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This paper examines and reviews the fundamental challenges that academicians face when using the event study methodology to assess the long-term consequences of financial events on the economy and to describe market reactions. Numerous studies have demonstrated that businesses can experience abnormal returns from 1 to 5 years after major financial events. Also, this paper investigates the long-run price performance of initial public offerings (IPOs) in Amman Stock Exchange (ASE). The sample period expands from 2018 to 2022. Various findings are obtained by employing several analytical methods. First, long-run price performance of IPOs is negative, and a strong evidence shows that the long-run performance is sensitive to the benchmark employed. To assess the long-term performance of IPOs, I used both cumulative abnormal returns (CAR) and buy-and-hold abnormal returns (BHAR) as aggregated models. I explained the methodology which is adopted in this study in detail for the event–time approach. However, I used the crucial values for the skewness-adjusted t-statistic to infer statistical tests. Even though BHAR provided weaker results, all methods indicated negative long-run abnormal returns for IPOs. Yet this performance varied when comparing the performance utilizing ASEI, Fama–French three-factor (FF3F), and matching firm (MF) as benchmarks.

As extended reality (XR) technologies become more accessible and technologically capable, exploring the potential of these rich mediums for technologically mediated communication is intriguing. In the commercial sector, technology corporations promise consumers that these XR technologies, augmented reality (AR), virtual reality (VR), and mixed reality (MR) will shape the future of social interaction. They claim that these technologies will allow users to get closer to their loved ones and connect on deeper levels. The effort of these corporations to reassure us that XR technologies will strengthen human connections has highlighted the importance of the concept of social presence. Social presence is the perception that the user is, in fact, together with another person, though they are interacting through a technological medium in disparate locations (Kreijns et al., 2022). Social presence research has focused on various technological mediums, like television and text messaging, and contexts, such as distance education and business communication. However, as XR technologies have become more readily available (Ziker et al., 2021), there is a need for more research into the affordances of these new mediums. Many companies claim that their XR products offer rich communicative benefits, but many of their claims are difficult to assess. Therefore, this paper proposes a system for evaluating the capacity of XR technologies to contribute to social presence. First, this paper will delve into the research on social presence and the role of technology in its mediation. Then, the author presents a baseline scenario to help gauge the degree to which technologies can promote social presence. Finally, the author will provide a basic conceptual framework for examining XR technologies and their capacity to facilitate social presence. The paper concludes by discussing future directions for social presence research with XR technologies.

The term Text Mining, which is given to the set of techniques used for the extraction, cleaning and processing of the information in texts, has become useful to provide valuable information to other algorithms and widely used with statistical and machine learning methods. By enabling the extraction of useful insights from textual data, Text Mining has become a potent tool in decision-making and knowledge discovery across many areas, including health care, government, education and industry. R is a mature open-source programming environment that has overstepped its initial scope of application for statistical computing and graphics to be used in pretty all the Data Science knowledge Area Groups. The objective of this paper is to present review and benchmarking analysis of packages for text mining techniques with R in computational systems. The paper reviews thirteen different packages comparing them on their execution time and memory used, for which new tests have been specifically designed. The results of this approach have been intended to be used over the most common tasks carried out when analyzing texts, and comparisons included allow R users to know which packages are best for each task and to improve their performance. Text mining package (tm) stands out particularly in Tokenization and Stemming techniques, while fastTextR is the best choice for Topic Modeling and Normalization. Also in the case of the Term Frequency-Inverse Document Frequency (TF-IDF) technique, the textir package is a clear choice. The other packages will depend on whether the technique is applied to a document-term matrix (DTM) or to plain text. In addition, there are packages that perform better in runtime than in memory usage and vice versa, making the choice more difficult. Packages such as udpipe can achieve better results working in parallel. Future works will include the same analysis for parallel computing, hybrid approaches, and novel algorithms.

Small object detection (SOD) is significant for many real-world applications, including criminal investigation, autonomous driving and remote sensing images. SOD has been one of the most challenging tasks in computer vision due to its low resolution and noise representation. With the development of deep learning, it has been introduced to boost the performance of SOD. In this paper, focusing on the difficulties of SOD, we analyze the deep learning-based SOD research papers from four perspectives, including boosting the resolution of input features, scale-aware training, incorporating contextual information and data augmentation. We also review the literature on crucial SOD tasks, including small face detection, small pedestrian detection and aerial image object detection. In addition, we conduct a thorough performance evaluation of generic SOD algorithms and methods for crucial SOD tasks on four well-known small object datasets. Our experimental results show that network configuring to boost the resolution of input features can enable significant performance gains on WIDER FACE and Tiny Person. Finally, several potential directions for future research in the area of SOD are provided.

