nils-herrmann/clean_scirepeval huggingface dataset:
Giant anisotropic magnetoresistance in a quantum anomalous Hall insulator

When a three-dimensional ferromagnetic topological insulator thin film is magnetized out-of-plane, conduction ideally occurs through dissipationless, one-dimensional (1D) chiral states that are characterized by a quantized, zero-field Hall conductance. The recent realization of this phenomenon, the quantum anomalous Hall effect, provides a conceptually new platform for studies of 1D transport, distinct from the traditionally studied quantum Hall effects that arise from Landau level formation. An important question arises in this context: how do these 1D edge states evolve as the magnetization is changed from out-of-plane to in-plane? We examine this question by studying the field-tilt-driven crossover from predominantly edge-state transport to diffusive transport in Crx(Bi,Sb)(2-x)Te3 thin films. This crossover manifests itself in a giant, electrically tunable anisotropic magnetoresistance that we explain by employing a Landauer-Büttiker formalism. Our methodology provides a powerful means of quantifying dissipative effects in temperature and chemical potential regimes far from perfect quantization.
T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction

Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in single scale, fail to capture the diverse two-dimensional patterns of trajectories in different spatial scales. In this paper, we propose T-CONV which models trajectories as two-dimensional images, and adopts multi-layer convolutional neural networks to combine multi-scale trajectory patterns to achieve precise prediction. Furthermore, we conduct gradient analysis to visualize the multi-scale spatial patterns captured by T-CONV and extract the areas with distinct influence on the ultimate prediction. Finally, we integrate multiple local enhancement convolutional fields to explore these important areas deeply for better prediction. Comprehensive experiments based on real trajectory data show that T-CONV can achieve higher accuracy than the state-of-the-art methods.
Anticipation and next action forecasting in video: an end-to-end model with memory

Action anticipation and forecasting in videos do not require a hat-trick, as far as there are signs in the context to foresee how actions are going to be deployed. Capturing these signs is hard because the context includes the past. We propose an end-to-end network for action anticipation and forecasting with memory, to both anticipate the current action and foresee the next one. Experiments on action sequence datasets show excellent results indicating that training on histories with a dynamic memory can significantly improve forecasting performance.
ZMP analysis for arm/leg coordination

This paper newly considers the ZMP(Zero Moment Point) of a humanoid robot under arm/leg coordination. By considering the infinitesimal displacement and the moment acting on the convex hull of the supporting points, we show that our method for determining the region of ZMP can be applicable to several cases of the arm/leg coordination tasks. We first express two kinds of ZMPs for such coordination tasks, i.e., the conventional ZMP, and the “Generalized Zero Moment Point (GZMP)” which is a generalization of the ZMP to the arm/leg coordination tasks. By projecting the edges of the convex hull of the supporting points onto the floor, we show that the position and the region of the GZMP for keeping the dynamical balance can be uniquely obtained. The effectiveness of the proposed method is shown by simulation results(see video).
M2: A Language for Mapping Spreadsheets to OWL

In this paper, we describe a mapping language for converting data contained in arbitrary spreadsheets into the Web Ontology Language (OWL). The developed language overcomes shortcomings with existing spreadsheet mapping techniques, including their restriction to well-formed spreadsheets reminiscent of a single relational database table and verbose syntax for expressing mapping rules when transforming spreadsheet contents into OWL. We additionally present an implementation of the mapping approach, Mapping Master, which is available as a plug-in for the Protégé ontology editor.
Data Splitting

In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out crossvalidation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type.
Task-oriented Dialogue System for Automatic Diagnosis

In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients’ self-reports and conversational data between patients and doctors. Then we propose a taskoriented dialogue system framework to make the diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.
Effects of soaking on yield and quality of agarwood oil.

