检索结果-内蒙古大学图书馆

2022 International conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Ahmed, Dewan Tanvir University of North Carolina at Charlotte Department of Computer Science CharlotteNC United States

ISBN: (纸本)9798350320282

Student learning outcomes (SLOs) are the specified knowledge, skills, and abilities that students are expected to achieve by the end of a course or program. Student learning outcomes describe what educators expect students to know and be able to do. SLO's specify an action of a student that must be observable, measurable and demonstrable. On the other hand, grades are objectives measure. This study attempts to quantify SLOs of a graduate level foundation course named algorithms and data structures. Three key aspects of the course are defined to address learning outcomes: a) Algorithm Design Skill, b) data Structure Selection and Design Skill and c) Algorithm Analysis Ability. For each aspect five achievement classes are specified in order to observe and quantify their knowledge, skills and ability. According to self-assessment, 86% students can a) determine the correct time and space complexities in an elegant and effective way, and b) correctly figure out time and space complexities of the designed algorithm but more elegant analysis exists. The study also shows 86% students can a) design elegant and robust algorithms and b) the designed algorithm is correct and efficient in most cases but it does not scale well in extreme cases. And finally, 79.7% students mentioned that a) data structure is well chosen (i.e., efficient) and well justified, and b) data structure is appropriate, but operations are not described sufficiently clearly. This clearly shows better success because of appropriate course modality, teaching pedagogy and engagement among many others. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

efficient and Optimized Small Organic Molecular Graph Generation Pathway Using a Quantum Generative Adversarial Network 5

Efficient and Optimized Small Organic Molecular Graph Genera...

引用

2024 International conference on Quantum Computing and Engineering

作者： Cui, Max Chang, Linda Chau, Adelina Mekuria, Hasset Adwankar, Leena Pendyala, Sriaditya McMahan, Larry Univ Toronto Toronto ON M5S 1A1 Canada Aspiring Scholars Directed Res Program Fremont CA USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798331541378

Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Generative Adversarial Networks, in particular, have shown immense potential for accelerating the drug discovery pipeline. However, these models consume a significant amount of computational resources, as they must perform adversarial training on expansive datasets to accurately sample the vast chemical space. Aiming for more efficient runtimes and rigorous analyses of high-dimensional molecular data with quantum computing, we previously developed a hybrid quantum-classical generative adversarial network (QGAN) to generate potential small organic drug candidates. Later, inspired by the classical NetGAN, we upgraded QGAN to a hybrid quantum-classical graph generative adversarial network (QNetGAN) which generated graph structures via random walks. This model was more efficient and had a higher accuracy rate (47.0%) than QGAN (2.3%). Most recently, we developed QNetGAN v2, an upgraded version of QNetGAN that features graph convolution. This model was able to generate 273 out of 300 structurally valid molecules that satisfy the octet rule and Lipinski's Rule of Five, yielding an 91.0% success rate and a training time of 35 minutes. This was made possible by integrating several post-processing algorithms into the original QNetGAN model, including the octet rule satisfaction validator, hydrogen addition algorithm, formal charge calculation, and molecular geometry (.XYZ file coordinate) calculation. In the future, we plan to use the QNetGAN v2 pathway to generate molecules with a wider range of elements and add functionality to calculate important molecular properties, ideally developing a third and more capable version of the QNetGAN molecular generation pathway that is able to outperform the accuracy of classical NetGAN.

关键词： Quantum Graph Generative Adversarial Network molecular generation graph convolution QNetGAN

来源：评论

学校读者我要写书评

暂无评论

PaC-Trees: Supporting Parallel and Compressed Purely-Functional Collections 2022

PaC-Trees: Supporting Parallel and Compressed Purely-Functio...

引用

43rd ACM SIGPLAN International conference on Programming Language Design and Implementation (PLDI)

作者： Dhulipala, Laxman Blelloch, Guy E. Gu, Yan Sun, Yihan Univ Maryland College Pk MD 20742 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Riverside Riverside CA USA

ISBN: (纸本)9781450392655

Many modern programming languages are shifting toward a functional style for collection interfaces such as sets, maps, and sequences. Functional interfaces offer many advantages, including being safe for parallelism and providing simple and lightweight snapshots. However, existing high-performance functional interfaces such as PAM, which are based on balanced purely-functional trees, incur large space overheads for large-scale data analysis due to storing every element in a separate node in a tree. This paper presents PaC-trees, a purely-functional data structure supporting functional interfaces for sets, maps, and sequences that provides a significant reduction in space over existing approaches. A PaC-tree is a balanced binary search tree which blocks the leaves and compresses the blocks using arrays. We provide novel techniques for compressing and uncompressing the blocks which yield practical parallel functional algorithms for a broad set of operations on PaC-trees such as union, intersection, filter, reduction, and range queries which are both theoretically and practically efficient. Using PaC-trees we designed CPAM, a C++ library that implements the full functionality of PAM, while offering significant extra functionality for compression. CPAM consistently matches or outperforms PAM on a set of microbenchmarks on sets, maps, and sequences while using about a quarter of the space. On applications including inverted indices, 2D range queries, and 1D interval queries, CPAM is competitive with or faster than PAM, while using 2.1-7.8x less space. For static and streaming graph processing, CPAM offers 1.6x faster batch updates while using 1.3-2.6x less space than the state-of-the-art graph processing system Aspen.

