nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing Authorship Attribution By Utilizing Syntax Tree Profiles

The aim of modern authorship attribution approaches is to analyze known authors and to assign authorships to previously unseen and unlabeled text documents based on various features. In this paper we present a novel feature to enhance current attribution methods by analyzing the grammar of authors. To extract the feature, a syntax tree of each sentence of a document is calculated, which is then split up into length-independent patterns using pq-grams. The mostly used pq-grams are then used to compose sample profiles of authors that are compared with the profile of the unlabeled document by utilizing various distance metrics and similarity scores. An evaluation using three different and independent data sets reveals promising results and indicate that the grammar of authors is a significant feature to enhance modern authorship attribution methods.
Compensation of vehicle-specific antenna radome effects at 5.9 GHz

Future vehicles are expected to integrate multiple radio access technologies to offer traffic information services for Intelligent Transportation Systems (ITS). Dedicated short range communication (DSRC) being designed exclusively for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is one of the major candidates to enable such services. The focus of this paper is to shed light on the integration aspects of this technology, especially in regards to the antenna design. A methodology is presented to overcome the difficulties occurring from the integration of the antenna in the radome on the vehicle rooftop. With help of simulations and measurements it is illustrated how the conventionally unwanted dielectric lens effect of the antenna radome resulting in nulls in the radiation pattern can be used to deliberately increase the antenna performance. The performance enhancement by using the antenna radome as a dielectric lens is validated with four prototypes in a semi-anechoic chamber. It is moreover shown that simplified geometrical shapes such as cuboids can be used to describe efficiently the behavior of geometrical more complex radome designs. The greatest advantage of our proposed methodology to enhance antenna performance is that it can be employed without changes of any electronic control units. Thus it can easily be included in a running production cycle without even altering any design elements of the vehicle. To demonstrate to which degree the achieved enhancement on component level translates into system level we also present simulation results of a test scenario including a vehicle and a road-side unit. The outcome of the system level simulations shows substantial improvements in the received signal strength (RSS).
Design & performance analysis of Fuzzy based MPPT control using two-switch non inverting Buck-Boost converter

The Solar energy conversion system is a very important substitute of energy among other renewable sources. The photovoltaic array shows a non-linear behavior pertaining to insolation and cell temperature. It is vital to elicit maximum power from the solar cell under irregular climatic conditions to elevate the efficiency of the solar energy conversion system. The maximum power point techniques (MPPT) are employed to maximize the output power of PV array by ceaselessly tracking maximum power point. This paper investigates the design of a two switch non inverting Buck-Boost converter using MPPT controller to store charge in a battery. The proposed system utilizes Fuzzy Logic based MPPT controller. The modeling of a Photovoltaic system is implemented using MATLAB/Simulink to inculcate the effectiveness of MPPT controller in terms of quick response time and improved efficiency of the system.
A 0.5V supply, 49nW band-gap reference and crystal oscillator in 40nm CMOS

This paper presents the co-design of 0.5V operational band-gap reference (BGR) and 32kHz crystal oscillator (XO) in 40nm CMOS process. The proposed BJT-based BGR offers 10× resistor area reduction compared to the conventional BGR. The proposed XO provides trans-conductance enhancement without the need for large coupling capacitor and uses a feedback loop to regulate its bias current, oscillation amplitude across PVT. The total power consumption is less than 49nW from −40°C to 120°C. Without any trimming, the BGR achieves temperature coefficients of 8ppm/°C and XO has temperature coefficient of 0.25ppm/°C. The BGR offers 5× lower area, 2× lower power consumption at 120°C compared to prior work on resistor-based, non-duty cycled BGR. The XO improves power consumption by at least 2× compared to prior work at 120°C, 0.5V supply.
Outlier detection by active learning

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.
The Growing Importance of Social Skills in the Labor Market

The slow growth of high-paying jobs in the U.S. since 2000 and rapid advances in computer technology have sparked fears that human labor will eventually be rendered obsolete. Yet while computers perform cognitive tasks of rapidly increasing complexity, simple human interaction has proven difficult to automate. In this paper, I show that the labor market increasingly rewards social skills. Since 1980, jobs with high social skill requirements have experienced greater relative growth throughout the wage distribution. Moreover, employment and wage growth has been strongest in jobs that require high levels of both cognitive skill and social skill. To understand these patterns, I develop a model of team production where workers “trade tasks” to exploit their comparative advantage. In the model, social skills reduce coordination costs, allowing workers to specialize and trade more efficiently. The model generates predictions about sorting and the relative returns to skill across occupations, which I test and confirm using data from the NLSY79. The female advantage in social skills may have played some role in the narrowing of gender gaps in labor market outcomes since 1980.  Thanks to Pol Antras, David Autor, Avi Feller, Lawrence Katz, Sandy Jencks, Richard Murnane, Doug Staiger and Lowell Taylor for reading early drafts of this paper and for providing insightful feedback. Thanks to Felipe Barrera-Osorio, Amitabh Chandra, Asim Khwaja, Alan Manning, Guy Michaels, Luke Miratrix, Todd Rogers, Marty West and seminar participants at LSE and PSE for helpful comments.  Chi, Madeleine Gelblum, Lauren Reisig and Stephen Yen provided superb research assistance. Special thanks to David Autor and Brendan Price for sharing their data and programs. Extra special thanks to Lisa Kahn and Chris Walters for “trading tasks” with me. All errors are my own.
A Multi-disciplinary Approach to Marketing Leveraging Advances in

