Reviewerly/exHarmony huggingface dataset:
Genetic Algorithm Based Probabilistic Model for Agile Project Success in Global Software Development

Software development organizations use agile practices in the global software development (GSD) environment to leverage these approaches in terms of low development cost, continuous project delivery, and high-quality product. Despite the benefits, using the agile manifesto in the GSD organizations is not a straightforward and easy task because the underlying projects inhibit additional risks factors that could lead agile projects to failure. GSD organizations are continuously improving their process management activities to increase the chances of project success. Project success can be achieved if the project manager or organization management can have preliminary information about the derailment of the project features by paying more attention and adopting the internationally accepted standard practices.
Smart healthcare using blockchain technologies: The importance, applications, and challenges

With the advent of the Internet of Things (IoT), its uses have been growing enormously in the smart healthcare sector. IoT devices in smart healthcare range from simple wristband devices capable of monitoring the heart rate, sleep pattern, and blood pressure to connected inhalers, ingestible sensors, glucose monitoring, and remote patient monitoring systems. To fulfill the requirements of smart healthcare, these devices need to have reliable connectivity and must comply with the security and privacy regulations. To resolve the connectivity issue in healthcare, 5G networks are being deployed. Implementation of 5G in blockchain-based smart healthcare will bring many advantages such as faster and seamless data transfer of patients’ medical records, real-time monitoring of patients at remote locations, 5G-powered telesurgery, and monitoring of smart IoT devices. This chapter provides a detailed literature review in this field to understand concepts of blockchain in the healthcare industry. Blockchain technology, its mechanisms, 5G technology, and Blockchain 5.0-based smart healthcare are described. Applications of Blockchain 5.0 in healthcare informatics are discussed in detail. A comparative analysis of existing models on electronic health records is performed and one case study is considered to understand the application of blockchain in healthcare. The challenges for 5G-enabled blockchain technology in the healthcare sector are also addressed, which provides an opportunity for researchers to resolve these issues for better performance of the system.
A Parallel Fuzzy Load Balancing Algorithm for Distributed Nodes Over a Cloud System

Cloud Computing is an IT organization concept that places the Internet at the heart of business activity, allowing it to use hardware resources. In recent years, Load Balancing has been an active area of research, and has played a very important role in the case of the Cloud environment. There is a wide range of improvements in this arena. In this paper, we propose a new Load Balancing algorithm, based on the weights of the nearest servers in the cloud platform. We use the fuzzy logic to represent the weight of the different nodes. Moreover, we implement separate requests in parallel, and use a token to dispatch tasks efficiently. The several scenarios in this paper are considered for experimentation and compare the result of existing Round Robin, Throttled Load Balancing, Equal Spread Load and proposed algorithm .The experiment results show that this approach improves the Load Balancing process effectively in terms of overall response time, data center processing time, total virtual machine cost, and total data transfer cost.
Real-Time Segregation of Encrypted Data Using Entropy

Encryption translates data into another form. It can be read only with the keys. Encrypted data is often known as ciphertext, whereas unencrypted data is known as plain text. Encryption protects files or encrypts them with a key, making them accessible only to those who have the keys to decrypt. The main idea is to prevent unauthorized parties from accessing the files. These days, one must protect information stored on their computers or communicated over the Internet against cyberattacks. Cryptographic methods come in a variety of shapes and sizes. Choosing a cryptographic process is mainly determined by application requirements such as reaction speed, bandwidth, integrity, and confidentiality. However, each cryptographic algorithm will have its own set of strengths and weaknesses. Here, we have segregated the encrypted data using the entropy as a measure. The encryption algorithms taken for analysis are 3DES, AES, RC4, and Blowfish.
An Intelligent Tree Extractive Text Summarization Deep Learning

In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classical models. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addresses memory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of the text-texts and provide better feature representation. It also incorporates an attention mechanism that offers an additional information source to conduct better summary extraction. The novel model addresses text summarization as a classification process, where the model calculates the probabilities of phrase and text-summary association. The model classification is divided into multiple features recognition such as information entropy, significance, redundancy and position. The model was assessed on two datasets, on the Multi-Doc Composition Query (MCQ) and Dual Attention Composition dataset (DAC) dataset. The experimental results prove that our proposed model has better summarization precision vs. other models by a considerable margin.
Is There Meaning in the Emoji Sequences Used on Social Media?

