Reviewerly/exHarmony huggingface dataset:
Pathway to achieving sustainable food security in Sub‐Saharan Africa: The role of agricultural mechanization

According to the World Health Organization (2020), many parts of the world have demonstrated potentials for acute hunger and famine. Many countries in Sub-Saharan Africa (SSA) actively feature in this category due to geopolitical crises and other humanitarian challenges. Despite efforts by SSA governments, agricultural productivity continues to be inadequate in meeting nutritional needs across Africa. Thus, in the presence of economic expansion, vast land, and labor resources, this study investigates the role of mechanization as an important factor for increased agricultural productivity in SSA. Data on 25 SSA countries over 17 years are used. Empirical results from System Generalized Method of Moments show that among other variables, mechanization is a significant factor influencing agricultural productivity. Consequently, in light of the bid for higher agricultural productivity, government investment in mechanization becomes a priority. Also, apart from the fact that many African countries are at the point where more land must be brought under development to satisfy expanded market needs, larger investments in mechanization appear imperative.
Effort Estimation Model for an Enterprise Software Upgrade

Determining the effort required to make a transition is one of the key factors that help in the decision-making of an enterprise software upgrade. In the last decade, extensive research has been carried out on Software Development Effort Estimation. The results show various models and approaches taken towards arriving at the effort needed during software development. However, to the best of our knowledge, no effective model has been studied that can estimate change efforts of upgrading a large productive enterprise system. In this paper, we propose a new model that uses both an algorithmic and non-algorithmic approach and comes up with efforts needed during the upgrade of an enterprise software. The proposed model has been evaluated through an extensive experimental validation and was applied to the upgrade of three enterprise systems of different domains (namely, Enterprise Resource Planning, Customer Relationship Management and Human Resource). The results obtained showed that the model was 91% accurate in providing effort estimates for the above mentioned three systems.
Information System Penetration Testing Using Web Attack Automated Simulation

The usage of information systems in the day-to-day operations of entrepreneurial companies opens enlarged opportunities for communication services provision and processing of information resources between relevant departments and organizations. At the same time, it opens the way for attackers to gain access to information that poses a real threat to information security and system safety. In addition, the possibility of relevant threat implementation increases through the extended availability of computer technology. As a consequence, the complexity of information system protection against related threats and their implementation ways (attacks) is significant. Every day new vulnerabilities of software could be identified and therefore new ways for attackers appear. Thereby, the new method of penetration time estimation for the information system is proposed in this research. It is grounded on using information about vulnerabilities and presents a preliminary and detailed study of possible options for the information attack implementation using a simulation model. Stochastic Petri net with informational arcs is used for model creation. The results which are obtained for different sets of vulnerabilities show a strong dependence of penetration time on system vulnerabilities.
Building a Model for finding Quality of Affirmation in a Discussion Forum

Education is an inherently social activity. People like to exchange thoughts and learn from each other: this is why we are interested in discussions. But discussions can be messy and vague. In order to make discussions meaningful, relevant mediations may need to be made, whenever discussions lose clarity. Discussion forum data is in the form of a sequence of questions, answers, and comments on the answers. Taken together, this data is called an affirmation. Knowing the clarity of an affirmation makes it possible to intervene in a discussion to steer it towards agreement or conclusion. We have built a model of clarity for a branch of an affirmation which is considered based on scores of agreements, disagreements, and partial answers. We used three classifier models in our experimentation, but the random forest classifier model gave an F1 score of 0.7315, which was better than the other two classifier models. For our model, the ratio of strong agreement to partial agreement was 1.44 for training data and 1.37 for testing data, which is quite close. In the future, we are planning to enhance our model at the sentence level to capture more details and find an aggregate score of clarity for an affirmation.
Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity

Pervasive computing environments are characterized by a plethora of sensing and communication-enabled devices that diffuse themselves among different users. Built-in sensors and telecommunication infrastructure allow co-presence detection. In turn, co-presence detection enables context-aware applications, like those for social networking among close-by users, and for modeling human behavior. We aim to support developers building better context-aware applications by a deepened understanding of which set of context information is appropriate for co-presence detection. We have gathered a multimodal dataset for proximity sensing, including several proximity verification sets, like Bluetooth, Wi-Fi, and GSM encounters, to be able to associate sensor's data with a spatial granularity. We show that sensor modalities are suitable to recognize the spatial adjacency of users with different spatio-temporal granularity. We find that individual user mobility has only a minor, negligible effect on co-presence detection. In contrast, the heterogeneity of device's sensor hardware has a major negative impact on co-presence detection. To reveal energy pitfalls with respect to usability, we perform an energy analysis pertaining to the usage stemming from different sensors for co-presence detection.
3D Multi Person Tracking With Dual 360° Cameras

Person tracking is an often studied facet of computer vision, with applications in security, automated driving and entertainment. However, despite the advantages they offer, few current solutions work for 360° cameras, due to projection distortion. This paper presents a simple yet robust method for 3D tracking of multiple people in a scene from a pair of 360° cameras. By using 2D pose information, rather than potentially unreliable 3D position or repeated colour information, we create a tracker that is both appearance independent as well as capable of operating at narrow baseline. Our results demonstrate state of the art performance on 360° scenes, as well as the capability to handle vertical axis rotation.
Fuzzy C- means Algorithm based Depth Estimation in Single Image

Depth estimation in single image aims to find the depth of different individual objects and the position of an object. Based on the Make3D dataset, first image segmentation is done using GMM and to describe their main features and attributes, the feature extraction is done using PCA, finally depth map will be generated with the help of FCM algorithm. Depth map provide as an excellent method to visualize distance of the surfaces of scene objects from a viewpoint. The platform used here is MATLAB R2018a. In our method, the final depth map is represented using different color where yellow color represents far objects and blue color indicates nearest objects. In the aforementioned perspective, the proposed model establishes a relationship between depths of different individual objects. It is very important for several computer vision applications like robot, obstacle avoidance, object recognition, self-driving car etc. The major finding of our study is to find the depth and position of different individual objects.
Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.
Personalized Fall Detection System

This paper explores the personalization of smartwatch-based fall detection models trained using a combination of deep neural networks with ensemble techniques. Deep neural networks face practical challenges when used for fall detection, which in general tend to have limited training samples and imbalanced datasets. Moreover, many motions generated by a wrist-worn watch can be mistaken for a fall. Obtaining a large amount of real-world labeled fall data is impossible as fall is a rare event. However, it is easy to collect a large number of non-fall data samples from users. In this paper, we aim to mitigate the scarcity of training data in fall detection by first training a generic deep learning ensemble model, optimized for high recall, and then enhancing the precision of the model, by collecting personalized false positive samples from individual users, via feedback from the SmartFall App. We performed real-world experiments with five volunteers and concluded that a personalized fall detection model significantly outperforms generic fall detection models, especially in terms of precision. We further validated the performance of personalization by using a new metric for evaluating the accuracy of the model via normalizing false positive rates with regard to the number of spikes of acceleration over time.
Towards a Knowledge Base for Activity Recognition of Diverse Users

This research work discusses a mathematical foundation based on probability theory and related disciplines for development of a knowledge base that would list the exhaustive ways or approaches, arising from universal diversity, by which any activity can be performed by a given user. The global challenge in this field is to address the needs associated with the constantly increasing population of the world who are diverse in multiple ways, through development of systems and frameworks that can serve as a long-term, robust, feasible, easily implementable, sustainable and economic solution for making the future of technology laden living environments ‘aware’ of the diverse ways by which users could be interacting with the environment and its components, in the context of performing Activities of Daily Living (ADLs), which are essential for their sustenance. Addressing this challenge serves as the main motivation for this work. Several case studies on different ADLs by application of the proposed framework were performed for development of the knowledge base and one study is presented here and discussed. In the context of making IoT-environments ‘Activity Aware’ for diverse users, such a knowledge base is expected to serve as a foundation to provide necessary information for various critical applications, for instance (1) user-centered activity recommendations by activity recommendation systems, (2) personalized behavior interventions for users with various forms of impairments – physical, cognitive etc. and (3) increasing the performance accuracy of the existing works in this field for adapting and responding to diverse interaction patterns exhibited by different users.
A Comparative Study of Library Information Storage System Through Blockchain Technology: A Case Study of Undergraduate Researches

