nils-herrmann/clean_scirepeval huggingface dataset:
Magnetic omnidirectional wheels for climbing robots

This paper describes design and development of omnidirectional magnetic climbing robots with high maneuverability for inspection of ferromagnetic 3D human made structures. The main focus of this article is design, analysis and implementation of magnetic omnidirectional wheels for climbing robots. We discuss the effect of the associated problems of such wheels, e.g. vibration, on climbing robots. This paper also describes the evolution of magnetic omnidirectional wheels throughout the design and development of several solutions, resulting in lighter and smaller wheels which have less vibration and adapt better to smaller radius structures. These wheels are installed on a chassis which adapts passively to flat and curved structures, enabling the robot to climb and navigate on such structures.
Personality Trait Change in Adulthood.

Recent longitudinal and cross-sectional aging research has shown that personality traits continue to change in adulthood. In this article, we review the evidence for mean-level change in personality traits, as well as for individual differences in change across the life span. In terms of mean-level change, people show increased selfconfidence, warmth, self-control, and emotional stability with age. These changes predominate in young adulthood (age 20-40). Moreover, mean-level change in personality traits occurs in middle and old age, showing that personality traits can change at any age. In terms of individual differences in personality change, people demonstrate unique patterns of development at all stages of the life course, and these patterns appear to be the result of specific life experiences that pertain to a person's stage of life.
A Methodology for Controlling the Size of a Test Suite

This paper presents a technique to select a representative set of test cases from a test suite that provides the same coverage as the entire test suite. This selection is performed by identifying, and then eliminating, the redundant and obsolete test cases in the test suite. The representative set replaces the original test suite and thus, potentially produces a smaller test suite. The representative set can also be used to identify those test cases that should be rerun to test the program after it has been changed. Our technique is independent of the testing methodology and only requires an association between a testing requirement and the test cases that satisfy the requirement. We illustrate the technique using the data flow testing methodology. The reduction that is possible with our technique is illustrated by experimental results.
Schema Profiling of Document Stores

In document stores, schema is a soft concept and the documents in a collection can have different schemata; this gives designers and implementers augmented flexibility but requires an extra effort to understand the rules that drove the use of alternative schemata when heterogeneous documents are to be analyzed or integrated. In this paper we outline a technique, called schema profiling, to explain the schema variants within a collection in document stores by capturing the hidden rules explaining the use of these variants; we express these rules in the form of a decision tree, called schema profile, whose main feature is the coexistence of value-based and schema-based conditions. Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles; to quantitatively assess these qualities we introduce a novel measure of entropy.  NoSQL, Schema Discovery, Decision Trees 1 Motivation and Outline Recent years have witnessed an erosion of the relational DBMS predominance to the benefit of DBMSs based on alternative representation models (e.g., documentoriented and graph-based) which adopt a schemaless representation for data. Schemaless databases are preferred to relational ones for storing heterogeneous data with variable schemata and structural forms; typical schema variants within a collection consist in missing or additional attributes, in different names or types for an attribute, and in different structures for instances. The absence of a unique schema grants flexibility to operational applications but adds complexity to analytical applications, in which a single analysis often involves large sets of data with different schemata. Dealing with this complexity requires a notable effort to understand the rules that drove the use of alternative schemata, plus an integration activity to identify a common schema to be adopted for analysis —which is quite hard when no documentation is available. In this paper we outline a technique to explain the schema variants within a collection in document stores by capturing the hidden rules explaining the use of these variants. We call this activity schema profiling. Schema profiling can be used for instance when trying to decode the behavior of an undocumented application that manages a document-base, or to support analytical applications ? This work was partly supported by the EU-funded project TOREADOR (contract n. H2020-688797). {	  	  "Ac&vityType"	  :	  "Walk", "User"	  : {	  	  "UserID"	  :	  23, "Age"	  :	  42 } } Ac&vityType
Current control techniques for three-phase voltage-source PWM converters: a survey

The aim of this paper is to present a review of recently used current control techniques for three-phase voltagesource pulsewidth modulated converters. Various techniques, different in concept, have been described in two main groups: linear and nonlinear. The first includes proportional integral stationary and synchronous) and state feedback controllers, and predictive techniques with constant switching frequency. The second comprises bang-bang (hysteresis, delta modulation) controllers and predictive controllers with on-line optimization. New trends in the current control—neural networks and fuzzy-logicbased controllers—are discussed, as well. Selected oscillograms accompany the presentation in order to illustrate properties of the described controller groups.
Anxiety in high-functioning children with autism.

