Reviewerly/exHarmony huggingface dataset:
The Effects of career preparation behaviors according to cooking University students"s Participation Motivation of Culinary Competition through Class Emersion

The purpose of this study is to investigate effect among Participation Motivation of Culinary Competition, Class Emersion and career prepar6ㄹation behavior by an analysis. For the field test, the North Gyeongsang Province, University of Busan students majoring in cooking from October 1st to December 31st, 2018 were selected as the research objects. The study result is summarized as follows. 1st, Culinary competition Participation Motivation of university students majoring in cooking impacted positively on Class Emersion. 2nd, Class Emersion of university students majoring in cooking had a positive influence on career preparation behaviors. In conclusion, Culinary Competition Participation Motivation of university students majoring in cooking affects Class Emersion and career preparation behavior. But motivation is impacted negatively by Class Emersion. It is expected that the research will improve the Employment Rate of university student by an analysis.
A Framework for Decoding Event-Related Potentials from Text

We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.
THE BENEFITS OF ARTBOTS ACTIVITIES IN EDUCATION &amp; SOCIETY

The aim of this research is to present the benefits of working with artbots in education and to investigate further benefits for society. The methods which are implemented in order to reach this aim are related to children's and other audiences' participation in contemporary art practices as well as educational activities which involve artbots. The se practices and activities are rich and diverse ranging from drawing with artbots (with or without coding) to producing small-scale theatrical pieces, happenings and films . However, all the activities presented in this paper are interdisciplinary and involve or connect the STEAM (Science, Technology, Engineering, Arts and Mathematics) fields. Another common characteristic of all the activities is that they communicate human values through messages of love and peace as well as through references to beauty, harmony, festivity, mobility, interconnectedness and other qualities of the contemporary living conditions. Which are the learning outcomes and what impact do these practices and activities have on the audiences? In order to give an answer to these questions it is important to take into consideration the different age and social groups as well as the contexts where these activities take place. It is also interesting to see these activities through and beyond STEAM education by highlighting the implementation of the Arts as a way to blaze new trails for fostering creative and innovative thinking. The artbots activities presented in this paper can be easily carried out in various intercultural, intergenerational and interdisciplinary contexts and they can bring together people by bridging many kinds of gaps. This means that artbots activities are a promising field of research and creation in education with multiple opportunities for original activities which benefit education and society
Annotating with Pros and Cons of Technologies in Computer Science Papers

This paper explores a task for extracting a technological expression and its pros/cons from computer science papers. We report ongoing efforts on an annotated corpus of pros/cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros/cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.
PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE

Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.
Alma Interested Users as a Service to Patrons and Librarians

In 2018, the KSU Library System’s Technical Services department piloted and developed workflows for the Interested Users function for monograph acquisitions. We analyzed the existing notification process and consulted with Access Services to incorporate the Interested User function into our workflows. Previously, “notify” meant that a user wanted to check out a resource as soon as it was received and processed. When a notification was placed on a book, the book had to be cataloged and processed individually by our Technical Services department. Because Technical Services is located in an off-site facility, getting the books to the owning library took additional time. We increased efficiency by using the Interested User function to automate the notification process, allowing books to receive shelf-ready processing and be shipped directly to the owning library. We also used the Interested User notification and Interested User hold functions to distinguish between requests from librarian liaisons for a notification when new resources were available and patrons who wanted to check out new books. A notification generated an automated email to the liaison, while a hold created a hold on the book using existing workflows. With the help of our Systems Librarian, we customized these email notifications. We integrated the overall process into our Alma API ordering workflow by adding interested users to purchase order lines before the lines went into auto-packaging. By attending this presentation, attendees will learn practical methods of using inter-departmental collaboration to increase patron notification efficiency and improve acquisitions processes in Alma.
Comparison of fuzzy search algorithms on the basis of the Russian language

Fuzzy search concept is realized by different algorithms, and is widely implemented in different domains of human activity. Thus, the problem of these algorithms comparison is a really relevant one. Most works in this area are made on the basis of English words and Latin alphabet. This research is oriented towards Russian words as primary data for experiments. It was revealed on the basis of experiments with Russian word data that the algorithm based on N-gram structures was the fastest one, but could deliver unreliable results.
AN APPROACH TO UNIVERSITY SOCIAL RESPONSIBILITY AS FORERUNNER OF STUDENTS’ SATISFACTION

