Reviewerly/exHarmony huggingface dataset:
PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal

In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively. Existing methods have achieved much advancement in constrained scenarios, but it is still very challenging for them to transfer makeup between images with large pose and expression differences, or handle makeup details like blush on cheeks or highlight on the nose. In addition, they are hardly able to control the degree of makeup during transferring or to transfer a specified part in the input face. These defects limit the application of previous makeup transfer methods to real-world scenarios. In this work, we propose a Pose and expression robust Spatial-aware GAN (abbreviated as PSGAN++). PSGAN++ is capable of performing both detail-preserving makeup transfer and effective makeup removal. For makeup transfer, PSGAN++ uses a Makeup Distill Network (MDNet) to extract makeup information, which is embedded into spatial-aware makeup matrices. We also devise an Attentive Makeup Morphing (AMM) module that specifies how the makeup in the source image is morphed from the reference image, and a makeup detail loss to supervise the model within the selected makeup detail area. On the other hand, for makeup removal, PSGAN++ applies an Identity Distill Network (IDNet) to embed the identity information from with-makeup images into identity matrices. Finally, the obtained makeup/identity matrices are fed to a Style Transfer Network (STNet) that is able to edit the feature maps to achieve makeup transfer or removal. To evaluate the effectiveness of our PSGAN++, we collect a Makeup Transfer In the Wild (MT-Wild) dataset that contains images with diverse poses and expressions and a Makeup Transfer High-Resolution (MT-HR) dataset that contains high-resolution images. Experiments demonstrate that PSGAN++ not only achieves state-of-the-art results with fine makeup details even in cases of large pose/expression differences but also can perform partial or degree-controllable makeup transfer. Both the code and the newly collected datasets will be released at https://github.com/wtjiang98/PSGAN.
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification

We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method - utilizing a nearest-subspace algorithm in the R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at [1].
Where and What? Examining Interpretable Disentangled Representations

Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement.
OctoPocus in VR: Using a Dynamic Guide for 3D Mid-Air Gestures in Virtual Reality

Bau and Mackays OctoPocus dynamic guide helps novices learn, execute, and remember 2D surface gestures. We adapt OctoPocus to 3D mid-air gestures in Virtual Reality (VR) using an optimization-based recognizer, and by introducing an optional exploration mode to help visualize the spatial complexity of guides in a 3D gesture set. A replication of the original experiment protocol is used to compare OctoPocus in VR with a VR implementation of a crib-sheet. Results show that despite requiring 0.9s more reaction time than crib-sheet, OctoPocus enables participants to execute gestures 1.8s faster with 13.8 percent more accuracy during training, while remembering a comparable number of gestures. Subjective ratings support these results, 75 percent of participants found OctoPocus easier to learn and 83 percent found it more accurate. We contribute an implementation and empirical evidence demonstrating that an adaptation of the OctoPocus guide to VR is feasible and beneficial.
The regime of ‘post-truth’: COVID-19 and the politics of knowledge

The emergence of 'post-truth' is often associated with the rise of conspiracy theories and the lack of trust in scientific knowledge. This article attempts to theorise the complex division of labour in this regime of 'post-truth', with reference to the COVID-19 pandemic/infodemic. First, we argue that the 'post-truth' condition mirrors what Foucault called the 'will to truth', and that this challenges the procedures and systems by which truth and knowledge are ordered. Second, through Basil Bernstein's extension of Foucault's work, we argue that the era of post-truth has two features regarding the condition of knowledge: (1) that conflicts in the field of knowledge recontextualisation, that is, the pedagogisation of knowledge, are becoming more intense and visible, and (2) that greater exposure to high-stakes, uncertain scientific knowledge, which grows at exponential rate, increases social anxieties and leads to biopoliticisation of neoliberal responsibilisation.
Beyond Clicktivism: What Makes Digitally Native Activism Effective? An Exploration of the Sleeping Giants Movement

This article explores how successful digitally native activism generates social change. Digitally native movements are initiated, organized, and coordinated online without any physical presence or pre-existing offline campaign. To do so, we explore the revelatory case of Sleeping Giants (SG)—an online movement that led more than 4,000 organizations to withdraw their programmatic advertising spend from Breitbart, a far-right publisher. Analyzing 3.5 million tweets related to the movement along with qualitative secondary data, we used a mixed method approach to investigate the conditions that favored SG emergence, the organizing and coordinating practices of the movement, and the strategic framing practices involved in the tuning of the movement’s language and rhetoric toward its targets. Overall, we contribute to research on online movements and shed light on the pivotal role of peer production work and of language in leading an impactful online movement that aimed to counter online disinformation and hate speech.
CASE: A Context-Aware Security Scheme for Preserving Data Privacy in IoT-Enabled Society 5.0

This article introduces the concept of context-aware attribute learning with cipher policy-attribute-based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled Society 5.0. The concept of Society 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserves privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely, attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named the context-aware attribute learning scheme (CASE), which autonomously learns users’ contextual information, exploiting edge intelligence, generates attributes, and reduces the post-encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over smart healthcare systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32%–33% average network delay, 33%–35% average energy consumption, and 31%–36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using the support vector machine (SVM), decision tree (DT), and naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification

Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged F1 of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.
Monitoring AI Services for Misuse

