Scale up the large language models to store vast amounts of knowledge within their parameters incur higher costs and training times. Thus, in this study, we aim to examine the effects of language models enhancing external knowledge and compare the performance of extractive and abstractive generation tasks in building the question-answering system. To ensure consistency in our evaluations, we modified the MS MARCO and MASH-QA datasets by filtering irrelevant support documents and enhancing contextual relevance by mapping the input question to the closest supported documents in our database setup. Finally, we materiality assess the performance in the health domain, our experience presents a promising result not only with information retrieval but also with retrieval augmentation tasks aimed at improving performance for future work.
The Internet of Things (IoT) is a network that connects a vast number of objects, enabling them to communicate and interact with each other with human intervention. The IoT is seeing rapid growth in the field of computing. However, it is important to acknowledge that IoT is very susceptible to many forms of assaults due to the hostile nature of the internet. In order to address this problem, it is necessary to implement practical steps to ensure the security of IoT networks, such as the implementation of network anomaly detection. While it is impossible to completely prevent assaults indefinitely, timely discovery of an attack is essential for effective defense. Because IoT devices have limited storage and processing power, standard high-end security solutions cannot protect them. In addition, IoT devices are now autonomously linked for extended durations. Consequently, it is necessary to create advanced network-based security solutions such as deep neural network solutions. While several researches have focused on the use of neural network methods for attack detection, there has been less emphasis on detecting assaults, especially in IoT networks. The objective of this research is to develop a gradient clipping long shortterm memory network (GC-LSTM) that can efficiently and promptly identify IoT network assaults. The Bot-IoT dataset is employed for evaluating various detection methodologies. The incorporation of additional features resulted in improved results. The GC-LSTM model, as proposed, achieves a remarkable accuracy of 99.98%, 97.67% of detection rate, 87.34% of TNR and 34.67% of FAR.
In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. Especially, the increasing amount of data and IoT devices require more storage space and computational power, which results in higher energy consumption. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This NLP techniques can analyze the text of questions and answers. This involves tokenization, lemmatization, and named entity recognition to identify terms and phrases related to energy consumption. This study examines the concerns of practitioners about energy consumption on Stack Overflow via the utilization of lexicon-based sentiment analysis, a concept in NLP, combined with RNNs. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners’ desire to start conversations in the field of energy is closely linked to the utilization of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users.
This research article presents the design and development of a location system using a low-power wireless data network (LoRaWAN). The device is designed for tracking locations and reporting the status of specific areas. The core principle of the system is that the central processing unit collects data from various sensors, including satellite location sensors, temperature sensors, and relative humidity sensors. This data is then transmitted via LoRaWAN technology to a server, where it is processed and displayed on a map, accessible through a web server. The system provides the geographical location, specifies latitude and longitude, and displays real-time temperature and relative humidity of the nodes.
This work deals with the problem of prison overcrowding in Senegal and the use of electronic bracelets to reduce this overcrowding. Electronic bracelets collect a variety of data such as location, movements, communication data and biometric data. However, data security is a major concern. The aim of the work is to protect this data by using Internet of Things (IoT) and Fog Computing technologies to limit the data collection perimeter, thereby reducing the transfer of massive amounts of data to remote data offices. The architecture implemented aims to collect only the necessary data from remand and correctional office controlled by departmental courts, to comply with data protection laws and to implement security policies to prevent external attacks. This approach aims to guarantee data confidentiality while enabling the use of electronic bracelets to improve the prison situation in Senegal.
This study explores the application and fine-tuning of You Only Look Once (YOLOv8) models for real-time tomato recognition using drone imagery in greenhouse environments, with a focus on practical optimization strategies. Our evaluation of YOLO’s speed, robustness, and adaptability revealed that varying batch sizes and epochs had minimal impact on performance. Notably, the YOLOv8n model matched the performance of the YOLOv8x model while reducing training time by up to 60 times. Further fine-tuning identified the final learning rate (lrf) and dataset annotation quality as critical factors for model performance. Optimizing the lrf and enhancing dataset annotations significantly improved accuracy, underscoring their importance in effective YOLO model deployment. Our results demonstrate YOLOv8’s superiority over YOLOv5, with the optimized YOLOv8n model being ready for deployment in future tomato recognition tasks, paving the way for more efficient agricultural monitoring. This work provides valuable insights into object detection and offers practical guidance for researchers addressing similar challenges.
The maximum likelihood (ML) technique offers high performance for the direction-of-arrival (DOA) estimation but is computational expensive. Conventionally, this approach uses the sample covariance matrix (SCM) of the array output. The computation of SCM relies on the array size and available snapshots which consequently leads to a huge computational burden for large array and/or snapshot samples. If calculation of the SCM can be avoided, the reduction of computation complexity is evidently achievable. To circumvent this issue, a modified ML version is proposed. Exploiting the Nyström method allows us to eliminate the SCM computation. The resulting low-rank matrices can be used to construct an accurate signal subspace without calculating the SCM and its eigenvalue decomposition (EVD). Furthermore, the replacement of the SCM by the signal subspace establishes the modified ML function. Regarding to the computation complexity, the complex multiplications between matrices are compared. Several simulation results such as spatial spectrum, root mean squared error (RMSE) and simulation time are included to confirm the tradeoff between the computational time and DOA estimation performance.
