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The Cross-field DBN for Image Recognition

The Cross-field DBN for Image Recognition

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2015 ieee international conference on progress in informatics and computing(PIC 2015)

作者： Mingxi Cheng International school Beijing University of Posts and Telecommunications

In this paper, a new cross-field deep belief network(DBN) model is designed to recognize images. The bottom layer of model is shared to capture low-level features while the highlayer neurons are detached to capture high-level features of the specific fields. The cross-field unlabeled sample sets are used to train the model due to the shared neurons, which can improve the recognition performance in current internet environment with large scale cross-field unlabeled images. The cross-field DBN is realized by Numpy and Theano, and tested by Mixed National Institute of Standards and Technology(MNIST) data set of handwriting characters and Columbia Object Image Library(COIL) data set of object images. The simulation shows that the cross-field DBN has better performance than traditional DBN due to the help of cross-field unlabeled training data. In addition, the training of sample set after binarization can greatly improve recognition performance.

关键词： cross-field deep belief network layer feature

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IMAGE BIT-DEPTH ENHANCEMENT VIA MAXIMUM-A-POSTERIORI ESTIMATION OF GRAPH AC COMPONENT

IMAGE BIT-DEPTH ENHANCEMENT VIA MAXIMUM-A-POSTERIORI ESTIMAT...

引用

ieee international conference on Image Processing

作者： Pengfei Wan Gene Cheung Dinei Florencio Cha Zhang Oscar C. Au Hong Kong University of Science and Technology National Institute of Informatics Japan Microsoft Research Redmond

While modern displays offer high dynamic range (HDR) with large bit-depth for each rendered pixel, the bulk of legacy image and video contents were captured using cameras with shallower bit-depth. In this paper, we study the bit-depth enhancement problem for images, so that a high bit-depth (HBD) image can be reconstructed from an input low bit-depth (LBD) image. The key idea is to apply appropriate smoothing given the constraints that reconstructed signal must lie within the per-pixel quantization bins. Specifically, we first define smoothness via a signal-dependent graph Laplacian, so that natural image gradients can nonetheless be interpreted as low frequencies. Given defined smoothness prior and observed LBD image, we then demonstrate that computing the most probable signal via maximum a posteriori (MAP) estimation can lead to large expected distortion. However, we argue that MAP can still be used to efficiently estimate the AC component of the desired HBD signal, which along with a distortion-minimizing DC component, can result in a good approximate solution that minimizes the expected distortion. Experimental results show that our proposed method outperforms existing bit-depth enhancement methods in terms of reconstruction error.

关键词： Image reconstruction Quantization (signal) Transforms Distortion Laplace equations Estimation

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Conversation Analysis (CA)-Informed Algorithmic Approach to Augmenting Closed Captions with Stressed Syllables to Help Thai EFL Learners Improve Their English Pronunciation

Conversation Analysis (CA)-Informed Algorithmic Approach to ...

引用

ieee international Smart Cities conference (ISC2)

作者： Prawit Boonmee Kemtong Sinwongsuwat Kanuengnij Kubola Prajaks Jitngernmadan Ponlawat Chophuk Shehenaz Shaik Sittisak Saechueng Jiranan Kongsomjit Faculty of Informatics Burapha University Chonburi Thailand Faculty of Liberal Arts Prince of Songkla University Songkla Thailand Faculty of Computing East Tennessee State University Johnson City Tennessee USA

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Closed captions have been proven to be effective resources for English as a Foreign Language (EFL) teaching. Informed by Conversation Analysis (CA) research, this study seeks to enhance closed captioning features to assist EFL learners in recognizing stress and intonation patterns in spoken English. Using an algorithmic approach, CA-informed closed captions (CC) were generated by underlining stressed syllables and capitalizing pitch accents. Our goal is to enable EFL learners, particularly those from non-stress-timed languages such as Thai, to articulate English with greater clarity and speak with higher intelligibility. Evaluation results demonstrate that the proposed approach achieved an accuracy of 63 %, with a precision of 90 % and recall of 61 %. Further work is in progress to evaluate and improve the usability of the application by EFL teachers and learners.

