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2008 ASEE Annual Conference and Exposition

作者： Morell, Lueny Borri, Claudio Rajala, Sarah Ramakrishna, Seeran Quadrado, Jose Carlos Petrie, Maria M. Larrondo Fraser, Duncan Laporte, Bruno Garboan, Adriana Fouger, Xavier Hoyer, Hans Hewlett-Packard Department of Chemical Engineering University of Puerto Rico Mayagüez Puerto Rico Universita' di Firenze Department of Computational Mechanics of Structures University of Florence Italy SEFI IFEES Within Socrates II Mississippi State University Department of Electrical and Computer Engineering Mississippi State University National University of Singapore Instituto Superior de Engenharia de Lisboa Portugal ISEL Research and Project Center on Electrical Machines Control and Applications Florida Atlantic University Academic and International Affairs for the College of Engineering and Computer Science Florida Atlantic University International Federation of Engineering Education Societies Latin American and Caribbean Consortium of Engineering Institutions University of Cape Town Academic Development Faculty of Engineering and the Built Environment UCT World Bank Human Development Development Group World Bank Institute Politechnica University of Bucharest Politehnica University of Bucharest Faculty of Electronics and Telecommunications Dassault Systemes Dassault Systemes Global Learning Leads the PLM Academy American Society for Engineering Education International Programs and Strategy ASEE

Globalization is making both developed and developing countries think about effective and efficient strategies that will advance their economies and social development. Throughout the history of civilization, engineering has played a critical role in economic development. Engineers are key not only in solving local problems but also in knowledge creation and knowledge transfer. Thus, it is imperative that technical know-how be supplemented with professional skills to develop an 'adaptive leader' who is capable of addressing the multiple challenges of an ever changing world. The key-question posed by the 21st century global economy to engineering educators and stake-holders is this: "How can education in science and technology help to reduce poverty, boost socio-economic development, and take the right decisions for sustainable and environmental compatible development?" To answer these questions, a global approach is needed: and this can only be accomplished by a "team" which has its roots in all regions of the world, i.e. which is enabled to think globally and then act locally. There's a need to establish effective engineering education processes of high quality around the world to assure a global supply of well-prepared engineering graduates. This paper describes the reasons for the creation in October 2006 of IFEES - the International Federation of Engineering Education Societies. It will describe the vision, mission, and key strategies of IFEES. It will also describe some of the initiatives currently underway and how the various engineering education stakeholders can leverage and benefit from engaging with IFEES, thus strengthening the organizational capacity of engineering education societies throughout the world. The paper will share plans to help members learn from each other's best practices and even failures and how those organizations that have been in existence for quite some time (ASEE was founded in the late 19th century) can contribute to the institutio

关键词： Engineering education

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Decision Making on Optimal Selection of Advertising Agencies using Machine Learning

Decision Making on Optimal Selection of Advertising Agencies...

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International Conference on Information Management (ICIM)

作者： M. Clement Joe Anand Nivetha Martin Arockiasamy Clementking S. Rani Sujitha Priyadharshini S. Siva Department of Mathematics Mount Carmel College (Autonomous) Bengaluru Karnataka India Department of Mathematics Arul Anandar College (Autonomous) Karumathur Tamil Nadu India Human Resource Development Mount Carmel College (Autonomous) Bengaluru Karnataka India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India CSST Academy of Research and Development (CARD) Pvt. Ltd Bengaluru Karnataka India Department of Computer Science & Applications Reva University Bengaluru Karnataka India

The decision makers of every business firm take strenuous efforts to achieve the target of sales promotion using different marketing strategies. In recent times, advertising is becoming one of the most preferred and adopted marketing techniques to reach customers at larger magnitude. The business firms irrespective of their production sizes and scales associate with various advertising agencies to familiarize their products to the customers. The advertising strategic approach of marketing also gives rise to a decision-making problem on the optimal choice of advertising agencies as the wrong selection of the agencies harms the sales. This research paper intends to propose a decision making (DM) model integrating both multi–criteria decision methods and machine learning algorithms to make optimal selection of advertising agencies. The proposed DM model considers 25 alternatives of advertising agencies, six criteria, linguistic analytic hierarchy process (LAHP) and random forest algorithm for ranking the advertising agencies. The machine learning (ML) algorithm identifies the feasible alternatives with the help of criterion weights obtained using the method of LAHP. This method is more compatible as it minimizes the risks of decision making by grouping the alternatives into two groups of acceptance and rejection based on their likelihood. The computations are done using Python programming language especially in Google Colabs.

