Reviewerly/exHarmony huggingface dataset:
How Developers Optimize Virtual Reality Applications: A Study of Optimization Commits in Open Source Unity Projects

Virtual Reality (VR) is an emerging technique that provides immersive experience for users. Due to the high computation cost of rendering real-time animation twice (for both eyes) and the resource limitation of wearable devices, VR applications often face performance bottlenecks and performanceoptimization plays an important role in VR software develop-ment. Performance optimizations of VR applications can be very different from those in traditional software as VR involves more elements such as graphics rendering and real-time animation. In this paper, we present the first empirical study on 183 real-world performance optimizations from 45 VR software projects. In particular, we manually categorized the optimizations in to 11 categories, and applied static analysis to identify how they affect different life-cycle phases of VR applications. Furthermore, we studied the complexity and design / behavior effects of performance optimizations, and how optimizations are different between large organizational software projects and smaller personal software projects. Our major findings include: (1) graphics simplification (24.0%), rendering optimization (16.9%), language / API optimization (15.3%), heap avoidance (14.8%), and valuecaching (12.0%) are the most common categories of performance optimization in VR applications; (2) game logic updates (30.4%) and before-scene initialization (20.0%) are the most common life-cycle phases affected by performance issues; (3) 45.9% of the optimizations have behavior and design effects and 39.3% of the optimizations are systematic changes; (4) the distributionsof optimization classes are very different between organizational VR projects and personal VR projects.
Techniques of Use: Confronting Value Systems of Productivity, Progress, and Usefulness in Computing and Design

This paper turns to one of HCI's central value systems, i.e. its commitments to usefulness and the ideal that technology enables social progress, productivity, and excellence. Specifically, we examine how the seemingly "positive" ideal to make technology "useful" – i.e. to build systems and devices that advance social and technological progress – masks various forms of violence and injustice such as colonial othering, racist exclusions, and exploitation. Drawing from ethnographic research, we show how design and computing methods from design thinking to agile theory and entrepreneurial approaches in tech production and higher education are the latest techniques in the cultivation of useful bodies on behalf of the state, the corporation, the university, and the economy. Aligning with feminist, critical race and critical computing commitments, this paper offers a genealogical approach to show how injustice and violence endure, despite and because of a narrative of progress and positive change.
Face Mask Recognition with Realistic Fabric Face Mask Data Set: A Combination Using Surface Curvature and GLCM

Wearing a mask is a requirement in the Covid-19 pandemic for the general public. While it is one of the several must-do actions to prevent forward spread in the Covid-19 infections, at the same time, the effect of wearing a mask in naïve face recognition systems have shown lower system performance in several cases and conditions. Simultaneously, only a handful of research studies have focused on a non-medical face mask with realistic images data set. This research proposed a new data set of realistic fabric face mask data set to be evaluated using surface curvature and gray level co-occurrence matrix (GLCM). The classification applied support vector machine (SVM). One hundred seventy-six images in the data set were analyzed with various properties, resulting in several experiments. The experiments' parameters were color properties, approaches in surface curvature, i.e., Gaussian, mean and principal curvature, angle and distance in GLCM, GLCM properties, i.e., contrast, homogeneity, correlation and energy, also kernel functions in SVM. The best accuracy result, 87.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , was derived from the combinations of these parameters. This research also improved the running time of the recognition process while maintaining the system's performance.
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experiment results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models.
Image Steganography Based on Iterative Adversarial Perturbations Onto a Synchronized-Directions Sub-Image

Nowadays a steganography has to face challenges to both feature-based staganalysis and convolutional neural network (CNN) based steganalysis. In this paper, we present a novel steganographic scheme to incorporate synchronizing modification directions and iterative adversarial perturbations to enhance steganographic performance. Firstly an existing steganographic function is employed to compute initial costs. Then the secret message bits are embedded following clustering modification directions profile. If the target CNN classifier discriminates the resulting stego image as the correct class, we change costs in adversarial manners, and then choose a sub-image to re-embed message with changed costs. Adversarial intensity will be iteratively increased until the adversarial stego image can deceive the target CNN classifier, which guarantees that applied adversarial perturbations are minimal and it is unnecessary to search the optimal adversarial intensity. Experiments demonstrate that the proposed method effectively enhances security to counter both feature-based classifiers and CNN classifiers, no matter they are targeted or non-targeted.
Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition

This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm applied on a search space defined by discrete values of hyperparameters. The best model resulted was trained and evaluated using FER2013 database, obtaining an accuracy of 72.16%.
Utilizing GCN and Meta-Learning Strategy in Unsupervised Domain Adaptation for Pancreatic Cancer Segmentation

