Reviewerly/exHarmony huggingface dataset:
Recent Advances in Reservoir Computing With A Focus on Electronic Reservoirs

Reservoir Computing is a subset of recurrent neural networks which can compute temporal-spatial tasks efficiently. In reservoir computing inputs are randomly connected to fixed untrained nodes in the reservoir layer. From the reservoir layer signals are mapped to an output layer from which they are separated into different classes. The major advantage with reservoir computing is that the training complexity is greatly simplified by training only the output layer. This way the output weights can be trained using a simple training algorithm such as a linear classifier. This allows feasibility in hardware implementations as well. The working procedure of the different subsets of reservoir computing including echo-state networks, liquid state machines and delayed feedback reservoir computing are covered in this review. This review focuses on the current trends in reservoir computing with a focus on electronic reservoir computing with analog circuits, FPGAs, VLSIs and memristors along with some applications of reservoir computing.
Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans

Fully annotated data sets play important roles in medical image segmentation and evaluation. Expense and imprecision are the two main issues in generating ground truth (GT) segmentations. In this paper, in an attempt to overcome these two issues jointly, we propose a method, named SparseGT, which exploit variability among human segmenters to maximally save manual workload in GT generation for evaluating actual segmentations by algorithms. Pseudo ground truth (p-GT) segmentations are created by only a small fraction of workload and with human-level perfection/imperfection, and they can be used in practice as a substitute for fully manual GT in evaluating segmentation algorithms at the same precision. p-GT segmentations are generated by first selecting slices sparsely, where manual contouring is conducted only on these sparse slices, and subsequently filling segmentations on other slices automatically. By creating p-GT with different levels of sparseness, we determine the largest workload reduction achievable for each considered object, where the variability of the generated p-GT is statistically indistinguishable from inter-segmenter differences in full manual GT segmentations for that object. Furthermore, we investigate the segmentation evaluation errors introduced by variability in manual GT by applying p-GT in evaluation of actual segmentations by an algorithm. Experiments are conducted on ∼500 computed tomography (CT) studies involving six objects in two body regions, Head & Neck and Thorax, where optimal sparseness and corresponding evaluation errors are determined for each object and each strategy. Our results indicate that creating p-GT by the concatenated strategy of uniformly selecting sparse slices and filling segmentations via deep-learning (DL) network show highest manual workload reduction by ∼80-96% without sacrificing evaluation accuracy compared to fully manual GT. Nevertheless, other strategies also have obvious contributions in different situations. A non-uniform strategy for slice selection shows its advantage for objects with irregular shape change from slice to slice. An interpolation strategy for filling segmentations can achieve ∼60-90% of workload reduction in simulating human-level GT without the need of an actual training stage and shows potential in enlarging data sets for training p-GT generation networks. We conclude that not only over 90% reduction in workload is feasible without sacrificing evaluation accuracy but also the suitable strategy and the optimal sparseness level achievable for creating p-GT are object- and application-specific.
Pitfalls of assessing extracted hierarchies for multi-class classification

Using hierarchies of classes is one of the standard methods to solve multi-class classification problems. In the literature, selecting the right hierarchy is considered to play a key role in improving classification performance. Although different methods have been proposed, there is still a lack of understanding of what makes a hierarchy good and what makes a method to extract hierarchies perform better or worse. To this effect, we analyze and compare some of the most popular approaches to extracting hierarchies. We identify some common pitfalls that may lead practitioners to make misleading conclusions about their methods. To address some of these problems, we demonstrate that using random hierarchies is an appropriate benchmark to assess how the hierarchy's quality affects the classification performance. In particular, we show how the hierarchy's quality can become irrelevant depending on the experimental setup: when using powerful enough classifiers, the final performance is not affected by the quality of the hierarchy. We also show how comparing the effect of the hierarchies against non-hierarchical approaches might incorrectly indicate their superiority. Our results confirm that datasets with a high number of classes generally present complex structures in how these classes relate to each other. In these datasets, the right hierarchy can dramatically improve classification performance.
UnchartIt

Charts are commonly used for data visualization. Generating a chart usually involves performing data transformations, including data pre-processing and aggregation. These tasks can be cumbersome and time-consuming, even for experienced data scientists. Reproducing existing charts can also be a challenging task when information about data transformations is no longer available.
Sports motional characteristics modeling by leveraging multi-modal image technique

