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Pair-wise selective classification with dynamic sampling for shipment importer prediction 23

Pair-wise selective classification with dynamic sampling for...

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15th international conference on machine learning and computing, icmlc 2023

作者： Chen, Xi Tan, Daniel Stanley Gupta, Prakash Bromuri, Stefano Computer Science Department Open University of the Netherlands Heerlen Netherlands Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands

ISBN: (纸本)9781450398411

Whenever a shipped package crosses the border, logistic companies have to declare importer information for the clearance process. This information is not always provided by the customer, causing delays and additional expenses. Fortunately, importer information can often be inferred from historical shipments. The current technical standard, even in big companies, is to use a feature-weighted nearest neighbor approach based on domain knowledge. Nearest neighbors assume that each sample point is represented independently and is fixed in some high dimensional space. This makes it difficult to integrate higher-order pair-wise relationships such as transaction frequency from shipper to receiver transaction, because the features are now changing dependent on the pairs. This would require a complex ad-hoc feature engineering and metric learning to capture pairwise relationships properly with nearest neighbors. In this paper, we propose a framework for importer prediction based on a pair-wise classification approach that allows us to capture higher order pair-wise relationships. We also incorporate an auxiliary neural network that can reliably reject shipments that our model could not predict well such as shipments with new importers that are not in the historical data. This allows us to pass the difficult cases to a human agent instead of naively making an incorrect prediction. Our proposed pair-wise solution outperforms the industry standard by a significant margin of precision across a wide range of recall values. © 2023 Owner/Author.

关键词： Forecasting

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learning-based Vulnerability Detection in Binary Code 2022

Learning-based Vulnerability Detection in Binary Code

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14th international conference on machine learning and computing, icmlc 2022

作者： Aumpansub, Amy Huang, Zhen School of Computing DePaul University United States

ISBN: (纸本)9781450395700

Cyberattacks typically exploit software vulnerabilities to compromise computers and smart devices. To address vulnerabilities, many approaches have been developed to detect vulnerabilities using deep learning. However, most learning-based approaches detect vulnerabilities in source code instead of binary code. In this paper, we present our approach on detecting vulnerabilities in binary code. Our approach uses binary code compiled from the SARD dataset to build deep learning models to detect vulnerabilities. It extracts features on the syntax information of the assembly instructions in binary code, and trains two deep learning models on the features for vulnerability detection. From our evaluation, we find that the BLSTM model has the best performance, which achieves an accuracy rate of 81% in detecting vulnerabilities. Particularly the F1-score, recall, and specificity of the BLSTM model are 75%, 95% and 75% respectively. This indicates that the model is balanced in detecting both vulnerable code and non-vulnerable code. © 2022 ACM.

关键词： Binary codes

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A Robust Framework Based on Mutual Information and Contrastive learning for Multimodal Violence Detection in Videos 23

A Robust Framework Based on Mutual Information and Contrasti...

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15th international conference on machine learning and computing, icmlc 2023

作者： Xiong, Guoding Zhou, Yongmei Zhou, Dong Lin, Nankai School of Information Science and Technology Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangdong University of Foreign Studies Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781450398411

With the development of modern information, the security of online videos content is receiving more and more *** work often ignores the correlation between different modalities. In order to solve the above problem, the task of violence detection is proposed to identify and locate violent frames in videos. In this paper, we propose a robust framework based on mutual information and contrastive learning for multimodal violence detection in videos. We use mutual information maximization to improve the correlation between audio and visual modalities. Meanwhile, we leverage contrastive learning to control modality fusion to capture modality invariance. We propose a robust framework consisting of three modules: 1) utilize a graph convolutional neural network module to capture global relationships across multiple video frames;2) demonstrate a mutual information module to strengthen the correlation between audio and visual modalities;3) utilize the contrastive learning module to capture the modality invariance to facilitate modality fusion. For comparison with previous studies, we conduct extensive experiments on a public XD-Violence dataset. Experimental results show that our method achieves state-of-the-art performance. © 2023 ACM.

