Miracle-dz/UniFAR-FTUs huggingface dataset:
Citation Count Prediction Based on Academic Network Features

Citation count is an important factor to measure the influence of academic publications. Identifying future citation count in advance can help scientists to find references and research area. There are many academic network features which are related to citation count. However, these features have not been completely explored in the existing studies. In this paper, we propose a citation count prediction model based on academic network features. Firstly, some important features are introduced and analyzed in detail. Then, we verify the importance of each feature and use a neural network model to select a set of optimal features. Finally, we present several machine learning methods and one multiple linear regression strategy to predict a paper's future citation. Experimental results on real datasets demonstrate that our model significantly outperforms the baseline method.
Extension of Partial Gene Transcripts by Iterative Mapping of RNA-Seq Raw Reads

Many non-model organisms lack reference genomes and the sequencing and de novo assembly of an organisms transcriptome is an affordable means by which to characterize the coding component of its genome. Despite the advances that have made this possible, assembling a transcriptome without a known reference usually results in a collection of full-length and partial gene transcripts. The downstream analysis of genes represented as partial transcripts then often requires further experimental work in the laboratory in order to obtain full- length sequences. We have explored whether partial transcripts, encoding genes of interest present in de novo assembled transcriptomes of a model and non-model insect species, could be further extended by iterative mapping against the raw transcriptome sequencing reads. Partial sequences encoding cytochrome P450s and carboxyl/cholinesterase were used in this analysis, because they are large multigene families and exhibit significant variation in expression. We present an effective method to improve the contiguity of partial transcripts in silico that, in the absence of a reference genome, may be a quick and cost-effective alternative to their extension by laboratory experimentation. Our approach resulted in the successful extension of incompletely assembled transcripts, often to full length. We experimentally validated these results in silico and using real-time PCR and sequencing.
A Multi-path Extension to RDV Routing Scheme for Static-node-Assisted Vehicular Networks

Vehicular Ad-hoc NETworks (VANETs) in sparse vehicles scenarios can be regarded as a kind of Delay Tolerant Networks (DTNs), and how to provide reliable and efficient communications over them has been extensively studied. Past studies such as SADV and RDV showed that the assistance of lowcost unwired static nodes located at intersections, which work as routers to provide distance-vector or link-state routing functions, significantly improves the communication performance such as delivery ratio and delivery delay. Especially, RDV provides any previously configured value of expected packet delivery ratio by creating the required number of duplicated packet copies on the shortest paths. Despite of the high delivery ratio achievement, RDV still has problems, that is, traffic concentration in the shortest paths and its large delivery delay. In this paper, we extend RDV by using multiple paths to avoid packet concentration as well as to improve delivery delay while preserving the function to provide the preconfigured expected delivery ratio. Evaluation results show that the proposed method MP-RDV (Multi-Path RDV) achieves high load-balancing performance to provide better network capacity, lower delivery delay, and higher fault tolerance against topology changes.
A Study of the Extraction of Bug Judgment and Correction Times from Open Source Software Bug Logs

Cost and time effective solution for software reliability analysis is becoming the biggest concern for open source software development. The open source software development process is a bug driven development and its life cycle cost is mainly incurred in bug correction process. Bug reports, with its various features, play an important role in improving the quality of software products. Unfortunately, less attention is paid towards extraction of important attributes from bug logs. This paper is focused on the comment feature of bug repositories to extract bug judgment and correction times for the open source software reliability model building. A bug report life cycle model is presented and the method to compute the two time points is proposed. The study used datasets from ten official releases of Apache 2.0 to draw empirical results. The proposition is expected to assist in open source software reliability model building.
In-network Self-Learning Algorithms for BEMS Through a Collaborative Fog Platform

