检索结果-内蒙古大学图书馆

Understanding and improving fairness in cognitive diagnosis

Science China(Information Sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

来源：评论

学校读者我要写书评

暂无评论

A Framework for Multiclass Contour Visualization

引用

IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

KVSAgg: Secure Aggregation of Distributed Key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

引用

Journal of computer Science & Technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

来源：评论

学校读者我要写书评

暂无评论

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph databases 39

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Qiu, Rui Ming, Yi Hong, Yisen Li, Haoyu Yang, Tong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peking University Academy for Advanced Interdisciplinary Studies China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases. © 2023 IEEE.

关键词： Graph databases

来源：评论

学校读者我要写书评

暂无评论

REncoder: A Space-Time Efficient Range Filter with Local Encoder 39

REncoder: A Space-Time Efficient Range Filter with Local Enc...

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Wang, Ziwei Zhong, Zheng Guo, Jiarui Wu, Yuhan Li, Haoyu Yang, Tong Tu, Yaofeng Zhang, Huanchen Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798350322279

A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters. © 2023 IEEE.

关键词： data structures

来源：评论

学校读者我要写书评

暂无评论

Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing 24

Bit-mask Robust Contrastive Knowledge Distillation for Unsup...

引用

33rd ACM Web Conference, WWW 2024

作者： He, Liyang Huang, Zhenya Liu, Jiayu Chen, Enhong Wang, Fei Sha, Jing Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://***/hly1998/BRCD. © 2024 Owner/Author.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets 9

Temporal Transformation Network Based On Scale Sequences for...

引用

9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Zhao, Bo Dong, Zhiwei Dong, Lingyun Li, Danyang School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9798350317251

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline. © 2023 IEEE.

关键词： Video recording

来源：评论

学校读者我要写书评

暂无评论

Oriented Object Detection in Aerial Images with Double Midpoint Vectors 9

Oriented Object Detection in Aerial Images with Double Midpo...

引用

9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Hu, Qi Chen, Xiaopan Yuan, Caihong Li, Minghao Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350317251

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Online Q&A for Machine Learning Courses Based on Formula Recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

引用

4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：