Implementation Of Architecture Research Articles

Investigation and implementation of digital software architecture based on internet of things

Application systems are becoming increasingly complex, and software system architecture plays an increasingly important role in the software development process. In view of the problems of the traditional software architecture, such as slow data processing speed, poor stability of the architecture and low accuracy of data classification, this paper studied the digital software architecture based on the Internet of Things (IoT) technology, and conducted data pre-processing on the data collected by sensors, mainly including data reading, data cleaning, data transformation and data reduction. Through experiments, it could be found that the accuracy of digital software architecture using data preprocessing for data classification was more than 93.52%, and the average accuracy of 10 experiments was 94.55%. At the same time, the digital software architecture based on the IoT was tested, and it was found that both the execution time and the original memory were better than other digital software architecture. The overall performance of digital software architecture based on the IoT was better.

Real-time Arabic Video Captioning Using CNN and Transformer Networks Based on Parallel Implementation

Video captioning techniques have practical applications in fields like video surveillance and robotic vision, particularly in real-time scenarios. However, most of the current approaches still exhibit certain limitations when applied to live video, and research has predominantly focused on English language captioning. In this paper, we introduced a novel approach for live real-time Arabic video captioning using deep neural networks with a parallel architecture implementation. The proposed model primarily relied on the encoder-decoder architecture trained end-to-end on Arabic text. Video Swin Transformer and deep convolutional network are employed for video understanding, while the standard Transformer architecture is utilized for both video feature encoding and caption decoding. Results from experiments conducted on the translated MSVD and MSR-VTT datasets demonstrate that utilizing an end-to-end Arabic model yielded better performance than methods involving the translation of generated English captions to Arabic. Our approach demonstrates notable advancements over compared methods, yielding a CIDEr score of 78.3 and 36.3 for the MSVD and MSRVTT datasets, respectively. In the context of inference speed, our model achieved a latency of approximately 95 ms using an RTX 3090 GPU for a temporal video segment with 16 frames captured online from a camera device.

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Design and Implementation of an NoC-Based Convolution Architecture With GEMM and Systolic Arrays

Design and Implementation of an NoC-Based Convolution Architecture With GEMM and Systolic Arrays

Practical Implementation of a Blockchain-Enabled SDN for Large-Scale Infrastructure Networks

The network function virtualization (NFV) feature lies at the core of modern networking, and it allows on-demand real-time integration of new network functions, which is a great benefit for large-scale infrastructure networks. In contrast to the functional benefits, NFV introduces software complexity and computational overhead through additional abstraction layers. The current article addresses the function validation problem in large-scale infrastructure networks of Internet Service Providers (ISPs) and proposes the utilization of blockchain as a validation technology, as opposed to implementing a custom validation solution. The current work showcases a practical architecture implementation to address the service validation in service provider large-scale networks. The POX-based solution to control software-defined networks (SDN) for NFV is extended to offer additional blockchain capabilities. Thus, a blockchain node is integrated and executed in the POX SDN controller. Transaction experiments are performed between two endpoints located in remote locations on the Internet, and the detailed results are presented to validate the utilization of the blockchain technology used on SDNs’ control plane.

Hate speech detection: A comprehensive review of recent works

AbstractThere has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset.

A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents.

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).

Comprehensive analysis of energy efficiency and performance of ARM and RISC-V SoCs

Over the past few years, ARM has been the dominant player in embedded systems and System-on-Chips (SoCs). With the emergence of hardware platforms based on the RISC-V architecture, a practical comparison focusing on their energy efficiency and performance is needed. In this study, our goal is to comprehensively evaluate the energy efficiency and performance of ARM and RISC-V SoCs in three different systems. We will conduct benchmark tests to measure power consumption and overall system performance. The results of our study are valuable to developers and researchers looking for the most appropriate hardware platform for energy-efficient computing applications. Our observations suggest that RISC-V Instruction Set Architecture (ISA) implementations may demonstrate lower average power consumption than ARM, but this does not automatically imply a superior performance per watt ratio for RISC-V. The primary focus of the study is to evaluate and compare these ISA implementations, aiming to identify potential areas for enhancing their energy efficiency. Furthermore, to ensure the practical applicability of our findings, we will use the Computational Fluid Dynamics software OpenFOAM. This step serves to validate the relevance of our results in real-world scenarios. It allows us to fine-tune execution parameters based on the insights gained from our initial study. By doing so, we aim not only to provide meaningful conclusions but also to investigate the transferability of our results to practical applications. Our analysis will also scrutinize the capabilities of these SoCs when handling nonsynthetic software workloads, thereby broadening the scope of our evaluation.

