Reviewerly/exHarmony huggingface dataset:
An Investigation on Image Conversions and Edge Detection Patterns in Image Processing

Nowadays color image processing is widely utilized in medical field, multimedia, computer vision, graphics and video broadcasting. In order to identify the perceived qualities of image we utilized the color spacing. It provides a sagacious method to manipulate and effectively display the object color. Generally each pixel in a color image is described by intensities like red, green and blue (RGB). This deals with introduction of an advanced approach to convert the RGB form of image to various other forms such as grayscale, black & white, hue saturation value (HSV), yellow chrominal blue chrominal red (YCbCr). The article also emphasizes on edge detection which incorporates a collection of mathematical techniques that focuses at distinguishing points in a digital image at which image brightness changes sprucely and has disruption. The similar issue of locating signal disruptions over time is termed as edge detection.
Data Controllability for Risk Management in Smart and Intelligent Systems

Information has become a major asset in companies that have based their business on the production and exploitation of this information, but also in traditional companies that exploit their information with a view to continuously improving their processes.This is the case in collaborative systems where companies are interconnected but also in intelligent systems that have many information exchanges with there environment.It is important that companies keep control of the information they import, process and distribute.In this article, in the context of a risk management approach, we present a new security criterion: controllability.
EMS3: An Improved Algorithm for Finding Edit-Distance Based Motifs

Discovering patterns in biological sequences is a crucial step to extract useful information from them. Motifs can be viewed as patterns that occur exactly or with minor changes across some or all of the biological sequences. Motif search has numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity among families of proteins, etc. The general problem of motif search is intractable. One of the most studied models of motif search proposed in literature is Edit-distance based Motif Search (EMS). In EMS, the goal is to find all the patterns of length l that occur with an edit-distance of at most d in each of the input sequences. EMS algorithms existing in the literature do not scale well on challenging instances and large datasets. In this paper, the current state-of-the-art EMS solver is advanced by exploiting the idea of dimension reduction. A novel idea to reduce the cardinality of the alphabet is proposed. The algorithm we propose, EMS3, is an exact algorithm. I.e., it finds all the motifs present in the input sequences. EMS3 can be also viewed as a divide and conquer algorithm. In this paper, we provide theoretical analyses to establish the efficiency of EMS3. Extensive experiments on standard benchmark datasets (synthetic and real-world) show that the proposed algorithm outperforms the existing state-of-the-art algorithm (EMS2).
Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria

Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration.This is achieved by integrating multiple geoscience datasets using mathematical tools to determine spatial relationships with known mineral occurrences in a GIS environment to produce mineral prospectivity map.The study area lies within Latitudes 9° 00ʹ N to 9° 15ʹ N and 6° 45ʹ to 7° 00ʹ E and is underlain by rocks belonging to the Basement Complex of Nigeria which include migmatitc gneiss, schist, granite and alluvium.The datasets used in this study consist of aeromagnetic, aeroradiometric, structural, satellite remote sensing and geological datasets.Published geologic map of the Sheet 185 Paiko SE was used to extract lithologic and structural information.Landsat images were used to delineate hydroxyl and iron-oxide alterations to identify linear structures and prospective zones at regional scales.ASTER images were used to extract mineral indices of the OH-bearing minerals including alunite, kaolinite, muscovite and montmorillonite to separate mineralized parts of the alteration zones.Aeromagnetic data were interpreted and derivative maps of First Vertical Derivative, Tilt derivative and Analytic signal were used to map magnetic lineaments and other structural attributes while the aeroradiometric dataset was used to map hydrothermally altered zones.These processed datasets were then integrated using Fuzzy Logic modelling to produce a final mineral prospectivity map of the area.The result of the model accurately predicted known deposits and highlighted areas where further detailed exploration may be conducted.
NASA Space Apps COVID-19 Challenge Winners Share Stories of Innovation

From a fourth grader in Canada to a respiratory specialist in Australia, from a college student in Michigan to a volunteer firefighter in Chile, the winners of this year’s Space Apps COVID-19 Challenge represent a rich diversity of culture, skills, and life experiences.
Improving Oral Reading Fluency of Struggling ESL Readers with Assisted Repeated Reading Using Graded Readers

