Reviewerly/exHarmony huggingface dataset:
Presence of Books for Children in the Households of Bangladesh: A District-wise Distribution

Health policies and public health studies in Bangladesh primarily focus on physical aspects of health, thus creating a gap in the literature regarding the assessment of the emotional-social environment for children and their developmental vulnerabilities. Interactions though literacy activities, such as shared reading times between child and parents, are possible ways to address the developmental and cognitive needs of children. This study explores the district-wise presence of books for children (aged 0–4 years) in households and identifies vulnerable households by exploring the association between sociodemographic status and household book ownership. The Bangladesh Multiple Indicator Cluster Survey 2012–13 was used to map the spatial heterogeneity of average availability of children’s books, which revealed that ownership of age-appropriate books was clustered around divisional cities, with one exception. Around 65% of the households did not have any suitable books for children. The presence of children’s books was significantly (p < .005) associated with children’s age, mother’s age and education, financial status of the household, education of the head of the house, mother’s mobile phone ownership, and mother’s access to media (newspaper and television). Parents in high-income households and with highly educated mothers were nearly three times more likely to own children’s books. Similarly, parents in a household with the mother having a mobile phone were 42% more likely to own children’s books. The findings suggest that a household’s lack of financial capacity to purchase books, the role of public health media in promoting the mother’s awareness of the need for children’s books, and a lack of understanding of children’s cognitive developmental needs could be more effectively addressed in Bangladesh.
Experimental Determination of the Passability Characteristics of Mobile Robotic Complex with Adaptive Wheels

In this paper, a generalized structural scheme of a mobile robotic complex with adaptive support-movable wheels, which is capable of changing its geometric characteristics in the process of movement, based on surmounted surface without operator participation was proposed. The algorithm and software which has been tested on the assembled microprocessor module with the element forming the wheel frame has been developed. As a result, the program provides data about the presence of an obstacle and the possibility of overcoming it and generates a control signal for the power body to change the geometric characteristics of the mover. The calculation and selection of the engine to provide the necessary speed and torque was made. On the basis of experimental data from the full-scale mover model, the main geometric characteristics of the complex were determined and the passability for two types of soils - clay and dry sand, was calculated.
Longitudinal predictions using regression-corrected grouping to reduce regression to the mean

[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] The education of physicists requires annual progress in mathematics through grades K–12, continuing on to high school physics, undergraduate instruction, and often many years of postgraduate study. The traditional domain of physics education research is the study of pedagogical techniques for a single lesson or a single course, but the physics and physics education research communities will need to study the complete educational system to increase performance and participation, especially for underrepresented groups. Analysis of long-term student outcomes and policy impacts requires predictive longitudinal techniques. We begin with concepts from fluid and statistical mechanics—trajectories and streamlines—to visualize longitudinal outcomes and make predictions. Drawing on coarse-graining procedures to model the movement of particles in a fluid, the longitudinal data are sorted into score bins to depict the flow of scores through time: trajectories depict the average scores over time for initial score bins and streamlines provide an approximate way to calculate the flow of student scores over many years based on only two consecutive years of data. However, due to the partially stochastic nature of observed scores, the coarse-graining procedure that sorts students into score bins amplifies a statistical phenomenon known as regression to the mean (RTM). As a result, streamlines do not provide an accurate prediction for the future performance of students. Here we discuss a new coarse-graining procedure, regression-corrected (RC) grouping, which reduces RTM in the streamlines. We explain the idea of RC streamlines through a toy model of freshman physics performance and then apply them to the realistic setting of the Texas State Longitudinal Data System, which contains standardized testing data for students throughout primary and secondary school since 2003. We show that the RC streamlines accurately predict trajectories, using 2 or 3 years of data. Therefore RC streamlines can be used to identify the effects of academic interventions on a time scale comparable to that of policy changes. We illustrate this assertion by examining a particular policy intervention, Texas’s Student Success Initiative.6 MoreReceived 27 August 2018DOI:https://doi.org/10.1103/PhysRevPhysEducRes.15.020109Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentEducational policyResearch methodologyProfessional TopicsK-12 studentsPhysics Education Research
Suitability Evaluation of Blockchain–Based Systems Using Fuzzy ANP- A Case Study in a Logistics Company

