nils-herrmann/clean_scirepeval huggingface dataset:
ROS-Based SLAM for a Gazebo-Simulated Mobile Robot in Image-Based 3D Model of Indoor Environment

At present, robot simulators have robust physics engine, high-quality graphics, convenient customer and graphical interfaces, that gives rich opportunities to substitute the real robot by its simulation model, providing the calculation of a robot locomotion by odometry and sensor data. This paper aims at describing a Gazebo simulation approach of simultaneous localization and mapping (SLAM) based on the Robotic operating system (ROS) for a simulated mobile robot with a system of two scanning lasers, which moves in a 3D model of realistic indoor environment. The image-based 3D model of a real room with obstacles was obtained by camera shots and reconstructed by Autodesk 123D Catch software with meshing in MeshLab software. We use the existing Gazebo simulation of the Willow Garage Personal Robot 2 (PR2) with its sensor system, which facilitates the simulation of robot locomotion and sensor measurements for SLAM and navigation tasks. The ROS-based SLAM approach applies Rao-Blackwellized particle filters and laser data to locate the PR2 robot in unknown environment and build a map. The Gazebo simulation of the PR2 robot locomotion, sensor data and SLAM algorithm is considered in details. The results qualitatively demonstrate the fidelity of the simulated 3D room with obstacles to the ROS-calculated map obtained from the robot laser system. It proves the feasibility of ROS-based SLAM of a Gazebo-simulated mobile robot to its usage in camera-based 3D model of a realistic indoor environment. This approach can be spread to further ROS-based robotic simulations with Gazebo, e.g. concerning a Russian android robot AR-601M.
Personality-Based User Modeling for Music Recommender Systems

Applications are getting increasingly interconnected. Although the interconnectedness provide new ways to gather information about the user, not all user information is ready to be directly implemented in order to provide a personalized experience to the user. Therefore, a general model is needed to which users’ behavior, preferences, and needs can be connected to. In this paper we present our works on a personality-based music recommender system in which we use users’ personality traits as a general model. We identified relationships between users’ personality and their behavior, preferences, and needs, and also investigated different ways to infer users’ personality traits from user-generated data of social networking sites (i.e., Facebook, Twitter, and Instagram). Our work contributes to new ways to mine and infer personality-based user models, and show how these models can be implemented in a music recommender system to positively contribute to the user experience.
Internet retail store design: How the user interface influences traffic and sales

Given the resources needed to launch a retail store on the Internet or change an existing online storefront design, it is important to allocate product development resources to interface features that actually improve store traffic and sales. We identified features that impact store traffic and sales using regression models of 1996 store traffic and dollar sales as dependent variables and interface design features such as number of links into the store, hours of promotional ads, number of products, and store navigation features as the independent variables. Product list navigation features that reduce the time to purchase products online account for 61% of the variance in monthly sales. Other factors explaining the variance in monthly sales include: number of hyperlinks into the store (10%), hours of promotion (4%) and customer service feedback (1%). These findings demonstrate that the user interface is an essential link between the customer and the retail store in Web-based shopping environments.
Channel estimation techniques based on pilot arrangement in OFDM systems

The channel estimation techniques for OFDM systems based on pilot arrangement are investigated. The channel estimation based on comb type pilot arrangement is studied through different algorithms for both estimating channel at pilot frequencies and interpolating the channel. The estimation of channel at pilot frequencies is based on LS and LMS while the channel interpolation is done using linear interpolation, second order interpolation, low-pass interpolation, spline cubic interpolation, and time domain interpolation. Time-domain interpolation is obtained by passing to time domain through IDFT, zero padding and going back to frequency domain through DFT. In addition, the channel estimation based on block type pilot arrangement is performed by sending pilots at every sub-channel and using this estimation for a specific number of following symbols. We have also implemented decision feedback equalizer for all sub-channels followed by periodic block-type pilots. We have compared the performances of all schemes by measuring bit error rate with 16QAM, QPSK, DQPSK and BPSK as modulation schemes, and multipath rayleigh fading and AR based fading channels as channel models.
Recent Progress in Deep Learning for NLP

