nils-herrmann/clean_scirepeval huggingface dataset:
An Introduction to Active Shape Models ∗

Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect the presence of particular structures in such images can be a daunting task. The inherent variability will thwart naive schemes. However, by using models which can cope with the variability it is possible to successfully analyse complex images. Here we will consider a number of methods where the model represents the expected shape and local grey-level structure of a target object in an image. Model-based methods make use of a prior model of what is expected in the image, and typically attempt to find the best match of the model to the data in a new image. Having matched the model, one can then make measurements or test whether the target is actually present. This approach is a ‘top-down’ strategy, and differs significantly from ‘bottomup’ methods. In the latter the image data is examined at a low level, looking for local structures such as edges or regions, which are assembled into groups in an attempt to identify objects of interest. Without a global model of what to expect, this approach is difficult and prone to failure. A wide variety of model based approaches have been explored (see the review below). This chapter will concentrate on a statistical approach, in which a model is built from analysing the appearance of a set of labelled examples. Where structures vary in shape or texture, it is possible to learn what are plausible variations and what are not. A new image can be interpreted by finding the best plausible match of the model to the image data. The advantages of such a method are that
Few-shot learning of neural networks from scratch by pseudo example optimization

In this paper, we propose a simple but effective method for training neural networks with a limited amount of training data. Our approach inherits the idea of knowledge distillation that transfers knowledge from a deep or wide reference model to a shallow or narrow target model. The proposed method employs this idea to mimic predictions of reference estimators that are more robust against overfitting than the network we want to train. Different from almost all the previous work for knowledge distillation that requires a large amount of labeled training data, the proposed method requires only a small amount of training data. Instead, we introduce pseudo training examples that are optimized as a part of model parameters. Experimental results for several benchmark datasets demonstrate that the proposed method outperformed all the other baselines, such as naive training of the target model and standard knowledge distillation.
Spatial Diversity 24-GHz FMCW Radar With Ground Effect Compensation for Automotive Applications

In automotive radar applications, a realistic road surface can cause multipath reflection, degrade the received power, and reduce detection probability. This paper proposes a one-transmitter–two-receiver (1T2R) 24-GHz frequency-modulation continuous-wave (FMCW) radar architecture with spatial diversity and ground effect compensation for automotive applications. A ray-tracing technique was developed to evaluate the multipath effect of the road surface. In the proposed design, suitable spacing between the two receiving antennas compensated for the multipath effect of the road surface and improved the signal-to-ground clutter ratio of the receivers by a minimum of 20 dB at certain distances. The measurement and calculation results of the proposed radar are in close agreement.
Robust low cost open-ended coaxial probe for dielectric spectroscopy in laboratory and in-situ applications

High-frequency electromagnetic (HF-EM) measurement techniques, e.g. dielectric spectroscopy, give inside into soil physical properties which are related to soil/water interactions such as the soil water characteristics. The open-ended coaxial line technique offers a non destructive determination of dielectric spectra of fine grained soil. In this study, a commercially available Agilent high temperature open ended coaxial probe (Agilent 85070E Dielectric Probe Kit) was compared to a robust 3.8 mm openended coaxial probe (Sequid probe). The experimental results demonstrate that the Sequid probe is suitable for accurate determination of high resolution dielectric spectra in the Frequency range from 50 MHz to 10 GHz and thus a robust low cost alternative to the Agilent high temperature probe in laboratory and in-situ applications.
Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area
A high PSRR, ultra-low power 1.2V curvature corrected Bandgap reference for wearable EEG application

A high PSRR, ultra-low power 1.2V voltage supply (VDD) curvature corrected Bandgap reference for Wearable EEG application is described in this paper. The proposed bandgap reference can operate with supply as low as 1V, and provides a supply regulation of 0.113%/V with VDD range of 1.01-2.62V. Piecewise curvature compensation is employed to reduce the temperature coefficient (TC) of bandgap reference from 22.84ppm /°C to 2.295ppm/°C, with a temperature range -10~110°C. The bandgap reference circuit was designed in standard 0.18um CMOS technology where a proportional to absolute temperature (PTAT) and a complementary to absolute temperature (CTAT) current generation circuit were used to generate first order bandgap reference. A non-linear current was generated using PTAT current and CTAT voltage generation circuit and a power supply rejection ratio (PSRR) of 84.62dB (at DC) was achieved to reduce the interference from power supply noise, in order to meet the specifications for wearable wireless EEG sensing systems. The total current consumption of the whole bandgap reference including biasing and startup circuit is only 4.691uA which fits the requirement of battery powered wearable wireless sensing applications.
Development of a Whole-Sensitive Teleoperated Robot Arm using Torque Sensing Technique

