nils-herrmann/clean_scirepeval huggingface dataset:
Sensitivity analysis of a hierarchical model of mobile cloud computing

Mobile cloud computing is a new paradigm that uses cloud computing resources to overcome the limitations of mobile computing. Due to its complexity, dependability and performance studies of mobile clouds may require composite modeling techniques, using distinct models for each subsystem and combining state-based and non–state-based formalisms. This paper uses hierarchical modeling and four different sensitivity analysis techniques to determine the parameters that cause the greatest impact on the availability of a mobile cloud. The results show that distinct approaches provide similar results regarding the sensitivity ranking, with specific exceptions. A combined evaluation indicates that system availability may be improved effectively by focusing on a reduced set of factors that produce large variation on the measure of interest. The time needed to replace a fully discharged battery in the mobile device is a parameter with high impact on steady-state availability, as well as the coverage factor for the failures of some cloud servers. This paper also shows that a sensitivity analysis through partial derivatives may not capture the real level of impact for some parameters in a discrete domain, such as the number of active servers. The analysis through percentage differences, or the factorial design of experiments, fulfills such a gap.
An efficient approach for parameter estimation of PV model using DE and fuzzy based MPPT controller

Photovoltaic (PV) panel can be represented by an equivalent electric circuit where the major problem with this PV model is the determination of the model parameters. In this paper, PV parameter estimation problem is converted to an optimization problem where Differential Evolution (DE) as an efficient optimizing technique is employed to estimate the model parameters at standard test condition (STC) (1000 W/m2 and 25°C) using the data provided by the manufacturer. A complete equivalent electric circuit model of the PV panel is developed in the MATLAB/Simulink and estimated parameters values are verified by comparing the determined I-V curves with the experimental data given in the data sheet. The developed PV model has been examined under different operating conditions and its accuracy has been verified. An efficient maximum power point tracking (MPPT) controller based on the Fuzzy Logic is also proposed. Analysis and comparison shows that the FLC based controller is more efficient and overcome the shortcomings of the conventional methods.
Illumination invariant face recognition based on improved Local Binary Pattern

Local Binary Pattern (LBP) is a kind of discriminative texture descriptor for characterization of face patterns. However, the value of LBP operator is greatly changed under non-monotonic intensity transformations. As a result, the recognition performance of LBP descriptor for face images with significant illumination variations is severely dropped. In this paper, a novel illumination-invariant face recognition algorithm that applies LBP descriptor is proposed to overcome the performance degradation of LBP descriptor caused by varying illumination conditions. In our proposed algorithm, illumination variation is first compensated by the so called Dynamic Morphological Quotient Image (DMQI) which generates quotient image after morphological filtering. Then, powerful LBP operator is applied to the DMQI to derive a distinctive and robust representation for face patterns in images. We compared the recognition accuracy of the proposed algorithm with that of traditional PCA-based, LDA-based and raw LBP-based method on Yale face dataset B which contains face images with severe lighting variations. Evaluation result demonstrates that our proposed algorithm outperforms the PCA-based, LDA-based and raw LBP-based method by 22.5%, 17.4%, and 5%, respectively, in terms of the recognition accuracy on the first rank. Another advantage of our algorithm is its computational simplicity. It only takes 0.48 seconds on a Pentium IV 3.0G CPU, so it is very suitable for real-time manipulation.
Polar format algorithm for bistatic SAR

Matched filtering (MF) of phase history data is a mathematically ideal but computationally expensive approach to bistatic synthetic aperture radar (SAR) image formation. Fast backprojection algorithms (BPAs) for image formation have recently been shown to give improved O(N/sup 2/ log/sub 2/N) performance. An O(N/sup 2/ log/sub 2/N) bistatic polar format algorithm (PFA) based on a bistatic far-field assumption is derived. This algorithm is a generalization of the popular PFA for monostatic SAR image formation and is highly amenable to implementation with existing monostatic image formation processors. Limits on the size of an imaged scene, analogous to those in monostatic systems, are derived for the bistatic PFA.
A Study on Optimal Design of the Triangle Type Permanent Magnet in IPMSM Rotor by Using the Box–Behnken Design

This paper proposes an optimal rotor design method of an interior permanent magnet synchronous motor (IPMSM) by using a permanent magnet (PM) shape. An IPMSM is a structure in which PMs are buried in an inner rotor. The torque, torque ripple, and safety factor of IPMSM can vary depending on the position of the inserted PMs. To determine the optimal design variables according to the placement of the inserted PMs, parameter analysis was performed. Therefore, a response surface methodology, which is one of the statistical analysis design methods, was used. Among many other response surface methodologies, Box-Behnken design is the most commonly used. For the purpose of this research, Box-Behnken design was used to find the design parameter that can achieve minimum experimental variables of objective function. This paper determines the insert position of the PM to obtain high-torque, low-torque ripple by using a finite-element-method, and this paper obtains an optimal design by using a mechanical stiffness method in which a safety factor is considered.
Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)

