Reviewerly/exHarmony huggingface dataset:
The Improvement of Students’ Information Literacy-based on the Association of College and Reseach Libraries (ACRL) using Empowering 8

The result of a preliminary study conducted by the researcher showed that the information literacy of students at the Elementary School Teacher (PGSD) Program was low. Therefore, this research was done to understand the effect of Empowering 8 model application in improving students’ information literacy. This research used the quasi-experimental method with a pretest-posttest control group design. The research participants were 66 students, consisting of 33 students in the experimental class and 33 students in the control class. This research utilized some instruments, including pretest, posttest, questionnaires and observation sheets. The results obtained were: the students’ information literacy ability based on ARCL experimental class was in the high category. It revealed that application of Empowering 8 model had a positive effect on the students’ information literacy.
An Efficient Analysing -N Looked based Adverse

Mutual unobtrusive components and taught structures have earned constrained side enthusiasm from each steganographers and futurists within the end a couple years. Given the a la mode reputation of amicable start, authorities genuinely need the examination of neighbourhood-request frameworks. We depict an evaluation of checksums, which we perceive DaedalSai.
An Exhaustive Survey on Meta-Heuristic Algorithms for Unimodal and Multimodal Optimization

A review of the global feature color comprising of seven different metrics are discussed here. The color is the most powerful feature for describing the images. This study contains four sections. The first section explains the related techniques used in various papers. The second section explains the two different kinds of metrics. (1) Similarity metrics such as Cosine and Correlation and (2) Dissimilarity metrics such as Euclidean, Manhattan, Bhattacharyya, Chi-Squared and Intersection. The third section explains experiment results using CALTECHUCSD Birds-200 image library. The fourth section gives the conclusion and future work. In this experiment, the query image can be divided into trained (indexed) or untrained (non-indexed). In the similarity metric analysis, the experimental results show that the cosine similarity gives better similarity score than correlation. Similarly, in the dissimilarity metric analysis, the Bhattacharyya gives a better result than other distance metrics.
Implementing Agile Methodologies in a Project-Based Learning Laboratory

In this study, students at Engineering Dynamics, a distance-learning course offered to engineering technology students, were engaged in a design-based laboratory assignment.Teams of students from multiple campuses worked on design, manufacturing, and testing of a dynamic mechanism.Due to the cross-campus collaborative nature of this project and in the absence of little to no face-to-face interaction between team members, a key factor in success of the team was to manage team effort through an effective project management strategy.As a solution, ideas from agile way of thinking were incorporated in this design-based project.Agile rituals such as stand-up meetings, retrospectives and iterative reviews were implemented for transparent teamwork and timely feedback.The effectiveness of how this intervention helped students was assessed through multiple tools, such as online surveys and targeted design-based questions during mid-semester exams.
Performance Assay of Big IoT Data Analytics Framework

Evaluation of Internet of Things (IoT) technologies in real life has scaled the enumeration of data in huge volumes and that too with high velocity, and thus a new issue has come into picture that is of management &amp; analytics of this BIG IOT STREAM data. In order to optimize the performance of the IoT Machines and services provided by the vendors, industry is giving high priority to analyze this big IoT Stream Data for surviving in the competitive global environment. Thses analysis are done through number of applications using various Data Analytics Framework, which require obtaining the valuable information intelligently from a large amount of real-time produced data. This paper, discusses the challenges and issues faced by distributed stream analytics frameworks at the data processing level and tries to recommend a possible a Scalable Framework to adapt with the volume and velocity of Big IoT Stream Data. Experiments focus on evaluating the performance of three Distributed Stream Analytics Here Analytics frameworks, namely Apache Spark, Splunk and Apache Storm are being evaluated over large steam IoT data on latency &amp; throughput as parameters in respect to concurrency. The outcome of the paper is to find the best possible existing framework and recommend a possible scalable framework.
A universal compression strategy using sorting transformation

