nils-herrmann/clean_scirepeval huggingface dataset:
Anytime navigation with Progressive Hindsight optimization

In multi-robot systems, efficiently navigating in a a partially-known environment is an ubiquitous but challenging task, as each robot must account for the uncertainty introduced, for example, by other moving robots. This uncertainty makes pre-computed plans not always applicable, and often hinders the desired efficient use of the robot's resources. In this work, we present a local anytime approach for robot motion planning that accounts for the uncertainty of the environment by generating `snapshots' of possible future scenarios. Our approach adapts the Hindsight optimization technique to allow robots to plan their immediate motion based on long-term efficiency. We validate our approach by comparing the efficiency on the paths executed against a state-of-the art navigation technique in a variety of scenarios, and show that by accounting for the uncertainty in the environment, agents can improve their time- and energy-efficient motions.
Privacy in e-commerce: examining user scenarios and privacy preferences

Privacy is a necessary concern in electronic commerce. It is difficult, if not impossible, to complete a transaction without revealing some personal data – a shipping address, billing information, or product preference. Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened. Fortunately, there are technologies to help users protect their privacy. P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology. However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies. Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy. This paper presents both the findings from that study as well as their design implications.
Configuration Tool for ARINC 653 Operating Systems Eu

ARINC 653 Specification defines a standardized interface of real-time operating systems and an Application Executive (APEX) to develop the reliable applications for avionics based on Integrated Modular Avionics (IMA). The requirements of system platform based on ARINC 653 Standard are defined as configuration data and are integrated to the XML configuration file(s) in the real-time operating system. Unfortunately, existing configuration tools for integrating requirements do not provide checking the syntax errors of XML and verifying the integrity of input data for partitioning. This paper presents a configuration tool for ARINC 653 OS that consist of Wizard module which generates the basic configuration data for IMA based on XML Scheme of ARINC 653 Standard, XML and Partition Editor for the partitioning system of IMA, and Verification module which checks the integrity of input data and XML syntax with its visualization.
Simple sequence repeat markers that identify Claviceps species and strains

Claviceps purpurea is a pathogen that infects most members of Pooideae, a subfamily of Poaceae, and causes ergot, a floral disease in which the ovary is replaced with a sclerotium. When the ergot body is accidently consumed by either man or animal in high enough quantities, there is extreme pain, limb loss and sometimes death. This study was initiated to develop simple sequence repeat (SSRs) markers for rapid identification of C. purpurea. SSRs were designed from sequence data stored at the National Center for Biotechnology Information database. The study consisted of 74 ergot isolates, from four different host species, Lolium perenne, Poa pratensis, Bromus inermis, and Secale cereale plus three additional Claviceps species, C. pusilla, C. paspali and C. fusiformis. Samples were collected from six different counties in Oregon and Washington over a 5-year period. Thirty-four SSR markers were selected, which enabled the differentiation of each isolate from one another based solely on their molecular fingerprints. Discriminant analysis of principle components was used to identify four isolate groups, CA Group 1, 2, 3, and 4, for subsequent cluster and molecular variance analyses. CA Group 1 consisting of eight isolates from the host species P. pratensis, was separated on the cluster analysis plot from the remaining three groups and this group was later identified as C. humidiphila. The other three groups were distinct from one another, but closely related. These three groups contained samples from all four of the host species. These SSRs are simple to use, reliable and allowed clear differentiation of C. humidiphila from C. purpurea. Isolates from the three separate species, C. pusilla, C. paspali and C. fusiformis, also amplified with these markers. The SSR markers developed in this study will be helpful in defining the population structure and genetics of Claviceps strains. They will also provide valuable tools for plant breeders needing to identify resistance in crops or for researchers examining fungal movements across environments.
Speech emotion recognition based on Gaussian Mixture Models and Deep Neural Networks

Recognition of speaker emotion during interaction in spoken dialog systems can enhance the user experience, and provide system operators with information valuable to ongoing assessment of interaction system performance and utility. Interaction utterances are very short, and we assume the speaker's emotion is constant throughout a given utterance. This paper investigates combinations of a GMM-based low-level feature extractor with a neural network serving as a high level feature extractor. The advantage of this system architecture is that it combines the fast developing neural network-based solutions with the classic statistical approaches applied to emotion recognition. Experiments on a Mandarin data set compare different solutions under the same or close conditions.
Gap Trap : A Pothole Detection and Reporting System Utilizing Mobile Devices

