nils-herrmann/clean_scirepeval huggingface dataset:
Design and realization of a process and temperature compensated CMOS ring oscillator

This paper describes the design and realization of a process and temperature compensated CMOS ring oscillator. The proposed circuit employs a current-starved ring oscillator with a compensated bias circuit, which generates an adaptive control voltage to maintain a fixed oscillation frequency against temperature and process variations. Simulation results using process parameters from a 0.18-μm CMOS technology and 1.8-V power supply voltage showed that the worst-case frequency variation of 4.49% and 2.29% could be obtained at the oscillation frequencies of 100 MHz and 150 MHz, respectively, over the temperature range of - 40°C to 125°C. The overall circuit consumes 437μW at 100MHz and 537μW at 150MHz.
Omnidirectional Circularly Polarized Antennas – a Small Antenna Perspective

1 Abstract—The paper discusses recent developments and challenges in the design of small omnidirectional circularly-polarized (CP) antennas. Although omnidirectional CP coverage is easily achievable using antenna arrays, it is just recently that small and low-cost antennas delivered this functionality. The paper addresses practical design problems for these antennas, not reported in previous publications. This includes selection of the omnidirectional plane relative to the ground plane and measurement challenges. Future perspectives on how these antennas can provide electronically steerable beams are investigated using simulated results.
A lightweight implementation of generics and dynamics

The recent years have seen a number of proposals for extending statically typed languages by dynamics or generics. Most proposals --- if not all --- require significant extensions to the underlying language. In this paper we show that this need not be the case. We propose a particularly lightweight extension that supports both dynamics and generics. Furthermore, the two features are smoothly integrated: dynamic values, for instance, can be passed to generic functions. Our proposal makes do with a standard Hindley-Milner type system augmented by existential types. Building upon these ideas we have implemented a small library that is readily usable both with Hugs and with the Glasgow Haskell compiler.
Centralized PID control by decoupling for TITO processes

This paper presents a new methodology to design multivariable PID controllers for two-input and two-output systems. The proposed control strategy, which is centralized, combines four PID controllers plus two possible delays and two P controllers. The proportional gains in the P controllers act as tuning parameters in order to modify the behavior of the loops almost independently. The design procedure consists of three steps: first, an ideal decoupler including integral action is determined. Second, the decoupler is approximated with four PID controllers plus two possible delays. Third, the proportional gains are tuned to achieve the specified performance. The proposed method is applied to three representative processes.
A longitudinal examination of the link between youth physical fitness and academic achievement.

Childhood obesity has been linked with other persistent health problems, but research is just beginning to examine its relationship with academic performance. This article tracks students longitudinally to examine the ways student physical fitness and changes in fitness align with school performance.



Using matched administrative data and individual growth modeling, we examine the relationship between academic achievement and overall physical fitness longitudinally from fourth to seventh and sixth to ninth grades for students in a California community.



Comparing those who are persistently fit to those who are persistently unfit, we find disparities in both math and English language arts test scores. These academic disparities begin even before students begin fitness testing in fifth grade and are larger for girls and Latinos. Overall physical fitness is a better predictor of academic achievement than obesity as measured by body mass index. Socioeconomic status acts as a buffer for those who have poor physical fitness but strong academic performance.



The findings indicate the presence of a physical fitness achievement gap that has consequences for potential students' future educational and health outcomes. This gap begins as early as fourth grade, which is before physical fitness testing begins in California.
Design, Implementation and Evaluation of Congestion Control for Multipath TCP

Multipath TCP, as proposed by the IETF working group mptcp, allows a single data stream to be split across multiple paths. This has obvious benefits for reliability, and it can also lead to more efficient use of networked resources. We describe the design of a multipath congestion control algorithm, we implement it in Linux, and we evaluate it for multihomed servers, data centers and mobile clients. We show that some ‘obvious’ solutions for multipath congestion control can be harmful, but that our algorithm improves throughput and fairness compared to single-path TCP. Our algorithm is a drop-in replacement for TCP, and we believe it is safe to deploy.
Extracting Social Network and Character Categorization From Bengali Literature

Literature network analysis is an emerging area in the computational research domain. Literature network is a type of social network with various distinct features. The analysis explores significance of human behavior and complex social relationships. The story consists of some characters and creates an interconnected social system. Each character of the literature represents a node and the edge between any two nodes offered the interaction between them. An annotation and a novel character categorization method are developed to extract interactive social network from the Bengali drama. We analyze Raktakarabi and Muktodhara, two renowned Bengali dramas of Rabindranath Tagore. Weighted degree, closeness, and betweenness centrality analyze the correlation among the characters. We propose an edge contribution-based centrality and diversity metric of a node to determine the influence of one character over others. High diverse nodes show low clustering coefficient and vice versa. We propose a novel idea to analyze the characteristics of protagonist and antagonist from the influential nodes based on the complex graph. We also present a game theory-based community detection method that clusters the actors with a high degree of relationship. Evaluation on real-world networks demonstrates the superiority of the proposed method over the other existing algorithms. Interrelationship of the actors within the drama is also shown from the detected communities, as underlying theme of the narrations is identical. The analytical results show that our method efficiently finds the protagonist and antagonist from the literature network. The method is unique, and the analytical results are more accurate and unbiased than the human perspective. Our approach establishes similar results compared with the benchmark analysis available in Tagore’s Bengali literature.
Software Framework for Topic Modelling with Large Corpora

Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.
Author ' s personal copy The loci and mechanisms of leadership : Exploring a more comprehensive view of leadership theory

Available online 15 October 2011 Through a qualitative review of the leadership literature, we derive two fundamental principles for codifying the last century of leadership theory and research: the locus and mechanism of leadership. Our systematic review and categorization of past theories suggests that further development of the follower, collective and context loci, and the affect mechanism is needed. Building on these insights, we propose that by simultaneously considering all five loci and four mechanisms in their theories, leadership scholars can advance a more comprehensive and integrative understanding of the leadership phenomenon. We then demonstrate the potential of using the locus and mechanism framework for examining and expanding current and future leadership theory including work on shared and strategic leadership.
DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking

This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.
An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement

24-h blood pressure (BP) has significant prognostic value for cardiovascular risk screening, but the present BP devices are mainly cuff-based, which are unsuitable for long-term BP measurement, especially during nighttime. In this paper, we developed an armband wearable pulse transit time (PTT) system for 24-h cuff-less BP measurement and evaluated it in an unattended out-of-laboratory setting. Ten healthy young subjects participated in this ambulatory study, where PTT was measured at 30-min interval by this wearable system and the reference BP was measured by a standard oscillometric ambulatory BP monitor. Due to the misalignment of BP and PTT on their recording time caused by the different measurement principles of the two BP devices, a new estimation method has been proposed: transients in PTT were removed from the raw data by defined criteria, and then evenly interpolated, low-pass filtered, and resampled to synchronize at the time when BP was recorded. The results show that with the proposed method, the correlation between PTT and systolic BP (SBP) during nighttime with dynamic range of 21.8 ± 14.2 mmHg has improved from -0.50 ± 0.24 to -0.62 ± 0.20 (p<;0.1), and the difference between the estimated and reference SBP has improved from 0.7 ± 10.7 to 2.8 ± 8.2 mmHg with root mean square error reduced from 10.7 to 8.7 mmHg. In addition, the correlation between a very low frequency component of SBP and PTT obtained from the proposed method during nighttime is -0.80 ± 0.10 and the difference is 2.4 ± 5.7 mmHg for a dynamic BP range of 13.5 ± 8.0 mmHg. It is therefore concluded from this study that the proposed wearable system has great potential to be used for overnight SBP monitoring, especially to measure the averaged SBP over a long period.
Credit Card Fraud Detection: A case study

In this research, a technique for `Credit Card Fraud Detection' is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.
Learning grounded finite-state representations from unstructured demonstrations

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian nonparametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, comprised of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multistep robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.
Randomized clinical trial of cognitive behavioral therapy (CBT) versus acceptance and commitment therapy (ACT) for mixed anxiety disorders.

Randomized comparisons of acceptance-based treatments with traditional cognitive behavioral therapy (CBT) for anxiety disorders are lacking. To address this gap, we compared acceptance and commitment therapy (ACT) to CBT for heterogeneous anxiety disorders.



One hundred twenty-eight individuals (52% female, mean age = 38, 33% minority) with 1 or more DSM-IV anxiety disorders began treatment following randomization to CBT or ACT; both treatments included behavioral exposure. Assessments at pre-treatment, post-treatment, and 6- and 12-month follow-up measured anxiety-specific (principal disorder Clinical Severity Ratings [CSRs], Anxiety Sensitivity Index, Penn State Worry Questionnaire, Fear Questionnaire avoidance) and non-anxiety-specific (Quality of Life Index [QOLI], Acceptance and Action Questionnaire-16 [AAQ]) outcomes. Treatment adherence, therapist competency ratings, treatment credibility, and co-occurring mood and anxiety disorders were investigated.



CBT and ACT improved similarly across all outcomes from pre- to post-treatment. During follow-up, ACT showed steeper linear CSR improvements than CBT (p < .05, d = 1.26), and at 12-month follow-up, ACT showed lower CSRs than CBT among completers (p < .05, d = 1.10). At 12-month follow-up, ACT reported higher AAQ than CBT (p = .08, d = 0.42; completers: p < .05, d = 0.56), whereas CBT reported higher QOLI than ACT (p < .05, d = 0.42). Attrition and comorbidity improvements were similar; ACT used more non-study psychotherapy at 6-month follow-up. Therapist adherence and competency were good; treatment credibility was higher in CBT.



