nils-herrmann/clean_scirepeval huggingface dataset:
Renewable Energy Systems With Photovoltaic Power Generators: Operation and Modeling

A substantial increase of photovoltaic (PV) power generators installations has taken place in recent years, due to the increasing efficiency of solar cells as well as the improvements of manufacturing technology of solar panels. These generators are both grid-connected and stand-alone applications. We present an overview of the essential research results. The paper concentrates on the operation and modeling of stand-alone power systems with PV power generators. Systems with PV array-inverter assemblies, operating in the slave-and-master modes, are discussed, and the simulation results obtained using a renewable energy power system modular simulator are presented. These results demonstrate that simulation is an essential step in the system development process and that PV power generators constitute a valuable energy source. They have the ability to balance the energy and supply good power quality. It is demonstrated that when PV array- inverters are operating in the master mode in stand-alone applications, they well perform the task of controlling the voltage and frequency of the power system. The mechanism of switching the master function between the diesel generator and the PV array-inverter assembly in a stand-alone power system is also proposed and analyzed. Finally, some experimental results on a practical system are compared to the simulation results and confirm the usefulness of the proposed approach to the development of renewable energy systems with PV power generators.
Relative age effect in Spanish association football: its extent and implications for wasted potential.

Spain is one of the largest and most successful powers in international youth football, but this success has not extended to the national team. This lack of continued success seems to indicate a loss of potential. The relative age effect has been detected in football in many countries. Understanding the extent of this bias in the youth teams of Spanish elite clubs may help to improve selection processes and reduce the waste of potential. Comparisons between players from: the Spanish Professional Football League, all age categories of these clubs' youth teams, the Under-17 to Under-21 national teams, the national team, and the Spanish population, show a constant tendency to under-represent players from the later months of the selection year at all age groups of youth and Under-17 to Under-21 national teams. Professional and national team players show a similar but diminished behaviour that weakens with ageing, which suggests that talent identification and selection processes can be improved to help better identify potential talent early on and minimize wasted potential.
Adaptive methods for job recommendation based on user clustering

Job recommender systems are designed to suggest a ranked list of jobs that could be associated with employee's interest. Most of existing systems use only one approach to make recommendation for all employees, while a specific method normally is good enough for a group of employees. Therefore, this study proposes an adaptive solution to make job recommendation for different groups of user. The proposed methods are based on employee clustering. Firstly, we group employees into different clusters. Then, we select a suitable method for each user cluster based on empirical evaluation. The proposed methods include CB-Plus, CF-jFilter and HyR-jFilter have applied for different three clusters. Empirical results show that our proposed methods is outperformed than traditional methods.
A time series approach for profiling attack

The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack are reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the time series nature of the power traces. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives.
On Loss Functions for Deep Neural Networks in Classification

Deep neural networks are currently among the most commonly used classifiers. Despite easily achieving very good performance, one of the best selling points of these models is their modular design – one can conveniently adapt their architecture to specific needs, change connectivity patterns, attach specialised layers, experiment with a large amount of activation functions, normalisation schemes and many others. While one can find impressively wide spread of various configurations of almost every aspect of the deep nets, one element is, in authors’ opinion, underrepresented – while solving classification problems, vast majority of papers and applications simply use log loss. In this paper we try to investigate how particular choices of loss functions affect deep models and their learning dynamics, as well as resulting classifiers robustness to various effects. We perform experiments on classical datasets, as well as provide some additional, theoretical insights into the problem. In particular we show that L1 and L2 losses are, quite surprisingly, justified classification objectives for deep nets, by providing probabilistic interpretation in terms of expected misclassification. We also introduce two losses which are not typically used as deep nets objectives and show that they are viable alternatives to the existing ones.
Mobile Learning projects - a critical analysis of the state of the art

This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control, communication, subject and objective. Although a significant number of projects have ventured to incorporate the physical context into the learning experience, few projects include a socializing context. Tool support ranges from pure content delivery to content construction by the learners. Although few projects explicitly discuss the Mobile Learning control issues, one can find all approaches from pure teacher control to learner control. Despite the fact that mobile phones initially started as a communication device, communication and collaboration play a surprisingly small role in Mobile Learning projects. Most Mobile Learning projects support novices, although one might argue that the largest potential is supporting advanced learners. All results show the design space and reveal gaps in Mobile Learning research.
Multi-View Image Generation from a Single-View

How to generate multi-view images with realistic-looking appearance from only a single view input is a challenging problem. In this paper, we attack this problem by proposing a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs). It generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produces coarse images of different views. Conditioned on the generated coarse images, it then performs adversarial learning to fill details consistent with the input and generate the fine images. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that the generated images with the proposed VariGANs are more plausible than those generated by existing approaches, which provide more consistent global appearance as well as richer and sharper details.
Big Data Framework

