Reviewerly/exHarmony huggingface dataset:
Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games

In a networked anagram game, each team member is given a set of letters and members collectively form as many words as possible. They can share letters through a communication network in assisting their neighbors in forming words. There is variability in behaviors of players, e.g., there can be large differences in numbers of letter requests, of replies to letter requests, and of words formed among players. Therefore, it is of great importance to understand uncertainty and variability in player behaviors. In this work, we propose versatile uncertainty quantification (VUQ) of behaviors for modeling the networked anagram game. Specifically, the proposed methods focus on building contrastive models of game player behaviors that quantify player actions in terms of worst, average, and best performance. Moreover, we construct agent-based models and perform agent-based simulations using these VUQ methods to evaluate the model building methodology and understand the impact of uncertainty. We believe that this approach is applicable to other networked games.
Bayesian Mass Averaging in Rigs and Engines

Abstract This paper introduces the Bayesian mass average and details its computation. Owing to the complexity of flow in an engine and the limited instrumentation and the precision of the sensor apparatus used, it is difficult to rigorously calculate mass averages. Building upon related work, this paper views any thermodynamic quantity’s spatial variation at an axial plane in an engine (or a rig) as a Gaussian random field. In cases where the mass flowrate is constant in the circumferential direction but can be expressed via a polynomial or spline radially, this paper presents an analytical calculation of the Bayesian mass average. In cases where the mass flowrate itself can be expressed as a Gaussian random field, a sampling procedure is presented to calculate the Bayesian mass average. Examples of the calculation of the Bayesian mass average for temperature are presented, including with a real engine case study where velocity profiles are inferred from stagnation pressure measurements.
Metrology for Measuring Bumps in a Protection Layer Based on Phase Shifting Fringe Projection

We developed a telecentric 3D measurement system based on the fringe projection technique to measure phases corresponding to the 3D shape of bumps on a surface. A measurement algorithm based on an area scan camera with a flexibly programmable region of interest (ROI) was applied to reconstruct the 3D model. Only the pixel information within the ROI is stored, which reduces the data volume and processing and accelerates the area scan acquisition. Experiments showed that the fringe projection system provided good linearity between the calibration and optical data with excellent accuracy. A reflectometry sensor was implemented to provide a correction offset to the bump height in a protection layer in a subsequent high-speed inspection.
Visual question answering with gated relation‐aware auxiliary

The great advances in computer vision and natural language processing make significant progress in visual question answering. In the visual question answering task, the visual representation is essential for understanding the image content. However, traditional methods rarely exploit the context information of the visual feature related to the question and the relation-aware information to capture valuable visual representation. Therefore, a gated relation-aware model is proposed to capture the enhanced visual representation for desiring answer prediction. The gated relation-aware module can learn relation-aware information between the visual feature and the context, and a certain object of an image, respectively. In addition, the proposed module can filter out the unnecessary relation-aware information through the gate guided by the question semantic representation. The results of the conducted experiments show that the gated relation-aware module makes a significant improvement on all answer categories.
Grammatical Tagging for the Kannada Text Documents using Hybrid Bidirectional Long-Short Term Memory Model

Kannada is one of the most spoken languages in India. Despite the large usage base, like other major Indian languages, there exist minimal linguistic resources for computing and processing. Rich morphology and agglutinative nature of this language pose a great challenge to even the most basic of natural language processing applications like lemmantization, parts of speech tagging, summarization etc. In this paper, we have discussed a deep learning based perspective} for the grammatical tagging by utilizing hybrid models of bidirectional long short term memory(BDLSTM) and linear chain conditional random fields(CCRF). A database of Kannada documents with 15500 manually tagged words is used for this task. Proposed hybrid model shows a promising result of 81.02%.
Parallel Processing of Signals in Local Spline Methods