关键词： Smart cities Generative AI Education Closed captioning Oral communication Libraries Pattern recognition Multimedia communication Usability Stress

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Enhanced HIP-based micro-mobility and macro-mobility management by proactive signaling scheme

Enhanced HIP-based micro-mobility and macro-mobility managem...

引用

international conference on Parallel and Distributed Systems (ICPADS)

作者： Ibrahima Ngom Ousmane Sadio Claude Lishou Hamadou Saliah-Hassane Informatics Engineering Department Ecole Supérieure Polytechnique (ESP) Dakar Senegal TELUQ Université du Québec Montréal Canada

ISBN: (纸本)9781479976164

In current HIP (Host Identity Protocol) architecture, the mobility procedure consist of a three-way handshake between the mobile node (MN) and its peers. This procedure lead to a stopping communication between MN and its correspondent's nodes (CN), which results in packets loss and delay if the procedure takes some time. HIP is not optimized for micro-mobility. In the literature, several solutions have been proposed, but each requires a signaling procedure during handover. In this paper, the proposed scheme does not require any signaling at the micro-mobility and reduces signaling overhead during macro-mobility. Indeed our mobility scheme is looked ahead-based, thus the mobile node is able to know his prospective network prefix before link on associated future point of attachment. Three signaling types have been introduced and mobility information exchange is before Handover. The performance evaluation is based on an analytical model and tests are in a real environment. OpenHIP is implemented for testing performance of the proposed mobility scheme, unlike other schemes are content to do their tests in a simulated environment.

关键词： Manganese IP networks Hip Handover Peer-to-peer computing Relays

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Flexible and reconfigurable system on chip for wireless sensor network

Flexible and reconfigurable system on chip for wireless sens...

引用

international conference on Information Technology Systems and Innovation (ICITSI)

作者： A Sumarudin Trio Adiono Willy Permana Putra Department of Informatics Politeknik Negeri Indramayu Jalan Indonesia Electrical Engineering Institute Teknologi Bandung Bandung Indonesia

This paper proposed a reconfigurable and flexible SoC architecture for various applications of wireless sensor network (WSN). The hardware-software system reconfiguration can be easy done because the systems utilize FPGA based SoC as a platform. The system also supports various network topologies and interfaces. The supported interface consists of I2C, SPI, USART, etc. The main processing system of SoC is based on ARM v7 processor that has high computing capability. The system utilizes Ubuntu 12.04 LTS as operating system. As consequences, we can easily interface the system with many external devices and create various program applications, such as monitoring system, sensor data warehouse, etc. The system has been successfully implemented with communication interface and PH, temperature, and humidity sensors. The system also has been verified using designed monitoring application for PH, temperature and humidity data that are transferred into gateway.

关键词： System-on-chip Temperature sensors Monitoring Logic gates ieee 802.11 Standards Computer architecture Field programmable gate arrays

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A Capacity Oriented Resource Allocation Algorithm for Device-to-Device Communication in Mobile Cellular Networks

A Capacity Oriented Resource Allocation Algorithm for Device...

引用

ieee international conference on Communications

作者： Xuejia Cai Jun Zheng Yuan Zhang Hidekazu Murata The State Key Laboratory of Integrated Services Networks Xidian University Shanxi China National Mobile Communications Research Laboratory Southeast University Jiangsu China Graduate School of Informatics Kyoto University Kyoto Japan

This paper proposes a Capacity Oriented Resource ALlocation (CORAL) algorithm for resource allocation in device-to-device (D2D) communication underlaying mobile cellular networks. To achieve high system capacity, CORAL allows a D2D pair to share the resources of more than one cellular user. Meanwhile, it introduces the concept of a Capacity-Oriented REstricted (CORE) region for a D2D pair to determine a candidate cellular user set for the D2D pair in resource allocation, which excludes those cellular users that introduce a negative system capacity gain, and thus can help increase the system capacity and reduce the computational complexity of CORAL as well. Moreover, CORAL attempts to avoid the situation where different D2D pairs simultaneously choose the same optimal cellular user, which would otherwise cause severe inteferences between the different D2D pairs and larger interference on the optimal cellular user. In addition, CORAL can ensure that each D2D pair can be allocated the resources of at least one cellular user. Simulation results show that CORAL can achieve a good performance in terms of both the system capacity and the rate loss of all cellular users.

