nils-herrmann/clean_scirepeval huggingface dataset:
Building and evaluating web corpora representing national varieties of English

Corpora are essential resources for language studies, as well as for training statistical natural language processing systems. Although very large English corpora have been built, only relatively small corpora are available for many varieties of English. National top-level domains (e.g., .au, .ca) could be exploited to automatically build web corpora, but it is unclear whether such corpora would reflect the corresponding national varieties of English; i.e., would a web corpus built from the .ca domain correspond to Canadian English? In this article we build web corpora from national top-level domains corresponding to countries in which English is widely spoken. We then carry out statistical analyses of these corpora in terms of keywords, measures of corpus comparison based on the Chi-square test and spelling variants, and the frequencies of words known to be marked in particular varieties of English. We find evidence that the web corpora indeed reflect the corresponding national varieties of English. We then demonstrate, through a case study on the analysis of Canadianisms, that these corpora could be valuable lexicographical resources.
COMPARISON OF LOSSLESS DATA COMPRESSION ALGORITHMS FOR TEXT DATA

Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.
Improving Relation Extraction by Pre-trained Language Representations

Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.
The Iowa Model of Evidence-Based Practice to Promote Quality Care.

The UIHC Department of Nursing is nationally known for its work on use of research to improve patient care. This reputation is attributable to staff members who continue to question "how can we improve practice?" or "what does the latest evidence tell us about this patient problem?" and to administrators who support, value, and reward EBP. The revisions made in the original Iowa Model are based on suggestions from staff at UIHC and other practitioners across the country who have implemented the model. We value their feedback and have set forth this revised model for evaluation and adoption by others.
Task switching

Everyday life requires frequent shifts between cognitive tasks. Research reviewed in this article probes the control processes that reconfigure mental resources for a change of task by requiring subjects to switch frequently among a small set of simple tasks. Subjects' responses are substantially slower and, usually, more error-prone immediately after a task switch. This 'switch cost' is reduced, but not eliminated, by an opportunity for preparation. It seems to result from both transient and long-term carry-over of 'task-set' activation and inhibition as well as time consumed by task-set reconfiguration processes. Neuroimaging studies of task switching have revealed extra activation in numerous brain regions when subjects prepare to change tasks and when they perform a changed task, but we cannot yet separate 'controlling' from 'controlled' regions.
IoT Goes Nuclear: Creating a ZigBee Chain Reaction

Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes. It enables the attacker to turn all the city lights on or off, to permanently brick them, or to exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lamps in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already). To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key (for each device type) that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product.
Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

For many forms of e-learning environments, the system’s behavior can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take. Pedagogical strategies are policies to decide the next system action when there are multiple ones available. In this project we present a Reinforcement Learning (RL) approach for inducing effective pedagogical strategies and empirical evaluations of the induced strategies. This paper addresses the technical challenges in applying RL to Cordillera, a Natural Language Tutoring System teaching students introductory college physics. The algorithm chosen for this project is a model-based RL approach, Policy Iteration, and the training corpus for the RL approach is an exploratory corpus, which was collected by letting the system make random decisions when interacting with real students. Overall, our results show that by using a rather small training corpus, the RL-induced strategies indeed measurably improved the effectiveness of Cordillera in that the RL-induced policies improved students’ learning gains significantly.
Leading Indicators of Currency Crises '

This paper examines the empirical evidence on currency crises and proposes a specific early warning system. This system involves monitoring the evolution of several indicators that tend to exhibit an unusual behavior in the periods preceding a crisis. When an indicator exceeds a certain threshold value, this is interpreted as a warning “signal” that a currency crises may take place within the following 24 months. The variables that have the best track record within this approach include: exports, deviations of the real exchange rate from trend, the ratio of broad money to gross international reserves, output, and equity prices.
Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy.
Microcontroller based drip irrigation system using smart sensor

In the past couple of decades, there is rapid growth in terms of technology in the field of agriculture. Different monitoring and controlled systems are installed in order to increase the yield. The yield rate may deceases due to numerous factors. Disease is one of the key factors that cause the degradation of yield. So the developed monitoring system mainly focuses on predicting the start of germination of the disease. Sensor module is used to detect different environmental condition across the farm and the sensed data is displayed on Liquid crystal display using microcontroller. Microcontroller wirelessly transmits different environment conditions across the farm to central unit where data is stored, and analysed. Central unit checks the present data with disease condition and if matches then it commands microcontroller to operate relay. Sensor module is tested for different temperature range and it is found that there are little variations in recorded values. Wireless data transfer is tested with the introduction of various obstacles like wall, metal body, magnet, etc. and it is found that same data is transferred to central unit but with some amount of delay in it. The developed system nearly predicts the start of germination of disease.
Modeling User Acceptance of Internet Banking in Malaysia : A Partial Least Square ( PLS ) Approach

This study is the first empirical research that compares three well known technology adoption models in the in the field of e-banking. It aims to determine the dominant factor(s) which influence the user intention to use Internet banking. Three models (TAM, TPB, and TRA) were used to test the impact of five factors (perceived ease of use, perceived usefulness, attitude, subjective norms, and perceived behavioral control) on intention to adopt e-banking by 239 individual bank customers in Malaysia. Survey questions from prior studies were adopted and customized. Partial least Square (PLS) SmartPLS M2 Version 2.0 was used for data analysis. Results reveal that the five factors have a direct positive effect on behavioral intention to use Internet banking. However, attitude toward behavior has the highest beta, followed by perceived usefulness, and subjective norm, while perceived behavioral control exerts the weakest effect. In testing the explanatory power of the different models, results found TAM model has the best explanatory power, followed by TPB and TRA models. Findings of the study should benefit banks in improving their use of e-banking technologies as a strategic weapon, and can be used to target more potential customers.
Analysis and Design of a Low-Voltage Low-Power Double-Tail Comparator