In this paper, we concentrate on the design of a new whole-sensitive robot arm enabling torque measurement in each joint by means of developed optical torque sensors. When the contact of arm with an object occurs, local impedance algorithm provides active compliance of corresponding robot arm joint. Thus, the whole structure of the manipulator can safely interact with unstructured environment. The detailed design procedure of the 4-DOF robot arm and optical torque sensors is described in the paper. The gravity compensation algorithm was elaborated and verified by implementation of the local impedance control of a simple two-link arm manipulator
Document concept lattice for text understanding and summarization

We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.
A DECISION TREE APPROACH FOR ACHIEVING HIGH CUSTOMER SATISFACTION AT URBAN INTERCHANGES

1 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074133,  marttsami@gmail.com 2 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074158, Fax: +302421074131, E-mail: giadamos@civ.uth.gr 3 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074164,  enath@uth.gr 4 Transport and Telecommunication Institute Lomonosova Street 1, LV-1019 Riga, Latvia Ph.: +37167100544, Fax: +37167100660, E-mail: evelina.budilovich@gmail.com 5 Transport and Telecommunication Institute Lomonosova Street 1, LV-1019 Riga, Latvia Ph.: +37167100544, Fax: +37167100660, E-mail: Jackiva.I@tsi.lv 6 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074133,
Quality assessment of Median filtering techniques for impulse noise removal from digital images

Impulse noise still poses challenges in front of researchers today. The removal of impulse noise brings blurring which leads to edges being distorted and image thus being of poor quality. Hence the need is to preserve edges and fine details during filtering. The proposed method consists of noise detection and then removal of detected noise by Improved Adaptive Median Filter using pixels that are not noise themselves in gray level as well as colour images. The pixels are split in two groups, which are noise-free pixels and noisy pixels. In removing out Impulse noise, only noisy pixels are processed. The noiseless pixels are then sent directly to the output image. The proposed method adaptively changes the masking matrix size of the median filter based on the count of the noisy pixels. Computer simulation and analysis have been carried out eventually to analyse the performance of the proposed method with that of Simple Median Filter (SMF), Simple Adaptive Median Filter (SAMF) and Adaptive Switched Median Filter (ASMF). The proposed filter proves to be more efficient in terms of both objective and subjective parameters.
Communication issues in requirements elicitation: a content analysis of stakeholder experiences

The gathering of stakeholder requirements comprises an early, but continuous and highly critical stage in system development. This phase in development is subject to a large degree of error, influenced by key factors rooted in communication problems. This pilot study builds upon an existing theory-based categorisation of these problems through presentation of a four-dimensional framework on communication. Its structure is validated through a content analysis of interview data, from which themes emerge, that can be assigned to the dimensional categories, highlighting any problematic areas. The paper concludes with a discussion on the utilisation of the framework for requirements elicitation exercises.
Improvement of the on-body performance of a dual-band textile antenna using an EBG structure

A new dual-band wearable textile antenna designed for body-centric communications is introduced. The antenna is fully characterized in free space and on the body model, with and without an electromagnetic band gap (EBG) substrate. The band gap array consists of 3 × 3 elements and is used to reduce the interaction with human tissues. With the EBG back reflector, the radiation into the body is reduced by more than 15 dB. Increases of 5.2 dB and 3 dB gain are noticed at 2.45 GHz and 5.5 GHz, respectively. The results are presented for the return loss, radiation pattern, efficiency, and specific absorption rate (SAR).
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
From Visual Data Exploration to Visual Data Mining: A Survey

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.
Multi objective outbound logistics network design for a manufacturing supply chain

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm—II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network. N. C. Hiremath · S. Sahu · M. K. Tiwari (B) Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, 721302 West Bengal, India e-mail: mkt09@hotmail.com N. C. Hiremath e-mail: nchiremath@gmail.com S. Sahu e-mail: sahus@mech.iitkgp.ernet.in
Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking

A new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care. Patient-driven health care can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care though new services. This paper examines three categories of novel health services: health social networks, consumer personalized medicine and quantified self-tracking.
International Telecommunication Union. Systems Management: Summarization function. Recommendation X.738

