Reviewerly/exHarmony huggingface dataset:
Insulator defect detection algorithm based on improved YOLOv5s

As one of the extremely important components on the transmission tower, the insulator has two functions of electrical insulation and wire fixing, which directly affects the operation of the power system. Defects in insulators can impair the service life of transmission lines. UAV aerial photography of electric power towers has problems such as small number of defective insulator samples, small area, large aspect ratio of insulator strings, and variable inclination angle, coupled with the influence of environmental factors such as light, interference, distance, etc., which lead to low detection accuracy of insulator defects. Aiming at the above problems, an improved YOLOv5 insulator defect detection algorithm is proposed. First, screen the aerial images and use data augmentation to obtain a sufficient number of defective insulator images to enrich the dataset and avoid model overfitting. Secondly, the convolutional attention module CBAM is introduced to improve the expression ability of defect insulator features and strengthen the network's ability to identify targets. Finally, the Leaky ReLU activation function of the hidden layer of the original YOLOv5 algorithm is replaced by the Mish function to improve the generalization ability of the network. The experimental results show that compared with the original YOLOv5 algorithm, the average precision mAP (IOU=0.5) of the improved algorithm is increased by 7.8%, which effectively improves the problems of false detection and missed detection in the original algorithm. Compared with other mainstream object detection algorithms, the algorithm proposed in this paper has better detection effect on insulator defects.
Quantum-Assisted Greedy Algorithms

We show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use QAs that sample from the ground state of a problem-dependent Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin of the Ising model as a random variable and contract all problem variables whose corresponding uncertainties are negligible. Our empirical results on a D-Wave 2000Q quantum proces-sor demonstrate that the proposed quantum-assisted greedy algorithm (QAGA) scheme can find notably better solutions compared to the state-of-the-art techniques in the realm of quantum annealing.
What Is Common to Transportation and Health in Machine Learning Education? The Domain Neglect Bias

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: 1) the complexity of using these algorithms in the context of real-life situations correctly and 2) the comprehension of related social and ethical issues. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Question:</i> To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.
A Generalized Attack on the Multi-prime Power RSA

The Multi-Prime Power RSA is an efficient variant of the RSA cryptosystem with a modulus of the form $$N=p^rq^s$$ and $$r>s\ge 2$$ . It can be used with a public exponent e and a private exponent d satisfying $$e\equiv \frac{1}{d}\pmod {p^{r-1}q^{s-1}(p-1)(q-1)}$$ . In 2017, Lu, Peng and Sarkar showed that one can factor the modulus $$N=p^rq^s$$ if $$d<N^{1-\frac{3r+s}{(r+s)^2}}$$ . In this paper, we propose a generalization of this attack to the situation where the public exponent e is of the form $$e\equiv \frac{z_0}{x_0}\pmod {p^{r-1}q^{s-1}(p-1)(q-1)}$$ . We show that for $$x_0=N^\delta $$ and $$|z_0|=N^\gamma $$ , one can factor the modulus $$N=p^rq^s$$ if $$\delta +\gamma <1+\frac{2(r-s)}{r(r+s)^2}\sqrt{s(r+s)}-\frac{2(2r-s)}{r(r+s)}$$ . As a consequence, our method can break the Multi-Prime Power RSA variant even if the private exponent d is of arbitrarily large size.
Analyzing the Potential of Remote Collaboration in Industrial Mixed and Virtual Reality Environments

Remote assistance tools in customer support allow for fast response times and reduced service expenses. Video and screen-based applications are widely used in various areas but lack spatial interactivity due to the limitation to a two-dimensional screen. Mixed reality-supported remote collaboration is frequently addressed in research as a way to extend the interaction space. The presented systems often require complicated device setups, calibration, or precomputation for reconstruction of the physical environment. This work investigates a novel approach for location-independent remote assistance and collaboration utilizing state-of-the-art mixed reality devices without any additional hardware modifications. In our approach a local user scans the environment using a HoloLens' standard depth camera. The data is transferred to a remote user equipped with a Virtual Reality headset, where the environment is reconstructed in real-time. The users can interact in the same shared environment. In a user study, the perceived workload was measured for both sides to examine the practical applicability of our approach. The results show that for local users the tasks set resulted in a low mental load, but also indicate that the coarse representation of the environment was mentally demanding for the remote users to work with. With our work we want to make remote collaboration easier and viable for a broader range of applications and facilitate interactions between globally distributed users.
Video Extrapolation in Space and Time

