nils-herrmann/clean_scirepeval huggingface dataset:
Next-Best Stereo: Extending Next-Best View Optimisation For Collaborative Sensors

Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable for large datasets. This work proposes an approach to intelligently filter large amounts of data for 3D reconstructions of unknown scenes using monocular cameras. Our contributions are twofold: First, we present a novel approach to efficiently optimise the Next-Best View (NBV) in terms of accuracy and coverage using partial scene geometry. Second, we extend this to intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find the NBV’s best stereo pair to perform reconstruction. Both contributions are extremely efficient, taking 0.8ms and 0.3ms per pose, respectively. Experimental evaluation shows that the proposed method allows efficient selection of stereo pairs for reconstruction, such that a dense model can be obtained with only a small number of images. Once a complete model has been obtained, the remaining computational budget is used to intelligently refine areas of uncertainty, achieving results comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little as 3.8% of the views.
Gremlin: Systematic Resilience Testing of Microservices

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices. Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.
Design and Development of a Class EF$_2$ Inverter and Rectifier for Multimegahertz Wireless Power Transfer Systems

This paper presents the design and implementation of a Class EF2 inverter and Class EF2 rectifier for two -W wireless power transfer (WPT) systems, one operating at 6.78 MHz and the other at 27.12 MHz. It will be shown that the Class EF2 circuits can be designed to have beneficial features for WPT applications such as reduced second-harmonic component and lower total harmonic distortion, higher power-output capability, reduction in magnetic core requirements and operation at higher frequencies in rectification compared to other circuit topologies. A model will first be presented to analyze the circuits and to derive values of its components to achieve optimum switching operation. Additional analysis regarding harmonic content, magnetic core requirements and open-circuit protection will also be performed. The design and implementation process of the two Class-EF2-based WPT systems will be discussed and compared to an equivalent Class-E-based WPT system. Experimental results will be provided to confirm validity of the analysis. A dc-dc efficiency of 75% was achieved with Class-EF2-based systems.
Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis

Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on community question answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.
Detecting Kernel-Level Rootkits Using Data Structure Invariants

Rootkits affect system security by modifying kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify noncontrol data. Most prior techniques for rootkit detection have focused solely on detecting control data modifications and, therefore, fail to detect such rootkits. This paper presents a novel technique to detect rootkits that modify both control and noncontrol data. The main idea is to externally observe the execution of the kernel during an inference phase and hypothesize invariants on kernel data structures. A rootkit detection phase uses these invariants as specifications of data structure integrity. During this phase, violation of invariants indicates an infection. We have implemented Gibraltar, a prototype tool that infers kernel data structure invariants and uses them to detect rootkits. Experiments show that Gibraltar can effectively detect previously known rootkits, including those that modify noncontrol data structures.
SURF-based Detection of Copy-Move Forgery in Flat Region

Techniques for digital image tampering are becoming widespread for the availability of low cost technology in which the image could be easily manipulated. Copy-move forgery is one of the tampering techniques that are frequently used and has recently received significant attention. But the existing methods, including block-matching and key point matching based methods, are not able to be used to solve the problem of detecting image forgery in both flat region and non-flat region. In this paper, combining the thinking of these two types of methods, we develop a SURF-based method to tackle this problem. In addition to the determination of forgeries in non-flat region through key point features, our method can be used to detect flat region in images in an effective way, and extract FMT features after blocking the region. By using matching algorithms of similar blocked images, image forgeries in flat region can be determined, which results in the completing of the entire image tamper detection. Experimental results are presented to demonstrate the effectiveness of the proposed method.
Evolution of a Subsumption Architecture Neurocontroller

An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this task is compared to (standard) monolithic evolution, incremental and modularised evolution. To corroborate the hypothesis that a layered controller performs at least as well as an integrated one, the evolved layers are merged back into a single network. On the grounds of the test results, it is argued that layered evolution provides a superior approach for many tasks, and it is suggested that this approach may be the key to scaling up evolutionary robotics.
Development and validation of a brief form of the Multidimensional Personality Questionnaire.

The Multidimensional Personality Questionnaire (MPQ; A. Tellegen, 1982, in press) provides for a comprehensive analysis of personality at both the lower order trait and broader structural levels. Its higher order dimensions of Positive Emotionality, Negative Emotionality, and Constraint embody affect and temperament constructs, which have been conceptualized in psychobiological terms. The MPQ thus holds considerable potential as a structural framework for investigating personality across varying levels of analysis, and this potential would be enhanced by the availability of an abbreviated version. This article describes efforts to develop and validate a brief (155-item) form, the MPQ-BF. Success was evidenced by uniformly high correlations between the brief- and full-form trait scales and consistency of higher order structures. The MPQ-BF is recommended as a tool for investigating the genetic, neurobiological, and psychological substrates of personality.
Prototype Design of a Modular Ultrawideband Wavelength-Scaled Array of Flared Notches