High-fidelity nuclear data libraries and neutronics simulation tools are essential for the development of fast reactors. The IAEA coordinated research project on “Neutronics Benchmark of CEFR Start-Up Tests” offers valuable data for the qualification of nuclear data libraries and neutronics codes. This paper focuses on the verification and validation of the CEFR start-up modelling using OpenMC Monte-Carlo code against the experimental measurements. The OpenMC simulation results agree well with the measurements in criticality, control rod worth, sodium void reactivity, temperature reactivity, subassembly swap reactivity, and reaction distribution. In feedback coefficient evaluations, an additional state method shows high consistency with lower uncertainty. Among 122 relative errors in the benchmark of the distribution of nuclear reaction, 104 errors are less than 10% and 84 errors are less than 5%. The results demonstrate the high reliability of OpenMC for its application in fast reactor simulations. In the companion paper, the influence of cross-section libraries is investigated using neutronics modelling in this paper.

Python is currently the number one language in the TIOBE index and has been the second most popular language on GitHub for years. But so far, there are only a few bug databases that contain bugs for Python projects and even fewer in which bugs can be reproduced. In this paper, we present a manually curated database of reproducible Python bugs called PyBugHive. The initial version of PyBugHive is a benchmark of 149 real, manually validated bugs from 11 Python projects. Each entry in our database contains the summary of the bug report, the corresponding patch, and the test cases that expose the given bug. PyBugHive features a rich command line interface for accessing both the buggy and fixed versions of the programs and provides the abstraction for executing the corresponding test cases. The interface facilitates highly reproducible empirical research and tool comparisons in fields such as testing, automated program repair, or bug prediction. The usage of our database is demonstrated through a use case involving a large language model, GPT-3.5. First, we evaluated the bug detection capabilities of the model with the help of the bug repository. Using multiple prompts, we found out that GPT-3.5 was able to detect 67 out of 149 bugs (45%). Furthermore, we leveraged the constructed bug dataset in assessing the automatic program repair capabilities of GPT-3.5 by comparing the generated fixes with the real patches contained in the dataset. However, its performance was far worse in this task compared to bug detection, as it was able to fix only one of the detected issues.

Ultra-wideband (UWB) localization has emerged in GPS-denied environments as a crucial facilitator for diverse industries, including logistics, healthcare applications, and societal domains. Despite notable progress, UWB algorithms from the scientific literature are often evaluated in isolation in very specific conditions and hence difficult to compare. This paper introduces a novel benchmark platform designed to assess the performance of 11 prominent UWB accuracy-enhancing algorithms, both independently and in combination. A key feature of the platform is its incorporation of multiple diverse evaluation metrics, including mean average error, latency, and spatial error. We showcase the significance of adopting alternative metrics such as spatial error, which often, depending on the use case, offers greater relevance than the prevalently used mean average error. Furthermore, we show that “more is better” does not hold true when combining multiple accuracy-improving algorithms for UWB systems: combining multiple accuracy-improving algorithms reveals instances of diminishing returns and can even result in overall performance decline. Additionally, we caution against blind reliance on accuracy outcomes reported in the scientific literature when designing UWB systems for use cases that are different in terms of requirements or environment. Finally, we also provide algorithmic recommendations for distinct surroundings, exemplary applications, and usage contexts, assisting in driving efficient design in future UWB research and adoption.

Abstract WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting.

Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.

Molten Salt Reactors (MSRs) have recently gained resurged research and development interest in the advanced reactor community. Several computational tools are being developed to capture the strong neutronics/thermal-hydraulics coupling effect in this special reactor configuration. This paper presents a consistent one-dimensional (1D) multigroup neutron diffusion model for MSR analysis, with the primary aim for fast and accurate calculations for long transients, as well as sensitivity and uncertainty analysis of the reactor. A fictitious radial leakage cross section is introduced in the model to properly account for the radial leakage effects of the reactor. The leakage cross section and other consistent neutronics parameters are generated with the Monte Carlo code Serpent using high-fidelity three-dimensional (3D) models. The accuracy of the 1D consistent model is verified by the reference solution from the Monte Carlo model on the Molten Salt Reactor Experiment (MSRE) configuration. The 1D consistent model successfully reproduced the integrated flux from the 3D model and the reactor multiplication factor keff with the error in the range of 95 to 397 pcm (per cent mille), depending on discretized energy group structures. The developed model is also extended to estimate the reactivity loss due to fuel circulation in MSRE. The estimate of reactivity loss in dynamics analysis is in great agreement with the experimental data. This model functions as the first step in the development of a 1D fully neutronics/thermal-hydraulics coupled model for short- and long-term MSRE transient analysis.

The paper presents the statement of the problem of dynamical system „crane-load” optimal control. The acceleration period is under consideration and control must meet the minimum duration condition as well as load oscillations elimination. The objective function, which ensures the final condition satisfaction, is developed and analyzed in terms of its topology features. It includes three arguments and their searching is the essence of the benchmark problem. Two variants of the problem are proposed with varied objective function parameters. Twelve agent-based optimization algorithms have been applied to find solutions to a bunch of problems. A brief analysis of the performance of the algorithms reveals their weaknesses and advantages. Thus, the proposed real-world problem may be exploited to estimate the optimization algorithms’ search performance.

Abstract A recent paper by Jiang et al. in BMC Bioinformatics presented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly compared to other tools, mostly due to low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3× to 20×) as specified by Jiang et al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jiang et al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiang et al. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiang et al. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jiang et al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community.

