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This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results. [ABSTRACT FROM AUTHOR]

Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have turned issues such as delay and energy consumption into major challenges. However, fog computing technology has emerged as a promising technology in this field. Methods: In this paper, service/energy management approaches are generally surveyed. Then, we explain our motivation for the systematic literature review procedure (SLR) and how to select the related works. Results: This paper introduces four domains of service management and energy management, including Architecture, Resource Management, Scheduling management, and Service Management. Scheduling management has been used in 38% of the papers. Therefore, they have the highest service management and energy management. Also, Resource Management is the second domain that has been able to attract about 26% of the papers in service management and energy management. Conclusion: About 81% of the fog computing papers simulated their approaches, and the others implemented their schemes using a testbed in the real environment. Furthermore, 30% of the papers presented an architecture or framework for their research, along with their research. In this systematic literature review, papers have been extracted from five valid databases, including IEEE Xplore, Wiley, Science Direct (Elsevier), Springer Link, and Taylor & Francis, from 2013 to 2022. We obtained 1596 papers related to the discussed subject. We filtered them and achieved 47 distinct studies. In the following, we analyze and discuss these studies; then we review the parameters of service quality in the papers, and ultimately, we present the benefits, drawbacks, and innovations of each study. [ABSTRACT FROM AUTHOR]

This report describes The University of Texas Student Cluster Competition team’s effort to reproduce the results of “MemXCT: memory-centric X-ray CT reconstruction with massive parallelization” (Hidayetoğlu et al., 2019). The article details a new memory-centric approach that reconstructs X-ray computed tomography (XCT) from noisy raw data. In our reproduction experiments, we utilized Microsoft Azure’s CycleCloud tool to provision, orchestrate, and manage our computing cluster in the cloud. In particular, we scheduled and benchmarked reconstruction workloads using Azure’s CPU-based HC44rs and GPU-based NC12s v2 virtual machine (VM) types to evaluate the scalability properties of the reconstruction approach and the performance differences between architectures. The HC44rs VMs contained 44 Intel Xeon Platinum cores, while the NC12s v2 VM was equipped with two NVIDIA P100 GPUs. We used a recent version of Intel’s compiler stack with the MKL library for our CPU code along with CUDA 11.1 on GPUs. Overall, our results confirm the findings of the original article, demonstrating similar acceleration on GPUs and scalability properties on CPUs. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108 [ABSTRACT FROM AUTHOR]

A. Kireeva et al. contributed another paper in the field of cellular automata: "Parallel simulation of drift-diffusion-recombination by cellular automata and global random walk algorithm". Parallel computing technologies enable the solution of large-scale numerical simulation and data processing problems in science and industry. The previously proposed contention-aware scheduling algorithm is formally described with this model, modifications of the algorithm are proposed: look-ahead technique, chunked data transfers, data caching, and peer-to-peer data transfers. [Extracted from the article]

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]

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]

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 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]

In this paper, we consider the complicated dynamics of a delay-based photonic reservoir computing system. Since conventional computer architectures are approaching their limit, it is imperative to find new, efficient and fast ways of data processing. Photonic reservoir computing (RC) is a promising way which combines the computational capabilities of recurrent neural networks with high processing speed and energy efficiency of photonics. As the RC system is very promising, we analyze its dynamics so that we can make better use of it. In this paper, we mainly focus on its double Hopf bifurcation. We first analyze the existence of double Hopf bifurcation points. Then we use DDE-BIFTOOL to draw the bifurcation diagrams with respect to two bifurcation parameters, i.e. feedback strength η and delay τ , and give a clear picture of the double Hopf bifurcation points of the system. These figures show stability switches and the existence of double Hopf bifurcation points. Finally, we employ the method of multiple scales to obtain their normal forms, use the method of normal form to unfold and classify their local dynamics. The classification and unfolding of these double Hopf bifurcation points are obtained. Three types of double Hopf bifurcations are found. We verify the results by numerical simulations and find its complicated behavioral dynamics. For example, there exist stable equilibrium, stable periodic and quasi-periodic solutions in distinct regions. The discovered rich dynamical phenomena can help us to choose suitable values of parameters to achieve excellent performance of RC. [ABSTRACT FROM AUTHOR]

Background and Objectives: With the increase in population in the world along with the decrease in natural resources, agricultural land, and the increase of unpredictable environmental conditions, causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture uses the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, and processing delay. The Computational paradigm is a key solution to solve the problems of time delays, security, storage space management, and real-time analysis. Computing paradigms include cloud, fog, and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications. Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges. Results: Computational paradigms have significant advantages. Combining these paradigms in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture. Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges. [ABSTRACT FROM AUTHOR]

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems. [ABSTRACT FROM AUTHOR]

Purpose: Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods: In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results: The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model. Conclusions: The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy. [ABSTRACT FROM AUTHOR]

Recently, deep learning has exhibited outstanding performance in various fields. Even though artificial intelligence achieves excellent performance, the amount of energy required for computations has increased with its development. Hence, the need for a new energy-efficient computer architecture has emerged, which further leads us to the neuromorphic computer. Although neuromorphic computing exhibits several advantages, such as low-power parallelism, it exhibits lower accuracy than deep learning. Therefore, the major challenge is to improve the accuracy while maintaining the neuromorphic computing-specific energy efficiency. In this paper, we propose a novel method of the inference process that considers the probability that after completing the learning process, a neuron can react to multiple target labels. Our proposed method can achieve improved accuracy while maintaining the hardware-friendly, low-power-parallel processing characteristics of a neuromorphic processor. Furthermore, this method converts the spike counts occurring in the learning process into probabilities. The inference process is conducted to implement the interaction between neurons by considering all the spikes that occur. The inferring circuit is expected to show a significant reduction in hardware cost and can afford an algorithm exhibiting a competitive computing performance. [ABSTRACT FROM AUTHOR]

This paper proposes a Software-Defined Delay Tolerant Networking (SDDTN) architecture as a solution to managing large Delay Tolerant Networking (DTN) networks in a scalable manner. This work is motivated by the planned deployments of large DTN networks on the Moon and beyond in deep space. Current space communication involves relatively few nodes and is heavily deterministic and scheduled, which will not be true in the future. It is unclear how these large space DTN networks, consisting of inherently intermittent links, will be able to adapt to dynamically changing network conditions. In addition to the proposed SDDTN architecture, this paper explores data plane programming and the Programming Protocol-Independent Packet Processors (P4) language as a possible method of implementing this SDDTN architecture, enumerates the challenges of this approach, and presents intermediate results. [ABSTRACT FROM AUTHOR]

The tridiagonal solver is an important kernel and is widely supported in mainstream numerical libraries. While parallel algorithms have been studied for many-core architectures, the performance of current algorithms and implementations is still hindered by input size sensitivity and cross-platform portability. In this paper, we propose a novel algorithm WM-pGE for the batched solution of diagonally dominant tridiagonal systems. The algorithm balances the key design objectives, including computation complexity, memory complexity, parallelism, and input size sensitivity, better than existing algorithms. Moreover, an elegant formulation is presented to show the implementation and cross-platform optimization without loss of efficiency and generality, by extracting the platform-dependent works into only four vector operators. The results from our batched tridiagonal experiments show that the proposed algorithm outperforms the prior work PCR-pThomas by 25% and 12% on NVIDIA Tesla V100 in single and double precision, respectively. On Intel KNL, our method achieves a 10% improvement in performance over PCR-pThomas in double precision. [ABSTRACT FROM AUTHOR]

This paper presents a deep architecture, called pyramidal semantic correspondence networks (PSCNet), that estimates locally-varying affine transformation fields across semantically similar images. To deal with large appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where the affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed. Different from the previous methods which directly estimate global or local deformations, our method first starts to estimate the transformation from an entire image and then progressively increases the degree of freedom of the transformation by dividing coarse cell into finer ones. To this end, we propose two spatial pyramid models by dividing an image in a form of quad-tree rectangles or into multiple semantic elements of an object. Additionally, to overcome the limitation of insufficient training data, a novel weakly-supervised training scheme is introduced that generates progressively evolving supervisions through the spatial pyramid models by leveraging a correspondence consistency across image pairs. Extensive experimental results on various benchmarks including TSS, Proposal Flow-WILLOW, Proposal Flow-PASCAL, Caltech-101, and SPair-71k demonstrate that the proposed method outperforms the lastest methods for dense semantic correspondence. [ABSTRACT FROM AUTHOR]