This paper proposes a method of constructing cubic spline functions of a local character with equal distances between nodes. Previous studies have shown precise models for digital processing of signals using interpolated cubic splines, with concrete examples showing the high degree of accuracy of the approximation of interpolated functions. In this work, the size of the calculations required to find the parameters to be determined during the construction of the spline function does not depend on the number of node points. Local-based splines are used to build such spline functions. Our body constantly provides information about our health. That is, it provides information about the heart rate, blood pressure, oxygen saturation level, blood glucose level, digestive activity, brain activity, and so on the activities of our other organs. Today, this information can be obtained in the form of signals by physiological means. Traditionally, such measurements are made at specific points in time and are given in tabular form. In most cases, physicians 'diagnosis and treatment will be based on the results in this table. Biomedical signal processing contributes greatly to providing useful information about how physicians make decisions about patients 'illnesses. This plays an important role in the development of the medical field. Today, scientists are exploring new ways to process these signals using a variety of mathematical models and algorithms. One of these methods is the problem of restoring the signal received in tabular form by spline functions. As an example, the first experimental data of the gastroenterological signal were taken. Thus, on the basis of the spline-function model discussed in the article, digital processing of the gastroenterological signal was performed and error results in the process of signal interpolation were obtained.
Efficient Multi-Channel Thermal Monitoring and Temperature Prediction Based on Improved Linear Regression

The early prediction and accurate tracking of the thermal characteristics of the CPU of a server can help avoid thermal failure and runaway due to defects in its thermal design. This study proposes a method of thermal monitoring and temperature prediction based on infrared thermocouples. A nine-channel whole-machine thermal monitoring system based on a thermal module is designed. It collects the real-time temperature data of each channel by using the Raspberry Pi platform through the I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C protocol and a visual digital interface that can intuitively display the dynamic distribution of the related temperatures. Based on data on the multi-channel historical thermal distribution as well as continuous training on real-time temperature data, an improved linear regression algorithm-based thermal prediction model is proposed to obtain rules governing the thermal characteristics of the CPU during operation. The results of experiments show that the accuracy of the temperature data collected using our proposed method of thermal monitoring can be controlled to within a range of error of 0.15%, with an average range of 0.62%. In addition, the improved linear regression algorithm predicts a good fitness with the empirical values, with a data coincidence of up to 0.96 compared with the traditional linear regression algorithm. The proposed method to monitor the thermal characteristics and predict the temperature of the CPU can provide a reference for the design and thermal diagnosis of high-performance servers.
A Method for Solving Quasi-Identifiers of Single Structured Relational Data

Quasi-identifier is a set of attributes used to identify the specific entity in structured data, which can provide an inference path for query attacks. Improper selection of quasi-identifiers leads to the failure of current privacy-preserving data publishing. In this paper, we propose a method of solving quasi-identifiers based on functional dependency to ensure the accuracy and completeness of the selected quasi-identifiers for relational data publishing. First, we partition the identifying attributes and sensitive attributes in the relational scheme of relational data published according to the semantic relationship and publishing requirements. Second, we mine the dependencies on identifying attributes with other attributes in the relational schema according to semantics and instance data in relational data, subsequently we can obtain complete quasi-identifiers. Finally, we implement the algorithm for solving quasi-identifiers in Python language, and solve quasi-identifiers on three actual data sets of different sizes, and afterward use the model of 3-anonymity, 2-diversity, and 1-differential privacy for privacy protection experiments. The results demonstrate that the anonymous data based on solving quasi-identifiers have less average group records and privacy disclosure probability, so the accuracy and completeness of our method are better than other five methods.
Carving of the OOXML document from volatile memory using unsupervised learning techniques

With the drastic rise in digital crimes, emphasis is laid on the digital forensics to solve crimes. Evidences that are being extracted from the digital devices are now considered enough to prove a criminal guilty in the court room. While gathering the proofs from a system, a significant venue to look at is the primary memory (commonly named as RAM). A RAM holds the information about the current state of the system. It often occurs that the metadata of a file is deleted or is not available to the digital investigator. In such cases, the retrieval of the file from RAM becomes very challenging as data is placed at random locations in RAM and their allocation tables are not valid if power supply to the system is removed. While carving these instances that are randomly scattered across the RAM, their usefulness in law process cannot be ensured. Microsoft Open Office XML file Format (OOXML FF) is one of the most widely used format, yet it is not much explored in forensics (and carving). Our research intends to improve the technique of carving of an OOXML file where we have employed clustering to collect the chunks of same data based on some similarity feature. Numerous OOXML files are used in our experiments, where we have extracted and rearranged their textual contents using clustering techniques i.e. K mean and Hierarchical clustering. The results are quite encouraging and show that our proposed method can be used for carving of OOXML format. Our technique of extraction of OOXML document from the RAM reduces the hassle and saves ample time of the digital investigator who would otherwise have to go through every document available on the system to find the concerned document.
Improving UAV Obstacle Avoidance by Environment Dependent Cost for Path Planning