nor fazila k & ku Halim kH. 2012. Effects of soaking on yield and quality of agarwood oil. The aims of this study were to investigate vaporisation temperature of agarwood oil, determine enlargement of wood pore size, analyse chemical components in soaking solvents and examine the chemical composition of agarwood oil extracted from soaked and unsoaked agarwood. Agarwood chips were soaked in two different acids, namely, sulphuric and lactic acids for 168 hours at room temperature (25 °C). Effects of soaking were determined using thermogravimetric analysis (TGA), scanning electron microscope (SEM) and gas chromatography-mass spectrum analysis. With regard to TGA curve, a small portion of weight loss was observed between 110 and 200 °C for agarwood soaked in lactic acid. SEM micrograph showed that the lactic acid-soaked agarwood demonstrated larger pore size. High quality agarwood oil was obtained from soaked agarwood. In conclusion, agarwood soaked in lactic acid with concentration of 0.1 M had the potential to reduce the vaporisation temperature of agarwood oil and enlarge the pore size of wood, hence, improving the yield and quality of agarwood oil.
Optimizing Failure Prediction to Maximize Availability

Availability of autonomous systems can be enhanced with self-monitoring and fault-tolerance methods based on failures prediction. With each correct prediction, proactive actions may be taken to prevent or to mitigate a failure. On the other hand, incorrect predictions will introduce additional downtime associated with the overhead of a proactive action that may decrease availability. The total effect on availability will depend on the quality of prediction (measured with precision and recall), the overhead of proactive actions (penalty), and the benefit of proactive actions when prediction is correct (reward). In this paper, we quantify the impact of failure prediction and proactive actions on steady-state availability. Furthermore, we provide guidelines for optimizing failure prediction to maximize availability by selecting a proper precision and recall trade-off with respect to penalty and reward. A case study to demonstrate the approach is also presented.
Model-Based API Testing of Apache ZooKeeper

Apache ZooKeeper is a distributed data storage that is highly concurrent and asynchronous due to network communication, testing such a system is very challenging. Our solution using the tool "Modbat" generates test cases for concurrent client sessions, and processes results from synchronous and asynchronous callbacks. We use an embedded model checker to compute the test oracle for non-deterministic outcomes, the oracle model evolves dynamically with each new test step. Our work has detected multiple previously unknown defects in ZooKeeper. Finally, a thorough coverage evaluation of the core classes show how code and branch coverage strongly relate to feature coverage in the model, and hence modeling effort.
Exploiting Functional Decomposition for Efficient Parallel Processing of Multiple Data Analysis Queries

Reuse is a powerful method for improving system performance. In this paper, we examine functional decomposition for improving data reuse, and therefore overall query execution performance, in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated during the execution of a previous query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using the techniques presented in the paper.
Designing gaze-supported multimodal interactions for the exploration of large image collections

While eye tracking is becoming more and more relevant as a promising input channel, diverse applications using gaze control in a more natural way are still rather limited. Though several researchers have indicated the particular high potential of gaze-based interaction for pointing tasks, often gaze-only approaches are investigated. However, time-consuming dwell-time activations limit this potential. To overcome this, we present a gaze-supported fisheye lens in combination with (1) a keyboard and (2) and a tilt-sensitive mobile multi-touch device. In a user-centered design approach, we elicited how users would use the aforementioned input combinations. Based on the received feedback we designed a prototype system for the interaction with a remote display using gaze and a touch-and-tilt device. This eliminates gaze dwell-time activations and the well-known Midas Touch problem (unintentionally issuing an action via gaze). A formative user study testing our prototype provided further insights into how well the elaborated gaze-supported interaction techniques were experienced by users.
Measurement of Hyoid and Laryngeal Displacement in Video Fluoroscopic Swallowing Studies: Variability, Reliability, and Measurement Error

Video fluoroscopy is commonly used in the study of swallowing kinematics. However, various procedures used in linear measurements obtained from video fluoroscopy may contribute to increased variability or measurement error. This study evaluated the influence of calibration referent and image rotation on measurement variability for hyoid and laryngeal displacement during swallowing. Inter- and intrarater reliabilities were also estimated for hyoid and laryngeal displacement measurements across conditions. The use of different calibration referents did not contribute significantly to variability in measures of hyoid and laryngeal displacement but image rotation affected horizontal measures for both structures. Inter- and intrarater reliabilities were high. Using the 95% confidence interval as the error index, measurement error was estimated to range from 2.48 to 3.06 mm. These results address procedural decisions for measuring hyoid and laryngeal displacement in video fluoroscopic swallowing studies.
Symmetry and Human Facial Attractiveness

Symmetry may act as a marker of phenotypic and genetic quality and is preferred during mate selection in a variety of species. Measures of human body symmetry correlate with attractiveness, but studies manipulating human face images report a preference for asymmetry. These results may reflect unnatural feature shapes and changes in skin textures introduced by image processing. When the shape of facial features is varied (with skin textures held constant), increasing symmetry of face shape increases ratings of attractiveness for both male and female faces. These findings imply facial symmetry may have a positive impact on mate selection in humans.
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.