Although traditional universal compression algorithms can effectively utilise repetition located in a slide window, they cannot take their own advantages for some message source in which similar messages are distributed uniformly. In this paper, we come up with a universal segmenting-sorting compression algorithm to solve this problem. The key idea is to reorder the message source before compressing it with Lz77 algorithm. We design transformation methods for two common data types, corpus of webpages and access log. The experimental results show that segmenting-sorting transformation is truly beneficial to compression ratio. Our new algorithm is able to make compression ratio 20% to 50% lower than naive Lz77 algorithm does and takes almost the same decompression time. For some read-heavy source segmenting-sorting compression can reduce space cost while guaranteeing throughput.
Preface of “The Eighth Symposium on Advanced Computation and Information in Natural and Applied Sciences”

First Page
Semantic Recognition Method of Innovation Points in Chinese Science and Technology Abstracts

The published Chinese Science and Technology Abstracts provide reliable semantic fact data of problems, methods and results in scientific research activities, and lay a solid foundation for the dissemination of innovation and interdisciplinary new knowledge discovery. How to accurately identify and separate them will be the key problem to extract innovation points by using artificial intelligence technology. This paper presents an innovative semantic recognition and classification method. In this method, the scientific and technological abstracts are processed by five classification algorithms according to the syntactic and semantic functions, and then the sentence type and sentence semantic position structure characteristics of the results of the five classification algorithms are deeply analyzed. The problems, methods and results of the innovative points of scientific and technological abstracts are classified faster, and the classification accuracy is 98.7%. The effectiveness of semantic recognition and classification algorithm is verified by manual test. Experimental results show that this method has the advantages of fast algorithm, high classification accuracy and good universality.
The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling

The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input including . The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules.The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.
An Interdisciplinary Approach To Information Systems Security Education: A Case Study

More specifically, he is interested in software security, network/cyber security, security management particularly in small and medium-sized organizations, software architecture,Architecture Description Languages (ADLs), object-oriented software development, formal methods, and requirements engineering.He has a significant industry experience (Sprint and IBM) in architecting and implementing secure, high-performance software for large-scale network management systems.
An Associative Based Approach to Analyzing an Online Learning Environment

Recent years have shown an increase in both in the number and use of online educational learning environments.Correspondingly, there is a greater availability of rich data sets that describe both the learners themselves and their interactions with the online learning environment.In this paper, we demonstrate the use of a data mining tool, association analysis, to analyze this data.We demonstrate its applicability in understanding how learners use a particular online learning environment and for the identification of learner interactions with the environments that are associated with particular learning outcomes.The methodology is first described and then is demonstrated as a case study through its application to the CareerWISE online learning environment.
Orthogonal Vector-Valued Wavelets on $$\mathbb {R}_{+}$$

We have considered the notion of vector-valued multiresolution analysis (VMRA) on positive half line $$\mathbb {R}_+$$ and studied associated vector-valued wavelets and wavelet packets. Xia and Suter in 1996 generalized the concept of multiresolution analysis (MRA) on $$\mathbb {R}$$ to vector-valued multiresolution analysis (VMRA) on $$\mathbb {R}$$ and studied associated vector-valued wavelets. Farkov (Farkov in Orthogonal p-wavelets on $$R^{+}$$ . St. Petersburg University Press, Saint Petersburg, p. 426, 2005) introduced MRA on $$\mathbb {R}_+$$ . In this chapter, we have introduced vector-valued multiresolution analysis (VMRA) on $$\mathbb {R}_+$$ , where the associated subspace $$V_0$$ of $$L^{2}(\mathbb {R}_+,\mathbb {C}^N)$$ has an orthonormal basis, a family of translates of a vector-valued function $$\varPhi $$ , i.e, $$\{\varPhi (x\ominus l)\}_{l\in \mathbb {Z}_+}$$ , where $$\mathbb {Z}_+$$ is the set of nonnegative integers. The necessary and sufficient condition for the existence of associated vector-valued wavelets has been obtained and the construction of vector-valued multiresolution analysis (VMRA) on $$\mathbb {R}_{+}$$ has been presented.
Mobile App Development: A Cross-Discipline Team-based Approach to Student and Faculty Learning

Entrepreneurship Centers for teaching innovations in a team-taught multi-disciplinary course.
Mediating Collective Discussions Using an Intelligent Argumentation-Based Framework

