nils-herrmann/clean_scirepeval huggingface dataset:
On Stress and Linguistic

In this essay a new theory of stress and linguistic rhythm will be elaborated, based on the proposals of Liberman (1975).' It will be argued that certain features of prosodic systems like that of English, in particular the phenomenon of "stress subordination", are not to be referred primarily to the properties of individual segments (or syllables), but rather reflect a hierarchical rhythmic structuring that organizes the syllables, words, and syntactic phrases of a sentence. The character of this structuring, properly understood, will give fresh insight into phenomena that have been apprehended in terms of the phonological cycle, the stress-subordination convention, the theory of disjunctive ordering, and the use of crucial variables in phonological rules. Our theory will employ two basic ideas about the representation of traditional prosodic concepts: first, we represent the notion relative prominence in terms of a relation defined on constituent structure; and second, we represent certain aspects of the notion linguistic rhythm in terms of the alignment of linguistic material with a "metrical grid". The perceived "stressing" of an utterance, we think, reflects the combined influence of a constituent-structure pattern and its grid alignment. This pattern-grid combination is reminiscent of the traditional picture of verse scansion, so that the theory as a whole deserves the name "metrical". We will also use the expression "'metrical theory" as a convenient term for that portion of the theory which deals with the assignment of relative prominence in terms of a relation defined on constituent structure. Section 1 will apply the metrical theory of stress-pattern assignment to the system of English phrasal stress, arguing this theory's value in rationalizing otherwise arbitrary characteristics of stress features and stress rules. Section 2 will extend this treatment to the domain of English word stress, adopting a somewhat traditional view of the assignment of the feature [+stress], but explaining the generation of word-level * We would like to thank
Antibacterial activity of black pepper ( Piper nigrum Linn.) with special reference to its mode of action on bacteria

During present study the antibacterial activity of black pepper ( and its mode of action on bacteria were done. The extracts of black pepper were evaluated for antibacterial activity by disc diffusion method. The minimum inhibitory concentration (MIC) was determined by tube dilution method and mode of action was studied on membrane leakage of UV260 and UV280 absorbing material spectrophotometrically. The diameter of the zone of inhibition against various Gram positive and Gram negative bacteria was measured. The MIC was found to be 50-500ppm. Black pepper altered the membrane permeability resulting the leakage of the UV260 and UV280 absorbing material i.e., nucleic acids and proteins into the extra cellular medium. The results indicate excellent inhibition on the growth of Gram positive bacteria like Staphylococcus aureus, followed by Bacillus cereus and Streptococcus faecalis. Among the Gram negative bacteria Pseudomonas aeruginosa was more susceptible followed by Salmonella typhi and Escherichia coli.
Application of Progressive Neural Networks for Multi-Stream Wfst Combination in One-Pass Decoding

Many state-of-the-art automatic speech recognition (ASR) systems adopt system combination techniques to improve recognition performance. In this paper, we investigate the possibility of transferring knowledge between models for different noisy speech domains and integrating these models via system combination. The first contribution of our work is the use of progressive neural networks for modeling the acoustic features of noisy speech. We train progressive neural networks on subdivided noisy data to achieve knowledge transfer between different noise conditions. Our second contribution is an improved multi-stream WFST framework that combines the output of the progressive networks at longer timescales (e.g., word hypotheses). The score fusion is performed by a trained LSTM at the word boundary on the decoding lattice. By adopting both knowledge transfer and system combination techniques, we achieve improved performance compared with independently trained deep neural networks.
Deep Generative Learning via Variational Gradient Flow