A prototype modular ultrawideband wavelength- scaled array of flared notches has been designed, built, measured and validated with full-wave modeling tools. Wavelength-scaled arrays operate over ultrawide bandwidths with significantly-reduced element counts, maintaining a relatively-constant beam size by utilizing phased-array radiators of different size. The prototype phased array presented here is designed to operate over an 8:1 bandwidth (1-8 GHz), demonstrating a 12-degree beam capacity at 2 GHz, 4 GHz, and 8 GHz. The architecture achieves a reduction in element count by a factor of 6.4-only 160 elements per polarization as compared to a conventional 1024-element phased array of the same aperture size-at the cost of reduced beamwidth capacity in the higher frequency range. Performance metrics (active VSWR and radiation characteristics) of the wavelength-scaled array are measured and validated against full-wave simulations. The technology is presented as a viable alternative to more expensive conventional ultrawideband arrays with dense uniform element layouts.
Spectra of "real-world" graphs: beyond the semicircle law.

Many natural and social systems develop complex networks that are usually modeled as random graphs. The eigenvalue spectrum of these graphs provides information about their structural properties. While the semicircle law is known to describe the spectral densities of uncorrelated random graphs, much less is known about the spectra of real-world graphs, describing such complex systems as the Internet, metabolic pathways, networks of power stations, scientific collaborations, or movie actors, which are inherently correlated and usually very sparse. An important limitation in addressing the spectra of these systems is that the numerical determination of the spectra for systems with more than a few thousand nodes is prohibitively time and memory consuming. Making use of recent advances in algorithms for spectral characterization, here we develop methods to determine the eigenvalues of networks comparable in size to real systems, obtaining several surprising results on the spectra of adjacency matrices corresponding to models of real-world graphs. We find that when the number of links grows as the number of nodes, the spectral density of uncorrelated random matrices does not converge to the semicircle law. Furthermore, the spectra of real-world graphs have specific features, depending on the details of the corresponding models. In particular, scale-free graphs develop a trianglelike spectral density with a power-law tail, while small-world graphs have a complex spectral density consisting of several sharp peaks. These and further results indicate that the spectra of correlated graphs represent a practical tool for graph classification and can provide useful insight into the relevant structural properties of real networks.
Automatic repair of buggy if conditions and missing preconditions with SMT

We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.
Trust in recommender systems

Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.
Blacksheep: detecting compromised hosts in homogeneous crowds

The lucrative rewards of security penetrations into large organizations have motivated the development and use of many sophisticated rootkit techniques to maintain an attacker's presence on a compromised system. Due to the evasive nature of such infections, detecting these rootkit infestations is a problem facing modern organizations. While many approaches to this problem have been proposed, various drawbacks that range from signature generation issues, to coverage, to performance, prevent these approaches from being ideal solutions.
 In this paper, we present Blacksheep, a distributed system for detecting a rootkit infestation among groups of similar machines. This approach was motivated by the homogenous natures of many corporate networks. Taking advantage of the similarity amongst the machines that it analyses, Blacksheep is able to efficiently and effectively detect both existing and new infestations by comparing the memory dumps collected from each host.
 We evaluate Blacksheep on two sets of memory dumps. One set is taken from virtual machines using virtual machine introspection, mimicking the deployment of Blacksheep on a cloud computing provider's network. The other set is taken from Windows XP machines via a memory acquisition driver, demonstrating Blacksheep's usage under more challenging image acquisition conditions. The results of the evaluation show that by leveraging the homogeneous nature of groups of computers, it is possible to detect rootkit infestations.
Conceptual Modeling through Linguistic Analysis Using LIDA

Despite the advantages that object technology can provide to the software development community and its customers, the fundamental problems associated with identifying objects, their attributes, and methods remain: it is a largely manual process driven by heuristics that analysts acquire through experience. While a number of methods exist for requirements development and specification, very few tools exist to assist analysts in making the transition from textual descriptions to other notations for object-oriented analysis and other conceptual models. In this paper we describe a methodology and a prototype tool, Linguistic assistant for Domain Analysis (LIDA), which provide linguistic assistance in the model development process. We first present our methodology to conceptual modeling through linguistic analysis. We give an overview of LIDA's functionality and present its technical design and the functionality of its components. We also provide a comparison of LIDA's functionality with that of other research prototypes. Finally, we present an example of how LIDA is used in a conceptual modeling task.
An Injection Island GA for Flywheel Design Optimization

