Reviewerly/exHarmony huggingface dataset:
An Outlier Detection and Exclusion Method Applying Property of Fast M-Estimation Gaussian Filter and Gaussian Filter

If an outlier exists, an accurate roughness evaluation is not possible. The solution of this problem is insufficient only by calculating a mean line and roughness profile by robust filter. In addition to it, excluding of the outlier region is necessary. Fast M-estimation based Gaussian filter (FMGF) is the robust Gaussian filter using the fast M-estimation method that is developed so far by authors. The FMGF shows robustness only for the outliers such as spike noise, then the output agrees with the Gaussian filter output when no outlier exists. In this paper, the region of the outlier is extracted from the roughness profile by using the characteristic of FMGF and GF. And the roughness parameter is calculated from the data that excluded the spike noise, without needing manual data processing.
Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model.
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models

Clinical time series are known for irregular, highly-sporadic and strongly-complex structures and are consequently difficult to model by traditional state-space models. In this paper, we investigate the potential of applying variational recurrent neural networks (VRNNs) for forecasting clinical time series extracted from electronic health records (EHRs) of patients. Variational recurrent neural networks (VRNNs) combine recurrent neural networks (RNNs) and variational inference (VI) and are state-of-the-art methods to model highly-variable sequential data such as text, speech, time series and multimedia signals in a generative fashion. We propose to incorporate multiple correlated time series to improve the forecasting of VRNNs. The selection of these correlated time series is based on the similarity of the supplementary medical information e.g., disease diagnostics, ethnicity and age etc. between the patients. We evaluate the effectiveness of utilizing such supplementary information with root mean square error (RMSE), on clinical benchmark data-set "Medical Information Mart for Intensive Care (MIMIC III)" for multi-step-ahead prediction. We further perform subjective analysis to highlight the effects of the similarity of the supplementary medical information on individual temporal features e.g., Systolic Blood Pressure (SBP), Heart Rate (HR) etc. of the patients from the same data-set. Our results clearly show that incorporating the correlated time series based on the supplementary medical information can help improving the accuracy of the VRNNs for clinical time series forecasting.
Supporting robotic software migration using static analysis and model-driven engineering

The wide use of robotic systems contributed to developing robotic software highly coupled to the hardware platform running the robotic system. Due to increased maintenance cost or changing business priorities, the robotic hardware is infrequently upgraded, thus increasing the risk for technology stagnation. Reducing this risk entails migrating the system and its software to a new hardware platform. Conventional software engineering practices such as complete re-development and code-based migration, albeit useful in mitigating these obsolescence issues, they are time-consuming and overly expensive. Our RoboSMi model-driven approach supports the migration of the software controlling a robotic system between hardware platforms. First, RoboSMi executes static analysis on the robotic software of the source hardware platform to identify platform-dependent and platform-agnostic software constructs. By analysing a model that expresses the architecture of robotic components on the target platform, RoboSMi establishes the hardware configuration of those components and suggests software libraries for each component whose execution will enable the robotic software to control the components. Finally, RoboSMi through code-generation produces software for the target platform and indicates areas that require manual intervention by robotic engineers to complete the migration. We evaluate the applicability of RoboSMi and analyse the level of automation and performance provided from its use by migrating two robotic systems deployed for an environmental monitoring and a line following mission from a Propeller Activity Board to an Arduino Uno.
Processing and Application of Substation Alarm Information Based on Semantic Framework

When the power grid is abnormal or faulty, the substation monitoring system will generate a large amount of alarm information. There is a problem that a lot of alarm information cannot be used in depth. In view of the above problems, this paper proposes a data preprocessing method based on the semantic framework of alarm information. It provides data support for power grid fault diagnosis. Firstly, the semantic slot of the semantic frame is filled with the Horspool algorithm to realize the automatic classification and extraction of alarm information by constructing a semantic frame. Secondly, the alarm information extracted by automatic classification is applied to the fault diagnosis program of the power grid. The data preprocessing part of the power grid fault diagnosis architecture based on data mining technology has been solved. Finally, the reliability of the proposed method is verified in the proposed case.
Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060