High-functioning children with autism were compared with two control groups on measures of anxiety and social worries. Comparison control groups consisted of children with specific language impairment (SLI) and normally developing children. Each group consisted of 15 children between the ages of 8 and 12 years and were matched for age and gender. Children with autism were found to be most anxious on both measures. High anxiety subscale scores for the autism group were separation anxiety and obsessive-compulsive disorder. These findings are discussed within the context of theories of autism and anxiety in the general population of children. Suggestions for future research are made.
A 233-MHz 80%-87% efficient four-phase DC-DC converter utilizing air-core inductors on package

We demonstrate an integrated buck dc-dc converter for multi-V/sub CC/ microprocessors. At nominal conditions, the converter produces a 0.9-V output from a 1.2-V input. The circuit was implemented in a 90-nm CMOS technology. By operating at high switching frequency of 100 to 317 MHz with four-phase topology and fast hysteretic control, we reduced inductor and capacitor sizes by three orders of magnitude compared to previously published dc-dc converters. This eliminated the need for the inductor magnetic core and enabled integration of the output decoupling capacitor on-chip. The converter achieves 80%-87% efficiency and 10% peak-to-peak output noise for a 0.3-A output current and 2.5-nF decoupling capacitance. A forward body bias of 500 mV applied to PMOS transistors in the bridge improves efficiency by 0.5%-1%.
A study of user behavior on an online dating site

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a real-world dateset from a major online dating site in China. Our study provides a firsthand account of the user online dating behaviors in China, a country with a large population and unique culture. The results can provide valuable guidelines to the design of recommendation engine for potential dates.
Collaborative Filtering Bandits

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.
White Blood Cells Segmentation and Classification to Detect Acute Leukemia Ms

In order to improve patient diagnosis various image processing software are developed to extract useful information from medical images. Hematologist makes the microscopic study of human blood which led to a need of methods, including microscope colour imaging, segmentation, classification, and clustering that can allow the identification of patients suffering from leukemia. Leukemia is related with blast white blood cell (WBC). The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis so hematologist also find difficulty for blast cell classification hence manual classification of blood cells is time-consuming and susceptible to error. Therefore fast, accurate and automatic identification of different blood cells is required. This paper has proposed automatic Otsu's threshold blood cell segmentation method along with image enhancement and arithmetic for WBC segmentation. kNN classifier has been utilized to classify blast cells from normal lymphocyte cells. The system is applied for 108 images available in public image dataset for the study of leukemia. This method gives 93% accuracy.
Community interventions and effective prevention.

The prevalence of pregnancy, substance abuse, violence, and delinquency among young people is unacceptably high. Interventions for preventing problems in large numbers of youth require more than individual psychological interventions. Successful interventions include the involvement of prevention practitioners and community residents in community-level interventions. The potential of community-level interventions is illustrated by a number of successful studies. However, more inclusive reviews and multisite comparisons show that although there have been successes, many interventions did not demonstrate results. The road to greater success includes prevention science and newer community-centered models of accountability and technical assistance systems for prevention.
Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.
Mobile-phone addiction in adolescence : The Test of Mobile Phone Dependence ( TMD )