关键词： Resource management Interference Mobile communication Receivers Transmitters Mobile computing Signal to noise ratio

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Quantum Entanglement Based Sentence Similarity Computation

Quantum Entanglement Based Sentence Similarity Computation

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Yan Yu Dong Qiu Ruiteng Yan Chongqing University of Posts and Telecommunications Chongqing China Chongqing University of Posts and Telecom Chongqing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Sentence representation is one of the foundations of natural language processing. A quantum entanglement-based approach is provided to determine the sentence similarity. The sentence embedding based on quantum computation considers the semantic structure of a sentence, tensor product and attentive mechanism. Main components of modifiers of a complete sentence are obtained by using the attentive mechanism combining with quantum entanglement of two consecutive notional words. The semantic features and syntactic structures of sentences are extracted by the tensor product of two consecutive notional words without any big-corpus or prior knowledge. Experimental results implemented on 17 datasets with an excellent effect show that taking the quantum entanglement into the sentence embedding yields improvements on mining semantic relations and syntactic structures. These results shed new light on sentence representation with limited resources, and represent a new step towards the quantum entanglement-based application in natural language processing.

关键词： Tensors Quantum computing Quantum entanglement Semantics Syntactics Quantum state Natural language processing

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Generalizability of Human Activity Recognition Machine Learning Models from non-Parkinson's to Parkinson's Disease Patients

Generalizability of Human Activity Recognition Machine Learn...

引用

Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Shreyas Aswar Vedasree Yerrabandi Megha M Moncy Sahithi Reddy Boda Josette Jones Saptarshi Purkayastha Department of Human-Centered Computing IUPUI Department of BioHealth Informatics Indiana University-Purdue University Indianapolis Indianapolis Indiana USA

Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a personalized measure. Recent progress in automated Human Activity Recognition using machine learning (ML) models shows potential for better monitoring of PD patients. However, ML models must be calibrated to ignore tremors and accurately identify activity and its intensity. We report findings from a study where we trained ML models using data from medically validated triple synchronous sensors connected to 8 non-PD subjects performing 32 exercises. We then tested the models to identify exercises performed by 8 PD patients at different stages of the disease. Our analysis shows that better data preprocessing before modeling can provide some model generalizability. However, it is extremely challenging, as the models work with high accuracy on one group (Healthy or PD patients) (F1=0.88-0.94) but not on both *** relevance-Patients with Parkinson's and other motor-generative diseases can now accurately measure physical activity with machine learning approaches. Clinicians, caregivers, and apps can make accurate, personalized exercise recommendations to augment medications that reduce tremors and stiffness.

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

引用

ieee international conference on Service Operations and Logistics, and informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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Intelligent and adaptive services for a smart campus

Intelligent and adaptive services for a smart campus

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international conference on Cognitive Infocommunications (CogInfoCom)

作者： Attila Adamkó Tamãs Kãdek Mãrk Kósa Department of Information Technology University of Debrecen Debrecen Hungary Department of Information Technology Faculty of Informatics University of Debrecen Debrecen Hungary

In the last few years semantic aspects earned more and more interest and have ever more applications. Ontologies and semantic frameworks are started applied more fields than we could imagine. Nowadays, when Future Internet and Internet of Things (IoT) research became an integral part of the rise of computing, we need to react to new challenges utilizing these aspects. Our University is not an exception, it is a perfect place to study and apply these possibilities. Furthermore, a new computing concept has appeared which had a vision where computing is made to appear everywhere and anywhere. These technological advancements made possible to collect and process a wide range of data. Several ideas are applied at our University Campus where we established a framework which could provide value-added services for various people living or working on the Campus. The main goals of our project - inside the Future Internet field - are twofold. First, establishing an intelligent platform as an extensible architecture which is used to drive the central part (as we call Smart Campus Central Intelligence - SCCI) through an extensible and open interface with data. That data is used not only for data analysis but recommendations as well. Second, the interface should be open for the crowd to made available crowdsourcing inside the system for creating new services. This paper highlights our applied usage scenarios and our vision where we could continue.

关键词： Communities Sensors Educational institutions Databases Calendars conferences Semantics

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