Reviewerly/exHarmony huggingface dataset:
Black-Box Transformations from Passive to Covert Security with Public Verifiability

In the context of secure computation, protocols with security against covert adversaries ensure that any misbehavior by malicious parties will be detected by the honest parties with some constant probability. As such, these protocols provide better security guarantees than passively secure protocols and, moreover, are easier to construct than protocols with full security against active adversaries. Protocols that, upon detecting a cheating attempt, allow the honest parties to compute a certificate that enables third parties to verify whether an accused party misbehaved or not are called publicly verifiable. In this work, we present the first generic compilers for constructing two-party protocols with covert security and public verifiability from protocols with passive security. We present two separate compilers, which are both fully blackbox in the underlying protocols they use. Both of them only incur a constant multiplicative factor in terms of bandwidth overhead and a constant additive factor in terms of round complexity on top of the passively secure protocols they use. The first compiler applies to all two-party protocols that have no private inputs. This class of protocols covers the important class of preprocessing protocols that are used to setup correlated randomness among parties. We use our compiler to obtain the first secret-sharing based two-party protocol with covert security and public verifiability. Notably, the produced protocol achieves public verifiability essentially for free when compared with the best known previous solutions based on secret-sharing that did not provide public verifiability. Our second compiler constructs protocols with covert security and public verifiability for arbitrary functionalities from passively secure protocols. It uses our first compiler to perform a setup phase, which is independent of the parties' inputs as well as the protocol they would like to execute. Finally, we show how to extend our techniques to obtain multiparty computation protocols with covert security and public verifiability against arbitrary constant fractions of corruptions.
Come as You Are

Decades of work on censorship evasion have resulted in myriad ways to empower clients with the ability to access censored content, but to our knowledge all of them have required some degree of client-side participation. Having to download and run anti-censorship software can put users at risk, and does not help the many users who do not even realize they are being censored in the first place.
A Benchmark of Security Metrics in Cyber-Physical Systems

The usage of connected devices and their role within our daily- and business life gains more and more impact. In addition, various derivations of Cyber-Physical Systems (CPS) reach new business fields, like smart healthcare or Industry 4.0. Although these systems do bring many advantages for users by extending the overall functionality of existing systems, they come with several challenges, especially for system engineers and architects. One key challenge consists in achieving a sufficiently high level of security within the CPS environment, as sensitive data or safety-critical functions are often integral parts of CPS. Being system of systems (SoS), CPS complexity, unpredictability and heterogeneity complicate analyzing the overall level of security, as well as providing a way to detect ongoing attacks. Usually, security metrics and frameworks provide an effective tool to measure the level of security of a given component or system. Although several comprehensive surveys exist, an assessment of the effectiveness of the existing solutions for CPS environments is insufficiently investigated in literature. In this work, we address this gap by benchmarking a carefully selected variety of existing security metrics in terms of their usability for CPS. Accordingly, we pinpoint critical CPS challenges and qualitatively assess the effectiveness of the existing metrics for CPS systems.
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras

We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras. To accurately label this large dataset (600 hours of video footage), we also develop a semi-automated annotation method. An effective MCTF model should proactively anticipate where and when a person will reappear in the camera network. In this paper, we consider the task of predicting the next camera a pedestrian will reappear after leaving the view of another camera, and present several base-line approaches for this. The labeled database is available online: https://github.com/olly-styles/Multi-Camera-Trajectory-Forecasting.
SoK

Cyber situational awareness is an essential part of cyber defense that allows the cybersecurity operators to cope with the complexity of today's networks and threat landscape. Perceiving and comprehending the situation allow the operator to project upcoming events and make strategic decisions. In this paper, we recapitulate the fundamentals of cyber situational awareness and highlight its unique characteristics in comparison to generic situational awareness known from other fields. Subsequently, we provide an overview of existing research and trends in publishing on the topic, introduce front research groups, and highlight the impact of cyber situational awareness research. Further, we propose an updated taxonomy and enumeration of the components used for achieving cyber situational awareness. The updated taxonomy conforms to the widely-accepted three-level definition of cyber situational awareness and newly includes the projection level. Finally, we identify and discuss contemporary research and operational challenges, such as the need to cope with rising volume, velocity, and variety of cybersecurity data and the need to provide cybersecurity operators with the right data at the right time and increase their value through visualization.
Searching for activation functions using a self-adaptive evolutionary algorithm

The introduction of the ReLU function in neural network architectures yielded substantial improvements over sigmoidal activation functions and allowed for the training of deep networks. Ever since, the search for new activation functions in neural networks has been an active research topic. However, to the best of our knowledge, the design of new activation functions has mostly been done by hand. In this work, we propose the use of a self-adaptive evolutionary algorithm that searches for new activation functions using a genetic programming approach, and we compare the performance of the obtained activation functions to ReLU. We also analyze the shape of the obtained activations to see if they have any common traits such as monotonicity or piece-wise linearity, and we study the effects of the self-adaptation to see which operators perform well in the context of a search for new activation functions. We perform a thorough experimental study on datasets of different sizes and types, using different types of neural network architectures. We report favorable results obtained from the mean and standard deviation of the performance metrics over multiple runs.
An overview of LCS research from IWLCS 2019 to 2020