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with $L$ L layers have $L$ L connections—one between each layer and its subsequent layer—our network has $\frac{L(L+1)}{2}$ L (L + 1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, encourage feature reuse and substantially improve parameter efficiency. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less parameters and computation to achieve high performance. [ABSTRACT FROM AUTHOR]

The nine papers in this special section focus on new developments in agile software and reports on applications for its use. A key aspect for the extensive use of agile software is that it supports developers with coping with the growing uncertainty and turbulence in technological and market environments. Feedback and change are at the core of Agile for a dynamic, evolving, and organic, rather than static, predefined, and mechanistic development process advocated by waterfall management. To create timely, high-quality, cost-efficient, and innovative solutions, Agile developers organized in small, colocated, autonomous teams, build and test software in rapid iterative cycles, actively involving users to gather feedback, updating the project scope, and plan "on-the-fly," using face-to-face communication as opposed to documentation. These papers contribute to the state-of-the-art of agile research by offering a rich, up-todate account of the dynamics occurring when expanding Agile into "not-just-software" contexts of the key challenges and perils related to the scaling and of the possible solutions to them. [ABSTRACT FROM AUTHOR]

This paper studies the device activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, in which the active devices transmit signature sequences to multiple base stations (BSs) that are connected to a central unit (CU), and the BSs cooperate across multiple cells to detect the active devices based on the sample covariance matrices at the BSs. This paper demonstrates the importance of exploiting the knowledge of channel large-scale fadings in this cooperative detection setting through a phase transition analysis, which characterizes the length of signature sequences needed for successful device activity detection in the massive MIMO regime. It is shown that when the large-scale fadings are known, the phase transition for the multi-cell scenario is approximately the same as that of a single-cell system. In this case, the length of the signature sequences required for reliable activity detection in the multi-cell system can be made to be independent of the number of cells through cooperation, in contrast to the case where the large-scale fadings are not known. Further, this paper considers the case in which the fronthaul links between the BSs and the CU have capacity constraints and proposes a novel cooperation scheme based on the quantization of preliminary detection results at the BSs and the reconstruction of the sample covariance matrices at the CU. Simulations show that the proposed method significantly outperforms the scheme of directly quantizing the sample covariance matrices. [ABSTRACT FROM AUTHOR]

While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this paper, we introduce Auto-PyTorch, which brings together the best of these two worlds by jointly and robustly optimizing the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several tabular benchmarks by combining multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data. To thoroughly study our assumptions on how to design such an AutoDL system, we additionally introduce a new benchmark on learning curves for DNNs, dubbed LCBench, and run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network. Side operation is introduced for bypassing (non-sampled) channels to guarantee the performance of searched architectures under extremely low sampling rates. In the spatial level, input features are down-sampled to eliminate spatial redundancy and enhance the efficiency of the mixed computation for operation selection. Furthermore, edge normalization is developed to maintain the consistency of edge selection based on channel sampling with the architectural parameters for edges. Theoretical analysis shows that partial channel connection and parameterized side operation are equivalent to regularizing the super-network on the weights and architectural parameters during bilevel optimization. Experimental results demonstrate that the proposed approach achieves higher search speed and training stability than DARTS. PC-DARTS obtains a top-1 error rate of 2.55 percent on CIFAR-10 with 0.07 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.1 percent on ImageNet (under the mobile setting) within 2.8 GPU-days. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

