nils-herrmann/clean_scirepeval huggingface dataset:
Comparative study of MPPT algorithms under variable resistive load

This paper proposed a fuzzy logic control (FLC) based maximum power point tracking (MPPT) of a photovoltaic (PV) system under standard temperature condition (STC) and variable connected load. The PV system including a PV module BP SX150S, buck-boost DC-DC converter, MPPT, and resistive load is modeled and simulated using Matlab/Simulink package. In order to compare the performance of the conventional perturb and observe (P&O) and FLC based MPPT methods at STC (G=1000 W/m2 and T=25 °C) and rapid variation at connected load, two times, rising time (tr) and recovering time, are calculated. From the simulation results we can observe that the FLC based MPPT method can track the MPP of the PV module faster and with less tr and recovering time at transient state and effectively eliminates the power oscillation around the MPP of the PV module at steady state, as compared with the perturb and observe (P&O) method, using the duty cycle (D) of the DC-DC converter as a control variable. Hence, the PV system with the proposed tracking method has better transient and steady performances.
A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS

In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.
Diagnosis Related Groups in Europe

Diagnosis-Related Group (DRG) systems were introduced in Europe to increase the transparency of services provided by hospitals and to incentivize greater efficiency in the use of resources invested in acute hospitals. In many countries, these systems were also designed to contribute to improving – or at least protecting – the quality of care. After more than a decade of experience with using DRGs in Europe, this book considers whether the extensive use of DRGs has contributed towards achieving these objectives.
Solid-state light sources getting smart.

More than a century after the introduction of incandescent lighting and half a century after the introduction of fluorescent lighting, solid-state light sources are revolutionizing an increasing number of applications. Whereas the efficiency of conventional incandescent and fluorescent lights is limited by fundamental factors that cannot be overcome, the efficiency of solid-state sources is limited only by human creativity and imagination. The high efficiency of solid-state sources already provides energy savings and environmental benefits in a number of applications. However, solid-state sources also offer controllability of their spectral power distribution, spatial distribution, color temperature, temporal modulation, and polarization properties. Such "smart" light sources can adjust to specific environments and requirements, a property that could result in tremendous benefits in lighting, automobiles, transportation, communication, imaging, agriculture, and medicine.
Overview of PI-Based Solutions for the Control of DC Buses of a Single-Phase H-Bridge Multilevel Active Rectifier

The H-bridge-based multilevel active rectifier is an attractive topology that allows feeding of multiple dc loads. The main control problem is to manage state variables (one current plus dc voltages) with only switching functions. In this paper, three PI-based solutions are discussed, and the tuning of the best solution is addressed in case resonant controllers are used instead of classical integrators. Moreover, practical problems such as the implementation and the influence of phase-locked-loop systems and of analog filters (first order and Butterworth) on the measurements are discussed.
An efficient deep neural networks training framework for robust face recognition

In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization. A deep architecture based on the softmax loss function is designed to initialize the DNN. 2) The adaptive fine-tuning. Based on the trained model, a set of high-quality triplet samples is generated and used to fine-tune the network, where an adaptive triplet loss function is introduced to improve the discriminative ability of DNN. Experimental results show that, the model obtained by the proposed DNN training framework achieves 97.3% accuracy on the LFW benchmark with low training complexity, which verifies the efficiency and effectiveness of the proposed framework.
A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms

Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.
Peak Detection based Spread Spectrum Audio Watermarking using Discrete Wavelet Transform

This paper proposes a robust spread spectrum based audio watermarking scheme using Discrete wavelet transform (DWT). Here we use a peak detection algorithm to obtain high robustness. In this watermarking scheme, watermarks are embedded into the peak value of detail coefficients of, transformed audio signal. It is a blind watermarking technique which is used for providing copyright protection and content authentication for digital content.
Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping

Electronic commerce typically lacks human warmth and sociability, since it is more impersonal, anonymous and automated than traditional face-to-face commerce. This paper explores how human warmth and sociability can be integrated through the web interface to positively impact consumer attitudes towards online shopping. An empirical study was undertaken to investigate the impact of various levels of socially rich text and picture design elements on the perception of online social presence and its subsequent effect on antecedents of attitudes towards websites. Higher levels of perceived social presence are shown to positively impact the perceived usefulness, trust and enjoyment of shopping websites, leading to more favourable consumer attitudes. Implications of these finding for practitioners and future research are outlined.
A PDF Wrapper for Table Processing

We propose a PDF document wrapper system that is specifically targeted at table processing applications. We (i) review the PDF specifications and identify particular challenges from the table processing point of view, (ii) specify a table-oriented document model containing the required atomic elements for table extraction and understanding applications. Our evaluation showed that the wrapper was able to detect important features such as page columns, bullets and numbering in all measures, recording over 90% accuracy, leading to better table locating and segmenting.
Plantar fasciitis: diagnosis and therapeutic considerations.