Neural network­based methods have been viewed as one of the major driving force in the recent development of natural language processing (NLP). We all have witnessed with great excitement how this subfield advances: new ideas emerge at an unprecedented speed and old ideas resurge in unexpected ways. In a nutshell, there are two major trends: ● Ideas and techniques from other fields of machine learning and artificial intelligence (A.I.) have increasing impact on neural network­based NLP methods. ● With end­to­end models taking on more complex tasks, the design of architecture and mechanisms often needs more domain knowledge from linguists and other domain experts. Both trends are important to researchers in the computational linguistics community. Fundamental ideas like external memory or reinforcement learning, although introduced to NLP only recently, have quickly lead to significant improvement on tasks like natural language generation and question answering. On the other hand, with complicated neural systems with many cooperating components, it calls for linguistic knowledge in designing the right mechanism, architecture, and sometimes training setting. As a simple example, the introducing of automatic alignment in neural machine translation, has quickly led to the state­of­the­art performance in machine translation and triggered a large body of sequence­to­sequence models. It is therefore important to get the researchers in computational linguistics community acquainted with the recent progress in deep learning for NLP. We will focus on the work and ideas strongly related to the core of natural language and yet not so familiar to the majority of the community, which can be roughly categorized into: 1) the differentiable data­structures, and 2) the learning paradigms for NLP. Differentiable data­structures, starting with the memory equipped with continuous operations in Neural Turing Machine, have been the foundation of deep models with sophisticated operations. Some members of it, such as Memory Network, have become famous on tasks like question answering and machine translation, while other development in this direction, including those with clear and important application in NLP, are relatively new to this community. Deep learning, with its promise on end­to­end learning, not only enables the training of complex NLP models from scratch, but also extends the training setting to include remote and indirect supervision. We will introduce not only the end­to­end learning in its general notion, but also newly emerged
Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games

A major difficulty in a recommendation system for groups is to use a group aggregation strategy to ensure, among other things, the maximization of the average satisfaction of group members. This paper presents an approach based on the theory of noncooperative games to solve this problem. While group members can be seen as game players, the items for potential recommendation for the group comprise the set of possible actions. Achieving group satisfaction as a whole becomes, then, a problem of finding the Nash equilibrium. Experiments with a MovieLens dataset and a function of arithmetic mean to compute the prediction of group satisfaction for the generated recommendation have shown statistically significant results when compared to state-of-the-art aggregation strategies, in particular, when evaluation among group members are more heterogeneous. The feasibility of this unique approach is shown by the development of an application for Facebook, which recommends movies to groups of friends.
Let's Play My Way: Investigating Audience Influence in User-Generated Gaming Live-Streams

We investigate how the audience of gaming live-streams can influence the content. We conducted two case studies on streams in which audience influence is central and in which the audience can directly participate: First, we review an existing format of the Rocket Beans TV channel and describe how the audience can influence its course of action. With this, we illustrate current practices for integrating the audience. Second, we report the results of our investigation of a "Twitch Plays Pokémon" (TPP)-like setting in which the audience shares the control of the main character through aggregated chat messages. We explored a wider range of techniques than the original TPP offered and found that this can help the audience to organize itself in more nuanced ways. From both case studies, we synthesize results that are of relevance for streams that want to give the audience more influence.
A probabilistic approach to liquid level detection in cups using an RGB-D camera

Robotic assistants have the potential to greatly improve our quality of life by supporting us in our daily activities. A service robot acting autonomously in an indoor environment is faced with very complex tasks. Consider the problem of pouring a liquid into a cup, the robot should first determine if the cup is empty or partially filled. RGB-D cameras provide noisy depth measurements which depend on the opaqueness and refraction index of the liquid. In this paper, we present a novel probabilistic approach for estimating the fill-level of a liquid in a cup using an RGB-D camera. Our approach does not make any assumptions about the properties of the liquid like its opaqueness or its refraction index. We develop a probabilistic model using features extracted from RGB and depth data. Our experiments demonstrate the robustness of our method and an improvement over the state of the art.
Big Data Benchmark Compendium

The field of Big Data and related technologies is rapidly evolving. Consequently, many benchmarks are emerging, driven by academia and industry alike. As these benchmarks are emphasizing different aspects of Big Data and, in many cases, covering different technical platforms and uses cases, it is extremely difficult to keep up with the pace of benchmark creation. Also with the combinations of large volumes of data, heterogeneous data formats and the changing processing velocity, it becomes complex to specify an architecture which best suits all application requirements. This makes the investigation and standardization of such systems very difficult. Therefore, the traditional way of specifying a standardized benchmark with pre-defined workloads, which have been in use for years in the transaction and analytical processing systems, is not trivial to employ for Big Data systems. This document provides a summary of existing benchmarks and those that are in development, gives a side-by-side comparison of their characteristics and discusses their pros and cons. The goal is to understand the current state in Big Data benchmarking and guide practitioners in their approaches and use cases.
Business Process Management: A Comprehensive Survey

