Reviewerly/exHarmony huggingface dataset:
System movement space and system mapping theory for reliability of IoT

The Internet of things (IoT) is ubiquitous, and its system structure is becoming increasingly complex. Hence, its reliability has become a concern for people. Scientific research on system reliability, fault processes and fault big data in industrial engineering, industrial informatization and IoT is complex. It must be conducted by combining information science, system science, intelligence science and data science. In this paper, we propose an analysis method of the reliability and fault big data for the study of IoT that is abstracted as a system. The paper introduces information ecological methodology into fault big data processing to complete fault information transformation. Regarding system reliability as an objective, the power, representations and measurement of system movement are investigated. The system movement space (SMS) and system mapping theory (SMT) are proposed and used for the identification of the system structure. Finally, the correspondence relationship between the artificial system and the natural system, their characteristics and mapping methods, and the system characteristics that are consistent with various philosophical views are identified. The paper provides a theoretical basis and analytical methods for the study of system movement and change processes, especially for the study of the system reliability via fault big data analysis.
Generative model chatbot for Human Resource using Deep Learning

Human Resource is the working environment inside a business that is in charge of everything master related which unites selecting, checking, picking, verifying, on boarding, preparing, advancing, paying, and terminating delegates and freely utilized substances. Human Resource is besides the working environment that stays over new request controlling how experts should be treated amidst the selecting, working, and consummation process. Here we will focus on the enrolling some bit of Human Resource. A Chatbot is an automated structure expected to begin a dialog with human customers or diverse Chatbots that gives through text. The Chatbots which is being proposed for Human Resource is Artificial Intelligence based Chatbot for major measurement profiling of contenders for the explicit task. The learning strategy utilized for the Chatbot here is assorted neural structure which includes deep learning techniques like recurrent neural network.
A Fast Iterative Method for Removing Impulsive Noise From Sparse Signals

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The main contribution of our algorithm is its low complexity; it has much lower run-time than most other algorithms. The reconstruction quality of our algorithm is both objectively (in terms of PSNR and SSIM) and subjectively better or comparable to other state-of-the-art algorithms. We provide a cost function for our problem, present an iterative method to find its local minimum, and provide the analysis of the algorithm. As an application of this problem, we apply our algorithm for Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise (RVIN) removal from images and compare our results with other notable algorithms in the literature. Furthermore, we apply our algorithm for removing clicks from audio signals. Simulation results show that our algorithms are simple and fast, and it outperforms other state-of-the-art methods in terms of reconstruction quality and/or complexity.
Hierarchical Spatiotemporal Context-Aware Correlation Filters for Visual Tracking

Discriminative correlation filters (DCF)-based trackers have been increasingly applied to visual tracking due to their high precision while running at high frame rates. However, most recent DCF-based methods solely concentrate on learning the correlation filter with spatial information and thus do not have sufficient descriptive power to discriminate the target from the background in the complex circumstances, such as full occlusion (OCC) and rapid target variation. In this article, we introduce a novel tracking framework that exploits the relationship between the target and its spatiotemporal context to improve tracking accuracy and robustness. Especially, we present our spatiotemporal context model in a hierarchical way, where each layer of the context pyramid is a spatial correlation filter learned from different temporal instances. For gaining an accurate spatiotemporal model, we propose an optimization fusion approach that can adaptively and efficiently learn the effect of each hierarchical layer and exploit these multiple temporal levels of correlation filters for visual tracking. Moreover, an adaptive model update strategy for correlation filters is introduced into the framework to dynamically select proper hierarchical layers, which boosts the temporal diversity of the target appearance, while radically reduces the number of model parameters and guarantees the real-time performance of the tracking method. The experimental results show that, with conventional handcrafted features, our tracker achieves the best success rates among available state-of-the-art trackers with handcrafted features, and provides state-of-the-art performance comparable to those of deep-learning-based trackers on OTB-2013, OTB-2015, VOT-2016, and UAV-20L benchmarks but runs significantly faster than deep trackers.
SVMDF: A Secure Virtual Machine Deployment Framework to Mitigate Co-Resident Threat in Cloud