We review our work towards achieving competitive performance (classification accuracies) for on-chip machine learning (ML) of large-scale artificial neural networks (ANN) using Non-Volatile Memory (NVM)-based synapses, despite the inherent random and deterministic imperfections of such devices. We then show that such systems could potentially offer faster (up to 25×) and lower-power (from 120-2850×) ML training than GPU-based hardware.
Generating word images using deep generative adversarial networks

As one of the most important research topic of nowadays, deep learning attracts researchers' attention with applications of convolutional (CNNs) and recurrent neural networks (RNNs). By pioneers of the deep learning community, generative adversarial training, which has been working for especially last two years, is defined as the most exciting topic of computer vision for the last 10 years. With the influence of these views, a new training approach is proposed to combine generative adversarial network (GAN) architecture with a cascading training. Using CVL database, text images can be generated in a short training time as a different application from the existing GAN examples.
Impaired Judgments of Sadness But Not Happiness Following Bilateral Amygdala Damage

Although the amygdala's role in processing facial expressions of fear has been well established, its role in the processing of other emotions is unclear. In particular, evidence for the amygdala's involvement in processing expressions of happiness and sadness remains controversial. To clarify this issue, we constructed a series of morphed stimuli whose emotional expression varied gradually from very faint to more pronounced. Five morphs each of sadness and happiness, as well as neutral faces, were shown to 27 subjects with unilateral amygdala damage and 5 with complete bilateral amygdala damage, whose data were compared to those from 12 braindamaged and 26 normal controls. Subjects were asked to rate the intensity and to label the stimuli. Subjects with unilateral amygdala damage performed very comparably to controls. By contrast, subjects with bilateral amygdala damage showed a specific impairment in rating sad faces, but performed normally in rating happy faces. Furthermore, subjects with right unilateral amygdala damage performed somewhat worse than subjects with left unilateral amygdala damage. The findings suggest that the amygdala's role in processing of emotional facial expressions encompasses multiple negatively valenced emotions, including fear and sadness.
The balance between facilitation and competition in mixtures of Eucalyptus and Acacia changes as stands develop

The balance between facilitation and competition is likely to change with age due to the dynamic nature of nutrient, water and carbon cycles, and light availability during stand development. These processes have received attention in harsh, arid, semiarid and alpine ecosystems but are rarely examined in more productive communities, in mixed-species forest ecosystems or in long-term experiments spanning more than a decade. The aim of this study was to examine how inter- and intraspecific interactions between Eucalyptus globulus Labill. mixed with Acacia mearnsii de Wildeman trees changed with age and productivity in a field experiment in temperate south-eastern Australia. Spatially explicit neighbourhood indices were calculated to quantify tree interactions and used to develop growth models to examine how the tree interactions changed with time and stand productivity. Interspecific influences were usually less negative than intraspecific influences, and their difference increased with time for E. globulus and decreased with time for A. mearnsii. As a result, the growth advantages of being in a mixture increased with time for E. globulus and decreased with time for A. mearnsii. The growth advantage of being in a mixture also decreased for E. globulus with increasing stand productivity, showing that spatial as well as temporal dynamics in resource availability influenced the magnitude and direction of plant interactions.
Leadership style and organizational learning : the mediate effect of school vision

Fundamentally, the success of schools depends on first-rate school leadership, on leaders reinforcing the teachers’ willingness to adhere to the school’s vision, creating a sense of purpose, binding them together and encouraging them to engage in continuous learning. Leadership, vision and organizational learning are considered to be the key to school improvement. However, systematic empirical evidence of a direct relationship between leadership, vision and organizational learning is limited. The present study aims to explore the influence of principals’ leadership style on school organizational learning, using school vision as a mediator.  The data were collected from 1,474 teachers at 104 elementary schools in northern Israel, and aggregated to the school level.  Mediating regression analysis demonstrated that the school vision was a significant predictor of school organizational learning and functioned as a partial mediator only between principals’ transformational leadership style and school organizational learning. Moreover, the principals’ transformational leadership style predicted school organizational vision and school organizational learning processes. In other words, school vision, as shaped by the principal and the staff, is a powerful motivator of the process of organizational learning in school.  The research results have implications for the guidance of leadership practice, training, appraisal and professional development.  The paper explores the centrality of school vision and its effects on the achievement of the school’s aims by means of organizational learning processes.
Hepatoblastoma as a Cause of Precocious Puberty

