Reviewerly/exHarmony huggingface dataset:
A two steps method of resources utilization predication for large Hadoop data center

Summary With the increase of data processing and Hadoop data center construction requirements, the performance of Hadoop data center is limited by inappropriate resources utilization. This paper introduces a new method to predict utilization for large‐scale Hadoop clusters. The new method adopts a two steps model, which includes Hadoop applications' performance simulation and resources utilization prediction. For performance simulation, a new simulator, which integrates baseline test and multilayered network model, is introduced and implemented. A resources utilization predictor is proposed in the second step. By analyzing the pattern of resources utilization, a single task model is proposed. A parallel‐batch‐task‐based (PBT) model, which represents the behavior of real Hadoop applications by integrating the single task model, is introduced. Two test scenarios are configured to verify the performance of our method. For the data center scenario, Terasort, Wordcount, and Hive are selected as benchmarks. In the virtual machines scenario, Terasort is used as benchmark. The experiments show that the error comparing between the simulator results and experimental environment results in most cases is less than 10%. The results confirm that we can locate the resource bottleneck for Hadoop clusters, meanwhile we can agilely configure clusters for applications with massive data.
Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.
Robust single target tracking using determinantal point process observations

Abstract The efficiency and robustness of modern visual tracking systems are largely dependent on the object detection system at hand. Bernoulli and Multi-Bernoulli filters have been proposed for visual tracking without explicit detections (image observations). However, these previous approaches do not fully exploit discriminative features for tracking. In this paper, we propose a novel Bernoulli filter with determinantal point processes observations. The proposed observation model can select groups of detections with high detection scores and low correlation among the observed features; thus achieving a robust filter.
Application of Displacement and Rotating Angle Measurement in Time Series Using Sampling Moire Method to a Plant Structure

A sampling moire method is applied to displacement and rotating angle measurement in time series of a practical conveyor belt used in a steel plant in this paper. It is required to develop efficient inspection methods of the healthiness. Authors recently proposed a sampling moire method to measure displacement distribution from a single shot two-dimensional grating image. The sampling moire method is one of the convenient phase analysis methods. The features of the method are high precision, non-contacting, and calibration free. Authors also proposed a rotating angle measurement method using the sampling moire method. Measuring displacement and rotating angle of a part of the structure using a camera in time series for recognizing the dynamic behavior is one of the efficient inspection methods. The purpose of this study is to confirm that the measurement methods of displacement and rotating angle in time series have ability to be applied to inspection of the plant structure healthiness with recognizing the dynamic behavior. In this experiment, displacements and rotating angles at two posts supporting belt rollers are measured using two sampling moire cameras in several conditions. The results suggest that the differences of structure properties can be inspected from measured displacements and rotating angles.
An Ontology for Decision-Making Support in Air Traffic Management

Decision-making processes of Air Traffic Management (ATM) need to organize information from different data sources. For example, reroute requires four-dimensional (4D) trajectory and weather information. Future ATM Decision-Support Systems (DSS) are expected to perform trustworthy complex decision-making automatically. This paper proposes an approach to DSS in ATM based on an ontology including concepts and instances of trajectories and meteorology. Temporal and spatial relationships are then set in this ontology. Reasoning rules are also build to represent knowledge in DSS. A case study shows a reroute scenario during a thunderstorm. In this scenario, information of the flight and the weather is combined to support decision-making by the proposed approach.
An Exploration on Cloud Computing Security Strategies and Issues

Cloud computing is revolutionizing many ecosystems by providing organization with computing resources that feature easy connectivity, deployment, automation, and scalability. In the recent past, the attractive features of cloud computing fuel the consolidation of cloud environment in the industry, which has been consequently motivating research on the related technologies by both the academia and industry. Regardless of its advantages, computing paradigm raises security concerns in transition phase, which have subjected several studies. Cloud computing tends to offer scalable on-demand services to the consumer with greater flexibility and lesser infrastructure investment. Since cloud services are delivered using classical network protocols and formats over the Internet, implicit vulnerabilities existing in these protocols as well as threats introduced by newer architectures have raised many security and privacy concerns. In this paper, we have focused on the data security issues found in the cloud computing. In addition to this, we discovered an appropriate solution and a private cloud domain.
Region Proposal Oriented Approach for Domain Adaptive Object Detection

