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arXiv 2022年

作者： Luo, Haokuan Yue, Albert Hong, Zhang-Wei Agrawal, Pulkit Improbable AI Lab The Department Of Electrical Engineering And Computer Science MIT United States

We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task of navigating to a target object in indoor environments. Our method is motivated from primary failure modes of prior state-of-the-art: poor exploration, inaccurate object identification, and agent getting trapped due to imprecise map construction. We make three contributions to mitigate these issues: (i) First, we show that existing map-based methods fail to effectively use semantic clues for exploration. We present a semantic-agnostic exploration strategy (called STUBBORN) without any learning that surprisingly outperforms prior work. (ii) We propose a strategy for integrating temporal information to improve object identification. (iii) Lastly, due to inaccurate depth observation the agent often gets trapped in small regions. We develop a multi-scale collision map for obstacle identification that mitigates this issue. Website: https://***/Improbable-AI/Stubborn. © 2022, CC BY.

关键词： Semantics

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Scalable and Independent Learning of Nash Equilibrium Policies in n-Player Stochastic Games with Unknown Independent Chains

arXiv

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arXiv 2023年

作者： Qin, Tiancheng Etesami, S. Rasoul The Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Lab University of Illinois Urbana-Champaign UrbanaIL61801 United States

We study a subclass of n-player stochastic games, namely, stochastic games with independent chains and unknown transition matrices. In this class of games, players control their own internal Markov chains whose transitions do not depend on the states/actions of other players. However, players’ decisions are coupled through their payoff functions. We assume players can receive only realizations of their payoffs, and that the players can not observe the states and actions of other players, nor do they know the transition probability matrices of their own Markov chain. Relying on a compact dual formulation of the game based on occupancy measures and the technique of confidence set to maintain high-probability estimates of the unknown transition matrices, we propose a fully decentralized mirror descent algorithm to learn an ϵ-NE for this class of games. The proposed algorithm has the desired properties of independence, scalability, and convergence. Specifically, under no assumptions on the reward functions, we show the proposed algorithm converges in polynomial time in a weaker distance (namely, the averaged Nikaido-Isoda gap) to the set of ϵ-NE policies with arbitrarily high probability. Moreover, assuming the existence of a variationally stable Nash equilibrium policy, we show that the proposed algorithm converges asymptotically to the stable ϵ-NE policy with arbitrarily high probability. In addition to Markov potential games and linear-quadratic stochastic games, this work provides another subclass of n-player stochastic games that, under some mild assumptions, admit polynomial-time learning algorithms for finding their stationary ϵ-NE policies. Copyright © 2023, The Authors. All rights reserved.

关键词： Mirrors

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Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection

arXiv

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arXiv 2024年

作者： Zhuang, Wenhao Mao, Yuyi He, Hengtao Xie, Lei Song, Shenghui Ge, Yao Ding, Zhi The Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Continental-NTU Corporate Lab Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of California at Davis DavisCA United States

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical. Although graph neural network (GNN)-based data detectors can achieve decent detection accuracy at reasonable computational cost, they fail to best harness prior information of transmitted data. To further minimize the data detection error of OTFS systems, this letter develops an AMP-GNN-based detector, leveraging the approximate message passing (AMP) algorithm to iteratively improve the symbol estimates of a GNN. Given the inter-Doppler interference (IDI) symbols incur substantial computational overhead to the constructed GNN, learning-based IDI approximation is implemented to sustain low detection complexity. Simulation results demonstrate a remarkable bit error rate (BER) performance achieved by the proposed AMP-GNN-based detector compared to existing baselines. Meanwhile, the proposed IDI approximation scheme avoids a large amount of computations with negligible BER degradation. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

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ECHO: An Approach to Enhance Use Case Quality Exploiting Large Language Models

ECHO: An Approach to Enhance Use Case Quality Exploiting Lar...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Gabriele De Vito Fabio Palomba Carmine Gravino Sergio Di Martino Filomena Ferrucci Department of Computer Science Software Engineering (SeSa) Lab University of Salerno Italy Department of Electrical Engineering and Information Technologies University of Naples "Federico II" Italy

UML use cases are commonly used in software engineering to specify the functional requirements of a system since they are an effective tool for interacting with stakeholders thanks to the use of natural languages. However, producing high-quality use cases can be challenging due to the lack of precise guidelines and suitable tools. This can lead to problems, e.g. inaccuracy and incompleteness, in the derived software artifacts and the final product. Recent advancements in Natural Language Processing and Large Language Models (LLMs) can provide the premises for developing tools supporting activities based on natural languages. In this paper, we propose ECHO, a novel approach for supporting software engineers in enhancing the quality of UML use cases using LLMs. Our approach consists of a co-prompt engineering approach and an iterative and interactive process with the LLM to improve the quality of use cases, based on practitioners’ feedback. To prove the feasibility of the proposal, we instantiated the approach using ChatGPT and performed a controlled experiment to assess its effectiveness by involving seven software engineering professionals. Three were part of the experimental group and used ECHO to improve the quality of the use cases. Three others were the control group and enhanced the quality of use cases manually. Finally, the last participant acted as an oracle, blind w.r.t. the groups, and evaluated the quality of the enhanced use cases, both qualitatively by means of a questionnaire, and quantitatively, by means of the Use Case Points metric. Results show that ECHO can effectively support software engineers to improve use cases’ quality thanks to the prompts suitably designed to interact with ChatGPT.

