Reviewerly/exHarmony huggingface dataset:
Real-Time Traffic Sign Recognition using YOLOv3 based Detector

Increase in the number of vehicles on road necessitates the use of automated systems for driver assistance. These systems form important components of self-driving vehicles also. Traffic Sign Recognition system is such an automated system which provides the required contextual awareness for the self-driving vehicle. CNN based methods like Faster R-CNN for object detection provide human level accuracy and real time performance and are proven successful in Traffic Sign Recognition systems [1]. Single stage detection systems such as YOLO [2] and SSD [3], despite offering state-of-the-art real-time detection speed, are not preferred for traffic sign detection problem due to its reduced accuracy and small object detection issues. The enhanced YOLO versions, YOLOv2 and YOLOv3 have shown promising results with respect to accuracy and speed required for object detection problems. YOLOv3 uses specialized network architecture inspired from feature pyramid network and has several design changes over previous versions to tackle the low accuracy and small object detection problems. In this paper, an approach for traffic sign recognition system based on YOLOv3 is presented with comparative analysis of its performance with Faster R-CNN based sign detector [1]. YOLOv3 forms the traffic sign detection network and a CNN-based classifier forms the traffic sign class recognizer. The network training and evaluation are done using the German Traffic Sign Detection Benchmark (GTSDB) [5] dataset and the classifier performance is verified using German Traffic Sign Recognition Benchmark (GTSRB) [6] dataset.
Contextual deconvolution network for semantic segmentation

In this paper, we propose a Contextual Deconvolution Network (CDN) and focus on context association in decoder network. Specifically, in upsampling path, we introduce two types of contextual modules to model the interdependencies of features in channel and spatial dimensions respectively. The channel contextual module captures image-level semantic information by aggregating the feature maps across spatial dimensions, and clarifies global ambiguity of features. Meanwhile, the spatial contextual module obtains patch-level semantic context by learning a spatial weight map, and enhance the feature discrimination. We embed the two contextual modules into individual components of the decoder network, thus improving the representation power and gaining more precise segment results. Thorough evaluations are performed on four challenging datasets, i.e., PASCAL VOC 2012, ADE20K, PASCAL-Context and Cityscapes dataset. Our approach achieves competitive performance with state-of-the-art models on PASCAL VOC 2012,ADE20K and Cityscapes dataset, and new state-of-the-art performance on PASCAL-Context dataset.
Sentiment analysis on IMDB using lexicon and neural networks

To find out what other people think has been an essential part of information-gathering behaviors. And in the case of movies, the movie reviews can provide an intricate insight into the movie and can help decide whether it is worth spending time on. However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving on time. Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews. The area of analysis of sentiments is related closely to natural language processing and text mining. It can successfully be used to determine the attitude of the reviewer in regard to various topics or the overall polarity of the review. In the case of movie reviews, along with giving a rating in numeric to a movie, they can enlighten us on the favorableness or the opposite of a movie quantitatively; a collection of those then gives us a comprehensive qualitative insight on different facets of the movie. Opinion mining from movie reviews can be challenging due to the fact that human language is rather complex, leading to situations where a positive word has a negative connotation and vice versa. In this study, the task of opinion mining from movie reviews has been achieved with the use of neural networks trained on the “Movie Review Database” issued by Stanford, in conjunction with two big lists of positive and negative words. The trained network managed to achieve a final accuracy of 91%.
Permissioned Blockchain-Based Double-Layer Framework for Product Traceability System

Blockchain-based product traceability systems are receiving increasing attention from both industry and academia. Existing systems make full use of the traceability and non-modification characteristics of blockchain technology and realize the openness and transparency of product traceability information in the entire supply chain. However, existing systems do not consider government regulation, cannot protect enterprise sensitive private data effectively, and have performance bottlenecks. To address these problems, this paper proposes a product traceability scheme based on the permissioned blockchain within a double-layer framework. We introduce the double-layer framework and describe its advantages in detail. We also describe the smart contracts (chain code) in the double-layer framework. Finally, we test the performance of the proposed scheme through simulation experiments. The simulation results demonstrate the performance of nodes in the main layer, which is very important for consumers to obtain product traceability information, is optimized.
A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms

This paper proposes a hybrid optimization technique combining genetic and exchange market algorithms. These algorithms are two evolutionary algorithms that facilitate finding optimal solutions for different optimization problems. The genetic algorithm's high execution time decreases its efficiency. Because of the genetic algorithm's strength in surveying solution space, it can be combined with a proper exploitation-based algorithm to improve the optimization efficiency. The exchange market algorithm is an optimization algorithm that can effectively find the global optimum of the objective functions in an efficient manner. According to the trade's inherent situation, the stock market works under unbalanced and balanced modes. In order to gain maximum profit, shareholders take specific decisions based on the existing conditions. The exchange market algorithm has two searching and two absorbent operators for acquiring the best-simulated form of the stock market. Simulations on twelve benchmarks with the different dimensions and variables prove the effectiveness of this algorithm compared to eight optimization algorithms.
A Novel Step Optimal Path Planning Algorithm for the Spherical Mobile Robot Based on Fuzzy Control

In order to improve the ability of the spherical mobile robot to navigate and move autonomously in an unknown environment. This paper proposed a novel step optimal path planning method based on fuzzy control. Firstly, by analyzing the motion model of the spherical mobile robot, the arrangement and debugging of the ultrasonic sensors (HC-SR04) were completed. Then, through multi-sensor fusion technology and D-H parameter method, a fuzzy controller for the spherical mobile robot was designed. Finally, the proposed fuzzy control method was applied to the path planning of spherical mobile robot in unknown environment, and tested by a series of simulations and experiments. The results showed that the step optimal method based on fuzzy control could ensure that the spherical mobile robot completed the path planning. The experimental results also verified the effectiveness and practicability of the proposed novel path planning method.
Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling

Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic gradient descent (SGD) is one of the most popular stochastic optimization methods for training deep networks, it shows a slow convergence rate due to the large noise in the gradient approximation. In this article, we attempt to remedy this problem by building a more efficient batch selection method based on typicality sampling, which reduces the error of gradient estimation in conventional minibatch SGD. We analyze the convergence rate of the resulting typical batch SGD algorithm and compare the convergence properties between the minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex minibatch SGD variants can benefit from the proposed batch selection strategy.
A Cuboid CNN Model With an Attention Mechanism for Skeleton-Based Action Recognition

The introduction of depth sensors such as Microsoft Kinect have driven research in human action recognition. Human skeletal data collected from depth sensors convey a significant amount of information for action recognition. While there has been considerable progress in action recognition, most existing skeleton-based approaches neglect the fact that not all human body parts move during many actions, and they fail to consider the ordinal positions of body joints. Here, and motivated by the fact that an action's category is determined by local joint movements, we propose a cuboid model for skeleton-based action recognition. Specifically, a cuboid arranging strategy is developed to organize the pairwise displacements between all body joints to obtain a cuboid action representation. Such a representation is well structured and allows deep CNN models to focus analyses on actions. Moreover, an attention mechanism is exploited in the deep model, such that the most relevant features are extracted. Extensive experiments on our new Yunnan University-Chinese Academy of Sciences-Multimodal Human Action Dataset (CAS-YNU MHAD), the NTU RGB+D dataset, the UTD-MHAD dataset, and the UTKinect-Action3D dataset demonstrate the effectiveness of our method compared to the current state-of-the-art.
Toward a Decentralized, Trust-Less Marketplace for Brokered IoT Data Trading Using Blockchain

As data marketplaces are becoming common place, it is also becoming clear that data streams generated from Internet of Things (IoT) devices hold value for potential third party consumers. We envision a marketplace for IoT data streams that can unlock such potential value in a scalable way, by enabling any pairs of data providers and consumers to engage in data exchange transactions without any prior assumption of mutual trust. We present a marketplace model and architecture to support trading of streaming data, from the advertising of data assets to the stipulation of legally binding trading agreements, to their fulfilment and payment settlement. Crucially, we show that by using blockchain technology and Smart Contracts in particular, we can offer participants a trade-off between the cost of transactional data exchange, and the risk of data loss when trading with untrusted third parties. We experimentally assess such trade-offs on a testbed using Ethereum Smart Contracts.
Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Data Sets

