Reviewerly/exHarmony huggingface dataset:
Real Time Air Quality Monitoring System in Three Sites (Bogor, Cibeureum and Serpong)

Abstract Center for Climate Risk and Opportunity Management of Bogor Agricultural University in collaboration with National Institute for Environmental Studies-Japan developed an on-line system to monitor real time air quality and greenhouse gases for three sites surrounding Jakarta area, i.e. Bogor (Center of Bogor city), Serpong (Jakarta suburb) and Cibeureum (mountainous area, background-like site). The system uses standards air-quality monitoring equipped with an array of gaseous and meteorological sensors. The system records data every minute and stores in local storage and sent automatically to a Dropbox every 6 hours. The data in the Dropbox is accessed using API and being processed and analysed using several modules to be converted into useful information and presented in a website. The website will display real time air quality for ozone (O 3 ) and particulate matters (PM 2.5 and PM 10 ) and greenhouse gas data (CO 2 , CH 4 ), Carbon monoxide (CO) Nitrogen Oxide (NO x ), and Sulfur Dioxide (SO 2 ), in the past 30-day including local weather data for the past 7 days. These data are then converted into an index indicating the status of the quality of the air surrounding the stations. The index is divided into five categories based on EPA air quality standard, namely good, moderate, unhealthy for sensitive group, unhealthy and very unhealthy. The system can provide the status of air quality index on a six hours basis and easy to be accessed by any stakeholder. The system allows local authorities, communities and industries to easily access and use the information for air quality management purposes and early warning.
A Framework for Strategic Cloud Migration

This paper presents a novel framework for organisations to carry out a structured feasibility study on Cloud migration and to decide Cloud Migration Strategy. Following the framework helps an organisation to decide whether Cloud migration is a feasible option for them, and if so, the best strategic approach towards Cloud migration. It is a crucial and sensitive part for any organisation to decide whether they should move to Cloud Computing platform. The decision requires strategic approach with proper feasibility study. Several technological, human, security and financial factors are involved in decision making process to move to the Cloud. The proposed framework helps an organisation to carry out a feasibility study to decide whether to move to the Cloud, and if so, what would be the best approach towards Cloud migration. The proposed framework addresses the factors that an organisation must explore to decide on Cloud migration. Cloud Computing has its own pros and cons. A whimsical decision to move to the Cloud may be disastrous for an organisation. Following the proposed framework will help organisations to carry out a structured and integrated feasibility study deal with the decision on Cloud migration.
Learning to Segment Unseen Category Objects using Gradient Gaussian Attention

Existing semantic segmentation models are trapped in the categories of training sets. Unfortunately, public datasets provide pixel-level annotations only for a small quantity of images and few categories. In this paper, we propose a novel cross-category supervised object segmentation network to explore the similar features among different categories, which can transfer the learned segmentation knowledge from categories with mask annotations to unseen categories that only have bounding boxes. Specifically, we fuse gaussian attention map of an object with guided gradient back-propagation map as an extra input, which gives localizable and discriminative prior cues to obtain precise object segmentation from the bounding box. In addition to computing a segment for each box, we also fuse segments to generate pixel-level labels. Then, without modifying the segmentation training program, the generated labels are still sufficient and achieve about 98.2% of the fully supervised model, in the case of only 50% categories with pixel-level annotations on PASCAL VOC 2012. The proposed method is also effective for interactive segmentation and salient object detection.
Keep Your Host Language Object and Also Query it

As a result of prolific growth in data science and machine learning applications, modern relational database management systems (RDBMS) are experimenting with various approaches to facilitate advanced analytical computations, in addition to the relational operations that they traditionally support. The most common approach has been to integrate an embedded high level language (HLL) interpreter into the RDBMS along with any additional libraries that specialize in numerical computations. Such implementations, e.g., user defined functions (UDFs), follow generally a black-box setup, and for many complex workflows that require datasets to be passed and processed back-and-forth between the query execution engine and the embedded HLL interpreter, optimization opportunities are not fully explored yet. In this paper, we propose and implement the concept of virtual tables that can be used to expose data set objects maintained by the embedded HLL interpreter to the query engine for executing relational operations. Unlike prevalent solutions, our approach minimizes the need for performing data copies and conversions, performing them lazily when required. It also facilitates better optimization opportunities for the execution of SQL queries as the RDBMS is able to analyze the data characteristics of the HLL objects before producing an execution plan. The approach is also programmer friendly, allowing for a more intuitive implementation of computational workflows. We perform evaluations over a variety of workloads which demonstrate the performance and programming benefits of virtual tables.
Research on Personalized Document Retrieval and Ranking Strategy

