Reviewerly/exHarmony huggingface dataset:
An intelligent shopping support robot: understanding shopping behavior from 2D skeleton data using GRU network

Abstract In supermarkets or grocery, a shopping cart is a necessary tool for shopping. In this paper, we have developed an intelligent shopping support robot that can carry a shopping cart while following its owners and provide the shopping support by observing the customer’s head orientation, body orientation and recognizing different shopping behaviors. Recognizing shopping behavior or the intensity of such action is important for understanding the best way to support the customer without disturbing him or her. This system also liberates elderly and disabled people from the burden of pushing shopping carts, because our proposed shopping cart is essentially a type of autonomous mobile robots that recognizes its owner and following him or her. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning towards a merchandise shelf. From the robot’s video stream, a DNN-based human pose estimator called OpenPose is used to extract the skeleton of 18 joints for each detected body. Using this extracted body joints information, we built a dataset and developed a novel Gated Recurrent Neural Network (GRU) topology to classify different actions that are typically performed in front of shelves: reach to shelf, retract from shelf, hand in shelf, inspect product, inspect shelf. Our GRU network model takes series of 32 frames skeleton data then gives the prediction. Using cross-validation tests, our model achieves an overall accuracy of 82%, which is a significant result. Finally, from the customer’s head orientation, body orientation and shopping behavior recognition we develop a complete system for our shopping support robot.
An improved first quantization matrix estimation for nonaligned double compressed JPEG images

Estimation of the first quantization matrix of a nonaligned double compressed JPEG (NA-DJPG) image remains a challenging problem. To solve this problem, a novel method for estimating the original quantization steps of NA-DJPG images is proposed in this paper. Our method is separated into two phases: estimation of the misalignment parameters and estimation of the quantization steps. In the first phase, since the texture of the image has great influence on the traces of the first compression grid, an appropriate block selection strategy is proposed that takes the interference of texture into consideration, and this is followed by a differential boundary analysis to estimate the misalignment parameters between the first and second compression grids. In the second phase, a DCT coefficient histogram rearrangement strategy is first proposed to make the periodicity of the histogram more apparent. This is followed by a histogram frequency spectrum analysis to estimate the period of the histogram, which is close to the first quantization step. To further suppress the interference of the second compression, several quantization step candidates around the rounded period value are selected, and finally, the quantization step for each band is estimated via a comparison strategy of periodic bin accumulation. The experimental results and a comparison with existing methods demonstrate the effectiveness and superiority of the proposed method.
Hate Speech Detection in Code-switched Text Messages

Not only does it happen in America, but also in Asia, in Africa and all over the world: Hate Speech. The exponential growth of user-generated content on social media bordering hate speech is increasingly alarming. Several efforts to monitor this phenomenon by social media network companies and the research community are on-going with various degrees of success. One gap in previous studies that this study addresses is the identification of hate speech in code-switched text messages. The alternation of words in different languages within a message is a common occurrence among multilingual persons or communities. The study explored the performance of different features across various machine learning algorithms and established that character-level Term Frequency-Inverse Document Frequency, performed best given a code-switched dataset of 25k annotated tweets using support vector machine algorithm as compared to six other conventional and two deep learning algorithms.
Scale-aware spatial pyramid pooling with both encoder-mask and scale-attention for semantic segmentation

This paper focuses on semantic segmentation of scenes by capturing the appropriate scale, rich detail and contextual dependencies in a feature representation. Semantic segmentation is a pixel-level classification task and has made steady progress on the basis of fully convolutional networks (FCNs). However, we find there is still room for improvement in the following aspects. The first is that the pixel itself has not enough information for semantic prediction, it needs to look around to determine which category it belongs to. However, the fixed size of the receptive field defined by the network is not suitable for all pixels when an image contains objects with various scales. The second is that the extracted scale-aware features do not handle sharper object boundaries due to low-resolution. The final aspect is regarding the ability to model long-range dependencies. In order to solve the above challenges, in this paper we propose three modules: Scale-aware spatial pyramid pool module, Encoder mask module and Scale-attention module (SSPP-ES). Extensive experiments on the Cityscapes and ADE20K benchmarks demonstrate the effectiveness of our approach for semantic segmentation.
Gender and the Digital Divide Across Urban Slums of New Delhi, India: Cross-Sectional Study

