nils-herrmann/clean_scirepeval huggingface dataset:
Exact robot navigation using artificial potential functions

We present a new methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design. Complete information about the freespace and goal is encoded in the form of a special artificial potential function a navigation function that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. Since navigation functions exist for any robot and obstacle course, our methodology is completely general in principle. However, this paper is mainly concerned with certain constructive techniques for a particular class of motion planning problems. Specifically, we present a formula for navigation functions that guide a point-mass robot in a generalized sphere world. The simplest member of this family is a space obtained by puncturing a disc by an arbitrary number of smaller disjoint discs representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation that we show how to build. Our constructions exploit these coordinate transformations to adapt a navigation function on the model space to its more geometrically complicated (but topologically equivalent) instances. The formula that we present admits sphere-worlds of arbitrary dimension and is directly applicable to configuration spaces whose forbidden regions can be modeled by such generalized discs. We have implemented these navigation functions on planar scenarios, and simulation results are provided throughout the paper.  Reprinted from  This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.  At the time of publication, Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member at the School of Engineering of the University of Pennsylvania.  http://repository.upenn.edu/ese_papers/323
How to stop spread of misinformation on social media: Facebook plans vs. right-click authenticate approach

One of the key features of social networks is that users are able to share information, and through cascades of sharing information, this information may reach a large number of individuals. The high availability of user-provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. With lack of means to verify information, social media has been accused of becoming a hot bed for sharing of misinformation. Facebook, as one of the largest social networking services, has been facing widespread criticism on how its newsfeed algorithm is designed thus amplifying dissemination of misinformation. In late 2016, Facebook revealed plans to address fake news on Facebook newsfeeds. In this work, we study the methods Facebook has proposed to combat the spread of misinformation and compare it with our previously proposed approach called ‘Right-click Authenticate’. By analyzing the Business Process Modeling and Notation of both approaches, this paper suggests some key weaknesses and improvements social media companies need to consider when tackling the spread of misinformation online.
Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing

There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.
Occupational therapy: evidence-based interventions for stroke.

Persons with stroke are commonly provided rehabilitation by occupational therapists. Occupational therapy endeavors to restore the person's independence in activities of daily living, skills in functional performance, infuse evidence-based practice to assure that interventions are efficacious and cost effective. This article reviews the literature and describes a variety of evidence-based interventions. This review will provide physicians and other health care provider's insights about the best practices in occupational therapy.
Chatting with teenagers: Considering the place of chat technologies in teen life

In the last few years, teenagers have been on the forefront of adopting short message service (SMS), a mobile phone-based text messaging system, and instant messaging (IM), a computer-based text chat system. However, while teenage adoption of SMS had led to a series of studies examining the reasons for its popularity, IM use in the teenage population remains understudied. This omission becomes significant given the increasing interest in domestic computing among human-computer interaction (HCI) and computer-supported cooperative work (CSCW) researchers. Further, because of the dearth of empirical work on teenage use of IM, we find that IM and SMS are sometimes incorrectly assumed to share the same features of use. To address these concerns, we revisit our own studies of SMS and IM use and reexamine them in tandem with other published studies on teenage chat. We consider similarities and differences in styles of SMS and IM use and how chat technologies enable the pursuit of teenage independence. We examine how differences are born out of technological differences and financial cost structures. We discuss how SMS and IM are used in concert to provide increased awareness and to coordinate inter-household communications, and how privacy is regulated within the individual household as a means of maintaining these communications.
Natural methods for robot task learning: instructive demonstrations, generalization and practice

Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach people use when teaching each other: first give a demonstration, then allow the learner to refine the acquired capabilities by practicing under the teacher's supervision, involving a small number of trials. Depending on the quality of the learned task, the teacher may either demonstrate it again or provide specific feedback during the learner's practice trial for further refinement. Also, as people do during demonstrations, the teacher can provide simple instructions and informative cues, increasing the performance of learning. Thus, instructive demonstrations, generalization over multiple demonstrations and practice trials are essential features for a successful human-robot teaching approach. We implemented a system that enables all these capabilities and validated these concepts with a Pioneer 2DX mobile robot learning tasks from multiple demonstrations and teacher feedback.
Short-Circuit Detection for Electrolytic Processes Employing Optibar Intercell Bars

This paper presents a method to detect metallurgical short circuits suitable for Optibar intercell bars in copper electrowinning and electrorefining processes. One of the primary achievements of this bar is to limit short-circuit currents to a maximum of 1.5 p.u. of the actual process current. However, low-current short circuits are more difficult to detect. Thus, conventional short-circuit detection instruments like gaussmeters and infrared cameras become ineffective. To overcome this problem, the proposed method is based on detecting the voltage drop across anode-cathode pairs. The method does not affect the operation of the process and does not require modifications of the industrial plant. In order to verify the performance of this proposal, experimental measurements done over a period of four months at a copper refinery are presented. A 100% success rate was obtained.
Introducing MVTec ITODD — A Dataset for 3D Object Recognition in Industry

