Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Orderly Spanning Trees with Applications

We introduce and study the {\em orderly spanning trees} of plane graphs. This algorithmic tool generalizes {\em canonical orderings}, which exist only for triconnected plane graphs. Although not every plane graph admits an orderly spanning tree, we provide an algorithm to compute an {\em orderly pair} for any connected planar graph $G$, consisting of a plane graph $H$ of $G$, and an orderly spanning tree of $H$. We also present several applications of orderly spanning trees: (1) a new constructive proof for Schnyder's Realizer Theorem, (2) the first area-optimal 2-visibility drawing of $G$, and (3) the best known encodings of $G$ with O(1)-time query support. All algorithms in this paper run in linear time.Comment: 25 pages, 7 figures, A preliminary version appeared in Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2001), Washington D.C., USA, January 7-9, 2001, pp. 506-51

A Framework for Enterprise Security Architecture and Its Application in Information Security Incident Management

An enterprise architecture (EA) plan is a long-term view or blueprint for an organization. It is a very important blueprint for balancing business and Information Technology (IT) and for adding value to an organization. Security is also nowadays an essential dimension for enterprises. It can prevent confidential information from being leaked, and/or stolen, lost succumbing to other serious disasters. There are many studies focusing on EA or on specific aspects of security. However, there are very few studies focusing on enterprise security architecture. This paper focuses on integrating the security dimension into the Zachman EA framework (Zachman, 2007) and is intended to serve as an enterprise security framework (ESA) to assist an organization in successfully and effectively implementing security. The efficacy of the ESA implementation is illustrated through an application in an organization

Why people adopt VR English language learning systems: An extended perspective of task-technology fit

Virtual Reality (VR) techniques involving immersion, interaction, and imagination, not only can improve conventional teaching methods, but also can enhance the transmission of education training contents through the interaction and simulation characteristics of VR. Incorporating information technology (IT) with English teaching has become an important issue in the academic field. Emerging after computer-assisted teaching, interactive network learning, distance education, and mobile learning in the early days, virtual reality techniques have been regarded as a new trend of merging technology with education. To explore the factors affecting users’ adoption intention of VR English language learning systems (VRELLS), this study has sought to build a theoretical framework based on the task-technology fit theory (extrinsic motivation) combining users’ needs (internal and external needs) and satisfaction to put forward an integrated research model (perceived needs-technology fit model), which explicates people’s adoption behaviors of VRELLS. An online questionnaire was employed to collect empirical data. A total of 291 samples were analyzed using a structural equation modeling (SEM) approach. The results of the study showed that both perceived needs-technology fit and satisfaction play a significant role in the user’ adoption intention of VRELLS services. In addition, the utilitarian and hedonic needs have a positive impact on the user’s perceived needs-technology fit. Also, it was found that relative advantage, service compatibility and complexity are important factors in influencing individuals’ perceived needs-technology fit. The implications of these findings are discussed along with suggestions for future research

Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model

The goal of this paper is to report certain scientific discoveries about a Seq2Seq model. It is known that analyzing the behavior of RNN-based models at the neuron level is considered a more challenging task than analyzing a DNN or CNN models due to their recursive mechanism in nature. This paper aims to provide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq model without attention can achieve token-positioning. We found four different types of neurons: storing, counting, triggering, and outputting and further uncover the mechanism for these neurons to work together in order to produce the right token in the right position.Comment: 9 pages (excluding reference), 10 figure

Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification

Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and inherent imbalanced distributions, due to difficulties in data collection and variations in disease prevalence across different types. In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS), which collectively target the imbalance classification issue at both the instance level and the class level. The OIS module alleviates the data insufficiency problem by generating representative samples tailored for online training of the classifier. On the other hand, the AAS module dynamically balances the synthesized samples among various classes, targeting those with low training accuracy. To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets. The results obtained demonstrate the superiority of our approach over state-of-the-art methods as well as the effectiveness of each component. The source code will be released upon acceptance