In order to provide appropriate text information in line with users' cognitive level, a new personalized retrieval method is proposed in this paper. At first user's personalized cognitive structure should be established and with this structure user's cognitive level can be represented formally. Then related concept can be extended around the keywords, and queried documents should be ranked according to the user's cognitive level and the content of the document. Experiments show that different users will get different search results even if they use the same keywords.
Applying Modified SHA256 on Blockchain using Challenge Response and Off-chain Signatures Patterns

This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Eberhardt J., Tai S. (2017) On or Off the Blockchain? Insights on Off-Chaining Computation and Data. In: De Paoli F., Schulte S., Broch Johnsen E. (eds) Service-Oriented and Cloud Computing. ESOCC 2017. Lecture Notes in Computer Science, vol 10465. Springer, Cham https://link.springer.com/chapter/10.1007/978-3-319-67262-5_1
Personalized Prediction Based on a Regression Model with User-Item Specific Interactions

Personalized information filtering is based on the user’s ideological similarity or other information, extracts information that is specific to the user, and predicts his/her preference for a large number of other items, which is called personalized prediction. Based on the cumulative logistic model, the article proposes a possibility method to emphasize the interaction between related variables, and uses the Low-rank decomposition method to theoretically discuss the problem of personalized prediction. This method is simple and easy to understand, while improving calculation and prediction accuracy.
Procedural system assisted authoring of open-world content for Marvel's Spider-Man

Crimes and vignettes are placed throughout the game city space using procedural systems to find appropriate locations. Editing of roads or buildings necessitates re-authoring the placement of dynamic encounters in that area of the environment. In many cases the dynamic encounters may have already undergone "final" manual adjustment to the space. To accommodate iterations on city layout, we add three main improvements to our procedural systems: preserve hand-authored work if it continues to meet specifications; place encounter components with higher fidelity; and provide the artists and designers guidance for crimes and vignettes needing more attention.
Ar-powered educational application based upon convolutional neural network

Abstract This research aims to justify the importance of using convolutional neural networks (CNN) in AR-powered educational applications to provide the learning process with impressive visualization of studied objects. Demonstration of the developed application proves neural network to be its basic part that allows for high-quality accuracy of object localization and image recognition in AR systems. As these systems provide a more informative and impressive way of learning, the application can be used for data mining and big data analysis disciplines to demonstrate real-time operation of localization, clustering and segmentation algorithms.
Feature Extraction for Eye Movement Video Data

Random objects in videos are common stimuli in eye tracker based studies and their locations and time of appearance need to be detected in related research such as depression detection. In this paper, we propose a new method to extract features in eye movement video data captured by the SMI eye tracker. Firstly, we provide a feature extraction method by using the circle Hough transform and the Douglas–Peucker algorithm to extract the feature for each frame of the eye movement video data, and verify its validity in eye movement video data processing. Secondly, because the storage time of the eye tracker is more accurate than the on-screen time of the exported video, we choose to extract the storage time of the eye tracker to improve the quality of feature extraction. Finally, we add batch processing function to improve the efficiency of the experiment. Experimental results show that the method can extract the eye movement features in the eye movement video accurately and effectively.
Cropping Region Proposal Network Based Framework for Efficient Object Detection on Large Scale Remote Sensing Images

It is very difficult to directly detect objects on the entire large scale remote sensing image, due to the limited GPU memory. Moreover, there are no objects of interest in most areas of such a huge image, thus a lot of computational costs is wasted in dealing with these vain areas. Therefore, this paper proposes a Cropping Region Proposal Network (CRPN), which includes a weak semantic RPN for quickly locating interesting regions, and a dual-scale strategy for generating effective cropping regions. Cropping regions consist of small and large cropping scales for detecting various-scale objects including very small and very large objects, which is hard for existing methods. CRPN helps to detect effective regions of remote sensing image. Meanwhile, it is also modularized and can be easily connected with mainstream detectors to form an end-to-end detecting framework. Experiments on public DOTA dataset show that our CRPN is effective for filtering invalid regions to greatly reduce the computation burden, and helps to achieve more accurate object detection on large scale remote sensing images.
Convergent Evolution of Innovative Teams in Technical Services

