nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of MPP Voltage and Power of mc-Si PV Modules in Partial Shading Conditions

Photovoltaic (PV) system performance is influenced by several factors, including irradiance, temperature, shading, degradation, mismatch losses, soiling, etc. Shading of a PV array, in particular, either complete or partial, can have a significant impact on its power output and energy yield, depending on array configuration, shading pattern, and the bypass diodes incorporated in the PV modules. In this paper, the effect of partial shading on multicrystalline silicon (mc-Si) PV modules is investigated. A PV module simulation model implemented in P-Spice is first employed to quantify the effect of partial shading on the I-V curve and the maximum power point (MPP) voltage and power. Then, generalized formulae are derived, which permit accurate enough evaluation of MPP voltage and power of mc-Si PV modules, without the need to resort to detailed modeling and simulation. The equations derived are validated via experimental results.
Semi-supervised word polarity identification in resource-lean languages

Sentiment words, as fundamental constitutive parts of subjective sentences, have a substantial effect on analysis of opinions, emotions and beliefs. Most of the proposed methods for identifying the semantic orientations of words exploit rich linguistic resources such as WordNet, subjectivity corpora, or polarity tagged words. Shortage of such linguistic resources in resource-lean languages affects the performance of word polarity identification in these languages. In this paper, we present a method which exploits a language with rich subjectivity analysis resources (English) to identify the polarity of words in a resource-lean foreign language. The English WordNet and a sparse foreign WordNet infrastructure are used to create a heterogeneous, multilingual and weighted semantic network. To identify the semantic orientation of foreign words, a random walk based method is applied to the semantic network along with a set of automatically weighted English positive and negative seeds. In a post-processing phase, synonym and antonym relations in the foreign WordNet are used to filter the random walk results. Our experiments on English and Persian languages show that the proposed method can outperform state-of-the-art word polarity identification methods in both languages.
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
Strategies for Combating Dark Networks

Our goal in this paper is to explore two generic approaches to disrupting dark networks: kinetic and nonkinetic. The kinetic approach involves aggressive and offensive measures to eliminate or capture network members and their supporters, while the non-kinetic approach involves the use of subtle, non-coercive means for combating dark networks. Two strategies derive from the kinetic approach: Targeting and Capacity-building. Four strategies derive from the non-kinetic approach: Institution-Building, Psychological Operations, Information Operations and Rehabilitation. We use network data from Noordin Top’s South East Asian terror network to illustrate how both kinetic and non-kinetic strategies could be pursued depending on a commander’s intent. Using this strategic framework as a backdrop, we strongly advise the use of SNA metrics in developing alterative counter-terrorism strategies that are contextdependent rather than letting SNA metrics define and drive a particular strategy.
Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.
Origami Theory and Its Applications: A Literature Review

This paper presents the fundamentals of Origami engineering and its application in nowadays as well as future industry. Several main cores of mathematical approaches such as HuzitaHatori axioms, Maekawa and Kawasaki’s theorems are introduced briefly. Meanwhile flaps and circle packing by Robert Lang is explained to make understood the underlying principles in designing crease pattern. Rigid origami and its corrugation patterns which are potentially applicable for creating transformable or temporary spaces is discussed to show the transition of origami from paper to thick material. Moreover, some innovative applications of origami such as eyeglass, origami stent and high tech origami based on mentioned theories and principles are showcased in section III; while some updated origami technology such as Vacuumatics, self-folding of polymer sheets and programmable matter folding which could greatlyenhance origami structureare demonstrated in Section IV to offer more insight in future origami. Origami, origami application, origami engineering, origami technology, rigid origami.
Optimal Design Method for Large-Scale YASA Machines

The objective of this paper is to develop an analytical optimal design tool to determine a megawatt-scale yokelss and segmented armature (YASA) machine design that fulfills the application requirements and constraints. This analytical tool considers both electromagnetic and structural designs. Different designs that provide similar performance will have emerged from this analytical process. A design reference map that graphically shows the relationships and tradeoffs between each objective function is introduced. A multicriteria optimization process is applied to determine a design optimum. In the optimization process, the design objectives considered in this study are to minimize the outer diameter, to minimize the structural mass of the machine, to minimize the copper and iron losses, and to minimize the active materials cost. Three variables considered in calculating the objective functions are the air-gap flux density, the ratio of outer-to-inner machine diameter, and the current loading. The optimization method uses a pseudoweight vector to provide the flexibility to prioritize one or more objective functions, dependant on the specific application requirements.
Evaluating the intangible benefits of Business Intelligence: review and research agenda

