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Stock Trend Prediction Assisted by Automatically Defined Polarity Scores of News Articles

Journal of Information science and Engineering 2024年第4期40卷 717-743页

作者： Jeng, Shuen-Lin Liu, Chiau-Hsuan Guan, Chin Min Department of Statistics Institute of Data Science Center for Innovative FinTech Business Models National Cheng Kung University Tainan City701 Taiwan

This study thoroughly integrated news information into the daily stock price up/down prediction. The predictors included the carefully selected technical features of stock prices, the word level and sentence level sentiment features, and the automatically defined polarity scores of news articles. The main goals of this study are to establish the functional polarity scores of news articles, build models with high prediction accuracy, and identify the important news feature effect for the price up/down prediction. Compared with the Long Short-Term Memory (LSTM) model of Recurrent Neural Networks, we take advantage of Multivariate Adaptive Regression Splines (MARS) as our primary prediction model for its capability in local feature building and its interpretability of selected features. An empirical analysis was carried out on the selected 100 individual stocks in Standard & Poor's 500 from 2019 to 2021. The overall average prediction accuracy for 100 stocks by the proposed MARS model was 0.653 which was significantly higher than 0.505 by the LSTM model. © 2024 Institute of Information science. All rights reserved.

关键词： Long short-term memory

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A Linked List Encryption Scheme for Image Steganography without Embedding

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computer Modeling in Engineering & sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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Performance Evaluation of a Multi-input Interleaved Boost Converter with a Tuned Proportional-integral Controller

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Chinese Journal of Electrical Engineering 2024年第4期10卷 119-128页

作者： Sucharita Pal Dola Sinha Mou Das Mahapatra Saibal Majumder Sovan Bhattacharya Chan Byopadhyay Department of Electrical Engineering Asansol Engineering CollegeAsansol 713305India Department of Electrical Engineering Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering(Data Science) Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering University of BremenBremen 28359Germany

The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.

关键词： Multi-input converter interleaved boost converter pulse-width modulation proportional-integral controller

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Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes 38

Provably Optimal Memory Capacity for Modern Hopfield Models:...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jerry Yao-Chieh Wu, Dennis Liu, Han Center for Foundation Models and Generative AI United States Department of Computer Science United States Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States

We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories. We present a tight analysis by establishing a connection between the memory configuration of KHMs and spherical codes from information theory. Specifically, we treat the stored memory set as a specialized spherical code. This enables us to cast the memorization problem in KHMs into a point arrangement problem on a hypersphere. We show that the optimal capacity of KHMs occurs when the feature space allows memories to form an optimal spherical code. This unique perspective leads to: (i) An analysis of how KHMs achieve optimal memory capacity, and identify corresponding necessary conditions. Importantly, we establish an upper capacity bound that matches the well-known exponential lower bound in the literature. This provides the first tight and optimal asymptotic memory capacity for modern Hopfield models. (ii) A sub-linear time algorithm U-Hop+ to reach KHMs' optimal capacity. (iii) An analysis of the scaling behavior of the required feature dimension relative to the number of stored memories. These efforts improve both the retrieval capability of KHMs and the representation learning of corresponding transformers. Experimentally, we provide thorough numerical results to back up theoretical findings. © 2024 Neural information processing systems foundation. All rights reserved.

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ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation 8

ICCVAE: Item Concept Causal Variational Auto-Encoder for top...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Feng, Jingyun Wang, Qianqian Huang, Zhejun Yang, Lili Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China

ISBN: (纸本)9798350302455

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100. © 2023 IEEE.

关键词： Recommender systems

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A Causal Inference Method Based on Front-Door Criterion and Difference in Differences for Analyzing Traffic Conditions 4

A Causal Inference Method Based on Front-Door Criterion and ...

