Reviewerly/exHarmony huggingface dataset:
Hyperspectral Image Restoration Combining Intrinsic Image Characterization With Robust Noise Modeling

In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs and the embedded noise, leading to suboptimal performance in practice. In this article, we propose a novel HSI restoration method by fully considering the intrinsic image structures and the complex noise characteristics. For HSIs, the global correlation is captured by the Kronecker-basis-representation-based tensor low-rankness measure, which integrates the insights delivered by both CP and Tucker decompositions; the local regularity is depicted by a plug-and-play spatial-spectral convolutional neural network with strong fitting ability to complex image features. For realistic noise, its statistical characteristics are encoded by a nonidentical and nonindependent distributed mixture of Gaussians distribution with flexible fitting capability. Then, we incorporate these image and noise priors into a probabilistic model based on the maximum a posteriori principle, and develop a solving scheme by combining expectation-maximization and alternating direction method of multipliers. Extensive experimental results on both simulated and real scenarios demonstrate the effectiveness of the proposed method and its superiority over the compared state-of-the- arts.
Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.
A Representation Learning Approach to Animal Biodiversity Conservation

Generating knowledge from natural language data has aided in solving many artificial intelligence problems.Vector representations of words have been the driving force behind the majority of natural language processing tasks.This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings.We use two different vector embeddings generated using representation learning on Wikipedia text and animal taxonomy data.We generate name embeddings for all species in the animal kingdom using unsupervised learning and build a model on the IUCN Red List dataset to classify species into endangered or least-concern.To our knowledge, this is the first work that makes use of learnt features instead of handcrafted features for this task and achieves competitive results.Based on the high confidence results of our model, we also predict the conservation status of data deficient species whose conservation status is still unknown, steering more focus towards them for protection.These embeddings have also been made publicly available here 1 .We believe this will greatly help in solving various downstream tasks and further advance research in the cross-domain involving natural language processing, conservation biology, and life sciences.
Detection of follicular regions in actin-stained whole slide images of the human lymph node by shock filter

Abstract Human lymph nodes play a central part of immune defense against infection agents and tumor cells. Lymphoid follicles are compartments of the lymph node which are spherical, mainly filled with B cells. B cells are cellular components of the adaptive immune systems. In the course of a specific immune response, lymphoid follicles pass different morphological differentiation stages. The morphology and the spatial distribution of lymphoid follicles can be sometimes associated to a particular causative agent and development stage of a disease. We report our new approach for the automatic detection of follicular regions in histological whole slide images of tissue sections immuno-stained with actin. The method is divided in two phases: (1) shock filter-based detection of transition points and (2) segmentation of follicular regions. Follicular regions in 10 whole slide images were manually annotated by visual inspection, and sample surveys were conducted by an expert pathologist. The results of our method were validated by comparing with the manual annotation. On average, we could achieve a Zijbendos similarity index of 0.71, with a standard deviation of 0.07.
AGMC-Based Robust Cubature Kalman Filter for SINS/GNSS Integrated Navigation System With Unknown Noise Statistics

A new robust cubature Kalman filter is proposed using adaptive generalized maximum correntropy (AGMC) criterion rather than the conventional MMSE criterion in this paper. In the proposed method, the adaptive generalized maximum correntropy (AGMC) criterion is firstly constructed from an adaptive forgetting correntropy based cost function, which is rather robust with respect to the process uncertainty and non-Gaussian noise. On this basis, a new robust cubature Kalman filter is further derived, where the predicted state vector and received measurements are processed simultaneously based on the regression form derived via the statistical linearization approach. An adaptive forgetting scheme is then proposed in combination with the AGMC-CKF to update the parameters of the AGMC adaptively in real time. Taking advantage of the AGMC, the unknown noise statistics caused by the process uncertainty and non-Gaussian noise can be effectively suppressed. Simulations and car-mounted experiments demonstrate that the proposed filter is superior in terms of estimation accuracy and robustness as compared with the related state-of-art methods.
Efficient Graph Neural Architecture Search

