Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in Sustaining Internal Communication in Corporate Sector: The Mediation of Two-way Communication Perspective of PR

Corporate sector organizations widely prefer incorporating technology in their everyday functioning. However, when it comes to communication, they offer even advanced technological support to upgrade operations. In this regard, this article examines the role of an Artificial Intelligence-based system in facilitating organizational communication in the UAE. The research selected a sample of n= 30 from corporate sector organizations currently working countrywide. The paper proposes a conceptual model supported by the Two-way Communication Model of communication and Public Relations. Results revealed that incorporating Artificial Intelligence has a significant effect on Organizational Communication (p>. 000) and Organizational Members (p>. 000). Besides, Organizational Communication is also affecting Organizational Members in a significant manner (p>. 000). However, the indirect effects of Two-way Communication on Organizational Communication (p>. 000) and Organizational Members (p> .003) also remained significant. Overall, the results indicate that Artificial Intelligence, accompanied by Two-way Communication, is widely contributing to creating and sustaining communication among the organizational members. Consequently, members feel confident that their voice is shared and heard by the other members, especially the stakeholders and the policymakers. Thus, it is concluded that Artificial Intelligence facilitates communication at many levels. Both vertical and horizontal level communication is possible and even improved by Artificial Intelligence. Existing literature on communication and Artificial Intelligence also witness the role of AI in facilitating communication. Besides, the relevant literature also witnessed AI being equipped with two-way communication, as affirmed by the current research.
A 3D Q-Learning Algorithm for Offline UAV Path Planning with Priority Shifting Rewards

Autonomous navigational robotics is a field of great importance due to its vast array of applications such as exploration, transportation, industry or defense. When it comes to theses scenarios, Unmanned Aerial Vehicles (UAV), can enable different approaches that can increase the task's efficiency and/or flexibility. In this paper we propose an offline path planning for static 3D environments using Q-Learning. The reward shaping is done in such a fashion that is able to account for three different priorities, namely path length, energy consumption and safety, that can be tuned freely by the user to suit the desired application. The proposed algorithm is able to guide the agent towards the goal from anywhere in the map, which can be helpful in scenarios where internal or external instabilities that can lead the agent stray from its main path may be expected. Scalability tests where also done to benchmark the proposed method's performance for larger maps.
POLAR: Performance-aware On-device Learning Capable Programmable Processing-in-Memory Architecture for Low-Power ML Applications

Improving the performance of real-time Traffic Sign Recognition (TSR) applications using Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNN) on software platforms is challenging due to the sheer computational complexity of these algorithms. In this work, we adopt a hardware-software combined approach to address this issue. We introduce a data-centric Processing-in-Memory (PIM) architecture that leverages Look-up-Table (LUT)-based processing for minimal data movement and superior performance and efficiency. Despite the superior performance, the limited available memory in PIM makes it complex to deploy deep CNNs. We propose merging CNN layers in this work to meet the limited resource constraints. One specific challenge in the TSR is the continuous change in the deployed environment, which makes a CNN model train over static data, leading to performance degradation over time. To address these challenges, we introduce a lightweight, performance-aware Generative Adversarial Network (GAN)-based on-device learning on PIM architecture. This compact CNN on PIM architecture attains data-level parallelism and reduces pipelining delays and makes it easier for on-device training and inference. Evaluation is performed on multiple state-of-the-art DL networks such as LeNet, AlexNet, ResNet using the German Traffic Sign Recognition Benchmark (GTSRB) Dataset, and the Belgium Traffic Sign Dataset (BTSD). With the proposed learning technique, it is observed to achieve maximum accuracy of 92.8% and 89.27% on GTSRB, and BTSD datasets. Also, it is observed the proposed mechanism maintains an average accuracy to be above 85% despite changes in the environment on all the CNNs deployed on the PIM accelerator.
A Smart Floor Device of an Exergame Platform for Elderly Fall Prevention

The high risk of falls in the elderly and their severe consequences makes research to prevent them an important priority for public health. Technologies that enable exercise in the form of games (exergames) can improve both cognitive and physical functions, which is a prerequisite to reduce fall risk. However, the key question is whether such tools are easy to use by the elderly. In this work, the development of a smart floor device for exergames that can motivate elderly to perform physical exercises is presented. The underlying hypothesis is that technologies that do not require the use of a complex user interaction environment such as the proposed smart floor are more suitable for the elderly to use for improving their physical and cognitive functions. The design and development of the smart floor leverages on features from the Internet of Things domain and follows the design principles for system composability using artifact tiles as building blocks. The tile mounting and circuit diagrams are discussed as well as the microcontroller selection to support both distributed and centralized game development models. Recurring training programs with stepping exercises can be deployed in the smart floor as appropriate interventions for fall prevention. Furthermore, the inherent ability to evaluate measures that can predict the risk of falling, such as the choice stepping reaction time, can promote the smart floor as a diagnostic tool. A qualitative assessment was performed with positive results on the perceived usefulness of the proposed exergame device.
Hybrid Post-Quantum Enhanced TLS 1.3 on Embedded Devices

