nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Agile Development: Using Scrum in a Large Project

While seemingly incompatible, combining large-scale global software development and agile practices is a challenge undertaken by many companies. Case study reports on the successful use of agile practices in small distributed projects already exist. How these practices could be applied to larger projects, however, remains unstudied. This paper reports a case study on agile practices in a 40- person development organization distributed between Norway and Malaysia. Based on seven interviews in the development organization, we describe how Scrum practices were successfully applied, e.g., using teleconference and Web cameras for daily scrum meetings, synchronized 4- week sprints and weekly scrum-of-scrums. Additional agility supporting practices for distributed projects were identified, e.g., frequent visits, unofficial distributed meetings and annual gatherings are described.
Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation

A state estimator for a quadrocopter is presented, using measurements from an accelerometer, angular rate gyroscope, and a set of ultra-wideband ranging radios. The estimator uses an extended aerodynamic model for the quadrocopter, where the full 3D airspeed is observable through accelerometer measurements. The remaining quadrocopter states, including the yaw orientation, are rendered observable by fusing ultra-wideband range measurements, under the assumption of no wind. The estimator is implemented on a standard microcontroller using readily-available, low-cost sensors. Performance is experimentally investigated in a variety of scenarios, where the quadrocopter is flown under feedback control using the estimator output.
Introduction to Derivative-Free Optimization

Derivative-Free Optimization Andrew R. Conn, Katya Scheinberg, and Luis N. Vicente The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. 2009 · xii + 277 pages · Softcover · ISBN 978-0-898716-68-9 List Price $73.00 · RUNDBRIEF Price $51.10 · Code MP08
VoR-Tree: R-trees with Voronoi Diagrams for Efficient Processing of Spatial Nearest Neighbor Queries

A very important class of spatial queries consists of nearestneighbor (NN) query and its variations. Many studies in the past decade utilize R-trees as their underlying index structures to address NN queries efficiently. The general approach is to use R-tree in two phases. First, R-tree’s hierarchical structure is used to quickly arrive to the neighborhood of the result set. Second, the R-tree nodes intersecting with the local neighborhood (Search Region) of an initial answer are investigated to find all the members of the result set. While R-trees are very efficient for the first phase, they usually result in the unnecessary investigation of many nodes that none or only a small subset of their including points belongs to the actual result set. On the other hand, several recent studies showed that the Voronoi diagrams are extremely efficient in exploring an NN search region, while due to lack of an efficient access method, their arrival to this region is slow. In this paper, we propose a new index structure, termed VoR-Tree that incorporates Voronoi diagrams into R-tree, benefiting from the best of both worlds. The coarse granule rectangle nodes of R-tree enable us to get to the search region in logarithmic time while the fine granule polygons of Voronoi diagram allow us to efficiently tile or cover the region and find the result. Utilizing VoR-Tree, we propose efficient algorithms for various Nearest Neighbor queries, and show that our algorithms have better I/O complexity than their best competitors.
Autonomous door opening and plugging in with a personal robot

We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.
Detecting and correcting malicious data in VANETs

In order to meet performance goals, it is widely agreed that vehicular ad hoc networks (VANETs) must rely heavily on node-to-node communication, thus allowing for malicious data traffic. At the same time, the easy access to information afforded by VANETs potentially enables the difficult security goal of data validation. We propose a general approach to evaluating the validity of VANET data. In our approach a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present. Explanations that are consistent with the node's model of the VANET are scored and the node accepts the data as dictated by the highest scoring explanations. Our techniques for generating and scoring explanations rely on two assumptions: 1) nodes can tell "at least some" other nodes apart from one another and 2) a parsimony argument accurately reflects adversarial behavior in a VANET. We justify both assumptions and demonstrate our approach on specific VANETs.
Active Learning and the Total Cost of Annotation

Active learning (AL) promises to reduce the cost of annotating labeled datasets for trainable human language technologies. Contrary to expectations, when creating labeled training material for HPSG parse selection and latereusing it with other models, gains from AL may be negligible or even negative. This has serious implications for using AL, showing that additional cost-saving strategies may need to be adopted. We explore one such strategy: using a model during annotation to automate some of the decisions. Our best results show an 80% reduction in annotation cost compared with labeling randomly selected data with a single model.
ALMA: a layered model of affect

