OpenRaiser/Intern-Atlas huggingface dataset:
Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden
Venue: arXiv cs.CY 2020
Year: 2020

Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the plaintiff succeed against a public authority as defendant.
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
Venue: arXiv cs.CY 2020
Year: 2020

In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation such as trains. In this paper, we aim to bridge the gap by introducing FRSign, a large-scale and accurate dataset for vision-based railway traffic light detection and recognition. Our recordings were made on selected running trains in France and benefited from carefully hand-labeled annotations. An illustrative dataset which corresponds to ten percent of the acquired data to date is published in open source with the paper. It contains more than 100,000 images illustrating six types of French railway traffic lights and their possible color combinations, together with the relevant information regarding their acquisition such as date, time, sensor parameters, and bounding boxes. This dataset is published in open-source at the address \url{https://frsign.irt-systemx.fr}. We compare, analyze various properties of the dataset and provide metrics to express its variability. We also discuss specific challenges and particularities related to autonomous trains in comparison to autonomous cars.
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
Venue: arXiv cs.CY 2020
Year: 2020

Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds on Titanic. People can talk with dr_ant about different aspects of the model to understand the rationale behind its predictions. Having collected a corpus of 1000+ dialogues, we analyse the most common types of questions that users would like to ask. To our knowledge, it is the first study which uses a conversational system to collect the needs of human operators from the interactive and iterative dialogue explorations of a predictive model.
Conditional Disclosure of Secrets: A Noise and Signal Alignment Approach
Venue: arXiv cs.IT 2020
Year: 2020

In the conditional disclosure of secrets (CDS) problem, Alice and Bob (each holds an input and a common secret) wish to disclose, as efficiently as possible, the secret to Carol if and only if their inputs satisfy some function. The capacity of CDS is the maximum number of bits of the secret that can be securely disclosed per bit of total communication. We characterize the necessary and sufficient condition for the extreme case where the capacity of CDS is the highest and is equal to 1/2. For the simplest instance where the capacity is smaller than 1/2, we show that the linear capacity is 2/5.
The Efficiency of Human Cognition Reflects Planned Information Processing
Venue: arXiv cs.AI 2020
Year: 2020

Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their actions. Put another way, people should also "plan their plans". Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people's reaction times reflect a planned use of information processing, consistent with our account. This formulation of planning to plan provides new insight into the function of hierarchical planning, state abstraction, and cognitive control in both humans and machines.
Compensation of Linear Attacks to Cyber Physical Systems through ARX System Identification
Venue: arXiv cs.CR 2020
Year: 2020

Cyber-Physical Systems (CPSs) are vastly used in today's cities critical infrastructure. The cyber part of these systems usually has a network component through which cyber attacks can be launched. In this paper, we first design an intrusion detection system (IDS) by identifying the plant. We assume the initial operation period of the CPS is attack-free and learn the plant model. Then, we compare the expected output found via the identifier with the real one coming through the feedback link. Any difference greater than a threshold is deemed to be an anomaly. To compensate, once the IDS flags a change in the loop, we restart the system identification to find the new transfer function. With the estimation of the new transfer function at hand, a new controller is designed to keep the system stable. To test the idea, we took a DC motor as the plant and employed ARX identifier. MATLAB Simulink environment was used to test the proposed intrusion detection and compensation framework. We applied a set of deception attacks to the forward channel in our experiments. The obtained results prove that our detection strategy works well and timely reacts to anomalies. Moreover, they show that the compensation strategy is also effective and keeps the system stable under such attacks.
Statistical Learning with Conditional Value at Risk
Venue: arXiv cs.LG 2020
Year: 2020

We propose a risk-averse statistical learning framework wherein the performance of a learning algorithm is evaluated by the conditional value-at-risk (CVaR) of losses rather than the expected loss. We devise algorithms based on stochastic gradient descent for this framework. While existing studies of CVaR optimization require direct access to the underlying distribution, our algorithms make a weaker assumption that only i.i.d.\ samples are given. For convex and Lipschitz loss functions, we show that our algorithm has $O(1/\sqrt{n})$-convergence to the optimal CVaR, where $n$ is the number of samples. For nonconvex and smooth loss functions, we show a generalization bound on CVaR. By conducting numerical experiments on various machine learning tasks, we demonstrate that our algorithms effectively minimize CVaR compared with other baseline algorithms.
An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset
Venue: arXiv cs.CV 2020
Year: 2020

