nils-herrmann/clean_scirepeval huggingface dataset:
Substrate Integrated Waveguide Cavity-Backed Self-Triplexing Slot Antenna

In this letter, a novel substrate integrated waveguide (SIW) cavity-backed self-triplexing slot antenna is proposed for triple-band communication. It is realized on a single-layer printed circuit board. The proposed antenna consists of a pair of bow-tie slots etched on an SIW cavity, and three different resonant modes are excited by two microstrip lines and a coaxial probe to operate at three distinct frequencies (7.89, 9.44, and 9.87 GHz). The band around 9.48 GHz is obtained due to radiation from one bow-tie slot fed by adjacent microstrip feedline and band around 9.87 GHz is due to radiation from other bow-tie slot fed by its adjacent microstrip feedline. On the other hand, lower band around 7.89 GHz is achieved because of radiation from both bow-tie slots excited by coaxial probe feedline. The measured isolation between any two feedlines is better than 22.5 dB. The measured realized gain is more than 7.2 dB at all the bands. Cross polarization below 36.5, 29.3, and 24.45 dB in broad sight direction and high front-to-back ratio of more than 17.3 dB at each resonant frequency are obtained.
Benchmarking Image Segmentation Algorithms

We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.
An improved formula for lamination core loss calculations in machines operating with high frequency and high flux density excitation

For electrical machine designers, core loss data are usually provided in the form of tables or curves of total loss versus flux density or frequency. These can be used to extract the loss coefficients of the core loss formulas. In this paper, three currently available formulas are discussed and compared with the loss data supplied by lamination steel manufacturers. It is found that the dynamic hysteresis loop plays an important role in the total loss calculation, especially at high flux densities and high frequencies, and the loss coefficients should change with frequency. A new modified formula is proposed to represent the coefficient changes. The new curve is applied to the measured manufacturer's data, with acceptable accuracy.
Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges

This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build truly, high-performance and highly-flexible big data management systems. We report on state-of-the-art approaches, methodologies and trends, and finally conclude by providing open problems and challenging research directions to be considered by future efforts.
Feedback specificity, exploration, and learning.

Greater feedback specificity is generally considered to be beneficial for performance and learning, but the evidence for this generalization is limited. The authors argue that increasing the specificity of feedback is beneficial for initial performance but discourages exploration and undermines the learning needed for later, more independent performance. The results of their transfer experiment demonstrate that increasing the specificity of feedback positively affected practice performance, but its benefits did not endure over time or modification of the task. In addition, feedback specificity negatively affected levels of exploration during practice and interacted with exploration strategies to affect learning. The results suggest that those who received feedback of varying specificity may have learned through different but equally beneficial mechanisms.
Making papercraft toys from meshes using strip-based approximate unfolding

We propose a new method for producing unfolded papercraft patterns of rounded toy animal figures from triangulated meshes by means of strip-based approximation. Although in principle a triangulated model can be unfolded simply by retaining as much as possible of its connectivity while checking for intersecting triangles in the unfolded plane, creating a pattern with tens of thousands of triangles is unrealistic. Our approach is to approximate the mesh model by a set of continuous triangle strips with no internal vertices. Initially, we subdivide our mesh into parts corresponding to the features of the model. We segment each part into zonal regions, grouping triangles which are similar topological distances from the part boundary. We generate triangle strips by simplifying the mesh while retaining the borders of the zonal regions and additional cut-lines. The pattern is then created simply by unfolding the set of strips. The distinguishing feature of our method is that we approximate a mesh model by a set of continuous strips, not by other ruled surfaces such as parts of cones or cylinders. Thus, the approximated unfolded pattern can be generated using only mesh operations and a simple unfolding algorithm. Furthermore, a set of strips can be crafted just by bending the paper (without breaking edges) and can represent smooth features of the original mesh models. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling -Geometric algorithms, languages, and systems
Contraction Moves for Geometric Model Fitting

This paper presents a new class of moves, called α-expansioncontraction, which generalizes α-expansion graph cuts for multi-label energy minimization problems. The new moves are particularly useful for optimizing the assignments in model fitting frameworks whose energies include Label Cost (LC), as well as Markov Random Field (MRF) terms. These problems benefit from the contraction moves’ greater scope for removing instances from the model, reducing label costs. We demonstrate this effect on the problem of fitting sets of geometric primitives to point cloud data, including real-world point clouds containing millions of points, obtained by multi-view reconstruction.
An Experimental Study of Genetic Crossover Operators for the Job Shop Scheduling Problem

The aim of this paper is to show the influence of genetic crossover operators on the performance of a genetic algorithm (GA). The GA is applied to the job shop scheduling problem (JSSP). To achieve this aim an experimental study of a set of crossover operators is presented. The experimental study is based on a decision support system (DSS). To compare the abilities of different crossover operators, the DSS was designed giving all the operators the same opportunities. The genetic crossover operators are tested on a set of standard instances taken from the literature. The makespan is the measure used to evaluate the genetic crossover operators. The main conclusion is that there is a crossover operator having the best average performance on a specific set of solved instances. The DSS developed can be utilized in a common industrial or construction environment.  Genetic algorithms, crossover operators, scheduling, heuristics, JSSP.
Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey

Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.
Routines , reconfiguration and the contribution of business analytics to organisational performance

Availability of large volumes of data in the firms has given a rise in interest in the potential use of business analytics applications. Research has investigated the potential of business analytics to deliver improved performance and competitive advantage at the firm level. However, a theoretical framework identifying the organisational factors which enables the firms to realise those performance gains and competitive advantage has not been clearly articulated. This paper proposes a theoretical framework identifying the organisational factors involved in realising performance gains and competitive advantage from business analytics. This paper draws on the foundational works of dynamic capabilities, routines and effective use to develop a research model. The data collected to test the research model is described along with the analytical strategies to test the model. Implications for research and practice are discussed. Disciplines Engineering | Science and Technology Studies Publication Details Anand, A., Sharma, R. & Kohli, R. (2013). Routines, reconfiguration and the contribution of business analytics to organisational performance. 24th Australasian Conference on Information Systems (pp. 1-10).  RMIT University. This conference paper is available at Research Online: http://ro.uow.edu.au/eispapers/2545 Information Systems: Transforming the Future 24 Australasian Conference on Information Systems, 4-6 December 2013, Melbourne Proudly sponsored by 24th Australasian Conference on Information Systems Effects of Business Analytics on Performance 4-6 Dec 2013, Melbourne Anand et al. Routines, Reconfiguration and the Contribution of Business Analytics to Organisational Performance Abhijith Anand School of Information Systems and Technology Faculty of Engineering and Information Sciences University of Wollongong New South Wales,  aas188@uowmail.edu.au Rajeev Sharma School of Information Systems and Technology Faculty of Engineering and Information Sciences University of Wollongong New South Wales,  The College of William & Mary Mason School of Business Williamsburg, Virginia, USA Email: rajiv.kohli@mason.wm.edu
A Compact GPS Antenna for Artillery Projectile Applications

This letter presents a compact antenna suitable for the reception of GPS signals on artillery projectiles over 1.57-1.60 GHz. Four inverted-F-type elements are excited by a series feed network in equal magnitude and successive 90° phase difference. The shape and form factor of the antenna is tailored so that the antenna can be easily installed inside an artillery fuze. Measurements show that the proposed antenna has a gain of 2.90-3.77 dBic, an axial ratio of 1.9-2.86 dB, and a reflection coefficient of less than -10 dB over 1.57-1.62 GHz.
Dcm-Ar: A fast Flash-based Web-PACS viewer for displaying large DICOM images

Using Web-PACS has become an attractive option that brings access to medical imaging databases from remote hosts. However, the big size of the medical images to be transmitted impacts negatively on the teleradiologist experience by increasing the dead times elapsed between study request and visualization on the screen. In this context, it is of the upmost interest to implement strategies to optimize image transmission. In this work a system (Dcm-Ar) for remote visualization of DICOM files is presented, which strongly reduces the time-to-display (TTD), thus improving the teleradiologist comfort. Dcm-Ar is made up of a Server interacting with a PACS and a remote Client. The Client is a DICOM viewer, which is based on the Adobe® Flash™ virtual machine. This fact ensures easy and costless dissemination of the technology.
A current-starved inverter-based differential amplifier design for ultra-low power applications

As silicon feature sizes decrease, more complex circui try arrays can now be contrived on a single die. This increase in the number of on-chip devices per unit area results in increased power dissipation per unit area. In order to meet certain power and operating temperature specifications, circuit design necessitates a focus on power efficiency, which is especially important in systems employing hundreds or thousands of instances of the same device. In large arrays, a slight increase in the power efficiency of a single component is heightened by the number of instances of the device in the system. This paper proposes a fully differential, low-power current-starving inverter-based amplifier topology designed in a commercial 0.18μm process. This design achieves 46dB DC gain and a 464 kHz uni ty gain frequency with a power consumption of only 145.32nW at 700mV power supply vol tage for ultra-low power, low bandwidth applications. Higher bandwidth designs are also proposed, including a 48dB DC gain, 2.4 MHz unity-gain frequency amplifier operating at 900mV wi th only 3.74μW power consumption.
THE RESTORATIVE BENEFITS OF NATURE : TOWARD AN INTEGRATIVE

Directed attention plays an important role in human information processing; its fatigue, in turn, has far­ reaching consequences. Attention Restoration Theory provides an analysis of the kinds of experiences that lead to recovery from such fatigue. Natural environments turn out to be particularly rich in the character­ istics necessary for restorative experiences. An integrative framework is proposed that places both directed attention and stress in the larger context of human-environment relationships.
Service Performance and Analysis in Cloud Computing

Cloud computing is a new cost-efficient computing paradigm in which information and computer power can be accessed from aWeb browser by customers. Understanding the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver Quality of Services (QoS) guaranteed services is crucial. In this paper, we present an approach for studying computer service performance in cloud computing. Specifically, in an effort to deliver QoS guaranteed services in such a computing environment, we find the relationship among the maximal number of customers, the minimal service resources and the highest level of services. The obtained results provide the guidelines of computer service performance in cloud computing that would be greatly useful in the design of this new computing paradigm.
Edge Computing and IoT Based Research for Building Safe Smart Cities Resistant to Disasters

