Reviewerly/exHarmony huggingface dataset:
Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving

Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect anomalies is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in the real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.
A Single Timescale Stochastic Approximation Method for Nested Stochastic Optimization

We study constrained nested stochastic optimization problems in which the objective function is a composition of two smooth functions whose exact values and derivatives are not available. We propose a single timescale stochastic approximation algorithm, which we call the nested averaged stochastic approximation (NASA), to find an approximate stationary point of the problem. The algorithm has two auxiliary averaged sequences (filters) which estimate the gradient of the composite objective function and the inner function value. By using a special Lyapunov function, we show that the NASA achieves the sample complexity of ${\cal O}(1/\epsilon^{2})$ for finding an $\epsilon$-approximate stationary point, thus outperforming all extant methods for nested stochastic approximation. Our method and its analysis are the same for both unconstrained and constrained problems, without any need of batch samples for constrained nonconvex stochastic optimization. We also present a simplified parameter-free variant of the NASA method for solving constrained single-level stochastic optimization problems, and we prove the same complexity result for both unconstrained and constrained problems.
A Survey of Robot-Assisted Language Learning (RALL)

Robot-assisted language learning (RALL) is becoming a more commonly studied area of human-robot interaction (HRI). This research draws on theories and methods from many different fields, with researchers utilizing different instructional methods, robots, and populations to evaluate the effectiveness of RALL. This survey details the characteristics of robots used—form, voice, immediacy, non-verbal cues, and personalization—along with study implementations, discussing research findings. It also analyzes robot effectiveness. While research clearly shows that robots can support native and foreign language acquisition, it has been unclear what benefits robots provide over computer-assisted language learning. This survey examines the results of relevant studies from 2004 (RALL's inception) to 2017. Results suggest that robots may be uniquely suited to aid in language production, with apparent benefits in comparison to other technology. As well, research consistently indicates that robots provide unique advantages in increasing learning motivation and in-task engagement, and decreasing anxiety, though long-term benefits are uncertain. Throughout this survey, future areas of exploration are suggested, with the hope that answers to these questions will allow for more robust design and implementation guidelines in RALL.
LWE with Side Information: Attacks and Concrete Security Estimation

We propose a framework for cryptanalysis of lattice-based schemes, when side information-in the form of "hints"-about the secret and/or error is available.Our framework generalizes the so-called primal lattice reduction attack, and allows the progressive integration of hints before running a final lattice reduction step.Our techniques for integrating hints include sparsifying the lattice, projecting onto and intersecting with hyperplanes, and/or altering the distribution of the secret vector.Our main contribution is to propose a toolbox and a methodology to integrate such hints into lattice reduction attacks and to predict the performance of those lattice attacks with side information.While initially designed for side-channel information, our framework can also be used in other cases: exploiting decryption failures, or simply exploiting constraints imposed by certain schemes (LAC, Round5, NTRU).We implement a Sage 9.0 toolkit to actually mount such attacks with hints when computationally feasible, and to predict their performances on larger instances.We provide several end-to-end application examples, such as an improvement of a single trace attack on Frodo by Bos et al. (SAC 2018).In particular, our work can estimates security loss even given very little side information, leading to a smooth measurement/computation trade-off for side-channel attacks.The research of L. Ducas and M. Rossi
Improved Primitives for MPC over Mixed Arithmetic-Binary Circuits

This work introduces novel techniques to improve the translation between arithmetic and binary data types in secure multi-party computation. We introduce a new approach to performing these conversions using what we call extended doubly-authenticated bits (edaBits), which correspond to shared integers in the arithmetic domain whose bit decomposition is shared in the binary domain. These can be used to considerably increase the efficiency of non-linear operations such as truncation, secure comparison and bit-decomposition. Our edaBits are similar to the daBits technique introduced by Rotaru et al. (Indocrypt 2019). However, we show that edaBits can be directly produced much more efficiently than daBits, with active security, while enabling the same benefits in higher-level applications. Our method for generating edaBits involves a novel cut-and-choose technique that may be of independent interest, and improves efficiency by exploiting natural, tamper-resilient properties of binary circuits that occur in our construction. We also show how edaBits can be applied to efficiently implement various non-linear protocols of interest, and we thoroughly analyze their correctness for both signed and unsigned integers. The results of this work can be applied to any corruption threshold, although they seem best suited to dishonest majority protocols such as SPDZ. We implement and benchmark our constructions, and experimentally verify that our technique yields a substantial increase in efficiency. EdaBits save in communication by a factor that lies between 2 and 60 for secure comparisons with respect to a purely arithmetic approach, and between 2 and 25 with respect to using daBits. Improvements in throughput per second slightly lower but still as high as a factor of 47. We also apply our novel machinery to the tasks of biometric matching and convolutional neural networks, obtaining a noticeable improvement as well.
Blockchain-Based Certificate Transparency and Revocation Transparency

