Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Inception Network for Cross-Database Micro-Expression Recognition

This paper presents the technique for our contribution to 2019 Micro-Expression Grand Challenge (MEGC 2019). One sub-challenge of MEGC 2019 named Cross-Database (Cross-DB) challenge aims to classify three main classes (Negative, Positive and Surprise) in the task of Composite Database Evaluation (CDE). Our proposed method utilizes Inception technique to overcome the challenge for the cross-database micro-expression recognition and can be divided into three steps. (1) In the preprocessing stage, onset and mid-position frames of each micro-expression sample are selected for the feature extraction. (2) TV-L1 optical flow features are calculated by the two frames obtained in the first step. (3) The horizontal and vertical components of TV-L1 optical flow features are fed to a Dual-Inception network for the micro-expression recognition. Our experiment results on three benchmark databases show that our proposed mechanism archives the overall unweighted F1 score (UF1) of 0.7322 and unweighted average recall (UAR) of 0.7278, which significantly outperform those metrics of the baseline method ( UF1: 0.5882, UAR: 0.5785). Code is publicly available on GitHub: https://github.com/xly135846/MEGC2019
Nonlinear Quantization Method for Wavelet-Based Video Codec

This paper is dedicated to the research and development of quantization methods for wavelet-based video codec. Uniform, nonlinear quantization method and their potential applications for wavelet-based video codec Dirac have been investigated. The optimized quantization method for wavelet-based video codec have been proposed based on analysis of the processing results for various video types using quantization methods mentioned above. The processing results for various types of video show that the proposed method provides up to 4.2% bitrate saving for low distortion level in the intra-frame coding mode, up to 9.8% bitrate reduction for low distortion level and up to 11.2% bitrate saving for medium (acceptable) distortion level in the inter-frame coding mode compared to the uniform quantization method implemented in video codec Dirac.
Deformable Registration of Whole Brain Zebrafish Microscopy Using an Implementation of the Flash Algorithm Within Ants

Recent advancements in microscopy, protein engineering, and genetics have rendered the larval zerbrafish a powerful model system for which whole brain, real time, functional neuroimaging at cellular resolution is accessible. Supplementing functional data with additional modalities in the same fish such as structural connectivity and transcriptomics will enable interpretation of structure-function relationships across the entire brains of individual animals. However, proper identification of corresponding cells in the large image volumes produced depends on accurate and efficient deformable registration. To address this challenge, we implemented the Fourier-approximated Lie Algebras for Shooting (FLASH) algorithm within the well-known Advanced Normalization Tools (ANTs) package. This combines the speed of FLASH with the extensive set of image matching functionals and multi-staging multi-resolution capabilities of ANTs. We registered longitudinal data from nine fish, using a line that uniquely identifies subsets of neurons in an independent channel. We validate our approach by demonstrating accurate cell-to-cell correspondence while requiring significantly less time and memory than the Symmetric Normalization (SyN) implementation in ANTs, and without compromising the theoretical foundations of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model.
Radiomic-Based Framework for Early Diagnosis of Lung Cancer

This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively.
Graph Convolutional Network Analysis for Mild Cognitive Impairment Prediction

Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), which is a neurodegenerative disease. Functional connectivity networks (FCN) provide an effective method for analyzing brain functional regions connectivity. However, most methods only considered the neuroimaging information and focused on group relationship without the subjects’ individual features, and ignored the demographic relationship. To handle it, in this paper, we introduce a novel method based on graph convolutional networks (GCN), which combines image and other information for MCI prediction tasks. The proposed model is capable of representing the individual features and data associations among subjects from potentially populations simultaneously. Specifically, we use different collection devices and gender information to build a graph called MCI-graph and modify convolutional neural networks (CNN) to construct GCN for MCI prediction. The experimental results demonstrate that our proposed method has achieved remarkable prediction performance.
Low Complexity Beam Selection Scheme for Terahertz Systems: A Machine Learning Approach