Business Process Management (BPM) research resulted in a plethora of methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. This survey aims to structure these results and provides an overview of the state-of-the-art in BPM. In BPM the concept of a process model is fundamental. Process models may be used to configure information systems, but may also be used to analyze, understand, and improve the processes they describe. Hence, the introduction of BPM technology has both managerial and technical ramifications, and may enable significant productivity improvements, cost savings, and flow-time reductions. The practical relevance of BPM and rapid developments over the last decade justify a comprehensive survey.
A proportional share resource allocation algorithm for real-time, time-shared systems

We propose and analyze a proportional share resource allocation algorithm for realizing real-time performance in time-shared operating systems. Processes are assigned a weight which determines a share (percentage) of the resource they are to receive. The resource is then allocated in discrete-sized time quanta in such a manner that each process makes progress at a precise, uniform rate. Proportional share allocation algorithms are of interest because (1) they provide a natural means of seamlessly integrating realand nonreal-time processing, (2) they are easy to implement, (3) they provide a simple and e ective means of precisely controlling the real-time performance of a process, and (4) they provide a natural mean of policing so that processes that use more of a resource than they request have no ill-e ect on well-behaved processes. We analyze our algorithm in the context of an idealized system in which a resource is assumed to be granted in arbitrarily small intervals of time and show that our algorithm guarantees that the di erence between the service time that a process should receive in the idealized system and the service time it actually receives in the real system is optimally bounded by the size of a Supported by GAANN fellowship. Dept. of CS, Old Dominion Univ., Norfolk, VA 23529-0162 (stoica@cs.odu.edu). ySupported by NSF grant CCR 95{9313857. Dept. of CS, Old Dominion Univ., Norfolk, VA 23529-0162 (wahab@cs.odu.edu). zSupported by grant from the IBM & Intel corps and NSF grant CCR 95{10156. Dpt. of CS, Univ. of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175, (jeffay@cs.unc.edu). xSupported by NSF under Research Initiation Award CCR{ 9596282. Dept. of CS,  (sanjoy@cs.uvm.edu).  Austin, TX 78712-1188 (plaxton@cs.utexas.edu). time quantum. In addition, the algorithm provides support for dynamic operations, such as processes joining or leaving the competition, and for both fractional and non-uniform time quanta. As a proof of concept we have implemented a prototype of a CPU scheduler under FreeBSD. The experimental results shows that our implementation performs within the theoretical bounds and hence supports real-time execution in a general purpose operating system.
Fast, Compact, and Discriminative: Evaluation of Binary Descriptors for Mobile Applications

Local feature descriptors underpin many diverse applications, supporting object recognition, image registration, database search, 3D reconstruction, and more. The recent phenomenal growth in mobile devices and mobile computing in general has created demand for descriptors that are not only discriminative, but also compact in size and fast to extract and match. In response, a large number of binary descriptors have been proposed, each claiming to overcome some limitations of the predecessors. This paper provides a comprehensive evaluation of several promising binary designs. We show that existing evaluation methodologies are not sufficient to fully characterize descriptors’ performance and propose a new evaluation protocol and a challenging dataset. In contrast to the previous reviews, we investigate the effects of the matching criteria, operating points, and compaction methods, showing that they all have a major impact on the systems’ design and performance. Finally, we provide descriptor extraction times for both general-purpose systems and mobile devices, in order to better understand the real complexity of the extraction task. The objective is to provide a comprehensive reference and a guide that will help in selection and design of the future descriptors.
Force Generation by Parallel Combinations of Fiber-Reinforced Fluid-Driven Actuators

The compliant structure of soft robotic systems enables a variety of novel capabilities in comparison to traditional rigid-bodied robots. A subclass of soft fluid-driven actuators known as fiber-reinforced elastomeric enclosures (FREEs) is particularly well suited as actuators for these types of systems. FREEs are inherently soft and can impart spatial forces without imposing a rigid structure. Furthermore, they can be configured to produce a large variety of force and moment combinations. In this letter, we explore the potential of combining multiple differently configured FREEs in parallel to achieve fully controllable multidimensional soft actuation. To this end, we propose a novel methodology to represent and calculate the generalized forces generated by soft actuators as a function of their internal pressure. This methodology relies on the notion of a state dependent fluid Jacobian that yields a linear expression for force. We employ this concept to construct the set of all possible forces that can be generated by a soft system in a given state. This force zonotope can be used to inform the design and control of parallel combinations of soft actuators. The approach is verified experimentally with the parallel combination of three carefully designed actuators constrained to a 2DOF test platform. The force predictions matched measured values with a root-mean-square error of less than 1.5 N force and  $\mathbf {8 \times 10^{-3}}$ Nm moment, demonstrating the utility of the presented methodology.
Counterparty Risk and Funding : The Four Wings of the TVA

