nils-herrmann/clean_scirepeval huggingface dataset:
Substance use and associated factors among Debre Berhan University students, Central Ethiopia

Being a global burden of youths, substances use is unhealthy behavior that exposes youths to health and social problems. Knowledge of the prevalence and predictors of substance use behavior among university students is important for designing periodic and locally appropriate interventions. This study is conducted to assess the prevalence and predicators of substances among Debre Berhan University students.



Cross-sectional quantitative study was employed in May 2016. Stratified two-stage sampling technique was applied to choose 695 students. Substance use behaviors were assessed using tools derived from World Health Organization Model Students' Substance Use Core Questionnaire.



The lifetime utilization of alcohol, khat and cigarette among students was found to be 36.3%, 10.9% and 7.4% respectively. The lifetime utilization of shisha and cannabis was 4.2% and 4.5% respectively. About 17%, 5.7%, and 3.1% of students are currently using alcohol, Khat and Cigarette respectively. Using multivariate binary logistic regression, being male, feeding out of the university café, being from private preparatory school, having higher monthly income, having substance user families and friends were found to be variables significantly associated with students' substance use behaviors.



The current prevalence of substances use among Debre Berhan University students is low comparing to other Ethiopian and African universities. Youth are starting substance use at lower grades especially at preparatory schools. Substance use behaviors are affected by complex factors at individual, family, school, social, and environmental factors. Therefore, strategies to alleviate youth substance use problems should focus on changing individual perception, knowledge, and intention towards substances. There is a need for further research with more powerful sample size and weighted estimates using complex analysis. Reasons for lower prevalence of substance use from other Ethiopian universities shall be further explored using qualitative study.
Compact low-voltage power-efficient operational amplifier cells for VLSI

“Some Design Aspects of a Two-Stage Rail-to-Rail CMOS Op Amp' by Sander L. J. Gierkink, Peter J. Holzmann, Remco J. Wiegerink and Roelof F. Wassenaar, Analog Integrated Circuits and Signal Processing, Vol. 21, No. 2, Nov. 1999, pp 143-152. “Compact Low-Voltage Power-Efficient Operational Amplifier Cells for VLSI by Klaas-Jan de Langen and Johan H. Huijsing,  1998,  Aspects of a Rail-to-Rail CMOS Op Amp' by Glierkink et al., Mesa Research institute, ECT-97-36, pp. 23-28.
Qualitative research methods in mental health.

As the evidence base for the study of mental health problems develops, there is a need for increasingly rigorous and systematic research methodologies. Complex questions require complex methodological approaches. Recognising this, the MRC guidelines for developing and testing complex interventions place qualitative methods as integral to each stage of intervention development and implementation. However, mental health research has lagged behind many other healthcare specialities in using qualitative methods within its evidence base. Rigour in qualitative research raises many similar issues to quantitative research and also some additional challenges. This article examines the role of qualitative methods within mental heath research, describes key methodological and analytical approaches and offers guidance on how to differentiate between poor and good quality qualitative research.
External localization system for mobile robotics

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.
A mixed reality system for virtual glasses try-on

In this paper we present an augmented reality system for automatic try-on of 3D virtual eyeglasses. The user can select from various virtual models of eyeglasses for trying-on and the system will automatically fit the selected virtual glasses on the user's face. The user can see his/her face as in a mirror with the 3D virtual glasses fitted on it. We also propose a method for handling the occlusion problem, to display only those parts of the glasses that are not occluded by the face. This system can be used for online shopping, or short listing a large set of available models to a few before physical try-on at a retailer's site.
Neuroaesthetics: a review

Neuroaesthetics is a relatively young field within cognitive neuroscience, concerned with the neural underpinnings of aesthetic experience of beauty, particularly in visual art. Neuroscientific investigations have approached this area using imaging and neurophysiological techniques, such as functional magnetic resonance (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG). The results produced so far are very heterogeneous. Nonetheless, an overall view of the findings suggests that the aesthetic experience of visual artworks is characterized by the activation of: sensorimotor areas; core emotional centres; and reward-related centres. In the present review, we discuss the functional relevance of these activations and propose that aesthetic experience is a multilevel process exceeding a purely visual analysis of artworks and relying upon visceromotor and somatomotor resonance in the beholder.
Enhanced LSB technique for audio steganography