We are constantly being told that we live in the Information Era - the Age of BIG data. It is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is, current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Paper addresses this lacunae by characterising the facets of Big Data and proposing a framework in which Big Data applications can be developed. The framework consists of three Stages and seven Layers to divide Big Data application into modular blocks. The aim is to enable organizations to better manage and architect a very large Big Data application to gain competitive advantage by allowing management to have a better handle on data processing.
Software defined networking for security enhancement in wireless mobile networks

In recent years we have seen a fast change in the networking industry: leading by the Software Defined Networking (SDN) paradigm that separates the control plane from the data plane to enable programmability and centralized control of the network infrastructure, the SDN design not only simplifies the network management but also accelerates the innovation speed of deploying advanced network applications. Meanwhile, the landscape of the wireless and mobile industry is changing dramatically as well. Given the advance of wireless technologies such as 4G and WiFi offering a pervasive Internet access, the traffic growth from the smartphone-alike devices has placed an increasing strain on the mobile network infrastructure and infringed the profit. Since the demand is increasing together with the growth of mobile users, the incumbent legacy infrastructure is already calling for an upgrade to overcome its existing limitations in terms of network management and security. In this paper, we advocate that the way forward is to integrate SDN and fully utilize its feature to solve the problem. As the security issue has raise serious concern in the networking community recently, we focus on the security aspect and investigate how to enhance the security with SDN for the wireless mobile networks.
A DSSS superregenerative receiver with tau-dither loop

This paper describes a direct-sequence spread-spectrum superregenerative receiver using a PN code synchronization loop based on the tan-dither technique. The receiver minimizes overall complexity by using a single signal processing path for data detection and PN code synchronization. An analytical study on the loop dynamics is presented, and the conditions for optimum performance are examined. Experimental results in the 433 MHz European ISM band confirm the receiver ability to perform acquisition and tracking, achieving a sensitivity of -103 dBm and an input dynamic range of 65 dB.
Performance Enhancement of Novel InAs/Si Hetero Double-Gate Tunnel FET Using Gaussian Doping

In this paper, for the first time, an InAs/Si heterojunction double-gate tunnel FET (H-DGTFET) has been analyzed for low-power high-frequency applications. For this purpose, the suitability of the device for low-power applications is investigated by extracting the threshold voltage of the device using a transconductance change method and a constant current method. Furthermore, the effects of uniform and Gaussian drain doping profile on dc characteristics and analog/RF performances are investigated for different channel lengths. A highly doped layer is placed in the channel near the source-channel junction, and this decreases the width of the depletion region, which improves the ON-current (ION) and the RF performance. Furthermore, the circuit-level performance assessment is done by implementing a common source amplifier using the H-DGTFET; a 3-dB roll-off frequency of 230.11 GHz and a unity-gain frequency of 5.4 THz were achieved.
Voice over IP performance monitoring

We describe a method for monitoring Voice over IP (VoIP) applications based upon a reduction of the ITU-T's E-Model to transport level, measurable quantities. In the process, 1) we identify the relevant transport level quantities, 2) we discuss the tradeoffs between placing the monitors within the VoIP gateways versus placement of the monitors within the transport path, and 3) we identify several areas where further work and consensus within the industry are required. We discover that the relevant transport level quantities are the delay, network packet loss and the decoder's de-jitter buffer packet loss. We find that an in-path monitor requires the definition of a reference de-jitter buffer implementation to estimate voice quality based upon observed transport measurements. Finally, we suggest that more studies are required, which evaluate the quality of various VoIP codecs in the presence of representative packet loss patterns.
LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification

The key challenge of intelligent fault diagnosis is to develop features that can distinguish different categories. Because of the unique properties of mechanical data, predetermined features based on prior knowledge are usually used as inputs for fault classification. However, proper selection of features often requires expertise knowledge and becomes more difficult and time consuming when volume of data increases. In this paper, a novel deep learning network (LiftingNet) is proposed to learn features adaptively from raw mechanical data without prior knowledge. Inspired by convolutional neural network and second generation wavelet transform, the LiftingNet is constructed to classify mechanical data even though inputs contain considerable noise and randomness. The LiftingNet consists of split layer, predict layer, update layer, pooling layer, and full-connection layer. Different kernel sizes are allowed in convolutional layers to improve learning ability. As a multilayer neural network, deep features are learned from shallow ones to represent complex structures in raw data. Feasibility and effectiveness of the LiftingNet is validated by two motor bearing datasets. Results show that the proposed method could achieve layerwise feature learning and successfully classify mechanical data even with different rotating speed and under the influence of random noise.
Learning joint demosaicing and denoising based on sequential energy minimization

Demosaicing is an important first step for color image acquisition. For practical reasons, demosaicing algorithms have to be both efficient and yield high quality results in the presence of noise. The demosaicing problem poses several challenges, e.g. zippering and false color artifacts as well as edge blur. In this work, we introduce a novel learning based method that can overcome these challenges. We formulate demosaicing as an image restoration problem and propose to learn efficient regularization inspired by a variational energy minimization framework that can be trained for different sensor layouts. Our algorithm performs joint demosaicing and denoising in close relation to the real physical mosaicing process on a camera sensor. This is achieved by learning a sequence of energy minimization problems composed of a set of RGB filters and corresponding activation functions. We evaluate our algorithm on the Microsoft Demosaicing data set in terms of peak signal to noise ratio (PSNR) and structured similarity index (SSIM). Our algorithm is highly efficient both in image quality and run time. We achieve an improvement of up to 2.6 dB over recent state-of-the-art algorithms.
A fast probabilistic ego-motion estimation framework for radar

