Reviewerly/exHarmony huggingface dataset:
Analytical Aircraft State and IMU Signal Generator From Smoothed Reference Trajectory

Generating aircraft position, velocity, and attitude along a reference trajectory is useful in many path and mission planning applications. In applications simulating an inertial navigation system the body-frame accelerations and angular rates are also required. This work presents a method for generating a smoothed continuous-curvature reference trajectory from a series of waypoints using line, arc, clothoid, and Fermat spiral segments in a fillet smoothing framework. The geometry of the segments are used to generate the position, velocity, and attitude of an aircraft following the reference trajectory. A primary contribution of this article is the use of the path geometry, coordinated turn model, and curvilinear motion theory to obtain analytical solutions for the body-frame accelerations and angular rates of the aircraft along the reference trajectory.
Quantitative Assessment of the Dynamics of Socioeconomic Processes

The efficiency of correlation-regression analysis would significantly expand if both of its essential variablesa dependent and an independentconveyed the information on the dynamic rather than static state of a phenomenon under consideration.For this objective, the dynamic development of the socioeconomic processes should be based on the quantitative assessment.Existing methodologies call for improvement as they do not fully reflect the state of particular phenomena.In this article, authors provide the quantitative assessment methodology to analyse the dynamics of socioeconomic processes.It was applied for assessing real situations, which confirmed adequacy and applicability of this methodology.
Radar Command Group Time Entropy Signature as a Visual Monitoring Enhancement for Air Traffic Controllers

The activity of generic flight path monitoring of an aircraft on the radar screen is one the major problem which results in air traffic controllers (ATCOs) losing awareness of it. A novel eye signature, known as the radar command group (RCG) time entropy signature, is thus presented in this paper. This signature seeks to model the monitoring behavior of ATCOs using eye-tracking technique during this activity. Acquisition and representation of this monitoring behavior is achieved by first identifying the respective fixation count and mouseover label movement on the aircraft to establish a RCG. The regularity of a RCG exhibited on an aircraft is then calculated using the information entropy formula. Real time simulations are conducted for 88 one-hour experimental sessions with licensed and non-licensed participants comprising of three expertise levels, using scenarios that mimic actual air traffic, consisting of various flight path configuration. Test results of the RCG time entropy signature showed that, an aircraft flying on a flight path with longer distance and no change in altitude likely results in a more regular generic flight path monitoring. Furthermore, it can also be used to differentiate the novice participants from intermediates and experts, as novices lack the training. An ATCO's expertise level can thus be determined and benchmarked accordingly, allowing this signature to be applied on future ATCO training.
Certification of continuous-variable gates using average channel-fidelity witnesses

We introduce witnesses for the average channel fidelity between a known target gate and an arbitrary unknown channel, for continuous-variable (CV) systems. These are observables whose expectation value yields a tight lower bound to the average channel fidelity in question, thus constituting a practical tool for certification of experimental CV gates. Our framework applies to a broad class of target gates. Here, we focus on three specific types of targets: multi-mode Gaussian unitary channels, single-mode coherent state amplifiers, and the single-mode (non-Gaussian) cubic phase gate, which is a crucial ingredient for CV universal quantum computation. Our witnesses are experimentally-friendly as they rely exclusively on Gaussian measurements, even for the non-Gaussian-target case. Moreover, in all three cases, they can be measured efficiently in the estimation error $\epsilon$ and failure probability $\Delta$, as well as in the number of modes $m$ for the Gaussian-target case. To end up with, our approach for the Gaussian-target case relies on an improved measurement scheme for Gaussian state-fidelity witnesses, which is polynomially and exponentially more efficient in $m$ and $\Delta$, respectively, than previous schemes. The latter constitutes an interesting byproduct result on its own. Our findings are relevant to the experimental validation of many-body quantum technologies.
Sparse Spectrum Gaussian Process for Bayesian Optimization

We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization (BO). Whilst the current sparse spectrum methods provide desired approximations for regression problems, it is observed that this particular form of sparse approximations generates an overconfident GP, i.e., it produces less epistemic uncertainty than the original GP. Since the balance between the predictive mean and variance is the key determinant to the success of BO, the current methods are less suitable for BO. We derive a new regularized marginal likelihood for finding the optimal frequencies to fix this overconfidence issue, particularly for BO. The regularizer trades off the accuracy in the model fitting with targeted increase in the predictive variance of the resultant GP. Specifically, we use the entropy of the global maximum distribution (GMD) from the posterior GP as the regularizer that needs to be maximized. Since the GMD cannot be calculated analytically, we first propose a Thompson sampling based approach and then a more efficient sequential Monte Carlo based approach to estimate it. Later, we also show that the Expected Improvement acquisition function can be used as a proxy for it, thus making the process further efficient.
Are we there yet? Estimating Training Time for Recommendation Systems