In this paper we present a new method and implementation for detecting and reporting roadway potholes. Potholes are a major problem that cost cities and motorists around the world billions of dollars per year. Gap Trap addresses the issue by automatically discovering and reporting potholes so that motorists may avoid them, and cities may quickly repair them.  Trap with similarly designed systems, and goes on to discuss the implemented prototype system that demonstrates the usefulness of this idea.
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial.
Simplified Optimal Trajectory Control (SOTC) for LLC Resonant Converters

In this paper, a simplified optimal trajectory control (SOTC) for the LLC resonant converter is proposed. During the steady state, a linear compensator such as proportional-integral or proportional-integral-derivative (PID) is used, controlling the switching frequency fs to eliminate the steady-state error. However, during load transients, the SOTC method takes over, immediately changing the pulse widths of the gate-driving signals. Using the state-plane analysis, the pulse widths are estimated, letting the state variables track the optimal trajectory locus in the minimum period of time. The proposed solution is implemented in a digital controller, and experimental results show that while the digital logic requirement is very small, the performance improvement is significant.
Estimating types in binaries using predictive modeling

Reverse engineering is an important tool in mitigating vulnerabilities in binaries. As a lot of software is developed in object-oriented languages, reverse engineering of object-oriented code is of critical importance. One of the major hurdles in reverse engineering binaries compiled from object-oriented code is the use of dynamic dispatch. In the absence of debug information, any dynamic dispatch may seem to jump to many possible targets, posing a significant challenge to a reverse engineer trying to track the program flow. We present a novel technique that allows us to statically determine the likely targets of virtual function calls. Our technique uses object tracelets – statically constructed sequences of operations performed on an object – to capture potential runtime behaviors of the object. Our analysis automatically pre-labels some of the object tracelets by relying on instances where the type of an object is known. The resulting type-labeled tracelets are then used to train a statistical language model (SLM) for each type.We then use the resulting ensemble of SLMs over unlabeled tracelets to generate a ranking of their most likely types, from which we deduce the likely targets of dynamic dispatches.We have implemented our technique and evaluated it over real-world C++ binaries. Our evaluation shows that when there are multiple alternative targets, our approach can drastically reduce the number of targets that have to be considered by a reverse engineer.
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

Electronic health records (EHRs) are only a first step in capturing and utilizing healthrelated data—the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients’ health conditions and responses to treatment over time.  We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data.  For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70% to 72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level—Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1+2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services.  This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that ‘‘learn’’ over time. & 2012 Fellowship of Postgraduate Medicine.  2211-8837/$ see front matter & 2012 Fellowship of Postgraduate Medicine.  Corresponding author at: Department of Informatics, Centerstone Research Institute, 365 South Park Ridge Road, Suite 103, Bloomington, IN 47401, United States. Tel.: +1 812 337 2302. E-mail addresses: Casey.Bennett@CenterstoneResearch.org (C.C. Bennett), Tom.Doub@CenterstoneResearch.org (T.W. Doub), RebeccaSelove@CenterstoneResearch.org (R. Selove). Health Policy and Technology (2012) 1, 105–114 Author's personal copy
Understanding the HyperLogLog : a Near-Optimal Cardinality Estimation Algorithm

The HyperLogLog algorithm (HLL) is a method to estimate the number of distinct elements in large datasets i.e. cardinality, in a single pass, and using a very small amount of memory. The HLL algorithm is an optimization of the method presented in 2003 by Durand and Flajolet in the paper LogLog Counting of Large Cardinalities. In this report we analyze the so called cornerstone of Big Data infrastructures, give a detailed description of the algorithm and explain the mathematical intuition behind it.
A tutorial on simulation modeling in six dimensions