With the emergence and widespread use of emojis in social network messages, some authors have discussed the possibility of grammar related to emoticons. In this field, some authors believe that emojis can be used to write sentences; others believe that emojis are just digital gestures without relevant grammar. This research proposes a model to analyse the co-occurrence of emoji in Twitter messages and the semantic context of using these sequences. The way is to understand whether these co-occurrences are meaningful, whether they vary from topic to topic, and whether they will change over time. The co-occurrence of emoticons will be stored in the graph database. Key phrases will be extracted and stored along with the tweet in Elasticsearch’s database from the tweet’s text. The tweets to be analysed are related to Brexit, the U.S. election and Covid-19. We will identify prominent emojis, the most commonly used emojis that appear simultaneously and are not repeated, and the associated key phrases for each discussion topic. The study outlined in this paper aims to understand if there is meaning in co-occurrences of emojis - sequences of two emojis - in messages exchanged on social networks as a starting point for a future study on syntax in emojis.
Relating Machine Learning to the Real-World: Analogies to Enhance Learning Comprehension

Machine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. Similarity has been substantially explored in machine learning algorithms such as in the K-nearest neighbors, Kernel methods, Support Vector Machines, but not so much in human learning, particularly when it comes to teaching machine learning. In the course of teaching the subject to undergraduate, graduate, and general pool of students, the author found that relating the concepts to real-world examples greatly enhances student comprehension and makes the topics much more approachable despite the math and the methods involved. This paper relates some of the concepts, artifacts, and algorithms in machine learning such as overfitting, regularization, and Generative Adversarial Networks to the real world using illustrative examples. Most of the analogies included in the paper were well appreciated by the students in the course of the author’s teaching and acknowledged as enhancing comprehension. It is hoped that the material presented in this paper will benefit larger audiences, drawing more learners to the field, resulting in enhanced contributions to the area. The paper concludes by suggesting deep learning for automatically generating similarities and analogies as a future direction.
Crow-Water Wave Optimization Algorithm for Privacy-Preserved Collaborative Data Publishing

The main motive of this research is the development of CWWO-based encoding algorithm for conserving the privacy of medical data. The privacy preservation of published data relies on the four privacy constraints. While publishing the record, the Service provider(SP) suggests the information to the unauthorized person in which the privacy needs to be preserved. The privacy preservation of medical record is relies on the constraints, like the k-anonymity, l-diversity, the m-privacy and f-sensitive. These privacy constraints are must be satisfied while publishing the data, and in case it is not satisfied, the C-mixture enhances these security constraints. Once the privacy parameters are satisfied, then the fitness value is computed. The fitness with minimum value is considered as best value for publishing the information and hence the privacy is to be enhanced.
A Trilateral Filter for Video SAR Speckle Noise Reduction

This letter proposes a trilateral filtering algorithm for speckle reduction in video synthetic aperture radar (video SAR). The novel filter takes traditional bilateral filter as the basic framework, so as to fully exploit the similarities of gray levels and the spatial location of neighboring pixels. Moreover, the proposed trilateral filter additionally exploits the temporal correlation information among adjacent image frames of the SAR videos and effectively reduces the interference of redundant information by using an adaptive similar frame selection technology. Comprehensively considering the three-dimensional correlation information of spatial, temporal, and gray-scale, a triple-similarity kernel is specifically developed for video-SAR de-speckling. The proposed trilateral filter can effectively smooth the speckle noise while greatly sustain the details of each image frame of the SAR videos. Experiments show that the proposed algorithm has better de-speckling performance compared with other algorithms.
Bayesian Belief Networks

Abstract This chapter overviews Bayesian Belief Networks, an increasingly popular method for developing and analysing probabilistic causal models. We go into some detail to develop an accessible and clear explanation of what Bayesian Belief Networks are and how you can use them. We consider their strengths and weaknesses, outline a brief history of the method, and provide guidance on useful resources and getting started, including an overview of available software.
Robust Image Tampering Detection Technique Using K-Nearest Neighbors (KNN) Classifier

