Reviewerly/exHarmony huggingface dataset:
VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People

We introduce the first visual privacy dataset originating from people who are blind in order to better understand their privacy disclosures and to encourage the development of algorithms that can assist in preventing their unintended disclosures. It includes 8,862 regions showing private content across 5,537 images taken by blind people. Of these, 1,403 are paired with questions and 62\% of those directly ask about the private content. Experiments demonstrate the utility of this data for predicting whether an image shows private information and whether a question asks about the private content in an image. The dataset is publicly-shared at http://vizwiz.org/data/.
The Necessary Nine: Design Principles for Embodied VR and Active Stem Education

This chapter explores the two profound affordances of VR for learning; namely (1) the sense of presence attendant with immersive VR, and (2) the active learning associated with movement/gestures in a three-dimensional virtual world. The chapter highlights several theories supporting embodied education and two examples of mediated STEM lessons which have been designed to maximize active learning. The first example explores the journey of redesign when a 2D tablet game is transformed into a 3D immersive VR lesson. The second example optimizes the new generation of hand controllers in VR and uses constructivism as the primary design foundation. The chapter ends with a set of optimal design principles for immersive VR in STEM education. The most important are called the Necessary Nine.
Building an Expert Recommender Chatbot

This paper presents our experience in implementing a chatbot for expert recommendation tasks. The chatbot was developed for the Pharo software ecosystem, and is integrated with the Discord chat service, which is used by the Pharo Community. We also report on a preliminary evaluation for which; the recommendation system was welcomed, though the conversational behavior was not; users expected a fully conversational chatbot, capable of following the conversation flow that the user handles. We discuss that such expectations might be hard to meet because of the uncanny valley effect.
Attitudes Toward Open Science and Public Data Sharing

Abstract. Central values of science are, among others, transparency, verifiability, replicability, and openness. The currently very prominent Open Science (OS) movement supports these values. Among its most important principles are open methodology (comprehensive and useful documentation of methods and materials used), open access to published research output, and open data (making collected data available for re-analyses). We here present a survey conducted among members of the German Psychological Society ( N = 337), in which we applied a mixed-methods approach (quantitative and qualitative data) to assess attitudes toward OS in general and toward data sharing more specifically. Attitudes toward OS were distinguished into positive expectations (“hopes”) and negative expectations (“fears”). These were un-correlated. There were generally more hopes associated with OS and data sharing than fears. Both hopes and fears were highest among early career researchers and lowest among professors. The analysis of the open answers revealed that generally positive attitudes toward data sharing (especially sharing of data related to a published article) are somewhat diminished by cost/benefit considerations. The results are discussed with respect to individual researchers’ behavior and with respect to structural changes in the research system.
Hybrid Key Encapsulation Mechanisms and Authenticated Key Exchange

Concerns about the impact of quantum computers on currently deployed public key cryptography have instigated research into not only quantum-resistant cryptographic primitives but also how to transition applications from classical to quantum-resistant solutions. One approach to mitigate the risk of quantum attacks and to preserve common security guarantees are hybrid schemes, which combine classically secure and quantum-resistant schemes. Various academic and industry experiments and draft standards related to the Transport Layer Security (TLS) protocol already use some form of hybrid key exchange; however sound theoretical approaches to substantiate the design and security of such hybrid key exchange protocols are missing so far. We initiate the modeling of hybrid authenticated key exchange protocols, considering security against adversaries with varying levels of quantum power over time, such as adversaries who may become quantum in the future or are quantum in the present. We reach our goal using a three-step approach: First, we introduce security notions for key encapsulation mechanisms (KEMs) that enable a fine-grained distinction between different quantum scenarios. Second, we propose several combiners for constructing hybrid KEMs that correspond closely to recently proposed Internet-Drafts for hybrid key exchange in TLS 1.3. Finally, we present a provably sound design for hybrid key exchange using KEMs as building blocks.
LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation

We present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the tri-modal confidence features. The attention inference networks encode the importance of tri-modal confidence features and then concatenate them using the attention maps in an adaptive and dynamic fashion. This enables us to make an optimal fusion of the heterogeneous features, compared to a simple concatenation technique that is commonly used in conventional approaches. In addition, to encode the confidence features with locally-varying receptive fields, the scale inference networks learn the scale map and warp the fused confidence features through convolutional spatial transformer networks. Finally, the confidence map is progressively estimated in the recursive refinement networks to enforce a spatial context and local consistency. Experimental results show that this model outperforms the state-of-the-art methods on various benchmarks.
Text Entry in Virtual Reality: A Comprehensive Review of the Literature

