nils-herrmann/clean_scirepeval huggingface dataset:
Design of Compact Dual Band High Directive Electromagnetic Bandgap (EBG) Resonator Antenna Using Artificial Magnetic Conductor

A compact high directive EBG resonator antenna operating in two frequency bands is described. Two major contributions to this compact design are using single layer superstrate and using artificial surface as ground plane. To obtain only the lower operating frequency band using superstrate layer is enough, but to extract the upper operating frequency band both superstrate layer and artificial surface as ground plane are necessary. Therefore, design of a superstrate to work in two frequency bands is very important. Initially, using appropriate frequency selective surface (FSS) structure with square loop elements, we design an optimum superstrate layer for each frequency band separately to achieve maximum directivity. Also, to design an artificial surface to work in the upper frequency band we use a suitable FSS structure over dielectric layer backed by PEC. Next, by using the idea of FSS structure with double square loop elements we propose FSS structure with modified double square loop elements, so that it operates in both of the desired operating frequency bands simultaneously. Finally, the simulation results for two operating frequency bands are shown to have good agreement with measurements.
Increased corpus callosum size in musicians

Using in-vivo magnetic resonance morphometry it was investigated whether the midsagittal area of the corpus callosum (CC) would differ between 30 professional musicians and 30 age-, sex- and handedness-matched controls. Our analyses revealed that the anterior half of the CC was significantly larger in musicians. This difference was due to the larger anterior CC in the subgroup of musicians who had begun musical training before the age of 7. Since anatomic studies have provided evidence for a positive correlation between midsagittal callosal size and the number of fibers crossing through the CC, these data indicate a difference in interhemispheric communication and possibly in hemispheric (a)symmetry of sensorimotor areas. Our results are also compatible with plastic changes of components of the CC during a maturation period within the first decade of human life, similar to those observed in animal studies.
RGB-H-CbCr skin colour model for human face detection

While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. This paper presents a novel skin colour model, RGB-H-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin colour distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin colour and background environment. The results are also comparable to that of the AdaBoost face classifier.
Wireless Relay Communications with Unmanned Aerial Vehicles: Performance and Optimization

In this paper, we investigate a communication system in which unmanned aerial vehicles (UAVs) are used as relays between ground-based terminals and a network base station. We develop an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle. To quantify link performance, we define the ergodic normalized transmission rate (ENTR) for the links between the ground nodes and the relay, and derive a closed-form expression for it in terms of the eigenvalues of the channel correlation matrix. We show that the ENTR can be approximated as a sinusoid with an offset that depends on the heading of the UAV. Using this observation, we develop a closed-form expression for the UAV heading that maximizes the uplink network data rate while keeping the rate of each individual link above a certain threshold. When the current UAV relay assignments cannot meet the minimum link requirements, we investigate the deployment and heading control problem for new UAV relays as they are added to the network, and propose a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.
On the role of linguistic descriptions of data in the building of natural language generation systems

This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field — and, more specifically, the data-to-text sub-field — and the linguistic descriptions of data field. Both approaches are explained in a detailed description which includes: i) a methodological revision of both fields including basic concepts and definitions, models and evaluation procedures; ii) the most relevant systems, use cases and real applications described in the literature. Some reflections about the current state and future trends of each field are also provided, followed by several remarks that conclude by hinting at some potential points of mutual interest and convergence between both fields.
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition

We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 100,000 words directly using deep bi-directional LSTM RNNs with CTC loss. The model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. We show that the CTC word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. We demonstrate that the CTC word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
Stadium Hashing: Scalable and Flexible Hashing on GPUs

Hashing is one of the most fundamental operations that provides a means for a program to obtain fast access to large amounts of data. Despite the emergence of GPUs as many-threaded general purpose processors, high performance parallel data hashing solutions for GPUs are yet to receive adequate attention. Existing hashing solutions for GPUs not only impose restrictions (e.g., inability to concurrently execute insertion and retrieval operations, limitation on the size of key-value data pairs) that limit their applicability, their performance does not scale to large hash tables that must be kept out-of-core in the host memory. In this paper we present Stadium Hashing (Stash) that is scalable to large hash tables and practical as it does not impose the aforementioned restrictions. To support large out-of-core hash tables, Stash uses a compact data structure named ticket-board that is separate from hash table buckets and is held inside GPU global memory. Ticket-board locally resolves significant portion of insertion and lookup operations and hence, by reducing accesses to the host memory, it accelerates the execution of these operations. Split design of the ticket-board also enables arbitrarily large keys and values. Unlike existing methods, Stash naturally supports concurrent insertions and retrievals due to its use of double hashing as the collision resolution strategy. Furthermore, we propose Stash with collaborative lanes (clStash) that enhances GPU's SIMD resource utilization for batched insertions during hash table creation. For concurrent insertion and retrieval streams, Stadium hashing can be up to 2 and 3 times faster than GPU Cuckoo hashing for in-core and out-of-core tables respectively.
Multiuser Millimeter Wave Communications With Nonorthogonal Beams