Abstract Backgound Efforts to measure performance and identify its driving factors among clinicians are needed for building a high-quality clinician workforce. The availability of data is the most challenging thing. This paper presented a summary performance measure for clinicians and its application on examining factors that influence performance using routine patient-based records. Methods Perfomance indicators and difficulty score were extracted from electronic medical records (EMRs). Difficulty adjustment and standardized processing were used to obtain indicators which were comparable between specialties. Principal component analysis (PCA) was used to estimate the summary performance measure. The performance measure was then used to examine the influence of person-job fit and burnout through a mediator effect model and cluster analysis. Results A valid sample of 404 clinicians were included in this study, and 244 of them had valid response in the questionnaire. PCA explained 79.37% of the total variance presented by the four adjusted performance indicators. Non-performance attributes and performance driving factors help distinguish different clusters of clinicians. Burnout mediates the relationship between person-job fit and performance in a specific group of clinicians (β = 0.120, p = 0.008). Conclusions We demonstrated the analytical steps to estimate clinicians’ performance and its practical application using EMRs. Our findings provide insight into personnel classified management. Such practice can be applied in countries where electronic medical record systems are relatively less developed to continuously improve the application of performance management.

This paper presents a benchmark analysis of NVIDIA Jetson platforms when operating deep learning-based 3D object detection frameworks. Three-dimensional (3D) object detection could be highly beneficial for the autonomous navigation of robotic platforms, such as autonomous vehicles, robots, and drones. Since the function provides one-shot inference that extracts 3D positions with depth information and the heading direction of neighboring objects, robots can generate a reliable path to navigate without collision. To enable the smooth functioning of 3D object detection, several approaches have been developed to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detectors and analyze their performance on the NVIDIA Jetson series that contain an onboard graphical processing unit (GPU) for deep learning computation. Since robotic platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. The Jetson series satisfies such requirements with a compact board size and suitable computational performance for autonomous navigation. However, a proper benchmark that analyzes the Jetson for a computationally expensive task, such as point cloud processing, has not yet been extensively studied. In order to examine the Jetson series for such expensive tasks, we tested the performance of all commercially available boards (i.e., Nano, TX2, NX, and AGX) with state-of-the-art 3D object detectors. We also evaluated the effect of the TensorRT library to optimize a deep learning model for faster inference and lower resource utilization on the Jetson platforms. We present benchmark results in terms of three metrics, including detection accuracy, frame per second (FPS), and resource usage with power consumption. From the experiments, we observe that all Jetson boards, on average, consume over 80% of GPU resources. Moreover, TensorRT could remarkably increase inference speed (i.e., four times faster) and reduce the central processing unit (CPU) and memory consumption in half. By analyzing such metrics in detail, we establish research foundations on edge device-based 3D object detection for the efficient operation of various robotic applications.

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.

Abstract Music inpainting is a sub-task of automated music generation that aims to infill incomplete musical pieces to help musicians in their musical composition process. Many methods have been developed for this task. However, we observe a tendency for each method to be evaluated using different datasets and metrics in the papers where they are presented. This lack of standardization hinders an adequate comparison of these approaches. To tackle these problems, we present MUSIB, a new benchmark for musical score inpainting with standardized conditions for evaluation and reproducibility. MUSIB evaluates four models: Variable Length Piano Infilling (VLI), Music InpaintNet, Music SketchNet, and AnticipationRNN, and over two commonly used datasets: JSB Chorales and IrishFolkSong. We also compile, extend, and propose metrics to adequately quantify note attributes such as pitch and rhythm with Note Metrics, but also higher-level musical properties with the introduction of Divergence Metrics, which operate by comparing the distance between distributions of musical features. Our evaluation shows that VLI, a model based on Transformer architecture, is the best performer on a larger dataset, while VAE-based models surpass this Transformer-based model on a relatively small dataset. With MUSIB, we aim at inspiring the community towards better reproducibility in music generation research, setting an example for strongly founded comparisons among SOTA methods.

The paper analyzes the key European benchmarks in the field of compiling and submitting sustainability reports. The analysis concerns the disclosure of their features in the context of considering the introduction in Ukraine to increase transparency, accountability and investment attractiveness of Ukrainian enterprises. Based on content and comparative analyses, a comparison was made of the key provisions of sustainability reporting issued by various standards-setters (ISSB (International Sustainability Standards Board), EFRAG (European Financial Reporting Advisory Group), SEC (The United States Securities and Exchange Commission), GRI (Global Reporting Initiative), and IIRC (International Integrated Reporting Council)) as a methodological level of the system of such benchmarks. The global impact of the specified benchmarks is complemented by an analysis of the impact of Directive 2014/95/EU (Non-Financial Reporting Directive – NFRD) and the new Directive 2022/2464/EU (Corporate Sustainability Reporting Directive – CSRD) on the introduction of the sustainability reporting. It is proved that in the context of the formation of the Ukrainian accounting system on the way to European integration, the transposition of the requirements of these Directives is the first step towards streamlining the regulatory framework for companies’ sustainability reporting. A two-level sustainability reporting benchmark system is presented, which at the operational level is based on the EU directives on disclosure of non-financial information and sustainability reporting, and at the methodological level – on the European Sustainability Reporting Standards and other generally accepted standards. Acknowledgment Inna Makarenko gratefully acknowledges support from the Supreme Council of Ukraine (0122U201796).