Simulation involves modeling and analysis of real-world systems. This tutorial will provide a broad overview of the modeling practice within simulation by introducing the reader to modeling choices found using six dimensions: abstraction, complexity, culture, engineering, environment, and process. Modeling can be a daunting task even for the seasoned modeling and simulation professional, and so my goal is to introduce modeling in two ways: 1) to use one specific type of model (Petri Net) as an anchor for cross-dimensional discussion, and 2) to provide a follow up discussion, with additional non Petri Net examples, to clarify the extent of each dimension. For example, in the abstraction dimension, one must think about scale, refinement, and hierarchy when modeling regardless of the type of modeling language. The reader will come away with a broad framework within which to understand the possibilities of models and of modeling within the practice of simulation.
Capacitive Touch Systems With Styli for Touch Sensors: A Review

This paper presents the latest progress in development and applications of the capacitive touch systems (CTSs) with styli. The CTSs, which include the touch sensor, analog front-end (AFE) integrated circuit (IC), and micro-controller unit, are reviewed along with the passive and active styli. The architecture of the CTS is explained first, followed by an exploration of the touch sensors: (1) types of touch sensors, such as in-cell, on-cell, and add-on types according to the size and material, (2) AFE IC with the driving and sensing methods, and (3) passive and active styli for the CTS. Finally, the future perspectives of the CTS are given from the viewpoint of the technical developments.
ReLiDSS: Novel lie detection system from speech signal

Lying is among the most common wrong human acts that merits spending time thinking about it. The lie detection is until now posing a problem in recent research which aims to develop a non-contact application in order to estimate physiological changes. In this paper, we have proposed a preliminary investigation on which relevant acoustic parameter can be useful to classify lie or truth from speech signal. Our proposed system in is based on the Mel Frequency Cepstral Coefficient (MFCC) commonly used in automatic speech processing on our own constructed database ReLiDDB (ReGIM-Lab Lie Detection DataBase) for both cases lie detection and person voice recognition. We have performed on this database the Support Vector Machines (SVM) classifier using Linear kernel and we have obtained an accuracy of Lie and Truth detection of speech audio respectively 88.23% and 84.52%.
Disconnecting outcomes and evaluations: the role of negotiator focus.

Three experiments explored the role of negotiator focus in disconnecting negotiated outcomes and evaluations. Negotiators who focused on their target prices, the ideal outcome they could obtain, achieved objectively superior outcomes compared with negotiators who focused on their lower bound (e.g., reservation price). Those negotiators who focused on their targets, however, were less satisfied with their objectively superior outcomes. In the final experiment, when negotiators were reminded of their lower bound after the negotiation, the satisfaction of those negotiators who had focused on their target prices was increased, with outcomes and evaluations becoming connected rather than disconnected. The possible negative effects of setting high goals and the temporal dimensions of the disconnection and reconnection between outcomes and evaluations are discussed.
Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy.

To determine whether breast cancer subtype is associated with outcome after breast-conserving therapy (BCT) consisting of lumpectomy and radiation therapy.



We studied 793 consecutive patients with invasive breast cancer who received BCT from July 1998 to December 2001. Among them, 97% had pathologically negative margins of resection, and 90% received adjuvant systemic therapy. No patient received adjuvant trastuzumab. Receptor status was used to approximate subtype: estrogen receptor (ER) or progesterone receptor (PR) positive and human epidermal growth factor receptor 2 negative = luminal A; ER+ or PR+ and HER-2+ = luminal B; ER-and PR -and HER-2+ = HER-2; and ER-and PR -and HER-2-= basal. Competing risks methodology was used to analyze time to local recurrence and distant metastases.



Median follow-up was 70 months. The overall 5-year cumulative incidence of local recurrence was 1.8% (95% CI, 1.0 to 3.1); 0.8% (0.3, 2.2) for luminal A, 1.5% (0.2, 10) for luminal B, 8.4% (2.2, 30) for HER-2, and 7.1% (3.0, 16) for basal. On multivariable analysis (MVA) with luminal A as baseline, HER-2 (adjusted hazard ratio [AHR] = 9.2; 95% CI, 1.6 to 51; P = .012) and basal (AHR = 7.1; 95% CI, 1.6 to 31; P = .009) subtypes were associated with increased local recurrence. On MVA, luminal B (AHR = 2.9; 95% CI, 1.3 to 6.5; P = .007) and basal (AHR = 2.3; 95% CI, 1.1 to 5.2; P = .035) were associated with increased distant metastases.



