nils-herrmann/clean_scirepeval huggingface dataset:
First response fire combat: Deep leaning based visible fire detection

Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based approach, based on Google's Inception v3 and evaluate how the use of different optimizers, learning rates and reduction functions impact the convergence time and the results obtained. The experiment used a balanced two-class dataset, composed of images from videos captured through hand-held and drone attached cameras on real fire hazard situations. The results show that using any choice of algorithms, the Inception v3 architecture is able to converge and obtain results that surpass the state-of-the-art fire detection methods. We show that using a proper choice of learning rate, the network is able to achieve a 99% accuracy in less than one hundred interactions.
Hidost: a static machine-learning-based detector of malicious files

Malicious software, i.e., malware, has been a persistent threat in the information security landscape since the early days of personal computing. The recent targeted attacks extensively use non-executable malware as a stealthy attack vector. There exists a substantial body of previous work on the detection of non-executable malware, including static, dynamic, and combined methods. While static methods perform orders of magnitude faster, their applicability has been hitherto limited to specific file formats. This paper introduces Hidost, the first static machine-learning-based malware detection system designed to operate onmultiple file formats. Extending a previously published, highly effective method, it combines the logical structure of files with their content for even better detection accuracy. Our system has been implemented and evaluated on two formats, PDF and SWF (Flash). Thanks to its modular design and general feature set, it is extensible to other formats whose logical structure is organized as a hierarchy. Evaluated in realistic experiments on timestamped datasets comprising 440,000 PDF and 40,000 SWF files collected during several months, Hidost outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.
A Study on Feature Selection and Classification Techniques for Automatic Genre Classification of Traditional Malay Music

Machine learning techniques for automated musical genre classification is currently widely studied. With large collections of digital musical files, one approach to classification is to classify by musical genres such as pop, rock and classical in Western music. Beat, pitch and temporal related features are extracted from audio signals and various machine learning algorithms are applied for classification. Features that resulted in better classification accuracies for Traditional Malay Music (TMM), in comparison to western music, in a previous study were beat related features. However, only the J48 classifier was used and in this study we perform a more comprehensive investigation on improving the classification of TMM. In addition, feature selection was performed for dimensionality reduction. Classification accuracies using classifiers of varying paradigms on a dataset comprising ten TMM genres were obtained. Results identify potentially useful classifiers and show the impact of adding a feature selection phase for TMM genre classification.
Deep Subspace Clustering with Sparsity Prior

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly seeking a subspace to fit each group. Most of existing methods are based on a shallow linear model, which may fail in handling data with nonlinear structure. In this paper, we propose a novel subspace clustering method – deeP subspAce clusteRing with sparsiTY prior (PARTY) – based on a new deep learning architecture. PARTY explicitly learns to progressively transform input data into nonlinear latent space and to be adaptive to the local and global subspace structure simultaneously. In particular, considering local structure, PARTY learns representation for the input data with minimal reconstruction error. Moreover, PARTY incorporates a prior sparsity information into the hidden representation learning to preserve the sparse reconstruction relation over the whole data set. To the best of our knowledge, PARTY is the first deep learning based subspace clustering method. Extensive experiments verify the effectiveness of our method.
Spatiotemporal Multiplier Networks for Video Action Recognition

This paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features. Our model combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end. We theoretically motivate multiplicative gating functions for residual networks and empirically study their effect on classification accuracy. To capture long-term dependencies we inject identity mapping kernels for learning temporal relationships. Our architecture is fully convolutional in spacetime and able to evaluate a video in a single forward pass. Empirical investigation reveals that our model produces state-of-the-art results on two standard action recognition datasets.
SiC MOSFET Switching Waveform Profiling Through Passive Networks

The use of Wide Band Gap (WBG) devices in high performance power converters is becoming more common, and the range of techniques for managing the high speed switching transitions is broadening. In particular, there is now a greater interest in applying techniques for shaping the switching transients to improve the converter performance. This paper explores the current state of the art in waveform profiling gate drivers and their application to WBG devices to determine the optimum gate waveform profile. This profile is then analysed with the goal of creating a passive gate drive network capable of replicating the performance. A hardware prototype of this gate driver is demonstrated and experimental results from it are compared with those from a standard drive and a fully active driver. The active gate driver shows a 31% decrease in current overshoot or a 35.4% reduction in turn-on switching losses.
Preference for Nature in Urbanized Societies : Stress , Restoration , and the Pursuit of Sustainability