Sport motional characteristic modeling is a hot research topic in human–computer interaction (HCI). Traditional sport motional characteristic based on single signal cannot achieve satisfactory performance. To solve this problem, we propose a multi-model feature fusion-based method for sport motional feature control. More specifically, we leverage Kinect sensor to acquire real-time video stream, where the main goal is to capture hand gesture as well as arm movements. HSV-based image segmentation is used for hand image patches extraction and recognition. To improve the effectiveness of sport motional characteristics control, we design audio-based HCI system to assist sport control. Our experiment is conducted on a quadruped robot platform with manipulator. Experimental results show the effectiveness of our proposed method.
Decontextualized learning for interpretable hierarchical representations of visual patterns

Apart from discriminative modeling, the application of deep convolutional neural networks to basic research utilizing natural imaging data faces unique hurdles. Here, we present decontextualized hierarchical representation learning (DHRL), designed specifically to overcome these limitations. DHRL enables the broader use of small datasets, which are typical in most studies. It also captures spatial relationships between features, provides novel tools for investigating latent variables, and achieves state-of-the-art disentanglement scores on small datasets. DHRL is enabled by a novel preprocessing technique inspired by generative model chaining and an improved ladder network architecture and regularization scheme. More than an analytical tool, DHRL enables novel capabilities for virtual experiments performed directly on a latent representation, which may transform the way we perform investigations of natural image features, directly integrating analytical, empirical, and theoretical approaches.
Competence of Slovenian art teachers in working with pupils with special educational needs

Comparisons of performance in the Slovenian national assessment of knowledge at the end of primary school show lower results for pupils with special educational needs (SEN) in all school subjects and in arts education. Several authors highlight the hardships teachers face when working with pupils with SEN. Therefore, in the study we analysed the self-evaluation of competence for working with pupils with SEN among Slovenian art teachers in primary and secondary schools. The results show that the biggest challenges for Slovenian art teachers are the blind and visually impaired pupils and pupils with autistic disorders. They often offer the pupils adjustment in terms of achieving psychomotor goals. However, they are less able to identify pupils’ problems in the socio-emotional sphere and to use adjustment in relation to specific groups of pupils with SEN. Our research provides an insight into practice and thus a basis for further research in this area.
System Approach in Mathematical Support of Modeling Methods in Quality Management Problems of Complex Systems and Processes

The article raises the problem of the formation of a common base of mathematical modelling of the tasks of quality management systems and processes of a complex nature. Analysis of the problems that arise in the mathematical modeling of control problems of objects of great complexity shows the systematization of modeling methods is possible through the use of the concept of synthetic qualimetry. The significance of the proposed approach is determined by the fact that synthetic qualimetry is based on the synthesis of existing measurement theories. This makes it possible to carry out a systematic analysis of modeling methods and the choice of mathematical structures for objects of different nature within the framework of special qualimetry. This determines the novelty of the proposed approach. Identifies ways to build a common base of the formal description of the process of control of complex objects and processes.
Generation of Energy Optimal Speed Profiles for a Differential Drive Mobile Robot with Payload on Straight Trajectories

Day by day mobile robots are being used more often in the industry and even by households. Energy is one of the running costs of robots and it can be quite significant specifically due to heavy usage. In such cases energy consumption can be reduced by programming the robots to utilize more efficient motion profiles. This paper develops energy minimizing motion profiles for two wheel differential drive robots. The approach utilizes an objective function that uses a power model that combines the dynamic models of the robot and the motors. The problem, which also takes into account variable payload masses, is solved using a Hamiltonian function approach leading to a solution in closed form for the speed profiles. Energy consumption of the generated speed profiles are experimentally compared to trapezoidal speed profiles and significant savings in energy are found.
Automated class diagram elicitation using intermediate use case template

Class diagrams, being more illustrative, provide an easier way of understanding software requirements compared to use case descriptions. Both manual and automated methods are used for the extraction of class diagrams from requirements. The automated techniques employ certain extraction rules and natural language processing methods. Each use case description template introduces a small set of extraction rules. However, when all types of templates are considered, the number of rules becomes large and the procedure becomes tedious. Thus, researchers restricted the class diagram extraction procedure to some specific use case description templates. However, such a restriction negatively affects the software developers as they get restricted to working with only limited templates. The proposed work in this paper strives to remove this restriction on developers by introducing an intermediate template. The traditional use case description templates get transformed into the intermediate template and the rule extraction procedure is then applied to this intermediate template. This reduces the total number of extraction rules and hence, brings down the extraction complexity. The class diagrams extracted from use case description templates of different domains using proposed technique show more accuracy in terms of completeness and correctness when compared with the state of the art approaches.
Privacy in time of a pandemic

