nils-herrmann/clean_scirepeval huggingface dataset:
DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability

Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.
Open Source Digital Forensics Tools : The Legal Argument

This paper addresses digital forensic analysis tools and their use in a legal setting. To enter scientific evidence into a United States court, a tool must be reliable and relevant. The reliability of evidence is tested by applying “Daubert” guidelines. To date, there have been few legal challenges to digital evidence, but as the field matures this will likely change. This paper examines the Daubert guidelines and shows that open source tools may more clearly and comprehensively meet the guidelines than closed source tools.
Design and evaluation of a delay-efficient RPL routing metric

The Routing Protocol for Low power and Lossy Networks (RPL) is the IETF standard for IPv6 routing in low-power wireless sensor networks. It is a distance vector routing protocol that builds a Destination Oriented Directed Acyclic Graph (DODAG) rooted towards one sink (the DAG root), using an objective function and a set of metrics/constraints to compute the best path. In this paper, we propose a routing metric which minimizes the delay towards the DAG root, assuming that nodes run with very low duty cycles (e.g., under 1%) at the MAC layer. We evaluate the proposed routing metric with the Contiki operating system and compare its performance with that of the Expected Transmission Count (ETX) metric. Moreover, we propose some extensions to the ContikiMAC radio duty cycling protocol to support different sleeping periods of the nodes.
The square-root unscented Kalman filter for state and parameter-estimation

Over the last 20-30years,the extendedKalmanfilter (EKF) has becomethealgorithmof choicein numerousnonlinearestimation andmachinelearningapplications.Theseincludeestimatingthe stateof a nonlineardynamicsystemaswell estimatingparameters for nonlinearsystemidentification(e.g., learningthe weightsof a neuralnetwork). The EKF appliesthe standardlinear Kalman filter methodologyto a linearizationof the truenonlinearsystem. This approachis sub-optimal,andcaneasily leadto divergence. Julier et al.  proposedthe unscentedKalman filter (UKF) as a derivative-freealternati ve to the extendedKalman filter in the framework of state-estimation.This wasextendedto parameter estimationby Wan andvan der Merwe [2, 3]. The UKF consistently outperformstheEKF in termsof predictionandestimation error, at an equalcomputationalcomplexity of 1 for generalstate-spaceproblems.WhentheEKF is appliedto parameter estimation,the specialform of the state-space quationsallows for an implementation.This paperintroducesthesquareroot unscentedKalmanfilter (SR-UKF)which is also for generalstate-estimationand for parameter estimation(note theoriginal formulationof theUKF for parameter -estimationwas ). In addition,thesquare-root formshave theaddedbenefit of numericalstabilityandguaranteedpositivesemi-definiteness of thestatecovariances.
Ontologies and Knowledge Bases. Towards a Terminological Clarification

The word “ontology” has recently gained a good popularity within the knowledge engineering community. However, its meaning tends to remain a bit vague, as the term is used in very different ways. Limiting our attention to the various proposals made in the current debate in AI, we isolate a number of interpretations, which in our opinion deserve a suitable clarification. We elucidate the implications of such various interpretations, arguing for the need of clear terminological choices regarding the technical use of terms like “ontology”, “conceptualization” and “ontological commitment”. After some comments on the use “Ontology” (with the capital “o”) as a term which denotes a philosophical discipline, we analyse the possible confusion between an ontology intended as a particular conceptual framework at the knowledge level and an ontology intended as a concrete artifact at the symbol level, to be used for a given purpose. A crucial point in this clarification effort is the careful analysis of Gruber’ s definition of an ontology as a specification of a conceptualization.
Scalable, graph-based network vulnerability analysis

Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graph-based algorithms to generate attack trees (or graphs). Either structure represents all possible sequences of exploits, where any given exploit can take advantage of the penetration achieved by prior exploits in its chain, and the final exploit in the chain achieves the attacker's goal. The most recent approach in this line of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems, and there is good reason to doubt whether a model checker can handle directly a realistic set of exploits for even a modest-sized network. In this paper, we revisit the idea of attack graphs themselves, and argue that they represent more information explicitly than is necessary for the analyst. Instead, we propose a more compact and scalable representation. Although we show that it is possible to produce attack trees from our representation, we argue that more useful information can be produced, for larger networks, while bypassing the attack tree step. Our approach relies on an explicit assumption of monotonicity, which, in essence, states that the precondition of a given exploit is never invalidated by the successful application of another exploit. In other words, the attacker never needs to backtrack. The assumption reduces the complexity of the analysis problem from exponential to polynomial, thereby bringing even very large networks within reach of analysis
A 0.5-to-2.5 Gb/s Reference-Less Half-Rate Digital CDR With Unlimited Frequency Acquisition Range and Improved Input Duty-Cycle Error Tolerance

A reference-less highly digital half-rate clock and data recovery (CDR) circuit with improved tolerance to input duty cycle error is presented. Using a chain of frequency dividers, the pro posed frequency detector produces a known sub-harmonic tone from the incoming random data. A digital frequency-locked loop uses the extracted tone, and drives the oscillator to any sub-rate of the input data frequency. The early/late outputs of a conventional half-rate bang-bang phase detector are used to determine the duty-cycle error in the incoming random data and adjust the oscillator clock phases to maximize receiver timing margins. Fabricated in 0.13 μm CMOS technology, the prototype digital CDR op erates without any errors from 0.5 Gb/s to 2.5 Gb/s. At 2 Gb/s, the prototype consumes 6.1 mW power from a 1.2 V supply. The pro posed clock-phase calibration is capable of correcting upto ±20% of input data duty-cycle error.
Development and validation of the childhood narcissism scale.

