nils-herrmann/clean_scirepeval huggingface dataset:
Polarimetric SAR interferometry

In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/XSAR data collected during October 8–9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.
A Secure and Efficient Uniqueness-and-Anonymity-Preserving Remote User Authentication Scheme for Connected Health Care

Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al.,  Syst 37:9902, 2013). Their scheme uses the user’s personal biometrics along with his/her password with the help of the smart card. The user’s biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.’s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.’s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the user and the server, they establish a secret session key shared between them for future secure communication.
NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data

We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN’s performance and scalability.
Wireless temperature sensor network based on DS18B20, CC2420, MCU AT89S52

Wireless sensor network (WSN) has extensive applications including temperature testing. Thus, this paper utilizes three fundamental chips, i.e., DS18B20, CC2420, MCU AT89S52, to design the wireless temperature sensor network (WTSN) via the ZigBee technology. The novel network sequentially transmits the temperature information to the substation, node, computer, thus realizing the wireless temperature monitoring. The WTSN holds prominent advantages regarding the high detection precision, the low power consumption, the high cost performance, and the high reliability. Thus, it could widely apply to the Internet of things field to effectively perform the temperature testing.
Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database

To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address these limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, has been specified to serve as a common basis for further works on this subject. Our work confirms the conclusions made by Cagli et al. at CHES 2017 about the high potential of convolutional neural networks. Interestingly, it shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis.
The Treatment of Missing Values and its Effect on Classifier Accuracy

The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used most frequently deletes instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve datasets to evaluate the effect on the misclassification error rate of four methods for dealing with missing values: the case deletion method, mean imputation, median imputation, and the KNN imputation procedure. The classifiers considered were the Linear Discriminant Analysis (LDA) and the KNN classifier. The first one is a parametric classifier whereas the second one is a non parametric classifier.
The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents

This study was designed to investigate the revised and short version of the smartphone addiction scale and the proof of its validity in adolescents. In addition, it suggested cutting off the values by gender in order to determine smartphone addiction and elaborate the characteristics of smartphone usage in adolescents.



A set of questionnaires were provided to a total of 540 selected participants from April to May of 2013. The participants consisted of 343 boys and 197 girls, and their average age was 14.5 years old. The content validity was performed on a selection of shortened items, while an internal-consistency test was conducted for the verification of its reliability. The concurrent validity was confirmed using SAS, SAPS and KS-scale. Receiver operating characteristics analysis was conducted to suggest cut-off.



The 10 final questions were selected using content validity. The internal consistency and concurrent validity of SAS were verified with a Cronbach's alpha of 0.911. The SAS-SV was significantly correlated with the SAS, SAPS and KS-scale. The SAS-SV scores of gender (p<.001) and self-evaluation of smartphone addiction (p<.001) showed significant difference. The ROC analysis results showed an area under a curve (AUC) value of 0.963(0.888-1.000), a cut-off value of 31, sensitivity value of 0.867 and specificity value of 0.893 in boys while an AUC value of 0.947(0.887-1.000), a cut-off value of 33, sensitivity value of 0.875, and a specificity value of 0.886 in girls.



The SAS-SV showed good reliability and validity for the assessment of smartphone addiction. The smartphone addiction scale short version, which was developed and validated in this study, could be used efficiently for the evaluation of smartphone addiction in community and research areas.
Probabilistic graph and hypergraph matching

We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyper-edges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyper-edge weight matrix and the desired vertex-to-vertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn  row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straight-forward scalability from graphs to hyper-graphs.
Enabling network programmability in LTE/EPC architecture using OpenFlow

Nowadays, mobile operators face the challenge to sustain the future data tsunami. In fact, today's increasing data and control traffic generated by new kinds of network usage puts strain on mobile operators', without creating any corresponding equivalent revenue. In our previous work, we analyzed the 3GPP LTE/EPC architecture and showed that a redesign of this architecture is needed to suit future network usages and to provide new revenue generating services. Moreover, we proposed a new control plane based on the OpenFlow (OF) protocol for the LTE/EPC architecture that enables flexibility and programmability aspects. In this paper, we are interested in the programmability aspect. We show how the data plane can be easily configured thanks to OF. In addition, we evaluate the signaling load of our proposed architecture and compare it to that of 3GPP LTE/EPC architecture. The preliminary findings suggest that managing the data plane with OF has little impact on the signaling load while the network programmability is improved.
DISASTER: Dedicated Intelligent Security Attacks on Sensor-Triggered Emergency Responses

Rapid technological advances in microelectronics, networking, and computer science have resulted in an exponential increase in the number of cyber-physical systems (CPSs) that enable numerous services in various application domains, e.g., smart homes and smart grids. Moreover, the emergence of the Internet-of-Things (IoT) paradigm has led to the pervasive use of IoT-enabled CPSs in our everyday lives. Unfortunately, as a side effect, the number of potential threats and feasible security attacks against CPSs has grown significantly. In this paper, we introduce a new class of attacks against CPSs, called dedicated intelligent security attacks against sensor-triggered emergency responses (DISASTER). DISASTER targets safety mechanisms deployed in automation/monitoring CPSs and exploits design flaws and security weaknesses of such mechanisms to trigger emergency responses even in the absence of a real emergency. Launching DISASTER can lead to serious consequences for three main reasons. First, almost all CPSs offer specific emergency responses and, as a result, are potentially susceptible to such attacks. Second, DISASTER can be easily designed to target a large number of CPSs, e.g., the anti-theft systems of all buildings in a residential community. Third, the widespread deployment of insecure sensors in already-in-use safety mechanisms along with the endless variety of CPS-based applications magnifies the impact of launching DISASTER. In addition to introducing DISASTER, we describe the serious consequences of such attacks. We demonstrate the feasibility of launching DISASTER against the two most widely-used CPSs: residential and industrial automation/monitoring systems. Moreover, we suggest several countermeasures that can potentially prevent DISASTER and discuss their advantages and drawbacks.
Vision Assisted SCARA Manipulator Design and Control Using Arduino and LabVIEW

