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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Navid Pasiar Arvin Mohammadi Sajjad Ahangar Navid Asadi Khomami Mehdi Tale Masouleh Department of Mechanical Eng. Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Eng. University of Tehran Tehran Iran

Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel Robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point.

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Security Enhancement in Consumer Enterprises using Neural Nets within the SIEM Framework

Security Enhancement in Consumer Enterprises using Neural Ne...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Arora, Saksham Kumar, Sudhakar Singh, Sunil K. Garg, Sahil Gupta, Brij B. Bansal, Shavi Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh India Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences Warsaw Poland Insights2Techinfo India Hong Kong Metropolitan University Hong Kong

ISBN: (纸本)9798331521165

With internet popularity increasing every day and more physical activities being brought to the online spectrum, Security Information and Event Management (SIEM) tools have emerged as the main way to tackle any threat intrusions in the network infrastructure of organizations. Threats are evolving and becoming more advanced with the advancements in technology, and detecting and preventing the attacks need more human intervention than ever. To prevent these attacks, artificial intelligence and neural networks are a great way to tackle the increasing needs of human intervention. This article, thus, proposes a novel framework that uses a neural network named FeedForward to develop a model for anomaly and threat protection. It then prompts the user and takes input when a threat is detected. On confirmation of a threat, the feedback is sent back to the neural network to train itself better for future threats and keep itself updated. Therefore, this article proposes a supervised learning and retraining framework on the neural network. The dataset chosen has 763,144 instances that are divided into 80% training and 20% testing dataset. On running the neural network on the dataset, we achieved an astonishing 99.2% accuracy. © 2025 IEEE.

关键词： Feedforward neural networks

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Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention 7

Saliency Region Detection in Complex Scenes Based on Multi-s...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wang, Yifeng Huo, Zhenfeng Liu, Aoli Zhao, Lin Wang, Di Xidian University The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shanxi Province Shanxi Province 710071 China Nanjing University of Science and Technology School of Computer Science and Engineering 210094 China

ISBN: (纸本)9781665441490

This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance. © 2021 IEEE.

关键词： Attention mechanism Channel weight attention Saliency detection Spatial weight attention

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Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing

arXiv

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arXiv 2023年

作者： Zhu, Guangming Wang, Siyuan Wu, Tianci Zhang, Liang School of Computer Science and Technology Xidian University China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China Xi’an Key Laboratory of Intelligent Software Engineering China

Free-hand sketches are appealing for humans as a universal tool to depict the visual world. humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category. For example, an airplane should at least have a fuselage and wings. Based on this analysis, a semantic component-level memory module is constructed and embedded in the proposed structured sketch recognition network in this paper. The memory keys representing semantic components of each sketch category can be self-learned and enhance the recognition network’s explainability. Our proposed networks can deal with different situations of sketch recognition, i.e., with or without semantic components labels of strokes. Experiments on the SPG and SketchIME datasets demonstrate the memory module’s flexibility and the recognition network’s explainability. The code and data are available at https://***/GuangmingZhu/SketchESC. © 2023, CC BY.

关键词： Semantics

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HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration

arXiv

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arXiv 2022年

作者： Fuhl, Wolfgang Department of Human Computer Interaction University Tübingen Tübingen72076 Germany

In this paper, we present a new approach for decomposing scan paths and its utility for generating new scan paths. For this purpose, we use the K-Means clustering procedure to the raw gaze data and subsequently iteratively to find more clusters in the found clusters. The found clusters are grouped for each level in the hierarchy, and the most important principal components are computed from the data contained in them. Using this tree hierarchy and the principal components, new scan paths can be generated that match the human behavior of the original data. We show that this generated data is very useful for generating new data for scan path classification but can also be used to generate fake scan paths. https://***/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FHPCGen&mode=list. Copyright © 2022, The Authors. All rights reserved.

关键词： K-means clustering

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Pistol: Pupil Invisible Supportive Tool to extract Pupil, Iris, Eye Opening, Eye Movements, Pupil and Iris Gaze Vector, and 2D as well as 3D Gaze

arXiv

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arXiv 2022年

作者： Fuhl, Wolfgang Weber, Daniel Eivazi, Shahram Department of Human Computer Interaction University Tübingen Tübingen72076 Germany

This paper describes a feature extraction and gaze estimation software, named Pistol that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector, eye movement types from pupil and iris velocities, marker detection, marker distance, 2D gaze estimation for the pupil center, iris center, pupil vector, and iris vector using Levenberg Marquart fitting and neural networks. The gaze signal is computed in 2D for each eye and each feature separately and for both eyes in 3D also for each feature separately. We hope this software helps other researchers to extract state-of-the-art features for their research out of their recordings. Link: https://***/d/8e2ab8c3fdd444e1a135/?p=%2FPISTOL&mode=list. © 2022, CC BY.

