nils-herrmann/clean_scirepeval huggingface dataset:
ARRAYS AND AMPLITUDE AND SPATIAL

This paper presents a review of the advances in strong motion recording since the early 1930s, based mostly on the experiences in the United States. A particular emphasis is placed on the amplitude and spatial resolution of recording, which both must be “adequate” to capture the nature of strong earthquake ground motion and response of structures. The first strong motion accelerographs had optical recording system, dynamic range of about 50 dB and useful life longer than 30 years. Digital strong motion accelerographs started to become available in the late 1970’s. Their dynamic range has been increasing progressively, and at present is about 135 dB. Most models have had useful life shorter than 5 to 10 years. One benefit from a high dynamic range is early trigger and anticipated ability to compute permanent displacements. Another benefit is higher sensitivity and hence a possibility to record smaller amplitude motions (aftershocks, smaller local earthquakes and distant large earthquakes), which would augment significantly the strong motion databases. The present trend of upgrading existing and adding new stations with high dynamic range accelerographs has lead to deployment of relatively small We dedicate this paper to Donald E. Hudson (1916-1999), a pioneer in the field of Earthquake Engineering, and our teacher and mentor. His contributions to academic research and development of earthquake instrumentation are without parallel. With a rare ability to attract, motivate and support yo ung scientists, he created a long and impressive list of Ph.D. graduates who are now professors, researchers and leaders in Earthquake Engineering.
PID Controller Tuning in Reverse Osmosis System based on Particle Swarm Optimization

This paper describes PID Controller tuning based on Particle Swarm Optimization in the Reverse Osmosis application. Particle Swarm Optimization is powerful method for tuning PID controller comparison to other conventional optimization techniques. It has some advance features such as easy implementation, stable characteristics; self-tuning, flexible design In this paper a model has presented that focus on the PH and Conductivity control in the Reverse Osmosis (RO). PSO algorithm is presented to tune the parameters of PID controller used in the RO Model. Computer Simulation in terms of a virtual process is designed to check the algorithm and compare the results with other optimization techniques. Index Terms — Particle Swarm Optimization (PSO), Reverse osmosis (RO), PID Tuning, Membrane
Travel Recommender Systems

Recommender systems are commonly defined as applications that e-commerce sites exploit to suggest products and provide consumers with information to facilitate their decision-making  They implicitly assume that we can map user needs and constraints, through appropriate recommendation algorithms, and convert them into product selections using knowledge compiled into the intelligent recommender. Knowledge is extracted from either domain experts (contentor knowledge-based approaches) or extensive logs of previous purchases (collaborative-based approaches). Furthermore, the interaction process, which turns needs into products, is presented to the user with a rationale that depends on the underlying recommendation technology and algorithms. For example, if the system funnels the behavior of other users in the recommendation, it explicitly shows reviews of the selected products or quotes from a similar user. Recommender systems are now a popular research area2 and are increasingly used by e-commerce  For travel and tourism,3 the two most successful recommender system technologies  are Triplehop’s TripMatcher (used by www. ski-europe.com, among others) and VacationCoach’s expert advice platform, MePrint (used by travelocity.com). Both of these recommender systems try to mimic the interactivity observed in traditional counselling sessions with travel agents when users search for advice on a possible holiday destination. From a technical viewpoint, they primarily use a content-based approach, in which the user expresses needs, benefits, and constraints using the offered language (attributes). The system then matches the user preferences with items in a catalog of destinations (described with the same language). VacationCoach exploits user profiling by explicitly asking the user to classify himself or herself in one profile (for example, as a “culture creature,” “beach bum,” or “trail trekker”), which induces implicit needs that the user doesn’t provide. The user can even input precise profile information by completing the appropriate form. TripleHop’s matching engine uses a more sophisticated approach to reduce user input. It guesses importance of attributes that the user does not explicitly mention. It then combines statistics on past user queries with a prediction computed as a weighted average of importance assigned by similar
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters​ ​than​ ​SegNet.
In-class laptop use and its effects on student learning

