nils-herrmann/clean_scirepeval huggingface dataset:
Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

Identity verification is an increasingly important process in our daily lives. Whether we need to use our own equipment or to prove our identity to third parties in order to use services or gain access to physical places, we are constantly required to declare our identity and prove our claim. Traditional authentication methods fall into two categories: proving that you know something (i.e., password-based authentication) and proving that you own something (i.e., token-based authentication). These methods connect the identity with an alternate and less rich representation, for instance a password, that can be lost, stolen, or shared. A solution to these problems comes from biometric recognition systems. Biometrics offers a natural solution to the authentication problem, as it contributes to the construction of systems that can recognize people by the analysis of their anatomical and/or behavioral characteristics. With biometric systems, the representation of the identity is something that is directly derived from the subject, therefore it has properties that a surrogate representation, like a password or a token, simply cannot have (Jain et al. (2006; 2004); Prabhakar et al. (2003)). The strength of a biometric system is determined mainly by the trait that is used to verify the identity. Plenty of biometric traits have been studied and some of them, like fingerprint, iris and face, are nowadays used in widely deployed systems. Today, one of the most important research directions in the field of biometrics is the characterization of novel biometric traits that can be used in conjunction with other traits, to limit their shortcomings or to enhance their performance. The aim of this chapter is to introduce the reader to the usage of heart sounds for biometric recognition, describing the strengths and the weaknesses of this novel trait and analyzing in detail the methods developed so far and their performance. The usage of heart sounds as physiological biometric traits was first introduced in Beritelli & Serrano (2007), in which the authors proposed and started exploring this idea. Their system is based on the frequency analysis, by means of the Chirp z-Transform (CZT), of the sounds produced by the heart during the closure of the mitral tricuspid valve and during the closure of the aortic pulmonary valve. These sounds, called S1 and S2, are extracted from the input 11
Energy Harvesting for Online Condition Monitoring of High Voltage Overhead Power Lines

For electric power transmission, high voltage overhead power lines play an important role as the costs for power transmission are comparatively low. However, the environmental conditions in many geographical regions can change over a wide range. Due to the high voltages, adequate distances between the conductors and objects in the environment have to be ensured for safety reasons. However, sag of the conductors (e.g. due to temperature variations or aging, icing of conductors as a result of extreme weather conditions) may increase safety margins and limit the operability of these power lines. Heavy loads due to icing or vibrations excited by winds increase the risk of line breakage. With online condition monitoring of power lines, critical states or states with increased wear for the conductor may be detected early and appropriate counter measures can be applied. In this paper we investigate possibilities for monitoring devices that are directly mounted onto a conductor. It is demonstrated that such a device can be powered from the electric field around the conductor and that electronic equipment can be protected from the strong electric and magnetic fields as well as transient signals due to partial discharge events.
Language, logic and ontology: uncovering the structure of commonsense knowledge

The purpose of this paper is twofold: ( i) we argue that the structure of commonsense knowledge must be discovered, rather than invented; and ( ii) we argue that natural language, which is the best known theory of our (shared) commo nsense knowledge, should itself be used as a guide to discovering the structure o f commonsense knowledge. In addition to suggesting a systematic method to the discovery of the structure of commonsense knowledge, the method we propose seems to also provide an explanation for a number of phenomena in natural language, such as me t phor, intensionality, and the semantics of nominal compounds. Admittedl y, our ultimate goal is quite ambitious, and it is no less than the systematic ‘dis overy’ of a well-typed ontology of commonsense knowledge, and the subsequent formulation of the longawaited goal of a meaning algebra.
Linear chirp generator based on direct digital synthesis and frequency multiplication for airborne FMCW snow probing radar

