Program Paths Research Articles

Determining a Fuzzy Model of Time Buffer Size in Critical Chain Project Management

Abstract Elaborating and applying a new model for estimating the time buffer size of a project programme, which shall guarantee a 90 % probability of timely project execution. The research included source text analysis to provide information on a research gap and the identification of the research problem. The research problem was identified: the time buffer size in a critical path programme does not guarantee a 90 % probability of timely project execution. A new model was then elaborated to estimate the buffer size; it was applied in a technical production preparation project. An additional comparative analysis was performed using the following methods to verify the model more accurately: half of the time total of a path, the sum of squares (SSQ), and the root square error method (RSEM). The application of the fuzzy model to estimate the buffer size in a critical chain programme offers can shorten the total planned project duration. It has a higher probability of timely project execution than other methods for estimating the buffer size. It guarantees a 90 % probability of timely project execution, keeping aggressive task times, which eliminates unwanted situations such as student syndrome, Parkinson’s law, overestimating task duration, and multitasking. Project programming is an inherent part of the project planning stage in project management. Recently, project management has been increasingly developing, which has been confirmed by the article’s source literature analysis. The analysis revealed a research gap in models estimating project buffer size, which might guarantee a 90 % probability of timely project execution. Thus, a fuzzy model for estimating time buffer size in a critical chain was developed, constituting added value to the science of management and quality of production engineering (currently, mechanical engineering). The fuzzy model for estimating time buffer size was applied in one Polish enterprise in a project for a new product’s technical production preparation. The fuzzy model for estimating time buffer size permits the shortening of the duration of tasks to aggressive times, guaranteeing a 90 % probability of project timely execution. The elaborated model for estimating time buffer size may be applied further in practice in projects programmed using the critical chain method.

Damage-programmable design of metamaterials achieving crack-resisting mechanisms seen in nature

The fracture behaviour of artificial metamaterials often leads to catastrophic failures with limited resistance to crack propagation. In contrast, natural materials such as bones and ceramics possess microstructures that give rise to spatially controllable crack path and toughened material resistance to crack advances. This study presents an approach that is inspired by nature’s strengthening mechanisms to develop a systematic design method enabling damage-programmable metamaterials with engineerable microfibers in the cells that can spatially program the micro-scale crack behaviour. Machine learning is applied to provide an effective design engine that accelerate the generation of damage-programmable cells that offer advanced toughening functionality such as crack bowing, crack deflection, and shielding seen in natural materials; and are optimised for a given programming of crack path. This paper shows that such toughening features effectively enable crack-resisting mechanisms on the basis of the crack tip interactions, crack shielding, crack bridging and synergistic combinations of these mechanisms, increasing up to 1,235% absorbed fracture energy in comparison to conventional metamaterials. The proposed approach can have broad implications in the design of damage-tolerant materials, and lightweight engineering systems where significant fracture resistances or highly programmable damages for high performances are sought after.

WCET Analysis Based on Micro-Architecture Modeling for Embedded System Security

To ensure the timely execution of hard real-time applications, scheduling analysis techniques must consider safe upper bounds on the possible execution durations of tasks or runnables, which are referred to as Worst-Case Execution Times (WCET). Bounding WCET requires not only program path analysis but also modeling the impact of micro-architectural features present in modern processors. In this paper, we model the ARMv8 ISA and micro-architecture including instruction cache, branch predictor, instruction prefetching strategies, out-of-order pipeline. We also consider the complex interactions between these features (e.g., cache misses caused by branch predictions and branch misses caused by instruction pipelines) and estimate the WCET of the program using the Implicit Path Enumeration Technique (IPET) static WCET analysis method. We compare the estimated WCET of benchmarks with the observed WCET on two ARMv8 boards. The ratio of estimated to observed WCET values for all benchmarks is greater than 1, demonstrating the security of the analysis.

