nils-herrmann/clean_scirepeval huggingface dataset:
Basic effects of pulp refining on fiber properties--a review.

The requirement for high quality pulps which are widely used in paper industries has increased the demand for pulp refining (beating) process. Pulp refining is a promising approach to improve the pulp quality by changing the fiber characteristics. The diversity of research on the effect of refining on fiber properties which is due to the different pulp sources, pulp consistency and refining equipment has interested us to provide a review on the studies over the last decade. In this article, the influence of pulp refining on structural properties i.e., fibrillations, fine formation, fiber length, fiber curl, crystallinity and distribution of surface chemical compositions is reviewed. The effect of pulp refining on electrokinetic properties of fiber e.g., surface and total charges of pulps is discussed. In addition, an overview of different refining theories, refiners as well as some tests for assessing the pulp refining is presented.
Fractional order PID control of a DC-motor with elastic shaft: a case study

In this paper, a fractional order PID controller is investigated for a position servomechanism control system considering actuator saturation and the shaft torsional flexibility. For actually implementation, we introduced a modified approximation method to realize the designed fractional order PID controller. Numerous simulation comparisons presented in this paper indicate that, the fractional order PID controller, if properly designed and implemented, will outperform the conventional integer order PID controller
Design analysis of TuskBot: Universal stair climbing 4-wheel indoor robot

There are two major challenges in universal stair climbing: stairs without riser and with nose, and stairs with various dimensions. In this study, we proposed an indoor robot platform to overcome these challenges. First, to create an angle of attack, the Tusk, a passive, protruded element, was added in front of a 4-wheel robot. For design analysis and optimization of the Tusk, a simplified model of universal stair climbing was applied. To accommodate stairs without risers and with nose, the assistive track mechanism was applied. To climb the stair regardless of its dimension, length-adjustable mechanism was added. The results indicated the robot with these mechanisms successfully overcame each challenge. The performance was better than most conventional stair-climbing robots in terms of the range of compatible stairs. We expect these new approaches to expand the range of indoor robot operation with minimal cost.
Synthetic Aperture Radar

The general theory of side-looking synthetic aperture radar systems is developed. A simple circuit-theory model is developed; the geometry of the system determines the nature of the prefilter and the receiver (or processor) is the postfilter. The complex distributed reflectivity density appears as the input, and receiver noise is first considered as the interference which limits performance. Analysis and optimization are carried out for three performance criteria (resolution, signal-to-noise ratio, and least squares estimation of the target field). The optimum synthetic aperture length is derived in terms of the noise level and average transmitted power. Range-Doppler ambiguity limitations and optical processing are discussed briefly. The synthetic aperture concept for rotating target fields is described. It is observed that, for a physical aperture, a side-looking radar, and a rotating target field, the azimuth resolution is λ/α where α is the change in aspect angle over which the target field is viewed, The effects of phase errors on azimuth resolution are derived in terms of the power density spectrum of the derivative of the phase errors and the performance in the absence of phase errors.
2 Cognitive-Behavioral Theory

C therapy (CBT) approaches are rooted in the fundamental principle that an individual’s cognitions play a significant and primary role in the development and maintenance of emotional and behavioral responses to life situations. In CBT models, cognitive processes, in the form of meanings, judgments, appraisals, and assumptions associated with specific life events, are the primary determinants of one’s feelings and actions in response to life events and thus either facilitate or hinder the process of adaptation. CBT includes a range of approaches that have been shown to be efficacious in treating posttraumatic stress disorder (PTSD). In this chapter, we present an overview of leading cognitive-behavioral approaches used in the treatment of PTSD. The treatment approaches discussed here include cognitive therapy/reframing, exposure therapies (prolonged exposure [PE] and virtual reality exposure [VRE]), stress inoculation training (SIT), eye movement desensitization and reprocessing (EMDR), and Briere’s selftrauma model (1992, 1996, 2002). In our discussion of each of these approaches, we include a description of the key assumptions that frame the particular approach and the main strategies associated with the treatment. In the final section of this chapter, we review the growing body of research that has evaluated the effectiveness of cognitive-behavioral treatments for PTSD.
A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.
Generation of global political priority for early childhood development: the challenges of framing and governance