关键词： Convolutional neural networks

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Analyze Hand Motor Capabilities for Potential Autistic Intervention: A Feasibility Study

Analyze Hand Motor Capabilities for Potential Autistic Inter...

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international conference on machine learning and Cybernetics (icmlc)

作者： Carrie M. Toptan Dinghuang Zhang Honghai Liu School of Computing University of Portsmouth Portsmouth United Kingdom

This paper aims to explore differences in hand motor capabilities with a focus for potential autistic intervention. In order to achieve that, a predefined drawing scenario is introduced with a metric of three complexity levels and predefined motion protocols. Then, a vision-based acquisition system for hand motor capability is proposed. It is evident that detecting differences in hand motor capabilities has a huge potential to detect hand motion difficulties in Autism. Hence, this study paves the way for the use of technology for early diagnosis and potential intervention in Autism.

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Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

Optimal Minimum Energy Path Planning of Mobile Robot Based o...

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2023 international conference on machine learning and Cybernetics, icmlc 2023

作者： Du, Jiang Da-Lin, Zhou Gong-Fa, Li Bao-Li, Lu Jun-Tong, Yun Ying, Liu Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Yichang443002 China Wuhan University of Science and Technology Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9798350303780

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing. © 2023 IEEE.

关键词： Genetic algorithms

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ZeroLess-DARTS: Improved Differentiable Architecture Search with Refined Search Operation and Early Stopping 23

ZeroLess-DARTS: Improved Differentiable Architecture Search ...

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15th international conference on machine learning and computing, icmlc 2023

作者： Fayyazifar, Najmeh Ahderom, Selam Samadiani, Najmeh Maiorana, Andrew Dwivedi, Girish Harry Perkins Institute of Medical Research University of Western Australia Perth Australia School of Allied Health Faculty of Health Sciences Curtin University WA Australia School of Science Edith Cowan University Perth Australia CSIRO Manufacturing Private Bag 10 Clayton Australia

ISBN: (纸本)9781450398411

Differentiable architecture search (DARTS) method has gained noticeable popularity in neural architecture search (NAS) domain as it reduces the required search time compared to reinforcement learning and evolutionary based NAS algorithms. However, some further studies have indicated that the search algorithm of DARTS may be suboptimal, and its performance may deteriorate over the search process. In this paper, we provided a detailed performance analysis of the DARTS search algorithm (on the CIFAR10 image classification task) from different aspects such as changes in accuracies of derived architectures at each search epoch, the trend of changes in strengths of different operations over successive epochs, and the number of skip connections per normal cells. We propose ZeroLess-DARTS that considerably improves original DARTS performance on the CIFAR10 dataset (Cohen's D = 2.213), by refining the operation space in the search procedure and introducing an early stopping criterion. We show that our approach is generalizable to time series classification tasks by evaluating the performance of our model on one-dimensional ECG signals for WCT (Wide Complex Tachycardia) classification (Cohen's D = 1.706). © 2023 ACM.

关键词： learning algorithms

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A Review of Systems for Educational User Data Storage and Security Through Decentralised Blockchain Storage Systems

A Review of Systems for Educational User Data Storage and Se...

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international conference on machine learning and Cybernetics (icmlc)

作者： Dylan Warman David Tien Muhammad Ashad Kabir School of Computing and Mathematics Charles Sturt University Bathurst New South Wales Australia

This paper provides a comprehensive review of the significant literature in the area of user data security with a specific focus on how this integrates with the area of blockchain technologies and zero-knowledge proofs within the context of educational data storage. The overall objective of this review is to provide an in-sight into what has already been made available, with regards to implementation, and how these form an overall direction for the field. The purpose of which is to assist in the efforts to provide a solution that unifies the existing solutions and frameworks and provides real world benefits by targeting the highlighted research gaps.

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Transfer learning Model for Target Segment Generation on Sequential Behavior and Advertisement Interaction History 23

Transfer Learning Model for Target Segment Generation on Seq...

