Reviewerly/exHarmony huggingface dataset:
Distance Estimation for Quantum Prototypes Based Clustering

Quantum machine learning is a new area of research with the recent work on quantum versions of supervised and unsupervised algorithms. In recent years, many quantum machine learning algorithms have been proposed providing a speed-up over the classical algorithms. In this paper, we propose an analysis and a comparison of three quantum distances for protoptypes-based clustering techniques. As an application of this work, we present a quantum K-means version which gives a good classification just like its classical version, the difference resides in the complexity: while the classical version of K-means takes polynomial time, the quantum version takes only logarithmic time especially in large datasets. Finally, we validate the benefits of the proposed approach by performing a series of empirical evaluations regarding the quantum distance estimation and its behavior versus the stability of finding the nearest centers in the right order.
“Detection, identification and tracking of objects during the motion”

This paper intends to introduce discovery of various objects, object classification, object tracking algorithms including analysis and a comparison of different techniques used for different stages of tracking. The purpose of tracking objects is segmenting a region of interest from a video scene and continuing tracking movement, positioning, and match. Detection of the object and classification are the steps preceding the object tracking in a sequence of images. Object detection is performed to control the existence of objects in the video and find that object, and then the detected object can be classified into many categories such as people, vehicles, floating trees, trees and other moving objects. In this paper is elaborated the autonomous tracking system with the aim of opposing movement in the event of a demonstration of human tracking, as such may enable the former to be distributed as it comprises more than one control center, key component parts of the system include:The camera and its 'interface, arduino viewing boards., PC - Controlled Center.
Continuous Identification of Driver Model Parameters via the Unscented Kalman Filter

Advanced Driver-Assistance Systems (ADAS) have become an essential part of modern cars. Among the solutions proposed, haptic shared control of the steering wheel is increasingly being studied. A fundamental question is how drivers adapt their behavior to these systems. This article proposes to use the Unscented Kalman Filter (UKF) to identify the variation over time in the psychological and neuromuscular parameters of a driver structured model. The goal here is to understand how the driver adapts to changes, whether regarding the behavior of the steering system, the visibility or the road conditions. The LPV system considered for identification is known as the cybernetic driver model. Two experiments carried out respectively with Simulink© and on a driving simulator provide the data. The methodology proposed for tuning the UKF is studied from the results obtained with those data. A multi-UKF strategy is also considered. The methodology reveals useful when a compromise between rapidity and precision has to be achieved for parameters estimation. It opens the way to a detailed analysis of the driver’s parameter variations within the multi-UKF framework.
Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations

Self-supervised learning is one of the most promising approaches to learnrepresentations capturing semantic features in images without any manualannotation cost. To learn useful representations, a self-supervised modelsolves a pretext-task, which is defined by data itself. Among a number ofpretext-tasks, the rotation prediction task (Rotation) achieves betterrepresentations for solving various target tasks despite its simplicity of theimplementation. However, we found that Rotation can fail to capture semanticfeatures related to image textures and colors. To tackle this problem, weintroduce a learning technique called multiple pretext-task for self-supervisedlearning (MP-SSL), which solves multiple pretext-task in addition to Rotationsimultaneously. In order to capture features of textures and colors, we employthe transformations of image enhancements (e.g., sharpening and solarizing) asthe additional pretext-tasks. MP-SSL efficiently trains a model by leveraging aFrank-Wolfe based multi-task training algorithm. Our experimental results showMP-SSL models outperform Rotation on multiple standard benchmarks and achievestate-of-the-art performance on Places-205.
Grossing Technology Today and Tomorrow

Abstract Objective To describe the perspective of grossing technology and highlight the prospective of its development in histology laboratory. Methods Analysis of different components of grossing technology. Results Increased requirements for a specimen’s turnaround time and the advancements in modern processing equipment make the triage of workflow a significant part of a grossing person’s responsibilities. The implementation of digital pathology in morphology studies practice requires standardization of fixation, the thickness of gross section, and optimal embedding orientation. To meet tomorrow’s challenges, grossing technology should work on embedding automation and gross digital pathology to record gross sections corresponding the microscope slide. Specialization of grossing stations might be beneficial to the quality of processing and smooth workflow productivity. The emerging grossing technologist subspecialty requires development of a special training program. Conclusion Grossing technology can contribute to new challenges in modern histology laboratory, assuring high-quality microscope slides for the pathologist’s diagnosis and research evaluation.
Competitive equilibria between staking and on-chain lending

