nils-herrmann/clean_scirepeval huggingface dataset:
Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques

⎯ Quality control in ceramic tile manufacturing is hard, labor intensive and it is performed in a harsh industrial environment with noise, extreme temperature and humidity. It can be divided into color analysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defects detection is still based on the judgment of human operators while most of the other manufacturing activities are automated so, our work is a quality control enhancement by integrating a visual control stage using image processing and morphological operation techniques before the packing operation to improve the homogeneity of batches received by final users. Keywords⎯ Quality control, Defects detection, Visual control, Image processing, Morphological operation.
Object scene flow for autonomous vehicles

This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which cannot be handled by existing methods.
Statecharts: A Visual Formalism for Complex Systems

We present a broad extension of the conventional formalism of state machines and state diagrams, that is relevant to the specification and design of complex discrete-event systems, such as multi-computer real-time systems, communication protocols and digital control units. Our diagrams, which we call statecharts, extend conventional state-transition diagrams with essentially three elements, dealing, respectively, with the notions of hierarchy, concurrency and communication. These transform the language of state diagrams into a highly structured and economical description language. Statecharts are thus compact and expressiv-small diagrams can express complex behavior-as well as compositional and modular. When coupled with the capabilities of computerized graphics, statecharts enable viewing the description at different levels of detail, and make even very large specifications manageable and comprehensible. In fact, we intend to demonstrate here that statecharts counter many of the objections raised against conventional state diagrams, and thus appear to render specification by diagrams an attractive and plausible approach. Statecharts can be used either as a stand-alone behavioral description or as part of a more general design methodology that deals also with the system’s other aspects, such as functional decomposition and data-flow specification. We also discuss some practical experience that was gained over the last three years in applying the statechart formalism to the specification of a particularly complex system.
Shape Descriptors for Non-Rigid Shapes with a Single Closed Contour

The Core Experiment CE-Shape-1 for shape descriptors performed for the MPEG-7 standard gave a unique opportunity to compare various shape descriptors for non-rigid shapes with a single closed contour. There are two main differences with respect to other comparison results reported in the literature: (1) For each shape descriptor, the experiments were carried out by an institute that is in favor of this descriptor. This implies that the parameters for each system were optimally determined and the implementations were throughly tested. (2) It was possible to compare the performance of shape descriptors based on totally different mathematical approaches. A more theoretical comparison of these descriptors seems to be extremely hard. In this paper we report on the MPEG-7 Core Experiment CE-Shape1.
A survey of mobility models for ad hoc network research

In the performance evaluation of a protocol for an ad hoc netw ork, the protocol should be tested under realistic conditions including, but not limited to, a sensible transm ission range, limited buffer space for the storage of messag e , representative data traffic models, and realistic movement s of he mobile users (i.e., a mobility model). This paper is a survey of mobility models that are used in the simulations o f ad hoc networks. We describe several mobility models that represent mobile nodes whose movements are independen t of each other (i.e., entity mobility models) and several mobility models that represent mobile nodes whose movement s are dependent on each other (i.e., group mobility models). The goal of this paper is to present a number of mobil ity models in order to offer researchers more informed choices when they are deciding upon a mobility model to use in their performance evaluations. Lastly, we present simulation results that illustrate the importance of choos ing a mobility model in the simulation of an ad hoc network protocol. Specifically, we illustrate how the performance r esults of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.
Polylingual Topic Models

Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools that can characterize content in many languages. We introduce a polylingual topic model that discovers topics aligned across multiple languages. We explore the model’s characteristics using two large corpora, each with over ten different languages, and demonstrate its usefulness in supporting machine translation and tracking topic trends across languages.
A Comparison of Push and Pull Techniques for AJAX

AJAX applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic Web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, AJAX still suffers from the limitations of the Web's request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for AJAX technology. It also shows the results of an empirical study comparing push and pull.
Collaborative Filtering for Implicit Feedback Datasets

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.
Analytic Error Modeling for Imprecise Arithmetic Circuits