We introduce the MVTec Industrial 3D Object Detection Dataset (MVTec ITODD), a public dataset for 3D object detection and pose estimation with a strong focus on objects, settings, and requirements that are realistic for industrial setups. Contrary to other 3D object detection datasets that often represent scenarios from everyday life or mobile robotic environments, our setup models industrial bin picking and object inspection tasks that often face different challenges. Additionally, the evaluation citeria are focused on practical aspects, such as runtimes, memory consumption, useful correctness measurements, and accuracy. The dataset contains 28 objects with different characteristics, arranged in over 800 scenes and labeled with around 3500 rigid 3D transformations of the object instances as ground truth. Two industrial 3D sensors and three high-resolution grayscale cameras observe the scene from different angles, allowing to evaluate methods that operate on a variety of different modalities. We initially evaluate 5 different methods on the dataset. Even though some show good results, there is plenty of room for improvement. The dataset and the results are publicly available1, and we invite others to submit results for evaluation and for optional inclusion in the result lists on the dataset's website.
Applied spatial data analysis with R

Do you need the book of Applied Spatial Data Analysis with R pdf with ISBN of 9781461476177? You will be glad to know that right now Applied Spatial Data Analysis with R pdf is available on our book collections. This Applied Spatial Data Analysis with R comes PDF and EPUB document format. If you want to get Applied Spatial Data Analysis with R pdf eBook copy, you can download the book copy here. The Applied Spatial Data Analysis with R we think have quite excellent writing style that make it easy to comprehend.
Accurate, Dense, and Robust Multiview Stereopsis

This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark  shows that the proposed method outperforms all others submitted so far for four out of the six data sets.
Deep Scattering Spectrum

A scattering transform defines a locally translation invariant representation which is stable to time-warping deformation. It extends MFCC representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A frequency transposition invariant representation is obtained by applying a scattering transform along log-frequency. State-the-of-art classification results are obtained for musical genre and phone classification on GTZAN and TIMIT databases, respectively.
Dual-Polarized Broadband Array Antenna With BOR-Elements, Mechanical Design and Measurements

A dual polarized broadband phased array antenna designed for the frequency range 6-18 GHz, a 45deg conical grating lobe free scan volume and equipped with BOR-elements developed by Saab is presented. The aim with this array element is to bring about a dual polarized broadband array antenna that is easy to assemble, disassemble and connect to active microwave modules. Disassembling may be important for maintenance and upgrade reasons. Mechanical design and electromagnetic performance in the form of active reflection coefficient, calculated from measured mutual coupling coefficients, and measured active gain element pattern for a central and an edge element is presented. Edge effects in the array, which may be severe in small broadband arrays, are considered in this paper
A Focus on Recent Developments and Trends in Underwater Imaging

■ Compact, efficient and easy to program digital signal processors execute algorithms once too computationally expensive for real-time applications. ■ Modeling and simulation programs more accurately predict the effects that physical ocean parameters have on the performance of imaging systems under different geometric configurations. ■ Image processing algorithms that handle data from multiple synchronous sources and that can extract and match feature points from each such source derive accurate 3-D scene information. ■ Digital compression schemes provide high-quality standardizations for increased data transfer rates (i.e. streaming video) and reduced storage requirements. This paper reports developments over the past three years in the following topics: ■ Image formation and image processing methods; ■ Extended range imaging techniques;
A Robust Minimax Approach to Classification

When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and covariance matrix of each class are assumed to be known. No further assumptions are made with respect to the class-conditional distributions. Misclassification probabilities are then controlled in a worst-case setting: that is, under all possible choices of class-conditional densities with given mean and covariance matrix, we minimize the worst-case (maximum) probability of misclassification of future data points. For a linear decision boundary, this desideratum is translated in a very direct way into a (convex) second order cone optimization problem, with complexity similar to a support vector machine problem. The minimax problem can be interpreted geometrically as minimizing the maximum of the Mahalanobis distances to the two classes. We address the issue of robustness with respect to estimation errors (in the means and covariances of the classes) via a simple modification of the input data. We also show how to exploit Mercer kernels in this setting to obtain nonlinear decision boundaries, yielding a classifier which proves to be competitive with current methods, including support vector machines. An important feature of this method is that a worst-case bound on the probability of misclassification of future data is always obtained explicitly.
VARAN the Unbelievable: An Efficient N-version Execution Framework