This paper presents a linear chirp generator for synthesizing ultra-wideband signals for use in an FM-CW radar being used for airborne snow thickness measurements. Ultra-wideband chirp generators with rigorous linearity requirements are needed for long-range FMCW radars. The chirp generator is composed of a direct digital synthesizer and a frequency multiplier chain. The implementation approach combines recently available high-speed digital, mixed signal, and microwave components along with a frequency pre-distortion technique to synthesize a 6-GHz chirp signal over 240 μs with a <;0.02 MHz/μs deviation from linearity.
Probabilistic reachability for parametric Markov models

Given a parametric Markov model, we consider the problem of computing the rational function expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression is computed. Afterwards, this expression is evaluated to a closed form function representing the reachability probability. This paper investigates how this idea can be turned into an effective procedure. It turns out that the bottleneck lies in the growth of the regular expression relative to the number of states (n Θ(log n)). We therefore proceed differently, by tightly intertwining the regular expression computation with its evaluation. This allows us to arrive at an effective method that avoids this blow up in most practical cases. We give a detailed account of the approach, also extending to parametric models with rewards and with non-determinism. Experimental evidence is provided, illustrating that our implementation provides meaningful insights on non-trivial models.
Decision Support for Watershed Management Using Evolutionary Algorithms

An integrative computational methodology is developed for the management of nonpoint source pollution from watersheds. The associated decision support system is based on an interface between evolutionary algorithms (EAs) and a comprehensive watershed simulation model, and is capable of identifying optimal or near-optimal land use patterns to satisfy objectives. Specifically, a genetic algorithm (GA) is linked with the U.S. Department of Agriculture’s Soil and Water Assessment Tool (SWAT) for single objective evaluations, and a Strength Pareto Evolutionary Algorithm has been integrated with SWAT for multiobjective optimization. The model can be operated at a small spatial scale, such as a farm field, or on a larger watershed scale. A secondary model that also uses a GA is developed for calibration of the simulation model. Sensitivity analysis and parameterization are carried out in a preliminary step to identify model parameters that need to be calibrated. Application to a demonstration watershed located in Southern Illinois reveals the capability of the model in achieving its intended goals. However, the model is found to be computationally demanding as a direct consequence of repeated SWAT simulations during the search for favorable solutions. An artificial neural network (ANN) has been developed to mimic SWAT outputs and ultimately replace it during the search process. Replacement of SWAT by the ANN results in an 84% reduction in computational time required to identify final land use patterns. The ANN model is trained using a hybrid of evolutionary programming (EP) and the back propagation (BP) algorithms. The hybrid algorithm was found to be more effective and efficient than either EP or BP alone. Overall, this study demonstrates the powerful and multifaceted role that EAs and artificial intelligence techniques could play in solving the complex and realistic problems of environmental and water resources systems. CE Database subject headings: Algorithms; Neural networks; Watershed management; Pollution control; Calibration;
Pathophysiology of traumatic brain injury.

The knowledge of the pathophysiology after traumatic head injury is necessary for adequate and patient-oriented treatment. As the primary insult, which represents the direct mechanical damage, cannot be therapeutically influenced, target of the treatment is the limitation of the secondary damage (delayed non-mechanical damage). It is influenced by changes in cerebral blood flow (hypo- and hyperperfusion), impairment of cerebrovascular autoregulation, cerebral metabolic dysfunction and inadequate cerebral oxygenation. Furthermore, excitotoxic cell damage and inflammation may lead to apoptotic and necrotic cell death. Understanding the multidimensional cascade of secondary brain injury offers differentiated therapeutic options.
A Miniaturized Marchand Balun Model With Short-End and Capacitive Feeding

