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Recently, convolutional neural networks (CNNs) have achieved great success in the field of artificial intelligence, including speech recognition, image recognition, and natural language processing. CNN architecture plays a key role in CNNs' performance. Most previous CNN architectures are hand-crafted, which requires designers to have rich expert domain knowledge. The trial-and-error process consumes a lot of time and computing resources. To solve this problem, researchers proposed the neural architecture search, which searches CNN architecture automatically, to satisfy different requirements. However, the blindness of the search strategy causes a 'loss of experience' in the early stage of the search process, and ultimately affects the results of the later stage. In this paper, we propose a self-adaptive mutation neural architecture search algorithm based on ResNet blocks and DenseNet blocks. The self-adaptive mutation strategy makes the algorithm adaptively adjust the mutation strategies during the evolution process to achieve better exploration. In addition, the whole search process is fully automatic, and users do not need expert knowledge about CNNs architecture design. In this paper, the proposed algorithm is compared with 17 state-of-the-art algorithms, including manually designed CNN and automatic search algorithms on CIFAR10 and CIFAR100. The results indicate that the proposed algorithm outperforms the competitors in terms of classification performance and consumes fewer computing resources. [ABSTRACT FROM AUTHOR]

A directory service keeps track of the location and status of mobile objects in a distributed system. This paper describes our experience implementing two distributed directory protocols as part of the Aleph toolkit, a distributed shared object system implemented in Java. One protocol is a conventional home-based protocol, in which a fixed node keeps track of the object's location and status. The other is a novel Arrow protocol, based on a simple path-reversal algorithm. We were surprised to discover that the Arrow protocol outperformed the home protocol, sometimes substantially, across a range of system sizes. This paper describes a series of experiments testing whether the discrepancy is due to an artifact of the Java run-time system (such as differences in thread management or object serialization costs), or whether it is something inherent in the protocols themselves. In the end, we use insights gained from these experimental results to design a new directory protocol that combines advantages of both. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The problem of resource-saving scheduling in a fog environment is considered in this paper. The objective function of the problem in question presupposes the fog nodes' reliability function maximizing. Therefore, to create a schedule, the following is required: the history of the fog devices' state changes and the search space, which consists of preselected nodes of the cloud-fog broker neighbourhood. The obvious approach to providing the scheduler with this information is to poll the fog nodes, yet this can consume the unacceptable time because of the QoS requirements. In this paper, the system architecture and general methods for efficient resource-saving scheduling is presented. The system is based on distributed ledger element usage, which provides the nodes with the proper awareness about the surroundings. The usage of the distributed ledger allows not only for the creation of the resource-saving schedule but also the reduction of the scheduling problem-solving time, which frees addition time that can be used for the solving of user tasks. The latter also affects the overall resource-saving via reliability. The novelty of this paper consists in the development of the hybrid ledger-based system, which integrates and arranges the elements of various ledger types to solve the newly formulated problem. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

The current article discusses Sierpinski networks, which are fractal networks with certain applications in computer science, physics, and chemistry. These networks are typically used in complicated frameworks, fractals, and recursive assemblages. The results derived in this study are in mathematical and graphical format for particular classes of these networks of two distinct sorts with two invariants, K-Banhatti Sombor (KBSO) and Dharwad, along with their reduced forms. These results can facilitate the formation, scalability, and introduction of novel interconnection network topologies, chemical compounds, and VLSI processor circuits. The mathematical expressions employed in this research offer modeling insights and design guidelines to computer engineers. The derived simulation results demonstrate the optimal ranges for a certain network. The optimization assistant tool deployed in this work provides a single maximized value representing the maximum optimized network. These ranges can be put into service to dynamically establish a network according to the requirements of this paper. [ABSTRACT FROM AUTHOR]