It has recently been shown in a brain-computer interface experiment that motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters. In particular, it was shown that the three-dimensional tuning curves of neurons whose decoding parameters were reassigned changed more than those of neurons whose decoding parameters had not been reassigned. In this article, we propose a simple learning rule that can reproduce this effect. Our learning rule uses Hebbian weight updates driven by a global reward signal and neuronal noise. In contrast to most previously proposed learning rules, this approach does not require extrinsic information to separate noise from signal. The learning rule is able to optimize the performance of a model system within biologically realistic periods of time under high noise levels. Furthermore, when the model parameters are matched to data recorded during the brain-computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments.
Analysis of a Very Large Web Search Engine Query Log

In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.
Power Efficient Scattered Pilot Channel Estimation for FBMC/OQAM

Filter bank multicarrier transmission with offsetQAM (FBMC/OQAM) is a promising candidate waveform for the next mobile communication systems as it is well suited for many new scenarios and challenges like improved spectral efficiency, spectrum sharing approaches or high mobility scenarios. It does not require a cyclic prefix (CP) leading to a higher spectral efficiency than orthogonal frequency division multiplexing with CP (CP-OFDM) and the flexibility of the transmit and receive filters enables higher throughput in spectral sharing and high mobility scenarios. One aspect to be considered is efficient channel estimation that is needed in order to realize these gains. As the classical channel estimation used for CP-OFDM cannot be applied directly to FBMC/OQAM, new competitive solutions are needed. One promising solution for the pilot design suited for FBMC introduces an auxiliary pilot (precoding symbol) that nullifies the intrinsic interference at the pilot position, but this leads to increased power on these auxiliary pilots. In this paper, a new non-linear scattered pilot design and channel estimation technique is proposed that addresses the problem of increased power of the auxiliary pilot. In addition to the reduced transmit power, we also observe that the receive power at the pilot position is increased with this new proposal which leads to an improved channel estimation performance.
Delay Tolerant Networking-A Tutorial

Delay Tolerant Networking has been a hot topic of interest in networking since the start of the century, and has sparked a significant amount of research in the area, particularly in an age where the ultimate goal is to provide ubiquitous connectivity, even in regions previously considered inaccessible. Protocols and applications in popular use on the Internet are not readily applicable to such networks, that are characterized by long delays and inconsistent connectivity. In this paper, we summarize the wealth of literature in this field in the form of a concise, but comprehensive tutorial. The paper is designed to bring researchers new to the field with a general picture of the state of the art in this area, and motivate them to begin exploring problems in the field quickly.
Stars and Misfits: Self-Employment and Labor Market Frictions

Recent evidence has shown that entrants into self-employment are disproportionately drawn from the tails of the earnings and ability distributions. This observation is explained by a multi-task model of occupational choice in which frictions in the labor market induces mismatches between firms and workers, and mis-assignment of workers to tasks. The model also yields distinctive predictions relating prior work histories to earnings and to the probability of entry into self-employment. These predictions are tested with the Korean Labor and Income Panel Study, from which we find considerable support for the model.
Systematic Review of Self-Report Family Assessment Measures.