We propose a general framework to learn deep generative models via Variational Gradient Flow (VGrow) on probability spaces. The evolving distribution that asymptotically converges to the target distribution is governed by a vector field, which is the negative gradient of the first variation of the f -divergence between them. We prove that the evolving distribution coincides with the pushforward distribution through the infinitesimal time composition of residual maps that are perturbations of the identity map along the vector field. The vector field depends on the density ratio of the pushforward distribution and the target distribution, which can be consistently learned from a binary classification problem. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning. We also evaluated several commonly used divergences, including KullbackLeibler, Jensen-Shannon, Jeffrey divergences as well as our newly discovered “logD” divergence which serves as the objective function of the logD-trick GAN. Experimental results on benchmark datasets demonstrate that VGrow can generate high-fidelity images in a stable and efficient manner, achieving competitive performance with stateof-the-art GANs. ∗Yuling Jiao (yulingjiaomath@whu.edu.cn) †Can Yang (macyang@ust.hk) 1 ar X iv :1 90 1. 08 46 9v 2 [ cs .L G ] 7 F eb 2 01 9
Creating firewall rules with machine learning techniques

The war against cybercrime is a constant battle. While cyber criminals keep using the same basic attack techniques [M.v.j.], the amount and diversity of malware grows [M. Fossi]. This renders security defenses ineffective such that millions of computers are infected with malware in the form of computer viruses, internet worms and Trojan horses. These cybercrimes cost the society money [G. Lovet, M. Clement, C. Kanich] and are a threat to our privacy. Intrusion detection is a critical component when fighting cybercrime in the area of network security. Intrusion detection systems (IDS) look at characteristics of network packets or series of packets to determine whether the packets are malicious or not. The goal of this thesis is to show how better and more complex firewall rules can be created with the use of machine learning algorithms. These new firewall rules contribute to better computer intrusion detection systems. This thesis looks at existing machine learning methods such as random forests [L. Breiman] and neural networks [K. Hornik] and how firewall rules can be extracted from the resulting models. The models are trained and tested on a novel labeled network data set containing malicious and normal packets. This data set was created for this thesis and has not been used before. How the data set is created is also presented in this thesis. However, as machine learning techniques work well on networks with similar traffic such as SCADA networks, a special chapter in Appendix A is included to this thesis on SCADA networks.
Elemental Spatiotemporal Variations of Total Suspended Particles in Jeddah City

Elements associated with total suspended particulate matter (TSP) in Jeddah city were determined. Using high-volume samplers, TSP samples were simultaneously collected over a one-year period from seven sampling sites. Samples were analyzed for Al, Ba, Ca, Cu, Mg, Fe, Mn, Zn, Ti, V, Cr, Co, Ni, As, and Sr. Results revealed great dependence of element contents on spatial and temporal variations. Two sites characterized by busy roads, workshops, heavy population, and heavy trucking have high levels of all measured elements. Concentrations of most elements at the two sites exhibit strong spatial gradients and concentrations of elements at these sites are higher than other locations. The highest concentrations of elements were observed during June-August because of dust storms, significant increase in energy consumption, and active surface winds. Enrichment factors of elements at the high-level sites have values in the range >10~60 while for Cu and Zn the enrichment factors are much higher (~0->700) indicating that greater percentage of TSP composition for these three elements in air comes from anthropogenic activities.
Translation and Ideology : A Critical Discourse Analysis of Chomsky ’ s “ Media Control ” and its Arabic Translation

There are many factors that influence the translators while translating a text. Amongst these factors is the notion of ideology transmission through the translated texts. This paper is located within the framework of Descriptive Translation Studies (DTS) and Critical Discourse Analysis (CDA). It investigates the notion of ideology with particular use of critical discourse analysis. The purpose is to highlight the relationship between language and ideology in translated texts. It also aims at discovering whether the translator’s socio-cultural and ideology constraints influence the production of his/her translations. As a mixed research method study, the corpus consists of two different Arabic translated versions of the English book “Media Control” by Noam Chomsky. The micro-level contains the qualitative stage where detailed description and comparison -contrastive and comparativeanalysis will be provided. The micro-level analysis should include the lexical items along with the grammatical items (passive verses. active, nominalisation vs. de-nominalisation, moralisation and omission vs. addition). In order to have more reliable and objective data, computed frequencies of the ideological significance occurrences along with percentage and Chi-square formula were conducted through out the data analysis stage which then form the quantitative part of the current study. The main objective of the mentioned data analysis methodologies is to find out the dissimilarity between the proportions of the information obtained from the target texts (TTs) and their equivalent at the source text (ST). The findings indicts that there are significant differences amongst the two TTs in relation to International Journal of Linguistics ISSN  syntactic structure compared by the ST. These significant differences indicate some ideological transmission through translation process of the two TTs. Therefore, and to some extent, it can be stated that the differences were also influenced by the translators’ socio-cultural and ideological constraints.
Optimizing ETL processes in data warehouses

Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. Usually, these processes must be completed in a certain time window; thus, it is necessary to optimize their execution time. In this paper, we delve into the logical optimization of ETL processes, modeling it as a state-space search problem. We consider each ETL workflow as a state and fabricate the state space through a set of correct state transitions. Moreover, we provide algorithms towards the minimization of the execution cost of an ETL workflow.
A Comparative Study of Classifying Legal Documents with Neural Networks

In recent years, deep learning has shown promising results when used in the field of natural language processing (NLP). Neural networks (NNs) such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for various NLP tasks including sentiment analysis, information retrieval, and document classification. In this paper, we the present the Supreme Court Classifier (SCC), a system that applies these methods to the problem of document classification of legal court opinions. We compare methods using traditional machine learning with recent NN-based methods. We also present a CNN used with pre-trained word vectors which shows improvements over the state-of-the-art applied to our dataset. We train and evaluate our system using the Washington University School of Law Supreme Court Database (SCDB). Our best system (word2vec + CNN) achieves 72.4% accuracy when classifying the court decisions into 15 broad SCDB categories and 31.9% accuracy when classifying among 279 finer-grained SCDB categories.
Direct pose estimation for planar objects

Estimating six degrees of freedom poses of a planar object from images is an important problem with numerous applications ranging from robotics to augmented reality. While the state-of-the-art Perspective-n-Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on target objects with rich texture. In this work, we propose a two-step robust direct method for six-dimensional pose estimation that performs accurately on both textured and textureless planar target objects. First, the pose of a planar target object with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Second, each object pose is refined and disambiguated using a dense alignment scheme. Extensive experiments on both synthetic and real datasets demonstrate that the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under varying conditions. Furthermore, we show that the proposed dense alignment scheme can also be used for accurate pose tracking in video sequences.
Understanding the determinants of cloud computing adoption

The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high-tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure.  A questionnaire-based survey was used to collect data from 111 firms belonging to the high-tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis.  The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing.  The research was conducted in the high-tech industry, which may limit the generalisability of the findings.  The findings offer cloud computing service providers with a better understanding of what affects cloud computing adoption characteristics, with relevant insight on current promotions.  The research contributes to the application of new technology cloud computing adoption in the high-tech industry through the use of a wide range of variables. The findings also help firms consider their information technologies investments when implementing cloud computing.
Toward Personalized Relational Learning

Relational learning exploits relationships among instances manifested in a network to improve the predictive performance of many network mining tasks. Due to its empirical success, it has been widely applied in myriad domains. In many cases, individuals in a network are highly idiosyncratic. They not only connect to each other with a composite of factors but also are often described by some content information of high dimensionality specific to each individual. For example in social media, as user interests are quite diverse and personal; posts by different users could differ significantly. Moreover, social content of users is often of high dimensionality which may negatively degrade the learning performance. Therefore, it would be more appealing to tailor the prediction for each individual while alleviating the issue related to the curse of dimensionality. In this paper, we study a novel problem of Personalized Relational Learning and propose a principled framework PRL to personalize the prediction for each individual in a network. Specifically, we perform personalized feature selection and employ a small subset of discriminative features customized for each individual and some common features shared by all to build a predictive model. On this account, the proposed personalized model is more human interpretable. Experiments on realworld datasets show the superiority of the proposed PRL framework over traditional relational learning methods.
How to pitch a brilliant idea.