Recent studies have shown that co-resident attacks have aroused great security threat in cloud. Since hardware is shared among different tenants, malicious tenants can launch various co-resident attacks, such as side channel attacks, covert channel attacks and resource interference attacks. Existing countermeasures have their limitations and can not provide comprehensive defense against co-resident attacks. This paper combines the advantages of various countermeasures and proposes a complete co-resident threat defense solution which consists of co-resident-resistant VM allocation (CRRVA), analytic hierarchy process-based threat score mechanism (AHPTSM) and attack-aware VM reallocation (AAVR). CRRVA securely allocates VMs and also takes load balance and power consumption into consideration to make the allocation policy more practical. According to the intrinsic characteristics of co-resident attacks, AHPTSM evaluates VM’s threat score which denotes the probability that a VM is suffering or conducting co-resident attacks based on analytic hierarchy process. And AAVR further migrates VMs with extremely high threat scores and separates VM pairs which are likely to be malicious to each other. Extensive experiments in CloudSim have shown that CRRVA can greatly reduce the allocation co-resident threat as well as balancing the load for both CSPs and tenants with little impact on power consumption. In addition, guided by threat score distribution, AAVR can effectively guarantee runtime co-resident security by migrating high threat score VMs with less migration cost.
Towards Improving Robustness of Deep Neural Networks to Adversarial Perturbations

Deep neural networks have presented superlative performance in many machine learning based perception and recognition tasks, where they have even outperformed human precision in some applications. However, it has been found that human perception system is much more robust to adversarial perturbation, as compared to these artificial networks. It has been shown that a deep architecture with a lower Lipschitz constant can generalize better and tolerate higher level of adversarial perturbation. Smooth regularization has been proposed to control the Lipschitz constant of a deep architecture and in this work, we show how a deep convolutional neural network (CNN), based on non-smooth regularization of convolution and fully connected layers, can present enhanced generalization and robustness to adversarial perturbation, simultaneously. We propose two non-smooth regularizers that present specific features for adversarial samples with different levels of signal-to-noise ratios. The regularizers build direct interconnections for the weight matrices in each layer, through which they control the Lipschitz constant of architecture and improve the consistency of input-output mapping of the network. This leads to more reliable and interpretable network mapping and reduces abrupt changes in the networks output. We develop an efficient algorithm to solve the non-smooth learning problems, which presents a gradual complexity addition property. Our simulation results over three benchmark datasets signify the superiority of the proposed formulations over previously reported methods for improving the robustness of deep architecture, towards human robustness to adversarial samples.
Securing Smart Contracts in Blockchain

Blockchain is an emerging technology that underlies creation and exchange of the digital assets, including cryptocurrency such as Bitcoin and Ether, without the need for a central authority. It provides a public ledger for recording sequence of transactions in blocks that are linked as a chain. Smart contracts are computer programs governing participant agreements that are automatically enforced by consensus protocols in the blockchain. Together, blockchain and smart contracts revolutionize efficient transaction stores, services and workflows that work even among distrusting participants and without a trusted authority. Unfortunately, like most software, smart contracts are vulnerable as evidenced by a recent Decentralized Autonomous Organization (DAO) attack that lost cryptocurrency then-valued about $60 million. Correctness of executions alone is not sufficient to guarantee security of smart contracts. This paper addresses how we can apply model checking, a well-established formal verification technique, to help alleviate security issues in smart contract development. Most existing studies have focused on verification of smart contracts on a specific language and specific platform. Smart contracts may have hidden operational side effects that impact software behaviors. Thus, applying model checking to smart contracts is not necessarily straightforward. This paper presents a general technique for building the core functional models applicable for model checking to identify all possible executions that lead to security breaches. It also shows how resulting executions can be systematically analyzed to help identify security issues. The models are language and system independent in that they can represent any smart contract in any language or any platform. We illustrate and evaluate the technique with a widely used example of a smart contract in a financial system along with experimental results using a well-known model checker, NuSMV in various scenarios.
Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes

In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online and real-time visual object tracking. Our method, SiamMask_E, improves the bounding box fitting procedure of the state-of-the-art object tracking algorithm SiamMask and still retains a fast-tracking frame rate (80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). We tested our approach on the visual object tracking datasets (VOT2016, VOT2018, and VOT2019) that were labeled with rotated bounding boxes. By comparing with the original SiamMask, we achieved an improved Accuracy of 64.5% and 30.3% EAO on VOT2019, which is 4.9% and 2% higher than the original SiamMask. The implementation is available on GitHub: https://github.com/baoxinchen/siammask_e.
Multiscale clustering of nonparametric regression curves

In a wide range of modern applications, one observes a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modeled by a nonparametric regression equation, one may in particular assume that the observed time series can be partitioned into a small number of groups whose members share the same nonparametric regression function. We develop a bandwidth-free clustering method to estimate the unknown group structure from the data. More precisely speaking, we construct multiscale estimators of the unknown groups and their unknown number which are free of classical bandwidth or smoothing parameters. In the theoretical part of the paper, we analyze the statistical properties of our estimators. Our theoretical results are derived under general conditions which allow the data to be dependent both in time series direction and across different time series. The technical analysis of the paper is complemented by simulated and real-data examples.
City Tracker: Multiple Object Tracking in Urban Mixed Traffic Scenes

Multiple object tracking (MOT) in urban traffic is exciting since there is large variation of road user appearance. In order to overcome that variation while tracking the objects, a deep learning detection-based tracking framework is implemented to employ classification information and associate different objects. In this paper, a method called City Tracker is proposed for MOT task in urban mixed traffic using urban traffic dataset. City Tracker contains two components which are YoloV3 detection and DeepSORT tracking. This method compares favorably to a current feature-based tracker for urban traffic scenes and achieves up to 0.8989 in MOTP and 0.4265 in MOTA.
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving

We present a training pipeline for the autonomous driving task given the current camera image and vehicle speed as the input to produce the throttle, brake, and steering control output. The simulator Airsim's [1] convenient weather and lighting API provides a sufficient diversity during training which can be very helpful to increase the trained policy's robustness. In order to not limit the possible policy's performance, we use a continuous and deterministic control policy setting. We utilize ResNet-34 [2] as our actor and critic networks with some slight changes in the fully connected layers. Considering human's mastery of this task and the high-complexity nature of this task, we first use imitation learning to mimic the given human policy and then leverage the trained policy and its weights to the reinforcement learning phase for which we use DDPG [3]. This combination shows a considerable performance boost comparing to both pure imitation learning and pure DDPG for the autonomous driving task.
HeyTeddy

Physical computing is a complex activity that consists of different but tightly coupled tasks: programming and assembling hardware for circuits. Prior work clearly shows that this coupling is the main source of mistakes that unfruitfully take a large portion of novices' debugging time. While past work presented systems that simplify prototyping or introduce novel debugging functionalities, these tools either limit what users can accomplish or are too complex for beginners. In this paper, we propose a general-purpose prototyping tool based on conversation. HeyTeddy guides users during hardware assembly by providing additional information on requests or by interactively presenting the assembly steps to build a circuit. Furthermore, the user can program and execute code in real-time on their Arduino platform without having to write any code, but instead by using commands triggered by voice or text via chat. Finally, the system also presents a set of test capabilities for enhancing debugging with custom and proactive unit tests. We codesigned the system with 10 users over 6 months and tested it with realistic physical computing tasks. With the result of two user studies, we show that conversational programming is feasible and that voice is a suitable alternative for programming simple logic and encouraging exploration. We also demonstrate that conversational programming with unit tests is effective in reducing development time and overall debugging problems while increasing users' confidence. Finally, we highlight limitations and future avenues of research.
Non-commutative graphs and quantum error correction for a two-mode quantum oscillator

An important topic in quantum information is the theory of error correction codes. Practical situations often involve quantum systems with states in an infinite-dimensional Hilbert space, for example, coherent states. Motivated by these practical needs, we develop the theory of non-commutative graphs, which is a tool to analyze error correction codes, to infinite-dimensional Hilbert spaces. As an explicit example, a family of non-commutative graphs associated with the Schrödinger equation describing the dynamics of a two-mode quantum oscillator is constructed and maximal quantum anticliques for these graphs are found.
Parameter-Free Table Detection Method