1 University of Trás-Os-Montes and Alto Douro, Cetrad Research Unit (PORTUGAL)2 Universidade Europeia (PORTUGAL)3 University of Lisbon, CAPP Research Unit / University of Trás-os-Montes and Alto Douro (PORTUGAL)
USE OF AI IN THE CLASSROOM AND THE “ZERO-TECH” TEACHING METHOD

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 2191-2196ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0594Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
2015 개정 교육과정 생명과학 I 교과서 글쓰기 문항 분석 및 교수-학습 자료 개발

The importance of science writing is emphasized in the 2015 revision curriculum. Various teaching materials should be developed to encourage students to express themselves more creatively. Therefore, the purpose of this study is to develop teaching-learning materials for the class of writing at high schools based on the 2015 revision curriculum. The teaching-learning materials were developed based on the key concepts and sections of 『Life Science I』 under the revised curriculum in 2015. Prior to this, we analyzed materials suggesting scientific writing in eight types of textbooks, including 2015 revision 『Life Science I』. The analytical framework was divided into descriptive and demonstrative formats, which were subdivided into nine formats. The analysis results showed that the most explained forms of science writing were presented in descriptive form, and among the analytical framework, the most descriptive sub-categories of task resolution, causal presentation. Based on the analysis results, the 2015 revision to the section of 『Life Science I』 Life Sciences Ito present various forms of science writing was developed. We hope developed materials will be utilized for practical learning, so that students can use them as Life Science writing materials for creative problem solving. Future research is expected to produce a suitable science writing and learning material in junior high school 『Science』 along with 『Life Science II』 textbooks.
IMPROVING STUDENTS’ READING COMPREHENSION USING LEARNING CELL TECHNIQUE

This research is talking about  investigating whether learning cell technique can improve students’ reading comprehension in recount text. The research was conducted at SMK PGRI Indramayu. The population of this research were the students at X RPL grade of SMK PGRI Indramayu and the samples of this research consisted of 24 students. This research was conducted by using Classroom Action Research (CAR). The technique of collecting data used test and observation. This research was conducted only two cycles. The test  included an objective 10 multiple choice, 5 true/false statements and 5 essay. The research finding showed that the data from two instruments, in preliminary test showed that the students reading comprehension was very low, the average in preliminary test was 50, 5. The result of students reading comprehension in cycle I was improved it was 66, 5. The result in cycle II showed improvement the student’s average score was 78. Based on the result of the research, it can be concluded that Learning Cell technique can improve the students ‘reading comprehension.
The Differential Impact of Corporate Leaders' Death on Firm Temporal Orientation

Strategy scholars have increasingly documented the evidence that corporate leader death affects strategic decisions. They have neglected, however, the impact on a firm’s decision choices between short-term or long-term investments and the importance of group boundary separating leaders. To address these questions, the present study focuses on how a corporate leader’s death can exert differential magnitude of impact on firm investment horizon in the period from 1992 to 2012. We argue that risk aversion and attention to immediate gains resulting from the psychological anxiety when the corporate leaders are exposed to peer death lead corporate leaders to prefer short-term investments. This effect is stronger for a non-CEO executive death than an independent director death, but the impact of an independent director death will be reinforced when board members and executives engage in more frequent interactions. We discuss the implications of our theory for the research on firm long-term orientation and leader turnover.
A Model-based Framework for Context-aware Augmented Reality Applications

Augmented Reality (AR) is a technique that enables users to interact with their physical environment through the overlay of digital information. With the spread of AR applications in various domains (e.g. product design, manufacturing or maintenance) and the introduction of concepts such as Pervasive Augmented Reality (PAR), the aspect context-awareness started to play an important role. By sensing the user’s current context and adapting the AR application accordingly, an adequate user experience can be achieved. Due to the complex structure and composition of AR applications, their development is a challenging task. Although, context-awareness for AR systems was addressed to some extent, a systematic method for development of context-aware AR applications is not fully covered yet. Therefore, in this paper, we identify the main challenges for development of context-aware AR applications and sketch our solution idea for a model-based development framework.
Electoral Uncertainty, Political Constraints, and Political Capabilities in Cross-Border Investment

