nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing Paxos with batching and pipelining

Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput. We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these resultswith the prediction from themodel. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latencyWANbut also inmany LAN scenarios.
Road Sign Classification without Color Information

The robust and general method for the recognition of traffic devices like road signs in traffic scene images is necessary for the creation of Driver Support System. Color may be used as a useful attribute for the decomposition of classification problem into several apriori defined road sign groups/subproblems. In this paper, the colorless method for the road sign classification is presented working on gray-level images and allowing the same problem decomposition as its color-based counterpart. The method may be used in combination with the color-independent sign detection algorithms. The road sign recognition system then works entirely without the color which may be used as an alternative procedure when the input traffic scene images lacks good color information.
Credit risk assessment with a multistage neural network ensemble learning approach

In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage. In the second stage, the different neural network models are created with different training subsets obtained from the previous stage. In the third stage, the generated neural network models are trained with different training datasets and accordingly the classification score and reliability value of neural classifier can be obtained. In the fourth stage, a decorrelation maximization algorithm is used to select the appropriate ensemble members. In the fifth stage, the reliability values of the selected neural network models (i.e., ensemble members) are scaled into a unit interval by logistic transformation. In the final stage, the selected neural network ensemble members are fused to obtain final classification result by means of reliability measurement. For illustration, two publicly available credit datasets are used to verify the effectiveness of the proposed multistage neural network ensemble model.
Technical Report on Autonomous Mobile Robot navigation

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ADMIRAL: A Data Mining Based Financial Trading System

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions
Replication and Data Placement in Distributed Key-Value Stores

MapReduce is a programming model widely used to process large amounts of data. Its its known for good scalability, ease of use, and fault-tolerance. However, it still exhibits some performance limitations when accessing the data to be processed, being far from reaching the performance of a well-configured parallel Database Management Systems (DBMSs). In this report we survey the most relevant optimisations that have been proposed for MapReduce, including indexing, data layouts, and co-location. We propose a novel system that, combining these three techniques, aims at boosting the execution time of MapReduce jobs by providing more efficient access to input data. Finally, we briefly describe how we plan to conduct the experimental evaluation of our system using
Alternative placement of bispectral index electrode for monitoring depth of anesthesia during neurosurgery.

In neurosurgery in particular, the recommended placement of electrodes for monitoring depth of anesthesia during surgery sometimes conflicts with the surgical site or patient positioning. Therefore, we proposed this study to evaluate the agreement and correlation of bispectral index values recorded from the usual frontal area and the alternate, post-auricular areas in neurosurgery patients. Thirty-four patients scheduled for neurosurgery under general anesthesia were included. Bispectral index (BIS) sensors were placed at both the frontal and post-auricular areas. The anesthesia given was clinically adjusted according to the frontal (standard) BIS reading. The BIS values and impedance were recorded;Pearson's correlation and Bland-Altman plots were analyzed. The bias±2SD for the electrode placement before, during, and post-anesthesia were 0±23.32, 1.5±10.69, and 2.1±13.52, while the limits of agreement were -23.3 to 23.3, -12.2 to 9.2, and -17.7 to 13.5, respectively. The correlation coefficient between frontal- and post-auricular-area electrodes was 0.74 with a p-value<0.001.The post-auricular placement of a BIS electrode is a practical alternative to frontal lobe placement. Nevertheless, proper electrode location is important to minimize error.
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set

We address a cross-scenario clothing retrieval problem- given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the human pose discrepancy by locating 30 human parts detected by a well trained human detector. Then, founded on part features, we propose a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos: 1) the within-scenario one-to-many similarities between a query daily photo and an extra auxiliary set are derived by direct sparse reconstruction; 2) by a cross-scenario many-to-many similarity transfer matrix inferred offline from the auxiliary set and the online shopping set, the reliable cross-scenario one-to-many similarities between the query daily photo and all online shopping photos are obtained.
Physical Limitations of Omnidirectional Antennas Physical Li£itations of Omnidirectional Antennas

The physical limitations of omnidirectional antennas are considered. With the use of the spherical wave functions to describe the field, the directivity gain G and the Q of an unspecified antenna are calculated under idealized conditions. To obtain the optimum performance, three criteria are used: (1) maximum gain for a given complexity of the antenna structure, (2) minimum Q, (3) maximum ratio of G/Q. It is found that an antenna of which the maximum dimension is 2a has the potentiality of a broad bandwidth provided that the gain is equal to or less than 4a/. To obtain a gain higher than this value, the Q of the antenna increases at an astronomical rate. The antenna which has potentially the broadest bandwidth of all omnidirectional antennas is one which has a radiation pattern corresponding to that of an infinitesimally small dipole.
(Leader/Randomization/Signature)-free Byzantine Consensus for Consortium Blockchains

