nils-herrmann/clean_scirepeval huggingface dataset:
Does leadership matter ? Examining the Relationship Among Transformational Leadership , School Climate , and Student Achievement

The purpose of this correlational study was to examine the relationship between transformational leadership, school climate, and student mathematics and reading achievement. Survey data were collected from a purposeful sample of elementary school principals and a convenience sample of his or her respective teachers located in a small suburban school district in southeast Texas. The Multifactor Leadership Questionnaire (MLQ-5X) was used to measure the degree to which a principal displays the factors of a transformational leader based on teacher perceptions and was used by the principals surveyed to self-assess. The School Climate Inventory-Revised (SCI-R) survey was used to measure teacher perceptions of school climate. Findings indicated a positive relationship between transformational leadership and school climate. However, a relationship was not found to exist between transformational leadership and student achievement nor between school climate and student achievement. When determining whether a relationship existed between the campus principal’s perceptions of their own transformational leadership qualities and his/her teachers’ perceptions of those same qualities, only two out of the 25 correlations were found to be statistically significant.
Wordnet. an Electronic Lexical Database. Edited by Christiane Fellbaum, with a Preface By

This is a landmark book. For anyone interested in language, in dictionaries and thesauri, or natural language processing, the introduction, Chapters 14, and Chapter 16 are must reading. (Select other chapters according to your special interests; see the chapter-by-chapter review). These chapters provide a thorough introduction to the preeminent electronic lexical database of today in terms of accessibility and usage in a wide range of applications. But what does that have to do with digital libraries? Natural language processing is essential for dealing efficiently with the large quantities of text now available online: fact extraction and summarization, automated indexing and text categorization, and machine translation. Another essential function is helping the user with query formulation through synonym relationships between words and hierarchical and other relationships between concepts. WordNet supports both of these functions and thus deserves careful study by the digital library community.
SAC : G : 3-D Cellular Automata based PRNG

Random numbers are critical in many areas like security, simulation, gaming and even gambling. Random numbers are basically of two types: true random numbers and pseudorandom numbers. In this paper we propose a three dimensional cellular automata (3-D CA) based pseudo-random number generator (PRNG). Cellular Automata (CA) is used in pseudo-random number generators to produce high-rate random numbers. However, the randomness of such numbers directly depends upon the CA rules and the number of neighbor cells. The two-dimensional (2-D) CA have several limitations such as finding the best CA rules, boundary cell problems, etc. To address the problems existing in the 2-D CA, we propose a random number generator based on 3-D cellular automata. The proposed generator is based on the rule numbers 43, 85, 170 and 201, and on incremental boundary conditions. The output bits are then passed to the well-known Diehard, ENT and NIST test suites to test its randomness. The results reveal that the bit stream generated by the proposed scheme passes all the tests present in the test suites.
Web Usage Mining for a Better Web-Based Learning Environment

Web-based technology is often the technology of choice for distance education given the ease of use of the tools to browse the resources on the Web, the relative affordability of accessing the ubiquitous Web, and the simplicity of deploying and maintaining resources on the WorldWide Web. Many sophisticated web-based learning environments have been developed and are in use around the world. The same technology is being used for electronic commerce and has become extremely popular. However, while there are clever tools developed to understand on-line customer’s behaviours in order to increase sales and profit, there is very little done to automatically discover access patterns to understand learners’ behaviour on web-based distance learning. Educators, using on-line learning environments and tools, have very little support to evaluate learners’ activities and discriminate between different learners’ on-line behaviours. In this paper, we discuss some data mining and machine learning techniques that could be used to enhance web-based learning environments for the educator to better evaluate the leaning process, as well as for the learners to help them in their learning endeavour.
Accident Detection and Smart Rescue System using Android Smartphone with Real-Time Location Tracking

