The ?rst SKLOIS Conference on Information Security and Cryptography(CISC 2005) was organized by the statekeylaboratory of Information Security of the Chinese Academy of sciences. It was held in Beijing, China, Decem...
详细信息
ISBN:
(数字)9783540324249
ISBN:
(纸本)9783540308553
The ?rst SKLOIS Conference on Information Security and Cryptography(CISC 2005) was organized by the statekeylaboratory of Information Security of the Chinese Academy of sciences. It was held in Beijing, China, December 15-17,2005andwassponsoredbytheInstituteofSoftware,theChineseAcademy of sciences, the Graduate School of the Chinese Academy of sciences and the National science Foundation of China. The conference proceedings, represe- ing invited and contributed papers, are published in this volume of Springer’s Lecture Notes in computerscience (LNCS) series. The area of research covered by CISC has been gaining importance in recent years, and a lot of fundamental, experimental and applied work has been done, advancing the state of the art. The program of CISC 2005 covered numerous ?elds of research within the general scope of the conference. The International Program Committee of the conference received a total of 196 submissions (from 21 countries). Thirty-three submissions were selected for presentation as regular papers and are part of this volume. In addition to this track, the conference also hosted a short-paper track of 32 presentations that were carefully selected as well. All submissions were reviewed by experts in the relevant areas and based on their ranking and strict selection criteria the papers were selected for the various tracks. We note that stricter criteria were applied to papers co-authored by program committee members. We further note that, obviously, no member took part in in?uencing the ranking of his or her own submissions.
th The 24 computer Graphics International Conference (CGI 2006) was held during June 26–28, 2006, in Hangzhou, China. This volume contains 39 full papers and 39 short papers accepted by CGI 2006. CGI conference was i...
详细信息
ISBN:
(数字)9783540356394
ISBN:
(纸本)9783540356387
th The 24 computer Graphics International Conference (CGI 2006) was held during June 26–28, 2006, in Hangzhou, China. This volume contains 39 full papers and 39 short papers accepted by CGI 2006. CGI conference was initially founded by the computer Graphics Society in 1983 and has now become a widely recognized, high-quality academic conference in the field of computer graphics. Recent CGI conferences were held in New York (2005), Crete (2004), Tokyo (2003), Bradford (2002), Hong Kong (2001) and Geneva (2000). The CGI 2006 Program Committee received an overwhelming 387 submissions from many countries worldwide. China and Korea contributed many enthusiastic submissions. Based on the strict review comments of international experts, we selected 38 full papers and 37 short papers for presentations. The main topics covered by the papers in this volume include: • Digital geometry processing and meshes • Physically based animation • Figure modeling and animation • Geometric computing and processing • Non-photorealistic rendering • Image-based techniques • Visualization We are grateful to all the authors who submitted their papers to CGI 2006, to the international Program Committee members and external reviewers for their valuable time and effort spent in the review process, and members of the Organizing Committee for their hard work which made this conference successful. Finally, we would like to thank the National Natural science Foundation of China and K. C. Wong Education Foundation, Hong Kong, for their financial support.
We report on magnetic and magnetotransport properties of single-crystalline Co2MnSi films grown by using conventional pulsed laser deposition (PLD) method on moderately heated semiconductor GaAs(001) substrates. The f...
详细信息
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achiev...
详细信息
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achievements of node embedding-based Graph Neural Networks (GNNs), we explore the upper bounds of expressiveness inherent to embedding-based methodologies, and tackle the challenges by reframing the CF task as a graph-signal processing problem. To this end, we propose PolyCF, a flexible graph signal filter that leverages polynomial graph filters to process interaction signals. PolyCF exhibits the capability to capture spectral features across multiple eigenspaces through a series of Generalized Gram filters, and is able to approximate the optimal polynomial response function for recovering missing interactions. A graph optimization objective and a pair-wise ranking objective are jointly used to optimize the parameters of the convolution kernel. Experiments on three widely adopted datasets demonstrate the superiority of PolyCF over the state-of-the-art CF methods.
Large Language Models (LLMs) have shown impressive In-Context Learning (ICL) ability in code generation. LLMs take a prompt context consisting of a few demonstration examples and a new requirement as input, and output...
详细信息
Large Language Models (LLMs) have shown impressive In-Context Learning (ICL) ability in code generation. LLMs take a prompt context consisting of a few demonstration examples and a new requirement as input, and output new programs without any parameter update. Existing studies have found that the performance of ICL-based code generation heavily depends on the quality of demonstration examples and thus arises research on selecting demonstration examples: given a new requirement, a few demonstration examples are selected from a candidate pool, where LLMs are expected to learn the pattern hidden in these selected demonstration examples. Existing approaches are mostly based on heuristics or randomly selecting examples. However, the distribution of randomly selected examples usually varies greatly, making the performance of LLMs less robust. The heuristics retrieve examples by only considering textual similarities of requirements, leading to sub-optimal *** fill this gap, we propose a Large language model-Aware selection approach for In-context-Learning-based code generation named LAIL. LAIL uses LLMs themselves to select examples. It requires LLMs themselves to label a candidate example as a positive example or a negative example for a requirement. Positive examples are helpful for LLMs to generate correct programs, while negative examples are trivial and should be ignored. Based on the labeled positive and negative data, LAIL trains a model-aware retriever to learn the preference of LLMs and select demonstration examples that LLMs need. During the inference, given a new requirement, LAIL uses the trained retriever to select a few examples and feed them into LLMs to generate desired programs. We apply LAIL to four widely used LLMs and evaluate it on five code generation datasets. Extensive experiments demonstrate that LAIL outperforms the state-of-the-art (SOTA) baselines by 11.58%, 3.33%, and 5.07% on CodeGen-Multi-16B, 1.32%, 2.29%, and 1.20% on CodeLlama-3
暂无评论