nils-herrmann/clean_scirepeval huggingface dataset:
Learning styles and formative assessment strategy: enhancing student achievement in Web-based learning

The purpose of this research was to investigate the effects of formative assessment and learning style on student achievement in a Web-based learning environment. A quasi-experimental research design was used. Participants were 455 seventh grade students from 12 classes of six junior high schools. A Web-based course, named BioCAL, combining three different formative assessment strategies was developed. The formative assessment strategies included Formative Assessment Module of the Web-Based Assessment and Test Analysis system (FAM-WATA) (with six Web-based formative assessment strategies), Normal Module of Web-Based Assessment and Test Analysis system (N-WATA) (only with partial Web-based formative assessment strategy) and Paper and Pencil Test (PPT) (without Web-based formative assessment strategy). Subjects were tested using Kolb’s Learning Style Inventory, and assigned randomly by class into three groups. Each group took Web-based courses using one of the formative assessment strategies. Preand post-achievement testing was carried out. A one-way ANCOVA analysis showed that both learning style and formative assessment strategy are significant factors affecting student achievement in a Webbased learning environment. However, there is no interaction between these two factors. A post hoc comparison showed that performances of the FAM-WATA group are higher than the N-WATA and PPT groups. Learners with a ‘Diverger’ learning style performed best followed by, ‘Assimilator’, ‘Accommodator’, and ‘Converger’, respectively. Finally, FAMWATA group students are satisfied with six strategies of the FAM-WATA.
Confidence intervals for estimates based on linear regression in forensic anthropology.

Forensic anthropologists commonly use simple linear regression to estimate the value of a dependent variable, such as stature, for a single specimen where the value of the independent variable, such as humerus length, is known. Published studies providing regression equations for such use almost invariably include the standard error of estimate. Unfortunately, it is exceptional for forensic anthropologists to use the standard error to calculate correctly the confidence limits for their single predicted value. We attempt to show why this may be and provide explicit guidelines for the proper construction of confidence interval in such circumstances.
DETERMINANTS OF INWARD FDI IN MONGOLIA : AN APPLICATION OF THE ARDL BOUNDS TESTING APPROACH TO COINTEGRATION

The study at hand is the first of its kind that aimed to provide a comprehensive analysis of the determinants of foreign direct investment (FDI) in Mongolia by analyzing their short-run, long-run, and Granger causal relationships. In doing so, we methodically used a series of econometric methods to ensure reliable and robust estimation results that included the augmented Dickey-Fuller and Phillips-Perron unit root tests, the most recently advanced autoregressive distributed lag (ARDL) bounds testing approach to cointegration, fully modified ordinary least squares, and the Granger causality test within the vector error-correction model (VECM) framework. Our findings revealed domestic market size and human capital to have a U-shaped relationship with FDI inflows, with an initial positive impact on FDI in the short-run, which then turns negative in the long-run. Macroeconomic instability was found to deter FDI inflows in the long-run. In terms of the impact of trade on FDI, imports were found to have a complementary relationship with FDI; while exports and FDI were found to be substitutes in the short-run. Financial development was also found to induce a deterring effect on FDI inflows in both the shortand long-run; thereby also revealing a substitutive relationship between the two. Infrastructure level was not found to have a significant impact on FDI on any conventional level, in either the shortor long-run. Furthermore, the results have exhibited significant Granger causal relationships between the variables; thereby, ultimately stressing the significance of policy choice in not only attracting FDI inflows, but also in translating their positive spill-over benefits into long-run economic growth.
CORPORATE RESPONSIBILITY AND FINANCIAL PERFORMANCE : THE ROLE OF INTANGIBLE RESOURCES

This paper examines the effects of a firm’s intangible resources in mediating the relationship between corporate responsibility and financial performance. We hypothesize that previous empirical findings of a positive relationship between social and financial performance may be spurious because the researchers failed to account for the mediating effects of intangible resources. Our results indicate that there is no direct relationship between corporate responsibility and financial performance—merely an indirect relationship that relies on the mediating effect of a firm’s intangible resources. We demonstrate our theoretical contention with the use of a database comprising 599 companies from 28 countries.
Lipid metabolism in Alzheimer’s disease

Since the metabolic disorder may be the high risk that contribute to the progress of Alzheimer's disease (AD). Overtaken of High-fat, high-glucose or high-cholesterol diet may hasten the incidence of AD in later life, due to the metabolic dysfunction. But the metabolism of lipid in brain and the exact effect of lipid to brain or to the AD's pathological remain controversial. Here we summarize correlates of lipid metabolism and AD to provide more foundation for the daily nursing of AD sensitive patients.
Internet of Things : Features , Challenges , and Vulnerabilities Authors