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction

arXiv

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arXiv 2024年

作者： Chen, Yanshuo Hu, Zhengmian Chen, Wei Huang, Heng Department of Computer Science University of Maryland College ParkMD20742 United States Center of Bioinformatics and Computational Biology College ParkMD20740 United States Department of Biostatistics University of Pittsburgh PittsburghPA15261 United States Department of Pediatrics UPMC Children’s Hospital of Pittsburgh PittsburghPA15224 United States Department of Human Genetics University of Pittsburgh PittsburghPA15261 United States

Predicting single-cell perturbation responses requires mapping between two unpaired single-cell data distributions. Optimal transport (OT) theory provides a principled framework for constructing such mappings by minimizing transport cost. Recently, Wasserstein-2 (W2) neural optimal transport solvers (e.g., CellOT) have been employed for this prediction task. However, W2 OT relies on the general Kantorovich dual formulation, which involves optimizing over two conjugate functions, leading to a complex min-max optimization problem that converges slowly. To address these challenges, we propose a novel solver based on the Wasserstein-1 (W1) dual formulation. Unlike W2, the W1 dual simplifies the optimization to a maximization problem over a single 1-Lipschitz function, thus eliminating the need for time-consuming min-max optimization. While solving the W1 dual only reveals the transport direction and does not directly provide a unique optimal transport map, we incorporate an additional step using adversarial training to determine an appropriate transport step size, effectively recovering the transport map. Our experiments demonstrate that the proposed W1 neural optimal transport solver can mimic the W2 OT solvers in finding a unique and "monotonic" map on 2D datasets. Moreover, the W1 OT solver achieves performance on par with or surpasses W2 OT solvers on real single-cell perturbation datasets. Furthermore, we show that W1 OT solver achieves 25 ~ 45× speedup, scales better on high dimensional transportation task, and can be directly applied on single-cell RNA-seq dataset with highly variable genes. Our implementation and experiments are open-sourced at https://***/poseidonchan/w1ot. © 2024, CC BY-NC-SA.

关键词： Cell culture

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Invariant inter-subject relational structures in the human visual cortex

arXiv

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arXiv 2024年

作者： Lipman, Ofer Grossman, Shany Friedman, Doron Hel-Or, Yacov Malach, Rafael Efi Arazi School of Computer Science Reichman University Herzliya Israel Max Planck Institute for Human Development Berlin Germany Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Institute of Psychology Universitsät Hamburg Germany Sammy Ofer School of Communications Reichman University Herzliya Israel Department of Brain Sciences Weizmann Institute of Science Rehovot Israel

It is a fundamental of behavior that different individuals see the world in a largely similar manner. This is an essential basis for humans’ ability to cooperate and communicate. However, what are the neuronal properties that underlie these inter-subject commonalities of our visual world? Finding out what aspects of neuronal coding remain invariant across individuals’ brains will shed light not only on this fundamental question but will also point to the neuronal coding scheme at the basis of visual perception. Here we address this question by obtaining intracranial recordings from three cohorts of patients taking part in a different visual recognition task (overall 19 patients and 244 high-order visual contacts included in the analyses) and examining the neuronal coding scheme that was most consistent across individuals’ visual cortex. Our results highlight relational coding – expressed by the set of similarity distances between profiles of pattern activations – as the most consistent representation across individuals. Alternative coding schemes, such as population vector coding or linear coding, failed to achieve similar inter-subject consistency. Our results thus support relational coding as the central neuronal code underlying individuals’ shared perceptual content in the human brain. © 2024, CC0.

