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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Multilingual Synopses of Movie Narratives: A dataset for Vision-Language Story Understanding

Multilingual Synopses of Movie Narratives: A Dataset for Vis...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Yidan Yu, Jianfei Li, Boyang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761681

Story video-text alignment, a core task in computational story understanding, aims to align video clips with corresponding sentences in their descriptions. However, progress on the task has been held back by the scarcity of manually annotated video-text correspondence and the heavy concentration on English narrations of Hollywood movies. To address these issues, in this paper, we construct a large-scale multilingual video story dataset named Multilingual Synopses of Movie Narratives (M-SYMON), containing 13,166 movie summary videos from 7 languages, as well as manual annotation of fine-grained video-text correspondences for 101.5 hours of video. Training on the human annotated data from M-SYMON outperforms the SOTA methods by 15.7 and 16.2 percentage points on Clip Accuracy and Sentence IoU scores, respectively, demonstrating the effectiveness of the annotations. As benchmarks for future research, we create 6 baseline approaches with different multilingual training strategies, compare their performance in both intra-lingual and cross-lingual setups, exemplifying the challenges of multilingual video-text alignment. The dataset is released at: https://***/insundaycathy/M-SyMoN. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Correspondence Learning via Group-wise Motion Enhancement 14

Correspondence Learning via Group-wise Motion Enhancement

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Zhang, Yifan Kang, Zheng Lin, Xiaolang Lai, Taotao Lin, Lihui Li, Zuoyong Fuzhou University College of Computer and Data Science Fuzhou China Fujian Agriculture and Forestry University College of Computer and Information Sciences Fuzhou China Fujian University of Technology School of Transportation Fuzhou China Minjiang University School of Computer and Big Data Fuzhou China Wuyi University School of Mathematics and Computer Science Nanping China

ISBN: (纸本)9798350356670

The outlier removal methods are usually based on Multi-Layer Perceptron (MLP) for capturing context, which neglect the underlying motion information in images. Recently, CNN-based methods have attempted to address this issue by estimating motion fields and have made significant progress. However, CNNs tend to diminish the discontinuities of motion fields, making it difficult for the network to capture multiple motion patterns in complex scenes. Therefore, we propose a Group-wise Motion Enhancement (GME) block that groups motion fields along the channel dimension to learn multiple motion patterns. This enables the network to identify inliers under different motion patterns more accurately. Experiments show that compared with several existing methods, our method achieves advanced performance on the relative pose estimation task. © 2024 IEEE.

关键词： Motion estimation

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Blockchain-based trust management for data exchange in internet of vehicle network

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Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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LegoDroid: flexible Android app decomposition and instant installation

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science China(Information sciences) 2023年第4期66卷 155-173页

作者： Yi LIU Yun MA Xusheng XIAO Tao XIE Xuanzhe LIU School of Computer Science Peking University Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education Institute for Artificial Intelligence Peking University Department of Computer and Data Sciences Case Western Reserve University

Current mobile applications(apps) have become increasingly complicated with increasing features that are represented on the graphical user interface associated with application programming interfaces(APIs) to access backend functionality and data. Meanwhile, apps suffer from the “software bloat” in volume. Some app features may be redundant, with respect to those features(from other apps) that the users already desirably and frequently use. However, the current app release model forces users to download and install a full-size installation package rather than optionally choosing only their desired features. Large-size apps can not only increase the local resource consumption, such as CPU, memory, and energy, but also inevitably compromise the user experience, such as the slow load time in the app. In this article, we first conduct an empirical study to characterize the app feature usage when users interact with Android apps,and surprisingly find that users access only a very small subset of app features. Based on these findings,we design a new approach named Lego Droid, which automatically decomposes an Android app for flexible loading and installation, while preserving the expected functionality with a fast and instant app load. With such a method, a slimmer bundle will be downloaded and host the target APIs inside the original app to satisfy users' requirements. We implement a system for Lego Droid and evaluate it with 1000 real-world Android apps. Compared to the original full-size apps, apps optimized by Lego Droid can substantially improve the load time by reducing the base bundle and feature bundles by 13.06% and 10.93%, respectively,along with the app-package installation size by 44.17%. In addition, we also demonstrate that Lego Droid is quite practical with evolving versions, as it can produce substantial reusable code to alleviate the developers' efforts when releasing new app versions.

