Reviewerly/exHarmony huggingface dataset:
Research Trends on University Creativity Education Program

본 연구의 목적은 국내에서 진행된 대학생 창의성 교육프로그램 연구 동향을 분석하고 향후 연구 및 설계 시사점을 제공하는 것이다. 이를 위해 분석 대상은 국내 학술지 전체를 대상으로 2015년 1월부터 2019년 6월까지 게재된 논문으로 ‘대학생’, ‘창의성’을 중심어로 1차 검색을 하고, 그 다음에 대학생 창의성 교육 프로그램과 관련된 논문 중 질적 연구를 제외한 논문들을 2차적으로 추출하였다. 최종 선정된 50개의 논문을 연도 별, 연구방법 별, 프로 램 별, 측정변인 별, 평가도구 별로 연구동향을 분석한 결과는 다음과 같다. 창의성 교육 프로그램 연구는 2015년부터 지금까지 꾸준히 이루어졌고, 특히 2016년의 연구수가 제일 많은 것으로 나타났다. 또한, 연구방법으로는 양적연구가 많이 이루어졌으며, 프로그램에 사용된 교수학습모형으로는 창의적 문제해결 수업(CPS)이 가장 많이 사용되었다. 프로그램의 효과를 측정하는 변인으로 창의성, 문제해결력, 창의적 성향이 많이 사용되었다. 특히 가장 많이 사용된 평가도구는 인지&#8228;지각검사인 K-ICT인 것으로 나타났다. 이러한 연구결과들을 토대로 추후 창의성 교육 프로그램 연구 및 실행 시 고려해야 할 요소들을 제언하였다.The purpose of this study is to analyze the recent trends in the research of the university creativity education program in Korea and to derive implications for research and design of university creativity education program. The subjects of the analysis are the articles published from January 2015 to June 2019 for all journals and the first search was conducted based on “university students” and “creativity”. Second, the qualitative research articles were excluded. The results of analyzing the research trends of the 50 selected papers by year, research method, program, measurement variable, and evaluation tool are as follows. Creativity education program research has been conducted steadily since 2015, with the highest number of studies in 2016. In addition, quantitative research as a research method, and CPS (Creative Problem Solving) model was most frequently utilized as a instructional model used. Creativity, problem-solving ability and creative personality were used as variables to measure the effectiveness of the university creativity education program. Especially, the most widely used assessment tool was K-ICT, which is a cognitive·perceptual test. Based on these findings, this study suggested the factors to be considered in the future research and implementation of creativity education programs.
Polynomial time in untyped elementary linear logic

We show how to represent polynomial time computation in an untyped version of proof-nets for elementary linear logic. This follows previous work by P. Baillot but which was developed in a typed and affine setting. We describe how these two properties can be adapted.
Optimal locally private estimation under $\ell_{p}$ loss for $1\le p\le 2$

We consider the minimax estimation problem of a discrete distribution with support size $k$ under locally differential privacy constraints. A privatization scheme is applied to each raw sample independently, and we need to estimate the distribution of the raw samples from the privatized samples. A positive number $\epsilon$ measures the privacy level of a privatization scheme. In our previous work (IEEE Trans. Inform. Theory, 2018), we proposed a family of new privatization schemes and the corresponding estimator. We also proved that our scheme and estimator are order optimal in the regime $e^{\epsilon}\ll k$ under both $\ell_{2}^{2}$ (mean square) and $\ell_{1}$ loss. In this paper, we sharpen this result by showing asymptotic optimality of the proposed scheme under the $\ell_{p}^{p}$ loss for all $1\le p\le 2.$ More precisely, we show that for any $p\in[1,2]$ and any $k$ and $\epsilon,$ the ratio between the worst-case $\ell_{p}^{p}$ estimation loss of our scheme and the optimal value approaches $1$ as the number of samples tends to infinity. The lower bound on the minimax risk of private estimation that we establish as a part of the proof is valid for any loss function $\ell_{p}^{p},p\ge 1.$
Cyber Space Features – Security and Data Protection Requirements

