Reviewerly/exHarmony huggingface dataset:
Deep Learning for Content-Based Personalized Viewport Prediction of 360-Degree VR Videos

In this paper, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model. Simulation results show that the proposed approach yields 16.1\% improvement in terms of prediction accuracy compared to a baseline approach that relies only on the position data.
A new lossless secret color image sharing scheme with small shadow size

Though there have been many research works carried out on the grayscale secret image sharing schemes, there is not sufficient research work available on the field of color secret image sharing schemes, especially on the lossless secret image sharing schemes. In the current work, we propose a new lossless secret color image sharing scheme with small shadow size. We directly deal with the three components of the intensities of the RGB values of each color pixel of the secret image. Using a mathematical transformation, we first embed the RGB intensity values of each secret pixel as an element of a suitably chosen finite field F. Then the whole mathematical operations are carried over the finite field F to generate shares using k-1 degree polynomials in the polynomial ring F[x]. Unlike most of the image sharing schemes, we do not have the preprocessing stage in which the secret image is transferred into random image either by using Arnold Cat map or by using some chaotic maps to avoid residual image effect. From experimental simulations, it is evident that the measure of randomness of the shares generated using our algorithm is same as that of the shares generated using the preprocessing stage in which Arnold Cat map or chaotic map is used, resulting our scheme simpler and efficient. Moreover, our scheme produces shares having smaller size than the secret image. Finally the recovery of the secret image by the qualified set of participants is lossless.
An Unsupervised Framework for Anomaly Detection in a Water Treatment System

Current Cyber-Physical Systems (CPSs) are sophisticated, complex, and equipped with networked sensors and actuators. As such, they have become further exposed to cyber-attacks. Recent catastrophic events have demonstrated that standard, human-based management of anomaly detection in complex systems is not efficient enough and have underlined the significance of automated detection, intelligent and rapid response. Nevertheless, existing anomaly detection frameworks usually are not capable of dealing with the dynamic and complicated nature of the CPSs. In this study, we introduce an unsupervised framework for anomaly detection based on an Attention-based Spatio-Temporal Autoencoder. In particular, we first construct statistical correlation matrices to characterize the system status across different time steps. Next, a 2D convolutional encoder is employed to encode the patterns of the correlation matrices, whereas an Attention-based Convolutional LSTM Encoder-Decoder (ConvLSTM-ED) is used to capture the temporal dependencies. More precisely, we introduce an input attention mechanism to adaptively select the most significant input features at each time step. Finally, the 2D convolutional decoder reconstructs the correlation matrices. The differences between the reconstructed correlation matrices and the original ones are used as indicators of anomalies. Extensive experimental analysis on data collected from all six stages of Secure Water Treatment (SWaT) testbed, a scaled-down version of a real-world industrial water treatment plant, demonstrates that the proposed model outperforms the state-of-the-art baseline techniques.
Research on edge detection algorithm based on improved sobel operator

In order to solve the shortcomings of traditional Sobel edge detection operator, such as low accuracy of image edge location and rough edge extracted, an improved edge detection algorithm based on Sobel operator is proposed. Firstly, in the aspect of the accuracy of edge detection, the direction template is increased from two directions of horizontal and vertical to eight directions; secondly, in terms of the rough edge extracted and the noise sensitivity, combined with the operation of mathematical morphology, the edge can be refined, and at the same time, some part of the noise can be suppressed. The experiment results show that the improved edge detection algorithm has a better accuracy in image positioning, and a certain ability to suppress noise. Finally, the detected image edge of the image is more delicate and clear than the traditional Sobel operator.
A New Clustering Algorithm and Its Application in Assessing the Quality of Underground Water

Cluster analysis, which is to partition a dataset into groups so that similar elements are assigned to the same group and dissimilar elements are assigned to different ones, has been widely studied and applied in various fields. The two challenging tasks in clustering are determining the suitable number of clusters and generating clusters of arbitrary shapes. This paper proposes a new concept of “epsilon radius neighbors” which plays an essential role in the cluster-forming process, thereby determining both the number of clusters and the shape of clusters, automatically. Based on “epsilon radius neighbors,” a new clustering algorithm in which the epsilon radius value is adapted to the characteristics of each cluster in the current partition is proposed. Recently, clustering has been widely applied in environmental applications, including underground water quality monitoring. However, the existing studies have simply applied conventional clustering techniques, in which the abovementioned two challenging tasks have not been solved already. Therefore, in this paper, the proposed clustering algorithm is applied in assessing the underground water quality in Phu My Town, Ba Ria-Vung Tau Province, Vietnam. The experimental results on benchmark datasets demonstrate the effectiveness of the proposed algorithm. For the quality of underground water, the new algorithm results in four clusters with different characteristics. Through this application, we found that the new algorithm might provide valuable reference information for underground water management.
Arabic Speech Recognition System Based on MFCC and HMMs

