nils-herrmann/clean_scirepeval huggingface dataset:
Automated Epileptic Seizure Detection Methods: A Review Study

Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population (Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and transient disturbances of perception or behaviour resulting from excessive synchronization of cortical neuronal networks; it is a neurological condition in which an individual experiences chronic abnormal bursts of electrical discharges in the brain. The hallmark of epilepsy is recurrent seizures termed "epileptic seizures". Epileptic seizures are divided by their clinical manifestation into partial or focal, generalized, unilateral and unclassified seizures (James, 1997; Tzallas, Tsipouras & Fotiadis, 2007a, 2009). Focal epileptic seizures involve only part of cerebral hemisphere and produce symptoms in corresponding parts of the body or in some related mental functions. Generalized epileptic seizures involve the entire brain and produce bilateral motor symptoms usually with loss of consciousness. Both types of epileptic seizures can occur at all ages. Generalized epileptic seizures can be subdivided into absence (petit mal) and tonic-clonic (grand mal) seizures (James, 1997).
Static spatial effects in motor cortex and area 5: Quantitative relations in a two-dimensional space

We describe the relations between active maintenance of the hand at various positions in a two-dimensional space and the frequency of single cell discharge in motor cortex (n = 185) and area 5 (n = 128) of the rhesus monkey. The steady-state discharge rate of 124/185 (67%) motor cortical and 105/128 (82%) area 5 cells varied with the position in which the hand was held in space (“static spatial effect”). The higher prevalence of this effect in area 5 was statistically significant. In both structures, static effects were observed at similar frequencies for cells that possessed as well as for those that lacked passive driving from the limb. The results obtained by a quantitative analysis were similar for neurons of the two cortical areas studied. It was found that of the neurons with a static effect, the steady-state discharge rate of 78/124 (63%) motor cortical and 63/105 (60%) area 5 cells was a linear function of the position of the hand across the two-dimensional space, so that the neuronal “response surface” was adequately described by a plane (R2 ≥ 0.7, p < 0.05, F-test in analysis of variance). The preferred orientations of these response planes differed for different cells. These results indicate that individual cells in these areas do not relate uniquely a particular position of the hand in space. Instead, they seem to encode spatial gradients at certain orientations. A unique relation to position in space could be signalled by the whole population of these neurons, considered as an ensemble. This remains to be elucidated. Finally, the similarity of the quantitative relations observed in motor cortex and area 5 suggests that these structures may process spatial information in a similar way.
Comment on "Wandering minds: the default network and stimulus-independent thought".

Mason et al. (Reports, 19 January 2007, p. 393) attributed activity in certain regions of the "resting" brain to the occurrence of mind-wandering. However, previous research has demonstrated the difficulty of distinguishing this type of stimulus-independent thought from stimulus-oriented thought (e.g., watchfulness). Consideration of both possibilities is required to resolve this ambiguity.
RELATIONSHIP-BASED APPROACH TO LEADERSHIP : DEVELOPMENT OF LEADER-MEMBER EXCHANGE ( LMX ) THEORY OF LEADERSHIP OVER 25 YEARS : APPLYING A MULTI-LEVEL MULTI-DOMAIN PERSPECTIVE

Research into Leader-Member Exchange (LMX) theory has been gaining momentum in recent years, with a multitude of studies investigating many aspects of LMX in organizations. Theoretical development in this area also has undergone many refinements, and the current theory is far different from the early Vertical Dyad Linkage (VDL) work. This article uses a levels perspective to trace the development of LMX through four evolutionary stages of theorizing and investigation up to the present. The article also uses a domains perspective to develop a new taxonomy of approaches to leadership, and LMX is discussed within this taxonomy as a relationship-based approach to leadership. Common questions and issues concerning LMX are addressed, and directions for future research are provided.
Achievement Goals and Achievement Emotions : Testing a Model of Their Joint Relations With Academic Performance

The authors propose a theoretical model linking achievement goals and achievement emotions to academic performance. This model was tested in a prospective study with undergraduates (N 213), using exam-specific assessments of both goals and emotions as predictors of exam performance in an introductory-level psychology course. The findings were consistent with the authors’ hypotheses and supported all aspects of the proposed model. In multiple regression analysis, achievement goals (mastery, performance approach, and performance avoidance) were shown to predict discrete achievement emotions (enjoyment, boredom, anger, hope, pride, anxiety, hopelessness, and shame), achievement emotions were shown to predict performance attainment, and 7 of the 8 focal emotions were documented as mediators of the relations between achievement goals and performance attainment. All of these findings were shown to be robust when controlling for gender, social desirability, positive and negative trait affectivity, and scholastic ability. The results are discussed with regard to the underdeveloped literature on discrete achievement emotions and the need to integrate conceptual and applied work on achievement goals and achievement emotions.
Ultra-thin , planar , Babinet-inverted plasmonic metalenses

