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arXiv 2025年

作者： Erdogan, Enes Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Information Technology Halmstad University Halmstad Sweden

Recognition of human manipulation actions in real-time is essential for safe and effective human-robot interaction and collaboration. The challenge lies in developing a model that is both lightweight enough for real-time execution and capable of generalization. While some existing methods in the literature can run in real-time, they struggle with temporal scalability, i.e., they fail to adapt to long-duration manipulations effectively. To address this, leveraging the generalizable scene graph representations, we propose a new Factorized Graph Sequence Encoder network that not only runs in real-time but also scales effectively in the temporal dimension, thanks to its factorized encoder architecture. Additionally, we introduce Hand Pooling operation, a simple pooling operation for more focused extraction of the graph-level embeddings. Our model outperforms the previous state-of-the-art real-time approach, achieving a 14.3% and 5.6% improvement in F1-macro score on the KIT Bimanual Action (Bimacs) Dataset and Collaborative Action (CoAx) Dataset, respectively. Moreover, we conduct an extensive ablation study to validate our network design choices. Finally, we compare our model with its architecturally similar RGB-based model on the Bimacs dataset and show the limitations of this model in contrast to ours on such an object-centric manipulation dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen Huazhong Agricultural University College of Informatics Hubei Wuhan China Charles Sturt University School of Computing Mathematics & Engineering Wagga WaggaNSW Australia Charles Sturt University Artificial Intelligence & Cyber Futures Institute BathurstNSW Australia University of Mines and Technology Faculty of Engineering Tarkwa Ghana Xinyu University School of Mathematics and Computer Science China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. © 2024 IEEE.

关键词： Contrastive Learning

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods 3

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier. © 2023 IEEE.

关键词： Support vector machines

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Augmented Lagrangian Method for Linear Programming Using Smooth Approximation 6th

Augmented Lagrangian Method for Linear Programming Using Sm...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Moosaei, Hossein Ketabchi, Saeed Syed, Mujahid N. Bazikar, Fatemeh Department of Informatics Faculty of Science Jan Evangelista Purkyně University Ústí nad Labem Czech Republic Department of Applied Mathematics Faculty of Mathematical Sciences University of Guilan Rasht Iran Department of Industrial and Systems Engineering Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals KSA Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran

ISBN: (纸本)9783031503191

The augmented Lagrangian method can be used for finding the least 2 - norm solution of a linear programming problem. This approach’s primary advantage is that it leads to the minimization of an unconstrained problem with a piecewise quadratic, convex, and differentiable objective function. However, this function lacks an ordinary Hessian, which precludes the use of a fast Newton method. In this paper, we apply the smoothing techniques and solve an unconstrained smooth reformulation of this problem using a fast Newton method. Computational results and comparisons are illustrated through multiple numerical examples to show the effectiveness of the proposed algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Newton-Raphson method

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Operation Performance of a Ground Source Heat Pump System in the Mediterranean Climate Zone. First Results

SSRN

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SSRN 2023年

作者： Christodoulides, Paul Christou, Christakis Aresti, Lazaros Stylianou, Iosifina I. Florides, Georgios A. Faculty of Engineering and Technology Cyprus University of Technology Limassol Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

This paper presents the configuration and performance of a Low Enthalpy Geothermal System successfully installed and utilized in the Mediterranean climate zone. Additionally, it examines the performance of different types of Ground Heat Exchangers (GHE), all installed in the same System. The Ground Source Heat Pump (GSHP) of the system consists of vertical ground heat exchangers (GHEs) in five different configurations, one double helicoidal coil in a well and an open loop (well) system. The entire system is constantly monitored by a Building Management System (BMS) that records the energy, flow, volume, incoming and outgoing temperature at critical points of the system. Based on the recorded values, the performance of the System was analyzed in a winter and summer working period, after examining the power flows in and out from critical points of the System. Results show higher heat exchange values inside the open well, both in winter and summer mode suggesting the usage of this type of GHE, where applicable. Additionally, the electric power consumed by the chillers which are the largest electricity consumers within the System, is approximately five times lower than the power placed in the building by the Geothermal System (COPsys between 4.5 and 5). In terms of primary energy savings, we can say with confidence the GSHP systems working under Mediterranean climate zone conditions, can be consider as high efficiency solutions. © 2023, The Authors. All rights reserved.

关键词： Geothermal heat pumps

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Towards a Multilingual Platform for Gamified Morphology Learning 7

Towards a Multilingual Platform for Gamified Morphology Lear...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Bektas, Fatih Dereli, Bihter Hayta, Furkan Sahin, Erkin Ali, Ubey Eryigit, Gulsen Istanbul Technical University Department of Artificial Intelligence & Data Engineering Istanbul Turkey Istanbul Turkey Nlp Research Group Istanbul Technical University Faculty of Computer & Informatics Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Mobile-assisted language learning is an emerging trend in language education. Recently, the use of gamification for complex morphology learning has been perceived positively by morphologically rich language learners (namely, Turkish as a foreign language learners). Generating automatic explicit grammar exercises with the use of finite-state transducers (FST) in a gamified environment is a new and practical approach in this field. The integration of such platforms into new languages should be easily implementable in order to reduce the development efforts. This study is a first attempt towards this goal. The paper introduces an architecture for a multilingual platform supporting gamified morphology learning. As a test case, the French language module is developed in addition to Turkish. Experiences with these languages are explained and the stages for adding additional languages are described. The study reveals that morphological complexity is an important issue to consider, and the morphological differences of the target language should be considered while designing a mobile language learning application. © 2022 IEEE.

关键词： Morphology

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology 16

Pending Interest Table (PIT) Performance Analysis in Named D...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Sucipto, Adi Jupriyadi Ahdan, Syaiful Arifin, Hasan Nur Zaki Hamidi, Eki Ahmad Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40132 Indonesia

ISBN: (纸本)9781665455947

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests. © 2022 IEEE.

关键词： Topology

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Badieah Sam Farisa Chaerul Haviana Sri Mulyono Channa Abdullah Salim Muhammad Fatih Adli Moh Taufik Febriansah Wiktasari Noor Suhana Sulaiman Azliza Yaqob Informatics Engineering Department Universitas Islam Sultan Agung Semarang Indonesia Computer Engineering Technology Politeknik Negeri Semarang Semarang Indonesia Faculty of Computer Media and Technology Management University College of TATI Terengganu Malaysia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model.

关键词： computer science Biological system modeling Computational modeling Semantics computer architecture Transformers Data models Encoding Recommender systems Context modeling

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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