Reviewerly/exHarmony huggingface dataset:
Pedestrian Trajectory Prediction with Structured Memory Hierarchies

This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient information from multiple modalities to automatically store and retrieve important information for decision making without any supervision. We evaluate the effectiveness of the proposed models on a novel multimodal dataset that we introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from a radar system and a CCTV camera system installed in a public place. The performance is also evaluated on the publicly available New York Grand Central pedestrian database. In both settings, the proposed models demonstrate their capability to better anticipate future pedestrian motion compared to existing state of the art. Data related to this paper are available at: https://github.com/qutsaivt/SAIVTMultiSpectralTrajectoryDataset .
Quantum codes from cyclic codes over the ring Fp[u]/〈u3 − u〉

In this paper, we study nonbinary quantum codes from cyclic codes over the ring [Formula: see text] for odd prime [Formula: see text]. We give the constructional properties of linear and cyclic codes over the ring [Formula: see text]. As an applications of these classes of codes, we obtain quantum codes over [Formula: see text] using self-orthogonal property. Moreover, we find some new nonbinary quantum codes with better parameters than the known quantum codes over [Formula: see text].
Control strategies for Hopf bifurcation in a chaotic associative memory

This paper presents a set of control approaches that can deal with unwanted dynamics such as the Hopf Bifurcation in a Chaotic Associative Memory (CAM) for two distinct goals: the bifurcation critical point shift to anticipate or to postpone the start of a Hopf bifurcation; or to extinguish it. A Washout Filter Control, a Polynomial Control, and an adaptive control for data-driven called the Model-Free Adaptive Control were used in this study. We present the results of numerical simulations in a CAM with three neurons (a system with six dimensions), which were trained classically to retrieve a set of memories. Our analysis includes the dynamics of the bifurcated system, the ability of the network to recover the memories learned and how it is affected by the control strategies. The generality of the proposed control approaches was tested to control a different system and for a different behavior (chaotic dynamics).
Scientific Visualization as a Microservice

In this paper, we propose using a decoupled architecture to create a microservice that can deliver scientific visualization remotely with efficiency, scalability, and superior availability, affordability and accessibility. Through our effort, we have created an open source platform, Tapestry, which can be deployed on Amazon AWS as a production use microservice. The applications we use to demonstrate the efficacy of the Tapestry microservice in this work are: (1) embedding interactive visualizations into lightweight web pages, (2) creating scientific visualization movies that are fully controllable by the viewers, (3) serving as a rendering engine for high-end displays such as power-walls, and (4) embedding data-intensive visualizations into augmented reality devices efficiently. In addition, we show results of an extensive performance study, and suggest how applications can make optimal use of microservices such as Tapestry.
Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net

Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated approach using 2D deep residual dilated networks which captures rich context information of different tissues for the segmentation of eight brain structures. The proposed system was evaluated in the MICCAI Brain Segmentation Challenge ( http://mrbrains18.isi.uu.nl/ ) and ranked 9 $$^{th}$$ out of 22 teams. We further compared the method with traditional U-Net using leave-one-subject-out cross-validation setting on the public dataset. Experimental results shows that the proposed method outperforms traditional U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35 mm vs 11.59 mm in averaged robust Hausdorff distance) and is computationally efficient.
A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Gunshot backspatters

This is a data set of blood spatter patterns scanned at high resolution, generated in controlled experiments. The spatter patterns were generated with a rifle or a handgun with varying ammunition. The resulting atomized blood droplets travelled opposite to the bullet direction, generating a gunshot backspatter on a poster board target sheet. Fresh blood with anticoagulants was used; its hematocrit and temperature were measured. The main parameters of the study were the bullet shape, size and speed, and the distance between the blood source and target sheet. Several other parameters were explored in a less systematic way. This new and original data set is suitable for training or research purposes in the forensic discipline of bloodstain pattern analysis.
Leaking your engine speed by spectrum analysis of real-Time scheduling sequences

This paper identifies and studies a new security/privacy issue for automobile vehicles. Specifically, attackers can infer the engine speed of a vehicle by observing and analyzing the real-time scheduling sequences on the Engine Control Unit (ECU). First, we present the problem model of engine-triggered task executed on ECU. And then, we introduce two Engine-triggered Task Period Tracing methods (DFT-based ETPT and FRSP-based ETPT) to infer the period variation of engine-triggered task. Finally, simulation experiments are conducted to demonstrate the effect of this new timing side-channel information leakage with our proposed methods.
Memory-Efficient Adaptive Optimization for Large-Scale Learning.

