Reviewerly/exHarmony huggingface dataset:
Anomaly Detection on Time Series with Wasserstein GAN applied to PHM

Modern vehicles are more and more connected. For instance, in the aerospace industry, newer aircraft are already equipped with data concentrators and enough wireless connectivity to transmit sensor data collected during the whole flight to the ground, usually when the airplane is at the gate. Moreover, platforms that were not designed with such capability can be retrofitted to install devices that enable wireless data collection,as is done on Airbus A320 family. For military and heavy helicopters, HUMS (Health and Usage Monitoring System) also allows the collection of sensor data. Finally, satellites send continuously to the ground sensor data, called telemetries. Most of the time, fortunately, the platforms behave normally, faults and failures are thus rare. In order to go beyond corrective or preventive maintenance, and anticipate future faults and failures, we have to look for any drift, any change, in systems’ behavior, in data that is normal almost all the time. Moreover, collected sensor data is time series data. The problem is then anomaly detection or novelty detection in time series data. Among machine learning techniques that can be used to analyze data, Deep Learning, especially Convolutional Neural Networks, is very popular since it has surpassed human capacities for image classification and object detection. In this field, Generative Adversarial Networks are a technique to generate data similar to a potentially high dimension original dataset. In our case, generate new data could be useful to enrich the learning dataset with generated abnormal data to make it less unbalanced. Yet we are more interested in the potential of such techniques to perform anomaly detection for high dimensional data, comparing newly observed data with data that could have been generated from a distribution built from normal examples.
Theory Exploration Powered by Deductive Synthesis

Abstract This paper presents a symbolic method for automatic theorem generation based on deductive inference. Many software verification and reasoning tasks require proving complex logical properties; coping with this complexity is generally done by declaring and proving relevant sub-properties. This gives rise to the challenge of discovering useful sub-properties that can assist the automated proof process. This is known as the theory exploration problem, and so far, predominant solutions that emerged rely on evaluation using concrete values. This limits the applicability of these theory exploration techniques to complex programs and properties. In this work, we introduce a new symbolic technique for theory exploration, capable of (offline) generation of a library of lemmas from a base set of inductive data types and recursive definitions. Our approach introduces a new method for using abstraction to overcome the above limitations, combining it with deductive synthesis to reason about abstract values. Our implementation has shown to find more lemmas than prior art, avoiding redundant lemmas (in terms of provability), while being faster in most cases. This new abstraction-based theory exploration method is a step toward applying theory exploration to software verification and synthesis.
Evaluating Performance and Gameplay of Virtual Reality Sickness Techniques in a First-Person Shooter Game

In virtual reality (VR) games, playability and immersion levels are important because they affect gameplay, enjoyment, and performance. However, they can be adversely affected by VR sickness (VRS) symptoms. VRS can be minimized by manipulating users' perception of the virtual environment via the head-mounted display (HMD). One extreme example is the Teleport mitigation technique, which lets users navigate discretely, skipping sections of the virtual space. Other techniques are less extreme but still rely on controlling what and how much users see via the HMD. This research examines the effect on players' performance and gameplay of these mitigation techniques in fast-paced VR games. Our focus is on two types of visual reduction techniques. This study aims to identify specifically the trade-offs these techniques have in a first-person shooter game regarding immersion, performance, and VRS. The main contributions in this paper are (1) a deeper understanding of one of the most popular techniques (Teleport) when it comes to gameplay; (2) the replication and validation of a novel VRS mitigation technique based on visual reduction; and (3) a comparison of their effect on players' performance and gameplay.
A Sketch-Transformer Network for Face Photo-Sketch Synthesis

We present a face photo-sketch synthesis model, which converts a face photo into an artistic face sketch or recover a photo-realistic facial image from a sketch portrait. Recent progress has been made by convolutional neural networks (CNNs) and generative adversarial networks (GANs), so that promising results can be obtained through real-time end-to-end architectures. However, convolutional architectures tend to focus on local information and neglect long-range spatial dependency, which limits the ability of existing approaches in keeping global structural information. In this paper, we propose a Sketch-Transformer network for face photo-sketch synthesis, which consists of three closely-related modules, including a multi-scale feature and position encoder for patch-level feature and position embedding, a self-attention module for capturing long-range spatial dependency, and a multi-scale spatially-adaptive de-normalization decoder for image reconstruction. Such a design enables the model to generate reasonable detail texture while maintaining global structural information. Extensive experiments show that the proposed method achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.
Efficient Neural Network Verification via Layer-based Semidefinite Relaxations and Linear Cuts