Urbanicity presents a challenge for the pursuit of sustainability. High settlement density may offer some environmental, economic, and social advantages, but it can impose psychological demands that people find excessive. These demands of urban life have stimulated a desire for contact with nature through suburban residence, leading to planning and transportation practices that have profound implications for the pursuit of sustainability. Some might dismiss people’s desire for contact with nature as the result of an anti-urban bias in conjunction with a romantic view of nature. However, research in environmental psychology suggests that people’s desire for contact with nature serves an important adaptive function, namely, psychological restoration. Based on this insight, we offer a perspective on an underlying practical challenge: designing communities that balance settlement density with satisfactory access to nature experience. We discuss research on four issues: how people tend to believe that nature is restorative; how restoration needs and beliefs shape environmental preferences; how well people actually achieve restoration in urban and natural environments; and how contact with nature can promote health. In closing, we consider urban nature as a design option that promotes urban sustainability.
Dropped head as an unusual presenting sign of myasthenia gravis

Prominent or isolated weakness of cervical extensor muscles is a relatively rare clinical sign. Commonly, this is known as “dropped-head syndrome”. This abnormal flexion of the head may occur in a variety of neuromuscular diseases and in a few non-neurological disorders as well. The case we describe concerns a 61-year-old woman with dropped-head syndrome as the unique complaint of myasthenia gravis.
Effects of the metacognitive computer-tool met.a.ware on the web search of laypersons

Today, laypersons often consult the Internet to inform themselves about health-related issues. However, the competent use of these often complex and heterogeneous information provisions cannot be taken for granted, because many Internet users are lacking the necessary metacognitive prerequisites. Therefore, we developed the metacognitive computer-tool met.a.ware, which supports laypersons’ Internet research for medical information by the means of metacognitive prompting and ontological classification. In an experimental investigation of met.a.ware a total of 118 participants with little medical knowledge were asked to conduct an Internet research on a medical topic. Participants were randomly assigned to four experimental groups that worked with met.a.ware and either received evaluation prompts, monitoring prompts, both types of prompts, or no prompts. All experimental conditions were additionally provided with ontological classification. One control group took paper and pencil notes. A further control group took notes using a blank text window. Results showed that laypersons receiving evaluation prompts outperformed controls in terms of knowledge about sources and produced more arguments commenting on the source of information in an essay task. In addition, laypersons receiving monitoring prompts acquired significantly more knowledge about facts, but did not perform better on a comprehension test than the controls. The availability of ontological categories helped to structure the notes laypersons in the conditions working with ontological classification took during Internet research. Analyses of the notes further demonstrated that the availability of ontological categories guided information search in direction of the selected categories. It is concluded, that met.a.ware is an effective tool that supports laypersons’ Internet research.  E-mail address: stadtlm@uni-muenster.de (M. Stadtler). M. Stadtler, R. Bromme / Computers in Human Behavior 24 (2008) 716–737 717
A Domain Model for the Internet of Things

By bringing together the physical world of real objects with the virtual world of IT systems, the Internet of Things has the potential to significantly change both the enterprise world as well as society. However, the term is very much hyped and understood differently by different communities, especially because IoT is not a technology as such but represents the convergence of heterogeneous - often new - technologies pertaining to different engineering domains. What is needed in order to come to a common understanding is a domain model for the Internet of Things, defining the main concepts and their relationships, and serving as a common lexicon and taxonomy and thus as a basis for further scientific discourse and development of the Internet of Things. As we show, having such a domain model is also helpful in design of concrete IoT system architectures, as it provides a template and thus structures the analysis of use cases.
Constant voltage control of high voltage high frequency power supply for ozone quantity adjustment