Speech recognition allows the machine to turn the speech signal into text through identification and understanding process.Extract the features, predict the maximum likelihood, and generate the models of the input speech signal are considered the most important steps to configure the Automatic Speech Recognition System (ASR).In this paper, an automatic Arabic speech recognition system was established using MATLAB and 24 Arabic words Consonant-Vowel Consonant-Vowel Consonant-Vowel (CVCVCV) was recorded from 19 Arabic native speakers, each speaker uttering the same word 3 times (total 1368 words).In order to test the system, 39-features were extracted by partitioning the speech signal into frames ~ 0.25 sec shifted by 0.10 sec. in back-end, the statistical models were generated by separated the features into number of states between 4 to 10, each state has 8-gaussian distributions.The data has 48 k sample rate and 32-bit depth and saved separately in a wave file format.The system was trained in phonetically rich and balanced Arabic speech words list (10 speakers * 3 times * 24 words, total 720 words) and tested using another word list (24 words * 9 speakers * 3 times *, total 648 words).Using different speakers similar words, the system obtained a very good word recognition accuracy results of 92.92% and a Word Error Rate (WER) of 7.08%.
A Taxonomy of Treemap Visualization Techniques

A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based “on the property of containment” (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.
Approximate information state for partially observed systems

The standard approach for modeling partially observed systems is to model them as partially observable Markov decision processes (POMDPs) and obtain a dynamic program in terms of a belief state. The belief state formulation works well for planning but is not ideal for online reinforcement learning because the belief state depends on the model and, as such, is not observable when the model is unknown.In this paper, we present an alternative notion of an information state for obtaining a dynamic program in partially observed models. In particular, an information state is a sufficient statistic for the current reward which evolves in a controlled Markov manner. We show that such an information state leads to a dynamic programming decomposition. Then we present a notion of an approximate information state and present an approximate dynamic program based on the approximate information state. Approximate information state is defined in terms of properties that can be estimated using sampled trajectories. Therefore, they provide a constructive method for reinforcement learning in partially observed systems. We present one such construction and show that it performs better than the state of the art for three benchmark models.
FootAndBall: Integrated Player and Ball Detector

The paper describes a deep neural network-based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches.The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution.It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players' positions and bounding boxes.The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field.This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification.Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO.This allows real-time processing of high resolution input video stream.
Comparison of Classification Algorithms for Detection of Phishing Websites

Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center.In literature, different generations of phishing websites detection methods have been observed.The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites.More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs.These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general.The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design.Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets.Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test.The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.
Distributed SGD With Flexible Gradient Compression

We design and evaluate a new algorithm called FlexCompressSGD for training deep neural networks over distributed datasets via multiple workers and a central server.In FlexCompressSGD, all gradients transmitted between workers and the server are compressed, and the workers are allowed to flexibly choose a compressing method different from that of the server.This flexibility significantly helps reduce the communication cost from each worker to the server.We mathematically prove that FlexCompressSGD converges with convergence rate 1/ √ MT where M is the number of distributed workers and T is the number of training iterations.We experimentally demonstrate that FlexCompressSGD obtains competitive top-1 testing accuracy on the ImageNet dataset while being able to reduce more than 70% communication cost from each worker to the server when compared with the state-of-the-art.
Robust Speaker Recognition Using Unsupervised Adversarial Invariance

In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pretrained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions. We analyze the robustness of the proposed embeddings to various sources of variability present in the signal for speaker verification and unsupervised clustering tasks on a large-scale speaker recognition corpus. Our analyses show that the proposed system substantially outperforms the baseline in a variety of challenging acoustic scenarios. Furthermore, for the task of speaker diarization on a real-world meeting corpus, our system shows a relative improvement of 36% in the diarization error rate compared to the state-of-the-art baseline.
Edge-based sequential graph generation with recurrent neural networks

Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task.
Deep-sea sediments of the global ocean

