Reviewerly/exHarmony huggingface dataset:
Image-Based Automatic Watermeter Reading under Challenging Environments

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.
A reliable ensemble based approach to semi-supervised learning

Semi-supervised learning (SSL) methods attempt to achieve better classification of unseen data through the use of unlabeled data than can be achieved by learning from the available labeled data alone. Most SSL methods require the user to familiarize themselves with novel, complex concepts and to ensure the underlying assumptions made by these methods match the problem structure, or they risk a decrease in predictive performance. In this paper, we present the reliable semi-supervised ensemble learning (RESSEL) method, which exploits unlabeled data by using it to generate diverse classifiers through self-training and combines these classifiers into an ensemble for prediction. Our method functions as a wrapper around a supervised base classifier and refrains from introducing additional problem dependent assumptions. We conduct experiments on a number of commonly used data sets to prove its merit. The results show RESSEL improves significantly upon the supervised alternatives, provided that the base classifier which is used is able to produce adequate probability-based rankings. It is shown that RESSEL is reliable in that it delivers results comparable to supervised learning methods if this requirement is not met, while the method also broadens the range of good parameter values. Furthermore, RESSEL is demonstrated to outperform existing self-labeled wrapper approaches.
Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography

Interval cancers are a commonly seen problem in organized breast cancer screening programs and their rate is measured for quality assurance. Artificial intelligence algorithms have been proposed to improve mammography sensitivity, in which case it is likely that the interval cancer rate would decrease and the quality of the screening system could be improved. Interval cancers from negative screening in 2011 and 2012 of one regional unit of the national German breast cancer screening program were classified by a group of radiologists, categorizing the screening digital mammography with diagnostic images as true interval, minimal signs, false negative and occult cancer. Screening mammograms were processed using a detection algorithm based on deep learning. Of the 29 cancer cases available, artificial intelligence identified eight out of nine of those classified as minimal signs, all six false negatives and none of the true interval and occult cancers. Sensitivity for lesions judged to be already present in screening mammogram was 93% (95% confidence interval 68-100) and sensitivity for any interval cancer was 48% (95% confidence interval 29-67). Using an artificial intelligence algorithm as an additional reading tool has the potential to reduce interval cancers. How and if this theoretical advantage can be reached without a negative effect on recall rate is a challenge for future research.
Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events

Having observed the unsatisfactory state-of-the-art performance in detecting abnormal events, this paper describes an iterative self-supervised learning method for such purpose. The proposed solution is composed of two experts that - at each step - find the most confidently classified instances to augment the amount of data available for the next iteration. Our contributions are four-fold: 1) we describe the iterative learning framework composed of experts working in the weak/self-supervised paradigms and providing learning data to each other, with the novel instances being filtered by a Bayesian framework; 2) upon Sultani et al. [14]'s work, we suggest a novel term the loss function that spreads the scores in the unit interval and is important for the performance of the iterative framework; 3) we propose a late decision fusion scheme, in which an ensemble of Decision Trees learned from bootstrap samples fuses the scores of the top-3 methods, reducing the EER values about 20% over the state-of-the-art; and 4) we announce the “Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/.
Survey on Automatic Text Summarization and Transformer Models Applicability

This survey talks about Automatic Text Summarization. Information explosion, the problem caused by the rapid growth of the internet, increased more and more necessity of powerful summarizers. This article briefly reviews different methods and evaluation metrics. The main attention is on the applications of the latest trends, neural network-based, and pre-trained transformer language models. Pre-trained language models now are ruling the NLP field, as one of the main down-stream tasks, Automatic Text Summarization is quite an interdisciplinary task and requires more advanced techniques. But there is a limitation of input and context length results in that the whole article cannot be encoded completely. Motivated by the application of recurrent mechanism in Transformer-XL, we build an abstractive summarizer for long text and evaluate how well it performs on dataset CNN/Daily Mail. The model is under general sequence to sequence structure with a recurrent encoder and stacked Transformer decoder. The obtained ROUGE scores tell that the performance is good as expected.
Enhancing the blockchain voting process in IoT using a novel blockchain Weighted Majority Consensus Algorithm (WMCA)

Internet of Things (IoT) is expected to improve our lifestyle in a noticeable way. However, although the IoT holds a lot of chances, it contains a lot of serious risks. This leads to a focus on developing security techniques that can increase the security level of IoT. Blockchain is considered as an innovative technique for securing IoT and sharing data in a secure and tamperproof way. The blockchain is a peer-to-peer connection system that performs transactions securely by using consensus algorithms with no need for a trusted third party. Blockchain proved its applicability in securing IoT networks, and the research in this area is still enticing researchers to delve deeper and deeper. Decentralized voting is considered the fundamental principle that blockchain relies on for making the appropriate decision that would offer a proper security level for IoT. In this research a novel decentralized blockchain Weighted Majority Consensus Algorithm is proposed. The algorithm is inspired by the well-known weighted majority voting algorithm in the ensemble data mining learning approach. A java implementation of WMCA has been created for testing several scenarios with the aim of confirming the applicability of the proposed WMCA and the results were very promising.
The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation

Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more efficient design than its two-stage counterparts. Besides, its global mask representation often leads to superior accuracy to the two-stage Mask R-CNN which has been dominant thus far. Despite the promising advances in single-stage methods, finer delineation of instance boundaries still remains unexcavated. Indeed, boundary information provides a strong shape representation that can operate in synergy with the fully-convolutional mask features of the single-stage segmenter. In this work, we propose Boundary Basis based Instance Segmentation(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods that are often lacking high-frequency details. Besides, we devise a unified quality measure of both mask and boundary and introduce a network block that learns to score the per-instance predictions of itself. When applied to the strongest baselines in single-stage instance segmentation, our B2Inst leads to consistent improvements and accurately parse out the instance boundaries in a scene. Regardless of being single-stage or two-stage frameworks, we outperform the existing state-of-the-art methods on the COCO dataset with the same ResNet-50 and ResNet-101 backbones.
Special issue on system research on artificial intelligence

Systems Research and Behavioral ScienceVolume 38, Issue 1 p. 187-187 CALL FOR PAPERS Special issue on system research on artificial intelligence Li Da Xu, Corresponding Author Li Da Xu lidxu@hotmail.com Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, USA Correspondence Li Da Xu, Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA. Email: lidxu@hotmail.comSearch for more papers by this author Li Da Xu, Corresponding Author Li Da Xu lidxu@hotmail.com Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, USA Correspondence Li Da Xu, Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA 23529, USA. Email: lidxu@hotmail.comSearch for more papers by this author First published: 03 January 2021 https://doi.org/10.1002/sres.2776Citations: 9Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume38, Issue1January/February 2021Pages 187-187 RelatedInformation
Vehicle Detection Using Image Processing

Nowadays, detection of the vehicles and their classification is very essential and also it has a lot of importance because of its use in many applications. One of the main applications is controlling and managing traffic. Vehicle detection and tracking plays a major role in the project of preventing road accidents using image processing. Tracking of moving objects is important in monitoring surveillance videos and capturing human motion. Taking its importance into consideration, an efficient algorithm is proposed to detect vehicles in an image using image processing. The image is captured from the front view of the vehicles. So, this algorithm detects vehicles using the front view. Each vehicle is detected based on its size. The two major techniques used in this algorithm are edge detection and morphological processing. Edge detection as well as morphological processing are important applications in image processing because of their wide range of uses. Edge detection is used to enhance the objects in image. Morphological operations are used to remove noise and as well as to adjust image in such a way to detect objects in an image. The simulation work of this algorithm is done in MATLAB which is a very strong scientific tool.
Hand gesture intention-based identity recognition using various recognition strategies incorporated with VGG convolution neural network-extracted deep learning features