A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model.

The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.

Design and Architectural Implementation of Consortium Blockchain Based Framework for Open Banking Customer Consent and Data Handling

Design and Architectural Implementation of Consortium Blockchain Based Framework for Open Banking Customer Consent and Data Handling

Design and implementation of anchor coprocessor architecture for wireless node localization applications

Design and implementation of anchor coprocessor architecture for wireless node localization applications

Penerapan Enterprise Architecture untuk Integrasi Sistem Informasi Pendidikan: Studi Literatur

Education is one of the fields that plays a significant role in the development of individuals and society. In some educational institutions, there are still issues, such as challenges in managing student data, lack of educator performance, and others. Therefore, the implementation of information technology has a profound impact on improving the management of university and school systems. Each educational institution should have standards to enhance business agreements and strategies. Enterprise Architecture can be utilized by schools and universities to facilitate the student admission process through the implementation of various systems. The results of the journal evaluation show that the majority of research studies use TOGAF (The Open Group Architecture Framework) as a framework to generate an Enterprise Architecture blueprint that can be utilized for integrating information systems, thus demonstrating that the implementation of enterprise architecture can influence the improvement of business process efficiency in the education sector. Additionally, there are differences in frameworks among previous studies, such as the utilization of Enterprise Architecture Planning and Federal Enterprise Architecture Framework. Each research journal also presents certain strengths and weaknesses, such as the ineffectiveness of including required cost estimates, among others.

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.

High-rate, high-resolution single photon X-ray imaging: Medipix4, a large 4-side buttable pixel readout chip with high granularity and spectroscopic capabilities

The Medipix4 chip is the latest member in the Medipix/Timepix family of hybrid pixel detector chips aimed at high-rate spectroscopic X-ray imaging using high-Z materials. It can be tiled on all 4 sides making it ideal for constructing large-area detectors with minimal dead area. The chip is designed to read out a sensor of 320 × 320 pixels with dimensions of 75 μm× 75 μm or 160 × 160 pixels with dimensions of 150 μm× 150 μm. The readout architecture features energy binning of the single photons, which includes charge sharing correction for hits with energy spread over adjacent pixels. This paper presents the specifications, architecture, and circuit implementation of the chip, along with the first electrical measurements.

The AGORA High-resolution Galaxy Simulations Comparison Project. VI. Similarities and Differences in the Circumgalactic Medium

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

Predictive assessment of learners through initial interactions with encoding techniques in deep learning

<p>Recent academic research has prominently focused on predicting student achievements in online learning. However, ongoing challenges persist for teachers and researchers, primarily revolving around the selection of relevant features. To address this issue, the study aims to predict the correlation between the use of a Virtual Learning Environment (VLE) and students’ performance based solely on their initial interactions with the platform during the initial segment of the course module within the annual study sequence of the academic year 2014–2015. The assessment employed a specialized data model that integrates the chronological order of a learner’s interactions with the educational materials provided on the online learning platform throughout the sequence employing data from the Open University Learning Analytics Dataset (OULAD), which included 32,593 undergraduates in all courses. Innovative methods, such as the VLE, and Long Short-Term Memory (LSTM) neural network rooted in deep learning, were applied to ensure the prediction of student achievement. Preliminary findings demonstrated a 60% accuracy of the model using only learner interaction data within the first third of the course duration and employing various variable encoding techniques. The successful integration of the esteemed OULAD, combined with the implementation of an LSTM neural network architecture in our data model, proved to be a highly effective strategy. This approach yielded valuable perceptions facilitating the identification of crucial data points essential for predicting student success and contributing to informed decision-making in formulating strategies to assess and support student performance effectively.</p>

Multimedia Identification and Analysis Algorithm of Piano Performance Music Based on Deep Learning

