Reviewerly/exHarmony huggingface dataset:
F2SRGAN: A Lightweight Approach Boosting Perceptual Quality in Single Image Super-Resolution via a Revised Fast Fourier Convolution

With the successful development of deep learning, single image super-resolution (SISR) has advanced significantly in recent years. However, in practice, excessive convolutions limit super-resolution applications on platforms with limited resources like mobile devices or embedded systems. Besides, existing lightweight models have a problem with small receptive fields and only consider local features for the reconstruction. Previous models try to stack more convolutions layers to address this problem, but this prolongs the execution time due to increasing the number of parameters. Therefore, this paper proposes a novel approach named F2SRGAN using a revised Fast Fourier Convolution to enlarge the receptive field, enabling this model to learn global features better, neither introducing too many parameters nor prolonging the model inference time. The experimental results show that our proposed F2SRGAN significantly improves perceptual image quality among the lightweight SISR methods while maintaining an acceptable inference time.
Compartive Analysis of Different Block Chain Technology to Improve the Security in Social Network

Social networking sites have given users unprecedented opportunities for the generation and dissemination of content. Block chain Technology as defined the decentralized system for distributed registers which are used to record data transactions on multiple computers. So a variety of social networking sites exist for different purposes, to afford users a range of anonymous and non-anonymous options for self-expression, and the ability to be a part of a virtual community. Sometimes a misinformation, propagated by users and group can create chaos or in some cases, might leads to cases of riots. Therefore, a robust and new system is required to check the information authenticity within the network, to stop the propagation of misinformation. In this paper, propose of block chain based framework is for sharing the information securely at the peer level. In the block chain model, a chain is created by combining blocks of information. I analyze real data by exploiting one of the most well-known DApps sites (decentralized applications), and also compare current technologies in order to get better algorithm or tool to secure our information. such as Facebook.
Automated Healthcare System Using AI Based Chatbot

Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing.
Public Blockchain-Based Data Integrity Verification for Low-Power IoT Devices

The integration of sensor nodes with public blockchains is possible with the help of low-power communication networks that use Bluetooth low energy and long range. However, power-consuming Wi-Fi is still the main means of communication for the existing sensor nodes, especially in urban environments. Typically high power consumption, private key disclosure, and high transaction fees are the issues that prevent battery-powered sensor nodes from being integrated with a public blockchain. Therefore, this article proposes a data protection protocol that is able to secure the data integrity of the stored sensor data, help to reduce transaction fees, and prolong battery life for IoT devices that are used with public blockchain networks. A proof of concept is presented using an ESP32S2 device to evaluate and verify the performance of the proposed data storage protocol. A smart contract is designed and analyzed using a formal smart contract analysis tool. A decentralized Web application is designed to display and verify the sensor data extracted from the public blockchain. The power consumption, memory usage, and security of the proposed solution are evaluated. The evaluation results show that data integrity can be achieved even for low-power sensor nodes that connect to public blockchains via Wi-Fi network.
Towards the Development of a Blockchain-based Decentralized Digital Credential System using Hyperledger Fabric for Participatory Governance

The Philippines’ ICT agenda to promote participatory e-Governance, industry and countryside development, and ICT user protection and information security can be addressed by Blockchain. This study used Hyperledger Fabric to develop a Blockchain network for a secured and validated system for issuing, distributing, and sharing digital credentials. For the initial prototype, the network was tested on two use cases: national identification system and academic credentials. Chaincodes were developed for each type of digital credential and deployed into designated Channels in the network. Initial results show that a Blockchain-based system using the Hyperledger Fabric framework is feasible for developing a secure digital credential platform. The current architecture can support the implementation of the initial use cases and can also accommodate the addition of new use cases in the future. The current iteration of our work provides the groundwork for developing a production-level ready platform. Future work for this study includes the development of the end-user web application where users can interact with the network and initiate transactions. The deployment of the Blockchain network may pave the way toward decentralized e-Participatory governance.
Contractor selection for Project execution using multi-attribute decision making