The International Workshop on Learning Classifier Systems (IWLCS) is a yearly workshop at the GECCO conference where new concepts and results all around Learning Classifier Systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intented to provide an easy entry point to the most recent progress and achievements. As organizers of the upcoming IWLCS, we take the underlying idea a bit further and accompany the presentation with the present workshop paper, hoping that this recurs as well. We give an overview of all the LCS-related publications since the last IWLCS, that is, since 13th July 2019 and until 10th March 2020. The 33 publications we review are clustered into six overall topics: Formal theoretic advances, approaches that combine LCSs with neural networks, contributions to LCS-based reinforcement learning, improvements to existing LCSs, new LCS architectures and applications of LCSs.
You Are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter

Automated social media bots have existed almost as long as the social media platforms they inhabit. Although efforts have long existed to detect and characterize these autonomous agents, these efforts have redoubled in the recent months following sophisticated deployment of bots by state and non-state actors. This research will study the differences between human and bot social communication networks by conducting an account snow ball data collection, and then evaluate network, content, temporal, and user features derived from this communication network in several bot detection machine learning models. We will compare this model to the other models of the bot-hunter toolbox as well as current state of the art models. In the evaluation, we will also explore and evaluate relevant training data. Finally, we will demonstrate the application of the bot-hunter suite of tools in Twitter data collected around the Swedish National elections in 2018.
Student Engagement in Mobile Learning via Text Message

Mobile learning is expanding rapidly due to its accessibility and affordability, especially in resource-poor parts of the world. Yet how students engage and learn with mobile learning has not been systematically analyzed at scale. This study examines how 93,819 Kenyan students in grades 6, 9, and 12 use a text message-based mobile learning platform that has millions of users across Sub-Saharan Africa. We investigate longitudinal variation in engagement over a one-year period for students in different age groups and check for evidence of learning gains using learning curve analysis. Student engagement is highest during school holidays and leading up to standardized exams, but persistence over time is low: under 25% of students return to the platform after joining. Clustering students into three groups based on their level of activity, we examine variation in their learning behaviors and quiz performance over their first ten days. Highly active students exhibit promising trends in terms of quiz completion, reattempts, and accuracy, but we do not see evidence of learning gains in this study. The findings suggest that students in Kenya use mobile learning either as an ad-hoc resource or a low-cost tutor to complement formal schooling and bridge gaps in instruction.
CommtPst: Deep learning source code for commenting positions prediction

Existing techniques for automatic code commenting assume that the code snippet to be commented has been identified, thus requiring users to provide the code snippet in advance. A smarter commenting approach is desired to first self-determine where to comment in a given source code and then generate comments for the code snippets that need comments. To achieve the first step of this goal, we propose a novel method, CommtPst, to automatically find the appropriate commenting positions in the source code. Since commenting is closely related to the code syntax and semantics, we adopt neural language model (word embeddings) to capture the code semantic information, and analyze the abstract syntax trees to capture code syntactic information. Then, we employ LSTM (long short term memory) to model the long-term logical dependency of code statements over the fused semantic and syntactic information and learn the commenting patterns on the code sequence. We evaluated CommtPst using large data sets from dozens of open-source software systems in GitHub. The experimental results show that the precision, recall and F-Measure values achieved by CommtPst are 0.792, 0.602 and 0.684, respectively, which outperforms the traditional machine learning method with 11.4% improvement on F-measure.
Tangle Ledger for Decentralized Learning

Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes.Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface.We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.
Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots

For mobile robots to operate in real environments, it is essential that basic tasks such as localization, mapping and navigation are performed properly. These tasks strongly rely on an adequate perception of the environment, which may be challenging in some cases due to the nature of the scene itself, the limited operation of some sensors, or even both. A mobile robot should be able to intelligently identify and overcome abnormal situations efficiently in order to avoid sensory malfunctioning. We propose in this work a novel methodology based on Bayesian networks, which enables to naturally represent complex relationships among sensors, to integrate heterogeneous sources of knowledge, to deduct the presence of sensory anomalies, and finally to recover from them by using the available information. The high computational cost of inference is addressed by a new algorithm that takes advantage of our model structure. Our proposal has been assessed in several simulations and has also been tested in a real environment with a mobile robot. The obtained results show that it achieves better performance and accuracy compared to other existing methods, while enhancing the robustness of the whole sensory system.
Introducing a Framework to Assess Newly Created Questions with Natural Language Processing

Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions’ text on difficulty and discrimination.
Severity Prediction with Machine Learning Methods