Recently, millimeter-wave (mmWave) and even terahertz wireless (with higher frequency) networks have attracted significant research interests as alternatives to support the formidable growth of wireless communication services. Normally, directional beamforming (BF) is shown as a promising technique to compensate its high path loss. We focus on mmWave communications and assume that both mmWave base stations (MBSs) and user equipments (UEs) can support directional BF. As mmWave spectrum has short wavelength, massive antenna arrays can be deployed at MBSs to form multiple directional beams through BF training. Then, an MBS can transmit simultaneously to multiple UEs (SUEs) with different beams in the networks. However, the beams that serve different SUEs may transmit (almost) in the same path, especially when SUEs are distributed densely. Thus, they are not in perfect orthogonal beams. Due to the leakage of transmission power, the interference among these beams may be severe. To address this problem, typically the MBS could serve these SUEs in time division multiplex. This will degrade the spectral efficiency. In this context, we investigate the effect of nonorthogonal beam interference and then propose two novel solutions (i.e., dynamic beam switching and static beam selection) to coordinate the transmitting beams effectively. Then, an improved downlink multiuser simultaneous transmission scheme is introduced. In the scheme, an MBS can serve multiple SUEs simultaneously with multiple orthogonal and/or nonorthogonal beams to guarantee SUEs’ Quality of Service. The theoretical and numerical results have shown that our scheme can largely improve the performance of the achievable rate and, meanwhile, can serve lots of SUEs simultaneously.
Conscious intention and brain activity

The problem of free will lies at the heart of modern scientific studies of consciousness. An influential series of experiments by Libet has suggested that conscious intentions arise as a result of brain activity. This contrasts with traditional concepts of free will, in which the mind controls the body. A more recent study by Haggard and Eimer has further examined the relation between intention and brain processes, concluding that conscious awareness of intention is linked to the choice or selection of a specific action, and not to the earliest initiation of action processes. The exchange of views in this paper further explores the relation between conscious intention and brain activity.
Influence And Dissemination Of Sentiments In Social Network Communication Patterns

Previous research suggests the existence of sentiments in online social networks. In comparison to real life human interaction, in which sentiments have been shown to have an influence on human behaviour, it is not yet completely understood which mechanisms explain how sentiments influence users in online environments. We develop a theoretical framework that tries to bridge the gap between social influence theories that focus on offline interactions on one hand and online interaction in social networks on the other hand. We then test our hypothesis about the influence and dissemination of sentiments in a quantitative analysis that is based on retrieved textual messages of communication patterns in over 12000 online social networks. Our empirical results suggest a general influence of sentiments on node communication patterns that is evidenced by increased occurrences of subsequent messages that express the same sentiment polarization. We interpret these findings and suggest future research to advance our currently limited theories that assume perceived and generalized social influence to path-dependent social influence models that consider actual behaviour.
Ranking Scientific Articles in a Dynamically Evolving Citation Network

Scientific ranking has long been a hot and important topic in both computer science and scientometrics. A lot of statistics-based and graph-based methods have been proposed for calculating a prestige value as the assessment of each paper's scientific influence. However, being ignorant of the dynamic nature of scientific publication and science evolution, all these methods present a biased point of view of scientific influence. Besides, the ranking results of these methods are not accessible to users because of lack of an explainable model. As an alternative to the state-of-the-art, this paper proposes a cognitively explainable model by integrating three factors in scientific development including knowledge accumulation by individual papers, knowledge diffusion through citation behaviour and knowledge decay with time elapse. Evaluated on ACL Anthology Network using the reference lists of four textbooks or handbooks as the gold standard, the proposed model is proved to be effective in scientific ranking and potential for new insights into the definition and measurement of scientific influence.
Fuzzy sliding mode control of unicycle robot

This paper proposes a single wheel balanced robot. In this paper, unicycle robot is implemented by the mobile inverted pendulum control method for pitch axis and the reaction wheel pendulum control method for roll axis. We assumed that both Roll dynamics and Pitch dynamics are decoupled. So, we obtained, respectively, the Roll dynamics and Pitch dynamics. And we designed the controller separately. Robot's angle data is obtained by a fusion of gyro sensor and accelerometer. Experiment results show the performance of the controller.
Characterization of Printed Moisture Sensors in Packaging Surveillance Applications

This work presents a study on the performance of printed low-cost moisture sensors fabricated using conductive ink on paper substrates. The sensors are intended to add value to the surveillance of packages. Two different kinds of sensors are evaluated and characterized. The two sensors have similar geometrical shapes, but different measuring principles are employed. The first sensor is measuring the moisture content in cellulose-based substrates, while the second is measuring high levels of relative humidity in the surroundings. The sensors have been developed so that they can be integrated into radio frequency identification (RFID) systems for surveillance in logistic chains. A laboratory setup of a RFID tag with sensor capability based on an ordinary passive RFID tag has been shown.
CRADLE: An Online Plan Recognition Algorithm for Exploratory Domains

In exploratory domains, agents’ behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users’ activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents’ interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.
Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements.

