Adaptive multi-vehicle motion counting

Phung

2022 IEEE International Conference on Services Computing (SCC)

et al. 2022

Sharing real-time data originating from connected devices is crucial to real-world intelligent Internet of Things (IoT) applications, i.e., based on artificial intelligence/machine learning (AI/ML). Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. It is still an open challenge to support multiple parties (aka tenants) with these dynamic contracts based on the data value for their specific contextual purposes.This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT Data Hub owners to define extensible rules and metrics to govern the tenants in accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have developed a proof-of-concept prototype for sharing sensitive data such as surveillance camera videos to illustrate our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time.

Section: Discussion On Edge-based Services and Policies Evolutionmentioning

confidence: 99%

Section: Context Sensingmentioning

confidence: 99%

Context-driven Policies Enforcement for Edge-based IoT Data Sharing-as-a-Service

Phung

2022 IEEE International Conference on Services Computing (SCC)

et al. 2022

“…For instance, Dundas [20] proposed a custom LOC measure that could be used to evaluate code quality by counting so-called simple lines, condition-based lines, looped-based lines, and exceptionhandling lines. Nguyen et al [13] discussed how logical and physical lines should be counted for different programming languages. Finally, Jones [21] proposed a list of guidelines for counting LOC for different purposes (e.g., counting reusable code, scaffold code, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Although the standard LOC measures are widely used, they are also often criticized as being sensitive to programming style, programming language, or the presence of generated code [10]- [12]. The community has also observed problems with inconsistency and lack of transparency in the counting algorithms (and tools) [11], [13], [14]. Finally, the study by Barb et al [15] questioned the ability of standard LOC measures to proxy for complexity, size, productivity, and effort.…”

Section: Introductionmentioning

confidence: 99%

Mining Task-Specific Lines of Code Counters

Ochodek,

Durczak,

Nawrocki

et al. 2023

IEEE Access

Context: Lines of code (LOC) is a fundamental software code measure that is widely used as a proxy for software development effort or as a normalization factor in many other softwarerelated measures (e.g., defect density). Unfortunately, the problem is that it is not clear which lines of code should be counted: all of them or some specific ones depending on the project context and task in mind? Objective: To design a generator of task-specific LOC measures and their counters mined directly from data that optimize the correlation between the LOC measures and variables they proxy for (e.g., code-review duration). Method: We use Design Science Research as our research methodology to build and validate a generator of task-specific LOC measures and their counters. The generated LOC counters have a form of binary decision trees inferred from historical data using Genetic Programming. The proposed tool was validated based on three tasks, i.e., mining LOC measures to proxy for code readability, number of assertions in unit tests, and code-review duration. Results: Task-specific LOC measures showed a "strong" to "very strong" negative correlation with code-readability score (Kendall's τ ranging from −0.83 to −0.76) compared to "weak" to "strong" negative correlation for the best among the standard LOC measures (τ ranging from −0.36 to −0.13). For the problem of proxying for the number of assertions in unit tests, correlation coefficients were also higher for task-specific LOC measures by ca. 11% to 21% (τ ranged from 0.31 to 0.34). Finally, task-specific LOC measures showed a stronger correlation with code-review duration than the best among the standard LOC measures (τ = 0.31, 0.36, and 0.37 compared to 0.11, 0.08, 0.16, respectively). Conclusions: Our study shows that it is possible to mine task-specific LOC counters from historical datasets using Genetic Programming. Task-specific LOC measures obtained that way show stronger correlations with the variables they proxy for than the standard LOC measures.INDEX TERMS software measurement, software size, lines of code, LOC

Cross-Industry AI Applications

Deep Learning-Based Vehicle Detection and Classification in Traffic Management for Intelligent Transportation Systems

Hemalakshmi,

Muthukumaravel

2024

For intelligent transportation systems (ITSs) and planning that makes use of exact location intelligence, accurate vehicle classification and detection are topics that are becoming more vital. Although computer vision and deep learning (DL) are smart techniques, there remain issues with effective real-time detection and categorization. The requirement for a large training dataset and the domain-shift problem are two prevalent issues in this area. This research proposes the use of the YOLOv3 (you only look once) algorithm to provide an effective and efficient framework for vehicle recognition and classification from traffic video surveillance data. Along with the other deep learning-based algorithms like faster RCNN and VGG16 pre-trained model, a machine learning model using bag of features (BoF) + support vector machine (SVM) is also compared and analyzed for detecting and classifying vehicles.