In December 2019, the world was struck by the unprecedented medical uncertainty that we now know as COVID-19, forcing many people into quarantine conditions. While the physiological impact of the pandemic is serious, this paper explores how privacy has been affected by COVID-19. In this paper, we explore the ways the ongoing pandemic has affected privacy ranging from the changes in privacy policy and regulation, through use of personal data for contact tracing and privacy in the technologies that have taken centre stage as we all work from home. The articles cited in this paper form an empirical basis of the observations we make. We use the information available to us to analyse the privacy concerns and draw conclusions.
Detection and Tracking of Multipath Channel Parameters Using Belief Propagation

We present a belief propagation (BP) algorithm with probabilistic data association (DA) for detection and tracking of specular multipath components (MPCs). In real dynamic measurement scenarios, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for specular MPC detection and joint estimation problem, and represent it by a factor graph which enables the use of BP for efficient computation of the marginal posterior distributions. A parametric channel estimator is exploited to estimate at each time step a set of MPC parameters, which are further used as noisy measurements by the BP-based algorithm. The algorithm performs probabilistic DA, and joint estimation of the time-varying MPC parameters and mean false alarm rate. Preliminary results using synthetic channel measurements demonstrate the excellent performance of the proposed algorithm in a realistic and very challenging scenario. Furthermore, it is demonstrated that the algorithm is able to cope with a high number of false alarms originating from the prior estimation stage.
Dataset Fault Tree Analysis for Systematic Evaluation of Machine Learning Systems

Recently, machine learning, particularly deep learning, is attracting much interest and is applied in various systems. Applications include not only entertainment systems, but safety-critical systems such as those found in autonomous vehicles. The reliability of such safety-critical systems must be guaranteed before they are released into society. However, methods for ensuring the safety of machine learning-based systems have yet to be established. In this paper, we propose a method for systematically evaluating the safety of such systems. The method consists of dataset-based safety analysis and statistical evaluation of testing results. In the safety analysis, we extend the widely used fault tree analysis to deal with datasets. In the testing, we use statistical estimation to guarantee recognition rates obtained in the safety analysis. We conducted experiments using a handwritten character recognition system implemented as a CNN to demonstrate the feasibility and effectiveness of our method.
Face Detection with Improved Face R-CNN Training Method

Faster R-CNN is a popular method in object detection applications. Based on Faster R-CNN, this paper proposes a facial feature enhancement method similar to the attention mechanism, and gets an improved model Face R-CNN. Face R-CNN combines with the syntax-guided network (SG-NET) to merge the generated image with the original convolutional features, which enhances the focus on the face area in the feature map and can effectively achieve face detection in the case of large-scale occlusion .Face R-CNN is trained and tested on the Wider Face dataset. Through the analysis of the experimental results, it can be concluded that Face R-CNN has a more obvious detection effect on occluding faces, and its average accuracy rate is 3.5% higher than Faster R-CNN, which can effectively improve the detection accuracy of occlusion faces.
A universal decentralized authentication and authorization protocol based on Blockchain

Authentication and authorization (A&A) are the most important properties to guarantee the security of Internet activities today. However, popular authentication and authorization protocols are all centralized nowadays which brings extra security issues such as personal data leak, unauthorized access, identity theft, etc. In this paper, we proposed a new universal decentralized authentication and authorization protocol based on the Blockchain technology to reduce the security risks caused by centralization.
Vulnerability discovery strategies used in software projects

Malicious users can exploit undiscovered software vulnerabilities i.e., undiscovered weaknesses in software, to cause serious consequences, such as large-scale data breaches. A systematic approach that synthesizes strategies used by security testers can aid practitioners to identify latent vulnerabilities. The goal of this paper is to help practitioners identify software vulnerabilities by categorizing vulnerability discovery strategies using open source software bug reports. We categorize vulnerability discovery strategies by applying qualitative analysis on 312 OSS bug reports. Next, we quantify the frequency and evolution of the identified strategies by analyzing 1,632 OSS bug reports collected from five software projects spanning across 2009 to 2019. The five software projects are Chrome, Eclipse, Mozilla, OpenStack, and PHP.
Generalized Expanded-Blaum-Roth Codes and Their Efficient Encoding/Decoding