Faster R-CNN has become a standard model in deep-learning based object detection. However, in many cases, few annotations are available for images in the application domain referred as the target domain whereas full annotations are available for closely related public or synthetic datasets referred as source domains. Thus, a domain adaptation is needed to be able to train a model performing well in the target domain with few or no annotations in this target domain. In this work, we address this domain adaptation problem in the context of object detection in the case where no annotations are available in the target domain. Most existing approaches consider adaptation at both global and instance level but without adapting the region proposal sub-network leading to a residual domain shift. After a detailed analysis of the classical Faster R-CNN detector, we show that adapting the region proposal sub-network is crucial and propose an original way to do it. We run experiments in two different application contexts, namely autonomous driving and ski-lift video surveillance, and show that our adaptation scheme clearly outperforms the previous solution.
Radar Emitter Identification Based on Improved Convolutional Neural Network

Aiming at the problem that the traditional pulse description word is difficult to effectively identify complex radar emitters, a method of extracting time-frequency characteristics of radar emitters and using improved convolutional neural network for recognition is proposed. Firstly, two-dimensional time-frequency images are obtained by time-frequency analysis of radar signals. Then, the convolution layer of convolutional neural network is used to extract time-frequency features, the full connection layer is used to integrate features, and softmax is used for classification and recognition. Five kinds of common radar signals are identified by simulation experiments, which proves the validity of this method.
Vulnerability as a driver of the police response to fraud

Purpose Frameworks for understanding victim harm and vulnerability have become central to priority-setting and resource allocation for decision-makers in the police and government in the UK. This paper aims to look at the meaning of vulnerability in the context of fraud. Design/methodology/approach The research took a mixed methods approach, including analysis of national crime data ( n = 61,902), qualitative data collected from interviews with practitioners ( n = 107) and a survey of strategic lead officers in the police ( n = 32). Findings There was a lack of clarity across practitioners and organisations in their understanding of vulnerability and the way it informed the police response to fraud, and a lack of resources and capability for identifying it. Research limitations/implications The authors invite reconsideration of the approach to fraud victims which have for too long been forgotten by response and support agencies. Practical implications We need to standardise and agree the definition of “vulnerability”; rethink eligibility levels; and refocus police on fraud victims taking vulnerability as a meaningful criterion in deciding who to support. Originality/value There is very little research on vulnerability and fraud victims; this paper, based on original research, fills this gap.
Perceptions and Experiences of Mobile-Assisted Language Learning for IELTS Preparation: A Case Study of Indonesian Learners

This qualitative case study investigated Indonesian English as Foreign Language (EFL) learners' perceptions and experiences of IELTS m-learning preparation.It aimed to explore the learners' perceived advantages and challenges of IELTS m-learning, self-directed m-learning strategies, and the extent to which m-learning supports their IELTS preparation.Data collected through interviews were analyzed following the Framework for the Rational Analysis of Mobile Education (FRAME).The findings revealed that there are some correlations between the advantages and the challenges of IELTS mobile learning adoption.This study also found that the learners' learning strategies for IELTS preparation were mostly related to motivation in which it drove them to expand some efforts towards their IELTS practices.IELTS mobile learning was also found to have supported their learning experience to a great extent. Index Terms-The FRAME model, IELTS,
Professional and educational strategies of modern applicants

The article deals with approaches to the understanding of the profession and professional choice in domestic and foreign scientific thought. The key issues of the study of vocational guidance and professional choice are presented in the historical perspective. The interrelation of choosing future life path, professional future and that educational institution, which in the future will allow youth not only to get a profession, but also serves as a launching pad for future career, is considered particularly acute by the end of school before each graduate. Today, the choice of the educational institution by the applicants remains a significant problem for them and in this regard the researchers face the task of characterizing the educational behavior of modern high school students, future applicants. To answer these questions, the staff of Faculty of sociology of the Lomonosov Moscow State University was conducted among senior pupil of Moscow schools. The article presents the results of studies of different years of satisfaction with the level of received education, the motives for choosing the future education of senior pupils, and value orientations of youth. The main factors influencing the choice of the desired level of education and educational institution, as well as the basic model-goals of education used by youth in choosing their life path, were highlighted. The significance of higher education for future applicants and a steady increase in the trend of the spread of consumer behavior of youth, orientation in their life strategies towards success and high material wealth, a certain social status, are confirmed on the basis of the obtained data.
Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation

There is a cold-start problem in the recommendation system field, which is how to profile new users and new items.The popular recommendation algorithm is an important solution to the coldstart problem.In this paper, we propose a new joint deep network model with auxiliary semantic learning for the popular recommendation algorithm (DMPRA).First, we define the items with a large quantity of review data and high ratings as the popular recommended items.Second, we introduce text analysis into the popular recommendation algorithm.We use the optimized CharCNN networks to learn the auxiliary semantic vectors from the users' reviews.Then, we use the Factorization Machine (FM) component and deep component to learn the corresponding vector representations of the items' attribute features.We use convolution to simulate the interaction of hidden latent vectors.This method can make the vectors interact more satisfactorily than traditional interactive representation methods.Finally, we provide the users with a reasonable popular recommendation list.The experimental results show that our algorithm can improve the AUC (area under the ROC curve) and Logloss (cross-entropy) of the popular items' prediction.In addition, we provide relevant explanations for some useful phenomena.
Interpreting Interpretations: Organizing Attribution Methods by Criteria

Motivated by distinct, though related, criteria, a growing number of attribution methods have been developed tointerprete deep learning. While each relies on the interpretability of the concept of "importance" and our ability to visualize patterns, explanations produced by the methods often differ. As a result, input attribution for vision models fail to provide any level of human understanding of model behaviour. In this work we expand the foundationsof human-understandable concepts with which attributionscan be interpreted beyond "importance" and its visualization; we incorporate the logical concepts of necessity andsufficiency, and the concept of proportionality. We definemetrics to represent these concepts as quantitative aspectsof an attribution. This allows us to compare attributionsproduced by different methods and interpret them in novelways: to what extent does this attribution (or this method)represent the necessity or sufficiency of the highlighted inputs, and to what extent is it proportional? We evaluate our measures on a collection of methods explaining convolutional neural networks (CNN) for image classification. We conclude that some attribution methods are more appropriate for interpretation in terms of necessity while others are in terms of sufficiency, while no method is always the most appropriate in terms of both.
Virtual Reality Applied to Astronomy Education in Primary and Middle School

Astronomy is the mother of sciences. Experiencing distant outer space through virtual reality can draw students closer to it. Students can also learn to recognize the stars in the sky on the plane images displayed that create a 3D space, thus expanding their knowledge of the sky. This study is grounded in STEM education principles, and it introduces a 6E learning model design process. Students can experience the mystery of gravity through interactive games in a virtual space, which also stimulates their motivation to learn about astronomy, physics, and mathematics.
Automatic Data Standardization for the Global Cryosphere Watch Data Portal

The Global Cryosphere Watch (GCW) was initiated by the World Meteorological Organization (WMO) as a mechanism to support the delivery of Earth System monitoring, modelling and prediction services focused on the cryosphere. GCW fosters international coordination and partnerships with the goal of providing authoritative, clear and usable data, information and analyses on the past, current and future state of the cryosphere. It fosters sustained and mutually beneficial partnerships between research and operational institutions, by linking research and operations as well as scientists and practitioners. This is important as most available cryospheric data come from the scientific community. It is generally managed by research institutes which often do not have the infrastructure, the resources, nor the mandate to enable FAIR data management, which is necessary for interoperability and discovery at data level. This implies that data do not fit into standardized systems or dataflows for broader data access and exchange (as exists at the WMO) and thus have been unavailable for operational meteorological and climate applications. This lack of standardization also impairs the reuse of data within the scientific community. GCW is bridging this gap through a data portal and software stack enabling the transformation of sparsely documented and highly variable data into standardized and well documented data suitable for downstream applications with data level interoperability. A processing engine converts raw data provided by the data producers into NetCDF-CF standard files with NetCDF Attribute Convention for Dataset Discovery (ACDD) metadata. The data portal web front end harvests the metadata necessary for its search engine through an OPeNDAP server so no manual editing of the medatadata is necessary. When a user downloads some data from the web portal, it gets the requested data through the OPeNDAP server.
VR-based Eye and Head Motion Collection for Modeling Their Coordination

This paper proposes a VR system that efficiently collects data of gaze behaviors and head motions required for modeling of the eye-head coordination. It consists of a 6-DoF type VR goggle, controllers, and eye trackers attachable to the goggle. This system makes it possible to collect data containing many natural gaze behaviors by imposing the spot-the-difference trials in various VR scenes on a user. Besides, we construct the eye-head coordination model by applying the existing modeling method to the collected data and evaluated the performance of the model.
Developing mathematical skills and moral behavior through cultural artifacts: a study of math trail activities at Patan Durbar Square in Nepal

