nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Discovery of Attribute Synonyms Using Query Logs and Table Corpora

Attribute synonyms are important ingredients for keywordbased search systems. For instance, web search engines, recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents. However, users often refer to an attribute in their e+a query differently from how it is referred in the web table or text passage. In such cases, search engines may fail to return relevant answers. To address that problem, we propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in web tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from web table attribute name co-occurrences. We formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and web table schemas as weighted edges. We develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-theart.
The evolution of synthetic aperture radar systems and their progression to the EOS SAR

The spaceborne imaging radar program of the National Aeronautics and Space Administration (NASA) has evolved primarily through Seasat and the Shuttle Imaging Radar (SIR) to a multifrequency, multipolarization system capable of monitoring the Earth with a variety of imaging geometries, resolutions, and swaths. In particular, the ability to digitally process and calibrate the data has been a key factor in developing the algorithms which will operationally provide geophysical and biophysical information about the surface of the Earth using synthetic aperture radar (SAR). This paper describes the evolution of the spaceborne imaging radar starting with the Seasat SAR, through the SIR-A, SIR-B, and SIR-CI X-SAR missions, to the EOS SAR which is scheduled for launch as part of the Earth Observing System (EOS) at the end of the 1990s. A summary of the planned international missions, which may produce a permanent active microwave capability in space starting as early as 1991, is also presented, along with a description of the airborne systems which will be essential to the algorithm development and long-term calibration of the spaceborne data. Finally, a brief summary of the planetary missions utilizing SAR and a comparison of their imaging capabilities with those available on Earth is presented.
PMSM laboratory stand for investigations on advanced structures of electrical drive control

This paper presents design, structure and experimental tests of a laboratory stand for the investigation on advanced control algorithms for electrical drive. The main propulsion unit is a permanent magnet synchronous motor, which is connected with a brushless DC motor operating as a dynamically adjustable load. System is controlled by a DSP with the dedicated speed and position measurement extension boards. Availability of an external DSP bus allowed to incorporate FPGA system to the control layer. The paper describes also an innovative, object-oriented approach to the implementation of the control algorithms in DSP system.
Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication

 \ N\| N\wOEN^|< e\ eNNN¡  ^  N¢X\£<¤ N¥N ¦||§N| ̈\<©RN\dV\a | aN ¦|<§ « \\NX¡;aUN\ O¢X\ N¬  ^  \NW \0\|aUNN­@®u7 | a^NN¡e¤o  O¤ \ a \ aN\`¦||a |\ ̄ 0§N WJ§N|\§X a ¢N<N <£  | 7 §Xa  | \| O « aO¤ I J O¢X¦ \X§° a |± aNN­ 20\NO¢NN¡¤@\ O¤3 O¤¬ a\O3 ^\X \¦| \| §X ^9| §N\^\\NU| &&N¦  \a\w\£<\Na4­e ́ * WXjaμ§N \N*  N¤  \<§N¦|  ` N| |a  ¦|NaUN<\\| |·¶[\u3 _ ̧ 1 oe­R®u¦|\  O¢<μaN9N»<^N\|aUN<\¦[\N ¦|\  O¢ | @\ a a <£1⁄4j\  aN  0¦||a |\1⁄43 \|  O \N ¦||§N\| §NJ^4O¢μaN <£  μa \ a    μ\ \X§° a |± aNN­
Design of an experimental setup for evaluation of inductive transcutaneous energy transfer systems

Work objective — is to develop an experimental setup for evaluation characteristics of inductive transcutaneous energy transfer systems, such as comparative studies of various topological solutions; studies of systems' thermal safety; influence of geometry of a coils couple on the parameters of energy transfer. The system consists of planar spiral coils, device for positioning, receiving and transmitting circuit boards, frequency generator, and National Instruments USB-6361 DAQ. Mathematical apparatus has been developed that makes it possible to obtain self-inductance and the internal equivalent resistance of the coils from the measured voltage drop in the transmitting circuit, as well as the dependence of the mutual inductance on displacements. LabVIEW virtual instrument for automation of measurements is designed. The values of the self-inductances of planar spiral coils and the practical dependences of the mutual inductance of spiral coils on displacements without and with using automation tools were experimentally found. The results corresponds theoretical calculations.
Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

Social media has become one of the most popular marketing channels for many companies, which aims at maximizing their influence by various marketing campaigns conducted from their official accounts on social networks. However, most of these marketing accounts merely focus on the contents of their tweets. Less effort has been made on understanding tweeting time, which is a major contributing factor in terms of attracting customers' attention and maximizing the influence of a social marketing campaign. To that end, in this paper, we provide a focused study of temporal retweeting patterns and their influence on social media marketing campaigns. Specifically, we investigate the users' retweeting patterns by modeling their retweeting behaviors as a generative process, which considers temporal, social, and topical factors. Moreover, we validate the predictive power of the model on the dataset collected from Sina Weibo, the most popular micro blog platform in China. By discovering the temporal retweeting patterns, we analyze the temporal popular topics and recommend tweets to users in a time-aware manner. Finally, experimental results show that the proposed algorithm outperforms other baseline methods. This model is applicable for companies to conduct their marketing campaigns at the right time on social media.
Adversarial Structure Matching Loss for Image Segmentation

