nils-herrmann/clean_scirepeval huggingface dataset:
Domo: a force sensing humanoid robot for manipulation research

Humanoid robots found in research and commercial use today typically lack the ability to operate in unstructured and unknown environments. Force sensing and compliance at each robot joint can allow the robot to safely act in these environments. However, these features can be difficult to incorporate into robot designs. We present a new force sensing and compliant humanoid under development in the humanoid robotics group at MIT CSAIL. The robot, named Domo, is to be a research platform for exploring issues in general dexterous manipulation, visual perception, and learning. In this paper we describe aspects of the design, detail proposed research directions for the robot, and illustrate how the design of humanoid robots can be informed by the desired research goals.
Regularized clustering for documents

In recent years, document clustering has been receiving more and more attentions as an important and fundamental technique for unsupervised document organization, automatictopic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine all those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods.
Snoopertrack: Text detection and tracking for outdoor videos

In this work we introduced SnooperTrack, an algorithm for the automatic detection and tracking of text objects — such as store names, traffic signs, license plates, and advertisements — in videos of outdoor scenes. The purpose is to improve the performances of text detection process in still images by taking advantage of the temporal coherence in videos. We first propose an efficient tracking algorithm using particle filtering framework with original region descriptors. The second contribution is our strategy to merge tracked regions and new detections. We also propose an improved version of our previously published text detection algorithm in still images. Tests indicate that SnooperTrack is fast, robust, enable false positive suppression, and achieved great performances in complex videos of outdoor scenes.
Neural Autoregressive Collaborative Filtering for Implicit Feedback

This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback( e.g. click/watch/browse behaviors). We first convert a user's implicit feedback into a "like" vector and a confidence vector, and then model the probability of the "like" vector, weighted by the confidence vector. The training objective of implicit CF-NADE is to maximize a weighted negative log-likelihood. We test the performance of implicit CF-NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how to convert watch counts into implicit "relative rating", and feed into implicit CF-NADE. Then we compare the performance of implicit CF-NADE model with the popular implicit matrix factorization approach. Experimental results show that implicit CF-NADE significantly outperforms the baseline.
WaveFunctionCollapse is constraint solving in the wild

Maxim Gumin's WaveFunctionCollapse (WFC) algorithm is an example-driven image generation algorithm emerging from the craft practice of procedural content generation. In WFC, new images are generated in the style of given examples by ensuring every local window of the output occurs somewhere in the input. Operationally, WFC implements a non-backtracking, greedy search method. This paper examines WFC as an instance of constraint solving methods. We trace WFC's explosive influence on the technical artist community, explain its operation in terms of ideas from the constraint solving literature, and probe its strengths by means of a surrogate implementation using answer set programming.
Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace

One of the problems often associated with online anonymity is that it hinders social accountability, as substantiated by the high levels of cybercrime. Although identity cues are scarce in cyberspace, individuals often leave behind textual identity traces. In this study we proposed the use of stylometric analysis techniques to help identify individuals based on writing style. We incorporated a rich set of stylistic features, including lexical, syntactic, structural, content-specific, and idiosyncratic attributes. We also developed the Writeprints technique for identification and similarity detection of anonymous identities. Writeprints is a Karhunen-Loeve transforms-based technique that uses a sliding window and pattern disruption algorithm with individual author-level feature sets. The Writeprints technique and extended feature set were evaluated on a testbed encompassing four online datasets spanning different domains: email, instant messaging, feedback comments, and program code. Writeprints outperformed benchmark techniques, including SVM, Ensemble SVM, PCA, and standard Karhunen-Loeve transforms, on the identification and similarity detection tasks with accuracy as high as 94% when differentiating between 100 authors. The extended feature set also significantly outperformed a baseline set of features commonly used in previous research. Furthermore, individual-author-level feature sets generally outperformed use of a single group of attributes.
Kernel Change-point Analysis

