nils-herrmann/clean_scirepeval huggingface dataset:
Wireless communication technologies for ITS applications [Topics in Automotive Networking]

Intelligent transport systems are the rising technology in the near future to build cooperative vehicular networks in which a variety of different ITS applications are expected to communicate with a variety of different units. Therefore, the demand for highly customized communication channel for each or sets of similar ITS applications is increased. This article explores the capabilities of available wireless communication technologies in order to produce a win-win situation while selecting suitable carrier( s) for a single application or a profile of similar applications. Communication requirements for future ITS applications are described to select the best available communication interface for the target application(s).
An ensemble method for extracting adverse drug events from social media

Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media.



We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter).



When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines.



Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness.
Initial study on in vivo conductivity mapping of breast cancer using MRI.

To develop and apply a method to measure in vivo electrical conductivity values using magnetic resonance imaging (MRI) in subjects with breast cancer.



A recently developed technique named MREPT (MR electrical properties tomography) together with a novel coil combination process was used to quantify the conductivity values. The overall technique was validated using a phantom study. In addition, 90 subjects were imaged (50 subjects with previously biopsy-confirmed breast tumor and 40 normal subjects), which was approved by our institutional review board (IRB). A routine clinical protocol, specifically a T2 -weighted FSE (fast spin echo) imaging data, was used for reconstruction of conductivity.



By employing the coil combination, the relative error in the conductivity map was reduced from ~70% to 10%. The average conductivity values in breast cancers regions (0.89 ± 0.33S/m) was higher compared to parenchymal tissue (0.43 S/m, P < 0.0001) and fat (0.07 S/m, P < 0.00005) regions. Malignant cases (0.89 S/m, n = 30) showed increased conductivity compared to benign cases (0.56 S/m, n = 5) (P < 0.05). In addition, invasive cancers (0.96 S/m) showed higher mean conductivity compared to in situ cancers (0.57 S/m) (P < 0.0005).



This study shows that conductivity mapping of breast cancers is feasible using a noninvasive in vivo MREPT technique combined with a coil combination process. The method may provide a tool in the MR diagnosis of breast cancer.
Hadoop Based Defense Solution to Handle Distributed Denial of Service (DDoS) Attacks

Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an enormous number of packets, hence exhausting the resources and preventing the legitimate users to access them. After having standard DDoS defense mechanism, still attackers are able to launch an attack. These inadequate defense mechanisms need to be improved and integrated with other solutions. The purpose of this paper is to study the characteristics of DDoS attacks, various models involved in attacks and to provide a timeline of defense mechanism with their improvements to combat DDoS attacks. In addition to this, a novel scheme is proposed to detect DDoS attack efficiently by using MapReduce programming model.
A novel outlier cluster detection algorithm without top-n parameter

Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. Outlier detection has been widely focused and studied in recent years. The concept about outlier factor of object is extended to the case of cluster. Although many outlier detection algorithms have been proposed, most of them face the top-n problem, i.e., it is difficult to know how many points in a database are outliers. In this paper we propose a novel outlier cluster detection algorithm called ROCF based on the concept of mutual neighbor graph and on the idea that the size of outlier clusters is usually much smaller than the normal clusters. ROCF can automatically figure out the outlier rate of a database and effectively detect the outliers and outlier clusters without top-n parameter. The formal analysis and experiments show that this method can achieve good performance in outlier detection.
Arbitrary dual-band components using composite right/left-handed transmission lines

Arbitrary dual-band microstrip components using composite right/left-handed (CRLH) transmission lines (TLs) are presented. Theory, synthesis procedure, and implementation of the dual-band quarter-wave (/spl lambda//4) CRLH TL are presented. Arbitrary dual-band operation is achieved by the frequency offset and the phase slope of the CRLH TL. The frequency ratio of the two operating frequencies can be a noninteger. The dual-band /spl lambda//4 open/short-circuit stub, dual-band branch-line coupler (BLC), and dual-band rat-race coupler (RRC) are also demonstrated. The performances of these dual-band components are demonstrated by both simulated and measured results. Insertion loss is larger than 23 dB for the shunt /spl lambda//4 CRLH TL open-circuit stub and less than 0.25 dB for the shunt /spl lambda//4 CRLH TL short-circuit stub at each passband. The dual-band BLC exhibits S/sub 21/ and S/sub 31/ larger than -4.034 dB, return losses larger than 17 dB, isolations larger than 13 dB, phase differences 90/spl deg//spl plusmn/1.5/spl deg/, and gain imbalance less than 0.5 dB at each passband. The dual-band RRC exhibits S/sub 21/ and S/sub 31/ larger than -4.126 dB, return losses larger than 12 dB, isolations larger than 30 dB, phase difference 180/spl deg//spl plusmn/4/spl deg/, and gain imbalance less than 0.2 dB at each passband.
Using vanishing points for camera calibration and coarse 3D reconstruction from a single image