We experimentally demonstrate the focusing of visible light with ultra-thin, planar metasurfaces made of concentrically perforated, 30-nm-thick gold films. The perforated nano-voids—Babinet-inverted (complementary) nano-antennas—create discrete phase shifts and form a desired wavefront of cross-polarized, scattered light. The signal-to-noise ratio in our complementary nano-antenna design is at least one order of magnitude higher than in previous metallic nano-antenna designs. We first study our proof-of-concept ‘metalens’ with extremely strong focusing ability: focusing at a distance of only 2.5 mm is achieved experimentally with a 4-mm-diameter lens for light at a wavelength of 676 nm. We then extend our work with one of these ‘metalenses’ and achieve a wavelength-controllable focal length. Optical characterization of the lens confirms that switching the incident wavelength from 676 to 476 nm changes the focal length from 7 to 10 mm, which opens up new opportunities for tuning and spatially separating light at different wavelengths within small, micrometer-scale areas. All the proposed designs can be embedded on-chip or at the end of an optical fiber. The designs also all work for two orthogonal, linear polarizations of incident light.  Science & Applications (2013) 2, e72; doi:10.1038/lsa.2013.28; published online 26 April 2013
A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption.
Mechanism-Aware Neural Machine for Dialogue Response Generation

To the same utterance, people’s responses in everyday dialogue may be diverse largely in terms of content semantics, speaking styles, communication intentions and so on. Previous generative conversational models ignore these 1-to-n relationships between a post to its diverse responses, and tend to return high-frequency but meaningless responses. In this study we propose a mechanism-aware neural machine for dialogue response generation. It assumes that there exists some latent responding mechanisms, each of which can generate different responses for a single input post. With this assumption we model different responding mechanisms as latent embeddings, and develop a encoder-diverter-decoder framework to train its modules in an end-to-end fashion. With the learned latent mechanisms, for the first time these decomposed modules can be used to encode the input into mechanism-aware context, and decode the responses with the controlled generation styles and topics. Finally, the experiments with human judgements, intuitive examples, detailed discussions demonstrate the quality and diversity of the generated responses with 9.80% increase of acceptable ratio over the best of six baseline methods.
Channel plasmon subwavelength waveguide components including interferometers and ring resonators

Photonic components are superior to electronic ones in terms of operational bandwidth, but the diffraction limit of light poses a significant challenge to the miniaturization and high-density integration of optical circuits. The main approach to circumvent this problem is to exploit the hybrid nature of surface plasmon polaritons (SPPs), which are light waves coupled to free electron oscillations in a metal that can be laterally confined below the diffraction limit using subwavelength metal structures. However, the simultaneous realization of strong confinement and a propagation loss sufficiently low for practical applications has long been out of reach. Channel SPP modes—channel plasmon polaritons (CPPs)—are electromagnetic waves that are bound to and propagate along the bottom of V-shaped grooves milled in a metal film. They are expected to exhibit useful subwavelength confinement, relatively low propagation loss, single-mode operation and efficient transmission around sharp bends. Our previous experiments showed that CPPs do exist and that they propagate over tens of micrometres along straight subwavelength grooves. Here we report the design, fabrication and characterization of CPP-based subwavelength waveguide components operating at telecom wavelengths: Y-splitters, Mach–Zehnder interferometers and waveguide–ring resonators. We demonstrate that CPP guides can indeed be used for large-angle bending and splitting of radiation, thereby enabling the realization of ultracompact plasmonic components and paving the way for a new class of integrated optical circuits.
CENTRAL PROBLEMS IN THE MANAGEMENT OF INNOVATION *

Innovation is defined as the development and implementation of new ^^eas by people who over time engage in transactions with others within an institutional order. Thxs defmUion focuses on four basic factors (new ideas, people, transactions, and ms itut.onal context)^An understanding of how these factors are related leads to four basic problems confronting most general managers: (1) a human problem of managing attention, (2) a process probleni in manlgng new ideas into good currency, (3) a structural problem of managing part-whole TelatLnships, and (4) a strategic problem of institutional leadership. This paper discusses thes four basic problems and concludes by suggesting how they fit together into an overall framework to guide longitudinal study of the management of innovation.
Advances in Multi-GPU Smoothed Particle Hydrodynamics Simulations

We present a multi-GPU version of GPUSPH, a CUDA implementation of fluid-dynamics models based on the smoothed particle hydrodynamics (SPH) numerical method. The SPH is a well-known Lagrangian model for the simulation of free-surface fluid flows; it exposes a high degree of parallelism and has already been successfully ported to GPU. We extend the GPU-based simulator to run simulations on multiple GPUs simultaneously, to obtain a gain in speed and overcome the memory limitations of using a single device. The computational domain is spatially split with minimal overlapping and shared volume slices are updated at every iteration of the simulation. Data transfers are asynchronous with computations, thus completely covering the overhead introduced by slice exchange. A simple yet effective load balancing policy preserves the performance in case of unbalanced simulations due to asymmetric fluid topologies. The obtained speedup factor (up to 4.5x for 6 GPUs) closely follows the expected one (5x for 6 GPUs) and it is possible to run simulations with a higher number of particles than would fit on a single device. We use the Karp-Flatt metric to formally estimate the overall efficiency of the parallelization.
Secure management of personal health records by applying attribute-based encryption

