Reviewerly/exHarmony huggingface dataset:
Digital Skills of Public Sector Employees for Digital Transformation

Digital skills are essential for driving digital transformation efforts in organisations. The objective of the study was to investigate the digital skills of employees within the public sector to support digital transformation. The study aimed to understand the barriers and enablers towards the acquisition of digital skills by public sector employees. The study adopted a case study approach, and data was collected using a questionnaire and interviews with government organisations. The study found that there is a lack of advanced digital skills such as those related to Artificial Intelligence (AI), Big Data and Internet of Things (IoT) as compared to a lower proportion of a lack of intermediate digital skills amongst employees. It was also noted that there is a shortage of qualified people with the appropriate digital skills to fill various positions in the sector. The results highlighted key enablers for developing digital skills such as financial resources, access to technology tools and formal training. The barriers to acquiring digital skills were noted as budget and infrastructure constraints, as well as a lack of agility in organisations. The study offers several recommendations for driving digital skills development in the public sector to support digital transformation.
An Efficient Approach to Reduce the Encryption and Decryption Time Based on the Concept of Unique Values

Data security has become the most important issue in every institution or company. With the existence of hackers, intruders, and third parties on the cloud, securing data has become more challenging. This paper uses a hybrid encryption method that is based on the Elliptic Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). ECC is used as a lightweight encryption algorithm that can provide a good level of security. Besides, FHE is used to enable data computation on the encrypted data in the cloud. In this paper, the concept of unique values is combined with the hybrid encryption method. Using the concept of unique values contributes to decreasing the encryption and decryption time obviously. To evaluate the performance of the combined encryption method, the provided results are compared with the ones in the encryption method without using the concept of unique values. Experiments show that the combined encryption method can reduce the encryption time up to 43% and the decryption time up to 56%.
AMF-MSPF: A retrospective analysis with online object tracking algorithms

Surveillance systems employ object tracking methods, a class of automatic data analysis algorithms, to accurately locate the target or its trajectory under a wide range of constraints in an uncontrolled environment. The best object tracking algorithm should be able to track under a range of constraints over long periods of time. Moreover, since object tracking algorithms are preliminary stages to these higher-level applications, they must be assigned a fraction of system resources to save computational power for more intensive higher-level decision-making tasks. Object tracking algorithms depend on a pipeline of components such as preprocessing stage, object detection, feature extraction/selection and fusion, and the search mechanism for object tracking. These multidimensional components are the tuning parameters for providing a balance between robustness, accuracy, and real-time performance of surveillance systems. In this review paper, the focus is on the detection-free class of tracking, where unlike the track-by-detection and end-to-end tracking, the object is initialized manually. The first aim of this review study is: a literature review on the multi-dimensional research trends with a focus on improving online object-tracking algorithms, and the second part is an experimental evaluation of our previously proposed Adaptive Multi-Feature Framework for MSPF (AMF-MSPF). AMF-MSPF is an MSPF-based adaptive framework that works under an online feature selection mechanism. However, a limited sequence of videos was used from the CAVIAR and PETS datasets to evaluate the AMF-MSPF. Therefore, we felt that an extensive evaluation and comparison, with online state-of-the-art tracking methods in the detection-free category, are necessary on a richer dataset to thoroughly investigate the effectiveness of AMF-MSPF and highlight its pitfalls. Twenty sequences were selected from the Amsterdam Library of Ordinary Video (ALOV++), a popular dataset with the tracking community, were selected to compare the results of the AMF-MSPF with reference methods in the online category of tracking. ALOV++ is a publically available dataset that is popular for its broad constraints, and more importantly the results of the reference methods are also publically available on this dataset. While the extensive experimentation has demonstrated the effectiveness of the AMF-MSPF against many challenging scenarios, nevertheless, this experimental analysis has highlighted some performance measures where the AMF-MSPF framework fell short of achieving even moderately satisfactory results. This research paper points toward these observations to caution researchers and advise them on what future research should concentrate on while developing generalized real-time tracking systems that would be effective under many scenarios. This research would benefit researchers and engineers working on surveillance systems to identify and tune these parameters for performance gains.
Using Entropy for Trust Measure in Collaborative Filtering