Today, image tampering has become very common due to easily available inexpensive electronic gadgets and various online and offline sophisticated multimedia editing tools. Many people edit the contents of the original image to hide the truth and circulate it on social media without knowing its implications on the feeling of individuals and society. Also, many times people used to produce the tampered images as proof of documents to deceive and take advantage of online facilities. Therefore, we have proposed a robust tampered image detection system to recognize whether the given input image is tampered or not. The proposed scheme uses an orthogonal transform, i.e., the discrete cosine transform (DCT) and singular value decomposition (SVD) to extract significant image features and k-nearest neighbors (KNN) classifier to classify forged from un-forged images accurately. To validate the robustness of the proposed system, we have considered image samples from two publicly available benchmark datasets, i.e., copy-move forgery dataset (CMFD) [1] and the Columbia image splicing dataset [2]. Based on the results obtained during training and testing phase, the proposed system conforms better detection results in terms of $${F_1}$$ -score, i.e., 92.89 and 93.73, for CMFD and Columbia image splicing dataset, respectively.
FPGA Implementation of Elliptic-Curve Diffie Hellman Protocol

This paper presents an efficient crypto processor architecture for key agreement using ECDH (Elliptic-curve Diffie Hellman) protocol over . The composition of our key-agreement architecture is expressed in consisting of the following: (i) Elliptic-curve Point Multiplication architecture for public key generation (DESIGN-I) and (ii) integration of DESIGN-I with two additional routing multiplexers and a controller for shared key generation (DESIGN-II). The arithmetic operators used in DESIGN-I and DESIGN-II contain an adder, squarer, a multiplier and inversion. A simple shift and add multiplication method is employed to retain lower hardware resources. Moreover, an essential inversion operation is operated using the Itoh-Tsujii algorithm with similar hardware resources of used squarer and multiplier units. The proposed architecture is implemented in a Verilog HDL. The implementation results are given on a Xilinx Virtex-7 FPGA (field-programmable gate array) device. For DESIGN-I and DESIGN-II over , (i) the utilized Slices are 3983 and 4037, (ii) the time to compute one public key and a shared secret is 553.7 μs and 1170.7 μs and (iii) the consumed power is 29 μW and 57 μW. Consequently, the achieved area optimized and power reduced results show that the proposed ECDH architecture is a suitable alternative (to generate a shared secret) for the applications that require low hardware resources and power consumption.
A Preliminary Study on Application of Tangible User Interface and Augmented Reality Technology with Table Game and Hand-Eye Coordination Operation Tasks in the Fields of Memory and Visuospatial Perception for the Elderly

As the elderly ages, their memory, judgement and spatial orientation and cognition gradually decline. These declining abilities can result in different levels of problems in everyday life and eventually lead to a decline in the quality of life of the elderly. Therefore, this study focuses on discussing the ways to strengthen the memory and visuospatial perception skills of the elderly through simple game mechanisms. It is known from previous works that the application of a “Tangible User Interface” combined with “Augmented Reality” (Tangible Augmented Reality, TAR) technology, together with game tasks and hand-eye coordination exercises, will help gamers to achieve better interface operability and comprehension. However, it remains to be seen whether such advantages can be equally reflected in the ‘memory’ and ‘visuospatial perception’ abilities of the elderly. Therefore, a total of six subjects of middle-advanced age over 60 years old were recruited for this study and each of them was provided with each: (1) Tablet computer game without TAR operation (the game is operated entirely on the tablet computer), and (2) An interactive game with TAR operation (which combines virtual and physical operational objects). The participants were asked to complete six cognitive scales (mental load, game operation, cognitive load, self-expression, visuospatial perception, and memory degree) after completing the two kinds of games for the purpose of assessing whether the TAR game task was equally operative and comprehensible for the elderly in terms of “memory” and “visuospatial perception”. The results show that TAR games do have better operability and is more easily comprehensible, which is also reflected in the performance of the elderly in terms of “memory” and “visuospatial perception”, with an average score of 5.55 or higher (out of 7) in memory, judgement and orientation cognition. The reason is that TAR games with physical operation is conducive to improving the spatial cognition and cognitive ability of the operator through hand-eye coordination, and this advantage is indirectly reflected by the operation and performance of games that require invoking memory, judgement and directional cognition. Therefore, it can be concluded that TAR games have advantages over non-TAR tablet games in terms of memory, spatial cognition and perceptual judgement and processing training for middle-advanced age users. More complete data support shall be gained for such result by a larger number of case recruitment trials, which is to be promoted and fulfilled in future studies.
Video-Based Pedestrian Re-identification with Non-local Attention Module