Contractor evaluation and selection is one of the most important issues in implementing a project. In order to eliminate unqualified contractors having low capabilities to perform the project, it is necessary a method for contractor evaluation and selection not only based on the lowest bid but also in attributes describing their abilities to carry out the project. The evaluation process represents the initial stages that an owner should employ in order to gain further assurance of professional conduct from the contractors that seem capable of achieving the performance requirements of the contract being tendered. During the pre-qualification stage, not only qualitative attributes, such as technical experience or management capability, but also quantitative attributes, such as liquidity or debt ratios, that reflect the financial soundness of the contractor being evaluated, are used. In this paper a multi-attribute decision-making method combining cloud and utility theory is proposed in order to evaluate a group of six contractors.
Towards the Development of a Blockchain System for Philippine Government Processes for Enhanced Transparency and Verifiability

There are cases of corruption and fraud within the Philippine government that have gone under the radar, often due to a lack of transparency and verifiability. The objective of this study is to prototype a blockchain network that can run a government process as a decentralized application such that it can enhance transparency and verifiability in the public sector. This can be accomplished by identifying a government process that would be converted into a decentralized application. One of these processes would be converted into a decentralized application. Afterwards, a blockchain framework should be identified — one which can create a public permissioned blockchain network. This framework can be used to design and implement the prototype blockchain network which the decentralized application can run on. The final prototype constitutes of smart contracts deployed on an Ethereum test network with a web frontend to easily interact with it. Mechanisms of the application that are deemed necessary to transparency and verifiability of the system are also identified. Finally, the variable cost of the system and possible limitation is explored in the paper. In this regard, the prototype offers a foundation with which other decentralized applications can follow and build upon. This is to promote transparency and verifiability within and among government processes.
Introduction to an agile framework for the management of technology transfer projects

Technology transfer offices play a valuable role in protecting and commercialising intellectual property developed at universities, for social and economic benefit around the world. University College London (UCL) Business Ltd is a technology transfer office that commercialises technology arising from the world-renowned research at UCL. PRINCE2 and agile approaches are amongst the popular choices of project management methodologies, however current frameworks are rigid and do not work very well within the technology transfer space, specifically in the biotechnology sector, due to the unpredictable nature of the projects. More organisations are adopting a flexible approach to delivering projects and becoming more agile in their management style. In a translational environment specifically, having a flexible response to change is highly encouraged. Current agile frameworks such as Scrum and Kanban, work extremely well within the IT and engineering-based projects, however for technology transfer projects (TTPs) they are slightly out of scope or too rigid in their methodology. An alternative agile framework is proposed to better suit the biotechnology sector and academic-research environment. This focuses on the breakdown of the project plan in separate work-packages, to manage the deliverables in separate development cycles using a system that splits the management into three gears based on the type of work and the length of time required to reach the deliverables within the project plan. This allows for the management of projects in stages against the original project plan, which in turn de-risks the project in stages. This framework is proposed as an alternative project management tool for biotechnology TTPs within a technology transfer office as well as the biotechnology industry.
Project Management Maturity Models: Proposal of a Framework for Models Comparison

With the evolutions of projects, it is necessary that companies understand the importance of project management, since this idea allows them to plan and monitor a project properly, through a set of knowledge, techniques, and tools. However, the implementation of project management alone is not enough. It is necessary that there is a continuous improvement in project management and an increase in maturity that accompanies the changes and needs of companies. These improvements and increases in maturity level are possible with the application of maturity models in project management. When well used, they allow companies to understand where they fail and where they need to improve. And if the measures taken from the models are applied, companies benefit positively in the delivery of the intended results, on time and on budget. As well as, in standing out compared to other companies in the market. Therefore, this study proposed a framework to compare project management maturity models. It allows the consideration of a set of variables when choosing a model. The results show that the Prado PMMM is typically the most suitable model for evaluation.
Modeling and Evaluation of a Data Center Sovereignty with Software Failures

