nils-herrmann/clean_scirepeval huggingface dataset:
Millimetre-wave T-shaped antenna with defected ground structures for 5G wireless networks

This paper presents a T-shaped antenna at millimetre-wave (MMW) frequency ranges to offer a number of advantages including simple structure, high operating bandwidth, and high gain. Defected ground structures (DGS) have been symmetrically added in ground in order to produce multiple resonating bands, accompanied by partial ground plane to achieve continuous operating bandwidth. The antenna consists of T-shaped radiating patch with a coplanar waveguide (CPW) feed. The bottom part has a partial ground plane loaded with five symmetrical split-ring slots. Measured results of antenna prototype show a wide bandwidth of 25.1-37.5 GHz. Moreover, simulation evaluation of peak gain of the antenna is 9.86 dBi at 36.8 GHz, and efficiency is higher than 80% in complete range of operation. The proposed antenna is considered as a potential candidate for the 5G wireless networks and applications.
DESIGN ANALYSIS OF HIGH GAIN WIDEBAND L-PROBE FED MICROSTRIP PATCH ANTENNA

A new high gain wideband L-probe fed inverted EE-H shaped slotted (LEE-H) microstrip patch antenna is presented in this paper. The design adopts contemporary techniques; L-probe feeding, inverted patch structure with air-filled dielectric, and EE-H shaped patch. The integration of these techniques leads to a new patch antenna with a low profile as well as useful operational features, as the broadband and high gain. The measured result showed satisfactory performance with achievable impedance bandwidth of 21.15% at 10 dB return loss (VSWR ≤ 2) and a maximum gain of 9.5 dBi. The antenna exhibits stable radiation pattern in the entire operating band.
Market States and Momentum

We test overreaction theories of short-run momentum and long-run reversal in the cross section of stock returns. Momentum profits depend on the state of the market, as predicted. From 1929 to 1995, the mean monthly momentum profit following positive market returns is 0.93 percent, whereas the mean profit following negative market returns is negative 0.37 percent. The up-market momentum reverses in the long-run. Our results are robust to the conditioning information in macroeconomic factors. Moreover, we find that macroeconomic factors are unable to explain momentum profits after simple methodological adjustments to take account of microstructure concerns.
A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle associated with a three-phase version of the hybridge rectifier

This paper proposes the use of a three-phase version of the hybridge rectifier in the three-phase zero-voltage switch (ZVS) DC/DC converter with asymmetrical duty cycle. The use of this new rectifier improves the efficiency of the converter because only three diodes are responsible for the conduction losses in the secondary side. The current in the secondary side of the transformer is half the output current. In addition to this, all the advantages of the three-phase DC/DC converter, i.e., the increased frequency of the output and input currents, the improved distribution of the losses, as well as the soft commutation for a wide load range, are preserved. Therefore, the resulting topology is capable of achieving high efficiency and high power density at high power levels. The theoretical analysis, simulation, and experimental results obtained from a 6-kW prototype, and also a comparison of the efficiency of this converter with the full-bridge rectifier are presented.
Abnormal image detection in endoscopy videos using a filter bank and local binary patterns

Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician's time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.
Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.  10.4018/978-1-4666-2047-6.ch006
A Convolutional Learning System for Object Classification in 3-D Lidar Data

In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.
A mobile-based home automation system

The rapidly advancing mobile communication technology and the decrease in costs make it possible to incorporate mobile technology into home automation systems. We propose a mobile-based home automation system that consists of a mobile phone with Java capabilities, a cellular modem, and a home server. The home appliances are controlled by the home server, which operates according to the user commands received from the mobile phone via the cellular modem. In our proposed system the home server is built upon an SMS/GPRS (short message service/general packet radio service) mobile cell module and a microcontroller, allowing a user to control and monitor any variables related to the home by using any Java capable cell phone. This paper presents the design and implementation of AT modem driver, text based command processing software, and power failure resilient output for a microcontroller to facilitate in sending and receiving data via the cell module, together with the design of Java application to enable the cell phone to send commands and receive alerts through the cell module
A Novel Active Snubber for High-Power Boost Converters

A technique which improves the performance of the boost converter by reducing the reverse-recovery-related losses in the boost switch and rectifier with an active snubber that is implemented with a minimum number of components is presented. This minimum-component-count snubber consists of a snubber inductor, an auxiliary switch, and a rectifier. The proposed technique reduces the reverse-recovery-related losses by controlling the turn-off di/dt rate of the rectifier current with the snubber inductor connected in series with the boost switch and rectifier. The voltage and current stresses of the components in the proposed active-snubber boost converter are similar to those in its conventional “hard-switched” counterpart.
The Radical Plasticity Thesis: How the Brain Learns to be Conscious

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the "Radical Plasticity Thesis." In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves "signal detection on the mind"; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks.
Cortical mechanisms of loss of consciousness: insight from TMS/EEG studies.