For decades, neuroscience has greatly contributed to our foundational understanding of human behavior. More recently, the findings and methods of neuroscience have been applied to study the process of decision-making in order to offer advanced insights into the neural mechanisms that influence economic and consumer choices. In this thesis, I will address how customized marketing strategies can be enriched through the integration of consumer neuroscience, an integrative field anchored in the biological, cognitive and affective mechanisms of consumer behavior. By recognizing and utilizing these multidisciplinary interdependencies, marketers can enhance their advertising and promotional mix to elicit desired neural and affective consumer responses and measure these reactions in order to enhance purchasing decisions. The principal objective of this thesis is to present a comprehensive review of consumer neuroscience and to elucidate why it is an increasingly important area of study within the framework of human behavior. I will also describe how the insights gained from this emerging field can be leveraged to optimize marketing activities. Finally, I propose an experiment that illuminates key research questions, which may have considerable impact on the discipline of consumer neuroscience as well as the marketing industry.
Finding community structure in very large networks.

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.
"I care about him as a pal": conceptions of robotic pets in online AIBO discussion forums

In this study, we analyzed people's conceptions of AIBO, a robotic pet, through thier spontaneous postings in online AIBO discussion forums. Results showed that AIBO psychologically engaged this group of participants, particularly by drawing forth conceptions of essences (79%), agency (60%), and social standing (59%). However, participants seldom attributed moral standing to AIBO (e.g., that AIBO deserves respect, has rights, or can be held morally accountable for action). Our discussion focuses on the societal implications of these results.
Sparse Bayesian Learning and the Relevance Vector Machine

This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classi­ cation tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vector machine’ (RVM), a model of identical functional form to the popular and state-of-the-art `support vector machine’ (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while o¬ering a number of additional advantages. These include the bene­ ts of probabilistic predictions, automatic estimation of `nuisance’ parameters, and the facility to utilise arbitrary basis functions (e.g. non-`Mercer’ kernels). We detail the Bayesian framework and associated learning algorithm for the RVM, and give some illustrative examples of its application along with some comparative benchmarks. We o¬er some explanation for the exceptional degree of sparsity obtained, and discuss and demonstrate some of the advantageous features, and potential extensions, of Bayesian relevance learning.
Opinion Mining with Deep Recurrent Neural Networks

Recurrent neural networks (RNNs) are connectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, “depth in space” — as an orthogonal notion to “depth in time” — in RNNs has been investigated by stacking multiple layers of RNNs and shown empirically to bring a temporal hierarchy to the architecture. In this work we apply these deep RNNs to the task of opinion expression extraction formulated as a token-level sequence-labeling task. Experimental results show that deep, narrow RNNs outperform traditional shallow, wide RNNs with the same number of parameters. Furthermore, our approach outperforms previous CRF-based baselines, including the state-of-the-art semi-Markov CRF model, and does so without access to the powerful opinion lexicons and syntactic features relied upon by the semi-CRF, as well as without the standard layer-by-layer pre-training typically required of RNN architectures.
NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as NoSQL, NewSQL and Search-based systems. This paper motivation is to provide classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.
Towards a Linguistic Linked Open Data cloud: The Open Linguistics Working Group

The Open Linguistics Working Group (OWLG) is an initiative of experts from different fields concerned with linguistic data, including academic linguistics (e.g. typology, corpus linguistics), applied linguistics (e.g. computational linguistics, lexicography and language documentation), and NLP (e.g. from the Semantic Web community). The primary goals of the working group are 1) promoting the idea of open linguistic resources, 2) developing means for their representation, and 3) encouraging the exchange of ideas across different disciplines. To a certain extent, the activities of the Open Linguistics Working Group converge towards the creation of a Linguistic Linked Open Data cloud, which is a topic addressed from different angles by several members of the Working Group. In this article, some of these currently on-going activities are presented and described. RÉSUMÉ. Le groupe OWLG est une initiative d’experts provenant de différents domaines linguistiques, comprenant la linguistique académique (typologie, corpus), la linguistique appliquée (linguistique computationelle, lexicographie, documentation de langues) et le traitement automatique des langues (p.ex. Web Sémantique). Les objectifs principaux de ce groupe sont 1) la promotion de l’idée de ressources ouvertes et accessibles, 2) le développement de moyens pour représenter lesdites ressources et 3) la stimulation d’échanges entre les diverses disciplines et subdisciplines. Les activités de l’OWLG sont pour la plupart liées à la création d’un nuage de données linguistiques Linked Open Data. Les membres du groupe abordent ce thème sous des aspects différents, dont nous présenterons quelques-uns ci-dessous.
Arabic Sentiment Analysis Using Supervised Classification