The recommendation system achieves user preferences by analyzing users’ historical behaviors, find user groups similar to target users, predict target users’ ratings of items of interest and make recommendations. Therefore, various techniques have been proposed to develop similarity measures. Research on recommendation algorithms that integrate user trust information has made considerable progress. However, only the definition of trust relationship based on the degree of social relationship can’t truly reflect the trust relationship between users. Focusing on the shortcomings of current trust algorithms, this paper proposes an implicit trust calculation method that integrates user entropy. By using the information entropy of user ratings to improve the previous similarity measurement, the user’s trust degree is integrated to make more accurate selection of neighborhoods, and the situation of high trust and low interest similarity is reduced. The effectiveness of the algorithm is verified through experiments, it is proved that it is superior to the previous similarity measure, and compared with the traditional recommendation algorithm, the prediction accuracy has been improved.
Path Planning for Mobile Robots Based on Improved Ant Colony Algorithm

In a two-dimensional environment, the traditional ant colony algorithm path planning is prone to problems, such as many turning points, easily falling into a local minimum, and the path is not smooth. To address these problems, a new improved ant colony algorithm is proposed to improve the path optimization performance. First, according to the position of the current grid relative to the start point and the end point, a non-uniform initial pheromone strategy is proposed, so that the closer the dominant grid is, the higher the pheromone concentration is, avoiding blind search by ants and reducing invalid search, and then the introduction of an angular guidance factor to increase the guidance to the end point and to avoid the probability of path zigzagging due to small differences in adjacent grid pheromones, next the pheromone update strategy with a reward and punishment mechanism, the enhancement and decay factors are introduced to adjust the pheromone values adaptively to improve the convergence of the algorithm, final the improved ant colony algorithm and genetic algorithm are fused, and the path is smoothed using the piecewise B-spline curve strategy. The experimental results show that the improved algorithm has greatly improved both the optimization finding ability and the convergence ability.
Research on the Elements of Future Holographic Scene Design Based on Card Sorting-Fuzzy Comprehensive Evaluation Method

With the future application and deployment of holographic, it becomes essential to study the design elements of holographic scene. This article aims to explore holographic scene design elements and measure their impact on user experience. The integrated method of card sorting and fuzzy comprehensive evaluation can provide direction decision for future scene design. Based on obtaining the design elements, firstly, we used the card sorting method to analyze the attribute classification of each design element; secondly, we used the fuzzy comprehensive evaluation method to evaluate the importance of the design elements. Finally, we obtained the comprehensive weight of future holographic scene design elements through calculation. The results show that “Naturally reasonable function” has the highest impact on the future experience of holographic scenes, followed by “Interactive perception range”, “Multi-sensory fusion”, and “Intelligent prediction”.
Dynamic Adjustment of the Learning Rate Using Gradient

Gradient descent method is the preferred method to optimize neural networks and many other machine learning algorithms. Especially with the wide use of deep learning in recent years, gradient descent algorithm has become more and more important. In gradient descent algorithm, learning rate is a very important parameter. The setting of learning rate directly affects the performance of the final model. The existing learning rate optimization algorithms adjusts learning rate based on the idea of step-by-step reduction. Different from this idea, this paper based on human walking law proposes a new optimization algorithm, the consolidate step-by-step algorithm (CSBS), which determines the learning rate according to the gradient of each iteration. In this paper, MNIST data set is used to verify the performance of the algorithm. The experimental results show that the CSBS algorithm accelerates the convergence speed of the model and reduces the sensitivity to the initial parameters.
Hybridization of Sine-Cosine Algorithm with K-Means for Pathology Image Clustering

The imperative source of diagnostic and prognostic information is pathology image and obtaining an entirely labeled pathology datasets is often challenging. However, with the arrival of whole slide scanner technologies, pathology slides are been digitized making it imaginable and feasible to accumulate and investigate digital pathology images making use of computer systems. Nuclei segmentation is the primary step in quantitative analysis of imaging data and a significant step in cancer analysis, diagnosis and classifying as it extremely hinges on the quality of segmented nuclei. The traditional K-Means algorithm has been gradually mounting since the year 1957 after the partitional clustering procedure was announced. A new era in the field of cluster analysis commenced as the nature inspired metaheuristics were applied and merged with various forms of existing clustering approaches resulting in several novel algorithms that is being widely used and accepted. With this motivation in mind, the proposed work aims in designing and developing improved hybrid procedures for visual information extraction and clustering using Nature Inspired Optimization Algorithm (NIOA) and Machine Learning techniques that takes full advantage of the solving power of underlying Sine Cosine (SC) and K-Means (KM) algorithms. The proposed hybrid Sine Cosine-K-Means algorithm (SC-KM) has been developed to surmount the demerits of SC and KM algorithms in a significant manner for pathology image clustering. Further, the SC-KM is additionally compared with traditional K-Means, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Sine Cosine (SC) algorithm and delivers superior results compared to other tested clustering models.
Optimization of Random Forest Hyperparameter Using Improved PSO for Handwritten Digits Classification