hardware challenges the traditional notion that correctness is an immutable priority, by systematically trading off efficacy (precision) against efficiency (power, area, performance, and cost). Evaluating the impact of such tradeoffs on output quality using, e.g., Monte Carlo simulations is a time-consuming and non-deterministic process. This paper presents two analytic modeling techniques for evaluating error properties and output quality in imprecise arithmetic circuits, based on Interval Arithmetic and Affine Arithmetic. Experiments show that these techniques offer significant speedups over previous methods, as well as promising estimation accuracy. I. INTRODUCTION ower has become the limiting factor in digital systems. The demand for higher computing capacity continues to rise, but power has reached a limit due to thermal and delivery issues. Imprecise hardware (IHW), also known as stochastic computation [1, 2], or Better than Worst-, has been proposed as a solution to tackle this problem. Compared to traditional hardware designed to always compute correctly, IHW allows occasional computation errors but achieves higher performance and/or lower power. Any resulting errors can be relegated to dedicated error correction circuits and software, or even left uncorrected, given the error-tolerant nature of many applications. This paper focuses on deterministic arithmetic IHW, which alters the logic function of the performed computation either at design time, or dynamically at runtime. Examples include supporting only a subset of the input combinations [4, 5], voltage overscaling [1, 2], and data width reduction. The introduced errors can be uniquely determined by the input but the mapping function is not easy to determine. The benefits of IHW are largely determined by (1) the level of performance/power improvement offered by imprecise computation, and (2) the amount of output quality degradation caused by errors. Significant power reduction and performance increase from IHW have been reported [4, 5, 6], and new IHW techniques are being developed. However, the consequences of IHW on output quality require further investigation. Typically a system with IHW components is simulated with random inputs in order to obtain an output profile with acceptable quality. This is a time-consuming task, especially for complex systems. Imprecise hardware expands the design space by relaxing the correctness requirement, thus faster error estimation is crucial to the effective exploration of the space. Moreover, simulations are usually nondeterministic and unreliable as output profiles can be influenced by the size and scope of the simulations. This paper presents two methods that enable analytic modeling of the statistical …
Characteristics of Higher-Level Languages for Software Architecture.

As the size and complexity of software systems increases, the design and speci cation of overall system structure|or software architecture|emerges as a central concern. Architectural issues include the gross organization of the system, protocols for communication and data access, assignment of functionality to design elements, and selection among design alternatives. Currently system designers have at their disposal two primary ways of de ning software architecture: they can use the modularization facilities of existing programming languages and module interconnection languages; or they can describe their designs using informal diagrams and idiomatic phrases (such as \client-server organization"). In this paper we explain why neither alternative is adequate. We consider the nature of architectural description as it is performed informally by systems designers. Then we show that regularities in these descriptions can form the basis for architectural description languages. Next we identify speci c properties that such languages should have. Finally, we illustrate how current notations fail to satisfy those properties.
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.
On Summarizing Large-Scale Dynamic Graphs

How can we describe a large, dynamic graph over time? Is it random? If not, what are the most apparent deviations from randomness – a dense block of actors that persists over time, or perhaps a star with many satellite nodes that appears with some fixed periodicity? In practice, these deviations indicate patterns – for example, research collaborations forming and fading away over the years. Which patterns exist in real-world dynamic graphs, and how can we find and rank their importance? These are exactly the problems we focus on. Our main contributions are (a) formulation: we show how to formalize this problem as minimizing an information theoretic encoding cost, (b) algorithm: we propose TIMECRUNCH, an effective and scalable method for finding coherent, temporal patterns in dynamic graphs and (c) practicality: we apply our method to several large, diverse real-world datasets with up to 36 million edges and introduce our auxiliary ECOVIZ framework for visualizing and interacting with dynamic graphs which have been summarized by TIMECRUNCH. We show that TIMECRUNCH is able to compress these graphs by summarizing important temporal structures and finds patterns that agree with intuition.
Learning to rank relevant files for bug reports using domain knowledge

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.
Anatomy of a Crowdsourcing Platform - Using the Example of Microworkers.com

Since Jeff Howe introduced the term "crowdsourcing" in 2006 for the first time, crowd sourcing has be come a growing market in the current Internet. Thousands of workers categorize images, write articles or perform other small tasks on platforms like Amazon Mechanical Turk (MTurk), Micro workers or Short Task. In this work, we want to give an inside view of the usage data from Micro workers and show that there are significant differences to the well studied MTurk. Further, we have a look at Micro workers from the perspective for a worker, an employer and the platform owner, in order to answer their most important questions: What jobs are most paid? How do I get my work done most quickly? When are the users of my platform active?
Imperforate Hymen Presenting as Acute Urinary Retention in A 14-Year-Old Nigerian Girl

Acute urinary retention in adolescent females is rare, just like imperforate hymen. We present a case of acute urinary retention secondary to imperforate hymen in a 14-year-old Nigerian girl. Its diagnosis and treatment are discussed with a brief review of literature. We highlight the need for a thorough evaluation in the female patient presenting with acute urinary retention, and also the need to provide better health facilities in rural areas in developing countries such as ours.
Networks in Cognitive Science

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.
Film Critics : Influencers or Predictors ?

Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market performance of entertainment services and the role of critics. The authors do so here. They show empirically that critical reviews correlate with late and cumulative box office receipts but do not have a significant correlation with early box office receipts. Although still far from any definitive conclusion, this finding suggests that critics, at least from an aggregate-level perspective, appear to act more as leading indicators than as opinion leaders.
Applied Nonlinear Control of Unmanned Vehicles with Uncertain Dynamics

The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system’s dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced.
Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.