With regards to Advanced Driver Assistance Systems in vehicles, vision and image-based ADAS is profoundly well known since it utilizes Computer vision algorithms, for example, object detection, street sign identification, vehicle control, impact cautioning, and so on., to aid sheltered and smart driving. Deploying these algorithms directly in resource-constrained devices like mobile and embedded devices etc. is not possible. Reduced Mobilenet V2 (RMNv2) is one of those models which is specifically designed for deploying easily in embedded and mobile devices. In this paper, we implemented a real-time RMNv2 image classifier in NXP Bluebox 2.0 and NXP i.MX RT1060. Because of its low model size of 4.3MB, it is very successful to implement this model in those devices. The model is trained and tested with the CIFAR10 dataset.
Towards Semantically Scalable Image Coding using Semantic Map

We propose an image coding scheme that compresses image into semantically scalable bitstream using deep neural networks. This scheme is expected to support intelligent analysis when the bitstream is partially decoded, as well as high-fidelity reconstruction of image when the bitstream is completely decoded. We implement such a semantically scalable image coding scheme based on semantic map. In the proposed scheme, the original image is firstly semantically segmented and the semantic map is compressed as the base layer. Then, the original image is segmented into several individual objects according to the semantic map, and each object is coded separately. A recurrent neural network-based encoder is used to compress these objects at several quality levels. At the decoder side, the semantic map can be directly applied for intelligent analysis. A generative adversarial network is used to synthesize a rough image using the semantic map. If user is interested in a certain object, more bits can be transmitted to enhance the quality of the object. Experimental results show that the proposed method achieves comparable compression performance with JPEG2000 at high bit rates, while facilitates intelligent analysis at low bit rates.
A New Majority Weighted Minority Oversampling Technique for Classification of Imbalanced Datasets

Classification problem is one of the essential tasks in data mining. Traditional classification strategies are predominantly via cost-insensitive equilibrium data. They tend to be concentrated on the overall accuracy of a model, and such classifiers are improper for unbalanced sample data. Hence, optimizing unbalanced samples to improve classifier performance is an issue worthy of discussion. Based on the information-rich minority samples that are difficult to learn, Majority Weighted Minority Oversampling Technique (MWMOTE) uses the clustering method to generate synthetic samples from the weighted information samples. However, the accuracy of the clustering should be optimized. To this end, a method called NC_Link_MWMOTE is presented for efficiently handling imbalanced learning problems. We propose a solution by using NC_Link-based hierarchical clustering method to synthesize different samples from a small number of samples, thus optimizing the clustering effect. NC_Link_MWMOTE was evaluated on six different levels of equilibrium data sets. The simulation results show that our method is effective and outperforms competitive baseline method in terms of various assessment metrics, such as Fl-score and Area Under Curve (AUC).
Infinite Bayesian Max-Margin Discriminant Projection

In this article, considering the supervised dimensionality reduction, we first propose a model, called infinite Bayesian max-margin linear discriminant projection (iMMLDP), by assembling a set of local regions, where we make use of Bayesian nonparametric priors to handle the model selection problem, for example, the underlying number of local regions. In each local region, our model jointly learns a discriminative subspace and the corresponding classifier. Under this framework, iMMLDP combines dimensionality reduction, clustering, and classification in a principled way. Moreover, to deal with more complex data, for example, a local nonlinear separable structure, we extend the linear projection to a nonlinear case based on the kernel trick and develop an infinite kernel max-margin discriminant projection (iKMMDP) model. Thanks to the conjugate property, the parameters in these two models can be inferred efficiently via the Gibbs sampler. Finally, we implement our models on synthesized and real-world data, including multimodally distributed datasets and measured radar image data, to validate their efficiency and effectiveness.
Precursors to CDA and Important Foundational Concepts

This chapter describes precursors to CDA, and important foundational concepts and theories. We first review briefly the ideas of the British linguist, John Rupert Firth, and his anthropologist colleague, Bronislaw Malinowski, and then discuss Michael Halliday’s Systemic Functional Linguistics (SFL), Systemic Functional Grammar (SFG) and Language as Social Semiotic (SocSem) and some ideas originated by James Martin—including his stratal-functional model, notions of text and context (and register and genre), the three metafunctions, grammatical metaphor and ‘appliable linguistics’. Next, we describe critical linguistics (CritLing): its relation to other approaches, its definition, important works such as Language and Control and Language as Ideology, its interdisciplinarity and ‘useability’ as an approach, and its further development in Kress (1985b/1989), Linguistic Processes in Sociocultural Practice. We then discuss the complex relationship between CritLing and SocSem, the further development of SocSem in Hodge and Kress (1988) and especially in Kress and Theo van Leeuwen’s work on the ‘grammar’ of visual design in Reading Images (), including the three metafunctions and other facets of the visual. We conclude with the development of multimodality—and a short discussion of Kress and van Leeuwen’s Multimodal Discourse in relation to CritLing, SocSem, and CDA.
Face Authentication From Grayscale Coded Light Field

