Reviewerly/exHarmony huggingface dataset:
Developing Entrepreneurial Competencies in Students and Teachers by Stimulating Creativity

Fostering entrepreneurship as a competence leads to divergence from the traditional education paradigms and the teachers and students focus on new, contemporary educational concepts.Teaching staff and students are seen as motivated, decisive, innovative and creative individuals, willing to contribute to a higher quality of everyday life whereby entrepreneurial skills are fostered and integrated in all segments of life.Creativity is regarded as one of the crucial skills related to the entrepreneurial spirit.A creative attitude towards life entails an individual (or society) who is conscious, nonconformist, independent and capable of critical thinking, ready to take risks in order to implement their ideas.Therefore, the aim of this paper is primarily to indicate the importance of stimulating creativity in children from an early age, and then to consider ways of fostering and cultivating creativity in both students and teachers as a prerequisite for the development of entrepreneurial skills.For the purpose of this paper, we analyzed the Arts Education and Serbian Language Curricula for primary schools and considered the compatibility of the teaching topics with the development of entrepreneurial competencies.Practical implications of integrating entrepreneurial skills into Arts Education and Serbian Language Curricula will be represented in the paper.
Securing Route Origin Authorization with Blockchain for Inter-Domain Routing

The inter-domain routing with BGP is highly vulnerable to malicious attacks, due to the lack of a secure means of verifying authenticity and legitimacy of inter-domain routes. Resource Public Key Infrastructure (RPKI) is a new security infrastructure to verify that an IP address block holder has authorized an Autonomous System (AS) to originate routes by maintaining a Route Origin Authorization (ROA) repository, preventing the most devastating prefix hijacks in BGP. However, RPKI is a centralized hierarchical architecture that may empower the centralized authorities to unilaterally revoke or compromise any IP prefixes under their control. To eliminate the risks of RPKI, we present ROAchain, a novel BGP security infrastructure based on blockchain. Different from RPKI, ROAchain is a decentralized architecture, in which each AS maintains a globally consistent and tamper-proof ROA repository, authenticating the legitimacy of route origin and preventing BGP prefix hijacks. In ROAchain, a novel consensus algorithm is proposed to guarantee the strong consistency, scalability, and security of the system. Moreover, an incremental deployment scheme is designed without changing the current BGP protocol. Finally, ROAchain is implemented in Golang and validated on the Google Cloud.
Autonomous learning of english as a foreign language in a culturally integrated b-learning ecosystem

The low level of oral skills in learning English as a foreign language seems to be related to the lack of spaces and opportunities to interact in dynamic learning environments since the texts and study materials are not related to the context in which the student lives. This research answers the question of whether a b-learning ecosystem with devices for monitoring learning and integrated into cultural dimensions of the students’ environment improves the learning of oral skills in learning English. The proposal is based on advances in research on ecosystems of learning and embodied cognition. A system is designed from the specification of learning spaces integrated into a spiral structure. An online learning environment integrates with an ecosystem with elements of Boyacá cuisine to develop communicative interactions and autonomous learning activities. The proposal is validated by taking as population, grade 11 students from the Colombian system and two equivalent samples of selected students, based on previous performance in the English subject in the current school year. Statistical analysis of results supports the positive answer to the research question and supports the importance of the cultural integration of learning b- learning ecosystems in foreign language learning.&#x0D;
LQ non-Gaussian Control with I/O packet losses

The paper concerns the Linear Quadratic non-Gaussian (LQnG) sub-optimal control problem when the input and output signals travel through an unreliable network, namely Gilbert-Elliot channels. In particular, the input/output packet losses are modeled by Bernoulli sequences, and we assume that the moments of the non-Gaussian noises up to the fourth order are known. By mean of a suitable rewriting of the system through an intermittent output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, a simple solution is provided by substituting the Kalman predictor with intermittent observations of the LQG control law with a quadratic optimal predictor. Numerical simulations show the effectiveness of the proposed method.
SmartSharing: A CDN with Smart Contract-based Local OTT Sharing

A content delivery network (CDN) uses distributed cache servers to reduce the content delivery latency to end users. In recent years, CDN providers adopt a new content caching strategy that allows end users to share their storage/bandwidth resources. Two core questions need to answer in this strategy: (1) how to incentivize end users to contribute their resources? (2) how to facilitate transparent, secure content trading among end users?We propose a new CDN solution, called SmartSharing, where users contribute their over-the-top (OTT) devices as mini-cache servers. To incentivize end users to contribute resources, SmartSharing uses game theory and an Expectation-Maximization (EM) algorithm to determine the content delivery schedule and the pricing scheme. To facilitate content trading among end users, SmartSharing uses smart contracts in Ethereum to create a transparent and safe transaction platform. We thoroughly evaluate the performance of SmartSharing with real-world trace-driven simulation as well as a prototype using content metadata and the derived pricing scheme.
Modular Digital Exposition and Education Platform: Concept and Its Implementation into the Educational Process of Archaeology Based on Novosibirsk State University

