nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods

The product safety of food-waste feed is the key factor limiting the development of its industrial chain. In this paper, we construct a method based on data from the testing of food-waste feed with comprehensive evaluation of its product safety by integrating fuzzy mathematics effectively, i.e., the entropy method (EM), and the model of the analytic hierarchy (AHP) process. Furthermore, a hierarchical three-level evaluation-index system including biological-safety and chemical-safety considerations is first established via data analysis, data surveys and expert experiential investigation as well, with an actual case in China being fully applied. In addition, we apply the EM and AHP process to calculate the weights of the individual evaluation indices. Finally, through the dimensionless treatment of test data from samples, we determine the degree of membership of each test value relative to the different levels of safety using a trapezoidal membership function. By adopting the developed three-level model of fuzzy mathematics for comprehensive evaluation, we derive the safety grades of tested samples. The comprehensive evaluation method developed in this paper can effectively overcome the shortcomings of traditional single-factor evaluation and offer the qualitative and quantitative advantages of expert survey and basic data research as well. As a result, it is considerably applicable for the product-safety analysis and production control of animal feed generated from food waste.
Feature extraction for classifying students based on their academic performance

Developing tools to support students and learning in a traditional or online setting is a significant task in today’s educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student’s performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of humaninterpretable features that quantify these factors. These features were derived from the students’ grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance.
The Impact of Brand Awareness on Consumer Purchase Intention : The Mediating Effect of Perceived Quality and Brand Loyalty

The purposes of the study are to explore the effects among brand awareness, perceived quality, brand loyalty and customer purchase intention and mediating effects of perceived quality and brand loyalty on brand awareness and purchase intention. The samples are collected from cellular phone users living in Chiyi, and the research adopts regression analysis and mediating test to examine the hypotheses. The results are: (a) the relations among the brand awareness, perceived quality and brand loyalty for purchase intention are significant and positive effect, (b) perceived quality has a positive effect on brand loyalty, (c) perceived quality will meditate the effects between brand awareness and purchase intention, and (d) brand loyalty will mediate the effects between brand awareness and purchase intention. The study suggests that cellular phone manufacturers ought to build a brand and promote its brand awareness through sales promotion, advertising, and other marketing activities. When brand awareness is high, its brand loyalty will also increase. Consumers will evaluate perceived quality of a product from their purchase experience. As a result, brand loyalty and brand preference will increase and also purchase intention.
The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence.

"Is there a biology of intelligence which is characteristic of the normal human nervous system?" Here we review 37 modern neuroimaging studies in an attempt to address this question posed by Halstead (1947) as he and other icons of the last century endeavored to understand how brain and behavior are linked through the expression of intelligence and reason. Reviewing studies from functional (i.e., functional magnetic resonance imaging, positron emission tomography) and structural (i.e., magnetic resonance spectroscopy, diffusion tensor imaging, voxel-based morphometry) neuroimaging paradigms, we report a striking consensus suggesting that variations in a distributed network predict individual differences found on intelligence and reasoning tasks. We describe this network as the Parieto-Frontal Integration Theory (P-FIT). The P-FIT model includes, by Brodmann areas (BAs): the dorsolateral prefrontal cortex (BAs 6, 9, 10, 45, 46, 47), the inferior (BAs 39, 40) and superior (BA 7) parietal lobule, the anterior cingulate (BA 32), and regions within the temporal (BAs 21, 37) and occipital (BAs 18, 19) lobes. White matter regions (i.e., arcuate fasciculus) are also implicated. The P-FIT is examined in light of findings from human lesion studies, including missile wounds, frontal lobotomy/leukotomy, temporal lobectomy, and lesions resulting in damage to the language network (e.g., aphasia), as well as findings from imaging research identifying brain regions under significant genetic control. Overall, we conclude that modern neuroimaging techniques are beginning to articulate a biology of intelligence. We propose that the P-FIT provides a parsimonious account for many of the empirical observations, to date, which relate individual differences in intelligence test scores to variations in brain structure and function. Moreover, the model provides a framework for testing new hypotheses in future experimental designs.
A Mobility-Aware and Quality-Driven Retransmission Limit Adaptation Scheme for Video Streaming over VANETs