We introduce a kernel-based method for change-point analys is within a sequence of temporal observations. Change-point analysis of an unla belled sample of observations consists in, first, testing whether a change in the di stribution occurs within the sample, and second, if a change occurs, estimating the ch ange-point instant after which the distribution of the observations switches f rom one distribution to another different distribution. We propose a test statisti c based upon the maximum kernel Fisher discriminant ratio as a measure of homogeneit y b tween segments. We derive its limiting distribution under the null hypothes is (no change occurs), and establish the consistency under the alternative hypoth esis (a change occurs). This allows to build a statistical hypothesis testing proce dur for testing the presence of a change-point, with a prescribed false-alarm proba bility and detection probability tending to one in the large-sample setting. If a ch nge actually occurs, the test statistic also yields an estimator of the change-po int location. Promising experimental results in temporal segmentation of mental ta sks from BCI data and pop song indexation are presented.
Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista

Many factors are believed to increase the vulnerability of software system; for example, the more widely deployed or popular is a software system the more likely it is to be attacked. Early identification of defects has been a widely investigated topic in software engineering research. Early identification of software vulnerabilities can help mitigate these attacks to a large degree by focusing better security verification efforts in these components. Predicting vulnerabilities is complicated by the fact that vulnerabilities are, most often, few in number and introduce significant bias by creating a sparse dataset in the population. As a result, vulnerability prediction can be thought of us preverbally “searching for a needle in a haystack.” In this paper, we present a large-scale empirical study on Windows Vista, where we empirically evaluate the efficacy of classical metrics like complexity, churn, coverage, dependency measures, and organizational structure of the company to predict vulnerabilities and assess how well these software measures correlate with vulnerabilities. We observed in our experiments that classical software measures predict vulnerabilities with a high precision but low recall values. The actual dependencies, however, predict vulnerabilities with a lower precision but substantially higher recall.
Integrating computational and creative thinking to improve learning and performance in CS1

Our research is based on an innovative approach that integrates computational thinking and creative thinking in CS1 to improve student learning performance. Referencing Epstein's Generativity Theory, we designed and deployed a suite of creative thinking exercises with linkages to concepts in computer science and computational thinking, with the premise that students can leverage their creative thinking skills to "unlock" their understanding of computational thinking. In this paper, we focus on our study on differential impacts of the exercises on different student populations. For all students there was a linear "dosage effect" where completion of each additional exercise increased retention of course content. The impacts on course grades, however, were more nuanced. CS majors had a consistent increase for each exercise, while non-majors benefited more from completing at least three exercises. It was also important for freshmen to complete all four exercises. We did find differences between women and men but cannot draw conclusions.
MiBench: A free, commercially representative embedded benchmark suite

This paper examines a set of commercially representative embedded programs and compares them to an existing benchmark suite, SPEC2000. A new version of SimpleScalar that has been adapted to the ARM instruction set is used to characterize the performance of the benchmarks using configurations similar to current and next generation embedded processors. Several characteristics distinguish the representative embedded programs from the existing SPEC benchmarks including instruction distribution, memory behavior, and available parallelism. The embedded benchmarks, called MiBench, are freely available to all researchers.
The Case for Mobile Edge-Clouds

Current mobile applications treat the end-user device as a "thin client," with all of the heavy computations being offloaded to an infrastructure cloud. However, the computational capabilities of mobile devices are constantly improving, and it is worthwhile considering whether an edge-cloud that consists purely of mobile devices (operating effectively as "thick clients") can perform as well as, or even better than, an infrastructure cloud. In this paper, we study the trade-offs between offloading computation to an infrastructure cloud versus retaining the computation within a mobile edge-cloud. To this end, we develop and run two classes of applications on both types of clouds, and we analyze the performance of the two clouds in terms of the time taken to run the application, along with the total amount of battery power consumed in both cases. Our results indicate that there are indeed classes of applications where an edge-cloud can outperform an infrastructure cloud in terms of both latency and battery power.
Wireless-control strategy for parallel operation of distributed generation inverters