Overall, the 5-year local recurrence rate after BCT was low, but varied by subtype as approximated using ER, PR, and HER-2 status. Local recurrence was particularly low for the luminal A subtype, but was less than 10% at 5 years for all subtypes. Although further follow-up is needed, these results may be useful in counseling patients about their anticipated outcome after BCT.
A Wideband Dual-Polarized L-Probe Stacked Patch Antenna Array

A 2 times 1 dual-polarized L-probe stacked patch antenna array is presented. It has employed a novel technique to achieve high isolation between the two input ports. The proposed antenna has a 14-dB return loss bandwidth of 19.8%, which is ranged from 0.808 to 0.986 GHz, for both ports. Also, it has an input port isolation of more than 30 dB and an average gain of 10.5 dBi over this bandwidth. Moreover, its radiation patterns in the two principal planes have cross-polarization level of less than -15 dB within the 3-dB beamwidths across the passband. Due to these features, this antenna array is highly suitable for the outdoor base station that is required to cover the operating bandwidths of both CDMA800 and GSM900 mobile communication systems.
Revealing Event Saliency in Unconstrained Video Collection

Recent progresses in multimedia event detection have enabled us to find videos about a predefined event from a large-scale video collection. Research towards more intrinsic unsupervised video understanding is an interesting but understudied field. Specifically, given a collection of videos sharing a common event of interest, the goal is to discover the salient fragments, i.e., the curt video fragments that can concisely portray the underlying event of interest, from each video. To explore this novel direction, this paper proposes an unsupervised event saliency revealing framework. It first extracts features from multiple modalities to represent each shot in the given video collection. Then, these shots are clustered to build the cluster-level event saliency revealing framework, which explores useful information cues (i.e., the intra-cluster prior, inter-cluster discriminability, and inter-cluster smoothness) by a concise optimization model. Compared with the existing methods, our approach could highlight the intrinsic stimulus of the unseen event within a video in an unsupervised fashion. Thus, it could potentially benefit to a wide range of multimedia tasks like video browsing, understanding, and search. To quantitatively verify the proposed method, we systematically compare the method to a number of baseline methods on the TRECVID benchmarks. Experimental results have demonstrated its effectiveness and efficiency.
Telework: Existing Research and Future Directions

Telework, which is defined as work performed at home or a satellite office to reduce commuting, is attracting much attention as an alternative way to organize work. Numerous studies have pointed out a variety of advantages of telework for individuals, organizations, and society. Current telework research, however, displays many weaknesses that inhibit use of this alternative as an effective vehicle to promote distributive organizational design. This study was undertaken to characterize existing telework research, improve understanding of problems and issues of telework, and guide future research directions. A review of the relevant literature and a characterization of telework were conducted from 3 different angles: the research methodology, the focus of existing telework studies, and the research paradigm. First, an overall lack of robust research methodology was found in many studies. Second, although telework is an organizational phenomenon, disproportionate attention has been given to teleworker-related personal issues. Finally, the current telework paradigm was discovered to be characterized by suitability-based planning that selects appropriate persons and tasks and by ad hoc implementation in response to local needs. We suggest that future research could be enriched with more rigorous research methodology, more balanced focus for studies, and more flexible perspectives in the research paradigm.
Modeling of magnetically biased graphene patch frequency selective surface (FSS)

A free-standing magnetically biased graphene patch frequency selective surface (FSS) is modelled in this paper. Its transmission coefficients of co- and cross-polarizations can be obtained with an equivalent tensorial surface conductivity. Then, the rotation angle for normal incidence is explored with different values of biasing magnetic field B0. The maximum rotation angle provided by the magnetically biased graphene patch FSS is 43° at 4.7 THz with B0=2.5 T, which is much larger than the rotation angle provided by a graphene sheet. This is very promising for THz nano-devices based on the giant Faraday rotation of graphenes.
A non-projective greedy dependency parser with bidirectional LSTMs

The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kiperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some ‘suffixed’ treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding ‘unsuffixed’ treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at https://github.com/CoNLLUD-2017/LyS-FASTPARSE