nils-herrmann/clean_scirepeval huggingface dataset:
DUCCI-SEQUENCES AND PASCAL ' S TRIANGLE

2T4 :=(\al-a2l\a2-a3l...,\a„-al\). The w-tuple Ai+l is called the (direct) successor of 4 , whereas 4 i the predecessor of Ai+l. As the maximum entry of the w-tuples cannot increase under the application of 2T and therefore the number of successors of any 4) * bounded, the sequence always leads to a cycle of repeating w-tuples or to the n-tuple (0,..., 0). If an w-tuple 4) gives rise to the latter, it is usually called vanishing. First, it will be shown in this article that the Ducci-sequences are closely related to Pascal's triangle and many properties of their cyclic structures can be found and proved considering Pascal's triangle modulo 2. In the second part we will examine whether, for a given n GN there is such anMthat 2 = -1 mod n, which is crucial for some properties of the Ducci-sequences. We would like to thank the referee for a number of valuable suggestions.
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which replace dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. ChannelNets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolutional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents the first attempt to compress the fully-connected classification layer, which usually accounts for about 25% of total parameters in compact CNNs. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods.
A roadmap for parametric CAD efficiency in the automotive industry

3D CAD systems are used in product design for simultaneous engineering and to improve productivity. CAD tools can substantially enhance design performance. Although 3D CAD is a widely used and highly effective tool in mechanical design, mastery of CAD skills is complex and time-consuming. The concepts of parametric–associative models and systems are powerful tools whose efficiency is proportional to the complexity of their implementation. The availability of a framework for actions that can be taken to improve CAD efficiency can therefore be highly beneficial. Today, a clear and structured approach does not exist in this way for CAD methodology deployment. The novelty of this work is therefore to propose a general strategy for utilizing the advantages of parametric CAD in the automotive industry in the form of a roadmap. The main stages of the roadmap are illustrated by means of industrial use cases. The first results of his research are discussed and suggestions for future work are given.
Impact of Job Related Stress on Employee Performance : A Review and Research Agenda

Impact of occupational stress on employee performance has been recognized as an important area of concern for organizations. Negative stress affects the physical and mental health of the employees that in turn affects their performance on job. Research into the relationship between stress and job performance has been neglected in the occupational stress literature (Jex, 1998). It is therefore significant to understand different Occupational Stress Inducers (OSI) on one hand and their impact on different aspects of job performance on the other. This article reviews the available literature to understand the phenomenon so as to develop appropriate stress management strategies to not only save the employees from variety of health problems but to improve their performance and the performance of the organization. 35 Occupational Stress Inducers (OSI) were identified through a comprehensive review of articles and reports published in the literature of management and allied disciplines between 1990 and 2014. A conceptual model is proposed towards the end to study the impact of stress on employee job performance. The possible data analysis techniques are also suggested providing direction for future research.
‘Webagogy’ - the next milestone after pedagogy and andragogy?

Traditional university benches are gradually being replaced with the intensification of Open and Distance Learning (ODL) mode. With more courses going online, the main challenge is to bridge the `separation' between distance learners and tutors. In this respect the Discussion Board (DB) is gathering great interest and has become the locus of considerable research. The objective of this paper is to assess the readiness of ODL learners to embrace the discussion board as an e-learning platform using multi-pronged qualitative approaches. It investigates if learning expectations are legitimate and if the Mauritian ODL learner is ready to break away with traditional practices and become beneficiaries of a learning system which is highly mediated by the functionalities of ICT. This case study involving ten students reading for a Master's degree at the Open University of Mauritius revealed that the DB is a valuable learning tool with great potential for peer tutoring, tutor/learner interaction and development of reflective and analytical skills. On the other hand there is a need for both tutors and leaners to familiarize themselves with the functionalities of this tool. This study purports that learners must be encouraged to develop their writing skills in order to make the most of the DB platform. The acronym “TELEPHONE” is proposed in this paper to describe some key facets of DB and online learning.
Estimating Trade Flows : Trading Partners and Trading Volumes