In recent years, we have witnessed an increase in data transfer rates, which requires the development of new communication methods that can handle high-speed data transfer at challenging communication channels. One of the needs is the transmission of communication over serializer deserializer (SerDes) printed circuit boards (PCBs). which are used to transmit data between chips at high speeds of 10 Gbps and above, using the pulse amplitude modulation with four levels (PAM-4) encoding method, which enables lower losses and relatively low cost. Significant signal degradation is present in high-speed communication systems at SerDes, and inter-symbol interference (ISI) distortion dominates. One of the most effective methods to mitigate ISI distortion is the use of equalizers. The goal of this research is to study the performance of communication between two chips (transmitter/receiver) over SerDes PCB at 100 Gbps using the PAM-4 encoding method with an integrated continuous time linear equalizer (CTLE), feedforward equalizer (FFE), and decision feedback equalizer (DFE). The analysis includes a transmitter/receiver with PAM-4 encoding including the PCB channel response. Further, testing the performance of the combination of different equalizers while defining relevant values and parameters (rate, transmission, convergence rate, and equalizer coefficients). Performance are evaluated using signal-to-noise ratio (SNR) and bit error rate (BER) metrics. We investigated the BER performance for five PCBs of different lengths with analog CTLE and digital FFE-DFE equalizers and found that: For a small number of taps in FFE-DFE, a specific CTLE configuration is optimal, but for an optimal combination of FFEDFE, a different configuration of the CTLE is the best for all PCB lengths. We also show that the longer the PCB length, the more coefficients of the FFE-DFE are needed, consequently, more power is required to compensate for a longer PCB length. The components of your paper [title, text, heads, etc.] are already defined in its style sheet. *CAUTION: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)
Many engineering optimization problems may be rephrased in terms of equivalent binary problems, and these can be effectively tackled with evolutionary algorithms. Unfortunately, the fitness function computation may be extremely timeconsuming when dealing with antenna designs. Therefore, it is of paramount importance to speed up the convergency and to improve the performance of this kind of algorithm. The recent introduction and the increasing availability of quantum computing may be very effective in accelerating the design process, even though new approaches and algorithms are needed in order to exploit the specificity of these instruments. In this paper, a new version of a novel quantum crossover operator for binary Genetic Algorithm (bGA) has been introduced and compared with its previous version. They have been successfully tested on different mathematical benchmark functions and on a preliminary thinned array design.
In vehicular ad hoc network (VANETs), vehicles travel at high speed from one place to another that create certain issues like congestion, delay and high power consumption. Reducing the delay and power consumption is the primary task to achieve an effective performance among the vehicles. For that purpose in this article cluster head ($\mathbf{C H}$) selection and the data dissemination with multicast protocol (CHDMV) is concentrated. The subsections of this model are the vehicles clustering process, cluster maintenance and multicast protocol, with the presence of this processes the efficiency of the network is improved even if the vehicle travels at high speed with rapidly changing topology. The parameters which are measured to analyze the outcomes are packet delivery ratio, network throughput, average delay, energy efficiency and routing overhead. From the calculator result it is shown that the CHDMV achieves maximum performance in terms of the efficiency and delivery ratio.
Mainly to improve the efficiency to reduce the power utilization in earlier researches the clustering model is concentrated. It is the formation of clusters in the network which increases the connection degree fundamentally and with the leaders the network stability is also increased. With the presence of a huge number of devices even the earlier clustering models consists of certain drawbacks like data loss and delay. Overcome such drawbacks in this article a hybrid multiagent adaptive algorithm with whale optimization (HMACWO) is developed so that an optimal clustering is introduced which is able to increase the efficiency of the vehicular network. The core modules which are present in this article are an efficient clustering process and whale optimization with an improved clustering model. Experimental demonstration of this model is done in NS3 software and using certain parameters such as the cluster efficiency, CH lifetime, packet delivery ratio, network throughput, and average delay the performance of the network is analyzed. From the results, it is shown that the HMACWO attains a maximum cluster efficiency and $\mathbf{C H}$ lifetime when compared with the earlier methods.
In vehicular ad hoc networks (VANETs) due to high-speed vehicles several challenges occurred like vehicle integrity management, speed management and effective data collection with road traffic event management. To monitor the huge number of vehicles in an efficient way in a large-scale coverage area in recent times unmanned aerial vehicles (UAVs) are developed. This technology provides effective communication among the ground level and the air medium. To achieve highquality communication of the vehicles in this article dynamic mobility-based effective load balancing and QoS-aware network selection (DELQNU),UAVs are constructed. The core modules of this proposed DELQNU are UAV network model, mobility model and QoS model. Using these ideas monitoring a huge number of vehicles even at the densely populated and highspeed technology becomes possible and highly efficient. The implementation analysis are packet delivery ratio, throughput, delay, routing overhead and energy efficiency. From the final outcome it is proven that DELQNU attains maximum results in terms of delivery ratio and throughout when compared with the earlier baseline methodologies.