Recently, a debate has begun over whether in-class laptops aid or hinder learning. While some research demonstrates that laptops can be an important learning tool, anecdotal evidence suggests more and more faculty are banning laptops from their classrooms because of perceptions that they distract students and detract from learning. The current research examines the nature of in-class laptop use in a large lecture course and how that use is related to student learning. Students completed weekly surveys of attendance, laptop use, and aspects of the classroom environment. Results showed that students who used laptops in class spent considerable time multitasking and that the laptop use posed a signiWcant distraction to both users and fellow students. Most importantly, the level of laptop use was negatively related to several measures of student learning, including self-reported understanding of course material and overall course performance. The practical implications of these Wndings are discussed.
Quality Evaluation of Image Steganography Techniques: A Heuristics based Approach

Steganography refers to techniques that hide information inside innocuous looking objects known as “Cover Objects” such that its very existence remains concealed to any unintended recipient. Images are pervasive in day to day applications and have high redundancy in representation. Thus, they are appealing contenders to be used as cover objects. There are a large number of image steganography techniques proposed till date but negligible research has been done on the development of a standard quality evaluation model for judging their performance. Existence of such a model is important for fueling the development of superior techniques and also paves the way for the improvement of the existing ones. However, the common quality parameters often considered for performance evaluation of an image steganography technique are insufficient for overall quantitative evaluation. This paper proposes a rating scale based quality evaluation model for image steganography algorithms that utilizes both quantitative parameters and observation heuristics. Different image steganography techniques have been evaluated using proposed model and quantitative performance scores for each of the techniques have been derived. The scores have been observed to be in accordance with actual literature and the system is simple, efficient and flexible.
Factorization Models for Forecasting Student Performance

Predicting student performance, one of the tasks in educational data mining, has been taken into account recently [Toscher and Jahrer 2010; Yu et al. 2010; Cetintas et al. 2010; Thai-Nghe et al. 2011]. It was selected as a challenge task for the KDD Cup 2010 [Koedinger et al. 2010]. Concretely, predicting student performance is the task where we would like to know how the students learn (e.g. generally or narrowly), how quickly or slowly they adapt to new problems or if it is possible to infer the knowledge requirements to solve the problems directly from student performance data [Corbett and Anderson 1995; Feng et al. 2009], and eventually, we would like to know whether the students perform the tasks (exercises) correctly (or with some levels of certainty). As discussed in Cen et al. [2006], an improved model for predicting student performance could save millions of hours of students’ time and effort in learning algebra. In that time, students could move to other specific fields of their study or doing other things they enjoy. From educational data mining point of view, an accurate and reliable model in predicting student performance may replace some current standardized tests, and thus, reducing the pressure, time, as well as effort on “teaching and learning for examinations” [Feng et al. 2009; Nghe et al. 2011]. To address the problem of predicting student performance, many papers have been published but most of them are based on traditional classification/regression techniques [Cen et al. 2006; Feng et al. 2009; Yu et al. 2010; Pardos and Heffernan 2010]. Many other works can be found in Romero et al. [2010]. Recently, [Thai-Nghe et al. 2010; Toscher and Jahrer 2010; Thai-Nghe et al. 2011] have proposed using recommendation techniques, e.g. matrix factorization, for predicting student performance. The authors have shown that predicting student performance can be considered as rating prediction since the student, task, and performance would become user, item, and rating in recommender systems, respectively. We know that learning and problem-solving are complex cognitive and affective processes that are different to shopping and other e-commerce transactions, however, as discussed in Thai-Nghe et al. [2011], the factorization models in recommender systems are implicitly able to encode latent factors of students and tasks (e.g. “slip” and “guess”), and especially in case where we do not have enough meta data about students and tasks (or even we have not enough background knowledge of the domain), this mapping is a reasonable approach.
Selected Bibliography of Scholarship in (Comparative) Cultural Studies and Popular Culture