Face verification is a fast-growing authentication tool for everyday systems, such as smartphones. While current 2D face recognition methods are very accurate, it has been suggested recently that one may wish to add a 3D sensor to such solutions to make them more reliable and robust to spoofing, e.g., using a 2D print of a person's face. Yet, this requires an additional relatively expensive depth sensor. To mitigate this, we propose a novel authentication system, based on slim grayscale coded light field imaging. We provide a reconstruction free fast anti-spoofing mechanism, working directly on the coded image. It is followed by a multi-view, multi-modal face verification network that given grayscale data together with a low-res depth map achieves competitive results to the RGB case. We demonstrate the effectiveness of our solution on a simulated 3D (RGBD) version of LFW, which will be made public, and a set of real faces acquired by a light field computational camera.
Usability evaluation of component based software system using software metrics

Component Based Software Engineering (CBSE) provides a way to create a new Component Based Software System (CBSS) by utilizing the existing components. The primary reason for that is to minimize the software development time, cost and effort. CBSS also increases the component reusability. Due to these advantages, software industries are working on CBSS and continuously trying to provide quality product. Usability is one of the major quality factors for CBSS. It should be measured before delivering the software product to the customer, so that if there are any usability flaws, it can be removed by software development team. In this paper, work has been done to evaluate the usability of CBSS based on major usability sub-factors (learnability, operability, understandability and configurability). For this purpose, firstly software metrics are identified for each usability sub-factor and the value of each sub-factor is evaluated for a component based software project. Secondly, overall usability of the software project is evaluated by using the calculated value of each usability sub-factor. Usability for the same project was also evaluated using Fuzzy approach in MATLAB to validate the experimental work of this research paper. It was identified that the value of usability obtained from software metrics and fuzzy model was very similar. This research work will be useful for the software developer to evaluate the usability of any CBSS and will also help them to compare different version of any CBSS in term of their usability.
Aerial War Game Simulation

War gaming is a tool to enhance human decision-making acumen during actual war time. War games are analytic games that simulate warfare at the tactical, operational or strategic level. They are used to analyze war fighting concepts, train and prepare commanders and subordinates, explore scenarios and evaluate how planning affects outcomes. Such simulations are very useful to develop tactical, strategic and doctrinal solutions. It also provides the participants with insight into decision making process and stress management. Our paper presents a framework that supports an aerial war game simulation in which two opposing forces (red and blue) participate and follow rules and strategies depicting an actual or real life situation. War is emulated and commanders and other officers perform war duties without any troops involved in this training. Our paper presents a proposal of aerial warfare training through simulation and discusses the nature of war gaming, its uses, underlying architecture and the functional modules visualized with their roles, game design i.e. the various stages into which the whole war game has been split and explains the states of the missions being planned by teams to accomplish the objectives given to them. Also, the paper addresses the implementation level details and overview of future scope visualized. This aerial war game solution is developed using java API's for 3D based GIS display, QT and Oracle 11g.
A DPA-Resistant Asynchronous-Logic NoC Router with Dual-Supply-Voltage-Scaling for Multicore Cryptographic Applications

We propose a 5-port asynchronous-logic Network-on-Chip (ANoC) router based on the Sense-Amplifier Half-Buffer (SAHB) approach for cryptographic processing cores to counteract side channel attack differential power analysis (DPA) in multicore platform. There are three features in the proposed DPA-resistant ANoC router. First, the proposed ANoC router embodies dual-supply-voltage SAHB cells, where the non-critical subsidiary supply voltage is adjustable from 0.3V to 1.2V, increasing the noise variance and hence reducing the Signal-to-Noise (SNR) ratio to hide the information leakage. Second, the proposed ANoC router performs as a noise engine by increasing the number of power-on IO ports, further randomizing the overall power dissipation. Third, the proposed ANoC router can switch between DPA-resistant mode and energy-efficient nominal (non-secure) mode, saving the power dissipation when the DPA secure countermeasure is unnecessary. Based on 65nm CMOS process, the multicore platform embedded with the proposed ANoC router is implemented, and the experiment is demonstrated by running the advanced encryption standard (AES) cryptography operation. When benchmarked against the nominal mode, the noise power variance of the proposed ANoC router increases by 2.3× in the DPA-resistant mode, reducing the overall SNR ratio by 56%. When comparing to other reported noise engines, our proposed ANoC router is one of the most DPA-secure, area-efficient and power-efficient designs for multicore cryptographic applications.
Globally Spatial-Temporal Perception: a Long-Term Tracking System