We develop a simple model of international trade with heterogeneous firms that is consistent with a number of stylized features of the data. In particular, the model predicts positive as well as zero trade flows across pairs of countries, and it allows the number of exporting firms to vary across destination countries. As a result, the impact of trade frictions on trade flows can be decomposed into the intensive and extensive margins, where the former refers to the trade volume per exporter and the latter refers to the number of exporters. This model yields a generalized gravity equation that accounts for the self-selection of firms into export markets and their impact on trade volumes. We then develop a two-stage estimation procedure that uses an equation for selection into trade partners in the first stage and a trade flow equation in the second. We implement this procedure parametrically, semiparametrically, and nonparametrically, showing that in all three cases the estimated effects of trade frictions are similar. Importantly, our method provides estimates of the intensive and extensive margins of trade. We show that traditional estimates are biased and that most of the bias is due not to selection but rather due to the omission of the extensive margin. Moreover, the effect of the number of exporting firms varies across country pairs according to their characteristics. This variation is large and particularly so for trade between developed and less developed countries and between pairs of less developed countries.
Stress, Social Support, and Burnout Among Long-Term Care Nursing Staff.

Long-term care nursing staff are subject to considerable occupational stress and report high levels of burnout, yet little is known about how stress and social support are associated with burnout in this population. The present study utilized the job demands-resources model of burnout to examine relations between job demands (occupational and personal stress), job resources (sources and functions of social support), and burnout in a sample of nursing staff at a long-term care facility (N = 250). Hierarchical linear regression analyses revealed that job demands (greater occupational stress) were associated with more emotional exhaustion, more depersonalization, and less personal accomplishment. Job resources (support from supervisors and friends or family members, reassurance of worth, opportunity for nurturing) were associated with less emotional exhaustion and higher levels of personal accomplishment. Interventions to reduce burnout that include a focus on stress and social support outside of work may be particularly beneficial for long-term care staff.
XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework

Predicting the next activity of a running process is an important aspect of process management. Recently, artificial neural networks, so called deep-learning approaches, have been proposed to address this challenge. This demo paper describes a software application that applies the Tensorflow deep-learning framework to process prediction. The software application reads industry-standard XES files for training and presents the user with an easy-to-use graphical user interface for both training and prediction. The system provides several improvements over earlier work. This demo paper focuses on the software implementation and describes the architecture and user interface.
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

Incorporating prior knowledge like lexical constraints into the model’s output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc. However, existing RNN-based models incrementally generate sentences from left to right via beam search, which makes it difficult to directly introduce lexical constraints into the generated sentences. In this paper, we propose a new algorithmic framework, dubbed BFGAN, to address this challenge. Specifically, we employ a backward generator and a forward generator to generate lexically constrained sentences together, and use a discriminator to guide the joint training of two generators by assigning them reward signals. Due to the difficulty of BFGAN training, we propose several training techniques to make the training process more stable and efficient. Our extensive experiments on two large-scale datasets with human evaluation demonstrate that BFGAN has significant improvements over previous methods.
Sarcasm Detection : Building a Contextual Hierarchy

The conundrum of understanding and classifying sarcasm has been dealt with by the traditional theorists as an analysis of a sarcastic utterance and the ironic situation that surrounds it. The problem with such an approach is that it is too narrow, as it is unable to sufficiently utilize the two indispensable agents in making such an utterance, viz. the speaker and the listener. It undermines the necessary context required to comprehend a sarcastic utterance. In this paper, we propose a novel approach towards understanding sarcasm in terms of the existing knowledge hierarchy between the two participants, which forms the basis of the context that both agents share. The difference in relationship of the speaker of the sarcastic utterance and the disparate audience found on social media, such as Twitter, is also captured. We then apply our model on a corpus of tweets to achieve significant results and consequently, shed light on subjective nature of context, which is contingent on the relation between the speaker and the listener.
Multi-Physical Domain Modeling of a DFIG Wind Turbine System using PLECS ®

