Reviewerly/exHarmony huggingface dataset:
A <scp>multi‐objective</scp> particle swarm optimization for matching domain ontologies

As a generic conceptual model for describing domain knowledge, ontology is able to provide a formal and concrete description on the concepts in different domains. Since different ontology engineers have diverse viewpoints on the world, leading to the heterogeneity issue among different ontologies. To solve this problem, this paper investigates how to make certain the correspondences of entities between different domain ontologies, that is, domain ontology matching problem. When solving this problem, how to effectively aggregate different similarity measures to differentiate entities and obtain high‐quality alignment is the kernel issue. In this paper, we first introduce two statistical metrics for measuring ontology alignment's quality, then, an optimization model is established for the multi‐objective ontology matching problem, and a Multi‐Objective Particle Swarm Optimization algorithm (MOPSO) is proposed to optimize the quality of domain ontology alignment. The experimental results illustrate that it outperforms OAEI's participants with respect to both recall and precision.
HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems

Graphical Processing Units (GPUs) are increasingly being incorporated to High-performance computing (HPC) systems alongside Central Processing Units (CPUs). As a result, HPC systems turned into heterogeneous systems.The unequal distribution of computing loads to the devices of a heterogeneous system is a major issue. It is caused by the variation of computing power between these devices. Balancing the load of such systems is a very complex task.In this context, we provide HBalancer, a CPU-GPU heterogeneous system resource manager. HBalancer distributes computing loads on the devices at runtime in a manner that minimizes the system imbalance. We define a new metric to estimate the system imbalance using execution time prediction. We provide mathematical formulas to calculate it.Experiments were conducted on a CPU-GPU heterogeneous system. We used multiple OpenCL applications as workload. Results show that HBalancer outperforms the Device Suitability and Round Robin approaches in load balance and execution time.
Implementation of Blockchain Technologies to Avoid Fraud in Supply Chain

In recent times, with technological advancements, businesses are looking at the digital transformation to create lucrative and sustainable methodologies to gain a competitive advantage over the competitors. With the awakening of consumers and stakeholders about the visible fraud in the operational activities and supply chain, the expectations from consumers regarding the supply chain are rapidly changing. Supply chain transparency, traceability, sustainability, and resilience have gained a lot of attention in recent years to end-users which potentially enables them to trace the goods and services flow and all the processes involved in manufacturing the raw materials to the final products. Maintaining a transparent supply chain has become a necessity rather than an additional service provided to the government, consumers, and other stakeholders. Companies around the globe are shifting towards technological advancements in the supply chain for effective utilization of resources and processes to gain an advantage by provisioning visibility in the Supply Chain Management (SCM). With the ability to maintain an immutable, distributed, unalterable ledger, Blockchain can be considered as one of the revolutionizing technologies in digital supply chain management. Blockchain technology can be used to overcome potential threats and vulnerabilities by channelizing the flow of information, thereby enhancing transparency and traceability in the process. Its potential can drive the SCM operations less costly and enrich the customer experience through transparency and traceability. In this paper we have proposed a novel framewok to avoid fraud in supply chain by using blockchain technology. Blockchain technology is considered more secure, however certain vulnerabilities may be possible due to service layers provided on the top of blockchain technology and it is not because of the core technology. Further scope to implement the proposed framework in the field of supply chain management.
Review on Reinforcement Learning in CartPole Game

Last recent years, reinforcement learning has been one of the machine learning approaches commonly used in many fields. Reinforcement learning applications can be in games, resource management, personalized recommendations, and robotics. The applications of RL implementation in the game increase rapidly. Therefore, ensuring the best performance of RL applications in games is one of the challenges that should be considered. The reinforcement learning methods can be used to achieve the highest scores in the game in the minimum time possible to win. This work presents an overview of RL with focusing on Q-learning and State Action Reward State Action (SARSA) models. Also, this work describes RL on CartPole Game and applies an experiment to measure and compare the implementation performance of Q-learning and SARSA in CartPole Game.
Performance Measurement of Popular Sorting Algorithms Implemented using Java and Python