Abstract. Although the deep-sea floor accounts for approximately 60 % of Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable, and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially explicit measure of confidence in the predictions, and probabilities for the occurrence of five lithology classes (calcareous sediment, clay, diatom ooze, lithogenous sediment, and radiolarian ooze). These map products were derived by the application of the random-forest machine-learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas, and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).
Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima

Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, due to the nonlinearity in value function approximation, such a coupling leads to nonconvexity and even divergence in optimization. As a result, the global convergence of neural TD remains unclear. In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for policy evaluation. In particular, we show how such global convergence is enabled by the overparametrization of neural networks, which also plays a vital role in the empirical success of neural TD. Beyond policy evaluation, we establish the global convergence of neural (soft) Q-learning, which is further connected to that of policy gradient algorithms.
No amount of “AI” in content moderation will solve filtering’s prior-restraint problem

Contemporary policy debates about managing the enormous volume of online content have taken a renewed focus on upload filtering, automated detection of potentially illegal content, and other “proactive measures”. Often, policymakers and tech industry players invoke artificial intelligence as the solution to complex challenges around online content, promising that AI is a scant few years away from resolving everything from hate speech to harassment to the spread of terrorist propaganda. Missing from these promises, however, is an acknowledgement that proactive identification and automated removal of user-generated content raises problems beyond issues of “accuracy” and overbreadth--problems that will not be solved with more sophisticated AI. In this commentary, I discuss how the technical realities of content filtering stack up against the protections for freedom of expression in international human rights law. As policymakers and companies around the world turn to AI for communications governance, it is crucial that we recall why legal protections for speech have included presumptions against prior censorship, and consider carefully how proactive content moderation will fundamentally re-shape the relationship between rules, people, and their speech.
An evaluation method of PE classroom teaching quality in colleges and universities based on grey system theory

This study aims to explore the quality evaluation of PE classroom teaching in colleges and universities. The evaluation index system of college PE class-room teaching quality was built by using the application of the analysis hierarchy process (AHP) to determine the weight of each level index, comb ined with gray system theory and fuzzy comprehensive evaluation method to establish the evaluation method for the study. Taking a college as an example, according to the above methods, the quality of PE teaching was evaluated. The evaluation results show that the quality of PE teaching of the sample is in good and middle level, close to good level. The evaluation index system, index weight and evaluation method have higher applicability.
Inter-sentence and Implicit Causality Extraction from Chinese Corpus

Automatically extracting causal relations from texts is a challenging task in Natural Language Processing (NLP). Most existing methods focus on extracting intra-sentence or explicit causality, while neglecting the causal relations that expressed implicitly or hidden in inter-sentences. In this paper, we propose Cascaded multi-Structure Neural Network (CSNN), a novel and unified model that extract inter-sentence or implicit causal relations from Chinese Corpus, without relying on external knowledge. The model employs Convolutional Neural Network (CNN) to capture important features as well as causal structural pattern. Self-attention mechanism is designed to mine semantic and relevant characteristics between different features. The output of CNN and self-attention structure are concatenated as higher-level phrase representations. Then Conditional Random Field (CRF) layer is employed to calculate the label of each word in inter-sentence or implicit causal relation sentences, which improves the performance of inter-sentence or implicit causality extraction. Experimental results show that the proposed model achieves state-of-the-art results, improved on three datasets, when compared with other methods.
MRI Restoration Using Edge-Guided Adversarial Learning

Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D) images present challenges when reformatted in orthogonal planes due to sparser sampling in the through-plane direction. Restoring the "missing" through-plane slices, or regions of an MRI image damaged by acquisition artifacts can be modeled as an image imputation task. In this work, we consider the damaged image data or missing through-plane slices as image masks and proposed an edge-guided generative adversarial network to restore brain MRI images. Inspired by the procedure of image inpainting, our proposed method decouples image repair into two stages: edge connection and contrast completion, both of which used general adversarial networks (GAN). We trained and tested on a dataset from the Human Connectome Project to test the application of our method for thick slice imputation, while we tested the artifact correction on clinical data and simulated datasets. Our Edge-Guided GAN had superior PSNR, SSIM, conspicuity and signal texture compared to traditional imputation tools, the Context Encoder and the Densely Connected Super Resolution Network with GAN (DCSRN-GAN). The proposed network may improve utilization of clinical 2D scans for 3D atlas generation and big-data comparative studies of brain morphometry.