Despite progress, early childhood development (ECD) remains a neglected issue, particularly in resource-poor countries. We analyse the challenges and opportunities that ECD proponents face in advancing global priority for the issue. We triangulated among several data sources, including 19 semi-structured interviews with individuals involved in global ECD leadership, practice, and advocacy, as well as peer-reviewed research, organisation reports, and grey literature. We undertook a thematic analysis of the collected data, drawing on social science scholarship on collective action and a policy framework that elucidates why some global initiatives are more successful in generating political priority than others. The analysis indicates that the ECD community faces two primary challenges in advancing global political priority. The first pertains to framing: generation of internal consensus on the definition of the problem and solutions, agreement that could facilitate the discovery of a public positioning of the issue that could generate political support. The second concerns governance: building of effective institutions to achieve collective goals. However, there are multiple opportunities to advance political priority for ECD, including an increasingly favourable political environment, advances in ECD metrics, and the existence of compelling arguments for investment in ECD. To advance global priority for ECD, proponents will need to surmount the framing and governance challenges and leverage these opportunities.
Symptomatic neurocutaneous melanosis in a child.

A 3-YEAR-OLD BOY PREsented with focal seizures for 3 months. He had severe headache associated with nonprojectile vomiting. There was no history of altered sensorium or diminished vision. He was developmentally normal. Family history was unremarkable. The patient was irritable. There was a giant congenital nevus in “bathing-trunk” distribution with multiple satellite nevi (Figure 1A). There was atrophy of the left leg. There were no focal motor or sensory deficits. Muscle stretch reflexes were normal. The rest of the neurological and systemic examination was unremarkable. Magnetic resonance imaging of the brain (Figure 1B and C) showed a focal lesion with characteristics of melanocytic tumor in the left temporal lobe and another diffuse lesion in the pons. Magnetic resonance imaging of the spine was normal. The patient underwent left temporal craniectomy with excision of the temporal lobe lesion. Histopathologic examination of the surgical specimen  was consistent with an infiltrative melanocytic tumor. Follow-up radiotherapy was planned.
Unemployment of engineering graduates : the key issues Professor

Engineering employers say publicly at national level that they need more engineering graduates, with surveys by, for example, the Engineering Employers Federation, proving there is demand. This project investigated the apparent contradiction between this high demand for engineering graduates and an unemployment rate of about 13% amongst UK engineering graduates (HESA data, July 2010). Employability has received huge attention but there remains a distinct issue of why some engineers do not get graduate level work within a short time of graduation. This National HE STEM Programme project interviewed a selection of unemployed graduates, identified from the Destinations of Leavers from HE (DLHE) survey six months after graduation, in order to investigate their experiences and gain an understanding of factors impeding their entry into graduate engineering employment. Questions ranged from whether the graduate decided to put off looking for a graduate level job until after graduation (and therefore ‘missed the boat’), through to academic and personal skills attributes, motivation and regional location. The data was analysed in the context both of previous research, and of data from interviews with engineering employers and employed graduates. Emerging from this study is that there is no single reason for unemployment amongst engineering graduates, with key findings centring on the importance of: students’ early engagement with career planning and the final year application process; relevant work experience; the distinction between the MEng and the BEng in employers’ recruitment criteria; and the ability of graduates to articulate their skills and competences effectively.
Characterization of IGBT devices for use in series resonant inverter for induction heating applications

This paper aims to determine the maximum frequency achievable in a 25 kW series inverter for induction heating applications and to compare, in hard switching conditions, four fast transistors IGBTs 600A and 1200V modules encapsulated in 62mm from different suppliers. The comparison has been done at 25 and 125ºC in a set-up. Important differences between modules have been obtained depending on the die temperature.
A monocular pose estimation system based on infrared LEDs

We present an accurate, efficient, and robust pose estimation system based on infrared LEDs. They are mounted on a target object and are observed by a camera that is equipped with an infrared-pass filter. The correspondences between LEDs and image detections are first determined using a combinatorial approach and then tracked using a constant-velocity model. The pose of the target object is estimated with a P3P algorithm and optimized by minimizing the reprojection error. Since the system works in the infrared spectrum, it is robust to cluttered environments and illumination changes. In a variety of experiments, we show that our system outperforms state-of-the-art approaches. Furthermore, we successfully apply our system to stabilize a quadrotor both indoors and outdoors under challenging conditions. We release our implementation as open-source software.
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model