This paper briefly describes Amanda(l), a framework for mediating collective discussions in distance learning environments. The objective of this framework is to help tutors achieve better results from group discussions and improve knowledge transfer among the participants. The overall idea is to organize the group discussion in an argumentation tree and involve the participants in successive discussion activities. The coordination of the discussion is made by a set of intelligent mechanisms which reason over the discussion and propose new interactions among the participants. Amanda advances the discussion by generating progressive discussion cycles until a desired set of target conditions are observed. At each discussion cycle, the system redistributes discussion tasks among the participants to ensure a desired degree of agreement and participation among them. In this short paper we describe the underlying coordination principles and the use of knowledge models for producing natural language questions.
Reuse a "Software Reuse" Course

Software reuse is the use of existing software artifacts and knowledge to construct new software.Systematic reuse has always been a major goal in software engineering since it promises large gains in productivity, quality, and time-to-market reduction.One of the main reasons software reuse has not been systematically practiced is due to the lack of education: In a survey collected from 113 respondents from 29 organizations, primarily in the US, only 13% said they had learned about reuse in school 1 .This paper presents the creation of a graduate-level seminar course on software reuse in a US institution whose software engineering program aims to educate students with strong technical skills so they can start work as productive members on a software development team.Rather than reinventing the wheel in curriculum development, we adapted a software reuse course developed by Frakes at Virginia Tech 2 .This paper reviews the major challenges of software reuse education, describes the reuse of Frakes' course modules and assessments, and discusses the modifications we made in our course.In particular, we modified our course by incorporating two pedagogical principles: active learning and cooperative learning.Redesigning the course from a lecture format to a seminar format allowed the students to play active roles in leading the classes and in discovering term paper topics that suited to their own research interests.Fostering collaborations among students and interactions between students and instructor allowed the students to recognize their individual accountability to the success of the group and the entire course.This paper reports instructor experiences, lessons learned, and recommendations for other educators considering the application of an active and cooperative learning approach for their software reuse courses.
Epilogue: Summary and outlook

Open Journal of Big Data (OJBD) is an open access journal addressing aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents three articles in the second issue. The first paper is on Big Data in the Cloud. The second paper is on Statistical Machine Learning in Brain State Classification using EEG Data. The third article is on Data Transfers in Hadoop. OJBD has a rising reputation thanks to the support of research communities, which has helped us set up the First International Conference on Internet of Things and Big Data (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals.
Serials Control at Texas A&amp;M University: Dispersion or Integration?

At Texas A&M University, there is no distinct serials department. Rather, there are several units whose primary goals are to maintain serials control, whether by cost, acquisition, receipt, cataloging, or use. This article describes an alternate approach to that of the integrated serials department. Emphasis is placed on problems encountered with scattered units, communication processes used to solve problems and maintain some control over the complexities of serials and changes wrought in the serials operation with the implementation of an integrated library system
Singe Image Rain Removal with Unpaired Information: A Differentiable Programming Perspective

Single image rain-streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. Previous works solve this problem using various hand-designed priors or by explicitly mapping synthetic rain to paired clean image in a supervised way. In practice, however, the pre-defined priors are easily violated and the paired training data are hard to collect. To overcome these limitations, in this work, we propose RainRemoval-GAN (RRGAN), the first end-to-end adversarial model that generates realistic rain-free images using only unpaired supervision. Our approach alleviates the paired training constraints by introducing a physical-model which explicitly learns a recovered images and corresponding rain-streaks from the differentiable programming perspective. The proposed network consists of a novel multiscale attention memory generator and a novel multiscale deeply supervised discriminator. The multiscale attention memory generator uses a memory with attention mechanism to capture the latent rain streaks context at different stages to recover the clean images. The deeply supervised multiscale discriminator imposes constraints at the recovered output in terms of local details and global appearance to the clean image set. Together with the learned rainstreaks, a reconstruction constraint is employed to ensure the appearance consistent with the input image. Experimental results on public benchmark demonstrates our promising performance compared with nine state-of-the-art methods in terms of PSNR, SSIM, visual qualities and running time.
Saliency Detection via the Improved Hierarchical Principal Component Analysis Method

