nils-herrmann/clean_scirepeval huggingface dataset:
Training Spiking ConvNets by STDP and Gradient Descent

This paper proposes a new method for training multi-layer spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly use backpropagation method for non-spiking networks is not easily applied. Our method uses a novel version of layered spike-timing- dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for spiking neural networks (SNNs). The training process uses two components for unsupervised feature extraction and supervised classification. The first component is a new STDP rule for spike-based representation learning which trains convolutional filters. The second introduces a new STDP-based supervised learning rule for spike pattern classification via an approximation to gradient descent. Stacking these components implements a novel spiking CNN of integrate-and-fire (IF) neurons with performances comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for the MNIST handwritten digit classification. Our network architecture is the only high performance, spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.
Planar-fed folded notch (PFFN) arrays: A novel wideband technology for multi-function active electronically scanning arrays (AESAs)

This paper describes a new form of the wideband, wide scan notch array antenna which provides some distinct advantages. The Planar-Fed Folded Notch (PFFN) Array described herein differs from more traditional printed circuit board (PCB) and metal embodiments of the notch radiator array. PFFN array designs employ planar feeds that are incorporated on a single, common multi-layer PCB that is parallel to the array face. Furthermore, PFFN designs replace the vertical metal "fins" used in traditional notch arrays with multi-layered PCBs that consist of layers of printed metal patches interleaved within laminated foam or dielectric layers. PFFN radiator/array designs are also highly manufacturable, relatively inexpensive, and have a lower profile/height. The paper also provides a brief overview of demonstrated PFFN AESAs and passive arrays and describes associated RF, mechanical,/environmental and high power qualification testing.
A comparison of binarization methods for historical archive documents

This paper compares several alternative binarization algorithms for historical archive documents, by evaluating their effect on end-to-end word recognition performance in a complete archive document recognition system utilising a commercial OCR engine. The algorithms evaluated are: global thresholding; Niblack's and Sauvola's algorithms; adaptive versions of Niblack's and Sauvola's algorithms; and Niblack's and Sauvola's algorithms applied to background removed images. We found that, for our archive documents, Niblack's algorithm can achieve better performance than Sauvola's (which has been claimed as an evolution of Niblack's algorithm), and that it also achieved better performance than the internal binarization provided as part of the commercial OCR engine.
Speed control and electrical braking of axial flux BLDC motor

Axial flux brushless direct current motors (AFBLDC) are becoming popular in many applications including electrical vehicles because of their ability to meet the demand of high power density, high efficiency, wide speed range, robustness, low cost and less maintenance. In this paper, AFBLDC motor drive with single sided configuration having 24 stator poles and 32 permanent magnets on the rotor is proposed. It is driven by six pulse inverter that is fed from a single phase AC supply through controlled AC to DC converter. The speed control and braking methods are also proposed based on pulse width modulation technique. The overall scheme is simulated in MATLAB environment and tested under different operating conditions. A prototype of proposed AFBLDC motor drive is designed and fabricated. The control methods are implemented using DSC dsPIC33EP256MC202 digital signal controller. Tests are performed on this prototype to validate its performance at different speeds with and without braking mode. It is observed that the proposed scheme works effectively and can be used as wheel direct driven motor for electrical vehicle.
False Data Injection Attacks in Control Systems

This paper analyzes the effects of false data injection attacks on Control System. We assume that the system, equipped with a Kalman filter and LQG controller, is used to monitor and control a discrete linear time invariant Gaussian system. We further assume that the system is equipped with a failure detector. An attacker wishes to destabilize the system by compromising a subset of sensors and sending corrupted readings to the state estimator. In order to inject fake sensor measurements without being detected the attacker needs to carefully design its inputs to fool the failure detector, since abnormal sensor measurements usually trigger an alarm from the failure detector. We will provide a necessary and sufficient condition under which the attacker could destabilize the system while successfully bypassing the failure detector. A design method for the defender to improve the resilience of the CPS against such kind of false data injection attacks is also provided.
Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings

Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases.
Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation

This paper explores the use of a soft ground-truth mask (“soft mask”) to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data with a reduced confidence weight. A soft mask is constructed by morphological dilation of the binary segmentation mask provided by a given expert, where expert-marked voxels receive label 1 and voxels of the dilated region are assigned a soft label. In the methodology proposed, the FCNN is trained using the soft mask. On the ISBI 2015 challenge dataset, this is shown to provide a better precision-recall tradeoff and to achieve a higher average Dice similarity coefficient. We also show that by using this soft mask scheme we can improve the network segmentation performance when compared to a second independent expert.
Bio-inspired self-organising multi-robot pattern formation: A review

Self-organised emergent patterns can be widely seen in natural and man-made complex systems generated by interactions among local components without external or global control. This paper presents a survey of recent research advances in self-organising pattern formation in mobile multi-robot (or swarm robotic) systems. Relevant pattern formation methods are reviewed with a special focus on biologicallyinspired self-organising approaches inspired from macroscopic collective behaviours or microscopic multicellular developing mechanisms. As the ultimate goal of this review is to provide insight into pattern formation using real robots, limitations and considerations on dealing with a large number of robots are discussed. In addition, guided self-organisation is also discussed as a design strategy where the swarm robotic system may be endowed with local rules for generating desired global patterns.
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

