Reviewerly/exHarmony huggingface dataset:
Unsupervised Learning: What is a Sports Car?

This tutorial studies unsupervised learning methods. Unsupervised learning methods are techniques that aim at reducing the dimension of data (covariables, features), cluster cases with similar features, and graphically illustrate high dimensional data. These techniques do not consider response variables, but they are solely based on the features themselves by studying incorporated similarities. For this reason, these methods belong to the field of unsupervised learning methods. The methods studied in this tutorial comprise principal components analysis (PCA) and bottleneck neural networks (BNNs) for dimension reduction, K-means clustering, K-medoids clustering, partitioning around medoids (PAM) algorithm and clustering with Gaussian mixture models (GMMs) for clustering, and variational autoencoder (VAE), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), self-organizing maps (SOM) and Kohonen maps for visualizing high dimensional data.
Multilayer Incremental Hybrid Cost-Sensitive Extreme Learning Machine With Multiple Hidden Output Matrix and Subnetwork Hidden Nodes

Recently, multilayer extreme learning machine with subnetwork nodes and cost sensitive learning was applied to representation learning and classification. However, the existing ML-ELM methods suffer from several drawbacks: 1) manual tuning for the number of hidden nodes in each layer will influent the training speed of the model, the best optimal determine approach for the number of hidden nodes is still for probing; 2) random projection of the input weight and bias in each layer still suboptimal and the network structure hard to reach best generalization performance. Inspired by the Subnetwork nodes and cost sensitive methods, using multiple hidden output matrix, a novel ELM method which called Multilayer Incremental Hybrid Cost Sensitive Extreme Learning Machine with Multiple Hidden Output Matrix and Subnetwork Hidden Nodes is proposed in this paper. Whose contributions are 1) a hybrid hidden nodes optimization methods is given which based on ant clone method and multiple grey wolf optimization method; 2) a multiple hidden layer output matrices are being using through weighted calculation of different output matrices which can optimize the network architecture effectively; 3) a novel ELM structure is proposed by merging the merits of Cost Sensitive Extreme Learning Machine and Subnetwork Hidden Nodes, while the regularized parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> is not sensitive to the generalization performance. Extensive experiments results show that the proposed method achieves better performance than existing state-of-art ELM methods.
A Blind Image Watermarking Method for Personal Medical Data Security

Data security becomes a backbone that hinders the development of effective telemedicine applications. The data confidentiality and security are the most levels targets by attackers because telemedicine applications contain medical as well as personal data. Hence, transmitting of valuable and confidential data over internet make data at serious target for stealing or manipulating. Image watermarking presents a solid and compelling solution for protecting the patient information's. This paper presents a novel blind medical image watermarking which aims to ensure the personal medical data security and confidentiality. It's based on two main processes. The first one is for embedding the watermark data in the DWT-Schur coefficients using a new watermarking method. The second is focusing on the watermark extracting by the same steps in embedding that succeed in extracting the watermark without the needing of the original or watermarked image. Experimental evaluation results prove that the proposed method keep good robustness while preserving good image quality.
Influence of air pollution to incident photosynthetically active radiation during clear sky conditions in Ostrava, Czech Republic

This research paper focuses on with the effects of air pollutants PM10, NOx, and SO2 on the blue (400–510 nm), green (510–600 nm), and red (600–700 nm) spectral regions of photosynthetically active radiation (PAR). The measurements of solar radiation incident on a horizontal surface were taken at two locations in the city of Ostrava (Czech Republic) that differed in the source of air pollution. Linear regression models, with two explanatory variables (solar elevation angle and logarithm of individual pollutant concentration), were used as statistical tools for revealing the effect of air pollutants on incident solar radiation. The effect of air pollution on the Blue/PAR spectral ratio was ambiguous. There was a significant decrease in the Green/PAR spectral ratio, whereas there was a significant enhancement in the Red/PAR spectral ratio caused by the increasing air pollutant concentrations. The effects of air pollutants on these spectral ratios were significant from a statistical point of view with p-values smaller than 0.05, but the magnitude of the effect that the air pollutants had on the Blue/PAR and Red/PAR was small. In comparison to the above mentioned, the pollutants had a dominating effect on the Green/PAR spectral ratio. Pollution did not induce homogenous reduction of all PAR spectral regions, instead each pollutant had a specific effect on the contributions of blue, green, and red spectral regions to PAR irradiance. Possible causes of these effects are discussed.
Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
Service level agreement specification for end‐to‐end IoT application ecosystems

