Reviewerly/exHarmony huggingface dataset:
Self-Efficacy and Achievement of Students in the Government Schools of Rawalakot Azad Kashmir

Self-viability is a vigorously explored theme inside the instructive discipline and is an essential mark of extensive-term execution and adequacy. Our public preparing gadget needs great fledgling educators possessing awesome feelings of viability and genuine instructing limit. The exhibited understudy educator execution appraisal is being utilized the nation over as a method for evaluating the preparation of student instructors for a hit calling in tutoring. Since self-adequacy is most intensely associated with early audits, it's crucial to comprehend the researcher's self-viability. This ganders found that the connection between self-viability and achievement rating toward the finish of the test, understudies inside the public authority resources of Rawalakot is standard. Self-adequacy in sure understudies changed into saw to diminish later inward accommodation while given remarks on advancement and execution. All people had been demonstrated to increment in self-adequacy from the beginning of the student as far as possible accurate to form fundamentally dependent on the writing evaluation. Understudy mentor execution is contrarily related to the other in self-viability from the start to the stop of understudy training. These outcomes show that the more the exchange self-viability from the start of the instructing as far as possible, the reduction a researcher performs at the achievement. This likewise proposes that when the other in self-adequacy is less, satisfaction execution is extra.
Quantum Information Processes

Satellite-based quantum network is currently a key research topic. Working with a quantum network requires the understanding of many advanced fields, especially quantum electronics and communication. A quantum communication system utilizes quantum information processes using quantum bits or qubits, similar to a classical communication system using ordinary bits, but with a different underlying physics. The coupling of qubits based on quantum entanglements and superposition is a necessary process for manipulation of quantum information and, hence, quantum communication. Such processes are led by some logical operations with the help of quantum logic gates that transform qubit states. The proposed chapter moves in this direction and explores the operations and designs of quantum logic gates using qubits required for quantum communication processes. Algorithms are created and the quantum communication processes are utilized for their execution applicable to satellite-based quantum networks.
Swarm Intelligence Based Data Selection Mechanism for Reputation Generation in Social Cloud

Cloud computing has evolved in every area of service generation and has also become an area of interest for researchers for the last couple of years. A social cloud is a cloud network that is connected by users who know each other in some manner. When service orientation takes place between the people who are known to each other, trust or reputation becomes a serious issue because if the user trusts the provider due to any connection, and if the provided service is not done in a significantly good manner, the user will be disappointed at the end the service level agreement gets violated. To solve this issue, researchers have shown interest in finding meta-heuristics-based Swarm Intelligence based solutions and they are discussed in the article as well. This research article aims to find the best solution which fits into provider selection when it comes to providing the user the best experience. To do so, machine learning-based k-means is used and Artificial Bee Colony (ABC) is being enhanced. The proposed Enhanced ABC (E-ABC) algorithm is compared with state of art solutions in terms of successful and trustworthy communication.
PDC Place Athens: Grassroots Participatory Design and Digital Commons

In this event we are interested to highlight the political dimensions of participatory design, by surfacing community-led and values-driven participatory design practices, more specifically in relation to solidarity, cooperativism and digital commons. We intend to do this in two events: The first event will be focused on bringing together cooperative organisations and communities across various sectors, and more specifically cultural spaces, energy, digital and urban commons, and cooperative economies to share opportunities and challenges of designing together differently. In addition to the creation of potential synergies and the sharing of knowledge at this first event, we anticipate that in addition to the Participatory Design Conference proceedings, this first event will create a knowledge base to be used for the participatory designing of the commoning practices of individuals and organisations active in producing digital commons in Greece (second event). As such, in addition to highlighting the political potential of Participatory Design and a call for “democratizing innovation”, through this PDC Place, we aim to bring together and strengthen the participatory design and digital commons communities.
A Comprehensive Study of Various Software Process Models in Software Development

The software development models are critical components of the entire development process. It consists of several steps, including requirement collecting, designing, development, testing, and implementation. The software process models determine the success or failure of software development. The software engineering process can be divided into two stages: At the first level, actions connected to gathering information, developing software, and maintaining it; at the second level, activity related to dentition, operation, measurement, and upgrading.This paper presents a comparative study of various software development process modelsFurthermore, it would undoubtedly serve as a directing way for the researchers to investigate new study directions. A thorough examination of all eight process models is also provided. Each software process model includes a variety of features and parameters that are simply described so that researchers can readily select a process model based on their needs, resources, and experience.
Privacy-Preserving Mechanism for Data Analytics

