Execution Model Research Articles

Protecting Dynamically Obfuscated Scan Chain Architecture from DOSCrack with Trivium Pseudo-Random Number Generation

Design-for-test/debug (DfT/D) introduces scan chain testing to increase testability and fault coverage by inserting scan flip-flops. However, these scan chains are also known to be a liability for security primitives. In previous research, the dynamically obfuscated scan chain (DOSC) was introduced to protect logic-locking keys from scan-based attacks by obscuring test patterns and responses. In this paper, we present DOSCrack, an oracle-guided attack to de-obfuscate DOSC using symbolic execution and binary clustering, which significantly reduces the candidate seed space to a manageable quantity. Our symbolic execution engine employs scan mode simulation and satisfiability modulo theories (SMT) solvers to reduce the possible seed space, while obfuscation key clustering allows us to effectively rule out a group of seeds that share similarities. An integral component of our approach is the use of sequential equivalence checking (SEC), which aids in identifying distinct simulation patterns to differentiate between potential obfuscation keys. We experimentally applied our DOSCrack framework on four different sizes of DOSC benchmarks and compared their runtime and complexity. Finally, we propose a low-cost countermeasure to DOSCrack which incorporates a nonlinear feedback shift register (NLFSR) to increase the effort of symbolic execution modeling and serves as an effective defense against our DOSCrack framework. Our research effectively addresses a critical vulnerability in scan-chain obfuscation methodologies, offering insights into DfT/D and logic locking for both academic research and industrial applications. Our framework highlights the need to craft robust and adaptable defense mechanisms to counter evolving scan-based attacks.

A multi-core makespan model for parallel scientific workflow execution in cloud computational framework

Researchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan.

Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.

ЭСТЕТИКА ЖЕНСКОГО ДЕЛОВОГО КОСТЮМА В МОДЕ ХХ – НАЧАЛА ХХI вв.

The article is devoted to one of the aspects of fashion – the concept of power, which found vivid expression in the women’s business suit of the 1980s. The increasing demand for women in the business sphere, their desire to achieve success in it equal to men’s, led to the creation in the 1970s of the so-called dress for success, and with the advent of a female executive model, to the birth of a style that creates the image of an authoritarian personality. In the philosophical environment of these decades, the concept of symbolic power as a system of relations is maturing (M. Foucault, J. Lacan, P. Bourdieu), who replaced the model of disciplinary dominance. Through it, fashion can be studied as built on the establishment of symbolic values. Achieving the quintessence of a business woman’s style, a suit for success received in the early 1980s the name power-dressing – an imperious manner of clothing. Conditionally, from this time on, the direction of the so-called «domineering style» fashion develops, similar to the disciplinary power that forms behavioral skills, subordinating the body to itself. So, women try not to demonstrate their femininity, which they rightly believe is associated in society with their lack of business qualities; they are forced to exclude trousers from the composition of a business suit, and again put on skirts. The costume, despite the skirt included in it, was not supposed to demonstrate the femininity of its owner. That is, there is a continuation of the power of the patriarchate, resorting to other forms of subordination. High-ranking female politicians tried to find a way out between feminine and masculine ways of dressing. An inspiring example of a business woman’s costume was the costume of British Prime Minister Margaret Thatcher, designed in the aesthetic canons of classics and national traditions. By the beginning of the XXI century, there was no need to assert oneself through signs and symbols of masculinity in a women’s business suit, this allowed the business suit to become softer, freer, more comfortable, aesthetically diverse. But changeable in all respects, fashion remains unchanged in only one way – it always functions as an element of the power regulations, determining the style of behavior, changing some elements of the dictate to others.

A Task Parallel Accelerator with Dynamic Pipeline Balancing

Coarse-grained reconfigurable array(CGRA) based ac- celerator is a promising architecture to accelerate data processing workloads. CGRA-based accelerator features more flexibility in adopting various workloads while remaining powerful in comparison to the traditional application specified accelerator. However, the strength of CGRAs is limited by irregular data access and dependence patterns. The previously proposed task- based execution model enabled work-aware dynamic scheduling, but the effectiveness is still limited by the regular execution resources. To address these issues, we proposed a heterogeneous architecture to improve parallelism for pipeline-enabled task streaming. We enabled dynamic pipeline balancing on this architecture with minor modifications to the task stream annotation. We compare the execution result on various heterogeneous structures with the regular configuration. Overall, we find that our architecture can improve performance with the same overall resources.

