nils-herrmann/clean_scirepeval huggingface dataset:
Fifth Generation (5G) Wireless Technology “Revolution in Telecommunication”

After the up rise of 4G wireless mobile technology takes place; researchers, mobile operator industries representative, academic institutions have started to look into the advancement (technological) towards 5G communication networks due to some main demands that are meliorated data rates, better capacity, minimized latency and better QoS (Quality of Service). To established the 5G mobile communication technological foundation, various research works or projects entailing main mobile infrastructure manufacturers, academia and international mobile network operators have been introduced recently. Nevertheless, 5G mobile services to be made available for use, their architecture, and their performance have not been evidently explicated. In this paper, we represent thorough overview of 5G the next generation mobile technology. We mainly throws light on 5G network architecture, 5G radio spectrum, ultra-dense radio access networks (UDRAN), traffic offloading of mobile, cognitive radio (CR), software defined radio (SDR), software defined networking (SDN), mixed infrastructure, and 5G network impact on the society.
An Overview of Predictors for Intrinsically Disordered Proteins over 2010–2014

The sequence-structure-function paradigm of proteins has been changed by the occurrence of intrinsically disordered proteins (IDPs). Benefiting from the structural disorder, IDPs are of particular importance in biological processes like regulation and signaling. IDPs are associated with human diseases, including cancer, cardiovascular disease, neurodegenerative diseases, amyloidoses, and several other maladies. IDPs attract a high level of interest and a substantial effort has been made to develop experimental and computational methods. So far, more than 70 prediction tools have been developed since 1997, within which 17 predictors were created in the last five years. Here, we presented an overview of IDPs predictors developed during 2010-2014. We analyzed the algorithms used for IDPs prediction by these tools and we also discussed the basic concept of various prediction methods for IDPs. The comparison of prediction performance among these tools is discussed as well.
Class-D Audio Amplifiers with Negative Feedback

There are many different designs for audio amplifiers. Class-D, or switching, amplifiers generate their output signal in the form of a high-frequency square wave of variable duty cycle (ratio of on time to off time). The square-wave nature of the output allows a particularly efficient output stage, with minimal losses. The output is ultimately filtered to remove components of the spectrum above the audio range. Mathematical models are derived here for a variety of related class-D amplifier designs that use negative feedback. These models use an asymptotic expansion in powers of a small parameter related to the ratio of typical audio frequencies to the switching frequency to develop a power series for the output component in the audio spectrum. These models confirm that there is a form of distortion intrinsic to such amplifier designs. The models also explain why two approaches used commercially succeed in largely eliminating this distortion; a new means of overcoming the intrinsic distortion is revealed by the analysis.
Double side redistribution layer process on embedded wafer level package for package on package (PoP) applications

In this paper, an embedded wafer level package with Cu through mold via (TMV) interconnects was developed for package on package (PoP) application. Cu pillar interconnects for different heights were fabricated on daisy chain test chips and sacrificial chips. The daisy chain test chips were then stacked onto the sacrificial chips using die attach film, and the chip stacks were picked and placed onto a molding tape for mold encapsulation to form a re-configured wafer. The reconfigured wafer was mechanically backgrinded on both sides to remove sacrificial chips and to expose the Cu TMV. The thinned re-configured wafer was temporarily bonded to a stiff Si carrier using a temporary adhesive, in order to reduce the wafer warpage to enable wafer level processing of the Cu redistribution layers (RDL). After front side RDL processing, the re-configured wafer is de-bonded and re-bonded for backside RDL processing. The warpage value of the reconfigured wafer was measured during different process steps and through-scan was performed using a scanning acoustic microscope to inspect the quality of temporary bonding of re-configured wafers to a Si carrier. Electrical test shows good connectivity between front and back side RDL with Cu TMV, thus enabling embedding wafer level package for PoP application.
Analysis of Current Crowding Effects in Multiturn Spiral Inductors

The effective trace resistance of a multiturn spiral inductor operating at high frequencies is known to increase dramatically above its dc value, due to proximity effect or current crowding. This phenomenon, which dominates resistance increases due to skin effect, is difficult to analyze precisely and has generally required electromagnetic simulation for quantitative assessment. Current crowding is studied in this paper through approximate analytical modeling, and first-order expressions are derived for predicting resistance as a function of frequency. The results are validated through comparisons with electromagnetic simulations and compared with measured data taken from a spiral inductor implemented in a silicon-on-sapphire process.
The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores.

