Reviewerly/exHarmony huggingface dataset:
Geomorphological Structuring and Tectonic Control in the Southeastern Brazilian Stepped Reliefs: Relation with the Evolution of Crystalline Scarps

The strong tectonic control acting on the relief evolution in the context of the southeastern Brazilian rift margin has been treated by several methodological prisms. The great escarpments of the Brazilian passive margin present important crustal elements acting in its geomorphological evolution and structuring, being the present work in charge of discussing the relations between the structural and tectonic control in the evolution of such geomorphological systems, focusing on specific compartments of Serra do Mar and Serra da Mantiqueira, the two main orographic steps of the Brazilian Atlantic margin. Through the elaboration of paleotopographic models and structural and geochronological analysis, the results pointed out strong neotectonic deformational effects superimposed on Precambrian structures reactivated in the Cretaceous-Paleocene, reinforcing the action of a post-Miocene tectonics influencing the maintenance of scarps and enhancing local and regional amplitudes.
An Evaluative Study on Resources, Training and Level of Knowledge in Handling Crime Scene Photography amongst Police Officials at Sikar District of Rajasthan

Crime scene photography is one aspect that should be upgraded with time for better and significant results in crime scene management.The present study focuses on the resources, training and level of knowledge about crime scene photography existing among police officials in Sikar district of Rajasthan.There is a great need to evaluate the resources, training and knowledge existing among police officials about crime scene photography, so that our criminal justice system is able to obtain sufficient and significant results from crime scene and helps in delivering justice.
Noisy quantum computation modeled by quantum walk: universality without ancillas

Abstract The universal quantum-walk-based quantum computation model by Childs has opened the door for a new way of studying the limitations and advantages of quantum computation, as well as for its intermediate-term simulation. In recent years, the growing interest in noisy intermediate-scale quantum (NISQ) computers has lead to intense efforts being directed at understanding the computational advantages of open quantum systems. In this work, we extend the quantum walk model to open noisy systems in order to provide such a tool for the study of NISQ computers. Our method does not use explicit purification, and allows to ignore the environment degrees of freedom and obtain a much more efficient implementation (linear rather than exponential in the runtime), which employs no ancillas, hence provides direct access to the entanglement properties of the system. In our scheme, the quantum walk amplitudes represent elements of the density matrix rather than the wavefunction of a pure state. Despite the non-trivial manifestation of the normalization requirement in this setting, we model the application of general unitary gates and nonunitary channels, with an explicit implementation protocol for channels that are commonly used in noise models.
Impact of digitalisation on medical sector employees

The present era is known as digitalisation era. There are various sectors in which digitalisation has impacted and has also increased the performance of employees as well as employers’ capacity and satisfaction. It is also important to explain the way, tools and techniques to understand and implement these technological challenges at their workplace. This technological change also shifting their employees from one phase of learning to second phase of learning. During the pandemic period, each saw increase in work from home opportunities this was because people could not go out for work when lockdown was declared. Work from home was possible because of digital presence across the country and various apps like Microsoft Teams, google duo, zoom etc. We were also not able to go to colleges, schools, coaching centers for studying then also due to digitally powerful base we were able to cope up with our studies. People were able to order food, grocery, clothes, and stuffs at their doorsteps using various shopping apps.
An Empirical Study on Secondary Vocational School Students’ Enhancement of Self-worth and Perceived School Climate

The relationship between boosting self-worth and perceived school climate is quite close, yet it has received little attention in the academic community.It was decided to use the Adolescent Self-Esteem Scale and the Perceived School Climate Scale.Using descriptive statistics, ANOVA, correlation analysis, and regression, the association between self-esteem and school climate was examined.The study revealed that the overall self-worth of secondary vocational school students was poor, as was their assessment of the school climate, and that there was a positive association between their self-worth and their perception of the school climate.The perception of school climate is higher than the perception of school climate.
Locating and categorizing inefficient communication patterns in HPC systems using inter-process communication traces

High Performance Computing (HPC) systems are used in a variety of industrial and research sectors to solve complex problems that require powerful computing platforms. For these systems to remain reliable, we should be able to debug and analyze their behavior in order to detect root causes of potential poor performance. Execution traces hold important information regarding the events and interactions among communicating processes, which are essential for the debugging of inter-process communication. Traces, however, tend to be considerably large, hindering their applicability. In previous work, we presented an approach for automatically detecting communication patterns and segmenting large HPC traces into execution phases. The goal is to reduce the effort of analyzing traces by allowing software analysts to focus on smaller parts of interest. In this paper, we propose an approach for detecting and localizing inefficient communication patterns using statistical and trace segmentation methods. In addition, we use the Analytic Hierarchy Process to categorize slow communication patterns based on their severity and complexity levels. Using our approach, an analyst can quickly locate slow communication patterns that may be the cause of important performance problems. We show the effectiveness of our approach by applying it to large traces from three HPC systems.
Investigation on the blurred image restoration based on brain‐inspired model

Though relatively good effect has been achieved by the image de-blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain-inspired image restoration model based on human attention and “fine vision” is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain-inspired model consists of dual-channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super-resolution, as well as looking results by visual inspection.
A novel color image steganography algorithm based on bit counting and multiple-base system

