Reviewerly/exHarmony huggingface dataset:
Extended by Design: A Toolkit for Creation of XR Experiences

Through the last decade, the creation of extended reality (XR) solutions has significantly increased due to the advent of cheaper, more advanced, and accessible instruments like smartphones, headsets, platforms, development kits, and engines. For instance, the number of GitHub repositories for XR related projects jumped from 51 in 2010 to over 15,000 in 2020. At the same time, the developer community approaches the creation of XR applications using inherited design processes and methods from past mainstream platforms such as web, mobile, or even product design. Unfortunately, those platforms do not consider the spatial aspects of these applications. In this paper, we present a revisited design process and a toolkit focused on the challenges innate to XR, that aims to help beginners and experienced teams in the creation of applications and interactions in Virtual, Augmented, and Mixed Reality. We also present a compendium of 113 techniques and 118 guidelines and a set of canvases that guides users through the process, preventing them from skipping important tasks and discoveries. At last, we present a pilot case where we accompany a team with developers and designers running our process and using our toolkit for the first time, showing the benefits of a process that strikes specific issues of XR apps.
Social-sensed Image Aesthetics Assessment

Image aesthetics assessment aims to endow computers with the ability to judge the aesthetic values of images, and its potential has been recognized in a variety of applications. Most previous studies perform aesthetics assessment purely based on image content. However, given the fact that aesthetic perceiving is a human cognitive activity, it is necessary to consider users’ perception of an image when judging its aesthetic quality. In this article, we regard users’ social behavior as the reflection of their perception of images and harness these additional clues to improve image aesthetics assessment. Specifically, we first merge the raw social interactions between users and images into clusters as the social labels of images, so the collective social behavioral information associated with an image can be well represented over a structured and compact space. Then, we develop a novel deep multi-task network to jointly learn social labels in different modalities from social images and apply it to common web images. In this manner, our approach is readily generalized to web images without social behavioral information. Finally, we introduce a high-level fusion sub-network to the aesthetics model, in which the social and visual representations of images are well balanced for aesthetics assessment. Experimental results on two benchmark datasets well verify the effectiveness of our approach and highlight the benefits of different types of social behavioral information for image aesthetics assessment.
Learning Event Representations for Temporal Segmentation of Image Sequences by Dynamic Graph Embedding

Recently, self-supervised learning has proved to be effective to learn representations of events suitable for temporal segmentation in image sequences, where events are understood as sets of temporally adjacent images that are semantically perceived as a whole.However, although this approach does not require expensive manual annotations, it is data hungry and suffers from domain adaptation problems.As an alternative, in this work, we propose a novel approach for learning event representations named Dynamic Graph Embedding (DGE).The assumption underlying our model is that a sequence of images can be represented by a graph that encodes both semantic and temporal similarity.The key novelty of DGE is to learn jointly the graph and its graph embedding.
A Collaborative Modeling Approach for Crossover Services

Nowadays, consumers have a variety of service requirements which are strongly correlated. A single service provider has not enough capabilities to meet various functional and nonfunctional requirements, thus giving rise to Crossover Services. Traditional modeling approaches focus more on common model elements and their relationships, but rarely make recommendations on what modeling methods are appropriate for specific service scenarios. Therefore, this paper summarizes the objective problems faced by Crossover Services modeling, and proposes a collaborative modeling solution, which aims to make full use of each participant's domain expertise and implement complete modeling efficiently and flexibly. Based on the global and local hierarchical dependency of crossover collaboration, the method divides the modeling process into the schema layer and the instance layer, then establishes mappings between global service choreography process and local business execution processes with a series of conversational interfaces exposed by different service participants, and finally models the hierarchical constraints from the abstract decision layer to the concrete implementation layer. At the end, through the analysis of a rural e-commerce service modeling case, it is shown that this method can make full use of the existing domain service specifications and enterprise service standards. Moreover, it has certain advantages in modeling accuracy and comprehensiveness, efficiency and flexibility.
Towards the Design of Adaptive Virtual Reality Horror Games: A Model of Players' Fears Using Machine Learning and Player Modeling