A systematic review of self-report family assessment measures was conducted with reference to their psychometric properties, clinical utility and theoretical underpinnings. Eight instruments were reviewed: The McMaster Family Assessment Device (FAD); Circumplex Model Family Adaptability and Cohesion Evaluation Scales (FACES); Beavers Systems Model Self-Report Family Inventory (SFI); Family Assessment Measure III (FAM III); Family Environment Scale (FES); Family Relations Scale (FRS); and Systemic Therapy Inventory of Change (STIC); and the Systemic Clinical Outcome Routine Evaluation (SCORE). Results indicated that five family assessment measures are suitable for clinical use (FAD, FACES-IV, SFI, FAM III, SCORE), two are not (FES, FRS), and one is a new system currently under-going validation (STIC).
A new prismatic series elastic actuator with compact size and high performance

This paper discusses design and control of a prismatic series elastic actuator with high mechanical power output in a small and lightweight form factor. We introduce a design that pushes the performance boundary of electric series elastic actuators by using high motor voltage coupled with an efficient drivetrain to enable large continuous actuator force while retaining speed. Compact size is achieved through the use of a novel piston-style ball screw support mechanism and a concentrically placed compliant element. We develop controllers for force and position tracking based on combinations of PID, model-based, and disturbance observer control structures. Finally, we demonstrate our actuator's performance with a series of experiments designed to operate the actuator at the limits of its mechanical and control capability.
Regulating pesticide risk reduction : the practice and dynamics of legal pluralism

Synthetic pesticides have become widely used in the past century to combat agricultural pests and diseases, and hence to achieve higher yields. However, their inherently toxic properties are also the reason that pesticides may cause harm to the environment and human health during the different stages of their life-cycle, that in its simplest form consists of the stages of production, marketing, use, and residues. In the production stage, site-specific emissions to the environment may occur as the result of production processes of pesticide substances and products, and workers in pesticide plants and people living in the neighbourhood of such production facilities may be at risk of exposure to hazardous emissions and industrial accidents.
Model-based ego-motion and vehicle parameter estimation using visual odometry

Ego-motion estimation based on images from a stereo camera has become a common function for autonomous mobile systems and is gaining increasing importance in the automotive sector. Unlike general robotic platforms, vehicles have a suspension adding degrees of freedom and thus complexity to their dynamics model. Some parameters of the model, such as the vehicle mass, are non-static as they depend on e.g. the specific load conditions and thus need to be estimated online to guarantee a concise and safe autonomous maneuvering of the vehicle. In this paper, a novel visual odometry based approach to simultaneously estimate ego-motion and selected vehicle parameters using a dual Ensemble Kalman Filter and a non-linear single-track model with pitch dynamics is presented. The algorithm has been validated using simulated data and showed a good performance for both the estimation of the ego-motion and of the relevant vehicle parameters.
On the Road to Benchmarking BPMN 2.0 Workflow Engines

Workflow Management Systems (WfMSs) provide platforms for delivering complex service-oriented applications that need to satisfy enterprise-grade quality of service requirements such as dependability and scalability. In this paper we focus on the case of benchmarking the performance of the core of WfMSs, Workflow Engines, that are compliant with the Business Process Model and Notation 2.0 (BPMN 2.0) standard. We first explore the main challenges that need to be met when designing such a benchmark and describe the approaches we designed for tackling them in the BenchFlow project. We discuss our approach to distill the essence of real-world processes to create from it processes for the benchmark, and to ensure that the benchmark finds wide applicability.
Generating Multilingual Personalized Descriptions from OWL Ontologies on the Semantic Web : the NaturalOWL System ∗

We present Naturalowl, an open-source multilingual natural language generation system that produces personalized descriptions of individuals and classes, starting from a linguistically annotated ontology. The generator is heavily based on ideas from ilex and m-piro, but it is in many ways simpler and it provides native support for owl dl ontologies, which we annotate with linguistic and user modelling resources in rdf. Naturalowl is written in Java and it is supported by m-piro’s authoring tool, as well as an alternative authoring plug-in for the Protégé ontology editor.
Building Context-Aware Recommendation Systems: A Software Engineering Point of View

Most publications of Context-Aware Recommender Systems (CARS) do not follow a Software Engineering process while building their systems, rather they focus on proposing and testing algorithms. We believe that CARS can be built following a structured development process, therefore this paper reviews Context-Aware Recommender System literature, identify the performed activities, and map such activities to the ISO/IEC 29110 Software Implementation activities. The results shows that even when no publication follows all the activities of the 29110 standard, all together, the publications cover most of the proposed activities of the ISO/IEC 29110 standard.