nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle routing with multiple deliverymen: Modeling and heuristic approaches for the VRPTW

0377-2217/$ see front matter 2011  A doi:10.1016/j.ejor.2011.12.005 ⇑ Corresponding author.  E-mail addresses: vpureza@dep.ufscar.br (V. (R. Morabito), marc.reimann@uni-graz.at (M. Reiman In real life distribution of goods, relatively long service times may make it difficult to serve all requests during regular working hours. These difficulties are even greater if the beginning of the service in each demand site must occur within a time window and violations of routing time restrictions are particularly undesirable. We address this situation by considering a variant of the vehicle routing problem with time windows for which, besides routing and scheduling decisions, a number of extra deliverymen can be assigned to each route in order to reduce service times. This problem appears, for example, in the distribution of beverage and tobacco in highly dense Brazilian urban areas. We present a mathematical programming formulation for the problem, as well as a tabu search and an ant colony optimization heuristics for obtaining minimum cost routes. The performance of the model and the heuristic approaches are evaluated using instances generated from a set of classic examples from the literature.
Extended Gardner Detector for Improved Symbol-Timing Recovery of $M$-PSK Signals

In a recent paper, the modified Gardner detector has been proposed for advanced symbol-timing recovery of M-ary phase-shift keying signals. Compared with the original algorithm, the self-noise jitter can be considerably reduced for highly bandlimited systems and smaller values of M. In this letter, a completely different approach is investigated, which improves the self-noise performance for larger values of M, as well
Why Scrum Works: A Case Study from an Agile Distributed Project in Denmark and India

Scrum seems to work extremely well as an agile project management approach. An obvious question is why. To answer that question, we carried out a longitudinal case study of a distributed project using Scrum across Denmark and India. In our analysis of case study data we used three selected theoretical frameworks. We conclude that Scrum works so well because it provides communication, social integration, control, and coordination mechanisms that are especially useful for distributed and agile project management.
Direct synthesis of van der Waals solids.

The stacking of two-dimensional layered materials, such as semiconducting transition metal dichalcogenides (TMDs), insulating hexagonal boron nitride (hBN), and semimetallic graphene, has been theorized to produce tunable electronic and optoelectronic properties. Here we demonstrate the direct growth of MoS2, WSe2, and hBN on epitaxial graphene to form large-area van der Waals heterostructures. We reveal that the properties of the underlying graphene dictate properties of the heterostructures, where strain, wrinkling, and defects on the surface of graphene act as nucleation centers for lateral growth of the overlayer. Additionally, we show that the direct synthesis of TMDs on epitaxial graphene exhibits atomically sharp interfaces. Finally, we demonstrate that direct growth of MoS2 on epitaxial graphene can lead to a 10(3) improvement in photoresponse compared to MoS2 alone.
Sexual Dimorphism in the Parietal Substrate Associated with Visuospatial Cognition Independent of General Intelligence

Sex differences in visuospatial cognition (VSC) with male advantage are frequently reported in the literature. There is evidence for sexual dimorphisms in the human brain, one of which postulates more gray matter (GM) in females and more white matter (WM) in males relative to total intracranial volume. We investigated the neuroanatomy of VSC independent of general intelligence (g) in sex-separated populations, homogenous in age, education, memory performance, a memory- and brain morphology-related gene, and g. VSC and g were assessed with the Wechsler adult intelligence scale. The influence of g on VSC was removed using a hierarchical factor analysis and the Schmid–Leiman solution. Structural high-resolution magnetic resonance images were acquired and analyzed with voxel-based morphometry. As hypothesized, the clusters of positive correlations between local volumes and VSC performance independent of g were found mainly in parietal areas, but also in pre- and postcentral regions, predominantly in the WM in males, whereas in females these correlations were located in parietal and superior temporal areas, predominantly in the GM. Our results suggest that VSC depends more strongly on parietal WM structures in males and on parietal GM structures in females. This sex difference might have to do with the increased axonal and decreased somatodendritic tissue in males relative to females. Whether such sex-specific implementations of the VSC network can be explained genetically as suggested in investigations into the Turner syndrome or as a result of structural neural plasticity upon different experience and usage remains to be shown.
Microstrip anti-phase Gysel power divider with unequal power division

