Reviewerly/exHarmony huggingface dataset:
An intelligent clustering method for devising the geochemical fingerprint of underground aquifers

<h2>Abstract</h2> Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting) are influenced by the anthropogenic impact and by the climate change. So, their monitoring is a tool of resilience and adaptation. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerprinting. In this paper we consider data from 57 wells located in the province of Ferrara (Italy), all belonging to the same geological group and separated into 4 different aquifers. The aquifer from which each well extracts its water is known only in 18 of the 57 cases, while in other 39 cases it can be only hypothesized based on geological considerations. We devise a novel technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted with a sufficiently high accuracy. Then, we experimentally prove that out method is sensibly more accurate than typical statistical approaches, such as principal component analysis, for this particular problem.
Application of Intel RealSense Cameras for Depth Image Generation in Robotics

This paper presents the applications of depth cameras in robotics. The aim is to test the capabilitiesof depth cameras in order to better detect objects in images based on depth information. In the paper, the IntelRealSense depth cameras are introduced briefly and their working principle and characteristics are explained.The use of depth cameras in the example of painting robots is shown in brief. The utilization of the RealSensedepth camera is a very important step in robotic applications, since it is the initial step in a series of roboticoperations, where the goal is to detect and extract an obstacle on a wall that is not intended for painting. Aseries of experiments confirmed that camera D415 provides much more precise and accurate depth informationthan camera D435.
Creating heralded hyper-entangled photons using Rydberg atoms

Abstract Entangled photon pairs are a fundamental component for testing the foundations of quantum mechanics, and for modern quantum technologies such as teleportation and secured communication. Current state-of-the-art sources are based on nonlinear processes that are limited in their efficiency and wavelength tunability. This motivates the exploration of physical mechanisms for entangled photon generation, with a special interest in mechanisms that can be heralded, preferably at telecommunications wavelengths. Here we present a mechanism for the generation of heralded entangled photons from Rydberg atom cavity quantum electrodynamics (cavity QED). We propose a scheme to demonstrate the mechanism and quantify its expected performance. The heralding of the process enables non-destructive detection of the photon pairs. The entangled photons are produced by exciting a rubidium atom to a Rydberg state, from where the atom decays via two-photon emission (TPE). A Rydberg blockade helps to excite a single Rydberg excitation while the input light field is more efficiently collectively absorbed by all the atoms. The TPE rate is significantly enhanced by a designed photonic cavity, whose many resonances also translate into high-dimensional entanglement. The resulting high-dimensionally entangled photons are entangled in more than one degree of freedom: in all of their spectral components, in addition to the polarization—forming a hyper-entangled state, which is particularly interesting in high information capacity quantum communication. We characterize the photon comb states by analyzing the Hong-Ou-Mandel interference and propose proof-of-concept experiments.
Crowd Behavior Monitoring and Analysis in Surveillance Applications: A Survey

In the research field of computer vision, crowd monitoring and analyzing the behavior is an open topic for researchers due to its importance. Over the last decade many methodology has been proposed to do these task. These methodologies supposed to perform various tasks for the crowd which includes finding the strength of crowd in number for the proper crowd management in time or for the security reasons, prediction of future behavior of the crowd etc. Although many complex methodologies have been implemented for analyzing the crowd but there is still open scope for the methodologies which analyze the crowd in real-time, especially for the non-organized crowd. This paper presents a literature survey of the methodologies, proposed for the crowd monitoring and behavior analyzing for the both organized crowd and non-organized crowd. We also included the dataset details, used for those proposed methods with advantages and disadvantages. We included the methodologies based on traditional approaches as well as modern deep learning concept. We have a faith with a motive that this paper will help in the research community to understand about the various state of art methodologies used for crowd monitoring and analyzing.
Development of a Hybrid Product Breakdown Structure and Variability Model