In this paper, we propose two parameter-free table detection methods: one for the closed tables and other for open tables. The unifying idea is multigaussian analysis. Multigaussian analysis of text height histograms classifies the document content into text and non-text blocks. Closed tables are classified as non-text and their identification from the non-text blocks is similar to many earlier methods that remove the separators. We do not need any parameters to identify rows and columns and discriminate them from text blocks because of multigaussian analysis. Open tables are initially classified as text blocks and are detected by extending the multigaussian analysis to the heights and widths of text blocks. The text-blocks are grouped into three categories by multigaussian analysis. These groups are used to classify table cells and distinguish them from text blocks. Table blocks are merged to obtain the table region. Evaluation on various Indic script newspapers and ICDAR2013 table competition dataset shows that our methods achieve more than 90% in table recognition. The strength of our algorithm is that it is a parameter-free approach and requires no training dataset.
A constraint-based approach for human–robot collision avoidance

In this paper, we present a software-based approach for collision avoidance that can be applied in human–robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance.
A Constructive Approach for General Video Game Level Generation

The following topics are dealt with: learning (artificial intelligence); Internet; computer games; public domain software; feature extraction; convolutional neural nets; human factors; robot programming; game theory; control engineering computing.
ICDM 2019 Knowledge Graph Contest: Team UWA

We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest. Our system uses a pipeline-based approach to extract a set of triples from a given document. It offers a simple and effective solution to the challenge of knowledge graph construction from domain-specific text. It also provides the facility to visualise useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.
Cross-Modal Prototype Learning for Zero-Shot Handwriting Recognition

In contrast to machine recognizers that rely on training with large handwriting data, humans can recognize handwriting accurately on learning from few samples, and can even generalize to handwritten characters from printed samples. Simulating this ability in machine recognition is important to alleviate the burden of labeling large handwriting data, especially for large category set as in Chinese text. In this paper, inspired by human learning, we propose a cross-modal prototype learning (CMPL) method for zero-shot online handwritten character recognition: for unseen categories, handwritten characters can be recognized without learning from handwritten samples, but instead from printed characters. Particularly, the printed characters (one for each class) are embedded into a convolutional neural network (CNN) feature space to obtain prototypes representing each class, while the online handwriting trajectories are embedded with a recurrent neural network (RNN). Via cross-modal joint learning, handwritten characters can be recognized according to the printed prototypes. For unseen categories, handwritten characters can be recognized by only feeding a printed sample per category. Experiments on a benchmark Chinese handwriting database have shown the effectiveness and potential of the proposed method for zero-shot handwriting recognition.
Maximum Information Bounds for Planning Active Sensing Trajectories

This paper considers the problem of planning trajectories for robots equipped with sensors whose task is to track an evolving target process in the world. We focus on processes which can be represented by a Gaussian random variable, which is known to reduce the general stochastic information acquisition problem to a deterministic problem, which is much simpler to solve. Previous work on solving the resulting deterministic problem focuses on computing a search tree by Forward Value Iteration and pruning uninformative nodes early on in the search via a domination criteria. In this work we formulate the Active Information Acquisition problem as a deterministic planning problem where algorithms like Dijkstra and A* can produce optimal solutions. To use A* effectively in long planning horizons we derive a consistent and admissible heuristic as a function of the sensor model which can be used in information acquisition tasks such as actively mapping static and moving targets in an environment with obstacles. We validate the results in several simulations indicating that the resulting heuristic informed algorithm can recover optimal solutions faster than existing search-based methods.
A Model-Based Human Activity Recognition for Human–Robot Collaboration

Human activity recognition is a crucial ingredient in safe and efficient human-robot collaboration. In this paper, we present a new model-based human activity recognition system called logical activity recognition system (LCARS). LCARS requires much less training data compared to learning-based works. Compared to other model-based works, LCARS requires minimal domain-specific modeling effort from users. The minimal modeling is for two reasons: i) we provide a systematic and intuitive way to encode domain knowledge for LCARS and ii) LCARS automatically constructs a probabilistic estimation model from the domain knowledge. Requiring minimal training data and modeling effort allows LCARS to be easily applicable to various scenarios. We verify this through simulations and experiments.