In this article, we describe the development and validation of a short (10 item) but comprehensive self-report measure of childhood narcissism. The Childhood Narcissism Scale (CNS) is a 1-dimensional measure of stable individual differences in childhood narcissism with strong internal consistency reliability (Studies 1-4). The CNS is virtually unrelated to conventional measures of self-esteem but is positively related to self-appraised superiority, social evaluative concern and self-esteem contingency, agentic interpersonal goals, and emotional extremity (Study 5). Furthermore, the CNS is negatively related to empathic concern and positively related to aggression following ego threat (Study 6). These results suggest that childhood narcissism has similar psychological and interpersonal correlates as adult narcissism. The CNS provides researchers a convenient tool for measuring narcissism in children and young adolescents with strong preliminary psychometric characteristics.
Optical nano-imaging of gate-tunable graphene plasmons

The ability to manipulate optical fields and the energy flow of light is central to modern information and communication technologies, as well as quantum information processing schemes. However, because photons do not possess charge, a way of controlling them efficiently by electrical means has so far proved elusive. A promising way to achieve electric control of light could be through plasmon polaritons—coupled excitations of photons and charge carriers—in graphene. In this two-dimensional sheet of carbon atoms, it is expected that plasmon polaritons and their associated optical fields can readily be tuned electrically by varying the graphene carrier density. Although evidence of optical graphene plasmon resonances has recently been obtained spectroscopically, no experiments so far have directly resolved propagating plasmons in real space. Here we launch and detect propagating optical plasmons in tapered graphene nanostructures using near-field scattering microscopy with infrared excitation light. We provide real-space images of plasmon fields, and find that the extracted plasmon wavelength is very short—more than 40 times smaller than the wavelength of illumination. We exploit this strong optical field confinement to turn a graphene nanostructure into a tunable resonant plasmonic cavity with extremely small mode volume. The cavity resonance is controlled in situ by gating the graphene, and in particular, complete switching on and off of the plasmon modes is demonstrated, thus paving the way towards graphene-based optical transistors. This successful alliance between nanoelectronics and nano-optics enables the development of active subwavelength-scale optics and a plethora of nano-optoelectronic devices and functionalities, such as tunable metamaterials, nanoscale optical processing, and strongly enhanced light–matter interactions for quantum devices and biosensing applications.
Regression Shrinkage and Selection via the Lasso

We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.
Argumentative zoning information extraction from scientific text

We present a new type of analysis for scientific text which we call Argumentative Zoning. We demonstrate that this type of text analysis can be used for generating usertailored and task-tailored summaries and for performing more informative citation analyses. We also demonstrate that our type of analysis can be applied to unrestricted text, both automatically and by humans. The corpus we use for the analysis (80 conference papers in computational linguistics) is a difficult test bed; it shows great variation with respect to subdomain, writing style, register and linguistic expression. We present reliability studies which we performed on this corpus and for which we use two unrelated trained annotators. The definition of our seven categories (argumentative zones) is not specific to the domain, only to the text type; it is based on the typical argumentation to be found in scientific articles. It reflects the attribution of intellectual ownership in scientific articles, expressions of authors’ stance towards other work, and typical statements about problem-solving processes. On the basis of sentential features, we use two statistical models (a Naive Bayesian model and an ngram model operating over sentences) to estimate a sentence’s argumentative status, taking the hand-annotated corpus as training material. An alternative, symbolic system uses the features in a rule-based way. The general working hypothesis of this thesis is that empirical discourse studies can contribute to practical document management problems: the analysis of a significant amount of naturally occurring text is essential for discourse linguistic theories, and the application of a robust discourse and argumentation analysis can make text understanding techniques for practical document management more robust.
Process Monitoring and Visualization Using Self-Organizing Maps

Analysis and control of complex nonlinear processes constitutes a diicult problem area. In complicated systems, as in chemical processes, it is not possible to predict all possible error types in advance. The self-organizing map algorithm can be used to investigate complex dependencies between various process parameters as well as input and output variables. It can also be utilised to create systems to monitor complicated, dynamical processes and to visualise the process development. 2.  The self-organizing map (SOM) algorithm 8]]9] creates a mapping from input patterns to map units. The key point in the applicability of the SOM algorithm is the topological nature of the mapping; similar signal patterns are mapped to nearby locations on the map. Such a mapping can be applied to pattern sequence analysis by nding the mapping locations of subsequent patterns and observing the trajectory, that is the curve of the locations in time. The pattern sequence in multi-dimensional input space can in that way be transformed into a two dimensional trajectory which can be used, for example, in process monitoring applications. In the following, the use of the self-organizing map in process state monitoring is described. Some applications in visualisation of the process operation
A k-mean clustering algorithm for mixed numeric and categorical data