A Business Intelligence (BI) system is a technology that provides significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these is easily recognised. High adoption rates and investment in BI software and services suggest that these systems are a principal provider of decision support in the current marketplace. Most business investments are screened using some form of evaluation process or technique. The benefits of BI are such that traditional evaluation techniques have difficulty in identifying the soft, intangible benefits often provided by BI. This paper, forming the first part of a larger research project, aims to review current evaluation techniques that address intangible benefits, presents issues relating to the evaluation of BI in industry, and suggests a research agenda to advance what is presently a limited body of knowledge relating to the evaluation of BI intangible benefits.
An improved K-nearest-neighbor algorithm for text categorization

Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naïve Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpuses show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naïve Bayes and Support Vector Machine classifiers. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it is valuable in practical application.
A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

With the abundance of exceptionally High Dimensional data, feature selection has become an essential element in the Data Mining process. In this paper, we investigate the problem of efficient feature selection for classification on High Dimensional datasets. We present a novel filter based approach for feature selection that sorts out the features based on a score and then we measure the performance of four different Data Mining classification algorithms on the resulting data. In the proposed approach, we partition the sorted feature and search the important feature in forward manner as well as in reversed manner, while starting from first and last feature simultaneously in the sorted list. The proposed approach is highly scalable and effective as it parallelizes over both attribute and tuples simultaneously allowing us to evaluate many of potential features for High Dimensional datasets. The newly proposed framework for feature selection is experimentally shown to be very valuable with real and synthetic High Dimensional datasets which improve the precision of selected features. We have also tested it to measure classification accuracy against various feature selection process.
Strategic Dialogue Management via Deep Reinforcement Learning

Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan—where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (‘bots’), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities.
A 6–18 GHz integrated HPA-DPDT GaN MMIC

In this contribution an integrated HPA-DPDT for next generation AESA TRMs is presented. The proposed circuit relies on a concurrent design technique merging switches and HPA matching network. Realized MMIC features a 3×5mm2 outline operating in the 6-18 GHz band with a typical output power of 2W, an associated PAE of 13% and 3dB insertion loss in RX mode.
Smart Mining for Deep Metric Learning

To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of this triplet model can be compromised by the fact that the vast majority of the training samples will produce gradients with magnitudes that are close to zero. This issue has motivated the development of methods that explore the global structure of the embedding and other methods that explore hard negative/positive mining. The effectiveness of such mining methods is often associated with intractable computational requirements. In this paper, we propose a novel deep metric learning method that combines the triplet model and the global structure of the embedding space. We rely on a smart mining procedure that produces effective training samples for a low computational cost. In addition, we propose an adaptive controller that automatically adjusts the smart mining hyper-parameters and speeds up the convergence of the training process. We show empirically that our proposed method allows for fast and more accurate training of triplet ConvNets than other competing mining methods. Additionally, we show that our method achieves new state-of-the-art embedding results for CUB-200-2011 and Cars196 datasets.
Theory of cellular automata: A survey

This article surveys some theoretical aspects of cellular automata CA research. In particular, we discuss classical and new results on reversibility, conservation laws, limit sets, decidability questions, universality and topological dynamics of CA. The selection of topics is by no means comprehensive and reflects the research interests of the author. The main goal is to provide a tutorial of CA theory to researchers in other branches of natural computing, to give a compact collection of known results with references to their proofs, and to suggest some open problems.
Machine-Learning Approaches to Power-System Security Assessment

This paper describes ongoing research and development of machine learning and other complementary automatic learning techniques in a framework adapted to the specific needs of power system security assessment. In the proposed approach, random sampling techniques are considered to screen all relevant power system operating situations, while existing numerical simulation tools are exploited to derive detailed security information. The heart of the framework is provided by machine learning methods used to extract and synthesize security knowledge reformulated in a suitable way for decision making. This consists of transforming the data base of case by case numerical simulations into a power system security knowledge base. The main expected fallouts with respect to existing security assessment methods are computational efficiency, better physical insight into non-linear problems, and management of uncertainties. The paper discusses also the complementary roles of various automatic learning methods in this framework, such as decision tree induction, multilayer perceptrons and nearest neighbor classifiers. Illustrations are taken from two different real large scale power system security problems : transient stability assessment of the Hydro-Québec system and voltage security assessment of the system of Electricité de France.
A quaternion-based model for optimal control of the SkySails airborne wind energy system