The idea of this paper is to invent a new strategy in Steganography to get the minimum effect in audio which is used to hide data into it. “Progress always involves risk” Fredrick Wilcox observed that technological progress of computer science and the Internet altered the way we lived, and will continue to cast our life[1] . In this paper we have presented a Steganography method of embedding text data in an audio file. The basic approach behind this paper is to provide a good, well-organized method for hiding the data and sent to the destination in safer manner. In the proposed technique first the audio file is sampled and then appropriate bit is modified. In selected sample one bit is modified at least significant bit .The remaining bits may be used but it may be cause noise. We have attempted to provide an overview, theoretical framework about audio Steganography techniques and a novel approach to hide data in an audio using least significant bit (LSB).
Ultra-High Input Impedance, Low Noise Integrated Amplifier for Noncontact Biopotential Sensing

Noncontact electrocardiogram/electroencephalogram/<?Pub _bookmark Command="[Quick Mark]"?>electromyogram electrodes, which operate primarily through capacitive coupling, have been extensively studied for unobtrusive physiological monitoring. Previous implementations using discrete off-the-shelf amplifiers have been encumbered by the need for manually tuned input capacitance neutralization networks and complex dc-biasing schemes. We have designed and fabricated a custom integrated noncontact sensor front-end amplifier that fully bootstraps internal and external parasitic impedances. DC stability without the need for external large valued resistances is ensured by an ac bootstrapped, low-leakage, on-chip biasing network. The amplifier achieves, without neutralization, input impedance of 60 fF<formula formulatype="inline"><tex Notation="TeX">$\Vert$</tex></formula>50 T<formula formulatype="inline"><tex Notation="TeX">$\Omega$</tex></formula>, input referred noise of 0.05 fA/<formula formulatype="inline"><tex Notation="TeX">$\sqrt{\rm Hz}$</tex></formula> and 200 nV/<formula formulatype="inline"><tex Notation="TeX">$\sqrt{\rm Hz}$</tex></formula> at 1 Hz, and power consumption of 1.5 <formula formulatype="inline"> <tex Notation="TeX">$\mu$</tex></formula>A per channel at 3.3 V supply voltage. Stable frequency response is demonstrated below 0.05 Hz with electrode coupling capacitances as low as 0.5 pF.
A Low-Cost Real-Time Embedded Stereo Vision System for Accurate Disparity Estimation Based on Guided Image Filtering

Stereo matching, a key element towards extracting depth information from stereo images, is widely used in several embedded consumer electronic and multimedia systems. Such systems demand high processing performance and accurate depth perception, while their deployment in embedded and mobile environments implies that cost, energy and memory overheads need to be minimized. Hardware acceleration has been demonstrated in efficient embedded stereo vision systems. To this end, this paper presents the design and implementation of a hardware-based stereo matching system able to provide high accuracy and concurrently high performance for embedded vision devices, which are associated with limited hardware and power budget. We first implemented a compact and efficient design of the guided image filter, an edge-preserving filter, which reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. The guided filter design is used in two parts of the stereo matching pipeline, showing that it can simplify the hardware complexity of the Adaptive Support Weight aggregation step, and efficiently enable a powerful disparity refinement unit, which improves matching accuracy, even though cost aggregation is based on simple, fixed support strategies. We implemented several variants of our design on a Kintex-7 FPGA board, which was able to process HD video (1,280 × 720) in real-time (60 fps), using ~57.5k and ~71k of the FPGA's logic (CLB) and register resources, respectively. Additionally, the proposed stereo matching design delivers leading accuracy when compared to state-of-the-art hardware implementations based on the Middlebury evaluation metrics (at least 1.5 percent less bad matching pixels).
NADA: New Arabic Dataset for Text Classification

In the recent years, Arabic Natural Language Processing, including Text summarization, Text simplification, Text Categorization and other Natural Language-related disciplines, are attracting more researchers. Appropriate resources for Arabic Text Categorization are becoming a big necessity for the development of this research. The few existing corpora are not ready for use, they require preprocessing and filtering operations. In addition, most of them are not organized based on standard classification methods which makes unbalanced classes and thus reduced the classification accuracy. This paper proposes a New Arabic Dataset (NADA) for Text Categorization purpose. This corpus is composed of two existing corpora OSAC and DAA. The new corpus is preprocessed and filtered using the recent state of the art methods. It is also organized based on Dewey decimal classification scheme and Synthetic Minority Over-Sampling Technique. The experiment results show that NADA is an efficient dataset ready for use in Arabic Text Categorization. Data collection; arabic natural language processing; arabic text categorization; dewey decimal classification; synthetic minority over-sampling
Least-Squares Fitting of Circles and Ellipses ∗