Novel view synthesis (NVS) and video prediction (VP) are typically considered disjoint tasks in computer vision. However, they can both be seen as ways to observe the spatial-temporal world: NVS aims to synthesize a scene from a new point of view, while VP aims to see a scene from a new point of time. These two tasks provide complementary signals to obtain a scene representation, as viewpoint changes from spatial observations inform depth, and temporal observations inform the motion of cameras and individual objects. Inspired by these observations, we propose to study the problem of Video Extrapolation in Space and Time (VEST). We propose a model that leverages the self-supervision and the complementary cues from both tasks, while existing methods can only solve one of them. Experiments show that our method achieves performance better than or comparable to several state-of-the-art NVS and VP methods on indoor and outdoor real-world datasets. (Project page: https://cs.stanford.edu/~yzzhang/projects/vest/ .)
A Robust Blind Watermarking Scheme Based on Attention Mechanism and Neural Joint Source-Channel Coding

Due to the importance of protecting digital images in the modern world, digital image watermarking draws the attention of academia because of its applications in proving the authenticity and integrity of digital images. The performance of current watermarking algorithms needs to be improved to cope with the growing demand for better imperceptibility, greater robustness, and larger capacity. In response to the above requirements, a new robust blind watermarking algorithm is proposed in this paper, which mainly utilizes convolutional neural networks to embed watermarks in DCT domain. An attention module is introduced to calculate the confidence that each image pixel block contains watermark information, thus making our algorithm robust enough to deal with Crop attack. In addition, the introduction of a learning based solution for joint source and channel coding enables our algorithm to take grayscale images with simple backgrounds as input watermarks while maintaining great imperceptibility and robustness. Extensive experiments demonstrate the superiority of our algorithm.
Delta Distillation for Efficient Video Processing

This paper aims to accelerate video stream processing, such as object detection and semantic segmentation, by leveraging the temporal redundancies that exist between video frames. Instead of propagating and warping features using motion alignment, such as optical flow, we propose a novel knowledge distillation schema coined as Delta Distillation. In our proposal, the student learns the variations in the teacher's intermediate features over time. We demonstrate that these temporal variations can be effectively distilled due to the temporal redundancies within video frames. During inference, both teacher and student cooperate for providing predictions: the former by providing initial representations extracted only on the key-frame, and the latter by iteratively estimating and applying deltas for the successive frames. Moreover, we consider various design choices to learn optimal student architectures including an end-to-end learnable architecture search. By extensive experiments on a wide range of architectures, including the most efficient ones, we demonstrate that delta distillation sets a new state of the art in terms of accuracy vs. efficiency trade-off for semantic segmentation and object detection in videos. Finally, we show that, as a by-product, delta distillation improves the temporal consistency of the teacher model.
DAS: Densely-Anchored Sampling for Deep Metric Learning

Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such as image retrieval and face recognition. However, the performance of DML methods often highly depends on sampling methods to choose effective data from the embedding space in the training. In practice, the embeddings in the embedding space are obtained by some deep models, where the embedding space is often with barren area due to the absence of training points, resulting in so called “missing embedding” issue. This issue may impair the sample quality, which leads to degenerated DML performance. In this work, we investigate how to alleviate the “missing embedding” issue to improve the sampling quality and achieve effective DML. To this end, we propose a Densely-Anchored Sampling (DAS) scheme that considers the embedding with corresponding data point as “anchor” and exploits the anchor’s nearby embedding space to densely produce embeddings without data points. Specifically, we propose to exploit the embedding space around single anchor with Discriminative Feature Scaling (DFS) and multiple anchors with Memorized Transformation Shifting (MTS). In this way, by combing the embeddings with and without data points, we are able to provide more embeddings to facilitate the sampling process thus boosting the performance of DML. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles. Extensive experiments on three benchmark datasets demonstrate the superiority of our method.
A demonstration of AutoOD

