nils-herrmann/clean_scirepeval huggingface dataset:
Interferometric Synthetic Aperture Radar (SAR) Missions Employing Formation Flying

This paper presents an overview of single-pass interferometric Synthetic Aperture Radar (SAR) missions employing two or more satellites flying in a close formation. The simultaneous reception of the scattered radar echoes from different viewing directions by multiple spatially distributed antennas enables the acquisition of unique Earth observation products for environmental and climate monitoring. After a short introduction to the basic principles and applications of SAR interferometry, designs for the twin satellite missions TanDEM-X and Tandem-L are presented. The primary objective of TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is the generation of a global Digital Elevation Model (DEM) with unprecedented accuracy as the basis for a wide range of scientific research as well as for commercial DEM production. This goal is achieved by enhancing the TerraSAR-X mission with a second TerraSAR-X like satellite that will be launched in spring 2010. Both satellites act then as a large single-pass SAR interferometer with the opportunity for flexible baseline selection. Building upon the experience gathered with the TanDEM-X mission design, the fully polarimetric L-band twin satellite formation Tandem-L is proposed. Important objectives of this highly capable interferometric SAR mission are the global acquisition of three-dimensional forest structure and biomass inventories, large-scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The sophisticated mission concept and the high data-acquisition capacity of Tandem-L will moreover provide a unique data source to systematically observe, analyze, and quantify the dynamics of a wide range of additional processes in the bio-, litho-, hydro-, and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics. Enabling technologies and techniques are described in detail. An outlook on future interferometric and tomographic concepts and developments, including multistatic SAR systems with multiple receivers, is provided.
SemEval-2016 Task 8: Meaning Representation Parsing

In this report we summarize the results of the SemEval 2016  Meaning Representation Parsing. Participants were asked to generate Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the news and discussion forum domains. Eleven sites submitted valid systems. The availability of state-of-the-art baseline systems was a key factor in lowering the bar to entry; many submissions relied on CAMR (Wang et al., 2015b; Wang et al., 2015a) as a baseline system and added extensions to it to improve scores. The evaluation set was quite difficult to parse, particularly due to creative approaches to word representation in the web forum portion. The top scoring systems scored 0.62 F1 according to the Smatch (Cai and Knight, 2013) evaluation heuristic. We show some sample sentences along with a comparison of system parses and perform quantitative ablative studies.
Benchmarking Lexical Simplification Systems

Lexical Simplification is the task of replacing complex words in a text with simpler alternatives. A variety of strategies have been devised for this challenge, yet there has been little effort in comparing their performance. In this contribution, we present a benchmarking of several Lexical Simplification systems. By combining resources created in previous work with automatic spelling and inflection correction techniques, we introduce BenchLS: a new evaluation dataset for the task. Using BenchLS, we evaluate the performance of solutions for various steps in the typical Lexical Simplification pipeline, both individually and jointly. This is the first time Lexical Simplification systems are compared in such fashion on the same data, and the findings introduce many contributions to the field, revealing several interesting properties of the systems evaluated.
OCR-based chassis-number recognition using artificial neural networks

The automatic detection and recognition of car number plates has become an important application of artificial vision systems. Since the license plates can be replaced, stolen or simply tampered with, they are not the ultimate answer for vehicle identification. The objective is to develop a system whereby vehicle identification number (VIN) or vehicle chassis number is digitally photographed, and then identified electronically by segmenting the characters from the embossed VIN. In this paper we present a novel algorithm for vehicle chassis number identification based on optical character recognition (OCR) using artificial neural network. The algorithm is tested on over thousand vehicle images of different ambient illumination. While capturing these images, the VIN was kept in-focus, while the angle of view and the distance from the vehicle varied according to the experimental setup. These images were subjected to pre-processing which comprises of some standard image processing algorithms. The resultant images were then fed to the proposed OCR system. The OCR system is a three-layer artificial neural network (ANN) with topology 504-600-10. The major achievement of this work is the rate of correct identification, which is 95.49% with zero false identification.
GTID: A Technique for Physical Device and Device Type Fingerprinting

In this paper, we introduce GTID, a technique that can actively and passively fingerprint wireless devices and their types using wire-side observations in a local network. GTID exploits information that is leaked as a result of heterogeneity in devices, which is a function of different device hardware compositions and variations in devices' clock skew. We apply statistical techniques on network traffic to create unique, reproducible device and device type signatures, and use artificial neural networks (ANNs) for classification. We demonstrate the efficacy of our technique on both an isolated testbed and a live campus network (during peak hours) using a corpus of 37 devices representing a wide range of device classes (e.g., iPads, iPhones, Google Phones, etc.) and traffic types (e.g., Skype, SCP, ICMP, etc.). Our experiments provided more than 300 GB of traffic captures which we used for ANN training and performance evaluation. In order for any fingerprinting technique to be practical, it must be able to detect previously unseen devices (i.e., devices for which no stored signature is available) and must be able to withstand various attacks. GTID is a fingerprinting technique to detect previously unseen devices and to illustrate its resilience under various attacker models. We measure the performance of GTID by considering accuracy, recall, and processing time and also illustrate how it can be used to complement existing security mechanisms (e.g., authentication systems) and to detect counterfeit devices.
A survey of main memory acquisition and analysis techniques for the windows operating system