In this paper we introduce ALMA - A Layered Model of Affect. It integrates three major affective characteristics: emotions, moods and personality that cover short, medium, and long term affect. The use of this model consists of two phases: In the preparation phase appraisal rules and personality profiles for characters must be specified with the help of AffectML - our XML based affect modeling language. In the runtime phase, the specified appraisal rules are used to compute real-time emotions and moods as results of a subjective appraisal of relevant input. The computed affective characteristics are represented in AffectML and can be processed by sub-sequent modules that control the cognitive processes and physical behavior of embodied conversational characters. ALMA is part of the VirtualHuman project which develops interactive virtual characters that serve as dialog partners with human-like conversational skills. ALMA provides our virtual humans with a personality profile and with real-time emotions and moods. These are used by the multimodal behavior generation module to enrich the lifelike and believable qualities.
Multi-camera object detection for robotics

Robust object detection is a critical skill for robotic applications in complex environments like homes and offices. In this paper we propose a method for using multiple cameras to simultaneously view an object from multiple angles and at high resolutions. We show that our probabilistic method for combining the camera views, which can be used with many choices of single-image object detector, can significantly improve accuracy for detecting objects from many viewpoints. We also present our own single-image object detection method that uses large synthetic datasets for training. Using a distributed, parallel learning algorithm, we train from very large datasets (up to 100 million image patches). The resulting object detector achieves high performance on its own, but also benefits substantially from using multiple camera views. Our experimental results validate our system in realistic conditions and demonstrates significant performance gains over using standard single-image classifiers, raising accuracy from 0.86 area-under-curve to 0.97.
A 77-GHz CMOS FMCW Frequency Synthesizer With Reconfigurable Chirps

This paper presents a 77-GHz CMOS frequency-modulated continuous-wave (FMCW) frequency synthesizer with the capability of reconfigurable chirps. The frequency-sweep range and sweep time of the chirp signals can be reconfigured every cycle such that the frequency-hopping random chirp signal can be realized for an FMCW radar transceiver. The frequency synthesizer adopts the fractional-N phase-locked-loop technique and is fully integrated in TSMC 65-nm digital CMOS technology. The silicon area of the synthesizer is 0.65 mm × 0.45 mm and it consumes 51.3 mW of power. The measured output phase noise of the synthesizer is -85.1 dBc/Hz at 1-MHz offset and the root-mean-square modulation frequency error is smaller than 73 kHz.
Paxos Quorum Leases: Fast Reads Without Sacrificing Writes

This paper describes quorum leases, a new technique that allows Paxos-based systems to perform reads with high throughput and low latency. Quorum leases do not sacrifice consistency and have only a small impact on system availability and write latency. Quorum leases allow a majority of replicas to perform strongly consistent local reads, which substantially reduces read latency at those replicas (e.g., by two orders of magnitude in wide-area scenarios). Previous techniques for performing local reads in Paxos systems either (a) sacrifice consistency; (b) allow only one replica to read locally; or (c) decrease the availability of the system and increase the latency of all updates by requiring all replicas to be notified synchronously. We describe the design of quorum leases and evaluate their benefits compared to previous approaches through an implementation running in five geo-distributed Amazon EC2 datacenters.
Neuromorphic Silicon Neuron Circuits

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.
Design and experiments on a new wheel-based cable climbing robot

This paper proposes an ameliorated wheel-based cable inspection robot, which is able to climb up a vertical cylindrical cable on the cable-stayed bridge. The newly-designed robot in this paper is composed of two equally spaced modules, which are joined by connecting bars to form a closed hexagonal body to clasp on the cable. Another amelioration is the newly-designed electric circuit, which is employed to limit the descending speed of the robot during its sliding down along the cable. For the safe landing in case of electricity broken-down, a gas damper with a slider-crank mechanism is introduced to exhaust the energy generated by the gravity when the robot is slipping down. For the present design, with payloads below 3.5 kg, the robot can climb up a cable with diameters varying from 65 mm to 205 mm. The landing system is tested experimentally and a simplified mathematical model is analyzed. Several climbing experiments performed on real cables show the capability of the proposed robot.
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies.
System-level ESD failure diagnosis with chip-package-system dynamic ESD simulation

A comprehensive chip-package-system (CPS) electrostatic discharge (ESD) simulation methodology is developed for addressing IEC61000-4-2 testing conditions. An innovative chip ESD compact model is proposed, combined with full-wave models of the ESD gun, ESD protection devices, PCB wires/vias and connectors for CPS analysis. Two examples of CPS ESD application are illustrated demonstrating good correlation with measurement.
Compact Root Bilinear CNNs for Content-Based Image Retrieval