This paper aims to propose innovative actions of advanced technological solutions and consequent necessary institutional decisions to achieve in a reasonable time the definitive confidential data protection and data sovereignty, based on available scientific results. Confidential data protection is a fundamental and strategic issue in next-generation Internet systems to guarantee data sovereignty and the respect of human rights as stated in the foundation of the United Nations. Even if presently many international regulations are decisive steps to guarantee data protection within normative contexts, they are not adequate to face new technologies, such as facial recognition, automatic profiling, position tracking, biometric data, AI applications, and many others in the future, as they are implemented without any awareness by the interested subjects. Therefore, a new approach to data protection is mandatory based on innovative and disruptive technological solutions. A recent OECD report highlighted the need for the so-called Privacy-Enhancing Technologies (PETs) for the effective protection of confidential data, even more urgent for the coexistence of privacy and data sharing in international contexts. A common feature of these technologies is the use of software methodologies that can run on currently available microprocessors and their present immaturity. More effective and definitive protection can be achieved with another methodological approach based on the paradigm of 'Data Usage Control'. This new concept guarantees data protection policy by default and initial design and it requires a new architecture of the data and a new HW&SW architecture of the computers. This contribution has a two-fold objective: first, to clarify why regulations alone and present technological proposals are not adequate for the effective and definitive protection of data and, second, to indicate the new necessary technological approach and the simultaneous institutional actions required to achieve the definitive protection and sovereignty of data in reasonable times, based on the results already available in the scientific literature. [ABSTRACT FROM AUTHOR]

In this paper, we first analyze a real storage system consisting of 72 SSDs utilizing either Hardware RAID (HW-RAID) or Software RAID (SW-RAID), and show that SW-RAID is up to 7× faster. We then reveal that with an increasing number of SSDs, the limited I/O parallelism in SAS controllers and multi-enclosure handshaking overheads cause a significant performance drop, minimizing the total I/O Per Second (IOPS) of a 144-SSD system to less than a single SSD. Second, we disclose the most important architectural parameters that affect a large-scale storage system. Third, we propose a framework that models a large-scale storage system and estimates the system IOPS and system resource usage for various architectures. We verify our framework against a real system and show its high accuracy. Lastly, we analyze a use case of a 240-SSD system and reveal how our framework guides architects in storage system scaling. [ABSTRACT FROM AUTHOR]

GPUs are widely used for diverse applications, particularly data-parallel tasks like machine learning and scientific computing. However, their efficiency is hindered by architectural limitations, inherited from historical RISC processors, in handling memory loads causing high register file contention. We observe that a significant number (around 26%) of values present in the register file are typically used only once, contributing to more than 25% of the total register file bank conflicts, on average. This paper addresses the challenge of single-use memory values in the GPU register file (i.e. data values used only once) which wastes space and increases latency. To this end, we introduce a novel mechanism inspired by CISC architectures. It replaces single-use loads with direct memory operands in arithmetic operations. Our approach improves performance by 20% and reduces energy consumption by 18%, on average, with negligible (<1%) hardware overhead. [ABSTRACT FROM AUTHOR]

Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning. [ABSTRACT FROM AUTHOR]

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices. [ABSTRACT FROM AUTHOR]

Distributed public ledgers, the key to modern cryptocurrencies and the heart of many novel applications, have scalability problems. Ledgers such as the blockchain underlying Bitcoin can process fewer than 10 transactions per second (TPS). The cost of transactions is high, and the time to confirm a transaction is in the minutes. We present the BlockGraph, a scalable distributed public ledger inspired by principles of computer architecture. The BlockGraph exploits the natural locality of transactions to allow publishing independent transactions in parallel. It extends the blockchain with three new transactions to create a unified consistent ledger out of essentially independent blockchains. The most important change is the introduction of the blockstamp transaction, which essentially checkpoints a local blockchain and secures it against attack. The result is a locality-based, simple, secure, sharding protocol which keeps all transactions readable. This paper introduces the BlockGraph protocol, proves that it is consistent and can achieve many thousands of TPS. Using our implementation (a small extension to Bitcoin core) we demonstrate that it, in practice, can significantly improve throughput. [ABSTRACT FROM AUTHOR]

In this study, we obtain a sex prediction algorithm based on CNN in two ways - building a red Convolutional Neural Network (CNN) model from scratch and via transfer learning. We built a model from scratch and compared it with fine-tuned EfficientNetB1. We use these models for gender determination using periocular images and compare the two models depending on the accuracy of the models. The CNN model proposed from scratch yields an accuracy of 94.46% while the fine-tuned EfficientNetB1 yields an accuracy of 97.94%. This paper is one of the first works in determining gender from periocular images in the visible spectrum using a CNN model built from the outset. [ABSTRACT FROM AUTHOR]