In machine learning, classification is one of the important methods used to train a model for the identification of each class on a labelled dataset. The performance of the algorithm can be enhanced using ensemble techniques such as bagging and random subsampling. Random forest (RF) is an ensemble machine learning algorithm to address multiclass classification problems. Several RF algorithms have been proposed to obtain various levels of accuracy on different datasets during classification. But it has been observed that the manual configuration of RF hyperparameter and its accuracy varies significantly depending upon different datasets. Manual configuration of hyperparameter is a time consuming job. Thus, the hyperparameter optimization process is used to generate optimal hyperparameter efficiently. Hyperparameter optimization also enhances the performance of machine learning algorithms. This paper proposes improved particle swarm optimization (PSO) method as hyperparameter optimizer to identify the optimal hyperparameter of the random forest algorithm. We compare the performance of RF model using improved PSO optimizer and existing RF model with default hyperparameter. The improved PSO outperforms the improvement of accuracy during the classification of each digit of the handwritten dataset.
Towards a Dynamic Reputation Management Scheme for Cross-Chain Transactions

Recently, research communities have dedicated their efforts to the development of versatile and innovative Blockchain-based application systems. These systems are usually independent and thus difficult to be integrated and cooperated with each other during cross-chain transactions in terms of the interoperability perspective. In addition, there may be potential risks once external malicious attackers desire to control or manipulate nodes in cross-chain systems. It is necessary to have a solution preventing users’ misbehaviors during the process of information and value exchange among different blockchains. In this paper, we propose a dynamic reputation management scheme which can effectively detect misbehaviors and identify potential security risks during cross-chain transactions. Our scheme conducts a mechanism evaluating reputation and trustworthiness of nodes and chains, respectively, through their current state and transactions history. This characteristic can help any cross-chains system well-react and be resistant against ongoing-endangered behaviors within a reasonable computation time.
Formal, Measurable Ontologies for Worldviews

Formal ontology has been used in a variety of projects in the humanities. Most interesting from our perspective is the work carried out to develop an ontology of Wittgenstein’s Nachlass since we want to take the first steps toward an ontology for the work of Arthur Norman Prior. We go through some of Prior’s early attempts to describe the logic involved in reformed theology. In The Analogy of Faith (1940), we suggest that Prior developed a knowledge graph of what he called “the logic of the bible.” We call this the Interpretative Community Graph and suggest that it can be adapted to a Prior Community Graph and used to develop a formal ontology of A.N. Prior’s Nachlass. We argue that this graph suggests a deeper conceptual structure concerning object properties for the ontology than the one provided by the Wittgenstein ontology. Based on this, we demonstrate how this deeper conceptual structure can be used to derive conclusions concerning philosophical views affirmed by Prior and the philosophers relevant to his work. Finally, we suggest that the project of providing a formal ontology for A.N. Prior could be used to develop formal ontologies for theology and systems of beliefs.
A Comparative Analysis of Machine Learning Based Sentiment Analysis

In today's world, everyone expresses themselves through social media. The most popular platforms for sharing opinions on any issue are Twitter, Facebook, Youtube, IMDB, etc. We can examine people's attitudes after evaluating messages, comments, responses, and reviews. Sentiment Analysis is a Natural Language Processing technique for analyzing texts and determining how people feel about them. The purpose of Sentiment Analysis is for the computer to be able to detect and express emotions. This work aimed to apply Machine Learning to discover the best accuracy for text-based sentiment Analysis. We have used two datasets in this project, one is a dataset of tweets, and the other is a dataset of movie reviews. We analyze each Machine Learning algorithm's accuracy and find which algorithm gives the best accuracy in each dataset.
AI Quality Engineering for Machine Learning Based IoT Data Processing