Global optimization algorithms have shown impressive performance in data-association-based multi-object tracking, but handling online data remains a difficult hurdle to overcome. In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking. We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames. More specifically, in the min-cost multi-commodity network flow, the target-specific similarities are online learned to enforce the local consistency for reducing the complexity of the global data association. Meanwhile, the global data association taking multiple video frames into account alleviates irrecoverable errors caused by the local data association between adjacent frames. To ensure the efficiency of online tracking, we give an efficient near-optimal solution to the proposed min-cost multi-commodity flow problem, and provide the empirical proof of its sub-optimality. The comprehensive experiments on real data demonstrate the superior tracking performance of our approach in various challenging situations.
Protein function in precision medicine: deep understanding with machine learning.

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
Visceral pain: the ins and outs, the ups and downs.

Visceral pain represents a major clinical problem, yet far less is known about its mechanisms compared with somatic pains, for example, from cutaneous and muscular structures.



In this review, we describe the neuroanatomical bases of visceral pain signalling in the peripheral and central nervous system, comparing to somatic pains and also the channels and receptors involved in these events. We include an overview of potential new targets in the context of mechanisms of visceral pain and hypersensitivity.



This review should inform on the recognition of what occurs in patients with visceral pain, why comorbidities are common and how analgesic treatments work.
You can't always get what you want: educational attainment, agency, and choice.

Using educational attainment to indicate socioeconomic status, the authors examined models of agency and effects of choice among European American adults of different educational backgrounds in 3 studies. Whereas college-educated (BA) participants and their preferred cultural products (i.e., rock music lyrics) emphasized expressing uniqueness, controlling environments, and influencing others, less educated (HS) participants and their preferred cultural products (i.e., country music lyrics) emphasized maintaining integrity, adjusting selves, and resisting influence. Reflecting these models of agency, HS and BA participants differently responded to choice in dissonance and reactance paradigms: BA participants liked chosen objects more than unchosen objects, but choice did not affect HS participants' preferences. Results suggest that HS and BA models of agency qualitatively differ, despite overlap between HS and BA worlds.
Backward compatible HDR stereo matching: a hybrid tone-mapping-based framework

Stereo matching under complex circumstances, such as low-textured areas and high dynamic range (HDR) scenes, is an ill-posed problem. In this paper, we introduce a stereo matching approach for real-world HDR scenes which is backward compatible to conventional stereo matchers. For this purpose, (1) we compare and evaluate the tone-mapped disparity maps to find the most suitable tone-mapping approach for the stereo matching purpose. Thereof, (2) we introduce a combining graph-cut based framework for effectively fusing the tone-mapped disparity maps obtained from different tone-mapped input image pairs. And finally, (3) we generate reference ground truth disparity maps for our evaluation using the original HDR images and a customized stereo matching method for HDR inputs. Our experiments show that, combining the most effective features of tone-mapped disparity maps, an improved version of the disparity is achieved. Not only our results reduce the low dynamic range (LDR), conventional disparity errors by the factor of 3, but also outperform the other well-known tone-mapped disparities by providing the closest results to the original HDR disparity maps.
A Machine Learning Approach to Anomaly Detection

Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.
Entity resolution using inferred relationships and behavior

We present a method for entity resolution that infers relationships between observed identities and uses those relationships to aid in mapping identities to underlying entities. We also introduce the idea of using graphlets for entity resolution. Graphlets are collections of small graphs that can be used to characterize the “role” of a node in a graph. The idea is that graphlets can provide a richer set of features to characterize identities. We validate our method on standard author datasets, and we further evaluate our method using data collected from Twitter. We find that inferred relationships and graphlets are useful for entity resolution.
Electronic switching in phase-change memories

A detailed investigation of electronic switching in chalcogenide-based phase-change memory devices is presented. An original bandgap model consistent with the microscopic structure of both crystalline and amorphous chalcogenide is described, and a physical picture of the switching mechanism is proposed. Numerical simulations provide, for the first time, a quantitative description of the peculiar current-voltage curve of a Ge/sub 2/Sb/sub 2/Te/sub 5/ resistor, in good agreement with measurements performed on test devices.
Low-Dimensional Representation of Spectral Envelope Without Deterioration for Full-Band Speech Analysis/Synthesis System

A speech coding for a full-band speech analysis/synthesis system is described. In this work, full-band speech is defined as speech with a sampling frequency above 40 kHz, whose Nyquist frequency covers the audible frequency range. In prior works, speech coding has generally focused on the narrowband speech with a sampling frequency below 16 kHz. On the other hand, statistical parametric speech synthesis currently uses the full-band speech, and low-dimensional representation of speech parameters is being used. The purpose of this study is to achieve speech coding without deterioration for full-band speech. We focus on a high-quality speech analysis/synthesis system and mel-cepstral analysis using frequency warping. In the frequency warping function, we directly use three auditory scales. We carried out a subjective evaluation using the WORLD vocoder and found that the optimum number of dimensions was around 50. The kind of frequency warping did not significantly affect the sound quality in the dimensions.
Development of a parametric kinematic model of the human hand and a novel robotic exoskeleton