Technology Sovereignty is the capacity of a state to provide a technology to its constituents by developing or out-sourcing the technology without causing a dangerous dependency on a particular contributor. There are several laws to monitor and govern networks that ensure the privacy and security of consumers. However, they do not control the infrastructure or hardware dependency associated with these networks. A robust end-to-end network requires reliable hardware, software, and network architecture to handle multiple failures, increased users, and increased traffic demands. To handle multiple failures efficiently, there must be little to no dependency on the manufacturers involved. Therefore, the robustness of a network requires an adequate degree of sovereignty. In this study, Data Center Networks (DCN) are considered to understand the impact of choosing the hardware and software manufacturers appropriately on the DCN robustness. The unavailability of a component from a manufacturer or the presence of a software bug in the switches of a DCN can affect several flows, leading to multiple failures and huge losses of data. Thus, it is rational to find the best combination of manufacturers to create a network with the least dependency on the manufacturer(s). In this research, the sovereignty of a DCN is evaluated by considering different multiple-failure scenarios based on hardware and software manufacturers' reliability. Design guidelines for DCN operators are provided based on the findings of the analyses.
Optimizing Speed and Accuracy Trade-off in Machine Learning Models via Stochastic Gradient Descent Approximation

Stochastic gradient descent (SGD) is a widely used optimization algorithm for training machine learning models. However, due to its slow convergence and high variance, SGD can be difficult to use in practice. In this paper, the author proposes the use of the 4th order Runge-Kutta-Nyström (RKN) method to approximate the gradient function in SGD and replace the Newton boosting and SGD found in XGBoost and multilayer perceptrons (MLPs), respectively. The new variants are called ASTRA-Boost and ASTRA perceptron, where ASTRA stands for “Accuracy-Speed Trade-off Reduction via Approximation”. Specifically, the ASTRA models, through the 4th order Runge-Kutta-Nyström, converge faster than MLP with SGD and they also produce lower variance outputs, all without compromising model accuracy and overall performance.
Exposing AI Generated Deepfake Images Using Siamese Network with Triplet Loss

Generative Adversarial Networks have gained popularity mainly due to their ability to create fake human faces. The remarkable detail with which such images have been created in the past few years has exceeded the ability of humans to differentiate between these fake images and real images. Such images have been known to be capable of deceiving the face recognition systems with certain success as well. Forensic systems being developed nowadays take into account adversarial attacks in order to create a more comprehensive detection approaches. Different GAN algorithms such as StackGAN, StyleGAN use different architectures to produce images. Since the underlying technique is different from one another it is difficult for any single detection algorithm trained on one kind of GAN to detect fake images generated from some other kind of GAN. In this research we use a siamese network with triplet loss function to provide a generic solution for detection of GAN generated images or deepfake images. Extensive experiments have been conducted to analyze the effectiveness of the proposed approach. The results show that the siamese triplet loss network performs significantly better than the contemporary approaches with accuracy exceeding 90 % in most experiments.
Flow Direction Algorithm for Feature Selection

Feature selection is a method used to decrease the number of features by removing unwanted, noisy and inconsistent data while maintaining classification accuracy. Most researchers have focused on using metaheuristic algorithms to select the best possible features to improve and simplify the dataset quality. However, the traditional optimization method tends to suffer from local optimality problems as the increasing of features in datasets. In this paper, an investigation is conducted to assess the performance of flow direction algorithm (FDA) in enhancing the classification accuracy by performing feature selection. Eight datasets obtained from UCI machine learning repository are used to perform comparative studies with existing algorithms known as differential evolution (DE), biogeography-based learning particle swarm optimization (BLPSO), henry gas solubility optimization (HGSO) and African vulture optimization algorithm (AVOA). The results reported that FDA obtains best mean accuracy in the comparative studies with the selected algorithms and the number of features selected.
Feature Selection of Medical Dataset Using African Vultures Optimization Algorithm