Purpose. The paper describes the experience of implementing a brand new interactive education platform at Novosibirsk State University (NSU). The purpose of the platform is to effectively use all potential communication channels between the object and the subject of knowledge in the system of higher professional education. The modular exposition and education project “Archaeology of Eurasia” developed by the education and research centre “New Archaeology” includes material sources and several digital stands designed following modern trends in education, latest information technologies and many years of experience in teaching archaeology at NSU. Results. The platform is based on primary principles of contemporary teaching approaches, such as brevity, imagery, diversity of sources, interactivity. Its active use in the educational process at the NSU Institute for the Humanities (IH) has shown to improve the quality of teaching and the level of learning skills. The project applies interdisciplinary methodology, which includes the best achievements in the field of educational and information technologies and archaeological science. Conclusion. For the NSU IH this project is a new experience to transfer from the usual classroom to a fundamentally different learning environment which provides an endless variety of opportunities for teaching and learning. Combination of the virtual exposition and real exhibits provides the opportunity to find the optimal algorithm of the interaction between the visitor and the expositional and educational space.
Didactic Digital Material for the Teaching of English to Second Grade Children

Abstract. -- Keywords. -- 1. Problem Statement. -- 2. Objectives. -- 2.1. General objective. -- 2.2. Specific objectives. -- 3. Literature review. -- 3.1 Material design. -- 3.2 Textbook. -- 3.3 Characteristics of a textbook. -- 3.4 Digital book. -- 3.5 Stories. -- 3.6 Communicative approach. -- 3.7 Integrated skills.-- 4. Method. -- 4.1. Context. -- 4.2. Participants. -- 4.3. Thematic unit. -- 4.4. Teaching English to children. -- 4.5. Research design. -- 4.5.1 Preliminary stage. -- 4.5.2 Classroom diagnosis. -- 5.Pedagogical Intervention. -- 6. Limitations. -- 7. Conclusions. -- 8. Recommendations. -- 9. Reference list. -- 10. Annexes.
MULTI LEVEL WAVELET AND CURVELET BASED VIDEO WATERMARKING FOR COPYRIGHTPROTECTION

Nowadays, security plays a vital role in every field of applications. The main aim of this researchwork is to increase the authentication and security by means of multi-level Video Watermarking using DiscreteWavelet Transform (DWT). The nature of watermarking used in this work is Invisible Watermarking. Ininvisible watermarking, the input data such as image, video and audio can be embedded into the original video.This invisible video watermarking has to be attained in multi-level by using DWT. DWT algorithm has to bewritten using MATLAB coding. In order to have more secure data, a secret key is used during an embeddedprocess. The original image can be extracted only if the correct secret key is matched at the output side. DWT isused to convert the spatial domain data into a spectral domain data. This algorithm may be used in variety ofapplications like copyright protection, defence applications, broadcasting applications, Meta  data applications,etc.Similar to Discrete Wavelet Transform (DWT), Discrete Curvelet Transform also used to increase theauthenticity of the input image with better quality.Curvelet transform is used for embedding the images into thevideo. In this research as a secondary, the procedure of watermarking is repeated with the Curvelet transform.The outcome of this research is to make secure digital data transmission that helps in variety of applications likebroadcast monitoring, ownership assertion, medical applications and mostly used in defense department.
An Empirical Analysis on Threat Intelligence: Data Characteristics and Real-World Uses