This paper proposes a novel color image steganography scheme using a compound sinusoidal discrete memristor map and bit counting method. Firstly, a new 2D compound sinusoidal discrete memristor map is designed to generate chaotic sequences with good randomness. Secondly, a new steganography algorithm combining bit counting and multiple-base system is proposed to reduce the distortion of the stego image. Finally, a new steganography framework based on the HSV model is designed to improve the security of the steganography scheme. Experimental and test results show that the proposed algorithm has good imperceptibility and can embed a large number of secret information.
FPGA implementation of feature detection and matching using ORB

Oriented FAST and Rotated BRIEF (ORB) is becoming the method of choice in real-time feature matching applications by offering performances comparable to other state-of-the-art descriptors at a fraction of the runtime and memory requirements. These characteristics have prompted multiple researchers to propose FPGA-based accelerators that allow for a significant increase in throughput compared to general-purpose processors. This increase is, however, achieved at the expense of the descriptor’s robustness as several approximations are necessary to map the algorithm efficiently into hardware which negatively affects its invariance. In this paper, we introduce an FPGA architecture capable of extracting ORB descriptors at high throughput with minimal impact to their invariance. Rotation of sample point coordinates is bypassed entirely, and methods for the accurate computation of the moments and descriptor generation in a single clock cycle are described. Moreover, a feature matching algorithm based on the cross-check criterion is proposed and implemented in hardware. The architecture achieved a throughput of 94 MPix/s using a 100 MHz clock while producing an almost identical performance compared to the reference implementation.
Recursive Feature Diversity Network for audio super-resolution

Deep learning methods have been successfully applied to audio super-resolution tasks. Although deep learning methods produce good performance, they are not practical for the real-world applications due to the large member of computations. To address this problem, we propose a Recursive Feature Diversity Networks (RFD-Nets), which is a lightweight model for achieving fast and accurate audio super-resolution. RFD-Nets are composed of a Recursive Feature Diversity (RFD) block and a Back-Projection (BP) block. Specifically, the RFD block is a recursive structure to iteratively refine and extract hierarchical audio feature. Subsequently, using an up-and-down sampling learner, the proposed BP block can effectively capture the deep relationships between High-Resolution (HR) and Low-Resolution (LR) audio pairs, thus producing high-quality audio reconstruction. Furthermore, we collect seven different types of complex audio datasets for training and comprehensively evaluating the proposed method. Extensive experiments demonstrate that our RFD-Nets can achieve superior accuracy on the proposed benchmark datasets against state-of-the-art methods while only requiring lower computation and memory. Datasets are released at https://github.com/JiangBoCS/RFD-Net.
An intelligent multivariate block steganography framework with enhanced PSO based on chaos mapping

To safeguard private information, image steganography is extensively used. Research is focused on ways to enhance steganographic technologies so that they may increase compression ratio while maintaining steganography image integrity. Because of its essential qualities such as security, scalability, and robustness, Steganography is a preferred way of communicating protected secret information to prevent hacking and misuse. This proposed research offers a steganography approach based on Enhanced Chaotic Particle Swarm Optimization (ECPSO), which uses chaos theory to determine the optimal pixel positions in the cover picture to hide confidential information when keeping the steganography quality in the images. Both the cover and secret pictures are separated into blocks to increase hiding capacity, with each component storing a sufficient quantity of secret data by mapping the pixels. The suggested ECPSO-Stegano system has better results with the criteria of Mean Square Error (MSE) of 0.00018%, Peak-Signal-to-Noise-Ratio (PSNR) of 79.66%, Bit Error Rate (BER) of 0.45% in average, and Structural Similarity Index (SSI) of 0.98 in average for various input size. It’s also robust to statistical threats.
Bilingual Corpus-based Hybrid POS Tagger for Low Resource Tamil Language: A Statistical approach

In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part-Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English-Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving one-time occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil.
Visible part prediction and temporal calibration for pedestrian detection

Despite their great advancement, current pedestrian detection methods focus on single static images, which fail to employ richer information available from the video sequences. Compared with still images, videos can offer temporal information of objects in the time dimension, thus providing the potential to obtain more robust detection performance. Here, a novel pedestrian detection method based on visible part detection and temporal calibration is proposed. Specifically, a part-aware module to predict the visible body part of each pedestrian instance, which enables us to obtain precise motion information of partially occluded pedestrians in a video sequence, is first developed. Then, the temporal coherence for each pedestrian instance based on the predicted motion information is constructed. After that, an adaptive temporal calibration method is introduced to effectively calibrate the final detection result. This method on two video pedestrian detection benchmarks, that is, Caltech-New and MOT17Det, is evaluated. Experimental results show that this method performs favourably against existing pedestrian detection approaches.
Transformational Leadership and Organisational Performance: The Combined Impact of Organisational Learning and Open Innovation

