Reviewerly/exHarmony huggingface dataset:
Active Multi-Information Source Bayesian Quadrature.

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here, we set the scene for active learning in BQ when multiple related information sources of variable cost (in input and source) are accessible. This setting arises for example when evaluating the integrand requires a complex simulation to be run that can be approximated by simulating at lower levels of sophistication and at lesser expense. We construct meaningful cost-sensitive multi-source acquisition rates as an extension to common utility functions from vanilla BQ (VBQ),and discuss pitfalls that arise from blindly generalizing. Furthermore, we show that the VBQ acquisition policy is a corner-case of all considered cost-sensitive acquisition schemes, which collapse onto one single de-generate policy in the case of one source and constant cost. In proof-of-concept experiments we scrutinize the behavior of our generalized acquisition functions. On an epidemiological model, we demonstrate that active multi-source BQ (AMS-BQ) allocates budget more efficiently than VBQ for learning the integral to a good accuracy.
Planning under LTL Environment Specifications

Planning domains represent what an agent assumes or believes about the environment it acts in. In the presence of nondeterminism, additional temporal assumptions, such as fairness, are often expressed as extra conditions on the domain. Here we consider environment specifications expressed in arbitrary LTL, which generalize many forms of environment specifications, including classical specifications of nondeterministic domains, fairness, and other forms of linear-time constraints on the domain itself. Existing literature typically implicitly or explicitly considers environment specifications as constraints on possible traces. In contrast, in spite of the fact that we use a linear-time formalism, we propose to consider environment specifications as specifications of environment strategies. Planning in this framework is the problem of computing an agent strategy that achieves its goal against all environment strategies satisfying the specification. We study the mathematical and computational properties of planning in this general setting. We observe that not all LTL formulas correspond to legitimate environment specifications, and formally characterize the ones that do. Moreover, we show that our notion of planning generalizes the classical notion of Church’s synthesis, and that in spite this one can still solve it optimally using classical Church’s synthesis.
Frames to Zones: Applying Mise-en-Scène Techniques in Cinematic Virtual Reality

Virtual reality (VR) is becoming increasingly commonplace and consumers, among other things, use the technology to access cinematic VR experiences. However, cinematic VR limits filmmakers' ability to effectively guide the audience's attention. This paper describes a between-groups study (n=60) exploring the use of a zone-division system to incorporate mise-en-scène in VR to guide attention. Participants were exposed to a cinematic VR experience including five points of interest (POIs). Half of the participants experienced a version containing visual cues arranged in the scene based on the zone-division system and the other half experienced no such guidance. The effectiveness of the intervention was assessed by measuring the angle between users' head orientation and the relevant POIs. Additionally, a questionnaire was used to determine whether the participants recalled the POIs. The results showed that participants exposed to additional visual cues orientated themselves significantly closer to the POIs than those in the other condition. However the questionnaire showed no significant differences between conditions.
Cache-Timing Attacks on RSA Key Generation

During the last decade, constant-time cryptographic software has quickly transitioned from an academic construct to a concrete security requirement for real-world libraries. Most of OpenSSL’s constant-time code paths are driven by cryptosystem implementations enabling a dedicated flag at runtime. This process is perilous, with several examples emerging in the past few years of the flag either not being set or software defects directly mishandling the flag. In this work, we propose a methodology to analyze security-critical software for side-channel insecure code path traversal. Applying our methodology to OpenSSL, we identify three new code paths during RSA key generation that potentially leak critical algorithm state. Exploiting one of these leaks, we design, implement, and mount a single trace cache-timing attack on the GCD computation step. We overcome several hurdles in the process, including but not limited to: (1) granularity issues due to word-size operands to the GCD function; (2) bulk processing of desynchronized trace data; (3) non-trivial error rate during information extraction; and (4) limited high-confidence information on the modulus factors. Formulating lattice problem instances after obtaining and processing this limited information, our attack achieves roughly a 27% success rate for key recovery using the empirical data from 10K trials.
Uncertainty-Aware Authentication Model for Fog Computing in IoT