One has to learn and master the four major language skills, namely listening, speaking, reading and writing, to be deemed proficient and fluent in the target language.In relation, reading skill is an essential language component and effective reading will allow for successful linguistic input so that comprehension, the ultimate outcome of reading skill, can be attained.Before comprehension can be established, it is important for readers to first read fluently.However, learning to read in a second or new language is often quite a challenging task.It is even more demanding for children with reading difficulties.Thus, this paper aims to explore the effectiveness of the reading intervention -assisted repeated reading using graded readers -in improving the oral reading fluency of struggling elementary-aged readers learning English as a second language (ESL).This action research based study was carried out with 7 struggling ESL readers from a primary school in the period of 30 days.The findings collected from three different data collection methods revealed that the participants were able to read with higher accuracy in word recognition and faster reading rate with improved prosodic reading.With the positive outcomes gathered from this study, it can be concluded that assisted repeated reading using graded readers did improve the oral reading fluency of struggling ESL readers.Further suggestions on how assisted repeated reading with graded readers can be adapted and applied to improve the oral reading fluency of struggling second language readers were also given.
A Complexity Theoretical Study of Fuzzy <i>K</i> -Means

The fuzzy K -means problem is a popular generalization of the well-known K -means problem to soft clusterings. In this article, we present the first algorithmic study of the problem going beyond heuristics. Our main result is that, assuming a constant number of clusters, there is a polynomial time approximation scheme for the fuzzy K -means problem. As a part of our analysis, we also prove the existence of small coresets for fuzzy K -means. At the heart of our proofs are two novel techniques developed to analyze the otherwise notoriously difficult fuzzy K -means objective function.
Emerging Trends and Research Issues on Blockchain Technology for 5G-Enabled Industrial IoT

A blockchain is a technology with an extensive range of potential industrial applications. Internet of Things (IoT) has detonated with a lot of interlinked gadgets, from centralized computers to coolers and indoor regulators. These gadgets offer guarantee for more prominent computerization and control as well as the capacity for miniaturized exchanges that didn’t exist earlier. The approach of blockchain offers a captivating way of overseeing circulated exchanges nowadays. Even the blockchain technology is an emerging trend; the utilization of blockchain in IoT applications and industrial IoT (IIoT) is moderately new, particularly at the lower end of the figuring range. Thus, the guide for what’s to come is muddled, and there are a few difficulties and questions that should be tended to, for example, trust, security, and proficiency. A portion of the applications related to blockchain in the field of IoT and IIoT is being actualized, including supply chains, more brilliant vitality, and social insurance. Various applications of IoT, along with IIoT and its usage with 5G technology, are being discussed. The procedures for defeating a large number of difficulties, which should prompt adequate appropriations of blockchain for IoT, are discussed. Finally, a preventative note on the research issues and recent trends in the field of blockchain has been made.
Combining Motion Primitives and Image-Based Visual Servo Control

Abstract Combining perception feedback control with learning-based open-loop motion generation for the robot’s end-effector control is an attractive solution for many robotic manufacturing tasks. For instance, while performing a peg-in-the-hole or an insertion task when the hole or the recipient part is not visible in the eye-in-the-hand camera, an open-loop learning-based motion primitive method can be used to generate end-effector path. Once the recipient part is in the field of view (FOV), visual servo control can be used to control the motion of the robot. Inspired by such applications, this paper presents a control scheme that switches between Dynamic Movement Primitives (DMPs) and Image-based Visual Servo (IBVS) control combining end-effector control with perception-based feedback control. A simulation result is performed that switches the controller between DMP and IBVS to verify the performance of the proposed control methodology.
Worry less about the algorithm, more about the sequence of events

<sec><b>Background</b>&nbsp;Many algorithms exist for learning network structure and parameters from data. Some of these algorithms include Grow Shrink, Incremental Association Markov Blanket, IAMB, Fast IAMB, and Interleaved IAMB, Hill Climbing, Restricted Maximization and Maximum-Minimum Hill Climbing. These algorithms optimize the fit to the data, while ignoring the order of occurrences of the variables in the network structure. </sec> <sec><b>Objective</b>&nbsp;This paper examines if sequence information (i.e., one variable occurs before another) can make algorithms for learning directed acyclical graph networks more accurate. </sec> <sec><b>Methods</b>&nbsp;A 13- variable network was simulated, where information on sequence of occurrence of some of the variables was assumed to be known. In each simulation 10,000 observations were generated. These observations were used by 4 conditional dependency and 4 search and score algorithms to discover the network from the simulated data. Partial sequence was used to prohibit a directed arc from a later variable to an earlier one. The Area under the Receiver Operating Curve (AROC) was used to compare the accuracy of the sequence-constrained and unconstrained algorithms in predicting the last node in the network. In addition, we examined the performance of sequence constrained algorithms in a real data set. We analyzed 1.3 million disability assessments done on 296,051 residents in Veterans Affairs nursing homes; where the sequence of occurrence of variables was inferred from the average age of occurrence of disabilities. We constructed three networks using Grow-Shrink algorithm, one without and the other two use two permutation of the observed sequence. The fit of these three models to data was examined using Bayesian Information Criterion (BIC). </sec> <sec><b>Results</b>&nbsp;In simulated data, algorithms that used sequenced constraints (AROC = 0.94, confidence intervals, C.I. = 0.86 to 1) were significantly more accurate than the same algorithm without use of sequence constraints (AROC = 0.74, C.I. = 0.65 to 0.83). The agreement between discovered and observed networks improved from range of 0.54 to 0.97 to range of 0.88 to 1. In the real data set, the Bayesian network constructed with use of sequence had 6% lower BIC scores. </sec> <sec><b>Conclusions</b>&nbsp;Sequence information improved accuracy of all eight learning algorithms and should be routinely examined in learning network structure from data. </sec>
A geotagged image dataset with compass directions for studying the drivers of farmland abandonment