Most of todays Internet connections are protected through the Transport Layer Security (TLS) protocol. Its client-server handshake mechanism provides authentication, privacy and data integrity between communicating applications. It is also the security base for the 5G connectivity. While currently considered secure, the dawn of quantum computing represents a threat for TLS. In order to prepare for such an event, TLS must integrate quantum-secure (post-quantum) cryptography (PQC). The use of hybrid approaches, that combines PQC and traditional cryptography are recommended by security agencies. Efficient PQC integration at TLS requires the exploration of a wide set of design parameters and platforms. To this end this work presents the following contributions. First, wide evaluation of PQC-enhanced TLS hybrid protocols, using end-to-end communication latency as metric. Second, the exploration and benchmarking in constrained embedded devices. Third, a wide traffic analysis, including the impact and behavior of PQC-enhanced hybrid TLS in real practical scenarios.
FPGA implementation of BIKE for quantum-resistant TLS

The recent advances in quantum computers impose the adoption of post-quantum cryptosystems into secure communication protocols. This work proposes two FPGA-based, client- and server-side hardware architectures to support the integration of the BIKE post-quantum KEM within TLS. Thanks to the parametric hardware design, the paper explores the best option between hardware and software implementations, given a set of available hardware resources and a realistic use-case scenario. The experimental evaluation comparing our client and server designs against the reference AVX2 and hardware implementations of BIKE highlighted two aspects. First, the proposed client and server architectures outperform the reference hardware implementation of BIKE by eight and four times, respectively. Second, the performance comparison between our client and server designs against the reference AVX2 implementation strongly depends on the available resource. Our solution is almost twice as fast as the AVX2 implementation while implemented on the Artix-7 200 FPGA, while it is up to six times slower when targeting smaller FPGAs, thus motivating a careful analysis of the available hardware resources and the optimization of the design's parallelism before opting for hardware support.
Project Management for Cloud Compute and Storage Deployment: B2B Model

This paper explains the project’s objectives, identifies the key stakeholders, defines the project manager’s authority and provides a preliminary breakdown of roles and responsibilities. For the project’s future, it acts as a source of authority. This paper’s objective is to record the justifications for starting the project, its goals, limitations, solution instructions and the names of the principal stakeholders. This manuscript is meant to be used as a “Project Management Plan Light” for small and medium-sized projects when it would be uneconomical to prepare an entire collection of documents that make up a project management plan. A global media cloud will be provided and managed by the ABC cloud company inside of a consumer’s current premises. In this paper, the authors explain the end-to-end delivery of cloud and compute services. The article mainly focuses on the delivery of virtual machines (VMs), graphics processing unit (GPUs), cloud storage, transcoding, packaging, 24/7 customer support and billing modules for the services used by end customers. The process starts with customer requirements gathering to initiate the feasibility check for the services desired or required by the clients. Pre-sale solution engineers capture all the customer requirements in the solution design document to review with the engineering and delivery team for the implementation. Based on the solution design document, the solution engineer needs to raise the system’s feasibility for the local loops, cross connects, VMs, GPUs, storage, transcoders and packagers required to meet the end customer expectations on the service delivery. The solution engineer must sign-off on the solution design document agreed with end customer from the engineering and technical team. The program manager and technical team review the solution design document and confirm the order ID requirement in the system for the sales team to share with the order entry team to log the orders for a signed customer order form (COF). The program manager will initiate the service delivery for these order IDs logged in to the system for these services. Once services are ready for customer delivery, a technical team will share the customer portal with the end customer and provide training to the teams at the customer end use the required resources for cloud, compute and storage uses. Along with the services mentioned above, customers can access the usage and billing information in the customer portal. Moreover, the program manager is to share the project closure document, including the information about the services, reference IDs to log the trouble ticket with the supplier’s 24/7 support team and billing start date for customer acceptance.
Quantum Fuzzy Support Vector Machine for Binary Classification

In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning.Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster algorithmic processing of data.However, FSVM and quantum computing are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner.This paper research and propose an efficient and accurate quantum fuzzy support vector machine (QFSVM) algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems.The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations (HHL algorithm) and the least-squares method to solve the quadratic programming problem in the FSVM.The proposed algorithm can determine whether a sample belongs to the positive or negative class while also achieving a good generalization performance.Furthermore, this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers, and achieve accurate classification of handwritten characters.When compared to FSVM, QFSVM's computational complexity decreases exponentially with the number of training samples.
A remote and cost‐optimized voting system using blockchain and smart contract

Abstract Traditional voting procedures are non‐remote, time‐consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters consider this voting system untrustworthy and manipulative, discouraging them from voting, and consequently, an election loses a significant number of participants. Although the inclusion of electronic voting systems (EVS) has increased efficiency; however, it has raised concerns over security, legitimacy, and transparency. To mitigate these problems, blockchain technology has been leveraged and smart contract facilities with a combination of artificial intelligence (AI) to propose a remote voting system that makes the overall voting procedure transparent, semi‐decentralized, and secure. In addition, a system that aids in boosting the number of turnouts in an election through an incentivization policy for the voters have also developed. Through the proposed virtual campaigning feature, the authority can generate a decent amount of revenue, which downsizes the overall cost of an election. To reduce the associated cost of transactions using smart contracts, this system implements a hybrid storage system where only a few cardinal data are stored in the blockchain network.
Dictionary Attacks on Speaker Verification