Traditional, persistent data-oriented approaches in computer forensics face some limitations regarding a number of technological developments, e.g., rapidly increasing storage capabilities of hard drives, memory-resident malicious software applications, or the growing use of encryption routines, that make an in-time investigation more and more difficult. In order to cope with these issues, security professionals have started to examine alternative data sources and emphasize the value of volatile system information in RAM more recently. In this paper, we give an overview of the prevailing techniques and methods to collect and analyze a computer's memory. We describe the characteristics, benefits, and drawbacks of the individual solutions and outline opportunities for future research in this evolving field of IT security.
A Forensic Taxonomy of SCADA Systems and Approach to Incident Response

Peter Eden Information Security Research group School of Computing and Mathematics Department of Computing, Engineering and Science University of South Wales Pontypridd, CF371DL UK peter.eden@southwales.ac.uk Andrew Blyth Information Security Research group School of Computing and Mathematics Department of Computing, Engineering and Science University of South Wales Pontypridd, CF371DL UK andrew.blyth@southwales.ac.uk
Anomaly detection for visual analytics of power consumption data

Commercial buildings are significant consumers of electrical power. Also, energy expenses are an increasing cost factor. Many companies therefore want to save money and reduce their power usage. Building administrators have to first understand the power consumption behavior, before they can devise strategies to save energy. Second, sudden unexpected changes in power consumption may hint at device failures of critical technical infrastructure. The goal of our research is to enable the analyst to understand the power consumption behavior and to be aware of unexpected power consumption values. In this paper, we introduce a novel unsupervised anomaly detection algorithm and visualize the resulting anomaly scores to guide the analyst to important time points. Different possibilities for visualizing the power usage time series are presented, combined with a discussion of the design choices to encode the anomaly values. Our methods are applied to real-world time series of power consumption, logged in a hierarchical sensor network. & 2013 Elsevier Ltd. All rights reserved.
Reducing Illumination Based on Nonlinear Gamma Correction

Illumination always affects image quality seriously in practice. To weaken illumination effect on image quality, this paper proposes an adaptive gamma correction method. First, a mapping between pixel and gamma values is built. The gamma values are then revised using two non-linear functions to prevent image distortion. Experimental results demonstrate that the proposed method performs better in readjusting image illumination condition and improving image quality.
Generating Diverse Numbers of Diverse Keyphrases

Existing keyphrase generation studies suffer from the problems of generating duplicate phrases and deficient evaluation based on a fixed number of predicted phrases. We propose a recurrent generative model that generates multiple keyphrases sequentially from a text, with specific modules that promote generation diversity. We further propose two new metrics that consider a variable number of phrases. With both existing and proposed evaluation setups, our model demonstrates superior performance to baselines on three types of keyphrase generation datasets, including two newly introduced in this work: STACKEXCHANGE and TEXTWORLD ACG. In contrast to previous keyphrase generation approaches, our model generates sets of diverse keyphrases of a variable number.
Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called `auditing-as-a-service' at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.
A mineralogical perspective on the apatite in bone

A crystalline solid that is a special form of the mineral apatite dominates the composite material bone. A mineral represents the intimate linkage of a three-dimensional atomic structure with a chemical composition, each of which can vary slightly, but not independently. The specific compositional–structural linkage of a mineral influences its chemical and physical properties, such as solubility, density, hardness, and growth morphology. In this paper, we show how a mineralogic approach to bone can provide insights into the resorption–precipitation processes of bone development, the exceedingly small size of bone crystallites, and the body’s ability to (bio)chemically control the properties of bone. We also discuss why the apatite phase in bone should not be called hydroxylapatite, as well as the limitations to the use of the stoichiometric mineral hydroxylapatite as a mineral model for the inorganic phase in bone. This mineralogic approach can aid in the development of functionally specific biomaterials. D 2005 Elsevier B.V. All rights reserved.
On the Personality Traits of StackOverflow Users

In the last decade, developers have been increasingly sharing their questions with each other through Question and Answer (Q&A) websites. As a result, these websites have become valuable knowledge repositories, covering a wealth of topics related to particular programming languages. This knowledge is even more useful as the developer community evaluates both questions and answers through a voting mechanism. As votes accumulate, the developer community recognizes reputed members and further trusts their answers. In this paper, we analyze the community's questions and answers to determine the developers' personality traits, using the Linguistic Inquiry and Word Count (LIWC). We explore the personality traits of Stack Overflow authors by categorizing them into different categories based on their reputation. Through textual analysis of Stack Overflow posts, we found that the top reputed authors are more extroverted compared to medium and low reputed users. Moreover, authors of up-voted posts express significantly less negative emotions than authors of down-voted posts.
Economic Shocks and Civil War