In modern technology where disciplines like data sciences, data Analytics, and machine learning are emerging and infrastructure being set for Internet of things, important operations like sorting and searching plays a vital role. Hence efficient sorting along with searching is still important for the efficiency of other algorithms. These emerging areas are using fourth generation languages(4GL) and fifth generation languages(5GL) languages for its implementation. Also, it is seen fever analysis is made on algorithms using 4GL languages though there exist sufficient analysis and results using languages like C, C++. In this paper we have made a thorough asymptotic analysis and performance measures using Python and Java languages. We conducted our experiments on Pseudo random data for average case analysis and sorted(descending) data for worst case analysis. The research shows that Merge sort doing well for its Python implementation instead of Quick sort which very much lacks in its Performance. Quicksort remains excellent in performance for Java implementation.
A Supervised Learning Classifier for Replayed Voice Attack Detection

Google’s voice assistant has the voice match feature, which can only recognize its user’s voice. However, it cannot distinguish between an authentic human voice or an audio-replayed replica of the same person’s voice. This work develops a Gaussian shallow learning Naive Bayes (GNB) voice-replay detector to add such a missing layer of verification. In the front-end feature extraction stage, the model extracts the Mel frequency Cepstrum Coefficients (MFCC) and Constant Q Cepstrum Coefficients (CQCC) from the input voice signal. The gathered attributes are given to the developed GNB classifier to classify the input speech as either genuine from a live source or replayed from a previously recorded source. The GNB classifier is trained using extensive datasets of labeled speech feature samples from both classes. The Equal Error Rate (%EER) statistic measures the classifier’s performance. The trained GNB classifier is exposed to extensive development and evaluation datasets to optimize performance in various reduction, normalization, and filtration situations and settings. The top %EER values for the GNB classifier are 14.3553% for the development set and 19.8722% for the evaluation set. A real-time experiment is conducted with the developed learning model to support the obtained performance results.
Blockchain-based Security for Internet of Medical Things Application

Internet of Things (IoT) refers to the network comprising of devices called "things" that are equipped with sensors, batteries, microcontrollers and radio transceivers for collecting and exchanging data with other devices and systems over the Internet. Internet of Medical Things (IoMT) signifies IoT applications in the healthcare sector and aims to improve the healthcare sector's technological, economic, and social aspects. An important concern in the IoMT is the security and privacy preservation of patient data. For IoMT to be widely adopted, assurance of data security and prevention of unauthorized access is of paramount importance. Presently IoMT applications incorporate centralized data storage which suffers from issues such as a single point of failure, a lack of strong protection against record tampering, hacking, data theft, trust and reliability. All of these issues can be resolved if IoMT adopts the blockchain technology leading to Blockchain-based IoT (BIoT) applications. Blockchain is a technology that aids in tracking, coordinating, and information sharing for a significant number of devices by maintaining ledgers of transactions on all connected peers, eliminating the need for a central server. This paper presents work related to the integration of multi-ledger blockchain architecture in IoMT and analyzes its security and resource implications. The results show that transition from centralized IoMT to decentralized BIoT introduces independence from the third parties, improves security, and removes a single point of failure. Additionally, the presented multi-ledger blockchain architecture with limited nodes demonstrates blockchain's scalability and adaptability with resource-limited IoMT devices.
A Secure and Efficient Access Control Scheme for Shared IoT Devices over Blockchain

The concept of shared IoT devices has attracted much attention from the industry sector, academia, and financial institutions, providing various benefits, such as saving resources, reducing personal expenses, and providing convenience. Although shared IoT devices facilitate people’s lives and work, the information exchange is over wireless networks that may suffer from some security attacks such as unauthorized access to a shared device or some private information of legitimate users being leaked. It makes the secure access control to the shared IoT devices become an intractable issue. In order to guarantee the access right of the legitimate users, to prevent the problems of privacy leakage and unnecessary economic disputes, a secure decentralized access control scheme for shared IoT devices is proposed leveraging the technologies of blockchain and a proposed authentication protocol in this paper. The new lightweight authentication protocol is proposed to perform mutual authentication between the user and the IoT device. To protect the privacy of the user, the instruction data are encrypted by a temporary session key negotiated between the user and the IoT device with the help of blockchain which enables nontamperable transactions and prevents central corruption and single point of failure. In our scheme, blockchain is maintained by the gateway nodes an acts as a distributed database and a smart contract for shared service is deployed on it. The smart contract has three functions in our scheme: (1) achieving the prepayment of users and settlement for the service contributor, (2) participating in a verification step during the key negotiation to prevent some malicious behaviour from users or devices, (3) recording the workload of the gateway. Finally, a comprehensive analysis on the safety and reliability of the entire scheme is carried out; extensive simulation experiments are conducted to reveal the authentication protocol is efficient and the scheme is feasible.
Depth Based Static Hand Gesture Segmentation and Recognition