Standardized calibrated severity scores (CSS) have been created for Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) Modules 1-4 as a metric of the relative severity of autism-specific behaviors. Total and domain CSS were created for the Toddler Module to facilitate comparison to other modules. Analyses included 388 children with ASD age 12-30 months and were replicated on 435 repeated assessments from 127 children with ASD. Compared to raw scores, associations between total and domain CSS and participant characteristics were reduced in the original sample. Verbal IQ effects on Social Affect-CSS were not reduced in the replication sample. Toddler Module CSS increases comparability of ADOS-2 scores across modules and allows studies of symptom trajectories to extend to earlier ages.
Multi-document Summarization Using Support Vector Regression

Most multi-document summarization systems follow the extractive framework based on various features. While more and more sophisticated features are designed, the reasonable combination of features becomes a challenge. Usually the features are combined by a linear function whose weights are tuned manually. In this task, Support Vector Regression (SVR) model is used for automatically combining the features and scoring the sentences. Two important problems are inevitably involved. The first one is how to acquire the training data. Several automatic generation methods are introduced based on the standard reference summaries generated by human. Another indispensable problem in SVR application is feature selection, where various features will be picked out and combined into different feature sets to be tested. With the aid of DUC 2005 and 2006 data sets, comprehensive experiments are conducted with consideration of various SVR kernels and feature sets. Then the trained SVR model is used in the main task of DUC 2007 to get the extractive summaries.
A Laminated Waveguide Magic-T With Bandpass Filter Response in Multilayer LTCC

A laminated waveguide magic-T with imbedded Chebyshev filter response is developed in a multilayer low-temperature co-fired ceramic technology by vertically stacked rectangular cavity resonators with highly symmetric coupling structures. The cavities provide even- and odd-symmetric field distributions by simultaneously resonating TE102 and TE201 modes at the center frequency of the magic-T. Thus, the in-phase and out-of-phase responses of the magic-T function are accomplished according to the induced mode. Meanwhile, the filter frequency response is realized by the cascaded cavities with proper coupling strengths according to the filter specification. With the degenerate, but orthogonal cavity modes, a fewer number of cavities are required and the circuit size is further reduced. A third-order bandpass magic-T is designed and fabricated to operate at 24 GHz with 6% fractional bandwidth. The highly symmetric structure of the implemented magic-T provides a low in-band magnitude imbalance ( ±0.25 dB) and phase imbalance (0°-6°). The sum and difference ports also provide an isolation greater than 30 dB in the operation frequency range. Measurement and simulated results are in good agreement, both validating the design concept.
Analyzing and labeling telecom communities using structural properties

Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:—rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work.
Towards the Implementation of IoT for Environmental Condition Monitoring in Homes

In this paper, we have reported an effective implementation for Internet of Things used for monitoring regular domestic conditions by means of low cost ubiquitous sensing system. The description about the integrated network architecture and the interconnecting mechanisms for the reliable measurement of parameters by smart sensors and transmission of data via internet is being presented. The longitudinal learning system was able to provide a self-control mechanism for better operation of the devices in monitoring stage. The framework of the monitoring system is based on a combination of pervasive distributed sensing units, information system for data aggregation, and reasoning and context awareness. Results are encouraging as the reliability of sensing information transmission through the proposed integrated network architecture is 97%. The prototype was tested to generate real-time graphical information rather than a test bed scenario.
TubeSpam: Comment Spam Filtering on YouTube