Blockchain practices apart from the finance are the current object of curiosity since blockchain is not only an information technology, but also an institutional technology with its new currency economics and distributed structures. Today, the areas such as supply chain, power and food/agriculture are promising in terms of capability of fitting the blockchain technology. Especially logistics corporations have been concentrating on developing efficient ways that provide cost advantages in integrated data, fleet management and communication issues. Given the current stage of industry evolution in Turkey, it is important to understand the suitability of the blockchain technology. Therefore, in this study, experts from a well known logistics company in Turkey contributed to the process of suitability evaluation of the blockchain-based systems. As for the analyzing method, Fuzzy Analytical Network Process (Fuzzy ANP) was used in order to determine and evaluate the interrelations between the suitability attributes. Besides, this method facilitated the prioritization of the blockchain-based alternatives.
A statistical procedure to adjust for time-interval mismatch in forensic voice comparison

The present paper describes a statistical modeling procedure that was developed to account for the fact that, in a forensic voice comparison analysis conducted for a particular case, there was a long time interval between when the questioned- and known-speaker recordings were made (six years), but in the sample of the relevant population used for training and testing the forensic voice comparison system there was a short interval (hours to days) between when each of multiple recordings of each speaker was made. The present paper also includes results of empirical validation of the procedure. Although based on a particular case, the procedure has potential for wider application given that relatively long time intervals between the recording of questioned and known speakers are not uncommon in casework.
Sensor Tag Detection, Tracking and Recognition for AR Application

Augmented reality (AR) applications are widely used in our world today. It may look at the first glance that AR is primarily used in entertainment and learning applications, but in fact, now big companies and corporations try to integrate AR technologies into their industrial processes. It reduces the number of unnecessary communication and increases the net profit of the company. Most companies use AR devices for frontend tasks and a server side for backend. This approach allows reducing the performance requirements for an AR device by implementing time-consuming operations on the powerful server. However, some operations should be implemented directly on the AR device and we have to implement them rationally in terms of performance. Also we have to make interaction between the AR device and a server more effectively. The most important and frequently used operations in AR applications are related to computer vision: image processing, objects detection, tracking and recognition. Therefore, it is important to compare the performance of these operations on AR devices and suggest those that will be implemented on the server side and on the AR device. This paper focuses on deciding these tasks for an AR client-side application for the Siemens company. This application provides a more effective way for monitoring the equipment at remote facilities. Different approaches for sensor tag detection and tracking on a real time video are considered and evaluated. The way for communication between the AR device and the server side is suggested.
Selection of Satisfied Association Rules via Aggregation of Linguistic Satisfied Degrees

Many association rule mining algorithms have been well-established, such as Apriori, Eclat, FP-Growth, or LCM algorithms. However, the challenge is that the huge size of association rules is extracted by using these algorithms, and it is difficult for users to select satisfied association rules from them. In this paper, a new method is proposed to select satisfied association rules, which is based on the aggregation of fuzzy linguistic satisfied degrees of extracted association rules. To this end, two problems must be solved, one is which interesting measures are utilized to obtain fuzzy linguistic satisfied degrees of association rules and the other is how to aggregate them. For the first problem, many objective and subjective interesting measures have been proposed, which are generally included in [0, 1] or others universes and easily calculated by support, confidence, or other measures, these interesting measures cannot be directly aggregated, because different interesting measures represent different satisfied degrees of association rules. In this paper, a new transformation function is proposed to transform these interesting measures into fuzzy linguistic satisfied degrees, such as dissatisfied, fair, satisfied, and so on. For the second problem, by considering different weights of objective and subjective interesting measures, linguistic aggregation operators are designed to aggregate these fuzzy linguistic satisfied degrees of association rules. Accordingly, satisfied association rules are selected by using order on the aggregation results of linguistic satisfied degrees. In cases' study, Apriori, Eclat, FP-Growth, and LCM algorithms are first utilized to extract association rules with higher support or confidence measures from Chess, Connect, Mushroom, and T40I10D100K databases, then the proposed method is applied to obtain fuzzy linguistic satisfied degrees of extracted association rules and aggregate and select satisfied association rules from those extracted association rules, and comparison and analysis show that the proposed method is a useful and alternative tool to select satisfied association rules from extracted association rules.
Analysis of Malignancy Using Enhanced GraphCut-Based Clustering for Diagnosis of Bone Cancer