CLCWeb: Comparative Literature and Culture, the peer-reviewed, full-text, and open-access learned journal in the humanities and social sciences, publishes new scholarship following tenets of the discipline of comparative literature and the field of cultural studies designated as "comparative cultural studies." Publications in the journal are indexed in the Annual Bibliography of English Language and Literature (Chadwyck-Healey), the Arts and Humanities Citation Index (Thomson Reuters ISI), the Humanities Index (Wilson), Humanities International Complete (EBSCO), the International Bibliography of the Modern Language Association of America, and Scopus (Elsevier). The journal is affiliated with the Purdue University Press monograph series of Books in Comparative Cultural Studies. Contact: <clcweb@purdue.edu>
Prospects for tropical forest biodiversity in a human-modified world.

The future of tropical forest biodiversity depends more than ever on the effective management of human-modified landscapes, presenting a daunting challenge to conservation practitioners and land use managers. We provide a critical synthesis of the scientific insights that guide our understanding of patterns and processes underpinning forest biodiversity in the human-modified tropics, and present a conceptual framework that integrates a broad range of social and ecological factors that define and contextualize the possible future of tropical forest species. A growing body of research demonstrates that spatial and temporal patterns of biodiversity are the dynamic product of interacting historical and contemporary human and ecological processes. These processes vary radically in their relative importance within and among regions, and have effects that may take years to become fully manifest. Interpreting biodiversity research findings is frequently made difficult by constrained study designs, low congruence in species responses to disturbance, shifting baselines and an over-dependence on comparative inferences from a small number of well studied localities. Spatial and temporal heterogeneity in the potential prospects for biodiversity conservation can be explained by regional differences in biotic vulnerability and anthropogenic legacies, an ever-tighter coupling of human-ecological systems and the influence of global environmental change. These differences provide both challenges and opportunities for biodiversity conservation. Building upon our synthesis we outline a simple adaptive-landscape planning framework that can help guide a new research agenda to enhance biodiversity conservation prospects in the human-modified tropics.
Realization with CCG

Here I give an overview of recent work on natural language realization with Combinatory Categorial Grammar, done by Michael White and his colleagues, with some more specific descriptions of the algorithms used, where they were unclear to me. In particular, I focus on the work presented in his 2007 paper , in which White et al describe a process for extracting a grammar from the CCGBank and using it to generate text based on semantic descriptions given in HLDS, the Hybrid Logic Dependency Semantics. I also give some background from his earlier work, a description of some background ideas.
Poor nutritional status of schoolchildren in urban and peri-urban areas of Ouagadougou (Burkina Faso)

Malnutrition is still highly prevalent in developing countries. Schoolchildren may also be at high nutritional risk, not only under-five children. However, their nutritional status is poorly documented, particularly in urban areas. The paucity of information hinders the development of relevant nutrition programs for schoolchildren. The aim of this study carried out in Ouagadougou was to assess the nutritional status of schoolchildren attending public and private schools.



The study was carried out to provide baseline data for the implementation and evaluation of the Nutrition Friendly School Initiative of WHO. Six intervention schools and six matched control schools were selected and a sample of 649 schoolchildren (48% boys) aged 7-14 years old from 8 public and 4 private schools were studied. Anthropometric and haemoglobin measurements, along with thyroid palpation, were performed. Serum retinol was measured in a random sub-sample of children (N = 173). WHO criteria were used to assess nutritional status. Chi square and independent t-test were used for proportions and mean comparisons between groups.



