nils-herrmann/clean_scirepeval huggingface dataset:
End to End Learning of Spiking Neural Network Based on R-STDP for a Lane Keeping Vehicle

Learning-based methods have demonstrated clear advantages in controlling robot tasks, such as the information fusion abilities, strong robustness, and high accuracy. Meanwhile, the on-board systems of robots have limited computation and energy resources, which are contradictory with state-of-the-art learning approaches. They are either too lightweight to solve complex problems or too heavyweight to be used for mobile applications. On the other hand, training spiking neural networks (SNNs) with biological plausibility has great potentials of performing fast computation and energy efficiency. However, the lack of effective learning rules for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem by introducing an end to end learning approach of spiking neural networks for a lane keeping vehicle. We consider the reward-modulated spike-timing-dependent-plasticity (R-STDP) as a promising solution in training SNNs, since it combines the advantages of both reinforcement learning and the well-known STDP. We test our approach in three scenarios that a Pioneer robot is controlled to keep lanes based on an SNN. Specifically, the lane information is encoded by the event data from a neuromorphic vision sensor. The SNN is constructed using R-STDP synapses in an all-to-all fashion. We demonstrate the advantages of our approach in terms of the lateral localization accuracy by comparing with other state-of-the-art learning algorithms based on SNNs.
RDF Data Storage and Query Processing Schemes: A Survey

The Resource Description Framework (RDF) represents a main ingredient and data representation format for Linked Data and the Semantic Web. It supports a generic graph-based data model and data representation format for describing things, including their relationships with other things. As the size of RDF datasets is growing fast, RDF data management systems must be able to cope with growing amounts of data. Even though physically handling RDF data using a relational table is possible, querying a giant triple table becomes very expensive because of the multiple nested joins required for answering graph queries. In addition, the heterogeneity of RDF Data poses entirely new challenges to database systems. This article provides a comprehensive study of the state of the art in handling and querying RDF data. In particular, we focus on data storage techniques, indexing strategies, and query execution mechanisms. Moreover, we provide a classification of existing systems and approaches. We also provide an overview of the various benchmarking efforts in this context and discuss some of the open problems in this domain.
Object oriented extensions of IEC 61131–3 as an enabling technology of software product lines

Software product lines (SPL) relying on UML technology have been a breakthrough in software reuse in the IT domain. In the industrial automation domain, SPL are not yet established in industrial practice. One reason for this is that conventional function block programming techniques do not adequately support SPL architecture definition and product configuration, while UML tools are not industrially accepted for control software development. In this paper, the use of object oriented (OO) extensions of IEC 61131–3 are used to bridge this gap. The SPL architecture and product specifications are expressed as UML class diagrams, which serve as straightforward specifications for configuring the IEC 61131–3 control application with OO extensions. A product configurator tool has been developed using PLCopen XML technology to support the generation of an executable IEC 61131–3 application according to chosen product options. The approach is demonstrated using a mobile elevating working platform as a case study.
D-FICCA : A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks

http://dx.doi.org/10.1016/j.measurement.2014.04.034 0263-2241/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author at: Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran,  fax: +6
The levels of physical activity and motor skills in young children with and without autism spectrum disorder, aged 2-5 years.

Autism spectrum disorder is the fastest growing developmental disability in the United States. As such, there is an unprecedented need for research examining factors contributing to the health disparities in this population. This research suggests a relationship between the levels of physical activity and health outcomes. In fact, excessive sedentary behavior during early childhood is associated with a number of negative health outcomes. A total of 53 children participated in this study, including typically developing children (mean age = 42.5 ± 10.78 months, n = 19) and children with autism spectrum disorder (mean age = 47.42 ± 12.81 months, n = 34). The t-test results reveal that children with autism spectrum disorder spent significantly less time per day in sedentary behavior when compared to the typically developing group ( t(52) = 4.57, p < 0.001). Furthermore, the results from the general linear model reveal that there is no relationship between motor skills and the levels of physical activity. The ongoing need for objective measurement of physical activity in young children with autism spectrum disorder is of critical importance as it may shed light on an often overlooked need for early community-based interventions to increase physical activity early on in development.
Novel High Gain Low Noise CMOS Instrumentation Amplifier for Biomedical Applications