A large number of deaths are caused by Traffic accidents worldwide. The global crisis of road safety can be seen by observing the significant number of deaths and injuries that are caused by road traffic accidents. In many situations the family members or emergency services are not informed in time. This results in delayed emergency service response time, which can lead to an individual’s death or cause severe injury. The purpose of this work is to reduce the response time of emergency services in situations like traffic accidents or other emergencies such as fire, theft/robberies and medical emergencies. By utilizing onboard sensors of a smartphone to detect vehicular accidents and report it to the nearest emergency responder available and provide real time location tracking for responders and emergency victims, will drastically increase the chances of survival for emergency victims, and also help save emergency services time and resources. Traffic accidents; accident detection; on-board sensor; accelerometer; android smartphones; real-time tracking; emergency services; emergency responder; emergency victim; SOSafe; SOSafe Go; firebase
The philosophy of chemistry.

Although chemistry is by far the largest scientific discipline according to any quantitative measure, it had, until recently, been virtually ignored by professional philosophers of science. They left both a vacuum and a one-sided picture of science tailored to physics. Since the early 1990s, the situation has changed drastically, such that philosophy of chemistry is now one of the most flourishing fields in the philosophy of science, like the philosophy of biology that emerged in the 1970s. This article narrates the development and provides a survey of the main topics and trends.
A review on thermoelectric renewable energy : Principle parameters that affect their performance

Developing thermoelectric materials with superior performance means tailoring interrelated thermoelectric physical parameters – electrical conductivities, Seebeck coefficients, and thermal conductivities – for a crystalline system. High electrical conductivity, low thermal conductivity, and a high Seebeck coefficient are desirable for thermoelectric materials. Therefore, knowledge of the relation between electrical conductivity and thermal conductivity is essential to improve thermoelectric properties. In general, research in recent years has focused on developing thermoelectric structures and materials of high efficiency. The importance of this parameter is universally recognized; it is an established, ubiquitous, routinely used tool for material, device, equipment and process characterization both in the thermoelectric industry and in research. In this paper, basic knowledge of thermoelectric materials and an overview of parameters that affect the figure of merit ZT are provided. The prospects for the optimization of thermoelectric materials and their applications are also discussed. & 2013 Elsevier Ltd. All rights reserved.
Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student’s mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-ofthe-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.
Multi-range Reasoning for Machine Comprehension

We propose MRU (Multi-Range Reasoning Units), a new fast compositional encoder for machine comprehension (MC). Our proposed MRU encoders are characterized by multi-ranged gating, executing a series of parameterized contractand-expand layers for learning gating vectors that benefit from long and short-term dependencies. The aims of our approach are as follows: (1) learning representations that are concurrently aware of long and short-term context, (2) modeling relationships between intra-document blocks and (3) fast and efficient sequence encoding. We show that our proposed encoder demonstrates promising results both as a standalone encoder and as well as a complementary building block. We conduct extensive experiments on three challenging MC datasets, namely RACE, SearchQA and NarrativeQA, achieving highly competitive performance on all. On the RACE benchmark, our model outperforms DFN (Dynamic Fusion Networks) by 1.5% − 6% without using any recurrent or convolution layers. Similarly, we achieve competitive performance relative to AMANDA  on the SearchQA benchmark and BiDAF [23] on the NarrativeQA benchmark without using any LSTM/GRU layers. Finally, incorporating MRU encoders with standard BiLSTM architectures further improves performance, achieving state-of-the-art results.
Comparing community structure identification

We compare recent approaches to community structure identification in terms of sensitivity and computational cost. The recently proposed modularity measure is revisited and the performance of the methods as applied to ad hoc networks with known community structure, is compared. We find that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods.
Reducing the harm of opioid overdose with the safe use of naloxone : a pharmacologic review.

Opioid overdose fatality has increased threefold since 1999. As a result, prescription drug overdose surpassed motor vehicle collision as the leading cause of unintentional injury-related death in the USA. Naloxone , an opioid antagonist that has been available for decades, can safely reverse opioid overdose if used promptly and correctly. However, clinicians often overestimate the dose of naloxone needed to achieve the desired clinical outcome, precipitating acute opioid withdrawal syndrome (OWS).