Convolutional Neural Networks (CNNs) have recently demonstrated a superior performance in computer vision applications; including image retrieval. This paper introduces a bilinear CNN-based model for the first time in the context of Content-Based Image Retrieval (CBIR). The proposed architecture consists of two feature extractors using a pre-trained deep CNN model fine-tuned for image retrieval task to generate a Compact Root Bilinear CNN (CRB-CNN) architecture. Image features are directly extracted from the activations of convolutional layers then pooled at image locations. Additionally, the output size of bilinear features is largely reduced to a compact but high descriminative image representation using kernal-based low-dimensional projection and pooling, which is a fundamental improvement in the retrieval performance in terms of search speed and memory size. An end-to-end training is applied by back-probagation to learn the parameters of the final CRB-CNN. Experimental results reported on the standard Holidays image dataset show the efficiency of the architecture at extracting and learning even complex features for CBIR tasks. Specifically, using a vector of 64-dimension, it achieves 95.13% mAP accuracy and outperforms the best results of state-of-the-art approaches.
A Fault Tolerant Wired/Wireless Sensor Network Architecture for Monitoring Pipeline Infrastructures

This paper proposes a new fault-tolerant sensor network architecture for monitoring pipeline infrastructures. This architecture is an integrated wired and wireless network. The wired part of the network is considered the primary network while the wireless part is used as a backup among sensor nodes when there is any failure in the wired network. This architecture solves the current reliability issues of wired networks for pipelines monitoring and control. This includes the problem of disabling the network by disconnecting the network cables due to artificial or natural reasons. In addition, it solves the issues raised in recently proposed network architectures using wireless sensor networks for pipeline monitoring. These issues include the issues of power management and efficient routing for wireless sensor nodes to extend the life of the network. Detailed advantages of the proposed integrated network architecture are discussed under different application and fault scenarios.
Design and Application of Surface Wave Sensors for Nonintrusive Power Line Fault Detection

This paper describes a novel conformal surface wave (CSW) launcher that can excite electromagnetic surface waves along unshielded power line cables nonintrusively. This CSW launcher can detect open circuit faults on power cables. Unlike conventional horn-type launchers, this CSW launcher is small, lightweight, and cost effective, and can be placed easily on a power cable. For a nonintrusive open fault detection, the error is <; 5% when the cable length is <; 10 m, which is comparable with other direct-connect fault-finding techniques. For a cable length of 15.14 m, 7.6% error is noted. Besides cable fault detection, the potential applications of the proposed launcher include broadband power line communication and high-frequency power transmission.
Using Association Rules for Product Assortment Decisions: A Case Study

It has been claimed that the discovery of association rules is well-suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. Moreover, recent work indicates that the discovery of interesting rules can in fact only be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fullyautomated convenience store is used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.
From ratings to trust: an empirical study of implicit trust in recommender systems

Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.
Face-Spoofing 2D-Detection Based on Moiré-Pattern Analysis

Biometric systems based on face recognition have been shown unreliable under the presence of face-spoofing images. Hence, automatic solutions for spoofing detection became necessary. In this paper, face-spoofing detection is proposed by searching for Moiré patterns due to the overlap of the digital grids. The conditions under which these patterns arise are first described, and their detection is proposed which is based on peak detection in the frequency domain. Experimental results for the algorithm are presented for an image database of facial shots under several conditions.
*-Box: Towards Reliability and Consistency in Dropbox-like File Synchronization Services

Cloud-based file synchronization services, such as Dropbox, have never been more popular. They provide excellent reliability and durability in their server-side storage, and can provide a consistent view of their synchronized files across multiple clients. However, the loose coupling of these services and the local file system may, in some cases, turn these benefits into drawbacks. In this paper, we show that these services can silently propagate both local data corruption and the results of inconsistent crash recovery, and cannot guarantee that the data they store reflects the actual state of the disk. We propose techniques to prevent and recover from these problems by reducing the separation between local file systems and synchronization clients, providing clients with deeper knowledge of file system activity and allowing the file system to take advantage of the correct data stored remotely.
Relational Collaborative Topic Regression for Recommender Systems

Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist relations (also known as networks) among the items which can be helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same model. Experiments on real-world datasets show that our model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation.