nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing Information Systems – A Systems Theory Perspective

Crowdsourcing harnesses the potential of large and open networks of people. It is a relatively new phenomenon and attracted substantial interest in practice. Related research, however, lacks a theoretical foundation. We propose a systemtheoretical perspective on crowdsourcing systems to address this gap and illustrate its applicability by using it to classify crowdsourcing systems. By deriving two principal dimensions from theory, we identify four fundamental types of crowdsourcing systems that help to distinguish important features of such systems. We analyse their respective characteristics and discuss implications and requirements for various aspects related to the design of such systems. Our results demonstrate that systems theory can inform the study of crowdsourcing systems. The identified system types and the implications on their design may prove useful for researchers to frame future studies and for practitioners to identify the right crowdsourcing systems for a particular purpose.
Effect of subjective perspective taking during simulation of action: a PET investigation of agency

Perspective taking is an essential component in the mechanisms that account for intersubjectivity and agency. Mental simulation of action can be used as a natural protocol to explore the cognitive and neural processing involved in agency. Here we took PET measurements while subjects simulated actions with either a first-person or a third-person perspective. Both conditions were associated with common activation in the SMA, the precentral gyrus, the precuneus and the MT/V5 complex. When compared to the first-person perspective, the third-person perspective recruited right inferior parietal, precuneus, posterior cingulate and frontopolar cortex. The opposite contrast revealed activation in left inferior parietal and somatosensory cortex. We suggest that the right inferior parietal, precuneus and somatosensory cortex are specifically involved in distinguishing self-produced actions from those generated by others.
A theory of visual attention.

A unified theory of visual recognition and attentional selection is developed by integrating the biased-choice model for single-stimulus recognition (Luce, 1963; Shepard, 1957) with a choice model for selection from multielement displays (Bundesen, Pedersen, & Larsen, 1984) in a race model framework. Mathematically, the theory is tractable, and it specifies the computations necessary for selection. The theory is applied to extant findings from a broad range of experimental paradigms. The findings include effects of object integrality in selective report, number and spatial position of targets in divided-attention paradigms, selection criterion and number of distracters in focused-attention paradigms, delay of selection cue in partial report, and consistent practice in search. On the whole, the quantitative fits are encouraging.
Selective Cluster-Based Document Retrieval

We address the long standing challenge of selective cluster-based retrieval; namely, deciding on a per-query basis whether to apply cluster-based document retrieval or standard document retrieval. To address this classification task, we propose a few sets of features based on those utilized by the cluster-based ranker, query-performance predictors, and properties of the clustering structure. Empirical evaluation shows that our method outperforms state-of-the-art retrieval approaches, including cluster-based, query expansion, and term proximity methods.
User Controller BYOC-VisorService Provider Domain Cloud Infrastructure VMs Switches Routing Storage Computing Logical Control Plane Control Plane Data Plane Control

The need of customized network functions for enterprises in Infrastructure-as-a-Service (IaaS) clouds is emerging. However, existing network functions in IaaS clouds are very limited, inflexible, and hard to control by the tenants. Recently, the introduction of Software-Defined Networking (SDN) technology brings the hope of flexible control of network flows and creation of diverse network functions. Unfortunately, enterprises lose access to the SDN controller when they move to clouds. Moreover, the cloud SDN controller is only managed by the provider administrators for security and performance reasons. To allow enterprise tenants to develop and deploy their own SDN apps in the cloud, in this paper, we introduce a new cloud usage paradigm: Bring Your Own Controller (BYOC). BYOC offers each tenant an individual SDN controller, where tenants can deploy SDN apps to manage their network. To manage these tenant SDN controllers, we propose BYOC-VISOR, a new SDNbased virtualization platform. BYOC-VISOR addresses several security and performance challenges which are specific to IaaS clouds. We show that BYOC-VISOR supports different controller platforms and diverse SDN security applications such as firewall, IDS, and access control. We implement a prototype system and the performance evaluation results show that our system has low overhead.
Model-based Security Testing: An Empirical Study on OAuth 2.0 Implementations

Motivated by the prevalence of OAuth-related vulnerabilities in the wild, large-scale security testing of real-world OAuth 2.0 implementations have received increasing attention lately [31,37,42]. However, these existing works either rely on manual discovery of new vulnerabilities in OAuth 2.0 implementations or perform automated testing for specific, previously-known vulnerabilities across a large number of OAuth implementations. In this work, we propose an adaptive model-based testing framework to perform automated, large-scale security assessments for OAuth 2.0 implementations in practice. Key advantages of our approach include (1) its ability to identify existing vulnerabilities and discover new ones in an automated manner; (2) improved testing coverage as all possible execution paths within the scope of the model will be checked and (3) its ability to cater for the implementation differences of practical OAuth systems/ applications, which enables the analyst to offload the manual efforts for large-scale testing of OAuth implementations. We have designed and implemented OAuthTester to realize our proposed framework. Using OAuthTester, we examine the implementations of 4 major Identity Providers as well as 500 top-ranked US and Chinese websites which use the OAuth-based Single-Sign-On service provided by the formers. Our empirical findings demonstrate the efficacy of adaptive model-based testing on OAuth 2.0 deployments at scale. More importantly, OAuthTester not only manages to rediscover various existing vulnerabilities but also identify several previously unknown security flaws and new exploits for a large number of eal-world applications implementing OAuth 2.0.
Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word
SinFP, unification of active and passive operating system fingerprinting

The ubiquity of firewalls using Network Address Translation and Port Address Translation (NAT/PAT), stateful inspection, and packet normalization technologies is taking its toll on today’s approaches to operating system fingerprinting. Hence, SinFP was developed attempting to address the limitations of current tools. SinFP implements new methods, like the usage of signatures acquired by active fingerprinting when performing passive fingerprinting. Furthermore, SinFP is the first tool to perform operating system fingerprinting on IPv6 (both active and passive modes). Thanks to its signature matching algorithm, it is almost superfluous to add new signatures to its current database. In addition, its heuristic matching algorithm makes it highly resilient against signatures that have been modified by intermediate routing and/or filtering devices in-between, and against TCP/IP customization methods. This document presents an in-depth explanation of techniques implemented by SinFP tool.
Simulation of Absolute Amplitudes of Ultrasound Signals Using Equivalent Circuits

Equivalent circuits for piezoelectric devices and ultrasonic transmission media can be used to cosimulate electronics and ultrasound parts in simulators originally intended for electronics. To achieve efficient system- level optimization, it is important to simulate correct, absolute amplitude of the ultrasound signal in the system, as this determines the requirements on the electronics regarding dynamic range, circuit noise, and power consumption. This paper presents methods to achieve correct, absolute amplitude of an ultrasound signal in a simulation of a pulse-echo system using equivalent circuits. This is achieved by taking into consideration loss due to diffraction and the effect of the cable that connects the electronics and the piezoelectric transducer. The conductive loss in the transmission line that models the propagation media of the ultrasound pulse is used to model the loss due to diffraction. Results show that the simulated amplitude of the echo follows measured values well in both near and far fields, with an offset of about 10%. The use of a coaxial cable introduces inductance and capacitance that affect the amplitude of a received echo. Amplitude variations of 60% were observed when the cable length was varied between 0.07 m and 2.3 m, with simulations predicting similar variations. The high precision in the achieved results show that electronic design and system optimization can rely on system simulations alone. This will simplify the development of integrated electronics aimed at ultrasound systems.
Why We Need a Granularity Concept for User Stories

User stories are a widespread instrument for representing requirements. They describe small user-oriented parts of the system and guide the daily work of developers. Often however, user stories are too coarse, so that misunderstandings or dependencies remain unforeseeable. Granularity of user stories needs to be investigated more, but at the same time is a hard-to-grasp concept. This paper investigates Expected Implementation Duration (EID) of a user story as a characteristic of granularity. We want to find out, whether it is suitable as a quality aspect and can help software teams improve their user stories. We have conducted a study with software engineering practitioners. There, many user stories had a relatively high EID of four or more days. Many developers state to have experienced certain problems to occur more often with such coarse user stories. Our findings emphasize the importance to reflect on granularity when working with user stories.
Millimetre-wave beam-switching rotman lens antenna designs on multi-layered LCP substrates

Compact and low-cost antenna arrays with beamsteering or beam-switching are needed for emerging millimeter-wave communication systems such as 5G and WLAN at the 60 GHz frequency band. The Rotman lens is a planar lens which can be used in conjunction with an antenna array to provide several switchable beam directions. The Rotman lens is a truetime delay device which makes it ideal for wideband systems. The proposed concept can be integrated on a printed circuit board construction based on liquid crystal polymer (LCP) base materials. The design, manufacturing and measurement results are presented for a 60 GHz Rotman lens implementation. The presented design can electrically switch between five different beam directions -30°, -15°, 0°, 15° and 30°.
An abstract-device interface for implementing portable parallel-I/O interfaces

We propose a strategy for implementing parallel I/O interfaces portably and efficiently. We have defined an abstract device interface for parallel I/O, called ADIO. Any parallel I/O API can be implemented on multiple file systems by implementing the API portably on top of ADIO, and implementing only ADIO on different file systems. This approach simplifies the task of implementing an API and yet exploits the specific high performance features of individual file systems. We have used ADIO to implement the Intel PFS interface and subsets of MPI-IO and IBM PIOFS interfaces on PFS, PIOFS, Unix, and NFS file systems. Our performance studies indicate that the overhead of using ADIO as an implementation strategy is very low.
Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.

Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogeneous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, such as social networks. As a practical application, we show how modularity optimization performs on our benchmark.
Single image super-resolution using Gaussian process regression

In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is predicted by its neighbors through the Gaussian process regression. We show that when using a proper covariance function, the Gaussian process regression can perform soft clustering of pixels based on their local structures. We further demonstrate that our algorithm can extract adequate information contained in a single low-resolution image to generate a high-resolution image with sharp edges, which is comparable to or even superior in quality to the performance of other edge-directed and example-based super-resolution algorithms. Experimental results also show that our approach maintains high-quality performance at large magnifications.
Recognizing Objects in Range Data Using Regional Point Descriptors

Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.
Speech responsive mobile robot for transporting objects of different weight categories

In this paper we present a robot with mechanical gripper which has the capability of transporting limited sized objects from one place to another with pick-up and drop capabilities. The robot's responses are based on speech recognition of verbal commands. In our project we have used Google speech recognition module in order to understand verbal commands. We also categorized the objects into six specific categories according to the amount of gripping force required to lift the objects. The categories are divided according to object stiffness. An android application was used to communicate with the robot through Bluetooth communication. The application decodes the human speech into an array of characters which are transmitted to the robot using Bluetooth technology. The robot uses microcontroller which decodes the messages into executable functions. In this project we have designed the robot such that it understands only fifteen distinct verbal commands and ignores others. Being a speech responsive mobile robot it can be effectively used to move objects from one place to another by people with disability or handicap.
Analysis of different PM machines with concentrated windings and flux barriers in stator core

The new stator structure with magnetic flux-barriers in the stator yoke or tooth region represents an efficient method for reducing the sub-harmonics of electric machines with fractional slots, tooth-concentrated windings. In this paper the both flux-barriers techniques are considered during the analysis of different PM machines. The 12-teeth single-layer and double-layer concentrated winding in combination with a 10-poles and 14-poles PM rotor are investigated. For the all machine topologies the new stator design is used to improve their performances and characteristics. The flux-barrier effects on the main machine parameters, such as in the air-gap flux density harmonics, dq-machine parameters, characteristic currents, electromagnetic torque, and so on, are studied carefully. Comparisons performed with the analogous conventional machines (with conventional stator) show that, the new stator design offers significant advantages.
Surface lightness influences perceived room height.

Surprisingly little scientific research has been conducted on the effects of colour and lightness on the perception of spaciousness. Practitioners and architects typically suggest that a room's ceiling appears higher when it is painted lighter than the walls, while darker ceilings appear lower. Employing a virtual reality setting, we studied the effects of the lightness of different room surfaces on perceived height in two psychophysical experiments. Observers judged the height of rooms varying in physical height as well as in the lightness of ceiling, floor, and walls. Experiment 1 showed the expected increase of perceived height with increases in ceiling lightness. Unexpectedly, the perceived height additionally increased with wall lightness, and the effects of wall lightness and ceiling lightness were roughly additive, incompatible with a simple effect of the lightness contrast between the ceiling and the walls. Experiment 2 demonstrated that the floor lightness has no significant effect on perceived height, and that the total brightness of the room is not the critical factor influencing the perceived height. Neither can the results be explained by previously reported effects of brightness on apparent depth or perceived distance.
Contextual Dueling Bandits

We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner’s goal is to find the best policy, or way of behaving, in some space of policies, although “best” is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.
A New Approach to Image-Based Estimation of Food Volume

A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92% on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.
Dual-Band Omnidirectional Circularly Polarized Antenna

A dual-band omnidirectional circularly polarized antenna is proposed. The antenna comprises back-to-back microstrip patches fed by a coplanar waveguide. A very low frequency ratio of 1.182 has been achieved, which can be easily tuned by adjusting four lumped capacitors incorporated into the antenna. An analysis of the omnidirectional circular polarization mechanism as well the dual band operation is provided and confirmed by numerical and experimental data. Key parameters to tune the resonant frequencies and the axial ratio have been identified. The prototype antenna provides omnidirectional circular polarization in one plane with cross polar isolation better than 12 dB for both frequency bands.
Dynamic resource allocation in urban settings: A “smart parking” approach

We propose a “smart parking” system for an urban environment based on a dynamic resource allocation approach. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the user's objective function that combines proximity to destination with parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. The allocation is updated at the next decision point ensuring that there is no resource reservation conflict, that no user is ever assigned a resource with higher than the current cost function value, and that a set of fairness constraints is satisfied. We add an event-driven mechanism to compensate for users with no assignment that are close to their destinations. Simulation results show that using this “smart parking” approach can achieve near-optimal resource utilization and significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems.