Reviewerly/exHarmony huggingface dataset:
Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people's opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information's contextual polarity.In social behavior, sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific, sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence, document level and feature levels.This work introduces a new Information Gain based Feature Selection (IGbFS) algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence, document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers, the suggested Information Gain based classifier resulted in an increased accuracy of 96% for document, 97.4% for sentence and 98.5% for feature levels respectively.Also, the proposed method is tested with IMDB, Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%, 96% and 98% for the proposed Information Gain based classifier for document, sentence and feature levels respectively compared to existing baseline classifiers.
Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

With an increasing number of web services on the Web, selecting appropriate services to meet the developer’s needs for mashup development has become a difficult task. To tackle the problem, various service recommendation methods have been proposed. However, there are still challenges, including the sparsity and imbalance of features, as well as the cold-start of mashups and services. To tackle these challenges, in this paper, we propose a Multigraph Convolutional Network enhanced Neural Factorization Machine model (MGCN-NFM) for service recommendation. It first constructs three graphs, namely, the collaborative graph, the description graph, and the tag graph. Each graph represents a different type of relation between mashups and services. Next, graph convolution is performed on the three graphs to learn the feature embeddings of mashups, services, and tags. Each node iteratively aggregates the information from its higher-order neighbors through message passing in each graph. Finally, the feature embeddings as well as the description features learned by Doc2vec are modeled by the neural factorization machine model, which captures the nonlinear and higher-order feature interaction relations between them. We conduct extensive experiments on the ProgrammableWeb dataset, and demonstrate that our proposed method outperforms state-of-the-art factorization machine-based methods in service recommendation.
Supervised learning

Machine learning models learn different tasks with different paradigms that effectively aim to get the models better through training. Supervised learning is a common form of machine learning training paradigm that has been used successfully in real-world machine learning applications. Typical supervised learning involves two phases. In phase 1 (commonly called training), we give input and expect output (also known as ground truth) and train a model with respect to a metric using an optimization algorithm. In phase 2 (commonly called testing), we deploy the model with unseen data and expect it to either classify or approximate the outputs. Although supervised learning is covered in almost all machine learning textbooks, we will introduce and explain supervised learning from an application point of view and its relationship to edge computing. All the algorithms will be explained from a mathematical and theoretical point of view and a programmer’s perspective. This will help in doing hands-on experience in implementing algorithms for a variety of problems.
Smart extensions to regular cameras in the industrial environment

Data mining from unstructured data can be skillfully employed to improve the performance of manufacturing or industrial processes. The main goal of this paper is to create a fast emergency aid system for object detection in SME industrial premises. The basic assumption is that SMEs do not have any IT-trained personnel, and the solution has to be unsupervised edge computing. We use several off-the-shelf models of deep neural networks pre-trained for smart city applications, ready for online object recognition and edge computing. Our system works without any retraining, additional annotation, or human intervention. Specifically, we present heuristics for the automated creation of PGT (Pseudo-Ground Truth). Based on PGT, we can automatically decide which model is the best in the specific environment. We present an application of fully automated enhancing image capture camera outputs to smarter ones. We evaluate our system in a controlled experiment. Low-resolution cameras and large areas cause problems for our method. We present a proof-of-concept for improving our system even in these challenging situations. The benefit is a knowledge extraction in a simple and inexpensive way to expand the organizations’ databases with information from unstructured data from CCTV/IP cameras.
Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model

The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously. However, the issues of irregular Pareto front, objective discontinuity, and population degeneration strongly limit the performances of conventional multiobjective evolutionary algorithms (MOEAs) on the specific problem. Therefore, a novel competitive decomposition-based MOEA is proposed in this study, which decomposes the original problem into several constrained subproblems, with neighboring subproblems sharing overlapping regions in the objective space. The solutions in the overlapping regions participate in environmental selection for the neighboring subproblems and then propagate the selection pressure throughout the entire population. Experimental results demonstrate that the proposed algorithm can possess a more powerful optimization ability than the state-of-the-art MOEAs. Furthermore, both the DNM itself and its hardware implementation can achieve very competitive classification performances when trained by the proposed algorithm, compared with numerous widely used machine-learning approaches.
Deep Bidirectional GRU network for human activity recognition using Wearable inertial sensors

Human activity recognition (HAR) has a wide range of applications in medical care, elderly care and so on. Due to the development of wearable sensor technology, HAR based on wearable sensors has gradually become a hot research topic. In terms of feature extraction of raw data, traditional methods usually use manually extracted features, which has many limitations. The method based on deep learning has gradually become the mainstream. In this paper, a deep bidirectional GRU network for human activity recognition is proposed, which uses few data preprocessing to classify and recognize daily activities. An accuracy rate of 95.69% is achieved on the UCL-HAR dataset which is an increase of 2.8% compared to the bidirectional LSTM network. In addition, the proposed method in this paper reduces the training time by 11.8 %, which will help the neural network to be deployed in real-time applications.
Effective uncertainty visualization for aftershock forecast maps

Abstract. Earthquake models can produce aftershock forecasts, which have recently been released to lay audiences. While visualization literature suggests that displaying forecast uncertainty can improve how forecast maps are used, research on uncertainty visualization is missing from earthquake science. We designed a pre-registered online experiment to test the effectiveness of three visualization techniques for displaying aftershock forecast maps and their uncertainty. These maps showed the forecasted number of aftershocks at each location for a week following a hypothetical mainshock, along with the uncertainty around each location's forecast. Three different uncertainty visualizations were produced: (1) forecast and uncertainty maps adjacent to one another; (2) the forecast map depicted in a color scheme, with the uncertainty shown by the transparency of the color; and (3) two maps that showed the lower and upper bounds of the forecast distribution at each location. We compared the three uncertainty visualizations using tasks that were specifically designed to address broadly applicable and user-generated communication goals. We compared task responses between participants using uncertainty visualizations and using the forecast map shown without its uncertainty (the current practice). Participants completed two map-reading tasks that targeted several dimensions of the readability of uncertainty visualizations. Participants then performed a Comparative Judgment task, which demonstrated whether a visualization was successful in reaching two key communication goals: indicating where many aftershocks and no aftershocks are likely (sure bets) and where the forecast is low but the uncertainty is high enough to imply potential risk (surprises). All visualizations performed equally well in the goal of communicating sure bet situations. But the visualization with lower and upper bounds was substantially better than the other designs at communicating surprises. These results have implications for the visual communication of forecast uncertainty both within and beyond earthquake science.
Large-Scale Distributed Deep Learning: A Study of Mechanisms and Trade-Offs with PyTorch

Artificial intelligence is a transforming technology for creating new scientific discoveries, services, and products. Its full potential is achieved when massive data repositories and large-scale computing systems are available. Both factors are becoming easier to obtain daily as sensor networks constantly create open-data archives, and Moore's law still makes supercomputing power more accessible. However, as deep learning models become larger to tackle data complexity, researchers must determine how to speed up training in those models. This paper uses an experimental approach to try to understand the algorithms and trade-offs associated with distributed deep learning. This study used the Summit supercomputer at Oak Ridge National Laboratory to determine that existing distributed deep learning mechanisms scale in execution time. However, as more nodes are used, accuracy degrades significantly. To solve this, several hyper-parameters must be tuned. The results show that optimizing those parameters is a nontrivial task. We also evaluated the impact of other scaling techniques, such as mixed precision and adaptive parameter optimization.
Ethereum Blockchain and Inter-Planetary File System (IPFS) based Application Model to Record and Share Patient Health Information: An Exemplary Case Study for e-Health Education in Nepal

In the last decade patient management systems or eHealth has gained huge attraction with researchers. eHealth spans from the remote access of medical records to actual data exchange of remote patient's data of their body sensors. In this paper we propose a decentralized application model that uses Ethereum private Blockchain, Inter-Planetary File System (IPFS), and associated web technologies that aim to help healthcare providers, policymakers, and research organizations in Nepal. It presents the outcome of a survey conducted among the general public and a focus group discussion with medical doctors in relation to their current practices and limitations in storing and sharing patients' medical histories for examinations or treatments. The paper concludes with why blockchain technologies can be a better option for public and private healthcare institutions as well as medical practitioners to store and share patient records to deliver timely and quality healthcare services in a resource-poor country like Nepal.
Algorithms and Service for Digital Processing of Two-Dimensional Geophysical Fields Using Octave Method

This paper covers new algorithms for digital signal processing of two-dimensional geophysical fields using octave method, which predicts the mineral value of the field in terms of signal energy value. In addition, it addresses and shows a functional scheme of the platform service based on cloud technologies. The essence of the work is that the geophysical data are two-dimensional, so their volume is very large. This requires the use of fast algorithms for digital processing of large amounts of data. Therefore, if we use the octave method effectively, the required result is obtained by calculating the value of the signal energy and comparing the finite difference of these values with its previous value.
State-of-the-Art Survey on Web Vulnerabilities, Threat Vectors, and Countermeasures

With the convergence of digital technology and communication, it has become easier to access and store information online. Nowadays, cloud services are gaining more attention for storing personal information such as media files, documents, account pins, bank details, login credentials etc,. As a result, it has become easier for hackers to gain access to the sensitive data via web applications. In this paper, we present a detailed review of the top three web application attacks namely, (1) SQL injection, (2) broken authentication, and session management and (3) Cross-site Scripting attack (XSS) according to the OSWAP top 10 vulnerabilities (owasp.org) where SQLi and XSS are still at the top since 2006. These attacks could deliberately affect company’s or individual’s private data used via accessing the internet and may lead to financial loss. In this article, we present the objectives with which hacker attack the websites, and also discusses prevention approaches for the aforementioned attack types.
An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Human Activity Recognition (HAR) plays an important role in life care and health monitoring since it involves examining various activities of patients at homes, hospitals, or offices.Hence, the proposed system integrates Human-Human Interaction (HHI) and Human-Object Interaction (HOI) recognition to provide in-depth monitoring of the daily routine of patients.We propose a robust system comprising both RGB (red, green, blue) and depth information.In particular, humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map.To track the movement of humans, we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray (LBG) algorithm for vector quantization.Then, the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network (ANN) while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification.There are two publicly available datasets used for experimentation on HHI recognition: Stony Brook University (SBU) Kinect interaction and the University of Lincoln's (UoL) 3D social activity dataset.Furthermore, two publicly available datasets are used for experimentation on HOI recognition: Nanyang Technological University (NTU) RGB-D and Sun Yat-Sen University (SYSU) 3D HOI datasets.The results proved the validity of the proposed system.
Development of an educational framework for promoting Digitalization in the RIS countries under the prism of Teaching and Learning Factories

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Mean Field Behavior of Collaborative Multiagent Foragers

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work, we focus on the problem of a biologically inspired multiagent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multiagent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviors and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multiagent system, which have impact on more general collaborative stochastic problems.
Calibration of ground surface albedo models

The emergence of bifacial photovoltaics as a serious option for large-scale power generation makes the accuracy of the in-situ estimation of ground surface albedo increasingly critical. Recent studies have evaluated the performance of different models but an investigation of how these models should be calibrated is missing. This paper proposes geographically generalised parametrisations for time-variant albedo models of vegetated and desert surfaces based on a worldwide database and reports their performance compared to site-specific, unbiased parametrisations. These geographically generalised formulations can be used as an alternative for lengthy calibration campaigns as their only parameters can be easily measured. The paper also presents a simple method for deriving white-sky albedo without the need for diffuse irradiance data. The method is founded both theoretically and empirically. If on-site measurements can be collected to calibrate a model, the duration of the campaign is usually a few weeks at the maximum. This paper first shows evidence of the strong impact of the timing on calibration performance and accordingly gives practical guidelines on the most suitable measurement seasons and weather conditions. In general, early-summer months appear to be the most appropriate time for calibrating most models. Atmospheric turbidity during the campaign period, however, should be either minimised or maximised depending on the site and the model. Finally, in case no flexibility exists in timing the calibration, simple models with few parameters should be deployed. Geometric mean shows the most robust performance at most sites.
Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering

In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. • In low-light conditions or due to camera lens limitations, images are of poor quality. • To increase the resolution of these images, upscaling is used. • When compared to a camera lens, this is a better value. • As a result, incorporating it into real-world applications is simple. • A simple method based on the concept of unsharp masking is proposed to increase the resolution of a low-resolution image.
Development of a Blockchain-Based Ad Listing Application

In today's time, ad-listing websites work as the main leads generator for businesses. Such websites are managed by a central authority called website admins. Because of the centralization, admins can change and remove some business information and manipulate the reviews and ratings to increase the benefits for other businesses. So, in this paper, we have developed a smart contract for an ad-listing website using Solidity on the Ethereal blockchain. Thus, Implementing a decentralized app architecture that uses Blockchain Technology. We have developed a decentralized web application that works on the Ethereal Ropsten test network and uses Infura platform as a virtual node. Virtual nodes provide the connection between our NodeJS app and the Blockchain, eliminating the need for running an Ethereal full node. By using such architecture, users don't need to sign in with a wallet extension or any Web3 component. From a user's perspective, the website is like any web app using a database. But, we had the advantage of utilizing the Blockchain test network for free as a data warehouse. Also, we calim that this app guarantees a more honest user experience because of the transparent nature of Blockchains.