This paper proposed a mechanism to maintain the data subject’s privacy while performing analytics on electricity billing data. First, this paper implemented privacy-preserving mechanisms such as generalisation, group shuffling, suppression and full masking in a mocked electricity billing dataset. This paper then calculates the data utility metric to prove that the data is adequately preserved. Finally, the data utility of the preserved data is evaluated to ensure the preserved data is still usable to perform analytics tasks. Among the three mechanisms examined in this article, the group shuffling mechanism achieved the most outstanding visibility; hence, it is the most suitable mechanism to be used in data analytics. Apart from that, group shuffling generates a very little loss of information.
Advanced Object Detection Methods for Drone Vision

The use of unmanned aerial vehicles (UAVs) is increasing by the day, be it in the public or the private sector. The use of UAVs requires a well-defined architecture at its core for coordinated and proper functioning; a framework called the internet of drones (IoD). Most UAVs are equipped with on-board cameras useful for monitoring of potential obstacles by the controller of the UAV from a remote location. Moreover, today we have work galore in the computer vision sector which focus on development of various deep learning (DL) algorithms for object detection and even image segmentation. Some researchers have even started speculating the challenges involved of using such algorithms in drone vision. This chapter describes the potential of such DL techniques applied on real-time footage gathered by the on-board cameras on the UAVs. These technologies will aid object tracking, self-navi-gation, and obstacle detection and collision avoidance capabilities. Machine learning (ML) algorithms have found immense applications in almost every area of modern research. A specialized branch of ML is DL which mainly deals with neural networks. These neural networks are designed to mimic the human brain and form the basis of many advanced artificial intelligence (AI) applications, such as smart computer vision. The DL computer vision architectures we study in this chapter rely heavily on the use of neural networks, which we shall describe in detail. In particular, we discuss three state-of-the-art DL computer vision architectures namely Faster-R convolutional neural networks (faster R-CNN), single shot detection (SSD) and you only look once (POLO). We discover how advanced computer vision can help UAVs maintain a real-time database of land-based entities with the use of different ways of annotations (2D annotations, 3D annotations, polygon annotation and semantic segmenta-tion). For example, many UAVs operating over the same zone with the same IoD architectures may, with the use of the above technologies of algorithms mentioned, enumerate vehicles on different major roads in an urban area to maintain a real time traffic intensity projection over different sectors of the urban area. In other instances, drones with such capabilities may detect birds and measure their distances and take necessary steps to avoid a collision. These algorithms are very popular in computer vision using DL today and have been used in many applications.
Harnessing Blockchain and Decentralized Finance in the Post-COVID-19 European Union

This chapter presents a singular convergence between a policy window of opportunity and changes in social habits triggered by the COVID-19 pandemic, which set timely conditions in the European Union to implement the green and digital transformation programs embodied in the Next Generation European Union (2021-2027). COVID-19 highlighted problems carried out decades ago, such as climate change and natural resources exhaustion, whereas prolonged lockdowns forged consciousness about pollution and overcrowded sites while accelerating the use of digital solutions. Under favorable circumstances to effect changes for a resilient, competitive, and inclusive European Union, the authors focus on the circular economy strategy and elaborate the case for blockchain and tokenization as a key enabling technology to sponsor agile networks for a swift transition to a green and digital society decoupled from resource extraction and waste production. The chapter expands the case for blockchain decentralized finance applications as a means to financialize tokenized green products.
Cryptography Techniques for Information Security

Nowadays cryptography has become a very popular technique for information security to everyone. It is used to convert a message into an unreadable form, so an unauthorized person will be able to recognize it. The process of converting a message into an unreadable form is known as encryption whereas the reverse of it is known as decryption. In order to provide security to the user, the main parameters used are authentication, confidentiality, integrity, and non-repudiation. This paper focuses on various techniques of cryptography and also contains the comparative analysis of these techniques.
Comparative Analysis of Forensic Digital Evidence on Android Smartphone based Instant Messaging Using NIST Framework