Plantar fasciitis is the most common cause of inferior heel pain. The pain and discomfort associated with this condition can have a dramatic impact on physical mobility. The etiology of this condition is not clearly understood and is probably multi-factorial in nature. Weight gain, occupation-related activity, anatomical variations, poor biomechanics, overexertion, and inadequate footwear are contributing factors. Although plantar fasciitis is generally regarded as a self-limited condition, it can take months to years to resolve, presenting a challenge for clinicians. Many treatment options are available that demonstrate variable levels of efficacy. Conservative therapies include rest and avoidance of potentially aggravating activities, stretching and strengthening exercises, orthotics, arch supports, and night splinting. Other considerations include use of anti-inflammatory agents, ultrasonic shockwave therapy, and, in the most extreme cases, surgery. This article reviews plantar fasciitis, presents the most effective treatment options currently available, and proposes nutritional considerations that may be beneficial in the management of this condition.
OFDM : Basic Concepts , Scope & its Applications

Orthogonal frequency division multiplexing (OFDM) is a special case of multicarrier transmission where a single DataStream is transmitted over a number of lower rate subcarriers. In July 1998, the IEEE standardization group decided to select OFDM as the basis for their new 5-GHz standard aiming a range of data stream from 6 up to 54 Mbps. This new standard is the first one to use OFDM in packetbased communications. In wireless communication, concept of parallel transmission of symbols is used to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and losses and also some of its application.  Orthogonal frequency division multiplexing(OFDM); BER; ISI; PAPR; DVB; DAB.
Discovering interpretable geo-social communities for user behavior prediction

Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.
Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.
Low-power high-performance SAR ADC with redundancy and digital background calibration

As technology scales, the improved speed and energy efficiency make the successiveapproximation-register (SAR) architecture an attractive alternative for applications that require high-speed and high-accuracy analog-to-digital converters (ADCs). In SAR ADCs, the key linearity and speed limiting factors are capacitor mismatch and incomplete digital-to-analog converter (DAC)/reference voltage settling. In this thesis, a sub-radix-2 SAR ADC is presented with several new contributions. The main contributions include investigation of using digital error correction (redundancy) in SAR ADCs for dynamic error correction and speed improvement, development of two new calibration algorithms to digitally correct for manufacturing mismatches, design of new architecture to incorporate redundancy within the architecture itself while achieving 94% better energy efficiency compared to conventional switching algorithm, development of a new capacitor DAC structure to improve the SNR by four times with improved matching, joint design of the analog and digital circuits to create an asynchronous platform in order to reach the targeted performance, and analysis of key circuit blocks to enable the design to meet noise, power and timing requirements. The design is fabricated in standard 1P9M 65nm CMOS technology with 1.2V supply. The active die area is 0.083mm with full rail-to-rail input swing of 2.4VP−P . A 67.4dB SNDR, 78.1dB SFDR, +1.0/-0.9 LSB12 INL and +0.5/-0.7 LSB12 DNL are achieved at 50MS/s at Nyquist rate. The total power consumption, including the estimated calibration and reference power, is 2.1mW, corresponding to 21.9fJ/conv.step FoM. This ADC achieves the best FoM of any ADCs with greater than 10b ENOB and 10MS/s sampling rate.  Duane S. Boning Title: Professor of Electrical Engineering and Computer Science Thesis Supervisor: Hae-Seung Lee Title: Professor of Electrical Engineering and Computer Science
Multi-label convolutional neural network based pedestrian attribute classification

Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database.
Fine-Grained Product Class Recognition for Assisted Shopping

Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, in shelves images taken with a smartphone in a grocery store. Our system consists of three components: (a) We automatically recognize useful text on product packaging, e.g., product name and brand, and build a mapping of words to product classes based on the large-scale GroceryProducts dataset. When the user populates the shopping list, we automatically infer the product class of each entered word. (b) We perform fine-grained product class recognition when the user is facing a shelf. We discover discriminative patches on product packaging to differentiate between visually similar product classes and to increase the robustness against continuous changes in product design.
Author ' s personal copy Better than my loved ones : Social comparison tendencies among narcissists

Narcissists pursue superiority and status at frequent costs to their relationships, and social comparisons seem central to these pursuits. Critically, these comparison tendencies should distinguish narcissism from healthy self-esteem. We tested this hypothesis in a study examining individual differences in everyday comparison activity. Narcissists, relative to those with high self-esteem, (1) made more frequent social comparisons, particularly downward ones, (2) were more likely to think they were better-off than other important individuals in their lives, and (3) perceived themselves superior to these important individuals on agentic traits. However, narcissists’ positive emotional reactions to these self-flattering comparisons were based on their high self-esteem. These results suggest that comparison processes play an important role in narcissists’ endless pursuit of status and admiration.
An Ontology-based Adaptive Personalized E-learning System, Assisted by Software Agents on Cloud Storage

E-Learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behaviour. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behaviour. Finally, various conclusions are drawn by exploring the learner’s behavior in an adaptive environment for the proposed e-learning system. e-learning, ontology, semantic web, cloud storage, software agents, adaptive.
A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.
Algorithm and Experiments in Testing Planar Graphs for Isomorphism

We give an algorithm for isomorphism testing of planar graphs suitable for practical implementation. The algorithm is based on the decomposition of a graph into biconnected components and further into SPQR-trees. We provide a proof of the algorithm’s correctness and a complexity analysis. We determine the conditions in which the implemented algorithm outperforms other graph matchers, which do not impose topological restrictions on graphs. We report experiments with our planar graph matcher tested against McKay’s, Ullmann’s, and SUBDUE’s (a graph-based data mining system) graph matchers. Article Type Communicated by Submitted Revised regular paper Giuseppe Liotta September 2003 February 2005 This research is sponsored by the Air Force Rome Laboratory under contract F3060201-2-0570. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of the Rome Laboratory, or the United States Government. J. Kukluk et al., Planar Graph Isomorphism, JGAA, 8(3) 313–356 (2004) 314
Seglearn: A Python Package for Learning Sequences and Time Series

seglearn is an open-source Python package for performing machine learning on time series or sequences. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. Sequences and series may be learned directly with deep learning models or via feature representation with classical machine learning estimators. This package is compatible with scikit-learn and is listed under scikit-learn ”Related Projects”. The package depends on numpy, scipy, and scikit-learn. seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage. Source code and documentation can be downloaded from https://github.com/dmbee/seglearn.