Recently, graph neural networks (GNN) have been demonstrated effective in various graph-based tasks. To obtain state-of-the-art (SOTA) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods. However, it remains to be a challenging problem to conduct efficient architecture search for GNN. In this work, we present a novel framework for Efficient GrAph Neural architecture search (EGAN). By designing a novel and expressive search space, an efficient one-shot NAS method based on stochastic relaxation and natural gradient is proposed. Further, to enable architecture search in large graphs, a transfer learning paradigm is designed. Extensive experiments, including node-level and graph-level tasks, are conducted. The results show that the proposed EGAN can obtain SOTA data-specific architectures, and reduce the search cost by two orders of magnitude compared to existing NAS baselines.
Deep CNN for Contrast-Enhanced Ultrasound Focal Liver Lesions Diagnosis

This paper investigates the huge potential provided by the deep learning paradigm associated with a convolutional neural network architecture for automatic diagnosis of focal liver lesions. The dataset was provided by the Department of Gastroenterology and Hepatology, "Victor Babes" University of Medicine and Pharmacy, Timisoara and contains a total of 285 contrast-enhanced ultrasound video sequences of five types of lesions. As elements of novelty of our work we could mention the use of 2D-Deep Convolutional Neural Network for implementing an automated diagnosis system which discriminates between an increased number of focal liver lesion types. Our experimental results demonstrated that the proposed principle achieves a competitive performance, 95.71% accuracy, compared to state-of-the-art methods.
Outlier Detection Methods and the Challenges for their Implementation with Streaming Data

Outlier detection has been a generally examined issue and highly used in a varied range of spaces. For example, transaction fraud, certain rise and fall in share market, sudden changes in weather, interruption detection for digital security, and fraud detection in security design patterns in data. Data mining is the rule of dealing with big amounts of data and choosing the important. Outlier detection is data mining procedures that identify uncommon occasions and special cases. This paper discusses fundamental concepts of outlier detection, the outlier types and the challenges in their detection. An in-depth presentation of outlier detection techniques is given which are divided into three major categories: supervised, semi supervised, and unsupervised. Special attention is given to unsupervised outlier detection. The existing algorithms and techniques in this category are elaborated in detail and the advantages and shortcomings of these techniques are summarized. The analyses of the existing algorithms for outlier detection show that no one of them completely satisfies all the requirements for scalability, work on high dimensional datasets with satisfactory time complexity and efficient memory usage especially when applied on streaming data. This is why the study suggests that there is a need of a hybrid approach that combines classical algorithms and artificial intelligence algorithm to provide efficient solution for outlier detection of streaming data with good key performance indicators.
Multi-Target Regression Rules With Random Output Selections

In this article, we address the task of multi-target regression (MTR), where the goal is to predict multiple continuous variables. We approach MTR by learning global models that simultaneously predict all of the target variables, as opposed to learning a separate model for predicting each of the target variables. Specifically, we learn rule ensembles by generating many candidate rules and assigning them weights that are then optimized in order to select the best performing subset of rules. Candidate rules are generated by transforming ensembles of generalized decision trees, called predictive clustering trees (PCTs), into rules. We propose to extend an existing multi-target regression rule learning method named FIRE by learning tree ensembles that use random output selections (ROS). Such ensembles force individual PCTs to focus only on randomly selected subsets of target variables. The rules obtained from the tree ensemble also focus on various subsets of the target variables (FIRE-ROS). We use three different ensemble methods to generate candidate rules: bagging and random forests of PCTs, and ensembles of extremely randomized PCTs. An experimental evaluation on a range of benchmark datasets has been conducted, where FIRE-ROS is compared to three interpretable methods, namely predictive clustering rules, MTR trees and the original FIRE method, as well as state-of-the-art MTR methods, in particular ensembles of extremely randomized PCTs with ROS, random linear combinations and extremely randomized MTR trees with random projections of the target space. The results show that FIRE-ROS can improve the predictive performance of the FIRE method and that it performs on par with state-of-the-art (non-interpretable) MTR methods.
Architecture Description Languages Taxonomies Review