The credit crisis and the ongoing European sovereign debt crisis have highlighted the native form of credit risk, namely the counterparty risk. The related Credit Valuation Adjustment (CVA), Debt Valuation Adjustment (DVA), Liquidity Valuation Adjustment (LVA) and Replacement Cost (RC) issues, jointly referred to in this paper as Total Valuation Adjustment (TVA), have been thoroughly investigated in the theoretical papers Crépey (2012a, 2012b). The present work provides an executive summary and numerical companion to these papers, through which the TVA pricing problem can be reduced to Markovian pre-default TVA BSDEs. The first step consists in the counterparty clean valuation of a portfolio of contracts, which is the valuation in a hypothetical situation where the two parties would be risk-free and funded at a risk-free rate. In the second step, the TVA is obtained as the value of an option on the counterparty clean value process called Contingent Credit Default Swap (CCDS). Numerical results are presented for interest rate swaps in the Vasicek, as well as in the inverse Gaussian Hull-White short rate model, also allowing one to assess the related model risk issue.
BUP: A Bottom-Up parser embedded in Prolog

A parser based on logic programming language (DCG) has very useful features; perspicuity, power, generality and so on. However, it does have some drawbacks in which it cannot deal with CFG with left recursive rules, for example. To overcome these drawbacks, a Bottom-Up parser embedded in Prolog (BUP) has been developed. In BUP, CFG rules are translated into Prolog clauses which work as a bottom-up left corner parser with top-down expectation. BUP is augmented by introducing a “link” relation to reduce the size of a search space. Furthermore, BUP can be revised to maintain partial parsing results to avoid computational duplication. A BUP translator and a BUP tracer which support the development of grammar rules are described.
Parallel matrix factorization for recommender systems

Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to compute matrix factorization, and there has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been thoroughly explored. In this paper, we show that coordinate descent-based methods have a more efficient update rule compared to ALS and have faster and more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updates rank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, with a synthetic dataset containing 14.6 billion ratings, on a distributed memory cluster with 64 processors, to deliver the desired test RMSE, CCD++ is 49 times faster than SGD and 20 times faster than ALS. When the number of processors is increased to 256, CCD++ takes only 16 s and is still 40 times faster than SGD and 20 times faster than ALS.
Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes

This paper reports the design principles and evaluation results of a new experimental hybrid intrusion detection system (HIDS). This hybrid system combines the advantages of low false-positive rate of signature-based intrusion detection system (IDS) and the ability of anomaly detection system (ADS) to detect novel unknown attacks. By mining anomalous traffic episodes from Internet connections, we build an ADS that detects anomalies beyond the capabilities of signature-based SNORT or Bro systems. A weighted signature generation scheme is developed to integrate ADS with SNORT by extracting signatures from anomalies detected. HIDS extracts signatures from the output of ADS and adds them into the SNORT signature database for fast and accurate intrusion detection. By testing our HIDS scheme over real-life Internet trace data mixed with 10 days of Massachusetts Institute of Technology/Lincoln Laboratory (MIT/LL) attack data set, our experimental results show a 60 percent detection rate of the HIDS, compared with 30 percent and 22 percent in using the SNORT and Bro systems, respectively. This sharp increase in detection rate is obtained with less than 3 percent false alarms. The signatures generated by ADS upgrade the SNORT performance by 33 percent. The HIDS approach proves the vitality of detecting intrusions and anomalies, simultaneously, by automated data mining and signature generation over Internet connection episodes
Motion planning for a mobile manipulator with imprecise locomotion

This paper presents a motion planning method for mobile manipulators for which the base locomotion is less precise than the manipulator control. In such a case, it is advisable to move the base to discrete poses from which the manipulator can be deployed to cover a prescribed trajectory. The proposed method finds base poses that not only cover the trajectory but also meet constraints on a measure of manipulability. We propose a variant of the conventional manipulability measure that is suited to the trajectory control of the end effector of the mobile manipulator along an arbitrary curve in three space. Results with implementation on a mobile manipulator are discussed.