Reviewerly/exHarmony huggingface dataset:
Towards Open-Set Semantic Segmentation Of Aerial Images

Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.
Leap Motion Hand Gesture Recognition Based on Deep Neural Network

This paper proposes a new gesture recognition system based on Deep Neural Network (DNN) and Leap Motion. The palm model is reconstructed to obtain the feature data, and then the feature data of all experimenters are obtained by the Leap Motion controller. The data are finally put into the DNN model for training to implement the recognition of specific hand gesture after being normalized. By testing with 30000 gesture frames of five volunteers, it is found that the recognition accuracy of the proposed scheme can reach 98%, which indicates that the scheme can complete the specific gesture recognition with a high average recognition rate.
Robust centralized and integrated covariance intersection fusion Kalman estimators for networked mixed‐uncertain systems

Summary For networked mixed uncertain time‐varying systems with uncertain noise variances, random one‐step measurement delay, state‐dependent and noise‐dependent multiplicative noises, and linearly dependent additive white noises, the robust local, centralized, and distributed fusion estimation problems are addressed. Three new approaches are presented, which include a new augmented state approach with fictitious white noises, an extended Lyapunov equation approach with two Lyapunov equations, and a universal integrated covariance intersection (ICI) fusion approach of integrating the minimax robust local Kalman estimators and their conservative cross‐covariances. They constitute a new important methodology of solving robust fusion estimation problems. Applying them, the local, centralized, and distributed ICI fusion time‐varying and steady‐state robust Kalman estimators (predictor, filter, and smoother) are presented in the sense that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. Their robustness, convergence, and accuracy relations are proved. Specially, the proposed ICI fusers improve the robust accuracies of the original covariance intersection fusers, and overcome their drawbacks, such that the local estimators and their conservative variances are assumed to be known, and the conservative cross‐variances are ignored. A simulation example with application to a vehicle suspension system shows the effectiveness of the proposed approaches and results.
Cross-Domain Missingness-Aware Time-Series Adaptation With Similarity Distillation in Medical Applications

Medical time series of laboratory tests has been collected in electronic health records (EHRs) in many countries. Machine-learning algorithms have been proposed to analyze the condition of patients using these medical records. However, medical time series may be recorded using different laboratory parameters in different datasets. This results in the failure of applying a pretrained model on a test dataset containing a time series of different laboratory parameters. This article proposes to solve this problem with an unsupervised time-series adaptation method that generates time series across laboratory parameters. Specifically, a medical time-series generation network with similarity distillation is developed to reduce the domain gap caused by the difference in laboratory parameters. The relations of different laboratory parameters are analyzed, and the similarity information is distilled to guide the generation of target-domain specific laboratory parameters. To further improve the performance in cross-domain medical applications, a missingness-aware feature extraction network is proposed, where the missingness patterns reflect the health conditions and, thus, serve as auxiliary features for medical analysis. In addition, we also introduce domain-adversarial networks in both feature level and time-series level to enhance the adaptation across domains. Experimental results show that the proposed method achieves good performance on both private and publicly available medical datasets. Ablation studies and distribution visualization are provided to further analyze the properties of the proposed method.
Unmanned Aerial Vehicle (UAV) Path Planning Based on Improved Pre-planning Artificial Potential Field Method

The improvement of Artificial Potential Field Method (APF) is proposed for the problem that the existence of local minimum severity of the traditional artificial force field method leads to the failure of path planning. This method introduces the Rapidly-exploring Random Trees (RRT) algorithm for path pre-planning and generates a pre-planned path from start to finish. This algorithm takes the pre-planned path node as the new gravitational source, weakens the role of target point and emphasizes the path coherence. In the process of path planning, the function of the repulsive potential field is improved to ensure the accessibility of the gravitational source and to guarantee the success of path planning. The simulation results show that the method can effectively avoid local minimum values and has good adaptability.
Probabilistic framework for solving visual dialog

In this paper, we propose a probabilistic framework for solving the task of ‘Visual Dialog’. Solving this task requires reasoning and understanding of visual modality, language modality, and common sense knowledge to answer. Various architectures have been proposed to solve this task by variants of multi-modal deep learning techniques that combine visual and language representations. However, we believe that it is crucial to understand and analyze the sources of uncertainty for solving this task. Our approach allows for estimating uncertainty and also aids a diverse generation of answers. The proposed approach is obtained through a probabilistic representation module that provides us with representations for image, question and conversation history, a module that ensures that diverse latent representations for candidate answers are obtained given the probabilistic representations and an uncertainty representation module that chooses the appropriate answer that minimizes uncertainty. We thoroughly evaluate the model with a detailed ablation analysis, comparison with state of the art and visualization of the uncertainty that aids in the understanding of the method. Using the proposed probabilistic framework, we thus obtain an improved visual dialog system that is also more explainable.
IDC Breast Cancer Detection Using Deep Learning Schemes