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach.
Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries

The ACL Anthology Network (AAN)1 is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm

The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the Singular Value Decomposition. However, in the presence of missing data and outliers this method is not applicable, and unfortunately, this is often the case in practice. In this paper we present a method for calculating the low-rank factorization of a matrix which minimizes the L1 norm in the presence of missing data. Our approach represents a generalization the Wiberg algorithm of one of the more convincing methods for factorization under the L2 norm. By utilizing the differentiability of linear programs, we can extend the underlying ideas behind this approach to include this class of L1 problems as well. We show that the proposed algorithm can be efficiently implemented using existing optimization software. We also provide preliminary experiments on synthetic as well as real world data with very convincing results.
A Geospatial Decision Support System for Drought Risk Management

We are developing an advanced Geospatial Decision Support System (GDSS) to improve the quality and accessibility of drought related data for drought risk management. This is part of a Digital Government project aimed at developing and integrating new information technologies for improved government services in the USDA Risk Management Agency (RMA) and the Natural Resources Conservation Service (NRCS). Our overall goal is to substantially improve RMA's delivery of risk management services in the near-term and provide a foundation and directions for the future. We integrate spatio-temporal knowledge discovery techniques into our GDSS using a combination of data mining techniques applied to rich, geospatial, time-series data. Our data mining objectives are to: 1) find relationships between user-specified target episodes and other climatic events and 2) predict the target episodes. Understanding relationships between changes in soil moisture regimes and global climatic events such as El Niño could provide a reasonable drought mitigation strategy for farmers to adjust planting dates, hybrid selection, plant populations, tillage practices or crop rotations. This work highlights the innovative data mining approaches integral to our project's success and provides preliminary results that indicate our system’s potential to substantially improve RMA's delivery of drought risk management services.
Residual Useful Life Prediction for Rolling Element Bearings Based on Multi-feature Fusion Regression

Residual useful life (RUL) prediction is critical in efficient implementation of condition-based maintenances for rolling element bearings (REBs). A multi-feature fusion regression method is reported for predicting the RUL of REBs in this work. In the proposed approach, locally linear embedding technique is employed to fuse original features for erecting a condition indicator of the REBs. The fused condition indicator is then modeled by an adaptive network-based fuzzy inference system for predicting the RUL. The present approach is applied to experimental data collected from an REB. Experimental results show that the proposed approach exhibits better performance than peer approaches. It is capable of accurately predicting the RUL of the REB.
A Brain-Computer Interface for classifying EEG correlates of chronic mental stress

In this paper, a Brain-Computer Interface (BCI) for classifying EEG correlates of chronic mental stress is proposed. Data from 8 EEG channels are collected from 26 healthy right-handed students during university examination period and after the examination whereby the former is considered to be relatively more stressful to students than the latter. The mental stress level are measured using the Perceived Stress Scale 14 (PSS-14) and categorized into stressed and stress-free groups. The proposed BCI is then used to classify the subjects' mental stress level on EEG features extracted using the Higuchi's fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Square Coherence Estimation (MSCE) between the EEG channels. Classification on the EEG features are then performed using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one-out validation. The results showed that the proposed BCI using features extracted by MSCE yielded a promising inter-subject validation accuracy of over 90% in classifying the EEG correlates of chronic mental stress.
Katyusha: the first direct acceleration of stochastic gradient methods

Nesterovâs momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterovâs momentum from providing similar acceleration, even if the underlying problem is convex. 
We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. It has a provably accelerated convergence rate in convex (off-line) stochastic optimization. The main ingredient is Katyusha momentum, a novel ânegative momentumâ on top of Nesterovâs momentum that can be incorporated into a variance-reduction based algorithm and speed it up. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially give Katyusha a hug.
A survey on algorithms of hole filling in 3D surface reconstruction

The surface reconstruction of 3D objects has attracted more and more attention for its widespread application in many areas, such as computer science, cultural heritage restoration, medical facilities, entertainment. However, due to occlusion, reflectance, the scanning angle, raw data preprocessing, it is inevitable to lose some point data, which leads to holes in the reconstruction surface, making it undesirable for various applications. Therefore, methods for filling holes in the process of surface reconstruction are critical to the final results of reconstruction. This paper makes a survey of existing well-known hole-filling algorithms, classifies the algorithms into two main categories, analyzes and compares these algorithms from the viewpoints of theories and experimental results to make a clear introduction of their performance. At the end, the paper points out the possible development direction of hole filling in the future and hopes to be a good guide for other researchers.