In recent years UNC-Chapel Hill Libraries, Duke University Libraries, and NC State University Libraries each formed a new team in response to the increasing need to repurpose and reuse data and metadata in and across domains, systems, and environments in new ways. During this session at NC Serials 2019 the speakers described their teams, how those teams came about, and their responsibilities; discussed similarities and differences across their teams; and shared communication strategies, lessons, and opportunities they’ve learned so far. This report captures the discussions that occurred during this session.
An Improved Chinese Search Engine Lingo Search Clustering Algorithm

Lingo algorithm is a clustering method based on latent semantic analysis based on STC algorithm. The main disadvantages of this algorithm are as follows: First, the time efficiency of SVD is too low, and there is a certain requirement for storage space. Second, the selection of K value is too large to increase the computation amount. If it is too small, the information will be lost. The article will analyze and improve the main factors affecting clustering, and finally through empirical verification analysis.
On the Structure of Discrete Metric Spaces Isometric to Circles

A metric space (X, D) is called circular if it is isometric to a subspace of a metric circle, that is, a circle in which distances are measured by the length of the shorter arc connecting them. We show that the following three conditions are equivalent: (1) X is circular, (2) every 4-point subset of X can be labeled as a, b, c, d so that $$D(a,b)+D(b,c)=D(a,c)$$ , $$D(b,c)+D(c,d)=D(b,d)$$ holds, and (3) every 4-point subset of X is circular.
Smart-Scrolling: Improving Information Access Performance in Linear Layout Views for Small-Screen Devices

We proposed an adaptive auto-scrolling technique, Smart-Scrolling, which determined the scanned information region and hid it automatically in a linear layout view. An experiment was conducted with 15 participants to explore the usability of the technique under various application conditions of different interaction methods, information list lengths, and screen sizes. The experimental results revealed that Smart-Scrolling outperformed the baseline with an improvement in task completion speed of 9.4% and accuracy of 42.9%, and reducing 51.4% manual scrolling. Smart-Scrolling performed better than the baseline under various application conditions of different interaction methods, information list lengths, and screen sizes.
Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications.

Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent: 1) an initially large learning rate accelerates training or helps the network escape spurious local minima; 2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation. Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex. We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity. And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets. We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.
Subjectivity of English connectives: A corpus and experimental investigation of result forward causality signals in written language

The current study sets out to investigate naturally produced English causal relations from the point of view of conceptual and linguistic features that contribute to their intended interpretations ...
Introduction to Graphics

Numbers are boring. It’s boring to have too many numbers in stories. All too often, stories on economic and corporate topics frequently rely on too many numbers and too little explanation.
Eliminating Linguistic Barriers as a Factor of Improving Language Education Quality

The paper is aimed at determining pedagogic means of improving education quality of teaching foreign languages. The ignorance of students’ linguistic barriers in the process of teaching foreign languages significantly decreases efficiency of language education. Therefore, this study is aimed at modeling pedagogic technology reducing students’ linguistic barriers. The main source is self-organizing intercultural adaptive abilities. The effectiveness of the authors’ pedagogic technology is achieved by means of applying person oriented, competence approaches and synergy methodology. The pedagogic approaches determined the choice of teaching methods and pedagogic observation methods. The main findings indicate that changeable communicative conditions eliminate linguistic barriers and enhance students’ language development. The article contributes to theoretical foundation and practical use of pedagogic technology models aimed at reducing linguistic barriers. Article is of practical use for teachers, students and as it reveals the main psychological reasons of linguistic barriers expressed as communicative tension, communicative hardships and points out the ways of eliminating linguistic barriers in education environment of technical university.
Summarization of User Reviews on E-Commerce Websites Using Hidden Markov Model

E-commerce is one of the most important reasons for the growth and sustenance of the internet, with the number of regular users of these sites increasing day by day. The user review forums on e-commerce websites help to get an overview about products of interest. However these reviews are not very organized with respect to whether features of the product, or service provided by the dealers is under discussion. These product reviews can further be positive, negative or a combination of both. This paper discusses a solution to the problem through summarization of product reviews across popular e-commerce websites. The Hidden Markov Model is used to find the component-feature pair, and the SentiWordNet libraries for Weight and Polarity Assignment. After aggregation, the final output is a graph which lists the top six aspects of the product discussed in the review forums along with the weighted polarity representation.
Non-strictly Sparse Source Modelling Using Heavy-tailed Distributions for DCT Coefficients