Hepatoblastoma is the most common primary hepatic tumor in children. Precocious puberty is a rare paraneoplastic syndrome that can occur in male children with hepatoblastoma as a result of elevated human chorionic gonadotropin (HCG). The clinical signs of precocious puberty may be detected months before the physical effects of tumor growth such as abdominal pain and distension. Therefore, consideration of hepatoblastoma in young boys presenting with precocious puberty can lead to earlier detection, diagnosis, treatment and thus arrest further virilization. This report describes the presentation, imaging and relevant laboratory findings in two pediatric patients with hepatoblastoma presenting with precocious puberty.
Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.
BPMN for Healthcare Processes

The nature of healthcare processes in a multidisciplinary hospital is inherently complex. In this paper, we identify particular problems of modeling healthcare processes with the de-facto standard process modeling language BPMN. We discuss all possibilities of BPMN adressing these problems. Where plain BPMN fails to produce nice and easily comprehensible results, we propose a new approach: Encorporating role information in process models using the color attribute of tasks complementary to the usage of lanes.
Transcranial magnetic stimulation (TMS) of the sensorimotor cortex and medial frontal cortex modifies human pain perception

Although recent neuroimaging studies have shown that painful stimuli can produce activity in multiple cortical areas, the question remains as to the role of each area in particular aspects of human pain perception. To solve this problem we used transcranial magnetic stimulation (TMS) as an 'interference approach' tool to test the consequence on pain perception of disrupting activity in several areas of cortex known to be activated by painful input.



Weak CO(2) laser stimuli at an intensity around the threshold for pain were given to the dorsum of the left hand in 9 normal subjects. At variable delays (50, 150, 250, 350 ms) after the onset of the laser stimulus, pairs of TMS pulses (dTMS: interpulse interval of 50 ms, and stimulus intensity of 120% resting motor threshold) were applied in separate blocks of trials over either the right sensorimotor cortex (SMI), midline occipital cortex (OCC), second somatosensory cortex (SII), or medial frontal cortex (MFC). Subjects were instructed to judge whether or not the stimulus was painful and to point to the stimulated spot on a drawing of subject's hand.



Subjects judged that the stimulus was painful on more trials than control when dTMS was delivered over SMI at 150-200 ms after the laser stimulus; the opposite occurred when dTMS was delivered over MFC at 50-100 ms. dTMS over the SII or OCC failed to alter the pain threshold.



These results suggest that TMS to SMI can facilitate whereas stimulation over MFC suppresses central processing of pain perception. Since there was no effect of dTMS at any of the scalp sites on the localization task, the cortical locus for point localization of pain may be different from that for perception of pain intensity or may involve a more complex mechanism than the latter.



This is the first report that TMS of SMI facilitates while that of MFC suppresses the central processing of pain perception. This raises the possibility of using TMS as a therapeutic device to control pain.
5 Hyperspectral Imaging Technology Used in Tongue Diagnosis

Traditional Chinese Medicine (TCM) is a range of medical practices used in China for more than four millenniums, a treasure of Chinese people (Lukman, He, & Hui, 2007). The important role of TCM and its profound influence on the health care system in China is well recognized. The West also has drawn the attention towards various aspects of TCM in the past few years (Chan, 1995). TCM consists of a systematized methodology of medical treatment and diagnosis (Watsuji, Arita, Shinohara, & Kitade, 1999). According to the basic concept of TCM, the different body-parts, zang-viscera and fu-viscera, the meridians of the body are linked as an inseparable whole. The inner abnormality can present on outer parts, while the outer disease can turn into the inner parts (Bakshi & Pal, 2010). Therefore, some diseases can be diagnosed from the appearance of the outer body. As the significant component part of TCM theory, TCM diagnostics includes two parts: TCM Sizhen (the four diagnosis methods) and differentiation of syndromes. The TCM physician experience the gravity of health condition of a sick person by means of the four diagnosis methods depending on the doctor's body "sensors" such as fingers, eyes, noses etc. Generally, TCM Sizhen consists of the following four diagnostic processes: inspection, auscultation and olfaction, inquiry, and pulse feeling and palpation (Nenggan & Zhaohui, 2004). In the inspection diagnostic process, TCM practitioners observe abnormal changes in the patient's vitality, colour, appearance, secretions and excretions. The vital signs encompass eyes, tongue, facial expressions, general and body surface appearance. The inter-relationship between the external part of the body such as face and tongue and the internal organ(s) is used to assist TCM doctors to predict the pathological changes of internal organs. In the auscultation and olfaction process, the doctor listen the patient's voice, breathing, and coughing used to judge the pathological changes in the interior of the patient's body. Inquiry diagnosis method is refer to query patient's family history, feelings in various aspects, such as chills and fever, perspiration, appetite and thirst, as well as pain in terms of its nature and locality. Palpation approach involves pulse diagnosis (Siu Cheung, Yulan, & Doan Thi Cam, 2007). The palpation diagnosis has been accepted as one of the most powerful method to give information for making diagnosis from ancient time till now. The pulse waves are measured at six points near the wrists of both hands. The waves are different each other and give us information about different organs (Hsing-Lin, Suzuki, Adachi, & Umeno, 1993). Tongue diagnosis is another inspection diagnostic method which
The Euler – Poincaré Equations and Semidirect Products with Applications to Continuum Theories