Highway companies engage in projects with governments through the concession model in which the governments’ aim is in fulfilling public interest while the highway companies aim to increase their financial stability. However, highway companies face various operational risks that would impede their financial excellency. Therefore, this paper aims to assess the financial performances of the highway companies with the goal programming (GP) model. The GP model is a powerful tool in solving multi-objective issues. This paper studies the listed highway companies, namely LITRAK, TALIWRK, and EDGENTA from 2017 to 2022 using financial data. Based on the optimal solution of GP model, all three companies have reached the goals for assets, equities, and optimum management items. LITRAK, TALIWRK, and EDGENTA have underachievement for the goal of liability management. The GP model has identified the potential improvements for these highway companies for further improvement based on the benchmark values.

The only common European Union (EU) legislation set up specifically to ensure the welfare of dairy cattle is for calves. As a consequence, there is wide diversity in how dairy cattle welfare is ensured in EU countries. A few countries have legal requirements for dairy cattle welfare, while in others, it is left to industry standards or niche production requirements, typically linked to various premium labels. In this paper, we compared animal welfare provisions in dairy cattle production across five countries with different combinations of legislative and other approaches: Denmark, Germany, the Netherlands, Sweden, and the United Kingdom. Firstly, we aimed to map the diversity of animal welfare initiatives. Secondly, we used the Benchmark method of expert valuations and weightings of the relative importance of individual welfare provisions. We found that Denmark and Sweden have the highest level of dairy cattle welfare provisions as measured by the Benchmark method, partly due to high legislative welfare requirements, followed by the United Kingdom, which has an extensive industry standard with very high uptake. Germany and the Netherlands, on the other hand, have lower levels of documented welfare provisions, and correspondingly a Benchmark score closer to a baseline defined by legal requirements at EU level. We also found differences in what elements of animal welfare were focussed on. Some initiatives emphasised fulfilling the social needs of cattle, while others focused more on space and freedom to move. Also, the countries with the highest Benchmark score had a relatively high level of production of organic and other specialty dairy products. We found the effect of national legislation or ambitious industry standards on dairy cattle welfare to be much larger than previous studies have found in either pigs or poultry. At a time when the EU is considering stepping up its efforts to improve animal welfare in terms of common minimum standards, the results of this study could have important policy implications. The diversity in the level of dairy cattle welfare standards found across countries may speak in favour of having shared minimum standards, both at EU level and globally. However, even among countries with a similar Benchmark score, we found a difference in the kinds of welfare provisions at work, which may make full harmonisation of standards more challenging.

Solving optimal control problems serves as the basic demand of industrial control tasks. Existing methods like model predictive control often suffer from heavy online computational burdens. Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible, high-accuracy solvers. Current Reinforcement learning (RL) solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills. Besides, many of them only support Python-based environments and limit to model-free algorithms. To address this gap, this paper develops General Optimal control Problems Solver (GOPS), an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields. GOPS is built with a highly modular structure that retains a flexible framework for secondary development. Considering the diversity of industrial control tasks, GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction, controller design, and performance validation. To handle large-scale problems, GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers. It offers a variety of common approximate functions for policy and value functions, including polynomial, multilayer perceptron, convolutional neural network, etc. Additionally, constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS. Several examples, including linear quadratic control, inverted double pendulum, vehicle tracking, humanoid robot, obstacle avoidance, and active suspension control, are tested to verify the performances of GOPS.

As the importance of data increases, data is continuously collected from diverse sources such as sensors, IoT devices, and edge computing devices. To manage these continuously monitored data, it is often organized chronologically with time which is referred as time-series data. By managing the data using time, data from different streams can be analyzed in a comprehensive manner with an identical index which is time. However, due to the unique characteristics of time-series data, it is essential for the underlying database systems to understand the characteristics of the time-series data. To handle this, time-series database systems, which specially target time-series data, are emerging. These database systems have different performance characteristics due to the unique characteristics of the data which should be investigated to efficiently store and analyze the data. In this paper, we analyze the time-series database from the perspective of I/O using various storage devices from HDD, SATA and NVMe SSD. First, we analyze the I/O characteristics such as runtime, throughput and size of total requests using various storage devices. In addition, we analyze the effect of unique time-series database features such as data chunk interval, compression and number of workers. Our analysis results show that adapting high-performance devices can greatly improve the performance of the database by up to $33.22\times $ .