This work describes a novel technique of designing a high gain low noise CMOS instrumentation amplifier for biomedical applications like ECG signal processing. A three opamp instrumentation amplifier have been designed by using two simple op-amps at the two input stages and a folded cascode opamp at the output stage. Both op-amps at the input and output are 2-stage. Most of the previous or earlier designed op-amp in literature uses same type of op-amp at the input and output stages of instrumentation amplifier. By using folded cascode op-amp at the output, we had achieved significant improvement in gain and CMRR. Transistors sizing plays a major role in achieving high gain and CMRR. To achieve a desirable common mode rejection ratio (CMRR), Gain and other performance metrics, selection of most appropriable op-amp circuit topologies & optimum transistor sizing was the main criteria for designing of instrumentation amplifier for biomedical applications. The complete instrumentation amplifier design is simulated using Cadence Spectre tool and layout is designed and simulated in Cadence Layout editor at 0.18μm CMOS technology. Each of the input two stage op-amp provides a gain and CMRR of 45dB and 72dB respectively. The output two stage folded cascode amplifier provides a CMRR of 92dB and a gain of 82dB. The design achieves an overall CMRR and gain of 92dB and 67db respectively. The overall power consumed by instrumentation amplifier is 263μW which is suitable for biomedical signal processing applications.
Leveraging the Path Signature for Skeleton-based Human Action Recognition

Human action recognition in videos is one of the most challenging tasks in computer vision. One important issue is how to design discriminative features for representing spatial context and temporal dynamics. Here, we introduce a path signature feature to encode information from intra-frame and inter-frame contexts. A key step towards leveraging this feature is to construct the proper trajectories (paths) for the data steam. In each frame, the correlated constraints of human joints are treated as small paths, then the spatial path signature features are extracted from them. In video data, the evolution of these spatial features over time can also be regarded as paths from which the temporal path signature features are extracted. Eventually, all these features are concatenated to constitute the input vector of a fully connected neural network for action classification. Experimental results on four standard benchmark action datasets, J-HMDB, SBU Dataset, Berkeley MHAD, and NTURGB+D demonstrate that the proposed approach achieves state-of-the-art accuracy even in comparison with recent deep learning based models.
Designing a Better Data Representation for Deep Neural Networks and Text Classification

Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human design. An alternative is to use deep learning to automatically learn features from raw text data. One promising deep learning approach is to use convolutional neural networks. These networks can learn abstract text concepts from character representations and be trained to perform discriminate tasks, such as classification. In this paper, we propose a new approach to encoding text for use with convolutional neural networks that greatly reduces memory requirements and training time for learning from character-level text representations. Additionally, this approach scales well with alphabet size allowing us to preserve more information from the original text, potentially enhancing classification performance. By training tweet sentiment classifiers, we demonstrate that our approach uses less computational resources, allows faster training for networks and achieves similar, or better performance compared to the previous method of character encoding.
Incremental updating approximations in probabilistic rough sets under the variation of attributes

The attribute set in an information system evolves in time when new information arrives. Both lower and upper approximations of a concept will change dynamically when attributes vary. Inspired by the former incremental algorithm in Pawlak rough sets, this paper focuses on new strategies of dynamically updating approximations in probabilistic rough sets and investigates four propositions of updating approximations under probabilistic rough sets. Two incremental algorithms based on adding attributes and deleting attributes under probabilistic rough sets are proposed, respectively. The experiments on five data sets from UCI and a genome data with thousand attributes validate the feasibility of the proposed incremental approaches.
Continuous passive motion in the prevention of deep-vein thrombosis: a randomised comparison in trauma patients.

There is a high risk of venous thromboembolism when patients are immobilised following trauma. The combination of low-molecular-weight heparin (LMWH) with graduated compression stockings is frequently used in orthopaedic surgery to try and prevent this, but a relatively high incidence of thromboembolic events remains. Mechanical devices which perform continuous passive motion imitate contractions and increase the volume and velocity of venous flow. In this study 227 trauma patients were randomised to receive either treatment with the Arthroflow device and LMWH or only with the latter. The Arthroflow device passively extends and plantarflexes the feet. Patients were assessed initially by venous-occlusion plethysmography, compression ultrasonography and continuous wave Doppler, which were repeated weekly without knowledge of the category of randomisation. Those who showed evidence of deep-vein thrombosis underwent venography for confirmation. The incidence of deep-vein thrombosis was 25% in the LMWH group compared with 3.6% in those who had additional treatment with the Arthroflow device (p < 0.001). There were no substantial complications or problems of non-compliance with the Arthroflow device. Logistic regression analysis of the risk factors of deep-vein thrombosis showed high odds ratios for operation (4.1), immobilisation (4.3), older than 40 years of age (2.8) and obesity (2.2).
Mutual Authentication Scheme in Secure Internet of Things Technology for Comfortable Lifestyle