Traffic, which became a part of our lives with the spread of cars, brought accidents and death with it. Safety and accident issues are a global problem in the world. This study aims to establish models to predict the accident severity levels of traffic accident injury records for possible accidents by using some data mining classification methods. The dataset used for this work is named Stats19, which has the traffic accident data from 2010 to 2012 in United Kingdom (UK), and it is collected by the UK government data service. The dataset was classified into three accident severity categories, which are fatal, serious, and slight. Classification algorithms use prior knowledge as training data to classify data objects into groups, which is good for us to work with. The models that we used are Multi-layer Perceptron (MLP), Decision Tree classifier, and Random Forest classifier and Naive Bayes classifier. The data extracted from the dataset will make sense to compare and predict a level degree. The tested classification algorithms come up with the results, the decision tree algorithm with an accuracy of 80.74%, the random forest classifier with an accuracy of 85.19%, the Naive Bayes algorithm with an accuracy of 83.40% and the MLP model with an accuracy of 86.67%. These factors of accidents can be important for estimating accident costs, increasing safety, and determining a strategy. Although it is not possible to stop accidents, it aims to reduce injury levels. This study is written in python programming language using Spyder integrated development environment (IDE).
PolygraphMR: Enhancing the Reliability and Dependability of CNNs

Deep neural networks (DNNs) are now starting to emerge in mission critical applications including autonomous vehicles and precision medicine. An important question is the dependability of DNNs and trustworthiness of their predictions. Considering the irreparable damage that can be caused by mispredictions, assessment of their potential misbehavior is necessary for safe deployment. In this paper, we first show the deficiency of current confidence-based methods as reliability measurement, and assess the effectiveness of traditional architecture reliability methods such as modular redundancy (MR). Then, we propose PolygraphMR and show that the combination of input preprocessing, smarter decision policies, and inclusion of prediction confidences can substantially improve the effectiveness of MR for DNNs. Next, we show how to prohibit explosive growth in the cost of MR by the help of reduced-precision designs and staged activations. Across six benchmarks, PolygraphMR detects an average of 33.5% of the baseline mispredictions with less than 2x overhead.
A Fast Parallel Particle Filter for Shared Memory Systems

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF applications have demanding accuracy and run-time constraints that can be addressed through parallel computing. However, an efficient parallelization of PFs can only be achieved by effectively parallelizing the bottleneck: resampling and its constituent redistribution step. A pre-existing implementation of redistribute on Shared Memory Architectures (SMAs) achieves O(NT log2N) time complexity over T parallel cores. This redistribute implementation is, however, highly computationally intensive and cannot be effectively parallelized due to the inherently limited number of cores of SMAs. In this paper, we propose a novel parallel redistribute on OpenMP 4.5 which takes O(N/T + log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N) steps and fully exploits the computational power of SMAs. The proposed approach is up to six times faster than the O(N/T log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N) one and its implementation on GPU provides a further three-time speed-up vs its equivalent on a 32-core CPU. We also show on an exemplary PF that our redistribution is no longer the bottleneck.
A New Approach of Minimizing Midas Touch Problem for a Tracer-Free Tongue-Controlled Assistive Technology

Tongue-controlled assistive technologies (ATs) have a great potential to improve the quality of life for people with severe disabilities. Current tongue-controlled ATs, however, require either a tracer to be attached on the tongue or a complicated gesture/mechanism to avoid issuing commands unintendedly during routine intraoral events (e.g., tongue resting, swallowing, and speaking), which poses a high entry barrier to potential users. In this study, we propose a tracer-free tongue-controlled AT using four optical distance sensors and one ambient light sensor with a multi-sensor data fusion algorithm to distinguish the intended tongue contacts from the unintended ones. Eleven healthy human subjects participated in the study to evaluate the performance using the proposed technology in masking unintendedly-issued commands and navigating a mouse cursor on a computer. The proposed multi-sensor data fusion algorithm could mask 100%, 86.99%, and 99.50% of unintendedly-issued commands during tongue resting, swallowing, and speaking, respectively. The efficacy of the tongue commands in mouse navigation was evaluated by command-issuing task and maze-navigation task. The participants could issue tongue commands in a random order with 97.16±0.94% of accuracy. The completion time and the sum of deviation of the maze-navigation task were 31.36±2.74 s and 9.38±0.85 pixel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The subjects' performance on computer-access tasks, using the proposed tracer-free tongue-controlled technology, is at the level of the performance measured with existing tracer-based tongue-controlled technologies, while securing robustness in masking commands, unintendedly issued during routine intraoral events. Results of this preliminary study suggest that the tracer-free implementation is feasible option for the tongue-controlled ATs.
Multivariate Hidden Markov Models for disease progression

Abstract Disease progression models are a powerful tool for understanding the development of a disease, given some clinical measurements obtained from longitudinal events related to a sample of patients. These models are able to give some insights about the disease progression through the analysis of patients histories and can be also used to predict the future course of the disease for an individual. In particular, Hidden Markov Models are suitable for disease progression since they model the latent unobservable states of the disease. In this work, we propose a HMM where the outcome is multivariate and its components are not independent; to accomplish our aim, since we do not make any usual normality assumptions, we model the outcome using copulas. We first test the performance of our model in a simulation setting and show the validity of the method. Then, we study the course of Heart Failure, applying our model to an administrative dataset from Lombardia Region in Italy, showing how episodes of hospitalization can give information about the disease status of a patient.