nils-herrmann/clean_scirepeval huggingface dataset:
A Low-Cost GPS / Inertial Attitude Heading Reference System ( AHRS ) for General Aviation Applications

An inexpensive Attitude Heading Reference System (AHRS) for General Aviation applications is developed by fusing low cost ($20-$1000) automotive grade inertial sensors with GPS. The inertial sensor suit consists of three orthogonally mounted solid state rate gyros. GPS is used for attitude determination in a triple antenna ultra short baseline configuration. Acomplemen-tary filter is used to combine the information from the inertial sensors with the attitude information derived from GPS. The inertial sensors provide attitude information at a sufficiently high bandwidth to drive an inexpensive glass-cockpit type display for pilot-in-the-loop control. The low bandwidth GPS attitude is used to calibrate the rate gyro biases on-line. In a series of ground and flight tests, it was shown that the system has an accuracy better than 0.2 degrees in yaw, pitch and roll. Data collected during laboratory testing is used to construct error models for the inertial sensors. Analysis based on these models shows that the system can coast through momentary GPS outages lasting 2 minutes with attitude errors less than 6 degrees. Actual performance observed during ground and flight tests with GPS off was found to be substantially better than that predicted by manufacturer supplied specification sheets. Based on this, it is concluded that off-line calibration combined with GPS based in-flight calibration can dramatically improve the performance of inexpensive automotive grade inertial sensors. Data collected from flight tests indicate that some of the automotive grade iner-tial sensors (180 deg/hr) can perform near the low end of tactical grade (10 deg/hr) sensors for short periods of time after being calibrated on-line by GPS.
A Novel Low-Cost, Large Curvature Bend Sensor Based on a Bowden-Cable

Bend sensors have been developed based on conductive ink, optical fiber, and electronic textiles. Each type has advantages and disadvantages in terms of performance, ease of use, and cost. This study proposes a new and low-cost bend sensor that can measure a wide range of accumulated bend angles with large curvatures. This bend sensor utilizes a Bowden-cable, which consists of a coil sheath and an inner wire. Displacement changes of the Bowden-cable's inner wire, when the shape of the sheath changes, have been considered to be a position error in previous studies. However, this study takes advantage of this position error to detect the bend angle of the sheath. The bend angle of the sensor can be calculated from the displacement measurement of the sensing wire using a Hall-effect sensor or a potentiometer. Simulations and experiments have shown that the accumulated bend angle of the sensor is linearly related to the sensor signal, with an R-square value up to 0.9969 and a root mean square error of 2% of the full sensing range. The proposed sensor is not affected by a bend curvature of up to 80.0 m(-1), unlike previous bend sensors. The proposed sensor is expected to be useful for various applications, including motion capture devices, wearable robots, surgical devices, or generally any device that requires an affordable and low-cost bend sensor.
Mobile apps for science learning: Review of research

This review examined articles on mobile apps for science learning published from 2007 to 2014. A qualitative content analysis was used to investigate the science mobile app research for its mobile app design, underlying theoretical foundations, and students' measured outcomes. This review found that mobile apps for science learning offered a number of similar design features, including technology-based scaffolding, location-aware functionality, visual/audio representations, digital knowledge-construction tools, digital knowledge-sharing mechanisms, and differentiated roles. Many of the studies cited a specific theoretical foundation, predominantly situated learning theory, and applied this to the design of the mobile learning environment. The most common measured outcome was students' basic scientific knowledge or conceptual understanding. A number of recommendations came out of this review. Future studies need to make use of newer, available technologies; isolate the testing of specific app features; and develop additional strategies around using mobile apps for collaboration. Researchers need to make more explicit connections between the instructional principles and the design features of their mobile learning environment in order to better integrate theory with practice. In addition, this review noted that stronger alignment is needed between the underlying theories and measured outcomes, and more studies are needed to assess students' higher-level cognitive outcomes, cognitive load, and skill-based outcomes such as problem solving. Finally, more research is needed on how science mobile apps can be used with more varied science topics and diverse audiences.
Nonspeech oral motor treatment issues related to children with developmental speech sound disorders.