The confidentiality of personal health records is a major problem when patients use commercial Web-based systems to store their health data. Traditional access control mechanisms have several limitations with respect to enforcing access control policies and ensuring data confidentiality. In particular, the data has to be stored on a central server locked by the access control mechanism, and the data owner loses control on the data from the moment when the data is sent to the server. Therefore, these mechanisms do not fulfill the requirements of data outsourcing scenarios where the third party storing the data should not have access to the plain data, and it is not trusted to enforce access policies. In this paper, we present a new variant of ciphertext-policy attribute-based encryption (CP-ABE) scheme which is used to enforce patient/organizational access control policies. In CP-ABE, the data is encrypted according to an access policy over a set of attributes. The access policy specifies which attributes a user needs to have in order to decrypt the encrypted data. Once the data is encrypted, it can be safely stored in an untrusted server such that everyone can download the encrypted data but only authorized users who satisfy the access policy can decrypt. The novelty of our construction is that attributes can be from two security domains: social domain (e.g. family, friends, or fellow patients) and professional domain (e.g. doctors or nurses).
Adaptive line tracking with multiple hypotheses for augmented reality

We present a real-time model-based line tracking approach with adaptive learning of image edge features that can handle partial occlusion and illumination changes. A CAD (VRML) model of the object to track is needed. First, the visible edges of the model with respect to the camera pose estimate are sorted out by a visibility test performed on standard graphics hardware. For every sample point of every projected visible 3D model line a search for gradient maxima in the image is then carried out in a direction perpendicular to that line. Multiple hypotheses of these maxima are considered as putative matches. The camera pose is updated by minimizing the distances between the projection of all sample points of the visible 3D model lines and the most likely matches found in the image. The state of every edge's visual properties is updated after each successful camera pose estimation. We evaluated the algorithm and showed the improvements compared to other tracking approaches.
A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In our work, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudoannotations. In this work, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts which generates high-quality pseudo-annotations in one forward path. The generated pseudoannotations lead to the segmentation results which achieve the state-of-the-art in weakly-supervised semantic segmentation.
Evolutional Data Quality: A Theory-Specific View

A data evolution life cycle typically consists of four stages of data activities: collection, organization, presentation, and application. During the cycle, data evolve as per the needs and specifications of a theory. In this paper we propose a concept of theory-specific data quality (DQ) that stipulates DQ is defined and measured as the extent to which data meet the needs and specifications of a theory. Depending on when a theory is applied during data evolution, we define DQ respectively at collection, organization, presentation, and application levels. We derive measurement attributes based on a fishbone cause-effect diagram. We compare our models with existing ones and point our future research directions to further validate and apply the proposed measurement models.
Bangla automatic number plate recognition system using artificial neural network

— Bangla automatic number plate recognition (ANPR) system using artificial neural network for number plate inscribing in Bangla is presented in this paper. This system splits into three major parts-number plate detection, plate character segmentation and Bangla character recognition. In number plate detection there arises many problems such as vehicle motion, complex background, distance changes etc., for this reason edge analysis method is applied. As Bangla number plate consists of two words and seven characters, detected number plates are segmented into individual words and characters by using horizontal and vertical projection analysis. After that a robust feature extraction method is employed to extract the information from each Bangla words and characters which is non-sensitive to the rotation, scaling and size variations. Finally character recognition system takes this information as an input to recognize Bangla characters and words. The Bangla character recognition is implemented using multilayer feed-forward network. According to the experimental result, (The abstract needs some exact figures of findings (like success rates of recognition) and how much the performance is better than previous one.) the performance of the proposed system on different vehicle images is better in case of severe image conditions.
On the design of a 3D LTE antenna for automotive applications based on MID technology

This paper presents the design of a long-term evolution (LTE) antenna and its integration on the 3D surface of the mounting compartment of an automotive roof-top antenna, using molded interconnect device (MID) technology. In the first step the design of the planar LTE antenna is shown. This antenna provides an input matching better than 10 dB in the desired frequency band and exhibits an omnidirectional radiation characteristic in the horizontal plane. Subsequently, this antenna is bent on the surface of a roof antenna housing. The effects of geometrical mapping of the planar antenna structure to the 3D surface in terms of input matching and radiation characteristics are analyzed. Based on these findings, a conformal and optimized two antenna system is introduced and discussed. A prototype realized by MID laser direct structuring (LDS) is presented and the measured antenna performance is compared to simulation results. An excellent agreement between measured and simulated results is observed. Finally, the prototype meets the specification requirements.
Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.