The availability of consumer-ready Virtual Reality (VR) Head-Mounted Displays (HMDs) has resulted in a surge in VR applications. It has also prompted the design and development of numerous text entry techniques for the paradigm. However, it is difficult to understand the mechanism of these techniques and extract meaningful average performance data from this body of work since they were evaluated in diverse experiment conditions and report different performance metrics. To remedy this, this paper classifies the existing text entry techniques for VR based on their input mechanism and discusses their strengths, limitations, and performance.
Turning It Off: Emotions in Digital-Free Travel

This article aims to theorize digitally disconnected travel experiences by investigating various emotional responses during the process of withdrawal and regain of technological affordances. The theoretical concepts of affordance and emotional episodes were adopted in this study to create a conceptual framework. Fifteen diaries and 18 interviews were collected from 24 participants’ reflections of their disconnected experiences. This study thus contributes a contextual update of emotional episodes by providing a detailed account of various emotions in the entire disconnecting/reconnecting travel experience. Also, this study contributes to the affordance literature by exploring the fluidity of technology affordances and environmental affordances. This article develops the Disconnected Emotions Model (DEM), a theoretical framework to provide an understanding of the changing relationship between human emotions and material affordances.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce.
Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks

Accurate cell segmentation is essential for computer-aided diagnosis of cervical precancerous lesions in cytology images. Automated segmentation poses a great challenge due to the presence of fuzzy and overlapping cells, noisy background, and poor cytoplasmic contrast. Deep learning diagnosis technology has showed its advantages in segmenting complex medical images. We present a new framework based on deep convolutional neural networks (DCNNs) to automatically segment overlapping cells in digital cytology. A double-window based cellular detection method is derived to correctly localize individual cells, in which TernausNet is adopted to classify the image pixels into nucleus, cytoplasm, or background. A modified DeepLab V2 model is applied to perform cytoplasm segmentation. To provide more training samples, a synthesis method is utilized to generate cell masses containing touching or overlapping cells. The presented method was tested on three independent data cohorts, including two public datasets. We achieved improved performance in terms of dice coefficient (DSC), false negative and false positive rates, with up to 15% improvement in DSC, compared with the state-of-the-art approaches. The results indicated that the DCNN based segmentation method could be useful in an image-based computerized analysis system for early detection of cervical cancer.
Dual Supervised Learning

In this chapter, we introduce dual supervised learning, which leverages structure duality in the supervised setting to enhance the learning from labeled data. We first describe the joint-probability principle, then introduce the dual supervised learning algorithm, and finally cover several applications.
Filter Design for Autoregressive Moving Average Graph Filters

In the field of signal processing on graphs, graph filters play a crucial role in processing the spectrum of graph signals. This paper proposes two different strategies for designing autoregressive moving average (ARMA) graph filters on both directed and undirected graphs. The first approach is inspired by Prony's method, which considers a modified error between the modeled and the desired frequency response. The second technique is based on an iterative approach, which finds the filter coefficients by iteratively minimizing the true error (instead of the modified error) between the modeled and the desired frequency response. The performance of the proposed algorithms is evaluated and compared with finite impulse response (FIR) graph filters, on both synthetic and real data. The obtained results show that ARMA filters outperform FIR filters in terms of approximation accuracy and they are suitable for graph signal interpolation, compression, and prediction.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and nonsmooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from most algorithms that have no convergence guarantees for nonstrongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate O(1=T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.
Comprehensive evaluation of twin SVM based classifiers on UCI datasets

In the past decade, twin support vector machine (TWSVM) based classifiers have received considerable attention from the research community. In this paper, we analyze the performance of 8 variants of TWSVM based classifiers along with 179 classifiers evaluated in Fernandez-Delgado et al. (2014) from 17 different families on 90 University of California Irvine (UCI) benchmark datasets from various domains. Results of these classifiers are exhaustively analyzed using various performance criteria. Statistical testing is performed using Friedman Rank (FRank). Our experiments show that two least square TWSVM based classifiers (ILSTSVM_m, and RELS-TSVM_m) are the top two ranked methods among 187 classifiers and they significantly outperform all other classifiers according to Friedman Rank. Overall, this paper bridges the evaluational benchmarking gap between various TWSVM variants and the classifiers from other families. Codes of this paper are provided on authors' homepages to reproduce the presented results and figures in this paper.
Insulator visual non-conformity detection in overhead power distribution lines using deep learning