Fitting circles and ellipses to given points in the plane is a problem that arises in many application areas, e.g. computer graphics , coordinate metrology [2], petroleum engineering [11], statistics [7]. In the past, algorithms have been given which fit circles and ellipses in some least squares sense without minimizing the geometric distance to the given points , . In this paper we present several algorithms which compute the ellipse for which the sum of the squares of the distances to the given points is minimal. These algorithms are compared with classical simple and iterative methods. Circles and ellipses may be represented algebraically i.e. by an equation of the form F (x) = 0. If a point is on the curve then its coordinates x are a zero of the function F . Alternatively, curves may be represented in parametric form, which is well suited for minimizing the sum of the squares of the distances. 1 Preliminaries and Introduction Ellipses, for which the sum of the squares of the distances to the given points is minimal will be referred to as “best fit” or “geometric fit”, and the algorithms will be called “geometric”. Determining the parameters of the algebraic equation F (x) = 0 in the least squares sense will be denoted by “algebraic fit” and the algorithms will be called “algebraic”. We will use the well known Gauss-Newton method to solve the nonlinear least squares problem (cf. ). Let u = (u1, . . . , un) T be a vector of unknowns and consider the nonlinear system of m equations f(u) = 0. If m > n, then we want to minimize
Magnetic integration of LCC compensated resonant converter for inductive power transfer applications

The aim of this paper is to present a novel magnetic integrated LCC series-parallel compensation topology for the design of both the primary and pickup pads in inductive power transfer (IPT) applications. A more compact structure can be realized by integrating the inductors of the compensation circuit into the coupled power-transmitting coils. The impact of the extra coupling between the compensated coils (inductors) and the power-transferring coils is modeled and analyzed. The basic characteristics of the proposed topology are studied based on the first harmonic approximation (FHA). High-order harmonics are taken into account to derive an analytical solution for the current at the switching instant, which is helpful for the design of soft-switching operation. An IPT system with up to 5.6kW output power for electric vehicles (EV) charger has been built to verify the validity of the proposed magnetic integrated compensation topology. A peak efficiency of 95.36% from DC power source to the battery load is achieved at rated operation condition.
Rethinking Visual Relationships for High-level Image Understanding

Relationships, as the bond of isolated entities in images, reflect the interaction between objects and lead to a semantic understanding of scenes. Suffering from visuallyirrelevant relationships in current scene graph datasets, the utilization of relationships for semantic tasks is difficult. The datasets widely used in scene graph generation tasks are splitted from Visual Genome by label frequency, which even can be well solved by statistical counting. To encourage further development in relationships, we propose a novel method to mine more valuable relationships by automatically filtering out visually-irrelevant relationships. Then, we construct a new scene graph dataset named Visually-Relevant Relationships Dataset (VrR-VG) from Visual Genome. We evaluate several existing methods in scene graph generation in our dataset. The results show the performances degrade significantly compared to the previous dataset and the frequency analysis do not work on our dataset anymore. Moreover, we propose a method to learn feature representations of instances, attributes, and visual relationships jointly from images, then we apply the learned features to image captioning and visual question answering respectively. The improvements on the both tasks demonstrate the efficiency of the features with relation information and the richer semantic information provided in our dataset.
A critical analysis of decision support systems research

This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal decision support systems, group support systems, negotiation support systems, intelligent decision support systems, knowledge managementbased DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals. Around two-thirds of DSS research is empirical, a much higher proportion than general IS research. DSS empirical research is 1 Acknowledgements: We would like to thank Gemma Dodson for research assistance, especially in obtaining articles and coding, and Peter O’Donnell for discussions about DSS history. The research was partially supported by a Monash University Small Grant and a Curtin University Visiting Professorship. Preliminary results from the empirical study were presented at the 2004 IFIP International Conference on Decision Support Systems, Prato, Italy and the 2004 Australasian Conference on Information Systems, Hobart,
Multi-Label Collective Classification

