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In this study, facial expression recognition is defined as a pair matching problem. Our objectives to formulate this talk in this way are to be able to decide whether the facial expressions of the unlabeled images of two people are the same or different and to benefit from the proposed pair matching methods that have been studied for many years in the face recognition field. The Extended Cohn-Kanade...
Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI...
Superpixel based methods have recently shown success in scene segmentation and labeling. In scene labeling, a superpixel algorithm is used first to segment the image into visually consistent small regions; then several feature descriptors are computed and classification is performed for each superpixel. In this paper, Kernel Codebook Encoding (KCB) of superpixel features is proposed. In KCB feature...
We propose a generalized construction for binary polar codes based on mixing multiple kernels of different sizes in order to construct polar codes of block lengths that are not only powers of integers. This results in a multi-kernel polar code with very good performance while the encoding complexity remains low and the decoding follows the same general structure as for the original Arikan polar codes...
In this paper, a new coding method called radical group encoding is used for protein secondary structure prediction. The radical group encoding is used to encode 20 common amino acids, it has 42 features and every feature shows the protein composition. We use the new encoding method to predict the secondary structure. Almost all amino acids can be represented by the coding, it contains the information...
Concatenated codes proposed by Forney are used extensively in digital communication. In this paper, concatenated kernel codes, a class of group codes is constructed with inner code and outer code. Binary and non — binary variants of concatenated kernel code is discussed with example. Constructed concatenated kernel code is represented over trellis. Minimal trellis representation is given for the concatenated...
Deep Convolutional Neural Networks based object detection has made significant progress recent years. However, detecting small scale objects is still a challenging task. This paper addresses the problem and proposes a unified deep neural network building upon the prominent Faster R-CNN framework. This paper has two main contributions. Firstly, an Atrous Region Proposal Network (ARPN) is proposed to...
Developing cross-corpus, cross-domain, and cross-language emotion recognition algorithm has becoming more prevalent recently to ensure the wide applicability of robust emotion recognizer. In this work, we propose a computational framework on fusing multiple emotion perspectives by integrating cross-lingual emotion information. By assuming that each data is ‘perceived’ not only by a main perspective...
Time-based Spiking Neural Network (SNN) has recently received increased attentions in neuromorphic computing system designs due to more bio-plausibility and better energy-efficiency. However, unleashing its potentials in realistic cognitive applications is facing significant challenges such as inefficient information representations and impractical learnings. In this work, we aim for exploring a practical...
This paper investigates the feasibility of a unified processor architecture to enable error coding flexibility and secure communication in low power Internet of Things (IoT) wireless networks. Error coding flexibility for wireless communication allows IoT applications to exploit the large tradeoff space in data rate, link distance and energy-efficiency. As a solution, we present a light-weight Galois...
In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works,...
In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works...
Activity recognition in videos is a challenging task, mainly if a scarce number of samples is available for modelling the problem. The task becomes even harder when using generative models such as mixture models or Hidden Markov Models (HMMs), as they demand a lot of samples to determinate their parameters. Additionally, these models rely on the appropriate selection of some parameters, for instance...
The prediction of molecule's properties through Quantitative Structure Activity (resp. Property) Relationships are two active research fields named QSAR and QSPR. Within these frameworks Graph kernels allow to combine a natural encoding of a molecule by a graph with classical statistical tools such as SVM or kernel ridge regression. Unfortunately some molecules encoded by a same graph and differing...
High accuracy fault diagnosis systems are extremely important for effective condition based maintenance (CBM) of rotating machines. In this work, we develop a fault diagnosis system using time and frequency domain statistical features as input to a backend support vector machine (SVM) classifier. We evaluate the performance of the baseline system for speed dependent and speed independent performance...
We propose a new transductive label propagation method, termed Adaptive Neighborhood Propagation (Adaptive-NP) by joint L2,1-norm regularized sparse coding, for semi-supervised classification. To make the predicted soft labels more accurate for predicting the labels of samples and to avoid the tricky process of choosing the optimal neighborhood size or kernel width for graph construction, Adaptive-NP...
This paper presents an acceleration method of the bilateral filter (BF) for multi-channel images. In most existing acceleration methods, the BF is approximated by an appropriate combination of convolutions. A major purpose under this framework is to achieve sufficient approximate accuracy by as few convolutions as possible. However, state-of-the-art methods for multi-channel images still requires...
In this paper, we propose the encoding and list decoding method of polar codes based on the four-dimensional Reed-Solomon (RS-4) kernel. In specific, an encoding table based method is employed to reduce the computational complexity of both encoder and decoder. In addition, a simplified method to update log-likelihood ratios (LLRs) which employs additions instead of exponential calculations is also...
A novel proposed approach, collaborative representation-based classification, has been developed for face recognition and recently used in image classification task owing to its simplicity and effectiveness. The major drawback of this method is the neglect of the spatial structure among the image representations. Inspired by the success of this technique and motivated by the power of spatial information...
Brain tumor segmentation, an essential but challenging task, has long attracted much attention from the medical imaging community. Recently, successful applications of sparse coding and dictionary learning has emerged in various vision problems including image segmentation. In this paper, a superpixel-based framework for automated brain tumor segmentation is introduced. The kernel trick is adopted...
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