Using vision systems with SCARA robotic manipulators is beneficial in many industrial applications specially in automated object picking and placing tasks. In this paper, the equations of forward and inverse kinematics of a SCARA manipulator are derived using D-H parameters convention. The mechanical structure, as well as the electrical, electronic and pneumatic components of the manipulator were designed and developed. The manipulator's control software, animation and simulation were developed using LabVIEW and Arduino. In addition to that, a vision system was developed and integrated with the manipulator system where a mobile phone camera was used to acquire images, then transmit them wirelessly to LabVIEW's Vision Assistant codes for processing and calibration. The developed vision system allowed detection and calculation of object positions within the manipulator's workspace. Finally, the manipulator's positioning errors were determined and discussed based on a number of repeatability experiments.
K-means clustering via principal component analysis

Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for performing unsupervised learning tasks. Here we prove that principal components are the continuous solutions to the discrete cluster membership indicators for K-means clustering. New lower bounds for K-means objective function are derived, which is the total variance minus the eigenvalues of the data covariance matrix. These results indicate that unsupervised dimension reduction is closely related to unsupervised learning. Several implications are discussed. On dimension reduction, the result provides new insights to the observed effectiveness of PCA-based data reductions, beyond the conventional noise-reduction explanation that PCA, via singular value decomposition, provides the best low-dimensional linear approximation of the data. On learning, the result suggests effective techniques for K-means data clustering. DNA gene expression and Internet newsgroups are analyzed to illustrate our results. Experiments indicate that the new bounds are within 0.5-1.5% of the optimal values.
Multiresolution splatting for indirect illumination

Global illumination provides a visual richness not achievable with the direct illumination models used by most interactive applications. To generate global effects, numerous approximations attempt to reduce global illumination costs to levels feasible in interactive contexts. One such approximation, reflective shadow maps, samples a shadow map to identify secondary light sources whose contributions are splatted into eye-space. This splatting introduces significant overdraw that is usually reduced by artificially shrinking each splat's radius of influence. This paper introduces a new, multi-resolution approach for interactively splatting indirect illumination. Instead of reducing GPU fill rate by reducing splat size, we reduce fill rate by rendering splats into a multi-resolution buffer. This takes advantage of the low-frequency nature of diffuse and glossy indirect lighting, allowing rendering of indirect contributions at low resolution where lighting changes slowly and at high resolution near discontinuities. Because this multi-resolution rendering occurs on a per-splat basis, we can significantly reduce fill rate without arbitrarily clipping splat contributions below a given threshold---those regions simply are rendered at a coarse resolution.
Whose Book is it Anyway ? Using Machine Learning to Identify the Author of Unknown Texts

In this paper, we present an implementation of a new method for text classification that achieves significantly faster results than most existing classifiers. We extract features that are computationally efficient and forgo more computationally expensive ones used by many other text classifiers, most notably n-grams (contiguous sequences of n words). We then analyze the feature vectors through a hybrid SVM technique that sequentially classifies them with a one-versus-all classifier and a sequence of binary classifiers. Our overall method finishes execution within seconds on large novels, with accuracy comparable to that of standard classifiers but with a significantly shorter runtime.
Microservices: a Language-based Approach

Microservices is an emerging development paradigm where software is obtained by composing autonomous entities, called (micro)services. However, microservice systems are currently developed using general-purpose programming languages that do not provide dedicated abstractions for service composition. Instead, current practice is focused on the deployment aspects of microservices, in particular by using containerization. In this chapter, we make the case for a languagebased approach to the engineering of microservice architectures, which we believe is complementary to current practice. We discuss the approach in general, and then we instantiate it in terms of the Jolie programming language.
Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data

Stock traders consider several factors or objectives in making decisions. Moreover, they differ in the importance they attach to each of these objectives. This requires a tool that can provide an optimal tradeoff among different objectives, a problem aptly solved by a multi-objective optimization (MOO) system. This paper aims to investigate the application of multi-objective optimization to end-of-day historical stock trading. We present a stock trading system that uses multi-objective particle swarm optimization (MOPSO) of financial technical indicators. Using end-of-day market data, the system optimizes the weights of several technical indicators over two objective functions, namely, percent profit and Sharpe ratio. The performance of the system was compared to the performance of the technical indicators, the performance of the market, and the performance of another stock trading system which was optimized with the NSGA-II algorithm, a genetic algorithm-based MOO method. The results show that the system performed well on both training and outof-sample data. In terms of percent profit, the system outperformed most, if not all, of the indicators under study, and, in some instances, it even outperformed the market itself. In terms of Sharpe ratio, the system consistently performed significantly better than all the technical indicators. The proposed MOPSO system also performed far better than the system optimized by NSGA-II. The proposed system provided a diversity of solutions for the two objective functions and is found to be robust and fast. These results show the potential of the system as a tool for making stock trading decisions.
Smart manufacturing, manufacturing intelligence and demand-dynamic performance

Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. It responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance; demand-driven supply chain services; and broad-based workforce involvement. ITenabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.
Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given <text, audio> pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-tosequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it’s substantially faster than sample-level autoregressive methods.