Sentiment analysis is a process during which the polarity (i.e. positive, negative or neutral) of a given text is determined. In general there are two approaches to address this problem, namely, machine learning approach or lexicon based approach. The current paper deals with sentiment analysis in Arabic reviews from a machine learning perspective. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naïve Bayes, SVM and K-Nearest Neighbor classifiers were run on this dataset. The results show that SVM gives the highest precision while KNN (K=10) gives the highest Recall.
Improving the Speed of Parallel Decimal Multiplication

Hardware support for decimal computer arithmetic is regaining popularity. One reason is the recent growth of decimal computations in commercial, scientific, financial, and Internet-based computer applications. Newly commercialized decimal arithmetic hardware units use radix-10 sequential multipliers that are rather slow for multiplication-intensive applications. Therefore, the future relevant processors are likely to host fast parallel decimal multiplication circuits. The corresponding hardware algorithms are normally composed of three steps: partial product generation (PPG), partial product reduction (PPR), and final carry-propagating addition. The state of the art is represented by two recent full solutions with alternative designs for all the three aforementioned steps. In addition, PPR by itself has been the focus of other recent studies. In this paper, we examine both of the full solutions and the impact of a PPR-only design on the appropriate one. In order to improve the speed of parallel decimal multiplication, we present a new PPG method, fine-tune the PPR method of one of the full solutions and the final addition scheme of the other; thus, assembling a new full solution. Logical Effort analysis and 0.13 mum synthesis show at least 13 percent speed advantage, but at a cost of at most 36 percent additional area consumption.
RACOG and wRACOG: Two Probabilistic Oversampling Techniques

As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing oversampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not represented well which leads to high misclassification error. We introduce two probabilistic oversampling approaches, namely RACOG and wRACOG, to synthetically generating and strategically selecting new minority class samples. The proposed approaches use the joint probability distribution of data attributes and Gibbs sampling to generate new minority class samples. While RACOG selects samples produced by the Gibbs sampler based on a predefined lag, wRACOG selects those samples that have the highest probability of being misclassified by the existing learning model. We validate our approach using nine UCI data sets that were carefully modified to exhibit class imbalance and one new application domain data set with inherent extreme class imbalance. In addition, we compare the classification performance of the proposed methods with three other existing resampling techniques.
A quantitative evaluation of usability in mobile applications: An empirical study

Developing high quality applications, in adequate time and low cost is the goal of using software engineering methods and processes. Over the years, a focus on usability has shown itself to be one of the cheapest and easiest ways to improve a system's quality (or more precisely, the user's perception of quality). This quality factors is an important issue in user experience of mobile applications. In this paper, our research question is if using various types of user guide affects the usability of mobile applications. Our contribution in this paper is focusing on quantitative evaluation via logging the user interaction automatically. Based on this point of view, the research question is defined in more details: if using animation or text user guide results a significant difference on timing factors of usability including task completion time, time spent using help, and time used to search a button to perform a specific function. To answer this question, we design an empirical study. In this study, a mobile application has been selected and two types of user guide (animation and text) are generated. To run the study, 68 audiences are participated that are divided into two independent groups. They use two versions of the mobile application: One contains an animated user guide for a functionality, while the other application has a text user guide that describes the same functionality. The activities of the users are logged and used to measure selected time factors. Applying the Mann-Whitney U Test on the results shows that animation user guide does not improve the time used to search a button to perform a specific function significantly, but it makes 76.9% improvement in time spent using help and 32.4% in task completion time. In the other words, the result of this study shows that animation user guide decreases the total time of task completion, and consequently it increases the usability of the function in our case study.
Orthogonal Coaxial Cavity Filters With Distributed Cross-Coupling

In this letter, six-pole filters are presented, in which the coaxial cavity resonators are arranged with 90 degree rotated spatial orientation. Cross-coupling between nonadjacent resonators will appear. In the new proposed filter topologies the cross-coupling elements are not arranged in separated triplet or quadruplet blocks. They interlace to form distributed cross-coupling. Synthesis of the distributed coupling matrix based on the symmetric canonical prototype is discussed. Two different bandpass filter topologies are presented. The first one has an inline arrangement of six orthogonal coaxial cavity resonators. Its distributed cross-coupling consists of four interlaced triplets, which generate two transmission zeros above the passband. The second filter design has a folded arrangement of two inline triplets with strong cross-coupling. An additional interlaced quadruplet pushes the resulting transmission zeros to the complex frequency domain. Measurement results of two corresponding filter prototypes confirm the predicted performance.
Google news personalization: scalable online collaborative filtering

Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.