Summary The paper presents an incremental updating algorithm to analyse streaming data sets using generalized linear models. The method proposed is formulated within a new framework of renewable estimation and incremental inference, in which the maximum likelihood estimator is renewed with current data and summary statistics of historical data. Our framework can be implemented within a popular distributed computing environment, known as Apache Spark, to scale up computation. Consisting of two data-processing layers, the rho architecture enables us to accommodate inference-related statistics and to facilitate sequential updating of the statistics used in both estimation and inference. We establish estimation consistency and asymptotic normality of the proposed renewable estimator, in which the Wald test is utilized for an incremental inference. Our methods are examined and illustrated by various numerical examples from both simulation experiments and a real world data analysis.
Deep Adversarial Data Augmentation for Extremely Low Data Regimes

Deep learning has revolutionized the performance of classification and object detection, but meanwhile demands sufficient labeled data for training. Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extremely low data regimes</i> : only a small set of labeled data are available, and nothing – including unlabeled data – else. Such regimes arise from practical situations where not only data labeling but also data collection itself is expensive. We propose a deep adversarial data augmentation (DADA) technique to address the problem, in which we elaborately formulate data augmentation as a problem of training a class-conditional and supervised generative adversarial network (GAN). Specifically, a new discriminator loss is proposed to fit the goal of data augmentation, through which both real and augmented samples are enforced to contribute to and be consistent in finding the decision boundaries. Tailored training techniques are developed accordingly. To quantitatively validate its effectiveness, we first perform extensive simulations to show that DADA substantially outperforms both traditional data augmentation and a few GAN-based options. We then extend experiments to three real-world small labeled classification datasets where existing data augmentation and/or transfer learning strategies are either less effective or infeasible. We also demonstrate that DADA to can be extended to the detection task. We improve the pedestrian synthesis work by substitute for our discriminator and training scheme. Validation experiment shows that DADA can improve the detection mean average precision (mAP) compared with some traditional data augmentation techniques in object detection. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SchafferZhang/DADA</uri> .
Engineering and Manufacturing on the Blockchain: A Systematic Review

This article presents the results of a systematic review of the literature pertaining to the use of blockchain technology in the engineering and manufacturing activities. The uses of blockchain have been growing quickly in the literature, and this fast pace can make it difficult for researchers to keep abreast of the state of the art. Using a systematic approach to search the primary electronic indices, the compiled list of papers is then analyzed and a number of themes are identified. The results of this article suggest that the literature surrounding blockchain and the engineering/manufacturing industry can be categorized into three primary groups: blockchain to protect data validity, blockchain to enhance inter and intraorganizational communications, and blockchain to increase the efficiency of the manufacturing process. Within each top-level theme, there exist some subthemes that provide a more finely defined categorization of the papers contained in that theme. These subthemes include specific areas of blockchain application such as additive manufacturing, cloud manufacturing, and building information models (BIMs). For each group of papers identified, each study is discussed briefly. This article concludes with a set of conclusions about the current state of the literature and suggestions for further research directions. The primary contribution of this article is to provide a foundation upon which additional research can be accomplished as well as to provide a resource for practitioners to help them understand the current state of the industry and help them determine how blockchain might be beneficial to their organization.
GlassesViewer: Open-source software for viewing and analyzing data from the Tobii Pro Glasses 2 eye tracker

Abstract We present GlassesViewer, open-source software for viewing and analyzing eye-tracking data of the Tobii Pro Glasses 2 head-mounted eye tracker as well as the scene and eye videos and other data streams (pupil size, gyroscope, accelerometer, and TTL input) that this headset can record. The software provides the following functionality written in MATLAB: (1) a graphical interface for navigating the study- and recording structure produced by the Tobii Glasses 2; (2) functionality to unpack, parse, and synchronize the various data and video streams comprising a Glasses 2 recording; and (3) a graphical interface for viewing the Glasses 2’s gaze direction, pupil size, gyroscope and accelerometer time-series data, along with the recorded scene and eye camera videos. In this latter interface, segments of data can furthermore be labeled through user-provided event classification algorithms or by means of manual annotation. Lastly, the toolbox provides integration with the GazeCode tool by Benjamins et al. (2018), enabling a completely open-source workflow for analyzing Tobii Pro Glasses 2 recordings.
Appropriateness of performance indices for imbalanced data classification: An analysis