Proof of Stake (PoS) is a burgeoning Sybil resistance mechanism that aims to have a digital asset (token) serve as security collateral in crypto networks. However, PoS has so far eluded a comprehensive threat model that encompasses both Byzantine attacks from distributed systems and financial attacks that arise from the dual usage of the token as a means of payment and a Sybil resistance mechanism. In particular, the existence of derivatives markets makes malicious coordination among validators easier to execute than in Proof of Work systems. We demonstrate that it is also possible for on-chain lending smart contracts to cannibalize network security in PoS systems. When the yield provided by these contracts is more attractive than the inflation rate provided from staking, stakers will tend to remove their staked tokens and lend them out, thus reducing network security. In this paper, we provide a simple stochastic model that describes how rational validators with varying risk preferences react to changes in staking and lending returns. For a particular configuration of this model, we provide a formal proof of a phase transition between equilibria in which tokens are predominantly staked and those in which they are predominantly lent. We further validate this emergent adversarial behavior (e.g. reduced staked token supply) with agent-based simulations that sample transitions under more realistic conditions. Our results illustrate that rational, non-adversarial actors can dramatically reduce PoS network security if block rewards are not calibrated appropriately above the expected yields of on-chain lending.
Design Thinking Methodology for Increasing Quality of Experience of Augmented Reality Educational Games

Integration of technology in education with adequate pedagogical approaches creates new opportunities for improving the quality of teaching and learning experiences, raising students' interest and motivation for the classroom activities at the same time. Game-based learning implemented with different technologies can utilize students' collaboration, energy, and enthusiasm. In order to increase the quality of experience of the learning process, elements of popular games (e.g., mobile games and augmented reality games) should be used in the educational context. This paper describes how design thinking methodology can be used to propose a model for the integration of games in education. The methodology defines the process of creating educational games starting from students' attitudes and needs and moving towards expected educational outcomes. As an example, a case study augmented reality prototype game was designed in order to illustrate the possibilities and benefits of the proposed methodology. The achieved results confirm that the proposed methodology ensures a strong gaming experience and high-quality learning experience.
Image Classification Based on SURF and KNN

The key of image recognition and classification based on machine learning is to extract image feature points effectively and classify image features correctly. In this paper, SURF (Speeded-Up Robust Features) is used to extract image feature points. The extracted feature points are clustered by K-means algorithm, and the center of clustering represents this kind of feature. The BOW (bag of word) trainer for each image is created according to the image features completed by clustering, and then the trained KNN (k-Nearest Neighbor) classifier is used to recognize and classify each image. The descriptor of SURF algorithm is 64-dimensional, which is faster than traditional feature extraction algorithm. KNN algorithm is suitable for multi-class label classification problem and has good generalization ability. The experimental results show that the accuracy of image classification reaches 90% when good parameters are selected, but there are some shortcomings for image classification with high similarity.
A Fully Decentralized Time-Lock Encryption System on Blockchain

To make a time capsule on the Internet, which will be opened at a planned time in the future, without third parties' involvement has always been a difficult problem. Although there are many researches worked on various time-lock systems, they may have some shortcomings like uncertainty in decryption time, not fully decentralized, hard to estimate the required computing resources. In this paper, we proposed a protocol and a reliable encryption scheme to make time-sensitive message be opened on time at a fully decentralized environment, which is then integrated with the blockchain to adapt to different computing power situations. The method also provides the capability of incorporating with appropriate incentives for encouraging participants to contribute their computing resources, which makes our system more suitable for real world applications.
Adaptive Real-Time Clustering Method for Dynamic Visual Tracking of Very Flexible Wings

Advancements in intelligent aircraft controller design, paired with increasingly flexible and efficient aircraft concepts, create the need for the development of novel (smart) adaptive sensing suitable for aeroelastic state estimation.In contrast to rigid states, aeroelastic state estimation requires more measurement points (displacements and forces) across the span to capture the vibrational shapes of the wing undergoing excitations.A potentially universal and non-invasive approach is visual tracking.However, many tracking methods require manual selection of initial marker locations as the start of a tracking sequence.This study is part of a larger study in the field of smart sensing and aims to cover the gap by investigating a robust machine learning approach for unsupervised automatic labelling of visual markers.The method utilizes fast DBSCAN and adaptive image segmentation pipeline with HSV colour filter to extract and label the marker centres under the presence of marker failure.A comparison is made with Disjoint-set data structure for clustering of the data.The segmentation-clustering pipeline with DBSCAN shows the capability to act as a visual tracking method on its own, capable of running real-time at 250 fps on an image sequence of a single camera with a resolution of 1088 × 600 pixels.To increase the robustness against noise, a novel formulation of DBSCAN better suited against noise, the inverse DBSCAN (DBSC AN -1 ), is proposed, allowing to cast the clustering problem into noise filtering problem with an additional MaxPts parameter.Furthermore, observations are made regarding the frequency content of the image pixel intensities across time, and how this can be utilized to estimate the natural frequency of the system and adjust the segmentation-clustering pipeline with a sliding DFT (Discrete Fourier Transform).
Deep Learning-Based Approach to Classify Praises or Complaints from Customer Reviews

