Reviewerly/exHarmony huggingface dataset:
Exploring augmented reality to support K-12 literacy: a case study

Traditionally literacy education has focused on teaching reading comprehension, writing, and effective communication. However, the nature of digital communications technology has so permeated the way we communicate that it has become more than a tool in many ways. This case study explored students' use of Augmented Reality (AR) to engage in literacy activities. Specifically, the purpose of the study was to explore students' and teachers' perceived benefits and challenges of using AR tools to support engagement with literacy activities. One hundred and thirty-seven students from five K-12 school districts participated in the AR activities during a university literacy festival. Data collection included student reflections, teacher interviews, and activity observations. The study indicated the use of AR tools promotes student engagement in literacy activities, teachers are eager to further explore the potential of AR tools to support engagement in literacy, AR tools have the capacity to bring literacy to life in a 21st century learning environment, and teachers need additional support to integrate AR tools for literacy instruction.
Trajectory distributed receding planning for UAV swarms subject to limited communication distance

For the security and efficiency issues of trajectory planning of unmanned aerial vehicle (UAV) swarms subject to the limited communication distance, this paper establishes the distributed receding planning framework and proposes the local-priority-decoupled sequential convex programming method (LPD-SCP). LPD-SCP divides swarm trajectory planning problems into a series of short-horizon single-UAV convex optimization subproblems, which can improve the computational efficiency and security of trajectories. This paper derives the length of feasible planning horizon to ensure the safety of trajectories. The communication-distance-limited local-priority decoupling mechanism is designed to adjust the priority of UAVs in the neighborhood according to the communication condition for achieving collision avoidance under communication distance limitation. In addition, the trajectory-time-consistency adjustment strategy is customized to realize the flight time consistency of UAVs in the neighborhood by updating the lower boundary constraint of flight time. It is theoretically guaranteed that the proposed LPD-SCP can obtain a swarm trajectory in several receding planning horizons subject to flight constraints. Simulation results demonstrate that LPD-SCP can plan the cooperative trajectories satisfying the flight performance, collision avoidance, and time consistency constraints under communication distance limitation, and the runtime of generating the short-horizon rendezvous trajectories for 15 UAVs is less than 4 s.
Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.
Instance Segmentation of Overlapping Cervical Cells Based on Boundary Tracking

The segmentation of cervical cell images with overlapping cells is a challenging topic. To solve this difficulty, this paper proposes a cell segmentation method by tracking the boundary for each cell based on the Semantic segmentation result. Inspired by the idea of target tracking to find the position of target in each frame, we try to find the radius of cell contour in each angle . First, use a improved U-net network to divide image pixels into cell nucleus, cytoplasm, background, and overlapping areas to achieve cell semantic segmentation. Then, use each cell nucleus as center to track the boundary point in different angle and finally get the cell contour. The experimental results show that the method in this paper can segment overlapping cervical cells effectively.
Measuring harmfulness of class imbalance by data complexity measures in oversampling methods

Many real world applications consist of skewed datasets which result in class imbalance problem. During classification, class imbalance cause underestimation of minority classes. Researchers have proposed a number of algorithms to deal with this problem. But recent research studies have shown that some skewed datasets are unharmful and applying class imbalance algorithms on these datasets lead to degenerated performance and increased execution time. In this research paper, we have pre-estimated the degree of harmfulness of class imbalance for skewed classification problems, using two of the data complexity measures: scatter matrix based class separability measure and ratio of intra-class versus inter-class nearest neighbours. Also the performance of oversampling-based class imbalance classification algorithms have been analysed with respect to these data complexity measures. The experiments are conducted using k-nearest neighbour (k-nn) and naive Bayes as the base classifiers for this study. The obtained results illustrate the usefulness of these measures by providing the prior information about the nature of the imbalance datasets that help us to select the more efficient classification algorithm.
GWKG: semi-automatic construction of the Great Wall Knowledge Graph

This study proposes to construct the Great Wall knowledge graph (GWKG) in a semi-automatic way. First, under the guidance of domain experts, we build the professional dictionary and ontology layer, where BERT is applied for Named Entity Recognition. The resulting entities are clustered by Word2Vec to automatically refine the ontology layer. Then, we conduct Relation Extraction based on semi-supervision, link entities to encyclopedia websites, and obtain semi-structured information by crawler technology for attribute filling. Finally, we visualize the GWKG and report the results of multiple data formats. The GWKG consists of 34 ontology concepts, 33 types of relations, more than 6,000 entities and attributes, and 720,000 Chinese corpora.
SAR image target recognition algorithm based on improved residual shrinkage network

Target recognition in SAR image has always been a research hotspot in the world. Aiming at the problem of low target recognition rate in SAR image, this paper proposes a neural network model suitable for SAR image classification, improves the residual shrinkage network, and uses two-channel one-dimensional convolution to improve the residual shrinkage network. On the premise of consuming only a small amount of computation, the information extraction degree of the module is improved, and it is used as the backbone to build the model. On the premise of low parameter quantity and complexity, the recognition rate is 99.4%.
Serving IIIF and DTS APIs specifications from TEI data via XQuery with support from a SPARQL Endpoint

This paper presents two modules, one serving the IIIF presentation API, and another the three Distributed Text Services API specifications (Collection, Navigation and Document), as well as an additional experimental Web Annotation and indexes API. These are all served from XML TEI data with a RESTxq XQuery module within an exist-db application which also benefits from direct access to a SPARQL Endpoint containing a serialization in RDF of some of the information in the XML. The setup is not uncommon: we have our data collaboratively edited in GitHub, indexed from there into exist-db and transformed with XSLT to RDF-XML. The RDF-XML is passed on to Apache Jena Fuseki on the same server and is indexed there as well, as RDF, so that the two datasets are parallel and updated synchronously. What I want to argue is that the setup itself and the code involved are integrating part of the knowledge being served. They make assumption on the existing data based on knowledge which is not in the data itself and build the additional representation using that with an additional set of inferences. I conclude the contribution with some examples of use of these modules and their functions.
Transliteration recognition of Tibetan person name based on Tibetan cultural knowledge

Institute of Computing Technology, Chinese Lexical Analysis System (ICTCLAS) is a common tool for Chinese word segmentation and named entity recognition. With this tool, the F1 value of person name recognition from Chinese texts in Tibetan culture is only 40%. We propose a method for transliteration recognition of Tibetan person name based on Tibetan cultural knowledge. Firstly, we leverage Tibetan cultural dictionary to improve the word segmentation performance of ICTCLAS. Then, special contextual features and naming rules of Tibetan person name in their culture are adopted to determine the boundary of Chinese transliteration of Tibetan person name. Finally, the transliteration candidates of Tibetan person name are filtered based on discrimination and reliability. Experiments on a 1.2 M Tibetan text in Tibetan culture show that the method can increase the F1 value of Chinese transliteration recognition of Tibetan person name from 40.08% to 87.92% in ICTCLAS.
2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering (AEMCME 2019)

Preface This issue of Proceedings gathers the papers presented at 2019 2nd International Conference on Advanced Electronic Materials, Computers and Materials Engineering (AEMCME 2019) held on April 19-21, 2019 in Changsha, China. AEMCME is an international conference covering research and development in the field of advanced electronic materials and computers and allow the participation from all over the world. More than 500 papers were submitted on the conference, and AEMCME finally accepted 316 papers after a double blinded peer review process by international reviewers and technical program committee members. Divided into 4 chapters, the papers provide a wide spectrum of researches on wide range of advanced electronic materials and computers. The chapters are devoted to Materials Engineering, Mechanical Engineering, Computer Programming and Industrial Design, as well as Software Engineering and Algorithm. Specific research results by conference participants were presented and examined in the light of the frameworks outlined above, which is of interest to academics, researchers and professionals in this field. Three keynote speeches were presented from Prof. Zhiqiang Hou, Xi’an University of Posts and Telecommunications, China; Prof. Zhezhao Zeng, Changsha University of Science &amp; Technology, China separately. All the talks were very impressive for the high level of professionalism, and in many cases original ideas and activities have been accomplished or proposed. List of Conference Chairs and Conference Technical Committees are available in this pdf.
Feature Engineering