This paper proposes the high frequency high voltage power supply for an ozone generator using a phase-shift pulse width modulation (PWM) full bridge inverter. The circuit operation is fully described. The high frequency transformer and ozone generator mathematical models are also included for preliminarily calculating of instantaneous voltages and currents. The proposed system simulation using the MATLAB /SIMULINK software package is given. In order to ensure that zero voltage switching (ZVS) mode always operates over a certain range of a frequency variation, a series-compensated resonant inductor is included. The advantage of the proposed system is a capability of varying ozone gas production quantity by varying the frequency and phase shift angle of the converter whilst the applied voltage to the electrodes is kept constant. The correctness of the proposed technique is verified by both simulation and experimental results.
Soil Microbial Resources for Improving Fertilizers Efficiency in an Integrated Plant Nutrient Management System

Tomorrow's agriculture, challenged by increasing global demand for food, scarcity of arable lands, and resources alongside multiple environment pressures, needs to be managed smartly through sustainable and eco-efficient approaches. Modern agriculture has to be more productive, sustainable, and environmentally friendly. While macronutrients such as nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) supplied by mineral fertilizers are vital to crop production, agriculturally beneficial microorganisms may also contribute directly (i.e., biological N2 fixation, P solubilization, and phytohormone production, etc.) or indirectly (i.e., antimicrobial compounds biosynthesis and elicitation of induced systemic resistance, etc.) to crop improvement and fertilizers efficiency. Microbial-based bioformulations that increase plant performance are greatly needed, and in particular bioformulations that exhibit complementary and synergistic effects with mineral fertilization. Such an integrated soil fertility management strategy has been demonstrated through several controlled and non-controlled experiments, but more efforts have to be made in order to thoroughly understand the multiple functions of beneficial microorganisms within the soil microbial community itself and in interaction with plants and mineral resources. In fact, the combined usage of microbial [i.e., beneficial microorganisms: N2-fixing (NF), P-solubilizing, and P mobilizing, etc.] and mineral resources is an emerging research area that aims to design and develop efficient microbial formulations which are highly compatible with mineral inputs, with positive impacts on both crops and environment. This novel approach is likely to be of a global interest, especially in most N- and P-deficient agro-ecosystems. In this review, we report on the importance of NF bacteria and P solubilizing/mobilizing microbes as well as their interactions with mineral P fertilization in improving crop productivity and fertilizers efficiency. In addition, we shed light on the interactive and synergistic effects that may occur within multi-trophic interactions involving those two microbial groups and positive consequences on plant mineral uptake, crop productivity, and resiliency to environmental constraints. Improving use of mineral nutrients is a must to securing higher yield and productivity in a sustainable manner, therefore continuously designing, developing and testing innovative integrated plant nutrient management systems based on relevant biological resources (crops and microorganisms) is highly required.
Identifying personal genomes by surname inference.

Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.
Incremental self-improvement for life-time multi-agent reinforcement learning

Previous approaches to multi-agent reinforcement learning are either very limited or heuris-tic by nature. The main reason is: each agent's or \animat's" environment continually changes because the other learning animats keep changing. Traditional reinforcement learning algorithms cannot properly deal with this. Their convergence theorems require repeatable trials and strong (typically Markovian) assumptions about the environment. In this paper, however, we use a novel, general, sound method for multiple, reinforcement learning \animats", each living a single life with limited computational resources in an unrestricted, changing environment. The method is called \incremental self-improvement" (IS | Schmidhuber, 1994). IS properly takes into account that whatever some animat learns at some point may aaect learning conditions for other animats or for itself at any later point. The learning algorithm of an IS-based animat is embedded in its own policy | the animat cannot only improve its performance, but in principle also improve the way it improves etc. At certain times in the animat's life, IS uses reinforce-ment/time ratios to estimate from a single training example (namely the entire life so far) which previously learned things are still useful, and selectively keeps them but gets rid of those that start appearing harmful. IS is based on an ee-cient, stack-based backtracking procedure which is guaranteed to make each animat's learning history a history of long-term reinforcement accelerations. Experiments demonstrate IS' eeectiveness. In one experiment, IS learns a sequence of more and more complex function approximation problems. In another, a multi-agent system consisting of three co-evolving, IS-based animats chasing each other learns interesting, stochastic predator and prey strategies.
Bandwidth formula for Linear FMCW radar waveforms