Rapid growth of modern technologies is bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Many data mining methods have been proposed for fraud detection. Nevertheless, there is always a contradiction that most methods are irrelevant to transaction sequence, yet sequence-related methods usually cannot learn information at single-transaction level well. In this paper, a new “within7between7within” sandwich-structured sequence learning architecture has been proposed by stacking an ensemble model, a deep sequential learning model and another top-layer ensemble classifier in proper order. Moreover, attention mechanism has also been introduced in to further improve performance. Models in this structure have been manifested to be very efficient in scenarios like fraud detection, where the information sequence is made up of vectors with complex interconnected features.
Automatic tuning matrix multiplication performance on graphics hardware

In order to utilize the tremendous computing power of graphics hardware and to automatically adapt to the fast and frequent changes in its architecture and performance characteristics, this paper implements an automatic tuning system to generate high-performance matrix-multiplication implementation on graphics hardware. The automatic tuning system uses a parameterized code generator to generate multiple versions of matrix multiplication, whose performances are empirically evaluated by actual execution on the target platform. An ad-hoc search engine is employed to search over the implementation space for the version that yields the best performance. In contrast to similar systems on CPUs, which utilize cache blocking, register tiling, instruction scheduling tuning strategies, this paper identifies and exploits several tuning strategies that are unique for graphics hardware. These tuning strategies include optimizing for multiple-render-targets, SIMD instructions with data packing, overcoming limitations on instruction count and dynamic branch instruction. The generated implementations have comparable performance with expert manually tuned version in spite of the significant overhead incurred due to the use of the high-level BrookGPU language.
Application of machine learning techniques to sentiment analysis

Today, we live in a ‘data age’. Due to rapid increase in the amount of user-generated data on social media platforms like Twitter, several opportunities and new open doors have been prompted for organizations that endeavour hard to keep a track on customer reviews and opinions about their products. Twitter is a huge fast emergent micro-blogging social networking platform for users to express their views about politics, products sports etc. These views are useful for businesses, government and individuals. Hence, tweets can be used as a valuable source for mining public's opinion. Sentiment analysis is a process of automatically identifying whether a user-generated text expresses positive, negative or neutral opinion about an entity (i.e. product, people, topic, event etc). The objective of this paper is to give step-by-step detail about the process of sentiment analysis on twitter data using machine learning. This paper also provides details of proposed approach for sentiment analysis. This work proposes a Text analysis framework for twitter data using Apache spark and hence is more flexible, fast and scalable. Naïve Bayes and Decision trees machine learning algorithms are used for sentiment analysis in the proposed framework.
3D geological modeling using sketches and annotations from geologic maps

Constructing 3D geological models is a fundamental task in oil/gas exploration and production. A critical stage in the existing 3D geological modeling workflow is moving from a geological interpretation (usually 2D) to a 3D geological model. The construction of 3D geological models can be a cumbersome task mainly because of the models' complexity, and inconsistencies between the interpretation and modeling tasks. To narrow the gap between interpretation and modeling tasks, we propose a sketched based approach. Our main goal is to mimic how domain experts interpret geological structures and allow the creation of models directly from the interpretation task, therefore avoiding the drawbacks of a separate modeling stage. Our sketch-based modeler is based on standard annotations of 2D geological maps and on geologists' interpretation sketches. Specific geological rules and constraints are applied and evaluated during the sketch-based modeling process to guarantee the construction of a valid 3D geologic model.
Conjunctival fibrosis following filtering glaucoma surgery.

Despite advances in surgical technique and postoperative care, fibrosis remains the major impediment to a marked reduction of intraocular pressure without the need of additional medication (complete success) following filtering glaucoma surgery. Several aspects specific to filtering surgery may contribute to enhanced fibrosis. Changes in conjunctival tissue structure and composition due to preceding treatments as well as alterations in interstitial fluid flow and content due to aqueous humor efflux may act as important drivers of fibrosis. In light of these pathophysiological considerations, current and possible future strategies to control fibrosis following filtering glaucoma surgery are discussed.
Multi-Label Informed Feature Selection

Multi-label learning has been extensively studied in the area of bioinformatics, information retrieval, multimedia annotation, etc. In multi-label learning, each instance is associated with multiple interdependent class labels, the label information can be noisy and incomplete. In addition, multi-labeled data often has noisy, irrelevant and redundant features of high dimensionality. As an effective data preprocessing step, feature selection has shown its effectiveness to prepare high-dimensional data for numerous data mining and machine learning tasks. Most of existing multi-label feature selection algorithms either boil down to solving multiple singlelabeled feature selection problems or directly make use of imperfect labels. Therefore, they may not be able to find discriminative features that are shared by multiple labels. In this paper, we propose a novel multi-label informed feature selection framework MIFS, which exploits label correlations to select discriminative features across multiple labels. Specifically, to reduce the negative effects of imperfect label information in finding label correlations, we decompose the multi-label information into a low-dimensional space and then employ the reduced space to steer the feature selection process. Empirical studies on real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.
Towards Collaborative Adaptive Autonomous Agents