Automated pancreatic cancer segmentation is highly crucial for computer-assisted diagnosis. The general practice is to label images from selected modalities since it is expensive to label all modalities. This practice brought about a significant interest in learning the knowledge transfer from the labeled modalities to unlabeled ones. However, the imaging parameter inconsistency between modalities leads to a domain shift, limiting the transfer learning performance. Therefore, we propose an unsupervised domain adaptation segmentation framework for pancreatic cancer based on GCN and meta-learning strategy. Our model first transforms the source image into a target-like visual appearance through the synergistic collaboration between image and feature adaptation. Specifically, we employ encoders incorporating adversarial learning to separate domain-invariant features from domain-specific ones to achieve visual appearance translation. Then, the meta-learning strategy with good generalization capabilities is exploited to strike a reasonable balance in the training of the source and transformed images. Thus, the model acquires more correlated features and improve the adaptability to the target images. Moreover, a GCN is introduced to supervise the high-dimensional abstract features directly related to the segmentation outcomes, and hence ensure the integrity of key structural features. Extensive experiments on four multi-parameter pancreatic-cancer magnetic resonance imaging datasets demonstrate improved performance in all adaptation directions, confirming our model's effectiveness for unlabeled pancreatic cancer images. The results are promising for reducing the burden of annotation and improving the performance of computer-aided diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/UDAseg, once this manuscript is accepted for publication.
Gaze During Locomotion in Virtual Reality and the Real World

How vision guides gaze in realistic settings has been researched for decades. Human gaze behavior is typically measured in laboratory settings that are well controlled but feature-reduced and movement-constrained, in sharp contrast to real-life gaze control that combines eye, head, and body movements. Previous real-world research has shown environmental factors such as terrain difficulty to affect gaze; however, real-world settings are difficult to control or replicate. Virtual reality (VR) offers the experimental control of a laboratory, yet approximates freedom and visual complexity of the real world (RW). We measured gaze data in 8 healthy young adults during walking in the RW and simulated locomotion in VR. Participants walked along a pre-defined path inside an office building, which included different terrains such as long corridors and flights of stairs. In VR, participants followed the same path in a detailed virtual reconstruction of the building. We devised a novel hybrid control strategy for movement in VR: participants did not actually translate: forward movements were controlled by a hand-held device, rotational movements were executed physically and transferred to the VR. We found significant effects of terrain type (flat corridor, staircase up, and staircase down) on gaze direction, on the spatial spread of gaze direction, and on the angular distribution of gaze-direction changes. The factor world (RW and VR) affected the angular distribution of gaze-direction changes, saccade frequency, and head-centered vertical gaze direction. The latter effect vanished when referencing gaze to a world-fixed coordinate system, and was likely due to specifics of headset placement, which cannot confound any other analyzed measure. Importantly, we did not observe a significant interaction between the factors world and terrain for any of the tested measures. This indicates that differences between terrain types are not modulated by the world. The overall dwell time on navigational markers did not differ between worlds. The similar dependence of gaze behavior on terrain in the RW and in VR indicates that our VR captures real-world constraints remarkably well. High-fidelity VR combined with naturalistic movement control therefore has the potential to narrow the gap between the experimental control of a lab and ecologically valid settings.
Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.
The application of machine learning to predictions of optical turbulence in the surface layer at Baikal Astrophysical Observatory

ABSTRACT In this study, we apply machine learning to predict optical turbulence in the surface layer at the Baikal Astrophysical Observatory. Advance knowledge of optical turbulence is important for maximizing the efficiency of adaptive optics systems, telescope operations, and the scheduling of the planned observations. Typically, optical turbulence is characterized by the structure constant of the refractive index of air $C_n^2$. The Monin–Obukhov similarity theory (MOST) provides a scientific basis for estimating the structure constant of the refractive index from meteorological variables in the surface layer. However, the MOST becomes unreliable for stable atmospheric conditions, which occurred for more periods regardless of the time of day at the Baikal Astrophysical Observatory. We propose the application of a neural network based on the group method of data handling (GMDH), one of the earliest deep-learning techniques, to predict the surface-layer refractive-index structure constant. The magnitudes of the predicted values of the structure constant of the refractive index and measurements are in agreement. Correlation coefficients ranging from 0.79–0.91 for a stably stratified atmosphere have been obtained. The explicit analytical expression is an advantage of the proposed approach, in contrast to many other machine-learning techniques that have a black-box model.
Application of compositional data analysis in geochemical exploration for concealed deposits: A case study of Ashele copper-zinc deposit, Xinjiang, China