In this paper we explore an under-researched dimension of political risk: electoral uncertainty. Using 56,996 M&A and greenfield investments into 55 countries, we show that close upcoming elections reduce the likelihood of foreign investments. Based on political cycle theory, we hypothesize and show empirically that countries with lower political constraints allow incumbent governments to offer sweet deals to foreign investors for their political benefit, thus moderating the negative effect of close upcoming elections. Finally, we argue and show that firms with previous experience in pre-election bargaining are more likely to invest under electoral uncertainty. Our paper offers several contributions combining the literatures on electoral uncertainty, political risk and political capabilities. Most importantly, our research adds an important and controversial contingency to the widely accepted dogma that low political constraints impede foreign direct investment.
An Improved Kernelized Correlation Filter with Redetection Mechanism for Visual Tracking

The correlation filters are the core components of most trackers which achieve the excellent results both on accuracy and real-time. However, the previous trackers are prone to drift away from the target when dealing with the challenging situations, e.g. motion blur and fast motion, which leads to the performance of trackers degration. To solve the above problems, in this paper, we propose a novel algorithm which adds the re-detection mechanism to the traditional kernelized correlation filter for judging and verifying the reliability of the detected target before updating the model. We employ the average peak-to-correlation energy to evaluate the confidence level of the candidate position. The experiment results show that proposed algorithm is more accurate and successful than the traditional algorithms.
Effects of Imperfect Observation on Quantum State Estimation

Imperfect observation is an indispensable but often neglected part for practical measurement. In this work, we mainly investigate its effect upon estimating quantum states by focusing on several specific examples. It is shown that the accuracy of the quantum state estimation will be greatly degraded in the presence of imperfect observation. In order to reduce its destructive influence, a cautious estimation method and the adaptive measurement strategy are attempted. However, it turns out that both of the methods cannot always overcome the performance deterioration due to the observation error. The results warn us that we have to seriously deal with the problem of imperfect observation in practical quantum information processing.
Sensitivity, affine transforms and quantum communication complexity

$\newcommand{\F}{\mathbb{F}}$We study the Boolean function parameters sensitivity ($s$), block sensitivity ($bs$), and alternation ($alt$) under specially designed affine transforms. For a function $f:\F_2^n\to \{0,1\}$, and $A=Mx+b$ for $M \in \F_2^{n\times n}$ and $b\in \F_2^n$, the result of the transformation $g$ is defined as $\forall x\in\F_2^n, g(x)=f(Mx+b)$. We study alternation under linear shifts ($M$ is the identity matrix) called the shift invariant alternation (denoted by $salt(f)$). We exhibit an explicit family of functions for which $salt(f)$ is $2^{\Omega(s(f))}$. We show an affine transform $A$, such that the corresponding function $g$ satisfies $bs(f,0^n) \le s(g)$, using which we proving that for $F(x,y)=f(x\land y)$, the bounded error quantum communication complexity of $F$ with prior entanglement, $Q^*_{1/3}(F)=\Omega(\sqrt{bs(f,0^n)})$. Our proof builds on ideas from Sherstov (2010) where we use specific properties of the above affine transformation. We show, * For a prime $p$ and $0<\epsilon<1$, any $f$ with $deg_p(f)\le(1-\epsilon)\log n$ must satisfy $Q^*_{1/3}(F) = \Omega(\frac{n^{\epsilon/2}}{\log n})$. Here, $deg_p(f)$ denotes the degree of the multilinear polynomial of $f$ over $\F_p$. * For any $f$ such that there exists primes $p$ and $q$ with $deg_q(f) \ge \Omega(deg_p(f)^\delta)$ for $\delta > 2$, the deterministic communication complexity - $D(F)$ and $Q^*_{1/3}(F)$ are polynomially related. In particular, this holds when $deg_p(f) = O(1)$. Thus, for this class of functions, this answers an open question (see Buhrman and deWolf (2001)) about the relation between the two measures. We construct linear transformation $A$, such that $g$ satisfies, $alt(f) \le 2s(g)+1$. Using this, we exhibit a family of Boolean functions that rule out a potential approach to settle the XOR Log-Rank conjecture via a proof of Sensitivity conjecture [Hao Huang (2019)].
An Optimized Memory Allocation and Deallocation Method while Programming in Python