Hand Gesture Recognition (HGR) refers to identifying various hand postures which helps in nonverbal communication with humans or machines. It also finds various applications in the area of Human Computer Interaction (HCI) like robotics control and Sign Language Recognition (SLR) for communication with specially abled people. Classification of hand gestures in varying lighting conditions and occlusion is still a challenging task. In this paper, a two-stage approach is proposed where hand region is segmented in first stage followed by classification of segmented hand gestures in the second stage. In the segmentation stage, a novel hybrid approach of coarse to fine hand segmentation is proposed where YOLO (You Only Look Once) network is used to detect the hand region at coarse which is further refined using Grabcut algorithm to obtain the fine boundary of hand region. In classification stage, hand segmented RGB and depth image are combined as 4-channel RGB-D input to the classification CNN model. Proposed method was evaluated on the OUHANDS dataset and achieved the validation accuracy of 98.75% and test accuracy of 86.50%.
Ensemble Architecture for Improved Image Classification

Image classification is one of the major research areas, to meet the needs of reliable and automatic image annotation system. Deep learning techniques have proved to be the solution for most of computer vision problems, with its self-learning ability and non-linear architecture it is able to learn the optimal parameters for the model. Transfer learning based pre-trained models are often used for its flexibility and to achieve good performance with lesser training time. The proposed Ensemble architecture is based on three pre-trained models - Vgg16, Inceptionv3 and Resnet50 Model, the features extracted are merged together using various methods and analyzed on Caltech-101 and Caltech-256 dataset. The conducted empirical study clearly shows that the proposed ensemble model with its merged features outperforms the performance of individual model with greater discriminating ability and with improved accuracy.
Augmented Reality for Cultural Heritage

Augmented reality applications for Cultural Heritage have been implemented in the last years. The use of AR is currently diffused for many purposes, from technical and managing activities to dissemination. On the communication side, the main potential of such approach is the extension of human sight as to cover simultaneously the current situation of a point of interest (monuments, archaeological sites, artifacts, etc.) and the reconstruction of its ancient condition in different historical periods. Moreover, it allows to compare different possible hypotheses and evaluate the reliability of each of them in the general context of the known data. Despite the current limits of AR (due to the approximation in device positioning), which still influence the use of applications for mobile use, such technology is very promising in the fields of tourism, education, and entertainment; it allows to enrich 3D scenarios with different types of content. In the next future, it is reasonable to imagine a huge amount of information, in different formats, potentially reachable by people simply having a look at the remains of the ancient past and choosing their favorite topics. At the same time, the connection of AR with other emerging technological infrastructures (such as IoT) will allow to collect, view, and manage simultaneously lots of real-time diagnostic data in the same framework. This will foster the research activity toward the definition of new communication metaphors and cognitive solutions.
Declarations of Software Engineering Project Managers Managing Remotely: Provisions for Hybrid Working

Owing to the landscape of the relevant work, software engineering organizations or teams are more prone to implement and benefit from remote working or hybrid working venues. The freshest pandemic brought about farther obligations and chances for organizations to practically experience and test the mode of working remotely. Correspondingly, for a noteworthy number of organizations, the new standard of working embraces remote working applications. Taking into account this actuality, the research has been conducted to comprehend the perspectives of software engineering project managers having experiences of managing remotely. Unambiguously, semi-structured interviews with 27 project managers from different industries were principally conducted to explore and extract the relevant involvements and interpretations. Outcomes revealed that the new standard of working for software development organizations ought to be the hybrid approach. Furthermore, via analyzing the collected pertinent data, foremost affirmations of software engineering project managers have been apprehended and commented on. The conclusions of this work are to be advantageous for relevant project managers, policymakers in software development organizations, and other scholars researching the evolving dynamics of workforce management applications in software engineering organizations.
Content-based Graph Privacy Advisor

