Reviewerly/exHarmony huggingface dataset:
Partial Diffusion Kalman Filtering for Distributed State Estimation in Multiagent Networks

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems.In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources.In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications.We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses.We also derive a closedform expression for the steady-state mean-square deviation criterion.Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable.
Real-Time Head Pose Estimation and Face Modeling From a Depth Image

We address the issues of 3-D head pose estimation and face modeling from a depth image. Given a depth image, random forests are effective for estimating the location and orientation of a person's head. However, the accuracy of the estimation is not high enough. We propose using corrected regression votes. The corrected votes are obtained by considering the cooperation of all trees, leading to significant improvement of head pose estimation accuracy. Based on the head pose estimator, we present a face modeling system. In our system, the face model is generated by aligning a deformable face model to a depth image using an iterative closest point (ICP) algorithm. The novelty of our approach is that an optimal weight for each vertex is incorporated into the ICP algorithm with point to plane constraints. Experiments show that our system can automatically estimate the head pose and generate a realistic face model from a single depth image. We also provide a detailed evaluation that shows the benefits of our approach.
Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.
An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-Based Smart Grid

Software-defined network (SDN) is widely used in smart grid for monitoring and managing the communication network. Big data analytics for SDN-based smart grid has got increasing attention. It is a promising approach to use machine learning technologies to analyze a large amount of data generated in SDN-based smart grid. However, the disclosure of personal privacy information must receive considerable attention. For instance, data clustering in user electricity behavior analysis may lead to the disclosure of personal privacy information. In this paper, an optimizing and differentially private clustering algorithm named ODPCA is proposed. In the ODPCA, the differentially private K-means algorithm and K-modes algorithm are combined to cluster mixed data in a privacy-preserving manner. The allocation of privacy budgets is optimized to improve the accuracy of clustering results. Specifically, the loss function that considers both the numerical and categorical attributes between true centroids and noisy centroids is analyzed to optimize the allocation the privacy budget; the number of iterations of clustering is set to a fixed value based on the total privacy budget and the minimal privacy budget allocated to each iteration. It is proved that the ODPCA can meet the differential privacy requirements and has better performance by comparing with other popular algorithms.
POWERT Channels: A Novel Class of Covert CommunicationExploiting Power Management Vulnerabilities

To be able to meet demanding application performance requirements within a tight power budget, runtime power management must track hardware activity at a very fine granularity in both space and time. This gives rise to sophisticated power management algorithms, which need the underlying system to be both highly observable (to be able to sense changes in instantaneous power demand timely) and controllable (to be able to react to changes in instantaneous power demand timely). The end goal is allocating the power budget, which itself represents a very critical shared resource, in a fair way among active tasks of execution. Fundamentally, if not carefully managed, any system-wide shared resource can give rise to covert communication. Power budget does not represent an exception, particularly as systems are becoming more and more observable and controllable. In this paper, we demonstrate how power management vulnerabilities can enable covert communication over a previously unexplored, novel class of covert channels which we will refer to as POWERT channels. We also provide a comprehensive characterization of the POWERT channel capacity under various sharing and activity scenarios. Our analysis based on experiments on representative commercial systems reveal a peak channel capacity of 121.6 bits per second (bps).
DiGraph

Many systems are recently proposed for large-scale iterative graph analytics on a single machine with GPU accelerators. Despite of many research efforts, for iterative directed graph processing over GPUs, existing solutions suffer from slow convergence speed and high data access cost, because many vertices are ineffectively reprocessed for lots of rounds so as to update their states according to other active vertices regardless of their dependencies. In this paper, we propose a novel and efficient iterative directed graph processing system on a machine with the support of multiple GPUs. Compared with existing systems, the unique feature of our system is that it takes advantage of the dependencies between vertices in three novel ways. First, it represents a directed graph into a set of disjoint hot/cold directed paths and takes the path as the basic parallel processing unit, so as to help efficient vertex state propagation along the paths over GPUs for faster convergence speed and higher utilization ratio of the loaded data. Second, it tries to dispatch the paths to GPUs for parallel processing according to the topological order of the dependency graph of them. Many paths then converge along such an order after processing them for exactly once, getting lower reprocessing overhead. Third, a path scheduling strategy is further developed on each streaming multiprocessor to enable the privileged execution of the paths (e.g., the hot paths) with greater impacts on vertex state propagation for shorter convergence time according to vertex dependency. Experimental results show that our approach speeds up iterative directed graph processing by up to 3.54 times in comparison with the state-of-the-art systems.
Classification of Melanoma Skin Cancer using Convolutional Neural Network

