Reviewerly/exHarmony huggingface dataset:
Fractal behaviour of distances between consecutive aftershocks: the examples of Landers, Northridge and Hector Mine Southern California mainshocks

Distances between consecutive aftershocks are analysed by means of mono- and multifractal theory with the aim of quantifying the complexity of the physical mechanism governing them, as well as their predictability and predictive instability. Hausdorff, Ha, and Hurst, H, exponents are determined by semivariograms and rescaled analysis, respectively. The exponent β of the power law describing power spectral contents is also quantified. These three parameters permit a generation of fractional Gaussian noise, fGn, simulating distances. The complexity and predictive instability of physical mechanism generating the series of distances is quantified by means of the correlation dimension, μ*, the Kolmogorov entropy, κ, and the Lyapunov exponents, λi, which are based on the reconstruction theorem formulation. Additionally, the multifractal detrended fluctuation analysis, MF-DFA, contributes with a different point of view to quantify the complexity of the series, in terms of fractal spectral width, W, spectral asymmetry, B, and the critical Hölder exponent, a0. By one hand, the MF-DFA is applied to the complete set of distances characterising the whole aftershock process. By the other hand, the MF-DFA is applied to segments of the series of distances with the aim of determining the evolution of the complexity since the mainshock up to the end of the stress relaxation process. Finally, an ARIMA multilinear regression process is applied to obtain some improvements, in comparison with fGn simulations, on the prediction of distances. The database for this analysis is obtained from the Southern California Seismic Network (SCSN) catalogue. Three series of aftershocks equalling to or exceeding magnitudes of 2.0, assuring seismic catalogue completeness, and associated with Landers (06/28/1992), Northridge (01/17/1994) and Hector Mine (10/16/1999) mainshocks are obtained. It is worth mentioning that common mono-multifractal behaviour for the three aftershocks series is not detected, whatever aftershock periods or segments of them are considered.
Fuzzy Systems: A Human Reasoning Approach Using Linguistic Variables

The term “Fuzzy” means vague or imprecise or uncertain or inexact. Fuzzy Sets enable us to accept the vagueness and lack of precision. Fuzzy Sets are used when classical/crisp representation cannot make the decision for a problem. Fuzzy Set Theory is a vast field and relies heavily on mathematical equations. This paper is an attempt to capture its essence without getting overwhelmed by the complexity of details. In this paper, we have confined our discussions about Fuzzy Sets and its operations in contrast to Classical Sets. We have started with Classical Sets followed by a discussion on how a Classical Set fails in some of the problems and how Fuzzy Sets overcome those issues. Then we have briefly discussed the Linguistic Variables and how it is more practical and realistic than a binary reasoning. For the sake of simplicity and understanding, we have tried to avoid mathematical equations wherever possible.
Scope-aware Code Completion with Discriminative Modeling

Code completion is a traditional popular feature for API access in integrated development environments (IDEs). It not only frees programmers from remembering specific details about an API but also saves keystrokes and corrects typographical errors. Existing methods for code completion usually suggest APIs based on statistics in code bases described by language models. However, they neglect the fact that the user's input is also very useful for ranking, as the underlying patterns can be used to improve the accuracy of predictions of intended APIs. In this paper, we propose a novel method to improve the quality of code completion by incorporating the users' acronym-like input conventions and the APIs' scope context into a discriminative model. The users' input conventions are learned using a logistic regression model by extracting features from collected training data. The weights in the discriminative model are learned using a support vector machine (SVM). To improve the real-time efficiency of code completion, we employ a trie to index and store the scope context information. An efficient top-k algorithm is developed. Experiments show that our proposed method outperforms the baseline methods in terms of both effectiveness and efficiency.
Nefele: Simplifying Application Development for the Cloud

This demo presents Nefele, a fully distributed, multi-tenant process orchestration system for cloud infrastructures. Nefele is inspired by the Single System Image (SSI) vision and its aim is to hide the heterogeneous and distributed nature of the available resources in a cluster and present them to users and applications as a single unified computing resource. In Nefele, a developer is equipped with a set of SSI-like features and programming interfaces to simplify the complexities of process deployment and Inter Process Communication (IPC) in distributed environments. The developer uses the APIs to simply deploy, execute, connect, and monitor tenants' processes spread over several nodes in a cluster.
Prototype Selection of Online Handwritten Telugu Characters Recognition Using Voronoi Tessellation