In the biometric biomedical applications, the study presents a pioneering research endeavor focused on the development of a multimedia identification and analysis algorithm tailored for piano performance music. With the power of deep learning techniques, this algorithm has been designed to address the intricate challenges posed by the convergence of musical data analysis and biometrics. The core objectives encompass the extraction, recognition, and interpretation of biometric information from piano performances, exploring the intricate patterns, nuances, and individualistic characteristics inherent to musicians. The study focuses on the development of advanced biometric authentication and identification systems capable of capturing and analyzing a user's unique behavioral patterns across diverse modalities. These biometric modalities offer the potential for highly secure and non-intrusive user identification. Hence, this paper developed an architecture of Marker Controlled Point (MCP) Estimation for the computation of the gesture in biometric-based applications. This architecture utilizes markers or reference points to precisely track and analyze user gestures, resulting in accurate and reliable biometric data. The research details the architecture's implementation, integrating advanced deep-learning techniques for feature extraction, pattern recognition, and authentication. This system finds versatile applications in various domains, from healthcare and cybersecurity to finance, where secure and user-friendly authentication is paramount. Experimental results underscore the system's effectiveness and robustness, demonstrating its potential for enhancing biometric authentication. The proposed model represents a significant stride in biometric technology, offering secure, non-intrusive user identification through the synergy of behavior biometrics and emerging modalities.

Retracted: A Smart Campus Implementation Architecture Based on Blockchain Technology

Retracted: A Smart Campus Implementation Architecture Based on Blockchain Technology

Selection of a Neural Network Architecture for Implementation of Cognitive Functions of Network Software for Control of a Group of Interacting Small Space Vehicles

The purpose of the research is to substantiate and select the architecture of a neural network for the possibility of implementing the cognitive functions of network software for controlling a grouping of interacting small spacecraft.Methods are based on the concepts of AI theory for managing the grouping of small spacecraft - the use of adaptive methods and tools that allow making decisions, similar to the mechanisms of human thinking. With regard to space communication systems with a heterogeneous structure, AI methods and technologies are aimed at the processes of predicting the state in communication channels between network nodes and automatic reconfiguration of the network of devices based on the learning processes of a neural network (NN).Results. In the learning and forecasting mode, it is necessary to use time series of parameters and coordinates of specific pairs of small spacecraft with non-zero line of sight. Especially for time series analysis, recurrent neural networks (RNN) are used, in particular, LSTM. The idea of RNN operation is to use as input data for the current forecast not only the state vectors of the SVs and their coordinates, but also the previous value of the communication quality, actual or predictive. The paper shows that the onboard computing power of a separate MSC does not allow performing forecasting and training on board. Therefore, a dedicated ground segment of forecasting and monitoring is required, which will collect a posteriori information, periodically train the cognitive model, use it to predict the quality of communication, and transmit the results to the network nodes to build data transmission routes.Conclusion. The analysis of modern solutions and the choice of neural network architecture for the implementation of the cognitive functions of the network software for controlling the grouping of interacting small spacecraft showed that the neural networks of the Transformer architecture, which are based on the mechanism of internal attention, most fully meet the requirements of the project. The Transformer architecture allows using the entirety of a priori data, has a high learning and forecasting speed.

Analisis Penerapan Konsep Desain Arsitektur Tropis pada Hotel Alila Seminyak, Bali

Title: Implementation’s Analysis of Tropical Architectural Design Concepts at Alila Seminyak Hotel, Bali Tropical architecture is born from efforts to adjust to the climate in the tropics. Tropical architectural design always shows the form of adaptation of buildings to the environment. Traditional Balinese houses that function as community residential units are one of the tropical architectural typologies with the main advantage of thermal comfort, especially on air circulation flow that flows well throughout the building. However, when reviewing further commercial buildings in Bali, especially star hotels, the implementation of tropical architecture is a theme that requires deep design thinking. Adjustment to Balinese regional regulations on buildings is very binding; limited sites and demands for comfort and suitability of building functions are challenging for architects to design an accommodation building with a tropical architectural concept. Alila Seminyak Hotel is one of the five-star hotels outside the ITDC Nusa Dua area, which tries to form a tropical architectural identity as the main attraction. This qualitative descriptive research takes the case study of Alila Seminyak Hotel. Assessment analysis was carried out on the physical form of the hotel building, which was then appropriated and compared with tropical architectural theory. The results of this study show that the implementation of tropical architecture in the Alila Seminyak hotel building can be classified into several main elements, namely, first is the form of laying the building period on-site, second is the orientation pattern of the building, and third is The physical design of the building emphasizes, that this building have three main concept designs, it is namely insulation, shading, and cross ventilation.