This study was aimed to develop a library information storage system through the blockchain technology to solve data security problems and reduce tools used to work with conventional systems such as security software, data collection programs, etc. Therefore, the library information storage systems have been developed by using open source technology. The research data were recorded in accordance with the standards of the bibliography of the MARC 21 format complying with the posting rules of the AACR2R in order to test and compare the performance of The Proof of Work ( PoW) and IPFS. The result revealed that the information storage system development could upload and download pdf files perfectly. Moreover, as the efficiency of proof of work (PoW) with IPFS were compared with each other, the upload and download speed in proof of work (PoW) was found to be faster than uploading information by PoW.
Secured Communication for Multimedia based Steganography

Secured communication on sensitive information between two parties need to consider information security as necessity. Steganography and cryptography can be used to enhance the information security. Cryptography discloses the availability of information, whereas steganography hides the availability of information so that no other than the two communicating parties can recognize it. With advancement in internet applications the information mainly consists of text, audio, image, and video type of information. This paper provides a detailed analysis and methodology for Multimedia based steganography of information. The performance of the proposed methodology is measured using mathematical model and comparisons with that of other methodologies are discussed.
Printed circuit boards isomorphism: An experimental study

We evaluate the discrimination power of different graph invariants in order to identify an appropriate measurement of similarity among printed circuit boards.We show that computationally expensive algorithmic evaluation of circuit boards equivalence based on the topology of the circuit boards is unnecessary in practice.Instead, the information about the circuit boards can be stored in terms of graph invariants, namely, number sequences characterizing the boards, which requires much less memory and enables quicker recognition of board equivalence.This straightforwardly leads to fast practical implementations of the search engines for the printed circuit boards.Our approach is multidisciplinary, as the tools were drawn from two fields; the industrial electronic engineering field, and the theoretic fields of graph theory and algorithms.A real case study on a search engine for printed circuit boards is conducted to illustrate the proposed approach.Finding an easily computable and complete set of graph invariants remains a challenging mathematical question.
A Multilayered Contextually Intelligent Activity Recognition Framework for Smart Home

The future of Internet of Things (IoT)-based living spaces would involve interaction, coordination, collaboration and communication between humans, machines, robots and other technology-laden systems in the context of users performing their daily routine tasks. Through this Contextually Intelligent Activity Recognition framework, this work proposes to develop a long-term, robust, feasible, easily implementable, sustainable and economic solution for Activity Recognition and Activity Monitoring, that would be able to track, monitor, evaluate, analyze and access human behavior in the context of the multimodal aspects of these user interactions including the spatial and temporal features. This Multilayered Contextually Intelligent Activity Recognition Framework envisions to take a holistic approach to improve the quality of life, enhance the user experience, acceptance and trust on technology during Activities of Daily Living (ADLs), in the context of human-computer, human-machine and human-robot interactions. The results presented uphold the relevance and demonstrate the feasibility for practical implementation of this framework in the future of IoT-based living spaces, for instance Smart Homes and Smart Cities.
Frustration Detection On Reviews Using Machine Learning