GPU Coroutines for Flexible Splitting and Scheduling of Rendering Tasks

We introduce coroutines into GPU kernel programming, providing an automated solution for flexible splitting and scheduling of rendering tasks. This approach addresses a prevalent challenge in harnessing the power of modern GPUs for complex, imbalanced graphics workloads like path tracing. Usually, to accommodate the SIMT execution model and latency-hiding architecture, developers have to decompose a monolithic mega-kernel into smaller sub-tasks for improved thread coherence and reduced register pressure. However, involving the handling of intricate nested control flows and numerous interdependent program states, this process can be exceedingly tedious and error-prone when performed manually. Coroutines, a building block for asynchronous programming in many high-level CPU languages, exhibit untapped potential for restructuring GPU kernels due to their versatility in control representation. By extending Luisa [Zheng et al. 2022], we implement an asymmetric, stackless coroutine model with programming language support and multiple built-in schedulers for modern GPUs. To showcase the effectiveness of our model and implementation, we examine them in different application scenarios, including path tracing, SDF rendering, and incorporation with custom passes.

GPU Parallel Computing Architectures : Unlocking the Power of Parallelism for High-Performance Applications

Graphics Processing Units (GPUs) have evolved from specialized graphics rendering hardware to become powerful parallel computing architectures, revolutionizing high-performance computing across diverse domains. This comprehensive article explores the fundamental principles of GPU parallel computing architectures, their design, and their impact on modern computational challenges. We begin by examining the multi-core structure, memory hierarchy, and data processing capabilities of GPUs, including the SIMD execution model and thread organization. The article then delves into prominent programming models like CUDA and OpenCL, discussing their features and comparative advantages. We explore how GPUs are leveraged for general-purpose computing in scientific simulations, machine learning, and big data analytics, while also addressing the challenges inherent in GPU parallel computing, such as data transfer bottlenecks and load balancing. Recent technological advancements, including tensor cores, unified memory architecture, and ray tracing acceleration, are analyzed for their transformative potential. The article concludes by examining future directions in GPU technology, including integration with emerging technologies like quantum computing, advancements in energy efficiency, and the potential impact on solving complex global challenges. Through this comprehensive analysis, we illustrate the pivotal role of GPU parallel computing architectures in shaping the future of high-performance computing and their potential to address some of the world's most pressing computational problems.

Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: A data-driven approach for improved classification

In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have also been developed for report labeling but lack adaptability, similar to rule-based systems. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on CXR reports. MAPLEZ extracts not only binary labels indicating the presence or absence of a finding but also the location, severity, and radiologists’ uncertainty about the finding. Over eight abnormalities from five test sets, we show that our method can extract these annotations with an increase of 3.6 percentage points (pp) in macro F1 score for categorical presence annotations and more than 20 pp increase in F1 score for the location annotations over competing labelers. Additionally, using the combination of improved annotations and multi-type annotations in classification supervision in a dataset of limited-resolution CXRs, we demonstrate substantial advancements in proof-of-concept classification quality, with an increase of 1.1 pp in AUROC over models trained with annotations from the best alternative approach. We share code and annotations.

Gambling-specific metacognitions, depression, and responsible gambling in Macao, China

The Self-regulatory Executive Function model elucidates how metacognitions link to the development and maintenance of gambling disorder, however whether, and how, metacognitions explain responsible gambling remains unknown. This study is the first to address this knowledge gap by: (a) examining whether both generic and gambling-specific metacognitions explain variances in responsible gambling; and (b) testing the mediating role of depression between generic/gambling-specific metacognitions and responsible gambling. A two-stage cluster random sampling method was used to obtain a probability sample of 837 Chinese adult gamblers (48.5 % men, mean age = 41.11, SD = 14.31) in Macao, China. Hierarchical regression analysis indicated that gambling-specific metacognitions contributed to an additional 23 % variance explained in responsible gambling after considering generic metacognitions and demographics. Path analysis further showed that the direct paths from generic, positive and negative gambling-specific metacognitions to responsible gambling were all significant while the positive type showing a stronger effect. Moreover, depression partially mediated the effects of both generic and gambling-specific metacognitions on responsible gambling. Findings suggest that responsible gambling campaigns should consider improving the awareness of metacognitions and emotion regulation in depression among Chinese adult gamblers.

