Reviewerly/exHarmony huggingface dataset:
Pandemic-Sanctioned AI Surveillance: Human Rights under the Threat of Algorithmic Injustice in the EU

Attention to algorithmic injustice has long characterised the perspective of European Union (EU) institutions toward artificial intelligence (AI), given the potential threats to citizens and democracies. From a global perspective, the EU has likewise championed in the pandemic context thanks to higher attention to concerns such as privacy in the deployment of technological solutions to help control the outbreak. Nevertheless, as digital tools became more and more pervasive, their proliferation far exceeded official contact tracing apps to include a multitude of public and private surveillance solutions. Our work considers the current European regulatory framework and it highlights how problematic pandemic surveillance digital tools in terms of privacy and data protection, digital accessibility, non-discrimination and social exclusion may fall through the cracks, especially within the private sector. The legal analysis complemented by empirical examples of COVID-19 related apps assesses how the pandemic offers a breeding ground for algorithmic injustice. Similarly, we evaluate the extent to which, in its current form, the European Commission Proposal for an AI Regulation (the AI Act) may fail to fully mitigate in practice such threats to human rights. Specifically, the contribution of the paper is to highlight how - even in a context such as the EU where notable attention is given to citizens' rights and their balancing against the need of protecting public health - COVID-19 and its algorithmic response poses a substantial risk to human rights. More broadly, the analysis offers a cautionary tale for post-pandemic societies in which AI surveillance is bound to remain a ubiquitous feature, for which current regulatory efforts may not prove sufficient guarantees.
Estimating the Confidence of Speech Spoofing Countermeasure

Conventional speech spoofing countermeasures (CMs) are designed to make a binary decision on an input trial. However, a CM trained on a closed-set database is theoretically not guaranteed to perform well on unknown spoofing attacks. In some scenarios, an alternative strategy is to let the CM defer a decision when it is not confident. The question is then how to estimate a CM’s confidence regarding an input trial. We investigated a few confidence estimators that can be easily plugged into a neural-network-based CM. On the ASVspoof2019 logical access database, the results demonstrate that an energy-based estimator and a neural-network-based one achieved acceptable performance in identifying unknown attacks in the test set. On a test set with additional unknown attacks and bona fide trials from other databases, the confidence estimators performed moderately well, and the CMs better discriminated bona fide and spoofed trials that had a high confidence score. Additional results also revealed the difficulty in enhancing a confidence estimator by adding unknown attacks to the training set.
A Fuzzy Logic Controller for Double Inverted Pendulum on a Cart

In this paper, it is discussed the control of a double inverted pendulum (DIP) on a cart with the use of a Fuzzy Logic Controller (FLC). The DIP is a classic problem in mechanics and the control procedure to stabilize around equilibrium position constitutes a classic criterion for the various control methods in Control Systems Theory and Robotics e.g., can serve as a representative model for the computation and stability of walking robots. To access the efficacy of the proposed method, a linear quadratic regulator (LQR) was used as a benchmark. The study showed that the proposed FLC can provide considerably better performance than LQR under different initial conditions of the system. This performance is remarkably significant in terms of the system outputs peak values reduction. The balancing and velocity tracking performance of the proposed controller was studied via simulations. The simulation result shows the effectiveness of our control scheme.
Exploring Spatial Diversity for Region-Based Active Learning

State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation costs, especially for dense pixel-level prediction tasks like semantic segmentation. We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance. In this setting, batches of informative image regions instead of entire images are selected for labeling. Importantly, we propose that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning. We apply this framework to the Cityscapes and PASCAL VOC datasets and demonstrate that the inclusion of spatial diversity effectively improves the performance of uncertainty-based and feature diversity-based active learning methods. Our framework achieves 95% performance of fully supervised methods with only 5 - 9% of the labeled pixels, outperforming all state-of-the-art region-based active learning methods for semantic segmentation.
Measuring Avalanche Properties on RC4 Stream Cipher Variants