Anomaly detection is a critical task in applications like preventing financial fraud, system malfunctions, and cybersecurity attacks. While previous research has offered a plethora of anomaly detection algorithms, effective anomaly detection remains challenging for users due to the tedious manual tuning process. Currently, model developers must determine which of these numerous algorithms is best suited for their particular domain and then must tune many parameters by hand to make the chosen algorithm perform well. This demonstration showcases AutoOD, the first unsupervised self-tuning anomaly detection system which frees users from this tedious manual tuning process. AutoOD outperforms the best un-supervised anomaly detection methods it deploys, with its performance similar to those of supervised anomaly classification models, yet without requiring ground truth labels. Our easy-to-use visual interface allows users to gain insights into AutoOD's self-tuning process and explore the underlying patterns within their datasets.
Layouts of 3D Data Visualizations Small Multiples around Users in Immersive Environments

Immersive 3D data visualizations have been used in many application areas recently. The importance of having multiple visualization in an immersive environment around the user, attract researchers attention to this question of how to layout different 3D data visualizations in Extended Reality (XR) environments as these visualizations may have natural connections and need to be organized in reasonable layouts around the user in a 3D space. In this paper, we describe a taxonomy for a design space of small multiples' layouts for immer-sive environments. We argue that these layouts are applicable across different interaction techniques, tasks, data visualization techniques. Our study demonstrates that there are many possible layouts that have not been used yet. These need to be investigated and evaluated.
FIDO Gets Verified: A Formal Analysis of the Universal Authentication Framework Protocol

The FIDO protocol suite aims at allowing users to log in to remote services with a local and trusted authenticator. With FIDO, relying services do not need to store user-chosen secrets or their hashes, which eliminates a major attack surface for e-business. Given its increasing popularity, it is imperative to formally analyze whether the security promises of FIDO hold. In this paper, we present a comprehensive and formal verification of the FIDO UAF protocol by formalizing its security assumptions and goals and modeling the protocol under different scenarios in ProVerif. Our analysis identifies the minimal security assumptions required for each of the security goals of FIDO UAF to hold. We confirm previously manually discovered vulnerabilities in an automated way and disclose several new attacks. Guided by the formal verification results, we also discovered two practical attacks on two popular Android FIDO apps, which we responsibly disclosed to the vendors. In addition, we offer several concrete recommendations to fix the identified problems and weaknesses in the protocol.
Use of a mobile learning application in the process of foreign vocabulary learning

Mobile devices and applications (apps), which have become an integral part of our lives, are gradually used for different purposes, including learning languages. Since vocabulary plays an important role in the process of foreign language learning, the aim of this study is to explore the use of the mobile application called Angličtina Today and its usefulness in learning English with special focus on developing vocabulary. The research was conducted at the Secondary vocational school of gastronomy and tourism, in Slovakia. Different quantitative and qualitative methods were used during the research period, and the results obtained showed that students using the mobile application achieved better results than students exposed to the traditional learning methods. At the same time, students' general satisfaction with using the mobile application was confirmed.
CFDMA: A Novel Click Fraud Detection Method in Mobile Advertising

While the Cost-Per-click mechanism ensures the prosperity of the mobile advertising ecosystem, click fraud becomes a significant threat that results in huge losses for advertisers. On the one hand, some existing studies identify click fraud in a predefined test environment on the client side. However, these works neglect the influence of user interaction on fraudulent behaviours. On the other hand, some studies employ binary classifiers to detect click fraud on the server side without considering the particularity of the click fraud scenario. This paper presents the design and implementation of CFDMA, a click fraud detection system that conducts both server side and client side detection. CFDMA makes connections between fraudulent behaviours and explicit user inputs through observing sensitive system classes on the client side. CFDMA constructs the publisher graph on the server side and uses its information to detect invalid traffic. We evaluate the performance of CFDMA on real click datasets. Our evaluation demonstrates that CFDMA is capable of identifying invalid click traffic and fraudulent behaviours.
Move to Design: Tactics and Challenges of Playful Movement-based Interaction Designers’ Experiences during the Covid-19 Pandemic