During the past few years, deep learning (DL) architectures are being employed in many potential areas such as object detection, face recognition, natural language processing, medical image analysis and other related applications. In these applications, DL has achieved remarkable results matching the performance of human experts. This paper presents a novel convolutional neural networks (CNN)-based approach for the detection of breast cancer in invasive ductal carcinoma tissue regions using whole slide images (WSI). It has been observed that breast cancer has been a leading cause of death among women. It also remains a striving task for pathologist to find the malignancy regions from WSI. In this research, we have implemented different CNN models which include VGG16, VGG19, Xception, Inception V3, MobileNetV2, ResNet50, and DenseNet. The experiments were performed on standard WSI slides data-set which include 163 patients of IDC. For performance evaluation, same data-set was divided into 113 and 49 images for training and testing, respectively. The testing was carried out separately over each model and the obtained results showed that our proposed CNN model achieved 83% accuracy which is better than the other models.
A lightweight AES algorithm implementation for encrypting voice messages using field programmable gate arrays

The security of data is a major concern in this digital era. In general text and image data are well protected with the help of existing standards, meanwhile, securing voice communications is a challenging task. In the literature, there are some methods found using oscillators and chaotic signals for protecting direct voice communications, but they have limited functionalities due to synchronization problems between chaotic signals and oscillators. This paper gives an idea of encrypting voice signals over peer to peer communication with the help of a modified and lightweight AES algorithm. This algorithm is similar to the traditional AES algorithm in most of the aspects but does not imply the use of mix column that is used in the traditional AES algorithm. The lightweight algorithm is tested on two different Field Programmable Gate Array (FPGA) and the designs are analyzed with respect to time delay and effective utilization of resources.
Revised DBSCAN Clustering Algorithm Based on Dual Grid

As one of the most important density-based clustering algorithms, DBSCAN has been widely used in many application fields due to its simplicity and good clustering effect. However, the original DBSCAN method exists two main problems, which include difficulty selecting the suitable external parameters and time consuming. In this paper, an improved version of the DBSCAN, namely DG-DBSCAN (dual grid-based DBSCAN) clustering algorithm, has been proposed. It adopts two types of grid (i.e. inner grid and outer gird) to solve the aforementioned problems respectively. Experimental results show that the new algorithm can not only determine the appropriate external input parameters, but also greatly reduce the running time. Therefore, the proposed DG-DBSCAN is successfully implemented as a novel clustering method.
Question Classification on Question-Answer System using Bidirectional-LSTM

The first step in the question-answer system is question analysis. This step was carried out with various processes, one of them was question classification. The question classification process can determine the accuracy of the answers generated by the system. Some approaches that have been used are Support Vector Machine (SVM), pattern matching, naïve bayes classification and Latent Dirichlet Allocation (LDA). Research on question classification using that approach has been widely carried out but still only work on certain sentence patterns. These problems can be solved using deep learning method, one of them is Bidirectional Long Short Term Memory (Bi-LSTM). Bi-LSTM does not depend on certain sentence patterns. Bi-LSTM has good accuracy in text classification. This study uses Bi-LSTM in the question classification process. Questions divided into three classes, that is greeting, daily conversation, and meetings. The classification results show the accuracy of 0.909 with loss of 0.316. Bi-LSTM has higher accuracy compared to basic LSTM and Recurrent Neural Network (RNN). Based on the results, Bi-LSTM can be used as question classification method for Question Answer System (QA System).
Human action recognition using deep rule-based classifier

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples.
VeTo: Expert Set Expansion in Academia

Expanding a set of known domain experts with new individuals, that have similar expertise, is a problem with many practical applications (e.g., adding new members to a conference program committee). In this work, we study this problem in the context of academic experts and we introduce VeTo, a novel method to effectively deal with it by exploiting scholarly knowledge graphs. In particular, VeTo expands the given set of experts by identifying researchers that share similar publishing habits with them, based on a graph analysis approach. Our experiments show that VeTo is more effective than existing techniques that can be applied to deal with the same problem.
Monetization using Blockchains for IoT Data Marketplace

The number of Internet of Things devices is growing dramatically, generating a huge amount of data which is becoming a valuable asset for data analysts. This trend culminates towards the creation of an IoT data marketplace, where streams of data from heterogeneous sources are sent in real time to various data consumers and are metered for monetization purposes. Publish/subscribe systems, such as Message Queuing Telemetry Transport (MQTT), are a promising solution to act as a transport layer for real-time data streams in a decoupled and large scale manner. However, pub/sub systems lack two key properties for an IoT data marketplace: (1) it does not provide any monetization logic; (2) it assumes that the pub/sub brokers are trusted entities, which is not the case in a decentralized or federated marketplace setting. In this paper, we address these issues using a reliable and transparent monetization system based on Distributed Ledger Technology (DLT) and smart contracts. We propose three monetization solutions and demonstrate the trade-off between the overhead of tracking IoT data on a blockchain vs. the accuracy of the monetization for data producers and consumers. In particular, we provide a Bloom filter-based solution for efficient verification of data exchange. We implement our system using Ethereum and Solidity and evaluate with respect to contract gas cost.
A framework for facial age progression and regression using exemplar face templates