A new equivalent circuit model of an electrical size-reduced coupled line radio frequency Marchand balun is proposed and investigated in this paper. It consists of two parts of coupled lines with significantly reduced electrical length. Compared with the conventional Marchand balun, a short-circuit ending is applied instead of the open-circuit ending, and a capacitive feeding is introduced. The electrical length of the proposed balun is reduced to around 1/3 compared with that of the conventional Marchand balun. Detailed mathematical analysis for this design is included in this paper. Groups of circuit simulation results are shown to verify the conclusions. A sample balun is fabricated in microstrip line type on the Teflon substrate, with low dielectric constant of 2.54. It has a dimension of  $0.189\lambda _{g} \times 0.066 \lambda _{g}$  with amplitude imbalance of 0.1 dB and phase imbalance of 179.09° ± 0.14°. The simulation and experiment results are in good agreement.
Cognitive radio networks: realistic or not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important aspects of commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. While on the one hand it is necessary to detect the existence of spectrum holes, on the other hand, simply sensing (cooperatively or not) the energy emitted from a primary transmitter cannot enable correct dynamic spectrum access. For example, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing solutions to the DSA problem no robust, implementable scheme has emerged. The set of assumptions that these schemes are built upon do not always hold in realistic, wireless environments. Specific settings are assumed, which differ significantly from how existing telecommunication networks work. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, namely TV and cellular, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments. The proposed study extends beyond cognitive radio networks, and further highlights the often existing gap between research and commercialization, paving the way to new thinking about how to accelerate commercialization and adoption of new networking technologies and services.
Collaborative Topic Ranking: Leveraging Item Meta-Data for Sparsity Reduction

Pair-wise ranking methods have been widely used in recommender systems to deal with implicit feedback. They attempt to discriminate between a handful of observed items and the large set of unobserved items. In these approaches, however, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this paper, we propose a novel hierarchical Bayesian framework which incorporates “bag-ofwords” type meta-data on items into pair-wise ranking models for one-class collaborative filtering. The main idea of our method lies in extending the pair-wise ranking with a probabilistic topic modeling. Instead of regularizing item factors through a zero-mean Gaussian prior, our method introduces item-specific topic proportions as priors for item factors. As a by-product, interpretable latent factors for users and items may help explain recommendations in some applications. We conduct an experimental study on a real and publicly available dataset, and the results show that our algorithm is effective in providing accurate recommendation and interpreting user factors and item factors.
Real-Time Energy Management in Microgrids

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.
High Gain Circularly Polarized Wire Antenna for DSRC Applications

In this communication, a low-cost circularly polarized wire antenna exhibiting improved gain performance for Dedicated Short Range Communications (DSRC), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is presented. The proposed antenna comprises a Y-shaped quarterwavelength monopole antenna surrounded by two iterations of eight conductive arched walls acting as parasitic elements to enhance the overall antenna gain and to shape the radiation pattern in the H-plane. A hemispherical radome shell is added to protect the antenna structure and its effect on the antenna performance is discussed. The designed antenna demonstrates antenna gain of 8.2 dB with omnidirectional far-field radiation pattern in the H-plane. The gain of the proposed antenna is also compared with the characteristic of the stand-alone Y-shaped monopole to highlight the advantages of the proposed approach. Circularly polarized, dedicated short-range communication, omnidirectional pattern, vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), Y-shaped wire monopole antenna.
A Survey of Machine Learning Methods for Big Data

Nowadays there are studies in different fields aimed to extract relevant information on trends, challenges and opportunities; all these studies have something in common: they work with large volumes of data. This work analyzes different studies carried out on the use of Machine Learning (ML) for processing large volumes of data (Big Data). Most of these datasets, are complex and come from various sources with structured or unstructured data. For this reason, it is necessary to find mechanisms that allow classification and, in a certain way, organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will also be reviewed.
MR lymphangiography in the treatment of lymphedema.

Lymphedema is a common condition frequently seen in cancer patients who have had lymph node dissection +/- radiation treatment. Traditional management is mainly non-surgical and unsatisfactory. Surgical treatment has relied on excisional techniques in the past. Physiologic operations have more recently been devised to help improve this condition. Assessing patients and deciding which of the available operations to offer them can be challenging. MRI is an extremely useful tool in patient assessment and treatment planning.
Operations Research ( OR ) in Service Industries : A Comprehensive Review

