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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Chang, Hong School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350310931

Artificial Intelligence (AI) has revolutionized various aspects of human life and transformed how people live, work, and interact. However, the development of AI also poses potential risks and ethical concerns. In this report, we aim to analyze the sentiment toward the AI industry using hybrid natural language processing techniques. To achieve our aim, we propose a model that draws upon a survey of related work. data collection involves gathering user-generated data from social media platforms. We then use hybrid natural language processing techniques to analyze the sentiment toward the AI industry. Our analysis reveals that the sentiment towards the AI industry is generally positive, with many people recognizing its potential benefits. However, there are also concerns about the potential risks and ethical implications of AI development. Some leading figures in the AI industry have expressed concerns about the potential misuse of AI and the need for ethical guidelines. In conclusion, our analysis highlights the transformative effects of AI on various industries and the potential risks associated with its development. We recommend that policymakers and industry leaders work together to develop ethical guidelines for the development and use of AI. This will help to ensure that the benefits of AI are maximized while minimizing the potential risks and ethical concerns. © 2023 IEEE.

关键词： Sentiment analysis

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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

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Journal of Computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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Scalable Transactional Stream Processing on Multicore Processors

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IEEE Transactions on Knowledge and data Engineering 2025年第7期37卷 4254-4269页

作者： Zhao, Jianjun Mao, Yancan Yang, Zhonghao Liu, Haikun Zhang, Shuhao Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore 119077 Singapore Nanyang Technological University 639798 Singapore

Transactional stream processing engines (TSPEs) are central to modern stream applications handling shared mutable states. However, their full potential, particularly in adaptive scheduling, remains largely unexplored. We present MorphStream, a TSPE designed to optimize parallelism and performance for transactional stream processing on multicores. Through a unique three-stage execution paradigm (i.e., planning, scheduling, and execution), MorphStream enables adaptive scheduling under varying workload characteristics. Building on this foundation, MorphStream is further enhanced with support for non-deterministic state access, employing a stateful task precedence graph to handle undefined read/write sets at runtime while guaranteeing transaction semantics. Additionally, MorphStream incorporates a generalized framework for managing window-based operations, enabling efficient tracking and maintenance of overlapping windows using multi-versioned state management. These extensions enhance the system’s ability to process dynamic and irregular workloads. Experimental results demonstrate up to 3.4 times higher throughput and 69.1% lower latency compared to state-of-the-art TSPEs, validating its scalability and adaptability in real-world streaming scenarios. © 1989-2012 IEEE.

关键词： Multicore programming

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Identification of Personality Traits by Machine Learning Analysis of Signatures and Handwriting

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SN Computer science 2024年第5期5卷 497页

作者： Sharma, Dinesh Parashar, Vivek Parashar, Amrita Department of Data Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computing Science and Engineering VIT Bhopal University Bhopal India

Personality plays a crucial role in personal growth across various aspects of life, professional pursuits, individual success, family dynamics, and love compatibility. Analyzing handwriting is a vital method for understanding one's personality traits. Yet, the significance of a signature surpasses regular handwriting, holding four times its weight. Personality encompasses thoughts, behaviors, emotions, and desires, profoundly shaping an individual's success. Graphology, a scientific discipline, explores these traits by examining both handwriting and signatures, offering valuable insights into human character. Graphologists utilize these analyses to gain deeper insights into an individual's character. Signatures serve as public representations of a person, offering valuable glimpses into their social and external lives, thereby aiding individuals in gaining a profound understanding of themselves. This paper aims to employ machine learning in conjunction with graphology techniques to predict personality traits and provide insights into one's life. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Personality traits Signature and handwriting analysis Signature identification

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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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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Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 IEEE.

关键词： Contrastive Learning

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural computing and Applications 2025年第16期37卷 10407-10423页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-14页

作者： Tan, Mingkui Chen, Guohao Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zhao, Peilin Niu, Shuaicheng South China University of Technology School of Software Engineering China Pazhou Laboratory Guangzhou China XVERSE China National University of Singapore School of Computing Singapore Tencent AI Lab China Nanyang Technological University College of Computing and Data Science Singapore

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy

关键词： Semantic Segmentation

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A New Carry Look-Ahead Adder Architecture Enabling Improved Speed and Energy Efficiency

A New Carry Look-Ahead Adder Architecture Enabling Improved ...

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2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM 2024

作者： Balasubramanian, Padmanabhan Maskell, Douglas College of Computing and Data Science Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (纸本)9798350362312

We present a new carry look-ahead adder (NCLA) architecture that makes use of non-uniform-size CLA modules in contrast to the existing CLA architecture which typically makes use of uniform-size CLA modules. This helps to achieve improved speed and energy efficiency for the NCLA architecture compared to the existing CLA architecture without incurring notable area and power penalties. To demonstrate the advantage of NCLA, several adders were implemented ranging from the slow ripple carry adder to the generally considered fastest parallel-prefix adder viz. the Kogge-Stone adder (KSA), and their performance metrics were compared. The 32-bit addition was considered an example, and the different adders were implemented in a semi-custom design method using a 28-nm CMOS standard digital cell library. Based on the synthesis results, the NCLA was found to facilitate significant optimizations in design metrics compared to its high-speed counterparts. To give some examples, the NCLA achieved: (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to a high-speed CLA while occupying slightly more area, (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder while occupying 8% more area, (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to a speed-optimized carry select adder while requiring 37% less area, and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the KSA. © 2024 IEEE.

关键词： CMOS integrated circuits

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Building Your Query Step by Step: A Query Wizard for the MY DGS - ANNIS Portal of the DGS Corpus 11

Building Your Query Step by Step: A Query Wizard for the MY ...

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11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources, sign-lang@LREC-COLING 2024

作者： Isard, Amy Institute of German Sign Language and Communication of the Deaf and House of Computing and Data Science University of Hamburg Germany

ISBN: (纸本)9782493814302

MY DGS - ANNIS makes the Public DGS Corpus available through the corpus query and visualization tool ANNIS. Due to the complex nature of the corpus, composing queries for advanced research questions can quickly become increasingly complicated. We present a Query Wizard which assists users in building valid queries for MY DGS - ANNIS. Complex queries are built up from smaller blocks, which can be linked to each other through context-sensitive connections. Blocks provide options specific to a given annotation tier and dynamically lead users through their construction while preventing the creation of invalid queries. Once completed, queries can be opened directly in MY DGS - ANNIS. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： ANNIS corpus query tool German Sign Language (DGS) query wizard

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