In this study heavy-tailed distributions including Pareto, Weibull, Levy, and Log-normal distributions are proposed to model the DCT coefficients of real-world sources. It will be shown that the proposed heavy-tailed distributions are more accurate to model non-strictly sparse sources rather than Laplace and Cauchy distributions. Maximum Likelihood Estimation (MLE) method is utilized to parameter estimation of the proposed distributions. Finally, the Shannon Lower Bound (SLB) on the rate distortion function of each model is calculated, accordingly a framework is provided that could be used to assess the rate distortion performance of any coding scheme that is supposed to compress DCT coefficients as outcome of a sparse source.
Local Feature Detection Based on Image Block Discrete Scale Space

Aiming at the complexity of scale space construction, this paper proposes a block-based discrete scale space representation method, which divides the image into overlapping blocks. Then each block independently constructs a block scale space and carries out subsequent feature detection, thus greatly reducing the space overhead. In addition, in view of the stability of feature detection caused by accelerated downsampling in scale space pyramid, the smoothing factor is introduced to reduce the impact of downsampling operation on the basis of no additional overhead in block scale space representation. Experiments show that the proposed discrete scale space representation method significantly reduces the time and space overhead in the process of scale space construction.
Clear the Page

Layout, how content is distributed on the page, has a huge impact on L2 readability. Writers and publishers can greatly enhance readability by paying attention to some key layout features. Unfortunately, modern printing practices often end up reducing overall readability in an attempt to save paper (Weiss in The Elements of International English Style. M. E. Sharpe, New York; London, 2004 [1]). Layout issues are especially problematic for L2 readers whose first language is typographically different from English. For example, languages that read from right to left, such as Arabic, and non-alphabet-based languages, such as Chinese and Japanese, look very different on the page. This makes clarity of page layout critical for many L2 readers. This chapter discusses how to reduce distractions on the page to enhance readability for L2 readers.
Hydrocarbon deposit mapping validation by the means of ground-based spectrometry, remote sensing and geophysical data

The probability estimation of oil and gas inside certain area is essential for decision making on the industrial exploitation of oil and gas bearing features. A quantitative assessment of the hydrocarbon contour mapping accuracy using ground-based spectrometric measurements, remote, geological and geophysical data requires a special validation procedure. Its purpose is to evaluate achieved accuracy and reliability as well as the conformance to specified requirements. The input data for validation of the hydrocarbon deposit contour by field spectrometry are the one points’ locations relative to the other contours detected by independent methods, such as remote, geological and geophysical. As the field spectrometry performed along spatial trace, the geometric drifts of other methods’ cross-points are estimated. The algorithm for the validation of hydrocarbon deposit contour mapping by field spectrometry, remote, geological and geophysical data is proposed in this paper. The algorithm was tested on over the Novotroitsky and East Rogintsy hydrocarbon deposits (Ukraine). Measurements along 14 spatial traces over the Novotroitsky’s deposit and 28 traces over the East Rogintsy’s one was carried out to perform validation. The average error probability was 0.28, which demonstrates an admissible reliability of hydrocarbon deposits contours’ mapping by field spectrometry data. The preliminary validation estimates engagement during the hydrocarbon deposits mapping provides the fact-based statistical consistency of the quantitative measurements received. In addition, it is possible to filter the outliers reasonable before final information product release, which will enhance the overall reliability.
An Enhanced Fuzzy Clustering with Cluster Density Immunity

Clustering is one of the well-known unsupervised learning methods that groups data into homogeneous clusters, and has been successfully used in various applications.Fuzzy C-Means(FCM) is one of the representative methods in fuzzy clustering.In FCM, however, cluster centers tend leaning to high density area because the sum of Euclidean distances in FCM forces high density clusters to make more contribution to clustering result.In this paper, proposed is an enhanced clustering method that modified the FCM objective function with additional terms, which reduce clustering errors due to density difference among clusters.Introduced are two terms, one of which keeps the cluster centers as far away as possible and the other makes cluster centers to be located in high density regions.The proposed method converges more to real centers than FCM, which can be verified with experimental results.