909 research outputs found
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid
The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
Towards Machine Learning-Based FPGA Backend Flow: Challenges and Opportunities
Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus on machine learning-based CAD tasks to suggest some pertinent research areas requiring more focus in CAD design. The development of open-source benchmarks optimized for an end-to-end machine learning experience, intra-FPGA optimization, domain-specific accelerators, lack of explainability and federated learning are the issues reviewed to identify important research spots requiring significant focus. The potential of the new cloud-based architectures to understand the application of the right ML algorithms in FPGA CAD decision-making steps is discussed, together with visualizing the scenario of incorporating more intelligence in the cloud platform, with the help of relatively newer technologies such as CAD as Adaptive OpenPlatform Service (CAOS). Altogether, this research explores several research opportunities linked with modern FPGA CAD flow design, which will serve as a single point of reference for modern FPGA CAD flow design
Machine Learning for Multi-Layer Open and Disaggregated Optical Networks
L'abstract è presente nell'allegato / the abstract is in the attachmen
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Instant delivery services, such as food delivery and package delivery, have
achieved explosive growth in recent years by providing customers with
daily-life convenience. An emerging research area within these services is
service Route\&Time Prediction (RTP), which aims to estimate the future service
route as well as the arrival time of a given worker. As one of the most crucial
tasks in those service platforms, RTP stands central to enhancing user
satisfaction and trimming operational expenditures on these platforms. Despite
a plethora of algorithms developed to date, there is no systematic,
comprehensive survey to guide researchers in this domain. To fill this gap, our
work presents the first comprehensive survey that methodically categorizes
recent advances in service route and time prediction. We start by defining the
RTP challenge and then delve into the metrics that are often employed.
Following that, we scrutinize the existing RTP methodologies, presenting a
novel taxonomy of them. We categorize these methods based on three criteria:
(i) type of task, subdivided into only-route prediction, only-time prediction,
and joint route\&time prediction; (ii) model architecture, which encompasses
sequence-based and graph-based models; and (iii) learning paradigm, including
Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively,
we highlight the limitations of current research and suggest prospective
avenues. We believe that the taxonomy, progress, and prospects introduced in
this paper can significantly promote the development of this field
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