Abstract Techniques for facial age progression and regression have many applications and a myriad of challenges. As such, automatic aged or de-aged face generation has become an important subject of study in recent times. Over the past decade or so, researchers have been working on developing face processing mechanisms to tackle the challenge of generating realistic aged faces for applications related to smart systems. In this paper, we propose a novel approach to try and address this problem. We use template faces based on the formulation of an average face of a given ethnicity and for a given age. Thus, given a face image, the target aged image for that face is generated by applying it to the relevant template face image. The resulting image is controlled by two parameters corresponding to the texture and the shape of the face. To validate our approach, we compute the similarity between aged images and the corresponding ground truth via face recognition. To do this, we have utilised a pre-trained convolutional neural network based on the VGG-face model for feature extraction, and we then use well-known classifiers to compare the features. We have utilised two datasets, namely the FEI and the Morph II, to test, verify and validate our approach. Our experimental results do suggest that the proposed approach achieves accuracy, efficiency and possess flexibility when it comes to facial age progression or regression.
An Introduction of CloudSim Simulation tool for Modelling and Scheduling

Cloud computing goals are to deliver secure, maintainable, fault-tolerant and reliable infrastructure for application services based on the net. To schedule and modeling the different services and applications on the cloud is a challenging job with different energy and load performances. The research on Cloud computing is still at the early stage and in order to resolve this problem CloudSim is introduced. CloudSim is a simulation tool, that has the ability to manage various services, scheduling the services and modeling infrastructure of the cloud. This paper also aims to provide the importance of simulation for performing various experimental beds with the case study of one of the simulation tools CloudSim. It also discussed its architecture, features with design and application. Last, this paper also supplies the installation steps to use CloudSim in NetBeans IDE and Eclipse.
Deep Networks With Detail Enhancement for Infrared Image Super-Resolution

Due to the limitation of hardware, infrared (IR) images have low-resolution (LR) and poor visual quality. Image super-resolution (SR) is a good solution to this problem. In this paper, we present a new convolution network (CNN) to improve the spatial resolution of infrared (IR) images. Our network is able to restore fine details by decomposing the input image into low-frequency and high-frequency domains. In low-frequency domains, we reconstruct image structure by deep networks. In high frequency domains, we reconstruct IR image details. Furthermore, we proposed another network to remove artifacts. Additionally, we propose a new loss function using visible (VIS) images to enhance the details of IR images. In training phase, we use VIS images to guide IR image restoration and in testing phase we get SR IR images with LR IR images input only. We optimize our deep network with a targeted function which penalizes images at different semantic levels using the corresponding terms. Besides, we build a dataset where paired LR-VIS images on the same scene are captured by a camera with both infrared and visible light sensors which both sensors have the same optical axis. Extensive experiments demonstrate that the proposed algorithm achieves superior performance and visual improvements against the state-of-the-arts.
Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.
Telepresent or not? Virtual reality, service perceptions, emotions and post-consumption behaviors

ABSTRACT The S-O-R framework (Stimulus-Organism-Response) is used to examine the influence of telepresence in virtual reality (VR) on service perceptions, positive emotions and post-consumption behaviors. Potential customers to a heritage bed and breakfast (B&B) hotel in Christchurch, New Zealand, experienced one of the guest rooms in VR. The telepresence dimension of spatial situation had a positive relationship with both service atmospherics and service quality. Positive emotions after the VR experience had a positive effect on overall attitude towards the hotel. Positive surprise had a significant relationship with intention to book the B&B hotel. The telepresence dimension of suspension of disbelief had a negative relationship with positive emotions. Implications for using VR as a marketing tool for B&Bs and hotels are offered.
Design and Implementation of Three-Dimensional Printable Optomechanical Components

Fused filament fabrication 3D printing is a process by which three-dimensional objects are created by depositing layers of a material onto a hard, flat surface by a robot. It is often referred to as an “additive manufacturing” technique because material is added in successive layers to create an object. Because many scientific applications require parts that are expensive to purchase or manufacture, 3D printing custom parts for scientific instrumentation can save (shipping and/or manufacturing) time and money and requires only one compact, computer-controlled robot. Thus, 3D printable scientific parts and equipment can drive down the costs of scientific teaching and research. Here, we present a library of 3D printable optomechanical components that are compatible with commercial optomechanical parts. These components were tested for their functionality in home-built optical systems constructed entirely from 3D printed optomechanical components, and we demonstrate that optical systems built using 3D printable optomechanical components are comparable to their more expensive, commercially available counterparts, albeit not as durable as commercial parts. Thus, we conclude that our library of 28 3D printable optomechanical components is appropriate for use in scientific teaching laboratories.