The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While~the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers' proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo's self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. In addition, a visualization tool is presented for verifying the performance of the model.
Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention
Venue: arXiv cs.CV 2020
Year: 2020

Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that shows high generalization performance and accuracy. To improve the generalization performance, we initially propose an auto-context algorithm in a single CNN. The proposed auto-context neural network exploits an effective high-level residual estimation to obtain the shape prior. Identical dual paths are effectively trained to represent mutual complementary features for an accurate posterior analysis of a liver. Further, we extend our network by employing a self-supervised contour scheme. We trained sparse contour features by penalizing the ground-truth contour to focus more contour attentions on the failures. The experimental results show that the proposed network results in better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance. We used 180 abdominal CT images for training and validation. Two-fold cross-validation is presented for a comparison with the state-of-the-art neural networks. Novel multiple N-fold cross-validations are conducted to verify the performance of generalization. The proposed network showed the best generalization performance among the networks. Additionally, we present a series of ablation experiments that comprehensively support the importance of the underlying concepts.
eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processing
Venue: arXiv cs.DC 2020
Year: 2020

Complex event processing systems process the input event streams on-the-fly. Since input event rate could overshoot the system's capabilities and results in violating a defined latency bound, load shedding is used to drop a portion of the input event streams. The crucial question here is how many and which events to drop so the defined latency bound is maintained and the degradation in the quality of results is minimized. In stream processing domain, different load shedding strategies have been proposed but they mainly depend on the importance of individual tuples (events). However, as complex event processing systems perform pattern detection, the importance of events is also influenced by other events in the same pattern. In this paper, we propose a load shedding framework called eSPICE for complex event processing systems. eSPICE depends on building a probabilistic model that learns about the importance of events in a window. The position of an event in a window and its type are used as features to build the model. Further, we provide algorithms to decide when to start dropping events and how many events to drop. Moreover, we extensively evaluate the performance of eSPICE on two real-world datasets.
MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
Venue: arXiv cs.CV 2020
Year: 2020

Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Venue: arXiv cs.CV 2020
Year: 2020

Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4-Dimensional (3-space + 1-time) tensors. And it is widely believed that the spatio-temporal patterns in fMRI manifests as behaviour and clinical symptoms. Because of the high dimensionality ($\sim$ 1 Million) of fMRI, and the added constraints of limited cardinality of data sets, extracting such patterns are challenging. A standard approach to overcome these hurdles is to reduce the dimensionality of the data by either summarizing activation over time or space at the expense of possible loss of useful information. Here, we introduce an end-to-end algorithm capable of extracting spatiotemporal features from the full 4-D data using 3-D CNNs and 3-D Convolutional LSTMs. We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from resting-state fMRI data. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively.
A Retrodirective Wireless Power Transfer Scheme for Ambient Backscatter Systems
Venue: arXiv cs.IT 2020
Year: 2020

One of the key challenges of the Internet of Things (IoT) is to sustainably power the large number of IoT devices in real-time. In this paper, we consider a wireless power transfer (WPT) scenario between an energy transmitter (ET) capable of retrodirective WPT and an energy receiver (ER) capable of ambient backscatter in the presence of an ambient source (AS). The ER requests WPT by backscattering signals from an AS towards the ET, which then retrodirectively beamforms an energy signal towards the ER. To remove the inherent direct-link ambient interference, we propose a scheme of ambient backscatter training. Specifically, the ER varies the reflection coefficient multiple times while backscattering each ambient symbol according to a certain pattern called the training sequence, whose design criterion we also present. To evaluate the system performance, we derive an analytical expression for the average harvested power at the ER. Our numerical results show that with the proposed scheme, the ER harvests tens of $μ$W of power, without any CSI estimation or active transmission from the ER, which is a significant improvement for low-power and low-cost ambient backscatter devices.
Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances
Venue: arXiv cs.SD 2020
Year: 2020

Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions according to the results obtained for early NIST SRE (Speaker Recognition Evaluation) datasets. From the practical point of view, taking into account the increased interest in virtual assistants (such as Amazon Alexa, Google Home, AppleSiri, etc.), speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances. For these purposes, we considered deep neural network architectures based on TDNN (TimeDelay Neural Network) and ResNet (Residual Neural Network) blocks. We experimented with state-of-the-art embedding extractors and their training procedures. Obtained results confirm that ResNet architectures outperform the standard x-vector approach in terms of speaker verification quality for both long-duration and short-duration utterances. We also investigate the impact of speech activity detector, different scoring models, adaptation and score normalization techniques. The experimental results are presented for publicly available data and verification protocols for the VoxCeleb1, VoxCeleb2, and VOiCES datasets.
A Simple 1-1/e Approximation for Oblivious Bipartite Matching
Venue: arXiv cs.DS 2020
Year: 2020