Airborne wind energy systems are capable of extracting energy from higher wind speeds at higher altitudes. The configuration considered in this paper is based on a tethered kite flown in a pumping orbit. This pumping cycle generates energy by winching out at high tether forces and driving a generator while flying figures-of-eight, or lemniscates, as crosswind pattern. Then, the tether is reeled in while keeping the kite at a neutral position, thus leaving a net amount of generated energy. In order to achieve an economic operation, optimization of pumping cycles is of great interest. In this paper, first the principles of airborne wind energy will be briefly revisited. The first contribution is a singularityfree model for the tethered kite dynamics in quaternion representation, where the model is derived from first principles. The second contribution is an optimal control formulation and numerical results for complete pumping cycles. Based on the developed model, the setup of the optimal control problem (OCP) is described in detail along with its numerical solution based on the direct multiple shooting method in the CasADi optimization environment. Optimization results for a pumping cycle consisting of six lemniscates show that the approach is capable to find an optimal orbit in a few minutes of computation time. For this optimal orbit, the power output is increased by a factor of two compared to a sophisticated initial guess for the considered test scenario.
An Improved Control Strategy of Limiting the DC-Link Voltage Fluctuation for a Doubly Fed Induction Wind Generator

The paper presents to develop a new control strategy of limiting the dc-link voltage fluctuation for a back-to-back pulsewidth modulation converter in a doubly fed induction generator (DFIG) for wind turbine systems. The reasons of dc-link voltage fluctuation are analyzed. An improved control strategy with the instantaneous rotor power feedback is proposed to limit the fluctuation range of the dc-link voltage. An experimental rig is set up to valid the proposed strategy, and the dynamic performances of the DFIG are compared with the traditional control method under a constant grid voltage. Furthermore, the capabilities of keeping the dc-link voltage stable are also compared in the ride-through control of DFIG during a three-phase grid fault, by using a developed 2 MW DFIG wind power system model. Both the experimental and simulation results have shown that the proposed control strategy is more effective, and the fluctuation of the dc-link voltage may be successfully limited in a small range under a constant grid voltage and a non-serious grid voltage dip.
LexPageRank: Prestige in Multi-Document Text Summarization

Multidocument extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Centrality is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We are now considering an approach for computing sentence importance based on the concept of eigenvector centrality (prestige) that we call LexPageRank. In this model, a sentence connectivity matrix is constructed based on cosine similarity. If the cosine similarity between two sentences exceeds a particular predefined threshold, a corresponding edge is added to the connectivity matrix. We provide an evaluation of our method on DUC 2004 data. The results show that our approach outperforms centroid-based summarization and is quite successful compared to other summarization systems.
A comprehensive simulation study of slotted CSMA/CA for IEEE 802.15.4 wireless sensor networks

In this paper, we analyze the performance limits of the slotted CSMA/CA mechanism of IEEE 802.15.4 in the beacon-enabled mode for broadcast transmissions in WSNs. The motivation for evaluating the beaconenabled mode is due to its flexibility for WSN applications as compared to the non-beacon enabled mode. Our analysis is based on an accurate simulation model of the slotted CSMA/CA mechanism on top of a realistic physical layer, with respect to the IEEE 802.15.4 standard specification. The performance of the slotted CSMA/CA is evaluated and analyzed for different network settings to understand the impact of the protocol attributes (superframe order, beacon order and backoff exponent) on the network performance, namely in terms of throughput (S), average delay (D) and probability of success (Ps). We introduce the concept of utility (U) as a combination of two or more metrics, to determine the best offered load range for an optimal behavior of the network. We show that the optimal network performance using slotted CSMA/CA occurs in the range of 35% to 60% with respect to an utility function proportional to the network throughput (S) divided by the average delay (D).
Bullying Detection

Data mining of Social networks is a new but interesting field within Data Mining. We leverage the power of sentiment analysis to detect bullying instances in Twitter. We are interested in understanding bullying in social networks, especially in Twitter. To best of our understanding, there is no previous work on using sentiment analysis to detect bullying instances. Our training data set consists of Twitter messages containing commonly used terms of abuse, which are considered noisy labels. These data are publicly available and can be easily retrieved by directly accessing the Twitter streaming API. For the classification of Twitter messages, also known as tweets, we use the Naïve Bayes classifier. It‟s accuracy was close to 70% when trained with “commonly terms of abuse” data. The main contribution of this paper is the idea of using sentiment analysis to detect bullying instances.
A Comparative Study of Various Routing Protocols in VANET

Vehicular Ad Hoc Networks (VANET) is a subclass of Mobile ad hoc networks which provides a distinguished approach for Intelligent Transport System (ITS). The survey of routing protocols in VANET is important and necessary for smart ITS. T his paper discusses the advantages / disadvantages and the applications of various routing protocols for vehicular ad hoc networks. It explores the motivation behind the designed, and traces the evolution of these routing protocols. F inally the paper concludes by a tabular comparison of the various routing protocols for VANET.