Adaptive autonomy enables agents operating in an environment to change, or adapt, their autonomy levels by relying on tasks executed by others. Moreover, tasks could be delegated between agents, and as a result decision-making concerning them could also be delegated. In this work, adaptive autonomy is modeled through the willingness of agents to cooperate in order to complete abstract tasks, the latter with varying levels of dependencies between them. Furthermore, it is sustained that adaptive autonomy should be considered at an agent’s architectural level. Thus the aim of this paper is two-fold. Firstly, the initial concept of an agent architecture is proposed and discussed from an agent interaction perspective. Secondly, the relations between static values of willingness to help, dependencies between tasks and overall usefulness of the agents’ population are analysed. The results show that a unselfish population will complete more tasks than a selfish one for low dependency degrees. However, as the latter increases more tasks are dropped, and consequently the utility of the population degrades. Utility is measured by the number of tasks that the population completes during run-time. Finally, it is shown that agents are able to finish more tasks by dynamically changing their willingness to cooperate.
Improved statistical analysis at low failure rates in Cu electromigration using an innovative multilink test structure

An innovative electromigration test structure is described in this paper. This new structure consisting of serial connected links is designed to address very early percentiles of lognormal electromigration failure time distribution and highlight extrinsic failures. The simplicity of implementation, data treatment and the correlation with elemental dual damascene test lines make this structure a pretty good candidate for the future of interconnects reliability.
Compressive sampling of ECG bio-signals: Quantization noise and sparsity considerations

Compressed sensing (CS) is an emerging signal processing paradigm that enables the sub-Nyquist processing of sparse signals; i.e., signals with significant redundancy. Electrocardiogram (ECG) signals show significant time-domain sparsity that can be exploited using CS techniques to reduce energy consumption in an adaptive data acquisition scheme. A measurement matrix of random values is central to CS computation. Signal-to-quantization noise ratio (SQNR) results with ECG signals show that 5- and 6-bit Gaussian random coefficients are sufficient for compression factors up to 6X and from 8X-16X, respectively, whereas 6-bit uniform random coefficients are needed for 2X-16X compression ratios.
Methods for Counseling Psychology

This article familiarizes counseling psychologists with qualitative research methods in psychology developed in the tradition of European phenomenology. A brief history includes some of Edmund Husserl’s basic methods and concepts, the adoption of existential-phenomenology among psychologists, and the development and formalization of qualitative research procedures in North America. The choice points and alternatives in phenomenological research in psychology are delineated. The approach is illustrated by a study of a recovery program for persons repeatedly hospitalized for chronic mental illness. Phenomenological research is compared with other qualitative methods, and some of its benefits for counseling psychology are identified.
TCP-Like Congestion Control for Layered Multicast Data Transfer

We present a novel congestion control algorithm suitable for use with cumulative, layered data streams in the MBone. Our algorithm behaves similarly to TCP congestion control algorithms, and shares bandwidth fairly with other instances of the protocol and with TCP flows. It is entirely receiver driven and requires no per-receiver status at the sender, in order to scale to large numbers of receivers. It relies on standard functionalities of multicast routers, and is suitable for continuous stream and reliable bulk data transfer. In the paper we illustrate the algorithm, characterize its response to losses both analytically and by simulations, and analyse its behaviour using simulations and experiments in real networks. We also show how error recovery can be dealt with independently from congestion control by using FEC techniques, so as to provide reliable bulk data transfer.
Measuring Attitudes Towards Telepresence Robots

Studies using Nomura et al.’s “Negative Attitude toward Robots Scale” (NARS)  as an attitudinal measure have featured robots that were perceived to be autonomous, independent agents. State of the art telepresence robots require an explicit human-in-the-loop to drive the robot around. In this paper, we investigate if NARS can be used with telepresence robots. To this end, we conducted three studies in which people watched videos of telepresence robots (n=70), operated telepresence robots (n=38), and interacted with telepresence robots (n=12). Overall, the results from our three studies indicated that NARS may be applied to telepresence robots, and culture, gender, and prior robot experience can be influential factors on the NARS score.