Disparities in access to specific technologies within gender groups have not been investigated. Slum settings provide an ideal population to investigate the contributing factors to these disparities.This study aimed to examine gender differences in mobile phone ownership, internet access, and knowledge of SMS text messaging among males and females living in urban slum settings.A convenience sampling approach was used in sample selection from 675 unnotified slums. A total of 38 slum sites were then selected across four geographic zones. Of these, 10% of the households in each slum site was selected from each zone. One household member was interviewed based on their availability and fulfillment of the eligibility criteria. Eligible individuals included those aged 18 years and above, residing in these slums, and who provided voluntary consent to participate in the study. Individuals with mental or physical challenges were excluded from the study.Our results showed that females were half as likely to own mobile phones compared with males (odds ratio [OR] 0.53, 95% CI 0.37-0.76), less likely to have internet access (OR 0.79, 95% CI 0.56-1.11), or know how to send text messages (OR 0.93, 95% CI 0.66-1.31). The predictors of mobile phone ownership, internet access, and text messaging between males and females included age, individual education, housing type, and the number of earning members in a household in the adjusted analysis. Among males, the number of earning members was a predictor of both mobile phone ownership and text messaging, whereas household education was a predictor of both internet access and text messaging. Age and individual education only predicted internet access, whereas housing type only predicted text messaging. Among females, household education was a predictor of all the technology outcomes. Age and type of toilet facility only predicted mobile phone ownership; housing type only predicted internet access whereas television ownership with satellite service and smoking behavior only predicted text messaging.Our study findings showing disparate access to technology within gender groups lend support for further research to examine the causal mechanisms promoting these differences to proffer significant solutions. Specifically, our study findings suggest that improving household education is crucial to address the disparate access and usage of mobile phones, the internet, and text messaging among women in slum settings. This suggestion is due to the consistency in household educational level as a predictor across all these technology indicators. In addition, the mechanisms by which the number of household earning members influences the disparate access to technology among men call for further exploration.
FPGA implementation of a wireless sensor node with built-in security coprocessors for secured key exchange and data transfer

The technological advances in wireless sensor nodes and the appearance of the Internet of Things (IoT) concept have reinforced interest in wireless sensor networks (WSN), as one of the basic elements, the IoT relies on Bhoyar et al. (2019) [1]. However, the security of data exchanged between nodes, which are susceptible to several types of attack, is still one of the challenges WSNs face. The aim of this work is to design and implement, in a field-programmable gate array (FPGA) circuit, a wireless sensor node with some customised built-in coprocessors to make data transfer and key-exchange operations secure and, especially, faster. These coprocessors allow us to establish secure communication using symmetric and asymmetric cryptography. In addition, we implement a reliable key-exchange protocol, to authenticate a new node before sharing the symmetric encryption key with it. This key exchange is based on the Diffie Hellman protocol, and enhanced with a lightweight defence mechanism against man-in-the-middle (MITM) attacks. The implemented security coprocessors are: the Advanced Encryption Standard (AES-128), to perform the symmetric cryptography; elliptic-curve cryptography (ECC-163), to perform elliptic-curves operations for asymmetric cryptography; and a secure hash algorithm (SHA-256), to perform a hash function that can be used for digital signature, authentication, and random-keys generation. All these coprocessors are supervised by the soft-core processor MicroBlaze. The hardware implementation of these algorithms makes their execution times shorter, which allows us to reduce the time needed for key exchange significantly to less than 12 ms and to accelerate the data encryption. The developed wireless node is equipped with the nRF24L01 + 2.4 GHz transceiver module, implemented and tested in an Artix-7 XC7A35T FPGA with a maximum resources consumption of less than 55%.
Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data

Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10,000x times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPE-AHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential.
Conditional Parallel Coordinates

Parallel Coordinates [11],[12] are a popular data visualization technique for multivariate data. Dating back to as early as 1880 [8] PC are nearly as old as John Snow's famous cholera outbreak map [18] of 1855, which is frequently regarded as a historic landmark for modern data visualization. Numerous extensions have been proposed to address integrity, scalability and readability. We make a new case to employ PC on conditional data, where additional dimensions are only unfolded if certain criteria are met in an observation. Compared to standard PC which operate on a flat set of dimensions the ontology of our input to Conditional Parallel Coordinates is of hierarchical nature. We therefore briefly review related work around hierarchical PC using aggregation or nesting techniques. Our contribution is a visualization to seamlessly adapt PC for conditional data under preservation of intuitive interaction patterns to select or highlight polylines. We conclude with intuitions on how to operate CPC on two data sets: an AutoML hyperparameter search log, and session results from a conversational agent.
Automotive cybersecurity: assessing a new platform for cybercrime and malicious hacking