Terahertz (THz) communication has been envisioned as a promising candidate to support ultra-broadband for future beyond fifth generation (5G). In the THz communication system, hybrid beamforming is essential for overcoming the serious attenuation due to the extremely high frequency in THz band. However, the current beam selection schemes in hybrid beamforming architectures still suffer the enormous computational complexity. To tackle this issue, we consider the beam selection problem as a multi-class classification model, and then propose a low complexity beam selection method based on random forest classification (RFC), which belongs to machine learning algorithms and sufficiently depends on training samples. Simulation results show that our proposed RFC based beam selection scheme is capable of providing a better tradeoff between sum-rate and complexity by choosing the appropriate parameter settings compared with some existing beam selection schemes.
Cooperative Impact-time-control Guidance of Multiple Flight Vehicles with Uncontrollable Velocity

Time-cooperative guidance of multiple flight vehicles can not only improve the accuracy of the guidance level, but also greatly improve the penetration capability. In this paper, a cooperative impact-time-control guidance law of multiple flight vehicles with uncontrollable velocity to realize simultaneous arrival is proposed. Firstly, the multi-agent consensus algorithm is introduced as basic theory. Secondly, a structure combined individual impact-time-control guidance with the cooperative control protocol is proposed, in which the coordination module is to generate coordination instructions, and local impact-time-control guidance law is adopted to execute instruction. Further, the finite-time time-cooperative guidance of multiple flight vehicles is studied to achieve the convergence of time-to-go instructions in finite time. Finally, the effectiveness of the above cooperative guidance law with uncontrollable velocity is verified by simulation.
Measurements and analysis of SER tolerant latch in a 90 nm dual-Vt CMOS process

We measured neutron soft error rate (SER) of hardened and standard latches in a 90 nm dual-Vt CMOS process. The hardened latch demonstrated over 10/spl times/ lower SER at no speed degradation. Energy penalty can be minimal for standard-latch transistor sizes at least twice the minimum size. We analyzed the effects of recovery time and leakage on the SER robustness.
Pixel Reconstruction For Speckle Reduction In 3D Optical Coherence Tomography Of Retina

Speckle noise reduction in optical coherence tomography (OCT) is important for better visualization and analysis in retinal imaging. This paper proposes a novel pixel reconstruction based method to reduce speckle noise in 3D OCT of retina. In the proposed method, each pixel is estimated as the sum of a noise-free part and a noise part, which are solved by axial-scan based alignment and a low rank matrix completion algorithm. Evaluated in a data set of 20 volumes, results show that the proposed method effectively reduces the noise. The technology is beneficial and can be used in OCT machines.
An Efficient Method of Eliminating Noisy Information in Web Pages for Data Mining

A commercial Web page typically contains many information blocks. Apart from the main content blocks, it usually has such blocks as navigation panels, copyright and privacy notices, and advertisements. We call these blocks that are not the main content blocks of the page the noisy blocks. We show that the information contained in these noisy blocks can seriously harm Web data mining. Eliminating these noises is thus of great importance. In this work, we propose a noise elimination technique. We propose a tree structure, called pattern tree, to capture the common presentation styles and the actual contents of the pages in a given Web site. By sampling the pages of the site, a pattern tree can be built for the site, which we call the site pattern tree (SPT). We then introduce an information-based measure to determine which parts of the SPT represent noises and which parts represent the main contents of the site. The SPT is employed to detect and eliminate noises in any Web page of the site by mapping this page to the SPT. The proposed technique is evaluated by a data-mining task that is Web clustering.
Speculative Execution Attack Methodologies (SEAM): An overview and component modelling of Spectre, Meltdown and Foreshadow attack methods

Speculative execution has made a significant impact in the processing speed of computing devices over the last circa 20 years. The functionality of modern CPU's to be able to ‘speculatively’ execute instructions ahead and ‘out-of-order’ of the normal process has increasingly optimized performance. However, vulnerabilities inherent in the speculative execution design are being increasingly discovered. This paper will provide an overview of some of the published works of these CPU vulnerabilities. In addition to providing an overview, this paper presents SEAM: Speculative Execution Attack Methodologies. Representing the attacks via Speculative Execution Attack Schemas (SE Attack Schemas) of the attack types provides further granularity to the attacks. The SE Schemas of existing exploits can potentially be used in future research to identify specific attack approaches and enable scrutiny of the underlying complexity in a more organized manner.
Fusion of Multi-frequency Polarimetric SAR and LISS-3 Optical Data for Classification of Various Land Covers

