Reviewerly/exHarmony huggingface dataset:
Ontology and Hybrid Optimization Based SVNN for Privacy Preserved Medical Data Classification in Cloud

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.
Efficient Multi-Object Tracking by Strong Associations on Temporal Window

In this paper, we present a multiple-object vehicle tracking system. We introduce a method to combine bounding boxes extracted from multiple CNNs-based detections as alight and accurate alternative to confidence-based detection methods such as non-maximum suppression and clustering approaches predicting a single bounding box. An intersection over union metric and a threshold value is proposed to determine whether a single detection or a connectivity graph between extracted bounding boxes exists. Affinity measurements are extracted from features representing bounding box geometry, appearance comparison, and changing scene properties. Then, data association is performed by solving the min-cost flow problem of the temporal windows' affinity network. An affinity network of a directed graph associates the objects and determines whether an existing tracklet is maintained, terminated, or a new tracklet is initiated. Our model is evaluated and tested by KITTI object tracking-Car class benchmark dataset. Overall, the proposed multiple object tracking performance is ranked second according to the multiple object tracking accuracy, mostly tracked, mostly lost statistical metric values assure lower fragmentation and less than half of the captured ID-switch in comparison with respect to the method reaching at the highest multiple object tracking accuracy metric. Furthermore, the runtime is six times faster.
GPU-based Graph Traversal on Compressed Graphs

Graph processing on GPUs received much attention in the industry and the academia recently, as the hardware accelerator offers attractive potential for performance boost. However, the high-bandwidth device memory on GPUs has limited capacity that constrains the size of the graph to be loaded on chip. In this paper, we introduce GPU-based graph traversal on compressed graphs, so as to enable the processing of graphs having a larger size than the device memory. Designed towards GPU's SIMT architecture, we propose two novel parallel scheduling strategies Two-Phase Traversal and Task-Stealing to handle thread divergence and workload imbalance issues when decoding the compressed graph. We further optimize our solution against power-law graphs by proposing Warp-centric Decoding and Residual Segmentation to facilitate parallelism on processing skewed out-degree distribution. Extensive experiments show that with 2x-18x compression rate, our proposed GPU-based graph traversal on compressed graphs (GCGT) achieves competitive efficiency compared with the state-of-the-art graph traversal approaches on non-compressed graphs.
Robust background generation based on an effective frames selection method and an efficient background estimation procedure (FSBE)

Background generation is a fundamental step for video surveillance. It is challenged by many effects such as illumination, intermittent motion and clutter. In this paper, we propose a robust background generation method which can estimate the background from frames which all contain foreground. Moreover, it has good robustness toward illumination, clutter and other effects. The value channel from the HSV color space is used to detect any illumination changes in order to initiate a procedure for retaining frames with close intensity. A Foreground Rate per Frame (FRF) measure is developed based on the motion pixels to reduce the effect of stationary foreground and dynamic background. To refine the motion pixels and reconstruct the background, a refinement algorithm is developed to select the frames which can lead to reliable background estimation. To confirm the performance of the proposed method, we hold experiments on the extensive benchmark SBMnet2016 which contains 79 videos and includes 8 different challenging effects. The proposed method ranks as the fourth , on average, compared to 30 state of art methods and it maintains competitive scores through all benchmark categories.
Learning Semantic Text Features for Web Text-Aided Image Classification

The good generalization performance of conventional pattern classifiers often relies on the size of training data labeled by costly human labor. These days, publicly available web resources grow explosively, and this allows us to easily obtain abundant and cheap web data. Yet, web data are usually not as cooperative as human labeled data. In this paper, we explore the use of web text data to aid image classification. Without requiring the previous collection of auxiliary data from the web, we directly retrieve the web text information with the aid of the powerful reverse image search engine. We develop a novel textual modeling method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic matching neural network</i> (SMNN) that is capable of learning semantic features from the associated text of web images. The SMNN text features have improved reliability and applicability, compared to the text features obtained from other methods. The SMNN text features and convolutional neural network (CNN) visual features are merged into a shared representation, which learns to capture the correlations between the two modalities. Experimental results on benchmark UIUC-Sports, Scene-15, Caltech-256, and Pascal VOC-2012 data sets show that the visual and text modalities of data from different sources are remarkably complementary and the fusion of them achieves substantial performance improvement.
An Efficient Sign Language Translator Device Using Convolutional Neural Network and Customized ROI Segmentation

Sign language is widely used by hearing impaired people all over the world. With the advancement of cutting-edge deep learning techniques, there has been immense attention given by the researchers for sign language conversion. But only a few works have been executed on the Bangla Sign Language conversion for hearing impaired people. This paper aims to demonstrate a user-friendly approach towards Bangla Sign language to text conversion through customized Region of Interest (ROI) segmentation and Convolutional Neural Network (CNN). 5 sign gestures are trained using custom image dataset and implemented in Raspberry Pi for portability. Using the ROI selection approach, the process shows better outcomes than conventional approaches in terms of accuracy level and real time detection from video streaming through webcam. Furthermore, this method serves to offer an efficient model which ultimately results in easy addition of more signs to the final prototype made using Raspberry Pi.
Local Deformable 3D Reconstruction with Cartan's Connections