Smartphones are currently experiencing rapid development along with technological developments. The rapid development of smartphones is also followed by the increasing use of social media and instant messaging. One of the well-known instant messaging applications, namely Whatsapp, issues a notification that its user content, whether uploading, storing, receiving or sending anything on WhatsApp, the company can use, reproduce, and display or distribute it. With this notification, WhatsApp users switch to more private instant messaging, namely Telegram and Signal Messenger. This study uses the National Institute of Standards and Technology (NIST) method, namely collection, examination, analysis, presentation. This study uses two smartphones with root and non-root conditions that have installed the whatsapp, telegram, and signal messenger applications. The case used in this study is a narcotics sale and purchase transaction. Digital evidence was obtained using four forensic tools, namely MOBILedit Forensic Express, Oxygen forensics, Belkasoft Evidence, and Magnet Axiom. This study produces digital evidence in the form of chat files, images, videos, perpetrator accounts, contacts, locations and conversations that have been deleted from smartphone devices. The calculation of the index result from the percentage of evidence obtained by the WhatsApp application on a smartphone with root conditions using the forensic tool Oxygen forensic got the result, namely 50%. Telegram gets 50% results using Oxygen forensic with root conditions. While the signal gets the result, which is 30% using MOBILedit with root conditions.
Japanese Multimodal Guidance Framework Based on Computer Network Ecological Environment

Based on the computer network ecological environment, this paper discusses the choice of modalities of self-directed learning before class and the coordination of multi-modality in classroom teaching. Studies have found that multimodal listening teaching can help improve Japanese comprehensive ability and Japanese listening ability; the use of moodle platform directly affects students’ psychological pressure and learning interest, and the principle of moderation should be followed; under the guidance, help and supervision of teachers, When instructing students to learn independently, teachers should break through the shackles of traditional teaching models and consciously set up modal combinations; starting from the characteristics of Japanese, make a good role positioning for the modalities applied to classroom teaching, and coordinate the relationship between the foreground and the background modalities.
Application of blockchain smart contracts in smart tenancies: A Malaysian perspective

The advancement in blockchain technology has enabled smart contracts to automate the execution of tenancy obligations, known as “smart tenancies”. This paper analyses the legal issues on the adoption of smart tenancies within Malaysia using legal doctrinal research method. We seek to answer these questions: (1) whether smart tenancies are enforceable in Malaysia; (2) whether parties to a smart tenancy can apply for an endorsement of tenancy under the National Land Code; (3) whether the legal profession can claim exclusivity in offering and maintaining smart tenancies services; and (4) whether there is room for self-help in resolving tenancy disputes using smart tenancies in Malaysia. The key findings are as follows: (1)(a) smart tenancies can and should be stamped when the user interface stipulates the information required for calculation of stamp duty; (1)(b) smart tenancies service provider have to comply with the Electronic Commerce Act 2006 to ensure that the system is reliable to attribute the electronic signatures to the contracting parties; (2) once the print-out of a smart tenancy is stamped, the tenant and landlord have an option to apply for endorsement of tenancy with the land registry under the National Land Code (Revised 2020); (3) the Legal Profession Act 1976 does not restrict the marketing, operation and maintenance of smart tenancies services to be done by law firms exclusively; and (4) there is no room for self-help eviction of a tenant in Malaysia, and the eviction process ought to be enforced with a court order.
An AES-Based Efficient and Valid QR Code for Message Sharing Framework for Steganography

Steganography is one of the methods that may be used to obscure information to facilitate information exchange. Generally speaking, it may be determined as a study of communication that focuses mostly on methods of hiding the message via the use of various mediums such as images, audio and so on. In this method, if the message is adequately concealed, it will be difficult to extract the message from attackers and eavesdroppers. In this study, we investigate the use of QR codes for message exchange in steganography, as well as the AES approach. The end findings’ performance will be assessed by comparing image file quality before or after data has been hidden from view in these files. For maximum difficulty in detecting that their data is hidden in these files depending upon the encryption and decryption processes using the AES method, the quality of original and stego image files must be similar or very close to one another. The robustness of the proposed approach is calculated by PSNR and error term with the steganographic attack.
Efficient Data Partitioning and Retrieval Using Modified ReDDE Technique

I.T. industries and private organizations generate a massive volume of data every day. Storing and processing Big data is challenging due to scalability and performance issues. Nowadays, a distributed architecture is used to process Big data. In a distributed architecture, several nodes/systems communicate to store and process data in a distributed architecture. Search engines use distributed architecture to store and retrieve documents for the user query. Elasticsearch is an open-source search engine, which uses distributed architecture. The main goal of this paper is to configure elastic search clusters, implement the shard selection algorithms, and perform the comparative study analysis of the existing shard selection techniques with the proposed shard selection technique. The sharding technique is applied to partition and retrieve relevant data from the nodes. Shards are created on each data node of the cluster. Shard is the small unit of storage in the memory of the data node. Data is horizontally partitioned according to topic-based and stored on different shards. This paper proposes a Modified ReDDE shard selection algorithm that enhances the throughput by searching only the relevant shards in the distributed processing architecture instead of all the shards. The results interpret that the Modified ReDDE algorithm improves the performance parameters compared to existing shard selection techniques by 26%.
Bio NFTs and Digital Liquidity