Melanoma cancer is a type of skin cancer and is the most dangerous one because it causes the most of skin cancer deaths.Melanoma comes from melanocyte cells, melaninproducing cells, so that melanomas are generally brown or black coloured.Melanomas are mostly caused by exposure to ultraviolet radiation that damages the DNA of skin cells.The diagnoses of melanoma cancer are often performed manually by using visuals of the skilled doctors, analyzing the result of dermoscopy examination and match it with medical sciences.Manual detection weakness is highly influenced by human subjectivity that makes it inconsistent in certain conditions.Therefore, a computer assisted technology is needed to help classifying the results of dermoscopy examination and to deduce the results more accurately with a relatively faster time.The making of this application starts with problem analysis, design, implementation, and testing.This application uses deep learning technology with Convolutional Neural Network method and LeNet-5 architecture for classifying image data.The experiment using 44 images data from the training results with a different number of training and epoch resulted the highest percentage of success at 93% in training and 100% in testing, which the number of training data used of 176 images and 100 epochs.This application was created using Python programming language and Keras library as Tensorflow back-end.
Context Aggregation Network for Semantic Labeling in Aerial Images

Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data.
Robust Obstacle Detection and Recognition for Driver Assistance Systems

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately locate the on-road obstacles, we propose an obstacle detection method based on the U-V disparity map generated from a stereo vision system. The proposed U-V disparity algorithm makes use of the V-disparity map that provides a good representation of the geometric content of the road region to extract the road features, and then detects the on-road obstacles using our proposed realistic U-disparity map that eliminates the foreshortening effects caused by the perspective projection of pinhole imaging. The proposed realistic U-disparity map greatly improves the detection accuracy of the distant obstacles compared with the conventional U-disparity map. Second, the detection results of our proposed U-V disparity algorithm are put into a context-aware Faster-RCNN that combines the interior and contextual features to improve the recognition accuracy of small and occluded obstacles. Specifically, we propose a context-aware module and apply it into the architecture of Faster-RCNN. The experimental results on two public datasets show that our proposed method achieves state-of-the-art performance under various driving conditions.
Adversarial Category Alignment Network for Cross-domain Sentiment Classification

Xiaoye Qu, Zhikang Zou, Yu Cheng, Yang Yang, Pan Zhou. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and region-based active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L2-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness.
Dense Dilated Network for Video Action Recognition

The ability to recognize actions throughout a video is essential for surveillance, self-driving, and many other applications. Although many researchers have investigated deep neural networks to get a better result in video action recognition, these networks usually require a large number of well-labeled data to train. In this paper, we introduce a dense dilated network to collect action information from snippet-level to global-level. The dilated dense network is composed of the blocks with densely connected dilated convolutions layers. Our proposed framework is capable of fusing outputs from each layer to learn high-level representations, and these representations are robust even with only a few training snippets. We study different spatial and temporal modality fusing configurations and introduce a novel temporal guided fusion upon the dense dilated network which can further boost the performance. We conduct extensive experiments on two popular video action datasets: UCF101 and HMDB51. The experiments demonstrate the effectiveness of our proposed framework.
Family of bound entangled states on the boundary of the Peres set

Bound entangled (BE) states are strange in nature: a nonzero amount of free entanglement is required to create them but no free entanglement can be distilled from them under local operations and classical communication (LOCC). Even though the usefulness of such states has been shown in several information processing tasks, there exists no simple method to characterize them for an arbitrary composite quantum system. Here we present a $(d\ensuremath{-}3)/2$-parameter family of BE states each with positive partial transpose (PPT). This family of PPT-BE states is introduced by constructing an unextendible product basis (UPB) in ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}$ with $d$ odd and $d\ensuremath{\ge}5$. The range of each such PPT-BE state is contained in a $2(d\ensuremath{-}1)$-dimensional entangled subspace, whereas the associated UPB subspace is of dimension ${(d\ensuremath{-}1)}^{2}+1$. We further show that each of these PPT-BE states can be written as a convex combination of $(d\ensuremath{-}1)/2$ rank-4 PPT-BE states. Moreover, we prove that these rank-4 PPT-BE states are extreme points of the convex compact set $\mathcal{P}$ of all PPT states in ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}$, namely, the Peres set. An interesting geometric implication of our result is that the convex hull of these rank-4 PPT-BE extreme points---the $(d\ensuremath{-}3)/2$ simplex---is sitting on the boundary between the set $\mathcal{P}$ and the set of non-PPT states. We also discuss consequences of our construction in the context of quantum state discrimination by LOCC.
A Boxology of Design Patterns forHybrid Learningand Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.
Deep model compression for mobile platforms: A survey