Author(s): Li, Guo | Advisor(s): Savage, Stefan; Levchenko, Kirill | Abstract: Threat Intelligence, both as a concept and a product, has been increasingly gaining prominence in the security industry. At a high-level, it is the “knowledge” that helps organizations understand and mitigate cyber-attacks. Most commonly, it refers to the collection of threat indicators—IP addresses, domain names, file hashes, etc. known to be associated with attacks. By compiling and distributing this information, it is believed that recipients will be able to better defend their systems from future attacks. Thus, there are now hundreds of vendors offering their threat intelligence solutions as a mix of public and commercial products.However, our understanding of this data, its characterization, and the extent to which it can meaningfully support its intended uses, is still quite limited. Furthermore, how the data is being used by organizations, how popular it is, and what impact it could have on the Internet are also not clear to our community. We lack an empirical assessment of real-world threat intelligence, both in terms of the data itself and its usage, and it is important to first understand the current status of threat intelligence, then can we reasonably discuss how to make improvements.In this dissertation, I take an empirical approach to study threat intelligence and try to address these gaps. In particular, I explore this topic from two perspectives: 1) Studying the characteristics of threat intelligence data itself and 2) Exploring how they are used in the real-world. In particular, I formally defined a set of metrics for analyzing threat intelligence data feeds and use these measures to systematically evaluate a broad range of public and commercial feeds. Further, I ground my quantitative assessments using external measurements to investigate issues of coverage and accuracy. Finally, I designed a method using the IP ID side channel to test if a remote host is blocking traffic from a given IP address. Using this technique, I measured over 220K U.S. hosts and tested whether they consistently block connections with IPs identified on popular IP blacklists. Beyond these blacklists, I also demonstrate the evidence for more widespread use of security related traffic blocking. Together, my work provides an in-depth look into the current picture of threat intelligence and augments the knowledge of our community on this topic.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs. This issue can significantly destabilize the feature embeddings and the predictions, rendering deep networks much less useful for the experts. This paper approaches this challenge by leveraging unit visualization techniques that enable the investigation of many-to-many relationships between dynamically varied multi-scalar aggregations of urban traffic data and neural network predictions. Through regular exchanges with a domain expert, we design and develop a visual analytics solution that integrates 1) a Bivariate Map equipped with an advanced bivariate colormap to simultaneously depict input traffic and prediction errors across space, 2) a Morans I Scatterplot that provides local indicators of spatial association analysis, and 3) a Multi-scale Attribution View that arranges non-linear dot plots in a tree layout to promote model analysis and comparison across scales. We evaluate our approach through a series of case studies involving a real-world dataset of Shenzhen taxi trips, and through interviews with domain experts. We observe that geographical scale variations have important impact on prediction performances, and interactive visual exploration of dynamically varying inputs and outputs benefit experts in the development of deep traffic prediction models.
ADAPTATION OF JOINT SOURCE-CHANNEL CODING TO OPTIMIZE THE BANDWIDTH TRANSMISSION VOICE OVER LTE NETWORK

In mobile communication systems over LTE, voice packets are transmitted over noisy channels that negatively affect the probability of losses. An important mechanism to combat packet loss is channel coding. The channel coding adds redundancy bits to the information packets to detect and correct errors in the detection process. This is the opposite of source coding that is responsible for eliminating re-abundance to minimize the rate of transmission. There is a compromise relationship between source and channel coding that influences the quality that users experience. In this research paper, a mechanism for optimizing the transmission ratio required in voice applications over LTE was implemented and evaluated based on finding an adequate relationship in the joint source-channel coding. The algorithm for coding selection is not original but has been modified so that it takes into account the qualitative evaluation ranges. The proposed changes have allowed an appreciable reduction in the transmission ratio required for a VoLTE connection concerning the original algorithm without changing the level in the qualitative scale of quality assessment.
VECTOR AUTOREGRESSIVE STABILITY CONDITION CHECK UNTUK PEMODELAN DAN PREDIKSI SUMBER PENERIMAAN PABEAN BELAWAN

Customs Intermediate are an institution that is responsible for regulating the flow of export and import trade activities in the Customs Area with the revenue coming from import duties and export duties. The time series data from the customs acceptance component import dan export which have a relationship between variables. Vector Autoregressive is a statistical method used in predicting and evaluating interrelationships between variables. The purpose of this study is to obtain a model for predicting import and export by using the VAR model and detecting the stability of the model. Model requirements are said to be stable if all modulus values from roots characteristic of coefficient matrices ≤ 1 that the predicted results can be verified. The data is divided into in sample data starting from January 2010 to June 2018 and out sample data starts from July 2018 until December 2018. The results of data analysis in this study, the model obtained for prediction is the VAR model (4) and there is a direct relationship between both variables. The VAR (4) residual model fulfills the assumption of white noise, while the assumption of multivariate normality is not fulfilled. Based on out sample the value of MAPE for import variables 18.42%, export 12.94% shows the VAR model (4) has good predictive capabilities that can be used for predicting future periods. Predicted results on import show fluctuations during the period of January to December 2019 while in the export shows increase during the period of January to December 2019.
Implementation of an educational model leveraging the power of incentive reward theory with Blockchain

Recent studies conducted to investigate the reasons for an alarming decline in the performance of undergraduate students have thrown up a few compelling facts. These necessitate our immediate attention. Demographic shuffling along with lack of intrinsic motivation stand out as the cardinal reasons among other miscellaneous ones. There is a need to introduce programs that perk up one’s persistence level and provide the missing tangible incentive for a purposeful engagement with our education system. The paper endeavours to fulfil this requirement through the use of Blockchain (cryptocurrency) guided by the principles of extrinsic motivation theory.
The structural Relationship among the Master teacher’s Facilitation Competency, Teachers’ Commitment and Teachers’ Learning Community in Elementary School