Osteosarcoma and Ewing’s sarcoma are very common bone tumors, and its biopsy is characterized with spatial distributions of osteoblasts, osteocytes, and osteoclasts. Vandana, B. S. Any abnormal growth found in these three cells can be either cancerous or benign. This paper presentsAntony, P. J. enhanced GraphCut-based clustering framework to ascertain malignancy level in hematoxylin and eosin (H&E)-stained histopathological images. This approach executes iterative GraphCut method to extract foreground objects from biopsy image. Alva, Sathyavathi R. Usually, iterative GraphCut needs user interaction to initialize segmentation process. But in enhanced GraphCut method, this initial data is manually generated using standard image processing tools. By doing this, experiment shows that quality of proposed segmentation result is improved. After segmentation of all tissue cells, its categorization is done through color and topological characteristics. Therefore, domain-specific methods such as color-based clustering, mathematical morphology, and active contour are used for feature extraction. This computed features are used to quantify the characteristics of malignancy and classify them as normal, benign, and cancerous using multiclass random forest framework. Proposed method is compared with earlier methods which yields 90% of classification accuracy.
Introduction to Non-Markovian Evolution of n-Level Quantum Systems

We analyze quantum dynamical maps and the corresponding master equations beyond the celebrated quantum Markovian master equation derived by Gorini, Kossakowski, Sudarshan, and Lindblad. In the Heisenberg picture such maps are represented by completely positive and unital maps, whereas in the Schrödinger picture by completely positive and trace-preserving maps. Both time-local equations governed by time dependent generators and time non-local equations of the Nakajima-Zwanzig form governed by the corresponding memory kernels are considered. We use the Schrödinger picture to discuss time-local case and Heisenberg picture for the non-local one. These equations describe quantum non-Markovian evolution that takes into account memory effects. Our analysis is illustrated by several simple examples.
An Eye-Tracking Database of Video Advertising

Reliably predicting where people look in images and videos remains challenging and requires substantial eye-tracking data to be collected and analysed for various applications. In this paper, we present an eye-tracking study where twenty-eight participants viewed forty still scenes of video advertising. First, we analyse human attentional behaviour based on gaze data. Then, we evaluate to what extent a machine - saliency model - can predict human behaviour. Experimental results show that there is a significant gap between human and machine in visual saliency. The resulting eye-tracking data would benefit the development of saliency models for video advertising or other relevant applications. The eye-tracking data are made publicly available to the research community.
Qualitative study of Riccati difference equation on maneuvering target tracking and fault diagnosis of wind turbine gearbox

Various performance analyses using Interacting Multiple Model (IMM) algorithm for the stability of tracking a maneuvering target are dealt with the state error covariance. A noble way of analyzing the stability of the IMM algorithm for a linear systems with the upper and lower bounds of the error covariance is analyzed. For this persuasion, a two-model Interacting Multiple Model Kalman Filter (IMMKF) with constant acceleration and constant jerk model has been applied for two different case studies. One is tracking a maneuvering target, and the another is tracking a vibration of wind turbine gearbox, which helps to identify failure component in wind energy system. The required data are collected from a radar and a defected gear box of a test wind turbine, and the efficiency of IMM algorithm is analyzed by simulation experiments.
An Ehrenfeucht-Fraïssé Game for Inquisitive First-Order Logic

