nils-herrmann/clean_scirepeval huggingface dataset:
Nicter: a Large-scale Network Incident Analysis System: Case Studies for Understanding Threat Landscape

We have been developing the Network Incident analysis Center for Tactical Emergency Response (nicter), whose objective is to detect and identify propagating malwares. The nicter mainly monitors darknet, a set of unused IP addresses, to observe global trends of network threats, while it captures and analyzes malware executables. By correlating the network threats with analysis results of malware, the nicter identifies the root causes (malwares) of the detected network threats. Through a long-term operation of the nicter for more than five years, we have achieved some key findings that would help us to understand the intentions of attackers and the comprehensive threat landscape of the Internet. With a focus on a well-knwon malware, i. e., W32.Downadup, this paper provides some practical case studies with considerations and consequently we could obtain a threat landscape that more than 60% of attacking hosts observed in our dark-net could be infected by W32.Downadup. As an evaluation, we confirmed that the result of the correlation analysis was correct in a rate of 86.18%.
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners

For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction. Code, data and more visual results will be made available at http://www.vision.ee.ethz.ch/ ̃heckers/Drive360.
Video quality assessment using structural distortion measurement

Objective image/video quality measures play important roles in various image/video processing applications, such as compression, communication, printing, analysis, registration, restoration and enhancement. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. In this paper, we follow a new philosophy in designing image/video quality metrics, which uses structural distortion as an estimation of perceived visual distortion. We develop a new approach for video quality assessment. Experiments on the video quality experts group (VQEG) test data set shows that the new quality measure has higher correlation with subjective quality measurement than the proposed methods in VQEG’s Phase I tests for full-reference video quality assessment.
On Automatic Plagiarism Detection Based on n-Grams Comparison

When automatic plagiarism detection is carried out considering a reference corpus, a suspicious text is compared to a set of original documents in order to relate the plagiarised text fragments to their potential source. One of the biggest difficulties in this task is to locate plagiarised fragments that have been modified (by rewording, insertion or deletion, for example) from the source text. The definition of proper text chunks as comparison units of the suspicious and original texts is crucial for the success of this kind of applications. Our experiments with the METER corpus show that the best results are obtained when considering low level word n-grams comparisons (n = {2, 3}).
Automated Torch Path Planning Using Polygon Subdivision for Solid Freeform Fabrication Based on Welding

A continuous path is one of the most important requirements for solid freeform fabrication (SFF) based on welding. This paper proposes a method for torch path planning applicable to SFF based on welding with an emphasis on minimum human intervention. The suggested approach describes a method based on the subdivision of a two-dimensional (2-D) polygonal section into a set of monotone polygons to generate a continuous path for material deposition. A two-dimensional contour is subdivided into smaller polygons (subpolygons). The path for each individual subpolygon is generated. The final torch path is obtained by connecting individual paths for all the subpolygons and trimming along the points of intersection of the paths for individual subpolygons. The final path is a closed loop; therefore, any point can be selected as the starting point for material deposition. The proposed method can be used to develop the toolpath for CNC milling.
Managing Platform Ecosystems

We examine how control over a technology platform can increase profits and innovation. By choosing how much to open and when to bundle enhancements, platform sponsors can influence choices of ecosystem partners. Platform openness invites developer participation but sacrifices direct sales. Bundling enhancements early drives developers away but bundling late delays platform growth. Ironically, developers can prefer sponsored platforms to unmanaged open standards despite giving up their applications. Results can inform antitrust law and innovation strategy.
Electrostatic Basis for Separation of Wheat Bran Tissues

New fractionation technologies are presently under investigation in order to enhance the wheat bran nutrient value. The explicit aim of this paper is to evaluate the possibility of using electric field forces for the separation of the fine particles generated from each wheat bran tissue during the milling process. Corona-charging and surface-potential-decay experiments were performed on samples of finely ground wheat bran and aleurone tissues (maximum size of 0.3 mm). Ground bran, which has a higher moisture content, more rapidly loses the charge imparted by corona discharge. For both kinds of samples, finer particles charged better than larger particles, and lyophilized samples were found to better preserve their charge than those not subjected to a freeze-drying process. An experiment carried out using a belt-type corona electrostatic separator validated the possibility of sorting these tissues based on the differences in their charge decay characteristics. Tribocharging experiments performed on the same tissues have also led to encouraging results: The charge/mass ratio of ground bran tissues was twice that of aleurone. These differences in the physical properties of the two types of tissues provide the technical basis for the development of electrostatic-separation technologies as part of more efficient bran-cracking strategies.
Low Cross-Polarization Vivaldi Arrays

This paper introduces a Vivaldi-type ultra-wideband antenna array with low cross-polarization, termed the Sliced Notch Antenna (SNA) array. High cross-polarization when scanning in the non-principal planes has long been a problem in Vivaldi arrays without a universal solution. In this paper, we shed light on the root cause of this long-standing issue, clarifying that a high ratio of vertical-to-horizontal current potentials along the upper radiator fin is primarily responsible for elevated cross-polarization (and not strictly the element profile). This finding therein motivates the main technical innovation in the present work—a simple reconfiguration of the upper Vivaldi fin into a series of coupled conductor segments to effectively control the aforementioned current ratio for significant reductions in cross-polarization, without reducing element profile or considerably hindering the excellent match and radiation efficiency of the original Vivaldi. Theory and design methodology for the SNA array are formulated, and followed by a concise set of design guidelines that are applied to reconfigure a representative 10:1 Vivaldi element into an SNA for practical reference, exhibiting a 20 dB reduction in peak cross-polarization ratio. This paper considers the case of single-polarized arrays as a first introduction to the SNA, though the method has been verified to work equally well (if not better) in dual-polarized arrays.
Machine learning applications in cancer prognosis and prediction

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
A Sparse Matrix Arithmetic based on H-Matrices . Part I : Introduction to H-Matrices ∗

A class of matrices (H-matrices) is introduced which have the following properties. (i) They are sparse in the sense that only few data are needed for their representation. (ii) The matrix-vector multiplication is of almost linear complexity. (iii) In general, sums and products of these matrices are no longer in the same set, but their truncations to the H-matrix format are again of almost linear complexity. (iv) The same statement holds for the inverse of an H-matrix. This paper is the first of a series and is devoted to the first introduction of the H-matrix concept. Two concret formats are described. The first one is the simplest possible. Nevertheless, it allows the exact inversion of tridiagonal matrices. The second one is able to approximate discrete integral operators.
FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning — answering image-related questions which require a multi-step, high-level process — a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-theart error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.
Function Representation of Solids Reconstructed from Scattered Surface Points and Contours

This paper presents a novel approach to the reconstruction of geometric models and surfaces from given sets of points using volume splines. It results in the representation of a solid by the inequality The volume spline is based on use of the Green’s function for interpolation of scalar function values of a chosen “carrier” solid. Our algorithm is capable of generating highly concave and branching objects automatically. The particular case where the surface is reconstructed from cross-sections is discussed too. Potential applications of this algorithm are in tomography, image processing, animation and CAD f o r bodies with complex surfaces.
What's in an Electronic Business Model?

An electronic business model is an important baseline for the development of e-commerce system applications. Essentially, it provides the design rationale for e-commerce systems from the business point of view. However, how an e-business model must be defined and specified is a largely open issue. Business decision makers tend to use the notion in a highly informal way, and usually there is a big gap between the business view and that of IT developers. Nevertheless, we show that conceptual modelling techniques from IT provide very useful tools for precisely pinning down what e-business models actually are, as well as for their structured specification. We therefore present a (lightweight) ontology of what should be in an e-business model. The key idea we propose and develop is that an e-business model ontology centers around the core concept of value, and expresses how value is created, interpreted and exchanged within a multi-party stakeholder network. Our e-business model ontology is part of a wider methodology for e-business modelling, called e3-valueTM , that is currently under development. It is based on a variety of industrial applications we are involved in, and it is illustrated by discussing a free Internet access service as an example.
Peer Assessment in MOOCs Using Preference Learning via Matrix Factorization

Evaluating in Massive Open Online Courses (MOOCs) is a difficult task because of the huge number of students involved in the courses. Peer grading is an effective method to cope with this problem, but something must be done to lessen the effect of the subjective evaluation. In this paper we present a matrix factorization approach able to learn from the order of the subset of exams evaluated by each grader. We tested this method on a data set provided by a real peer review process. By using a tailored graphical representation, the induced model could also allow the detection of peculiarities in the peer review process.
Investigation Into IGBT dV/dt During Turn-Off and Its Temperature Dependence

In many power converter applications, particularly those with high variable loads, such as traction and wind power, condition monitoring of the power semiconductor devices in the converter is considered desirable. Monitoring the device junction temperature in such converters is an essential part of this process. In this paper, a method for measuring the insulated gate bipolar transistor (IGBT) junction temperature using the collector voltage dV/dt at turn-OFF is outlined. A theoretical closed-form expression for the dV/dt at turn-OFF is derived, closely agreeing with experimental measurements. The role of dV/dt in dynamic avalanche in high-voltage IGBTs is also discussed. Finally, the implications of the temperature dependence of the dV/dt are discussed, including implementation of such a temperature measurement technique.
A computer forensic method for detecting timestamp forgery in NTFS

In this paper, we present a computer forensic method for detecting timestamp forgeries in the Windows NTFS file system. It is difficult to know precisely that the timestamps have been changed by only examining the timestamps of the file itself. If we can find the past timestamps before any changes to the file are made, this can act as evidence of file time forgery. The log records operate on files and leave large amounts of information in the $LogFile that can be used to reconstruct operations on the files and also used as forensic evidence. Log record with 0x07/0x07 opcode in the data part of Redo/Undo attribute has timestamps which contain past-and-present timestamps. The past-and-present timestamps can be decisive evidence to indicate timestamp forgery, as they contain when and how the timestamps were changed. We used file time change tools that can easily be found on Internet sites. The patterns of the timestamp change created by the tools are different compared to those of normal file operations. Seven file operations have ten timestamp change patterns in total by features of timestamp changes in the $STANDARD_INFORMATION attribute and the $FILE_NAME attribute. We made rule sets for detecting timestamp forgery based on using difference comparison between changes in timestamp patterns by the file time change tool and normal file operations. We apply the forensic rule sets for “.txt”, “.docx” and “.pdf” file types, and we show the effectiveness and validity of the proposed method. The importance of this research lies in the fact that we can find the past time in $LogFile, which gives decisive evidence of timestamp forgery. This makes the timestamp active evidence as opposed to simply being passive evidence. a 2012 Elsevier Ltd. All rights reserved.
3D-TV Content Creation: Automatic 2D-to-3D Video Conversion

Three-dimensional television (3D-TV) is the next major revolution in television. A successful rollout of 3D-TV will require a backward-compatible transmission/distribution system, inexpensive 3D displays, and an adequate supply of high-quality 3D program material. With respect to the last factor, the conversion of 2D images/videos to 3D will play an important role. This paper provides an overview of automatic 2D-to-3D video conversion with a specific look at a number of approaches for both the extraction of depth information from monoscopic images and the generation of stereoscopic images. Some challenging issues for the success of automatic 2D-to-3D video conversion are pointed out as possible research topics for the future.
Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts

Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media.



We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training.



Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision.



Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models.



ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.
Device-to-Device Communication Underlaying Cellular Communications Systems

In this article we propose to facilitate local peer-to-peer communication by a Device-to-Device (D2D) radio that operates as an underlay network to an IMT-Advanced cellular network. It is expected that local services may utilize mobile peer-to-peer communication instead of central server based communication for rich multimedia services. The main challenge of the underlay radio in a multi-cell environment is to limit the interference to the cellular network while achieving a reasonable link budget for the D2D radio. We propose a novel power control mechanism for D2D connections that share cellular uplink resources. The mechanism limits the maximum D2D transmit power utilizing cellular power control information of the devices in D2D communication. Thereby it enables underlaying D2D communication even in interference-limited networks with full load and without degrading the performance of the cellular network. Secondly, we study a single cell scenario consisting of a device communicating with the base station and two devices that communicate with each other. The results demonstrate that the D2D radio, sharing the same resources as the cellular network, can provide higher capacity (sum rate) compared to pure cellular communication where all the data is transmitted through the base station.
Ontology-Based Sentiment Analysis Model of Customer Reviews for Electronic Products

This article reports on a generalizable system model design that analyzes the unstructured customer reviews inside the posts about electronic products on social networking websites. For the purposes of this study, posts on social networking websites have been mined and the keywords are extracted from such posts. The extracted keywords and the ontologies of electronic products and emotions form the base for the sentiment analysis model, which is used to understand online consumer behavior in the market. In order to enhance system accuracy, negating and enhancing terms are considered in the proposed model. As a result, it allows online businesses to use query to analyze the market trends of each product accurately based on the comments from user posts in social networking sites.
MS-LSTM: A multi-scale LSTM model for BGP anomaly detection

Detecting anomalous Border Gateway Protocol (BGP) traffic is significantly important in improving both security and robustness of the Internet. Existing solutions apply classic classifiers to make real-time decision based on the traffic features of present moment. However, due to the frequently happening burst and noise in dynamic Internet traffic, the decision based on short-term features is not reliable. To address this problem, we propose MS-LSTM, a multi-scale Long Short-Term Memory (LSTM) model to consider the Internet flow as a multi-dimensional time sequence and learn the traffic pattern from historical features in a sliding time window. In addition, we find that adopting different time scale to preprocess the traffic flow has great impact on the performance of all classifiers. In this paper, comprehensive experiments are conducted and the results show that a proper time scale can improve about 10% accuracy of LSTM as well as all conventional machine learning methods. Particularly, MS-LSTM with optimal time scale 8 can achieve 99.5% accuracy in the best case.