We introduce an efficient and tight layer-based semidefinite relaxation for verifying local robustness of neural networks. The improved tightness is the result of the combination between semidefinite relaxations and linear cuts. We obtain a computationally efficient method by decomposing the semidefinite formulation into layerwise constraints. By leveraging on chordal graph decompositions, we show that the formulation here presented is provably tighter than current approaches. Experiments on a set of benchmark networks show that the approach here proposed enables the verification of more instances compared to other relaxation methods. The results also demonstrate that the SDP relaxation here proposed is one order of magnitude faster than previous SDP methods.
Logic Explained Networks

The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their decisions. In situations in which the machine is expected to support the decision of human experts, providing a comprehensible explanation is a feature of crucial importance. The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience. In this paper, we propose a general approach to Explainable Artificial Intelligence in the case of neural architectures, showing how a mindful design of the networks leads to a family of interpretable deep learning models called Logic Explained Networks (LENs). LENs only require their inputs to be human-understandable predicates, and they provide explanations in terms of simple First-Order Logic (FOL) formulas involving such predicates. LENs are general enough to cover a large number of scenarios. Amongst them, we consider the case in which LENs are directly used as special classifiers with the capability of being explainable, or when they act as additional networks with the role of creating the conditions for making a black-box classifier explainable by FOL formulas. Despite supervised learning problems are mostly emphasized, we also show that LENs can learn and provide explanations in unsupervised learning settings. Experimental results on several datasets and tasks show that LENs may yield better classifications than established white-box models, such as decision trees and Bayesian rule lists, while providing more compact and meaningful explanations.
Discourse-Level Event Temporal Ordering with Uncertainty-Guided Graph Completion

Learning to order events at discourse-level is a crucial text understanding task. Despite many efforts for this task, the current state-of-the-art methods rely heavily on manually designed features, which are costly to produce and are often specific to tasks/domains/datasets. In this paper, we propose a new graph perspective on the task, which does not require complex feature engineering but can assimilate global features and learn inter-dependencies effectively. Specifically, in our approach, each document is considered as a temporal graph, in which the nodes and edges represent events and event-event relations respectively. In this sense, the temporal ordering task corresponds to constructing edges for an empty graph. To train our model, we design a graph mask pre-training mechanism, which can learn inter-dependencies of temporal relations by learning to recover a masked edge following graph topology. In the testing stage, we design an certain-first strategy based on model uncertainty, which can decide the prediction orders and reduce the risk of error propagation. The experimental results demonstrate that our approach outperforms previous methods consistently and can meanwhile maintain good global consistency.
How Does the Use of the Augmented Reality Technology in Mathematics Education Affect Learning Processes?: A Systematic Review

This study aimed to examine the methodological trends of studies using the Augmented Reality (AR) technology in mathematics education and reveal basic findings obtained from the studies. In this context, a systematic review process was conducted in the Web of Science database and 86 studies obtained as a consequence were reviewed. In this systematic review study, the content analysis method has been used to analyze the data. As a result of analyzing the studies reviewed, it was observed that related studies increased as from 2010, and the qualitative method was the most frequently used. It was determined that materials in the studies were usually designed using the Unity3D and Vuforia platforms. In the studies reviewed, it was seen that the most frequently faced advantages of AR in mathematics education were that it supported learning and motivation and enhanced the spatial abilities of students. Additionally, the most frequent disadvantages of AR in mathematics education were that it caused technical inconveniences and it is difficult to develop materials through AR. Students show resistance against new technology, and it causes health problems in extended use. Finally, recommendations for researchers and practitioners have been presented based on the findings obtained from this systematic review.
MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering

Spoken question answering (SQA) has recently drawn considerable attention in the speech community. It requires systems to find correct answers from the given spoken passages simultaneously. The common SQA systems consist of the automatic speech recognition (ASR) module and text-based question answering module. However, previous methods suffer from severe performance degradation due to ASR errors. To alleviate this problem, this work proposes a novel multi-modal residual knowledge distillation method (MRD-Net), which further distills knowledge at the acoustic level from the audio-assistant (Audio-A). Specifically, we utilize the teacher (T) trained on manual transcriptions to guide the training of the student (S) on ASR transcriptions. We also show that introducing an Audio-A helps this procedure by learning residual errors between T and S. Moreover, we propose a simple yet effective attention mechanism to adaptively leverage audio-text features as the new deep attention knowledge to boost the network performance. Extensive experiments demonstrate that the proposed MRD-Net achieves superior results compared with state-of-the-art methods on three spoken question answering benchmark datasets.
Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation

Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.
A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value

Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature selection is a technical approach that can effectively reduce the data dimension. In the study of feature selection, the multi-objective optimization algorithm has shown an excellent global optimization performance. The Pareto relationship can handle contradictory objectives in the multi-objective problem well. Therefore, a Shapley value-fused feature selection algorithm for multi-label learning (SHAPFS-ML) is proposed. The method takes multi-label criteria as the optimization objectives and the proposed crossover and mutation operators based on Shapley value are conducive to identifying relevant, redundant and irrelevant features. The comparison of experimental results on real-world datasets reveals that SHAPFS-ML is an effective feature selection method for multi-label classification, which can reduce the classification algorithm’s computational complexity and improve the classification accuracy.
Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate

Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation.Emoji-based hate is an emerging challenge for automated detection.We present HATEMOJICHECK, a test suite of 3,930 short-form statements that allows us to evaluate performance on hateful language expressed with emoji.Using the test suite, we expose weaknesses in existing hate detection models.To address these weaknesses, we create the HATEMOJIBUILD dataset using a human-andmodel-in-the-loop approach.Models built with these 5,912 adversarial examples perform substantially better at detecting emoji-based hate, while retaining strong performance on text-only hate.Both HATEMOJICHECK and HATEMOJI-BUILD are made publicly available. 1 Content Warning This article contains examples of hateful language from HATEMOJICHECK to illustrate its composition.Examples are quoted verbatim, except for slurs and profanity in text, for which the first vowel is replaced with an asterisk.The authors oppose the use of hateful language.
OAuth 2.0 Authorization using Blockchain-based Tokens

OAuth 2.0 is the industry-standard protocol for authorization.It facilitates secure service provisioning, as well as secure interoperability among diverse stakeholders.All OAuth 2.0 protocol flows result in the creation of an access token, which is then used by a user to request access to a protected resource.Nevertheless, the definition of access tokens is transparent to the OAuth 2.0 protocol, which does not specify any particular token format, how tokens are generated, or how they are used.Instead, the OAuth 2.0 specification leaves all these as design choices for integrators.In this paper, we propose a new type of OAuth 2.0 token backed by a distributed ledger.Our construction is secure, and it supports proof-of-possession, auditing, and accountability.Furthermore, we provide added-value token management services, including revocation, delegation, and fair exchange, by leveraging smart contracts.We realized a proof-ofconcept implementation of our solution using Ethereum smart contracts and the ERC-721 token specification.
Object Removal Attacks on LiDAR-based 3D Object Detectors

LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and their safe operations.Recent works have demonstrated that it is possible to spoof LiDAR return signals to elicit fake objects.In this work we demonstrate how the same physical capabilities can be used to mount a new, even more dangerous class of attacks, namely Object Removal Attacks (ORAs).ORAs aim to force 3D object detectors to fail.We leverage the default setting of LiDARs that record a single return signal per direction to perturb point clouds in the region of interest (RoI) of 3D objects.By injecting illegitimate points behind the target object, we effectively shift points away from the target objects' RoIs.Our initial results using a simple random point selection strategy show that the attack is effective in degrading the performance of commonly used 3D object detection models.
Collision avoidance for autonomous vehicle using MPC and time varying Sigmoid safety constraints

This paper deals with the planning of collision-free trajectory for autonomous vehicle on highway. The problem is formulated using Model Predictive Control (MPC) where a simplified prediction model that integrates the relative positions and velocities between the surrounding vehicles and the ego vehicle is employed. The MPC provides the necessary lateral and longitudinal accelerations to the ego vehicle to track the reference path and to drive at the desired speed without hitting the road boundaries or colliding with the neighbouring vehicles. The collision avoidance is guaranteed through the integration of a time varying, Sigmoid-based, safety constraint. This constraint plays the role of an S-shaped barrier, enforcing the host vehicle to move apart and change the lane when the inter-vehicular safety distance is not kept. The proposed approach is focused on avoiding collision by performing lane change and overtaking maneuvers since in many traffic situations, an imminent collision cannot be avoided by braking. Satisfactory results are obtained from the set of the simulated scenarios.
Zero-error attack against coherent-one-way quantum key distribution

Abstract Coherent-one-way (COW) quantum key distribution (QKD) held the promise of distributing secret keys over long distances with a simple experimental setup. Indeed, this scheme is currently used in commercial applications. Surprisingly, however, it has been recently shown that its secret key rate scales at most quadratically with the system’s transmittance and, thus, it is not appropriate for long distance QKD transmission. Such pessimistic result was derived by employing a so-called zero-error attack, in which the eavesdropper does not introduce any error, but still the legitimate users of the system cannot distill a secure key. Here, we present an improved zero-error attack against COW-QKD that significantly outperforms previous approaches, and provides stronger restrictions on its maximum achievable distance in the absence of errors. This translates into an upper bound on its secret key rate that is more than an order of magnitude lower than previously known upper bounds.
Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation.
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation

Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation.However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation.To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation.The unique scheme of tree-structured reasoning views traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed.Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.