Overhead Power Distribution Lines (OPDLs) correspond to a large percentage of the medium-voltage electrical systems. In these networks, visual inspection activities are usually performed without resorting to automated systems, requiring a significant investment of time and human resources. We present a methodology to identify the defect and type of insulators using Convolutional Neural Networks (CNNs). More than 2500 photographs were collected both from inside a studio and from a realistic OPDL. A classification model is proposed to automatically recognize the insulators conformity. This model is able to learn from indoors photographs by augmenting these images with realistic details such as top ties and real-world backgrounds. Furthermore, Multi-Task Learning (MTL) was used to improve performance of defect detection by also predicting the insulator class. The proposed methodology is able to achieve an accuracy of 92% for material classification and 85% for defect detection, with F1-score of 0.75, surpassing available solutions.
Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models

Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images, end-to-end demonstrations of successful attacks, which manipulate the physical environment and result in physical consequences, are scarce. Moreover, attacks typically involve carefully constructed adversarial examples at the level of pixels. We demonstrate the first end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are surprisingly easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective, and others that are less vulnerable (e.g., driving straight). Further, we use network deconvolution to demonstrate that the attacks succeed by inducing activation patterns similar to entirely different scenarios used in training.
GVFOM: a novel external force for active contour based image segmentation

The GVF snake is effective on converging into concave areas and being insensitive to initialization. However, it fails to converge to areas with deep and narrow concavities or preserve weak edges. In this paper, a novel external force called gradient vector flow over manifold (GVFOM) is proposed for active contours. The proposed model takes the GVF vector as a 2D manifold embedded into a 4D Euclidean space, leading to the generalization of the Laplacian operator in Euclidean Space to that over manifold, and the two components of the external force are couples with each other to improve the properties of the GVF snake. The GVFOM snake has been tested with different kinds of images, and experimental results and comparison manifest that the GVFOM snake has better performance than the GVF and other state-of-the-art snakes on object separation, deep and narrow concavity convergence, weak edge protection, noise robustness while keeping some desirable properties of the GVF model such as initialization insensitivity and large capture range.
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems

Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption.
Data inequalities and why they matter for development

The 'data revolution' marks a time of growing interest and investment in data – big, small, or otherwise. Critical attention to data is also proliferating, exposing the diverse ways that data produces inequality of opportunity and harm in society. This paper draws the nascent field of critical data studies into conversation with emerging narratives in data-for-development (D4D) to advance the conceptualization of data inequalities, explaining how they both align with and diverge from core tropes of digital inequalities research – and why this matters for development. The paper examines the causes, consequences, and potential solutions to three 'data divides' – access to data, representation of the world as data, and control over data flows – through examples of digital identity systems and national data infrastructures, user-generated data, and personal behavioral data produced through corporate platforms. This understanding provides a basis for future research, practice, and policymaking on data-related (in)equalities in development contexts and beyond.
Switchable Normalization for Learning-to-Normalize Deep Representation

We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fig. 1</xref> ). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g., 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, MegaFace and Kinetics. Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/switchablenorms</uri> .
Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses

Facial Recognition possess the importance to give biometric authentication that is used in different applications especially in security. A stored database of the subjects is manipulated using image processing techniques to accomplish this task. This paper proposes a frame work of smart glasses that can recognize the faces. Implementing facial recognition using portable smart glasses can aid law enforcement agencies to detect a suspect’s face. The advantage over security cameras is their portability and good frontal view capturing. The techniques used for the whole process of face recognition are machine learning based because of their high accuracy as compared with other techniques. Face detection is the pre-step for face recognition that is performed using Haar-like features. Detection rate of this method is 98% using 3099 features. Face recognition is achieved using Deep Learning’s sub-field that is Convolutional Neural Network (CNN). It is a multi-layer network trained to perform a specific task using classification. Transfer learning of a trained CNN model that is AlexNet is done for face recognition. It has an accuracy of 98.5% using 2500 variant images in a class. These smart glasses can serve in the security domain for the authentication process.