The per-pixel cross-entropy loss (CEL) has been widely used in structured output prediction tasks as a spatial extension of generic image classification. However, its i.i.d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occuring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the adversarial structure matching loss (ASML). The structure analyzer is trained to maximize ASML, or to exaggerate recurring structural mistakes usually among co-occurring patterns. On the contrary, the structured output prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured output prediction networks using ASML reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASML outperforms its counterpart CEL especially in context and boundary aspects on figure-ground segmentation and semantic segmentation tasks with various base architectures, such as FCN, U-Net, DeepLab, and PSPNet.
Hadoop+Aparapi: Making heterogenous MapReduce programming easier

Lately, programmers have started to take advantage of GPU capabilities of cloud-based machines. Using the GPUs can decrease the number of nodes required to perform the computation by increasing the productivity per node. We combine Hadoop, a widely-used MapReduce framework, with Aparapi, a new Java-to-OpenCL conversion tool from AMD. We propose an easy-to-use API which allows easy implementation of MapReduce algorithms that make use of the GPU. Our API improves upon Hadoop by further hiding the complexity of GPU programming, thus allowing the programmer to concentrate on her algorithm. We also propose an accompanying refactoring that allows the programmer to specify the GPU part of their map computation by using very lightweight annotation.
FIRST AND SECOND ORDER METHODS FOR ONLINE CONVOLUTIONAL

Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage, and severely limits the training data that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods. This paper extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, and that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.
Thinking like a nurse: a research-based model of clinical judgment in nursing.

This article reviews the growing body of research on clinical judgment in nursing and presents an alternative model of clinical judgment based on these studies. Based on a review of nearly 200 studies, five conclusions can be drawn: (1) Clinical judgments are more influenced by what nurses bring to the situation than the objective data about the situation at hand; (2) Sound clinical judgment rests to some degree on knowing the patient and his or her typical pattern of responses, as well as an engagement with the patient and his or her concerns; (3) Clinical judgments are influenced by the context in which the situation occurs and the culture of the nursing care unit; (4) Nurses use a variety of reasoning patterns alone or in combination; and (5) Reflection on practice is often triggered by a breakdown in clinical judgment and is critical for the development of clinical knowledge and improvement in clinical reasoning. A model based on these general conclusions emphasizes the role of nurses' background, the context of the situation, and nurses' relationship with their patients as central to what nurses notice and how they interpret findings, respond, and reflect on their response.
A shape and image merging technique to solve jigsaw puzzles

This paper proposes an algorithm for solving subsets of typical (canonical) jigsaw puzzles. This algorithm combines shape and image matching with a cyclic ‘‘growth’’ process that tries to place pieces in their correct positions. First, the jigsaw pieces are extracted from the input image. Then, the corner points of the jigsaw pieces are detected. Next, piece classification and recognition are performed based on jigsaw piece models. Connection relationships between pieces are calculated and finally recovered by using boundary shape matching and image merging. We tested this algorithm by employing real-world images containing dozens of jigsaw pieces. The experiment s results show this algorithm is efficient and effective.
Security slicing for auditing XML, XPath, and SQL injection vulnerabilities

XML, XPath, and SQL injection vulnerabilities are among the most common and serious security issues for Web applications and Web services. Thus, it is important for security auditors to ensure that the implemented code is, to the extent possible, free from these vulnerabilities before deployment. Although existing taint analysis approaches could automatically detect potential vulnerabilities in source code, they tend to generate many false warnings. Furthermore, the produced traces, i.e. dataflow paths from input sources to security-sensitive operations, tend to be incomplete or to contain a great deal of irrelevant information. Therefore, it is difficult to identify real vulnerabilities and determine their causes. One suitable approach to support security auditing is to compute a program slice for each security-sensitive operation, since it would contain all the information required for performing security audits (Soundness). A limitation, however, is that such slices may also contain information that is irrelevant to security (Precision), thus raising scalability issues for security audits. In this paper, we propose an approach to assist security auditors by defining and experimenting with pruning techniques to reduce original program slices to what we refer to as security slices, which contain sound and precise information. To evaluate the proposed pruning mechanism by using a number of open source benchmarks, we compared our security slices with the slices generated by a state-of-the-art program slicing tool. On average, our security slices are 80% smaller than the original slices, thus suggesting significant reduction in auditing costs.
Reverse Data Hiding Using Histogram Comparison

