Reviewerly/exHarmony huggingface dataset:
QUOTIENT

Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also trained securely. The few prior works on secure DNN training have focused either on designing custom protocols for existing training algorithms, or on developing tailored training algorithms and then applying generic secure protocols. In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts. We present QUOTIENT, a new method for discretized training of DNNs, along with a customized secure two-party protocol for it. QUOTIENT incorporates key components of state-of-the-art DNN training such as layer normalization and adaptive gradient methods, and improves upon the state-of-the-art in DNN training in two-party computation. Compared to prior work, we obtain an improvement of 50X in WAN time and 6% in absolute accuracy.
Virtual Reality Experience: Immersion, Sense of Presence, and Cybersickness

Introduction Virtual reality (VR) is a recent topic in healthcare education. Little is known about the factors affecting the immersion and the sense of presence in VR. This study aimed to understand the elements that influence the sense of presence among undergraduate healthcare students and postgraduate (PG). Methods Undergraduate healthcare students (n = 42) and PG (n = 19) were immersed in a mass casualty incident–immersive simulation. Participants completed questionnaires before (immersion propensity, stress) and after immersion (sense of presence, stress, cybersickness, and satisfaction). The qualitative component of the study provided the context for the quantitative results. Results The sense of presence was high among both groups but higher among PG (p = .02) and was positively correlated to immersion propensity (r = 0.36; p < .001). The level of cybersickness was low despite a discomfort induced by the VR material. A significant group-by-time effect was found for stress (p < .001). Stress levels increased more in the PG through the immersion without effect on the sense of presence. Conclusion In accordance with the results, the mass casualty incident–immersive simulation induced a high level of sense of presence and a low level of cybersickness. The sense of presence was correlated with the individual immersion propensity.
Structured Prediction Helps 3D Human Motion Modelling

Human motion prediction is a challenging and important task in many computer vision application domains. Existing work only implicitly models the spatial structure of the human skeleton. In this paper, we propose a novel approach that decomposes the prediction into individual joints by means of a structured prediction layer that explicitly models the joint dependencies. This is implemented via a hierarchy of small-sized neural networks connected analogously to the kinematic chains in the human body as well as a joint-wise decomposition in the loss function. The proposed layer is agnostic to the underlying network and can be used with existing architectures for motion modelling. Prior work typically leverages the H3.6M dataset. We show that some state-of-the-art techniques do not perform well when trained and tested on AMASS, a recently released dataset 14 times the size of H3.6M. Our experiments indicate that the proposed layer increases the performance of motion forecasting irrespective of the base network, joint-angle representation, and prediction horizon. We furthermore show that the layer also improves motion predictions qualitatively. We make code and models publicly available at https://ait.ethz.ch/projects/2019/spl.
How does the pedagogical design of a technology-enhanced collaborative academic course promote digital literacies, self-regulation, and perceived learning of students?

The wide expansion of digital technologies in higher education has introduced the need for an examination of the added value of various technological tools for quality teaching and active individual and collaborative learning. The current study explored whether and how the pedagogical design of an academic course, which developed a variety of digital literacy competencies, supported students in regulating collaborative technology-enhanced learning and helped them cope with the sense of psychological ownership over collaborative learning outcomes. In addition, we examined how these issues were expressed in cognitive, emotional and social aspects of students' perceived learning (Caspi & Blau, 2011). During four semesters, we conducted a qualitative analysis on reflective learning diaries, written by 78 graduate students studying education (N = 1870 codes). The bottom-up analysis focused on learning processes that enabled the development of various digital literacies conceptualized by the Digital Literacy Framework (DLF; Eshet-Alkalai, 2012): photo-visual, information, reproduction, branching, social-emotional, and real-time thinking skills. Furthermore, findings highlighted the importance of self-regulation and learning new technologies as an integral part of digital literacies. In addition, social-emotional statements expressed the development of effective communication and collaboration that enable students to cope with a sense of ownership over learning outcomes, and present different levels of teamwork: sharing, cooperation, and collaboration. Qualitative coding provided a more granulated perspective on perceived learning by differentiating between positive and negative aspects of emotional and social retrospection during the learning process. The findings contribute to educational theory by extending DLF and by providing new insights to the literature on students' perceived learning. We discuss the implications for instructional design and adoption of innovative pedagogy in higher education.
BLiMP: The Benchmark of Linguistic Minimal Pairs for English

We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP), 1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands.
Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks

With wide deployment of machine learning (ML)-based systems for a variety of applications including medical, military, automotive, genomic, multimedia, and social networking, there is great potential for damage from adversarial learning (AL) attacks. In this article, we provide a contemporary survey of AL, focused particularly on defenses against attacks on deep neural network classifiers. After introducing relevant terminology and the goals and range of possible knowledge of both attackers and defenders, we survey recent work on test-time evasion (TTE), data poisoning (DP), backdoor DP, and reverse engineering (RE) attacks and particularly defenses against the same. In so doing, we distinguish robust classification from anomaly detection (AD), unsupervised from supervised, and statistical hypothesis-based defenses from ones that do not have an explicit null (no attack) hypothesis. We also consider several scenarios for detecting backdoors. We provide a technical assessment for reviewed works, including identifying any issues/limitations, required hyperparameters, needed computational complexity, as well as the performance measures evaluated and the obtained quality. We then delve deeper, providing novel insights that challenge conventional AL wisdom and that target unresolved issues, including: robust classification versus AD as a defense strategy; the belief that attack success increases with attack strength, which ignores susceptibility to AD; small perturbations for TTE attacks: a fallacy or a requirement; validity of the universal assumption that a TTE attacker knows the ground-truth class for the example to be attacked; black, gray, or white-box attacks as the standard for defense evaluation; and susceptibility of query-based RE to an AD defense. We also discuss attacks on the privacy of training data. We then present benchmark comparisons of several defenses against TTE, RE, and backdoor DP attacks on images. The article concludes with a discussion of continuing research directions, including the supreme challenge of detecting attacks whose goal is not to alter classification decisions, but rather simply to embed, without detection, “fake news” or other false content.
A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.
Contextual encoder–decoder network for visual saliency prediction

Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. Our model achieves competitive and consistent results across multiple evaluation metrics on two public saliency benchmarks and we demonstrate the effectiveness of the suggested approach on five datasets and selected examples. Compared to state of the art approaches, the network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources, such as (virtual) robotic systems, to estimate human fixations across complex natural scenes.
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective

Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes. We analyze this mismatch from a domain adaptation point of view. First of all, we connect existing class-balanced methods for long-tailed classification to target shift, a well-studied scenario in domain adaptation. The connection reveals that these methods implicitly assume that the training data and test data share the same class-conditioned distribution, which does not hold in general and especially for the tail classes. While a head class could contain abundant and diverse training examples that well represent the expected data at inference time, the tail classes are often short of representative training data. To this end, we propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach. We validate our approach with six benchmark datasets and three loss functions.
Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation

Referring expression comprehension (REC) and segmentation (RES) are two highly-related tasks, which both aim at identifying the referent according to a natural language expression. In this paper, we propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning of REC and RES for the first time. In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks. ASNLS supresses the response of unrelated regions in RES based on the prediction of REC. To validate our model, we conduct extensive experiments on three benchmark datasets of REC and RES, i.e., RefCOCO, RefCOCO+ and RefCOCOg. The experimental results report the significant performance gains of MCN over all existing methods, i.e., up to +7.13% for REC and +11.50% for RES over SOTA, which well confirm the validity of our model for joint REC and RES learning.
Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art

Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.
Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine

As 5G mobile networks are bringing about global societal benefits, the design phase for 6G has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery simultaneously for mass autonomy, human machine interfacing, and targeted healthcare. Trust will become increasingly critical for 6G as it manages a wide range of mission-critical services. As we migrate from traditional mathematical model-dependent optimization to data-dependent deep learning, the insight and trust we have in our optimization modules decrease. This loss of model explainability means we are vulnerable to malicious data, poor neural network design, and the loss of trust from stakeholders and the general public -- all with a range of legal implications. In this review, we outline the core methods of explainable artificial intelligence (XAI) in a wireless network setting, including public and legal motivations, definitions of explainability, performance vs. explainability trade-offs, and XAI algorithms. Our review is grounded in case studies for both wireless PHY and MAC layer optimization and provide the community with an important research area to embark upon.
Surprise: A Python library for recommender systems

Recommender systems aim at providing users with a list of recommendations of items that a service offers.For example, a video streaming service will typically rely on a recommender system to propose a personalized list of movies or series to each of its users.A typical problem in recommendation is that of rating prediction: given an incomplete dataset of useritem interations which take the form of numerical ratings (e.g. on a scale from 1 to 5), the goal is to predict the missing ratings for all remaining user-item pairs.
<scp>COVID</scp>‐19 pandemic cybersecurity issues

This paper studies the cybersecurity issues that have occurred during the coronavirus (COVID‐19) pandemic. During the pandemic, cyber criminals and Advanced Persistent Threat (APT) groups have taken advantage of targeting vulnerable people and systems. This paper emphasizes that there is a correlation between the pandemic and the increase in cyber‐attacks targeting sectors that are vulnerable. In addition, the growth in anxiety and fear due to the pandemic is increasing the success rate of cyber‐attacks. We also highlight that healthcare organizations are one of the main victims of cyber‐attacks during the pandemic. The pandemic has also raised the issue of cybersecurity in relation to the new normal of expecting staff to work from home (WFH), the possibility of state‐sponsored attacks, and increases in phishing and ransomware. We have also provided various practical approaches to reduce the risks of cyber‐attacks while WFH including mitigation of security risks related to healthcare. It is crucial that healthcare organizations improve protecting their important data and assets by implementing a comprehensive approach to cybersecurity.
A survey of visual analytics techniques for machine learning

Abstract Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.
Neural Point-Based Graphics

We present a new point-based approach for modeling the appearance of real scenes. The approach uses a raw point cloud as the geometric representation of a scene, and augments each point with a learnable neural descriptor that encodes local geometry and appearance. A deep rendering network is learned in parallel with the descriptors, so that new views of the scene can be obtained by passing the rasterizations of a point cloud from new viewpoints through this network. The input rasterizations use the learned descriptors as point pseudo-colors. We show that the proposed approach can be used for modeling complex scenes and obtaining their photorealistic views, while avoiding explicit surface estimation and meshing. In particular, compelling results are obtained for scenes scanned using hand-held commodity RGB-D sensors as well as standard RGB cameras even in the presence of objects that are challenging for standard mesh-based modeling.
Non-local Spatial Propagation Network for Depth Completion

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixed-local neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page ( https://github.com/zzangjinsun/NLSPN_ECCV20 ).