In this paper, we propose dictionary attacks against speaker verification -a novel attack vector that aims to match a large fraction of speaker population by chance.We introduce a generic formulation of the attack that can be used with various speech representations and threat models.The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population.The resulting master voice successfully matches a nontrivial fraction of people in an unknown population.Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%.By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders.We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems.
ISSA-ELM: A Network Security Situation Prediction Model

To resolve the problems of low prediction accuracy and slow convergence speed of traditional extreme learning machines in network security situation prediction methods, we combine a meta-heuristic search algorithm with neural networks and propose a prediction method based on the improved sparrow search algorithm optimization of an extreme learning machine. Firstly, the initial population is initialized by cat-mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm. Secondly, the Cauchy mutation and tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. The improvement not only guarantees the diversity of the population, but also makes up for the defect that the sparrow search algorithm is easily trapped in the local optima in later iterations, and greatly improves the accuracy of the network security situation prediction.
The Structure of Interdisciplinary Science: Uncovering and Explaining Roles in Citation Graphs

Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capable of recognising complex local structures. However, when working with large, real-world networks, it is difficult to interpret or validate a set of roles identified according to these methods. In this work, motivated by advancements in the field of explainable artificial intelligence (XAI), we propose a new framework for interpreting role assignments on large graphs using small subgraph structures known as graphlets. We demonstrate our methods on a large, multidisciplinary citation network, where we successfully identify a number of important citation patterns which reflect interdisciplinary research.
Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation

Image generation technology is currently one of the popular directions in computer vision research, especially regarding infrared imaging, bearing critical applications in the military field. Existing algorithms for generating infrared images from visible images are usually weak in perceiving the salient regions of images and cannot effectively highlight the ability to generate texture details in infrared images, resulting in less texture details and poorer generated image quality. In this study, a cycle generative adversarial network method based on gradient normalization was proposed to address the current problems of poor infrared image generation, lack of texture detail and unstable models. First, to address the problem of limited feature extraction capability of the UNet generator network that makes the generated IR images blurred and of low quality, the use of the residual network with better feature extraction capability in the generator was employed to make the generated infrared images highly defined. Secondly, in order to solve issues concerning severe lack of detailed information in the generated infrared images, channel attention and spatial attention mechanisms were introduced into the ResNet with the attention mechanism used to weight the generated infrared image features in order to enhance feature perception of the prominent regions of the image, helping to generate image details. Finally, to tackle the problem where the current training models of adversarial generator networks are insufficiently stable, which leads to easy collapse of the model, a gradient normalization module was introduced in the discriminator network to stabilize the model and render it less prone to collapse during the training process. The experimental results on several datasets showed that the proposed method obtained satisfactory data in terms of objective evaluation metrics. Compared with the cycle generative adversarial network method, the proposed method in this work exhibited significant improvement in data validity on multiple datasets.
Federated Learning Algorithms with Heterogeneous Data Distributions: An Empirical Evaluation

Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without centralizing raw data, and has recently received growing interest primarily as a solution to improve privacy guarantees for end users while still distilling knowledge from a population of devices (e.g., edge devices or edge gateways managing a local set of visiting devices). However, the performance of FL algorithms significantly drops in presence of heterogeneous data distributions among the learners in the federation – this setting is very common in real practical applications, with clients holding data related to their habits, preferences, or environment. Several algorithms have been recently proposed to try to deal with data heterogeneity in FL settings under different assumptions and with differentiated pros/cons. In this article, we originally provide a review of the most relevant related solutions in the literature to alleviate the harmfulness of non-identically and independently distributed (IID) data, highlighting the intuition behind these alternative strategies as well as their possible drawbacks. Furthermore, we propose an empirical comparison among a subset of such state-of-the-art solutions under different levels of data hetero-geneity running them in the same operating conditions. We end up identifying the most promising approaches considering both empirical performances and defining characteristics (e.g., assumptions the strategy possibly make). The code is available online at https://github.com/alessiomora/fI_algorithms_non_iid.
Recognition of Real-Time Hand Gestures using Mediapipe Holistic Model and LSTM with MLP Architecture

This study used computer vision to capture real-time hand gestures using the MediaPipe Holistic Model and LSTM with MLP architecture to bridge the communication gap between the hearing majority and the deaf minority. The structure of LSTM architecture was consist of 84 neurons with a 30, 1662 input vector that will be transmitted to the MLP model. It has five layers with corresponding nodes of 84, 56, 28,14, and 7 and a dropout with rate values of 0.4 in the first, second, and third layers, while 0.5 in fourth layer. The proposed method was trained and validated with 1000 epoch which achieved a 100 percent accuracy rate in the recognition of real-time hand gesture. Additionally, the researchers envisioned to incorporate hand gesture detection into a number of real-world applications for human-computer interaction in the coming years.