Survey on virtual coaching for older adults

Virtual coaching has emerged as a promising solution to extend independent living for older adults. A virtual coach system is an always-attentive personalized system that continuously monitors user's activity and surroundings and delivers interventions - that is, intentional messages - in the appropriate moment. This article presents a survey of different approaches in virtual coaching for older adults, from the less technically supported tools to the latest developments and future avenues for research. It focuses on the technical aspects, especially on software architectures, user interaction and coaching personalization. Nevertheless, some aspects from the fields of personality/social psychology are also presented in the context of coaching strategies. Coaching is considered holistically, including matters such as physical and cognitive training, nutrition, social interaction and mood.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 769830

5G Network Management, Orchestration, and Architecture: A Practical Study of the MonB5G project

The cellular device explosion in the past few decades has created many different opportunities for development for future generations. The 5G network offers a greater speed in the transmissions, a lower latency, and therefore greater capacity for remote execution. The benefits of AI for 5G network slicing orchestration and management will be discussed in this survey paper. We will study these topics in light of the EU-funded MonB5G project that works towards providing zero-touch management and orchestration in the support of network slicing at massive scales for 5G LTE and beyond

Low-cost, low-power FPGA implementation of ED25519 and CURVE25519 point multiplication

Twisted Edwards curves have been at the center of attention since their introduction by Bernstein et al. in 2007. The curve ED25519, used for Edwards-curve Digital Signature Algorithm (EdDSA), provides faster digital signatures than existing schemes without sacrificing security. The CURVE25519 is a Montgomery curve that is closely related to ED25519. It provides a simple, constant time, and fast point multiplication, which is used by the key exchange protocol X25519. Software implementations of EdDSA and X25519 are used in many web-based PC and Mobile applications. In this paper, we introduce a low-power, low-area FPGA implementation of the ED25519 and CURVE25519 scalar multiplication that is particularly relevant for Internet of Things (IoT) applications. The efficiency of the arithmetic modulo the prime number 2 255 − 19, in particular the modular reduction and modular multiplication, are key to the efficiency of both EdDSA and X25519. To reduce the complexity of the hardware implementation, we propose a high-radix interleaved modular multiplication algorithm. One benefit of this architecture is to avoid the use of large-integer multipliers relying on FPGA DSP modules

Genetic Operators Applied to Symmetric Cryptography

In this article, a symmetric-key cryptographic algorithm for text is proposed, which applies Genetic Algorithms philosophy, entropy and modular arithmetic. An experimental methodology is used over a deterministic system, which redistributes and modifies the parameters and phases of the genetic algorithm that directly affect its behavior, carrying out a constant evaluation using the fitness function, in order to optimize the results. An independent encryption is established for the auxiliary key, using a main key, in charge of increasing security. The tests are performed over different text sizes, manipulating the parameters and criteria proposed to obtain their appropriate values. Finally, a comparison is presented against the following cryptographic algorithms DES (Data Encryption Standard), RSA (Rivest, Shamir and Adleman) and AES (Advanced Encryption Standard), exposing factors such as processing time, scalability, key size, etc. It is shown that the proposed algorithm has a better performance

The Systematic Discovery of Services in Early Stages of Agile Developments: A Systematic Literature Review

In recent years, agile methodologies have been consolidated and extended in organizations that develop software in Web environments. For this reason, the development methodology of these organizations will not only be related to Services, but also to the Web Engineering paradigm. These organizations are heading for incorporating software development methodologies whose paradigm can allow integration, naturally and in the earlier stages of Web applications develop with the services of the organization that described and published in the Services Portfolio. The aim of this study will be to analyze the current state of the art in the process of discovering services in early stages of agile software development with focus on those identified requirements that could be covered with the services included in the Service Portfolio. We have identified 20 relevant papers through conducting a double systematic literature review (SLR). It is concluded that no study has been found that can solve the entire process of discovering candidate services within an organization that cover the requirements of a new application developed with agile methodologies. At the same time, guidelines have been found to formalize the solution to this problem and fill in that gap of knowledge by proposing in a single process, the formalization of a requirement based on agile techniques, which can be managed against a Services PortfolioMinisterio de Economía y Competitividad TIN2016-76956-C3-2-R (POLOLAS

Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning

Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset

Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

UAV Based 5G Network: A Practical Survey Study

Unmanned aerial vehicles (UAVs) are anticipated to significantly contribute to the development of new wireless networks that could handle high-speed transmissions and enable wireless broadcasts. When compared to communications that rely on permanent infrastructure, UAVs offer a number of advantages, including flexible deployment, dependable line-of-sight (LoS) connection links, and more design degrees of freedom because of controlled mobility. Unmanned aerial vehicles (UAVs) combined with 5G networks and Internet of Things (IoT) components have the potential to completely transform a variety of industries. UAVs may transfer massive volumes of data in real-time by utilizing the low latency and high-speed abilities of 5G networks, opening up a variety of applications like remote sensing, precision farming, and disaster response. This study of UAV communication with regard to 5G/B5G WLANs is presented in this research. The three UAV-assisted MEC network scenarios also include the specifics for the allocation of resources and optimization. We also concentrate on the case where a UAV does task computation in addition to serving as a MEC server to examine wind farm turbines. This paper covers the key implementation difficulties of UAV-assisted MEC, such as optimum UAV deployment, wind models, and coupled trajectory-computation performance optimization, in order to promote widespread implementations of UAV-assisted MEC in practice. The primary problem for 5G and beyond 5G (B5G) is delivering broadband access to various device kinds. Prior to discussing associated research issues faced by the developing integrated network design, we first provide a brief overview of the background information as well as the networks that integrate space, aviation, and land

Design and Performance Analysis of MCPC and P4 Waveforms for OFDM based Radar System

This study aimed to investigate the performance of Multicarrier Phase Coding (MCPC) and P4-encoded waveforms. Researchers explored the unique properties of these signals, focusing on aspects like phase distribution, autocorrelation, power spectral density for P4 encoding, and aperiodic autocorrelation and ambiguity function for MCPC signals. The findings identified optimal MCPC sequences with reduced peak-to-mean envelope power ratios (PMEPR), improving signal performance. Complementary codes based on permutation were also generated and analyzed for MCPC sequences. The study utilized an improved genetic algorithm to develop new and improved waveforms, underscoring the importance of techniques like optimal sequence permutation, complementary sequences, and classical window frequency weighting in enhancing signal performance

A deep CNN architecture with novel pooling layer applied to two Sudanese Arabic sentiment data sets

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5)