Horror games are designed to induce fear in players. Although fundamental fears, such as the unknown, are inherent to the human being, more specific fears, such as darkness and apparitions, are individual and can vary from person to person. When a game aims at intensifying the fear evoked in individual players, having useful information about the fears of the current player is vital to promote more frightening experiences. This paper explores fear modeling and presents a new method to identify what players fear in a virtual reality horror game. The proposed method uses machine learning and player modeling techniques to create a model of players' fears, which can be used to adapt in-game horror elements to intensify the fear evoked in players. The paper presents the proposed method and evaluates its accuracy and real-time performance in a virtual reality horror game.
Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for Image Denoising

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.
Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition

Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA). In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions. Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results. In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.
AdaDiffGrad: An Adaptive Batch Size Implementation Technique for DiffGrad Optimization Method

Stochastic Gradient Descent is a major contributor to the success of the deep neural networks.The gradient provides basic knowledge about the function direction and its rate of change.However, SGD changes the step size equally for all parameters irrespective of their gradient behavior.Recently, several efforts have been made to improve the SGD method, such as AdaGrad, RMSprop, Adam, and diffGrad.The diffGrad is an appropriate and enhanced technique that uses fraction constant based on previous gradient information for gradient calculation.This fraction constant decreases the momentum resulting in slow convergence towards an optimal solution.This paper addresses the slow convergence problem of the diffGrad algorithm and proposed a new adaDiffGrad algorithm.In adaDiffGrad an adoptive batch size is implemented for the diffGrad to overcome the problem of slow convergence.The proposed model is experimented for image categorization and classification over CIFAR10, CIFAR100, and FakeImage dataset.The results are compared with the state of art models, such as Adam, AdaGrad, DiffGrad, RMSprop, and, SGD.The results show that adaDiffGrad outperforms other optimizers and improves the accuracy of the diffGrad.
Detection of Medicine Information with Optical Character Recognition using Android

Healthcare sector is one of the most important and rapidly growing sectors in today's world. With the changing lifestyle people need more medicines to cope up with the stress or any other disease. As a result, a lot of money is spent on medicines. Also, the medical waste generated is immense. According to the World Health Organization (WHO) the Health Care Waste Generation Rate (HCWGR) in the world is 2.5kg/bed/day. In India this rate is 1.55kg/bed/day. A major part of this waste is the medicines that we throw away because we do not have any information about them. This medical waste is increasing day by day endangering the planet. In the wake of Covid-19, the economic situation has become graver than ever. People need to find ways to save money for improving their financial condition. Our project addresses the same problem. In this project we have developed an application that will detect the medicines with the help of Optical Character Recognition (OCR). On the application we have provided information such as the name of the medicine, for which disease the medicine can be used, its side effects, the generic equivalent, its price, expiry date, alternate brands and quick tips. Additionally, we have created a chat box wherein the users can contact each other and exchange the medicines.
Energy Efficient Workflow Scheduling of Cloud Services Using Chaotic Particle Swarm Optimization

Cloud computing is undoubtedly one of the most prominent and fastest growing distributed computing paradigm It enables virtualized software, platforms, computation and storage to be promptly provisioned, scaled and released instantaneously. Supported applications involve fields such as high-energy physics, astronomy, bioinformatics, structural biology, seismology, which are complex areas with tasks that need to be organized and processed as scientific workflows. In order to routinely allocate and deal with the execution of dependent tasks on connected resources, workflow scheduling should consider various criteria, such as minimizing cost and maximizing resource use while still meeting the user-specified overall deadlines. This paper aims to implement workflow scheduling using combined chaotic Particle Swarm optimization (PSO) heuristic to optimize the scheduling efficiency by (a) specifying a model for task-resource allocation to reduce the overall energy consumption using the Dynamic Voltage Scaling (DVS) technique; and (b) developing a heuristic that uses combined chaotic PSO to solve task resource allocation based on the proposed model. Our approach is simulated and validated using a complex workflow application.
Adversarial Attacks Against LipNet: End-to-End Sentence Level Lipreading