关键词： Vectors

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We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization 28th

We Are Sending You Back... to the Optimum! Fuzzy Time Trav...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Papetti, Daniele M. Tangherloni, Andrea Coelho, Vasco Besozzi, Daniela Cazzaniga, Paolo Nobile, Marco S. Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computer Science Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy Research Center Milan Italy

ISBN: (纸本)9783031900648

Particle Swarm Optimization (PSO) is a swarm intelligence meta-heuristics whose performance highly depends on the selection of its hyper-parameters, which control the particles’ exploration and exploitation capabilities during the search process. Since the tuning of the hyper-parameters is problem-dependent, settings-free methods are preferable. Fuzzy Self-Tuning PSO (FST-PSO) is a settings-free variant of PSO that exploits a Fuzzy Rule-Based System to determine the best hyper-parameter values for each particle dynamically. Despite this advantage, the optimization process might get stuck in local optima. Here, we propose a what-if strategy to generate and explore alternate swarm histories: what happens if we could go back in time and change something in the past so that one particle "was" different at the beginning of the optimization? Specifically, whenever the global best particle does not improve for a number of iterations, it is terminated and re-initialized, meanwhile sending the rest of the swarm back to the initial configuration. This approach, called "time travel" FST-PSO (FTT-PSO), works under the assumption that the convergence of any particle towards an optimum is strongly related to the influence of the global best particle. We compare the performance of FTT-PSO against FST-PSO on the benchmark suites used in IEEE CEC and GECCO competitions. Our results show that time traveling allows for outperforming both the standard and the multistart versions of FST-PSO. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Integrating Blended Learning in Teacher Training: A Comprehensive Review 1

Integrating Blended Learning in Teacher Training: A Comprehe...

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1st Mediterranean Smart Cities Conference, MSCC 2024

作者： Chekour, Mohammed Morabit, Nadir El Benqassou, Issam Lechhab, Abdelhamid El-Hars, Fouad Hafid, Moulay Mustapha Department of Computer Science Higher School of Education and Training Ibn Tofail University Kenitra Morocco Department of English Studies Faculty of Literature and Human Sciences Abdelmalek Essaadi University Tetouan Morocco Department of Physics Higher School of Education and Training Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9798350374001

This article provides a holistic overview comprising an analysis of blended learning`s role in teacher training, as its growing significance and influence have become increasingly diverse in vocational education. Therefore, to emphasize the importance of blended learning in the field of teacher training, this paper summarizes key findings from the most cited ten articles over the last two decades, which touch upon the various methods used and the main outcomes enhanced through the blended learning approach. With a combination of integrated traditional classroom settings and digital platforms, this study demonstrates that blended learning also contributes to the adaptation of teachers’ methods of training. The review highlights important trends, showcasing developed digital competencies and increased collaboration between teachers and teacher training institutions. Both pre-service and in-service training programs benefit from this integration. This synthetic study, which combines a review of current trends in blended learning with ongoing updates in the field under scrutiny, has become a key source of contemporary information. It not only aids researchers but also provides a platform for further development of ideas in vocational education. ©2024 IEEE.

关键词： blended learning digital skills in-service teachers pre-service teacher training traditional and online modes

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A multimodal emotion recognition model based on deep neural network with cross-media data feature fusion

A multimodal emotion recognition model based on deep neural ...

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2021 International Conference on Electronic Information Engineering and computer Communication, EIECC 2021

作者： Liu, Sheng-Tong Chen, Zhi-Hong Li, Peng-Hai Lu, Jia Shao, Xin-Wang Gao, Xiao-Qi Zhang, Hai-Wei Yang, Xiao-Ping Department of Optoelectronic Information Science and Engineering Institute of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China Institute of Brain-computer Interaction and Industrial Intelligent Network Tianjin University of Technology Tianjin China

ISBN: (数字)9781510653207

ISBN: (纸本)9781510653191

Affective computing is an interdisciplinary research area that includes machine learning and pattern recognition, psychology, and cognitive science. The aim is to research and develop theories, methods and systems that can recognize, interpret, process and simulate human emotions. In this article we propose a neural network model for multimodal emotion recognition based on cross-media data-feature fusion. Multimodal data fusion can effectively improve the accuracy of emotion recognition. We extract features from EEG data and facial images using a deep double-stream neural network and then merge them in a medium-term feature layer to identify three categories of emotions (sadness, calm, and happiness). The experimental results show that the detection accuracy can reach over 95%. Compared to the traditional single-modal emotion recognition method, the accuracy rate of emotion recognition based on EEG data and facial images has been significantly improved. It also proves that the multimodal medium-term feature layer fusion method has good applicability for emotion recognition. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Emotion Recognition

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Demand Forecasting in Transportation: A Graph Attention Networks for Predicting Bus Ridership

Demand Forecasting in Transportation: A Graph Attention Netw...

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International Conference on Advanced Communication Technology (ICACT)

作者： Tsetsentsengel Munkhbayar Zolzaya Dashdorj Zoljargal Jargalsaikhan Kang Tae-Koo Erdenebaatar Altangerel Department of Computer Science Mongolian University of Science and Technology Ulaanbaatar Mongolia Department of Human Intelligence and Robot Engineering Sangmyung University Seoul Republic of Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Demand prediction in transportation systems plays a critical role in optimizing resources and improving service efficiency. This study explores demand prediction for Ulaanbaatar's public transportation network using Graph Attention Networks (GATs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). GATs effectively capture spatial relationships, achieving the best performance while GANs struggle with stability and convergence issues. The findings emphasize the potential of using graph-based methods that incorporate key stations in the analysis of public transportation networks for predicting transit demand.

关键词： Deep learning Neural networks Demand forecasting Predictive models Generative adversarial networks Stability analysis Convolutional neural networks Resource management Public transportation Convergence

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