Self-adaptive distributed embedded systems can automatically adjust their behavior and/or structure at run time to respond to some predictable or unpredictable events. On the other hand, architecture description languages (ADLs) are qualified to be a convenient solution to model systems architecture as a set of components with well-defined interfaces and links. ADLs have been well-studied and applied in many engineering areas beyond the software and hardware engineering. This research work reviews the most relevant ADLs taxonomies and surveys from 2000 till now, selects the most suitable ADLs for self-adaptive embedded systems, and compares between standard and non-standard ADLs based on some key criteria. To do this, a search methodology was followed enabling a systematic review. Results showed that only a few standard ADL have been accepted by the embedded industry favoring domain-specific ADLs with a proved support of adaptivity, real time, energy consumption and security.
Fractures models comparison using GIS data around crater lakes in Cameroon volcanic line environment

This study assesses the contribution of Landsat 7 ETM + and Landsat 8 OLI images to observe existing fractures and faults of at least 12 km in length around the lakes Nyos, Monoun and Oku whose are three major crater lakes in the Cameroon Volcanic Line (CVL) environment. The results obtained using principal component analysis, directional filters and linear densities show many similarities and some differences. In fact, Landsat 7 ETM+ and Landsat 8 OLI images present 427 lineaments in common, including all known geological linear structures, certain geophysical lineaments and some new fractures. This proves that the area is much more fractured than previously known and that only the environments of lakes Nyos and Monoun are crossed by faults and fractures. Apart from the similarities, the differences observed relate to the lineaments specific to each image, their main direction, their length and the values ​​of the fracturing densities. Also, the results show that the Landsat 7 ETM+ and Landsat 8 OLI images are suitable for the structural research in the CVL environment. But, the Landsat 8 OLI images are more efficient as they have made it possible to reconstitute almost all known geological structures and their lengths.
Generating multi-layer nested chaotic attractor and its FPGA implementation*

Complex chaotic sequences are widely employed in real world, so obtaining more complex sequences have received highly interest. For enhancing the complexity of chaotic sequences, a common approach is increasing the scroll-number of attractors. In this paper, a novel method to control system for generating multi-layer nested chaotic attractors is proposed. At first, a piecewise (PW) function, namely quadratic staircase function, is designed. Unlike pulse signals, each level-logic of this function is square constant, and it is easy to realize. Then, by introducing the PW functions to a modified Chua’s system with cubic nonlinear terms, the system can generate multi-layer nested Chua’s attractors. The dynamical properties of the system are numerically investigated. Finally, the hardware implementation of the chaotic system is used FPGA chip. Experimental results show that theoretical analysis and numerical simulation are right. This chaotic oscillator consuming low power and utilization less resources is suitable for real applications.
Balancing Utility and Fairness against Privacy in Medical Data

There are numerous challenges when designing algorithms that interact with sensitive data, such as, medical records. One of these challenges is privacy. However, there is a tension between privacy, utility (model accuracy), and fairness. While de-identification techniques, such as generalisation and suppression, have been proposed to enable privacy protection, it comes with a cost, specifically to fairness and utility. Recent work on algorithmic fairness defines fairness as a guarantee of similar outputs for “similar” inputs. This notion is discussed in connection to de-identification. This research investigates the trade-off between privacy, fairness, and utility. In contrast, other work investigates the trade-off between privacy and overall utility. In this research, we investigate the effects of two de-identification techniques, k-anonymity and differential privacy, on both utility and fairness. We propose two measures to calculate the trade-off between privacy-utility and privacy-fairness. Other research has provided guarantees for privacy regarding utility; this research focuses on the trade-offs given set de-identification levels and relies on these guarantees. We discuss the effects of de-identification on data of different characteristics: class imbalance, and outcome imbalance. We evaluated these effects on synthetic datasets and real-world datasets. As a case study, we analysed the Medical Expenditure Panel Survey dataset.
Data hiding