An adaptive medium access control (MAC) retransmission limit selection scheme is proposed to improve the performance of IEEE 802.11p standard MAC protocol for video streaming applications over vehicular ad-hoc networks (VANETs). A multi-objective optimization framework, which jointly minimizes the probability of playback freezes and start-up delay of the streamed video at the destination vehicle by tuning the MAC retransmission limit with respect to channel statistics as well as packet transmission rate, is applied at road side unit (RSU). Periodic channel state estimation is performed at the RSU using the information derived from the received signal strength (RSS) and Doppler shift effect. Estimates of access probability between the RSU and the destination vehicle is incorporated in the design of the adaptive MAC scheme. The adaptation parameters are embedded in the user datagram protocol (UDP) packet header. Two-hop transmission is applied in zones in which the destination vehicle is not within the transmission range of any RSU. For multi-hop scenario, we discuss two-hop joint MAC retransmission adaptation and path selection. Compared with the non-adaptive IEEE 802.11p standard MAC, numerical results show that the proposed adaptive MAC protocol exhibits significantly fewer playback freezes while introduces only a slight increase in start-up delay.
A survey of methods for encrypted traffic classification and analysis

With the widespread use of encrypted data transport network traffic encryption is becoming a standard nowadays. This presents a challenge for traffic measurement, especially for analysis and anomaly detection methods which are dependent on the type of network traffic. In this paper, we survey existing approaches for classification and analysis of encrypted traffic. First, we describe the most widespread encryption protocols used throughout the Internet. We show that the initiation of an encrypted connection and the protocol structure give away a lot of information for encrypted traffic classification and analysis. Then, we survey payload and feature-based classification methods for encrypted traffic and categorize them using an established taxonomy. The advantage of some of described classification methods is the ability to recognize the encrypted application protocol in addition to the encryption protocol. Finally, we make a comprehensive comparison of the surveyed feature-based classification methods and present their weaknesses and strengths.
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context

Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.
Interferring Discourse Relations in Context

We investigate various contextual effects on text interpretation, and account for them by providing contextual constraints in a logical theory of text interpretation. On the basis of the way these constraints interact with the other knowledge sources, we draw some general conclusions about the role of domain-specific information, top-down and bottom-up discourse information flow, and the usefulness of formalisation in discourse theory. Introduct ion: T i m e S w i t c h i n g and A m e l i o r a t i o n Two essential parts of discourse interpretation involve (i) determining the rhetorical role each sentence plays in the text; and (ii) determining the temporal relations between the events described. Preceding discourse context has significant effects on both of these aspects of interpretation. For example, text (1) in vacuo may be a non-iconic explanation; the pushing caused the falling and so explains why Max fell. But the same pair of sentences may receive an iconic, narrative interpretat ion in the discourse context provided by (2): John takes advantage of Max's vulnerability while he is lying the ground, to push him over the edge of the cliff. (1) Max fell. John pushed him. (2) John and Max came to the cliff's edge. John applied a sharp blow to the back of Max's neck. Max fell. John pushed him. Max rolled over the edge of the cliff. a The support of the Science and Engineering Research Council through project number GR/G22077 is gratefully acknowledged. HCRC is supported by the Economic and SociM Research Council. We thank two anonymous reviewers for their helpful comments. Moreover, the text in (3) in vacuo is incoherent, but becomes coherent in (4)'s context.
A Robust Method for Identification of Paper Currency Using Otsu’s Thresholding

In this modern world, the correct identification of paper currency is an essential need for the automation of current systems. The automatic currency identification method is highly required to reduce the human power in recognizing the currency and its associated value. Sometimes, the damaged or blurred currency and the complex design of the currency make the task of identification much difficult. In this paper, a method is proposed that can identify paper currency by considering the identification marks given on the currency. Specifically, this study focuses on the identification of Pakistani paper currency. The proposed method makes use of the fundamental image processing techniques for differentiating the major denominations. Particularly, the proposed algorithm uses Otsu’s thresholding method and circulatory matrix criteria for locating the special identity marks given on the currency. The effectiveness of the proposed approach has been shown by performing various experiments on all of the major denominations of Pakistani paper currency with varying intensities. The Proposed method shows quite satisfactory results in terms of accuracy and efficiency.
Batch Normalization Sampling

Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration. Specifically, we model it as a statistical sampling problem and identify that by sampling less correlated data, we can largely reduce the requirement of the number of data for statistics estimation in BN, which directly simplifies the reduction operations. Based on this conclusion, we propose two sampling strategies, “Batch Sampling” (randomly select several samples from each batch) and “Feature Sampling” (randomly select a small patch from each feature map of all samples), that take both computational efficiency and sample correlation into consideration. Furthermore, we introduce an extremely simple variant of BN, termed as Virtual Dataset Normalization (VDN), that can normalize the activations well with few synthetical random samples. All the proposed methods are evaluated on various datasets and networks, where an overall training speedup by up to 20% on GPU is practically achieved without the support of any specialized libraries, and the loss on accuracy and convergence rate are negligible. Finally, we extend our work to the “micro-batch normalization” problem and yield comparable performance with existing approaches at the case of tiny batch size.
Ecological-economic analysis of wetlands : scientific integration for management and policy