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4th International Conference on computer, Big data and Artificial Intelligence, ICCBD+AI 2023

作者： Ding, Yixuan Wang, Qianqian Qi, Zimo Zhu, Liuxin Zhu, Youxin Luo, Lizhuo Yang, Lili Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China

ISBN: (纸本)9798350373233

In the period of pandemic, various regions implemented distinct control measures, leading to subtle changes in road traffic patterns. These fluctuations were further influenced by external factors such as public risk perception and weather conditions. Although prior research has employed observational methods to assess traffic states and applied causal inference techniques to passive data, the fusion of these approaches in the field of transportation remains relatively unexplored. This study endeavors to address this research gap by investigating the causal impact of pandemic containment policies on urban traffic in multiple cities across China. Our analytical framework combines the Additive Noise Model (ANM) algorithm with rigorous statistical tests designed to capture casual relationship. Specifically, we employ the front-door criterion and a difference-in-differences approach to validate these causal connections. Refutation tests and placebo tests are conducted. Ultimately, our methodology yields a generalized linear model optimized for predicting traffic patterns under varying intervention scenarios. Our research thus paves the way for incorporating causal inference methodologies into the formulation of resilient transportation strategies amid public health crises. © 2023 IEEE.

关键词： Additive noise

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Attention-based Bidirectional Long Short-Term Memory for Urban Traffic Prediction Using Multivariate data 3

Attention-based Bidirectional Long Short-Term Memory for Urb...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Yang, Mingyang Wang, Qianqian Yang, Lili Huang, Zhejun Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781510671485

With the continuous expansion of urban and population size, the external cost of traffic congestion is increasing daily. Urban traffic prediction using multivariate data is significant in solving these problems. However, traditional prediction methods are mostly based on merely statistical theory, which only includes a single variable, resulting in the precision accuracy not being guaranteed. Therefore, based on machine learning theory, this paper collects multivariate traffic data from the main sections of Shenzhen, China. With the help of the advantages of deep learning model in data analysis, urban traffic prediction using multivariate data is proposed based on considering the characteristics of multi-parameters, which solves the problem that the traffic prediction method is mainly based on a single variable and the auxiliary information is not considered enough. To fully consider the spatial-temporal characteristics of the traffic dataset, an ABi-LSTM prediction method concerning the influence of upstream and downstream road sections is proposed, solving the problem of low prediction accuracy caused by imperfect consideration of spatial-temporal characteristics in previous traffic predictions. The results provide an important reference for traffic management departments to alleviate traffic congestion effectively, moreover, it’s convenient for residents to understand road information more clearly and make reasonable travel choices. © 2023 SPIE.

关键词： Long short-term memory

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Peer Collaboration in DBLP Using Graph Convolutional Network

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SN computer science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP Graph convolutional network Graph mining Social networks

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Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis 38

Point-PRC: A Prompt Learning Based Regulation Framework for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sun, Hongyu Ke, Qiuhong Wang, Yongcai Chen, Wang Yang, Kang Li, Deying Cai, Jianfei Department of Computer Science Renmin University of China China Department of Data Science & AI Monash University Australia

This paper investigates the 3D domain generalization (3DDG) ability of large 3D models based on prevalent prompt learning. Recent works demonstrate the performances of 3D point cloud recognition can be boosted remarkably by parameter-efficient prompt tuning. However, we observe that the improvement on downstream tasks comes at the expense of a severe drop in 3D domain generalization. To resolve this challenge, we present a comprehensive regulation framework that allows the learnable prompts to actively interact with the well-learned general knowledge in large 3D models to maintain good generalization. Specifically, the proposed framework imposes multiple explicit constraints on the prompt learning trajectory by maximizing the mutual agreement between task-specific predictions and task-agnostic knowledge. We design the regulation framework as a plug-and-play module to embed into existing representative large 3D models. Surprisingly, our method not only realizes consistently increasing generalization ability but also enhances task-specific 3D recognition performances across various 3DDG benchmarks by a clear margin. Considering the lack of study and evaluation on 3DDG, we also create three new benchmarks, namely base-to-new, cross-dataset and few-shot generalization benchmarks, to enrich the field and inspire future research. Code and benchmarks are available at https://***/auniquesun/Point-PRC. © 2024 Neural information processing systems foundation. All rights reserved.

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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