In the real-world, it is virtually impossible to have non-causal knowledge of future events. Research in energy harvesting (EH) systems that assumes knowledge of future energy arrivals falls short in terms of practical utility, pointing to the need for online strategies. In addition, the modeling and analysis for EH transmitter and receiver are inherently different. Compared with EH transmitter, EH receiver has received less attention. In this paper, we formulate Markov decision process problems and perform online optimization to maximize the number of bits decoded for an EH receiver with a time-switching architecture, which harvests energy from both a dedicated transmitter and other sources. We consider both infinite and finite horizon scenarios. For the infinite horizon, we provide an upper bound on the average expected reward. Then, we find an optimal policy which can achieve performance arbitrarily close to this bound. For the finite horizon, we first provide a policy obtained from standard backward induction with space quantization. Its performance can be close to optimal online performance as the number of quantization intervals increases, at the cost of relatively high computational complexity. Then, by carefully restricting the state space, we present a computationally efficient policy, which achieves comparatively good performance. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports published within the journal including "Finding Maximal Cliques in Massive Networks," "Designing Fast Architecture-Sensitive Tree Search on Modern Multicore/Many-Core Processors," and "Characterizing Schema Mappings via Data Examples."

The article discusses deadlines for submission of manuscript of periodical on various topics related to architectures, algorithms and applications of software defined vehicular networks (SDVN). Topics discussed include security and privacy in SDVN; data offloading in SDVN and flexible architecture in SDVN.

This paper introduces an event‐based computing paradigm, where workers only perform computation in response to external stimuli (events). This approach is best employed on hardware with many thousands of smaller compute cores with a fast, low‐latency interconnect, as opposed to traditional computers with fewer and faster cores. Event‐based computing is timely because it provides an alternative to traditional big computing, which suffers from immense infrastructural and power costs. This paper presents four case study applications, where an event‐based computing approach finds solutions to orders of magnitude more quickly than the equivalent traditional big compute approach, including problems in computational chemistry and condensed matter physics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought about a profound transformation in the educational landscape in recent months. Educators worldwide have been challenged to tackle academic issues they could never have imagined. Among the most stressful situations faced by students and teachers is implementing online assessments. This paper proposes a system that includes exam prototypes for computer architecture modules at the higher education level. This system generates a wide range of questions and variations on the server side, supported by a set of simulators, resulting in many unique examination proposals. This system streamlines the monitoring process for the teacher, as it eliminates the possibility of two students receiving similar exams and reduces student stress by allowing them to practice with a limitless number of exam samples. This paper also highlights several indicators that demonstrate the advantages of this framework. [ABSTRACT FROM AUTHOR]

Background and Objectives: Big data is a combination of structured, semi-structured and unstructured data collected by organizations that must be stored and used for decision-making. Businesses that deal with the business intelligence system, as well as their data sources, have a major challenge in exploiting Big Data. The current architecture of business intelligence systems is not capable of incorporating and exploiting Big Data. In this paper, an architecture is developed to respond to this challenge. Methods: This paper focuses on the promotion of business intelligence to create an ability to exploit Big Data in business intelligence. In this regard, a new architecture is proposed to integrate both Business Intelligence and Big Data architectures. To evaluate the proposed architecture, we investigated business intelligence architecture and Big Data architecture. Then, we developed a Unified Modeling Language diagram for the proposed architecture. In addition, using the Colored Petri-Net, the proposed architecture is evaluated in a case study. Results: The results show that our architectural system has a higher efficiency in performing all steps, average time, and maximum time compared to business intelligence architecture. Conclusion: The proposed architecture can help companies and organizations gain more value from their data sources and better support managers and organizations in their decision-making. [ABSTRACT FROM AUTHOR]

This paper presents the theoretical keystone for a decentralization of model-based diagnosis by proving the equivalence between decentralized and centralized residual generation. The proof is based on structural analysis and graph-theoretical concepts. The second contribution of this paper is the design of a decentralized fault-focused residual generation scheme advantageously implementing a strategy of fault isolation on request. Algorithms are tested on the attitude determination and control system of a low Earth orbit satellite. [ABSTRACT FROM PUBLISHER]