Recommendation systems (RS) are a key component of modern commercial platforms, with Collaborative Filtering (CF) based RSs being the centrepiece. Relevant research has long focused on measuring and improving the effectiveness of such CF systems, but alas their efficiency - especially with regards to their time- and resource-consuming training phase - has received little to no attention. This work is a first step in the direction of addressing this gap. To do so, we first perform a methodical study of the computational complexity of the training phase for a number of highly popular CF-based RSs, including approaches based on matrix factorisation, k-nearest neighbours, co-clustering, and slope one schemes. Based on this, we then build a simple yet effective predictor that, given a small sample of a dataset, is able to predict training times over the complete dataset. Our systematic experimental evaluation shows that our approach outperforms state-of-the-art regression schemes by a considerable margin.
Identification of Mineralization in Geochemistry Along a Transect Based on the Spatial Curvature of Log-Ratios

Detecting subcropping mineralizations but also deeply buried mineralizations is one important goal in geochemical exploration. The identification of useful indicators for mineralization is a difficult task, as mineralization might be influenced by many factors, including location, investigated media and depth. Here, a statistical method is proposed which indicates chemical elements related to mineralization along a transect. Moreover, the method determines the potential area of the deposit along a transect. The identification is based on general additive models (GAMs) for the element concentrations across the spatial coordinate(s). The log-ratios of the GAM fits are taken to compute the curvature, where high and narrow curvature is supposed to indicate the mineralization area. By defining a measure for the quantification of high curvature, the log-ratios can be ranked, and elements can be identified that are indicative of the anomaly patterns.
Coders Hub ML Edu. Platform Systems

Coders Hub ML Edu. Platform is a digital repository system for learning the most trending technology/ field - Machine Learning. A properly dimensioned ML repository has the potential to increase the interest of students in learning the latest technologies. It is an ML integrated website where students can learn different machine learning algorithms, visualize them and can even get hands-on experience on some of the models. The main objective of the study is to develop a web-based digital repository for Machine learning algorithms combined with application models of machine learning for better learning. It has various models integrated in it like handwritten digit recognition system, Sentiment Analyser and many more. One of the models is of Facial Detection. We have used the Caffe Model of the DNN Module for facial detection. Unlike other facial detection models, it not only detects the person‟s front face but also detects the side face accurately. It gives very fast fps compared to other models.
Single depth map super-resolution via joint non-local self-similarity modeling and local multi-directional gradient-guided regularization

The development of consumer depth cameras makes it possible to acquire depth information of a scene in real-time. However, low resolution and low quality of a depth map has greatly constrained its applications. In this paper, we propose a novel framework for single depth map super-resolution, which considers local and non-local information jointly in the depth map. For the non-local constraint, group-based sparse representation is used to explore non-local self-similarity in the depth map. For the local constraint, a multi-directional gradient-guided regularization is proposed to describe the gradient of the depth map with spatially varying orientations. The former constraint contains the visual artifacts effectively, while the latter restores sharp edge and fine structure. Finally, the two complementary regularizers are jointly casted into a unified optimization framework, where a split Bregman-based technique is developed to tackle the optimization problem. Both quantitative and qualitative evaluations indicate that the proposed method can obtain better reconstruction performance compared with state-of-the-art methods.
Introduction of Project Orientated Style of Management into the Professional Education System

This article substantiates the necessity of project principals and methods application while managing educational organizations in conditions of professional education updating. Key factors, which influence the development of the professional education system with the inter- action of the state policy in educational sphere, are presented. It is defined that the providing with the constant development of any educational organization, the rise of the efficiency of its activity is possible if it is based on the outstripping character of the project management. The problems any management meets while creating the project educational organization system and the possible ways of resolving them are described.
An Augmentation Strategy to Mimic Multi-Scanner Variability in MRI

Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data.
Detection of Broken and Good Medical Tablets Using Various Machine Learning Models