Automatic detection of infeasible paths in large-scale program based on program summaries

The existence of infeasible paths in a program reduces the coverage of test cases and causes a waste of valuable testing resources. Detecting infeasible paths allows for focusing testing resources on feasible paths. This paper introduces a method for detecting infeasible paths based on program summaries. Our proposed method partitions the program into sequential statements, conditional statements and loop statements, and automatically generates statement summaries and function summaries. It analyzes the summaries to extract the path constraints and determines the feasibility of paths. We implemented a detection tool named DTSIP based on this method, and conducted experiments using a set of benchmark programs and open source projects. The results confirm the effectiveness of our method in detecting infeasible paths. It can detect both intraprocedural and interprocedural infeasible paths, demonstrating its broad applicability. Our method overcomes challenges associated with analyzing complex paths, achieving efficient feasibility determination while reducing processing time.

A cosmopolitan approach to international law (CAIL) reflection on the space development experience of Africa

Space power theory from the realist school states that the proliferation of space technology is a foe rather than a friend because it contributes to military and economic competition; and, above all, it empowers the exercise of threat of force in, through and from Outer Space. It is thus important to examine questions of justice in one's professional field and locate the analysis of one's practice within wider social, economic, and political context. While law is not justice and simply announces the form of justice sought, the approach taken in this paper is more precisely the lived experience or mental processes of consciousness and its aim begins a conversation to investigate this in a rigorous way. The cognitive act that characterizes the phenomenological reflection is “paying attention.” Therefore, this reflection contemplates the relevance of experience and reflective practice as a valuable methodology for using insights and learning from the past to support the assessment of the present and recommendations for the future. This is applied in the case of over 17 years of personal experience in following the development path of African space programs. The paper concludes that the African case confirms that multi-level governance is an approach that is of importance, and Africa is in a unique position to master this as stakeholders must think about how to engage in space development at all 4 levels: the global, regional, national, and local level.

Development of an Improved Communication Control System for ATV Electric Vehicles Using MRS Developers Studio

Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework.

Impact of Personalized Learning Paths on Student Motivation and Achievement in Online High School Programs in Pakistan

Purpose: The aim of the study was to investigate the impact of personalized learning paths on student motivation and achievement in online high school programs in Pakistan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Tailored learning experiences boost intrinsic motivation by offering autonomy and relevance, while goal-setting and progress tracking contribute to improved academic performance and engagement. These findings highlight the potential of personalized learning to meet diverse student needs and enhance educational outcomes in Pakistani online high school settings. Unique Contribution to Theory, Practice and Policy: Self-determination theory (SDT), social cognitive theory (SCT) & constructivist learning theory may be used to anchor future studies on the impact of personalized learning paths on student motivation and achievement in online high school programs in Pakistan. Implement ongoing professional development programs for educators to effectively implement and support personalized learning strategies. Develop and advocate for educational policies that support the implementation of personalized learning paths in online high school programs. Policies should address funding allocations, technology infrastructure upgrades, and guidelines for equitable access to personalized educational resources.

Adapting SEL interventions to meet student needs: A research-practice partnership supporting students with emotional disabilities

Implementing school-based social-emotional learning (SEL) interventions requires consideration of both evidence-based practices as well as contextual fit based on student needs. One key approach to adapting SEL interventions is through research-practice partnerships between researchers, practitioners, and school district leaders. The current paper explores the ways a research-practice partnership supports an inclusive education intervention, called the Path Program, designed to support elementary students with emotional disabilities through both structural elements and evidence-based practices. Using the research-practice exchange in the Path Program to support a continuous improvement approach to adaptation, the Path Program uses a Plan Do Study Act cycle to continually infuse SEL activities with a trauma-informed approach to meet student needs. The paper offers crucial information for SEL interventions on the strengths of leveraging research-practice partnerships for program adaptation as well as implementation. Impact statementThe current paper importantly sheds light on ways to leverage a research-practice partnership to support intervention adaptation and implementation to better meet student needs while maintaining evidence-based practices. Specifically, the paper details the importance of infusing SEL activities with trauma-informed practices to meet students with emotional disabilities’ needs and the ways a research-practice partnership supports that process. The paper informs researchers, practitioners, and school leaders on ways to use partnerships to develop stronger SEL interventions.

Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics (Extended Abstract)

Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics (Extended Abstract)

Optimization of fresh produce delivery path based on C-W saving algorithm

Because fresh agricultural products have the characteristics of difficulty in preserving freshness, short shelf life, and are perishable and fragile, so they have extremely high requirements for warehousing, distribution, and other multiple links, and their distribution costs have caused some economic pressure on Z. To reduce the distribution costs of Z's distribution centers and to maximize the satisfaction of customers' needs, it is very important to carry out reasonable planning of the distribution paths. How to scientifically and reasonably formulate the vehicle distribution plan and arrange the distribution path program, shorten the time of fresh agricultural products in transit and reduce the company's logistics and distribution costs, maximize customer satisfaction, and accurately and timely provide customers with high-quality fresh agricultural products and high-quality distribution services are of great significance. In this paper, by summarizing the existing literature on the optimization of the distribution path of fresh agricultural products cold chain logistics, the C.W saving algorithm is used to optimize the path of the distribution center of Z company.

Multi-task collaborative method based on manifold optimization for automated test case generation based on path coverage

Automated test case generation based on path coverage is not only a large-scale black-box optimization problem, but also a key scientific problem in software automatic testing technology. Evolutionary algorithms and other search-based algorithms are representative methods for this problem. However, existing research mainly focuses on the multi-function case, where generating test cases for multiple functions is difficult due to the combinatorial explosion of the path number. In this paper, we propose a multi-task collaborative method based on manifold optimization by considering the topological manifold relationship between the test case space (decision space) and the program path space (target space). This method achieves the goal of collaborative optimization for different function coverage tasks by coordinating the allocation of computing resources and knowledge transfer mechanisms among them. To verify the effectiveness of the proposed method, we compare it with general solution methods for single-function automated test case generation based on path coverage, such as the manifold-inspired search-based algorithm. The experimental results show that the proposed method outperforms the compared single-function optimization algorithms especially on programs with strong coding similarities. This study verifies the feasibility of collaborative optimization methods for solving large-scale black-box optimization problems, such as the automated test case generation based on path coverage, and expands their application scenarios.

‘Pavee Leaders’ – enablers and inhibitors to accessing higher education for Irish travellers

ABSTRACT Historically, Irish Travellers have experienced social exclusion and marked educational disadvantage. Less than 1% of Travellers are documented as being in third-level education, with only 167 adult Travellers reported as holding a third-level qualification (0.5%). This low rate of participation has been targeted by the Department of Further and Higher Education, Research, Innovation and Science (DFHERIS) Programme for Access to Higher Education (PATH). The PATH programme (Strand 3) funded partnership initiatives between higher education institutes in regional clusters. The SOAR partnership initiative in the South Cluster developed a Travellers in Education workstream which delivered three key initiatives: (1) two Leadership in the Community courses delivered at University College Cork (UCC); (2) a Mentoring in Education course delivered at Munster Technological University (MTU) Kerry Campus and (3) a Traveller Graduate Network (TGN) developed at Munster Technological University (MTU) Cork Campus. Two of these initiatives (1 and 2) are bespoke part-time courses for adult learners and the third is a network to support Travellers accessing full-time higher education. Drawing from evaluations of these three initiatives, this paper provides insights into Travellers’ experience of accessing and progressing through Higher Education and identifies supports and barriers.

WeBridge: Synthesizing Stored Procedures for Large-Scale Real-World Web Applications

Modern web applications use databases to store their data. When processing user requests, these applications retrieve and store data in the database server, which incurs network round trips. These round trips significantly increase the application's latency. Previous approaches have attempted to reduce these round trips by prefetching query results or batching database accesses. However, neither method can efficiently reduce the latency when some queries depend on previous queries' results. In real-world applications, nearly 50% of the queries depend on the result of other queries. This paper presents WeBridge, the first system capable of synthesizing stored procedures for large-scale real-world web applications. First, WeBridge employs concolic execution technique to analyze the applications and generate stored procedures for hot program paths. Then, it seamlessly integrates the stored procedures into the application by extending the database access library. Finally, it improves the efficiency of the stored procedures with speculative execution. Evaluation using real-world web applications and workloads show that WeBridge achieves up to 79.8% median latency reduction and up to 2× peak throughput.