Prepaid mobile phone plans are gaining in importance in the U.S. and are already well established in Europe. Aggregation of a certain number of Micropayments is the preferred business model for wireless carriers to leverage the existing payment infrastructure with a mobile payment solution. There exist various Payment Scenarios that are based on the Wallet metaphor, and in general, they can be used to implement flexible and scalable solutions. Person-to-person transactions are already very popular in the U.S. as a consequence of the widespread use a payment instrument for auctions at eBay. This scenario is most often discussed as a valuable alternative to check payments in the U.S. For several reasons, the U.S. has been slower to adopt wireless technology than Europe. First, landlines in the U.S. are dominant and high personal computer penetration prevents people from using mobile phones to communicate, either by voice calls or text messaging. Second, prices in the U.S. favor landlines, even though prices for mobile phone plans in the competitive market have been cut many times in the past. For mobile payment, the fact that social patterns matter could affect the adoption of this form of payment in Europe, because in several countries, solutions already exist. The factors that form the concept of Diffusion of Innovation Theory eventually can be seen as the critical success factors of mobile payment. ii Whereas some emerging and existing mobile payment systems will come along with their own processing infrastructure, several will be based on existing payment networks that have initially been designed for another purpose. In Europe, credit card operators launch mobile payment solutions to leverage their existing networks while in the U.S., operators of ATM networks and leading credit card processors even try to bypass existing networks. Unlike in Europe, where checks have de facto been banned from the market by charging high prices for the processing of this non-cash payment instrument, the U.S. partly supports innovative check processing solutions to leverage the ACH infrastructure. The European bank approach shows that customer's payment behavior can be influenced by a pricing policy. Meanwhile, European countries and the U.S. have switched to 2.5G networks. With the transition to 3G, interoperability within the U.S. should finally become reality. Commercial launches of 3G services have already started in both regions. Notably, the first U.S. carrier to launch a broadband wireless service seems to be attracting business customers and offering high …
Motion Capture and Retargeting of Fish by Monocular Camera

Accurate motion capture and flexible retargeting of underwater creatures such as fish remain to be difficult due to the long-lasting challenges of marker attachment and feature description for soft bodies in the underwater environment. Despite limited new research progresses appeared in recent years, the fish motion retargeting with a desirable motion pattern in real-time remains elusive. Strongly motivated by our ambitious goal of achieving high-quality data-driven fish animation with a light-weight, mobile device, this paper develops a novel framework of motion capturing and retargeting for a fish. We capture the motion of actual fish by a monocular camera without the utility of any marker. The elliptical Fourier coefficients are then integrated into the contour-based feature extraction process to analyze the fish swimming patterns. This novel approach can obtain the motion information in a robust way, with smooth medial axis as the descriptor for a soft fish body. For motion retargeting, we propose a two-level scheme to properly transfer the captured motion into new models, such as 2D meshes (with texture) generated from pictures or 3D models designed by artists, regardless of different body geometry and fin proportions among various species. Both motion capture and retargeting processes are functioning in real time. Hence, the system can simultaneously create fish animation with variation, while obtaining video sequences of real fish by a monocular camera.
DC and RF breakdown voltage characteristics of SiGe HBTs for WiFi PA applications

Breakdown voltage and RF characteristics relevant for RF power amplifiers (PA) are presented in this paper. Typically, DC collector-to-emitter breakdown voltage with base open (BVCEO) or DC collector-to-base breakdown with emitter open (BVCBO) has been presented as the metric for voltage limit of PA devices. In practical PA circuits, the RF envelope voltage can swing well beyond BVCEO without causing a failure. An analysis of output power swing limitations and DC breakdown is presented with attention to biasing and temperature.
Understanding the roles of self-esteem, self-compassion, and fear of self-compassion in eating disorder pathology: an examination of female students and eating disorder patients.

The present study examined the relative contributions of self-compassion, fear of self-compassion, and self-esteem in eating disorder pathology. One-hundred and fifty-five female undergraduate students and 97 females entering eating disorder treatment completed the Self-Compassion Scale, Fears of Compassion Scale, Rosenberg Self-Esteem Inventory, and Eating Disorder Examination Questionnaire. T-tests revealed that the patient group had lower mean self-compassion and higher mean fear of self-compassion than the student group. When controlling for self-esteem, high fear of self-compassion emerged as the strongest predictor of eating disorder pathology in the patient group, whereas low self-compassion was the strongest predictor in the student group. These preliminary results suggest that targeting fear of self-compassion may be important when intervening with individuals suffering from an eating disorder, whereas building self-compassion may be a valuable approach for eating disorder prevention.
Automatic common-centroid layout generation for binary-weighted capacitors in charge-scaling DAC

As the precision of the capacitance ratios among binary-weighted capacitors is the key to accuracy/performance of charge-scaling digital-to-analog converters, it is very important to generate a highly matched common-centroid layout with minimum routing-induced parasitics. However, most of the previous works only focused on common-centroid placement optimization with the consideration of random and systematic mismatch. This paper introduces a novel common-centroid capacitor layout generation approach to minimize the parasitic impact on circuit accuracy/performance. Experimental results show that, compared with the manual layout, the layout generated by the presented approach can achieve even smaller layout area and better circuit accuracy/performance within much shorter time.