In the last three decades, the RC4 has been the most cited stream cipher, due to a large amount of research carried out on its operation. In this sense, dissimilar works have been presented on its performance, security, and usability. One of the distinguishing features that stand out the most is the sheer number of RC4 variants proposed. Recently, a weakness has been reported regarding the existence of statistical dependence between the inputs and outputs of the RC4, based on the use of the strict avalanche criterion and the bit independence criterion. This work analyzes the influence of this weakness in some of its variants concerning RC4. The five best-known variants of RC4 were compared experimentally and classified into two groups according to the presence or absence of such a weakness.
Reward Conditioned Neural Movement Primitives for Population-Based Variational Policy Optimization

This paper aims to study the reward-based policy exploration problem in a supervised learning approach and enable robots to form complex movement trajectories in challenging reward settings and search spaces. For this, the experience of the robot, which can be bootstrapped from demonstrated trajectories, is used to train a novel Neural Processes-based deep network that samples from its latent space and generates the required trajectories given desired rewards. Our framework can generate progressively improved trajectories by sampling them from high reward landscapes, increasing the reward gradually. Variational inference is used to create a stochastic latent space to sample varying trajectories in generating a population of trajectories given target rewards. We benefit from Evolutionary Strategies and propose a novel crossover operation, which is applied in the self-organized latent space of the individual policies, allowing blending of the individuals that might address different factors in the reward function. Using a number of tasks that require sequential reaching to multiple points or passing through gaps between objects, we showed that our method provides stable learning progress and significantly higher sample efficiency compared to a number of state-of-the-art robotic reinforcement learning methods. Finally, we show the real-world suitability of our method through real robot execution involving obstacle avoidance.
Enhancing Detection of Malicious URLs Using Boosting and Lexical Features

A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage of having low false-positive rates regarding previously recognized malicious URLs. However, this method cannot recognize newly created malicious URLs. To solve this problem, many machine-learning models have been used. In this paper, we introduce an effective machine learning approach that uses an ensemble learner algorithm called AdaBoost (Adaptive Boosting), combined with different algorithms that enhance detection. For datasets filtration, we used CfsSubsetEval technique, which is an algorithm that searches for a subset of features that work well together. Datasets were collected from the UNB repository; divided into four categories: spam, phishing, malware, and defacement URLs; combined with benign URLs, dataset content is based on lexical features. The experimental results indicate that the proposed approach was successful in enhancing the detection accuracy of malicious URLs with less false-positive rates for all experimental algorithms.
When Face Completion Meets Irregular Holes: An Attributes Guided Deep Inpainting Network

Lots of convolutional neural network (CNN)-based methods have been proposed to implement face completion with regular holes. However, in practical applications, irregular holes are more common to see. Moreover, due to the distinct attributes and large variation of appearance for human faces, it is more challenging to fill irregular holes in face images while keeping content consistent with the rest region. Since facial attributes (e.g., gender, smiling, pointy nose, etc.) allow for a more understandable description of one face, they can provide some hints that benefit the face completion task. In this work, we propose a novel attributes-guided face completion network (AttrFaceNet), which comprises a facial attribute prediction subnet and a face completion subnet. The attribute prediction subnet predicts facial attributes from the rest parts of the corrupted images and guides the face completion subnet to fill the missing regions. The proposed AttrFaceNet is evaluated in an end-to-end way on commonly used datasets CelebA and Helen. Extensive experimental results show that our method outperforms state-of-the-art methods qualitatively and quantitatively especially in large mask size cases. Code is available at https://github.com/FVL2020/AttrFaceNet.
Analysis of cyber-security aspects both ashore and at sea

Cyberattacks on critical infrastructure facilities are becoming more complex, ranging from implementation into software codes and ending with powerful opportunities for indirect impact, for example, in the form of geodynamic threats. The maritime industry also suffers from cyberattacks. Information systems, equipment, remote control stations are available to hackers. They can obtain data about valuable cargoes and movement of ships taking them off course, making them run aground or even sinking them. This article elaborates the impact of various maritime cyber security standards. It covers multiple issues including blackmailing, extortion, taking-over ships at a distance. Messing with schedules, ship positioning and in and out traffic, and terminal operations also take place. Countermeasures must be calculated and worked out. It is recommended to have cyber-security drills and information security in maritime transport establishments.
Learning to Understand Traffic Signs

