nils-herrmann/clean_scirepeval huggingface dataset:
Feedforward friction compensation of Bowden-cable transmission via loop routing

Friction along the Bowden-cable transmission degenerates control performance unless it is properly compensated. Friction is produced when the bending angle of the Bowden-cable changes as the relative position of the actuator and the end-effector changes. This study proposes a method, termed loop routing, to compensate friction along the Bowden-cable. Loop routing involves making a one-round loop along the sheath that continuously maintains the sheath's bending angle at 2π regardless of the end-effector's position in 2-D space. This minimizes the bending angle change of the sheath as the end-effector translates in a 3-D workspace, which minimizes the friction change and enables feedforward friction compensation of the Bowden-cable without employing a sensor. An experiment in open-loop tension control of the Bowden-cable was conducted to evaluate the performance of the proposed method. Results show that the output tension follows the reference tension well, with an RMS error of 4.3% and a peak error of 13.3% of maximum reference.
Using CommonKADS Method to Build Prototype System in Medical Insurance Fraud Detection

At present, the false claim cases related to insurance fraud emerge endlessly. In respect of frequent fraud cases of social insurance undertaken by government organizations, the inspection procedure usually relies on experts’ experience for verification and experienced personnel in charge of checking. However, due to heavy work load insufficient manpower and lack of experience, the ratio of miscarriages of justice is very high, which leads to improper settlement of claims and the waste of social resources. In this paper, we used rule technology to improve the above inefficiency. We employ a knowledge engineering methodology to analyze problems and construct knowledge modesl, including the domain schema and rules. We implement the knowledge model along with the existing database applications. The benefits generated by the research are: (1) establishing a knowledge system with expertise reasoning to solve the review problems of massive cases, (2) significantly reducing the large labor cost and consumed time of the existing reviewing system, and (3) improving the application level of traditional database in the expert reasoning system.
Power System Control and Stability

This classic text offers you the key to understanding short circuits, open conductors and other problems relating to electric power systems that are subject to unbalanced conditions. Using the method of symmetrical components, acknowledged expert Paul M. Anderson provides comprehensive guidance for both finding solutions for faulted power systems and maintaining protective system applications. You'll learn to solve advanced problems, while gaining a thorough background in elementary configurations. Features you'll put to immediate use: Numerous examples and problems Clear, concise notation Analytical simplifications Matrix methods applicable to digital computer technology Extensive appendices
Real-Time Convex Optimization in Signal Processing

This article shows the potential for convex optimization methods to be much more widely used in signal processing. In particular, automatic code generation makes it easier to create convex optimization solvers that are made much faster by being designed for a specific problem family. The disciplined convex programming framework that has been shown useful in transforming problems to a standard form may be extended to create solvers themselves. Much work remains to be done in exploring the capabilities and limitations of automatic code generation. As computing power increases, and as automatic code generation improves, the authors expect convex optimization solvers to be found more and more often in real-time signal processing applications.
Teaching Phishing-Security: Which Way is Best?

Ever more processes of our daily lives are shifting into the digital realm. Consequently, users face a variety of IT-security threats with possibly severe ramifications. It has been shown that technical measures alone are insufficient to counter all threats. For instance, it takes technical measures on average 32 hours before identifying and blocking phishing websites. Therefore, teaching users how to identify malicious websites is of utmost importance, if they are to be protected at all times. A number of ways to deliver the necessary knowledge to users exist. Among the most broadly used are instructor-based, computer-based and text-based training. We compare all three formats in the security context, or to be more precise in the context of anti-phishing training.
A Collaborative Multiagent Taxi-Dispatch System

This paper presents a novel multiagent approach to automating taxi dispatch that services current bookings in a distributed fashion. The existing system in use by a taxi operator in Singapore and elsewhere, attempts to increase customer satisfaction locally, by sequentially dispatching nearby taxis to service customers. The proposed dispatch system attempts to increase customer satisfaction more globally, by concurrently dispatching multiple taxis to the same number of customers in the same geographical region, and vis-à-vis human driver satisfaction. To realize the system, a multiagent architecture is proposed, populated with software collaborative agents that can actively negotiate on behalf of taxi drivers in groups of size N for available customer bookings. Theoretically, an analysis of the boundary and optimal multiagent taxi-dispatch situations is presented along with a discussion of their implications. Experimentally, the operational efficiency of the existing and proposed dispatch systems was evaluated through computer simulations. The empirical results, obtained for a 1000-strong taxi fleet over a discrete range of N , show that the proposed system can dispatch taxis with reduction in customer waiting and empty taxi cruising times of up to 33.1% and 26.3%, respectively; and up to 41.8% and 41.2% reduction when a simple negotiation speedup heuristic was applied.
Towards Automatic Persona Generation Using Social Media

