Reviewerly/exHarmony huggingface dataset:
Online Self-Learning Fuzzy Discrete Event Systems

The fuzzy discrete event system theory is unique in that it is capable of modeling a class of event-driven systems as fuzzy automata with states and event-invoked state transitions being ambiguous. At present, the theory lacks a self-learning component, an important topic that has hardly been touched in the literature. In this article, we use stochastic gradient descent to develop online learning algorithms for the fuzzy automata. We uncover an inherent obstacle in the initial derived algorithms that fundamentally restricts their learning capability owing to dependences of the model parameters to be learned. We develop a novel mechanism to not only overcome the obstacle but also make the learning adaptive. Our final algorithms can learn an event transition matrix based on automaton's states before and after the occurrence of a fuzzy event, and learn the transition matrix and multidimensional Gaussian fuzzy sets yielding initial automaton states from relevant input variables and target states. Computer simulation results are presented to show learning performance of the final algorithms.
On signal reconstruction from FROG measurements

Phase retrieval refers to recovering a signal from its Fourier magnitude. This problem arises naturally in many scientific applications, such as ultra-short laser pulse characterization and diffraction imaging. Unfortunately, phase retrieval is ill-posed for almost all one-dimensional signals. In order to characterize a laser pulse and overcome the ill-posedness, it is common to use a technique called Frequency-Resolved Optical Gating (FROG). In FROG, the measured data, referred to as FROG trace, is the Fourier magnitude of the product of the underlying signal with several translated versions of itself. The FROG trace results in a system of phaseless quartic Fourier measurements. In this paper, we prove that it suffices to consider only three translations of the signal to determine almost all bandlimited signals, up to trivial ambiguities. In practice, one usually also has access to the signal's Fourier magnitude. We show that in this case only two translations suffice. Our results significantly improve upon earlier work.
Multi-Scale Deep Neural Networks for Real Image Super-Resolution

Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work. Firstly, due to the high computation complexity in high-resolution spaces, we process an input image mainly in two different downscaling spaces, which could greatly lower the usage of GPU memory. Then, to reconstruct the details of an image, we design a multi-scale residual network (MsRN) in the downscaling spaces based on the residual blocks. Besides, we propose a multi-scale dense network based on the dense blocks to compare with MsRN. Finally, our empirical experiments show the robustness of MsDNN for image SR when the upscaling factor is unknown. According to the preliminary results of NTIRE 2019 image SR challenge, our team (ZXHresearch@fudan) ranks 21-st among all participants. The implementation of MsDNN is released at: https://github.com/shangqigao/gsq-image-SR.
Web Text Categorization Based on Statistical Merging Algorithm in Big Data Environment

In the field of modern information technology, how to find information quickly, accurately and comprehensively that users really needed has become the focus of research in this field. In this article, a feature selection method based on a complex network is proposed for the structure and content characteristics of large-scale web text information. The preprocessed web text is converted into a complex network. The nodes in the network correspond to the entries in the text. The edges of the network correspond to the links between the entries in the text, and the degree of nodes and the aggregation system are used. Second, the text classification method is studied from the point of view of data sampling, and a text classification method based on density statistics is proposed. This method uses not only the density information of the text feature set in the classification process, but also the use of statistical merging criteria to get the text. The difference information of each feature has a better classification effect for large text collections.
Cryptanalyzing an image encryption based on a modified Henon map using hybrid chaotic shift transform

Recently, a novel image encryption scheme has been proposed based on a modified Henon map using hybrid chaotic shift transform. This paper analyzes the security of the original encryption scheme and finds it insecure against the chosen-plaintext attack. Meanwhile, an efficient strategy is proposed to break the original encryption scheme with several chosen-plaintext attacks. The experimental results show that all the keys can be revealed with a time complexity of only O(⌈MNlogc⁡(MN)⌉). Furthermore, some improvement suggestions are proposed.
A Study of Recent Recommender System Techniques

The influx of data in most domains is huge and dynamic, leading to big data and hence the need to build a recommender system grows stronger. This work is a comprehensive survey of the current status of different recommendation approaches, their limitations and extension which when applied may eradicate the incessant information overload problem of web entirely. Further, an investigation is conducted on the Google Scholar database, delineating the temporal distribution of different recommendation techniques. Several popular and most-used evaluation metrics, domain-specific applications, and data sets used in the recommendation are reviewed. By summarizing the current state-of-the-art, this work may help researchers in the field of recommendation system techniques and provides future directions highlighting issues that need to be focused on.
CC-NET: Image Complexity Guided Network Compression for Biomedical Image Segmentation

Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations. Searching for an acceptable compressed representation of the base CNN for a specific imaging application typically involves a series of time-consuming training/validation experiments to achieve a good compromise between network size and accuracy. To address this challenge, we propose CC-Net, a new image complexity-guided CNN compression scheme for biomedical image segmentation. Given a CNN model, CC-Net predicts the final accuracy of networks of different sizes based on the average image complexity computed from the training data. It then selects a multiplicative factor for producing a desired network with acceptable network accuracy and size. Experiments show that CC-Net is effective for generating compressed segmentation networks, retaining up to ≈ 95% of the base network segmentation accuracy and utilizing only ≈ 0.1% of trainable parameters of the full-sized networks in the best case.
Shortest Dubins path to a circle