The computer vision has played a very important role in the fields of medical science. Not only computer vision but also machine learning has been an important aspect in medical applications. One such application of both of them together is explained in paragraphs given below. Detection medicines of different shapes and size have already been done before. But, in this paper, we have proposed various machine learning models to segregate damaged tablets and capsules from the good ones and also compared accuracy of each models separately.
Multi-Robot Path Planning for Each Robot with Several Jobs in a Single Trip

The path planning problem is important and has been well-studied in multi-robot systems. Current approaches have a common assumption that each robot is required to move from a start point to another one, while implicitly ignoring the fact that a robot may be assigned several jobs in a single trip. In this paper, we take this fact into account and propose optimal path planning solutions. The studied problem is modeled as a max-flow problem of a network, and integer linear programming(ILP) solver can be used to find its optimal solution. However, the optimal solver suffers the problem of dimensional disaster and becomes impractical for a large-scale system. We thus propose a heuristic approach to find a sub-optimal solution with an acceptable computation time. Experimental results demonstrate the high effectiveness of our proposed approaches.
JustSpeak: Automated, User-Configurable, Interactive Agents for Speech Tutoring

Conversational agents are widely used in many situations, especially for speech tutoring. However, their contents and functions are often pre-defined and not customizable for people without technical backgrounds, thus significantly limiting their flexibility and usability. Besides, conventional agents often cannot provide feedback in the middle of training sessions because they lack technical approaches to evaluate users' speech dynamically. We propose JustSpeak: automated and interactive speech tutoring agents with various configurable feedback mechanisms, using any speech recordings with its transcription text as the template for speech training. In JustSpeak, we developed an automated procedure to generate customized tutoring agents from user-inputted templates. Moreover, we created a set of methods to dynamically synchronize speech recognizers' behavior with the agent's tutoring progress, making it possible to detect various speech mistakes dynamically such as being stuck, mispronunciation, and rhythm deviations. Furthermore, we identified the design primitives in JustSpeak to create different novel feedback mechanisms, such as adaptive playback, follow-on training, and passive adaptation. They can be combined to create customized tutoring agents, which we demonstrate with an example for language learning. We believe JustSpeak can create more personalized speech learning opportunities by enabling tutoring agents that are customizable, always available, and easy-to-use.
The Wealth of (Open Data) Nations? Examining the Interplay of Open Government Data and Country-level Institutions for Entrepreneurial Activity at the Country-level

The provision of Open Data (OD) has been promoted by governments around the world with the hope of fuelling entrepreneurial use of the data for new products or process innovations. However, these benefits are still far from being fully understood and realised, and it remains unclear to what extent OD leads to systematic benefits for entrepreneurship. This paper aims to shed light to this open question by providing novel empirical evidence on the relationship between OD publishing and entrepreneurial outcomes at country-level. We draw upon a longitudinal dataset comprising 90 countries observed over the period 2013-2016. We find a significant and positive association between OD adoption and entrepreneurship at the country-level. The results also show that OD adoption and entrepreneurship is strong in countries with high institutional quality. We argue that unless a country has quality institutions, publishing OD alone does not positively affect entrepreneurship for the digital economy. Publishing OD is not sufficient to improve entrepreneurship alone, so states need to move beyond a focus on OD initiatives and promotion, to focus on a broader set of policy initiatives that promote good governance.
Inverse Scale Space Iterations for Non-convex Variational Problems Using Functional Lifting

Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely one-homogeneous regularizer. In order to extend these approaches to general energies with non-convex data term, we apply the Bregman iteration to a lifted version of the functional with sublabel-accurate discretization. We provide a condition for the subgradients of the regularizer under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and non-convex case.
Cryptographic Pseudorandom Generators Can Make Cryptosystems Problematic

Randomness is an essential resource for cryptography. For practical randomness generation, the security notion of pseudorandom generators (PRGs) intends to automatically preserve (computational) security of cryptosystems when used in implementation. Nevertheless, some opposite case such as in computational randomness extractors (Barak et al., CRYPTO 2011) is known (but not yet systematically studied so far) where the security can be lost even by applying secure PRGs. The present paper aims at pushing ahead the observation and understanding about such a phenomenon; we reveal such situations at layers of primitives and protocols as well, not just of building blocks like randomness extractors. We present three typical types of such cases: (1) adversaries can legally see the seed of the PRGs (including the case of randomness extractors); (2) the set of “bad” randomness may be not efficiently recognizable; (3) the formulation of a desired property implicitly involves non-uniform distinguishers for PRGs. We point out that the semi-honest security of multiparty computation also belongs to Type 1, while the correctness with negligible decryption error probability for public key encryption belongs to Types 2 and 3. We construct examples for each type where a secure PRG (against uniform distinguishers only, for Type 3) does not preserve the security/correctness of the original scheme; and discuss some countermeasures to avoid such an issue.
Colored Tobit Kalman filter