Impulsiveness in Substance Users: Metacognitive Beliefs and Repetitive Negative Thinking as Potential Maintenance Factors.

Using the self-regulatory executive function model as a basis, this study explored whether, among substance users, metacognitive beliefs and repetitive negative thinking were associated with impulsiveness. A total of 100 substance users were recruited. Impulsiveness, metacognitive beliefs, rumination and worry were assessed. Correlation and hierarchal regression analyses were run. Beliefs about the need to control thoughts, lower cognitive self-consciousness and brooding rumination were found to be independent predictors of the total score on impulsiveness in the hierarchical regression analysis. Further regression analyses indicated that motor impulsiveness was predicted by a combination of beliefs about the need to control thoughts and brooding rumination, while non-planning impulsiveness was predicted by beliefs about the need to control thoughts and lower cognitive self-consciousness. Among substance users, higher impulsiveness is associated with the tendency to endorse beliefs about the need to control thoughts and brooding rumination. Among substance users, beliefs about the need to control thoughts and brooding rumination (and potentially cognitive self-consciousness) could be a suitable therapeutic targets to mitigate particularly motor impulsiveness and non-planning impulsiveness.

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article, lane change models for mixed traffic flow under cooperative adaptive cruise control (CACC) platoon formation are established. The analysis begins by examining the impact of lane changes on traffic flow stability. The influences of various factors such as lane change locations, timing, and the current traffic state on stability are discussed. In this analysis, it is assumed that the lane change location and the entry position in the adjacent lane have already been selected, without considering the specific intention behind the lane change. The speeds of the involved vehicles are adjusted based on an existing lane change model, and various conditions are analyzed for traffic flow disturbances, including duration, shock amplitude, and driving delays. Numerical calculations are provided to illustrate these effects. Additionally, traffic flow stability is factored into the lane change decision-making process. By incorporating disturbances to the fleet into the lane change income model, both a lane change intention model and a lane change execution model are constructed. These models are then compared with a model that does not account for stability, leading to the corresponding conclusions.

ASSOCIAÇÃO DE PROTEÇÃO ASSISTÊNCIA AOS CONDENADOS: DESAFIOS E PERSPECTIVAS DO MÉTODO COM OS RECUPERANDOS

This study examines the role of social workers in the Associations for the Protection and Assistance to the Convicted (APACs), exploring the challenges and perspectives of the humane method proposed by these institutions within the Brazilian penal context. Historically, the conventional prison system in Brazil has been characterized by overcrowding, violence, and inhumane conditions, reinforcing the need for alternative methods of resocialization. APACs emerge as an innovative alternative, proposing a penal execution model centered on human dignity and the social reintegration of the convicted. The work of social workers in APACs is multifaceted, ranging from psychosocial support to the implementation of educational and professional training programs. However, these professionals face significant challenges, such as a lack of resources, institutional resistance, and the stigmatization of convicts by society. The research employs a mixed methodological approach, including a literature review and interviews with social workers in APACs, to investigate how these professionals contribute to promoting social reintegration and what innovations can be adopted to optimize their practices. The results indicate that despite the difficulties, the role of social workers is crucial to the success of the APAC method, highlighting the importance of a holistic and personalized approach that respects the dignity of the convicted and promotes effective and humane resocialization.

Assessing the impact of meteorological factors and air pollution on respiratory disease mortality rates: a random forest model analysis (2017-2021).