A cost and energy efficient method of wind power generation is to connect the output of the turbine to a doubly-fed induction generator (DFIG), allowing operation at a range of variable speeds. While for electrical engineers the electromagnetic components in such a system, like the electric machine, power electronic converter and magnetic filters are of most interest, a DFIG wind turbine is a complex design involving multiple physical domains strongly interacting with each other. The electrical system, for instance, is influenced by the converter’s cooling system and mechanical components, including the rotor blades, shaft and gearbox. This means that during component selection and design of control schemes, the influence of domains on one another must be considered in order to achieve an optimized overall system performance such that the design is dynamic, efficient and cost-effective. In addition to creating an accurate model of the entire system, it is also important to model the real-world operating and fault conditions. For fast prototyping and performance prediction, computer-based simulation has been widely adopted in the engineering development process. Modeling such complex systems while including switching power electronic converters requires a powerful and robust simulation tool. Furthermore, a rapid solver is critical to allow for developing multiple iterative enhancements based on insight gained through system simulation studies.
ARCTIC: metadata extraction from scientific papers in pdf using two-layer CRF

Most scientific articles are available in PDF format. The PDF standard allows the generation of metadata that is included within the document. However, many authors do not define this information, making this feature unreliable or incomplete. This fact has been motivating research which aims to extract metadata automatically. Automatic metadata extraction has been identified as one of the most challenging tasks in document engineering. This work proposes Artic, a method for metadata extraction from scientific papers which employs a two-layer probabilistic framework based on Conditional Random Fields. The first layer aims at identifying the main sections with metadata information, and the second layer finds, for each section, the corresponding metadata. Given a PDF file containing a scientific paper, Artic extracts the title, author names, emails, affiliations, and venue information. We report on experiments using 100 real papers from a variety of publishers. Our results outperformed the state-of-the-art system used as the baseline, achieving a precision of over 99%.
A 79-GHz 2 × 2 MIMO PMCW Radar SoC in 28-nm CMOS

In this paper, the concept of phase modulated MIMO radars is explained and demonstrated with a 28 nm CMOS fully integrated 79 GHz radar SoC. It includes two transmitters, two receivers, and the mm-wave frequency generation. The receivers’ outputs are digitized by on-chip ADCs and processed by a custom designed digital core, which performs correlation and accumulation with a pseudorandom sequence used in transmission. The SoC consumes 1 W to achieve 7.5 cm range resolution. A module with antennas allows for 5° resolution over ± 60° elevation and azimuth scan in 2×2 code domain MIMO operation. A 4×4 MIMO system is also demonstrated by means of two SoCs mounted on the same module.
TECHNOLOGY AND ACCOUNTING INFORMATION SYSTEM IN THE NIGERIAN BANKING INDUSTRY

Information technology has tremendously stimulated expansion of the banking networks and range of the offered services during recent years. The information technology has become a critical business resource because its absence could result in poor decisions and ultimately business failure. This study intends to find out the information technology influence on accounting information production in the Nigerian banking industry. Both primary and secondary data were used and Analysis of Variance (ANOVA) was used to test the hypothesis. Judgmental sampling method was used to obtain a representative sample of the population. Although for all Nigerian banks the efficiency has increased, the improvement of cost of efficiency is relatively much smaller than in the case of profit efficiency. It is also observed that accounting information technology can improve banks performance by reducing operational cost and by facilitating transactions among customers within the same or different network. It is, therefore, concludedthat accounting information technology is relevant in simplifying issues and in the provision of quality information in the Nigerian banking industry. That explains why the banks spend a greater part of their resources on information technology and consider its application as a comparative edge in the competitive banking industry. This paper recommends that the impact of the progress in accounting information technology on banking service should not lead to a very strong increase of cost of their processing, which put in question possibility to achieve economy of scale by Nigerian banks. Also all Nigerian banks should continue to utilize and upgrade their information technology for efficient service delivery and profitability.
Designing ScratchJr: support for early childhood learning through computer programming

ScratchJr is a graphical programming language based on Scratch and redesigned for the unique developmental and learning needs of children in kindergarten to second grade. The creation of ScratchJr addresses the relative lack of powerful technologies for digital creation and computer programming in early childhood education. ScratchJr will provide software for children to create interactive, animated stories as well as curricula and online resources to support adoption by educators. This paper describes the goals and challenges of creating a developmentally appropriate programming tool for children ages 5-7 and presents the path from guiding principles and studies with young children to current ScratchJr designs and plans for future work.
Factors influencing the decision that women make on their mode of delivery: the Health Belief Model

Childbirth is regarded as an important life event for women, and growing numbers of them are making the choice to give birth by Caesarean Delivery. The aim of this study was to identify the factors influencing the decision that women make on their mode of delivery, underpinned by the Health Belief Model.