This paper compares various performance metrics of the conventional direct digital frequency synthesis (DDFS) with DDFS introducing the quarter method, which is most commonly used among ROM-based DDFS. The evaluation is implemented at the register-transfer level (RTL) in the low-power and general-purpose 65 nm CMOS technologies, and 180 nm CMOS technology. We have observed that phase-to-amplitude converter (PAC) becomes a key building block that mainly determines the performance of DDFS. At a 12-bit resolution, the PAC of the conventional DDFS occupies about 97% of the total cell area and accounts for approximately 83% of the total power consumption. On the other hand, the PAC of DDFS with the quarter method applied occupies about 80% of the total cell area and uses about 45% of the total power consumption. In addition, because the quarter method requires additional decoding logic to map the phase to the corresponding amplitude, this method has area and power consumption disadvantages at relatively small resolution targets below 10-bit. However, at relatively high-resolution targets beyond 11-bit, the quarter method has significant benefits in terms of various performance metrics (e.g., area, power consumption, and speed). Then, the quarter method has advantages at resolutions exceeding 7 bits based on figure of merit (FoM). Based on FoM, a similar pattern is evaluated despite changing the CMOS technology. As a result, the quarter method is the crucial structure in DDFS architecture. The designed DDFS is verified in implementation results using a field programmable gate array (FPGA) in 65 nm CMOS technology.

In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Exploration Challenges(SMAC-Exp), where agents learn to perform multi-stage tasks and to use environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. SMAC-Exp, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack. We investigate a total of twelve MARL algorithms under both sequential and parallel episode settings of SMAC-Exp and observe that recent approaches perform well in similar settings to the previous challenge, but we discover that current multi-agent approaches place relatively less emphasis on exploration perspectives. To a limited extent, we observe that an enhanced exploration method has a positive effect on SMAC-Exp, however, there is still a gap that state-of-the-art algorithms cannot resolve the most challenging scenarios of SMAC-Exp. Consequently, we propose a new axis for future research into Multi-Agent Reinforcement Learning studies.

Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.

IntroductionSpeech to text (STT) technology has seen increased usage in recent years for automating transcription of spoken language. To choose the most suitable tool for a given task, it is essential to evaluate the performance and quality of both open source and paid STT services.MethodsIn this paper, we conduct a benchmarking study of open source and paid STT services, with a specific focus on assessing their performance concerning the variety of input text. We utilizes ix datasets obtained from diverse sources, including interviews, lectures, and speeches, as input for the STT tools. The evaluation of the instruments employs the Word Error Rate (WER), a standard metric for STT evaluation.ResultsOur analysis of the results demonstrates significant variations in the performance of the STT tools based on the input text. Certain tools exhibit superior performance on specific types of audio samples compared to others. Our study provides insights into STT tool performance when handling substantial data volumes, as well as the challenges and opportunities posed by the multimedia nature of the data.DiscussionAlthough paid services generally demonstrate better accuracy and speed compared to open source alternatives, their performance remains dependent on the input text. The study highlights the need for considering specific requirements and characteristics of the audio samples when selecting an appropriate STT tool.

The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms.

The paper analyzes one of the most important and difficult stages of constructing a geological model of the Beregovoye field when calculating hydrocarbon reserves – the correlation of the productive strata. It considers the productive strata of the Pokur formation of continental origin, analyzes the conditions of sedimentation, recommends the main correlation benchmarks and examines their characteristic features and inherent patterns.

Abstract Background In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics. Results The results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods. Conclusions We recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly.

Hybrid Transactional/Analytical Processing (HTAP) databases are designed to execute real-time analytics and provide performance isolation for online transactions and analytical queries. Real-time analytics emphasize analyzing the fresh data generated by online transactions. And performance isolation depicts the performance interference between concurrently executing online transactions and analytical queries. However, HTAP databases are extreme lack micro-benchmarks to accurately measure data freshness. Despite the abundance of HTAP databases and benchmarks, there needs to be more thorough research on the performance isolation and real-time analytics capabilities of HTAP databases. This paper focuses on the critical designs of mainstream HTAP databases and the state-of-the-art and state-of-the-practice HTAP benchmarks. First, we systematically introduce the advanced technologies adopted by HTAP databases for real-time analytics and performance isolation capabilities. Then, we summarize the pros and cons of the state-of-the-art and state-of-the-practice HTAP benchmarks. Next, we design and implement a micro-benchmark for HTAP databases, which can precisely control the rate of fresh data generation and the granularity of fresh data access. Finally, we devise experiments to evaluate the performance isolation and real-time analytics capabilities of the state-of-the-art HTAP database. In our continued pursuit of transparency and community collaboration, we will soon make available our comprehensive specifications, meticulously crafted source code, and significant results for public access at https://www.benchcouncil.org/mOLxPBench.

The Darknet is a set of networks and technologies, having as fundamental principles anonymity and security. In many cases, they are associated with illicit activities, opening space for malware traffic and attacks to legitimate services. To prevent Darknet misuse is necessary to classify and characterize its existing traffic. In this paper, we characterize and classify the real Darknet traffic available from the CIC-Darknet2020 dataset. In that sense, we performed the feature extraction and grouped the possible subnets with an n-gram approach. Furthermore, we evaluated the relevance of the best features selected by the Recursive Feature Elimination method for the problem. Our results indicate that simple models, like Decision Trees and Random Forests, reach an accuracy above 99% on traffic classification. Our methodology represents a gain of up to 13% in comparison with the state-of-the-art.