The Internet of Things (IoT), which can be regarded as an enhanced version of machine-to-machine communication technology, was proposed to realize intelligent thing-to-thing communications by utilizing the Internet connectivity. In the IoT, "things" are generally heterogeneous and resource constrained. In addition, such things are connected to each other over low-power and lossy networks. In this paper, we propose an inter-device authentication and session-key distribution system for devices with only encryption modules. In the proposed system, unlike existing sensor-network environments where the key distribution center distributes the key, each sensor node is involved with the generation of session keys. In addition, in the proposed scheme, the performance is improved so that the authenticated device can calculate the session key in advance. The proposed mutual authentication and session-key distribution system can withstand replay attacks, man-in-the-middle attacks, and wiretapped secret-key attacks.
Mechanical assistance in the surgical treatment of massive abdominal pannus

A 32 year-old male patient was effectively immobilized secondary to morbid obesity. He underwent a gastric bypass which reduced his weight from 647 lbs to 376 lbs over 12 months. A massive abdominal panniculus persisted limiting his ability to pursue an effective exercise program and maintain appropriate hygiene. The patient was operated upon with the aid of Steinman pins to pierce the dependant portion of the pannus and an orthopaedic suspension device to facilitate its elevation. Operative excision of the 19.93 kg pannus proceeded in a rapid fashion with minimal difficulties in pannus manipulation. The technique of excision, its benefits and the case history are reviewed.
The Perpetual Music Track The Phenomenon of Constant Musical Imagery

The perpetual music track is a new concept that describes a condition of constant or near-constant musical imagery. This condition appears to be very rare even among composers and musicians. I present here a detailed self-analysis of musical imagery for the purpose of defining the psychological features of a perpetual music track. I have music running through my head almost constantly during waking hours, consisting of a combination of recently-heard pieces and distant pieces that spontaneously pop into the head. Imagery consists mainly of short musical fragments that get looped repeatedly upon themselves. Corporeal manifestations of imagery occur in the form of unconscious finger movements whose patterns correspond to the melodic contour of the imagined piece. Musical dreams occur every week or two, and contain a combination of familiar and originallycomposed music. These results are discussed in light of theories of imagery, consciousness, hallucination, obsessive cognition, and most especially the notion that acoustic consciousness can be split into multiple parallel streams.
Teaching and Learning with ICT Tools: Issues and Challenges from Teachers’ Perceptions

In this digital era, ICT use in the classroom is important for giving students opportunities to learn and apply the required 21st century skills. Hence studying the issues and challenges related to ICT use in teaching and learning can assist teachers in overcoming the obstacles and become successful technology users.  Therefore, the main purpose of this study is to analyze teachers’ perceptions of the challenges faced  in using  ICT tools  in classrooms. A quantitative research design was used to collect the data randomly from a sample of 100 secondary school teachers in the state of Melaka, Malaysia. Evidence has been collected through distribution of a modified‐ adopted survey questionnaire. Overall, the key issues and  challenges  found  to  be  significant  in  using  ICT  tools  by  teachers  were:  limited accessibility and network connection, limited technical support, lack of effective training, limited time and lack of teachers’ competency. Moreover, the results from independent t‐ test show that use of ICT tools by male teachers (M =2.08, SD = .997) in the classroom is higher compared to female teachers (M = 2.04, SD = .992). It is hoped that the outcome of this research provides proper information and recommendation to those responsible for integrating new technologies into the school teaching and learning process.
Lower third of the face: indications and limitations of the minimal access cranial suspension lift.