关键词： purely-functional data structures parallel data structures space-efficient data structures

来源：评论

学校读者我要写书评

暂无评论

Improving Real-Time data Processing Performance Through Machine Learning

Improving Real-Time Data Processing Performance Through Mach...

引用

2023 IEEE International conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sathya, S. Shewale, Chaitali R. Kar, Abhijeet Pandey, Megha Gosh, Srijani Sohal, Jagmeet Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350329773

Actual-time information processing technology have become more and more vital for the efficient operation of diverse enterprise tactics. System gaining knowledge of generation can help improve the overall performance of those information processing structures via allowing them to conform to changing conditions and styles. System gaining knowledge of algorithms may be used to discover tendencies and anomalies in information streams, detect patterns, and make predictions so that it will optimize the overall performance of the records processing gadget. The machine learning algorithms can be skilled and evaluated on ancient facts, or on streaming facts samples after which deployed on a records processing device. The overall performance of the statistics processing system may be progressed via adjusting parameters associated with information sampling frequency, facts granularity and the kind of features to be processed. Further, gadget mastering algorithms can be used to optimize facts preprocessing and publish-processing duties, together with feature extraction and information cleaning. Ultimately, gadget learning techniques can be utilized to optimize the scheduling and execution of records processing jobs, which includes identifying appropriate sources for a selected project and dynamically allocating sources and jobs as in line with the demand. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

efficient Incrementialization of Correlated Nested Aggregate Queries using Relative Partial Aggregate Indexes (RPAI) 22

Efficient Incrementialization of Correlated Nested Aggregate...

引用

International conference on Management of data (SIGMOD)

作者： Abeysinghe, Supun He, Qiyang Rompf, Tiark Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450392495

Incrementalization of queries is imperative in cases where data arrives as streams and output is latency-critical and/or desired before the full data has been received. Incremental execution computes the output at a given time by reusing the previously computed outputs or maintained views rather than re-evaluating the query from scratch. There are various approaches to perform this incrementalization ranging from query-specific algorithms and data structures (e.g., DYN, AJU) to general systems (e.g., DBToaster, Materialize). DBToaster is a state-of-the-art system that comes with an appealing theoretical background based on the idea of applying Incremental View Maintenance (IVM) recursively, maintaining a hierarchy of materialized views via delta queries. However, one key limitation of this approach is its inability to efficiently incrementalize correlated nested-aggregate queries due to an inefficient delta rule for such queries. Moreover, none of the other specialized approaches have shown efficient ways to optimize such queries either. Nonetheless, these types of queries can be found in many real-world application domains (e.g., finance), for which efficient incrementalization remains a crucial open problem. In this work, we propose an approach to incrementalize such queries based on a novel tree-based index structure called Relative Partial Aggregate Indexes (RPAI). Our approach is asymptotically faster than other systems and shows up to 1100x speedups in workloads of practical importance.

关键词： incremental processing nested aggregate queries aggregate indexes parent-relative trees

来源：评论

学校读者我要写书评

暂无评论

Odd-Even Hash Algorithm: A Improvement of Cuckoo Hash Algorithm 9

Odd-Even Hash Algorithm: A Improvement of Cuckoo Hash Algori...

引用

9th International conference on Advanced Cloud and Big data, CBD 2021

作者： Zhu, Haiting Wan, Junmei Li, Nan Deng, Yingying He, Gaofeng Guo, Jing Zhang, Lu Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China Nanjing Audit University Institute of Audit Science and Technology Nanjing China

ISBN: (纸本)9781665407458

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage. © 2022 IEEE.

关键词： data structures

来源：评论

学校读者我要写书评

暂无评论

Counter Pools: Counter Representation for efficient Stream Processing

arXiv

引用

arXiv 2025年

作者： Basat, Ran Ben Einziger, Gil Tyah, Bilal Vargaftik, Shay UCL United Kingdom BGU VMware Research

Due to the large data volume and number of distinct elements, space is often the bottleneck of many stream processing systems. The data structures used by these systems often consist of counters whose optimization yields significant memory savings. The challenge lies in balancing the size of the counters: too small, and they overflow;too large, and memory capacity limits their number. In this work, we suggest an efficient encoding scheme that sizes each counter according to its needs. Our approach uses fixed-sized pools of memory (e.g., a single memory word or 64 bits), where each pool manages a small number of counters. We pay special attention to performance and demonstrate considerable improvements for various streaming algorithms and workload characteristics. © 2025, CC BY.