이 연구는 초등학교 수석교사의 퍼실리테이션 역량과 교사의 교직헌신, 교사학습공동체와의 구조적 관계를 분석하는 데 그 목적이 있다. 이를 위해 전국 632명의 초등수석교사가 근무하는 학교의 교사를 대상으로 단순무선표집(simple random sampling)으로 설문 조사를 실시하였다. 회수된 854부 중 847명을 최종 분석대상으로 선정하여, SPSS 21과 AMOS 23프로그램을 사용하여 상관관계분석, 회귀분석 및 구조방정식 분석을 실시하였다. 연구 결과는 다음과 같다. 수석교사의 퍼실리테이션 역량과 교직헌신, 교사학습공동체 각각의 잠재변인과 세 잠재변인의 9개 하위변인 간에 모두 유의한 정적 상관이 있는 것으로 나타났다. 둘째, 수석교사의 퍼실리테이션 역량은 교사의 교직헌신과 교사학습공동체에 영향을 주었으며, 또한 교사의 교직헌신도 교사학습공동체에 영향을 주는 것으로 나타났다. 넷째, 수석교사의 퍼실리테이션 역량이 교사의 교직헌신 부분매개를 통해 교사학습공동체에 미치는 영향은 유의미하게 나타났다. 이에 수석교사의 퍼실리테이션 역량은 균형, 포용, 연계에 기반한 홀리스틱 교육의 원리를 반영하고 있기에 교직헌신과 학습공동체 구축의 가치와 의미를 부여할 수 있을 것이다.The purpose of this study is to analyze the relationship among the Master Teacher’s Facilitation Competency, Teachers’ Commitment, and Teachers Learning Community. For analyzing the result of the study, SPSS 23.0 and AMOS 23.0 were used as statistical processing programs. The major findings from this study were as follows: First, there was the meaningful positive correlation among the Master Teachers Facilitation Competency, Teachers Commitment, Teachers Learning Community, predictors of outcome of each and their 9 subfactors. Second, the Master Teachers Facilitation Competency influenced Teachers Commitment and Teachers Learning Community, and Teachers Commitment influenced Teachers Learning Community. Third, the Master Teachers Facilitation Competency had a significant impact on Teachers Learning Community through Teachers Commitment as partial mediation. According to the study, it was empirically established that the Master Teachers Facilitation Competency can vitalize Teachers Learning Community through Teachers Commitment. Therefore, the institutional condition for performing the Master Teachers Facilitation Competency need to be available to vitalize Teachers and to derive Teachers Commitment. In addition, various approaches and strategies are required to improve the Master Teachers Facilitation Competency.
Research Guides: Library Textbook Update: Library Account (Circulation) and Tech Help

Get in touch with library staff regarding circulation issues such as checkouts, overdue fees, library holds, and tech help.
The effectiveness of integrating graphic elements into e-learning

The importance of the use of graphic elements in e-learning in its ability to attract the attention of learners, and its impact in maintain the subject for a longer period in the mind of the learner. The philosophy of e-learning provides education for all individuals, and facilitate the provision of information leading to the principle of equal educational opportunities among all learners.
Associative3D: Volumetric Reconstruction from Sparse Views

This paper studies the problem of 3D volumetric reconstruction from two views of a scene with an unknown camera. While seemingly easy for humans, this problem poses many challenges for computers since it requires simultaneously reconstructing objects in the two views while also figuring out their relationship. We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well as an inter-view object affinity matrix. This information is then jointly reasoned over to produce the most likely explanation of the scene. We train and test our approach on a dataset of indoor scenes, and rigorously evaluate the merits of our joint reasoning approach. Our experiments show that it is able to recover reasonable scenes from sparse views, while the problem is still challenging. Project site: https://jasonqsy.github.io/Associative3D
LibGuides: Off Campus/Remote Library Access: Humanities

This guide is intended to be a quick, helpful place to answer basic questions about Library Resources -- and how you can access them off campus. Our Top Ten List of Humanities-related resources are found here, along with tips & tricks for accessing these materials off campus ...
An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, primarily through the use of text mining and search tools. This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data. While most research in search engines is performed in the academic field of information retrieval (IR), most academic search engines$\unicode{x2013}$though rigorously evaluated$\unicode{x2013}$are sparsely utilized, while major commercial web search engines (e.g., Google, Bing) dominate. This relates to COVID-19 because it can be expected that commercial search engines deployed for the pandemic will gain much higher traction than those produced in academic labs, and thus leads to questions about the empirical performance of these search tools. This paper seeks to empirically evaluate two such commercial search engines for COVID-19, produced by Google and Amazon, in comparison to the more academic prototypes evaluated in the context of the TREC-COVID track (Roberts et al., 2020). We performed several steps to reduce bias in the available manual judgments in order to ensure a fair comparison of the two systems with those submitted to TREC-COVID. We find that the top-performing system from TREC-COVID on bpref metric performed the best among the different systems evaluated in this study on all the metrics. This has implications for developing biomedical retrieval systems for future health crises as well as trust in popular health search engines.