Mean age of the children (48% boys) was 11.5 ± 1.2 years. Micronutrient malnutrition was highly prevalent, with 38.7% low serum retinol and 40.4% anaemia. The prevalence of stunting was 8.8% and that of thinness, 13.7%. The prevalence of anaemia (p = 0.001) and vitamin A deficiency (p < 0.001) was significantly higher in public than private schools. Goitre was not detected. Overweight/obesity was low (2.3%) and affected significantly more children in private schools (p = 0.009) and younger children (7-9 y) (p < 0.05). Thinness and stunting were significantly higher in peri-urban compared to urban schools (p < 0.05 and p = 0.004 respectively). Almost 15% of the children presented at least two nutritional deficiencies.



This study shows that malnutrition and micronutrient deficiencies are also widely prevalent in schoolchildren in cities, and it underlines the need for nutrition interventions to target them.
Boosting up Scene Text Detectors with Guided CNN

Deep CNNs have achieved great success in text detection. Most of existing methods attempt to improve accuracy with sophisticated network design, while paying less attention on speed. In this paper, we propose a general framework for text detection called Guided CNN to achieve the two goals simultaneously. The proposed model consists of one guidance subnetwork, where a guidance mask is learned from the input image itself, and one primary text detector, where every convolution and non-linear operation are conducted only in the guidance mask. The guidance subnetwork filters out non-text regions coarsely, greatly reducing the computation complexity. At the same time, the primary text detector focuses on distinguishing between text and hard non-text regions and regressing text bounding boxes, achieving a better detection accuracy. A novel training strategy, called background-aware block-wise random synthesis, is proposed to further boost up the performance. We demonstrate that the proposed Guided CNN is not only effective but also efficient with two state-of-the-art methods, CTPN [52] and EAST [64], as backbones. On the challenging benchmark ICDAR 2013, it speeds up CTPN by 2.9 times on average, while improving the F-measure by 1.5%. On ICDAR 2015, it speeds up EAST by 2.0 times while improving the F-measure by 1.0%.  The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. * Zhanghui Kuang is the corresponding author 2 YUE ET AL: BOOSTING UP SCENE TEXT DETECTORS WITH GUIDED CNN  Illustration of guiding the primary text detector. Convolutions and non-linear operations are conducted only in the guidance mask indicated by the red and blue rectangles. The guidance mask (the blue) is expanded by backgroundaware block-wise random synthesis (the red) during training. When testing, the guidance mask is not expanded.  Text appears very sparsely in scene images. The left shows one example image. The right shows the text area ratio composition of ICDAR 2013 test set. Images with (0%,10%], (10%,20%], (20%,30%], and (30%,40%] text region account for 57%, 21%, 11%, and 6% respectively. Only 5 % images have more than 40% text region. 57% 21% 11% 6% 5% (0.0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,1.0]
Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems.

We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.
Artificial Intelligence Techniques in Medicine and Healthcare

Now-a-days, researchers are increasingly looking into new and innovative techniques with the help of information technology to overcome the rapid surge in health care costs facing the community. Research undertaken in the past has shown that artificial intelligence (AI) tools and techniques can aid in the diagnosis of disease states and assessment of treatment outcomes. This has been demonstrated in a number of areas, including: help with medical decision support system, classification of heart disease from electrocardiogram (ECG) waveforms, identification of epileptic seizure from electroencephalogram (EEG) signals, ophthalmology to detect glaucoma disease, abnormality in movement pattern (gait) recognition for rehabilitation and potential falls risk minimization, assisting functional electrical stimulation (FES) control in rehabilitation setting of spinal cord injured patients, and clustering of medical images (Begg et al., 2003; Garrett et al., 2003; Masulli et al., 1998; Papadourokis et al., 1998; Silva & Silva, 1998). Recent developments in information technology and AI tools, particularly in neural networks, fuzzy logic and support vector machines, have provided the necessary support to develop highly efficient automated diagnostic systems. Despite plenty of future challenges, these new advances in AI tools hold much promise for future developments in AI-based approaches in solving medical and health-related problems. This article is organized as follows: Following an overview of major AI techniques, a brief review of some of the applications of AI in health care is provided. Future challenges and directions in automated diagnostics are discussed in the summary and conclusion sections.