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15th international conference on machine learning and computing, icmlc 2023

作者： Kim, Hong-Hoe Palczewski, Tomasz Zhu, Yingnan Zhao, Xiangyuan Samsung Research America Mountain ViewCA United States

ISBN: (纸本)9781450398411

Online advertisement is a key methodology to increase a company's RoI (Return on Investment). With the recent success of deep learning, many approaches generate target segments by predicting who will click the advertisement from their advertisement interaction history (e.g., user A clicked/did not click advertisement B at time t). However, from their sequential behavior history (e.g., user A interacted with item B with a rating value at time t), we can notice that users could click the advertisement out of short-period curiosity or by accident even though their rating on this specific item was low. Unfortunately, many recommendation approaches will still believe that this user has an interest in the item because in these models the user's sequential behavior history is neglected. We believe that considering both the user's sequential behavior on the advertised item and advertisement interaction behavior together will help us to decide the user's advertisement click willingness. Thus, in this paper, we propose a model that first learns the user's item preference (rating) information using the transformer model on the sequential behavior history. Finally, we transfer the learned item preference information and train the model to predict the item advertisement click willingness of users on advertisement interaction history. The experiment results on real-world data, containing information on more than twenty-eight million users, have verified that using both a sequential behavior and advertisement interaction history performs better than considering just advertisement interaction history. © 2023 ACM.

关键词： Deep learning

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QoE-Aware Adaptive Bitrate Algorithm Based on Subepisodic Deep Reinforcement learning for DASH 23

QoE-Aware Adaptive Bitrate Algorithm Based on Subepisodic De...

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15th international conference on machine learning and computing, icmlc 2023

作者： Yang, Dujia Song, Changjian Wang, Jian Zhu, Rangang Yang, Jun'An National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9781450398411

Recently, mobile video service is booming and its traffic accounts for the vast majority of network traffic. The adaptive bitrate (ABR) algorithm in dynamic adaptive streaming over HTTP (DASH) is the key technology to improve user's quality of experience (QoE). However, existing ABR algorithms still have some problems such as poor universality, high complexity, too much domain knowledge and lack of attention to initial buffering (IB). To this end, we propose a QoE-aware ABR algorithm based on subepisodic deep reinforcement learning (DRL) for DASH. Starting from the episodic attributes of video service, we introduce the efficient episodic DRL to model the ABR problem for the first time. Secondly, through analyzing problems of direct application of episodic DRL, a new algorithm with the idea of divide and conquer is designed creatively. The algorithm divides video session into two subepisodes (IB and formal playing), and models and executes the optimization strategy respectively. These two subepisodes are unified in the reward function with the same goal of maximizing users' QoE. Finally, simulation results show that compared with the ABR algorithms based on traditional DRL and direct application of episodic DRL, our algorithms significantly improve not only the sample efficiency in training, but also both users' subjective QoE and objective indicators of video playing quality in test, including the performance in the IB phase. © 2023 ACM.

关键词： Reinforcement learning

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The Title of the Paper: Research on insurance marketing application based on hash link-table improved association rule algorithm 2022

The Title of the Paper: Research on insurance marketing appl...

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14th international conference on machine learning and computing, icmlc 2022

作者： He, Xianmei Teng, Shaohua School of Computing Guangdong University of Technology China

ISBN: (纸本)9781450395700

Aiming at the problem of slow processing efficiency of Apriori algorithm when the amount of data is large, the association rule algorithm is combined with hash link-table, and an improved association rule algorithm based on hash link-table is proposed to solve the disadvantage of long average time-consuming of traditional association rule algorithm in finding frequent itemsets. The improved association rule algorithm through hash link-table is applied to the insurance marketing scenario with large data set, analyzes the insurance purchase behavior of a large number of customers in the insurance database, finds out the insurance product sales association rules, and accurately recommends the products they are interested in to customers, so as to increase the success rate of marketing. © 2022 ACM.

关键词： Association rules

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