One of the intelligent transportation system's critical tasks is to understand traffic signs and convey traffic information to humans. However, most related works are focused on the detection and recognition of traffic sign texts or symbols, which is not sufficient for understanding. Besides, there has been no public dataset for traffic sign understanding research. Our work takes the first step towards addressing this problem. First, we propose a "CASIA-Tencent Chinese Traffic Sign Understanding Dataset" (CTSU Dataset), which contains 5000 images of traffic signs with rich semantic descriptions. Second, we introduce a novel multi-task learning architecture that extracts text and symbol information from traffic signs, reasons the relationship between texts and symbols, classifies signs into different categories, and finally, composes the descriptions of the signs. Experiments show that the task of traffic sign understanding is achievable, and our architecture demonstrates state-of-the-art and superior performance. The CTSU Dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-Tencent%20CTSU/index.html.
A Comprehensive Workflow for High Resolution 3D Solar Photovoltaic Potential Mapping in Dense Urban Environment: A Case Study on Campus of Delft University of Technology

Photovoltaic (PV) technology is the most promising renewable energy source to be integrated on urban building surfaces. Modeling and simulating urban PV systems pose more challenges than the conventional ones installed in open field due to rich urban morphology. Herein, a comprehensive workflow to estimate urban solar PV potential is developed where TU Delft campus is used as a case study. This workflow only requires light detection and ranging data and building footprints as data inputs, and multiple levels of result can be delivered including accurate geo‐referenced 3D building models, annual solar irradiation map, annual DC/AC yield maps and classified roof segments according to the specific yield of mounted PV system. The study reports a total of ≈8.1 GWh year −1 of PV energy which can be collected from campus building roofs and facades. Given the total electricity demand on the entire campus being 82.6 GWh/year, this PV potential can cover roughly 10% of the current electricity demand. The results constitute an initial assessment of solar PV potential on TU Delft campus buildings that is currently being used to prioritize PV integration on buildings and accelerate the transition toward a climate‐neutral campus.
A QuadTree Image Representation for Computational Pathology

The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to be split up into image tiles or patches so modern convolutional neural networks (CNNs) can process them. In this work, we present a method to generate an interpretable image representation of computational pathology images using quadtrees and a pipeline to use these representations for highly accurate downstream classification. To the best of our knowledge, this is the first attempt to use quadtrees for pathology image data. We show it is highly accurate, able to achieve as good results as the currently widely adopted tissue mask patch extraction methods all while using over 38% less data.
Quantum Rényi Divergences and the Strong Converse Exponent of State Discrimination in Operator Algebras

The sandwiched Rényi $$\alpha $$ -divergences of two finite-dimensional quantum states play a distinguished role among the many quantum versions of Rényi divergences as the tight quantifiers of the trade-off between the two error probabilities in the strong converse domain of state discrimination. In this paper, we show the same for the sandwiched Rényi divergences of two normal states on an injective von Neumann algebra, thereby establishing the operational significance of these quantities. Moreover, we show that in this setting, again similarly to the finite-dimensional case, the sandwiched Rényi divergences coincide with the regularized measured Rényi divergences, another distinctive feature of the former quantities. Our main tool is an approximation theorem (martingale convergence) for the sandwiched Rényi divergences, which may be used for the extension of various further results from the finite-dimensional to the von Neumann algebra setting. We also initiate the study of the sandwiched Rényi divergences of pairs of states on a $$C^*$$ -algebra and show that the above operational interpretation, as well as the equality to the regularized measured Rényi divergence, holds more generally for pairs of states on a nuclear $$C^*$$ -algebra.
Representing Data on a Quantum Computer

This chapter presents one of the most important parts of quantum machine learning algorithms, namely strategies with which a single data point, or sometimes an entire dataset, can be encoded into quantum states. We present quantum routines for this task, discuss their runtimes and review their interpretation as feature maps known in classical machine learning.
Knowing Where and What to Write in Automated Live Video Comments: A Unified Multi-Task Approach