This article provides a comprehensive review of naloxone's pharmacologic properties and its clinical application to promote the safe use of naloxone in acute management of opioid intoxication and to mitigate the risk of precipitated OWS. Available clinical data on opioid-receptor kinetics that influence the reversal of opioid agonism by naloxone are discussed. Additionally, the legal and social barriers to take home naloxone programs are addressed.



Naloxone is an intrinsically safe drug, and may be administered in large doses with minimal clinical effect in non-opioid-dependent patients. However, when administered to opioid-dependent patients, naloxone can result in acute opioid withdrawal. Therefore, it is prudent to use low-dose naloxone (0.04 mg) with appropriate titration to reverse ventilatory depression in this population.
Combatting Online Fraud in Saudi Arabia Using General Deterrence Theory (GDT)

Online fraud, described as dubious business transactions and deceit carried out electronically, has reached an alarming rate worldwide and has become a major challenge to organizations and governments. In the Gulf region, particularly Saudi Arabia, where there is high Internet penetration and many online financial transactions, the need to put effective measures to deter, prevent and detect online fraud, has become imperative. This paper examines how online fraud control measures in financial institutions in Saudi Arabia are organized and managed. Through qualitative interviews with experts in Saudi Arabia, the study found that people’s perceptions (from their moral, social, cultural and religious backgrounds) have significant effect on awareness and fraud prevention and detection. It also argues that technological measures alone may not be adequate. Deterrence, prevention, detection and remedy activities, together making General Deterrence Theory (GDT) as an approach for systematically and effectively combatting online fraud in Saudi.
Seismic Analysis of High-Rise Building by Response Spectrum Method

Reinforced Concrete Frames are the most commonly adopted buildings construction practices in India. With growing economy, urbanisation and unavailability of horizontal space increasing cost of land and need for agricultural land, high-rise sprawling structures have become highly preferable in Indian buildings scenario, especially in urban. With high-rise structures, not only the building has to take up gravity loads, but as well as lateral forces. Many important Indian cities fall under high risk seismic zones, hence strengthening of buildings for lateral forces is a prerequisite. In this study the aim is to analyze the response of a high-rise structure to ground motion using Response Spectrum Analysis. Different models, that is, bare frame, brace frame and shear wall frame are considered in Staad Pro. and change in the time period, stiffness, base shear, storey drifts and top-storey deflection of the building is observed and compared.
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification

ive text summarization has achieved successful performance thanks to the sequence-to-sequence model (Sutskever, Vinyals, and Le 2014) and attention mechanism (Bahdanau, Cho, and Bengio 2014). Rush, Chopra, and Weston (2015) first used an attention-based encoder to compress texts and a neural network language decoder to generate summaries. Following this work, recurrent encoder was introduced to text summarization, and gained better performance (Lopyrev 2015; Chopra, Auli, and Rush 2016). Towards Chinese texts, Hu, Chen, and Zhu (2015) built a large corpus of Chinese short text summarization. To deal with unknown word problem, Nallapati et al. (2016) proposed a generator-pointer model so that the decoder is able to generate words in source texts. Gu et al. (2016) also solved this issue by incorporating copying mechanism. Besides, Ayana et al. (2016) proposes a minimum risk training method which optimizes the parameters with the target of rouge scores. Zhu, Bernhard, and Gurevych (2010) constructs a wikipedia dataset, and proposes a treebased simplification model, which is the first statistical simplification model covering splitting, dropping, reordering and substitution integrally. Woodsend and Lapata (2011) introduces a datadriven model based on quasi-synchronous grammar, which captures structural mismatches and complex rewrite operations. Wubben, van den Bosch, and Krahmer (2012) presents a method for text simplification using phrase based machine translation with re-ranking the outputs.
Deep Learning UI Design Patterns of Mobile Apps