People may be unaware of the privacy risks of uploading an image online. In this paper, we present Graph Privacy Advisor, an image privacy classifier that uses scene information and object cardinality as cues to predict whether an image is private. Graph Privacy Advisor simplifies a state-of-the-art graph model and improves its performance by refining the relevance of the information extracted from the image. We determine the most informative visual features to be used for the privacy classification task and reduce the complexity of the model by replacing high-dimensional image feature vectors with lower-dimensional, more effective features. We also address the problem of biased prior information by modelling object co-occurrences instead of the frequency of object occurrences in each class.
The Impact of Artificial Intelligence on a Learning Management System in a Higher Education Context: A Position Paper

This position paper provides an overview of the most important practices in the field of Artificial Intelligence (AI) used in educational contexts, with a focus on the main platforms used for teaching (LMS) to support the development of a research work at Eduardo Mondlane University (UEM) in Mozambique. To that end, definitions and descriptions of relevant terms, a brief historical overview of Artificial Intelligence (AI) in education and an overview of the common goals and practices of using computational methods in educational contexts are provided. The state of the art regarding the adaptation and use of Artificial Intelligence is presented and we discuss the potential benefits and the open challenges. The paper also presents the methodology and key steps which will be developed at UEM to achieve the research goals.
Education 5.0 Maturity Index: Concept and Prospects for Development

E-governance covers all areas of society where the state regulates activities, manages processes, and so on. Education is no exception. In the context of digital transformations and challenges of recent years, the state's managerial capabilities in the field of education have become even more relevant. Therefore, there is a need for a comprehensive understanding of changes in the field of education and identification of future directions for its development in order to take into account the development of digital technologies and future possible crises. This paper further developed the ideas of Education 4.0, as well as describes the author's vision of the new trend Education 5.0. The result of the research is a conceptual vision of the Education 5.0 Maturity Index, which is a comprehensive indicator consisting of the following components: Participants in the learning process; Knowledge acquisition; Location; Technical Learning tools and Learning outcomes. Participants in the learning process are the teacher and the learner. The "Knowledge acquisition" covers the sources of knowledge; the curriculum; methods and techniques of the learning process. The "Location" describes the location of the learner and teacher during the learning process. The "Technical learning tools" describes technical tools and learning technologies (EdTech) are used in the learning process. The "Learning outcomes" characterizes the type of education document and the obtained digital competencies proficiency level. The concept of the Education 5.0 Maturity Index presented in this paper requires further development towards providing its components with qualitative and quantitative characteristics.
A Blockchain Enabled Trusted Public Distribution Management System Using Smart Contract

Nowadays, a large population of hungry people still exists in many countries. The government body of most countries in the world tries to provide food security to every citizen. They have implemented many rules to provide food safety for citizens. They also run many Public Distribution Shops (PDS) to distribute food products and essential commodities at low prices. But existing systems face many challenges in terms of low accessibility like buying items from the same PDS, transaction transparency, PDS trust, centralized system, and much more. So, to overcome these limitations, a Blockchain-enabled trusted public distribution framework has been proposed using smart contracts. The design and development of smart contracts will not only provide food security but can deliver transparency in the system, which leads to building trust in the system. Therefore, the concerned authorities of the distribution system can build trust with each other efficiently and reliably. In the proposed framework, people can also buy commodities from different PDS creating high accessibility to the system. The proposed work also provides secure communication among authorities and transparency and security of the management data.
Graph Regression Based on Graph Autoencoders

We offer in this paper a trial of encoding graph data as means of efficient prediction in a parallel setup. The first step converts graph data into feature vectors through a Graph Autoencoder (G-AE). Then, derived vectors are used to perform a prediction task using both a Neural Network (NN) and a regressor separately. Results for graph property prediction of both models compared to one another and baselined against a classical graph regression technique i.e. Nearest Neighbours, showed that using embeddings for model fitting has a significantly lower computational cost while giving valid predictions. Moreover, the Neural Network fitting technique outperforms both the regression and Nearest Neighbours methods in terms of accuracy. Hence, it can be concluded that using a non-linear fitting architecture may be suitable for tasks similar to representing molecular compounds and predicting their energies, as results signify the G-AE's ability to properly embed each graph's features in the latent vector. This could be of particular interest when it comes to representing graph features for model training while reducing the computational cost.