The terminology Internet of Things (IoT) refers to a future where every day physical objects are connected by the Internet in one form or the other, but outside the traditional desktop realm. The successful emergence of the IoT vision, however, will require computing to extend past traditional scenarios involving portables and smart-phones to the connection of everyday physical objects and the integration of intelligence with the environment. Subsequently, this will lead to the development of new computing features and challenges. The main purpose of this paper, therefore, is to investigate the features, challenges, and weaknesses that will come about, as the IoT becomes reality with the connection of more and more physical objects. Specifically, the study seeks to assess emergent challenges due to denial of service attacks, eavesdropping, node capture in the IoT infrastructure, and physical security of the sensors. We conducted a literature review about IoT, their features, challenges, and vulnerabilities. The methodology paradigm used was qualitative in nature with an exploratory research design, while data was collected using the desk research method. We found that, in the distributed form of architecture in IoT, attackers could hijack unsecured network devices converting them into bots to attack third parties. Moreover, attackers could target communication channels and extract data from the information flow. Finally, the perceptual layer in distributed IoT architecture is also found to be vulnerable to node capture attacks, including physical capture, brute force attack, DDoS attacks, and node privacy leaks.
TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring

Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for nontarget prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog

This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog. Given a dialog in text and a passage containing factual descriptions about the dialog where mentions of the characters are replaced by blanks, the task is to fill the blanks with the most appropriate character names that reflect the contexts in the dialog. Since there is no dataset that challenges the task of passage completion in this genre, we create a corpus by selecting transcripts from a TV show that comprise 1,681 dialogs, generating passages for each dialog through crowdsourcing, and annotating mentions of characters in both the dialog and the passages. Given this dataset, we build a deep neural model that integrates rich feature extraction from convolutional neural networks into sequence modeling in recurrent neural networks, optimized by utterance and dialog level attentions. Our model outperforms the previous state-of-the-art model on this task in a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs. Our analysis shows the effectiveness of the attention mechanisms and suggests a direction to machine comprehension on multiparty dialog.
An Iterative Closest Point Method for Unsupervised Word Translation

Unsupervised word translation from nonparallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Our simple linear method is able to achieve better or equal performance to recent state-of-theart deep adversarial approaches and typically does a little better than the supervised baseline. Our method is also efficient, easy to parallelize and interpretable.
The neural bases of social cognition and story comprehension.

A great deal of research exists on the neural basis of theory-of-mind (ToM) or mentalizing. Qualitative reviews on this topic have identified a mentalizing network composed of the medial prefrontal cortex, posterior cingulate/precuneus, and bilateral temporal parietal junction. These conclusions, however, are not based on a quantitative and systematic approach. The current review presents a quantitative meta-analysis of neuroimaging studies pertaining to ToM, using the activation-likelihood estimation (ALE) approach. Separate ALE meta-analyses are presented for story-based and nonstory-based studies of ToM. The conjunction of these two meta-analyses reveals a core mentalizing network that includes areas not typically noted by previous reviews. A third ALE meta-analysis was conducted with respect to story comprehension in order to examine the relation between ToM and stories. Story processing overlapped with many regions of the core mentalizing network, and these shared regions bear some resemblance to a network implicated by a number of other processes.
The return of the chatbots

By all accounts, 2016 is the year of the chatbot. Some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps. But chatbots have a long history. So what’s new, and what’s different this time? And is there an opportunity here to improve how our industry does technology transfer? 1 The year of interacting conversationally This year’s most hyped language technology is the intelligent virtual assistant. Whether you call these things digital assistants, conversational interfaces or just chatbots, the basic concept is the same: achieve some result by conversing with a machine in a dialogic fashion, using natural language. Most visible at the forefront of the technology, we have the voice-driven digital assistants from the Big Four: Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Google’s new Assistant. Following up behind, we have many thousands of text-based chatbots that target specific functionalities, enabled by tools that let you build bots for a number of widely used messaging platforms. Many see this technology as heralding a revolution in how we interact with devices, websites and apps. The MIT Technology Review lists conversational interfaces as one of the ten breakthrough technologies of 2016. In January of this year, Uber’s Chris Messina wrote an influential blog piece declaring 2016 the year of conversational commerce. In March, Microsoft CEO Satya Nadella announced that chatbots were the next big thing, on a par with the graphical user interface, the web browser 1 https://www.technologyreview.com/s/600766/10-breakthrough-technologies2016-conversational-interfaces 2 https://medium.com/chris-messina/2016-will-be-the-year-of-conversationalcommerce-1586e85e3991 https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1351324916000243 Downloaded from https://www.cambridge.org/core. IP address: 54.70.40.11, on 03 Aug 2018 at 20:59:31, subject to the Cambridge Core terms of use, available at
Age Estimation by Multi-scale Convolutional Network

In the last five years, biologically inspired features (BIF) always held the state-of-the-art results for human age estimation from face images. Recently, researchers mainly put their focuses on the regression step after feature extraction, such as support vector regression (SVR), partial least squares (PLS), canonical correlation analysis (CCA) and so on. In this paper, we apply convolutional neural network (CNN) to the age estimation problem, which leads to a fully learned end-toend system can estimate age from image pixels directly. Compared with BIF, the proposed method has deeper structure and the parameters are learned instead of hand-crafted. The multi-scale analysis strategy is also introduced from traditional methods to the CNN, which improves the performance significantly. Furthermore, we train an efficient network in a multi-task way which can do age estimation, gender classification and ethnicity classification well simultaneously. The experiments on MORPH Album 2 illustrate the superiorities of the proposed multi-scale CNN over other state-of-the-art methods.
Demographic Word Embeddings for Racism Detection on Twitter