ABSTRACT In transforming from a project‐based engineering approach to a product line engineering (PLE) approach, the engineering teams must have support throughout the transition from evolving tools and methodologies. As an example, the Product Breakdown Structure (PBS) is traditionally a construction‐based decomposition of a complex system, where subsystems reflect a breakdown of the engineering elements with appropriate technical interfaces, subassemblies, and team responsibility delineations. However, when used for a product line with myriad variants, a traditional PBS format provides insufficient detail and structure. As such, using standard desktop software, the author developed a hybrid PBS‐Variability Model (VM), combining the familiar PBS structure with variability modeling aspects based on feature modeling and decision modeling approaches. This resulted in an engineering artifact recognizable as a PBS and easy to adapt to design evolution, yet sufficiently expansive to initially characterize variability. In this way, the traditional PBS evolves to the hybrid PBS‐VM before transitioning to a complete variability model, thereby supporting the engineering teams transitioning from a project‐based engineering approach to a PLE approach. In this paper, the author describes the traditional PBS limitations, the hybrid model development process with a custom‐developed syntax description, the resulting hybrid model, and conclusions on appropriate product line usage.
A Survey on Security on Medical Data and Images in Healthcare Systems

The development in the field of networking technologies and telecommunications has increased the popularity of telemedicine usage. In medical diagnosis, many processes have been proposed by researchers for securing patients’ records and medical images that are sent from one location to another. This paper represents a technical survey on various cryptographic and watermarking processes that are applied on different medical images for secure transmission. Deterioration in the quality of the medical data or images at the time of transmission may endanger the treatment of patients, therefore, lossless and reversible methods need to be emphasized. In this study, we have analyzed some of the most relevant existing works in this area. On the basis of this analysis, we have tried to identify the open issues in the field of secure medical image transmission in order to provide secure transmission and ensure enhanced quality of treatment of the patients.
Distribution-preserving data augmentation

In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it possible to develop a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, and sometimes not likely in some domains if not challenging. Therefore, researchers proposed data augmentation methods to increase dataset size and variety by creating variations of the existing data. For image data, variations can be obtained by applying color or spatial transformations, only one or a combination. Such color transformations perform some linear or nonlinear operations in the entire image or in the patches to create variations of the original image. The current color-based augmentation methods are usually based on image processing methods that apply color transformations such as equalizing, solarizing, and posterizing. Nevertheless, these color-based data augmentation methods do not guarantee to create plausible variations of the image. This paper proposes a novel distribution-preserving data augmentation method that creates plausible image variations by shifting pixel colors to another point in the image color distribution. We achieved this by defining a regularized density decreasing direction to create paths from the original pixels’ color to the distribution tails. The proposed method provides superior performance compared to existing data augmentation methods which is shown using a transfer learning scenario on the UC Merced Land-use, Intel Image Classification, and Oxford-IIIT Pet datasets for classification and segmentation tasks.
Clifford algebra from quantum automata and unitary Wilson fermions

We introduce a spacetime discretization of the Dirac equation that has the form of a quantum automaton and that is invariant upon changing the representation of the Clifford algebra, as the Dirac equation itself. Our derivation follows Dirac's original one: We required that the square of the discrete Dirac scheme be what we define as an acceptable discretization of the Klein-Gordon equation. Contrary to standard lattice gauge theory in discrete time, in which unitarity needs to be proven, we show that the quantum automaton delivers naturally unitary Wilson fermions for any choice of Wilson's parameter.
Quantum process tomography of a Mølmer-Sørensen gate via a global beam

We present a framework for quantum process tomography of two-ion interactions that leverages modulations of the trapping potential and composite pulses from a global laser beam to achieve individual-ion addressing. Tomographic analysis of identity and delay processes reveals dominant error contributions from laser decoherence and slow qubit frequency drift during the tomography experiment. We use this framework on two co-trapped $^{40}$Ca$^+$ ions to analyze both an optimized and an overpowered M{\o}lmer-S{\o}rensen gate and to compare the results of this analysis to a less informative Bell-state tomography measurement and to predictions based on a simplified noise model. These results show that the technique is effective for the characterization of two-ion quantum processes and for the extraction of meaningful information about the errors present in the system. The experimental convenience of this method will allow for more widespread use of process tomography for characterizing entangling gates in trapped-ion systems.
An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment

Bayesian networks (BNs) can be employed to cognitive diagnostic assessment (CDA). Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending (GD) learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints (greater skill should lead to better item performance) cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data contained in every CDA and continue to estimate the parameters of BN based on the EM or the GD algorithm regarding the parameters based on the IRP training method as informative priors. Both the simulation study and realistic data analysis demonstrated the validity and feasibility of the new method. The BN based on the new parameter estimating method exhibits promising statistical classification performance and even outperforms the G-DINA model in some conditions.
A Comprehensive Analysis of Cyber Data Breaches and Their Resulting Effects on Shareholder Wealth

Cyber Security data breaches are an evolving major risk that corporations, public and private alike, have to contend with as part of their risk management scenarios. Historically, studies evaluating shareholder ramifications of cyber data breaches have been limited by the lack of data available to the public regarding company specific cyber attack details. The few studies that have been done have been mainly limited to sample sizes of only 100 to 200 companies over various time frames with hand collected data. This study uses a comprehensive proprietary data set developed by Advisen Ltd., the leading provider of data, media, and technology solutions for the commercial property and casualty insurance market, to develop a representative sample of almost four thousand separate cases of public companies from all over the world experiencing cyber data breaches during the period 1990 to 2019. Using events study methodology, our results analyze both short term and long term shareholder cumulative abnormal returns around the public first notice date. The results, using the overall sample, do support smaller studies that show significantly negative short run returns for event windows that range from -1 up to 5 days after announcement date. However, our results are lower than many of the original studies that find much higher negative returns with smaller data sets. Segmenting the sample by industry and type of data breach shows a mixture of short run negative results for industries like the highly targeted service companies, while finance companies show an over correction, which result in significant positive short run, returns for investors by day 3 and 5. Returns associated with cyber attacks involving either Personal Financial Information (PFI) or Personal Identity Information (PII) support short run negative returns that are significant, but data related to Personal Health Information (PHI) show a significant overcorrection of short run excess returns. Long term results of the entire sample show significantly negative results for up to 250 trading days from announcement date of up to -7.46%.
Pronoun Resolution Task for Multilingual Machine Translation

Machine translation is leading towards a noticeable progress in the era of natural language processing. Started with monolingual, now it’s growing into multilingual translations. Though many devices now come with on the fly translations, the efforts required for low resource languages are incredible. We have developed a system for Indian languages which plays a major role in preserving the context after translation. Like a human, machines are not able to identify and preserve the context, check the dependency between different parts of speech etc. Our contribution is to resolve the anaphora resolution for multilingual, mainly English to Marathi and Hindi translation. Adding the external world knowledge noticeably improves the results for context preservation.
Investigation of the Influence of the Parameters of the VFH + Method on the Navigation Efficiency of the Mobile Robot

The article focuses on verifying the effects of the VFH + navigation method parameters, proving to be very effective for the robot's reactive navigation. This research is based on our long-standing knowledge of histogram methods used in robot navigation. The article focuses on verifying the influence of crucial parameters - thresholds in a binary histogram, the smax parameter defining wide and narrow valleys, constants setting the criterion function, and the impact of robot dynamics on navigation. Many experiments were performed in a ROS simulation environment, and the article lists those significant confirming certain assumptions in setting these parameters.
Enhancement of the U-net Architecture for MRI Brain Tumor Segmentation