Live video comments, or “danmu”, are an emerging social feature on Asian online video platforms. These time-synchronous comments are overlaid on the video playback and uniquely enrich the viewing experience, engaging hundreds of millions of users in rich community discussions. The presence of danmu comments has become a determining factor for video popularity. Recent work has proposed a model to automatically generate comments, but very little work has so far considered the problem of where to insert the comments in the video timeline. In this work, we propose to address both the what and where of automatic danmu generation, by jointly predicting the danmu comment content to be generated, as well as its optimal insertion point in the video timeline. Our model exploits the video visual content, subtitles, audio signals, and any existing surrounding comments, in one unified architecture and can handle scenarios where the videos are already heavily commented or when the video has no comments yet. Experiments show that our proposed unified framework is in general observed to outperform state-of-the-art comment generation methods.
TransFusion: Multi-Modal Fusion for Video Tag Inference via Translation-based Knowledge Embedding

Tag inference is an important task in the business of video platforms with wide applications such as recommendation, interpretation, and more. Existing works are mainly based on extracting video information from multiple modalities such as frames or music, and then infer tags through classification or object detection. This, however, does not apply to inferring generic tags or taxonomy that are less relevant to video contents, such as video originality or its broader category, which are important in practice. In this paper, we claim that these generic tags can be modeled through the semantic relations between videos and tags, and can be utilized simultaneously with the multi-modal features to achieve better video tagging. We propose TransFusion, an end-to-end supervised learning framework that fuses multi-modal embeddings (e.g., vision, audio, texts, etc.) with the knowledge embedding to derive the video representation. To infer the diverse tags following heterogeneous relations, TransFusion adopts a dual attentive approach to learn both the modality importance in fusion and relation importance in inference. Besides, it is general enough and can be used with the existing translation-based knowledge embedding approaches. Extensive experiments show that TransFusion outperforms the baseline methods with lowered mean rank and at least 9.59% improvement in [email protected] on the real-world video knowledge graph.
Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the new cases rate like maintaining social distancing via crowd estimation. This manuscript presents an efficient detection model for the crowd counting and social distancing between visitors in the two holy mosques, Al Masjid Al Haram in Mecca and the Prophet's Mosque in Medina. Also, the manuscript develops a secure crowd monitoring structure based on the convolutional neural network (CNN) model using real datasets of images for the two holy mosques. The proposed framework is divided into two procedures, crowd counting and crowd recognition using datasets of different densities. To confirm the effectiveness of the proposed model, some metrics are employed for crowd analysis, which proves the monitoring efficiency of the proposed model with superior accuracy. Also, it is very adaptive to different crowd density levels and robust to scale changes in several places.
The Stochastic Augmented Lagrangian method for domain adaptation

Among various topics explored in the transfer learning community, domain adaptation (DA) has been of primary interest and successfully applied in diverse fields. However, theoretical understanding of learning convergence in DA has not been sufficiently explored. To address such an issue, this paper presents the Stochastic Augmented Lagrangian method (SALM) to solve the optimization problem associated with domain adaptation. In contrast to previous works, the SALM is able to find the optimal Lagrangian multipliers, as opposed to manually selecting the multipliers which could result in significantly suboptimal solutions. Additionally, the SALM is the first algorithm which can find a feasible point with arbitrary precision for domain adaptation problems with bounded penalty parameters. We also observe that with unbounded penalty parameters, the proposed algorithm is able to find an approximate stationary point of infeasibility. We validate our theoretical analysis with several experimental results using benchmark data sets including MNIST, SYNTH, SVHN, and USPS.
Cross-Source Image Retrieval Based on Ensemble Learning and Knowledge Distillation for Remote Sensing Images

As different kinds of high-resolution remote sensing (HRRS) image data sources increase, the cross-source content-based image retrieval (CS-CBRSIR) is becoming an important and urgent task to be solved. Most existing methods focus on optimizing the common space features for dual-source effectively. The source discrepancy in classifier level, however, has been ignored. To handle this problem, we propose teacher-ensemble learning with the knowledge distillation method in this paper. The ensemble of source-shared and source-specific classifiers could construct an effective teacher model. The useful information can be transferred back with the knowledge distillation. Besides, the feature pyramid network is introduced to learn the multi-scale features from HRRS images, which can describe the complex contents of HRRS images well. The positive experimental results conducted on DSRSID illustrates the effectiveness of the proposed method.