The Dubins path problem had enormous applications in path planning for autonomous vehicles. In this paper, we consider a generalization of the Dubins path planning problem, which is to find a shortest Dubins path that starts from a given initial position and heading, and ends on a given target circle with the heading in the tangential direction. This problem has direct applications in Dubins neighborhood traveling salesman problem, obstacle avoidance Dubins path planning problem etc. We characterize the length of the four CSC paths as a function of angular position on the target circle, and derive the conditions which to find the shortest Dubins path to the target circle.
Cappuccino: Efficient CNN Inference Software Synthesis for Mobile System-on-Chips

Convolutional neural networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped Internet of Things devices permeate into every aspect of modern life, the ability to execute CNN inference, a computationally intensive application, on resource constrained devices has become increasingly important. In this context, we present Cappuccino, a framework for synthesis of efficient inference software targeting mobile system-on-chips (SoCs). We propose techniques for efficient parallelization of CNN inference targeting mobile SoCs, and explore the underlying tradeoffs. Experiments with different CNNs on three mobile devices demonstrate the effectiveness of our approach.
DAFE-FD: Density Aware Feature Enrichment for Face Detection

Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module. This module, inspired by recent crowd counting/density estimation techniques, performs the task of estimating the per pixel density of people/faces present in the image. Output of this module is employed to accentuate the feature maps from the backbone network using a feature enrichment module before being used for detecting smaller faces. The proposed approach can be used to complement recent anchor-design based novel methods to further improve their results. Experiments conducted on different datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of the proposed approach.
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters.To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset, treating the cluster cardinalities as a given input.We cast this problem as a mixed-integer linear program (MILP) that admits tractable semidefinite and linear programming relaxations.We propose deterministic rounding schemes that transform the relaxed solutions to feasible solutions for the MILP.We also prove that these solutions are optimal in the MILP if a cluster separation condition holds.
Refining Joint Locations for Human Pose Tracking in Sports Videos

The estimation of an athlete's pose in video footage enables the automation of athletic performance assessment, the prediction of motion kinematics and dynamics in sports videos and the possibility of technology-assisted, direct training feedback. Despite remarkable progress in the field of deep learning assisted human pose estimation, the performance of such systems decreases while noise and errors increase with the complexity of the scene. In this paper, we focus on aquatic training scenarios, where even novel pose estimators produce several types of orthogonal errors, including joint swaps and prediction outliers. In order to improve the estimation of an athlete's pose in swimming, we propose a graph partitioning problem that connects pose estimates over time and explicitly allows for joints to switch labels if their location better fits each other's trajectory. We optimize the problem using integer linear programming, which partitions the graph into the most probable joint trajectories. We show experimentally that our method of joint rectification improves the joint detection precision of swimmers in a swimming channel by 0.8%-4.8% PCK for anti-symmetrical motion and up to 1.8% PCK for symmetrical styles.
ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections

In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes.
Homogenized Chebyshev-Arnold Map and its Application to Color Image Encryption

In this paper, we propose a homogenized Chebyshev-Arnold map (HCAM) by homogenizing the linear coupling of Chebyshev map and Arnold map. The proposed HCAM has complex dynamical behaviors and can avoid the problems of the original Chebyshev map when used in image encryption. Based on the HCAM, we present a color image encryption algorithm that contains confusion and diffusion processes. In the confusion stage, we use the random chaotic matrix transform (RCMT) to randomize the shifting steps, which can eliminate the regular pattern of the original CMT and enhance the security level. In the diffusion stage, we use a SHA-512- and SHA-384-based fast pixel substitution scheme to perform the bit-level exclusive-or operation, which can obtain outstanding self-adaptiveness and high efficiency. The experimental results and security analysis demonstrate that the proposed algorithm has high level of security and robust to the potential attacks.
Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach

Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data. This work proposes a novel strategy using Autoencoder Deep Neural Networks to defend a machine learning model against two gradient-based attacks: The Fast Gradient Sign attack and the Fast Gradient attack. First we use an autoencoder to denoise the test data, which is trained with both clean and corrupted data. Then, we reduce the dimension of the denoised data using the hidden layer representation of another autoencoder. We perform this experiment for multiple values of the bound of adversarial perturbations, and consider different numbers of reduced dimensions. When the test data is preprocessed using this cascaded pipeline, the tested deep neural network classifier yields a much higher accuracy, thus mitigating the effect of the adversarial perturbation.
Deep Fundamental Matrix Estimation Without Correspondences