This article revisits the relationship between income per capita and civil conict. We establish that the empirical literature identies two di¤erent patterns. First, poor countries have a higher propensity to su¤er from civil war. Second, civil war occurs when countries su¤er negative income shocks. In a formal model we examine an explanation often suggested in the informal literature: civil wars occur in poor countries because the opportunity cost of ghting is small. We show that while this explanation fails to make sense of the rst empirical pattern, it provides a coherent theoretical basis for the second. We then enrich the model to allow for private imperfect information about the state of the economy and show that mutual fears exacerbate the problem caused by negative income shocks. Previous versions of this paper were presented at the Political Science Departments of MIT, Harvard, Princeton, Columbia, UC Berkeley and Washington University as well as conict conferences at Northwestern University and NYU. We are thankful for all the comments received and for helpful conversations with Edward Miguel and Robert Powell. All remaining errors are, of course, our own. yWoodrow Wilson School of Public and International A¤airs and Department of Economics. Chassang@princeton.edu zSTICERD and Department of Economics.
Constraints, lazy constraints, or propagators in ASP solving: An empirical analysis

Answer Set Programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications this approach is infeasible because the grounding of one or few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, that are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies. (Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.
CPAchecker: A Tool for Configurable Software Verification

Configurable software verification is a recent concept for expressing different program analysis and model checking approaches in one single formalism. This paper presents CPAchecker, a tool and framework that aims at easy integration of new verification components. Every abstract domain, together with the corresponding operations, is required to implement the interface of configurable program analysis (CPA). The main algorithm is configurable to perform a reachability analysis on arbitrary combinations of existing CPAs. The major design goal during the development was to provide a framework for developers that is flexible and easy to extend. We hope that researchers find it convenient and productive to implement new verification ideas and algorithms using this platform and that it advances the field by making it easier to perform practical experiments. The tool is implemented in Java and runs as command-line tool or as Eclipse plug-in. We evaluate the efficiency of our tool on benchmarks from the software model checker Blast. The first released version of CPAchecker implements CPAs for predicate abstraction, octagon, and explicit-value domains. Binaries and the source code of CPAchecker are publicly available as free software.
Wilkinson Power Divider With Complex Isolation Component and Its Miniaturization

By adding a complex isolation component between two 90 ° transmission lines at an arbitrary phase angle from the input terminal, a small Wilkinson power divider provides physical separation and electrical isolation between two output ports. General design equations for the complex isolation component are derived from even- and odd-mode analysis. Parallel and series RLC circuits are chosen to realize complex isolation components, respectively. Considering the bandwidths of S22, S33, and S32, inductors in both parallel and series RLC components are omitted to get the widest bandwidths; mathematical proof and design examples are also presented in this paper. A coupled line section with a compensating capacitor is introduced to reduce the circuit size, where characteristic impedances between even and odd mode are different in the coupled line section, and their electrical lengths are also different in inhomogeneous medium. Mathematical equations and simulation examples prove that the compensating capacitor compensates the characteristic impedance difference in the homogeneous medium and the electrical length difference in the inhomogeneous medium. Finally, an experimental circuit shows good agreement with the theoretical simulation.
Information-preserving hybrid data reduction based on fuzzy-rough techniques

Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats, and a unified data reducing technique for hybrid data is desirable. In this paper, an information measure is proposed to computing discernibility power of a crisp equivalence relation or a fuzzy one, which is the key concept in classical rough set model and fuzzy-rough set model. Based on the information measure, a general definition of significance of nominal, numeric and fuzzy attributes is presented. We redefine the independence of hybrid attribute subset, reduct, and relative reduct. Then two greedy reduction algorithms for unsupervised and supervised data dimensionality reduction based on the proposed information measure are constructed. Experiments show the reducts found by the proposed algorithms get a better performance compared with classical rough set approaches.
Red team vs. blue team hardware trojan analysis: detection of a hardware trojan on an actual ASIC

We infiltrate the ASIC development chain by inserting a small denial-of-service (DoS) hardware Trojan at the fabrication design phase into an existing VLSI circuit, thereby simulating an adversary at a semiconductor foundry. Both the genuine and the altered ASICs have been fabricated using a 180 nm CMOS process. The Trojan circuit adds an overhead of only 0.5% to the original design. In order to detect the hardware Trojan, we perform side-channel analyses and apply IC-fingerprinting techniques using templates, principal component analysis (PCA), and support vector machines (SVMs). As a result, we were able to successfully identify and classify all infected ASICs from non-infected ones. To the best of our knowledge, this is the first hardware Trojan manufactured as an ASIC and has successfully been analyzed using side channels.
Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control

The majority of projects dealing with monitoring and diagnosis of Cyber Physical Systems (CPSs) relies on models created by human experts. But these models are rarely available, are hard to verify and to maintain and are often incomplete. Data-driven approaches are a promising alternative: They leverage on the large amount of data which is collected nowadays in CPSs, this data is then used to learn the necessary models automatically. For this, several challenges have to be tackled, such as real-time data acquisition and storage solutions, data analysis and machine learning algorithms, task specific human-machine-interfaces (HMI) and feedback/control mechanisms. In this paper, we propose a cognitive reference architecture which addresses these challenges. This reference architecture should both ease the reuse of algorithms and support scientific discussions by providing a comparison schema. Use cases from different industries are outlined and support the correctness of the architecture.