This article examines nonspeech oral motor treatments (NSOMTs) in the population of clients with developmental speech sound disorders. NSOMTs are a collection of nonspeech methods and procedures that claim to influence tongue, lip, and jaw resting postures; increase strength; improve muscle tone; facilitate range of motion; and develop muscle control. In the case of developmental speech sound disorders, NSOMTs are employed before or simultaneous with actual speech production treatment.



First, NSOMTs are defined for the reader, and there is a discussion of NSOMTs under the categories of active muscle exercise, passive muscle exercise, and sensory stimulation. Second, different theories underlying NSOMTs along with the implications of the theories are discussed. Finally, a review of pertinent investigations is presented.



The application of NSOMTs is questionable due to a number of reservations that include (a) the implied cause of developmental speech sound disorders, (b) neurophysiologic differences between the limbs and oral musculature, (c) the development of new theories of movement and movement control, and (d) the paucity of research literature concerning NSOMTs.



There is no substantive evidence to support NSOMTs as interventions for children with developmental speech sound disorders.
Internet of things capability and alliance: Entrepreneurial orientation, market orientation and product and process innovation

Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation Xiaoyu Yu Bang Nguyen Yi Chen Article information: To cite this document: Xiaoyu Yu Bang Nguyen Yi Chen , (2016),"Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation", Internet Research, Vol. 26 Iss 2 pp. Permanent link to this document:
Building cubes with MapReduce

In the last years, the problems of using generic storage techniques for very specific applications has been detected and outlined. Thus, some alternatives to relational DBMSs (e.g., BigTable) are blooming. On the other hand, cloud computing is already a reality that helps to save money by eliminating the hardware as well as software fixed costs and just pay per use. Indeed, specific software tools to exploit a cloud are also here. The trend in this case is toward using tools based on the MapReduce paradigm developed by Google. In this paper, we explore the possibility of having data in a cloud by using BigTable to store the corporate historical data and MapReduce as an agile mechanism to deploy cubes in ad-hoc Data Marts. Our main contribution is the comparison of three different approaches to retrieve data cubes from BigTable by means of MapReduce and the definition of criteria to choose among them.
Exploiting packet-sampling measurements for traffic characterization and classification

The use of packet sampling for traffic measurement has become mandatory for network operators to cope with the huge amount of data transmitted in nowadays net works, powered by increasingly faster transmission technologies. Therefore, many networking ta sks must already deal with such reduced data, more available but less rich in information. In this work we a ssess the impact of packet sampling on various network monitoring activities, with a particular f ocus on traffic characterization and classification. We process an extremely heterogeneous dataset composed of f our packet level traces (representative of different access technologies and operational environmen ts) with a traffic monitor able to apply different sampling policies and rates to the traffic and extract severa l features both in aggregated and per-flow fashion, providing empirical evidences of the impact of packet sampl ing on both traffic measurement and traffic classification. First, we analyze feature distortion, quan tified by means of two statistical metrics: most features appear already deteriorated under low sampling st ep, no matter the sampling policy, while only a few remain consistent under harsh sampling conditions, which m ay even cause some artifacts undermining the correctness of measurements. Second, we evaluate the perfo rmance of traffic classification under sampling. The information content of features, even though deteriora ted, still allows a good classification accuracy, provided that the classifier is trained with data obtained at the same sampling rate of the target data. The accuracy is also due to a thoughtful choice of a smart samplin g olicy which biases the sampling towards packets carrying the most useful information.
Demand Response as a Load Shaping Tool in an Intelligent Grid With Electric Vehicles

As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration will cause overload conditions at the distribution transformer. This paper focuses on the impacts of charging EVs on residential distribution networks including the transformer. The cost to accommodate a large-scale EV penetration by upgrading distribution transformers can be prohibitive. To alleviate the potential new load peaks with minimal infrastructure investments, a demand response strategy is proposed as a load shaping tool that allows improvement in distribution transformer usage. With the proposed strategy, consumers' preferences, load priorities, and privacy can be taken into account.
Control of cascaded H-bridge multilevel inverter with individual MPPT for grid-connected photovoltaic generators