Expanded-Blaum-Roth (EBR) code encodes a (p - 1) × k information array into a p × p array such that any bit in a column can be recovered within the column and any k out of p columns can retrieve all (p - 1) × k information bits, where p is a prime number. In this paper, we generalize the construction of EBR code with a more flexible parameter, i.e., the number of bits stored in a column in the proposed construction can be not only a prime number but also an even number. In addition, we present an efficient encoding/decoding method for the proposed generalized EBR codes based on the LU factorization of Vandermonde matrix. We show that the proposed encoding/decoding method has less computational complexity than the existing method. Moreover, we show that the minimum symbol distance of generalized EBR codes is the same as that of EBR code for some parameters.
An Efficient Credit Card Fraud Detection System using Deep-learning based Approaches

During the past few years, use of e-commerce has been grown to a large scale. Due to which, the use of credit card has also been increased. Many people now use credit cards for online shopping, e-billing, and other online payments. This frequent use of credit cards is pushing the organizations and banks to implement credit card fraud detection systems to distinguish between illicit and legitimate transactions. These systems have been trained in pre-existed datasets and then applied to the new transactions. Many techniques are used to detect fraudulent transactions, such as Genetic Algorithm (GA), Support Vector Machine (SVM), and Artificial Immune System (AIS). In most of the techniques, the classification results are biased towards the majority class due to this biasness False Positive Rate (FPR) and False Negative Rate (FNR) are maximized. To overcome this problem, we have implemented three techniques, i.e., Naive Bayes (NB), Generative Adversarial Networks (GAN), and Neural Networks (NN). The final results are then compared in terms of accuracy, precision, recall, and f-measure. Our main objectives are to minimize the FPR and FNR, which ultimately improves the identification of fraudulent and legitimate transactions. The results show that NN outperforms NB and GAN in term of accuracy, precision, and f-measure.
Fully Bayesian Analysis of Relevance Vector Machine Classification With Probit Link Function for Imbalanced Data Problem

The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated Normal distribution. Compared with the original RVM classification model, our proposed one is a Fully Bayesian approach, and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem.
Telephony Speech Enhancement for Elderly People

One of the limiting factors of performance of any communication system is bandwidth. In telephone systems, the operational voice frequency ranges from 300 to 3400 Hz and is called Narrow Band (NB). This NB causes remarkable depreciation of speech in terms of quality and intelligibility, specifically for elderly persons. Speech Bandwidth Extension (SBE) technique is used to increase the quality and intelligibility of NB signal. In SBE method, additional data about the lost speech frequency components is transmitted along with NB signal. A better-quality Wide Band (WB) signal is reconstructed at the receiver end using the transmitted missing speech frequency components. This paper explores the ability of SBE to enhance the quality and intelligibility of NB signal for elderly persons. The enhancement in the quality and intelligibility of NB speech for elderly persons is reasserted by subjective listening and objective tests.
Spacecraft Custody Maintenance and Maneuver Detection Using Robotic Telescopes and Reachable Sets

The increased congestion of the orbital environment necessitates improved ground-based telescope tasking, most forms of which focus on covariance minimization. Limited work has been done on tactical sensor tasking aimed at resolving specific hypotheses of interest, which is the focus of this paper. The discernment of spacecraft maneuvers and maintenance of custody is imperative for improved long-term space domain awareness. As such, a novel telescope tasking algorithm is developed using decision making, evidential reasoning, and reachability techniques. Position reachable set overapproximations are continuously computed assuming that spacecraft are maneuvering using optimal control, which are used to determine whether search or tracking actions are optimal, resulting in improved custody maintenance against worst-case spacecraft maneuvers. Moreover, a new Mahalanobis distance-based binary hypothesis detection method is derived to characterize spacecraft anomaly from a nominal trajectory. A Monte Carlo study is designed to a specified statistical confidence level to examine the accuracy of the algorithm. Lastly, the algorithm is implemented on a robotic telescope in real-time. Thus, using reachable set overapproximations and searches, new evidence to hypothesis belief mappings, a Monte Carlo study, and a real-time demonstration, a novel telescope tasking strategy is presented.