This paper illustrated various types of prototype selection for Online Handwritten Telugu Characters. The prototypes can be regarded as diverse styles of writing or expressing a specific character. Subsequently, a novel algorithm is proposed for prototype selection, in which the number of prototypes will be independently governed by the Voronoi Tessellation. Later, the K-Nearest Neighbor algorithm is applied on these prototypes, using parameters like angle/direction and coordinates of the pen. The HP labs dataset hpl-Telugu-ISO-char-online-1.0 is utilized for evaluation of proposed algorithm, which is found to achieve a recognition accuracy of over 93%. The average throughput is found to be 0.18 characters per second. The HP labs dataset contains 166 character classes which are aggregated from numerous writers on ACECAD Digimemo, with the help of Acecad Digi memo DCT application. Each of 166 Telugu characters contains 270 samples, which are written by native Telugu writers.
Routing Problems for Reconnaissance Patrolling Missions

This article introduces routing problems that arise in reconnaissance patrolling missions, where the focus is on improving the fidelity of information gathered from targets. The general problem is a multi-stage stochastic program where at each stage, additional nearby satellite targets might have to be visited to collect more information depending on the uncertainty associated with the information gathered from the original targets. In this paper, a preliminary two-stage version of a simple variant of the general problem is formulated and solved. Statistical bounds on the quality of the solution obtained for the two-stage problem are computed and the approach is compared with a deterministic counterpart. Finally, a plethora of relevant problem variants and future research directions are discussed.
Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

The chapter presents an approach for the interactive definition of curves and motion paths based on Gaussian mixture model (GMM) and optimal control. The input of our method is a mixture of multivariate Gaussians defined by the user, whose centers define a sparse sequence of key-points, and whose covariances define the precision required to pass through these key-points. The output is a dynamical system generating curves that are natural looking and reflect the kinematics of a movement, similar to that produced by human drawing or writing. In particular, the stochastic nature of the GMM combined with optimal control is exploited to generate paths with natural variations, which are defined by the user within a simple interactive interface. Several properties of the Gaussian mixture are exploited in this application. First, there is a direct link between multivariate Gaussian distributions and optimal control formulations based on quadratic objective functions (linear quadratic tracking), which is exploited to extend the GMM representation to a controller. We then exploit the option of tying the covariances in the GMM to modulate the style of the calligraphic trajectories. The approach is tested to generate curves and traces that are geometrically and dynamically similar to the ones that can be seen in art forms such as calligraphy or graffiti.
A Method for Projecting Features from Observed Sets of Phonological Classes

Given a set of phonological features, we can enumerate a set of phonological classes. Here we consider the inverse of this problem: given a set of phonological classes, can we derive a feature system? We show that this is indeed possible, using a collection of algorithms that assign features to a set of input classes and differ in terms of what types of features are permissible. This work bears on theories of both language-specific and universal features, provides testable predictions of the featurizations available to learners, and serves as a useful component in computational models of feature learning.
Adaptive Gaussian Mixture-Probability Hypothesis Density Based Multi Sensor Multi-Target Tracking

This paper addresses a novel multiple target tracking (MTT) problem in a decentralized sensors network (DSN) framework. The algorithm jointly estimates the number of targets and the states of the targets from a noisy measurement in the presence of data association uncertainty and missed detection. The standard GM-PHD filters estimate the multi-targets in a cluttered environment with an assumption that the target birth intensity is known or homogeneous. It results in inefficient tracking for new, occluded or missed targets. The issue is addressed by the proposed adaptive Gaussian birth components based estimation. A method based on covariance intersection fusion is proposed to address inter-sensor target data association.
Training teachers: explorative activities measuring Planck’s constant

Abstract In recent years, Modern Physics has been included in the curricula of Italian secondary schools, so that many teachers are asking for specific didactical tools to allow their students to acquire the right skills to deal with final examinations. The Physics Department at University of Salerno (Italy) is, therefore, planning PLS (Piano Lauree Scientifiche / Scientific Degree Project) training courses to give some valid educational paths through new didactical strategies for Physics learning. Introducing quantum mechanics by experimental set-ups with commonly used materials is a rather interesting and challenging task. In fact, this practice allows teacher attending the course to reproduce the same experiments in their own class. Moreover, by using readily available material for the experimental set-up, a soft approach to Modern Physics can be achieved. In the present work, we propose the analysis of an experiment: the measurement of Planck’s constant, one of the most important fundamental quantities in Modern Physics, by means of Light Emitting Diodes (LEDs). The properties of LEDs can be explained by means of elementary quantum mechanics concepts. As a consequence, when describing the quantum behaviour of these commonly used physical systems, Planck’s constant needs to be considered. Therefore, to illustrate one possible way of measuring Planck’s constant to high-school students, we can make use of the current-voltage (I-V) characteristics of a LED. In fact, by opportunely interpreting the measured I-V curves of these systems, by means of a linear fitting the value of Planck’s constant can be obtained.
Event-Based State Estimation Using the Auxiliary Particle Filter

Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is not well suited for state estimation in event-based systems. We propose an extension to the auxiliary particle filter for systems with event-based measurements, in which certain existing techniques for finding an approximation of the fully adapted filter can easily be utilized. In a simulation study, we demonstrate that at new measurement events, the benefits of using the auxiliary particle filter increases when fewer measurements are being sent.
Revisiting Data Hiding Techniques for Apple File System

Data hiding is an important part of anti-forensic research since the continuous development of operating systems, file systems and other software may close some previously known vulnerabilities but will often inadvertently create new ones. Many of the currently used file systems such as FAT, NTFS or ext4 have been thoroughly analysed. There are quite a few theoretical approaches and also some practical tools that help us to hide data in the existing file systems in different ways. For the Apple File System (APFS), the new standard file system for all Apple devices, only part of the previous work is transferable. There are only a few published forensic analyses of APFS so far and some forensic tools like the Sleuthkit have at least partially adapted APFS functionality. However, anti-forensic techniques specific to APFS have not yet been explored.
Unsupervised Variational Learning of Finite Generalized Inverted Dirichlet Mixture Models with Feature Selection and Component Splitting

Variational learning of mixture models has proved to be effective in recent research. In this paper, we propose a generalized inverted Dirichlet based mixture model with an incremental variational algorithm. We incorporate feature selection and a component splitting approach for model selection within the variational framework. This helps us estimate the complexity of the data efficiently concomitantly eliminating the irrelevant features. We validate our model with two challenging applications; image categorization and dynamic texture categorization.
Sub-pixel Upsampling Decode Network for Semantic Segmentation

Many recent semantic segmentation frameworks use encoder-decoder design strategy. Most of them employ a relatively simple encoder with a complex stratified decoder. In this work, we explore the potential of combining a robust encoder with a simple one-layer but finely designed decoder. We refine the structure of the decoder module with larger effective receptive field and residual refinement connection. We also explore different strategies for fusing multi-stage contextual feature, to improve the effectiveness of the proposed framework. Specifically, we introduce a Sub-pixel Upsampling Decoder module to decode more detailed information by learning spacial downsampled label in different decoder channel groups. We test the proposed model in the dataset of PASCAL VOC 2012 segmentation task, and the results show the improvement of our method under our computational limitation.
Real-World Image Denoising via Weighted Low Rank Approximation

Most of existing denoising algorithms are based on the assumption of additive white Gaussian noise. As the realistic noise in color images captured by CCD or CMOS cameras is much more complex than additive white Gaussian noise, most methods will be not effective. In this paper, we present a weighted low rank approximation for real color image denoising, which effectively models the statistical property of the noise and intrinsic characteristic of the image. Specifically, we employ two weighted matrices to model the realistic noise property along channels and in the spatial dimension in consideration of their different statistics. The intrinsic characteristic of the image is explored via low rank regularization. Then, we formulate the denoising problem into a variational optimization model, which can be solved via the alternating direction method of multipliers (ADMM). Experiments on synthetic and real-world noisy color images show that our proposed method outperforms state-of-the-art denoising methods.
We See a Rainbow: Resource Sharing and Reference Services Working Together

The University of Massachusetts Boston, Joseph P. Healey Library, Resource Sharing and Document Delivery Department and Reference, Outreach and Instruction Department, established a cross-interdepartmental collaboration resulting in enhanced customer service and increased Interlibrary Loan (ILL) fill rate. This case study will review the rationale and the events that necessitated the establishment of the cross-interdepartmental collaboration. The collaborative partnership resulted in higher fulfilments rates increasing dramatically from 83% to 91.5%, knowledge sharing across departments, and heightened research skills. Future plans include extending the ILL/ROI collaboration model and piloting an Open Access initiative.
Convolutional neural network based tracking for human following mobile robot with LQG based control system

Visual object Tracking is one of the most challenging tasks in computer vision due to various complications like environmental clutter and object clutter. In this paper we propose the use of Masked RCNN and YoloV2 based CNN architecture to overcome the challenges of tracking and we have also compared their performance in real-time application on a Mobile Robot. The type of dataset required, and approach considered for each of the approach to increase the accuracy as well as implantability on real-time system is also discussed. A Skid Steer Mobile Robot (SSMR) is used to follow the human detected by the CNN algorithms. Te Robot Control is done by use of Linear Quadratic Gaussian Controller for velocity control.
Research on blurred image restoration based on generative adversarial networks