__________________________________________________________________________________________  The mobile phone is among the technological tools with the greatest presence in the market. During a period of scarcely 10 years, it has gone from being almost non-existent to being the device most used (and desired) by adolescents. Its physical characteristics as well as the psychological processes involved in its use explain both the fascination it elicits and the abuse or dependence it can come to provoke or encourage in adolescents. This study entailed the development and evaluation of a questionnaire designed to evaluate dependence on the mobile phone.  The items included in this instrument were developed based on criteria contained in the Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition-Text Revision (DSM-IV-TR; American Psychiatric Association, 2000) for dependence disorder. The questionnaires were administered to a total of 2,486 adolescents aged 12–18 years of age, and factor analyses were then performed.  The questionnaire is characterised by good psychometric properties as well as by the ability to discriminate between sexes and among age groups in an adolescent sample. The factors comprising this instrument are congruent with the concept of dependence as defined in the DSM-IV-TR. The process by which this questionnaire was developed is described, and the final version of the questionnaire is presented.  The Test of Mobile Phone Dependence (TMP) is a questionnaire built taking into account the dependence criteria of DSM-IVTR. The process by which this questionnaire was developed is described, and the final version of the questionnaire is presented.
BackFi: High Throughput WiFi Backscatter

We present BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter devices and WiFi APs using ambient WiFi transmissions as the excitation signal. Specifically, we show that it is possible to design devices and WiFi APs such that the WiFi AP in the process of transmitting data to normal WiFi clients can decode backscatter signals which the devices generate by modulating information on to the ambient WiFi transmission. We show via prototypes and experiments that it is possible to achieve communication rates of up to 5 Mbps at a range of 1 m and 1 Mbps at a range of 5 meters. Such performance is an order to three orders of magnitude better than the best known prior WiFi backscatter system [27,25]. BackFi design is energy efficient, as it relies on backscattering alone and needs insignificant power, hence the energy consumed per bit is small.
A new compact temperature-compensated CMOS current reference

This paper describes a new circuit integrated on silicon, which generates temperature-independent bias currents. Such a circuit is firstly employed to obtain a current reference with first-order temperature compensation, then it is modified to obtain second-order temperature compensation. The operation principle of the new circuits is described and the relationships between design and technology process parameters are derived. These circuits have been designed by a 0.35 /spl mu/m BiCMOS technology process and the thermal drift of the reference current has been evaluated by computer simulations. They show good thermal performance and in particular, the new second-order temperature-compensated current reference has a mean temperature drift of only 28 ppm//spl deg/C in the temperature range between -30/spl deg/C and 100/spl deg/C.
Superpixel-based video object segmentation using perceptual organization and location prior

In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly generate a coarse foreground segmentation, which provides predictions about motion regions by analyzing how superpixel segmentation changes in consecutive frames. We then exploit these location priors to refine the initial segmentation by optimizing an energy function based on appearance and perceptual organization, only on regions where motion is observed. We evaluated our method on complex and challenging video sequences and it showed significant performance improvements over recent state-of-the-art methods, being also fast enough to be used for “on-the-fly” processing.
Hardware obfuscation using PUF-based logic

There is a great need to develop universal and robust techniques for intellectual property protection of integrated circuits. In this paper, we introduce techniques for the obfuscation of an arbitrary circuit by using physical unclonable functions (PUFs) and programmable logic. Specifically, we introduce the notion of PUF-based logic which can be configured to be functionally equivalent to any arbitrary design, as well as a new architecture for wire merging that obfuscates signal paths exponentially. We systematically apply our techniques in such a way so as to maximize obfuscation while minimizing area and delay overhead. We analyze our techniques on popular benchmark circuits and show them to be resilient against very powerful reverse engineering attacks in which the adversary has knowledge of the complete netlist along with the ability to read and write to any flip-flop in the circuit.
Evaluating and Optimizing Autonomous Text Classification Systems

Text retrieval systems typically produce a ranking of documents and let a user decide how far down that ranking to go. In contrast, programs that filter text streams, software that categorizes documents, agents which alert users, and many other IR systems must make decisions without human input or supervision. It is important to define what constitutes good effectiveness for these autonomous systems, tune the systems to achieve the highest possible effectiveness, and estimate how the effectiveness changes as new data is processed. We show how to do this for binary text classification systems, emphasizing that different goals for the system lead to different optimal behaviors. Optimizing and estimating effectiveness is greatly aided if classifiers that explicitly estimate the probability of class membership are used.
Context dependent phone models for LSTM RNN acoustic modelling

