristics based on Nielsen's ten heuristics and adapted to the conversational interface context. The results point at the strengths (immediate feedback, familiar language, consistent wording and visual design) as well as shortcomings (little user control and freedom, missing permanent menu and help options, lack of context understanding and interaction management capabilities) of COVID-19 chatbots. The paper furthermore gives recommendations for chatbot design in similar contexts.
Security challenges of blockchain in Internet of things: Systematic literature review

Abstract Since security is the most important issue in the Internet of things (IoT) and blockchain (BC), this article aims to identify, analyze, and organize the literature about security in the IoT and BC context up to August 2020 and proposes an agenda for future research. The 39 related articles have been ranked into three main categories, smart city, data management and information, and architecture and framework. The findings showed that the distinctive technical aspects of BC might impressively find a solution for privacy and security problems encountering the IoT development, supply distributed storage, transparency, trust, and other support for IoT. It is conducted to form a valid, impressive, and secure distributed IoT network and supply a beneficial guarantee for the security of IoT users. Since the devices of IoT are simply reachable, a BC‐depended solution can be executed, which may certify the confidentiality and safety of the info preserved in the devices. In addition, BC technology facilitates the secure sharing of IoT datasets and secure IoT systems. However, blockchains require more computational strength and high bandwidth and are not perfectly proper for many resource‐restricted IoT devices.
Detection of XSS in web applications using Machine Learning Classifiers

Considering the amount of time we spend on the internet, web pages have evolved over a period of time with rapid progression and momentum. With such advancement, we find ourselves fronting a few hostile ideologies, breaching the security levels of webpages as such. The most hazardous of them all is XSS, known as Cross-Site Scripting, is one of the attacks which frequently occur in website-based applications. Cross-Site Scripting (XSS) attacks happen when malicious data enters a web application through an untrusted source. The spam attacks happen in the form of Wall posts, News feed, Message spam and mostly when a user is open to download content of webpages. This paper investigates the use of machine learning to build classifiers to allow the detection of XSS. Establishing our approach, we target the detection modus operandi of XSS attack via two features: URLs and JavaScript. To predict the level of XSS threat, we will be using four machine learning algorithms (SVM, KNN, Random forest and Logistic Regression). Proposing these classified algorithms, webpages will be branded as malicious or benign. After assessing and calculating the dataset features, we concluded that the Random Forest Classifier performed most accurately with the lowest False Positive Rate of 0.34. This precision will ensure a method much efficient to evaluate threatening XSS for the smooth functioning of the system.
Entanglement-Enhanced Communication Networks

Building quantum networks ultimately requires strong use cases. As today's design and use of the Internet solely rests on the interconnection of classical computing devices, the development of hardware should take this dependence on an existing market into account. One might think quantum secure communication would be such a use case, but the entire design of the current Internet is built on the end-to-end argument and may reject the idea of implementing security as a physical layer protocol. On the other hand, higher data rates and reduced latency have been successfully used as key arguments for the conception of new communication standards. We thus argue that exactly these two figures of merit should be used again. We define two new initial stages of development of the quantum Internet, where in the first phase entanglement is only generated and used between network nodes, and in second phase entanglement swapping and thus distribution of entanglement over increasing distances becomes possible. In both phases, we show by simulation how the available new protocols increase the network capacity. Interestingly, following this envisioned approach can serve the needs of current market participants while paving the road for fully quantum applications in the future.
Method for Ranking the Helpfulness of Online Reviews Based on SO-ILES TODIM

Online shopping has become a habit for consumers, who often make purchase decisions based on online reviews. However, the gradual accumulation of reviews has caused an issue associated with information redundancy. Therefore, recommending helpful reviews for consumers has become an urgent problem. Current research on the helpfulness of reviews is mostly at the level of analyzing influencing factors, and few studies have focused on the problem of ranking the helpfulness of reviews. Taking film reviews as the research object, we proposed a SO-ILES TODIM method (a TODIM method based on the intuitive language evaluation set of emotional and ontological features). This method takes into account both semantic indicators (emotional factors and ontological features) and statistical indicators (review length), considers comprehensive information in the review text and has better domain adaptability. First, an intuitive language evaluation set that considers emotional and ontological features was constructed based on statistical rules. Second, a quantitative calculation method that includes an index weight value based on the logit regression model was designed, and it can effectively avoid the subjectivity of the manual assignment method. Finally, based on the degree of membership deviation, the score function and the exact function were designed to realize a ranking of the helpfulness of reviews. Through a case simulation, we show that this method can prioritize reviews that directly evaluate the product. Through a comparative analysis and parameter sensitivity analysis, the stability and scientificity of the SO-ILES TODIM method was demonstrated. This paper broadens the research scope of reviews, enriches the research method of review helpfulness ranking and provides insights for merchants or third-party platforms to manage online reviews.
Kernel meets recommender systems: A multi-kernel interpolation for matrix completion

A primary research direction for recommender systems is matrix completion, which attempts to recover the missing values in a user–item rating matrix. There are numerous approaches for rating tasks, which are mainly classified into latent factor models and neighborhood-based models. Most neighborhood-based models seek similar neighbors by computing similarities in the original data space for final predictions. In this paper, we propose a new neighborhood-based interpolation model with a kernelized matrix completion framework, with the impact weights provided by neighbors computed in a new Hilbert space containing more features. In our model, the kernel function is combined with a similarity measurement to achieve better approximation for unknown ratings. Furthermore, we extend our model with a non-linear multi-kernel framework which learns weights automatically to improve the model. Finally, we conduct extensive experiments on several real-world datasets. The outcomes show that the proposed methods work effectively and improve the performance of the rating prediction task compared to both the traditional and state-of-the-art approaches.