This work deals with the effect of transformational leadership (TL) on organisational performance (OP) through the mediating roles of organisational learning (OL) and open innovation (OI). Data was collected from 202 respondents in Tunisian enterprises using non-probabilistic convenience sampling. Based on structural equation modelling, the findings demonstrate a positive effect of TL on OP through an OI mediation. The study highlighted the direct and indirect effects of TL on OP through a double partial mediation of OI and OL. The document sheds new light on the gaps between TL and entrepreneurial leadership, not as a continuum, but as coexisting entrepreneurial leadership styles.
Theories in Agile Software Development: Past, Present, and Future Introduction to the XP 2020 Special Section

Over the last two decades, agile software development has gained popularity among software engineering researchers and practitioners. However, the development and use of theories in agile research remain relatively low. While analyzing publications on agile software development in the Scopus database from the last decade, we found that only 7% of the papers used or developed a theory. This trend seems stable. However, it is promising that most theory-centric studies use or propose theories to address cognitive and behavioral aspects of people working in agile development. We argue that these aspects build fundamental pillars in agile software development. In this special section, we introduce extended versions of four papers selected from the XP2020 Conference. These papers make valuable contributions to aspects of learning and behavior in agile software development. We encourage researchers to be more theory-centric in their future empirical studies of agile methods and practices by familiarizing themselves with existing theories and applying and developing theories. This way, they can contribute to a reliable, evidence-based body of knowledge in our community.
Distribution of Hydrocarbon Resources by Fields of Different Sizes and by the Number of Pools in Each Field

Abstract —This study describes a procedure for estimating the distribution of hydrocarbon resources by fields with different sizes of reserves and by the number of pools in each field. Such an evaluation is important in predicting the efficiency of exploration. The procedure first uses a simulation model for generating a population of hydrocarbon pool sizes. Then the spatial distribution of pools over a petroleum basin is modeled as a nonstationary Poisson point field. When we generate a field population in the basin, each field is considered a geometric object consisting of several pools according to the principle of their mutual geometric location. The proposed model was used to estimate a two-dimensional distribution of the number of fields and resources contained in them by resource sizes and the number of pools in each field. The proposed assessment was made for oil resources in the West Siberian petroleum province.
Distributed Graph Estimation under Laplacian Constraints

In recent years, distributed estimation of graph Laplacian matrices for smooth graph signals has received much attention. Traditional methods for estimating the graph Laplacian matrices usually estimate the global parameters in a centralized manner, which is computationally intensive and hard to apply in large-scale networks. In this paper, in order to reduce the computational complexity and meanwhile maintain the estimation accuracy, we propose a distributed graph Laplacian matrix estimation method called the distributed combinatorial graph Laplacian estimation (DCGL). In our method, a local parameter estimation problem is first formulated for each vertex by maximizing the marginal likelihood of the data collected from the neighborhood of the vertex. Then, by discussing the connectivity and Laplacian property of the marginal precision matrix, the Laplacian and structural constraints are added to each local estimation problem to resolve the non-convexity between the local and global estimates. Finally, through a simple and single message-passing rule, the global graph Laplacian matrix is obtained by extracting, combining, and symmetrizing the locally estimated parameters. Experiments on synthetic and real datasets demonstrate that the proposed distributed estimator is asymptotically consistent in the classical regime while having advantages in the high-dimensional regime.
Advanced Edge Detection of AprilTags for Precise Docking Maneuvers of Mobile Robots

The development of modules and routines into an existing server-based control station to handle approach and docking maneuvers of mobile systems is the central achievement of this work. Efficient material flow in a digitized factory environment requires that goods are automatically loaded and unloaded onto transport robots. For this purpose, accurate positioning of the robot to a loading station is necessary or to a charging station, to automatically charge the robot's energy storage. Apart from these applications, robots are furthermore increasingly used for assembly work in production facilities. The possibility of using a transport robot as a mobile montage platform at assembly stations with manipulators is investigated in this work. Crucial for this is the ability to repeatedly acquire a position of sub-centimeter accuracy relative to the station and additionally to transmit the precise final position to the station. The critical component here is the estimation of the mobile system's pose, which is estimated using camera images of artificial markers attached to the stations. Since the precision of a visual localization strongly depends on the detection accuracy of the markers, an optimization problem is formulated to approximate the edges of the markers using an edge model function and thus determine their corners more precisely. Finally, a planar continuous trajectory planning is implemented to determine the segments of a possible path between any two poses
Critical Logical Link Assessment for Improving Industrial Wireless Communication Systems

Wireless communication is an important power for moving Industry 4.0 and intelligent manufacturing, where its dependability is challenging industry and academia. Dependability research has two phases: dependability assessment and control. Dependability control based on assessment feedback is the ideal mode for future complex industrial wireless communication systems (WCSs). However, the state-of-the-art automated control methods are focused on improving dependability indicators which ignore the boundary that only killing some logical links may improve the whole WCS. Therefore, this paper proposes the critical logical link assessment method with steps of measurement, experience-based labeling, feature extraction, link assessment, and critical link calculation. In particular, the feature extraction and link assessment are based on two deep neural networks and work together to obtain a correlation coefficient between two logical links. At last, we conducted experiments on realistic measurements in three WCSs which proves both usability and novelty.