Online reviews carry customers’ opinion about product or service and help the customers to make online purchase-related decisions. These reviews are analyzed by business organizations to understand customer sentiment w.r.t. product/service. The extreme opinions like praise and complaint sentences are a subset of positive and negative sentences and difficult to find. Praise sentences are more descriptive in nature. Praises contain more nouns, adjectives, and intensifiers as compared to plain positive sentences and complaint sentences contain more connectives, adverbs as compared to plain negative sentences. In the past machine, learning methods are used to identify extreme opinions but the accuracy of such methods is very limited. This paper proposes (1) linguistic feature-based approach for reviewing sentences filtering and (2) machine learning-based and deep learning-based approach to classifying review sentences as praises or complaints. These praise and complaint sentences can be further analyzed by business organizations to identify the reasons for customer satisfaction or dissatisfaction. It can also be used for creating automatic product description from online reviews in terms of pro and con of the product/service. The performance of the machine learning classifiers with proposed hybrid features and deep learning-based classifiers using dense neural network, CNN, and multichannel CNN was evaluated by training and testing the deep neural network with a set of important words such as nouns, adjectives, intensifiers, and verbs present in the sentence. Hotel domain reviews were evaluated using the parameters accuracy, precision, recall, and F1-score. The proposed method showed excellent results as compared to state-of–the-art classifiers.
Adaptive tape placement process control at geometrically changing substrates

The laser-assisted tape placement process faces thermal challenges at geometrically changing substrates. Due to changing optical conditions and thereby varying local energy absorption, local differences of the process temperature occur. These temperature inhomogeneities become even more pronounced due to restrictions with regard to machine dynamics. In particular, strong directional changes, which are necessary e.g. for small machining radii, result in the relative speed between workpiece and laser beam not being constant. The use of vertical-cavity surface-emitting lasers (short: VCSEL) as adaptive heat sources could improve the temperature control especially by use of application adapted intensity distributions that can be calculated through inverse simulations. In this work, the optical and thermal effects during tape placement on geometrically changing substrates are described and analyzed. A possible solution to the described challenges is shown by using application adapted intensity distributions, which aim at a suitable, constant process temperature. The simulations will consider the three-dimensional part geometry, specific intensity distributions of the VCSEL as well as machine dynamics. By the use of tape placement equipment of the Fraunhofer IPT the occurring local temperature changes and the resulting degree of process optimization are experimentally validated.
JTCR: Joint Trajectory Character Recognition for human action recognition

Action recognition is a challenging task in computer vision. In this paper, a new skeleton-based action recognition, which uses joint trajectory images and vision feature to model human action, is proposed. First, Openpose is applied to extract human skeleton data from RGB Camera. Human action in a single video is represented as the trajectory of human(s) skeleton joints in an image space. Inspired by the work of optical character recognition, the problem of action recognition can be regarded as the problem of trajectory image recognition.HOG combined with SVM method is applied to recognized action in this paper. Finally, in the experiment, the recognition performances are analyzed and compared. Our work can be helpful for researchers to try to use the joint trajectory images for action recognition.
Geometry-Modeling Based Flight Optimization for Autonomous Groups of UAVs

The authors present a problem of the optimization of Unmanned Aerial Vehicles flights for different tasks in the city. Using Central Drone Repeater in conjunction with geometrical modeling covering as the most suitable configuration of Unmanned Aerial Vehicles team (group) in the city, authors suggest improving the versatility, maintainability and operational safety of Unmanned Aerial Vehicles group via the introduction of a functional framework in the form of a functional-protocol stack, in remote analogy with Open Systems Interconnection model in telecommunications
Identification of Gender Based on Speech Signal