The MACS-lift technique, in the simple or extended variation, delivers a reproducible and natural rejuvenation of the face and neck with minimal morbidity and a swift recovery. Retro-auricular extension of the surgery is avoided by pure vertical redraping of the facial skin. In patients with an exceptionally bad skin quality, any vertical pleats below the lobule that may appear can easily be corrected with a limited posterior cervicoplasty.
DEMM: A Dynamic Energy-Saving Mechanism for Multicore Memories

Since main memory system contributes to a large and increasing fraction of server/datacenter energy consumption, there have been several efforts to reduce its power and energy consumption. DVFS schemes have been used to reduce the memory power, but they come with a performance penalty. In this work, we propose DEMM, an OS-based, high performance DVFS mechanism that reduces memory power by dynamically scaling individual memory channel frequencies/voltages. Our strategy also involves clustering the running applications based on their sensitivities to memory latency, and assigning memory channels to the application clusters. We introduce a new metric called Discrete Misses per Kilo Cycle (DMPKC) to capture the performance sensitivities of the applications to memory frequency modulation. DEMM allows us to save power in the memory system with negligible impact on performance. We demonstrate around 25% savings in the memory system energy and 10% savings in the total system energy, with only a 4% loss in workload performance.
Practical Guidelines for Testing Statistical Software

Schloss Reisensburg.   Traditional tests of the accuracy of statistical software have been based on a few limited paradigms for ordinary least squares regression. Test suites based on these criteria served the statistical computing community well when software was limited to a few simple procedures. Recent developments in statistical computing require both more and less sophisticated measures, however. We need tests for a broader variety of procedures and ones which are more likely to reveal incompetent programming. This paper summarizes these issues.
Proportional Fair Frequency-Domain Packet Scheduling for 3GPP LTE Uplink

With the power consumption issue of mobile handset taken into account, Single-carrier FDMA (SC-FDMA) has been selected for 3GPP Long-Term Evolution (LTE) uplink multiple access scheme. Like in OFDMA downlink, it enables multiple users to be served simultaneously in uplink as well. However, its single carrier property requires that all the subcarriers allocated to a single user must be contiguous in frequency within each time slot. This contiguous allocation constraint limits the scheduling flexibility, and frequency-domain packet scheduling algorithms in such system need to incorporate this constraint while trying to maximize their own scheduling objectives. In this paper we explore this fundamental problem of LTE SC-FDMA uplink scheduling by adopting the conventional timedomain Proportional Fair algorithm to maximize its objective (i.e. proportional fair criteria) in the frequency-domain setting. We show the NP-hardness of the frequency-domain scheduling problem under this contiguous allocation constraint and present a set of practical algorithms fine tuned to this problem. We demonstrate that competitive performance can be achieved in terms of system throughput as well as fairness perspective, which is evaluated using 3GPP LTE system model simulations.
A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features

We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently outperforms a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.
High-speed bounding with the MIT Cheetah 2: Control design and experiments

This paper presents the design and implementation of a bounding controller for the MIT Cheetah 2 and its experimental results. The paper introduces the architecture of the controller along with the functional roles of the subcomponents. The application of impulse scaling provides feedforward force profiles that automatically adapt across a wide range of speeds. A discrete gait pattern stabilizer maintains the footfall sequence and timing. Continuous feedback is layered to manage balance during the stance phase. Stable hybrid limit cycles are exhibited in simulation using simplified models, and are further validated in untethered 3D bounding experiments. Experiments are conducted both indoors and outdoors on various man-made and natural terrains. The control framework is shown to provide stable bounding in the hardware, at speeds of up to 6.4 m/s and with a minimum total cost of transport of 0.47. These results are unprecedented accomplishments in terms of efficiency and speed in untethered experimental quadruped machines.
Multi-threaded Simics SystemC Virtual Platform

The functional simulator Simics provides a co-simulation integration path with a SystemC simulation environment to create Virtual Platforms. With increasing complexity of the SystemC models, this platform suffers from performance degradation due to the single threaded nature of the integrated Virtual Platform. In this paper, we present a multi-threaded Simics SystemC platform solution that significantly improves performance over the existing single threaded solution. The two schedulers run independently, only communicating in a thread safe manner through a message interface. Simics based logging and checkpointing are preserved within SystemC and tied to the corresponding Simics' APIs for a seamless experience. The solution also scales to multiple SystemC models within the platform, each running its own thread with an instantiation of the SystemC kernel. A second multi-cell solution is proposed providing comparable performance with the multi-thread solution, but reducing the burden of integration on the SystemC model. Empirical data is presented showing performance gains over the legacy single threaded solution.