A single-phase cascaded H-bridge multilevel inverter for a grid-connected photovoltaic (PV) system with nonactive power compensation is presented in this paper. To maximize the solar energy extraction of each PV string, an individual maximum power point tracking (MPPT) control scheme is applied, which allows the independent control of each dc-link voltage. A generalized nonactive power theory is applied to generate the nonactive current reference. Within the inverter's capability, the local consumption of nonactive power is provided to realize power factor correction. A single-phase modular cascaded multilevel inverter prototype has been built. Each H-bridge is connected to a 195 W solar panel. Simulation and experimental results are presented to validate the proposed ideas.
Vehicle detection with sub-class training using R-CNN for the UA-DETRAC benchmark

Different types of vehicles, such as buses and cars, can be quite different in shapes and details. This makes it more difficult to try to learn a single feature vector that can detect all types of vehicles using a single object class. We proposed an approach to perform vehicle detection with Sub-Classes categories learning using R-CNN in order to improve the performance of vehicle detection. Instead of using a single object class, which is a vehicle in this experiment, to train on the R-CNN, we used multiple sub-classes of vehicles so that the network can better learn the features of each individual type. In the experiment, we also evaluated the result of using a transfer learning approach to use a pre-trained weights on a new dataset.
The neurophysiology of unmyelinated tactile afferents

CT (C tactile) afferents are a distinct type of unmyelinated, low-threshold mechanoreceptive units existing in the hairy but not glabrous skin of humans and other mammals. Evidence from patients lacking myelinated tactile afferents indicates that signaling in these fibers activate the insular cortex. Since this system is poor in encoding discriminative aspects of touch, but well-suited to encoding slow, gentle touch, CT fibers in hairy skin may be part of a system for processing pleasant and socially relevant aspects of touch. CT fiber activation may also have a role in pain inhibition. This review outlines the growing evidence for unique properties and pathways of CT afferents.
DeepID3: Face Recognition with Very Deep Neural Networks

The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net [10] and GoogLeNet  to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.
A SiC-based isolated DC/DC converter for high density data center applications

Data centers are increasing in number and size at astounding rates, while operational cost, thermal management, size, and performance continue to be the driving metrics for the power subsystems in the associated computing equipment. This paper presents a SiC-based phase-shifted full bridge (PSFB) converter designed for 10kW output power and targeted at data center applications. The design approach focused on tuning of the converter efficiency and minimizing the thermal management system, resulting in a high-density converter. A unique thermal management system has also been employed, resulting in both increased power density and better thermal management. In this paper, the implementation of this converter is described in detail, along with empirical results, both electrical and thermal.
Airport Taxi Scheduling Optimization Based on Genetic Algorithm

The increasing development of civil aviation transportation has caused serious congestion, which often results in low efficiency and potential risks of airport surface traffic system. In this paper, a new genetic encoding method different from previous ones is presented to solve the aircraft safe taxiing problems based on an optimization model with three types of conflict restrictions. The simulations are made by using the realistic data of the Chengdu Shuangliu airport and an illustrative example is given to show the validity of our approach by comparison with shortest path algorithm without safety constraint condition.
Automatic Acquisition of Linguistic Patterns for Conceptual Modeling

Conceptual modeling aims to capture the knowledge of a domain as perceived by the modeler and represent it in a way that will help further system analysis and design. Object-oriented methodology has been taken as a common practice for software system analysis and design because of its obvious advantages of handling the “software crisis” (Booch, 1994). Object-oriented analysis (OOA) is “a method of analysis that examines requirements from the perspectives of the classes and objects” (Booch, 1994). Object-oriented design (OOD) is a method of design “encompassing the process of OO decomposition and a notation for depicting both logical and physical as well as static and dynamic models of the system” (Booch, 1994).
Model-based clustering of high-dimensional data: A review