The push for technological enhancement and Internet connectivity in all aspects of modern life is radically transforming our use of devices and equipment that was not designed with this functionality. In fact, technological enhancements are transforming the automotive industry by increasing the number of components within the vehicle that are controlled by computers and which provide information to remote systems by various communications methods. These changes create new opportunities for cybercrime, particularly acts of cyber-trespass by computer hackers. This study considers how these technical threats may be limited through the use of criminological theory, specifically a routine activities perspective. The role of various actors in the automotive supply chain, and their potential function to deter and mitigate threats are discussed. The implications of this study for theory and practice are discussed in depth.
Collison Avoidance using Point Cloud Data Fusion from Multiple Depth Sensors: A Practical Approach

This paper presents a collision avoidance system based on vision sensors that is suitable for collaborative robotics scenarios. In fact, collaborative robotics foresees the possibility that humans and robots share the same workspace, so the safety of the human operators must be ensured. The collision avoidance algorithm here presented can modify the trajectory of the robot in order to avoid any collisions with a human operator. A fundamental element for the algorithm is the relative position between robot and human. In this work, the information of the position of a human operator is obtained by Microsoft Kinect sensor in the form of a point cloud. Two Microsoft Kinect are used and their point cloud data is merged to overcome the problems related to the possible occlusions of the sensors, obtaining a more reliable point cloud. Each Kinect works with a dedicated PC and the two PCs communicate via ethernet network in a master-slave mode. The layout of the acquiring system is described and the functions used for the communication between the PCs and the manipulation of the point clouds are presented. Results of simulation tests made to verify the performances of the system and collision avoidance based on point cloud compared with convex mesh are reported and discussed.
Anomaly Detection in Partially Observed Traffic Networks

This paper addresses the problem of detecting anomalous activity in traffic networks where the network is not directly observed. Given knowledge of what the node-to-node traffic in a network should be, any activity that differs significantly from this baseline would be considered anomalous. We propose a Bayesian hierarchical model for estimating the traffic rates and detecting anomalous changes in the network. The probabilistic nature of the model allows us to perform statistical goodness-of-fit tests to detect significant deviations from a baseline network. We show that due to the more defined structure of the hierarchical Bayesian model, such tests perform well even when the empirical models estimated by the EM algorithm are misspecified. We apply our model to both simulated and real datasets to demonstrate its superior performance over existing alternatives.
Feature selection and classification by minimizing overlap degree for class-imbalanced data in metabolomics

Learning from class-imbalanced data has gained increasing attention in recent years due to the massive growth of skewed data across many scientific fields such as metabolomics. Some researches show that it is not the imbalance itself which hinders the classification performance, but class overlapping do play an important role in the performance degradation when associated to class-imbalance. So alleviating the overlapping of the imbalanced data might be an effective way to improve the performance in class-imbalance learning. In this study, we propose two feature selection algorithms that aim to minimize the overlap degree between the majority and the minority, which is based on a simple assumption that decreasing overlap degree of a data set makes it more separable. The proposed MOSNS and MOSS methods are built via sparse regularization techniques. Simulation results indicate that our algorithms is effective in recognizing key features and control false discoveries for class-imbalance learning. Four class-imbalanced metabolomics data sets are also employed to test the performance of our algorithm, and a comparison with accuracy (ACC)-based and ROC-based selection procedures is performed. The result shows that our algorithms are highly competitive and can be an alternative feature selection strategy in class-imbalance learning.
Fourth Industrial Revolution for Development: The Relevance of Cloud Federation in Healthcare Support