Long Short Term Memory Recurrent Neural Networks (LSTM RNNs), combined with hidden Markov models (HMMs), have recently been show to outperform other acoustic models such as Gaussian mixture models (GMMs) and deep neural networks (DNNs) for large scale speech recognition. We argue that using multi-state HMMs with LSTM RNN acoustic models is an unnecessary vestige of GMM-HMM and DNN-HMM modelling since LSTM RNNs are able to predict output distributions through continuous, instead of piece-wise stationary, modelling of the acoustic trajectory. We demonstrate equivalent results for context independent whole-phone or 3-state models and show that minimum-duration modelling can lead to improved results. We go on to show that context dependent whole-phone models can perform as well as context dependent states, given a minimum duration model.
Gender and motivation

The role of gender in shaping achievement motivation has a long history in psychological and educational research. In this review, gender differences in motivation are examined using four contemporary theories of achievement motivation, including attribution, expectancy-value, selfefficacy, and achievement goal perspectives. Across all theories, findings indicate girls’ and boys’ motivation-related beliefs and behaviors continue to follow gender role stereotypes. Boys report stronger ability and interest beliefs in mathematics and science, whereas girls have more confidence and interest in language arts and writing. Gender effects are moderated by ability, ethnicity, socioeconomic status, and classroom context. Additionally, developmental research indicates that gender differences in motivation are evident early in school, and increase for reading and language arts over the course of school. The role of the home and school environment in the development of these gender patterns is examined. Important implications for school professionals are highlighted. D 2006 Society for the Study of School Psychology.
An experimental comparison of gender classification methods

Successful face analysis requires robust methods. It has been hard to compare the methods due to different experimental setups. We carried out a comparison study for the state-of-the-art gender classification methods to find out their actual reliability. The main contributions are comprehensive and comparable classification results for the gender classification methods combined with automatic real-time face detection and, in addition, with manual face normalization. We also experimented by combining gender classifier outputs arithmetically. This lead to increased classification accuracies. Furthermore, we contribute guidelines to carry out classification experiments, knowledge on the strengths and weaknesses of the gender classification methods, and two new variants of the known methods.
Duchenne smile, emotional experience, and autonomic reactivity: a test of the facial feedback hypothesis.

This study examined the modulatory function of Duchenne and non-Duchenne smiles on subjective and autonomic components of emotion. Participants were asked to hold a pencil in their mouth to either facilitate or inhibit smiles and were not instructed to contract specific muscles. Five conditions--namely lips pressing, low-level non-Duchenne smiling, high-level non-Duchenne smiling, Duchenne smiling, and control--were produced while participants watched videoclips that were evocative of positive or negative affect. Participants who displayed Duchenne smiles reported more positive experience when pleasant scenes and humorous cartoons were presented. Furthermore, they tended to exhibit different patterns of autonomic arousal when viewing positive scenes. These results support thefacial feedback hypothesis and suggest that facial feedback has more powerful effects when facial configurations represent valid analogs of basic emotional expressions.
A flow based approach for SSH traffic detection

The basic objective of this work is to assess the utility of two supervised learning algorithms AdaBoost and RIPPER for classifying SSH traffic from log files without using features such as payload, IP addresses and source/destination ports. Pre-processing is applied to the traffic data to express as traffic flows. Results of 10-fold cross validation for each learning algorithm indicate that a detection rate of 99% and a false positive rate of 0.7% can be achieved using RIPPER. Moreover, promising preliminary results were obtained when RIPPER was employed to identify which service was running over SSH. Thus, it is possible to detect SSH traffic with high accuracy without using features such as payload, IP addresses and source/destination ports, where this represents a particularly useful characteristic when requiring generic, scalable solutions.