&#x0D; &#x0D; &#x0D; The fireside chat features an in-depth discussion with the founder of Genobank.io regarding the future of NFT use in healthcare. NFTs are explored as a means to autonomy in health information. Citizens are slowly understanding the (digital) liquidity of thier confidential data to institutions such as payors and health systems.&#x0D; Enterprise blockchain in healthcare is slowly maturing and many institutions are adopting the tools for transormation.&#x0D; Questions to resolve are autonomous (asset) ownership of data, or paying a third party to catalogue an individual's data. &#x0D; &#x0D; &#x0D;
Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch

As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on two mutually delay-coupled semiconductor lasers (SLs) with optical injection. The results show that better prediction performance can be realized by setting appropriate parameter mismatch scenarios. Especially for the situation with large prediction errors encountered in the RC with identical laser parameters, a suitable parameter mismatch setting can achieve computing performance improvement of an order of magnitude. Our research is instructive for the hardware implementation of laser-based RC, where the parameter mismatch is unavoidable.
Dynamic Rendering-Aware VR Service Module Placement Strategy in MEC Networks

Combining multiaccess edge computing (MEC) technology and wireless virtual reality (VR) game is a promising computing paradigm. Offloading the rendering tasks to the edge node can make up for the lack of computing resources of mobile devices. However, the current offloading works ignored that when rendering is enabled at the MEC server, the rendering operation depends heavily on the environment deployed on this MEC serve. In this paper, we propose a dynamically rendering-aware service module placement scheme for wireless VR games over the MEC networks. In this scheme, the rendering tasks of VR games are offloaded to the MEC server and closely coupled with service module placement. At the same time, to further optimize the end-to-end latency of VR video delivery, the routing delay of the rendered VR video stream and the costs of the service module migration are jointly considered with the proposed placement scheme. The goal of this scheme is to minimize the sum of the network costs over a long time under satisfying the delay constraint of each player. We model our strategy as a high-order, nonconvex, and time-varying function. To solve this problem, we transform the placement problem into the min-cut problem by constructing a series of auxiliary graphs. Then, we propose a two-stage iterative algorithm based on convex optimization and graphs theory to solve our object function. Finally, extensive simulation results show that our proposed algorithm can ensure low end-to-end latency for players and low network costs over the other baseline algorithms.
Optical-Electrical Nonlinear Feedback Assisted Photonic Circuits for Temporal Pattern Recognition

We have proposed projection-based photonic circuits for nonlinear classification and here we extend our work to temporal pattern recognition by adding optical-electrical nonlinear feedback module to this photonic circuits. Pattern recognition is demonstrated by simulation.
Vehicle-Type Classification Using Capsule Neural Network

Vehicles include cars, bikes, buses, aeroplanes, space shuttles, cycles and many more. Vehicle detection and vehicle-type recognition is a practical application of machine learning concepts and is straight away applicable for different tasks in a surveillance system of the traffic contributing to a smart surveillance. We will broach the functioning of instinctive vehicle-type identification using static image datasets. We have put forward Capsule Neural Networks for the identification of vehicle types by consolidating capsules to overcome drawbacks posed by CNN and its counterparts where CNN pooling loses significant data while modern processes can overcome this still image reconstruction is a tedious task. Capsule Network takes into different consideration delineations of object orientation, possie and relates its approach with inverse graphics making object recognition more efficient and accurate and significantly increasing classification performance.
Trajectory Planning of Cable-Driven Parallel Robot with Mobile Cranes

Cable-Driven Parallel Robot (CDPR) with mobile crane can be an alternative solution for helping evacuation process in post-disaster sites. This paper aims to carry out the trajectory planning of CDPR with mobile crane and hence the reconfiguration of mobile crane can be executed to avoid instability. This instability occurs due to high cable tension. Trajectory was generated based on fifth-degree polynomial that provides position, velocity, and acceleration profiles of the end-effector. Then, the end-effector moves along the trajectory while the mobile crane adjusting its length. The prototype of CDPR with mobile crane was built to verify the theoretical results. Reconfiguration of CDPR is also proposed to lift higher payload capacity while maintaining stability of the mobile crane.