One of the top priorities of ore body exploration geochemistry is to effectively identifying “anomalies” and separating them from “background” in geochemical data analyses, during which an accurate understanding of geochemical distribution patterns of elements and anomaly separation has been the focus of ore body hunting. However, geochemical data are compositional data with closure effect and require necessary logratio transformation to open closed data for the following statistical analysis. The invested dataset of 13 elements derived from 2264 surface rock samples from the northwestern Ashele mining area was processed and respectively analyzed as the raw data, logarithmically-transformed data, and isometric logratio (ilr) transformed data with exploratory data analysis (EDA) technology. Of which, the ilr transformed data yields an ideal data structure and scale distribution. The data then was analyzed with principal component analysis (PCA) and robust principal component analysis (RPCA), along with the compositional biplots and the first principal component score maps to identify the spatial distribution of element assemblages. After that, a comparative study of the geochemical distribution patterns based on the raw data of mineralized elements and the principal component scores based on ilr transformation data is conducted to explore the connection with mineralization. As a result, four prospecting prediction targets of two types are delineated in the study area.
Evolving convolutional neural networks by symbiotic organisms search algorithm for image classification

Convolutional neural networks (CNNs) are important neural networks in the deep learning field. In general, CNN design requires manual iterations and adjustments based on the characteristics of the data and a priori knowledge of the application area, and such processes are time-consuming, labor-intensive, and weaken the generalizability of the CNN architecture. Due to the complexity of the CNN architecture, its automatic construction is difficult. Automating the design of a CNN architecture using evolutionary algorithms is viable, but the field is still in its infancy. We propose an architectural search algorithm for CNNs that employs a symbiotic organisms search (SOS) algorithm called sosCNN. First, a SOS algorithm with strong global optimization abilities was introduced for CNN architecture search, and this algorithm facilitates the automatic building of good CNN architectures. Then, a novel integrated coding update method is proposed that reduces the loss of convolutional layers, which results in a searched architecture with stronger feature extraction capability. Finally, three new non-numeric computational strategies, namely, binary segmentation, slack gain, and dissimilar mutation, were combined with the SOS algorithm. We tested the sosCNN algorithm against 24 algorithms, including state-of-the-art algorithms, on nine widely used image classification datasets. The experimental results show that the average classification error of sosCNN is reduced by 0.04% to 7.37% on the nine benchmark test sets compared to the state of art algorithms, which is a very promising result.
Private Set Operations from Oblivious Switching

Private set intersection reveals the intersection of two private sets, but many real-world applications require the parties to learn only partial information about the intersection. In this paper we introduce a new approach for computing arbitrary functions of the intersection, provided that it is safe to also reveal the cardinality of the intersection. In the most general case, our new protocol provides the participants with secret shares of the intersection, which can be fed into any generic 2PC protocol. Certain computations on the intersection can also be done even more directly and efficiently, avoiding this secret-sharing step. These cases include computing only the cardinality of intersection, or the "cardinality-sum" application proposed in Ion et al. (ePrint 2017). Compared to the state-of-the-art protocol for computing on intersection (Pinkas et al., Eurocrypt 2019), our protocol has about \(2.5-3\times \) less communication, and has faster running time on slower (50 Mbps) networks.
Cascaded panoptic segmentation method for high resolution remote sensing image

Great progress has been made for remote sensing image segmentation with the development of Deep Convolutional Neural Networks. However, Multiple convolutions significantly reduce the resolution and lead to the loss of many key information, the prediction accuracy of pixel categories is reduced. And DCNN accumulate context information on a large receptive field, which leads to blurred boundary segmentation of objects. This paper proposes a cascaded panoptic segmentation network to target the aforementioned problems. Firstly, a shared feature pyramid network backbone and a new hybrid task cascade framework are designed, which share the features and integrate the complementary features of different tasks in different stages, which can extract rich context information. Then, a functional module is designed to learn the mask quality of predicted instances in Mask R-CNN to calibrate the inconsistency between mask quality and mask score, thus to deal with the scale change of the object. Finally, a new Visual-saliency ranking module is designed to overcome the mutual occlusion problem between the prediction results, and strengthen robustness to illumination. The experimental results prove that our method still has significant advantages even compared with the most advanced methods, and ablation experiments also verify the effectiveness of our designed strategies.
A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.
Scaling Neuroscience Research Using Federated Learning

The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning is a promising approach to learn a joint model over data silos. This architecture does not share any subject data across sites, only aggregated parameters, often in encrypted environments, thus satisfying privacy and regulatory requirements. Here, we describe our Federated Learning architecture and training policies. We demonstrate our approach on a brain age prediction model on structural MRI scans distributed across multiple sites with diverse amounts of data and subject (age) distributions. In these heterogeneous environments, our Semi-Synchronous protocol provides faster convergence.