Frustration is a type of emotion that roots with disappointment or when the person is not satisfied with the product, service offered, the result of an experiment, an event that happened in his/her life. Purchasing products online is a new way of doing shopping, using e-commerce, or any digital platform. Generally, people write a review of the product they have bought and used. Surveys can be a positive review or a negative review or neutral review. In a negative review there is a deep sentiment of being get disappointed by the product bought and used[1]. Frustration is a feeling of being upset, getting annoyed, getting cheated, or unable to get the service that is being expected from the other side. In this paper, we have tried to detect the frustration of a user using the Machine Learning Algorithm on the data-set.
Natural Language Processing based Abstractive Text Summarization of Reviews

Humans are habituated to remember only relevant points of an article. The way synopsis of a book is used to understand the gist of it and decide whether it is interesting, similarly, it is required to analyze the product reviews before purchasing it. Thus, text summarization is a method to help people analyze a product by reading a concise version of all the reviews. In this paper Abstraction based summarization is used since Extraction-based picks up the words as it is and adds it in a summarized version. It will help in proposing a solution to the traditional product reviewing method using machine learning and natural language processing (NLP) such that the essence and sentiment of the reviews are still maintained. Along with text summarization, Sentimental Analysis is used to learn in-depth about the reviews and it will help the customers know what the overall review of the product is.
Role of internet of things for health-care monitoring during COVID-19 pandemic

Internet of Things (IoT) is already being implemented in the medical field to improve the treatment outcome. This technology also plays a significant role during the COVID-19 pandemic for the betterment of the infected patient. With such high levels of infection of the SARS-CoV-2 virus, organisations want to automate hospital facilities, so there is a need for this technology for improving hospital management and patient care system. A proper application & implementation of these technologies may reduce the chances of mistakes and provides effective control during the entire treatment process of COVID-19 patients. This short review identifies and discusses significant roles of IoT in taking up major healthcare challenges during the COVID-19 pandemic. IoT helps identify the symptoms and provides a rapid treatment process with the help of internet-connected devices and networks. It automatically helps to manage the medical staff to maintain quality supervision. There are an adequate flow and better safety of the patient in the internet-connected hospital. It helps provides real-time information to create transparency in the treatment. In the future, this technology can predict the upcoming situation and make appropriate forecasting regarding this virus.
Blockchain based Consensus Checking in Cloud Storage

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.
Vehicle Classification in Traffic Surveillance System using YOLOv3 Model

Vehicle detection and classification plays a vital role within the space of the traffic management system. There's an outsized area for the development during this system as associated with accuracy and exactness. Because of increasing traffic within the advanced occasions, it's basic to arrange a framework winning to keep up a record of vehicles going through a path or a street. Spontaneous identification of vehicle data has been broadly used in the vehicle identification and classification system. Applications of the system developed are often useful in the traffic signal controller, vehicle lane departure warning system. The techniques goal is to provide appropriate data about traveling vehicles with the exact count. The convolutional neural network technique models based on YOLOv3 is used. The input is given in the form of video and pre-processing is finished and also the output is gained i.e. the count of vehicles, classification of vehicles supported its sort and total variety of vehicle motion at a specific time.
Natural Language Description for Videos Using NetVLAD and Attentional LSTM

Video captioning infers the process of generating textual description from videos which describes the objects and actions present in it. The multimodal information is available on each frame based on texture and time in the video. In video captioning, the tremendous task is generating the caption automatically related to video content precisely. By using advancement in the field of deep learning technology, a model was developed to generates natural-language descriptions for activities in the video is proposed. In our proposed work, the first stage is extracting the key features for machine understandable about the video content using 2D and 3D CNN. The convolutional neural network(CNN) of 2D and 3D is used to extract both the spatial and temporal features respectively for transferring the videos into key features. The extracted features are preprocessed using NetVLAD. After NetVLAD preprocessing, the features are concatenated and given as input into attention based Long-Short Term Memory(aLSTM). aLSTM generates sentences in a sequential manner by selecting the salient features. The expected output of the model is a sentence to describe the contents of the video. The evaluation is done by using Bilingual Evaluation Understudy (BLEU) metrics.