Recently, several researches concerning with smart and connected communities have been studied. Soon the 4G / 5G technology becomes popular, and cellular base stations will be located densely in the urban space. They may offer intelligent services for autonomous driving, urban environment improvement, disaster mitigation, elderly/disabled people support and so on. Such infrastructure might function as edge servers for disaster support base. In this paper, we enumerate several research issues to be developed in the ICDCS community in the next decade in order for building safe, smart cities resistant to disasters. In particular, we focus on (A) up-to-date urban crowd mobility prediction and (B) resilient disaster information gathering mechanisms based on the edge computing paradigm. We investigate recent related works and projects, and introduce our on-going research work and insight for disaster mitigation.
A LSTM-based method for stock returns prediction: A case study of China stock market

The presented paper modeled and predicted China stock returns using LSTM. The historical data of China stock market were transformed into 30-days-long sequences with 10 learning features and 3-day earning rate labeling. The model was fitted by training on 900000 sequences and tested using the other 311361 sequences. Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable.
Minimal intervention dentistry: part 4. Detection and diagnosis of initial caries lesions

The detection of carious lesions is focused on the identification of early mineral changes to allow the demineralisation process to be managed by non-invasive interventions. The methods recommended for clinical diagnosis of initial carious lesions are discussed and illustrated. These include the early detection of lesions, evaluation of the extent of the lesion and its state of activity and the establishment of appropriate monitoring. The place of modern tools, including those based on fluorescence, is discussed. These can help inform patients. They are also potentially useful in regular control visits to monitor the progression or regression of early lesions. A rigorous and systematic approach to caries diagnosis is essential to establish a care plan for the disease and to identify preventive measures based on more precise diagnosis and to reduce reliance on restorative measures.
iShadow: design of a wearable, real-time mobile gaze tracker

Continuous, real-time tracking of eye gaze is valuable in a variety of scenarios including hands-free interaction with the physical world, detection of unsafe behaviors, leveraging visual context for advertising, life logging, and others. While eye tracking is commonly used in clinical trials and user studies, it has not bridged the gap to everyday consumer use. The challenge is that a real-time eye tracker is a power-hungry and computation-intensive device which requires continuous sensing of the eye using an imager running at many tens of frames per second, and continuous processing of the image stream using sophisticated gaze estimation algorithms. Our key contribution is the design of an eye tracker that dramatically reduces the sensing and computation needs for eye tracking, thereby achieving orders of magnitude reductions in power consumption and form-factor. The key idea is that eye images are extremely redundant, therefore we can estimate gaze by using a small subset of carefully chosen pixels per frame. We instantiate this idea in a prototype hardware platform equipped with a low-power image sensor that provides random access to pixel values, a low-power ARM Cortex M3 microcontroller, and a bluetooth radio to communicate with a mobile phone. The sparse pixel-based gaze estimation algorithm is a multi-layer neural network learned using a state-of-the-art sparsity-inducing regularization function that minimizes the gaze prediction error while simultaneously minimizing the number of pixels used. Our results show that we can operate at roughly 70mW of power, while continuously estimating eye gaze at the rate of 30 Hz with errors of roughly 3 degrees.
Reliability and validity of the Evaluation Tool of Children's Handwriting-Cursive (ETCH-C) using the general scoring criteria.

To determine the reliability and aspects of validity of the Evaluation Tool of Children's Handwriting-Cursive (ETCH-C; Amundson, 1995), using the general scoring criteria, when assessing children who use alternative writing scripts.



Children in Years 5 and 6 with handwriting problems and a group of matched control participants from their respective classrooms were assessed with the ETCH-C twice, 4 weeks apart.



Total Letter scores were most reliable; more variability should be expected for Total Word scores. Total Numeral scores showed unacceptable reliability levels and are not recommended. We found good discriminant validity for Letter and Word scores and established cutoff scores to distinguish children with and without handwriting dysfunction (Total Letter <90%, Total Word <85%).



The ETCH-C, using the general scoring criteria, is a reliable and valid test of handwriting for children using alternative scripts.
Information Processing in Medical Imaging

In this paper, we present novel algorithms to compute robust statistics from manifold-valued data. Specifically, we present algorithms for estimating the robust Fréchet Mean (FM) and performing a robust exact-principal geodesic analysis (ePGA) for data lying on known Riemannian manifolds. We formulate the minimization problems involved in both these problems using the minimum distance estimator called the L2E. This leads to a nonlinear optimization which is solved efficiently using a Riemannian accelerated gradient descent technique. We present competitive performance results of our algorithms applied to synthetic data with outliers, the corpus callosum shapes extracted from OASIS MRI database, and diffusion MRI scans from movement disorder patients respectively.
A trust-based bio-inspired approach for credit lending decisions

Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository. Due to the varying spectrum of trust levels, we are able to solve the problem of binary credit lending decisions. A trust-based credit scoring approach allows the financial institutions to grant credit-based on the level of trust in potential customers.