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Usability and Perceived Efficiency of an Adaptive Route Optimization Solution for Commercial Vehicles

IEEE ACCESS 2025年 13卷 61091-61108页

作者： Anghelache, Florian Goga, Nicolae Marian, Constantin Viorel Alexandru Mitrea, Dan Pavaloiu, Ionel-Bujorel iSYS Profess Res & Dev Dept Bucharest 020312 Romania Natl Univ Sci & Technol POLITEHN Bucharest Fac Engn Foreign Languages FILS Dept Engn Foreign Languages Bucharest 060042 Romania Univ Groningen Mol Dynam Grp NL-9747 AG Groningen Netherlands

This paper details the outcome of a European scientific research and development project, funded to prototype a commercial solution for vehicle fleets. While current route optimization solutions have data sources such as real-time road traffic and weather conditions, our solution was developed to offer companies a proposed plan with adapted optimized routes, considering critical operational requirements, local conditions, and drivers' driving experience. The iRoute solution consists of a module for receiving vehicle positions from GPS devices, an integration layer for end-customer applications, a route optimization engine, and applications for web and mobile users. Unlike other approaches, this innovative solution leverages historical, well-known, and familiar route data to enhance, refine, and adapt the optimized operational routes. This way, the drivers benefit from a more effective daily plan to follow based on business requirements such as locations, quantities, time, and distance. At the end of the day, adding these adjustments results in achieving up to a 20% improvement in the accuracy of executed routes, in terms of distance and time, compared to the planned route, outperforming standard optimization algorithms. This allows business owners to rely more confidently on the optimization results, providing a safer itinerary for their drivers. Also, due to lower incident rates on these known and preferred itineraries, the overall business will benefit from increased vehicle fleet availability and fewer days in service, with lower maintenance costs. The paper evaluates the usability and perceived efficiency value of the proposed solution by statistically analyzing feedback from dispatchers and business owners.

关键词： Vehicles Costs Clustering algorithms Business Routing Optimization Companies Usability software algorithms software Adaptive algorithms adaptive systems path planning roadmaps (technology planning) road traffic control vehicle routing

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Interval Mean Estimation Under (ε,δ)-Local Differential Privacy

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 2074-2086页

作者： Zhang, Junming Wang, Jingru Long, Shigong Li, Yanen Wang, Lun Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Peoples R China Guizhou Polytech Construct Guiyang 550014 Peoples R China

Local differential privacy (LDP) techniques obviate the need for trust in the data collector, as they provide robust privacy guarantees against untrusted data managers while simultaneously preserving the accuracy of statistical information derived from the privatized data. As a result, these methods have garnered considerable interest and research efforts. In particular, (epsilon,delta) -LDP schemes have been utilized across a range of statistical tasks. Nonetheless, existing (epsilon,delta) -LDP mechanisms for mean estimation suffer from challenges such as elevated estimation errors and diminished data utility. To address this problem, we propose two novel (epsilon,delta) -LDP algorithms for mean estimation. Specifically, we design a one-dimensional piecewise mean estimation algorithm, which perturbs the input data into intervals, thereby reducing noise addition and enhancing both accuracy and efficiency. Building on this foundation, we extend our approach to multi-dimensional data, resulting in a multi-dimensional piecewise mean estimation algorithm. Furthermore, we conduct a theoretical analysis to derive both the variance and error bounds for the proposed algorithms. Extensive experiments conducted on real datasets demonstrate the high practicality of our algorithms for data statistical tasks, showing significant improvements in data utility.

关键词： Privacy Protection Differential privacy Accuracy Perturbation methods Noise Estimation error Probabilistic logic Big Data software algorithms Privacy protection local differential privacy mean estimation data utility

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CURE: Simulation-Augmented Autotuning in Robotics

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2825-2842页

作者： Hossen, Md Abir Kharade, Sonam O'Kane, Jason M. Schmerl, Bradley Garlan, David Jamshidi, Pooyan Univ South Carolina Columbia SC 29208 USA Texas A&M Univ College Stn TX 77840 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact nontrivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e.g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e.g., optimization steps, allotted time) and lacking transferability. This article proposes causal understanding and remediation for enhancing robot performance (CURE)-a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and the robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e.g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation.

关键词： Robots Optimization Transfer learning Robot sensing systems Navigation Safety Computer bugs Bayes methods Tuning software algorithms Causal inference optimization robot testing robotics and cyber-physical systems

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CODEI: Resource-Efficient Task-Driven Codesign of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2727-2748页

作者： Milojevic, Dejan Zardini, Gioele Elser, Miriam Censi, Andrea Frazzoli, Emilio Swiss Fed Inst Technol Inst Dynam Syst & Control CH-8092 Zurich Switzerland Empa Swiss Fed Labs Mat Sci & Technol Chem Energy Carriers & Vehicle Syst Lab CH-8600 Dubendorf Switzerland MIT Lab Informat & Decis Syst Cambridge MA 02139 USA

This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using false negative rate and false positive rate across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an integer linear programming approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a codesign optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the codesign problem of mobile robots as CODEI, short for codesign of embodied intelligence. A case study on developing an autonomous vehicle for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.