Deep learning has recently gained popularity in the field of video anomaly detection, with the development of various methods for identifying abnormal events in visual data. The growing need for automated systems to monitor video streams for anomalies, such as security breaches and violent behaviours in public areas, requires the development of robust and reliable methods. As a result, there is a need to provide tools to objectively evaluate and compare the real-world performance of different deep learning methods to identify the most effective approach for video anomaly detection. Current state-of-the-art metrics favour weakly-supervised strategies stating these as the best-performing approaches for the task. However, the area under the ROC curve, used to justify this statement, has been shown to be an unreliable metric for highly unbalanced data distributions, as is the case with anomaly detection datasets. This paper provides a new perspective and insights on the performance of video anomaly detection methods. It reports the results of a benchmark study with state-of-the-art methods using a novel proposed framework for evaluating and comparing the different models. The results of this benchmark demonstrate that using the currently employed set of reference metrics led to the misconception that weakly-supervised methods consistently outperform semi-supervised ones.

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.

The creep and shrinkage of concrete play important roles for many nuclear power plant (NPP) and engineering structures. This paper benchmarks the standard and micromechanical models using a revamped and appended Northwestern University database of laboratory creep and shrinkage data with 4663 data sets. The benchmarking takes into account relevant concretes and conditions for NPPs using 781 plausible data sets and 1417 problematic data sets, which cover together 47% of the experimental data sets in the database. The B3, B4, and EC2 models were compared using the coefficient of variation of error (CoV) adjusted for the same significance for short-term and long-term measurements. The B4 model shows the lowest variations for autogenous shrinkage and basic and total creep, while the EC2 model performs slightly better for drying and total shrinkage. In addition, confidence levels at 5, 10, 90, and 95% are quantified in every decade. Two micromechanical models, Vi(CA)2T and SCK CEN, use continuum micromechanics for the mean field homogenization and thermodynamics of the water–pore structure interaction. Validations are carried out for the 28-day Young’s modulus of concrete, basic creep compliance, and drying shrinkage of paste and concrete. The Vi(CA)2T model is the second best model for the 28-day Young’s modulus and the basic creep problematic data sets. The SCK CEN micromechanical model provides good prediction for drying shrinkage.

A myriad of opportunities is available to collaborate via international benchmark exercises and experimental data preservation activities. Many such opportunities abound under the auspices of the Nuclear Science Committee of the Organisation for Economic Co-operation and Development Nuclear Energy Agency (NEA). Key projects and activities of relevance to the development of advanced reactors design include the International Criticality Safety Benchmark Evaluation Project (ICSBEP), the International Reactor Physics Experiment Evaluation Project (IRPhEP), the International Assay Data of Spent Nuclear Fuel Database (SFCOMPO), the Shielding Integral Benchmark and Archive Database (SINBAD), and The International Experimental Thermal HYdraulicS Database (TIETHYS), and various cooperative benchmark exercises. Interested participants are encouraged to contact the leadership and secretariat of the various Technical Working Groups and Working Parties to become more engaged. This paper provides a summary of the current benchmark exercises and experimental databases available for international participation.

One of the strategic goals for the Republic of Macedonia is membership in the European Union. At the end of 2011, the Commission launched a so-called High-Level Accession Dialogue for Macedonia, with a possibility to start the negotiations after the fulfillment of the Dialogue goals and benchmarks. For these reasons, the main goal of this paper will be to give an answer of the dilemma of whether the Accession Dialogue for Macedonia is an accelerator of the entrance in the European Union, or is just a sophisticated tool for delay of the start of the negotiations for final accession. The expected results will correspond with the future EU plans for Macedonia, but also for the other Western Balkan countries, i.e. we will try to examine whether these countries have a realistic perspective for entrance in the European Union, or they are just a “declarative décor” for the vocabulary of the Brussels diplomats and member countries representatives. That will help to determine i.e. to try to predict the next steps of these countries, connected with the European integration, regardless of the actual constellation in the European Union concerning the Enlargement policy. The descriptive method, content analysis method, comparative method, but also the inductive and deductive methods will be used in this paper.

One of the strategic goals for the Republic of Macedonia is membership in the European Union. At the end of 2011, the Commission launched a so-called High-Level Accession Dialogue for Macedonia, with a possibility to start the negotiations after the fulfillment of the Dialogue goals and benchmarks. For these reasons, the main goal of this paper will be to give an answer of the dilemma of whether the Accession Dialogue for Macedonia is an accelerator of the entrance in the European Union, or is just a sophisticated tool for delay of the start of the negotiations for final accession. The expected results will correspond with the future EU plans for Macedonia, but also for the other Western Balkan countries, i.e. we will try to examine whether these countries have a realistic perspective for entrance in the European Union, or they are just a “declarative décor” for the vocabulary of the Brussels diplomats and member countries representatives. That will help to determine i.e. to try to predict the next steps of these countries, connected with the European integration, regardless of the actual constellation in the European Union concerning the Enlargement policy. The descriptive method, content analysis method, comparative method, but also the inductive and deductive methods will be used in this paper.