This large volume of special issue includes all regular papers presented at Embedded Systems Week (ESWEEK) 2018 that brings together three leading conferences (CASES, CODES+ISSS, and EMSOFT) in the embedded systems area. ESWEEK is a unique premier event that covers all aspects of embedded systems design and hardware/software architectures. ESWEEK presents a wide range of topics unveiling state-of-the-art techniques as can be found in this special issue. Following the journal-integrated publication model started last year, the three conferences conducted the journal-like two-stage peer-reviewed process before final decision. Acceptance rates have been about 25.5% for all conferences with a total number of 270 submissions to the journal track. [ABSTRACT FROM AUTHOR]

This special issue is the eleventh in an important tradition in the computer architecture community: IEEE Micro's Top Picks from the Computer Architecture Conferences. This tradition provides a means for sharing a sample of the best papers published in computer architecture in 2013 with the IEEE Micro readership and researchers in the computer architecture community. [ABSTRACT FROM AUTHOR]

The ancient Vedic mathematics is well known for quicker handy multiplications but its recognition as an integrated circuit core against existing hardware multipliers is not established. As optimized hardware implementation of binary multiplier is one of the prominent unsolved problems in computer architecture, this paper proposes efficient Urdhava Tiryakbhyam Vedic multiplier architecture and compares it with the set of hierarchical multiplication algorithms which generate multiplication result in a single clock cycle. Two innovative algorithms are proposed here, one with a compact structure and another for faster execution. Also, its optimized transistor level layout is designed and implemented. To maintain homogeneity for comparison, all the algorithms are programmed on a common HDL language platform and analyzed with the same tool and technology. Final results indicate that the proposed architecture delivers 15.5% less power delay product (PDP) compared to closest competitor algorithm. [ABSTRACT FROM AUTHOR]

This special issue consists of papers ranging from both classical multi-view geometry to recent deep learning methods deep stereo matching networks, GAN and adversarial learning, and multi-task learning for video object segmentation. The idea for a special issue about architectures and theories for computer vision came from the ACCV conference held in 2018. We, the program chairs of ACCV 2018 and guest editors of this special issue, invited the authors of the ACCV'18 award-winning papers to submit extended manuscripts to this special issue. [Extracted from the article]

The Internet of Drones (IoD) has become a hot research topic in academia, industry, and management in current years due to its wide potential applications, such as aerial photography, civilian, and military. This paper presents a comprehensive survey of IoD and its applications, deployments, and integration. We focused in this review on two main sides; IoD Applications include smart cities surveillance, cloud and fog frameworks, unmanned aerial vehicles, wireless sensor networks, networks, mobile computing, and business paradigms; integration of IoD includes privacy protection, security authentication, neural network, blockchain, and optimization based-method. A discussion highlights the hot research topics and problems to help researchers interested in this area in their future works. The keywords that have been used in this paper are Internet of Drones. [ABSTRACT FROM AUTHOR]

Spintronics is one of the emerging fields for the next-generation nanoscale devices offering better memory and processing capabilities with improved performance levels. It demonstrates great potential in the post-Moore era. Ever since the discovery of Giant Magneto-Resistance (GMR) effect in 1988, spintronics has shown a rapid progress. Recent advances has expanded this technology to the entire electronics industry of sensors, memories, oscillators, quantum information processors, computer architecture, brain inspired computing and various other fields. Spintronics is now one of the most researched areas and is on the verge of becoming a mainstream technology. A hard disk drive (HDD) invented by IBM in 1956, now has a global market revenue of approximately $12bn. Other emerging field of application for this technology is magnetic field sensors that showcased a market revenue of~ $19b in 2018. The magnetic memory production at major foundries such as Samsung, Globalfoundries, Western Digital and TSMC marks the adoption of spintronics technology. However, in order to meet the ever-increasing demands of the industry, innovation in terms of modeling, design, materials, processes, circuits and applications are required. This Special Issue of the IEEE Transactions on Electron Devices will feature the most recent developments and the state-of-the-art in the field of spintronic devices, circuits and new architectures for high performance. [ABSTRACT FROM AUTHOR]

Call for Papers House Advertisement [ABSTRACT FROM PUBLISHER]