关键词： performance software bloat instant installation mobile applications program analysis

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A Machine Learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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An improved method for small object detection in aerial images based on YOLOv8

An improved method for small object detection in aerial imag...

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2024 International Conference on computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2024

作者： Ma, Liting Zhao, Junzhe Hsiang, Han-Cheng School of Data and Computer Science Xiamen Institute of Technology Xiamen China

ISBN: (纸本)9781510689275

In recent years, detecting objects in aerial images has emerged as a crucial area of study within the domain of computer vision. However, due to obstacles like the limited size of objects, dense distributions, and class imbalance in these images, traditional object detection methods often face issues like missed detections and low performance. In response to these challenges, this study introduces an enhanced YOLOv8s model designed to optimize small object detection. First, to enhance feature extraction capabilities for objects of different scales, the C2f structure within the backbone of the baseline YOLOv8s model is modified by PKI(Poly Kernel Inception)Block from PKINet, thereby strengthening its recognition capabilities across multi-scale targets. Second, to further elevate the efficiency of the neck network for detecting small targets, the MCAttention module is incorporated as part of the C2f design in the neck, leading to the enhanced C2fMCAttn module, which augments the neck network's effectiveness in modeling features from small object areas. Moreover, a 160×160 detection head is added to the detection layer, tailored to enhance the detection of small objects. The experiments were conducted on the VisDrone2019 drone aerial imagery dataset. The results indicate that the improved model outperformed the baseline YOLOv8s model. It achieved a 5.3% increase in precision, a 5% increase in recall and a 4.4% increase in mAP@50. Furthermore, the proposed model outperforms other mainstream object detection methods, exhibiting superior detection effectiveness. © 2025 SPIE.

关键词： MCAttention PKI Block Small object detection YOLOv8s

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K-Means Clustering with Local Distance Privacy

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Big data Mining and Analytics 2023年第4期6卷 433-442页

作者： Mengmeng Yang Longxia Huang Chenghua Tang the Data61 Commonwealth Scientific and Industrial Research OrganizationMelbourne 3168Australia the School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China. the School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541010China

With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real ***,these analyses are based on users’data,which disclose users’*** differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering ***,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy *** solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering *** of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each *** be specific,we generate a noisy distance first and then synthesize the high-dimensional data *** propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,***,we introduce a way to generate synthetic data records in high-dimensional *** experimental evaluation results show that our methods outperform the traditional methods significantly.

关键词： K-means clustering local differential privacy data analysis

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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

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High-Confidence Computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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Simple models are all you need: Ensembling stylometric, part-of-speech, and information-theoretic models for the ALTA 2024 Shared Task 22nd

Simple models are all you need: Ensembling stylometric, part...

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22nd Annual Workshop of the Australasian Language Technology Association, ALTA 2024

作者： Thomas, Joel Hoang, Gia Bao Mitchell, Lewis School of Computer and Mathematical Sciences Adelaide Data Science Centre The University of Adelaide SA5005 Australia

The ALTA 2024 shared task concerned automated detection of AI-generated text. Large language models (LLM) were used to generate hybrid documents, where individual sentences were authored by either humans or a state-of-the-art LLM. Rather than rely on similarly computationally expensive tools like transformer-based methods, we decided to approach this task using only an ensemble of lightweight "traditional" methods that could be trained on a standard desktop machine. Our approach used models based on word counts, stylometric features, readability metrics, part-of-speech tagging, and an information-theoretic entropy estimator to predict authorship. These models, combined with a simple weighting scheme, performed well on a held-out test set, achieving an accuracy of 0.855 and a kappa score of 0.695. Our results show that relatively simple, interpretable models can perform effectively at tasks like authorship prediction, even on short texts, which is important for democratisation of AI as well as future applications in edge computing. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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