This paper presents a new resilience optimal Byzantine consensus algorithm targeting consortium blockchains. To this end, it first revisits the consensus validity property by requiring that the decided value satisfies a predefined predicate, which does not systematically exclude a value proposed only by Byzantine processes, thereby generalizing the validity properties found in the literature. Then the paper presents a simple and modular Byzantine consensus algorithm that relies neither on a leader, nor on signatures, nor on randomization. It consists of a reduction of multivalued Byzantine consensus to binary Byzantine consensus satisfying this validity property. This reduction terminates after a constant-sized sequence of binary Byzantine consensus instances. The idea is to spawn concurrent instances of binary consensus but to decide only after a sequence of two of these instances. The binary consensus instances result in a bitmask that the reduction applies to a vector of multivalued proposals to filter out a valid proposed value that is decided. The paper then presents an underlying binary Byzantine consensus algorithm that assumes eventual synchrony to terminate.
Areas of Attention for Image Captioning

We propose “Areas of Attention”, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.
Balancing acts: walking the Agile tightrope

Self-organizing teams are one of the critical success factors on Agile projects - and yet, little is known about the self-organizing nature of Agile teams and the challenges they face in industrial practice. Based on a Grounded Theory study of 40 Agile practitioners across 16 software development organizations in New Zealand and India, we describe how self-organizing Agile teams perform balancing acts between (a) freedom and responsibility (b) cross-functionality and specialization, and (c) continuous learning and iteration pressure, in an effort to maintain their self-organizing nature. We discuss the relationship between these three balancing acts and the fundamental conditions of self-organizing teams - autonomy, cross-fertilization, and self-transcendence.
Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control (MiTDC)

For physical human-robot interaction (pHRI), it has been an important issue to control the output force of actuators. The aim of this study was to apply a new control strategy, named model-inverse time delay control (MiTDC), to series elastic actuators (SEA) in a lower extremity exoskeleton, even in the presence of uncertainties from pHRI. The law for time delay control (TDC) is derived and implementation issues are discussed, including the design of the state observer and the selection of the nominal value of the control distribution coefficient. Additionally, a new concept, a new reference position using the inverse of model dynamics, is introduced to realize satisfactory tracking performance without delay. Experimental results showed that the suggested controller achieved satisfactory performance without accurate information on system parameters, requiring only a nominal value of the control distribution coefficient.
A visual editor for language-independent scripting for BPMN modeling

This paper tackles a scripting problem associated with script tasks in BPMN modeling. Usually a BPMN process modeling tool provides a plain text editor without any assisting features for writing a script for a BPMN script task. This is not very convenient even for skilled programmers. There is also a limitation on exporting the script written in one language to different process modeling and execution runtimes that support other scripting languages. To provide easy-to-use and platform-independent scripting, we present a visual block programming editor for defining a visual script for a BPMN script task. The internal representation of the visual script is an XML-based business process script, or XBPScript, from which executable scripts for different target runtimes can be generated. This paper discusses, in particular, how the editor supports a complex function block that can be used in building a script. It also discusses, as an example, how to integrate the visual editor and XBPScript with open source business process management software. An evaluation of the visual editor productivity in comparison with plain text editing is presented.
Personalization in the public sector: An inventory of organizational and user obstacles towards personalization of electronic services in the public sector

The public sector more and more deploys personalized e-government services. Personalization offers great opportunities to make communication more effective and efficient, to infer and predict citizens’ behavior and to even influence behavior. However, some drawbacks must be considered. Important organizational barriers hinder the implementation of personalized e-government services and important user obstacles, such as access, trust, control, and privacy, have to be overcome to make fruitful use of those personalized e-government services. D 2006 Elsevier Inc. All rights reserved.
Free space range measurements with Semtech Lora™ technology

Although short range wireless communication explicitly targets local and very regional applications, range continues to be an extremely important issue. The range directly depends on the so called link budget, which can be increased by the choice of modulation and coding schemes. Especially, the recent transceiver generation comes with extensive and flexible support for Software Defined Radio (SDR). The SX127x family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview into the technologies to support Long Range (LoRa ™), describes the outdoor setup at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, shows detailed measurement results and discusses the strengths and weaknesses of this technology.
Switching-Type H∞ Filter Design for T - S Fuzzy Systems With Unknown or Partially Unknown Membership Functions

This paper is concerned with the H∞ filter design problem for Takagi-Sugeno (T-S) fuzzy systems with unknown or partially unknown membership functions. If the membership functions are allowed to be unknown or partially unknown, then a fuzzy system may describe a wide class of nonlinear systems. However, in this case, the filter design of fuzzy systems, which is based on parallel distributed compensator strategy, is infeasible. To tackle this difficulty, a switching mechanism, which depends on the lower and upper bounds of the unknown membership functions, is introduced to construct the H∞ filter with varying gains. Some examples given in the simulation section show that the proposed method can achieve a better disturbance attenuation performance than a simple fixed-gain filter design approach.
Development and validation of the mindful eating questionnaire.