Most local path planning algorithms rely on several cost functions representing different aspects of the planned path, e.g., path smoothness and deviation from goal, to evaluate alternative flight paths and define the next waypoint(s). The cost functions are weighed against each other by fixed or user predefined weighting parameters. Previous investigations showed that an optimal choice of weights differs significantly between various environments. This work presents a method to automatically choose weighting parameters for flight velocity, minimum obstacle distance, yaw, and pitch cost depending on the environment. The presented strategies are formulated such that they fit various path planning algorithms. The performance of the proposed method is tested with the 3DVFH* in the px4-Avoidance implementation. The adaptions show significant improvement in failure probability for environments with small to medium-sized, rectangular obstacles and environments containing different kinds of forests. The failure probability of city-like environments is very sensitive to the correct implementation of the avoidance strategies. However, a proper choice leads to a reduction of failure probability as well.
Developing capacity for impactful use of Earth Observation data: Lessons from the AfriCultuReS project

An increasing number of products and services based on satellite Earth Observation (EO) data are being developed for use by decision-makers in African agricultural contexts, providing information such as weather and climate forecasts, crop yields and water availability. Capacity development to support impactful use of EO data is a key component of many EO-for-development initiatives, but there is little consensus over where or how capacity should be developed. Our goal in this piece is to provide a critical perspective on the capacity development required to support the creation of more impactful EO data services. Drawing on a capacity needs assessment carried out as part of the AfriCultuReS project (a major EO-for-development initiative), we identify proximate factors which inhibit the success of EO data services such as flawed communication strategies, low relevance in African agricultural contexts, duplication of existing products, and lack of financial sustainability. We link these proximate challenges to deeper issues such as unequal access to funding and resources, fragmentation in the EO field, and relational asymmetries of power, all of which combine to exclude important forms of knowledge from decision-making. Based on this needs assessment, we argue that capacity development requires broader systems-based approaches which develop the capacities of all actors (including those in the Global North) to respect different forms of knowledge, use and participate in co-design approaches, and recognise and challenge the asymmetries of power which currently limit the involvement of certain groups in processes of EO data service design.
Updated GOES-13 Heliosat-2 Method for Global Horizontal Irradiation in the Americas

Increasing the utilization of renewable energy is at the center of most sustainability policies. Solar energy is the most abundant resource of this type on Earth, and optimizing its use requires the optimal estimation of surface solar irradiation. Heliosat-2 is one of the most popular methods of global horizontal irradiation (GHI) estimation. Originally developed for the Meteosat satellite, Heliosat-2 has been modified in previous work to deal with GOES-13 data and named here GOES_H2. This model has been validated through the computation of indicators and irradiation maps for the Guiana Shield. This article proposes an improved version of GOES_H2, which has been combined with a radiative transfer parameterization (RTP) and the McClear clear-sky model (MC). This new version, hereafter designated RTP_MC_GOES_H2, was tested on eight stations from the Baseline Surface Radiation Network, located in North and South America, and covered by GOES-13. RTP_MC_GOES_H2 improves the hourly GHI estimates independently of the type of sky. This improvement is independent of the climate, no matter the station, the RTP_MC_GOES_H2 gives better results of MBE and RMSE than the original GOES_H2 method. Indeed, the MBE and RMSE values, respectively, change from −11.93% to −2.42% and 23.24% to 18.24% for North America and from −4.35% to 1.79% and 19.97% to 17.37 for South America. Moreover, the flexibility of the method may allow to improve results in the presence of snow cover and rainy/variable weather. Furthermore, RTP_MC_GOES_H2 results outperform or equalize those of other operational models.
Distributed State Estimation for Uncertain Linear Systems With a Recursive Architecture

This paper investigates distributed state estimation for a class of discrete-time linear systems, with dynamics subject to parameter perturbation and Gaussian noise simultaneously. A sensor network is adopted, where each sensor estimates the system state by fusing its own measurements and its neighbors’ information. A novel distributed state estimator is proposed, where the estimator gains are designed by using a state-error augmented technique. To check whether the estimator is effective on the infinite horizon, a very simple criterion only on system parameters is developed. Moreover, a relation between the estimation error covariance and the system state covariance is revealed, which shows that a performance index similar to the signal-to-noise ratio is ensured by the designed estimator. Compared with the existing methods in the literature, such as augmented methods and linear matrix inequalities-based methods, the estimation method proposed in this paper is much more computationally efficient. Finally, several numerical simulation results are demonstrated to illustrate the effectiveness of the new estimator.
FFCD: A Fast-and-Frugal Coherence Detection Method