Collective classification in relational data has become an important and active research topic in the last decade, where class labels for a group of linked instances are correlated and need to be predicted simultaneously. Collective classification has a wide variety of real world applications, e.g. hyperlinked document classification, social networks analysis and collaboration networks analysis. Current research on collective classification focuses on single-label settings, which assumes each instance can only be assigned with exactly one label among a finite set of candidate classes. However, in many real-world relational data, each instance can be assigned with a set of multiple labels simultaneously. In this paper, we study the problem of multi-label collective classification and propose a novel solution, called Icml (Iterative Classification of Multiple Labels), to effectively assign a set of multiple labels to each instance in the relational dataset. The proposed Icml model is able to capture the dependencies among the label sets for a group of related instances and the dependencies among the multiple labels within each label set simultaneously. Empirical studies on real-world tasks demonstrate that the proposed multi-label collective classification approach can effectively boost classification performances in multilabel relational datasets.
Images of Eyes Enhance Investments in a Real-Life Public Good

A key issue in cooperation research is to determine the conditions under which individuals invest in a public good. Here, we tested whether cues of being watched increase investments in an anonymous public good situation in real life. We examined whether individuals would invest more by removing experimentally placed garbage (paper and plastic bottles) from bus stop benches in Geneva in the presence of images of eyes compared to controls (images of flowers). We provided separate bins for each of both types of garbage to investigate whether individuals would deposit more items into the appropriate bin in the presence of eyes. The treatment had no effect on the likelihood that individuals present at the bus stop would remove garbage. However, those individuals that engaged in garbage clearing, and were thus likely affected by the treatment, invested more time to do so in the presence of eyes. Images of eyes had a direct effect on behaviour, rather than merely enhancing attention towards a symbolic sign requesting removal of garbage. These findings show that simple images of eyes can trigger reputational effects that significantly enhance on non-monetary investments in anonymous public goods under real life conditions. We discuss our results in the light of previous findings and suggest that human social behaviour may often be shaped by relatively simple and potentially unconscious mechanisms instead of very complex cognitive capacities.
Old School vs. New School: Comparing Transition-Based Parsers with and without Neural Network Enhancement

In this paper, we attempt a comparison between "new school" transitionbased parsers that use neural networks and their classical "old school" counterpart. We carry out experiments on treebanks from the Universal Dependencies project. To facilitate the comparison and analysis of results, we only work on a subset of those treebanks. However, we carefully select this subset in the hope to have results that are representative for the whole set of treebanks. We select two parsers that are hopefully representative of the two schools; MaltParser and UDPipe and we look at the impact of training size on the two models. We hypothesize that neural network enhanced models have a steeper learning curve with increased training size. We observe, however, that, contrary to expectations, neural network enhanced models need only a small amount of training data to outperform the classical models but the learning curves of both models increase at a similar pace after that. We carry out an error analysis on the development sets parsed by the two systems and observe that overall MaltParser suffers more than UDPipe from longer dependencies. We observe that MaltParser is only marginally better than UDPipe on a restricted set of short dependencies.
Metaphor Comprehension : From Comparison to Categorization

In this paper, we explore the relationship between metaphor and polysemy . We begin by discussing how novel metaphoric mappings can create new word meanings in the form of domain-general representations. Turning next to consider the implications of this view for the on-line comprehension of figurative language, we suggest that there is a shift from comparison processing to categorization processing as metaphors are conventionalized . Finally, we describe a series of experimental findings that support the proposed account.
DESIGNING A LAYOUT USING THE MODIFIED TRIANGLE METHOD , AND GENETIC ALGORITHMS

This paper describes the use of genetic algorithms (GA) for solving the facility layout problem (FLP) within manufacturing systems’ design. The paper considers a specific heuristic layout planning method, known as the modified triangle method. This method seeks for an optimal layout solution based on the degrees of flows between workstations. The search for an optimal solution is extremely time-consuming and not suitable for larger systems; therefore we have developed a system based on evolutionary computation. Our paper presents a system, based on GA, and the results obtained regarding several numerical cases from the literature. We propose the usage of this system by presenting numerous advantages over other methods of solving FLP for problem representation and evolutionary computation for solution search.