Reviewerly/exHarmony huggingface dataset:
Training data independent image registration using generative adversarial networks and domain adaptation

Medical image registration is an important task in automated analysis of multimodal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However, DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining. To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest X-ray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation.
Analog errors in quantum annealing: doom and hope

Abstract Quantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to $$J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>J</mml:mi> </mml:math> -chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that $$J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>J</mml:mi> </mml:math> -chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.
Google Scholar as a Data Source for Research Assessment

The launch of Google Scholar ( Google Scholar (GS) ) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search. Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products that leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses. The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation. First, we present a general overview of how GS works. Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate). Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance. Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data. We conclude that GS presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.
Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This paper proposes a mini-batch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: 1) uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier; and, 2) batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy classifiers to expedite the convergence and improve the generalization performance. Experiments on 12 UCI datasets from various application domains, with varying size and dimensionality, demonstrated that UR and BN are effective individually, and integrating them can further improve the classification performance.
Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.
Drone Detection in Long-Range Surveillance Videos

The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.
Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases

Abstract Solar energy generated by grid-connected photovoltaic (GCPV) systems is considered an important alternative electric energy source because of its clean energy production system, easy installation, and low operating and maintenance costs. This has led to it becoming more popular compared with other resources. However, finding optimal sites for the construction of solar farms is a complex task with many factors to be taken into account (environmental, social, legal and political, technical-economic, etc.), which classic site selection models do not address efficiently. There are few studies on the criteria that should be used when identifying sites for solar energy installations (large grid-connected photovoltaic systems which have more than 100 kWp of installed capacity). It is therefore essential to change the way site selection processes are approached and to seek new methodologies for location analysis. A geographic information system (GIS) is a tool which can provide an effective solution to this problem. Here, we combine legal, political, and environmental criteria, which include solar radiation intensity, local physical terrain, environment, and climate, as well as location criteria such as the distance from roads and the nearest power substations. Additionally, we use GIS data (time series of solar radiation, digital elevation models (DEM), land cover, and temperature) as further input parameters. Each individual site is assessed using a unique and cohesive approach to select the most appropriate locations for solar farm development in the Valencian Community, a Spanish region in the east of Spain.
ERL-Net: Entangled Representation Learning for Single Image De-Raining

Despite the significant progress achieved in image de-raining by training an encoder-decoder network within the image-to-image translation formulation, blurry results with missing details indicate the deficiency of the existing models. By interpreting the de-raining encoder-decoder network as a conditional generator, within which the decoder acts as a generator conditioned on the embedding learned by the encoder, the unsatisfactory output can be attributed to the low-quality embedding learned by the encoder. In this paper, we hypothesize that there exists an inherent mapping between the low-quality embedding to a latent optimal one, with which the generator (decoder) can produce much better results. To improve the de-raining results significantly over existing models, we propose to learn this mapping by formulating a residual learning branch, that is capable of adaptively adding residuals to the original low-quality embedding in a representation entanglement manner. Using an embedding learned this way, the decoder is able to generate much more satisfactory de-raining results with better detail recovery and rain artefacts removal, providing new state-of-the-art results on four benchmark datasets with considerable improvement (i.e., on the challenging Rain100H data, an improvement of 4.19dB on PSNR and 5% on SSIM is obtained). The entanglement can be easily adopted into any encoder-decoder based image restoration networks. Besides, we propose a series of evaluation metrics to investigate the specific contribution of the proposed entangled representation learning mechanism. Codes are available at 〈https://github.com/RobinCSIRO/ERL-Net-for-Single-Image-Deraining〉.
Parameter identification for symbolic regression using nonlinear least squares

Abstract In this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquardt algorithm for parameter optimization and calculate gradients via automatic differentiation. We provide examples where the parameter identification succeeds and fails and highlight its computational overhead. Using an extensive suite of symbolic regression benchmark problems we demonstrate the increased performance when incorporating nonlinear least squares within genetic programming. Our results are compared with recently published results obtained by several genetic programming variants and state of the art machine learning algorithms. Genetic programming with nonlinear least squares performs among the best on the defined benchmark suite and the local search can be easily integrated in different genetic programming algorithms as long as only differentiable functions are used within the models.
Explainability as a Non-Functional Requirement

Recent research efforts strive to aid in designing explainable systems. Nevertheless, a systematic and overarching approach to ensure explainability by design is still missing. Often it is not even clear what precisely is meant when demanding explainability. To address this challenge, we investigate the elicitation, specification, and verification of explainablity as a Non-Functional Requirement (NFR) with the long-term vision of establishing a standardized certification process for the explainability of software-driven systems in tandem with appropriate development techniques. In this work, we carve out different notions of explainability and high-level requirements people have in mind when demanding explainability, and sketch how explainability concerns may be approached in a hypothetical hiring scenario. We provide a conceptual analysis which unifies the different notions of explainability and the corresponding explainability demands.
Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data

In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data preprocessing technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naïve Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different preprocessing techniques on the data collected from Twitter using various techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data preprocessing technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.
Modelling and simulation of security-aware task scheduling in cloud computing based on Blockchain technology