A comparative assessment of optical and microwave data is essential to find out the complementarity of data for classification of various land features. In this paper, the classification accuracy of LISS-3 (IRS-P6) optical data over Mumbai is compared with multi-frequency PolSAR data to study the synergy between both the remote sensing techniques. SVM classifier is applied on optical and PolSAR data. Before applying SVM classifier, optical data is registered with PolSAR data. The overall accuracy of optical IRS data (87.31%) is less than that of fully polarized SAR data (93.54%). Optical data cannot differentiate well between grasslands and mangroves and also saltpans and wetlands, whereas PolSAR data gave high classification accuracy. However, forest is well classified with optical data. Since the forest is on hilly area, foreshortening and layover in PolSAR data affect the classification accuracy. It is observed that the combination of both optical and PolSAR gives better results (98.62%).
Pain Expression Recognition Using Occluded Faces

Frequent pain monitoring in intensive care unit (ICU) settings has been shown to have achieved improvement in patient outcomes. Specifically, the nursing staff is required to assess the pain of the patients approximately every four hours, and make necessary pain medication adjustments. Shortage and overburdening of nursing staff is well acknowledged in many hospital settings worldwide. In this paper, we present a preliminary study on automatic recognition of facial expression of pain in ICU settings. There has been considerable work on computer based pain recognition for settings beyond the ICU, wherein, usually the entire face of the person is visible without occlusions. The ICU setting, however, brings along unique challenges for recognizing facial expression of pain – the patient’s face is often covered by a respirator mask, the face is partially occluded by accessories attached to the respirator, and the patient is occasionally in a transient state of consciousness. In this paper we investigate the use of computer vision techniques for recognizing pain from partially visible faces. To establish a proof-of-concept, we simulate the occlusions most likely to happen in an ICU setting by masking out the nose, cheeks, and mouth regions, and use only a narrow band around the eyes of the patient as an input to our method. We generate these simulated occlusions using data from the UNBC-McMaster pain expression archive −a previously used dataset for pain expression recognition based on fully visible faces. We investigate multiple feature representations by extracting features only on the basis of the eye-region and train classifiers to detect the presence of pain using leave-one-person-out cross validation. Our results suggest a potential viability of automatic pain monitoring in the ICU settings involving face occlusions.
Tracking Control via Iterative Learning for High-Speed Trains With Distributed Input Constraints

This paper investigates the tracking control problem of high-speed trains in the presence of the input constraints caused by the distribution and output capacity of power systems. By virtue of the repetitive operation pattern of trains and the backstepping technique, an adaptive iterative learning control (ILC) strategy based on the multi-particle model is proposed to drive the train to track the given reference displacement and velocity, where the unknown time-varying parameters are learned and adjusted between successive operations, and an input-dependent auxiliary system is introduced to compensate the influence of input constraints. During the design of the controller, the Lyapunov function and composite energy function (CEF) are constructed to ensure the stability of the closed-loop system and the convergence of tracking errors for high-speed trains. Furthermore, numerical simulation is performed to confirm the effectiveness of the proposed scheme. The three main contributions of this work lie in: 1) Integrating the multi-particle model and ILC framework, which can more accurately reveal the dynamics of the train, and take full advantage of the repetitive operation pattern; 2) Following the backstepping procedure to devise the learning controller, where the parameter uncertainties and modeling inaccuracies are deliberately handled, and; 3) Solving the issues of distributed input constraints for the control system of high-speed trains.
Hybrid Stochastic and Interval Power Flow Considering Uncertain Wind Power and Photovoltaic Power

It is an important means for knowing something about the influence of uncertain factors on the power flow state to calculate uncertain power flow. In this paper, the uncertain power flow problem considering wind power and photovoltaic power generation is studied. The interval distribution models of wind power and photovoltaic power generation are established by expressing the natural factors such as wind speed and light intensity as interval variables, and an uncertain power flow model of hybrid stochastic and interval variables is established by expressing the node load as the stochastic variable to obey the Gaussian distribution. A double-layer Monte Carlo method is proposed to solve it. The numerical results obtained by the IEEE-30 bus system show that the proposed model and method are effective, by which the maximum and minimum cumulative probability functions of the power flow state can be obtained, thus determining whether the probabilistic interval of the power flow state is out-of-limit (maximum probability and minimum probability).
A New Low-Complexity Cipher Class for Clone-Resistant Identities