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HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees

arXiv

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arXiv 2024年

作者： García-Castellanos, Alejandro Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Bekkers, Erik J. Kragic, Danica Amsterdam Machine Learning Lab University of Amsterdam Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Engineering Sciences KTH Royal Institute of Technology Sweden

We propose HyperSteiner – an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the Delaunay triangulation. The central idea is rephrasing Steiner tree problems with three terminals as a system of equations in the Klein-Beltrami model. Motivated by the fact that hyperbolic geometry is well-suited for representing hierarchies, we explore applications to hierarchy discovery in data. Results show that HyperSteiner infers more realistic hierarchies than the Minimum Spanning Tree and is more scalable to large datasets than Neighbor Joining. © 2024, CC BY.

关键词： Trees (mathematics)

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Enhanced Redundant Weighted Clustered Scheme for Damaged S-UAV

Enhanced Redundant Weighted Clustered Scheme for Damaged S-U...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

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Multiple Redundant K-Means Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

Multiple Redundant K-Means Clustered Scheme Based on Weighte...

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IEEE Conference on Global Communications (GLOBECOM)

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATlab. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

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Language Models for Code-switch Detection of te reo Maori and English in a Low-resource Setting

Language Models for Code-switch Detection of te reo Maori an...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： James, Jesin Yogarajan, Vithya Shields, Isabella Watson, Catherine Keegan, Peter J. Jones, Peter-Lucas Mahelona, Keoni Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand Strong AI Lab School of Computer Science The University of Auckland New Zealand Te Puna Wananga The University of Auckland New Zealand Te Hiku Media New Zealand

ISBN: (纸本)9781955917766

Te reo Maori, New Zealand's only indigenous language, is code-switched with English. Maori speakers are atleast bilingual, and the use of Maori is increasing in New Zealand English. Unfortunately, due to the minimal availability of resources, including digital data, Maori is under-represented in technological advances. cloud-based multilingual systems such as Google and Microsoft Azure support Maori language detection. However, we provide experimental evidence to show that the accuracy of such systems is low when detecting Maori. Hence, with the support of Maori community, we collect Maori and bilingual data to use natural language processing (NLP) to improve Maori language detection. We train bilingual sub-word embeddings and provide evidence to show that our bilingual embeddings improve overall accuracy compared to the publiclyavailable monolingual embeddings. This improvement has been verified for various NLP tasks using three bilingual databases containing formal transcripts and informal social media data. We also show that BiLSTM with pretrained Maori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Maori language. However, this research uses large models 'as is' for transfer learning, where no further training was done on Maori-English data. The best accuracy of 87% was obtained using BiLSTM with bilingual embeddings to detect Maori-English codeswitching points. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Embeddings

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Classification of Brain Tumors using MRI images based on Convolutional Neural Network and Supervised Machine Learning Algorithms 6

Classification of Brain Tumors using MRI images based on Con...

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Ali, Rawaa Al-Jumaili, Saif Duru, Adil Deniz Ucan, Osman Nuri Boyaci, Aytug Duru, Dilek Goksel Altinbas University Information Technologies Istanbul Turkey Altinbas University Electrical and Computer Engineering Istanbul Turkey Marmara University Neuroscience and Psychology Research in Sports Lab Istanbul Turkey Air Force Academy Department of Computer Engineering Istanbul Turkey German University Faculty of Science Turkish Istanbul Turkey

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Brain tumor is abnormal cells that originate from cranial tissue and is considered one of the most destructive diseases, and lead to the cause of death, where the early diagnosis is crucial for accelerating the therapy of brain tumors. Examining the patient's MRI scans is one traditional way of distinguishing brain cancers. The conventional approaches take a long time and are prone to human error, especially when dealing with huge amounts of data and diverse brain tumor classes. Artificial Intelligence (AI) is extremely useful for the strict detection and classification of several diseases in the brain. Convolutional Neural Network (CNN) is one of the modes techniques which act as a tumor classifier due to it shows high effectiveness for diagnosing brain tumors. That's why, in this research, we presented a hybrid method that merged a group of pre-Trained deep learning CNN patterns with a group of supervised classifiers in machine learning called, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA). We used an MRI image that consist of images of four brain tumor classes, namely glioma, meningioma, pituitary, and no tumor. We deduced the features extracted from the images by hiring three types of CNN called (GoogleNet, Shuffle-Net, and NasNet-Mobile). Depending upon the experimental consequences, ShuffleN et with SVM achieved the highest results according to the four categories of metrics evaluation that are Accuracy of 98.40%, Precision of 97%, Recall of 96.75%, and Fl-Score of 96.75%. Finally, we compared our results with different state-of-The-Art papers recently published and our proposed method show outperforms compared them. © 2022 IEEE.

关键词： Support vector machines

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