Building Energy Management System (BEMS) is a vital approach in constructing a global energy-efficient environment. It can be operated by analyzing data collected from sensors located in designated indoor areas. The key is to improve the data processing results while reducing the total data processing/communication volume required in the whole Internet of Things (IoT) networks as much as possible. In this work, a novel in-network self-learning algorithm for BEMS through a collaborative Fog platform is proposed. In particular, we devise an emerging Fog computing enabled IoT network architecture, where most of data can be processed in the Sensor-to-Fog and Fog-to-Fog layers. Data processing on Cloud is only required if anomalous sensor data are detected, and thus, the energy consumption due to heavy data processing on Cloud will be significantly reduced. The proposed algorithm makes the best use of Fog node capability to realize distributed data collection and processing. Via Fog-to-Fog connections, it can examine the sensor data by collecting them from different search ranges, whose values are meanwhile optimized. Numerical experiments conducted in a real indoor environment demonstrate that our algorithm achieve a high prediction accuracy for anomaly detection even with relatively small sensor data for processing. The effectiveness of Fog node placement is also verified. The overall scheme is expected to be a feasible solution to construct a cost-effective IoT network to minimize energy consumption while maximizing the indoor user's comfort, from the perspective of achieving a high prediction accuracy in BEMS data monitoring.
A New Cross-Layer Approach for MIMO Amplify and Forward Relay System

In this paper, we investigate the joint queuing at the data link layer and orthogonal frequency division multiplexing (OFDM) adaptive modulation at the physical layer for multiple-input-multiple-output (MIMO) relay system. A finite state Markov chain (FSMC) for this scenario has been proposed. Moreover, we consider an amplify and forward (AF) cooperative system with finite-length queue at the data link layer which leads to lower delay quantities. Finally, the packet loss rate and average spectral efficiency (ASE) of this system are evaluated in which the numerical results confirm the benefits of using MIMO nodes in our proposed cross-layer design and demonstrates that by employing cross-layer scheme the ASE decreases with respect to the case without queuing. Also, there is a good match between simulation and theoretical results.
Forensic Speaker Identification Using Speech Quality Data

The performance of speaker recognition systems based on Gaussian mixture models is often impaired both by the low quality and by the short duration of test speech samples. In literature, a large number of best material selection criteria were described, suitable for the scoring stage in forensic automatic speaker recognition systems. An application of quality-based speaker features is described in the present paper which outperforms forensic speaker recognition systems that assume uniform quality of speech during model training and scoring.
Cyber Attack Detection in Platoon-Based Vehicular Networked Control Systems

This paper is concerned with cyber attack detection problem in a platoon-based vehicular networked control system. In such a system, the information among vehicles is transmitted through a shared wireless communication network and also each vehicle has access to its own information measured by local sensors. These kind of systems are highly vulnerable to cyber attacks and therefore, cyber-security issues need to be properly addressed to ensure the safety of the systems. Among various cyber-security aspects, reliable attack detection is of utmost importance as the ability to detect cyber attacks in a timely manner can reduce the damage to the systems. Therefore, we present a cyber attack detection algorithm that is capable of detecting attacks violating both measurements and control command data. This algorithm is based on an ellipsoidal set-membership filtering approach which consists of two sets: prediction ellipsoid set and an estimation ellipsoid set calculated through updating the prediction ellipsoid set with the measurement data. The detection method depends on the existence of intersection between these two sets computed by the filter. Simulation results for some possible cyber attacks are provided to demonstrate the effectiveness of the proposed method.
Process optimization of ultrasound-assisted treatment for soya bean protein isolate/polyacrylamide composite film

In this paper, composite films composed of soya bean protein isolate (SPI) and polyacrylamide (PAM) were prepared under variations of ultrasonic power, treatment time and heating temperature. The effects of the major processing parameters related to ultrasonic-assisted treatment were evaluated and optimized through the single-factor analysis and Box–Behnken design (BBD), respectively, when the tensile strength of composite films was considered as the response value. The single-factor analysis was carried out to study the effects of ultrasonic power, treatment time and heating temperature on the viscosity and cohesion of the slurry and the tensile strength of SPI/PAM composite films, which also provided a reasonable data range of each factor for further optimization. Experiment results indicated that these three factors play a significant role in the tensile strength of films. Then BBD was applied to optimize the treatment conditions of these three factors, using the tensile strength of films as the response value. According to the interactive second-order polynomial model of three factors and the three-dimensional response surface, the maximum tensile strength of films was obtained under the optimal condition. To verify the reliability of the model, the experiment with the optimal condition was conducted, and results demonstrated that the observed tensile strength was in agreement with the predicated one. Also, the morphology and water solubility of the films showed that the film can be coated on the yarns evenly and removed clearly.
Label-Free Biomolecule Detection in Physiological Solutions With Enhanced Sensitivity Using Graphene Nanogrids FET Biosensor