Since the term “Fog Computing” has been coined by Cisco Systems in 2012, security and privacy issues of this promising paradigm are still open challenges. Among various security challenges, Access Control is a crucial concern for all cloud computing-like systems (e.g. Fog computing, Mobile edge computing) in the IoT era. Therefore, assigning the precise level of access in such an inherently scalable, heterogeneous and dynamic environment is not easy to perform. This work defines the uncertainty challenge for authentication phase of the access control in fog computing because on one hand fog has a number of characteristics that amplify uncertainty in authentication and on the other hand applying traditional access control models does not result in a flexible and resilient solution. Therefore, we have proposed a novel prediction model based on the extension of Attribute Based Access Control (ABAC) model. Our data-driven model is able to handle uncertainty in authentication. It is also able to consider the mobility of mobile edge devices in order to handle authentication. In doing so, we have built our model using and comparing four supervised classification algorithms namely as Decision Tree, Naïve Bayes, Logistic Regression and Support Vector Machine. Our model can achieve authentication performance with 88.14% accuracy using Logistic Regression.
Contributions to Clinical Named Entity Recognition in Portuguese

Having in mind that different languages might present different challenges, this paper presents the following contributions to the area of Information Extraction from clinical text, targeting the Portuguese language: a collection of 281 clinical texts in this language, with manually-annotated named entities; word embeddings trained in a larger collection of similar texts; results of using BiLSTM-CRF neural networks for named entity recognition on the annotated collection, including a comparison of using in-domain or out-of-domain word embeddings in this task.Although learned with much less data, performance is higher when using in-domain embeddings.When tested in 20 independent clinical texts, this model achieved better results than a model using larger out-of-domain embeddings.
Autoencoder based novelty detection for generalized zero shot learning

The problem of generalized zero-shot learning deals with the classification of test examples for which training data may or may not be available. Existing baseline algorithms connect the seen and unseen set of categories by learning functions to project the image data into the attribute space or vice versa. However, since the classification framework is trained only on the seen set of categories, the recognition performance is typically biased and algorithms have great difficulty in recognizing novel classes. In this work, we investigate the usefulness of a novelty detector to recognize a given data as coming from the seen or novel set. The proposed novelty detector is based on an autoencoder network structure with reconstruction and triplet cosine embedding losses which can be effectively trained using only the seen data and its categories. Experiments over a variety of benchmark datasets and zero-shot algorithms show the efficacy of the proposed approach.
Subtopic-driven Multi-Document Summarization

In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper.That is, the underlying topic can be collectively represented by all the documents in the set.Meanwhile, different documents may cover various different subtopics and the same subtopic can be across several documents.Inspired by topic model, the underlying topic of a document set can also be viewed as a collection of different subtopics of different importance.In this paper, we propose a summarization model called STDS.The model generates the underlying topic representation from both document view and subtopic view in parallel.The learning objective is to minimize the distance between the representations learned from the two views.The contextual information is encoded through a hierarchical RNN architecture.Sentence salience is estimated in a hierarchical way with subtopic salience and relative sentence salience, by considering the contextual information.Top ranked sentences are then extracted as a summary.Note that the notion of subtopic enables us to bring in additional information (e.g., comments to news articles) that is helpful for document summarization.Experimental results show that the proposed solution outperforms state-of-the-art methods on benchmark datasets.
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy.Previous works have shown that significant improvements can be reached by injecting information about the confusion between clean and noisy labels in this additional training data into the classifier training.However, for noise estimation, these approaches either do not take the input features (in our case word embeddings) into account, or they need to learn the noise modeling from scratch which can be difficult in a low-resource setting.We propose to cluster the training data using the input features and then compute different confusion matrices for each cluster.To the best of our knowledge, our approach is the first to leverage feature-dependent noise modeling with pre-initialized confusion matrices.We evaluate on low-resource named entity recognition settings in several languages, showing that our methods improve upon other confusion-matrix based methods by up to 9%.
A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety

Use of cooperative information, distributed by road-side units, offers large potential for intelligent vehicles (IVs), As vehicle automation progresses and cooperative perception is used to fill the blind spots of onboard sensors, the question of reliability of the data becomes increasingly important in safety considerations (SOTIF, Safety of the Intended Functionality). This paper addresses the problem to estimate the reliability of cooperative information for in-vehicle use. We propose a novel method to infer the reliability of received data based on the theory of Subjective Logic (SL). Using SL, we fuse multiple information sources, which individually only provide mild cues of the reliability, into a holistic estimate, which is statistically sound through an end-to-end modeling within the theory of SL. Using the proposed scheme for probabilistic SL-based fusion, IVs are able to separate faulty from correct data samples with a large margin of safety. Real world experiments show the applicability and effectiveness of our approach.
"I don't see why I would ever want to use it"

Passwords are an often unavoidable authentication mechanism, despite the availability of additional alternative means. In the case of smartphones, usability problems are aggravated because interaction happens through small screens and multilayer keyboards. While password managers (PMs) can improve this situation and contribute to hardening security, their adoption is far from widespread. To understand the underlying reasons, we conducted the first empirical usability study of mobile PMs, covering both quantitative and qualitative evaluations. Our findings show that popular PMs are barely acceptable according to the standard System Usability Scale, and that there are three key areas for improvement: integration with external applications, security, and user guidance and interaction. We build on the collected evidence to suggest recommendations that can fill this gap.
Any-Cubes

Here we present Any-Cubes, a prototype toy with which children can intuitively and playfully explore and understand machine learning as well as Internet of Things technology. Our prototype is a combination of deep learning-based image classification [12] and machine-to-machine (m2m) communication via MQTT. The system consists of two physical and tangible wooden cubes. Cube 1 ("sensor cube") is inspired by Google's teachable machine [14,15]. The sensor cube can be trained on any object or scenery. The machine learning functionality is directly implemented on the microcontroller (Raspberry Pi) by a Google Edge TPU Stick. Via MQTT protocol, the microcontroller sends its current status to Cube 2, the actuator cube. The actuator cube provides three switches (relays controlled by an Arduino board) to which peripheral devices can be connected. This allows simple if-then functions to be executed in real time, regardless of location. We envision our system as an intuitive didactic tool for schools and maker spaces.
Filtering Malicious JavaScript Code with Doc2Vec on an Imbalanced Dataset

Drive-by download attacks are one of main threats on the Internet. Several detection methods are to build run-time environments that allow JavaScript code to run and track its behavior while it runs. Dynamic analysis requires too much time to examine all the web pages a client accesses. Hence, lightweight filtering methods to detect unseen malicious JavaScript snippets are required. Static analysis often extracts statistical and lexical features from the associated JavaScript code of each web page in order to build detection models. In general, static analysis imposes no runtime overhead. These methods are, however, vulnerable to code obfuscation techniques. Some researchers attempt to detect obfuscated VBA macros with Natural Language Processing (NLP) techniques. In these methods, neural networks extract the features automatically in contrast with traditional approaches. In addition, since several methods are evaluated with a balanced dataset, the practical performance is still open to discussion. To evaluate the practical performance, these methods have to be evaluated with imbalanced datasets. In this paper, we attempt to detect unseen malicious JavaScript snippets with Doc2Vec, an unsupervised algorithm to generate vectors for documents with neural networks. To mitigate the class imbalance problem, our method uses a clustering-based undersampling technique. Furthermore, we build a web crawler and generate an imbalanced dataset with over 20,000 samples. The evaluation result shows that our method achieves a F-measure of 0.71.
Graph Learning with Partial Observations: Role of Degree Concentration