With fast developments of artificial intelligence, human behaviors can be further acknowledged by means of the biometric information of hand gesture actions made by the person. Such hand gesture information revealing the specific intention of the person will be undoubtedly a critical clue to cognize human behaviors. Furthermore, identity recognition of the hand gesture-making person is one of the most important technique issues in hand gesture recognition applications. This work explores hand gesture intention-based identity recognition where various deep learning recognition strategies are presented. The well-know image sensor of Leap Motion Controller (LMC) is employed in this work for acquisitions of active hand gesture data. This paper presents four different deep learning strategies for hand gesture intention-based identity recognition, all of which are based on the deep learning model of the visual geometry group (VGG)-type convolution neural network (CNN). The presented deep learning strategies to perform hand gesture intention-based identity recognition are typical VGG-16 CNN deep learning, dynamic time warping (DTW) classifications with VGG-16 CNN extracted deep learning features, DTW classifications by VGG-16 CNN extracted deep learning features with principal component analysis (PCA) data reduction, and PCA centroid classifications using VGG-16 CNN extracted deep learning features with PCA. Compared with traditional hand gesture recognition by classifications of only the geometrical space feature of LMC 3D-(x, y, z) data without any deep learning, most of presented VGG-CNN based deep learning approaches have more outstanding performances on recognition accuracy. In the situation of real-time recognition that considers both of recognition accuracy and computation time, PCA centroid classifications by VGG-16 CNN extracted deep learning features with PCA reduction, FC1-PCA and FC2-PCA features that are estimated from the first and the second fully connected (FC) layer of VGG-CNN respectively (i.e. FC1 and FC2 layers) and then significantly reduced the data dimension by PCA, apparently performs best among all presented deep learning strategies.
Image encryption algorithm using wavelet packets and multiple chaotic maps

This paper presents an improved image encryption algorithm using Haar wavelet packets decomposition and four chaotic maps. For the wavelet packets scrambling we propose a permutation based on the Baker chaotic map. To increase the entropy of the ciphered image and the key space of the cryptosystem two chaotic maps - the three-dimensional Arnold map, applied twice, and the tent map combined with the logistic map - are used. Matlab simulations show that, in each step, these chaotic maps increase the entropy of the encrypted images and the pixels' decorrelation. Another benefit obtained using the four maps is the augmentation of the key space.
Business Applications for Current Developments in Big Data Clustering: An Overview

"The world's most valuable resource is no longer oil, but data" announces the headline of the May 6th, 2017 edition of The Economist; the digital revolution is here to stay. The primary currency of this movement is big data. The complexity of big data is defined as the relationships and how the data can be arranged with one another. Facebook has 30 billion pieces of unique information shared each month; this data's sheer size can cause an immeasurable amount of combinations for relational data. Analyzing this big data can reveal various useful insights for decision-makers. With the adoption of clustering analysis, patterns and hidden information can be developed from big raw data that can be used across many business problems and applications. In this paper, an overview of the state of the art of clustering analysis and its adoption in business applications in the era of big data is presented.
SCSSnet: Learning Spatially-Conditioned Scene Segmentation on LiDAR Point Clouds

This work proposes a spatially-conditioned neural network to perform semantic segmentation and geometric scene completion in 3D on real-world LiDAR data. Spatially-conditioned scene segmentation (SCSSnet) is a representation suitable to encode properties of large 3D scenes at high resolution. A novel sampling strategy encodes free space information from LiDAR scans explicitly and is both simple and effective. We avoid the need for synthetically generated or volumetric ground truth data and are able to train and evaluate our method on semantically annotated LiDAR scans from the Semantic KITTI dataset. Ultimately, our method is able to predict scene geometry as well as a diverse set of semantic classes over a large spatial extent at arbitrary output resolution instead of a fixed discretization of space. Our experiments confirm that the learned scene representation is versatile and powerful and can be used for multiple downstream tasks. We perform point-wise semantic segmentation, point-of-view depth completion and ground plane segmentation. The semantic segmentation performance of our method surpasses the state of the art by a significant margin of 7% mIoU.
Systematic Review of Issues and Solutions for Security in E-commerce