In this paper, a method for the parallel operation of inverters in an ac-distributed system is proposed. The paper explores the control of active and reactive power flow through the analysis of the output impedance of the inverters and its impact on the power sharing. As a result, adaptive virtual output impedance is proposed in order to achieve a proper reactive power sharing regardless of the line impedance unbalances. A soft-start operation is also included, avoiding the initial current peak, which results in a seamless hot-swap operation. Active power sharing is achieved by adjusting the frequency in load transient situations only, thanks to which the proposed method obtains constant steady-state frequency and amplitude. As opposed to the conventional droop method, the transient response can be modified by acting on the main control parameters. Linear and nonlinear loads can be properly shared due to the addition of a current harmonic loop in the control strategy. Experimental results are presented from a two 6-kVA parallel-connected inverters system, showing the feasibility of the proposed approach.
A General Approach to Network Configuration Analysis

We present an approach to detect network configuration errors, which combines the benefits of two prior approaches. Like prior techniques that analyze configuration files, our approach can find errors proactively, before the configuration is applied, and answer “what if” questions. Like prior techniques that analyze data-plane snapshots, our approach can check a broad range of forwarding properties and produce actual packets that violate checked properties. We accomplish this combination by faithfully deriving and then analyzing the data plane that would emerge from the configuration. Our derivation of the data plane is fully declarative, employing a set of logical relations that represent the control plane, the data plane, and their relationship. Operators can query these relations to understand identified errors and their provenance. We use our approach to analyze two large university networks with qualitatively different routing designs and find many misconfigurations in each. Operators have confirmed the majority of these as errors and have fixed their configurations accordingly.
Inkjet Printing of Novel Wideband and High Gain Antennas on Low-Cost Paper Substrate

A complete characterization of the inkjet printing process using metallic nanoparticle inks on a paper substrate for microwave frequencies up to 12.5 GHz as well as its application to low-cost, high gain and wideband antenna design are demonstrated in this work. Laser and heat sintering of metallic nanoparticles are compared on paper substrate for the first time which demonstrate immense cost and time benefits of laser sintering. The antennas fabricated using the characterized process include a Vivaldi for the UWB band which exhibits a significantly higher gain of up to 8 dBi as compared to the currently published inkjet printed antennas, and a novel slow-wave log periodic dipole array which employs a new miniaturization technique to show 20% width reduction.
Automated Statistics Collection in DB2 UDB

The use of inaccurate or outdated database statistics by the query optimizer in a relational DBMS often results in a poor choice of query execution plans and hence unacceptably long query processing times. Configuration and maintenance of these statistics has traditionally been a time-consuming manual operation, requiring that the database administrator (DBA) continually monitor query performance and data changes in order to determine when to refresh the statistics values and when and how to adjust the set of statistics that the DBMS maintains. In this paper we describe the new Automated Statistics Collection (ASC) component of IBM® DB2® Universal DatabaseTM (DB2 UDB). This autonomic technology frees the DBA from the tedious task of manually supervising the collection and maintenance of database statistics. ASC monitors both the update-delete-insert (UDI) activities on the data as well as query feedback (QF), i.e., the results of the queries that are executed on the data. ASC uses these two sources of information to automatically decide which statistics to collect and when to collect them. This combination of UDI-driven and QF-driven autonomic processes ensures that the system can handle unforeseen queries while also ensuring good performance for frequent and important queries. We present the basic concepts, architecture, and key implementation details of ASC in DB2 UDB, and present a case study showing how the use of ASC can speed up a query workload by orders of magnitude without requiring any DBA intervention.
DELTA: Delta encoding for less traffic for apps

The number of applications (or apps) in the Android Market exceeded 450,000 in 2012 with more than 11 billion total downloads. The necessity to fix bugs and add new features leads to frequent app updates. For each update, a full new version of the app is downloaded to the user's smart phone; this generates significant traffic in the network. We propose to use delta encoding algorithms and to download only the difference between two versions of an app. We implement delta encoding for Android using the bsdiff and bspatch tools and evaluate its performance. We show that app update traffic can be reduced by about 50%, this can lead to significant cost and energy savings.
A Multiscale Model of Adaptation and Spatial Vision for Realistic Image Display