Most social media platforms grant users freedom of speech by allowing them to freely express their thoughts, beliefs, and opinions. Although this represents incredible and unique communication opportunities, it also presents important challenges. Online racism is such an example. In this study, we present a supervised learning strategy to detect racist language on Twitter based on word embedding that incorporate demographic (Age, Gender, and Location) information. Our methodology achieves reasonable classification accuracy over a gold standard dataset (F1=76.3%) and significantly improves over the classification performance of demographic-agnostic models.
Amodel of HIV / AIDS population dynamics including ARV treatment and pre-exposure prophylaxis

*Correspondence: pwitbooi@uwc.ac.za Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville, 7535, Republic of South Africa Abstract Antiretroviral treatment (ART) and oral pre-exposure prophylaxis (PrEP) have recently been used efficiently in management of HIV infection. Pre-exposure prophylaxis consists in the use of an antiretroviral medication to prevent the acquisition of HIV infection by uninfected individuals. We propose a new model for the transmission of HIV/AIDS including ART and PrEP. Our model can be used to test the effects of ART and of the uptake of PrEP in a given population, as we demonstrate through simulations. The model can also be used to estimate future projections of HIV prevalence. We prove global stability of the disease-free equilibrium. We also prove global stability of the endemic equilibrium for the most general case of the model, i.e., which allows for PrEP individuals to default. We include insightful simulations based on recently published South-African data.
Estimating the Impact of the Death Penalty on Murder

This paper reviews the econometric issues in efforts to estimate the impact of the death penalty on murder, focusing on six recent studies published since 2003. We highlight the large number of choices that must be made when specifying the various panel data models that have been used to address this question. There is little clarity about the knowledge potential murderers have concerning the risk of execution: are they influenced by the passage of a death penalty statute, the number of executions in a state, the proportion of murders in a state that leads to an execution, and details about the limited types of murders that are potentially susceptible to a sentence of death? If an execution rate is a viable proxy, should it be calculated using the ratio of last year’s executions to last year’s murders, last year’s executions to the murders a number of years earlier, or some other values? We illustrate how sensitive various estimates are to these choices. Importantly, the most up-to-date OLS panel data studies generate no evidence of a deterrent effect, while three 2SLS studies purport to find such evidence. The 2SLS studies, none of which shows results that are robust to clustering their standard errors, are unconvincing because they all use a problematic structure based on poorly measured and theoretically inappropriate pseudo-probabilities that are
Intellectual Capital and Its Measurement

Intellectual capital is becoming the preeminent resource for creating economic wealth. Tangible assets such as property, plant, and equipment continue to be important factors in the production of both goods and services. However, their relative importance has decreased through time as the importance of intangible, knowledge-based assets has increased. This shift in importance has raised a number of accounting questions critical for managing assets such as brand names, trade secrets, production processes, distribution channels, and work-related competencies. This paper develops a working definition of intellectual capital and a framework for identifying and classifying the various components of intellectual capital. In addition, methods of measuring intellectual capital at both the individual-component and organization levels are presented. This provides an exploratory foundation for accounting systems and processes useful for meaningful management of intellectual assets. INTELLECTUAL CAPITAL AND ITS MEASUREMENT
Turning scientists into data explorers

Nowadays scientists receive increasingly large volumes of data daily. These volumes and accompanying metadata that describes them are collected in scientific file repositories. Today's scientists need a data management tool that makes these file repositories accessible and performs a number of exploration steps near-instantly. Current database technology, however, has a long data-to-insight time, and does not provide enough interactivity to shorten the exploration time. We envision that exploiting metadata helps solving these problems. To this end, we propose a novel query execution paradigm, in which we decompose the query execution into two stages. During the first stage, we process only metadata, whereas the rest of the data is processed during the second stage. So that, we can exploit metadata to boost interactivity and to ingest only required data per query transparently. Preliminary experiments show that up-front ingestion time is reduced by orders of magnitude, while query performance remains similar. Motivated by these results, we identify the challenges on the way from the new paradigm to efficient interactive data exploration.
Neural Sequence-Labelling Models for Grammatical Error Correction

We propose an approach to N -best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.
A SURVEY OF VARIOUS SCHEDULING ALGORITHM IN CLOUD COMPUTING ENVIRONMENT

Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing.
Using dialogue corpora to train a chatbot

This paper presents two chatbot systems, ALICE and Elizabeth, illustrating the dialogue knowledge representation and pattern matching techniques of each. We discuss the problems which arise when using the Dialogue Diversity Corpus to retrain a chatbot system with human dialogue examples. A Java program to convert from dialog transcript to AIML format provides a basic implementation of corpusbased chatbot training.. We conclude that dialogue researchers should adopt clearer standards for transcription and markup format in dialogue corpora to be used in training a chatbot system more effectively.
Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review

Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult.  To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence.  Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014.  The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context.  Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.