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA). An iiGA in combination with a finite element code is used to search for shape variations to optimize the Specific Energy Density (SED) of elastic flywheels. SED is defined as the amount of rotational energy stored per unit mass. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate sub-populations (islands). Solutions are sought first at low levels of refinement with an axisymmetric plane stress finite element code for high speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that uses an axisymmetric three dimensional finite element model to “ fine-tune” the flywheel designs. In true multi -objective optimization, various “sub-fitness” functions can be defined that represent “good” aspects of the overall fitness function. Solutions can be sought for these various “sub-fitness” functions on different nodes and injected into a node that evaluates the overall fitness. Allowing subpopulations to explore different regions of the fitness space simultaneously allows relatively robust and efficient exploration in problems for which fitness evaluations are costly.  This paper will describe the advantages of searching with an axisymmetric plane stress finite element model (with a “sub-fitness” function) to quickly find building blocks needed to inject into an axisymmetric three-dimensional finite element model through use of an iiGA. An optimal annular composite flywheel shape will be sought by an iiGA and, for comparison, by a “ring” topology parallel GA. The flywheel is modeled as a series of concentric rings . The thickness of each ring varies linearly in the radial direction with the possibilit y for a diverse set of material choices for each ring. Figure 2 shows a typical flywheel model in which symmetry is used to increase computational eff iciency. The overall fitness function for the genetic algorithm GALOPPS was the specific energy density (SED) of a flywheel, which is defined as: SED I mass = 1 2 2 ω 1.) where ω is the angular velocity of the flywheel (“sub-fitness” function), I is the mass moment of inertia defined by:
Adaptive sampling for sensor networks

A distributed data-stream architecture finds application in sensor networks for monitoring environment and activities. In such a network, large numbers of sensors deliver continuous data to a central server. The rate at which the data is sampled at each sensor affects the communication resource and the computational load at the central server. In this paper, we propose a novel adaptive sampling technique where the sampling rate at each sensor adapts to the streaming-data characteristics. Our approach employs a Kalman-Filter (KF)-based estimation technique wherein the sensor can use the KF estimation error to adaptively adjust its sampling rate within a given range, autonomously. When the desired sampling rate violates the range, a new sampling rate is requested from the server. The server allocates new sampling rates under the constraint of available resources such that KF estimation error over all the active streaming sensors is minimized. Through empirical studies, we demonstrate the flexibility and effectiveness of our model.
Modeling the World from Internet Photo Collections

There are billions of photographs on the Internet, comprising the largest and most diverse photo collection ever assembled. How can computer vision researchers exploit this imagery? This paper explores this question from the standpoint of 3D scene modeling and visualization. We present structure-from-motion and image-based rendering algorithms that operate on hundreds of images downloaded as a result of keyword-based image search queries like “Notre Dame” or “Trevi Fountain.” This approach, which we call Photo Tourism, has enabled reconstructions of numerous well-known world sites. This paper presents these algorithms and results as a first step towards 3D modeling of the world’s well-photographed sites, cities, and landscapes from Internet imagery, and discusses key open problems and challenges for the research community.
TEXplorer: keyword-based object search and exploration in multidimensional text databases

We propose a novel system TEXplorer that integrates keyword-based object ranking with the aggregation and exploration power of OLAP in a text database with rich structured attributes available, e.g., a product review database. TEXplorer can be implemented within a multi-dimensional text database, where each row is associated with structural dimensions (attributes) and text data (e.g., a document). The system utilizes the text cube data model, where a cell aggregates a set of documents with matching values in a subset of dimensions. Cells in a text cube capture different levels of summarization of the documents, and can represent objects at different conceptual levels.
 Users query the system by submitting a set of keywords. Instead of returning a ranked list of all the cells, we propose a keyword-based interactive exploration framework that could offer flexible OLAP navigational guides and help users identify the levels and objects they are interested in. A novel significance measure of dimensions is proposed based on the distribution of IR relevance of cells. During each interaction stage, dimensions are ranked according to their significance scores to guide drilling down; and cells in the same cuboids are ranked according to their relevance to guide exploration. We propose efficient algorithms and materialization strategies for ranking top-k dimensions and cells. Finally, extensive experiments on real datasets demonstrate the efficiency and effectiveness of our approach.
What's decidable about hybrid automata?

Hybrid automata model systems with both digital and analog components, such az embedded control programs. Many verification tasks for such programs can be expressed as reachability problems for hybrid automata. By improving on previous decidability and undecidability results, we identify the precise boundary between decidability and undecidability of the reachability problem for hybrid automata. On the positive side, we give an (optimal) PSPACE reachability algorithm for the case of initialized rectangular automata, where all analog variables follow trajectories within piecewise-linear envelopes and are reinitialized whenever the envelope changes. Our algorithm is based on a translation of an initialized rectangular automaton into a timed automaton that defines the same timed language. The translation has practical significance for verification, because it guarantees the termination of symbolic procedures for the reachability analysis of initialized rectangular automata. On the negative side, we show that several slight generalizations of initialized rectangular automata lead to an undecidable reachability problem. In particular, we prove that the reachability problem is undecidable for timed automata with a single stopwatch.