In this work we consider the problem of learning an Erdös-Rényi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections linking the accessible nodes (local structure learning). We propose three matrix estimators, namely, the Granger, the one-lag correlation, and the residual estimators, which, when followed by a universal clustering algorithm, are shown to retrieve the true subgraph in the limit of large network sizes. Remarkably, it is seen that a fundamental role is played by the uniform concentration of node degrees, rather than by sparsity.
Contrastive Relevance Propagation for Interpreting Predictions by a Single-Shot Object Detector

Object detection is a widely-used computer vision task, in which we identify a bounding box around each object in an image and classify the object into one of pre-defined classes. Single Shot MultiBox Detector (SSD) is a real-time object detector based on a single convolutional neural network. SSD is popular and known for high speed and accuracy, but its black-box nature is not ignorable when it is applied to critical systems. In this paper, we propose Contrastive Relevance Propagation (CRP), an extension of Layer-wise Relevance Propagation (LRP) tailored for SSD. CRP can consistently deal with SSD's heterogeneous output, i.e. confidences for object classes and location offsets, and create a heatmap that highlights a crucial part of the input, which is not available with a standard use of LRP. By experiments with the Pascal VOC 2012 dataset, we confirmed the quality of heatmaps created by CRP, and with such heatmaps, we conducted a simple analysis on prediction errors made by SSD.
BotSlayer: real-time detection of bot amplification on Twitter

by the bad actors vary from astroturf (Ratkiewicz,
Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement

The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.
Single Image Reflection Removal Based on GAN With Gradient Constraint

When we take a picture through glass windows, the photographs are often degraded by undesired reflections.To separate reflection layer and background layer is an important problem for enhancing image quality.However, single-image reflection removal is a challenging process because of the ill-posed nature of the problem.In this paper, we propose a single-image reflection removal method based on generative adversarial networks.Our network is an end-to-end trained network with four types of losses.It includes pixel loss, feature loss, adversarial loss, and gradient constraint loss.We propose a novel gradient constraint loss in order to separate the background layer and the reflection layer clearly.Gradient constraint loss is applied in a gradient domain and it minimizes the correlation between the background and reflection layer.Owing to the novel loss and our new synthetic dataset, our reflection removal method outperforms state-of-the-art methods in PSNR and SSIM, especially in real world images.
Design and Implementation of an Embedded Real-Time System for Guiding Visually Impaired Individuals

Computer vision aims to provide computers with vision capabilities similar to humans. Humans use their eyes and their brains to see and understand the world and objects around them. For visually impaired individuals, these capabilities are lost or damaged in different degrees. Their eyes cannot discharge vision responsibilities. This paper aims to design and implement a portable system to help visually impaired individuals in perceiving objects and people around them and estimating their distance precisely. The proposed system uses a CNN-based real-time object detection technique called YOLO (You Look Only Once) with a single camera mounted on Raspberry Pi board. The system also estimates the distance of the detected objects and deliver these data to visually impaired person in audible form. The results show that the system can detect a person and predict his/her distance with 98.8% accuracy.
Supervised Classifiers to Identify Hate Speech on English and Spanish Tweets

Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. More precisely, we do an empirical study that analyzes the influence of two types of linguistic features (n-grams, word embeddings) when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), Decision Tree (DT), Nearest Neighbors (KN), Random Forest (RF) and Neural Network (NN). The experiments we have carried out show that CNB, SVM, and RF are better than the rest classifiers in English and Spanish languages by taking into account all features.
Building a Conference Recommender System Based on SciGraph and WikiCFP

SciGraph is a Linked Open Data graph published by Springer Nature which contains information about conferences and conference publications. In this paper, we discuss how this dataset can be utilized to build a conference recommendation system, yielding a recall@10 of up to 0.665, and a MAP of up to 0.540, generating recommendations based on authors, abstracts, and keywords. Furthermore, we show how the dataset can be linked to WikiCFP to recommend upcoming conferences.