Feature engineering represents the methodological framework that allows to design and generate informative and discriminant feature sets for the machine learning algorithms. These design and development tasks exploit the information and knowledge belonging to the application-specific domain in order to properly define, extract, and evaluate the more informative sets of variables that should be further processed and sometimes transformed during the more advanced stages of running the machine learning algorithms. Feature engineering includes feature design, feature transformation, and feature selection, with various algorithms that can be used according to the application requirements as concerning the discriminant performance, execution speed, and application-specific constraints.
A novel entanglement criterion of two-qubit system

The “separability problem” in quantum information theory is a quite important and well-known hard problem. The low-dimensional system satisfies the PPT criterion. However, the high-dimensional system problem has been shown to be NP-hard problem. In general, it is very difficult to find the analytic solution of the density matrix for the high-dimensional system. Therefore, getting an analytic solution for two-qubit system is an interesting and useful problem. We propose a novel criterion for separability and entanglement-verification of two-qubit system. We expressed the density matrix by a sum of a principal density matrix and six separable density matrices. The necessary and sufficient conditions for the two-qubit system include that if the four involved coefficients [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the principal density matrix [Formula: see text] are separable, then the two-qubit system is separable, otherwise the two-qubit system is entangled. Finally, our criterion results in a totally different conclusion compared to Horodecki’s criterion. We believe that the new criterion is more stringent than existing PPT methods.
SIFT Image Feature Extraction for an Efficient Image Registration

This paper presents an image registration algorithm based on SIFT (Scale Invariant Feature Transform).The obtained descriptors and key points by the SIFT confirms that, the algorithm is very robust to scaling, noise, translation and rotation. At the beginning, the key points are extracted from the image. Later to Match the obtained points, dot products between the unit vectors are calculated. Finally, transformation matrix is obtained by applying RANSAC algorithm. Experimental results shows that the algorithm extracts the better key points, which can be used for used for image registration applications.
Economic Issues in Document Delivery: Access versus Ownership and Library Consortia

In theory, a consortium of libraries that offers members joint collection development and priority access to interlibrary loan can provide savings to its members when compared to commercial document delivery, traditional interlibrary loan, or ownership of journal subscriptions. This paper presents an economic analysis of library consortium, the potential savings from consortium delivery of scholarly articles and joint collection development, and the effects of changes in subscription prices and copyright fees on consortium decision making. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Design of cloud computing-based foreign language teaching management system based on parallel computing

In view of the long response time of the traditional foreign language teaching management system and the inability to guide students to improve their learning interest, a foreign language teaching management system based on parallel computing is proposed. In this method, the cloud architecture of the foreign language teaching system is given under the cloud computing environment, on which the parallel computing method is adopted to design the foreign language teaching management system hardware, which is scalable and flexible. The parallel algorithm is designed and the communication between the modules of the system is implemented with C# language. The experimental results show that response time for each of the four scores of 'Zhang' is 1s in reference (Huang, 2017), the response time for each of them is only 0.6 s in this paper. The system can shorten the response time for query and improve the development speed of web-based foreign language teaching, effectively promotes the development of web-based foreign language teaching and students' interests in foreign language learning.
Design and Analysis of Optimized Stooge Sort Algorithm