Raw source data can be made accessible in the form of processable information through Data-as-a-Service (DaaS) architectures. Machine learning is one possible way that allows to produce meaningful information and knowledge based on this raw source data. Thus, quality is a major concern that applies to raw data as well as to information provided by ML-generated models. Quality management is a major concern of AI Engineering – an attempt to systematically produce quality AI solutions. As the core of our solution, we define a conceptual framework that links input data quality and the machine learned data service quality, specifically inferring raw data problems as root causes from observed data service deficiency symptoms. This will allow to identify the hidden origins of quality problems that might be observed by users of DaaS offerings. We analyse the quality framework using a real-world case study from an edge cloud and Internet-of-Things-based traffic application. We identify quality assessment techniques for symptom and cause analysis.
Character Recognition in Scene Images Using MSER and CNN

Text detection in Scene images has procured significance in recent decade. Due to its diversified applications in blind navigation assistance for Visually impaired, traffic monitoring, Automatic driving assistance systems etc., Text detection has stimulated new research avenues in area of computer vision Text detection is a trivial task because of varying color, font face and size, orientation of text against complex background. A diversity of deep learning techniques are introduced by researchers for graphical text detection in images. The article proposed method consisting of 3 stages. First, we use Otsu’s method for text separation from background. Secondly Text ROI’s are extracted using Maximally stable Ensemble method (MSER). Finally, each extracted text ROI is classified using ConvNets. CNN classifier have been trained to recognize Scene Text Characters.
Artificial Neural Network in Pathology: Basic Principles and Applications

An artificial neural network (ANN) is a special computer program that has the ability to perform many difficult and complex functions. The ANN has the ability to learn from the environment and it can adapt to the changing circumstances. Convolution neural network (CNN) is a type of neural network that can extract the features and can diagnose from the extracted features. In this chapter, the basic principles of ANN and CNN have been described along with their application in the field of pathology.
Embodied Interaction on Constrained Interfaces for Augmented Reality

Wearable computers have seen a recent resurgence in interest and popularity in which augmented reality (AR) smartglasses are poised to influence the way we complete our work and daily tasks. Nowadays, industrial applications of these smartglasses are focused on interior designs, remote collaborations, as well as e-commerce. Under five key constraints on AR smartglasses such as miniature touch interface, small-screen real estate, user mobility, limited computational resource, and short battery life, existing user interaction paradigms designed for desktop computers and smartphones are obsolete and incompatible with the scenarios of AR smartglasses. The cumbersome and difficult interaction with the AR smartglasses becomes a hurdle to their wider industrial applications. Thus, an unmet demand for designing interaction techniques on AR smartglasses is undoubtedly critical. In this chapter, we present three interaction techniques, namely, TiPoint, HIBEY, and TOFI, in order to enhance object manipulation and text entry in the constrained environment of AR smartglasses. These techniques are devised in a way that leverage on advantageous features of human body and experiences such as the dexterity of fingertip, lexicographical order ingrained in our memory, proprioception, as well as opposable thumbs. We thoroughly address the key constraints on AR smartglasses and explore different modalities with various hardware and peripherals.
Privacy and Security Issues and Solutions for Mixed Reality Applications

Mixed-reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. Despite this, most of the research and development has been focused on delivering the promise of MR; concerns on potential security and privacy risks are continuously being pointed out, and only a few are working on the privacy and security implications of the technology. We put into light these risks and look into the latest security and privacy work on MR. In this chapter, we present an exposition and categorization of the latest security and privacy work on MR.
A Bibliometric Analysis of Agile Software Development Publications

Software development has nowadays reached a wide adoption due to increasing demands from varying kinds of organizations and it, as a complex domain, adopts increasingly more Agile approaches. This interest is parallel to what the academic literature on Agile poses. However, despite this prevalence, there is a lack of bibliometric research on Agile. Considering this gap, we performed a quantitative bibliometric analysis of Agile publications worldwide using Scrum as the method. We provide a summary of relevant academic studies that emerged in Agile research by focusing on many aspects including bibliometric properties of papers, researchers, affiliations, and thematic contents. After delivering the results based on these aspects, we discussed the results and findings of our study.
Swarm optimization for Arabic word sense disambiguation based on English pre-trained word embeddings

In this article, we present a new approach to word sense disambiguation for Arabic language based on the notion of local and global algorithms. We are going to use LESK defined on a distributional semantic space to compute the gloss-context overlap for disambiguation of words in the local context and the Cuckoo Optimization Algorithm to propagate local measures at the upper level. This task needs lexical resources and since Arabic lacks them, we are using English pre-trained word embeddings. Experimental results show that the proposed WSD approach significantly improves the base-line word sense disambiguation method. Furthermore, it will be easier to compare our results to other methods. In addition, we compared different pre-existing word embeddings model in our approach.