The paper deals with some problems, determined in the contemporary digital age which are closely connected with the collecting, using and disseminating user's personal data. The discussion is based on a short presentation of the digital age features and contemporary cyber technologies related to the user's privacy. A summarization of the main challenges of the cyber space is made and some requirements for protection of privacy and user's security are formulated.
Evaluating Missing Values for Software Defect Prediction

Software defect prediction (SDP) models help software development teams to identify defected modules. SDP models use historical data collected from different software repositories. This data may contain certain missing values which make data unfit for SDP model training. This study identifies the best imputation technique used to handle missing values in SDP dataset. Also we investigated for the best imputation technique along with feature selection method. Results showed that the linear regression followed by correlation-based feature is the best combination for building SDP models.
Three-dimensional shape measurement of aspheric mirrors with null phase measuring deflectometry

A null phase measuring deflectometry (PMD) is presented to measure the three-dimensional shape of aspheric mirrors with radial symmetry. Curved fringes displayed on a mobile screen are reflected by the surface under test, and a camera captures the fringe patterns. By analyzing the captured fringe patterns, the deviations along the normal of the mirror’s surface can be restored by integrating the slopes of these deviations, and then the surface under test can be reconstructed. Compared with non-null PMD, the qualitative information of surface in null PMD can be shown fast and easily for a surface with large deviations in the grinding stage. Computer simulation and experiment are presented to validate the presented method.
Maximum smoothed likelihood component density estimation in mixture models with known mixing proportions

Mixture models appear in many research areas. In genetic and epidemiology applications, sometimes the mixture proportions may vary but are known. For such data, the existing methods for the underlying component density estimation may produce undesirable results: negative values in the density estimates. In this paper, we propose a maximum smoothed likelihood method to estimate these component density functions. The proposed estimates maximize a smoothed log likelihood function which can inherit all the important properties of probability density functions. A majorization-minimization algorithm is suggested to compute the proposed estimates numerically. We show that, starting from any initial value, the algorithm converges. Furthermore, we establish the asymptotic convergence rate of the $L_{1}$ errors of our proposed estimators. Our method provides a general framework for dealing with many similar mixture model problems. An adaptive procedure is suggested for choosing the bandwidths in our estimation procedure. Simulation studies show that the proposed method is very promising and can be much more efficient than the existing method in terms of the $L_{1}$ errors. A malaria data application shows the advantages of our method over others.
Oblivious PRF on Committed Vector Inputs and Application to Deduplication of Encrypted Data

Ensuring secure deduplication of encrypted data is a very active topic of research because deduplication is effective at reducing storage costs. Schemes supporting deduplication of encrypted data that are not vulnerable to content guessing attacks (such as Message Locked Encryption) have been proposed recently [Bellare et al. 2013, Li et al. 2015]. However in all these schemes, there is a key derivation phase that solely depends on a short hash of the data and not the data itself. Therefore, a file specific key can be obtained by anyone possessing the hash. Since hash values are usually not meant to be secret, a desired solution will be a more robust oblivious key generation protocol where file hashes need not be kept private. Motivated by this use-case, we propose a new primitive for oblivious pseudorandom function (OPRF) on committed vector inputs in the universal composable (UC) framework. We formalize this functionality as $$\mathcal {F}_\mathsf {OOPRF}$$ , where $$\mathsf {OOPRF}$$ stands for Ownership-based Oblivious PRF. $$\mathcal {F}_\mathsf {OOPRF}$$ produces a unique random key on input a vector digest provided the client proves knowledge of a (parametrisable) number of random positions of the input vector. To construct an efficient $$\mathsf {OOPRF}$$ protocol, we carefully combine a hiding vector commitment scheme, a variant of the PRF scheme of Dodis-Yampolskiy [Dodis et al. 2005] and a homomorphic encryption scheme glued together with concrete, efficient instantiations of proofs of knowledge. To the best of our knowledge, our work shows for the first time how these primitives can be combined in a secure, efficient and useful way. We also propose a new vector commitment scheme with constant sized public parameters but $$(\log n)$$ size witnesses where n is the length of the committed vector. This can be of independent interest.
Preprocessing Analysis of Brain Images with Atherosclerosis