Estimating fundamental matrices is a classic problem in computer vision. Traditional methods rely heavily on the correctness of estimated key-point correspondences, which can be noisy and unreliable. As a result, it is difficult for these methods to handle image pairs with large occlusion or significantly different camera poses. In this paper, we propose novel neural network architectures to estimate fundamental matrices in an end-to-end manner without relying on point correspondences. New modules and layers are introduced in order to preserve mathematical properties of the fundamental matrix as a homogeneous rank-2 matrix with seven degrees of freedom. We analyze performance of the proposed models using various metrics on the KITTI dataset, and show that they achieve competitive performance with traditional methods without the need for extracting correspondences.
Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay Attacks

Fake audio detection is expected to become an important research area in the field of smart speakers such as Google Home, Amazon Echo and chatbots developed for these platforms. This paper presents replay attack vulnerability of voice-driven interfaces and proposes a countermeasure to detect replay attack on these platforms. This paper presents a novel framework to model replay attack distortion, and then use a non-learning-based method for replay attack detection on smart speakers. The reply attack distortion is modeled as a higher-order nonlinearity in the replay attack audio. Higher-order spectral analysis (HOSA) is used to capture characteristics distortions in the replay audio. Effectiveness of the proposed countermeasure scheme is evaluated on original speech as well as corresponding replayed recordings. The replay attack recordings are successfully injected into the Google Home device via Amazon Alexa using the drop-in conferencing feature.
OSP

When running in Parameter Server (PS), the Distributed Stochastic Gradient Descent (SGD) incurs significant communication delays because after pushing their updates, computing nodes (workers) have to wait for the global model to be communicated back from the master in every iteration. In this paper, we devise a new synchronization parallel mechanism named overlap synchronization parallel (OSP), in which the waiting time is removed by conducting computation and communication in an overlapped manner. We theoretically prove that our mechanism could achieve the same convergence rate compared to the sequential SGD for non-convex problems. Evaluations show that our mechanism significantly improves performance over the state-of-the-art ones, e.g., by 4× for both AlexNet and ResNet18 in terms of convergence speed.
Direct inversion of the nonequispaced fast Fourier transform

Various applications such as MRI, solution of PDEs, etc. need to perform an inverse nonequispaced fast Fourier transform (NFFT), i.e., compute M Fourier coefficients from given N nonequispaced data. In the present paper we consider direct methods for the inversion of the NFFT. We introduce algorithms for the setting M = N as well as for the underdetermined and overdetermined cases. For the setting M = N a direct method of complexity O ( N log ⁡ N ) is presented, which utilizes Lagrange interpolation and the fast summation. For the remaining cases, we use the matrix representation of the NFFT to deduce our algorithms. Thereby, we are able to compute an inverse NFFT up to a certain accuracy by means of a modified adjoint NFFT in O ( M log ⁡ M + N ) arithmetic operations. Finally, we show that these approaches can also be explained by means of frame approximation.
Making AI meaningful again

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.
List-Decodable Zero-Rate Codes

We consider list decoding in the zero-rate regime for two cases-the binary alphabet and the spherical codes in Euclidean space. Specifically, we study the maximal τ ∈ [0, 1] for which there exists an arrangement of M balls of relative Hamming radius τ in the binary hypercube (of arbitrary dimension) with the property that no point of the latter is covered by L or more of them. As M → ∞ the maximal τ decreases to a well-known critical value τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> . In this paper, we prove several results on the rate of this convergence. For the binary case, we show that the rate is Θ(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) when L is even, thus extending the classical results of Plotkin and Levenshtein for L = 2. For L = 3, the rate is shown to be Θ(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-(2/3)</sup> ). For the similar question about spherical codes, we prove the rate is Ω(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) and O(M- <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2L/L(2)-L+2)</sup> ).
UAV Payload Transportation via RTDP Based Optimized Velocity Profiles

This paper explores the application of a real-time dynamic programming (RTDP) algorithm to transport a payload using a multi-rotor unmanned aerial vehicle (UAV) in order to optimize journey time and energy consumption. The RTDP algorithm is developed by discretizing the journey into distance interval horizons and applying the RTDP sweep to the current horizon to get the optimal velocity decision. RTDP sweep requires the current state of the UAV to generate the next best velocity decision. To the best of the authors knowledge, this is the first time that such real-time optimization algorithm is applied to multi-rotor based transportation. The algorithm was first tested in simulations and then experiments were performed. The results show the effectiveness and applicability of the proposed algorithm.
Automatic Temporally Coherent Video Colorization

Greyscale image colorization for applications in image restoration has seen significant improvements in recent years. Many of these techniques that use learning-based methods struggle to effectively colorize sparse inputs. With the consistent growth of the anime industry, the ability to colorize sparse input such as line art can reduce significant cost and redundant work for production studios by eliminating the in-between frame colorization process. Simply using existing methods yields inconsistent colors between related frames resulting in a flicker effect in the final video. In order to successfully automate key areas of large-scale anime production, the colorization of line arts must be temporally consistent between frames. This paper proposes a method to colorize line art frames in an adversarial setting, to create temporally coherent video of large anime by improving existing image to image translation methods. We show that by adding an extra condition to the generator and discriminator, we can effectively create temporally consistent video sequences from anime line arts.