nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional neural networks with multi-valued neurons

Convolutional neural network (CNN) has been successfully used in many fields including image recognition. CNN is composed of input, convolution, pooling, hidden and output layers, and the weights and biases between layers except the ones between convolution and pooling layers are acquired by learning. In comparison to the conventional neural networks, the learning cost of CNN is higher, and the learning time is longer especially when hidden layer(s) are added. Recently, complex- and quaternion-valued neural networks have drawn much attention. In complex-valued neural networks, inputs, weights, biases and outputs are complex numbers, and in quaternion-valued neural networks, these parameters are quaternions. It has been shown that both methods exhibit excellent accuracy in various applications such as classification and function approximation problems with less computational burden. In this study, we propose CNNs with complex- and quaternion-valued neurons where complex and quaternion numbers are used between pooling, hidden and output layers. We here show that CNNs with complex- and quaternion-valued neurons have higher learning ability in handwritten digit image classification with the MNIST data than the real-valued counterpart.
TRANSRECTAL ULTRASONOGRAPHY OF EQUINE LUMBOSACRAL NERVES: PILOT STUDY IN 28 HEALTHY WARMBLOOD HORSES.

A noninvasive method for visualizing lumbosacral nerves would be helpful for horses with suspected lumbosacral plexopathy or injury. The aim of this prospective descriptive pilot study was to characterize the ultrasonographic appearance of the lumbosacral nerves in a sample of healthy horses, and expand on the technique for image acquisition. Horses were recruited for inclusion if they were determined to be healthy and sound based on clinical and lameness evaluation. Transrectal ultrasound images of the lumbosacral nerves (L6, S1, and S2) were obtained for both sides. Landmarks for localization, and techniques for nerve identification and measurement were described. Effects of sex, age, side, and nerve on measured thickness were statistically tested. Twenty-eight warmblood horses were sampled (15 males and 13 females). Ages ranged from 5 to 15 years. Ventral nerve roots from L6 to S2 appeared as tubular structures with a characteristic hyperechoic linear echo pattern. There was no significant difference in nerve vertical diameter between left and right sides. A three-way interaction was found among sex, age and lumbosacral nerve. The L6 nerve in males was significantly larger than S1 only in the youngest group. The S2 nerve was significantly smaller than L6 or S1 regardless of age group or sex. In conclusion, transrectal ultrasound was a feasible method for visualizing and measuring equine lumbosacral nerves. Wide ranges of sizes for each nerve were found between horses, however nerves were bilaterally symmetrical within horses. Side comparison is therefore recommended when nerve pathology is suspected.
Large Margin Methods for Structured and Interdependent Output Variables

Learning general functional dependencies between arbitra ry input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representa tions, this paper addresses the complementary issue of designing classification algorithms that c an deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider pr oblems involving multiple dependent output variables, structured output spaces, and classifica tion problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulat ion. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem i n polynomial time for a large class of problems. The proposed method has important applications i n areas such as computational biology, natural language processing, information retrieval/extr action, and optical character recognition. Experiments from various domains involving different types o f output spaces emphasize the breadth and generality of our approach.
Cognitive Smart Healthcare for Pathology Detection and Monitoring

We propose a cognitive healthcare framework that adopts the Internet of Things (IoT)–cloud technologies. This framework uses smart sensors for communications and deep learning for intelligent decision-making within the smart city perspective. The cognitive and smart framework monitors patients’ state in real time and provides accurate, timely, and high-quality healthcare services at low cost. To assess the feasibility of the proposed framework, we present the experimental results of an EEG pathology classification technique that uses deep learning. We employ a range of healthcare smart sensors, including an EEG smart sensor, to record and monitor multimodal healthcare data continuously. The EEG signals from patients are transmitted via smart IoT devices to the cloud, where they are processed and sent to a cognitive module. The system determines the state of the patient by monitoring sensor readings, such as facial expressions, speech, EEG, movements, and gestures. The real-time decision, based on which the future course of action is taken, is made by the cognitive module. When information is transmitted to the deep learning module, the EEG signals are classified as pathologic or normal. The patient state monitoring and the EEG processing results are shared with healthcare providers, who can then assess the patient’s condition and provide emergency help if the patient is in a critical state. The proposed deep learning model achieves better accuracy than the state-of-the-art systems.
Protection motivation and deterrence: a framework for security policy compliance in organisations