Cerebral Atherosclerosis is narrowing of the arteries caused by a buildup of plaque in the blood vessels of the brain. It is a form of stroke seen in young and middle age group, especially in women which can cause acute neurological deterioration with increased morbidity and mortality if not diagnosed in early stage. To detect Cerebral Atherosclerosis from MRI images image enhancement technique is required. To obtain accurate results from enhanced image it is necessary to do preprocessing technique. Among number of filters used in preprocessing technique of image processing, a comparative analysis of commonly used filters like mean, median, alpha trimmed mean, weighted median, adaptive median, bilateral, Wiener filter is carried out on MRI brain images and optimal PSNR and MSE is determined.
Cryptocurrencies and Code Before the Courts

In the rapidly developing cyber sphere dominated by cryptocurrencies and code, it is perhaps not uncommon for firms to focus on cutting-edge technological developments leaving the law behind as an ...
Getting creative in everyday life: Investigating arts and crafts hobbyists' information behavior

ABSTRACT While there has been increasing interest in how creative professionals find information to drive creative outputs, previous information behavior research has largely ignored how arts and crafts hobbyists look for information sources in their everyday lives. To fill this literature gap, we conducted interviews and observations with arts and crafts hobbyists to find out how they conceive potential DIY projects. The findings highlight three themes: the dearth of human sources, the prevalence of domain‐specific information, and the use of self‐curated information. In addition to empirical results, this work also broadens the understanding of information behavior in an arts and crafts context by studying populations beyond professional artists.
Perceiving QUIC: do users notice or even care?

QUIC, as the foundation for HTTP/3, is becoming an Internet reality. A plethora of studies already show that QUIC excels beyond TCP+ TLS+HTTP/2. Yet, these studies compare a highly optimized QUIC Web stack against an unoptimized TCP-based stack. In this paper, we bring TCP up to speed to perform an eye-level comparison. Instead of relying on technical metrics, we perform two extensive user studies to investigate QUIC's impact on the quality of experience. First, we investigate if users can distinguish two protocol versions in a direct comparison, and we find that QUIC is indeed rated faster than TCP and even a tuned TCP. Yet, our second study shows that this perceived performance increase does mostly not matter to the users, and they rate QUIC and TCP indistinguishable.
Reflection and Security

Abstract “Reflection and Security” introduces a distinctive notion of default assumptions, one that applies in human performance domains generally, and uses that notion to extend virtue epistemology in new directions.
Comparison of Two Value-Modeling Methods: e<sup>3</sup> value and SEAM

In the last few decades, several value-modeling methods have emerged in requirements engineering for IS research. We compare two value-modeling methods, e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> value and SEAM. We illustrate their use with an example of the exchange of the Intellectual Property Rights (IPR) on music. In the process, we propose a comparison framework. The results of our study show that e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> value and SEAM are similar value-modeling techniques: both model services in networked systems and focus on value exchanges. They differ, however, in the way value is conceptualized: the market viability of the service system in e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> value versus the subjective value and lack of market profitability analysis in SEAM. e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> value shows how value flows from one actor to another, whereas SEAM shows the relative importance of different value propositions and how they are constructed by the service network. These results can be used by modelers to select a value-modeling method for their purposes by proposing explicit selection criteria. The comparison framework, which is in its early stages of development, can be used to compare other modeling methods.
Camera Model Identification Based on Residual Extraction Module and SqueezeNet

Camera model identification is a hot topic in the field of image forensics. In this paper, a patch-level camera model identification method based on convolutional neural network is proposed. Firstly, inspired by pre-processing method in traditional camera model identification method, an automatic residual extraction module is designed in order to avoid subsequent convolutional neural network being affected by image content. Secondly, a modified SqueezeNet is proposed to extract the camera model related features within image patches. Finally, the effectiveness of the proposed method is verified under a strict patch-level evaluation protocol, which is designed based on the largest public image forensic Dresden database.
Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival.
Information Security from a Scientometric Perspective