The International Telecommunications Union provides recommendations regarding spectral emission bounds for primary radar systems. These bounds are currently in review and are defined in terms of spectral occupancy, necessary bandwidth, 40dB bandwidth and out-of-band roll-off rates. Here we derive out-of-band domain spectral envelopes, bandwidth formula and roll-off rates, for various Linear FMCW radar waveforms including sawtooth (LFMCW), Quadratic Phase Coded LFMCW, LFM Pulse Train, and Hann amplitude tapered LFMCW.
Automatic Generation of 3 D Thermal Maps of Building Interiors

Most existing approaches to characterizing thermal properties of buildings and heat emissions from their elements rely on manual inspection and as such are slow, and labor intensive. This is often a daunting task, which requires several days of on-site inspection. In this paper, we propose a fully automatic approach to construct a 3D thermal point cloud of the building interior reflecting the geometry including walls, floors, and ceilings, as well as structures such as furniture, lights, windows, and plug loads. Our approach is based on a wearable ambulatory backpack comprising multiple sensors such as Light Detection And Ranging (LiDAR) scanners, and Infrared and optical cameras. As the operator wearing the backpack walks through the building, the LiDAR scans are collected and processed in order to compute the 3D geometry of the building. Furthermore, the Infrared cameras are calibrated intrinsically and extrinsically such that the captured images are registered to the captured geometry. Thus, the temperature data in the Infrared images is associated with the geometry resulting in a “thermal 3D point cloud”. The same process can be repeated using optical imagery resulting in a “visible 3D point cloud”. By visualizing the two point clouds simultaneously in interactive rendering tools, we can virtually walk through the thermal and optical 3D point clouds, toggle between them, identify and annotate, “hot” regions, objects, plug loads, thermal and moisture leaks, and document their location with fine spatial granularity in the 3D point clouds.
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.



In this article, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0-1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods.



The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip CONTACT: yangzh@dlut.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
An Improved Design of Planar Elliptical Dipole Antenna for UWB Applications

In this letter, an enhanced planar elliptical dipole antenna design for ultrawideband (UWB) communication and impulse radar systems is presented. The printed-circuit-elliptical (PCE) antenna has been investigated to be an effective radiator for UWB applications. To enhance gain and return loss bandwidth of the antenna, elliptical slots are used on the dipole arms. The gain performance of the antenna has been increased by means of elliptical slots in the frequency range from 2.7 to 11 GHz. The standing wave ratio is less than 2 (SWR < 2) along 94.4% of operation bandwidth from 1.1 to 11 GHz. The radiation pattern in E- and H-plane for certain frequencies, the return loss, and the gain performance are presented with the experimental and simulation results. Moreover, the time domain analysis of the antenna is presented. The antenna introduces low-level ringing and pulse distortion. Consequently, the antenna is very useful for impulse and UWB communication systems.
Ethnicity , Insurgency , and Civil War Revisited ∗

∗This work was supported by the Center for Statistics and the Social Sciences at the University of Washington with funds from the University Initiatives Funds. In addition, Quinn acknowledges research support under NSF Grant SES 01-36676. †Department of Political Science and CSSS, University of Washington, quinn@stat.washington.edu ‡Department of Sociology, University of Washington, hechter@u.washington.edu §Department of Political Science, University of Washington, ewibbels@u.washington.edu
Design and Analysis of Stepped Impedance Microstrip Low Pass Filter Using ADS Simulation Tool for Wireless Applications

There is an increasing demand for microwave systems to meet the emerging telecommunication challenges with respect to size, performance and cost. This project describes a general design technique for micro strip low pass filters that are used to convey microwave frequency signals. The parasitic problems of Xband can be adjusted through impedance ratio K, which can enhance the performance of harmonic suppression. The ADS simulation tool is used to design an X-band stepped impedance low pass filter of range 8-12 GHz. This simulation results show that the filter works on 10GHz at the center frequency and achieves attenuation of 60dB, which effectively suppresses the parasitic bands. To attain the filter with these characteristics, Insertion Loss Method is performed. Compared to other filter types, this design works very well with excellent harmonic suppression performance.