This paper reports the integration of a kinematic model of the human hand during cylindrical grasping, with specific focus on the accurate mapping of thumb movement during grasping motions, and a novel, multi-degree-of-freedom assistive exoskeleton mechanism based on this model. The model includes thumb maximum hyper-extension for grasping large objects (∼>50mm). The exoskeleton includes a novel four-bar mechanism designed to reproduce natural thumb opposition and a novel synchro-motion pulley mechanism for coordinated finger motion. A computer aided design environment is used to allow the exoskeleton to be rapidly customized to the hand dimensions of a specific patient. Trials comparing the kinematic model to observed data of hand movement show the model to be capable of mapping thumb and finger joint flexion angles during grasping motions. Simulations show the exoskeleton to be capable of reproducing the complex motion of the thumb to oppose the fingers during cylindrical and pinch grip motions.
An ontology-based data integration approach for web analytics in e-commerce

Web analytics has emerged as one of the most important activities in e-commerce, since it allows companies and e-merchants to track the behavior of customers when visiting their web sites. There exist a series of tools for web analytics that are used not only for tracking and measuring web traffic, but also for analyzing the commercial activity. However, most of these tools focus on low level web attributes and metrics, making other sophisticated functionalities and analyses only available for commercial (non-free) versions. In this context, the SME-Ecompass European initiative aims at providing e-commerce SMEs with accessible tools for high level web analytics. These software facilities should use different sources of data coming from digital footprints allocated in e-shops, to fuse them together in a coherent way, and to make them available for advanced data mining procedures. This motivated us to propose in this work an ontology-based approach to collect, integrate and store web analytics data, from many sources of popular and commercial digital footprints. As article’s main impact, we obtain enriched and semantically annotated data that is used to properly train an intelligent system, involving data mining procedures, for the analysis of customer behavior in real e-commerce sites. In concrete, for the validation of our semantic approach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocated in 15 e-shops of different commercial sectors and countries (UK, Spain, Greece and Germany), throughout several months of activity. The obtained results show different perspectives in customer’s behavior analysis that go one step beyond the most popular web analytics tools in the current market.
PUBLIC-KEY STEGANOGRAPHY BASED ON MODIFIED LSB METHOD

Steganography is the art and science of invisible communication. It is a technique which keeps the existence of the message secret. This paper proposed a technique to implement steganogaraphy and cryptography together to hide the data into an image. This technique describes as: Find the shared stego-key between the two communication parties by applying Diffie-Hellman Key exchange protocol, then encrypt the data using secret stego-key and then select the pixels by encryption process with the help of same secret stegokey to hide the data. Each selected pixel will be used to hide 8 bits of data by using LSB method.
A Survey on Natural Language Text Copy Detection

Copy detection has very important application in both intellectual property protection and information retrieval. Currently, copy detection concentrates on document copy detection mainly. In early days, document copy detection concentrated on program plagiarism detection mainly and now the most studies are on text copy detection. In this paper, a comprehensive survey on natural language text copy detection is given, the developments of copy detection is introduced. The approaches and features of a variety of existing text copy detection systems or prototypes are reviewed in detail. Then some key detection techniques are listed and compared with each other. In the end, the future trend of text copy detection is discussed.
How are WEEE doing? A global review of the management of electrical and electronic wastes.

This paper presents and critically analyses the current waste electrical and electronic equipment (WEEE) management practices in various countries and regions. Global trends in (i) the quantities and composition of WEEE; and (ii) the various strategies and practices adopted by selected countries to handle, regulate and prevent WEEE are comprehensively examined. The findings indicate that for (i), the quantities of WEEE generated are high and/or on the increase. IT and telecommunications equipment seem to be the dominant WEEE being generated, at least in terms of numbers, in Africa, in the poorer regions of Asia and in Latin/South America. However, the paper contends that the reported figures on quantities of WEEE generated may be grossly underestimated. For (ii), with the notable exception of Europe, many countries seem to be lacking or are slow in initiating, drafting and adopting WEEE regulations. Handling of WEEE in developing countries is typified by high rate of repair and reuse within a largely informal recycling sector. In both developed and developing nations, the landfilling of WEEE is still a concern. It has been established that stockpiling of unwanted electrical and electronic products is common in both the USA and less developed economies. The paper also identifies and discusses four common priority areas for WEEE across the globe, namely: (i) resource depletion; (ii) ethical concerns; (iii) health and environmental issues; and (iv) WEEE takeback strategies. Further, the paper discusses the future perspectives on WEEE generation, treatment, prevention and regulation. Four key conclusions are drawn from this review: global amounts of WEEE will continue unabated for some time due to emergence of new technologies and affordable electronics; informal recycling in developing nations has the potential of making a valuable contribution if their operations can be changed with strict safety standards as a priority; the pace of initiating and enacting WEEE specific legislation is very slow across the globe and in some cases non-existent; and globally, there is need for more accurate and current data on amounts and types of WEEE generated.