User interface (UI) is one of the most important components of a mobile app and strongly influences users' perception of the app. However, UI design tasks are typically manual and time-consuming. This paper proposes a novel approach to (semi)-automate those tasks. Our key idea is to develop and deploy advanced deep learning models based on recurrent neural networks (RNN) and generative adversarial networks (GAN) to learn UI design patterns from millions of currently available mobile apps. Once trained, those models can be used to search for UI design samples given user-provided descriptions written in natural language and generate professional-looking UI designs from simpler, less elegant design drafts.
Wireless Body Area Network & ITS APPLICATION

WBAN based medical-health technologies have great potential for continuous monitoring in ambulatory settings, early detection of abnormal conditions, and supervised rehabilitation. They can provide patients with increased confidence and a better quality of life, and promote healthy behavior and health awareness. Continuous monitoring with early detection likely has the potential to provide patients with an increased level of confidence, which in turn may improve quality of life. In addition, ambulatory monitoring will allow patients to engage in normal activities of daily life, rather than staying at home or close to specialized medical services. Last but not least, inclusion of continuous monitoring data into medical databases will allow integrated analysis of all data to optimize individualized care and provide knowledge discovery through integrated data mining. Indeed, with the current technological trend toward integration of processors and wireless interfaces, we will soon have coin-sized intelligent sensors. They will be applied as skin patches, seamlessly integrated into a personal monitoring system, and worn for extended periods of time.
Clinical Periodontal Response to Anterior All-Ceramic Crowns with Either Chamfer or Feather-edge Subgingival Tooth Preparations: Six-Month Results and Patient Perception.

Subgingival margins are often required for biologic, mechanical, or esthetic reasons. Several investigations have demonstrated that their use is associated with adverse periodontal reactions, such as inflammation or recession. The purpose of this prospective randomized clinical study was to determine if two different subgingival margin designs influence the periodontal parameters and patient perception. Deep chamfer and feather-edge preparations were compared on 58 patients with 6 months follow-up. Statistically significant differences were present for bleeding on probing, gingival recession, and patient satisfaction. Feather-edge preparation was associated with increased bleeding on probing and deep chamfer with increased recession; improved patient comfort was registered with chamfer margin design. Subgingival margins are technique sensitive, especially when feather-edge design is selected. This margin design may facilitate soft tissue stability but can expose the patient to an increased risk of gingival inflammation.
IoT Security Techniques Based on Machine Learning

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.
GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv ”grid” architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.
A Survey and Taxonomy of Graph Sampling

Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. It has a wide spectrum of applications, e.g. survey hidden population in sociology [54], visualize social graph [29], scale down Internet AS graph [27], graph sparsification , etc. In some scenarios, the whole graph is known and the purpose of sampling is to obtain a smaller graph. In other scenarios, the graph is unknown and sampling is regarded as a way to explore the graph. Commonly used techniques are Vertex Sampling, Edge Sampling and Traversal Based Sampling. We provide a taxonomy of different graph sampling objectives and graph sampling approaches. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided. Although being smaller in size, sampled graphs may be similar to original graphs in some way. We are particularly interested in what graph properties are preserved given a sampling procedure. If some properties are preserved, we can estimate them on the sampled graphs, which gives a way to construct efficient estimators. If one algorithm relies on the perserved properties, we can expect that it gives similar output on original and sampled graphs. This leads to a systematic way to accelerate a class of graph algorithms. In this survey, we discuss both classical text-book type properties and some advanced properties. The landscape is tabularized and we see a lot of missing works in this field. Some theoretical studies are collected in this survey and simple extensions are made. Most previous numerical evaluation works come in an ad hoc fashion, i.e. evaluate different type of graphs, different set of properties, and different sampling algorithms. A systematical and neutral evaluation is needed to shed light on further graph sampling studies.