3D reconstruction of deformable objects using inter-image visual motion from monocular images has been studied under Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM). Most methods have been developed for simple deformation models, primarily isometry. They may treat a surface as a discrete set of points and draw constraints from the points only or they may use a non-parametric representation and use both points and differentials to express constraints. We propose a differential framework based on Cartan's theory of connections and moving frames. It is applicable to SfT and NRSfM, and to deformation models other than isometry. It utilises infinitesimal-level assumptions on the surface's geometry and mappings. It has the following properties. 1) It allows one to derive existing solutions in a simpler way. 2) It models SfT and NRSfM in a unified way. 3) It allows us to introduce a new skewless deformation model and solve SfT and NRSfM for it. 4) It facilitates a generic solution to SfT which does not require deformation modeling. Our framework is complete: it solves deformable 3D reconstruction for a whole class of algebraic deformation models including isometry. We compared our solutions with the state-of-the-art methods and show that ours outperform in terms of both accuracy and computation time.
E-Government and the Challenge of Cybercrime in Nigeria

Data driven government has become significant in the quest for quality service delivery in the public sector. Countries all over the world are now using Information Communication Technologies (ICTs)to enhance the quality of their governance. ICTs have been found to improve efficiency and effectiveness of public administration and enhance citizens access to government services, hence, improvement of the quality of governance. The increasing development of ICTs however, presents new and emerging threats for government to contend with. The issue of cybercrime has become a critical factor in digital government practice in recent times. The internet which provides free access to information has also led to the abuse of technology in perpetrating crimes capable of undermining a nations security. This study examines the implications of cybercrimes on the implementation digital government in Nigerias public sector. The paper makes use of qualitative secondary data gathered from publications of peer reviewed journals and international bodies, books, internet materials to interrogate the implication of cybercrimes in Nigerias quest for digital government adoption. Data gathered were analysed through a systematic literature review with the Routine Activity theory serving as the theoretical framework. Findings of the study reveal the great threat that the increasing spate of cybercrimes pose for digital government implementation in the Nigeria public sector. The paper argues for a data driven government in Nigeria and the political will for its implementation and as well, a vigorous public awareness campaign and decisive application of cyber laws in the country.
A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter

This paper presents a strategy for providing reliable state estimates that allow a group of robots to realize a formation even when communication fails and the tracking data alone is insufficient for maintaining a stable formation. Furthermore, the tracking information does not provide the identity of the robot, therefore a simple fusion of tracking and communication data is not possible. We extend a Gaussian mixture probability hypothesis density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Our method of combining communicated data, information about the formation and sensory detections is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication is sporadic or suffers from short-term outage. The proposed method is validated using a high-fidelity simulator in scenarios with a formation of up to five robots. The results show that the proposed tracking strategy allows for sustaining formations in cluttered environments, with high measurement uncertainty and low quality communication.
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring

In manufacturing, a golden batch is an idealized realization of the perfect process to produce the desired item, typically represented as a multidimensional time series of pressures, temperatures, flow-rates and so forth. The golden batch is sometimes produced from first-principle models, but it is typically created by recording a batch produced by the most experienced engineers on carefully cleaned and calibrated machines. In most cases, the golden batch is only used in post-mortem analysis of a product with an unexpectedly inferior quality, as plant managers attempt to understand where and when the last production attempt went wrong. In this work, we make two contributions to golden batch processing. We introduce an online algorithm that allows practitioners to understand if the process is currently deviating from the golden batch in real-time, allowing engineers to intervene and potentially save the batch. This may be done, for example, by cooling a boiler that is running unexpectedly hot. In addition, we show that our ideas can greatly expand the purview of golden batch monitoring beyond industrial manufacturing. In particular, we show that golden batch monitoring can be used for anomaly detection, attention focusing, and personalized training/skill assessment in a host of novel domains.
An automated approach for the recognition of intended messages in grouped bar charts

Abstract Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents.
Dynamics of solitons for nonlinear quantum walks

Abstract We present some numerical results for nonlinear quantum walks (NLQWs) studied by the authors analytically (Maeda et al 2018 Discrete Contin. Dyn. Syst. 38 3687–3703; Maeda et al 2018 Quantum Inf. Process. 17 215). It was shown that if the nonlinearity is weak, then the long time behavior of NLQWs are approximated by linear quantum walks. In this paper, we observe the linear decay of NLQWs for range of nonlinearity wider than studied in (Maeda et al 2018 Discrete Contin. Dyn. Syst. 38 3687–3703). In addition, we treat the strong nonlinear regime and show that the solitonic behavior of solutions appears. There are several kinds of soliton solutions and the dynamics becomes complicated. However, we see that there are some special cases so that we can calculate explicit form of solutions. In order to understand the nonlinear dynamics, we systematically study the collision between soliton solutions. We can find a relationship between our model and a nonlinear differential equation.
Understanding automated and human-based technical debt identification approaches-a two-phase study

The technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools could be complementary and, also, how human-based identification compares with them. To understand how to effectively elicit TD from humans, to investigate several types of tools for TD identification, and to understand the developers' point of view about TD indicators and items reported by tools. We asked developers to identify TD items from a real software project. We also collected the output of three tools to automatically identify TD and compared the results in terms of their locations in the source code. Then, we collected developers' opinions on the identification process through a focus group. Aggregation seems to be an appropriate way to combine TD reported by developers. The tools used cannot help in identifying many important TD types, so involving humans is necessary. Developers reported that the tools would help them to identify TD faster or more accurately and that project priorities and current development activities are important to be considered together, along with the values of principal and interest, when deciding to pay off a debt. This work contributes to the TD landscape, which depicts an understanding between different TD types and how they are best discovered.
PyGOP: A Python library for Generalized Operational Perceptron algorithms

PyGOP provides a reference implementation of existing algorithms using Generalized Operational Perceptron (GOP), a recently proposed artificial neuron model. The implementation adopts a user-friendly interface while allowing a high level of customization including user-defined operators, custom loss function, custom metric functions that requires full batch evaluation such as Precision, Recall or F1. Besides, PyGOP supports different computation environments (CPU/GPU) on both single machine and cluster using SLURM job scheduler. In addition, since training GOP-based algorithms might take days, PyGOP automatically saves checkpoints during computation and allows resuming to the last checkpoint in case the script got interfered in the middle during the progression.
Smartdata: Data preprocessing to achieve smart data in R

As the amount of data available exponentially grows, data scientists are aware that finding the value in the data is key to a successful data exploiting. However, the data rarely presents itself in a ordered, clean way. In opposition to dealing with raw data, the term smart data is becoming more and more visible both in the specialized literature and companies. While software packages publicly exist to deal with raw data, there is no unified framework that encompasses all the required fields to transform such raw data to smart data. In this paper, the novel smartdata package is introduced. Written in R and available at CRAN repository, it includes the most recent and relevant algorithms to treat raw data from multiple perspectives, now unified under a simple yet powerful API, which enables the data scientist to easily pipeline their application. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript.
Average case constant factor time and distance optimal multi-robot path planning in well-connected environments

Fast algorithms for optimal multi-robot path planning are sought after in real-world applications. Known methods, however, generally do not simultaneously guarantee good solution optimality and good (e.g., polynomial) running time. In this work, we develop a first low-polynomial running time algorithm, called SplitAndGroup (SaG), that solves the multi-robot path planning problem on grids and grid-like environments, and produces constant factor makespan optimal solutions on average over all problem instances. That is, SaG is an average case O(1)-approximation algorithm and computes solutions with sub-linear makespan. SaG is capable of handling cases when the density of robots is extremely high - in a graph-theoretic setting, the algorithm supports cases where all vertices of the underlying graph are occupied. SaG attains its desirable properties through a careful combination of a novel divide-and-conquer technique, which we denote as global decoupling, and network flow based methods for routing the robots. Solutions from SaG, in a weaker sense, are also a constant factor approximation on total distance optimality.
Multi-sensor capture and network processing for virtual reality conferencing

Recent developments in key technologies like 5G, Augmented and Virtual Reality (AR/VR) and Tactile Internet result in new possibilities for communication. Particularly, these key digital technologies can enable remote communication and collaboration in remote experiences. In this demo, we work towards 6-degrees of freedom (DoF) photo-realistic shared experiences by introducing a multi-view multi-sensor capture end-to-end system. Our system acts as a baseline end-to-end system for capture, transmission and rendering of volumetric video of user representations. To handle multi-view video processing in a scalable way, we introduce a Multi-point Control Unit (MCU) to shift processing from end devices into the cloud. MCUs are commonly used to bridge videoconferencing connections, and we design and deploy a VR-ready MCU to reduce both upload bandwidth and end-device processing requirements. In our demo, we focus on a remote meeting use case where multiple people can sit around a table to communicate in a shared VR environment.
Towards privacy-preserving data trading for web browsing history

The trading of social media data has attracted wide research interests over years. Especially the trading for web browsing histories probably produces tremendous economic value for data consumers when being applied to targeted advertising. However, the disclosure of entire browsing histories, even in form of anonymous datasets poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer's utility. In this paper, we propose PEATSE, a new Privacy-prEserving dAta Trading framework for web browSing historiEs. It takes users' diverse privacy preferences and the utility of their web browsing histories into consideration. PEATSE perturbs users' detailed browsing times on released browsing records to protect user privacy, while balancing the privacy-utility tradeoff. Through real-data based experiments, our analysis and evaluation results demonstrate PEATSE indeed achieves user privacy protection, the data consumer's accuracy requirement, and truthfulness, individual rationality as well as budget balance.
A Decentralized Cryptographic Blockchain Approach for Health Information System

With the rise of cyber attacks and the advancement of technology providing easy access to personal data and information in real time using networks and other services such as the cloud. These advancements have placed sensitive data such as medical information under threat. Due to the ease in accessing and modifying such data leading to little or no trace of such wrongdoings. This paper proposes a cryptographic blockchain approach using the md5 hash algorithm to verify and validate the medical data of the health information system infrastructure. The approach makes it difficult for data to be altered without detection and adopts a distributed approach coupled with blockchain.