E-commerce popularity is growing rapidly all over the world, driven by technology and customer's needs. However, the development of e-commerce is not in line with the security of the platform. Recently, many security-related cases have occurred in e-commerce, such as hacking, data breach, fraud, or identity theft. Such security incidents cause high impact in terms of security risk; from financial loss to loss of customers' trust. This shows that security incidents are still occurring and causing a great impact on e-commerce platforms, besides the fact that many studies have been discussing security risk in e-commerce. Therefore, studying security risk in e-commerce platforms is still highly relevant, mainly due to advances in cyber-attacks coupled with the lack of security awareness from the e-commerce platform's stakeholders, including the owner and the end-users. Through a systematic literature review using Kitchenham; the aim of this study is to identify and enhance a comprehensive understanding of the security issues in e-commerce and present the potential solution. Findings showed that the most common security issue is credit card fraud. The other findings will be discussed later. Furthermore, the outcome of this study will contribute to increasing the understanding and awareness of stakeholders about security issues in e-commerce. Thus, stakeholders can improve the security aspects of e-commerce and can avoid hacking, data breach, fraud, or identity theft. Finally, it can increase the trust and willingness of customers to do transaction in e-commerce.
Reputation-Based Miner Node Selection in Blockchain-Based Vehicular Edge Computing

Vehicular edge computing (VEC) is one of the most efficient ways to have high functionality in resource-constrained connected vehicles. As the edge nodes in VEC holds paramount importance and highly sensitive data, we need to make sure the information exchange remains tamper-free. Although blockchain provides immutable and verifiable data storage solutions, the nodes that enable blockchain might be under attack such as a denial of service from various malicious actors. One way to nullify these types of attacks is to find the nodes under attack and remove them from the blockchain consensus process to ensure the proper functioning of the system, i.e., miner node selection. Similarly, in order to provide the decision on which node is to be removed from the network, we need a smart decision-making technique such as an artificial neural network (ANN). The ANN is used to calculate the reputation of the miner nodes. In this article, we propose a reputation-based miner node selection (RbMNS) in blockchain-based vehicular edge computing (BloVEC). We have used ANN and hyperledger fabric (HF) to implement the RbMNS and blockchain model in the system. We obtained the average transactional latency of 128 ms/109 ms with/without our implementation, respectively.
A semantics for Hybrid Probabilistic Logic programs with function symbols

Probabilistic Logic Programming (PLP) is a powerful paradigm for the representation of uncertain relations among objects. Recently, programs with continuous variables, also called hybrid programs, have been proposed and assigned a semantics. Hybrid programs are capable of representing real-world measurements but unfortunately the semantics proposal was imprecise so the definition did not assign a probability to all queries. In this paper, we remedy this and formally define a new semantics for hybrid programs. We prove that the semantics assigns a probability to all queries for a large class of programs.
Spatial presence depends on ‘coupling’ between body sway and visual motion presented on head-mounted displays (HMDs)

This study investigated the effects of simulating self-motion via a head-mounted display (HMD) on standing postural sway and spatial presence. Standing HMD users viewed simulated oscillatory self-motion in depth. On a particular trial, this naso-occipital visual oscillation had one of four different amplitudes (either 4, 8, 12 or 16 m peak-to-peak) and one of four different frequencies (either 0.125, 0.25, 0.5 or 1 Hz). We found that simulated high amplitude self-oscillation (approximately 16 m peak-to-peak) at either 0.25 Hz or 0.5 Hz: 1) generated the strongest effects on postural sway; and 2) made participants feel more spatially present in the virtual environment. Our findings provide insight into the parameters of simulated self-motion that generate the strongest postural responses within virtual environments. These postural constraints have valuable implications for improving our understanding of sensory processes underlying the ergonomic experience of virtual environments simulated using HMDs.