We studied single neurons in the frontal eye fields of awake macaque monkeys and compared their activity with the saccadic eye movements elicited by microstimulation at the sites of these neurons. Saccades could be elicited from electrical stimulation in the cortical gray matter of the frontal eye fields with currents as small as 10 microA. Low thresholds for eliciting saccades were found at the sites of cells with presaccadic activity. Presaccadic neurons classified as visuomovement or movement were most associated with low (less than 50 microA) thresholds. High thresholds (greater than 100 microA) or no elicited saccades were associated with other classes of frontal eye field neurons, including neurons responding only after saccades and presaccadic neurons, classified as purely visual. Throughout the frontal eye fields, the optimal saccade for eliciting presaccadic neural activity at a given recording site predicted both the direction and amplitude of the saccades that were evoked by microstimulation at that site. In contrast, the movement fields of postsaccadic cells were usually different from the saccades evoked by stimulation at the sites of such cells. We defined the low-threshold frontal eye fields as cortex yielding saccades with stimulation currents less than or equal to 50 microA. It lies along the posterior portion of the arcuate sulcus and is largely contained in the anterior bank of that sulcus. It is smaller than Brodmann's area 8 but corresponds with the union of Walker's cytoarchitectonic areas 8A and 45. Saccade amplitude was topographically organized across the frontal eye fields. Amplitudes of elicited saccades ranged from less than 1 degree to greater than 30 degrees. Smaller saccades were evoked from the ventrolateral portion, and larger saccades were evoked from the dorsomedial portion. Within the arcuate sulcus, evoked saccades were usually larger near the lip and smaller near the fundus. Saccade direction had no global organization across the frontal eye fields; however, saccade direction changed in systematic progressions with small advances of the microelectrode, and all contralateral saccadic directions were often represented in a single electrode penetration down the bank of the arcuate sulcus. Furthermore, the direction of change in these progressions periodically reversed, allowing particular saccade directions to be multiply represented in nearby regions of cortex.(ABSTRACT TRUNCATED AT 400
A conversational intelligent tutoring system to automatically predict learning styles

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.
Leadership style and patient safety: implications for nurse managers.

The purpose of this study was to explore the relationship between nurse manager (NM) leadership style and safety climate.



Nursing leaders are needed who will change the environment and increase patient safety. Hospital NMs are positioned to impact day-to-day operations. Therefore, it is essential to inform nurse executives regarding the impact of leadership style on patient safety.



A descriptive correlational study was conducted in 41 nursing departments across 9 hospitals. The hospital unit safety climate survey and multifactorial leadership questionnaire were completed by 466 staff nurses. Bivariate and regression analyses were conducted to determine how well leadership style predicted safety climate.



Transformational leadership style was demonstrated as a positive contributor to safety climate, whereas laissez-faire leadership style was shown to negatively contribute to unit socialization and a culture of blame.



Nursing leaders must concentrate on developing transformational leadership skills while also diminishing negative leadership styles.
iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing: Percentile Based Binarization

End-to-end Optical Character Recognition (OCR) systems are heavily used to convert document images into machine-readable text. Commercial and open-source OCR systems (like Abbyy, OCRopus, Tesseract etc.) have traditionally been optimized for contemporary documents like books, letters, memos, and other end-user documents. However, these systems are difficult to use equally well for digitizing historical document images, which contain degradations like non-uniform shading, bleed-through, and irregular layout; such degradations usually do not exist in contemporary document images.
 The open-source anyOCR is an end-to-end OCR pipeline, which contains state-of-the-art techniques that are required for digitizing degraded historical archives with high accuracy. However, high accuracy comes at a cost of high computational complexity that results in 1) long runtime that limits digitization of big collection of historical archives and 2) high energy consumption that is the most critical limiting factor for portable devices with constrained energy budget. Therefore, we are targeting energy efficient and high throughput acceleration of the anyOCR pipeline. Generalpurpose computing platforms fail to meet these requirements that makes custom hardware design mandatory. In this paper, we are presenting a new concept named iDocChip. It is a portable hybrid hardware-software FPGA-based accelerator that is characterized by low footprint meaning small size, high power efficiency that will allow using it in portable devices, and high throughput that will make it possible to process big collection of historical archives in real time without effecting the accuracy.
 In this paper, we focus on binarization, which is the second most critical step in the anyOCR pipeline after text-line recognizer that we have already presented in our previous publication . The anyOCR system makes use of a Percentile Based Binarization method that is suitable for overcoming degradations like non-uniform shading and bleed-through. To the best of our knowledge, we propose the first hardware architecture of the PBB technique. Based on the new architecture, we present a hybrid hardware-software FPGA-based accelerator that outperforms the existing anyOCR software implementation running on i7-4790T in terms of runtime by factor of 21, while achieving energy efficiency of 10 Images/J that is higher than that achieved by low power embedded processors with negligible loss of recognition accuracy.