Traditional X.509 public key infrastructures (PKIs) depend on trusted certification authorities (CAs) to sign certificates, used in SSL/TLS to authenticate web servers and establish secure channels. However, recent security incidents indicate that CAs may (be compromised to) sign fraudulent certificates. In this article, we propose blockchain-based certificate transparency (CT) and revocation transparency (RT) to balance the absolute authority of CAs. Our scheme is compatible with X.509 PKIs but significantly reinforces the security guarantees of a certificate. The CA-signed certificates and their revocation status information of an SSL/TLS web server are published by the subject (i.e., the web server) as a transaction in the global certificate blockchain. The certificate blockchain acts as append-only public logs to monitor CAs&#x2019; certificate signing and revocation operations, and an SSL/TLS web server is granted with the cooperative control on its certificates. A browser compares the certificate received in SSL/TLS negotiations with the ones in the public certificate blockchain, and accepts it only if it is published and not revoked. We implement the prototype system with Firefox and Nginx, and the experimental results show that it introduces reasonable overheads.
Gaussian Distribution Based Oversampling for Imbalanced Data Classification

The imbalanced data classification problem widely exists in many real-world applications. Data resampling is a promising technique to deal with imbalanced data through either oversampling or undersampling. However, the traditional data resampling approaches simply take into account the local neighbor information to generate new instances in linear ways, leading to the generation of incorrect and unnecessary instances. In this study, we propose a new data resampling technique, namely, Gaussian Distribution based Oversampling (GDO), to handle the imbalanced data for classification. In GDO, anchor instances are selected from the minority class instances in a probabilistic way by taking into account the density and distance information carried by the minority instances. Then new minority instances are generated following a Gaussian distribution model. The proposed method is validated in experimental study by comparing with seven imbalanced learning approaches on 40 data sets from the KEEL repository and 10 large data sets from the UCI repository. Experimental results show that our method outperforms the other compared methods in terms of AUC, G-mean and memory usage with an increase in running time. We also apply GDO to deal with two real imbalanced data classification problems: Internet video traffic identification and metastasis detection of esophageal cancer. The experimental results once again validate the effectiveness of our approach.
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking

User demand for blocking advertising and tracking online is large and growing.Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution.Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g.URL patterns, code structure), making existing approaches susceptible to evasion.In this work we present ADGRAPH, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web.ADGRAPH differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.Because ADGRAPH considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches.We evaluate ADGRAPH on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists.We implement ADGRAPH as a modification to Chromium.ADGRAPH adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites.Overall, we conclude that ADGRAPH is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.
Characterizing pollution and source identification of heavy metals in soils using geochemical baseline and PMF approach

Abstract It is necessary to establish local geochemical baseline concentrations (GBCs) due to the lack or the inapplicability of regional background values in the study area. The establishment of GBCs of heavy metal (HM) in soil helps in making the accurate assessment of pollution, and then provides a basis for pollution control. Based on this, a case study was undertaken to study the GBCs of the Jiedong District, Guangdong Province, China. In this research, cumulative frequency distribution curves were utilized to determine the local GBCs in the subsoils. The determined GBCs of Cr, Hg, As, Pb, Ni, Cd, Cu, Zn, Co and V were 39.91, 0.072, 11.48, 47.62, 12.70, 0.17, 14.22, 64.54, 6.31, and 68.14 mg/kg, respectively. The average concentrations of Hg, As, Pb, Cd, Cu and Zn in the topsoils exceeded the corresponding baseline concentrations. In particular, the contents of Cd and Hg were 1.53 and 2.22 times higher than GBCs. According to this baseline criterion, enrichment factor (EF), pollution load index (PLI) and ecological risk index (RI) were applied to assessing HM pollution. EF and PLI suggested that most areas were under moderate contamination, while Hg and Cd pollution was more serious. And the RI values presented that the potential ecological risks were low in most parts of the study area. The possible origins of HMs were identified by combining positive matrix factorization with EF and geostatistics. Comprehensive analysis indicated that Hg and Cd were related to industrial activities, such as textile and garment processing, plastic and rubber production and metal manufacturing. Arsenic and part of Cu mainly came from agricultural activities, namely the use of pesticides, fertilizers and livestock manures. Lead and Zn were mainly attributed to traffic emissions. Chromium, Ni, V, Co, and part of Cu were originated from natural source controlled by parent materials. The corresponding contributions of these sources were 20.61%, 24.20%, 19.22% and 35.97%, respectively. This work provides information to prevent and control the soil HM pollution by proposing the efficient management of anthropogenic sources.
A Novel Medical Image Signcryption Scheme Using TLTS and Henon Chaotic Map