Despite the rapid development of mobile and embedded hardware, directly executing computation-expensive and storage-intensive deep learning algorithms on these devices' local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-the-art deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compression techniques, we quantify their storage and computation tunable by layer compression techniques and discuss their practical challenges and possible improvements. Then, we implement Android projects using TensorFlow Mobile to test these 10 compression methods and compare their practical performances in terms of accuracy, parameter size, intermediate feature size, computation, processing latency, and energy consumption. To further discuss their advantages and bottlenecks, we test their performance over four standard recognition tasks on six resource-constrained Android smartphones. Finally, we survey two types of run-time Neural Network (NN) compression techniques which are orthogonal with the layer compression techniques, run-time resource management and cost optimization with special NN architecture, which are orthogonal with the layer compression techniques.
Strong quantum nonlocality in multipartite quantum systems

Recently, based on some quantum states which are locally irreducible in all bipartitions, strong quantum nonlocality has been presented by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]. However, the remaining questions are whether a set of quantum states can reveal strong quantum nonlocality when these states are locally irreducible in all tripartitions or more multipartitions and how to construct strong nonlocality of orthogonal product states in more than tripartite quantum systems. Here we present a general definition of strong quantum nonlocality based on the local irreducibility. Then, using a $3\ensuremath{\bigotimes}3\ensuremath{\bigotimes}3$ quantum system as an example, we show three sets of locally indistinguishable orthogonal product states to explain the general definition of strong quantum nonlocality. Furthermore, in $3\ensuremath{\bigotimes}3\ensuremath{\bigotimes}3\ensuremath{\bigotimes}3$, we construct a set of strong nonlocality of orthogonal product states; these states do not form a complete basis. Our results demonstrate the phenomenon of strong nonlocality without entanglement for the multipartite quantum systems.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text

There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.In this paper we introduce a new anomaly detection method-Context Vector Data Description (CVDD)-which builds upon word embedding models to learn multiple sentence representations that capture multiple semantic contexts via the self-attention mechanism.Modeling multiple contexts enables us to perform contextual anomaly detection of sentences and phrases with respect to the multiple themes and concepts present in an unlabeled text corpus.These contexts in combination with the self-attention weights make our method highly interpretable.We demonstrate the effectiveness of CVDD quantitatively as well as qualitatively on the wellknown Reuters, 20 Newsgroups, and IMDB Movie Reviews datasets.
Tree Communication Models for Sentiment Analysis

Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously.However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents.In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree.Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.
Attention Is (not) All You Need for Commonsense Reasoning

The recently introduced BERT model exhibits strong performance on several language understanding benchmarks.In this paper, we describe a simple re-implementation of BERT for commonsense reasoning.We show that the attentions produced by BERT can be directly utilized for tasks such as the Pronoun Disambiguation Problem and Winograd Schema Challenge.Our proposed attention-guided commonsense reasoning method is conceptually simple yet empirically powerful.Experimental analysis on multiple datasets demonstrates that our proposed system performs remarkably well on all cases while outperforming the previously reported state of the art by a margin.While results suggest that BERT seems to implicitly learn to establish complex relationships between entities, solving commonsense reasoning tasks might require more than unsupervised models learned from huge text corpora.
Label-Agnostic Sequence Labeling by Copying Nearest Neighbors

Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest.However, much recent work merely conditions on retrieved structures (e.g., in a sequence-to-sequence framework), rather than explicitly manipulating them.We show we can perform accurate sequence labeling by explicitly (and only) copying labels from retrieved neighbors.Moreover, because this copying is label-agnostic, we can achieve impressive performance in zero-shot sequencelabeling tasks.We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors, which allows for controlling the number of distinct (copied) segments used to form a prediction, and leads to both more interpretable and accurate predictions.