"Mindful eating" describes a nonjudgmental awareness of physical and emotional sensations associated with eating. This article reports the development of a mindful eating questionnaire (MEQ) to support rigorous scientific inquiry into this concept. An item pool was developed based on hypothesized domains of mindful eating. A cross-sectional survey examined associations of MEQ scores with demographic and health-related characteristics. The MEQ was distributed to seven convenience samples between January and May 2007, with an overall response rate of 62% (n=303). Participants were mostly women (81%) and white (90%), and had a mean age of 42+/-14.4 years (range 18 to 80 years). Exploratory factor analysis was used to identify factors, which were defined as the mean of items scored one to four, where four indicated higher mindfulness; the mean of all factors was the summary MEQ score. Multiple regression analysis was used to measure associations of demographic characteristics, obesity, yoga practice, and physical activity with MEQ scores. Domains of the final 28-item questionnaire were: disinhibition, awareness, external cues, emotional response, and distraction. The mean MEQ score was 2.92+/-0.37, with a reliability (Chronbach's alpha) of .64. The covariate-adjusted MEQ score was inversely associated with body mass index (3.02 for body mass index <25 vs 2.54 for body mass index >30, P<0.001). Yoga practice, but neither walking nor moderate/intense physical activity, was associated with higher MEQ score. In this study sample, the MEQ had good measurement characteristics. Its negative association with body mass index and positive association with yoga provide evidence of construct validity. Further evaluation in more diverse populations is warranted.
Aggregating Machine Learning and Rule Based Heuristics for Named Entity Recognition

This paper, submitted as an entry for the NERSSEAL-2008 shared task, describes a system build for Named Entity Recognition for South and South East Asian Languages. Our paper combines machine learning techniques with language specific heuristics to model the problem of NER for Indian languages. The system has been tested on five languages: Telugu, Hindi, Bengali, Urdu and Oriya. It uses CRF (Conditional Random Fields) based machine learning, followed by post processing which involves using some heuristics or rules. The system is specifically tuned for Hindi and Telugu, we also report the results for the other four languages.
Standing mobility vehicle with passive exoskeleton assisting voluntary postural changes

This study proposes a novel personal mobility vehicle for supporting and assisting people with disabled lower limbs. The developed mobile platform is capable of assisting voluntary postural transition between standing and sitting, called the Passively Assistive Limb (PAL) mechanism, in addition to high mobility with upright posture. The device consists of gas-spring-powered passive exoskeleton for postural support and two in-wheel motors for mobility support. The developed robot system allows a user to sit down on chairs and beds, and stand up through easy operations. In addition, a user can move the system in the standing posture without using their hands. In this paper, the development and assessments of the standing mobility vehicle are described.
Automatic classification of handwritten and printed text in ICR boxes

Machine printed and handwritten texts intermixed appear in the ICR cells of variety of documents. Recognition techniques for machine printed and handwritten text in these document images are significantly different. It is necessary to separate these two types of texts and feed them to the respective engine - OCR (Optical Character Recognition) and ICR (Intelligent Character Recognition) engine to achieve optimal performance. This paper addresses the problem of classification of machine printed and handwritten text from acquired document images. Document processors can increase their productivity and classify handwritten and printed characters inside the ICR cells and feed their images to the appropriate OCR or ICR engine for better accuracy. The algorithm is tested on variety of forms and the recognition rate is calculated to be over 91%.
Emotional stability, core self-evaluations, and job outcomes: A review of the evidence and an agenda for future research

In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.
JAutomate: A Tool for System- and Acceptance-test Automation

System- and acceptance-testing are primarily performed with manual practices in current software industry. However, these practices have several issues, e.g. they are tedious, error prone and time consuming with costs up towards 40 percent of the total development cost. Automated test techniques have been proposed as a solution to mitigate these issues, but they generally approach testing from a lower level of system abstraction, leaving a gap for a flexible, high system-level test automation technique/tool. In this paper we present JAutomate, a Visual GUI Testing (VGT) tool that fills this gap by combining image recognition with record and replay functionality for high system-level test automation performed through the system under test's graphical user interface. We present the tool, its benefits compared to other similar techniques and manual testing. In addition, we compare JAutomate with two other VGT tools based on their static properties. Finally, we present the results from a survey with industrial practitioners that identifies test-related problems that industry is currently facing and discuss how JAutomate can solve or mitigate these problems.