Air pollution poses a significant threat to the health of all living beings on our planet. It has been scientifically established as a crucial factor affecting mortality rates, respiratory illnesses, mental well-being, and overall health. This study aimed to investigate the impact of air pollution and meteorological factors on respiratory disease mortality rates in Mashhad in 2017-2021 using a Random Forest (RF) model. At first, the daily statistics of meteorological parameters (pressure, humidity, temperature, solar radiation) during 2017-2021 were collected. The information related to pollutants pollutants such as PM2.5 (which is defined as particulate matter with less than 2.5 micrometer diameter and potentially harmful to humans), PM10 (Particles with a diameter of 10 micrometers or less that can negatively impact both human health and environmental conditions.), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) was collected. the mortality statistics from respiratory diseases were collected from the Health system registaration (Sina). Then we used the RF regression model in Excel and Python software to investigate the interaction between the mentioned variables. The escalating trend of air pollution in Mashhad has led to an expected increase in respiratory-related hospitalizations. This necessitates urgent air pollution control measures. Concurrently, the study of pollutants and climatic elements, as substantiated by global epidemiological studies, is crucial. In Mashhad, the second most polluted city in Iran, climatic factors like humidity, sunshine duration, temperature, pressure, and sunlight intensity exacerbate atmospheric pollution levels, impacting human health and ecosystems. The R2, RSME, and MAE of RF model are 0.73, 2.52, and 2 which indicate that the model has successfully identified the relationship between input variables and the target variable, and it will exhibit high accuracy in predictions. In this study, the most influential factor was identified when the Variance Inflation (VI) factor reached a value of 0.548. This indicates a strong correlation between this factor and the death rate of patients during the specified period. Furthermore, we analyzed by excluding the day and month plans from our model. The results showed that the factor with the highest coefficient in the executive model was related to pressure, with a VI value of 0.049. This suggests that pressure plays a significant role in our model and has a substantial effect on the death rate of patients. In the study of various pollutants, it was found that PM10 had the most significant impact on the mortality rate of patients with respiratory conditions, with a VI of 0.039. Following PM10, the pollutants with the next highest coefficients of importance were NO2 (VI = 0.034), SO2 (VI = 0.033), PM2.5 (VI = 0.029), and CO (VI = 0.025), respectively. Furthermore, the study observed a notable increase in the mortality rate of respiratory patients over time. Specifically, for every five days, the death rate increased by 35.5%. Indeed, climate change and air pollution significantly contribute to the mortality rate from respiratory diseases. Therefore, it is crucial for individuals, particularly those with respiratory conditions, to heed meteorological warnings.

Operation-based versioning as a foundation for live executable models

Operation-based versioning as a foundation for live executable models

Enhancing Jakarta Faces Web App with AI Data-Driven Python Data Analysis and Visualization

Python is widely used in artificial intelligence (AI) and machine learning (ML) because of its flexibility, adaptability, rich libraries, active community, and broad environment, which makes it a popular choice for AI development. Python compatibility has already been examined with Java using TCP socket programming on both non-graphical and graphical user interfaces, which is highly essential to implement in the Jakarta Faces web application to grab potential competitive advantages. Python data analysis library modules such as numpy, pandas, and scipy, as well as visualization library modules such as Matplotlib and Seaborn, and machine-learning module Scikit-learn, are intended to be integrated into the Jakarta Faces web application. The research method uses similar TCP socket programming for the enhancement process, which allows instruction and data exchange between Python and Jakarta Faces web applications. The outcome of the findings emphasizes the significance of modernizing data science and machine learning (ML) workflows for Jakarta Faces web developers to take advantage of Python modules without using any third-party libraries. Moreover, this research provides a well-defined research design for an execution model, incorporating practical implementation procedures and highlighting the results of the innovative fusion of AI from Python into Jakarta Faces.