In this paper, we show how to calibrate a camera and to recover the geometry and the photometry (textures) of objects from a single image. The aim of this work is to make it possible walkthrough and augment reality in a 3D model reconstructed from a single image. The calibration step does not need any calibration target and makes only four assumptions: (1) the single image contains at least two vanishing points, (2) the length (in 3D space) of one line segment (for determining the translation vector) in the image is known, (3) the principle point is the center of the image, and (4) the aspect ratio is fixed by the user. Each vanishing point is determined from a set of parallel lines. These vanishing points help determine a 3D world coordinate system R o. After having computed the focal length, the rotation matrix and the translation vector are evaluated in turn for describing the rigid motion between R o and the camera coordinate system R c. Next, the reconstruction step consists in placing, rotating, scaling, and translating a rectangular 3D box that must fit at best with the potential objects within the scene as seen through the single image. With each face of a rectangular box, a texture that may contain holes due to invisible parts of certain objects is assigned. We show how the textures are extracted and how these holes are located and filled. Our method has been applied to various real images (pictures scanned from books, photographs) and synthetic images.
Big data team process methodologies: A literature review and the identification of key factors for a project's success

This paper reports on our review of published research relating to how teams work together to execute Big Data projects. Our findings suggest that there is no agreed upon standard for executing these projects but that there is a growing research focus in this area and that an improved process methodology would be useful. In addition, our synthesis also provides useful suggestions to help practitioners execute their projects, specifically our identified list of 33 important success factors for executing Big Data efforts, which are grouped by our six identified characteristics of a mature Big Data organization.
Delta-n Hinge: Rotation-Invariant Features for Writer Identification

This paper presents a method for extracting rotation-invariant features from images of handwriting samples that can be used to perform writer identification. The proposed features are based on the Hinge feature , but incorporating the derivative between several points along the ink contours. Finally, we concatenate the proposed features into one feature vector to characterize the writing styles of the given handwritten text. The proposed method has been evaluated using Fire maker and IAM datasets in writer identification, showing promising performance gains.
Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data

Change-point detection is the problem of finding abrupt changes in time-series, and it is attracting a lot of attention in the artificial intelligence and data mining communities. In this paper, we present a supervised learning based change-point detection approach in which we use the separability of past and future data at time t (they are labeled as +1 and 1) as plausibility of change-points. Based on this framework, we propose a detection measure called the additive Hilbert-Schmidt Independence Criterion (aHSIC), which is defined as the weighted sum of the HSIC scores between features and its corresponding binary labels. Here, the HSIC is a kernelbased independence measure. A novel aspect of the aHSIC score is that it can incorporate feature selection during its detection measure estimation. More specifically, we first select features that are responsible for an abrupt change by using a supervised approach, and then compute the aHSIC score by employing the selected features. Thus, compared with traditional detection measures, our approach tends to be robust as regards noise features, and so the aHSIC is suitable for a use with high-dimensional time-series change-point detection problems. We demonstrate that the proposed change-point detection method is promising through extensive experiments on synthetic data sets and a real-world human activity data set.
Nanosecond Pulse Generator with Variable Pulse Duration for the Study of Pulse Induced Biological Effects

We have developed a Blumlein line pulse generator which utilizes the superposition of electrical pulses launched from two individually switched pulse forming lines. By using a fast power MOSFET as a switch on each end of the Blumlein line, we were able to generate pulses with amplitudes of 1 kV across a 100-Omega load. Pulse duration and polarity can be controlled by the temporal delay in the triggering of the two switches. In addition, the use of identical switches allows us to overcome pulse distortions arising from the use of non-ideal switches in the traditional Blumlein configuration. With this pulse generator, pulses with durations between 8 and 300 ns were applied to Jurkat cells (a leukemia cell line) to investigate the pulse dependent increase in calcium levels. The development of the calcium levels in individual cells was studied by spinning-disc confocal fluorescent microscopy with the calcium indicator, fluo-4. With this fast imaging system, fluorescence changes, representing calcium mobilization, could be resolved with an exposure of 5 ms every 18 ms. For a 60-ns pulse duration, each rise in intracellular calcium was greater as the electric field strength was increased from 25 kV/cm to 100 kV/cm. Only for the highest electric field strength is the response dependent on the presence of extracellular calcium. The results complement ion-exchange mechanisms previously observed during the charging of cellular membranes, which were suggested by observations of membrane potential changes during exposure.
A 65fJ/Conversion-Step 0-to-50MS/s 0-to-0.7mW 9b Charge-Sharing SAR ADC in 90nm Digital CMOS

A fully dynamic SAR ADC is proposed that uses passive charge-sharing and an asynchronous controller to achieve low power consumption. No active circuits are needed for high-speed operation and all static power is removed, offering power consumption proportional to sampling frequency from 50MS/s down to 0. The prototype implementation in 90nm digital CMOS achieves 7.8 ENOB, 49dB SNDR at 20MS/s consuming 290 muW. This results in a FOM of 65fJ/conversion-step.
Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images

Semantic segmentation is a fundamental problem in biomedical image analysis. In biomedical practice, it is often the case that only limited annotated data are available for model training. Unannotated images, on the other hand, are easier to acquire. How to utilize unannotated images for training effective segmentation models is an important issue. In this paper, we propose a new deep adversarial network (DAN) model for biomedical image segmentation, aiming to attain consistently good segmentation results on both annotated and unannotated images. Our model consists of two networks: (1) a segmentation network (SN) to conduct segmentation; (2) an evaluation network (EN) to assess segmentation quality. During training, EN is encouraged to distinguish between segmentation results of unannotated images and annotated ones (by giving them different scores), while SN is encouraged to produce segmentation results of unannotated images such that EN cannot distinguish these from the annotated ones. Through an iterative adversarial training process, because EN is constantly “criticizing” the segmentation results of unannotated images, SN can be trained to produce more and more accurate segmentation for unannotated and unseen samples. Experiments show that our proposed DAN model is effective in utilizing unannotated image data to obtain considerably better segmentation.
Securing Internet of Things with Lightweight IPsec

Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. In some cases it may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between an IP enabled sensor nodes and a device on traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec on Contiki. Our extension supports both IPsec’s Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms.
Reasoning based on property propagation on CIDOC-CRM and CRMdig based repositories

Reasoning on provenance information and property propagation is of significant importance in e-science since it helps scientists manage derived metadata in order to understand the source of an object, reproduce results of processes and facilitate quality control of results and processes. In this paper we introduce a simple, yet powerful reasoning mechanism based on property propagation along the transitive part-of and derivation chains, in order to trace the provenance of an object and to carry useful inferences. We apply our reasoning in semantic repositories using the CIDOC-CRM conceptual schema and its extension CRMdig, which has been develop for representing the digital and empirical provenance of digi-
Good Friends, Bad News - Affect and Virality in Twitter

The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. The basic measure of virality in Twitter is the probability of retweet and we are interested in which dimensions of the content of a tweet leads to retweeting. We hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. Our findings may be summarized ’If you want to be cited: Sweet talk your friends or serve bad news to the public’.
Secure two-party computations in ANSI C

The practical application of Secure Two-Party Computation is hindered by the difficulty to implement secure computation protocols. While recent work has proposed very simple programming languages which can be used to specify secure computations, it is still difficult for practitioners to use them, and cumbersome to translate existing source code into this format. Similarly, the manual construction of two-party computation protocols, in particular ones based on the approach of garbled circuits, is labor intensive and error-prone.
 The central contribution of the current paper is a tool which achieves Secure Two-Party Computation for ANSI C. Our work is based on a combination of model checking techniques and two-party computation based on garbled circuits. Our key insight is a nonstandard use of the bit-precise model checker CBMC which enables us to translate C programs into equivalent Boolean circuits. To this end, we modify the standard CBMC translation from programs into Boolean formulas whose variables correspond to the memory bits manipulated by the program. As CBMC attempts to minimize the size of the formulas, the circuits obtained by our tool chain are also size efficient; to improve the efficiency of the garbled circuit evaluation, we perform optimizations on the circuits. Experimental results with the new tool CBMC-GC demonstrate the practical usefulness of our approach.
Document clustering using locality preserving indexing

We propose a novel document clustering method which aims to cluster the documents into different semantic classes. The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality. By using locality preserving indexing (LPI), the documents can be projected into a lower-dimensional semantic space in which the documents related to the same semantics are close to each other. Different from previous document clustering methods based on latent semantic indexing (LSI) or nonnegative matrix factorization (NMF), our method tries to discover both the geometric and discriminating structures of the document space. Theoretical analysis of our method shows that LPI is an unsupervised approximation of the supervised linear discriminant analysis (LDA) method, which gives the intuitive motivation of our method. Extensive experimental evaluations are performed on the Reuters-21578 and TDT2 data sets.
Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization

That the initialization can have a significant impact on the performance of evolutionary algorithms (EAs) is a well known fact in the empirical evolutionary computation literature. Surprisingly, it has nevertheless received only little attention from the theoretical community.
 We bridge this gap by providing a thorough runtime analysis for a simple iterated random sampling initialization. In the latter, instead of starting an EA with a random sample, it is started in the best of k search points that are taken from the search space uniformly at random. Implementing this strategy comes at almost no cost, neither in the actual coding work nor in terms of wall-clock time.
 Taking the best of two random samples already decreases the Θ(n log n) expected runtime of the (1+1)~EA and Randomized Local Search on OneMax by an additive term of order √n. The optimal gain that one can achieve with iterated random sampling is an additive term of order √n log n}. This also determines the best possible mutation-based EA for OneMax, a question left open in [Sudholt,  2013].
 At the heart of our analysis is a very precise bound for the maximum of k independent Binomially distributed variables with success probability 1/2.
Impact of organizational structure on distributed requirements engineering processes: lessons learned

The requirements engineering program at Siemens Corporate Research has been involved with process improvement, training and project execution across many of the Siemens operating companies. We have been able to observe and assist with process improvement in mainly global software development efforts. Other researchers have reported extensively on various aspects of distributed requirements engineering, but issues specific to organizational structure have not been well categorized. Our experience has been that organizational and other management issues can overshadow technical problems caused by globalization. This paper describes some of the different organizational structures we have encountered, the problems introduced into requirements engineering processes by these structures, and techniques that were effective in mitigating some of the negative effects of global software development.