Nepalese education is often inclined towards rote memorization and teacher centered instruction and learning processes. In this regard, how can instruction be actively made more effective as a key idea as researchers take this into account when conducting research in this area. This study forms an ethnographic case study of one of the schools that is transforming itself from a traditional book-based teaching and learning approach to a curriculum specific child friendly learning process. As researchers and teacher trainers we have been involved in teacher training programs for numbers of schools in Nepal. What we have observed during the four years of involvement is that the training solely do not make changes in the school system. This program is helping teachers in making instructional activities that allows them to galvanize their work for increased student involvement. This research also indicates how moral behavior can be developed through math trail activities where students do field work in groups for different objectives. Children develop their understanding through day to day activities where the perspective of different contexts, culture and ethnic groups play a vital role. At this point, we started working on excursion-based instruction. The change in instruction forces them to design activities and projects that demand children to work in outside school context with curricular objectives. It ensured opportunities for participating in hands-on activities and experiential learning. Finally, this research supports the assumption that mathematical knowledge, skill and moral behavior can be developed through different cultural project-based instructional activities and cultural artifacts and practices are key to ensure effectiveness of learning.&#x0D;
A Mixed-Norm Fidelity Model for Hyperspectral Image Denoising under Gaussian-Impulse Noise

In this paper, we evaluate an optimization problem to denoise high-dimensional hyperspectral data. We formulate the problem as a variational technique for simultaneously handling mixed Gaussian and impulse noise which is the characteristic of hyperspectral data. Vectorization of data and subsequent application of Total Variation as a regularization term exploit correlation along spatial and spectral dimension. We then use a mixed data fidelity model to simultaneously handle the effect of mixed Gaussian-impulse noise. The resultant optimization problem is solved using gradient descent. Experimental results on synthetic and real hyperspectral data support our argument of using a mixed-norm fidelity term.
Active Learning - Competency Development Strategy

The study is devoted to the development of legal competence in the study of the discipline “Jurisprudence”. To enhance the learning activities were used interactive forms of practical exercises. The use of business and role-playing games, work with cases, the construction of Synquain (the word “synquain” comes from the French word “five” and means “a poem consisting of five lines”), the analysis of situations occurring in real professional activity leads not only to quality of the educational process, but also forms the legal consciousness of students. They increase their professional identity and actively participate in legal events.
A Study on Media Literacy of Industrial Education

Even in our information-oriented society, school education has not been able to fully utilize the media.In order to secure the knowledge of electrical power generating to students, it is necessary to consider how to use media more effectively.To that end, we aim to develop future educational activities after organizing and grasping information literacy, media literacy, technology literacy, etc… in current school education.In addition, in the process of practicing the presentation making full use of media, the students aim to foster their thinking, judgment and expression.
An efficiently generated family of binary de Bruijn sequences

We study how to generate binary de Bruijn sequences efficiently from the class of simple linear feedback shift registers with feedback function f(x0,x1,…,xn−1)=x0+x1+xn−1 for n≥3, using the cycle joining method. Based on the properties of this class of LFSRs, we propose two new generic successor rules, each of which produces at least 2n−3 de Bruijn sequences. These two classes build upon a framework proposed by Gabric et al. (2018). Here we introduce new useful choices for the uniquely determined state in each cycle to devise valid successor rules. In each class, the next bit costs O(n) time and O(n) space for a fixed n.
k-Cut: A Simple Approximately-Uniform Method for Sampling Ballots in Post-election Audits

We present an approximate sampling framework and discuss how risk-limiting audits can compensate for these approximations, while maintaining their “risk-limiting” properties. Our framework is general and can compensate for counting mistakes made during audits. Moreover, we present and analyze a simple approximate sampling method, “k-cut”, for picking a ballot randomly from a stack, without counting. Our method involves doing k “cuts,” each involving moving a random portion of ballots from the top to the bottom of the stack, and then picking the ballot on top. Unlike conventional methods of picking a ballot at random, k-cut does not require identification numbers on the ballots or counting many ballots per draw. We analyze how close the distribution of chosen ballots is to the uniform distribution, and design mitigation procedures. We show that $$k=6$$ cuts is enough for a risk-limiting election audit, based on empirical data, which provides a significant increase in sampling efficiency. This method has been used in pilot RLAs in Indiana and is scheduled to be used in Michigan pilot audits in December 2018.