This paper presents a fast, joint spatial- and Doppler velocity-based, probabilistic approach for ego-motion estimation for single and multiple radar-equipped robots. The normal distribution transform is used for the fast and accurate position matching of consecutive radar detections. This registration technique is successfully applied to laser-based scan matching. To overcome discontinuities of the original normal distribution approach, an appropriate clustering technique provides a globally smooth mixed-Gaussian representation. It is shown how this matching approach can be significantly improved by taking the Doppler information into account. The Doppler information is used in a density-based approach to extend the position matching to a joint likelihood optimization function. Then, the estimated ego-motion maximizes this function. Large-scale real world experiments in an urban environment using a 77 GHz radar show the robust and accurate ego-motion estimation of the proposed algorithm. In the experiments, comparisons are made to state-of-the-art algorithms, the vehicle odometry, and a high-precision inertial measurement unit.
An effective log mining approach for database intrusion detection

Organizations spend a significant amount of resources securing their servers and network perimeters. However, these mechanisms are not sufficient for protecting databases. In this paper, we present a new technique for identifying malicious database transactions. Compared to many existing approaches which profile SQL query structures and database user activities to detect intrusions, the novelty of this approach is the automatic discovery and use of essential data dependencies, namely, multi-dimensional and multi-level data dependencies, for identifying anomalous database transactions. Since essential data dependencies reflect semantic relationships among data items and are less likely to change than SQL query structures or database user behaviors, they are ideal for profiling data correlations for identifying malicious database
A 6.4-Gb/s CMOS SerDes core with feed-forward and decision-feedback equalization

A 4.9-6.4-Gb/s two-level SerDes ASIC I/O core employing a four-tap feed-forward equalizer (FFE) in the transmitter and a five-tap decision-feedback equalizer (DFE) in the receiver has been designed in 0.13-/spl mu/m CMOS. The transmitter features a total jitter (TJ) of 35 ps p-p at 10/sup -12/ bit error rate (BER) and can output up to 1200 mVppd into a 100-/spl Omega/ differential load. Low jitter is achieved through the use of an LC-tank-based VCO/PLL system that achieves a typical random jitter of 0.6 ps over a phase noise integration range from 6 MHz to 3.2 GHz. The receiver features a variable-gain amplifier (VGA) with gain ranging from -6to +10dB in /spl sim/1dB steps, an analog peaking amplifier, and a continuously adapted DFE-based data slicer that uses a hybrid speculative/dynamic feedback architecture optimized for high-speed operation. The receiver system is designed to operate with a signal level ranging from 50 to 1200 mVppd. Error-free operation of the system has been demonstrated on lossy transmission line channels with over 32-dB loss at the Nyquist (1/2 Bd rate) frequency. The Tx/Rx pair with amortized PLL power consumes 290 mW of power from a 1.2-V supply while driving 600 mVppd and uses a die area of 0.79 mm/sup 2/.
Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint

Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learning method to augment Multinomial Naive Bayes (MNB) for text classification. Despite its success, MNB-EM is not stable, and may succeed or fail to improve MNB. We believe that this is because MNB-EM lacks the ability to preserve the class distribution on words. In this paper, we propose a novel method to augment MNBEM by leveraging the word-level statistical constraint to preserve the class distribution on words. The word-level statistical constraints are further converted to constraints on document posteriors generated by MNB-EM. Experiments demonstrate that our method can consistently improve MNBEM, and outperforms state-of-art baselines remarkably.
FAULT SENSING IN A REMOTE TRANSFORMER USING GSM & AUTOMATIC ON / OFF OF STREET LAMPS

Networked embedded systems have become quite important nowadays, especially for monitoring and controlling the devices. Advances in embedded system technologies have led to the development of residential gateway and automation systems. Apart from frequent power cuts, residential areas suffer from a serious problem that people are not aware about the power cuts due to power disconnections in the transformers, also power theft issues and power wastage in the street lamps during day time exists. So this paper presents a lifestyle system using GSM which transmits the status of the transformer.
Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization

The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.
Growing a Brain: Fine-Tuning by Increasing Model Capacity

CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that growing a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results.
Using Dependency Relations for Text Classification

We investigate the use of syntactically related pairs of words for the task of text classification. The set of all pairs of syntactically related words should intuitively provide a better description of what a document is about, than the set of proximity-based N-grams or selective syntactic phrases. We generate syntactically related word pairs using a dependency parser. We experimented with Support Vector Machines and Decision Tree learners on the 10 most frequent classes from the Reuters-21578 corpus. Results show that syntactically related pairs of words produce better results in terms of accuracy and precision when used alone or combined with unigrams, compared to unigrams alone.