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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 816-828页

作者： Yao, Yinghua Pan, Yuangang Tsang, Ivor W. Yao, Xin Centre for Frontier Ai Research 138632 Singapore Lingnan University School of Data Science Hong Kong University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom

Most previous methods for continuous image-to-image translation resorted to binary attributes with restrictive description ability and thus cannot achieve satisfactory performance. Some works proposed to use fine-grained semantic information, relative attributes (RAs), preferences over pairs of images on the strength of a specified attribute. However, they still failed to reconcile both goals for smooth translation and for high-quality generation simultaneously. In this work, we propose a new model continuous translation via adversarial preferences (CTAP) to coordinate these two goals for high-quality continuous translation based on RAs. In CTAP, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth nuanced changes w.r.t. the interested attributes;and a ranker that executes adversarial preferences consisting of the input image and the desired image. Particularly, adversarial preferences involve an adversarial ranking process: 1) the ranker thinks no difference between the desired image and the input image in terms of the interested attributes;2) the generator fools the ranker to believe the attributes of its output image changes as expect compared with the input image. RAs over pairs of real images are introduced to guide the ranker to rank image pairs regarding the interested attributes only. With an effective ranker, the generator would "win"the adversarial game by producing high-quality images that present smooth changes. The experiments on two face datasets and one shoe dataset demonstrate that our CTAP achieves state-of-art results in generating high-fidelity images which exhibit smooth changes over the interested attributes. © 2024 IEEE.

关键词： Generative adversarial networks

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Air Pollution Prediction using Random Forest Classifier: A Case Study of DKI Jakarta

Air Pollution Prediction using Random Forest Classifier: A C...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Muljana, Richie Ayuningtyas, Lintang Diah Daksa, Rayhan Prawira Djamhari, Simen Ferdinand Fiezayyan, Muhammad Ariiq Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

This research paper analyzes the Air Pollution Standard Index (APSI) in Jakarta, Indonesia, using the Random Forest Classifier (RFC). The study aims to predict the APSI level in Jakarta based on the concentrations of various pollutants, including particulate matter (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). The data used in the study was collected from a public website called Jakarta Open data from January to December 2020. The SMOTE-Tomek technique was used to handle an imbalanced dataset in this work. The results show that the RFC model accurately predicts the APSI level in Jakarta with an accuracy of 95%. In addition, RFC can identify ozone (O3) and particulate matter (PM) are the most critical factors influencing the APSI level. This study provides valuable insights into the factors influencing air pollution in Jakarta and can be used to inform city decision-making regarding air quality management. This paper discusses the finding's significance and potential future research directions. © 2023 IEEE.

关键词： Carbon monoxide

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Salary Classification & Prediction based on Job Field and Location using Ensemble Methods

Salary Classification & Prediction based on Job Field and Lo...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Jocelyn Verna Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary. © 2023 IEEE.

关键词： Decision trees

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A Study of data Leakage Prevention Techniques in Remote Collaborative Work Application 24

A Study of Data Leakage Prevention Techniques in Remote Coll...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Xu, Wenfang Zhao, Shusheng School of Data Science and Computer Engineering Xiamen Institute of Technology China

ISBN: (纸本)9798400718137

Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protection measures have increasingly become the focus of attention, posing a severe challenge to enterprise data security. To address this challenge, this paper proposes an innovative comprehensive data leakage prevention technical solution. This solution integrates cloud services and kernel technology and creates a set of data security protection systems for the remote collaboration working environment. This technical solution adopts a series of effective technical means, including asymmetric encryption, one-way data flow control, dynamic digital watermarking technology, injection attack defense, and program anti-hanging mechanism, significantly enhancing the security of data in the collaborative office system. The constructed framework includes multiple modules such as user management, service support, kernel driver, and user terminal. This technical solution can be integrated with enterprise remote collaboration office software, providing a higher level of security guarantee for the remote office environment, effectively curbing the risk of sensitive data leakage, and also providing solid technical support for the healthy and stable development of remote collaboration work. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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Comparison of Different Machine Learning Algorithms for Predicting Loan Risk Categories

Comparison of Different Machine Learning Algorithms for Pred...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sudjono, Salomo Hendrian Adrian, Favian H.N. Sunarya, Caroline Angelina Ariyanto, Gabrielle Felicia Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

There are many cases where borrowed money by debtors is not returned. It is because the company misjudged in determining the risk of lending. Thus, debtors cannot repay their debts and end up in losses on the company's side. Thus, this research aims to create an accurate classification model for determining loan risk categories to minimize business losses. We used the public dataset from kaggle website. Then we perform several data transformation and analysis methods are performed to obtain information from the data. We built 2 classification models to predict the original label. Unfortunately, the desired results did not achieve based on the f1-score. Therefore, the clustering method using K-Means is applied to make a new label for the dataset and determine the behavior of each cluster. Each cluster is labeled then build a model to perform cluster classification. We created several classification models and found that gradient tree boosting is the most accurate predictor of the loan risk category based on the accuracy. We used clustering to create a classification model that can have high accuracy. The classification model we built can have high accuracy because it uses clustering. The empirical results of this study and others pave the way for further in-depth future work to guarantee that the algorithm performs acceptably when confronted with more complicated real data. © 2023 IEEE.

关键词： K-means clustering

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Robust Deep One-Class Classification Time Series Anomaly Detection

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computers, Materials & Continua 2025年第6期83卷 5181-5197页

作者： Zhengdao Yang Xuewei Wang Yuling Chen Hui Dou Haiwei Sang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China College of Computer Science and Technology Weifang University of Science and TechnologyWeifang261000China School of Mathematics and Big Data Guizhou Education UniversityGuiyang550018China

Anomaly detection(AD)in time series data is widely applied across various industries for monitoring and security applications,emerging as a key research focus within the field of deep *** many methods based on different normality assumptions performwell in specific scenarios,they often neglected the overall normality *** feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection,leading to decreased ***,real-world time series samples are rarely free from noise,making them susceptible to outliers,which further impacts detection *** address these challenges,we propose a novel anomaly detection method called Robust One-Class Classification Detection(ROC).This approach utilizes an autoencoder(AE)to learn features while constraining the context vectors fromthe AE within a sufficiently small hypersphere,akin to One-Class Classification(OC)*** simultaneously optimizing two hypothetical objective functions,ROC captures various aspects of *** categorize the input raw time series into clean and outlier sequences,reducing the impact of outliers on compressed feature *** results on public datasets indicate that our approach outperforms existing baselinemethods and substantially improves model robustness.

关键词： Time series anomaly detection self-supervised learning robustness

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of computer science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of computer science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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Sexism Identification in Tweets using BERT and XLM - Roberta 25

Sexism Identification in Tweets using BERT and XLM - Roberta

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Usmani, Maha Siddiqui, Rania Rizwan, Samin Khan, Faryal Alvi, Faisal Samad, Abdul Computer Science Program Dhanani School of Science and Engineering Habib University Karachi Pakistan

The rapid growth of social media platforms has led to an increase in offensive content, often targeting specific demographic groups. This paper focuses on identifying and categorizing sexism in tweets collected from various social media platforms. We address three tasks from the EXIST 2024 lab, involving the classification of tweets in English and Spanish. These tasks include binary classification for sexism identification, source intention categorization of sexist tweets, and multi-label classification for different facets of sexism. Our approach employs BERT multilingual and XLM-RoBERTa models, along with an ensemble technique to enhance prediction accuracy. We evaluate the models using both hard labels, determined by majority vote, and soft labels, based on class probabilities. © 2022 Copyright for this paper by its authors.

关键词： Tweets

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