The use of personas is an interactive design technique with considerable potential for product and content development. However, personas have typically been viewed as fairly static. In this research, we implement an approach for creating personas in real time, based on automated analysis of actual social media data, integrating data from Facebook, Twitter, and YouTube channels for a large commercial organization. From Twitter, we gather user insights representing interests and viewpoints, leveraging approximately 195,000 follower profiles. From YouTube, we gather demographic data and topical interests, leveraging more than 188,000 subscriber profiles and millions of user interactions. From Facebook, we collect instances of hundreds of thousands of link sharing by more than 54,000 social media followers, specifically examining the domains these users share. We integrate the social media data from all three platforms in order to demonstrating that this data can be used to develop personas in real-time. The research results provide insights into competitive marketing, topical interests, and preferred system features for the users of the online news medium. Research implications are that personas can be generated in real-time, instead of being the result of a laborious, time-consuming development process.
GaN based wideband T/R module for multi-function applications

The next generation of AESA antennas will be challenged with the need for enabling a combination of different operating modes within the same antenna front end, including radar, communication (data links), and jamming (electronic warfare, EW). This leads to enhanced demands especially with regard to the usable RF bandwidth. One main step to overcome this is the use of disruptive semiconductor materials for RF MMICs. As the RF section of today's T/R modules for AESA applications is typically based on GaAs technology, GaN and SiGe BiCMOS will challenge or even replace it, here. This paper will describe the design of a GaN-based T/R module using all-European MMICs and covering the X-band from 8 to 12 GHz and its realisation at Airbus Defence and Space in Ulm. It will show some measurement results and give an impression on achieved performance. Further this paper shall describe potential next steps and give an outlook towards future developments.
Similarity Measures

With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a deenition of similarity as an operation. In this paper we develop a similarity measure, based on fuzzy logic, that exhibit several features that match experimental ndings in humans. The model is dubbed Fuzzy Feature Contrast (FFC) and is an extension to a more general domain of the Feature Contrast model due to Tversky. We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties.
Rya: a scalable RDF triple store for the clouds

Resource Description Framework (RDF) was designed with the initial goal of developing metadata for the Internet. While the Internet is a conglomeration of many interconnected networks and computers, most of today's best RDF storage solutions are confined to a single node. Working on a single node has significant scalability issues, especially considering the magnitude of modern day data. In this paper we introduce a scalable RDF data management system that uses Accumulo, a Google Bigtable variant. We introduce storage methods, indexing schemes, and query processing techniques that scale to billions of triples across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases, our system outperforms existing distributed RDF solutions, even systems much more complex than ours.
Simultaneous calibration, localization, and mapping

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on environmental changes or on the wear of the devices. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the platform parameters. The proposed approach performs on-line estimation of the parameters and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real world data using different types of robotic platforms.
What do the Teachers Think?: Introducing Computational Thinking in the Primary School Curriculum

This paper reports on the preliminary results of an ongoing study examining the teaching of new primary school topics based on Computational Thinking in New Zealand. We analyse detailed feedback from 13 teachers participating in the study, who had little or no previous experience teaching Computer Science or related topics. From this we extract key themes identified by the teachers that are likely to be encountered when deploying a new curriculum, including unexpected opportunities for cross-curricula learning, development of students' social skills, and engaging a wide range of students. From here we articulate key concepts and issues that arise in the primary school context, based on feedback during professional development for the study, and direct feedback from teachers on the experience of delivering the new material in the classroom.
Credit-Scheduled Delay-Bounded Congestion Control for Datacenters

Small RTTs (~tens of microseconds), bursty flow arrivals, and a large number of concurrent flows (thousands) in datacenters bring fundamental challenges to congestion control as they either force a flow to send at most one packet per RTT or induce a large queue build-up. The widespread use of shallow buffered switches also makes the problem more challenging with hosts generating many flows in bursts. In addition, as link speeds increase, algorithms that gradually probe for bandwidth take a long time to reach the fair-share. An ideal datacenter congestion control must provide 1) zero data loss, 2) fast convergence, 3) low buffer occupancy, and 4) high utilization. However, these requirements present conflicting goals.
 This paper presents a new radical approach, called ExpressPass, an end-to-end credit-scheduled, delay-bounded congestion control for datacenters. ExpressPass uses credit packets to control congestion even before sending data packets, which enables us to achieve bounded delay and fast convergence. It gracefully handles bursty flow arrivals. We implement ExpressPass using commodity switches and provide evaluations using testbed experiments and simulations. ExpressPass converges up to 80 times faster than DCTCP in 10 Gbps links, and the gap increases as link speeds become faster. It greatly improves performance under heavy incast workloads and significantly reduces the flow completion times, especially, for small and medium size flows compared to RCP, DCTCP, HULL, and DX under realistic workloads.
Data-aware process mining: discovering decisions in processes using alignments

Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm—called GDBSCAN—can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes. In addition, four applications using 2D points (astronomy), 3D points (biology), 5D points (earth science) and 2D polygons (geography) are presented, demonstrating the applicability of GDBSCAN to real-world problems.
Discovering Opinion Spammer Groups by Network Footprints