Forging a Path to College Persistence: An Experimental Evaluation of the Detroit Promise Path Program

Detroit students who obtain a college degree overcome many obstacles to do so. This article reports the results of a randomized evaluation of a program meant to provide support to low-income community college students. The Detroit Promise Path program was designed to complement an existing College Promise scholarship, providing students with coaching, summer engagement, and financial incentives. The evaluation found that students offered the program enrolled in more semesters and earned more credits compared with those offered the scholarship alone. However, at the 3-year mark, there were no discernable impacts on degrees earned. This article examines systemic barriers to degree completion and offers lessons for the design of interventions to increase equity in postsecondary attainment.

Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted Models

Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted Models

A Novel Seed Generation Approach for Vulnerability Mining Based on Generative Adversarial Networks and Attention Mechanisms

Coverage-guided fuzzing has been widely applied in software error and security vulnerability detection. The fuzzing technique based on AFL (American Fuzzy Loop) is a common coverage-guided fuzzing method. The code coverage during AFL fuzzing is highly dependent on the quality of the initial seeds. If the selected seeds’ quality is poor, the AFL may not be able to detect program paths in a targeted manner, resulting in wasted time and computational resources. To solve the problems that the seed selection strategy in traditional AFL fuzzing cannot quickly and effectively generate high-quality seed sets and the mutated test cases cannot reach deeper paths and trigger security vulnerabilities, this paper proposes an attention mechanism-based generative adversarial network (GAN) seed generation approach for vulnerability mining, which can learn the characteristics and distribution of high-quality test samples during the testing process and generate high-quality seeds for fuzzing. The proposed method improves the GAN by introducing fully connected neural networks to balance the competitive adversarial process between discriminators and generators and incorporating attention mechanisms, greatly improving the quality of generated seeds. Our experimental results show that the seeds generated by the proposed method have significant improvements in coverage, triggering unique crashes and other indicators and improving the efficiency of AFL fuzzing.

XFuzz: Machine Learning Guided Cross-Contract Fuzzing

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present <small>xFuzz</small>, a machine learning guided smart contract fuzzing framework. The machine learning models are trained with novel features (e.g., word vectors and instructions) and are used to filter likely benign program paths. Comparing with existing static tools, machine learning model is proven to be more robust, avoiding directly adopting manually-defined rules in specific tools. We compare <small>xFuzz</small> with three state-of-the-art tools on 7,391 contracts. <small>xFuzz</small> detects 18 exploitable cross-contract vulnerabilities, of which 15 vulnerabilities are exposed for the first time. Furthermore, our approach is shown to be efficient in detecting non-cross-contract vulnerabilities as well&#x2014;using less than 20&#x0025; time as that of other fuzzing tools, <small>xFuzz</small> detects twice as many vulnerabilities.

Battling against Protocol Fuzzing: Protecting Networked Embedded Devices from Dynamic Fuzzers

N etworked E mbedded D evices (NEDs) are increasingly targeted by cyberattacks, mainly due to their widespread use in our daily lives. Vulnerabilities in NEDs are the root causes of these cyberattacks. Although deployed NEDs go through thorough code audits, there can still be considerable exploitable vulnerabilities. Existing mitigation measures like code encryption and obfuscation adopted by vendors can resist static analysis on deployed NEDs, but are ineffective against protocol fuzzing. Attackers can easily apply protocol fuzzing to discover vulnerabilities and compromise deployed NEDs. Unfortunately, prior anti-fuzzing techniques are impractical as they significantly slow down NEDs, hampering NED availability. To address this issue, we propose Armor—the first anti-fuzzing technique specifically designed for NEDs. First, we design three adversarial primitives—delay, fake coverage, and forged exception—to break the fundamental mechanisms on which fuzzing relies to effectively find vulnerabilities. Second, based on our observation that inputs from normal users consistent with the protocol specification and certain program paths are rarely executed with normal inputs, we design static and dynamic strategies to decide whether to activate the adversarial primitives. Extensive evaluations show that Armor incurs negligible time overhead and effectively reduces the code coverage (e.g., line coverage by 22%-61%) for fuzzing, significantly outperforming the state-of-the-art.

Discussion on the Upgrading Path of Engineering Programs in Local Applied Universities for New Economy

Discussion on the Upgrading Path of Engineering Programs in Local Applied Universities for New Economy

Career Path Program Evaluation for the Retention of Kawasaki Kisen Kaisha Ltd. Scholars

Career Path Program Evaluation for the Retention of Kawasaki Kisen Kaisha Ltd. Scholars