Automatic parallelization of sequential programs combined with auto-tuning is an alternative to manual parallelization. With wider research directions and the increased number of performance tuning tools that have been developed, it has become increasingly difficult to choose a particular tuning tool. This paper reviews the fundamentals of different performance optimization and tuning techniques. It also surveys several tuning frameworks and classifies them into different groups based on their criteria. Developing benchmarks for HPC and validating their accuracy are demanding tasks for computer architects, researchers, and application developers. In addition to providing a survey of performance-tuning tools, we also performed a detailed review of current benchmarks and discussed the requirements for future benchmarks. We performed a detailed comparison of these tuning tools based on other features such as speedup and infrastructure details. We believe that this study will be a very useful resource for parallel computing communities, especially for early-stage parallel computing and performance researchers to gain exposure to existing performance optimization options.

Synchronization is a crucial process in the operation of grid-connected distributed energy sources. Synchronization techniques are classified in closed-loop (CLS) and open-loop synchronization (OLS) techniques. Unlike the CLS technique, OLS techniques are recent and less known. This paper discusses a comparative analysis of the three prominent state-of-the-art OLS techniques. Accordingly, a benchmark model is proposed to systematically evaluate the performance of the OLS techniques under grid voltage disturbances such as harmonic distortions, voltage unbalance, DC offset, and low voltage ride through operation. Given the hardware resource utilization, the computational complexity and the execution time are also presented to judge the efficacy of the proposed model. Thus, engineers/or researchers can better understand the choice of hardware resources and the preferred dynamic performance of a synchronization scheme applied to control and protect a grid-tied power converter. Simulation and experimental results are presented and discussed to demonstrate the purpose of the proposed model.

Nowadays, the use of hardware accelerators to boost the performance of HPC applications is a consolidated practice, and among others, GPUs are by far the most widespread. More recently, some data centers have successfully deployed also FPGA accelerated systems, especially to boost machine learning inference algorithms. Given the growing use of machine learning methods in various computational fields, and the increasing interest towards reconfigurable architectures, we may expect that in the near future FPGA based accelerators will be more common in HPC systems, and that they could be exploited also to accelerate general purpose HPC workloads. In view of this, tools able to benchmark FPGAs in the context of HPC are necessary for code developers to estimate the performance of applications, as well as for computer architects to model that of systems at scale. To fulfill these needs, we have developed FER (FPGA Empirical Roofline), a benchmarking tool able to empirically measure the computing performance of FPGA based accelerators, as well as the bandwidth of their on-chip and off-chip memories. FER measurements enable to draw Roofline plots for FPGAs, allowing for performance comparisons with other processors, such as CPUs and GPUs, and to estimate at the same time the performance upper-bounds that applications could achieve on a target device. In this paper we describe the theoretical model on which FER relies, its implementation details, and the results measured on Xilinx Alveo accelerator cards.

The expansion of autonomous driving operations requires the research and development of accurate and reliable self-localization approaches. These include visual odometry methods, in which accuracy is potentially superior to GNSS-based techniques while also working in signal-denied areas. This paper presents an in-depth review of state-of-the-art visual and point cloud odometry methods, along with a direct performance comparison of some of these techniques in the autonomous driving context. The evaluated methods include camera, LiDAR, and multi-modal approaches, featuring knowledge and learning-based algorithms, which are compared from a common perspective. This set is subject to a series of tests on road driving public datasets, from which the performance of these techniques is benchmarked and quantitatively measured. Furthermore, we closely discuss their effectiveness against challenging conditions such as pronounced lighting variations, open spaces, and the presence of dynamic objects in the scene. The research demonstrates increased accuracy in point cloud-based methods by surpassing visual techniques by roughly 33.14% in trajectory error. This survey also identifies a performance stagnation in state-of-the-art methodologies, especially in complex conditions. We also examine how multi-modal architectures can circumvent individual sensor limitations. This aligns with the benchmarking results, where the multi-modal algorithms exhibit greater consistency across all scenarios, outperforming the best LiDAR method (CT-ICP) by 5.68% in translational drift. Additionally, we address how current AI advances constitute a way to overcome the current development plateau.

At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System (HDFS), Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.

Various substances that possess liquid states include drinking water, various types of fuel, pharmaceuticals, and chemicals, which are indispensable in our daily lives. There are numerous real-world applications for liquid content detection in transparent containers, for example, service robots, pouring robots, security checks, industrial observation systems, etc. However, the majority of the existing methods either concentrate on transparent container detection or liquid height estimation; the former provides very limited information for more advanced computer vision tasks, whereas the latter is too demanding to generalize to open-world applications. In this paper, we propose a dataset for detecting liquid content in transparent containers (LCDTC), which presents an innovative task involving transparent container detection and liquid content estimation. The primary objective of this task is to obtain more information beyond the location of the container by additionally providing certain liquid content information which is easy to achieve with computer vision methods in various open-world applications. This task has potential applications in service robots, waste classification, security checks, and so on. The presented LCDTC dataset comprises 5916 images that have been extensively annotated through axis-aligned bounding boxes. We develop two baseline detectors, termed LCD-YOLOF and LCD-YOLOX, for the proposed dataset, based on two identity-preserved human posture detectors, i.e., IPH-YOLOF and IPH-YOLOX. By releasing LCDTC, we intend to stimulate more future works into the detection of liquid content in transparent containers and bring more focus to this challenging task.