Recently, graphene nanogrid sensor has been reported to be capable of sub-femtomolar sensing of Hepatitis B (Hep-B) surface antigen in buffer. However, for such low concentration of Hep-B in serum, it has been observed during real-time operation that there is an overlap of around 50% in the drain–source current sensitivity values between different concentrations of the target biomolecule, in the range from 0.1 to 100 fM. This has been attributed to the fact that the concentration of non-specific antigen in serum being significantly higher than that of the target antigen, there is a considerable deviation in the number of captured target antigen for the same concentration. Further, this degree of overlap varies from one set to another set of sensor, depending on the statistical variations in the sensor fabrication process. This phenomenon challenges the quantification of target antigen for ultralow limit in physiological analyte. In this paper, we introduce probabilistic neural network (PNN) for quantification of Hep-B down to 0.1 fM in serum using graphene nanogrids field-effect transistor biosensor. The sensor has been operated in heterodyne mode in the frequency range of 100 kHz to 1 MHz applied between drain and source to overcome the problem of Debye screening effect. The application of PNN limits the quantification error within 10% in the range of 0.1 to 100 fM in contrast to 77% and 66% using polynomial fit and static neural network models, respectively. Further, the proposed methodology lowers the detection limit of Hep-B in serum by more than three orders of magnitude compared with the state-of-the-art, real-time, label-free sensors.
Measuring Energy Consumption of a Wireless Sensor Node During Transmission: panStamp

In most of the real-life application scenarios, where Wireless Sensor Networks (WSNs) are deployed, the energy is a limited and valuable resource. Many energy-based strategies were proposed in the literature to reduce the power consumption of sensor nodes and thus enhance the network lifetime. When those algorithms are developed and tested with some well-known network simulators like NS3, OPNET, the energy estimation aspect is not a key criterion, because these simulators have their own specific energy models. By implementing these algorithms in a real sensor node, the energy consumption can be measured and compared to the results provided by other previously implemented approaches. This paper proposes a method for estimating the energy consumption of a low power wireless node panStamp NRG 2.0. The proposed model calculates the consumed energy of the system, at run time for different operation cycles. Besides, since the biggest amount of energy is consumed during the communication, this paper aims to characterize the power consumption in the transmission state. A proper metric of the energy consumed per byte transmission pattern of the packets is provided. The results can be used to derive not only the energy consumption for other protocols but also the energy needed to transmit one byte as well as the payload size are given.
Genetic counseling prior to Assisted Reproductive Technology procedures in the era of cytogenomics

The possibility of sequencing hundreds of genes simultaneously and performing molecular karyotyping thanks to the introduction of novel genetic tools has expanded the use of preconception screening for blastocyst recessive mutations and aneuploidies before embryo transfer, with the ultimate purpose of increasing the proportion of normal healthy newborns. Since medically-assisted reproduction procedures are increasingly required to be eugenic, and the aforementioned genetic tests cover only half of the potential genetic diseases occurring at birth, it seems reasonable to incorporate genetic counseling in the practice of assisted reproduction to avoid prosecution for malpractice.
Maternal and Neonatal Characteristics for Late Foetal Death in Latvia between 2001 and 2014: Population-Based Study

Introduction Stillbirth is one of the most common adverse pregnancy outcomes worldwide. Late foetal death (LFD) rates are mostly used for international comparisons because of the large variations in stillbirth rates between countries. Objective To examine trends in LFD (including antepartum and intrapartum) by multiple births, birth weight, and maternal age in two time periods. Methods A retrospective cohort study was used to analyse data from the Medical Birth Register (2001–2014), divided into 2 periods of 7 years each. In total, data on 1,340 singletons were analysed. This study calculated LFD rates and rate ratios (RR). Results The overall LFD rate showed a slight statistically significant reduction (p < 0.001) of 18% between 2001–2007 and 2008–2014. There was a slight increase in the mortality rate from multiple pregnancies (RR 1.1/1000; 95% CI 0.6-1.9). There were no major differences in the LFD rate by maternal age during the time periods. Conclusions LFD decreased (RR 0.8/1000 births), as well as intrapartum LFD (RR 0.6/1000 births). Older maternal age influenced pregnancy outcomes, and higher LFD rates were observed in the age group ≥35 years. Substantial intrapartum stillbirths rates indicate problems with quality of intrapartum care and emergency obstetric care. Further research is needed to evaluate the strategies necessary to substantially reduce the number of stillbirths in the country.
Human action recognition using depth motion maps pyramid and discriminative collaborative representation classifier