Different from the previous applications of the process, a new approach is used. This method uses histogram shifting technique and also the modification of the prediction errors (MPE) algorithm by using the predictors. The embedding capacity and the output of the steganography-applied image directly depends on the efficiency of the predictors. This method uses two & three bins in the histogram to increase the embedding capacity of the image. The final and the initial image can be compared with their following histograms. Index Terms – Prediction, Prediction Error, Steganography, RC7, Histogram Shifting, Histogram, Reversible Data Hiding.
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.
Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation

We introduce a novel unsupervised loss function for learning semantic segmentation with deep convolutional neural nets (ConvNet) when densely labeled training images are not available. More specifically, the proposed loss function penalizes the L1-norm of the gradient of the label probability vector image , i.e. total variation, produced by the ConvNet. This can be seen as a regularization term that promotes piecewise smoothness of the label probability vector image produced by the ConvNet during learning. The unsupervised loss function is combined with a supervised loss in a semi-supervised setting to learn ConvNets that can achieve high semantic segmentation accuracy even when only a tiny percentage of the pixels in the training images are labeled. We demonstrate significant improvements over the purely supervised setting in the Weizmann horse, Stanford background and Sift Flow datasets. Furthermore, we show that using the proposed piecewise smoothness constraint in the learning phase significantly outperforms post-processing results from a purely supervised approach with Markov Random Fields (MRF). Finally, we note that the framework we introduce is general and can be used to learn to label other types of structures such as curvilinear structures by modifying the unsupervised loss function accordingly.
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

Program code is a precious asset to its owner. Due to the easyto-reverse nature of Java, code protection for Android apps is of particular importance. To this end, code obfuscation is widely utilized by both legitimate app developers andmalware authors, which complicates the representation of source code or machine code in order to hinder the manual investigation and code analysis. Despite many previous studies focusing on the obfuscation techniques, however, our knowledge on how obfuscation is applied by realworld developers is still limited. In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple thirdparty markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction.
K-Band GaAs MMIC Doherty Power Amplifier for Microwave Radio With Optimized Driver

In this paper, a Doherty power amplifier for K-band point-to-point microwave radio, developed in TriQuint GaAs 0.15-μm PWR pHEMT monolithic technology, is presented. Highly efficient driver stages on both the main and auxiliary branches have been designed and optimized to boost gain with minimal impact on power-added efficiency. The selected architecture enables a modular combination to reach higher power levels. Matching network structures have been designed, according to simple equivalent circuit approaches, to obtain the desired 10% fractional bandwidth. The fabricated power amplifier (PA) exhibits, at 24 GHz in continuous-wave conditions, an output power of 30.9 dBm, with a power-added efficiency of 38% at saturation and 20% at 6 dB of output power back-off, together with a gain of 12.5 dB. System-level characterization at 24 GHz, in very demanding conditions, with a 28-MHz channel 7.5-dB peak-to-average ratio modulated signal, showed full compliance with the standard emission mask, adopting a simple predistorter, with average output power of 23.5 dBm, and average efficiency above 14%. The measured performance favorably compare with other academic and commercial K-band PAs for similar applications.
Learning of lane information reliability for intelligent vehicles

Automated riving is becoming the focus of various research institutions and companies. In this context, road estimation is one of the most important tasks. Many works propose to realize this task by employing one or multiple of the following orthogonal information sources: road markings from optical lane recognition, leading vehicle, digital map. etc. Each of them has its own strength and drawbacks in different situations. However, many existing approaches assume that the sources are equally reliable. Incorporating reliability estimates into the fusion of theses sources can significantly increase the availability of automated driving in most scenarios. In this work, we propose a novel concept to define, measure, learn and integrate reliabilities into the road estimation task. We introduce a new error metric in which the reliability is defined as the angle discrepancy between the estimated road course and the manually driven trajectory. Based on a large database containing sensor and context information from different situations, a Bayesian Network and Random Forests are trained to learn the reliabilities. The estimated reliabilities are used to discard unreliable sources in the fusion process. Experimental results prove our concept.
Understanding the Role of Social presence in Crowdfunding: Evidence from Leading U.S. And German Platforms

As a novel opportunity to acquire capital from the masses, crowdfunding has attracted great attention in academia and practice. So far, little is known about the factors that promote the success of crowdfunding projects, however. In this paper, we examine in how far the social presence perceived on a project’s website influences the success of the respective crowdfunding project. Based on a data-driven analysis of 2.000 project websites from the largest crowdfunding platforms in the U.S. and Germany, we show that the perceived social presence has a significant influence on the success of crowdfunding projects. The obtained results indicate that using socially rich pictures and a socially rich description in the project presentation positively affects the success of a crowdfunding project. A socially rich profile page of the founder(s) in contrast appears to have a rather limited effect. The success of crowdfunding projects seems to be dependent on the participation behavior of the founder, however. Our results indicate that having backed other projects positively influences the success of one’s own initiative. The number of answered comments might have a negative effect on the success of the initiative, though.