Abstract Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we propose the first benchmark for digital out-of-home audience measurement that evaluates the vision-based tasks of audience localization and counting, and audience demographics. The benchmark is composed of a novel, dataset captured at multiple locations and a set of performance measures. Using the benchmark, we present an in-depth comparison of eight open-source algorithms on four hardware platforms with GPU and CPU-optimized inferences and of two commercial off-the-shelf solutions for localization, count, age, and gender estimation. This benchmark and related open-source codes are available at http://ava.eecs.qmul.ac.uk .

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

As the Internet of Things (IoT) industry expands, the demand for microprocessors and microcontrollers used in IoT systems has increased steadily. Benchmarks provide a valuable reference for processor evaluation. Different IoT application scenarios face different data scales, dimensions, and types. However, the current popular benchmarks only evaluate the processor’s performance under fixed data formats. These benchmarks cannot adapt to the fragmented scenarios faced by processors. This paper proposes a new benchmark, namely IoTBench. The IoTBench workloads cover three types of algorithms commonly used in IoT applications: matrix processing, list operation, and convolution. Moreover, IoTBench divides the data space into different evaluation subspaces according to the data scales, data types, and data dimensions. We analyze the impact of different data types, data dimensions, and data scales on processor performance and compare ARM with RISC-V and MinorCPU with O3CPU using IoTBench. We also explored the performance of processors with different architecture configurations in different evaluation subspaces and found the optimal architecture of different evaluation subspaces. The specifications, source code, and results are publicly available from https://www.benchcouncil.org/iotbench/.

Many emerging IoT–Edge–Cloud computing systems are not yet implemented or are too confidential to share the code or even tricky to replicate its execution environment, and hence their benchmarking is very challenging. This paper uses autonomous vehicles as a typical scenario to build the first benchmark for IoT–Edge–Cloud systems. We propose a set of distilling rules for replicating autonomous vehicle scenarios to extract critical tasks with intertwined interactions. The essential system-level and component-level characteristics are captured while the system complexity is reduced significantly so that users can quickly evaluate and pinpoint the system and component bottlenecks. Also, we implement a scalable architecture through which users can assess the systems with different sizes of workloads.We conduct several experiments to measure the performance. After testing two thousand autonomous vehicle task requests, we identify the bottleneck modules in autonomous vehicle scenarios and analyze their hotspot functions. The experiment results show that the lane-keeping task is the slowest execution module, with a tail latency of 77.49 ms for the 99th percentile latency. We hope this scenario benchmark will be helpful for Autonomous Vehicles and even IoT–edge–Cloud research. Now the open-source code is available from the official website https://www.benchcouncil.org/scenariobench/edgeaibench.html.

This paper describes benchmark calculations for the APR1400 nuclear reactor performed using the high-fidelity deterministic whole-core simulator MPACT compared to reference solutions generated by the Monte Carlo code McCARD. The methodology presented in this paper is a common approach in the field of nuclear reactor analysis, when measured data are not available for comparison, and may be more broadly applied in other simulation applications of energy systems. The benchmark consists of several problems that span the complexity of single pins to a hot full power cycle depletion. Overall, MPACT shows excellent agreement compared to the reference solutions. MPACT effectively predicts the reactivity for different geometries and several temperature and boron conditions. The largest deviation from McCARD occurs for cold zero conditions in which the fuel, moderator, and cladding are all 300 K. Possible reasons for this are discussed. Excluding these cases, the ρ reactivity difference from McCARD is consistently below 100 pcm. For single fuel pin problems, the highest error of 151 pcm occurs for the lowest fuel enrichment of 1.71 wt.% UO2, indicating possible, albeit small, enrichment bias in MPACT’s cross-section library. Furthermore, MOC and spatial mesh parametric studies indicate that default meshing parameters and options yield results comparable to finely meshed cases. Additionally, there is very good agreement of the radial and axial power distributions. RMS radial pin and assembly power differences for all cases are at or below 0.75%, and all RMS axial power differences are below 1.65%. These results are comparable to previous results from the VERA progression problems benchmark and meet generally accepted accuracy criteria for whole-core transport codes.

Eventual Consistency (EC) model is adopted by numerous large-scale distributed systems. To ensure performance and scalability, this model allows any replica to accept updates without remote synchronization. Nowadays, many EC algorithms are developed to control the behavior of the replicated data in the face of concurrent updates. Among them, those using a central server to order the updates, while others support the decentralization. In this paper, we focus on decentralized EC algorithms. Suitability of such algorithms under users and devices constraints such as execution time, memory requirements, messages size and quality of the result remains to be investigated under different conditions. Evaluate such algorithms in different context and under different parameters require a framework. In this paper, we propose a generic framework designed to evaluate diferent decentralized EC algorithms, in diferent context by controlling diferent parameters. Our framework provides a generic simulator that generates a runnable data following diferent parameters.