关键词： Neurons

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Blockchain Technology for Enhanced Transparency, Efficiency, and Security in Supply Chain Management

Blockchain Technology for Enhanced Transparency, Efficiency,...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Santhosh Krishna B V M. Vanitha Lakshmi S Sathyanarayanan Balaji L Harshith C M S. Kaliappan Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of ECE School of Engineering Presidency University Bengaluru Sri Sathya Sai University for Human Excellence Gulbarga Karnataka Dept of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Division of Research and Development Lovely Professional University Punjab

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Though innovation in barcodes, GPS, and IoT sensors has enhanced the scope of modern supply chains to track assets to a larger extent, it raises a critical challenge for centralized systems in terms of data breaches, unauthorized access, and manipulation. Blockchain technology serves as an optimum decentralized and tamper-proof solution for further improvement of data integrity, security, and transparency. This paper discusses the integration of blockchain into supply chains its benefits and challenges and its potential to change global supply chain operations at its very core. In a nutshell, the system proposed outlines a blockchain-based framework using IoT, GPS, and smart contracts that guarantee traceability, security, and efficiency throughout the supply chain in real time. The results are showing high improvement in transparency, security, and operational efficiency, which will certainly lead to its usage cases in varied sectors.

关键词： Technological innovation Supply chain management Data integrity Supply chains Smart contracts Transforms Blockchains Security Stakeholders Global Positioning System

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No Fear of Representation Bias Graph Contrastive Learning with Calibration and Fusion

SSRN

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SSRN 2024年

作者： Li, Jindong Liu, Yixin Xing, Qianli Wang, Qi Pan, Shirui The School of Artificial Intelligence Jilin University Changchun130012 China The Department of Data Science and AI Monash University MelbourneVIC3800 Australia The College of Computer Science and Technology Jilin University Changchun130012 China The School of Information and Communication Technology Griffith University Gold CoastQLD4222 Australia Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China

Graph contrastive learning (GCL) is receiving growing attention due to its promising self-supervised learning capability on graph-structured data. While GCL approaches have demonstrated significant potential, their inherited stochastic perturbation-based augmentation causes the representation bias on representation hyperspace. Such a serious issue, unfortunately, causes the representation drift between the distributions of original and augmented embeddings, misleading the contrastive training for the graph neural network encoders. Moreover, the information loss caused by bias can further result in incomplete representation problem, hindering the self-supervised framework from capturing comprehensive structural and attributive patterns. To address these challenges, this paper introduces the CAlibratioN aNd FusiON model (CANNON for short). CANNON introduces a cross-view calibrating contrast mechanism to enhance the agreement between the original and augmented representations, which effectively alleviates the representation drift. Concurrently, CANNON overcomes the incomplete representation problem with a fused-view calibrating contrast mechanism for contrastive learning application on the fused embeddings, where a transformer-based cross-view fusion block is used to integrate two augmented views into a comprehensive one. The extensive experimental results, executed on 9 real-world datasets, demonstrate that CANNON significantly outperforms existing state-of-the-art methods. The code is available at https://***/jindongli-Ai/CANNON. © 2024, The Authors. All rights reserved.

关键词： Calibration

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Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading

Self-supervised learning of a tailored Convolutional Auto En...

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European Signal Processing Conference (EUSIPCO)

作者： Zahra Tabatabaei Adrián Colomer Kjersti Engan Javier Oliver Valery Naranjo Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano HUMAN-tech Universitat Politècnica de València Spain Department of Artificial Intelligence Tyris Tech S.L. Valencia Spain ValgrAI - Valencian Graduate School and Research Network for Artificial Intelligence Department of Electrical Engineering and Computer Science University of Stavanger Norway

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct $128\times 128\times 3$ patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of the proposed SSL paradigm is the automatic grading of histopathological patches of prostate cancer. The presented framework reports promising results on the validation set, obtaining an overall accuracy of 83% and on the test set, achieving an overall accuracy value of 76% with F1-score of 77% in G4.

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Platoon-Based Distributed Control for Automated Material Handling Systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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