In this work, we present a dataset containing a collection of pictures taken during the fieldwork of a farmland abandonment study. Data was taken in 2010 with a compact camera that incorporates GPS and a digital compass sensor. The photographs were taken as part of a GIS database. Using their Exif metadata, we created a layer of geographic fields of view (geoFOVs) that can be used to perform specific spatial queries. The dataset contains 2,235 pictures and GIS layers of geoFOVs contextualising the agricultural plots being photographed. The dataset is hosted in a Zenodo dataset repository.
Knowledge Description Frame of Learning Resources for Recommendation System: From the Perspectives of Learning Psychology

The paper firstly analyzed the three common issues that learners encounter during online learning, namely information disorientation, cognitive overload and discontinuous flow from the perspective of learning psychology in the study. Then, to recommend accurate and essential learning resources to different learners, characteristic information of the learner model in Chinese national standards is extracted. The paper also explained how the characteristic information of learners influences the use and demand of learning resources among different learners as they encounter the aforementioned issues. Finally, the knowledge description frame adding the description of learning suggestion and learners’ characteristic information is constructed, in order that the recommendation of online learning resources can be more efficiently improved. In addition, a vision of how the frame can be applied in K12 educational database is provided.
Deep Learning based Pedestrian Tracking Framework using Body Posture Information

This paper presents a deep learning based (human) pedestrian position tracking methodology from a video. In a manufacturing shop floor, the moving trajectories of pedestrians can be utilized for various applications, such as layout optimization of facilities, and material flow analysis. To improve the accuracy of the proposed human tracking method, we make use of the body posture information of workers. The body posture information is extracted by using the ‘Open- Pose’ library which represents the first real-time multi-person system to jointly detect human body, hand, facial, and foot key points on single images. The proposed methodology consists of four major steps; 1) Pedestrian recognition from a video, 2) Pedestrian tracking by using the posture information, 3) correction of potential tracking errors, and 4) finding the moving trajectories of pedestrians. The proposed methodology has been implemented and test with various examples.
Digital Rock Core Images Super Resolution via SRCNN Based on Accelerated Bicubic Interpolation

The capability of Super Resolution Convolutional Neural Networks (SRCNN) has been proved to enhance resolution of images. We applied SRCNN to enhance digital rock core images that play an important role in analyzing rock core. In this process, we noticed that the bicubic interpolation algorithm that is the first step of SRCNN might improve the speed by adjusting the calculation strategy. We proposed an SRCNN based on accelerated bicubic interpolation and tested the performance with 2000 digital rock core images. The experiment demonstrated that the accelerated bicubic interpolation algorithm faster than improved region-based bicubic image interpolation algorithm and standard bicubic interpolation algorithm, and demonstrated the feasibility of SRCNN based on our proposed algorithm to produce higher resolution digital rock core images.
Luck is What Happens When Preparation Meets Opportunity: Building Israel’s Multilingual, Multiscript Authority Database

Mazal, the Israeli national authority file, is a multilingual, multiscript database combining authority control with identity management. Equivalent headings in each of the country's four languages of cataloging can retrieve all library holdings, in any language, connected to any heading in the authority record. This paper will describe the consortium of 20 academic, public, and special libraries, led by the National Library of Israel, which is the first communal authority file to be natively created within the Alma Community Zone. Even a worldwide pandemic couldn't slow Mazal down; lockdown proved to be an opportunity for significant improvements and new projects.
Cloud Computing: Overview of PaaS with Force.com Platform

Cloud Computing is middle-of-road for many technology companies for computing resources. Cloud computing is classified into three kinds Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS). Cloud platforms are pay-as-you-use, on-demand, easy to design, deploy, data storage, backup, and other utilities. In the year 2015 Platform-as-a-Service came into buzz it established its strong roots to provide technical service. This paper gives an overview of cloud computing and focuses on platform-as-a-Service (PaaS) along with an example of the Force.complatform.The paper also discusses the architecture, stack implementation, pros, and cons of Force.com.
Røys som fenomen. Rydning eller grav?