Although siamese trackers have achieved superior performance, these kinds of approaches tend to favour the local search mechanism and are thus prone to accumulating inaccuracies of predicted positions, leading to tracking drift over time, especially in long-term tracking scenario. To solve these problems, we propose a siamese tracker in the spirit of the faster RCNN's two-stage detection paradigm. This new tracker is dedicated to reducing cumulative inaccuracies and improving robustness based on a global perception mechanism, which allows the target to be retrieved in time spatially over the whole image plane. Since the very deep network can be enabled for feature learning in this two-stage tracking framework, the power of discrimination is guaranteed. What's more, we also add a CNN-based trajectory prediction module exploiting the target's temporal motion information to mitigate the interference of distractors. These two spatial and temporal modules exploit both the high-level appearance information and complementary trajectory information to improve the tracking robustness. Comprehensive experiments demonstrate that the proposed Globally Spatial-Temporal Perception-based tracking system performs favorably against state-of-the-art trackers.
Disentangled Representation Learning Based Multidomain Stain Normalization For Histological Images

Color variations of histological images due to multi-factor hinder the performance of computer-aided diagnosis (CAD) systems. Previous stain normalization methods have achieved excellent results. While in practice, a multidomain stain normalization method is still be needed when more than two color variations exist in dataset. In this paper, we propose a multidomain stain normalization model inspired by MUNIT [1], with the idea of disentangling the representations of content and style. We assume that the latent space of histological images can be decomposed into domain-shared content space and domain-specific style space. The stain normalization aims to transfer the styles cross domains and maintain the contents. In addition, we propose to use the earth mover’s distance(EMD) to evaluate the effectiveness of stain normalization. We evaluate our approach against the state-of-the-art methods quantitatively and qualitatively.
Zebra: Memory Bandwidth Reduction for CNN Accelerators with Zero Block Regularization of Activation Maps

The large amount of memory bandwidth between local buffer and external DRAM has become the speedup bottleneck of CNN hardware accelerators, especially for activation maps. To reduce memory bandwidth, we propose to learn pruning unimportant blocks dynamically with zero block regularization of activation maps (Zebra). This strategy has low computational overhead and could easily integrate with other pruning methods for better performance. The results show that the proposed method for Resnet-20 on Tiny-Imagenet can reduce 70% of memory bandwidth and further improve to 76% with the combination of Network Slimming, all within 1% accuracy drops.
Convolutional Neural Network with Inception Blocks for Image Compression Artifact Reduction

A convolutional neural network is proposed for the reduction of artifacts introduced by JPEG compression. In this work the existing DnCNN architecture has been considered as the basis network and it is extended by introducing inception blocks on top of a normal convolution block consisting of convolution, rectified linear unit and batch-normalization layers. Each inception block contains parallel convolution blocks with various dilation factors, essentially performing Atrous convolution. Compression artifacts result in multiple image neighborhoods or blocks having similar or redundant information. In spatial domain the Atrous convolution introduces a larger receptive field while ignoring redundant neighboring pixels due to the dilation, thus helping in a better reconstruction. The proposed network is trained on a small dataset as used by the basis network. Evaluation of this network on the standard test datasets shows a substantial quantitative and qualitative improvement, particularly for higher compression rates.
Development of AUV for Data Acquisition in Underwater Environment

In this project, we are designing and developing an acoustic unmanned vehicle to acquire images in the deep sea ocean environment for effective surveillance in a very low-cost manner, compared to the other models available in defense labs. Here, we are using a simple AUV that has been designed and implemented successfully for image acquisition. After the image acquisition is done, the AUV will be taken out from the underwater environment, and the data acquired during the movement of the vehicle will be retrieved. Then, the acquired image will be transferred to MATLAB for further processing. In this stage, the image will be enhanced using frequency domain-based image enhancement, as well as histograms, for cross verifying the performance of the image enhancement. The principal objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. Image enhancement processing methods fall into two broad categories: spatial domain methods and frequency domain methods, which are left to the end-user at the final stage.