Inefficient healthcare is a major concern among many African nations.It can be mitigated by building world-class infrastructure connecting different medical facilities for collaboration and resource sharing.Such infrastructure should support the exchange of medical data, enabling access to expertise not available locally.It should be equipped with technologies of the fourth industrial revolution, providing support to doctors thereby enabling African nations leapfrog from poorly equipped to medically prepared.Sadly, world-class healthcare facilities are a missing piece in African public health ecosystems.Medical facilities are either non-existent or prohibitively expensive.Being a collaborative model between Cloud providers, federated Clouds allow the execution of tasks on computing resources flexibly and cost efficiently.This paper aims to interconnect medical facilities across Africa by proposing a Cloud federation for healthcare using co-operative and competitive collaboration models.Simulations were carried out to test the efficiency of these models using two new allocation schemes: Genetic Algorithm-based VM Allocation (GAVA) and Stable Roommate Allocation (SRA).These schemes were bench-marked against First-Fit-Descending (FFD), Best-Fit-Descending (BFD), Binary-Search-Best-Fit (BSBF) allocation schemes; for both light and heavy workloads.Obtained results revealed that the co-operative model resulted in lower delays but higher resource utilization; while the competitive provided faster service delivery and better quality of service.Deployment considerations and potential business models for the African Cloud federation were also presented.
Dual-Purpose Method for Underwater and Low-Light Image Enhancement via Image Layer Separation

Underwater and low-light images possess different characteristics; hence, few approaches have been exploited to jointly improve the visibility of these images.Herein, a dual-purpose method that achieves satisfactory performance in enhancing the visibility of both underwater and low-light images is proposed.In our study, the formation of these two types of images is described in a unified manner.Subsequently, an objective function is formulated, and several novel regularization terms are imposed on our optimization algorithm to separate incident light and reflectance as well as suppress intensive noise simultaneously.Next, post-processing algorithms are implemented to correct the color distortion of the incident light and improve the contrast of the reflectance.Ultimately, an enhanced image with clear visibility and natural appearance can be achieved by integrating the processed reflectance and incident light.Additionally, comprehensive tests were performed to compare the proposed method with other outstanding methods.Experiments on images captured in various scenes demonstrated the effectiveness of the proposed method, as evident in enhanced underwater and low-light images. INDEX TERMS Dual-purpose
Detection of Lung Cancer from CT Scan Images using GLCM and SVM

Lung cancer is that kind of disease that can be defined as one of the threating one and has become very challenging these days. In many cases, the cells that carry cancer overlap with each other and its very hard to detect in the initial stage. But fact is, early detection bring solution; it can reduce the death for sure. Throughout the study, we use a technique (GLCM); The Grey Level Cooccurrence for the purpose of extracting the lung images of affected. The detection of abnormal Lung image can be detected by the Support Vector Machine (SVM).
Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review

Eye gaze tracking has been used to study the influence of visual stimuli on consumer behavior and attentional processes. Eye gaze tracking techniques have made substantial contributions in advertisement design, human computer interaction, virtual reality and disease diagnosis. Eye gaze estimation is considered critical for prediction of human attention, and hence indispensable for better understanding human activities. In this paper, Latent Semantic Analysis is used to develop an information model for identifying emerging research trends within eye gaze estimation techniques. An exhaustive collection of 423 titles and abstracts of research papers published during 2005-2018 were used. Five major research areas and ten research trends were classified based upon this study.
Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk

Segmentation of nuclei and cytoplasm inside the cellular clumps in cervical smear images is a difficult task because of the poor contrast and unpredictable shape of cytoplasm. This article addresses a new framework based on Attention U-Net (ATT U-Net) network and graph-based Random Walk (RW) to extract both nucleus and cytoplasm of each individual cell within an image of overlapping cervical cells. The proposed approach starts by separating nuclei from the cellular clumps through ATT U-Net architecture. Then, we remove fake nuclei that are usually much smaller than real nuclei. For each nucleus, a polar coordinate sampling matrix is generated. Each element in this matrix is realized by converting the image pixel from Cartesian coordinates to polar coordinates. After that, converted images would serve as the input of ATT U-Net for predicting cytoplasm. And finally, Graph-based RW is applied to extract the contour of cytoplasm. Because the features of cytoplasm boundaries in predicted maps are so obvious that the segmentation of every individual cell, including overlapping area, worked well under RW. We evaluate our framework on ISBI 2014 Challenge Dataset. The results reveal that our approach improves the performance on extracting individual cell from heavy overlapping cell clumps.