Cairns in one form or another are the most common type of prehistoric structure encountered during archaeological surveys in Rogaland County, southwestern Norway. In Norway, the responsibility for pre-development archaeological investigations is divided between county archaeologists and regional museums with the former undertaking site identification surveys and the latter carrying out subsequent excavations. Archaeological surveying is typically non-invasive and distinguishing burial cairns from clearance cairns often proves to be a difficult task. It is frequently argued that separating the two categories based on size and form alone is not sufficient. In this article, it is shown that, in addition to visual analysis, an understanding of the surrounding prehistoric landscape within which the cairn is situated is crucial when interpreting these structures. The interpretive utility of new methodologies such as Lidar and analysis of soil identification maps are also highlighted. Arguments are based primarily on data from localities containing cairns encountered during the E39 Ålgård–Hove project carried out in 2016 by Rogaland County, as well as a site excavated by the Museum of Archaeology, University of Stavanger in 2011.
Persuasion Act in the Proposal Movie

The act of persuasion happens anywhere and anytime. When we need a help, we persuade other to do what we need. In order to have a successful persuasion, we apply certain strategy. This pragmatic paper wants to analyse the effective strategies in order to have an effective persuasive acts. Pragmatics is chosen as the method since the analysis is about the language used in certain context, in this case persuasion. This paper elaborates how certain speech acts can help the speaker to perform persuasion successfully. Besides, the researcher also analyses how the attempts of persuading violate certain maxims stated by Grice. The researcher took a movie entitled The Proposal (2009) as the data since it shows how the main characters apply certain speech acts and strategies in order to achieve effective persuasion. In this movie, Margaret and Andrew are trying to persuade each other in order to get different advantages through faking marriage. The researcher takes the dialogue of those two characters as the data. Then, the data was analysed using speech act theory, implicature, and language function theory on persuasion. The result of the study is the successful persuasion relies on using appropriate speech act, flouting the maxim of quantity, and applying good persuasive strategy. Key words: Persuasion, Speech Act, Maxim, Strategy
Order in Chaos: Prioritizing Mobile App Reviews using Consensus Algorithms

The continuous growth of the mobile apps industry creates a competition among apps developers. To succeed, app developers must attract and retain users. User reviews provide a wealth of information about bugs to fix and features to add and can help app developers offer high-quality apps. However, apps may receive hundreds of unstructured reviews, which makes transforming them into change requests a difficult task. Approaches exist for analyzing and extracting topics from mobile app reviews, however, prioritizing these reviews has not gained much attention. In this study, we introduce the use of a consensus algorithm to help developers prioritize user reviews for the purpose of app evolution. We evaluate the usefulness of our approach and meaningfulness of its consensus rankings on four Android apps. We compare the rankings against reviews ranked by app developers manually and show that there is a strong correlation between the two (average Kendall rank correlation coefficient = 0.516). Thus, our approach can prioritize user reviews and help developers focus their time/effort on improving their apps instead of on identifying reviews to address in the next release.
Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model

The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition. Tasks such as lexical analysis need to be based on sentence segmentation because of the reason that a plenty of ancient books are not punctuated. However, step-by-step processing is prone to cause multi-level diffusion of errors. This paper designs and implements an integrated annotation system of sentence segmentation and lexical analysis. The BiLSTM-CRF neural network model is used to verify the generalization ability and the effect of sentence segmentation and lexical analysis on different label levels on four cross-age test sets. Research shows that the integration method adopted in ancient Chinese improves the F1-score of sentence segmentation, word segmentation and part of speech tagging. Based on the experimental results of each test set, the F1-score of sentence segmentation reached 78.95, with an average increase of 3.5%; the F1-score of word segmentation reached 85.73%, with an average increase of 0.18%; and the F1-score of part-of-speech tagging reached 72.65, with an average increase of 0.35%.
Future Teachers Forming Professional Expectations in Modern Educational Conditions

The practical implications of the study: The nuances of professional expectations development among future teachers have been identified. When organizing professional education, these nuances should be considered for increased effectiveness and for greater success of graduates in their professional activities.
Acquiring Custom OCR System with Minimal Manual Annotation

We describe a development of a custom OCR system, which is designed specifically for a linguistic analysis of texts printed during the early modern period. This analysis requires precise detection of individual graphemes, and we, therefore, could not apply standard approaches that transcribe whole lines in an end-to-end fashion. We also describe our use of synthetically generated images, which allow us to avoid manual annotation of a large training set.