关键词： Robot sensing systems Robots software Hardware Optimization software algorithms Planning Pipelines Mobile robots Decision making Codesign mobile robots sensor selection

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Candidate Selection algorithms for Hybrid IP/SDN Networks With Multi-Link Failures

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1219-1231页

作者： Vuppalapati, Navya Venkatesh, T. G. Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India

A longstanding problem in the context of computer networks is the issue of recovery from link failures. A promising solution to safeguard all single link failures (SLF) is through partial up gradation of certain IP routers (Internet Protocol(IP)) to software Defined Networking (SDN) switches using CSA (Candidate Selection algorithms). The resultant network comprises a combination of IP routers and SDN switches, forming a hybrid IP/SDN network. Most of the existing approaches primarily address SLFs, yet real-world scenarios may involve multiple link failures (MLF). Hence, it's crucial to investigate CSA for Hybrid IP/SDN with MLF. In this paper, we first introduce four CSA strategies tailored for hybrid IP/SDN networks with MLF. Subsequently, we present a CSA designed to accommodate network scaling or expansion. We emphasize the advantage of CSAs based on non-shortest path approach. Additionally, we propose a budget constraint-based CSA to aid in selecting suitable candidate switches within a specified budget. We evaluate the performance of hybrid IP/SDN networks employing the proposed CSAs, considering average repair path length (AMLU) and average maximum link utilization (AMLU) in the post-recovery network across diverse real-world network topologies. It will be shown that the hybrid IP/SDN with proposed CSA for MLFs will reduce the ARPL by 31.37%, 22.15%, and 18.64%, respectively, for NSF, Attmpls, and Abilene networks.

关键词： Switches IP networks Routing protocols Maintenance engineering software defined networking Tunneling Routing Network topology Topology software algorithms Candidate selection algorithm hybrid IP/SDN multi-link failures network scaling repair path length software defined networks

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Metaheuristic algorithms in Optimizing Deep Neural Network Model for software Effort Estimation

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IEEE ACCESS 2021年 9卷 60309-60327页

作者： Khan, Muhammad Sufyan Jabeen, Farhana Ghouzali, Sanaa Rehman, Zobia Naz, Sheneela Abdul, Wadood COMSATS Univ Dept Comp Sci Islamabad 44000 Pakistan King Saud Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11543 Saudi Arabia

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

关键词： software Estimation software algorithms Optimization Genetic algorithms Analytical models Market research software effort estimation meta-heuristic algorithms deep neural networks deep learning

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Fast time-frequency transform algorithms and their applications to real-time software implementation of AC-3 audio codec

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1998年第2期44卷 413-423页

作者： Chen, YC Tsai, CW Wu, JL Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10764 Taiwan

AC-3 audio coding technology is a kind of Perceptual Audio Coder (PAC) developed by the Dolby Company. Up to 5 full-bandwidth channels and one subwoofer channel (cutoff at 120Hz) are available in AC-3 to provide multi-channel, low bit rate, and high perceptual quality of audio. This explains why AC-3 has become the audio standard of many international standards. In this paper, we focus on the real-time software implementation issues of AC-3, Two fast algorithms of the time-frequency transform, one for memory economization and the other is for time domain subsampling, are presented. Meanwhile, we will state the current performance status of our AC-3 decoder.

关键词： Time frequency analysis software algorithms Application software Psychology Bit rate Psychoacoustic models Signal processing algorithms Audio coding Decoding Multimedia systems

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What Do AI/ML Practitioners Think About AI/ML Bias?

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IEEE software 2025年第1期42卷 114-118页

作者： Pant, Aastha Hoda, Rashina Turhan, Burak Tantithamthavorn, Chakkrit Monash Univ Clayton Vic 3800 Australia Monash Univ Fac Informat Technol Software Engn Clayton Vic 3800 Australia Univ Oulu Fac Informat Technol & Elect Engn Res Unit M3S Software Engn Oulu 90014 Finland

Presents a panel discussion on the topic of AI/ML Practitioners.

关键词： Artificial intelligence Ethics Companies Australia Training software engineering Collaboration software algorithms Interviews Information technology

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Enhancing Decomposition-Based algorithms by Estimation of Distribution for Constrained Optimal software Product Selection

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第2期24卷 245-259页

作者： Xiang, Yi Yang, Xiaowei Zhou, Yuren Huang, Han South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510006 Peoples R China

This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem.

关键词： Optimization software software algorithms Probabilistic logic Estimation software engineering Evolutionary computation Constrained multiobjective optimization decomposition-based multiobjective algorithm estimation of distribution (EoD) optimal software product selection (OSPS) search-based software engineering (SBSE)

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INTERACTIVE software FOR EVALUATING AND TEACHING DIGITAL RELAYING algorithms

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IEEE TRANSACTIONS ON POWER SYSTEMS 1990年第1期5卷 346-352页

作者： SACHDEV, MS NAGPAL, M ADU, T Power System Research Group University of Saskatchewan Saskatoon SAS Canada

An interactive software is presented for evaluating algorithms for digital relay designs. The software includes signal processing and protection modules that are used in digital relays. The software also includes facilities for generating data for testing the performance of digital relay designs. The performance of the designs can also be studied by using data recorded at a power system bus or generated by another software. The results can be displayed in the form of graphs for visual inspection. The software is interactive and can be used with ease.< >

关键词： Education Digital relays Signal processing algorithms Power system protection software performance Protective relaying software algorithms Algorithm design and analysis Digital signal processing software testing

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