Online reviews are an important source for consumers to evaluate products/services on the Internet (e.g. Amazon, Yelp, etc.). However, more and more fraudulent reviewers write fake reviews to mislead users. To maximize their impact and share effort, many spam attacks are organized as campaigns, by a group of spammers. In this paper, we propose a new two-step method to discover spammer groups and their targeted products. First, we introduce NFS (Network Footprint Score), a new measure that quantifies the likelihood of products being spam campaign targets. Second, we carefully devise GroupStrainer to cluster spammers on a 2-hop subgraph induced by top ranking products. Our approach has four key advantages: (i) unsupervised detection; both steps require no labeled data, (ii) adversarial robustness; we quantify statistical distortions in the review network, of which spammers have only a partial view, and avoid any side information that spammers can easily evade, (iii) sensemaking; the output facilitates the exploration of the nested hierarchy (i.e., organization) among the spammers, and finally (iv) scalability; both steps have complexity linear in network size, moreover, GroupStrainer operates on a carefully induced subnetwork. We demonstrate the efficiency and effectiveness of our approach on both synthetic and real-world datasets from two different domains with millions of products and reviewers. Moreover, we discover interesting strategies that spammers employ through case studies of our detected groups.
A Beam-Search Decoder for Grammatical Error Correction

We present a novel beam-search decoder for grammatical error correction. The decoder iteratively generates new hypothesis corrections from current hypotheses and scores them based on features of grammatical correctness and fluency. These features include scores from discriminative classifiers for specific error categories, such as articles and prepositions. Unlike all previous approaches, our method is able to perform correction of whole sentences with multiple and interacting errors while still taking advantage of powerful existing classifier approaches. Our decoder achieves an F1 correction score significantly higher than all previous published scores on the Helping Our Own (HOO) shared task data set.
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

FC (fully-connected) layer takes the pooling results, produces features FROI, Fcontext, Fframe, and feeds them into two streams, inspired by [BV16]. • Classification stream produces a matrix of classification scores S = [FCcls(FROI1); . . . ;FCcls(FROIK)] ∈ RK×C • Localization stream implements the proposed context-aware guidance that uses FROIk, Fcontextk, Fframek to produce a localization score matrix L ∈ RK×C.
Recruitment Framework for Participatory Sensing Data Collections

Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.
The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations

In order for neural networks to learn complex languages or grammars, they must have sufficient computational power or resources to recognize or generate such languages. Though many approaches have been discussed, one obvious approach to enhancing the processing power of a recurrent neural network is to couple it with an external stack memory in effect creating a neural network pushdown automata (NNPDA). This paper discusses in detail this NNPDA its construction, how it can be trained and how useful symbolic information can be extracted from the trained network. In order to couple the external stack to the neural network, an optimization method is developed which uses an error function that connects the learning of the state automaton of the neural network to the learning of the operation of the external stack. To minimize the error function using gradient descent learning, an analog stack is designed such that the action and storage of information in the stack are continuous. One interpretation of a continuous stack is the probabilistic storage of and action on data. After training on sample strings of an unknown source grammar, a quantization procedure extracts from the analog stack and neural network a discrete pushdown automata (PDA). Simulations show that in learning deterministic context-free grammars the balanced parenthesis language, 1 n0n, and the deterministic Palindrome the extracted PDA is correct in the sense that it can correctly recognize unseen strings of arbitrary length. In addition, the extracted PDAs can be shown to be identical or equivalent to the PDAs of the source grammars which were used to generate the training strings.
A Survey of Mobile Robots for Distribution Power Line Inspection

The purpose of this paper is to present the most important achievements in the field of distribution power line inspection by mobile robots. Stimulated by the need for fast, accurate, safe and low-cost power line inspection, which would increase the quality of power delivery, the field of automated power line inspection has witnessed rapid development over the last decade. This paper addresses automated helicopter inspection, inspection with flying robots and inspection with climbing robots. The first attempts to automate power line inspection were conducted in the field of helicopter inspection. In recent years, however, the research was mostly focused on flying and climbing robots. These two types of robots for automated power line inspection are critically assessed according to four important characteristics: design requirements, inspection quality, autonomy and universality of inspection. Besides, some general not yet identified problems and tasks of inspection robots, which should be addressed in the future, are presented. In conclusion, the two robot types have specific benefits and drawbacks so that none can currently be considered generally advantageous.
Likely to stop? Predicting Stopout in Massive Open Online Courses

Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and nontemporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-ofthe-art machine learning techniques and the analysis of feature importance by examining over 70000 models. We found that stop out prediction is a tractable problem. Our models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks’ data, the models attained AUCs of 0.7.
A Half-Sized Post-Wall Short-Slot Directional Coupler with Hollow Rectangular Holes in a Dielectric Substrate

The authors realize a 50% length reduction of short-slot couplers in a post-wall dielectric substrate by two techniques. One is to introduce hollow rectangular holes near the side walls of the coupled region. The difference of phase constant between the TE10 and TE20 propagating modes increases and the required length to realize a desired dividing ratio is reduced. Another is to remove two reflection-suppressing posts in the coupled region. The length of the coupled region is determined to cancel the reflections at both ends of the coupled region. The total length of a 4-way Butler matrix can be reduced to 48% in comparison with the conventional one and the couplers still maintain good dividing characteristics; the dividing ratio of the hybrid is less than 0.1 dB and the isolations of the couplers are more than 20 dB. key words: short-slot coupler, length reduction, Butler matrix, post-wall waveguide, dielectric substrate, rectangular hole