nils-herrmann/clean_scirepeval huggingface dataset:
A Dual-Band CPW-Fed Inductive Slot-Monopole Hybrid Antenna

A dual-band coplanar waveguide (CPW)-fed hybrid antenna consisting of a 5.4 GHz high-band CPW-fed inductive slot antenna and a 2.4 GHz low-band bifurcated F-shaped monopole antenna is proposed and investigated experimentally. This antenna possesses an appealing characteristic that the CPW-fed inductive slot antenna reinforces and thus improves the radiation efficiency of the bifurcated monopole antenna. Moreover, due to field orthogonality, one band resonant frequency and return loss bandwidth of the proposed hybrid antenna allows almost independent optimization without noticeably affecting those of the other band.
A theoretical argument for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with several complex-valued vectors followed by (2) taking the absolute value of every entry of the resulting vectors followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as “data-driven multiscale windowed power spectra,” “data-driven multiscale windowed absolute spectra,” “data-driven multiwavelet absolute values,” or (in their most general configuration) “data-driven nonlinear multiwavelet packets.” Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max. pooling, etc., do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). This note develops “data-driven multiscale windowed spectra” for certain stochastic processes that are common in the modeling of time series (such as audio) and natural images (including patterns and textures). We motivate the construction of such multiscale spectra in the form of “local averages of multiwavelet absolute values” or (in the most general configuration) “nonlinear multiwavelet packets” and connect these to certain “complex-valued convolutional networks.” A textbook treatment of all concepts and terms used above and below is given by . Further information is available in the original work of [7], , , , , , , [20], and [18], for example. The work of , [13], , , and [3] also develops complex-valued convolutional networks (convnets). Renormalization group theory and its connection to convnets is discussed by ; this connection is incredibly insightful, though we leave further discussion to the cited work. Our exposition relies on nothing but the basic signal processing treated by . For simplicity, we first limit consideration to the special case of a doubly infinite sequence of nonnegative random variables Xk, where k ranges over the integers. This input data will be the result of convolving an unmeasured independent and identically distributed (i.i.d.) sequence Zk, where k ranges over the integers, with an unknown sequence of real numbers fk, where k ranges over the integers (this latter sequence is known as a “filter,” whereas the i.i.d. sequence is known as “white noise”): Xj = ∞
A 128-Channel, 710 M Samples/Second, and Less Than 10 ps RMS Resolution Time-to-Digital Converter Implemented in a Kintex-7 FPGA

Recent investigations of Field Programmable Gate Array (FPGA)-based time-to-digital converters (TDCs) have predominantly focused on improving the time resolution of the device. However, the monolithic integration of multi-channel TDCs and the achievement of high measurement throughput remain challenging issues for certain applications. In this paper, the potential of the resources provided by the Kintex-7 Xilinx FPGA is fully explored, and a new design is proposed for the implementation of a high performance multi-channel TDC system on this FPGA. Using the tapped-delay-line wave union TDC architecture, in which a negative pulse is triggered by the hit signal propagating along the carry chain, two time measurements are performed in a single carry chain within one clock cycle. The differential non-linearity and time resolution can be significantly improved by realigning the bins. The on-line calibration and on-line updating of the calibration table reduce the influence of variations of environmental conditions. The logic resources of the 6-input look-up tables in the FPGA are employed for hit signal edge detection and bubble-proof encoding, thereby allowing the TDC system to operate at the maximum allowable clock rate of the FPGA and to achieve the maximum possible measurement throughput. This resource-efficient design, in combination with a modular implementation, makes the integration of multiple channels in one FPGA practicable. Using our design, a 128-channel TDC with a dead time of 1.47 ns, a dynamic range of 360 ns, and a root-mean-square resolution of less than 10 ps was implemented in a single Kintex-7 device.
A Quranic quote verification algorithm for verses authentication

The growth of Quranic digital publishing increases the need to develop a better framework to authenticate Quranic quotes with the original source automatically. This paper aims to demonstrate the significance of the quote authentication approach. We propose an approach to verify the e-citation of the Quranic quote as compared with original texts from the Quran. In this paper, we will concentrate mainly on discussing the Algorithm to verify the fundamental text for Quranic quotes.
A survey on wireless multimedia sensor networks

The availability of low-cost hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., networks of wirelessly interconnected devices that are able to ubiquitously retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, the state of the art in algorithms, protocols, and hardware for wireless multimedia sensor networks is surveyed, and open research issues are discussed in detail. Architectures for WMSNs are explored, along with their advantages and drawbacks. Currently off-the-shelf hardware as well as available research prototypes for WMSNs are listed and classified. Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations.
Cogging Torque Reduction in Permanent Magnet Machines

This paper examines two methods - magnet shifting and optimizing the magnet pole arc - for reducing cogging torque in permanent magnet machines. The methods were applied to existing machine designs and their performance was calculated using finite-element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly eliminated using the two methods. However, there was some residual cogging in the prototypes due to manufacturing difficulties. In both methods, the back electromotive force was improved by reducing harmonics while preserving the magnitude.
Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized PCFG. We also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. Empirical results demonstrate the utility of our methods in predicting human reading times.
Attribute-Based Access Control with Efficient Revocation in Data Outsourcing Systems