This was a cross-sectional study. Hong Kong Chinese women aged 18-45, who were pregnant or had given birth within the last three years were recruited. The participants were asked to complete a structured self-administered questionnaire consisting of 62 questions.



A total of 319 women were recruited, of whom 73 (22.9%) preferred to have a cesarean section delivery (CD). The results showed that women preferred CD because they were concerned about being pregnant at an advanced age, were worried about labor pain and perineum tearing, wanted to have a better plan for maternity leave, had chosen an auspicious date to deliver, and perceived that CD is a more convenience way to deliver. The perceived benefits and severity of a vaginal birth (VB), and the perceived benefits, severity, and cues to action of CD, affected the decision to undergo either a VB or CD.



The data indicated that the constructs of the Health Belief Model--perceived benefits, perceived severity, and cues to action--affect the decision that women make on their mode of delivery. This research indicates that there is value in designing educational programs for pregnant women to educate them on the benefits, risks, and severity of the two different modes of birth based on the constructs of HBM. This will enable women to be active participants in choosing the mode of birth that they believe is right for them.
Expression of affect in spontaneous speech: Acoustic correlates and automatic detection of irritation and resignation

The majority of previous studies on vocal expression have been conducted on posed expressions. In contrast, we utilized a large corpus of authentic affective speech recorded from real-life voice controlled telephone services. Listeners rated a selection of 200 utterances from this corpus with regard to level of perceived irritation, resignation, neutrality, and emotion intensity. The selected utterances came from 64 different speakers who each provided both neutral and affective stimuli. All utterances were further automatically analyzed regarding a comprehensive set of acoustic measures related to F0, intensity, formants, voice source, and temporal characteristics of speech. Results first showed that several significant acoustic differences were found between utterances classified as neutral and utterances classified as irritated or resigned using a withinpersons design. Second, listeners’ ratings on each scale were associated with several acoustic measures. In general the acoustic correlates of irritation, resignation, and emotion intensity were similar to previous findings obtained with posed expressions, though the effect sizes were smaller for the authentic expressions. Third, automatic classification (using LDA classifiers both with and without speaker adaptation) of irritation, resignation, and neutral performed at a level comparable to human performance, though human listeners and machines did not necessarily classify individual utterances similarly. Fourth, clearly perceived exemplars of irritation and resignation were rare in our corpus. These findings were discussed in relation to future research.
Learning scrum by doing real-life projects

Being Scrum the agile software development framework most commonly used in the software industry, its applicability is attracting great attention to the academia. That is why this topic is quite often included in computer science and related university programs. In this article, we present a course design of a Software Engineering course where an educational framework and an open-source agile project management tool were used to develop real-life projects by undergraduate students. During the course, continuous guidance was given by the teaching staff to facilitate the students' learning of Scrum. Results indicate that students find it easy to use the open-source tool and helpful to apply Scrum to a real-life project. However, the unavailability of the client and conflicts among the team members have negative impact on the realization of projects. The guidance given to students along the course helped identify five common issues faced by students through the learning process.
Information Privacy Concerns: Linking Individual Perceptions with Institutional Privacy Assurances

Organizational information practices can result in a variety of privacy problems that can increase consumers’ concerns for information privacy. To explore the link between individuals and organizations regarding privacy, we study how institutional privacy assurances such as privacy policies and industry self-regulation can contribute to reducing individual privacy concerns. Drawing on Communication Privacy Management (CPM) theory, we develop a research model suggesting that an individual’s privacy concerns form through a cognitive process involving perceived privacy risk, privacy control, and his or her disposition to value privacy. Furthermore, individuals’ perceptions of institutional privacy assurances -namely, perceived effectiveness of privacy policies and perceived effectiveness of industry privacy self-regulation -are posited to affect the riskcontrol assessment from information disclosure, thus, being an essential component of privacy concerns. We empirically tested the research model through a survey that was administered to 823 users of four different types of websites: 1) electronic commerce sites, 2) social networking sites, 3) financial sites, and 4) healthcare sites. The results provide support for the majority of the hypothesized relationships. The study reported here is novel to the extent that existing empirical research has not explored the link between individuals’ privacy perceptions and institutional privacy assurances. We discuss implications for theory and practice and provide suggestions for future research.