Indices quantifying the performance of classifiers under class-imbalance, often suffer from distortions depending on the constitution of the test set or the class-specific classification accuracy, creating difficulties in assessing the merit of the classifier. We identify two fundamental conditions that a performance index must satisfy to be respectively resilient to altering number of testing instances from each class and the number of classes in the test set. In light of these conditions, under the effect of class imbalance, we theoretically analyze four indices commonly used for evaluating binary classifiers and five popular indices for multi-class classifiers. For indices violating any of the conditions, we also suggest remedial modification and normalization. We further investigate the capability of the indices to retain information about the classification performance over all the classes, even when the classifier exhibits extreme performance on some classes. Simulation studies are performed on high dimensional deep representations of subset of the ImageNet dataset using four state-of-the-art classifiers tailored for handling class imbalance. Finally, based on our theoretical findings and empirical evidence, we recommend the appropriate indices that should be used to evaluate the performance of classifiers in presence of class-imbalance.
Secure Image Transmission Using Chaotic-Enhanced Elliptic Curve Cryptography

In this paper, a novel scheme for secure and real time image transmission is introduced. The scheme uses a block-based elliptic curve (BBEC) public key encryption as a first stage of encryption so it solves the key distribution and management problem of symmetric key encryption in an efficient method. Then the security of first stage BBEC encryption is enhanced through a second stage of encryption which XOR the BBEC first stage output with a pseudo random sequence generated by a new designed multi chaotic pseudo random generator STH. It merges Sine, Ten and Henon maps (STH) and so it has five secret control parameters which increase the key space and hence the scheme security against brute force attacks. Both the key of the first stage BBEC image encryption and the control parameters of the second stage STH depend on two parts: EC Diffie-Hellman shared secret key and the input plain image itself which provides a good randomness and achieves a strong resistance against chosen plaintext attacks. After the two stages of encryption, the encrypted image is digitally signed in an efficient method to achieve integrity, authentication and non-repudiation. The results prove that the proposed scheme is more secure and faster than some other recent EC based schemes. It has low correlation, huge key space; key-dependent pixel value replacement and can resist statistical, differential and noise attacks.
Attract or Distract: Exploit the Margin of Open Set

Open set domain adaptation aims to diminish the domain shift across domains, with partially shared classes. There exist unknown target samples out of the knowledge of source domain. Compared to the close set setting, how to separate the unknown (unshared) class from the known (shared) ones plays the key role. Whereas, previous methods did not emphasize the semantic structure of the open set data, which may introduce bias into the domain alignment and confuse the classifier around the decision boundary. In this paper, we exploit the semantic structure of open set data from two aspects: 1) Semantic Categorical Alignment, which aims to achieve good separability of target known classes by categorically aligning the centroid of target with the source. 2) Semantic Contrastive Mapping, which aims to push the unknown class away from the decision boundary. Empirically, we demonstrate that our method performs favourably against the state-of-the-art methods on representative benchmarks, e.g. Digits and Office-31 datasets.
Robust Visual Tracking via Constrained Multi-Kernel Correlation Filters

Discriminative Correlation Filter (DCF) based trackers are quite efficient in tracking objects by exploiting the circulant structure.The kernel trick further improves the performance of such trackers.The unwanted boundary effects, however, are difficult to solve in the kernelized correlation models.In this paper, we propose a novel Constrained Multi-Kernel Correlation tracking Filter (CMKCF), which applies spatial constraints to address this drawback.We build the multi-kernel models for multi-channel features with three different attributes, and then employ a spatial cropping operator on the semi-kernel matrix to address the boundary effects.For the constrained optimization solution, we develop an Alternating Direction Method of Multipliers (ADMM) based algorithm to learn our multi-kernel filters efficiently in the frequency domain.In particular, we suggest an adaptive updating mechanism by exploiting the feedback from high-confidence tracking results to avoid corruption in the model.Extensive experimental results demonstrate that the proposed method performs favorably on OTB-2013, OTB-2015, VOT-2016 and VOT-2018 dataset against several state-of-the-art methods.