In a recent series of experiments we recorded the electroencephalogram (EEG) response to a direct cortical stimulation in humans during wakefulness, NREM sleep, REM sleep and anesthesia by means of a combination of transcranial magnetic stimulation (TMS) and high-density EEG (hd-EEG). TMS/hd-EEG measurements showed that, while during wakefulness and REM sleep the brain is able to sustain long-range specific patterns of activation, during NREM sleep and Midazolam-induced anesthesia, when consciousness fades, this ability is lot: the thalamocortical system, despite being active and reactive, either breaks down in causally independent modules (producing a local slow wave), or it bursts into an explosive and non-specific response (producing a global EEG slow wave). We hypothesize that, like spontaneous sleep slow waves, the slow waves triggered by TMS during sleep and anaesthesia are due to bistability between upand down-states in thalamocortical circuits. In this condition, the inescapable occurrence of a silent, down state after an initial activation impairs the ability of thalamocortical circuits to sustain long-range, differentiated patterns of activation, a theoretical requisite for consciousness. According to animal experiments and computer simulations, thalamocortical bistability may result from increased K-currents, from alterations of the balance between excitation and inhibition and from partial cortical de-afferentation. We hypothesize that these factor may play an important role in determining loss, and recovery, of consciousness also in brain-injured subjects. If this is the case, some types of brain lesions may impair information transmission, above and beyond the associated anatomical disconnection, by inducing bistability in portions of the thalamocortical system that are otherwise healthy.
Linear model predictive control of the locomotion of Pepper, a humanoid robot with omnidirectional wheels

The goal of this paper is to present a new real-time controller based on linear model predictive control for an omnidirectionnal wheeled humanoid robot. It is able to control both the mobile base of the robot and its body, while taking into account dynamical constraints. It makes it possible to have high velocity and acceleration motions by predicting the dynamic behavior of the robot in the future. Experimental results are proposed on the robot Pepper made by Aldebaran, showing good performance in terms of robustness and tracking control, efficiently managing kinematic and dynamical constraints.
Human Action Recognition under Log-Euclidean Riemannian Metric

This paper presents a new action recognition approach based on local spatio-temporal features. The main contributions of our approach are twofold. First, a new local spatio-temporal feature is proposed to represent the cuboids detected in video sequences. Specifically, the descriptor utilizes the covariance matrix to capture the self-correlation information of the low-level features within each cuboid. Since covariance matrices do not lie on Euclidean space, the Log-Euclidean Riemannian metric is used for distance measure between covariance matrices. Second, the Earth Mover’s Distance (EMD) is used for matching any pair of video sequences. In contrast to the widely used Euclidean distance, EMD achieves more robust performances in matching histograms/distributions with different sizes. Experimental results on two datasets demonstrate the effectiveness of the proposed approach.
Network Traffic Management

The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.
Detection of Signals in Noise 10.1. Receiver Noise

Noise is the unwanted energy that interferes with the ability of the receiver to detect the wanted signal. It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver. In underwater sonar systems external acoustic noise is generated by waves and wind on the water surface, by biological agents (fish, prawns etc) and manmade sources such as engine noise. In radar and lidar sensors the external electromagnetic noise is generated by various natural mechanisms such as the sun and lightning amongst others. Manmade sources of electromagnetic noise are myriad, from car ignition systems and fluorescent lights through other broadcast signals. As discussed earlier, noise within the sensor is generated by the thermal motion of the conduction electrons in the ohmic portions of the receiver input stages. This is known as Thermal or Johnson Noise. Noise power P N is expressed in terms of the temperature T o of a matched resistor at the input of the receiver β o N kT P = W, (10.1) where: k – Boltzmann's Constant (1.38×10-23 J/K), T o – System Temperature (usually 290K), β – Receiver Noise Bandwidth (Hz). The noise power in practical receivers is always greater than that which can be accounted for by thermal noise alone. The total noise at the output of the receiver, N, can be considered to be equal to the noise power output from an ideal receiver multiplied by a factor called the Noise Figure, NF NF kT F P N o N N. β = = W.
Business model design : conceptualizing networked value co-creation

A common thread in the modern marketing theories, such as service-dominant logic (Vargo and Lusch, 2004) and viable systems approach VSA (Golinelli et al., 2002), is the notion value co-creation: the locus of value creation is n o longer perceived to reside within firm boundaries but value is considered to be co-created between various actors within the networked market. The evolution of value creation, from value cr ation by the manufacturing firm to value cocreation in a network, necessitates a corresponding change in the concepts used to depict value creation. The present research investigates busines s models as a broader conceptualization of value co-creation that captures this change.  The topic is approached by a combination of liter atu e review and interactive research (Gummesson, 2002a), including interactions with managers from 12 international companies.  Business models are defined as configurations of twelve interrelated elements, covering market, offering, operational, and management viewp oints. The effectiveness of a business model in value co-creation is defined by the internal config urational fit between all business model elements and the external configurational fit between provid er’s and customers’ business models.  A firm can radically improve the value co-creatio n by designing business models that have high degree of internal and extern al configurational fit.  For a scholarly audience the article contributes to the discussion on value cocreation by providing a conceptualization of the bu siness model construct depicting the value cocreation in a network. For a practitioner audience it offers ideas for improving business performance through conscious business model develo pm nt.
A relative entropy-based approach to image thresholding

In this paper, we present a new image thresholding technique which uses the relative entropy (also known as the Kullback-Leiber discrimination distance function) as a criterion of thresholding an image. As a result, a gray level minimizing the relative entropy will be the desired threshold. The proposed relative entropy approach is different from two known entropy-based thresholding techniques, the local entropy and joint entropy methods developed by N. R. Pal and S. K. Pal in the sense that the former is focused on the matching between two images while the latter only emphasized the entropy of the cooccurrence matrix of one image. The experimental results show that these three techniques are image dependent and the local entropy and relative entropy seem to perform better than does the joint entropy. in addition, the relative entropy can complement the local entropy and joint entropy in terms of providing different details which the others cannot. As far as computing saving is concerned, the relative entropy approach also provides the least computational complexity. Thresholding Relative entropy Local entropy Joint entropy Co-occurrence matrix
Required Capacity in 5 G Massive Multi-Antenna Techniques Non-Orthogonal Multiple Access Spectral Efficiency Interference Cancellation Cooperation

A range of efficient wireless processes and enabling techniques are put under a magnifier glass in the quest for exploring different manifestations of correlated processes, where sub-Nyquist sampling may be invoked as an explicit benefit of having a sparse transform-domain representation. For example, wide-band next-generation systems require a high Nyquistsampling rate, but the channel impulse response (CIR) will be very sparse at the high Nyquist frequency, given the low number of reflected propagation paths. This motivates the employment of compressive sensing based processing techniques for frugally exploiting both the limited radio resources and the network infrastructure as efficiently as possible. A diverse range of sophisticated compressed sampling techniques is surveyed and we conclude with a variety of promising research ideas related to large-scale antenna arrays, non-orthogonal multiple access (NOMA), and ultra-dense network (UDN) solutions, just to name a few.
Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions

The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.
Speech emotion recognition using hidden Markov models

In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is proposed. The proposed method makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier. The emotions are classified into six categories. The category labels used are, the archetypal emotions of Anger, Disgust, Fear, Joy, Sadness and Surprise. Adatabase consisting of 60 emotional utterances, each from twelve speakers is constructed and used to train and test the proposed system. Performance of the LFPC feature parameters is compared with that of the linear prediction Cepstral coefficients (LPCC) and mel-frequency Cepstral coefficients (MFCC) feature parameters commonly used in speech recognition systems. Results show that the proposed system yields an average accuracy of 78% and the best accuracy of 96% in the classification of six emotions. This is beyond the 17% chances by a random hit for a sample set of 6 categories. Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters.
Narrow assessments misrepresent development and misguide policy: comment on Steinberg, Cauffman, Woolard, Graham, and Banich (2009).

Intellectual and psychosocial functioning develop along complex learning pathways. Steinberg, Cauffman, Woolard, Graham, and Banich (see record 2009-18110-001) measured these two classes of abilities with narrow, biased assessments that captured only a segment of each pathway and created misleading age patterns based on ceiling and floor effects. It is a simple matter to shift the assessments to produce the opposite pattern, with cognitive abilities appearing to develop well into adulthood and psychosocial abilities appearing to stop developing at age 16. Their measures also lacked a realistic connection to the lived behaviors of adolescents, abstracting too far from messy realities and thus lacking ecological validity and the nuanced portrait that the authors called for. A drastically different approach to assessing development is required that (a) includes the full age-related range of relevant abilities instead of a truncated set and (b) examines the variability and contextual dependence of abilities relevant to the topics of murder and abortion.