With the widespread availability of multi-core processors, running multiple diversified variants or several different versions of an application in parallel is becoming a viable approach for increasing the reliability and security of software systems. The key component of such N-version execution (NVX) systems is a runtime monitor that enables the execution of multiple versions in parallel. Unfortunately, existing monitors impose either a large performance overhead or rely on intrusive kernel-level changes. Moreover, none of the existing solutions scales well with the number of versions, since the runtime monitor acts as a performance bottleneck.
 In this paper, we introduce Varan, an NVX framework that combines selective binary rewriting with a novel event-streaming architecture to significantly reduce performance overhead and scale well with the number of versions, without relying on intrusive kernel modifications.
 Our evaluation shows that Varan can run NVX systems based on popular C10k network servers with only a modest performance overhead, and can be effectively used to increase software reliability using techniques such as transparent failover, live sanitization and multi-revision execution.
Innovation in Analogical Design: A Model-Based Approach

Analogical reasoning, it is commonly accepted, plays an important role in innovation and creativity. Since design often is innovative, and can be creative, the design task provides a good context for exploring the role of analogy in innovation and creativity. In the IDEAL project, we are exploring the processes of innovation and creativity in the context of conceptual (or preliminary, qualitative) design of physical devices. In particular, we are developing a model-based approach to innovation and creativity in analogical design: our core hypothesis is that innovative analogical design involves both reasoning from past experiences in designing devices (in the form of analogs or cases) and comprehension of how those devices work (in the form of device models or theories). In this paper, we focus on the issues of understanding feedback on the performance of a design, discovery of new design constraints, and reformulation of the design problem, and describe how the model-based approach addresses these issues.
All Your Droid Are Belong to Us: A Survey of Current Android Attacks

In the past few years, mobile devices (smartphones, PDAs) have seen both their computational power and their data connectivity rise to a level nearly equivalent to that available on small desktop computers, while becoming ubiquitous. On the downside, these mobile devices are now an extremely attractive target for largescale security attacks. Mobile device middleware is thus experiencing an increased focus on attempts to mitigate potential security compromises. In particular, Android incorporates by design many well-known security features such as privilege separation. The Android security model also creates several new security sensitive concepts such as Android’s application permission system and the unmoderated Android market. In this paper we look to Android as a specific instance of mobile computing. We first discuss the Android security model and some potential weaknesses of the model. We then provide a taxonomy of attacks to the platform demonstrated by real attacks that in the end guarantee privileged access to the device. Where possible, we also propose mitigations for the identified vulnerabilities.
Elicitation of Security requirements for E-Health system by applying Model Oriented Security Requirements Engineering (MOSRE) Framework

E-health is a health care system which is supported by electronic process and communication. The information that is kept in the system must be accurate. In case of false information, it may cause harm to human life. So this system needs more security to protect the credential information. E-Health system is the most security sensitive process handled electronically. The highest achievable security is never too much for an E-Health system. So when system is being built, tasks such as Security Requirements Elicitation, Specification and Validation are essential to assure the Quality of the resulting secure E-Health system. By considering the Security requirements as functional requirements in the Requirement phase, the completeness of Security Requirements for E-Health system can be developed. In this paper we propose Model Oriented Security Requirements Engineering (MOSRE) Framework in the early phases of E-Health system development, to identify assets, threats and vulnerabilities. This helps in standardizing the Security Requirements for secure E-Health system without any security issues.
Real-Time Multi-Criteria Social Graph Partitioning: A Game Theoretic Approach

Graph partitioning has attracted considerable attention due to its high practicality for real-world applications. It is particularly relevant to social networks because it enables the grouping of users into communities for market analysis and advertising purposes. In this paper, we introduce RMGP, a type of real-time multi-criteria graph partitioning for social networks that groups the users based on their connectivity and their similarity to a set of input classes. We consider RMGP as an on-line task, which may be frequently performed for different query parameters (e.g., classes). In order to overcome the serious performance issues associated with the large social graphs found in practice, we develop solutions based on a game theoretic framework. Specifically, we consider each user as a player, whose goal is to find the class that optimizes his objective function. We propose algorithms based on best-response dynamics, analyze their properties, and show their efficiency and effectiveness on real datasets under centralized and decentralized scenarios.
Feasibility of monolithic and 3D-stacked DC-DC converters for microprocessors in 90nm technology generation

Rapidly increasing input current of microprocessors resulted in rising cost and motherboard real estate occupied by decoupling capacitors and power routing. We show by analysis that an on-die switching DC-DC converter is feasible for future microprocessor power delivery. The DC-DC converter can be fabricated in an existing CMOS process (90nm-180nm) with a back-end thin-film inductor module. We show that 85% efficiency and 10% output voltage droop can be achieved for 4:1, 3:1, and 2:1 conversion ratios, area overhead of 5% and no additional on-die decoupling capacitance. A 4:1 conversion results in 3.4x smaller input current and 6.8x smaller external decoupling.