Wetlands all over the world have been lost or are threatened in spite of various international agreements and national policies. This is caused by: (1) the public nature of many wetlands products and services; (2) user externalities imposed on other stakeholders; and (3) policy intervention failures that are due to a lack of consistency among government policies in different areas (economics, environment, nature protection, physical planning, etc.). All three causes are related to information failures which in turn can be linked to the complexity and ‘invisibility’ of spatial relationships among groundwater, surface water and wetland vegetation. Integrated wetland research combining social and natural sciences can help in part to solve the information failure to achieve the required consistency across various government policies. An integrated wetland research framework suggests that a combination of economic valuation, integrated modelling, stakeholder analysis, and multi-criteria evaluation can provide complementary insights into sustainable and welfare-optimising wetland management and policy. Subsequently, each of the various www.elsevier.com/locate/ecolecon * Corresponding author.  +46-8-6739540; fax: +46-8-152464. E-mail address: tore@beijer.kva.se (T. Söderqvist). 0921-8009/00/$ see front matter  S 0921 -8009 (00 )00164 -6 R.K. Turner et al. / Ecological Economics 35 (2000) 7–23 8 components of such integrated wetland research is reviewed and related to wetland management policy.
Human Learning in Atari

Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game dynamics, and goals presented to an agent. The last two years have seen a spate of research into artificial agents that use a single algorithm to learn to play these games. The best of these artificial agents perform at better-than-human levels on most games, but require hundreds of hours of game-play experience to produce such behavior. Humans, on the other hand, can learn to perform well on these tasks in a matter of minutes. In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mechanisms that lead to such rapid learning.
Evolution of web site design patterns

The Web enables broad dissemination of information and services; however, the ways in which sites are designed can either facilitate or impede users' benefit from these resources. We present a longitudinal study of web site design from 2000 to 2003. We analyze over 150 quantitative measures of interface aspects (e.g., amount of text on pages, numbers and types of links, consistency, accessibility, etc.) for 22,000 pages and over 1,500 sites that received ratings from Internet professionals. We examine characteristics of highly rated sites and provide three perspectives on the evolution of web site design patterns: (1) descriptions of design patterns during each time period; (2) changes in design patterns across the three time periods; and (3) comparisons of design patterns to those that are recommended in the relevant literature (i.e., texts by recognized experts and user studies). We illustrate how design practices conform to or deviate from recommended practices and the consequent implications. We show that the most glaring deficiency of web sites, even for sites that are highly rated, is their inadequate accessibility, in particular for browser scripts, tables, and form elements.
A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs

We introduce a manually-created, multireference dataset for abstractive sentence and short paragraph compression. First, we examine the impact of singleand multi-sentence level editing operations on human compression quality as found in this corpus. We observe that substitution and rephrasing operations are more meaning preserving than other operations, and that compressing in context improves quality. Second, we systematically explore the correlations between automatic evaluation metrics and human judgments of meaning preservation and grammaticality in the compression task, and analyze the impact of the linguistic units used and precision versus recall measures on the quality of the metrics. Multi-reference evaluation metrics are shown to offer significant advantage over single reference-based metrics.
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons

Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous in virtually all machine learning and computer vision challenges; however, advancements in CNNs have arguably reached an engineering saturation point where incremental novelty results in minor performance gains. Although there is evidence that object classification has reached human levels on narrowly defined tasks, for general applications, the biological visual system is far superior to that of any computer. Research reveals there are numerous missing components in feed-forward deep neural networks that are critical in mammalian vision. The brain does not work solely in a feed-forward fashion, but rather all of the neurons are in competition with each other; neurons are integrating information in a bottom up and top down fashion and incorporating expectation and feedback in the modeling process. Furthermore, our visual cortex is working in tandem with our parietal lobe, integrating sensory information from various modalities.
Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.
The ConceptMapper Approach to Named Entity Recognition

tool we created for classifying mentions in an unstructured text document based on concept terminologies and yielding named entities as output. It is implemented as a UIMA1 (Unstructured Information Management Architecture (IBM, 2004)) annotator, and concepts come from standardised or proprietary terminologies. ConceptMapper can be easily configured, for instance, to use different search strategies or syntactic concepts. In this paper we will describe ConceptMapper, its configuration parameters and their trade-offs, in terms of precision and recall in identifying concepts in a collection of clinical reports written in English. ConceptMapper is available from the Apache UIMA Sandbox, using the Apache Open Source license.
Improving Stock Movement Prediction with Adversarial Training

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a recurrent neural network model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with stationary price-based features (e.g., the closing price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of continuous price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution (Xu and Cohen 2018) with 3.11% relative improvements on average w.r.t. accuracy, verifying the usefulness of adversarial training for stock prediction task. Codes will be made available upon acceptance.