Yiting Xing, Ling Li, Zhuming Bi, Marzena Wilamowska-Korsak and Li Zhang* 1 School of Economics and Management, Chang’an University, Xi’an, China Old Dominion University, Norfolk, VA, USA Department of Engineering, Indiana University Purdue University Fort Wayne, Fort Wayne, IN, USA Safety Engineering Department, College of Engineering, Warmia and Mazury University at Olsztyn, Olsztyn, Poland 5 School of Economics and Management, Beijing Jiaotong University, Beijing, China
Multiple object detection using OpenCV on an embedded platform

Object detection has been attracting much interest due to the wide spectrum of applications that use it. Object detection technology has been driven by an increasing processing power available in software and hardware. In this work we present a developed application for multiple objects detection based on OpenCV libraries. The complexity-related aspects that were considered in the object detection using cascade classifier are described. Furthermore, we discuss the profiling and porting of the application into an embedded platform and comparing the results with the regular platform. The proposed application deals with real time systems implementation and the results give an indication of where the cases of object detection applications may be more complex and where it may be simpler.
Hierarchical dynamic power management using model-free reinforcement learning

Model-free reinforcement learning (RL) has become a promising technique for designing a robust dynamic power management (DPM) framework that can cope with variations and uncertainties that emanate from hardware and application characteristics. Moreover, the potentially significant benefit of performing application-level scheduling as part of the system-level power management should be harnessed. This paper presents an architecture for hierarchical DPM in an embedded system composed of a processor chip and connected I/O devices (which are called system components.) The goal is to facilitate saving in the system component power consumption, which tends to dominate the total power consumption. The proposed (online) adaptive DPM technique consists of two layers: an RL-based component-level local power manager (LPM) and a system-level global power manager (GPM). The LPM performs component power and latency optimization. It employs temporal difference learning on semi-Markov decision process (SMDP) for model-free RL, and it is specifically optimized for an environment in which multiple (heterogeneous) types of applications can run in the embedded system. The GPM interacts with the CPU scheduler to perform effective application-level scheduling, thereby, enabling the LPM to do even more component power optimizations. In this hierarchical DPM framework, power and latency tradeoffs of each type of application can be precisely controlled based on a user-defined parameter. Experiments show that the amount of average power saving is up to 31.1% compared to existing approaches.
Pretreatment methods of lignocellulosic biomass for anaerobic digestion

Agricultural residues, such as lignocellulosic materials (LM), are the most attractive renewable bioenergy sources and are abundantly found in nature. Anaerobic digestion has been extensively studied for the effective utilization of LM for biogas production. Experimental investigation of physiochemical changes that occur during pretreatment is needed for developing mechanistic and effective models that can be employed for the rational design of pretreatment processes. Various-cutting edge pretreatment technologies (physical, chemical and biological) are being tested on the pilot scale. These different pretreatment methods are widely described in this paper, among them, microaerobic pretreatment (MP) has gained attention as a potential pretreatment method for the degradation of LM, which just requires a limited amount of oxygen (or air) supplied directly during the pretreatment step. MP involves microbial communities under mild conditions (temperature and pressure), uses fewer enzymes and less energy for methane production, and is probably the most promising and environmentally friendly technique in the long run. Moreover, it is technically and economically feasible to use microorganisms instead of expensive chemicals, biological enzymes or mechanical equipment. The information provided in this paper, will endow readers with the background knowledge necessary for finding a promising solution to methane production.
DC fault current control of modular multilevel converter with SiC-based Power Electronics Building Blocks

The Medium Voltage dc (MVDC) distribution is actively being sought as the eventual structure for the next-generation shipboard electrical power systems and the Modular Multilevel Converter (MMC) has become a preferred choice for MVDC systems. However, the fault response is a big challenge for both the shipboard MVDC system and the MMC. A fault current control is proposed to work with MMCs composed of full-bridge Power Electronics Building Blocks (PEBBs). The converter is able to fast clear the fault current when a dc shortcircuit fault happens and easily restore the service after the fault is isolated. The fault response is verified by both simulation results and experimental results from a three-phase SiC PEBBs based MMC prototype.