The profitability promoted by Google in its brand new video distribution platform YouTube has attracted an increasing number of users. However, such success has also attracted malicious users, which aim to self-promote their videos or disseminate viruses and malwares. Since YouTube offers limited tools for comment moderation, the spam volume is shockingly increasing which lead owners of famous channels to disable the comments section in their videos. Automatic comment spam filtering on YouTube is a challenge even for established classification methods, since the messages are very short and often rife with slangs, symbols and abbreviations. In this work, we have evaluated several top-performance classification techniques for such purpose. The statistical analysis of results indicate that, with 99.9% of confidence level, decision trees, logistic regression, Bernoulli Naive Bayes, random forests, linear and Gaussian SVMs are statistically equivalent. Based on this, we have also offered the TubeSpam - an accurate online system to filter comments posted on YouTube.
Modeling Default Risk

Default risk is the uncertainty surrounding a firm's ability to service its debts and obligations. Prior to default, there is no way to discriminate unambiguously between firms that will default and those that won't. At best we can only make probabilistic assessments of the likelihood of default. As a result, firms generally pay a spread over the default-free rate of interest that is proportional to their default probability to compensate lenders for this uncertainty. AUTHORS Peter Crosbie Jeff Bohn 415-352-1279 jeff.bohn@mkmv.com CONTACTS Ahmet Kocagil 212-553-3889 ahmet.kocagil@mkmv.com  Credit Monitor®, EDFCalc®, Private Firm Model®, KMV®, CreditEdge, Portfolio Manager, Portfolio Preprocessor, GCorr, DealAnalyzer, CreditMark, the KMV logo, Moody's RiskCalc, Moody's Financial Analyst, Moody's Risk Advisor, LossCalc, Expected Default Frequency, and EDF are trademarks of MIS Quality Management Corp. Published by: Moody’s KMV Company To Learn More Please contact your Moody’s KMV client representative, visit us online at www.moodyskmv.com, contact Moody’s KMV via e-mail at info@mkmv.com, or call us at: NORTH AND SOUTH AMERICA, NEW ZEALAND AND AUSTRALIA, CALL: 1 866 321 MKMV (6568) or 415 296 9669 EUROPE, THE MIDDLE EAST, AFRICA AND INDIA, CALL: 44 20 7778 7400 FROM ASIA CALL: 813 3218 1160
Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis.

Biomechanical analysis is a powerful tool in the evaluation of movement dysfunction in orthopaedic and neurologic populations. Three-dimensional (3D) motion capture systems are widely used, accurate systems, but are costly and not available in many clinical settings. The Microsoft Kinect™ has the potential to be used as an alternative low-cost motion analysis tool. The purpose of this study was to assess concurrent validity of the Kinect™ with Brekel Kinect software in comparison to Vicon Nexus during sagittal plane gait kinematics. Twenty healthy adults (nine male, 11 female) were tracked while walking and jogging at three velocities on a treadmill. Concurrent hip and knee peak flexion and extension and stride timing measurements were compared between Vicon and Kinect™. Although Kinect measurements were representative of normal gait, the Kinect™ generally under-estimated joint flexion and over-estimated extension. Kinect™ and Vicon hip angular displacement correlation was very low and error was large. Kinect™ knee measurements were somewhat better than hip, but were not consistent enough for clinical assessment. Correlation between Kinect™ and Vicon stride timing was high and error was fairly small. Variability in Kinect™ measurements was smallest at the slowest velocity. The Kinect™ has basic motion capture capabilities and with some minor adjustments will be an acceptable tool to measure stride timing, but sophisticated advances in software and hardware are necessary to improve Kinect™ sensitivity before it can be implemented for clinical use.
Applying Deep Learning to Better Predict Cryptocurrency Trends

In this paper, we will create deep learning models using the Python library, Keras, to make predictions on Bitcoin. We designed two neural networks to make similar predictions with important differences. In our first approach, we created a simple neural net with one input layer and an additional dropout layer to generate a continuous value. This continuous value is the predicted price of the Bitcoin a week from the given input. For the second model, we attempted to predict whether or not the price of the Bitcoin would go up 3%, stay within 3%, or go down 3% a week from a given date. Instead of returning a single continuous value, the result contains an array of three values. Each value is a percentage of the likelihood that it will either go up 3%, stay within 3%, or go down 3%. We approached designing the models using many different optimizers, activation functions, number of neurons, and various quantities of layers before settling on the final models. We also compared three different optimizers for each problem: Stochastic Gradient Descent (SGD), Adam, and RMSProp. We compared the results of all three optimizers to determine which one was the most effective at creating the most accurate model.
Patients' Experience After a Fall and Their Perceptions of Fall Prevention: A Qualitative Study.