Inquisitive first-order logic, InqBQ, is an extension of classical first-order logic with questions. From a mathematical point of view, formulas in this logic express properties of sets of relational structures. In this paper we describe an Ehrenfeucht-Fraïssé game for InqBQ and show that it characterizes the distinguishing power of the logic. We exploit this result to show a number of undefinability results: in particular, several variants of the question how many individuals have property P are not expressible in InqBQ, even in restriction to finite models.
Rusty: Runtime System Predictability Leveraging LSTM Neural Networks

Modern cloud scale data-centers are adopting workload co-location as an effective mechanism for improving resource utilization. However, workload co-location is stressing resource availability in unconventional and unpredictable manner. Efficient resource management requires continuous and ideally predictive runtime knowledge of system metrics, sensitive both to workload demands, e.g., CPU, memory etc., as well as interference effects induced by co-location. In this paper, we present Rusty, a framework able to address the aforementioned challenges by leveraging the power of Long Short-Term Memory networks to forecast at runtime, performance metrics of applications executed on systems under interference. We evaluate Rusty under a diverse set of interference scenarios for a plethora of cloud workloads, showing that Rusty achieves extremely high prediction accuracy, up to 0.99 in terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value, satisfying at the same time the strict latency constraints to be usable at runtime.
Development of a New Airport Unusual-Weather Detection System With Aircraft Surveillance Information

This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance-broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases.
A Multi-Dimensional Resource Scheduling Strategy Based on Multilateral Complementarity

The diversification of computing resources and the increasing complexity of resource demand from applications in terms of type, granularity, and quantity call for more efficiency in resource scheduling. To meet this challenge, this paper proposes a resource description model based on a quantized polygon. It explores the theoretical basis for the multilateral complementarity strategy (MCS), analyzes the basic mechanisms and application architecture of the MCS, and, finally, proposes the multi-dimensional quantized polygon (MQP) algorithm-a multi-dimensional resource scheduling algorithm based on multilateral complementarity (MC-MDRS algorithm). The experiments show that the multi-dimensional resource scheduling strategy based on the MQP, when implemented in an environment in which multi-dimensional (3 ≤ d ≤ 6) computing resources are provisioned, can effectively respond to the requests for various computing resources by granular services and facilitate the deployment of granular application services arriving in batches. The analysis of the experimental results indicates that the MQP algorithm outperforms other multi-dimensional resource scheduling algorithms by 2%-5% in node utilization.
Human Action Pattern Recognition and Semantic Research Based on Embodied Cognition Theory

Human body motion pattern recognition in video images is an important research direction in the field of pattern recognition. It has a very broad application prospect in many fields such as intelligent video surveillance, human-computer interaction, motion analysis, video retrieval, etc. Research has also received extensive attention from scholars at home and abroad. Pattern recognition is essentially a branch of artificial intelligence. It has its unique role in the field of artificial intelligence. Accurate recognition of human body motion patterns in video images is of great help in image classification, retrieval, human tracking and video surveillance. Based on the human visual perception mechanism, this paper proposes a human behavior recognition algorithm based on semantic saliency map. Through the combination of sliding window and similarity measure, the behavioral region that best exhibits the semantic features of the image is found, which is the semantically significant region. The semantic significant region and the original image are used as the dual input source to study the human behavior recognition, and the image is enhanced. The utilization of significant regional information better reveals the identifiable area of the image and contributes to the recognition of human behavior.
Business Process Driven Trust-Based Task Scheduling

The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.
Multi-robot Multi-goal Motion Planning with Time and Resources

This paper addresses multi-robot multi-goal motion planning with temporal and resources constraints. It solves the vehicle routing problem for mobile robots that operate according to their system dynamics, and which have to visit a number of waypoints scattered in a two-dimensional map environment with obstacles, while satisfying time window and capacity constraints. We compute the shortest path distances between each pair of waypoints in advance, and Monte-Carlo search plans the vehicles’ tour through adaptation of a rollout policy, while adding the constraints to its optimization objective. Macro actions enable the vehicles to run in real-time with best actions being distributed to the individual controllers. We analyze how the simulation is affected by varying parameters such as the number of vehicles.