Safety Precaution Mechanism and Evaluation Model

Study’s Excerpt A safety management model tailored to the oil and gas industry, which offers a structured approach to mitigating workplace hazards is developed. Questionnaires to assess the hazardous nature of industry operations and evaluate safety performance across multiple firms are used to identify key areas for improvement. The model could enhance safety practices and reduce injury and fatality rates in this critical sector. Full Abstract The increase in the human population raises some fundamental questions about the human ability to protect and take care of its safety. From the foundation of time, humans have demonstrated skills for sustainability. In doing this, there is always a high level of concern about the hazardous nature of the environment. These hazards have hitherto led to severe injuries or death among workers. One key area of the world economy is the oil and gas of which Nigeria as a nation sustained its yearly budget. This model, as developed, addresses and proffers solutions to the safety of lives as it affects the oil and gas industry staff with a heavy focus on those directly on the field. To solve this, this model employed some research measures, such as a questionnaire, to ascertain the gravity of the hazardous nature of the oil and gas industries. The data obtained were subjected to critical evaluation, as seen in the result table 12, and the result shows an improved performance among the examined oil and gas firms in safety management. The data received aided the design and implementation of a safety precaution execution and evaluation model strong enough to mitigate hazards associated with the operational states of staff in the oil and gas industries.

Domain Specific Abstractions for the Development of Fast-by-Construction Dataflow Codes on FPGAs

FPGAs are popular in many fields but have yet to gain wide acceptance for accelerating HPC codes. A major cause is that whilst the growth of High-Level Synthesis (HLS), enabling the use of C or C++, has increased accessibility, without widespread algorithmic changes these tools only provide correct-by-construction rather than fast-by-construction programming. The fundamental issue is that HLS presents a Von Neumann-based execution model that is poorly suited to FPGAs, resulting in a significant disconnect between HLS’s language semantics and how experienced FPGA programmers structure dataflow algorithms to exploit hardware. We have developed the high-level language Lucent which builds on principles previously developed for programming general-purpose dataflow architectures. Using Lucent as a vehicle, in this paper we explore appropriate abstractions for developing application-specific dataflow machines on reconfigurable architectures. The result is an approach enabling fast-by-construction programming for FPGAs, delivering competitive performance against hand-optimised HLS codes whilst significantly enhancing programmer productivity.

Unveiling the Effectiveness of Multi-Classification Algorithms: A Comprehensive Investigation

Multi classes portray alishead to a ton of genuine computer-based intelligence applications that need the ability to perceive numerous different classes immediately. In multiclass arrangement issues, we really want to manage multiple classes which imply the calculation which we are utilizing ought to be equipped for working with different classes. The rear different models accessible for this and a few strategies are likewise accessible that can make support vector machines and Calculated relapse equipped for managing multiple classes. In this examination paper, we present a multiclass grouping strategy from AI (ML) to foresee the six classes of Dermatology sickness expectation. In particular, the One-Refrains One (OVO) and One-Sections Rest (OVR) systems are assessed under Support Vector Machine (SVM) and Logistic Regression (LR) calculations. The current review intends store cognize fitting finding class for dermatology patients through building a multi-class expectation model. Our exploratory outcomes showed that the overall model is LR under the OVR technique accomplishing the most noteworthy Exactness 98.2% when contrasted with LR with OVO and SVM with OVR strategy accomplishing normal precision when contrasted with OVO. This proposed model shows the similar and promising outcomes that can upgrade the expectation execution model formulate classification.

A New Concept for the Rapid Development of Digital Twin Core Models for Bioprocesses in Various Reactor Designs

In this research work, a new software tool concept and its application for the rapid and flexible development of mechanistic digital twin core models for bioprocesses in various reactor designs are presented. The newly developed software tool concept automatically combines user-selected submodels into an overall digital twin core model. The main part is a biokinetic submodel, of which three were designed for enzymatic, microbial and biocatalytic processes, which can be adapted to specific processes. Furthermore, the digital twin core model contains a physico-chemical submodel (e.g., calculating pH or oxygen transfer) and a reactor submodel. The basis of the reactor submodel is an ideally mixed stirred tank reactor. The biokinetic submodel is decoupled from the reactor submodels and enables an independent parameterisation of submodels. Connecting ideally mixed stirred tank reactor models allows for the simulation of different reactor designs. The implementation of an executable digital twin core model was accelerated, creating a new software tool concept. When the concept was applied, the development time and the computing time of digital twin core models for the cultivation of Saccharomyces cerevisiae in two coupled stirred tank reactors as well as for enzymatic hydrolysis processes in a packed-bed reactor were reduced by 90%.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.