Some of the most challenging issues in data outsourcing scenario are the enforcement of authorization policies and the support of policy updates. Ciphertext-policy attribute-based encryption is a promising cryptographic solution to these issues for enforcing access control policies defined by a data owner on outsourced data. However, the problem of applying the attribute-based encryption in an outsourced architecture introduces several challenges with regard to the attribute and user revocation. In this paper, we propose an access control mechanism using ciphertext-policy attribute-based encryption to enforce access control policies with efficient attribute and user revocation capability. The fine-grained access control can be achieved by dual encryption mechanism which takes advantage of the attribute-based encryption and selective group key distribution in each attribute group. We demonstrate how to apply the proposed mechanism to securely manage the outsourced data. The analysis results indicate that the proposed scheme is efficient and secure in the data outsourcing systems.
Deep image aesthetics classification using inception modules and fine-tuning connected layer

In this paper we investigate the image aesthetics classification problem, aka, automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition. Deep convolutional neural network (DCNN) methods have recently shown promising results for image aesthetics assessment. Currently, a powerful inception module is proposed which shows very high performance in object classification. However, the inception module has not been taken into consideration for the image aesthetics assessment problem. In this paper, we propose a novel DCNN structure codenamed ILGNet for image aesthetics classification, which introduces the Inception module and connects intermediate Local layers to the Global layer for the output. Besides, we use a pre-trained image classification CNN called GoogLeNet on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVA dataset . The experimental results show that the proposed ILGNet outperforms the state of the art results in image aesthetics assessment in the AVA benchmark.
A TorPath to TorCoin: Proof-of-Bandwidth Altcoins for Compensating Relays

The Tor network relies on volunteer relay operators for relay bandwidth, which may limit its growth and scaling potential. We propose an incentive scheme for Tor relying on two novel concepts. We introduce TorCoin, an “altcoin” that uses the Bitcoin protocol to reward relays for contributing bandwidth. Relays “mine” TorCoins, then sell them for cash on any existing altcoin exchange. To verify that a given TorCoin represents actual bandwidth transferred, we introduce TorPath, a decentralized protocol for forming Tor circuits such that each circuit is privately-addressable but publicly verifiable. Each circuit’s participants may then collectively mine a limited number of TorCoins, in proportion to the end-to-end transmission goodput they measure on that circuit.
Levenshtein distance for graph spectral features

Graph structures play a critical role in computer vision, but they are inconvenient to use in pattern recognition tasks because of their combinatorial nature and the consequent difficulty in constructing feature vectors. Spectral representations have been used for this task which are based on the eigensystem of the graph Laplacian matrix. However, graphs of different sizes produce eigensystems of different sizes where not all eigenmodes are present in both graphs. We use the Levenshtein distance to compare spectral representations under graph edit operations which add or delete vertices. The spectral representations are therefore of different sizes. We use the concept of the string-edit distance to allow for the missing eigenmodes and compare the correct modes to each other. We evaluate the method by first using generated graphs to compare the effect of vertex deletion operations. We then examine the performance of the method on graphs from a shape database.
Sentiment analysis: From binary to multi-class classification: A pattern-based approach for multi-class sentiment analysis in Twitter

Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.
Case Studies of Damage to Tall Steel Moment-Frame Buildings in Southern California during Large San Andreas Earthquakes

On 9 January 1857, a large earthquake of magnitude 7.9 occurred on the San Andreas fault, with rupture initiating at Parkfield in central California and propagating in a southeasterly direction over a distance of more than 360 km. Such a unilateral rupture produces significant directivity toward the San Fernando and Los Angeles basins. Indeed, newspaper reports of sloshing observed in the Los Angeles river point to long-duration (1–2 min) and long-period (2–8 sec) shaking. If such an earthquake were to happen today, it could impose significant seismic demand on present-day tall buildings. Using state-of-the-art computational tools in seismology and structural engineering, validated using data from the 17 January 1994, magnitude 6.7 Northridge earthquake, we determine the damage to an existing and a new 18story steel moment-frame building in southern California due to ground motion from two hypothetical magnitude 7.9 earthquakes on the San Andreas fault. Our study indicates that serious damage occurs in these buildings at many locations in the region in one of the two scenarios. For a north-to-south rupture scenario, the peak velocity is of the order of 1 m • sec 1 in the Los Angeles basin, including downtown Los Angeles, and 2 m • sec 1 in the San Fernando valley, while the peak displacements are of the order of 1 m and 2 m in the Los Angeles basin and San Fernando valley, respectively. For a south-to-north rupture scenario the peak velocities and displacements are reduced by a factor of roughly 2.
A critical review of "automatic patch generation learned from human-written patches": essay on the problem statement and the evaluation of automatic software repair

At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs. We strongly disagree with key points of this paper. Our critical review has two goals. First, we aim at explaining why we disagree with Kim and colleagues and why the reasons behind this disagreement are important for research on automatic software repair in general. Second, we aim at contributing to the field with a clarification of the essential ideas behind automatic software repair. In particular we discuss the main evaluation criteria of automatic software repair: understandability, correctness and completeness. We show that depending on how one sets up the repair scenario, the evaluation goals may be contradictory. Eventually, we discuss the nature of fix acceptability and its relation to the notion of software correctness.
Knowledge Building : Theory , Pedagogy , and Technology