In this paper we develop a computational model of adaptation and spatial vision for realistic tone reproduction. The model is based on a multiscale representation of pattern, luminance, and color processing in the human visual system. We incorporate the model into a tone reproduction operator that maps the vast ranges of radiances found in real and synthetic scenes into the small fixed ranges available on conventional display devices such as CRT’s and printers. The model allows the operator to address the two major problems in realistic tone reproduction: wide absolute range and high dynamic range scenes can be displayed; and the displayed images match our perceptions of the scenes at both threshold and suprathreshold levels to the degree possible given a particular display device. Although in this paper we apply our visual model to the tone reproduction problem, the model is general and can be usefully applied to image quality metrics, image compression methods, and perceptually-based image synthesis algorithms. CR Categories: I.3.0 [Computer Graphics]: General;
New Methods in Iris Recognition

This paper presents the following four advances in iris recognition: 1) more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and ldquorotatingrdquo the eye into orthographic perspective; 3) statistical inference methods for detecting and excluding eyelashes; and 4) exploration of score normalizations, depending on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented based on 200 billion iris cross-comparisons that were generated from 632 500 irises in the United Arab Emirates database to analyze the normalization issues raised in different regions of receiver operating characteristic curves.
DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation

Lindsay, R.K., B.G. Buchanan, E.A. Feigenbaum and J. Lederberg, DENDRAL: a case study of the first expert system for scientific hypothesis formation, Artificial Intelligence 61 (1993) 209-261. The DENDRAL Project was one of the first large-scale programs to embody the strategy of using detailed, task-specific knowledge about a problem domain as a source of heuristics, and to seek generality through automating the acquisition of such knowledge. This paper summarizes the major conceptual contributions and accomplishments of that project. It is an attempt to distill from this research the lessons that are of importance to artificial intelligence research and to provide a record of the final status of two decades of work. Correspondence to: R.K. Lindsay, University of Michigan, 205 Zina Pitcher Place, Ann Arbor, MI 48109, USA. Telephone: (313) 764-4227. Fax: (313) 747.4130. E-mail: lindsay@umich.edu. This research was supported by the National Aeronautics and Space Administrations, the Advanced Research Projects Agency of the Department of Defense, and the National Institutes of Health. This paper draws upon [37], expanding and updating that account in light of subsequent developments and the benefit of hindsight. Large portions of descriptive material are taken nearly verbatim to make the present paper self-contained since the book itself is no longer in print. OOO4-3702/93/$06.00
Sphere Detection in Kinect Point Clouds via the 3D Hough Transform

We introduce a fast, robust and accurate Hough Transform (HT) based algorithm for detecting spherical structures in 3D point clouds. To our knowledge, our algorithm is the first HT based implementation that detects spherical structures in typical in 3D point clouds generated by consumer depth sensors such as the Microsoft Kinect. Our approach has been designed to be computationally efficient; reducing an established limitation of HT based approaches. We provide experimental analysis of the achieved results, showing a robust performance against occlusion, and we show superior performance to the only other HT based algorithm for detecting spheres in point clouds available in literature.
Online simultaneous localization and mapping in dynamic environments

We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and with physical robots show the efficiency of our approach and show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.
Scheduling I/O in virtual machine monitors

This paper explores the relationship between domain scheduling in avirtual machine monitor (VMM) and I/O performance. Traditionally, VMM schedulers have focused on fairly sharing the processor resources among domains while leaving the scheduling of I/O resources as asecondary concern. However, this can resultin poor and/or unpredictable application performance, making virtualization less desirable for applications that require efficient and consistent I/O behavior.
 This paper is the first to study the impact of the VMM scheduler on performance using multiple guest domains concurrently running different types of applications. In particular, different combinations of processor-intensive, bandwidth-intensive, andlatency-sensitive applications are run concurrently to quantify the impacts of different scheduler configurations on processor and I/O performance. These applications are evaluated on 11 different scheduler configurations within the Xen VMM. These configurations include a variety of scheduler extensions aimed at improving I/O performance. This cross product of scheduler configurations and application types offers insight into the key problems in VMM scheduling for I/O and motivates future innovation in this area.