An exploratory descriptive study was conducted to explore the perspectives of patients who had fallen in the hospital; 100 patients were interviewed. An inductive content analysis approach was adopted. Six themes emerged: Apathetic toward falls, self-blame behavior, reluctance to impose on busy nurses, negative feelings toward nurses, overestimating own ability, and poor retention of information. Patients often downplayed the risks of falls and were reluctant to call for help.
Multi-fiber Networks for Video Recognition

In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9× and 13× less computations than the I3D  and R(2+1)D  models, respectively, yet providing higher accuracy.
2 Saddles in Deep Networks : Background and Motivation

Recent years have seen a growing interest in understanding deep neural networks from an optimization perspective. It is understood now that converging to low-cost local minima is sufficient for such models to become effective in practice. However, in this work, we propose a new hypothesis based on recent theoretical findings and empirical studies that deep neural network models actually converge to saddle points with high degeneracy. Our findings from this work are new, and can have a significant impact on the development of gradient descent based methods for training deep networks. We validated our hypotheses using an extensive experimental evaluation on standard datasets such as MNIST and CIFAR-10, and also showed that recent efforts that attempt to escape saddles finally converge to saddles with high degeneracy, which we define as ‘good saddles’. We also verified the famous Wigner’s Semicircle Law in our experimental results.
Robust Detection of Lines Using the Progressive Probabilistic Hough Transform

In the paper we present the progressive probabilistic Hough transform (PPHT). Unlike the probabilistic HT, where the standard HT is performed on a preselected fraction of input points, the PPHT minimizes the amount of computation needed to detect lines byexploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection are interleaved. The most salient features are likely to be detected first. While retaining its robustness, experiments show that the PPHT has, in many circumstances, advantages over the standard HT.
Design and Experimental Investigation of a Compact Circularly Polarized Integrated Filtering Antenna for Wearable Biotelemetric Devices

A compact circularly polarized (CP) integrated filtering antenna is reported for wearable biotelemetric devices in the 2.4 GHz ISM band. The design is based on a mutual synthesis of a CP patch antenna connected to a bandpass filter composed of coupled stripline open-loop resonators, which provides an integrated low-profile radiating and filtering module with a compact form factor of 0.44λ0 × 0.44λ0 × 0.04λ0. The optimized filtering antenna is fabricated and measured, achieving an , an axial ratio of less than 3 dB and gain higher than 3.5 dBi in the targeted ISM band. With the integrated filtering functionality, the antenna exhibits good out-of-band rejection over an ultra-wide frequency range of 1-6 GHz . Further full-wave simulations and experiments were carried out, verifying that the proposed filtering antenna maintains these desirable properties even when mounted in close proximity to the human body at different positions. The stable impedance performance and the simultaneous wide axial ratio and radiated power beam widths make it an ideal candidate as a wearable antenna for off-body communications. The additional integrated filtering functionality further improves utility by greatly reducing interference and crosstalk with other existing wireless systems.
Use of opposition method in the test of high-power electronic converters

The test and the characterization of medium or high-power electronic converters, under nominal operating conditions, are made difficult by the requirement of high-power electrical source and load. In addition, the energy lost during the test may be very significant. The opposition method, which consists of an association of two identical converters supplied by the same source, one operating as a generator, the other as a receptor, can be a better way to do these test. Another advantage is the possibility to realize accurate measurements of the different losses in the converters under test. In the first part of this paper, the characteristics of the method concerning loss measurements are compared to those of the electrical or calorimetric methods, then it is shown how it can be applied to different types of power electronic converters, choppers, switched mode power supplies, and pulsewidth modulation inverters. In the second part, different examples of studies conducted by the authors, and using this method, are presented. They have varying goals, from the test of soft-switching inverters to the characterization of integrated gate-commutated thyristor (IGCT) devices mounted into 2-MW choppers.