In the era of heavy emphasis on deep neural architectures with well-proven and impressive competencies albeit with massive carbon footprints, we present a simple and inexpensive solution founded on Bidirectional Encoder Representations from Transformers Next Sentence Prediction (BERT NSP) task to localize sequential discourse coherence in a text. We propose Fast and Frugal Coherence Detection (FFCD) method, which is an effective tool for the author to incarcerate regions of weak coherence at the sentence level and reveal the extent of overall coherence of the document in near real-time. We leverage the pre-trained BERT NSP model for sequential coherence detection at sentence-to-sentence transition and evaluate the performance of the proposed FFCD approach on coherence detection tasks using publicly available datasets. The mixed performance of our solution compared to state-of-the-art methods for coherence detection invigorates efforts to design explainable and inexpensive solutions downstream of the existing upscale language models.
Development of the professional culture of a music teacher among foreign students in the process of musical and instrumental training at a Russian pedagogical university

We examine some of the problems of the process of developing the professional culture of a music teacher among foreign students from the Chinese People’s Republic in accordance with modern trends in Russian pedagogy of music education; the concept of “culture of piano performance-intonation” is revealed as a necessary component of the professional culture of a music teacher. The goal is to substantiate the need to study the features of the development of professional culture among Chinese students, future music teachers, in the process of musical and instrumental training at a Russian pedagogical university. In the course of the research, the concept of “professional culture of a music teacher” is clarified; the features of the development of the professional culture of a music teacher, and, in particular, the culture of piano performance-intonation among Chinese students, as its separate component, are investigated; the conditions for the upbringing of the culture of piano performance and intonation among foreign students in the process of musical and instrumental training in a Russian pedagogical university are determined. The research methodology is based on the systematization of scientific and pedagogical knowledge in the field of musicology, pedagogy and psychology of music education, as well as the results of experimental work carried out over the past three years on the basis of the “Special Piano” Department of the Tambov State Musical Pedagogical Institute named after S.V. Rachmaninov and the Music and Methods of Teaching Music Department of Penza State University.
Cloudifying the Curriculum with AWS

This is an Innovate Practice Full Paper. The Cloud has become a principal paradigm of computing in the last ten years, and Computer Science curricula must be updated to reflect that reality. This paper examines simple ways to accomplish curriculum cloudification using Amazon Web Services (AWS), for Computer Science and other disciplines such as Business, Communication and Mathematics.
Capturing Multi-granularity Interests with Capsule Attentive Network for Sequential Recommendation

The sequential recommender system attempts to predict the next interaction based on user's historical behaviors, which is a challenging problem due to intricate sequential dependencies and user's various interests underneath the interactions. Existing works regard each item that the user interacts with as an interest unit and apply advanced deep learning techniques to learn a unified interest representation. However, user's interests vary in multiple granularities. An item mirrors preferences for a specific item, while a set of items reflect general user interests, which are barely captured by a unified representation at the same granularity level. Furthermore, since the unrelated items are treated at the same granularity level as these decisive items, the model cannot focus on the items that help make the accurate recommendation. In this paper, we propose a novel Capsule Attentive Network (CAN) for sequential recommendation, which integrates the dynamic routing algorithm to capture diverse user interests at coarse-grained levels with a transformer module to learn more informative embeddings at fined-grained levels. Experimental results on three datasets demonstrate that CAN achieves substantial improvement over state-of-the-art methods.
Performance evaluation of state-of-the-art CNN architectures for the on-board processing of remotely sensed images

Over the last few years, Convolutional Neural Networks (CNNs) have been extensively used in different remote sensing applications. However, for large networks the computation and memory requirements have brought many challenges into this field. Additionally, the computational capabilities of hardware devices available on-board satellites is limited, being this another constraint for these implementations. In this paper, the authors present the evaluation of nine different CNN architectures for ship and airplane detection, taking into consideration that the final use-case application will be an on-board system with target detection capabilities.