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional spaces. This is mainly due to the fact that model-based clustering methods are dramatically over-parametrized in this case. However, high-dimensional spaces have specific characteristics which are useful for clustering and recent techniques exploit those characteristics. After having recalled the bases of model-based clustering, this article will review dimension reduction approaches, regularization-based techniques, parsimonious modeling, subspace clustering methods and clustering methods based on variable selection. Existing softwares for model-based clustering of high-dimensional data will be also reviewed and their practical use will be illustrated on real-world data sets.
Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: (1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and (2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.
User Acceptance of Footfall Analytics with Aggregated and Anonymized Mobile Phone Data

Monitoring and analyzing pedestrian traffic in and around retail stores has become an important tool for discovering underutilized operational and marketing opportunities of retail localities. Since a large proportion of pedestrians nowadays carry mobile phones, visual observation methods of the past could give way to cell-tower and WiFi based capture of passers-by, optionally augmented by aggregated or anonymized demographic data about them coming from their service providers. A major mobile phone operator recently announced the introduction of such a service in Germany, the U.K. and Brazil, but had to cancel its plans for Germany since the revelation caused nationwide privacy uproar. We conducted an exploratory interview study to gauge whether and under what conditions German consumers would accept if their mobile phone providers disclosed their personal data to retail stores they walk by, in aggregated and anonymized individual form. Virtually all respondents wanted their data to remain private at an extent that goes considerably beyond the protections afforded by current privacy laws. Nearly everyone however also indicated an interest in financial incentives in return for their consent to the transfer of their data, and many of them at seemingly very reasonable terms.
Managing elasticity across multiple cloud providers

In the context of cloud computing, elasticity is the capacity to scale computing resources up and down easily. Currently, most Platforms as a Service (PaaS) manage application elasticity within a single cloud provider. However, the not so infrequent issue of cloud outages has become a concern that hinders the availability of cloud-based applications. The most promising solutions to this issue are those based on the federation of multiple clouds. In this paper, we present a Multi-Cloud-PaaS architecture. We show how this architecture can be used for managing elasticity across multiple cloud providers.
PHP FRAMEWORK FOR DATABASE MANAGEMENT BASED ON MVC PATTERN

PHP is a powerful language to develop dynamic and interactive web applications. One of the defining features of PHP is the ease for developers to connect and manipulate a database. PHP prepares the functions for database manipulation. However, database management is done by the Structure Query Language (SQL). Most novice programmers often have trouble with SQL syntax. In this paper, we present the PHP framework for database management based on the MVC pattern. The MVC pattern is very useful for the architecture of web applications, separating the model, view and controller of a web application. The PHP framework encapsulated, common database operations are INSERT, UPDATE, DELETE and SELECT. Developers will not be required to consider the specific SQL statement syntax, just to call it the method in the model module. In addition, we use White-Box testing for the code verification in the model module. Lastly, a web application example is shown to illustrate the process of the PHP framework.
Absence of nasal bone in fetuses with trisomy 21 at second trimester of pregnancy

To investigate the value of second-trimester ultrasound assessment of fetal nasal bone (NB) in predicting trisomy 21.  A retrospective analysis was performed on the test results of pregnant women who received amniocentesis besides their anomaly scan due to various reasons at the Prenatal Diagnosis Center of Fudan University Obstetrics & Gynecology Hospital from January 2012 to January 2016. The ultrasound screening and chromosome screening results of fetuses with absent NBs were recorded.  The rate of NB absence in the fetus group with trisomy 21 was 8.43%, significantly higher than the rate of the euploid fetus group, 0.36% (P < 0.05). The rate of trisomy 21 in the fetus group with non-isolated NB absence was 87.5% (14/16), far higher than the rate of the group with isolated NB absence, 4.5% (1/22) (P < 0.05).  Second-trimester ultrasound screening for absence of the fetal NB, especially for non-isolated NB absence is useful in detecting fetuses with trisomy 21.
AVFI: Fault Injection for Autonomous Vehicles

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.