A new large cipher class based on new two variable self-inverse-permutation deploying Golden S-Boxes is presented. The mapping has particular properties, in that it is both self-inverse and uses arithmetic over the ring of integers modulo $2^{{\mathrm {n}}}$. The key contribution of such cipher structures is in activating the use of passive hard-core multipliers (Math-Blocks) in modern FPGA devices. Such multipliers are often available for free when not utilized in for the system tasks. Standard ciphers do not usually use multiplications due to their high implementation complexity. The proposed cipher class is reactivating such free multipliers to create good quality ciphers at very low cost. The target application of such ciphers is in creating the so-called Secret Unknown Ciphers (SUCs). Such SUCs can serve as highly-resilient clone-resistant digital-identity modules in future smart self-reconfiguring non-volatile FPGA devices. The security level of the resulting ciphers is relatively high and scalable. The main future application areas of such structures are expected to be for vehicular security applications. A sample implementation prototype demonstrates the complexity of the resulting physical identity and its performance.
Emotion Classification Based on Convolutional Neural Network Using Speech Data

The human voice is the most frequently used mode of communication among people. It carries both linguistic and paralinguistic information. For an emotion classification task, it is important to process paralinguistic information because it describes the current affective state of a speaker. This affective information can be used for health care purposes, customer service enhancement and in the entertainment industry. Previous research in the field mostly relied on handcrafted features that are derived from speech signals and thus used for the construction of mainly statistical models. Today, by using new technologies, it is possible to design models that can both extract features and perform classification. This preliminary research explores the performance of a model that comprises a convolutional neural network for feature extraction and a deep neural network that performs emotion classification. The convolutional neural network consists of three convolutional layers that filter input spectrograms in time and frequency dimensions and two dense layers forming the deep part of the model. The unified neural network is trained and tested spectrograms of speech utterances from the Berlin database of emotional speech.
News2meme: An Automatic Content Generator from News Based on Word Subspaces from Text and Image

Internet users engage in content creation by using various media formats. One of the most popular forms is the “internet meme”, which often depicts the general opinion about events with an image and a catchphrase. In this paper, we propose news2meme, a method for automatically generating memes from a news article, where we aim to match words and images efficiently. We approach this as two multimedia retrieval problems with the same input news text: 1) An image retrieval task where the output is a meme image; 2) A text retrieval task where the output is a catchphrase. These two outputs are combined to generate the meme for the news article. We represent texts and catchphrases as sets of word vectors through the word2vec representation. To handle images similarly, we extract sets of tags from the images using a deep neural network. These tags are then translated to word vectors in the same vector space through word2vec. Finally, we represent the intrinsic variability of features in a set of word vectors with a word subspace. Through word subspaces comparison, we can directly compare images and texts, making retrieval across media formats possible. A preliminary experiment was performed to evaluate our framework.
Pattern recognition of a spiking neural network based on visual motion model

With the rapid development of artificial intelligence, deep learning which has been broadly applied on the image processing, pattern recognition and data mining. However, it requires huge amounts of data and computing power. As we all know, the human brain is very complex but effective with much lower energy consumption. It is of great significance to process information with reference to the brain processing mechanism which not only helps us to understand how the brain works, but also can build smart chips with lower power consumption. In this paper, images preprocessed by the visual motion model and mapped into the V2 layer with different orientations, and then, we train the connection between V2 and output by supervised STDP rule. The results show that we can achieve the same recognition accuracy with fewer training samples, which contributed by the visual model preprocessing. The visual preprocess can amplify the spatiotemporal information and highlight the feature of images.
Energy-Efficiency for Fixed-Wing UAV-Enabled Data Collection and Forwarding

Wireless communication with unmanned aerial vehicles (UAVs) becomes a hot topic because of thier mobility and low cost. This paper proposes a fixed-wing UAV-enabled communication system to collect and forward data from one node to another node, between which the direct link is blocked. As fixed-wing UAV cannot hover steadily and turn around quickly, this paper proposes a running track (RT) trajectory model which consists of straight part and circular part. The objective is to minimize the UAV's propulsion energy consumption while satisfying the throughput requirement by optimizing the RT trajectory. A variable discretization method is used to transform the problem into a discretized equivalent. And then the problem is transformed into a convex optimization problem which can obtain a locally optimal solution by the successive convex approximation (SCA) method. Numerical results show that the proposed RT model can obtain the optimal trajectory to collect and forward the data efficiently with the less propulsion energy, compared to the approximate straight trajectory and circular trajectory.