The magnetic resonance images (MRI) brain tumor segmentation is one of the most difficult medical images segmentation, and it has many challenges because the tumor has no specific shape or size, not found in a specific place of the brain and contains three sub-regions (full tumor FT, tumor core TC, and enhanced tumor ET). The manual segmentation is extremely difficult and prone to mistakes. In this research, the semantic segmentation is used by implementing the U-net model, which is a fully convolutional network (FCN) algorithm on BraTS 2018 that contains four modalities (T1, T2, T1c, Flair). The U-net model is used two time: The first is using 9-layers U-net with some enhancements on the original model for segment the full tumor, and the second is using 7-layers U-net model for segment the tumor core and enhanced tumor. The results were promised by segmenting the brain tumor within three sub-regions (full tumor, tumor core, and enhanced tumor), and the results were evaluated using standard brain tumor segmentation metrics. The proposed system achieves mean dice similarity coefficient metric; it is 0.87, 0.76, and 0.71 for full tumor, tumor core, and enhanced tumor, respectively. Additionally, the median dice similarity coefficient metric is 0.90, 0.84, and 0.80 for full tumor, tumor core, and enhanced tumor, respectively.
Tight finite-key analysis for quantum key distribution without monitoring signal disturbance

Abstract Unlike traditional communication, quantum key distribution (QKD) can reach unconditional security and thus attracts intensive studies. Among all existing QKD protocols, round-robin-differential-phase-shift (RRDPS) protocol can be running without monitoring signal disturbance, which significantly simplifies its flow and improves its tolerance of error rate. Although several security proofs of RRDPS have been given, a tight finite-key analysis with a practical phase-randomized source is still missing. In this paper, we propose an improved security proof of RRDPS against the most general coherent attack based on the entropic uncertainty relation. What’s more, with the help of Azuma’s inequality, our proof can tackle finite-key effects primely. The proposed finite-key analysis keeps the advantages of phase randomization source and indicates experimentally acceptable numbers of pulses are sufficient to approach the asymptotical bound closely. The results shed light on practical QKD without monitoring signal disturbance.
Features of managing teamwork of students in the context of distance learning using the Moodle platform

The distance form of the implementation of educational programs in university should not affect the quality of the competencies formed in the future graduate. One of the competencies of a graduate, the need for which is dictated by modern educational standards and labor market requirements, is the ability to fulfill his role in a team. When organizing teamwork of students in a distance learning environment, the problems of managing the activities of team members, tracking work results, assessing the contribution of each participant to the overall result arise. The article describes such an approach implemented on the basis of the LMS Moodle to form students into teams and to coordinate the actions of team members in a distance learning environment, which ensures the achievement of educational goals and the formation of teamwork skills. The possibilities of communication tools of the LMS Moodle for organizing teamwork of students are discussed. The conclusion is made about the advantages of the Wiki element, which allows each group of students to work on their project, and the teacher — to track the contribution of each participant to the achievement of the overall result. The conclusions presented in the article can be used in organizing teamwork in the context of distance learning, implemented using the Moodle platform.
A New Representation of Successor Features for Transfer across Dissimilar Environments

Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function changes, they have assumed that the dynamics of the environments remain consistent. Many real-world RL problems require transfer among environments with different dynamics. To address this problem, we propose an approach based on successor features in which we model successor feature functions with Gaussian Processes permitting the source successor features to be treated as noisy measurements of the target successor feature function. Our theoretical analysis proves the convergence of this approach as well as the bounded error on modelling successor feature functions with Gaussian Processes in environments with both different dynamics and rewards. We demonstrate our method on benchmark datasets and show that it outperforms current baselines.
An Improved Approach for Face Detection

Face recognition from an arbitrary image has been a standout amongst the most considered topic in image processing and computer vision. The human face is a convoluted multidimensional visual model and henceforth it is exceptionally hard to build up a computational model to recognize the face. This paper presents an approach depending on the attributes extracted from the image to identify the human face. The proposed approach combines both morphological image processing techniques and cascade object detector capabilities. This method is effective in face detection for arbitrary images.