nils-herrmann/clean_scirepeval huggingface dataset:
A Study of Text Preprocessing Tools for Arabic Text Categorization

Text preprocessing is an essential stage in text categorization (TC) particularly and text mining generally. Morphological tools can be used in text preprocessing to reduce multiple forms of the word to one form. There has been a debate among researchers about the benefits of using morphological tools in TC. Studies in the English language illustrated that performing stemming during the preprocessing stage degrades the performance slightly. However, they have a great impact on reducing the memory requirement and storage resources needed. The effect of the preprocessing tools on Arabic text categorization is an area of research. This work provides an evaluation study of several morphological tools for Arabic Text Categorization. The study includes using the raw text, the stemmed text, and the root text. The stemmed and root text are obtained using two different preprocessing tools. The results illustrated that using light stemmer combined with a good performing feature selection method enhances the performance of Arabic Text Categorization especially for small threshold values.
Cultural Fits and Misfits: Is ERP a Universal Solution?

E RP software packages that manage and integrate business processes across organizational functions and locations cost millions of dollars to buy, several times as much to implement, and necessitate disruptive organizational change. While some companies have enjoyed significant gains, others have had to scale back their projects and accept minimal benefits, or even abandon implementation of ERP projects . Historically, a common problem when adopting package software has been the issue of “misfits,” that is, the gaps between the functionality offered by the package and that required by the adopting organization [1, 3]. As a result, organizations have had to choose among adapting to the new functionality, living with the shortfall, instituting workarounds, or customizing the package. ERP software, as a class of package software, also presents this problematic choice to organizations. The problem is exacerbated because ERP implementation is more complex due to cross-module integration, data standardization, adoption of the underlying business model (“best practices”), compressed implementation schedule, and the involvement of a large number of stakeholders. The knowledge gap among implementation personnel is usually significant. Few organizational users underChristina Soh, Sia Siew Kien, and Joanne Tay-Yap
Math Anxiety in Elementary and Secondary School Students

We assessed math anxiety in 6ththrough 12th-grade children (N = 564) as part of a comprehensive longitudinal investigation of children's beliefs, attitudes, and values concerning mathematics. Confirmatory factor analyses provided evidence for two components of math anxiety, a negative affective reactions component and a cognitive component. The affective component of math anxiety related more strongly and negatively than did the worry component to children's ability perceptions, performance perceptions, and math performance. The worry component related more strongly and positively than did the affective component to the importance that children attach to math and their reported actual effort in math. Girls reported stronger negative affective reactions to math than did boys. Ninth-grade students reported experiencing the most worry about math and sixth graders the least.
A CMOS nested-chopper instrumentation amplifier with 100-nV offset

A CMOS nested-chopper instrumentation amplifier is presented with a typical offset of 100 nV. This performance is obtained by nesting an additional low-frequency chopper pair around a conventional chopper amplifier. The inner chopper pair removes the 1/f noise, while the outer chopper pair reduces the residual offset. The test chip is free from 1/f noise and has a thermal noise of 27 nV//spl radic/Hz consuming a total supply current of 200 /spl mu/A.
A Lane Detection, Tracking and Recognition System for Smart Vehicles

As important components of intelligent transportation system, lane detection and tracking (LDT) and lane departure warning (LDW) systems have attracted great interest from the computer vision community over the past few years. Conversely, lane markings recognition (LMR) systems received surprisingly little attention. This thesis proposed a real-time lane assisting framework for intelligent vehicles, which consists of a comprehensive module and simplified module. To the best of our knowledge, this is the first parallel architecture that considers not only lane detection and tracking, but also lane marking recognition and departure warning. A lightweight version of the Hough transform, PPHT is used for both modules to detect lines. After detection stage, for the comprehensive module, a novel refinement scheme consisting of angle threshold and segment linking (ATSL) and trapezoidal refinement method (TRM) takes shape and texture information into account, which significantly improves the LDT performance. Also based on TRM, colour and edge informations are used to recognize lane marking colors (white and yellow) and shapes (solid and dashed). In the simplified module, refined MSER blobs dramatically simplifies the preprocessing and refinement stage, and enables the simplified module performs well on lane detection and tracking. Several experiments are conducted in highway and urban roads in Ottawa. The detection rate of the LDT system in comprehensive module average 95.9% and exceed 89.3% in poor conditions, while the recognition rate depends on the quality of lane paint and achieves an average accuracy of 93.1%. The simplified module has an average detection rate of 92.7% and exceeds 84.9% in poor conditions. Except the conventional experimental methods, a novel point cluster evaluation and pdf analysis method have been proposed to evaluate the performance of LDT systems, in terms of the stability, accuracy and similarity to Gaussian distribution.
Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges

Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
The influence of business strategy on project portfolio management and its success — A conceptual framework

Firms are facing more difficulties with the implementation of strategies than with its formulation. Therefore, this paper examines the linkage between business strategy, project portfolio management, and business success to close the gap between strategy formulation and implementation. Earlier research has found some supporting evidence of a positive relationship between isolated concepts, but so far there is no coherent and integral framework covering the whole cycle from strategy to success. Therefore, the existing research on project portfolio management is extended by the concept of strategic orientation. Based on a literature review, a comprehensive conceptual model considering strategic orientation, project portfolio structuring, project portfolio success, and business success is developed. This model can be used for future empirical research on the influence of strategy on project portfolio management and its success. Furthermore, it can easily be extended e.g. by contextual factors.
A Compact Microstrip-Fed Triple Band-Notched UWB Monopole Antenna

In this letter, a novel low-profile microstrip-fed compact triple band-notched ultrawideband (UWB) antenna is proposed. Notch bands around the 3.3-3.8-GHz WiMAX and 5.15-5.85-GHz WLAN frequencies are obtained by etching out two elliptic single complementary split-ring resonators (ESCSRRs) of different dimensions from the radiating patch of the antenna. Furthermore, by placing two rectangular split-ring resonators near the feedline-patch junction of the antenna, rejection for the 7.9-8.4-GHz X-band frequencies is achieved. Design guidelines for implementing the notch-bands at the desired frequency regions are provided. The match between the simulated and experimental results suggests that the proposed antenna can be a good candidate for application in UWB communication systems.
Maxims or myths of beauty? A meta-analytic and theoretical review.

Common maxims about beauty suggest that attractiveness is not important in life. In contrast, both fitness-related evolutionary theory and socialization theory suggest that attractiveness influences development and interaction. In 11 meta-analyses, the authors evaluate these contradictory claims, demonstrating that (a) raters agree about who is and is not attractive, both within and across cultures; (b) attractive children and adults are judged more positively than unattractive children and adults, even by those who know them; (c) attractive children and adults are treated more positively than unattractive children and adults, even by those who know them; and (d) attractive children and adults exhibit more positive behaviors and traits than unattractive children and adults. Results are used to evaluate social and fitness-related evolutionary theories and the veracity of maxims about beauty.
Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE).

SHARE is a unique panel database of micro data on health, socio-economic status and social and family networks covering most of the European Union and Israel. To date, SHARE has collected three panel waves (2004, 2006, 2010) of current living circumstances and retrospective life histories (2008, SHARELIFE); 6 additional waves are planned until 2024. The more than 150 000 interviews give a broad picture of life after the age of 50 years, measuring physical and mental health, economic and non-economic activities, income and wealth, transfers of time and money within and outside the family as well as life satisfaction and well-being. The data are available to the scientific community free of charge at www.share-project.org after registration. SHARE is harmonized with the US Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA) and has become a role model for several ageing surveys worldwide. SHARE's scientific power is based on its panel design that grasps the dynamic character of the ageing process, its multidisciplinary approach that delivers the full picture of individual and societal ageing, and its cross-nationally ex-ante harmonized design that permits international comparisons of health, economic and social outcomes in Europe and the USA.
How to Make ad-hoc Polymorphism Less ad-hoc

This paper presents type classes, a new approach to ad-hoc polymorphism. Type classes permit overloading of arithmetic operators such as multiplication, and generalise the “eqtype variables” of Standard ML. Type classes extend the Hindley/Milner polymorphic type system, and provide a new approach to issues that arise in object-oriented programming, bounded type quantification, and abstract data types. This paper provides an informal introduction to type classes, and defines them formally by means of type inference rules.
A Multi-Facet Survey on Memetic Computation

Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this paper, a comprehensive multi-facet survey of recent research in memetic computation is presented.
Privacy-Preserving Classification on Deep Neural Network

Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however also gives rise to important data privacy risks. This observation motives Microsoft researchers to propose a framework, called Cryptonets. The core idea is to combine simplifications of the NN with Fully Homomorphic Encryptions (FHE) techniques to get both confidentiality of the manipulated data and efficiency of the processing. While efficiency and accuracy are demonstrated when the number of non-linear layers is small (eg 2), Cryptonets unfortunately becomes ineffective for deeper NNs which let the problem of privacy preserving matching open in these contexts. This work successfully addresses this problem by combining the original ideas of Cryptonets’ solution with the batch normalization principle introduced at ICML 2015 by Ioffe and Szegedy. We experimentally validate the soundness of our approach with a neural network with 6 non-linear layers. When applied to the MNIST database, it competes the accuracy of the best non-secure versions, thus significantly improving Cryptonets.
Generating Large Scale Image Datasets from 3 D CAD Models

Datasets power computer vison research and drive breakthroughs. Larger and larger datasets are needed to better utilize the exponentially increasing computing power. However, datasets generation is both time consuming and expensive as human beings are required for image labelling. Human labelling cannot scale well. How can we generate larger image datasets easier and faster? In this paper, we provide a new approach for large scale datasets generation. We generate images from 3D object models directly. The large volume of freely available 3D CAD models and mature computer graphics techniques make generating large scale image datasets from 3D models very efficient. As little human effort involved in this process, it can scale very well. Rather than releasing a static dataset, we will also provide a software library for dataset generation so that the computer vision community can easily extend or modify the datasets accordingly.
Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques

Data mining is the process of identifying the hidden patterns from large and complex data. It may provide crucial role in decision making for complex agricultural problems. Data visualisation is also equally important to understand the general trends of the effect of various factors influencing the crop yield. The present study examines the application of data visualisation techniques to find correlations between the climatic factors and rice crop yield. The study also applies data mining techniques to extract the knowledge from the historical agriculture data set to predict rice crop yield for Kharif season of Tropical Wet and Dry climatic zone of India. The data set has been visualised in Microsoft Office Excel using scatter plots. The classification algorithms have been executed in the free and open source data mining tool WEKA. The experimental results provided include sensitivity, specificity, accuracy, F1 score, Mathews correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error. General trends in the data visualisation show that decrease in precipitation in the selected climatic zone increases the rice crop yield and increase in minimum, average or maximum temperature for the season increases the rice crop yield. For the current data set experimental results show that J48 and LADTree achieved the highest accuracy, sensitivity and specificity. Classification performed by LWL classifier displayed the lowest accuracy, sensitivity and specificity results.
Distance Measures for Effective Clustering of ARIMA Time-Series

Many environmental and socioeconomic time–series data can be adequately modeled using Auto-Regressive Integrated Moving Average (ARIMA) models. We call such time–series ARIMA time–series. We consider the problem of clustering ARIMA time–series. We propose the use of the Linear Predictive Coding (LPC) cepstrum of time–series for clustering ARIMA time–series, by using the Euclidean distance between the LPC cepstra of two time–series as their dissimilarity measure. We demonstrate that LPC cepstral coefficients have the desired features for accurate clustering and efficient indexing of ARIMA time–series. For example, few LPC cepstral coefficients are sufficient in order to discriminate between time–series that are modeled by different ARIMA models. In fact this approach requires fewer coefficients than traditional approaches, such as DFT and DWT. The proposed distance measure can be used for measuring the similarity between different ARIMA models as well. We cluster ARIMA time–series using the Partition Around Medoids method with various similarity measures. We present experimental results demonstrating that using the proposed measure we achieve significantly better clusterings of ARIMA time–series data as compared to clusterings obtained by using other traditional similarity measures, such as DFT, DWT, PCA, etc. Experiments were performed both on simulated as well as real data.
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection

Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowdsourcing the process of paraphrase generation is an effective means of expanding natural language datasets, but there has been limited analysis of the trade-offs that arise when designing tasks. In this paper, we present the first systematic study of the key factors in crowdsourcing paraphrase collection. We consider variations in instructions, incentives, data domains, and workflows. We manually analyzed paraphrases for correctness, grammaticality, and linguistic diversity. Our observations provide new insight into the trade-offs between accuracy and diversity in crowd responses that arise as a result of task design, providing guidance for future paraphrase generation procedures.
Vaginal delivery of dicephalic parapagus conjoined twins: case report and literature review.