Abstract Memory management is memory allocation and deallocation for objects done automatically at run time in Python. Programmers need not allocate or deallocate memory explicitly. Python Virtual Machine (PVM) handles these activities. Every Python object is stored in its own way, that is, different objects are stored in different methods. Reference counting algorithm is used for memory management in Python. Keywords : Allocation, deallocation, PVM, reference counting Cite this Article S.S. Sugantha Mallika, I. Mettildha Mary, M. Priyadharshini, N. Balaji. An Optimized Memory Allocation and Deallocation Method while Programming in Python. Journal of Operating Systems Development & Trends . 2019; 6(1): 1–6p.
French medical named entity recognition: a hybrid approach

With the availability of a huge amount of medical textual documents in digital form, the automatic extraction of relevant information from these documents is becoming a very challenging task because of the volume and the heterogeneous structure of medical text, which contains complex vocabulary. Therefore, there is an urgent need for medical information extraction techniques. One of the most important of these techniques is named entity recognition (NER). In this paper, we propose a system of French medical NER using a hybrid approach. And since the medical domain contains various types of information, we have taken into account both clinical and biomedical data to generalise the performance of our proposed system.
Design and Development of a New Active Slider Crank Mechanism Based Step Climbing Wheelchair

In this paper, a novel, inexpensive and reliable stair climbing wheelchair mechanism is proposed. Large variation in inclination angle of a traditional wheelchair while moving on steps posses high risk to the rider’s safety. A new active Slider-crank mechanism is used to minimize any variation in inclination angle of the wheelchair during climbing up or down on stairs. This, in-turn, reduces the risk of wheelchair toppling on steps. Kinematic model of the proposed wheelchair is developed and MATLAB simulations are performed to inspect the behavior of wheelchair on stairs. In addition, the mechanism is designed in such a way that the rider can sit towards the motion direction, which adds additional safety to the rider. It is envisaged that the novel step climbing wheelchair will enhance the mobility of the elderly people significantly.
Logic Synthesis for Reversible Circuits

Automatic synthesis methods of reversible circuits are discussed in this chapter. First the basic characteristics of reversible circuits are reviewed, and the difference of logic synthesis methods for general CMOS circuits and reversible circuits are clarified. Then logic synthesis methods for reversible circuits based on exhaustive search, repetition of local circuit transformations, Binary Decision Diagram (BDD) based approaches, SAT-based methods, and hierarchical methods are presented in order. The chapter concludes with discussions on future research topics.
Enhancing Salient Object Segmentation Through Attention

Segmenting salient objects in an image is an important vision task with ubiquitous applications. The problem becomes more challenging in the presence of a cluttered and textured background, low resolution and/or low contrast images. Even though existing algorithms perform well in segmenting most of the object(s) of interest, they often end up segmenting false positives due to resembling salient objects in the background. In this work, we tackle this problem by iteratively attending to image patches in a recurrent fashion and subsequently enhancing the predicted segmentation mask. Saliency features are estimated independently for every image patch, which are further combined using an aggregation strategy based on a Convolutional Gated Recurrent Unit (ConvGRU) network. The proposed approach works in an end-to-end manner, removing background noise and false positives incrementally. Through extensive evaluation on various benchmark datasets, we show superior performance to the existing approaches without any post-processing.
Motivated Agents : Toward the Computational Modeling of Motivational Affordances

Video games routinely use procedural content generation, player modelling, and other forms of computational interaction that provide a good starting point for engaging computational interfaces. However, across these practices, games model environment (game content) and actor (player type) separately, which is out of tune with both basic and applied research. The ecological construct of motivational affordances, formalized as actor-environment system ratios, provides a promising alternative that could also prove fruitful for computational interaction in general.
Copyright and Student Publishing

The presentation was given at the Fourth Annual Student Journal Forum organized by the University of Toronto Libraries in January 2019.
Everything you need to know about Plan S, Elsevier, and the transition towards Open Science

Introduction at seminar for PhD Candidates and Postdocs. Panel debate moderator: Johanne Raade, UiT with contributions from Lene Ottesen, UiT and Kenneth Ruud, UiT. Event organiser: University Library, UiT in collaboration with TODOS (Tromso Doctoral Students Association). The entire seminar was streamed and can be found on the YouTube channel of TODOS - Tromso Doctoral Students Association: https://youtu.be/jLH6OqkGHbM.
LibGuides: Emerging Information Sources for Information Technology Professionals: Home

This guide lists sources of emerging information for IT professionals aimed at specific communities and technologies. Suggest a resource by contacting me at email in profile below.