Contact tracing is the cornerstone of epidemic control of infectious diseases, especially in the era of COVID-19. This labor-intensive task calls for the use of digital technology to help identify individuals who have potentially been exposed to the infection to deliver necessary interventions and treatment. Mobile applications based on different technologies and system architectures have been developed and widely used in concert with public health policies and regulations. Three main types of digital contact-tracing technologies, namely Bluetooth low energy (BLE), location tracking, and check-in, were adopted in contact-tracing apps and implemented with a centralized or decentralized system architecture to protect privacy and facilitate spatiotemporal co-occurrence matching. Passive data collection methods, including BLE and location tracking, could be contrasted with check-in-based apps, which require users' effort to record their whereabouts. Persuasive computing with a centralized system for collecting contact-tracing data from users might require additional legislation for authorization and privacy protection. Technology options should therefore be taken into account when designing enforceable public health policies on the use of contact-tracing apps. At the same time, public health policies also inform the information system's design. This paper aims to delineate and contrast current technologies and system architectures used for developing contact-tracing apps and examine the intertwined relationship between the design and implementation of public health policies and the design of digital contact-tracing systems. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) refers to the implementation of a damage detection strategy for structures. Fault occurrence in these structural systems during the operation is inevitable. Efficient, fast, and precise health monitoring methods are required to proactively perform the necessary repairs and maintenance on time before it is too late. The current structural health monitoring methods involve physically attached sensors or non-contact vision-based vibration measurements. However, these methods have significant drawbacks due to the low spatial resolution, weight influence on the lightweight structure, and time/labor consumption. Recently, computer-vison-based deep learning methods like convolutional neural network (CNN) and fully convolutional neural network (FCN) have been applied for defect detection and localization, which address the aforementioned problems and obtain high accuracy. This paper proposes a novel hybrid deep learning architecture comprising CNN and temporal convolutional networks (CNN-TCN) for the computer vision-based defect detection task. Various beam samples, consisting of five different materials and various structural defects, were used to evaluate the proposed deep learning algorithms' performance. The proposed deep learning methods treat each pixel of the video frame like a sensor to extract valuable features for defect detection. Through empirical results, we demonstrate that this 'pixel-sensor' approach is more efficient and accurate and can achieve a better defect detection performance on different beam samples compared with the current state-of-the-art approaches, including CNN-long short-term memory (LSTM), CNN-bidirectional long short-term memory (BiLSTM), multi-scale CNN-LSTM, and CNN-gated recurrent unit(GRU) methods. [ABSTRACT FROM AUTHOR]

The Cache plays an important role in computer architecture by reducing the access time of the processor and improving its performance. The hardware design of the Cache is complex and it is challenging to verify its functions, so the traditional Verilog-based verification method is no longer applicable. This paper proposes a comprehensive and efficient verification testbench based on the SystemVerilog language and universal verification methodology (UVM) for an instruction Cache (I-Cache) controller. Corresponding testcases are designed for each feature of the I-Cache controller and automatically executed using a python script on an electronic design automation (EDA) tool. After simulating a large number of testcases, the statistics reveal that the module's code coverage is 99.13%. Additionally, both the function coverage and the assertion coverage of the module reach 100%. Our results demonstrate that these coverage metrics meet the requirements and ensure the thoroughness of function verification. Furthermore, the established verification testbench exhibits excellent scalability and reusability, making it easily applicable to higher-level verification scenarios. [ABSTRACT FROM AUTHOR]

Based on branch prediction, value prediction has emerged as a solution for problems caused by true data dependencies in pipelined processors. While branch predictors have binary outcomes (taken/not taken), value predictors face a challenging task as their outcomes can take any value. Because of that, coverage is reduced to enhance high accuracy and minimise costly recovery from misprediction. This paper evaluates value prediction, focusing on instruction execution with imprecisely predicted operands whose result can still be correct. Two analytical models are introduced to represent instruction execution with value prediction. One model focuses on correctly predicted operands, while the other allows for imprecisely predicted operands as long as the instruction results remain correct. A trace-driven simulator was developed for simulation purposes, implementing well-known predictors and some of the predictors presented at the latest Championship Value Prediction. The gem5 simulator was upgraded to generate program traces of SPEC and EEMBC benchmarks that were used in simulations. Based on the simulation result, proposed analytical models were compared to reveal the conditions under which a model with imprecisely predicted operands, but still correct results, achieved better execution time than a model with correctly predicted operands. Analysis revealed that the accuracy of the correct instruction result based on the predicted operand, even when the predicted operand is imprecise, is higher than the accuracy of the correctly predicted operand. The accuracy improvement ranges from 0.8% to 44%, depending on the specific predictor used. [ABSTRACT FROM AUTHOR]