Given the surge in interest in AI, we now see the emergence of Artificial Intelligence as a Service (AIaaS). AIaaS entails service providers offering remote access to ML models and capabilities at arms-length', through networked APIs. Such services will grow in popularity, as they enable access to state-of-the-art ML capabilities, 'on demand', 'out of the box', at low cost and without requiring training data or ML expertise. However, there is much public concern regarding AI. AIaaS raises particular considerations, given there is much potential for such services to be used to underpin and drive problematic, inappropriate, undesirable, controversial, or possibly even illegal applications. A key way forward is through service providers monitoring their AI services to identify potential situations of problematic use. Towards this, we elaborate the potential for 'misuse indicators' as a mechanism for uncovering patterns of usage behaviour warranting consideration or further investigation. We introduce a taxonomy for describing these indicators and their contextual considerations, and use exemplars to demonstrate the feasibility analysing AIaaS usage to highlight situations of possible concern. We also seek to draw more attention to AI services and the issues they raise, given AIaaS' increasing prominence, and the general calls for the more responsible and accountable use of AI.
A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification.The system uses the smartphone's accelerometer and gyroscope to monitor the participants' motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller's location and activity before the fall.In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles.The system's performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.
Optimal path finding in stochastic quasi-dynamic environments using particle swarm optimization

Finding the optimal path for a mobile robot in complex environments is a crucial part of automation. In this application, we seek a path that avoids obstacles inside the scene. This paper discusses the path planning problem in cases where 2D obstacles change their areas in stochastic form. The trade-off between shortness and safety of the path is captured by two objective functions: distance-based and risk-based objective functions. The Risk-based objective function describes how likely the suggested path avoids the obstacles. Particle swarm optimization (PSO) is employed to find the best path. A new set of parameters are introduced to the problem model to capture the stochastic nature of the environment. The range and behavior of these parameters are studied under different difficulty-level scenarios. The results show that the proposed approach can predict the stochastic changes in obstacles and plan a path that is simultaneously safe and short, as compared to the regular approach with a standard distance-only cost function. These results manifest the use of the proposed approach in environments where there are elastic objects that can block the path between two points.
Proactive Failure-Aware Task Scheduling Framework for Cloud Computing

Cloud computing is a widely adopted platform for executing tasks of different application types that belong to the end users. In the cloud, application task is prone to failure for several reasons, such as software bug or exception, virtual or physical infrastructure failure. Cloud service providers are responsible for managing availability of scheduled computing tasks in order to provide high level QoS for their customers. Protecting task against failure is a challenging and not a trivial mission due to dynamic, heterogeneous and large distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedy (post) actions. In this work, we first study and analyze three publicly available large cluster datasets from Google, Alibaba, and Trinity, to characterize task failure in cloud computing platform. We then propose a failure-aware task scheduling framework that can predict the termination status for a set of given tasks during the runtime, and take the appropriate remedy actions. The framework uses deep learning methods named Artificial and Convolutional Neural Network, ANN and CNN, for different prediction purposes. In addition, we formalize the actions selection problem as Integer Linear Programming (ILP) model and propose a heuristic optimization solution that aims to minimize the failure probability of tasks and their resources usage. The results show ANN and CNN can achieve prediction accuracy of up to 94% and 92%, respectively using Google dataset. Moreover, the framework can protect up to 40% of tasks that are predicted as failed using Alibaba dataset by taking the appropriate remedy actions, and hence save many of cluster's resources such as CPU and RAM.
Fuzzified Contrast Enhancement for Nearly Invisible Images

Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.
12 Angry Developers - A Qualitative Study on Developers' Struggles with CSP

The Web has improved our ways of communicating, collaborating, teaching, and entertaining us and our fellow human beings. However, this cornerstone of our modern society is also one of the main targets of attacks, most prominently Cross-Site Scripting (XSS). A correctly crafted Content Security Policy (CSP) is capable of effectively mitigating the effect of those Cross-Site Scripting attacks. However, research has shown that the vast majority of all policies in the wild are trivially bypassable.
A track-before-detect algorithm for UWB radar sensor networks

Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by a multistatic radar network with localization errors of 25.4 cm and 19.7 cm, a detection rate of the two targets of 88.75%, and no false alarm trajectory.
Recognition of Unknown Radar Emitters With Machine Learning

Classifiers based on machine learning are usually trained to distinguish between several known classes. For an electronic intelligence application, however, it is of great importance to recognize if an intercepted signal belongs to an unknown radar emitter. In the machine learning literature, this task is called open-set recognition. This article investigates six approaches in several configurations to recognize unknown emitters. It is based on a hierarchical emission model that understands emissions as a language with an inherent hierarchical structure. We consider two general approaches, which are the “memoryless” Markov chain and the Long Short-Term Memory recurrent neural network, which is especially designed to “remember” the past. The performance is demonstrated with two evaluation metrics in ten scenarios that contain different combinations of known and unknown emitters. An evaluation with corrupted data provides an estimate on the methods’ accuracies under challenging conditions. The results show that unknown emitters that do not use known waveforms are reliably recognized even with corrupted data, while unknown emitters that are more similar to known ones are harder to detect.
Explainable AI and Adoption of Financial Algorithmic Advisors

We study whether receiving advice from either a human or algorithmic advisor, accompanied by five types of Local and Global explanation labelings, has an effect on the readiness to adopt, willingness to pay, and trust in a financial AI consultant. We compare the differences over time and in various key situations using a unique experimental framework where participants play a web-based game with real monetary consequences. We observed that accuracy-based explanations of the model in initial phases leads to higher adoption rates. When the performance of the model is immaculate, there is less importance associated with the kind of explanation for adoption. Using more elaborate feature-based or accuracy-based explanations helps substantially in reducing the adoption drop upon model failure. Furthermore, using an autopilot increases adoption significantly. Participants assigned to the AI-labeled advice with explanations were willing to pay more for the advice than the AI-labeled advice with "No-explanation" alternative. These results add to the literature on the importance of XAI for algorithmic adoption and trust.