During recent years, Information societies are continually confronted with threats and vulnerabilities. Literature analysis of major disciplines is one of the key tools available to policymakers of research institutions and organizations. Scientometrics is the field of study which concerns itself with measuring and analyzing scientific literature. The purpose of the present study was to evaluate the research trends of in the Middle-East and the world from a scientometric perspective. Descriptive and analytical statistics were used in this study. The Scopus citation database was used to collect the publication data, as the largest abstract and citation database of peer-reviewed literature. Therefore, all documents related to information security were retrieved and analyzed. The results showed that the majority of scientific publications in the field of were produced in the United States of America and China. Among the Middle-Eastern countries, Iran ranked the first in terms of scientific publications in the field of while ranked 23th among the countries of the world. It is apparent that paying special attention to the field of and data in terms of international scientific collaborations, using knowledge and experiences of the leading, and supporting research and development in this field can improve the quality and quantity of scientific publications in this field. The expertise gathered during the process will prove invaluable and effective in confronting the threats and vulnerabilities posed to societies.
Near Optimal Control With Reachability and Safety Guarantees

Control systems designed via learning methods, aiming at quasi-optimal solutions, typically lack stability and performance guarantees. We propose a method to construct a near-optimal control law by means of model-based reinforcement learning and subsequently verifying the reachability and safety of the closed-loop control system through an automatically synthesized Lyapunov barrier function. We demonstrate the method on the control of an anti-lock braking system. Here the optimal control synthesis is used to minimize the braking distance, whereas we use verification to show guaranteed convergence to standstill and formally bound the braking distance.
AI agents play hide-and-seek: An OpenAI project demonstrated "emergent behavior" by AI players - [News]

After 25 million games, the AI agents playing hide-and-seek with each other had mastered four basic game strategies. The researchers expected that part.
Neural models for information retrieval without labeled data

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval [9]. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or weakly supervised solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models [3] that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification [1, 2]. We further propose a standalone learning to rank model based on deep neural networks [5, 8]. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query [7]. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems [4]. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training [6]. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available.
Even Turing Should Sometimes Not Be Able to Tell: Mimicking Humanoid Usage Behavior for Exploratory Studies of Online Services

Online services such as social networks, online shops, and search engines deliver different content to users depending on their location, browsing history, or client device. Since these services have a major influence on opinion forming, understanding their behavior from a social science perspective is of greatest importance. In addition, technical aspects of services such as security or privacy are becoming more and more relevant for users, providers, and researchers. Due to the lack of essential data sets, automatic black box testing of online services is currently the only way for researchers to investigate these services in a methodical and reproducible manner. However, automatic black box testing of online services is difficult since many of them try to detect and block automated requests to prevent bots from accessing them. In this paper, we introduce a testing tool that allows researchers to create and automatically run experiments for exploratory studies of online services. The testing tool performs programmed user interactions in such a manner that it can hardly be distinguished from a human user. To evaluate our tool, we conducted—among other things—a large-scale research study on Risk-based Authentication (RBA), which required human-like behavior from the client. We were able to circumvent the bot detection of the investigated online services with the experiments. As this demonstrates the potential of the presented testing tool, it remains to the responsibility of its users to balance the conflicting interests between researchers and service providers as well as to check whether their research programs remain undetected.
A Comparative Study of Routing Methods in Capsule Networks

Recently, the deep neural network structure caps-net was proposed by Sabouret al. [11]. Capsule networks are designed to learn relative geometry betweenthe features of a layer and the features of t ...
Exposing Knowledge: Providing a Real-Time View of the Domain Under Study for Students

With the amount of information that exists online, it is impossible for a student to find relevant information or stay focused on the domain under study. Research showed that search engines have deficiencies that might prevent students from finding relevant information. To this end, this research proposes a technical solution that takes the personal search history of a student into consideration and provides a holistic view of the domain under study. Based on algorithmic approaches to assert semantic similarity, the proposed framework makes use of a user interface to dynamically assist students through aggregated results and wordcloud visualizations. The effectiveness of our approach is finally evaluated through the use of commonly used datasets and compared in line with existing research.