This article deals with the Tobit Kalman filtering (TKF) process when the one-dimensional measurements are censored and the noises of the state-space model are colored. Two improvements of the standard TKF process are proposed. Firstly, the exact moments of the censored measurements are calculated via the moment generating function of the censored measurements. Secondly, colored noises are considered in the proposed method in order to tackle real-life problems, where the white noises are not common. The designed process is evaluated using two experiments-simulations. The results show that the proposed method outperforms other methods in minimizing the Root Mean Square Error (RMSE) in both experiments.
The New Normal Tourism in Indonesia: The Expansion of the Wonosari AR Project to Enhance Holiday Experience

This chapter examines the expansion of Wonosari AR, an Augmented Reality (AR) application in Agrowisata Wonosari, Malang, Indonesia. Digital reality applications have been widely used in the tourism industry of developed countries. The advancement of Augmented Reality technology in the tourism field has helped to increase people's interest, however in developing countries, such as Indonesia, these approaches have not yet been used. During the COVID-19 pandemic, most tourist attractions were closed for a few months to prevent the spread of the virus, which caused a reduction in the number of tourists. This chapter comprises the introduction of the research problem, the previous Wonosari AR project, the Wonosari AR project during The New Normal Tourism, and the development of this new application. The authors designed three AR markers in playing cards, face masks, and tea packaging using the design thinking methodology. The combination of three different shapes of the AR marker fitted perfectly to promote future digital tourism. The chapter's discussion explores the prototyping and trial experiences, including the opportunity for future research in the use of AR for agrotourism.
A Detection Approach for Vulnerability Exploiter Based on the Features of the Exploiter

With the wide application of software system, software vulnerability has become a major risk in computer security. The on-time detection and proper repair for possible software vulnerabilities are of great importance in maintaining system security and decreasing system crashes. The Control Flow Integrity (CFI) can be used to detect the exploit by some researchers. In this paper, we propose an improved Control Flow Graph with Jump (JCFG) based on CFI and develop a novel Vulnerability Exploit Detection Method based on JCFG (JCFG-VEDM). The detection method of the exploit program is realized based on the analysis results of the exploit program. Then the JCFG is addressed through combining the features of the exploit program and the jump instruction. Finally, we implement JCFG-VEDM and conduct the experiments to verify the effectiveness of the proposed method. The experimental results show that the proposed detection method (JCFG-VEDM) is feasible and effective.
Algorithmic gaze classification for mobile eye-tracking

Mobile eye tracking traditionally requires gaze to be coded manually. We introduce an open-source Python package (GazeClassify) that algorithmically annotates mobile eye tracking data for the study of human interactions. Instead of manually identifying objects and identifying if gaze is directed towards an area of interest, computer vision algorithms are used for the identification and segmentation of human bodies. To validate the algorithm, mobile eye tracking data from short combat sport sequences were analyzed. The performance of the algorithm was compared against three manual raters. The algorithm performed with substantial reliability in comparison to the manual raters when it came to annotating which area of interest gaze was closest to. However, the algorithm was more conservative than the manual raters for classifying if gaze was directed towards an object of interest. The algorithmic approach represents a viable and promising means for automating gaze classification for mobile eye tracking.
Non-Rigid Registration Of Live Cell Nuclei Using Global Optical Flow with Elasticity Constraints

Non-rigid registration of cell nuclei in time-lapse microscopy images requires the estimation of nucleus deformation. We propose a new approach for deformation estimation and non-rigid registration of cell nuclei, which integrates elasticity constraints into a global optical flow-based method. We derive an elasticity prior on the deformation from the Navier equation. The common Markov random field prior in previous global methods is replaced by the elasticity prior to better regularize the estimated deformation fields. In addition, we introduce a scheme to exclude sub-cellular structures from estimating the nucleus deformation so that their relative motion does not deteriorate the registration result. Experiments on live cell microscopy image data demonstrate that the proposed method with elasticity constraints outperforms previous methods.