Data hiding provides a collection of techniques that can be used within a wider cybersecurity or privacy framework, making it possible, for example, to protect the users' privacy in streaming or broadcasting of multimedia contents or to detect cyberattacks in IoT networks when combined with machine learning solutions. However, criminals and terrorists are also aware and these techniques and can exploit them for malicious purposes. This contribution provides an overview of the traditional data hiding applications and the current trends in this field, pointing out prospective uses of data hiding in the context of information security and privacy, but also introducing potential threats for users and the society as a whole when they are applied for evil.
Uncovering Hidden Semantics of Set Information in Knowledge Bases

Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyze the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo.
Large Batch Simulation for Deep Reinforcement Learning

We accelerate deep reinforcement learning based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19,000 frames of experience per second on a single GPU (and up to 72,000 frames per second on a single eight-GPU machine). The key idea of our approach is to design a 3D renderer and environment simulator around the principle of “batch simulation”: accepting and executing large batches of requests simultaneously. Beyond exposing large amounts of work at once, batch simulation allows simulator implementations to amortize in-memory storage of scene assets, rendering work, data loading, and synchronization costs across many simulation requests, dramatically improving the number of simulated agents per GPU and overall simulation throughput. To balance DNN inference and training costs with faster simulation, we also build a computationally efficient policy DNN that maintains high task performance, and modify training algorithms to maintain sample efficiency when training with large mini-batches. By combining batch simulation and DNN performance optimizations, we demonstrate that PointGoal navigation agents can be trained in complex 3D environments on a single GPU in 1.5 days to 97% of the accuracy of agents trained on a prior state-of-the-art system using a 64-GPU cluster over three days. We provide open-source reference implementations of our batch 3D renderer and simulator to facilitate incorporation of these ideas into current and future RL systems.
A Survey and Analysis on Image Annotation

The rapid growth of archives of available visual content, such as photo or video sharing websites, has created a need for indexing techniques and multimedia information search, and more precisely images. Image annotation allows indexing and searching large collections of images in an easy and fast way. Annotation of images by keywords is a way to associate “semantics” with an image. It consists of assigning each image, a keyword or a set of keywords, intended to describe the semantic content of the image. In this paper, a detailed study of image annotation methods are devoted for distinguishing between manual, semi-automatic and automatic annotation. The evaluation measures of annotation systems and the description of utilized databases are also devoted in this survey. This paper conclude with a synthesis of state-of-the-art methods to motivate choices. This synthesis allowed us to argue choice for probabilistic graphic models as a method of annotation and the importance of integrating user feedback and a semantic hierarchy into image annotation model.
Regulating Bot Speech

We live in a world of artificial speakers with real impact. So-called “bots” foment political strife, skew online discourse, and manipulate the marketplace. Concerns over bot speech has led prominent figures in the world of technology to call for regulations in response to the unique threats bots pose. Recently, legislators have begun to heed these calls, drafting laws that would require online bots to clearly indicate that they are not human. This work is the first to consider how efforts to regulate bots might run afoul of the First Amendment. At first blush, requiring a bot to self-disclose raises little in the way of free speech concerns—it does not censor speech as such, nor does it unmask the identity of the person behind the automated account. However, a deeper analysis reveals several areas of First Amendment tension. Bot disclosure laws fit poorly with the state’s stated goals, risk unmasking anonymous speakers in the enforcement process, and create a scaffolding for censorship by private actors and other governments. Ultimately bots represent a diverse and emerging medium of speech. Their use for mischief should not overshadow their novel capacity to inform, entertain, and critique. We conclude by urging society to proceed with caution in regulating bots, lest we inadvertently curtail a new, unfolding form of expression.