Aiming at the problems of intensive background noise, low accuracy, and high computational complexity of the current significant object detection methods, the visual saliency detection algorithm based on Hierarchical Principal Component Analysis (HPCA) has been proposed in the paper. Firstly, the original RGB image has been converted to a grayscale image, and the original grayscale image has been divided into eight layers by the bit surface stratification technique. Each image layer contains significant object information matching the layer image features. Secondly, taking the color structure of the original image as the reference image, the grayscale image is reassigned by the grayscale color conversion method, so that the layered image not only reflects the original structural features but also effectively preserves the color feature of the original image. Thirdly, the Principal Component Analysis (PCA) has been performed on the layered image to obtain the structural difference characteristics and color difference characteristics of each layer of the image in the principal component direction. Fourthly, two features are integrated to get the saliency map with high robustness and to further refine our results; the known priors have been incorporated on image organization, which can place the subject of the photograph near the center of the image. Finally, the entropy calculation has been used to determine the optimal image from the layered saliency map; the optimal map has the least background information and most prominently saliency objects than others. The object detection results of the proposed model are closer to the ground truth and take advantages of performance parameters including precision rate (PRE), recall rate (REC), and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>F</mml:mi></mml:math>-measure (FME). The HPCA model’s conclusion can obviously reduce the interference of redundant information and effectively separate the saliency object from the background. At the same time, it had more improved detection accuracy than others.
An Effective Transition-based Model for Discontinuous NER

Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions.We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER.Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.
Measurements of Quantum Hamiltonians with Locally-Biased Classical Shadows

Abstract Obtaining precise estimates of quantum observables is a crucial step of variational quantum algorithms. We consider the problem of estimating expectation values of quantum Hamiltonians, obtained on states prepared on a quantum computer. We propose a novel estimator for this task, which is locally optimised with knowledge of the Hamiltonian and a classical approximation to the underlying quantum state. Our estimator is based on the concept of classical shadows of a quantum state, and has the important property of not adding to the circuit depth for the state preparation. We test its performance numerically for molecular Hamiltonians of increasing size, finding a sizable reduction in variance with respect to current measurement protocols that do not increase circuit depths.
Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation

Several geographical latent representation models that capture geographical influences among points-of-interest (POIs) have been proposed. Although the models improve POI recommendation performance, they depend on shallow methods that cannot effectively capture highly non-linear geographical influences from complex user-POI networks. In this paper, we propose a new graph-based geographical latent representation model (GGLR) which can capture highly non-linear geographical influences from complex user-POI networks. Our proposed GGLR considers two types of geographical influences: ingoing influences and outgoing influences. Based on a graph auto-encoder, geographical latent representations of ingoing and outgoing influences are trained to increase geographical influences between two consecutive POIs that frequently appear in check-in histories. Furthermore, we propose a graph neural network-based POI recommendation model (GPR) that uses the trained geographical latent representations of ingoing and outgoing influences for the estimation of user preferences. In the experimental evaluation on real-world datasets, we show that GGLR effectively captures highly non-linear geographical influences and GPR achieves state-of-the-art performance.
Image Defogging Quality Assessment: Real-World Database and Method

Fog removal from an image is an active research topic in computer vision. However, current literature is weak in the following two areas which in many ways are hindering progress for developing defogging algorithms. First, there is no true real-world and naturally occurring foggy image datasets suitable for developing defogging models. Second, there is no suitable mathematically simple and easy to use image quality assessment (IQA) methods for evaluating the visual quality of defogged images. We address these two aspects in this paper. We first introduce a new foggy image dataset called multiple real-world foggy image dataset (MRFID). MRFID contains foggy and clear images of 200 outdoor scenes. For each scene, one clear image and 4 foggy images of different densities defined as slightly foggy, moderately foggy, highly foggy, and extremely foggy, are manually selected from images taken from these scenes over the course of one calendar year. We then process the foggy images of MRFID using 16 defogging methods to obtain 12,800 defogged images (DFIs) and perform a comprehensive subjective evaluation of the visual quality of the DFIs. Through collecting the mean opinion score (MOS) of 120 subjects and evaluating a variety of fog-relevant image features, we have developed a new Fog-relevant Feature based SIMilarity index (FRFSIM) for assessing the visual quality of DFIs. We present extensive experimental results to show that our new visual quality assessment measure, the FRFSIM, is more consistent with the MOS than other IQA methods and is therefore more suitable for evaluating defogged images than other state-of-the-art IQA methods. Our dataset and relevant code are available at http://www.vistalab.ac.cn/MRFID-for-defogging/.