关键词： data streams

来源：评论

学校读者我要写书评

暂无评论

Electrical Big data’s Stream Management for efficient Energy Control 14th

Electrical Big Data’s Stream Management for Efficient Ener...

引用

14th EAI International conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Sarr, Jean Gane Bame, Ndiouma Boly, Aliou Cheikh Anta Diop University BP Dakar Fann5005 Senegal

ISBN: (纸本)9783031348952

Energy is crucial for any activity of a country. Therefore, electrical organizations need to analyze the data generated in the form of data streams by network equipment’s for decisions making. These data are voluminous and velocious, which make impossible to process or store them with conventional methods. Hence the need to have a tool that allows to exploit these electrical data from the user’s consumption and the network. In this paper, we propose a tool that summaries data streams by using data cubes structures and gives the ability to face the need to make the best decisions required by the customer as well as the supplier. This tool is composed by a data stream summary model and two algorithms used to create, load, and update the data cubes. This proposal was performed with Big data tools that give to this summary the capacity to scale up. To demonstrate the effectiveness of the proposition, a detailed experimental evaluation over a real electrical data stream is presented. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Approximate Range Thresholding 22

Approximate Range Thresholding

引用

International conference on Management of data (SIGMOD)

作者： Zhang, Zhuo Gan, Junhao Bao, Zhifeng Kazemi, Seyed Mohammad Hussein Chen, Guangyong Zhu, Fengyuan Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Univ Melbourne Melbourne Vic Australia RMIT Univ Sch Comp Technol Melbourne Vic Australia Zhejiang Lab Hangzhou Zhejiang Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Kaifeng Investment Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450392495

In this paper, we study the (approximate) Range Thresholding (RT) problem over streams. Each stream element is a d-dimensional point and with a positive integer weight. An RT query q specifies a d-dimensional axis-parallel rectangular range R(q) and a positive integer threshold tau(q). Once the query q is registered in the system, define s(q) as the total weight of the elements that satisfy: (i) they arrive after q's registration, and (ii) they fall in the range R(q). Given a real number 0 < epsilon < 1, the task of the system is to capture an arbitrary moment during the period between the first moment when s(q) >= (1 - epsilon) . tau(q) and the first moment when s(q) >= tau(q). The challenge is to support a large number of RT queries simultaneously while achieving sub-quadratic overall running time and near-linear space consumption all the time. We propose a new algorithm called FastRTS , which can reduce the exponent in the poly-logarithmic factor of the state-of-the-art QGT algorithm from d + 1 to d, yet slightly increasing the log term itself. Besides, we propose two extremely effective optimization techniques which significantly improve the practical performance of FastRTS . Experimental results show that FastRTS outperforms the competitors by up to three orders of magnitude in both running time and peak memory usage.

关键词： Range Thresholding streams data structures algorithms

来源：评论

学校读者我要写书评

暂无评论

GEOELECTRICAL SURVEYS FOR GEO-ENGINEERING HAZARDS ASSESSMENT IN FAULTED AREAS APPLYING GENETIC algorithms AND ADVANCED INVERSION METHOD 28

GEOELECTRICAL SURVEYS FOR GEO-ENGINEERING HAZARDS ASSESSMENT...

引用

28th European Meeting of Environmental and Engineering Geophysics, Held at the Near Surface Geoscience conference and Exhibition 2022, NSG 2022

作者： Attwa, M. Abdelraouf Elsayed, A.M. Zagazig University Egypt National Authority for Remote Sensing and Space Sciences Egypt

ISBN: (纸本)9789462824256

Direct current resistivity (DCR) surveys play an efficient role for near-surface applications, such as engineering geology. However, the conventional interpretation methods still have a degree of the exact-data non-uniqueness problem leading to erroneous geological interpretations essentially in highly deformed urban areas. This study presents the efficient use of linear and non-linear inversion methods for preliminary geo-hazards assessment in a highly structured urban area, Badr city, Egypt. Genetic algorithms are applied to robust the DCR soundings interpretation considering the borehole data and conventional inversion results. Based on DCR soundings inversion results, some geotechnical properties are predicted considering the consequences of traditional engineering measurements. Although the electrical resistivity tomography (ERT) is a remarkable method for mapping the rock discontinuities, the resistivity artefacts at shallow depths due to soil lateral heterogeneities can increase the effects of ill-posed inverse problem with missing shallow subsurface layers and structures. Accordingly, advanced inversion method is applied using a decoupling line and a robust inversion option to attenuate the near-surface resistivity artefacts especially with high conductive layers presence. Consequently, the shallow subsurface structures and shale layers can be resolved. The results demonstrate that our protocol is cost efficient for preliminary geo-hazards assessment in highly faulted urban areas. © 2022 NSG. All Rights Reserved.

关键词： Genetic algorithms

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：