Visual adversarial attacks inspired by Carlini-Wagner targeted audiovisual attacks can fool the state-of-the-art Google DeepMind LipNet model to subtitle anything with over 99% similarity. We explore several methods of visual adversarial attacks, including the vanilla fast gradient sign method (FGSM), the L∞ iterative fast gradient sign method, and the L2 modified Carlini-Wagner attacks. The feasibility of these attacks raise privacy and false information threats, as video transcriptions are used to recommend and inform people worldwide and on social media.
A Benchmark Study on Machine Learning Methods using Several Feature Extraction Techniques for News Genre Detection from Bangla News Articles &amp; Titles

Genre detection from news articles or news titles is one kind of text classification procedures where news articles or titles are categorized among different families. Nowadays, text classification has become a key research field in text mining and natural language understanding because of it's several applications, such as search engine, document filtering, keywords extraction, text summarizing, etc. Several studies have been conducted for detecting news genres in different languages, but little research has been done in Bangla language due to the lack of Bangla resources. Moreover, in the few works of Bangla language, a little number of news genres have been used for classifications and dataset has been created from a small number of news sources. In this study, we have shown comparative analysis of different traditional machine learning, advanced neural networks, attention-based supervised learning models by extracting several informative features including TF-IDF, category-based word frequency, word embedding, etc. We have implemented these categorization methods for both news article and news title classifications against ten mutually exclusive news genres. From our analysis, We have found that 'Bidirectional LSTM' with 'Word2vec' feature extraction technique using 'Skip-gram' method is the most robust method for both classifications, compared to other models.
GoThrough: a Tool for Creating and Visualizing Impossible 3D Worlds Using Portals

Portals are commonly used in video games (e.g., games like Portal, Antichamber, and the classic Asteroids). In this work we introduce GoThrough, a tool that enables users with little to no previous knowledge to add transformative portals to 3D scenes in the Unity game engine. We map the existing literature in portals, both in terms of academic works and web resources, as well as entertainment usages. Then, we introduce an approach for portals to work robustly both in terms of geometry and rendering, and explore common pitfalls (as well as how to handle them). The tool is shown to work in a variety of example scenarios, and has been evaluated quantitatively for performance, providing real-time performance in a variety of scenarios. User tests have also been conducted in order to analyse GoThrough qualitatively. With a SUS score of 87.5, we concluded that GoThrough is intuitive enough to be used by non-experts, making the process of creating impossible 3D worlds much less cumbersome.
Retrieve Content Images using Color Histogram, LBP and HOG

In this research work, Content-Based Image Retrieval is implemented using color histogram, Localized Binary Pattern, and Histogram calculated from oriented Gradients. The implementation consists of three steps preprocessing, feature extraction, classification. When recovering images based on content, extraction highlighting is an amazing test task. In this article, the highlights of a histogram, a local binary pattern to highlight surface components, and an ordered slope histogram to highlight inclusions in a shape. The machine classifier of the reinforcement vector is used for grouping. Research results show that a combination of each of the three key points is superior to a single element or a combination of two-component recovery methods.
Mono is Enough: Instance Segmentation from Single Annotated Sample

With the help of various Deep Neural Networks, instance segmentation has achieved significant progress. How-ever, these successes are heavily reliant on large-scale manually annotated samples, which are extremely time-consuming and expensive. To address this issue, we propose a highly efficient anisotropic data augmentation method, which generates high quality training data from a single manually annotated sample. Instead of equivalently modifying foreground and background like traditional data augmentation methods, we focus on enriching the diversities of foreground appearance and positional relation between foreground and background, which are beneficial for the classification and localization sub-tasks respectively. All foreground instances of the source annotated sample undergo various rotation, brightness change, rescale, distortion and frequency-component mixup (FCM). Then, these modified instances are randomly embedded into background, which serve as new training samples. Experiments on Cityscapes dataset show that our method significantly outperforms traditional data augmentation methods.
Traffic Congestion Detection Using Fixed-Wing Unmanned Aerial Vehicle (UAV) Video Streaming Based on Deep Learning