This paper studies a video-based person re-identification (Re-ID) model with non-local attention module. Firstly, on the basis of the residual module embedded in the 3D convolutional neural network, an non-local attention module is added. This module can associate the long-distance information among video frames, and establish connections between pixels at a certain distance, and enrich the pedestrian feature representations from local and global aspects; Then, many experiments on two public video-based datasets Mars and DukeMTMC-VideoREID proves that our proposed method is competitive with some recent video-based Re-ID methods.
Control of a Bi-axial Solar Tracker by Observing the Power Supplied by the MPPT

The solar tracker is a mechanism that helps the photovoltaic panel maximize its efficiency during the day while keeping it oriented towards direct solar radiation. To manipulate the movement of the solar tracker, most use a solar collector that specifies the position of the sun before operating the motors. The idea of this paper is to replace the solar collector with an observation system of the PV output power (between the PV and the MPPT). The follower control card observes the variation of this power as long as it is increased. The system continues the correction of the tracking and it stops only if it detects a decrease in this power. In this way, we will have a perfect follow-up that looks for the peak of the power provided by the photovoltaic panel.
A Linear-Time 2-Party Secure Merge Protocol

We present a linear-time, space and communication data-oblivious algorithm for securely merging two private, sorted lists into a single sorted, secret-shared list in the two party setting. Although merging two sorted lists can be done insecurely in linear time, previous secure merge algorithms all require super-linear time and communication. A key feature of our construction is a novel method to obliviously traverse permuted lists in sorted order. Our algorithm only requires black-box use of the underlying Additively Homomorphic cryptosystem and generic secure computation schemes for comparison and equality testing.
A Recommender System for EOSC. Challenges and Possible Solutions

European Open Science Cloud (EOSC) is a pan-European environment providing researchers with a plethora or publicly-available, open resources and services to help them conduct their research. Availability of publications, datasets, computational power, networks or storage allows researchers to concentrate on their research rather than the technical infrastructures. However, the plenitude and diversity of items offered in EOSC increases and becomes overwhelming for researchers who expect guidance and support. Recommender systems allow them to assign rankings to subject object, based on their value for specific end users, inferred from diverse data about them, their behaviour or various relationships between users and objects. In this paper we present architectural and functional challenges related to the EOSC Recommender System that could substantially improve the experience of researchers using EOSC offerings.
Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

Due to drastic increase in the generation of data, it is tedious to examine and derive high level knowledge from the data. The rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning processes. The feature selection procedure establishes a generally encountered issue of global combinatorial optimization. The FS process can lessen the number of features by the removal of unwanted and repetitive data. In this aspect, this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning (IHSFS-ODL) enabled classification model. The proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification outcomes. In addition, the IHSFS-ODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm (HSA) for global optimization. Besides, ODL based classifier including quantum behaved particle swarm optimization (QPSO) with gated recurrent unit (GRU) is applied for data classification process. The utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification performance. In order to demonstrate the enhanced outcomes of the IHSFS-ODL technique, a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.
A Hybrid Deep Learning Model for Human Activity Recognition Using Wearable Sensors

Human activity recognition is the most important field for supporting the everyday lives of elderly people and sometimes finding out suffering to several cognitive dysfunctions like Parkinson's disease and dementia. There are several techniques for studying human behavior, such as recent advancements in artificial intelligence and machine learning (ML) are also taken place. In this paper, a combination of NN with GRU-LSTM-CNN is proposed that enables the efficiency of their integration in daily recognition after classifying data automatically. Since LSTM is another integration of recurrent neural network (RNN), so it can be easily applied over temporal sequences. On the dataset (UCI-HAR) global average pooling layer (GAP), Flatten is applied over the connected layer in order to reduce down the model parameters. The datasets contain data from 18 everyday sensor-based activities as well as takes into account 6 actions within the proposed system. This paper proposed a hybrid model's gated recurrent unit, long short-term memory, convolutional neural network (GRU-LSTM-CNN) have been analyzed on the wearable sensors built-in for smartphones in order to recognize the human activities so to better understand the human behavior, respectively. The proposed technique is successful in providing a better accuracy level of 93.43% comparatively to the previous one.