Feature selection is one of the popular techniques used to reduce the number of features by eliminating noisy, unreliable, and unnecessary data without affecting the classification accuracy. Metaheuristic algorithms were widely incorporated by researchers to search for the best possible features in simplifying and enhancing dataset feature. This is because the traditional optimization techniques have drawback of suffering from entrapment into local optima when handling a dataset with large number of features. In this study, the capability of African vultures optimization algorithm (AVOA) in conducting feature selection on medical datasets while preserving classification accuracy is investigated. Eight medical datasets retrieved from UCI machine learning repository are used to evaluation performance of AVOA in feature selection and compare with other algorithms known as opposition-based differential evolution algorithms (CO-DE), particle swarm optimization (PSO), hybrid canonical differential evolutionary particle swarm optimization (hC-DEEPSO), and multi-verse optimizer (MVO). Comparative study reports that AVOA produces the best mean accuracy (i.e., 82.9%) in six out of eight medical datasets and lowest number of features (i.e., around 24 features) in four out of eight medical datasets. AVOA can outperform other competitive algorithm in the selected medical dataset as it has most of the mean accuracy and lowest number of features.
Online Path Decision of No-Fly Zones Avoidance for Hypersonic Vehicles Based on a Graph Attention Network

The path decision problem for a hypersonic vehicle has been recently formulated as it helps to determine a good initial guess for trajectory optimization with no-fly zone constraints. This path decision problem is a hybrid problem. Discrete variables optimize paths and continuous variables optimize dynamics, which consumes costly computation time and is hard to apply in online scenarios. To reduce the computation, instead of directly solving the hybrid problem, we design a heuristics approach taking advantage of the interpretability and flexibility of a graph attention network (GAT). The path decision is modeled by a directed graph and transformed into a GAT training problem, and the resulting GAT can directly output a path in online use. During this work, there are two innovations: 1) GAT customization and 2) offline training. First, we define the mask to express the graph structure, and model the problem-specific decoder process in the GAT, thus meeting the path decision logic of no-fly zones avoidance and ensuring the solutions are feasible. Second, we numerically integrate dynamics by a path-following guidance law, calculate the total control effort as the cost function, and use this cost to train the GAT based on the widely used REINFORCE, thus conforming to dynamics that are practical for hypersonic vehicles. Simulation results illustrate the high-accuracy path decision, much faster calculation, and generalization on no-fly zone layouts and numbers.
MedoidsFormer: A Strong 3D Object Detection Backbone by Exploiting Interaction With Adjacent Medoid Tokens

In this paper, we propose MedoidsFormer, a novel transformer-based backbone equipped with a self-attention mechanism that is tailored explicitly to LiDAR-based 3D object detection. Unlike 2D object detection, the proportion of target objects to the input scene is much smaller, and their distribution is significantly sparser in 3D object detection. Given these observations, we introduce a new self-attention mechanism called Medoids Attention, focusing on exploiting interactions within surrounding regions, which not only reduces computation and memory costs but obtains discriminative context information. Instead of aggregating tokens from adjacent areas, we present a dynamic semantic-aware token mining process through k-Medoids clustering to direct select representative tokens for attention modeling. Our proposed method shows consistent improvement over existing 3D object detectors through extensive experiments and achieves state-of-the-art performance on the large-scale Waymo Open Dataset. We also conduct comprehensive ablation studies to verify the efficacy of the new self-attention mechanism and provide thorough insights.
Gamers' Reaction to the Use of NFT in AAA Video Games

The use of non-fungible tokens (NFTs) in AAA games is a very controversial topic, which leads to negative reactions from the gamer community. The objective of this article is to relate some of these cases that presented visibility in the press and to analyze the reactions this theme generates. To achieve this, we present some cases that had more relevance in the specialized press and, in the sequence, we present a discussion about the main problems pointed out, such as the state of the art of blockchains, energy efficiency, frauds, and currency evasions. Finally, we present some hypotheses to glimpse how NFTs, and their use in games, may happen in the near future.