Summary With an ever‐increasing variety and complexity of Internet of Things (IoT) applications delivered by increasing numbers of service providers, there is a growing demand for an automated mechanism that can monitor and regulate the interaction between the parties involved in IoT service provision and delivery. This mechanism needs to take the form of a contract, which, in this context, is referred to as a service level agreement (SLA). As a first step toward SLA monitoring and management, an SLA specification is essential. We believe that current SLA specification formats are unable to accommodate the unique characteristics of the IoT domain, such as its multilayered nature. Therefore, we propose a grammar for a syntactical structure of an SLA specification for IoT. The grammar is built based on a proposed conceptual model that considers the main concepts that can be used to express the requirements for hardware and software components of an IoT application on an end‐to‐end basis. We followed the goal question metric approach to evaluate the generality and expressiveness of the proposed grammar by reviewing its concepts and their predefined lists of vocabularies against two use cases with a considerable number of participants whose research interests are mainly related to IoT. The results of the analysis show that the proposed grammar achieved 91.70% of its generality goal and 93.43% of its expressiveness goal.
Blockchain-based secure digital asset exchange scheme with QoS-aware incentive mechanism

As the Internet of things (IoT) is increasingly popular, the number of IoT devices such as sensors and smart equipments are growing at an astonishing rate and data generated by these devices is exploding. However, these massive IoT data, stored in the form of isolated data centers, can not be shared by others who also need it. Moreover, data exchange is now needing a secure and fair mechanism to guarantee the data provider's rights and data security. Data providers also lack the motivation to share their data, as no effective mechanism exists to reward this behavior. To solve these problems, we propose a digital asset exchange mechanism based on blockchain technology, in which we record the behavior of data publishing and exchanging into the blockchain, which can ensure the reliability and transparency of data exchange without the restriction of trusted third-party payment institutions. Especially, to inspire the data providers to share their high-quality data, we design an incentive mechanism based on QoS, which gives higher rewards to those who provide high-quality data. Experimental results of this prototype demonstrate that this mechanism is appropriate to be applied in practice.
Class Consistency Driven Unsupervised Deep Adversarial Domain Adaptation

In unsupervised deep domain adaptation (DA), the use of adversarial domain classifiers is popular in learning a shared feature space which reduces the distributions gap for a pair of source (with training data) and target (with only test data) domains. In the new space, a classifier trained on source training data is expected to generalize well for the target domain samples. We hypothesize that such a feature space obtained by aligning the domains globally ignores the category level feature distributions. This, in turn, leads to erroneous mapping for fine-grained classes. Besides, the discriminativeness of the shared space is not explicitly addressed. In order to resolve both the issues, we propose a novel adversarial approach which judiciously refines the space learned by the domain classifier by incorporating class level information. We follow an ensemble classifiers based approach to model the source domain and introduce a novel consistency constrain on the classifier's outcomes when evaluated on a held-out set of target domain samples. We further leverage the ensemble learning strategy during the inference, as opposed to the existing single classifier based methods. We find that our deep DA model is capable of producing a compact and better domain aligned feature space. Experimental results obtained on the Office-Home, Office-CalTech, MNIST-USPS, and a remote sensing dataset confirm the superiority of the proposed approach.
“She has promised never to use the N-word again”: Discourses of racism in a Swedish media debate

In Sweden, as in most European countries, antiracist discourses co-exist with new racism and right-wing populism. Self-images as antiracist are maintained through the strategy to deny racism and accusations of racism. This article addresses discourses of racism in a Swedish media debate that started with a YouTuber being accused of using the N-word. Through a categorization analysis of newspaper articles, that report on and debate the incident, the management of accusations and denials, as well as language ideologies and different perspectives on racism, are examined. The analysis shows how the YouTuber, and journalists, deny racism by claiming that the racist expression was an unintentional “gaffe”, thus categorizing the YouTuber as a non-racist. This understanding of racism, as an individual and intentional phenomenon, dominates the debate. However, some debaters advocate a structural discourse of racism, focusing on racist activities rather than racist individuals. Nevertheless, the findings suggest that the media context does not provide room for a nuanced discussion about structural racism and racist language. Instead, the category work indicates that the maintenance of social relations and an antiracist self-image is the core activity of the interaction.
Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning

Sensor Networks are very much vulnerable and prone to faults and external attacks. Sensor networks used for Healthcare Monitoring are termed as Wireless Body Area Networks (WBAN), which is used for collecting various vital physiological parameters of patients from remote locations. However, WBAN sensors are prone to failures because of noise, hardware misplacement, patient`s sweating. Sensed data from these sensors are sent from the Local Processing Unit to Medical Professionals. It would be very difficult for the Medical Professionals to diagnose correctly if the sensed data from these sensors are faulty or effected by the malicious third party. At times, even faulty data might lead to misdiagnosis or death of a patient. It motivated us to address this challenge by proposing a Machine Learning Paradigm to distinguish this anomalous data from the genuine sensed data. Firstly, we classify the health parameters as normal records or abnormal record. After the classification, we propose to apply regression technique for identifying the anomalous data and actual critical data. We use real patient`s vital physiological parameters for validating the robustness and reliability of our proposed approach.
Offline privacy preserving proxy re-encryption in mobile cloud computing

This paper addresses the always online behavior of the data owner in proxy re-encryption schemes for re-encryption keys issuing. We extend and adapt multi-authority ciphertext policy attribute based encryption techniques to type-based proxy re-encryption to build our solution. As a result, user authentication and user authorization are moved to the cloud server which does not require further interaction with the data owner, data owner and data users identities are hidden from the cloud server, and re-encryption keys are only issued to legitimate users. An in depth analysis shows that our scheme is secure, flexible and efficient for mobile cloud computing
Long Integral Time Continuous Panorama Scanning Imaging Based on Bilateral Control with Image Motion Compensation

Urban remote sensing with moving carriers enables comprehensive monitoring of an urban area. High spatial resolution and wide covering are always required to improve the performance and efficiency of remote sensing. Continuous scanning imaging is a feasible solution. However, imaging motion degrades the performance of a remote sensing system. Rotating motion leads to the loss of key urban morphology information of a panorama imaging. Image translation results in blurry images. For high spatial resolution and high efficiency imaging with low illumination condition, such as imaging at dusk, long-focus lens and long integral time must be further utilized, which makes the problem more severe. In this paper, a novel image motion compensation method is proposed to compensate for image rotation and image translation simultaneously. A quantitative description of image motion, including both image rotation and image translation, is first developed based on the principle of geometrical optics and then analyzed in detail through numerical simulations. Furthermore, a comprehensive image rotation compensation method is developed based on four-channel bilateral control with sliding mode controller, at the same time image translation compensation is performed according to the quantitative relationship of the motion of the scan mirror and image translation compensator. The experimental results show that the proposed method provides effective compensation for image rotation and image translation. This enables acquisition of high spatial resolution urban panoramic images.
Detecting Failures of Neural Machine Translation in the Absence of Reference Translations

Despite getting widely adopted recently, a Neural Machine Translation (NMT) system is often found to produce translation failures in the outputs. Developers have been relying on in-house system testing for quality assurance of NMT. This testing methodology requires human-constructed reference translations as the ground truth (test oracle) for example natural language inputs. The testing methodology has shown benefits of quickly enhancing an NMT system in early development stages. However, in industrial settings, it is desirable to detect translation failures without reliance on reference translations for enabling further improvements on translation quality in both industrial development and production environments. Aiming for a practical and scalable solution to such demand in the industrial settings, in this paper, we propose a new approach for automatically identifying translation failures without requiring reference translations for a translation task. Our approach focuses on a property of natural language translation that can be checked systematically by using information from both the test inputs (i.e., the texts to be translated) and the test outputs (i.e., the translations under inspection) of the NMT system. Our evaluation conducted on real-world datasets shows that our approach can effectively detect property violations as translation failures. By deploying our approach in the translation service of WeChat (a messenger app with more than one billion monthly active users), we show that our approach is both practical and scalable in the industrial settings.
Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the sub-iteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Lévy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Lévy flight updating method to keep the advantage of the Lévy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can.
Rethinking Location Privacy for Unknown Mobility Behaviors

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.
Extraction and Visual Analysis of Potential Vorticity Banners around the Alps

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.