After an unsuccessful midwife-assisted delivery in which a head was born but delivery could not be advanced, episiotomy performed at Sanliurfa Maternity Hospital allowed vaginal delivery of female conjoined twins. Visual and x-ray examination showed two heads, two vertebral columns, two feet, two arms, and fusion at the level of the pelvis. The baby was born dead, but the mother made an uneventful recovery. Parapagus (anterolaterally joined) dicephalus (two-headed) twins account for only 11-13% of all conjoined twins, and they rarely survive. Complex malformations of hearts, lungs and abdominal organs, duplication of the tracheae, upper gastrointestinal tract and spinal column, and either double or single versions of other organs have been reported in parapagus dicephalus cases. The incidence, anatomical, embryological, diagnostic, prognostic, obstetrical, perinatal, and ethical aspects of conjoined twins are reviewed, with a focus on parapagus dicephalus conjoined twins.
Kernel methods, syntax and semantics for relational text categorization

Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categorization. The main reason is the high reliability and effectiveness of language models, which are sufficient to accurately solve such retrieval tasks. However, when the latter involve the computation of relational semantics between text fragments simple statistical models may result ineffective. In this paper, we show that syntactic and semantic structures can be used to greatly improve complex categorization tasks such as determining if an answer correctly responds to a question. Given the high complexity of representing semantic/syntactic structures in learning algorithms, we applied kernel methods along with Support Vector Machines to better exploit the needed relational information. Our experiments on answer classification on Web and TREC data show that our models greatly improve on bag-of-words.
Nonconscious Consumer Psychology

What exactly is “nonconscious consumer psychology?” We use the term to describe a category of consumption behavior that is driven by processes that occur outside a consumer's conscious awareness. In other words, individuals engage in consumptionrelated cognition, motivation, decision making, emotion, and behavior without recognizing the role that nonconscious processes played in shaping them. A growing literature has documented that a wide range of consumption behaviors are strongly influenced by factors outside of people's conscious awareness. For instance, consumers are often unaware they have been exposed to an environmental cue that triggers a given consumption behavior, or are unaware of a mental process that is occurring outside conscious awareness, or are even unaware of the consumption-related outcome of such a nonconscious process (Chartrand, 2005). Such processes are often adaptive and highly functional, but at times can lead to undesirable outcomes for consumers. By shining a light on a wide range of nonconscious consumer psychology we hope to facilitate increased reliance on our unconscious systems in certain situations and equip consumers to defend themselves when unconscious processes can lead to negative outcomes. What exactly then is a nonconscious psychological process for a consumer? We define it as a subset of automatic processing (Bargh & Chartrand, 1999). An automatic process is one that, once set into motion, has no need of conscious intervention (Bargh, 1989). The labeling of automatic processes in social and cognitive psychology, including those set forth in dual process models, implies that processes are either automatic or they are not. Labels such as automatic/controlled, implicit/explicit, conscious/ nonconscious, spontaneous/deliberative, and System 1/System 2 by their dichotomous nature suggest that consumers are either in conscious decision making mode or have their unconscious driving their decision making entirely. However, there are
High performance true random number generator based on FPGA block RAMs

This paper presents a new method for creating TRNGs in Xilinx FPGAs. Due to its simplicity and ease of implementation, the design constitutes a valuable alternative to existing methods for creating single-chip TRNGs. Its main advantages are the high throughput, the portability and the low amount of resources it occupies inside the chip. Therefore, it could further extend the use of FPGA chips in cryptography. Our primary source of entropy is a True Dual-Port Block-RAM operating at high frequency, which is used in a special architecture that creates a concurrent write conflict. The paper also describes the practical issues which make it possible to convert that conflict into a strong entropy source. Depending on the users' requirements, it is possible to connect many units of this generator in parallel on a single FPGA device, thus increasing the bit generation throughput up to the Gbps level. The generator has successfully passed the major statistical test batteries.