Reviewerly/exHarmony huggingface dataset:
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors

Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work on explanations through feature importance of approximate linear models has moved from input-level features (pixels or segments) to features from mid-layer feature maps in the form of concept activation vectors (CAVs). CAVs contain concept-level information and could be learned via clustering. In this work, we rethink the ACE algorithm of Ghorbani et~al., proposing an alternative invertible concept-based explanation (ICE) framework to overcome its shortcomings. Based on the requirements of fidelity (approximate models to target models) and interpretability (being meaningful to people), we design measurements and evaluate a range of matrix factorization methods with our framework. We find that non-negative concept activation vectors (NCAVs) from non-negative matrix factorization provide superior performance in interpretability and fidelity based on computational and human subject experiments. Our framework provides both local and global concept-level explanations for pre-trained CNN models.
Grounded Language Learning Fast and Slow

Recent work has shown that large text-based neural language models acquire a surprising propensity for one-shot learning. Here, we show that an agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with conventional RL algorithms. After a single introduction to a novel object via visual perception and language (This is a dax), the agent can manipulate the object as instructed (Put the dax on the bed), combining short-term, within-episode knowledge of the nonsense word with long-term lexical and motor knowledge. We find that, under certain training conditions and with a particular memory writing mechanism, the agent's one-shot word-object binding generalizes to novel exemplars within the same ShapeNet category, and is effective in settings with unfamiliar numbers of objects. We further show how dual-coding memory can be exploited as a signal for intrinsic motivation, stimulating the agent to seek names for objects that may be useful later. Together, the results demonstrate that deep neural networks can exploit meta-learning, episodic memory and an explicitly multi-modal environment to account for 'fast-mapping', a fundamental pillar of human cognitive development and a potentially transformative capacity for artificial agents.
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets

Deep neural nets (DNNs) compression is crucial for adaptation to mobile devices. Though many successful algorithms exist to compress naturally trained DNNs, developing efficient and stable compression algorithms for robustly trained DNNs remains widely open. In this paper, we focus on a co-design of efficient DNN compression algorithms and sparse neural architectures for robust and accurate deep learning. Such a co-design enables us to advance the goal of accommodating both sparsity and robustness. With this objective in mind, we leverage the relaxed augmented Lagrangian based algorithms to prune the weights of adversarially trained DNNs, at both structured and unstructured levels. Using a Feynman-Kac formalism principled robust and sparse DNNs, we can at least double the channel sparsity of the adversarially trained ResNet20 for CIFAR10 classification, meanwhile, improve the natural accuracy by 8.69% and the robust accuracy under the benchmark 20 iterations of IFGSM attack by 5.42%.
An analysis of the graph processing landscape

The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one of many digital representations. Extracting information from graphs involves processing all their elements globally, which can be done with single-machine systems (with varying approaches to hardware usage), distributed systems (either homogeneous or heterogeneous groups of machines) and systems dedicated to high-performance computing (HPC). For these systems focused on processing the bulk of graph elements, common use-cases consist in executing for example algorithms for vertex ranking or community detection, which produce insights on graph structure and relevance of their elements. Many distributed systems (such as Flink, Spark) and libraries (e.g. Gelly, GraphX) have been built to enable these tasks and improve performance. This is achieved with techniques ranging from classic load balancing (often geared to reduce communication overhead) to exploring trade-offs between delaying computation and relaxing accuracy. In this survey we firstly familiarize the reader with common graph datasets and applications in the world of today. We provide an overview of different aspects of the graph processing landscape and describe classes of systems based on a set of dimensions we describe. The dimensions we detail encompass paradigms to express graph processing, different types of systems to use, coordination and communication models in distributed graph processing, partitioning techniques and different definitions related to the potential for a graph to be updated. This survey is aimed at both the experienced software engineer or researcher as well as the graduate student looking for an understanding of the landscape of solutions (and their limitations) for graph processing.
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies

Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. Our proposed RNN is based on a time-discretization of a system of second-order ordinary differential equations, modeling networks of controlled nonlinear oscillators. We prove precise bounds on the gradients of the hidden states, leading to the mitigation of the exploding and vanishing gradient problem for this RNN. Experiments show that the proposed RNN is comparable in performance to the state of the art on a variety of benchmarks, demonstrating the potential of this architecture to provide stable and accurate RNNs for processing complex sequential data.
Reduction of the molecular hamiltonian matrix using quantum community detection

Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree-Fock methods as future advances in quantum hardware become available.
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate

In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS observation protocol that is widely used in educational research. ACORN uses convolutional neural networks to analyze spectral audio features, the faces of teachers and students, and the pixels of each image frame, and then integrates this information over time using Temporal Convolutional Networks. The audiovisual ACORN's PC and NC predictions have Pearson correlations of $0.55$ and $0.63$ with ground-truth scores provided by expert CLASS coders on the UVA Toddler dataset (cross-validation on $n=300$ 15-min video segments), and a purely auditory ACORN predicts PC and NC with correlations of $0.36$ and $0.41$ on the MET dataset (test set of $n=2000$ videos segments). These numbers are similar to inter-coder reliability of human coders. Finally, using Graph Convolutional Networks we make early strides (AUC=$0.70$) toward predicting the specific moments (45-90sec clips) when the PC is particularly weak/strong. Our findings inform the design of automatic classroom observation and also more general video activity recognition and summary recognition systems.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for $$\times $$ 2, $$\times $$ 3 and $$\times $$ 4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
4D surface shape measurement system with high spectral resolution and great depth accuracy

A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems’ image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.
Application of Machine Learning in Industry 4.0

The fourth industrial revolution, Industry 4.0, is concerned with the ongoing changes and advances in the way of manufacturing and processes involved for the production of goods and their efficiency which is increasing rapidly with the advancement in the machine learning techniques and other technologies. Industry 4.0 encapsulates future industry development trends and techniques so as to achieve more advanced and intelligent manufacturing processes. Making use of smart, advanced, and automated machines, products are manufactured in more efficient, customized, and smarter way. It led a way of smart production lines which improved flexibility for manufacturers to experiment with different product designs and configurations—a strategic initiative introduced by the German government. It is a step toward transformation of industrial manufacturing through digitalization and exploitation of potentials of new approaches and technologies. With the access to usage data from the machine and product’s feedback, overall efficiency of production is improved. The effect of digitization and advancement in technology strives to make life simpler.
Gaze Estimation via a Differential Eyes’ Appearances Network with a Reference Grid

A person’s eye gaze can effectively express that person’s intentions. Thus, gaze estimation is an important approach in intelligent manufacturing to analyze a person’s intentions. Many gaze estimation methods regress the direction of the gaze by analyzing images of the eyes, also known as eye patches. However, it is very difficult to construct a person-independent model that can estimate an accurate gaze direction for every person due to individual differences. In this paper, we hypothesize that the difference in the appearance of each of a person’s eyes is related to the difference in the corresponding gaze directions. Based on this hypothesis, a differential eyes’ appearances network (DEANet) is trained on public datasets to predict the gaze differences of pairwise eye patches belonging to the same individual. Our proposed DEANet is based on a Siamese neural network (SNNet) framework which has two identical branches. A multi-stream architecture is fed into each branch of the SNNet. Both branches of the DEANet that share the same weights extract the features of the patches; then the features are concatenated to obtain the difference of the gaze directions. Once the differential gaze model is trained, a new person’s gaze direction can be estimated when a few calibrated eye patches for that person are provided. Because person-specific calibrated eye patches are involved in the testing stage, the estimation accuracy is improved. Furthermore, the problem of requiring a large amount of data when training a person-specific model is effectively avoided. A reference grid strategy is also proposed in order to select a few references as some of the DEANet’s inputs directly based on the estimation values, further thereby improving the estimation accuracy. Experiments on public datasets show that our proposed approach outperforms the state-of-the-art methods.
Practical Threshold Multi-Factor Authentication

Multi-factor authentication (MFA) has been widely used to safeguard high-value assets. Unlike single-factor authentication (e.g., password-only login), t-factor authentication ( tFA) requires a user always to carry and present t specified factors so as to strengthen the security of login. Nevertheless, this may restrict user experience in limiting the flexibility of factor usage, e.g., the user may prefer to choose any factors at hand for login authentication. To bring back usability and flexibility without loss of security, we introduce a new notion of authentication, called (t,n) threshold MFA, that allows a user to actively choose t factors out of n based on preference. We further define the “most-rigorous” multi-factor security model for the new notion, allowing attackers to control public channels, launch active/passive attacks, and compromise/corrupt any subset of parties as well as factors. We state that the model can capture the most practical security needs in the literature. We design a threshold MFA key exchange (T-MFAKE) protocol built on the top of a threshold oblivious pseudorandom function and an authenticated key exchange protocol. Our protocol achieves the “highest-attainable” security against all attacking attempts in the context of parties/factors being compromised/corrupted. As for efficiency, our design only requires 4+t exponentiations, 2 multi-exponentiations and <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</b> communication rounds. Compared with existing tFA schemes, even the degenerated (t,t) version of our protocol achieves the strongest security (stronger than most schemes) and higher efficiency on computational and communication. We instantiate our design on real-world platform to highlight its practicability and efficiency.
Logic-Consistency Text Generation from Semantic Parses

Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.This is challenging due to two reasons: (1) the complex and intensive inner logic with the data scarcity constraint, (2) the lack of automatic evaluation metrics for logic consistency.To address these two challenges, this paper first proposes SNOWBALL, a framework for logic consistent text generation from semantic parses that employs an iterative training procedure by recursively augmenting the training set with quality control.Second, we propose a novel automatic metric, BLEC, for evaluating the logical consistency between the semantic parses and generated texts.The experimental results on two benchmark datasets, Logic2Text and Spider, demonstrate the SNOWBALL framework enhances the logic consistency on both BLEC and human evaluation.Furthermore, our statistical analysis reveals that BLEC is more logically consistent with human evaluation than general-purpose automatic metrics including BLEU, ROUGE and, BLEURT.Our data and code are available at https://github.com/Ciaranshu/relogic.
Why Machine Reading Comprehension Models Learn Shortcuts?