A microstrip Gysel power divider with unequal power division and anti-phase feature is presented in this paper. By using transmission line theory, the proposed circuit is analyzed and the design equations are derived. For verification, a 4:1 prototype operating at 3GHz is designed, fabricated and measured. The measured insertion losses at the center frequency are about −1.15 dB and −7.23dB, respectively. The measured return losses are better than −20dB from 2.85 to 3.16GHz. The measured phase difference between two output ports is 180°±6° over the range of 2.8 to 3.2GHz, which show a good agreement with the simulated ones.
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Saliency prediction typically relies on hand-crafted (multiscale) features that are combined in different ways to form a "master" saliency map, which encodes local image conspicuity. Recent improvements to the state of the art on standard benchmarks such as MIT1003 have been achieved mostly by incrementally adding more and more hand-tuned features (such as car or face detectors) to existing models. In contrast, we here follow an entirely automatic data-driven approach that performs a large-scale search for optimal features. We identify those instances of a richly-parameterized bio-inspired model family (hierarchical neuromorphic networks) that successfully predict image saliency. Because of the high dimensionality of this parameter space, we use automated hyperparameter optimization to efficiently guide the search. The optimal blend of such multilayer features combined with a simple linear classifier achieves excellent performance on several image saliency benchmarks. Our models outperform the state of the art on MIT1003, on which features and classifiers are learned. Without additional training, these models generalize well to two other image saliency data sets, Toronto and NUSEF, despite their different image content. Finally, our algorithm scores best of all the 23 models evaluated to date on the MIT300 saliency challenge, which uses a hidden test set to facilitate an unbiased comparison.
Non-EEG seizure-detection systems and potential SUDEP prevention: State of the art

There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes are attached to the scalp. EEG analysis is also labour-intensive and has yet to be automated and adapted for real-time monitoring. It is therefore usually performed in a hospital setting, for a few days at the most. The goal of this article is to provide an overview of body signals that can be measured, along with corresponding methods, state-of-art research, and commercially available systems, as well as to stress the importance of a good detection system.



Narrative literature review.



A range of body signals can be monitored for the purpose of seizure detection. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important patho-physiological mechanism of SUDEP, and of movement, as many seizures have a motor component.



The most effective seizure detection systems are multimodal. Such systems should also be comfortable and low-power. The body signals and modalities on which a system is based should take account of the user's seizure types and personal preferences.
Bounding Gait in a Hybrid Wheeled-Leg Robot

This paper discusses the first implementation of a dynamically stable bounding gait on a hybrid wheeled-leg robot. Design of the robot is reviewed and the controllers which allow this mode of mobility to occur are discussed. Experimental results demonstrating the key dynamic characteristics of the gait, including footfall patterns, are given. The hypothesis that varying leg takeoff angles can lead to regulation of forward speed of the bounding gait is presented and verified. In addition, comparisons are made between the bounding gait which uses active wheel control and bounding which uses passive mechanical blocking of the wheels
Towards Dynamic Demand Response On Efficient Consumer Grouping Algorithmics

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in a provably optimal real-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online) randomized approximation algorithm for grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.
Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder

Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users. However, the high-order correlations between tow kind of data are always ignored by existing works. Towards this problem, we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations from both rating and trust data for Top-N recommendation. First, a novel deep learning model CoDAE, in which two mid-layers from separate stack denoising autoencoders are fused into one shared layer. Advancing previous works which utilize these data in shallow level, this model can effectively extract high-order correlations from low-level representations of these data for recommendation. Second, to further learn implicit corrections between these two autoencoders, we develop a novel correlative regulation to build the relations between other hidden layers of the two separate autoencoders. In this way, this model can learn correlations more effectively and thus improve the recommendation quality. Comprehensive experiments on two public datasets demonstrate that the CoDAE significantly outperforms other state-of-the-art approaches in top-N recommendation task.
Modelling the interaction of electromagnetic fields (10 MHz-10 GHz) with the human body: methods and applications.

Numerical modelling of the interaction between electromagnetic fields (EMFs) and the dielectrically inhomogeneous human body provides a unique way of assessing the resulting spatial distributions of internal electric fields, currents and rate of energy deposition. Knowledge of these parameters is of importance in understanding such interactions and is a prerequisite when assessing EMF exposure or when assessing or optimizing therapeutic or diagnostic medical applications that employ EMFs. In this review, computational methods that provide this information through full time-dependent solutions of Maxwell's equations are summarized briefly. This is followed by an overview of safety- and medical-related applications where modelling has contributed significantly to development and understanding of the techniques involved. In particular, applications in the areas of mobile communications, magnetic resonance imaging, hyperthermal therapy and microwave radiometry are highlighted. Finally, examples of modelling the potentially new medical applications of recent technologies such as ultra-wideband microwaves are discussed.
An OpenCCG-Based Approach to Question Generation from Concepts

Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user’s expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.
A Vehicle License Plate Detection and Recognition System

Automatic vehicle license plate detection and recog nition is a key technique in most of traffic related applications a d is an active research topic in the image process ing domain. Different methods, techniques and algorithm s ave been developed for license plate detection and recognitions.  Due to the varying characteristics of the license p late from country to country like numbering system, colors, language of characters, style (font) and sizes of license plate , further research is still needed in this area.  In most of the middle East countries, they use the combination of Arabic and English letters, plus th eir countries logo. Thus, it makes the localization of plate number, the differentiation between Arabic an d English letters and logo’s object and finally the recognition of those characters become more challen ging research task. The use of artificial neural network has proved itself beneficial for plate reco gnition, but it has not been applied for the plate detection. Radial Basis Function (RBF) neural netwo rk is used both for the detection and recognition of Saudi Arabian license plate.  The proposed approach has been tested on 200 front images of national license pla te of Saudi Arabia. A higher percentage of accuracy has been obtained to show that the significant of t his approach. The study could be further investigat ed on other middle east countries.
A Rule-Based System to Extract Financial Information

Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based information extraction methodology for the extraction of highly accurate financial information to aid investment decisions. The methodology includes a rule-based symbolic learning model trained by the Greedy Search algorithm and a similar model trained by the Tabu Search algorithm. The methodology has been found very effective in extracting financial information from NASDAQ-listed companies. Also, the Tabu Search based model performed better than some well-known systems. The simple rule structure makes the system portable and it should make parallel processing implementations easier.
On the usability of Hadoop MapReduce, Apache Spark & Apache flink for data science

Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks. This has led to the development of more advanced dataflow oriented platforms, most prominently Apache Spark and Apache Flink. Those platforms not only aim to improve performance through improved in-memory processing, but in particular provide built-in high-level data processing functionality, such as filtering and join operators, which should make data analysis tasks easier to develop than with plain Hadoop MapReduce. But is this indeed the case? This paper compares three prominent distributed data processing platforms: Apache Hadoop MapReduce; Apache Spark; and Apache Flink, from a usability perspective. We report on the design, execution and results of a usability study with a cohort of master students, who were learning and working with all three platforms in order to solve different use cases set in a data science context. Our findings show that Spark and Flink are preferred platforms over MapReduce. Among participants, there was no significant difference in perceived preference or development time between both Spark and Flink as platforms for batch-oriented big data analysis. This study starts an exploration of the factors that make Big Data platforms more — or less — effective for users in data science.
OSINT Analysis using Adaptive Resonance Theory for Conterterrorism Warnings

Open Source Intelligence (OSINT) is an extremely valuable source of data for intelligence analysts in identifying and analyzing potential terrorism warnings and indicators . A key problem is making sense of this large amount of data in time to prevent a catastrophic situation like what occurred on September 11, 2001. These events might been mitigated had U.S. intelligence agencies had better information technology tools for analyzing the situation according to the report of Congress’s Joint Inquiry into the events leading up to the Sept. 11 attacks . Improvements to Information and Communications Technologies (ICTs) are necessary to provide the Homeland Security with the proper support for their missions . Artificial Neural Networks (ANNs) have been around for over half a century and their biologically-inspired capability allows functionality similar to the human brain via simulated neurons that can make near-human choices. ANNs are in their genesis with future applications include finance, marketing, medicine and security in data mining . Data mining enables a large amount of data to be sifted and provide avenues to learn or generalize information about that data using feature extraction . Adaptive Resonance Theory (ART) may provide another tool for this analysis.
Online LCL filter compensation using embedded FRA

LCL filter control design is challenging because of presence of a resonant pole in the LCL filter transfer function. This paper presents a guideline for LCL filter design, which are often used in grid connected applications, with compensation based on complex poles and zeros. A Software Frequency Response Analyzer (SFRA) is integrated in the control software of the power converter. This enables periodical collection of frequency response data, which can be used to monitor movement of resonant poles due to aging, drifts and degradation of components. The information obtained from embedded SFRA can then be used to adjust the compensation. A feedback linearization method is illustrated to account for the grid voltage disturbances, which cannot be compensated by traditional PI controller. A solar micro inverter board is designed to prove the proposed scheme. Discussions about the embedded design challenges in integration of multiple power stage control, for a micro inverter using a single processor, are included and solutions presented.
A Tensor Network Kalman filter with an application in recursive MIMO Volterra system identification

This article introduces a Tensor Network Kalman filter, which can estimate state vectors that are exponentially large without ever having to explicitly construct them. The Tensor Network Kalman filter also easily accommodates the case where several different state vectors need to be estimated simultaneously. The key lies in rewriting the standard Kalman equations as tensor equations and then implementing them using Tensor Networks, which effectively transforms the exponential storage cost and computational complexity into a linear one. We showcase the power of the proposed framework through an application in recursive nonlinear system identification of high-order discrete-time multiple-input multiple-output (MIMO)Volterra systems. The identification problem is transformed into a linear state estimationproblem wherein the state vector contains all Volterra kernel coefficients and is estimated using the Tensor Network Kalman filter. The accuracy and robustness of the scheme are demonstrated via numerical experiments, which show that updating the Kalman filter estimate of a state vector of length 109 and its covariance matrix takes about 0.007 s on a standard desktop computer in Matlab.
Handwritten Digit Recognition with a Back-Propagation Network

We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
Mixed Type Audio Classification with Support Vector Machine

Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification of mixed type audio data, such as clips having speech with music as background, is still considered a difficult problem. In this paper, we present a mixed type audio classification system based on support vector machine (SVM). In order to capture characteristics of different types of audio data, besides selecting audio features, we also design four different representation formats for each feature. Our SVM-based audio classifier can classify audio data into five types: music, speech, environment sound, speech mixed with music, and music mixed with environment sound. The experimental results show that our system outperforms other classification systems using k nearest neighbor (k-NN), neural network (NN), and Naive Bayes (NB)