21 February 2008  15 August 2008   23 February 2009  Enterprises establish computer security policies to ensure the security of information resources; however, if employees and end-users of organisational information systems (IS) are not keen or are unwilling to follow security policies, then these efforts are in vain. Our study is informed by the literature on IS adoption, protection-motivation theory, deterrence theory, and organisational behaviour, and is motivated by the fundamental premise that the adoption of information security practices and policies is affected by organisational, environmental, and behavioural factors. We develop an Integrated Protection Motivation and Deterrence model of security policy compliance under the umbrella of Taylor-Todd’s Decomposed Theory of Planned Behaviour. Furthermore, we evaluate the effect of organisational commitment on employee security compliance intentions. Finally, we empirically test the theoretical model with a data set representing the survey responses of 312 employees from 78 organisations. Our results suggest that (a) threat perceptions about the severity of breaches and response perceptions of response efficacy, self-efficacy, and response costs are likely to affect policy attitudes; (b) organisational commitment and social influence have a significant impact on compliance intentions; and (c) resource availability is a significant factor in enhancing self-efficacy, which in turn, is a significant predictor of policy compliance intentions. We find that employees in our sample underestimate the probability of security breaches.  (2009) 18, 106–125. doi:10.1057/ejis.2009.6; published online 21 April 2009
Toward a Theory of Organizational Creativity

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SINGLE PHASE PWM RECTIFIER IN TRACTION APPLICATION

This research has been motivated by industrial demand for single phase PWM rectifier in traction application. This paper presents an advanced control structure design for a single phase PWM rectifier. The PWM rectifier is controlled to consume nearly sinusoidal current with power factor nearing to unity. The control structure consists of a DC-link voltage controller and a current controller implemented by a proportionalresonant controller using a fast phase angle and frequency estimator. The estimation algorithm is derived from the weighted least-squares estimation method. The feasibility of the proposed control structure is confirmed by experimental tests performed on designed laboratory prototype.
Attainment of early feeding milestones in preterm neonates

This study aimed to document the ages at which preterm neonates commence suckle-feeds and attain exclusive suckle-feeding, as well as the time taken to transition from commencement of suckle-feeds to exclusive suckle-feeding. It was hypothesized that gestational age (GA) at birth and degree of neonatal morbidity would influence the timing of these early feeding milestones.A chart review was conducted for all neonates born <37;0 weeks GA admitted to a tertiary level perinatal facility over a 12-month period (n=735). Complete data relating to attainment of feeding milestones were available on 472 neonates.Correlation analysis indicated that both a low GA at birth and a high neonatal morbidity rating were statistically significantly correlated with an increased transition time from commencement of suckle-feeds to exclusive suckle-feeding. Cox regression indicated that both of these variables were statistically significant risk factors for a delayed GA at attainment of exclusive suckle-feeding.Preterm neonates who were less mature at birth and/or who displayed a greater degree of neonatal morbidity took longer to transition from starting suckle-feeds to achieving independent suckle-feeding, and were more mature at attainment of independent suckle-feeding.
12 Artificial Intelligence for Space Applications

The ambitious short-term and long-term goals set down by the various national space agencies call for radical advances in several of the main space engineering areas, the design of intelligent space agents certainly being one of them. In recent years, this has led to an increasing interest in artificial intelligence by the entire aerospace community. However, in the current state of the art, several open issues and showstoppers can be identified. In this chapter, we review applications of artificial intelligence in the field of space engineering and space technology and identify open research questions and challenges. In particular, the following topics are identified and discussed: distributed artificial intelligence, enhanced situation self-awareness, and decision support for spacecraft system design.
Mobile Phishing Attacks and Mitigation Techniques

Health Information Technology (HIT) professionals are in increasing demand as healthcare providers need help in the adoption and meaningful use of Electronic Health Record (EHR) systems while the HIT industry needs workforce skilled in HIT and EHR development. To respond to this increasing demand, the School of Computing and Software Engineering at Southern Polytechnic State University designed and implemented a series of HIT educational programs. This paper summarizes our experience in the HIT curriculum development and provides an overview of HIT workforce development initiatives and major HIT and health information management (HIM) educational resources. It also provides instructional implications and experiences for positioning HIT programs and enhancing curriculum.
RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning

In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.
Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition.

Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.
Design and Performance of Electrical Propulsion System of Extended Range Electric Vehicle (EREV) Chevrolet Volt

This paper presents the design and performance details of the Chevrolet Volt electric propulsion system. The propulsion system has two machines: One machine is operating mostly as a motor while the other machine is operating mostly as a generator. Both machines of the Volt electric drive system are permanent-magnet ac synchronous machine types with the magnets buried inside the rotor. The motor has distributed windings. However, as opposed to a conventional stranded winding, the Chevrolet Volt motor has bar-wound construction to improve the motor performance, particularly in the low to medium speed range. At higher speed, the skin and proximity effects in the stator bars lead to increased stator winding losses but are addressed in the design. The bar-wound construction also has excellent thermal performance, in both the steady-state and transient conditions, necessary for full electric vehicle (EV) driving. The generator uses concentrated windings. The concentrated winding construction has good slot fill and short end-turn length. These features resulted in good performance in the intended operational region and were an enabler for machine packaging inside the transmission. Both the machines exhibit excellent efficiency and exceptionally smooth and quiet operation. The machine design and construction details, as well as the measured thermal, electromagnetic, and acoustic noise performances, are presented in this paper.
Recent Advances in Convolutional Neural Network Acceleration

In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN’s further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.
CLOUDS: A Decision Tree Classifier for Large Datasets

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifters, while maintaining the quality of the generated trees in terms of accuracy and tree size. We provide experimental results with a number of real and synthetic data~ets.
Head-bobbing of walking birds

Many birds show a rhythmic forward and backward movement of their heads when they walk on the ground. This so-called “head-bobbing” is characterized by a rapid forward movement (thrust phase) which is followed by a phase where the head keeps its position with regard to the environment but moves backward with regard to the body (hold phase). These head movements are synchronized with the leg movements. The functional interpretations of head-bobbing are reviewed. Furthermore, it is discussed why some birds do bob their head and others do not.
State of the art of virtual reality technology

In the past three years, the so-called second wave of Virtual Reality (VR) has brought us a vast amount of new displays and input devices. Not only new hardware has entered the consumer market providing affordable pricing models but also completely new technologies are being designed and developed. Additionally new concepts for handling existing problems on the hardware and software side of the VR technology are constantly being introduced. This software and hardware development is mainly lead by enthusiasts interested in the domain of VR opposed to the established scientific community, which already partially makes use of the newly available technology. Besides Head-Mounted Displays (HMDs), either cable-based or mobile, other devices like haptics devices, controllers, vests, omnidirectional treadmills, tracking technologies, as well as optical scanners for gesture-based interaction are gaining importance in the field of commodity VR. Most of these technologies are already precise and robust enough to be used for professional operation and scientific experiments. The topics discussed are the common issues with the new technologies including the approaches to solve them as for example motion-to-photon latency, barrel distortion, and low-persistence displays. Additionally an in-depth analysis of the available solutions expected to hit the market is provided. A taxonomy categorising the current developments with the chosen implementation approaches will be given. The paper analyses the state of technological advancements in the field and provides an extensive overview on the current development considering the upcoming devices and the advancements from the software side.
Single channel audio source separation using convolutional denoising autoencoders

Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.
DDoS Detection with Information Theory Metrics and Netflows - A Real Case

Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) constitute one of the main issues for critical Internet services. The widespread availability and simplicity of automated stressing tools has also promoted the voluntary participation to extensive attacks against known websites. Today the most effective (D)DoS detection schemes are based on information theory metrics, but their effectiveness is often evaluated with synthetic network traffic. In this work we present a comparison of the main metrics proposed in the literature carried on a huge dataset formed by real netflows. This comparison considers the ability of each metric to detect (D)DoS attacks at an early stage, in order to launch effective and timely countermeasures. The evaluation is based on a large dataset, collected from an Italian transit tier II Autonomous System (AS) located in Rome. This AS network is connected to all the three main network infrastructures present in Italy (Commercial, Research and Public Administration networks), and to several international providers (even for Internet transit purposes). Many attempted attacks to Italian critical IT infrastructures can be observed inside the network traffic of this AS. Several publicly declared attacks have been traced and many other malicious activities have been found by ex-post analysis.