Coming up with creative ideas is easy; selling them to strangers is hard. Entrepreneurs, sales executives, and marketing managers often go to great lengths to demonstrate how their new concepts are practical and profitable--only to be rejected by corporate decision makers who don't seem to understand the value of the ideas. Why does this happen? Having studied Hollywood executives who assess screenplay pitches, the author says the person on the receiving end--the "catcher"--tends to gauge the pitcher's creativity as well as the proposal itself. An impression of the pitcher's ability to come up with workable ideas can quickly and permanently overshadow the catcher's feelings about an idea's worth. To determine whether these observations apply to business settings beyond Hollywood, the author attended product design, marketing, and venture-capital pitch sessions and conducted interviews with executives responsible for judging new ideas. The results in those environments were similar to her observations in Hollywood, she says. Catchers subconsciously categorize successful pitchers as showrunners (smooth and professional), artists (quirky and unpolished), or neophytes (inexperienced and naive). The research also reveals that catchers tend to respond well when they believe they are participating in an idea's development. As Oscar-winning writer, director, and producer Oliver Stone puts it, screen-writers pitching an idea should "pull back and project what he needs onto your idea in order to make the story whole for him." To become a successful pitcher, portray yourself as one of the three creative types and engage your catchers in the creative process. By finding ways to give your catchers a chance to shine, you sell yourself as a likable collaborator.
Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook

Online social networks such as Friendster, MySpace, or the Facebook have experienced exponential growth in membership in recent years. These networks offer attractive means for interaction and communication, but also raise privacy and security concerns. In this study we survey a representative sample of the members of the Facebook (a social network for colleges and high schools) at a US academic institution, and compare the survey data to information retrieved from the network itself. We look for underlying demographic or behavioral differences between the communities of the network’s members and non-members; we analyze the impact of privacy concerns on members’ behavior; we compare members’ stated attitudes with actual behavior; and we document the changes in behavior subsequent to privacy-related information exposure. We find that an individual’s privacy concerns are only a weak predictor of his membership to the network. Also privacy concerned individuals join the network and reveal great amounts of personal information. Some manage their privacy concerns by trusting their ability to control the information they provide and the external access to it. However, we also find evidence of members’ misconceptions about the online community’s actual size and composition, and about the visibility of members’ profiles.
Neural Regression Trees

Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.
Adjective Intensity and Sentiment Analysis

For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives that share a common semantic property. In this paper, we present a semi-supervised approach to assign intensity levels to adjectives, viz. high, medium and low, where adjectives are compared when they belong to the same semantic category. For example, in the semantic category of EXPERTISE, expert, experienced and familiar are respectively of level high, medium and low. We obtain an overall accuracy of 77% for intensity assignment. We show the significance of considering intensity information of adjectives in predicting star-rating of reviews. Our intensity based prediction system results in an accuracy of 59% for a 5-star rated movie review corpus.
Using Neo4j graph database in social network analysis

This article describes how Neo4j database capabilities can be utilized to implement measures often used in social network analysis. It gives a brief overview of the concept of Neo4j graph database. The UML class diagram of domain model is presented and discussed in details. On the basis of implementation of degree centrality and local clustering coefficient measures, several Neo4j core features are presented. In the summary, some general comments on using this database as a tool in a social network analysis are provided.
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms

This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. Part I of this tutorial (this paper), describes the probabilistic form of the SLAM problem, essential solution methods and significant implementations. Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.
Style Transfer from Non-Parallel Text by Cross-Alignment

This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. The key challenge is to separate the content from other aspects such as style. We assume a shared latent content distribution across different text corpora, and propose a method that leverages refined alignment of latent representations to perform style transfer. The transferred sentences from one style should match example sentences from the other style as a population. We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word