The need for ultra low-power, area efficient, and high speed analog-to-digital converters is pushing toward the use of dynamic regenerative comparators to maximize speed and power efficiency. In this paper, an analysis on the delay of the dynamic comparators will be presented and analytical expressions are derived. From the analytical expressions, designers can obtain an intuition about the main contributors to the comparator delay and fully explore the tradeoffs in dynamic comparator design. Based on the presented analysis, a new dynamic comparator is proposed, where the circuit of a conventional double-tail comparator is modified for low-power and fast operation even in small supply voltages. Without complicating the design and by adding few transistors, the positive feedback during the regeneration is strengthened, which results in remarkably reduced delay time. Post-layout simulation results in a 0.18- μm CMOS technology confirm the analysis results. It is shown that in the proposed dynamic comparator both the power consumption and delay time are significantly reduced. The maximum clock frequency of the proposed comparator can be increased to 2.5 and 1.1 GHz at supply voltages of 1.2 and 0.6 V, while consuming 1.4 mW and 153 μW, respectively. The standard deviation of the input-referred offset is 7.8 mV at 1.2 V supply.
Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.
Evaluation of image stabilization algorithms

Several techniques for electronic image stabilization have recently been proposed, but very little research has been done to compare and evaluate such techniques. In this paper we propose a set of measures to evaluate image stabilization algorithms based of their fidelity, displacement range, and performance. These measures do not require calibration or ground truth, making the evaluation procedure very simple and flexible, i.e., it provides the means to compare techniques based on different motion models. We have used this procedure to compare several image stabilization algorithms and also evaluate the sensitivity of these algorithms to some of its parameters. This same procedure could also be used for the comparison and evaluation of motion estimation and image registration techniques.
Power Transformer Economic Evaluation in Decentralized Electricity Markets

Owing to deregulation, privatization, and competition, estimating financial benefits of electrical power system projects is becoming increasingly important. In other words, it is necessary to assess the project profitability under the light of new developments in the electricity market. In this paper, a detailed methodology for the least cost choice of a distribution transformer is proposed, showing how the higher price of a facility can be traded against its operational cost over its life span. The proposed method involves the incorporation of the discounted cost of transformer losses to their economic evaluation, providing the ability to take into account variable energy cost during the transformer operating lifetime. In addition, the influence of the variability in the energy loss cost is investigated, taking into account a potential policy intended to be adopted by distribution network operators. The method is combined with statistical and probabilistic assessment of electricity price volatility in order to derive its impact on the transformer purchasing policy.
A COMPARATIVE ANALYSIS OF DECISION MAKING METHODS FOR THE SEISMIC RETROFIT OF RC BUILDINGS

Several different technical options are nowadays available to seismically upgrade an existing building. Selecting the best one with reference to a given structure generally represents a complex problem. In fact many and generally conflicting are also the points of view by which each alternative has to be judged. Decision support systems like the so called Multi Criteria Decision Making (MCDM) methods may be very useful in order to make an as much as possible objective and rational choice. The paper investigates the applicability and effectiveness of different MCDM methods for the focused decision problem. Some of the most widely adopted and consolidated methods are considered and compared one each other, first in terms of suitability for the particular decision task, then in terms of simplicity of use, reliability of the results, degree of human (subjective) intervention in the process and other significant criteria. The comparison is also quantitatively made with reference to a case-study consisting of an underdesigned RC structure.
Variational Bayes with synthetic likelihood

Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Monte Carlo simulation from the model. In this article, we develop alternatives to Markov chain Monte Carlo implementations of Bayesian synthetic likelihoods with reduced computational overheads. Our approach uses stochastic gradient variational inference methods for posterior approximation in the synthetic likelihood context, employing unbiased estimates of the log likelihood. We compare the new method with a related likelihood free variational inference technique in the literature, while at the same time improving the implementation of that approach in a number of ways. These new algorithms are feasible to implement in situations which are challenging for conventional approximate Bayesian computation (ABC) methods, in terms of the dimensionality of the parameter and summary statistic.
Establishing Smart City Technical Standards and Guidance: A Way Forward

Solving technical problems with complex systems and integrating the many technologies employed in these multifaceted structures has been a recurring theme in Smart Cities research. This paper presents an analysis of the reason this problem has been so well explored but persists with no solution widely available. The problem is viewed as a combination of Smart City needs, governance, and increasingly technically difficult decisions. The paper describes the requirements that must be met to develop a framework that can address this seeming intractable and expanding integration concern, identifies the governance processes that can be used to address this problem, and to manage integration in Smart Cities, The solution proposed is a formalized accepted and managed technology regulated environment introduced by governance groups composed of city planners/managers, citizen, stake holders, and technology delivery organizations. The solution requirements dictate the establishment of a standard that would guide the development and usage of automated, autonomous components, integrating dynamically with software agents. All of this working to rapidly optimize shared resources through error handling processes executing largely at no cost except those of processing time, meeting safety guidelines, satisfying operational monitoring needs, and meeting post issue liability guidelines. This technical standard would obligate developers and vendors to meet safety standards and accept liability for malfeasance. As initiating steps, Smart City managers must come together and establish a basic understanding of the goals and regulations, and the methodologies for implementing them.
Modeling trend progression through an extension of the Polya Urn Process

Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.