Population growth in the region has led to increased use of roads that causes traffic congestion. Traffic congestion also occurs during long weekend in outsides city. The roads in the area are usually smooth traffic flow, it becomes very congested. One method of the smart road monitoring system is a fixed camera sensor as input and artificial intelligent as the analysis. However, these methods require a great infrastructure on highways such as: power supply, protective CPU, good power supply and stable computer network connections. This will be difficult to fulfill if applied on roads outside the city. To overcome the problem, we propose a system of vehicles detection and road density classification using Fixed-Wing Unmanned Aerial Vehicle (UAV) video streaming. We chose Fixed-Wing UAV for its advantages: wide range and fast flight speed. The proposed system detects and classifies vehicles. Vehicles are detected and calcified using CNN's Deep Learning which uses the YOLO architecture. The level of traffic density is determined by the area of the road covered by vehicles to road area. We tested the proposed system using YouTube video and UAV video streaming. Both experimental scenarios have almost the same results. Precisions of recording video UAV and streaming video are: 90.75% and 90%, respectively.
Paralleled attention modules and adaptive focal loss for Siamese visual tracking

Recently, Siamese-based trackers have drawn amounts of attention in visual tracking field because of their excellent performance on different tracking benchmarks. However, most Siamese-based trackers encounter difficulties under circumstances such as similar objects interference and background clutters. Besides, there exists an extreme foreground–background data imbalance that weakens the performance during training but few loss functions pay attention to it. The authors intend to address the issues mentioned above by introducing a module named paralleled spatial and channel attention (PSCA) and adaptive focal loss (AFL). Firstly, paralleled spatial and channel attention is proposed to enhance the extracted features and eliminate the noise information from both spatial and channel aspects. Secondly, adaptive focal loss is proposed as the loss function to make the model focus on hard samples that contribute more to training process. Finally, paralleled spatial and channel attention and modified ResNet are combined for extracting more powerful features. Experimental results show that the authors' method achieves outstanding performance in multiple benchmarks while keeping a beyond-real-time frame rate.
Kernel gradient descent algorithm for information theoretic learning

Information theoretic learning is a learning paradigm that uses concepts of entropies and divergences from information theory. A variety of signal processing and machine learning methods fall into this framework. Minimum error entropy principle is a typical one amongst them. In this paper, we study a kernel version of minimum error entropy methods that can be used to find nonlinear structures in the data. We show that the kernel minimum error entropy can be implemented by kernel based gradient descent algorithms with or without regularization. Convergence rates for both algorithms are deduced.
Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising

X-ray computed tomography (CT) imaging, which uses X-ray to acquire image data, is widely used in medicine. High X-ray doses may be harmful to the patient's health. Therefore, X-ray doses are often reduced at the expense of reduced quality of CT images. This paper presents a convolutional neural network model for low-dose CT image denoising, inspired by a recently introduced dialated residual network for despeckling of synthetic aparture radar images (SAR-DRN). In particular, batch normalization is added to some layers of SAR-DRN in order to adapt SAR-DRN for low-dose CT denoising. In addition, a preprocessing layer and a post-processing one are added in order to improve the receptive field and to reduce computational time. Moreover, the perceptual loss combined with MSE one are used in the training phase so that the proposed denoising model can preserve more subtle details of denoised images. Experimental results show that the proposed model can denoise low-dose CT images efficiently as compared to some state-of-the-art methods.
Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points

We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.
A Gap in Brain Gain for Emerging Countries: Evidence of International Immigration on Non-Resident Patents

Immigration is a controversial topic that draws much debate. From a human sustainability perspective, immigration is disadvantageous for home countries causing brain drains. Ample evidence suggests the developed host countries benefit from immigration in terms of diversification, culture, learning, and brain gains, yet less is understood for emerging countries. The purpose of this paper is to examine the presence of brain gains due to immigration for emerging countries, and explore any gaps as compared to developed countries. Using global data from 88 host and 109 home countries over the period from 1995 to 2015, we find significant brain gains due to immigration for emerging countries. However, our results show that there is still a significant brain gain gap between emerging and developed countries. A brain gain to the developed host countries is about 5.5 times greater than that of the emerging countries. The results hold after addressing endogeneity, self-selection, and large sample biases. Furthermore, brain gain is heterogenous by immigrant types. Skilled or creative immigrants tend to benefit the host countries about three times greater than the other immigrants. In addition, the Top 10 destination countries seem to attract the most creative people, thus harvest the most out of the talented immigrants. In contrast, we find countries of origin other than the Top 10 seem to send these creative people to the rest of the world.