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art.
A 2.5D Deep Learning-Based Approach for Prostate Cancer Detection on T2-Weighted Magnetic Resonance Imaging

In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.
Phase noise estimation using Bayesian inference for continuous-variable quantum key distribution

Excess noise induced by the phase drifts is a serious impairment for the continuous-variable quantum key distribution with locally generated local oscillator scheme, which is recently proposed to avoid the side channel attacks due to the transmitted local oscillator. Theoretical and experimental studies on the phase estimation have been widely reported, while two frequency-locked laser sources are indispensable to achieve quantum coherent detection. Moreover, the self-referenced phase estimation scheme requires to propagate the strong reference pulse through optical fiber, which opens a security loophole through the manipulation of the reference pulse amplitude. Based on the theoretical security and Bayes' theorem, we propose a phase estimation protocol, which does not require propagating the strong reference pulse for performing phase estimation. Compared to the other related work, the protocol can avoid the security problem caused by strong reference pulse. Moreover, this algorithm is an iterative progress for each of experiment to obtain the phase estimation and its uncertainty. We hope the proposed scheme could further promote the performance of continuous-variable quantum key distribution.
Online Subspace Learning from Gradient Orientations for Robust Image Alignment

Robust and efficient image alignment remains a challenging task, due to the massiveness of images, great illumination variations between images, partial occlusion, and corruption. To address these challenges, we propose an online image alignment method via subspace learning from image gradient orientations (IGOs). The proposed method integrates the subspace learning, transformed the IGO reconstruction and image alignment into a unified online framework, which is robust for aligning images with severe intensity distortions. Our method is motivated by a principal component analysis (PCA) from gradient orientations that provides more reliable low-dimensional subspace than that from pixel intensities. Instead of processing in the intensity-domain-like conventional methods, we seek alignment in the IGO domain, such that the aligned IGO of the newly arrived image can be decomposed as the sum of a sparse error and a linear composition of the IGO-PCA basis learned from previously well-aligned ones. The optimization problem is tackled by an iterative linearization that minimizes the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm of the sparse error. Furthermore, the IGO-PCA basis is adaptively updated based on incremental thin singular value decomposition, which takes the shift of IGO mean into consideration. The efficacy of the proposed method is validated on the extensive challenging datasets through image alignment, medical atlas construction, and face recognition. The experimental results demonstrate that our algorithm provides more illumination- and occlusion-robust image alignment than the state-of-the-art methods.
Human Action Recognition Based on Temporal Pose CNN and Multi-dimensional Fusion

To take advantage of recent advances in human pose estimation from images, we develop a deep neural network model for action recognition from videos by computing temporal human pose features with a 3D CNN model. The proposed temporal pose features can provide more discriminative human action information than previous video features, such as appearance and short-term motion. In addition, we propose a novel fusion network that combines temporal pose, spatial and motion feature maps for the classification by bridging the gap between the dimension difference between 3D and 2D CNN feature maps. We show that the proposed action recognition system provides superior accuracy compared to the previous methods through experiments on Sub-JHMDB and PennAction datasets.
Toward The New Mixed Reality Environment for Interior Design

The paper presents a computer-aided design (CAD) system for interior architecture design called Design3R. The main assumption of this project is the implementation of the achievements from the field of augmented reality (AR) and virtual reality (VR) in order to embed the design process in the first person perspective and perceptual scale. It allows to work with furniture and other interior elements in their real size, directly in the realistic view of the designed interior. Design3R is developed by the author of the paper as the cross-platform software, and what is more, as the cooperation in the field of research and development with 3R Studio Mobile company. Design3R project is aimed to take advantage of the possibilities offered by HTC Vive and Google Tango to create a design environment in which the real space of building interior and the emerging interior design are completely integrated. The paper claims that the presented methods support the design process and create a new, more intuitive perspective on the perception of the project.
An Argumentation‐Based Analysis of the Simonshaven Case

In an argumentation approach, legal evidential reasoning is modeled as the construction and attack of "trees of inference" from evidence to conclusions by applying generalizations to evidence or intermediate conclusions. In this paper, an argumentation-based analysis of the Simonshaven case is given in terms of a logical formalism for argumentation. The formalism combines abstract argumentation frameworks with accounts of the structure of arguments, of the ways they can be attacked and of ways to evaluate conflicting arguments. The purpose of this paper is not to demonstrate or argue that the argumentation approach to modeling legal evidential reasoning is feasible or even preferable but to have a fully worked-out example that can be used in the comparison with alternative Bayesian or scenario-based analyses.
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution operation while still maintaining representational efficiency. To show the effectiveness of our proposed convolution, we present extensive experimental results on the standard convolutional neural network (CNN) architectures such as VGG \cite{vgg2014very} and ResNet \cite{resnet}. We find that after replacing the standard convolutional filters in these architectures with our proposed HetConv filters, we achieve 3X to 8X FLOPs based improvement in speed while still maintaining (and sometimes improving) the accuracy. We also compare our proposed convolutions with group/depth wise convolutions and show that it achieves more FLOPs reduction with significantly higher accuracy.
A Novel Behaviour Profiling Approach to Continuous Authentication for Mobile Applications

The growth in smartphone usage has led to increased user concerns regarding privacy and security. Smartphones contain sensitive information, such as personal data, images, and emails, and can be used to perform various types of activity, such as transferring money via mobile Internet banking, making calls and sending emails. As a consequence, concerns regarding smartphone security have been expressed and there is a need to devise new solutions to enhance the security of mobile applications, especially after initial access to a mobile device. This paper presents a novel behavioural profiling approach to user identity verification as part of mobile application security. A study involving data collected from 76 users over a 1-month period was conducted, generating over 3 million actions based on users’ interactions with their smartphone. The study examines a novel user interaction approach based on supervised machine learning algorithms, thereby enabling a more reliable identity verification method. The experimental results show that users could be distinguished via their behavioural profiling upon each action within the application, with an average equal error rate of 26.98% and the gradient boosting classifier results prove quite compelling. Based on these findings, this approach is able to provide robust, continuous and transparent authentication.
Discriminative multi-source adaptation multi-feature co-regression for visual classification

Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in machine learning. While the advantages of multi-source adaptation have been widely recognized, there still exit three major limitations in extant methods. Firstly, how to effectively select the discriminative sources is yet an unresolved issue. Secondly, multiple different visual features on hand cannot be effectively exploited to represent a target object for boosting the adaptation performance. Last but not least, they mainly focus on either visual understanding or feature learning independently, which may lead to the so-called semantic gap between the low-level features and the high-level semantics. To overcome these defects, we propose a novel Multi-source Adaptation Multi-Feature (MAMF) co-regression framework by jointly exploring multi-feature co-regression, multiple latent spaces learning, and discriminative sources selection. Concretely, MAMF conducts the multi-feature representation co-regression with feature learning by simultaneously uncovering multiple optimal latent spaces and taking into account correlations among multiple feature representations. Moreover, to discriminatively leverage multi-source knowledge for each target feature representation, MAMF automatically selects the discriminative source models trained on source datasets by formulating it as a row-sparsity pursuit problem. Different from the state-of-the-arts, our method is able to adapt knowledge from multiple sources even if the features of each source and the target are partially different but overlapping. Experimental results on three challenging visual domain adaptation tasks consistently demonstrate the superiority of our method in comparison with the related state-of-the-arts.
Research on intelligent cognitive function enhancement of intelligent robot based on ant colony algorithm

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved.
Illumination Estimation and Compensation of Low Frame Rate Video Sequences for Wavelet-Based Video Compression

In this paper, we are interested in the compression of image sets or video with considerable changes in illumination. We develop a framework to decompose frames into illumination fields and texture in order to achieve sparser representations of frames which is beneficial for compression. Illumination variations or contrast ratio factors among frames are described by a full resolution multiplicative field. First, we propose a Lifting-based Illumination Adaptive Transform (LIAT) framework which incorporates illumination compensation to temporal wavelet transforms. We estimate a full resolution illumination field, taking heed of its spatial sparsity by a rate-distortion (R-D) driven framework. An affine mesh model is also developed as a point of comparison. We find the operational coding cost of the subband frames by modeling a typical t + 2D wavelet video coding system. While our general findings on R-D optimization are applicable to a range of coding frameworks, in this paper, we report results based on employing JPEG 2000 coding tools. The experimental results highlight the benefits of the proposed R-D driven illumination estimation and compensation in comparison with alternative scalable coding methods and non-scalable coding schemes of AVC and HEVC employing weighted prediction.
The effect of P- and S-phase arrivals on the accuracy of microseismic source location

In this paper, the conventional microseismic monitoring is classified into two types: body and line monitoring cases. And the phase arrival conditions are classified into three types: 1) Only including P-arrivals and P-picks being in error; 2) Both including P- and S-arrivals and P-picks being in error; 3) Both including P- and S-arrivals and S-picks being in error. Then the effect of P- and S-phase arrivals on the accuracy of source location is studied under six conditions. In body monitoring case, the location accuracy will be improved dramatically when S-picks are added. In line monitoring case, the convergence rate of location function is dramatically improved when S-picks are added. This kind of phenomena is called P-picks effect. Then a pair of mutually exclusive hypothesizes are put forward based on P-picks effect. And another set of numerical tests are designed to verify which one is true. The tests results show that the location accuracy and the iterative convergence rate of the objective function can be dramatically improved when S-picks are added. What's more, this effect cannot be offset by increasing P-picks. To understand this effect deeply, the location model is simplified further. The properties of location functions with or without S-picks are studied and compared. And the S-picks effect is analyzed mathematically based on the simplified location model and the properties of objective function. Two field data tests, 81,307# workface microseismic monitoring of Baode Coal Mine and 4# branch tunnel microseismic monitoring of Hanjiang-to-Weihe River Valley Water Diversion Project, are analyzed to verify the conclusion that the participation of S-picks can improve converge rate and location accuracy. In both cases, the location results with P- and S-picks are consistent with the rock mechanics theoretical results. Both of them confirm that S-picks are indispensable in source location.
Deep Learning for Predictive Analytics in Healthcare

Despite a recent wealth of data and information, the healthcare sector is lacking in actionable knowledge. The healthcare industry faces challenges in essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. It is necessary to reduce healthcare costs and the movement towards personalized healthcare. The rapidly expanding fields of deep learning and predictive analytics has started to play a pivotal role in the evolution of large volume of healthcare data practices and research. Deep learning offers a wide range of tools, techniques, and frameworks to address these challenges. Health data predictive analytics is emerging as a transformative tool that can enable more proactive and preventative treatment options. In a nutshell, this paper focus on the framework for deep learning data analysis to clinical decision making depicts the study on various deep learning techniques and tools in practice as well as the applications of deep learning in healthcare.