The article presents a gender identification based on speech signal with supervised machine learning implementation. At first, a database of speech signals in Polish language was collected. Next, a set of features from audio signal were calculated. The features were farther used to train a neural network. Audio signal processing and implementation of the neural network were performed in Python, and the calculation of features in the R language. Neural network training process was carried out using only CPU, then CPU with GPU and the times of the programs execution were compared. The obtained accuracy of gender recognition was 92.4%. The use of GPU accelerated the network learning process several times.
HiperJobViz: Visualizing Resource Allocations in High-Performance Computing Center via Multivariate Health-Status Data

Scheduling, visualizing, and balancing resource allocations in High-Performance Computing Centers are complicated tasks due to a large amount of data and the dynamic natures of the resource allocation problem. This paper introduces HiperJobViz, a visual analytic tool for visualizing the resource allocations of data centers for jobs, users, and usage statistics. The goals of this tool are: 1) to provide an overview of the current resource usages, 2) to track changes of resource usages by users, jobs, and hosts and 3) to provide a detailed view of the resource usage via multi-dimensional representation of health metrics, such as CPU temperatures, memory usage, and power consumption. To support these goals, our visual analytics tool provides a full range of interactive features, including details on demands, brushing and links, and filtering.
Parameter Distribution Balanced CNNs

Convolutional neural network (CNN) is the primary technique that has greatly promoted the development of computer vision technologies. However, there is little research on how to allocate parameters in different convolution layers when designing CNNs. We research mainly on revealing the relationship between CNN parameter distribution, i.e., the allocation of parameters in convolution layers, and the discriminative performance of CNN. Unlike previous works, we do not append more elements into the network, such as more convolution layers or denser short connections. We focus on enhancing the discriminative performance of CNN through varying its parameter distribution under strict size constraint. We propose an energy function to represent the CNN parameter distribution, which establishes the connection between the allocation of parameters and the discriminative performance of CNN. Extensive experiments with shallow CNNs on three public image classification data sets demonstrate that the CNN parameter distribution with a higher energy value will promote the model to obtain better performance. According to the motivated observation, the problem of finding the optimal parameter distribution can be transformed into an optimization problem of finding the biggest energy value. We present a simple yet effective guideline that uses balanced parameter distribution to design CNNs. Extensive experiments on ImageNet with three popular backbones, i.e., AlexNet, ResNet34, and ResNet101, demonstrate that the proposed guideline can make consistent improvements upon different baselines under strict size constraint.
Detecting Hate Speech Online: A Case of Croatian

This project proposes a NooJ algorithm with the task to find and categorize various slurs, insults and ultimately, hate speech in Croatian. The results also provide a more detailed insight into inappropriate language in Croatian. We strongly emphasize the ethical considerations of (mis) identifying hate speech and as a result, an unethical and undeserved censorship of inappropriate, but free speech. Thus, we tried to make a clear distinction between insults and hate speech. The test corpus consists of written online comments and remarks posted on five Croatian Facebook news pages during one week period. Given the differences between the standard Croatian grammar and syntax, and what is actually being used in informal on-line communication, the false negatives present the biggest difficulty since some variations (substandard usages of cases, spelling errors, colloquialisms) are impossible to predict, and therefore, extremely hard to implement into the algorithm.
Nonlinear improvement of qubit-qudit entanglement witnesses

The entanglement witness is an important and experimentally applicable tool for entanglement detection. In this paper, we provide a nonlinear improvement of any entanglement witness for $2\ensuremath{\bigotimes}d$ quantum systems. Compared with any existing entanglement witness, the improved separability criterion only needs two more measurements on local observables. Detailed examples are employed to illustrate the efficiency of the nonlinear improvement for general, optimal, and nondecomposable entanglement witnesses.
Rapid and precise phase retrieval from two-frame tilt-shift based on Lissajous ellipse fitting and ellipse standardization

A rapid and precise phase-retrieval method based on Lissajous ellipse fitting and ellipse standardization is demonstrated. It only requires two interferograms without pre-filtering, which reduces its complexity and shortens the processing time. The elliptic coefficients obtained by ellipse fitting are used for ellipse standardization. After compensating phase-shift errors by ellipse standardization, the phase distribution is extracted with high precision. It is suitable for fluctuation, noise, tilt-shift, simple and complex fringes. This method is effective for the number of fringes less than 1. The reliability of the method is verified by simulations and experiments, indicating high accuracy and less time consumption.