In tele-medicine, images based on patient diagnostic tests and reports are usually required to broadcast securely so that recipient can receive them without any error. For the secure communication of sensitive medical data, there is a need of authenticated and unforgeable cryptosystem. In this paper, we propose a novel signcryption technique for medical images that fulfills the necessary security requirements of sensitive medical data during its communication. The design of novel medical image signcryption scheme is empowered by the combination strategy of hybrid cryptography. It employs a technique of public key cryptography furnished with elliptic curve cryptography for the generation of secret encryption key. The proposed scheme uses elliptic curve for signcryption purpose and chaotic maps for performing encryption of the medical images. It gives a combined mechanism of key exchange, digital signature and chaotic image encryption. Image encryption phase contains permutation and diffusion for pixel scrambling. The proposed image signcryption scheme provides confidentiality, authentication, integrity, unforgability, forward secrecy and non-repudiation. The use of chaos-based symmetric image encryption scheme shows good results for key space analysis, key sensitivity, correlation analysis, number of pixel change rate, unified average changing intensity, entropy analysis and histogram analysis. On the basis of the results obtained from all these analyses, we believe that the proposed medical image signcryption scheme is efficient and robust, providing good security for sensitive medical images.
Domain Adaptation Based on Correlation Subspace Dynamic Distribution Alignment for Remote Sensing Image Scene Classification

Remote sensing image scene classification refers to assigning semantic labels according to the content of the remote sensing scenes. Most machine learning-based scene classification methods assume that training and testing data share the same distributions. However, in real application scenarios, this assumption is difficult to guarantee. Domain adaptation (DA) is a promising approach to address this problem by aligning the feature distribution of training and testing data. Inspired by the idea DA, in this article, we propose a correlation subspace dynamic distribution alignment (CS-DDA) method for remote sensing image scene classification. Aiming at the characteristics of remote sensing scenes, we introduce two strategies to balance the effects of source and target domains: subspace correlation maximization (SCM) and dynamic statistical distribution alignment (DSDA). On the one hand, SCM tries to avoid mapping source domain data into irrelevant subspace to preserve the representation information of the source domain. On the other hand, DSDA is proposed to reduce the data distribution discrepancy between aligned source and target domains. Specifically, DSDA is a dynamic adjustment process where an adaptive factor is learned to balance the interclass and intraclass distribution between domains. Moreover, we integrate SCM and DSDA into a uniform optimization framework, and the optimal solution can be converted to the generalized eigendecomposition problem by derivation. The experimental results indicate that the proposed method can generate better results when compared with other feature distribution alignment methods.
Self-Paced Clustering Ensemble

The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.
Access granted: Facebook’s free basics in Africa

This article explores one of the most notorious and controversial initiatives by tech corporations to increase connectivity across the Global South: Facebook’s Free Basics project. Public attention focused on its ban in India following nationwide protests about net neutrality. In Africa, however, Free Basics expanded without much public scrutiny to some 32 countries. This article traces this quiet expansion by using an innovative virtual private network (VPN)-based method and by calling for an analytical focus on the landscape of the digital civil society in Africa. Specifically, I outline two key, interrelated phenomena: (1) Facebook’s evolving strategy, including a greater engagement with civil society organizations and (2) the focus of digital rights activists in Africa on issues like Internet shutdowns, government surveillance, and the lack of data privacy frameworks. In the process, I illuminate broader trends in the digital industry including tech corporations’ growing investments in mobile social media, network infrastructures, and in civil society; the use of disadvantaged populations and unregulated territories for digital experiments and data extraction; and the mounting recognition of Facebook’s political role, both within and outside the corporation.
Towards a Complete 3D Morphable Model of the Human Head

Three-dimensional morphable models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: (i). use a regressor to complete missing parts of one model using the other, and (ii). use the Gaussian Process framework to blend covariance matrices from multiple models. Thus, we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color.
ScaIL: Classifier Weights Scaling for Class Incremental Learning

Incremental learning is useful if an AI agent needs to integrate data from a stream. The problem is non trivial if the agent runs on a limited computational budget and has a bounded memory of past data. In a deep learning approach, the constant computational budget requires the use of a fixed architecture for all incremental states. The bounded memory generates imbalance in favor of new classes and a prediction bias toward them appears. This bias is commonly countered by introducing a data balancing step in addition to the basic network training. We depart from this approach and propose simple but efficient scaling of past classifiers' weights to make them more comparable to those of new classes. Scaling exploits incremental state statistics and is applied to the classifiers learned in the initial state of classes to profit from all their available data. We also question the utility of the widely used distillation loss component of incremental learning algorithms by comparing it to vanilla fine tuning in presence of a bounded memory. Evaluation is done against competitive baselines using four public datasets. Results show that the classifier weights scaling and the removal of the distillation are both beneficial.
Adversarial Sampling for Active Learning

This paper proposes ASAL, a new GAN based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. To the best of our knowledge, ASAL is the first GAN based AL method applicable to multi-class problems that outperforms random sample selection. Another benefit of ASAL is its small run-time complexity (sub-linear) compared to traditional uncertainty sampling (linear). We present a comprehensive set of experiments on multiple traditional data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling). In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection.