nils-herrmann/clean_scirepeval huggingface dataset:
Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface

A synthetic aperture radar (SAR) can produce high-resolution two-dimensional images of mapped areas. The SAR comprises a pulsed transmitter, an antenna, and a phase-coherent receiver. The SAR is borne by a constant velocity vehicle such as an aircraft or satellite, with the antenna beam axis oriented obliquely to the velocity vector. The image plane is defined by the velocity vector and antenna beam axis. The image orthogonal coordinates are range and cross range (azimuth). The amplitude and phase of the received signals are collected for the duration of an integration time after which the signal is processed. High range resolution is achieved by the use of wide bandwidth transmitted pulses. High azimuth resolution is achieved by focusing, with a signal processing technique, an extremely long antenna that is synthesized from the coherent phase history. The pulse repetition frequency of the SAR is constrained within bounds established by the geometry and signal ambiguity limits. SAR operation requires relative motion between radar and target. Nominal velocity values are assumed for signal processing and measurable deviations are used for error compensation. Residual uncertainties and high-order derivatives of the velocity which are difficult to compensate may cause image smearing, defocusing, and increased image sidelobes. The SAR transforms the ocean surface into numerous small cells, each with dimensions of range and azimuth resolution. An image of a cell can be produced provided the radar cross section of the cell is sufficiently large and the cell phase history is deterministic. Ocean waves evidently move sufficiently uniformly to produce SAR images which correlate well with optical photographs and visual observations. The relationship between SAR images and oceanic physical features is not completely understood, and more analyses and investigations are desired.
Bed-tree: an all-purpose index structure for string similarity search based on edit distance

Strings are ubiquitous in computer systems and hence string processing has attracted extensive research effort from computer scientists in diverse areas. One of the most important problems in string processing is to efficiently evaluate the similarity between two strings based on a specified similarity measure. String similarity search is a fundamental problem in information retrieval, database cleaning, biological sequence analysis, and more. While a large number of dissimilarity measures on strings have been proposed, edit distance is the most popular choice in a wide spectrum of applications. Existing indexing techniques for similarity search queries based on edit distance, e.g., approximate selection and join queries, rely mostly on n-gram signatures coupled with inverted list structures. These techniques are tailored for specific query types only, and their performance remains unsatisfactory especially in scenarios with strict memory constraints or frequent data updates. In this paper
 we propose the Bed-tree, a B+-tree based index structure for evaluating all types of similarity queries on edit distance and normalized edit distance. We identify the necessary properties of a mapping from the string space to the integer space for supporting searching and pruning for these queries. Three transformations are proposed that capture different aspects of information inherent in strings, enabling efficient pruning during the search process on the tree. Compared to state-of-the-art methods on string similarity search, the Bed-tree is a complete solution that meets the requirements of all applications, providing high scalability and fast response time.
INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data

Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.
Software Defects and their Impact on System Availability: A Study of Field Failures in Operating Systems

In recent years, software defects have become the dominant cause of customer outage, and improvements in software reliability and quality have not kept pace with those of hardware. Yet, software defects are not well enough understood to provide a clear methodology for avoiding or recovering from them. To gain the necessary insight, we study defects reported between 1986 and 1889 from a on a high-end operating system product. We compare a typical defect ( regular) to one that corrupts a program’s memory ( overlay) given that overlays are considered by field services to be particularly hard to find and fix.
Feasibility of a serverless distributed file system deployed on an existing set of desktop PCs

We consider an architecture for a serverless distributed file system that does not assume mutual trust among the client computers. The system provides security, availability, and reliability by distributing multiple encrypted replicas of each file among the client machines. To assess the feasibility of deploying this system on an existing desktop infrastructure, we measure and analyze a large set of client machines in a commercial environment. In particular, we measure and report results on disk usage and content; file activity; and machine uptimes, lifetimes, and loads. We conclude that the measured desktop infrastructure would passably support our proposed system, providing availability on the order of one unfilled file request per user per thousand days.
Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

In this paper, we study aspects of single microphone speech enhancement SE based on deep neural networks DNNs. Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio SNR. Furthermore, we investigate how specialized DNN-based SE systems, which have been trained to be either noise type specific, speaker specific or SNR specific, perform relative to DNN based SE systems that have been trained to be noise type general, speaker general, and SNR general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error STSA-MMSE based SE algorithm. We show that DNN-based SE systems, when trained specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality SQ and speech intelligibility SI, when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system when exposed to unseen speakers, genders and noise types, given a large number of speakers and noise types have been used in the training of the system. In addition, we show that a DNN-based SE system that has been trained using a large number of speakers and a wide range of noise types outperforms a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI for others.
Challenges and opportunities: from big data to knowledge in AI 2.0

In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.
Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition

In this paper, we aim to construct a deep neural network which embeds high dimensional symmetric positive definite (SPD) matrices into a more discriminative low dimensional SPD manifold. To this end, we develop two types of basic layers: a 2D fully connected layer which reduces the dimensionality of the SPD matrices, and a symmetrically clean layer which achieves non-linear mapping. Specifically, we extend the classical fully connected layer such that it is suitable for SPD matrices, and we further show that SPD matrices with symmetric pair elements setting zero operations are still symmetric positive definite. Finally, we complete the construction of the deep neural network for SPD manifold learning by stacking the two layers. Experiments on several face datasets demonstrate the effectiveness of the proposed method.  Symmetric positive definite (SPD) matrices have shown powerful representation abilities of encoding image and video information. In computer vision community, the SPD matrix representation has been widely employed in many applications, such as face recognition (Pang, Yuan, and Li 2008; Huang et al. 2015; Wu et al. 2015; Li et al. 2015), object recognition (Tuzel, Porikli, and Meer 2006; Jayasumana et al. 2013; Harandi, Salzmann, and Hartley 2014; Yin et al. 2016), action recognition (Harandi et al. 2016), and visual tracking (Wu et al. 2015). The SPD matrices form a Riemannian manifold, where the Euclidean distance is no longer a suitable metric. Previous works on analyzing the SPD manifold mainly fall into two categories: the local approximation method and the kernel method, as shown in Figure 1(a). The local approximation method (Tuzel, Porikli, and Meer 2006; Sivalingam et al. 2009; Tosato et al. 2010; Carreira et al. 2012; ) locally flattens the manifold and approximates the SPD matrix by a point of the tangent space. The kernel method (Harandi et al. 2012; Wang et al. 2012; Jayasumana et al. 2013; Li et al. 2013; Quang, San Biagio, and Murino 2014; Yin et al. 2016) embeds the manifold into a higher dimensional Reproducing Kernel Hilbert Space (RKHS) via kernel functions. On new ∗corresponding author Copyright c
Learning to suggest: a machine learning framework for ranking query suggestions

We consider the task of suggesting related queries to users after they issue their initial query to a web search engine. We propose a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model which enables us to generalize to queries that have never occurred in the logs as well. The model is trained on co-occurrences mined from the search logs, with novel utility and relevance models, and the machine learning step is done without any labeled data by human judges. The learning step allows us to generalize from the past observations and generate query suggestions that are beyond the past co-occurred queries. This brings significant gains in coverage while yielding modest gains in relevance. Both offline (based on human judges) and online (based on millions of user interactions) evaluations demonstrate that our approach significantly outperforms strong baselines.
A Framework for Detection of Video Spam on YouTube

YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. It has been observed that the presence of opportunistic users post unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts). A method of mining YouTube to classify a video as spam or legitimate based on video attributes has been presented. The empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it to recognize video response spam.  video spam; spam detection; YouTube; TubeKit
Online Dynamic Graph Drawing

This paper presents an algorithm for drawing a sequence of graphs online. The algorithm strives to maintain the global structure of the graph and, thus, the user's mental map while allowing arbitrary modifications between consecutive layouts. The algorithm works online and uses various execution culling methods in order to reduce the layout time and handle large dynamic graphs. Techniques for representing graphs on the GPU allow a speedup by a factor of up to 17 compared to the CPU implementation. The scalability of the algorithm across GPU generations is demonstrated. Applications of the algorithm to the visualization of discussion threads in Internet sites and to the visualization of social networks are provided.
A prosody only decision-tree model for disfluency detection

Speech disfluencies (filled pauses, repe titions, repairs, and false starts) are pervasive in spontaneous speech. The ab ility to detect and correct disfluencies automatically is important for effective natural language understanding, as well as to improve speech models in general. Previous approaches to disfluency detection have relied heavily on lexical information, which makes them less applicable when word recognition is unreliable. We have developed a disfluency detection method using decision tree classifiers that use only local and automatically extracted prosodic features. Because the model doesn’t rely on lexical information, it is widely applicable even when word recognition is unreliable. The model performed significantly better than chance at detecting four disfluency types. It also outperformed a language model in the detection of false starts, given the correct transcription. Combining the prosody model with a specialized language model improved accuracy over either model alone for the detection of false starts. Results suggest that a prosody-only model can aid the automatic detection of disfluencies in spontaneous speech.
Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework

The post-adoption behaviors of online service users are critical performance factors for online service providers. To fill an academic gap that persists regarding bloggers’ switching behavior across online service substitutes, this empirical study investigates which factors affect bloggers who switch social network sites, in an attempt to understand specifically how push, pull, and mooring factors shape their switching intentions. The data to test the hypotheses come from an online survey of 319 bloggers, analyzed using partial least squares techniques. The results confirm positive influences of push and pull effects, a negative influence of mooring effects, and an interactive effect of push and mooring on switching intentions. The push–pull–mooring framework thus is a useful tool for comprehending the competing forces that influence the use of online service substitutes. In particular, perceptions of weak connections and writing anxiety push bloggers away, whereas relative enjoyment and usefulness pull bloggers to social network sites; switching cost and past experience also inhibit a change. These findings offer key insights and implications for the competitive strategy choices of online service providers.
DroidRay: a security evaluation system for customized android firmwares

Android mobile devices are enjoying a lion's market share in smartphones and mobile devices. This also attracts malware writers to target the Android platform. Recently, we have discovered a new Android malware distribution channel: releasing malicious firmwares with pre-installed malware to the wild. This poses significant risk since users of mobile devices cannot change the content of the malicious firmwares. Furthermore, pre-installed applications have " more permissions" (i.e., silent installation) than other legitimate mobile apps, so they can download more malware or access users' confidential information. To understand and address this new form of malware distribution channel, we design and implement "DroidRay": a security evaluation system for customized Android firmwares. DroidRay uses both static and dynamic analyses to evaluate the firmware security on both the application and system levels. To understand the impact of this new malware distribution channel, we analyze 250 Android firmwares and 24,009 pre-installed applications. We reveal how the malicious firmware and pre-installed malware are injected, and discovered 1,947 (8.1%) pre-installed applications have signature vulnerability and 19 (7.6%) firmwares contain pre-installed malware. In addition, 142 (56.8%) firmwares have the default signature vulnerability, five (2.0%) firmwares contain malicious hosts file, at most 40 (16.0%) firmwares have the native level privilege escalation vulnerability and at least 249 (99.6%) firmwares have the Java level privilege escalation vulnerability. Lastly, we investigate a real-world case of a pre-installed zero-day malware known as CEPlugnew, which involves 348,018 infected Android smartphones, and we show its degree and geographical penetration. This shows the significance of this new malware distribution channel, and DroidRay is an effective tool to combat this new form of malware spreading.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems1.
False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.
 In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.
Classification Algorithm for Feature Extraction using Linear Discriminant Analysis and Cross-correlation on ECG Signals

This paper develops a novel framework for feature extraction based on a combination of Linear Discriminant Analysis and cross-correlation. Multiple Electrocardiogram (ECG) signals, acquired from the human heart in different states such as in fear, during exercise, etc. are used for simulations. The ECG signals are composed of P, Q, R, S and T waves. They are characterized by several parameters and the important information relies on its HRV (Heart Rate Variability). Human interpretation of such signals requires experience and incorrect readings could result in potentially life threatening and even fatal consequences. Thus a proper interpretation of ECG signals is of paramount importance. This work focuses on designing a machine based classification algorithm for ECG signals. The proposed algorithm filters the ECG signals to reduce the effects of noise. It then uses the Fourier transform to transform the signals into the frequency domain for analysis. The frequency domain signal is then cross correlated with predefined classes of ECG signals, in a manner similar to pattern recognition. The correlated co-efficients generated are then thresholded. Moreover Linear Discriminant Analysis is also applied. Linear Discriminant Analysis makes classes of different multiple ECG signals. LDA makes classes on the basis of mean, global mean, mean subtraction, transpose, covariance, probability and frequencies. And also setting thresholds for the classes. The distributed space area is divided into regions corresponding to each of the classes. Each region associated with a class is defined by its thresholds. So it is useful in distinguishing ECG signals from each other. And pedantic details from LDA (Linear Discriminant Analysis) output graph can be easily taken in account rapidly. The output generated after applying cross-correlation and LDA displays either normal, fear, smoking or exercise ECG signal. As a result, the system can help clinically on large scale by providing reliable and accurate classification in a fast and computationally efficient manner. The doctors can use this system by gaining more efficiency. As very few errors are involved in it, showing accuracy between 90% 95%.
Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics

This paper focuses on the problem of pitch tracking in noisy conditions. A method using harmonic information in the residual signal is presented. The proposed criterion is used both for pitch estimation, as well as for determining the voicing segments of speech. In the experiments, the method is compared to six state-of-the-art pitch trackers on the Keele and CSTR databases. The proposed technique is shown to be particularly robust to additive noise, leading to a significant improvement in adverse conditions.