Design practices targeting playful movement-based interaction are changing rapidly with both technological and societal developments. In this paper, we provide a snapshot of movement-based interaction designers’ experiences during the Covid-19 pandemic, by interviewing designers working in diverse roles that integrate physical human body movement and digital technologies (i.e., exergame and wearable game designers, playground and landscape architects, sports and dance trainers, and Sports-HCI professionals). Using grounded theory, we have identified two tactics from the designers’ experiences: (i) the significance of face-to-face embodied interactions throughout the entire movement-based interaction design process, and (ii) the importance of positive, yet critical, attitudes to technology for designing bodily experiences, including mixed use of non-technical materials and tools for rapid prototyping and iteration. However, it was evident that such tactics are often not feasible without physical interaction between the designer and users. The restrictions imposed by Covid-19, therefore, further revealed the importance of body movement to designing a playful movement-based interaction — from ideation to execution and testing. This paper offers a worrying view of movement-based interaction design during the Covid-19 era, while calling for further investigation to enrich the discourse that could contribute to this field, possibly also beyond the circumstances of the Covid-19 pandemic.
VAFA: A Visually-Aware Food Analysis System for Socially-Engaged Diet Management

In this demo, we demonstrate a visually-aware food analysis (VAFA) system for socially-engaged diet management. VAFA is capable of receiving multimedia inputs, such as the images of food with/without a description to record a user’s daily diet. A set of AI algorithms for food classification, ingredient identification, and nutritional analysis are provided with this information to produce a nutrition report for the user. Furthermore, by profiling users’ eating habits, VAFA can recommend individualized recipes and detect social communities that share similar dietary appetites for them. With the support of state-of-the-art AI algorithms and a large-scale Chinese food dataset that includes 300K users, 400K recipes, and over 10M user-recipe interactions, VAFA has won several awards in China’s national artificial intelligence competitions.
Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation

3D hand pose estimation (HPE) is the process of locating the joints of the hand in 3D from any visual input. HPE has recently received an increased amount of attention due to its key role in a variety of human-computer interaction applications. Recent HPE methods have demonstrated the advantages of employing videos or multi-view images, allowing for more robust HPE systems. Accordingly, in this study, we propose a new method to perform Sequential learning with Transformer for Hand Pose (SeTHPose) estimation. Our SeTHPose pipeline begins by extracting visual embeddings from individual hand images. We then use a transformer encoder to learn the sequential context along time or viewing angles and generate accurate 2D hand joint locations. Then, a graph convolutional neural network with a U-Net configuration is used to convert the 2D hand joint locations to 3D poses. Our experiments show that SeTHPose performs well on both hand sequence varieties, temporal and angular. Also, SeTHPose outperforms other methods in the field to achieve new state-of-the-art results on two public available sequential datasets, STB and MuViHand.
Adversarial Attacks on Monocular Pose Estimation

Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adversarial attacks, thus presenting a challenge in reliable deployment. Two of the prominent tasks in 3D scene-understanding for robotics and advanced drive assistance systems are monocular depth and pose estimation, often learned together in an unsupervised manner. While studies evaluating the impact of adversarial attacks on monocular depth estimation exist, a systematic demonstration and analysis of adversarial perturbations against pose estimation are lacking. We show how additive imperceptible perturbations can not only change predictions to increase the trajectory drift but also catastrophically alter its geometry. We also study the relation between adversarial perturbations targeting monocular depth and pose estimation networks, as well as the transferability of perturbations to other networks with different architectures and losses. Our experiments show how the generated perturbations lead to notable errors in relative rotation and translation predictions and elucidate vulnerabilities of the networks.