Data sorting has many advantages and applications in software and web development. Search engines use sorting techniques to sort the result before it is presented to the user. The words in a dictionary are in sorted order so that the words can be found easily .There are many sorting algorithms that are used in many domains to perform some operation and obtain the desired output. But there are some sorting algorithms that take large time in sorting the data. This huge time can be vulnerable to the operation. Every sorting algorithm has the different sorting technique to sort the given data, Stooge sort is a sorting algorithm which sorts the data recursively. Stooge sort takes comparatively more time as compared to many other sorting algorithms. Stooge sort works recursively to sort the data element but the Optimized Stooge sort does not use recursive process. In this paper, we propose Optimized Stooge sort to reduce the time complexity of the Stooge sort. The running time of Optimized Stooge sort is very much reduced as compared to the Stooge sort algorithm. The existing research focuses on reducing the running time of Stooge sort. Our results show that the Optimized Stooge sort is faster than the Stooge sort algorithm.
A simple decision-making approach for information technology solution selection

Information technology (IT) is an indispensable tool for any organisation today, so the choice of adequate IT solutions is a critically important skill. In the literature, many methods for selecting IT solutions have been proposed, but often they use vague criteria that are very difficult to quantify and complex methods to compare alternatives. So, the application of these methods outside the theoretical articles is restricted, since practitioners need simpler approaches. We propose a simple method of the evaluation of alternative IT solutions based on five criteria, namely the cost of ownership, the time for the change, security risks, acceptance by users, and confidence in the supplier's ability to implement the solution. In accordance with the theory of probabilistic mental models, a reference class is proposed for each criterion and variables that can be measured quantitatively are chosen on its base. To simplify the decision-making process, a weighted production model is used for the comparison of alternatives.
Privacy Preservation in Information System

The information technology revolution has brought drastic change in the way data is collected or generated for decision mining. The accumulated data has no relevance unless it provides certain useful information pertaining to the interest of an organization. The real challenge lies in converting high dimensional data into knowledge and to use this knowledge for the development of the organization. On the other hand, hiding an organization's sensitive information is a major concern. Much research has been carried out in this direction. This chapter discusses various privacy preservation techniques that can be employed in an information system to safeguard the sensitive information of an organization. This chapter also highlights sensitive fuzzy association rules that can be generated from an information system. The authors provide illustrations wherever necessary to give a clear idea of the concepts developed.
Multiple Regression and General Linear Models

Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.
Entanglement degree of high-dimensional quantum low-density parity check codes

The important part of quantum information science is quantum error-correction and quantum computation. In thepractical application, some methods of quantum communication, such as the quantum communication based on the atmosphere transmission, require a mix ofmathematics-potentially to solve the problem. The quantum error-correction code is an efficient method to calculate quantum communication. However,since the construction of quantum error-correction codes depends on the degree of quantum entanglement, it is difficult to be widely used in practice.At present, the graph state is a promising solution to reduce the degree of codewords entanglement. But there are still many difficulties in constructingthe high-dimensional graph state. More importantly, the above difficulties can be skillfully solved by the upper and lower bounds of codewords entanglement.Based on the characteristics of the cyclic difference set of stabilizer codes and the combination of U and B of classical low-density parity check (LDPC) codes,a high-dimensional quantum low-density parity check (QLDPC) code is constructed creatively. By calculating the characteristics of the non-$Z$-type generators ofthe new codewords, the minimum number is obtained and the entanglement upper bound of the new code is acquired. And then, the rank of the check matrix of thenew code is calculated as the lower bound of entanglement. When the upper bound and lower bound of entanglement are different, the learning vectorquantization (LVQ) algorithm in machine learning can be used to simultaneously obtain the codewords entanglement and the coding complexity of the code,in which the relationship between them can be derived. In terms of calculating the running speed, compared with the iterative algorithm in the Lagrange multipliermethod, the running speed of LVQ algorithm is increased by $37.68%$. In this paper, a measurement of quantum codewords entanglement is proposed, which supports a helpful role in thedesign of quantum error-correction codes with higher decoding efficiency.