This paper presents an educational setting that attempts to enhance students' understanding and facilitate students' linking-inferencing skills. The proposed setting is structured in three stages. The first stage intends to explore students' prior knowledge. The second stage aims to help students tackle their difficulties and misconceptions and deepen their understanding of the topics under study. This is attempted through individual student engagement in suitably-designed activities and relative feedback. As recorded in previous research, students' difficulties feedback on the material development. The third stage of the educational setting exploits social interaction to help students reorganize their knowledge of the concepts under study. The web-based application of the proposed educational setting indicated improvement in first-year Computer Science (CS) students' understanding of fundamental Computer Architecture concepts and progress in students' linking-inference skills. These results encourage integration in the instructional process of interventions designed according to the proposed setting in order to support and enhance students' understanding of troublesome concepts and their interrelations. [ABSTRACT FROM AUTHOR]

Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs —approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power. [ABSTRACT FROM AUTHOR]

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design and train the shared-weight backbone network, a.k.a. the super-net. Since heuristics substantially vary across different methods and have not been carefully studied, it is unclear to which extent they impact super-net training and hence the weight-sharing NAS algorithms. In this paper, we disentangle super-net training from the search algorithm, isolate 14 frequently-used training heuristics, and evaluate them over three benchmark search spaces. Our analysis uncovers that several commonly-used heuristics negatively impact the correlation between super-net and stand-alone performance, whereas simple, but often overlooked factors, such as proper hyper-parameter settings, are key to achieve strong performance. Equipped with this knowledge, we show that simple random search achieves competitive performance to complex state-of-the-art NAS algorithms when the super-net is properly trained. [ABSTRACT FROM AUTHOR]

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it allows us to exploit the correlation between people flow and optical flow to further improve the results. We also show that leveraging people conservation constraints in both a spatial and temporal manner makes it possible to train a deep crowd counting model in an active learning setting with much fewer annotations. This significantly reduces the annotation cost while still leading to similar performance to the full supervision case. [ABSTRACT FROM AUTHOR]

In prior parts of this series, I analyzed the 1) number of all issued patents and issued computer architecture patents; 2) the prosecution time and effective patent term; 3) the number of claims, breakdown of independent and dependent claims, and effect that excess claim fees had on the numbers of total and independent claims; and 4) the type of claims (apparatus, method, or Beauregard), and effect that the Supreme Court's decision in Alice v. CLS Bank had on the number of independent and dependent method claims, for patents that were issued to 18 leading computer architecture companies and filed between 1996 and 2020. This article examines the 1) number of "backward" citations to previously issued/published U.S. patents and U.S. patent publications, foreign patents, and Other References and 2) the number of "forward" citations to a company's patent by another U.S. patent or U.S. patent publication. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity. [ABSTRACT FROM AUTHOR]

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations. [ABSTRACT FROM AUTHOR]

The separation of the data capture and analysis in modern vision systems has led to a massive amount of data transfer between the end devices and cloud computers, resulting in long latency, slow response, and high power consumption. Efficient hardware architectures are under focused development to enable Artificial Intelligence (AI) at the resource-limited sensing devices. One of the most promising solutions is to enable Processing-in-Pixel (PIP) scheme. However, the conventional schemes suffer from the low fill-factor issue. This paper proposes a PIP based Complementary Metal-Oxide-Semiconductor (CMOS) sensor architecture, which allows convolution operation before the column readout circuit to significantly reduce the overall power consumption while improving the resource utilization of the succeeding deep learning accelerator. The simulation results show that the proposed architecture could support the computing efficiency up to 3.37 TOPS/W at the 8-bit weight configuration, which is four times as high as the conventional schemes after normalization. The transistors required for each pixel are only 3.5T, significantly improving the fill-factor. [ABSTRACT FROM AUTHOR]

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework's performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers. [ABSTRACT FROM AUTHOR]

This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52\times $ hardware-efficient and $2.8\times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent detection performance in the real-world scenario. [ABSTRACT FROM AUTHOR]