Recent studies report that many machine reading comprehension (MRC) models can perform closely to or even better than humans on benchmark datasets.However, existing works indicate that many MRC models may learn shortcuts to outwit these benchmarks, but the performance is unsatisfactory in real-world applications.In this work, we attempt to explore, instead of the expected comprehension skills, why these models learn the shortcuts.Based on the observation that a large portion of questions in current datasets have shortcut solutions, we argue that larger proportion of shortcut questions in training data make models rely on shortcut tricks excessively.To investigate this hypothesis, we carefully design two synthetic datasets with annotations that indicate whether a question can be answered using shortcut solutions.We further propose two new methods to quantitatively analyze the learning difficulty regarding shortcut and challenging questions, and revealing the inherent learning mechanism behind the different performance between the two kinds of questions.A thorough empirical analysis shows that MRC models tend to learn shortcut questions earlier than challenging questions, and the high proportions of shortcut questions in training sets hinder models from exploring the sophisticated reasoning skills in the later stage of training.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications.However, we identify two biases in existing datasets for XDTS: (1) a high proportion of contextindependent questions and (2) a high proportion of easy SQL queries.These biases conceal the major challenges in XDTS to some extent.In this work, we present CHASE, a large-scale and pragmatic Chinese dataset for XDTS.It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are contextindependent, and 28% of SQL queries are easy.We experiment on CHASE with three state-ofthe-art XDTS approaches.The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that CHASE highlights the challenging problems of XDTS.We believe that CHASE can provide fertile soil for addressing the problems.
Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization algorithm and model predictive control.In the trajectory planning layer, the vehicle kinematics model suitable for the low-velocity parking scene is established.Considering the vehicle physical constraints, boundary condition constraints, and obstacle avoidance constraints during the parking process, the parking trajectory planning task is described as an optimal control problem, further transformed into a nonlinear programming problem by Gauss pseudo-spectral method.Taking the shortest parking completion time as the optimization objective function, the parking trajectories of the large, medium and small parking spaces are obtained, respectively.A parking trajectory tracking controller based on the model predictive control algorithm is designed in the trajectory tracking control layer.The linear error model is used as the prediction model, and the quadratic programming is adopted as the rolling optimization algorithm in the tracking controller.The velocity and front-wheel swing angle are obtained as control signals for parking trajectory tracking.Through CarSim and Simulink's co-simulation, the feasibility and effectiveness of the proposed parallel parking trajectory planning and tracking control method are verified.The co-simulation results show that the maximum tracking errors of horizontal and longitudinal positions are less than 0.15m.The maximum tracking errors of heading angle are less than 2 • under three different parking spaces.Real vehicle tests are carried out to verify the effectiveness of the proposed hierarchical control method.The test results show that the vehicle can park in the parking space safely, quickly and accurately when the actual parking space is detected.The proposed method can plan the parking trajectory with the constraints and the shortest time and control the vehicle to complete the parking operation accurately along the planned trajectory.
Cyberattacks against the health-care sectors during the COVID-19 pandemic

Purpose This paper aims to analyze the changes in cyberattacks against the health-care sector during the COVID-19 pandemic. Design/methodology/approach The changes in cyberattacks of the health-care sector are analyzed by examination of the number and essence of published news concerning cybersecurity attacks on the health-care sector during 2019 and compared them to those published during 2020, based on two main websites, which review such incidents. Findings This study found that there was a significant growth in reports of cyberattacks on the health-care sector. Moreover, the number of cyberattacks fit interestingly to the pattern of waves of the disease, which expanded worldwide. During the first wave the number of reports was doubled or even tripled, compared to the same period in 2019, a tendency that was slightly waned afterwards. Practical implications This study helps to deepen the awareness of information security implications of a potential global devastating crisis, even in the cybersecurity domain, and on the health-care sector, among various other affected sectors and domains. Social implications COVID-19 pandemic created long-term wide-range changes that affect every individual and sector, mainly owing to the shift to remote working model, which impose long-term new cybersecurity changes, among them to the health-care industry. Originality/value This paper extends the existing information on implication of remote working model on information security and of the COVID-19 pandemic on the cybersecurity of health-care institutions around the world.