There are substantial similarities between deep learning and the processes by which knowledge advances in the disciplines. During the 1960s efforts to exploit these similarities gave rise to learning by discovery, guided discovery, inquiry learning, and Science: A Process Approach (American Association for the Advancement of Science, 1967). Since these initial reform efforts, scholars have learned a great deal about how knowledge advances. A mere listing of keywords suggests the significance and diversity of ideas that have come to prominence since the 1960s: approaches have changed in response to some of these developments; there is a greater emphasis on collaborative rather than individual inquiry, the tentative nature of empirical laws is more often noted, and argumentation has become an important part of some approaches. But the new " knowledge of knowledge " has much larger educational implications: Ours is a knowledge-creating civilization. A growing number of " knowledge societies " (Stehr, 1994), are joined in a deliberate effort to advance all the frontiers of knowledge. Sustained knowledge advancement is seen as essential for social progress of all kinds and for the solution of societal problems. From this standpoint the fundamental task of education is to enculturate youth into this knowledge-creating civilization and to help them find a place in it. In light of this challenge, both traditional education, with its emphasis on knowledge transmission, and the newer constructivist methods, appear as limited in scope if not entirely missing the point. Knowledge building, as elaborated in this chapter, represents an attempt to refashion education in a fundamental way, so that it becomes a coherent effort to initiate students into a knowledge creating culture. Accordingly, it involves students not only
Semi-supervised distance metric learning based on local linear regression for data clustering

Distance metric plays an important role in many machine learning tasks. The distance between samples is mostly measured with a predefined metric, ignoring how the samples distribute in the feature space and how the features are correlated. This paper proposes a semi-supervised distance metric learning method by exploring feature correlations. Specifically, unlabeled samples are used to calculate the ability, that is, maximizing the between-class covariance and minimizing the within-class covariance. We then fuse the knowledge learned from both labeled and unlabeled samples into an overall objective function which can be solved by maximum eigenvectors. Our algorithm explores both labeled and unlabeled information as well as data distribution. Experimental results demonstrates the superiority of our method over several existing algorithms. & 2012 Elsevier B.V. All rights reserved.
Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining

Face detection constitutes a key visual information analysis task in Machine Learning. The rise of Big Data has resulted in the accumulation of a massive volume of visual data which requires proper and fast analysis. Deep Learning methods are powerful approaches towards this task as training with large amounts of data exhibiting high variability has been shown to significantly enhance their effectiveness, but often requires expensive computations and leads to models of high complexity. When the objective is to analyze visual content in massive datasets, the complexity of the model becomes crucial to the success of the model. In this paper, a lightweight deep Convolutional Neural Network (CNN) is introduced for the purpose of face detection, designed with a view to minimize training and testing time, and outperforms previously published deep convolutional networks in this task, in terms of both effectiveness and efficiency. To train this lightweight deep network without compromising its efficiency, a new training method of progressive positive and hard negative sample mining is introduced and shown to drastically improve training speed and accuracy. Additionally, a separate deep network was trained to detect individual facial features and a model that combines the outputs of the two networks was created and evaluated. Both methods are capable of detecting faces under severe occlusion and unconstrained pose variation and meet the difficulties of large scale real-world, real-time face detection, and are suitable for deployment even in mobile environments such as Unmanned Aerial Vehicles (UAVs).
Difficulties in simulating the internet

Simulating how the global Internet behaves is an immensely challenging undertaking because of the network's great heterogeneity and rapid change. The heterogeneity ranges from the individual links that carry the network's traffic, to the protocols that interoperate over the links, the "mix" of different applications used at a site, and the levels of congestion seen on different links. We discuss two key strategies for developing meaningful simulations in the face of these difficulties: searching for invariants and judiciously exploring the simulation parameter space. We finish with a brief look at a collaborative effort within the research community to develop a common network simulator.
Database technologies in the world of big data

Now we have a number of database technologies called usually NoSQL, like key-value, column-oriented, and document stores as well as search engines and graph databases. Whereas SQL software vendors offer advanced products with the capability to handle highly complex queries and transactions, NoSQL databases share rather characteristics concerning scaling and performance, as e.g. auto-sharding, distributed query support, and integrated caching. Their drawbacks can be a lack of schema or data consistency, difficulty in testing and maintaining, and absence of a higher query language. Complex data modelling and the SQL language as the only access tool to data are missing here. On the other hand, last studies show that both SQL and NoSQL databases have value for both for transactional and analytical Big Data. Top databases providers offer rearchitected database technologies combining row data stores with columnar in-memory compression enabling processing large data sets and analytical querying, often over massive, continuous data streams. The technological progress led to development of massively parallel processing analytic databases. The paper presents some details of current database technologies, their pros and cons in different application environments, and emerging trends in this area.
DTKI: A New Formalized PKI with Verifiable Trusted Parties

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.