Overall improvement was similar between ACT and CBT, indicating that ACT is a highly viable treatment for anxiety disorders.
ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach

-ADVISOR 2.1 is the latest version of the National Renewable Energy Laboratory’s advanced vehicle simulator. It was first developed in 1994 to support the U.S. Department of Energy hybrid propulsion system program, and is designed to be accurate, fast, flexible, easily sharable, and easy to use. This paper presents the model, focusing on its combination of forwardand backward-facing simulation approaches, and evaluates the model in terms of its design goals. ADVISOR predicts acceleration time to within 0.7% and energy use on the demanding US06 to within 0.6% for an underpowered series hybrid vehicle (0-100 km/h in 20 s). ADVISOR simulates vehicle performance on standard driving cycles between 2.6 and 8.0 times faster than a representative forward-facing vehicle model. Due in large part to ADVISOR’s powerful graphical user interface and Web presence, over 800 users have downloaded ADVISOR from 45 different countries. Many of these users have contributed their own component data to the ADVISOR library.
Generalized hesitant fuzzy sets and their application in decision support system

Hesitant fuzzy sets are very useful to deal with group decision making problems when experts have a hesitation among several possible memberships for an element to a set. During the evaluating process in practice, however, these possible memberships may be not only crisp values in [0,1], but also interval values. In this study, we extend hesitant fuzzy sets by intuitionistic fuzzy sets and refer to them as generalized hesitant fuzzy sets. Zadeh’s fuzzy sets, intuitionistic fuzzy sets and hesitant fuzzy sets are special cases of the new fuzzy sets. We redefine some basic operations of generalized hesitant fuzzy sets, which are consistent with those of hesitant fuzzy sets. Some arithmetic operations and relationships among them are discussed as well. We further introduce the comparison law to distinguish two generalized hesitant fuzzy sets according to score function and consistency function. Besides, the proposed extension principle enables decision makers to employ aggregation operators of intuitionistic fuzzy sets to aggregate a set of generalized hesitant fuzzy sets for decision making. The rationality of applying the proposed techniques is clarified by a practical example. At last, the proposed techniques are devoted to a decision support system.
A Survey of Personality Computing

Personality is a psychological construct aimed at explaining the wide variety of human behaviors in terms of a few, stable and measurable individual characteristics. In this respect, any technology involving understanding, prediction and synthesis of human behavior is likely to benefit from Personality Computing approaches, i.e. from technologies capable of dealing with human personality. This paper is a survey of such technologies and it aims at providing not only a solid knowledge base about the state-of-the-art, but also a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition (inference of the true personality of an individual from behavioral evidence), Automatic Personality Perception (inference of personality others attribute to an individual based on her observable behavior) and Automatic Personality Synthesis (generation of artificial personalities via embodied agents). Furthermore, the article highlights the issues still open in the field and identifies potential application areas.
Smart Car Parking System Based on IoT Concept

A major problem in day to day life is parking of vehicles especially the car parking at an appropriate place. And this issue indirectly leads to traffic congestion. This paper presents the basic concept of using server or cloud based smart parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This system will be accessible through a mobile app or through the webpage provided and can be used to monitor or find the empty slots in that area. Index Terms : Car parking, Connectivity(c), Constrained Application Protocol(CoAP), End node(e), Hypertext transfer protocol(HTTP), Internet of Things (IoT), Internet protocol(IP), Message queuing transport telemetry protocol(MQTT), Processing node(p), Smart cities, Transmission control protocol(TCP).
MobiLearn go: mobile microlearning as an active, location-aware game

Mobile technologies hold great potential to make studying both more effective and more enjoyable. In this work we present a mobile, microlearning application. Our system is designed with two goals: be flexible enough to support learning in any subject and encourage frequent short study sessions in a variety of contexts. We discuss the use of our application to assess the feasibility of microlearning for non-language learning and the relationship between the physical location of study sessions and information retention.
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations

This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the MultiGenre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al.. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genrematched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domainindependent representations for sentence meaning.
Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy upto 94.17% and 93.75% for male and female respectively.
Single-Fed Low Profile Broadband Circularly Polarized Stacked Patch Antenna

A simple technique is developed in this communication to increase the axial ratio (AR) bandwidth and achieve good impedance matching of a single-fed low profile circularly polarized (CP) stacked patch antenna. The proposed antenna is composed of a driven patch layer and a parasitic patch layer. The driven patch layer consists of a truncated main patch, a parasitic patch and a probe feeding structure while the stacked patch layer is comprised of five patches. The proposed antenna combines the attractive features such as low profile, wide impedance and AR bandwidths, high gain as well as easiness of design, manufacture and integration. The antenna operating at 6 GHz band is designed and fabricated on an FR4 substrate and the overall volume is 0.8 λ0 ×0.8 λ0 ×0.09 λ0. Measured results show that the antenna achieves an impedance bandwidth of more than 30% for dB, a 3-dB AR bandwidth of about 20.7%, and a gain level of over 7.9 dBi within the 3-dB AR bandwidth.