We study Euler–Poincaré systems (i.e., the Lagrangian analogue of LiePoisson Hamiltonian systems) defined on semidirect product Lie algebras. We first give a derivation of the Euler–Poincaré equations for a parameter dependent Lagrangian by using a variational principle of Lagrange d’Alembert type. Then we derive an abstract Kelvin-Noether theorem for these equations. We also explore their relation with the theory of Lie-Poisson Hamiltonian systems defined on the dual of a semidirect product Lie algebra. The Legendre transformation in such cases is often not invertible; thus, it does not produce a corresponding Euler–Poincaré system on that Lie algebra. We avoid this potential difficulty by developing the theory of Euler–Poincaré systems entirely within the Lagrangian framework. We apply the general theory to a number of known examples, including the heavy top, ideal compressible fluids and MHD. We also use this framework to derive higher dimensional Camassa-Holm equations, which have many potentially interesting analytical properties. These equations are Euler-Poincaré equations for geodesics on diffeomorphism groups (in the sense of the Arnold program) but where the metric is H rather than L. ∗Research partially supported by NSF grant DMS 96–33161. †Research partially supported by NSF Grant DMS-9503273 and DOE contract DE-FG0395ER25245-A000.
Detecting hidden enemy lines in IP address space

If an outbound flow is observed at the boundary of a protected network, destined to an IP address within a few addresses of a known malicious IP address, should it be considered a suspicious flow? Conventional blacklisting is not going to cut it in this situation, and the established fact that malicious IP addresses tend to be highly clustered in certain portions of IP address space, should indeed raise suspicions. We present a new approach for perimeter defense that addresses this concern. At the heart of our approach, we attempt to infer internal, hidden boundaries in IP address space, that lie within publicly known boundaries of registered IP netblocks. Our hypothesis is that given a known bad IP address, other IP address in the same internal contiguous block are likely to share similar security properties, and may therefore be vulnerable to being similarly hacked and used by attackers in the future. In this paper, we describe how we infer hidden internal boundaries in IPv4 netblocks, and what effect this has on being able to predict malicious IP addresses.
A wireless surveillance and safety system for mine workers based on Zigbee

This paper addresses a cost-effective, flexible solution of underground mine workers' safety. A module of MEMS based sensors are used for underground environment monitoring and automating progression of measurement data through digital wireless communication technique is proposed with high accuracy, smooth control and reliability. A microcontroller is used for collecting data and making decision, based on which the mine worker is informed through alarm as well as voice system. The voice system with both microphone and speaker, transforms into digital signal and effectively communicate wirelessly with the ground control centre computer. ZigBee, based on IEEE 802.15.4 standard is used for this short distance transmission between the hardware fitted with the mine worker and the ground control centre.
Mindfulness meditation-related pain relief: Evidence for unique brain mechanisms in the regulation of pain

The cognitive modulation of pain is influenced by a number of factors ranging from attention, beliefs, conditioning, expectations, mood, and the regulation of emotional responses to noxious sensory events. Recently, mindfulness meditation has been found attenuate pain through some of these mechanisms including enhanced cognitive and emotional control, as well as altering the contextual evaluation of sensory events. This review discusses the brain mechanisms involved in mindfulness meditation-related pain relief across different meditative techniques, expertise and training levels, experimental procedures, and neuroimaging methodologies. Converging lines of neuroimaging evidence reveal that mindfulness meditation-related pain relief is associated with unique appraisal cognitive processes depending on expertise level and meditation tradition. Moreover, it is postulated that mindfulness meditation-related pain relief may share a common final pathway with other cognitive techniques in the modulation of pain.