Although a lot of work has been done in the domain, tasks scheduling and resource allocation in cloud computing remain the challenging problems for both industry and academia. Security in scheduling in highly distributed computing environments is one of the most important criteria in the era of personalization of the cloud services. Blockchain became recently a promising technology for integration with the cloud clusters and improvement of the security of cloud transactions and access to data and application codes. In this paper, we developed a new model of the cloud scheduler based on the blockchain technology. Differently to the other similar models, we tried to offload the implementation of the blockchain modules. We developed a novel ’proof–of–schedule’ consensus algorithm (instead of ’proof–of–work’) and used the Stackelberg games for the improvement of the approval of the generated schedules. The developed model has been experimentally simulated and validated by using the new original cloud simulator. The proposed Blockchain Scheduler was also compared with other selected cloud schedulers. The experiments shows that the applied approach improved significantly the efficiency of prepared schedules, in most cases, simulator returns a schedule with better makespan than existing individual scheduling modules.
Measuring the impact of functional and experiential mixed reality elements on a museum visit

Although research on the impact of new realities on museum experience is receiving increasing attention, it remains mainly conceptual in nature and measuring the impact of mixed reality on a museum visit is still in its infancy. This paper attempts to shed light on this topic by applying the visitor experience model for mixed reality, which measures how 23 functional and experiential elements have been reshaping a traditional museum visit, enhancing visitor satisfaction. The empirical analysis interests ‘The Ara As It Was’, a mixed reality project installed in the iconic Ara Pacis Museum in Rome (Italy), an expression of Roman history and unique on the Italian and international panorama. The findings and conclusions open up new scenarios for future research on immersive museum experiences, combining cultural heritage with technological innovation. They suggest managerial implications to combine authenticity and innovation in the museum, generating unique experiences and redefining museum service models.
A two-fold transformation model for human action recognition using decisive pose

Human action recognition in videos is a tough task due to the complex background, geometrical transformation and an enormous volume of data. Hence, to address these issues, an effective algorithm is developed, which can identify human action in videos using a single decisive pose. To achieve the task, a decisive pose is extracted using optical flow, and further, feature extraction is done via a two-fold transformation of wavelet. The two-fold transformation is done via Gabor Wavelet Transform (GWT) and Ridgelet Transform (RT). The GWT produces a feature vector by calculating first-order statistics values of different scale and orientations of an input pose, which have robustness against translation, scaling and rotation. The orientation-dependent shape characteristics of human action are computed using RT. The fusion of these features gives a robust unified algorithm. The effectiveness of the algorithm is measured on four publicly datasets i.e. KTH, Weizmann, Ballet Movement, and UT Interaction and accuracy reported on these datasets are 96.66%, 96%, 92.75% and 100%, respectively. The comparison of accuracies with similar state-of-the-arts shows superior performance.
Towards Secure and Decentralized Sharing of IoT Data

The Internet of Things (IoT) bears unprecedented security and scalability challenges due to the magnitude of data produced and exchanged by IoT devices and platforms. Some of those challenges are currently being addressed by coupling IoT applications with blockchains. However, current blockchain-backed IoT systems simply use the blockchain to store access control policies, thereby underutilizing the power of blockchain technology. In this paper, we propose a new framework named Sash that couples IoT platforms with blockchain that provides a number of advantages compared to state of the art. In Sash, the blockchain is used to store access control policies and take access control decisions. Therefore, both changes to policies and access requests are correctly enforced and publicly auditable. Further, we devise a "data marketplace" by leveraging the ability of blockchains to handle financial transaction and providing "by design" remuneration to data producers. Finally, we exploit a special flavor of identity-based encryption to cater for cryptography-enforced access control while minimizing the overhead to distribute decryption keys. We prototype Sash by using the FIWARE open source IoT platform and the Hyperledger Fabric framework as the blockchain back-end. We also evaluate the performance of our prototype and show that it incurs tolerable overhead in realistic deployment settings.
End-to-End Optimized ROI Image Compression

Compressing an image with more bits automatically allocated to the region of interest (ROI) than to the background can both protect key information and reduce substantial redundancy. This paper models ROI image compression as an optimization problem of minimizing a weighted sum of the rate of the image and distortion of the ROI. The traditional framework solves this problem by cascading ROI prediction and ROI coding, through which achieving the optimized solution is impossible. To improve coding performance, we propose a novel deep-learning-based unified framework that can achieve rate distortion optimization for ROI compression. Specifically, the proposed framework includes a pair of ROI encoder and decoder convolutional neural networks and a learned entropy codec. The encoder network simultaneously generates multiscale representations that support efficient rate allocation and an implicit ROI mask that guides rate allocation. The proposed framework can automatically complete ROI image compression, and it can be optimized from data in an end-to-end manner. To effectively train the framework by back propagation, we develop a soft-to-hard ROI prediction scheme to make the entire framework differential. To improve visual quality, we propose a hierarchical distortion loss function to protect both pixel-level fidelity for ROI and structural similarity for the entire image. The proposed framework is implemented in two scenarios: salient-target and face-target ROI compression. Comparative experiments demonstrate the advantages of the proposed framework over the traditional framework, including considerably better subjective visual quality, significantly higher objective ROI compression performance and execution efficiency.