In order to realize the restoration of the blurred image by using the Generative Adversarial Networks, this paper proposes to add generator loss optimization and network depth optimization based on the generation of the Generative Adversarial Networks(GANs) with gradient penalty. This paper adds Perceptual Loss and ResNet. The perceptual loss is migrated from the image style migration network module as the second item added to the loss to the generator loss function, learning the clear image style and facilitating the correction generation. Add residual modules to the generator network to reduce network degradation while deepening network depth. The network structure model optimized in this paper shows relatively good test results in the subsequent experiments.
Recursive Bayesian Classification for Perception of Evolving Targets using a Gaussian Toroid Prediction Model

This paper proposes a probabilistic framework for classification of evolving targets, leveraging the principles of recursive Bayesian estimation in a perception-oriented context. By implementing a Gaussian toroid prediction model of the perception target's evolution, the proposed recursive Bayesian classification (RBC) scheme provides probabilistically robust classification. Appropriate features are extracted from the target, which is then probabilistically represented in a belief space. This approach is capable of handling high-dimensional belief spaces, while simultaneously allowing for multi-Gaussian representation of belief without computational complexity that hinders real-time analysis. The proposed technique is validated over several parameter values by thousands of simulated experiments, where it is shown to outperform naıve classification when high observational uncertainty is present.
An Efficient Method of PCB Images Stitching

In solder paste inspection, since the camera' field of view is small relative to the size of PCB, it is necessary to stitch the partial PCB images taken in different parts. Stitching PCB images requires real-time processing, while common existing methods are not satisfactory, so this paper proposes a novel method to further speed up image stitching. This study contributes in three ways: First, a 4-step improved method used for corner detection is introduced, which builds on the well-known FAST corner detection method. Second, the nearest and next nearest neighbor ratio method, bidirectional matching method and RANSAC are adopted to reduce the possibility of mismatching and optimize the performance of the corner matching. Third, several corner detection methods are compared. Performances in terms of quantitative and qualitative results are discussed so as to move towards the selection of the most appropriate method. The experiments demonstrate that the improved image stitching method outperforms other methods both in efficiency and accuracy.
Optical System That Forms a Mid-Air Image Moving at High Speed in the Depth Direction

Mid-air imaging technology expresses how virtual images move about in the real world. A conventional mid-air image display using a retro-transmissive optical element moves a light source the distance a mid-air image is moved. In conventional mid-air image displays, the linear actuator that moves a display as a light source makes the system large. In order to solve this problem, we designed an optical system that realizes high-speed movement of mid-air images without a linear actuator. We propose an optical system that moves the virtual image of the light source at a high speed by generating the virtual image of the light source with a rotating mirror and light source by the motor.
Safe and Fast Path Planning in Cluttered Environment using Contiguous Free-space Partitioning

The paper proposes a path planning algorithm for cluttered environment and maze. The proposed planning algorithm exploits the merit of convex optimization while forming the convex navigable free-spaces, ensuring safety of the vehicle. The contiguous convex free-spaces are iteratively computed from a random-walk based seed generation method to create a contiguous navigable geometry. Inside this contiguous navigable geometry an undirected graph is then created, whose each node and edge belong to at least one convex region which boils down the path planning problem into a graph search problem. In addition, the proposed multiple query planning algorithm can merge the user provided feasible initial and goal configuration with the existing undirected graph in each plan, without deteriorating the planning performance in terms of run-time and path length. Simulation and experimental results confirm the superiority of the proposed planning algorithm jointly in terms of both path length and run-time by a significant margin.
Radius of Turn and Flight Path Angle Estimation from Unmanned Aircraft Flight Trajectories

The Dubins vehicle model, with its parameters radius of turn and flight path angle, is commonly used for modeling unmanned aircraft trajectories. While Dubins model path planning is state of the art, the reverse direction, extracting Dubins model information from unmanned aircraft trajectories is poorly addressed. This article presents a method to fit the Dubins model parameters radius of turn and flight path angle to streamed aircraft trajectory data in a two-phase approach. In the first step flight phases are detected. Within the second step the corresponding radii of turn and flight path angles are estimated. The method is applied on various flight trajectories of different unmanned aircraft. The evaluation further regards the algorithms run time and states the feasibility for online applications.