We study the oblivious matching problem, which aims at finding a maximum matching on a graph with unknown edge set. Any algorithm for the problem specifies an ordering of the vertex pairs. The matching is then produced by probing the pairs following the ordering, and including a pair if both of them are unmatched and there exists an edge between them. The unweighted (Chan et al. (SICOMP 2018)) and the vertex-weighted (Chan et al. (TALG 2018)) versions of the problem are well studied. In this paper, we consider the edge-weighted oblivious matching problem on bipartite graphs, which generalizes the stochastic bipartite matching problem. Very recently, Gamlath et al. (SODA 2019) studied the stochastic bipartite matching problem, and proposed an (1-1/e)-approximate algorithm. We give a very simple algorithm adapted from the Ranking algorithm by Karp et al. (STOC 1990), and show that it achieves the same (1-1/e) approximation ratio for the oblivious matching problem on bipartite graph.
Benchmarking Knowledge Graphs on the Web
Venue: arXiv cs.DB 2020
Year: 2020

The growing interest in making use of Knowledge Graphs for developing explainable artificial intelligence, there is an increasing need for a comparable and repeatable comparison of the performance of Knowledge Graph-based systems. History in computer science has shown that a main driver to scientific advances, and in fact a core element of the scientific method as a whole, is the provision of benchmarks to make progress measurable. This paper gives an overview of benchmarks used to evaluate systems that process Knowledge Graphs.
Eat4Thought: A Design of Food Journaling
Venue: arXiv cs.HC 2020
Year: 2020

Food journaling is an effective method to help people identify their eating patterns and encourage healthy eating habits as it requires self-reflection on eating behaviors. Current tools have predominately focused on tracking food intake, such as carbohydrates, proteins, fats, and calories. Other factors, such as contextual information and momentary thoughts and feelings that are internal to an individual, are also essential to help people reflect upon and change attitudes about eating behaviors. However, current dietary tracking tools rarely support capturing these elements as a way to foster deep reflection. In this work, we present Eat4Thought -- a food journaling application that allows users to track their emotional, sensory, and spatio-temporal elements of meals as a means of supporting self-reflection. The application enables vivid documentation of experiences and self-reflection on the past through video recording. We describe our design process and an initial evaluation of the application. We also provide design recommendations for future work on food journaling.
FQuAD: French Question Answering Dataset
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In order to track the progress of French Question Answering models we propose a leader-board and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/.
Artificial Intelligence, connected products, virtual reality: potential impacts on consumer safety in terms of their physical and psychological ability or well-being
Venue: arXiv cs.CY 2020
Year: 2020

With the progressive digitalisation of a majority of services to communities and individuals, humankind is facing new challenges. While energy sources are rapidly dwindling and rigorous choices have to be made to ensure the sustainability of our environment, there is increasing concern in science and society about the safety of connected products and technology for the individual user. This essay provides a first basis for further inquiry into the risks in terms of potentially negative, short and long-term, effects of connected technologies and massive digitalisation on the psychological and/or physical abilities and well-being of users or consumers.
Docking Haptics: Extending the Reach of Haptics by Dynamic Combinations of Grounded and Worn Devices
Venue: arXiv cs.HC 2020
Year: 2020

Grounded haptic devices can provide a variety of forces but have limited working volumes. Wearable haptic devices operate over a large volume but are relatively restricted in the types of stimuli they can generate. We propose the concept of docking haptics, in which different types of haptic devices are dynamically docked at run time. This creates a hybrid system, where the potential feedback depends on the user's location. We show a prototype docking haptic workspace, combining a grounded six degree-of-freedom force feedback arm with a hand exoskeleton. We are able to create the sensation of weight on the hand when it is within reach of the grounded device, but away from the grounded device, hand-referenced force feedback is still available. A user study demonstrates that users can successfully discriminate weight when using docking haptics, but not with the exoskeleton alone. Such hybrid systems would be able to change configuration further, for example docking two grounded devices to a hand in order to deliver twice the force, or extend the working volume. We suggest that the docking haptics concept can thus extend the practical utility of haptics in user interfaces.