Abstract. Human action recognition is a challenging task in machine learning and pattern recognition. This paper presents an action recognition framework based on depth sequences. An effective feature descriptor named depth motion maps pyramid (DMMP) inspired by DMMs is developed. First, a series of DMMs with temporal scales are constructed to effectively capture spatial–temporal motion patterns of human actions. Then these DMMs are fused to obtain the final descriptor named DMMs pyramid. Second, we propose a discriminative collaborative representation classifier (DCRC), where an extra constraint on the collaborative coefficient is imposed to provide prior knowledge for the representation coefficient. In addition, we apply DCRC to encode the obtained features and recognize the human actions. The proposed framework is evaluated on MSR three-dimensional (3-D) action datasets, MSR hand gesture dataset, UTD-MHAD, and MSR daily Activity3D dataset, respectively. The experimental results indicate the effectiveness of our proposed method for human action recognition.
Measurement of Non-Circular PSFs in Single Pixel Cameras

This paper proposes an improved method for measuring Point Spread Function (PSF) of a camera. With this method the PSF is obtained by analyzing the image of a circular chart taken by the camera to be characterized. The main advantage over the existing methods is the capability to measure PSFs of various shapes not only circular. Single Pixel Camera is one of the imaging systems that could benefit of this method since depending of the light wavelength, the PSF can take either the shape of the aperture or that of Spatial Light Modulator cell. Other advantages of our methods are the capability to correct shear effects of the chart image and to infer the PSF from projections by Inverse Radon Transform. This last feature gives the possibility to also restore the images taken by the camera and not only to characterize the camera. The method is tested on synthetic images of the chart in the case of four different PSF shapes: circular, square, hexagonal and rounded square. The results show a correct estimation of the PSF shape and well restored images obtained by deconvolution with the measured PSF.
Dynamic Collaboration of Centralized & Edge Processing for Coordinated Data Management in an IoT Paradigm

Over the past decade, much focus in the area of Technology has deviated towards two relatively new areas; "The Internet of Things" and "Machine Learning". Although completely separate technologies, they have one major factor in common, Data. The IoT paradigm relies on sensor devices to ingest data and gain valuable insight on their surrounding environment. Data is often considered the newest natural resource. Analysing data instantaneously can give companies a leading edge in their market. Machine learning algorithms are helping companies achieve this feat in the most efficient way possible. In this paper, we propose a governance architecture for dynamic distributed data mining, utilizing a flow based programming inspired model. We illustrate a collaborative protocol between edge devices and central controllers where computation and distribution may be driven by factors including hardware limitations, latency, or energy consumption. Our proposed architecture is evaluated in a connected vehicle use case. To demonstrate the feasibility of our work, we present two scenarios; local real-time prediction of driver alertness, and task/computation offloading based on CPU usage of the edge device.
Triangle-Driven Community Detection in Large Graphs Using Propositional Satisfiability

Discovering the latent community structure is crucial to understanding the features of networks. Several approaches have been proposed to solve this challenging problem using different measures or data structures. Among them, detecting overlapping communities in a network is an usual way towards network structure discovery. It presents nice algorithmic issues, and plays an important role in complex network analysis. In this paper, we propose a new approach to detect overlapping communities in large complex networks. First, we introduce a novel subgraph concept based on triangles to capture the cohesion in social interactions, and propose an efficient approach to discover clusters in networks. Next, we show how the problem of detecting overlapping communities can be expressed as a Partial Max-SAT optimization problem. Our comprehensive experimental evaluation on publicly available real-life networks with ground-truth communities demonstrates the effectiveness and efficiency of our proposed method.
Electronic Health Record Use Among American Neurotology Society Members.

OBJECTIVE To describe the use of electronic health records (EHR) among members of the American Neurotology Society (ANS). STUDY DESIGN Cross-sectional. SETTING Active ANS members in November 2017. INTERVENTION Internet-based survey. MAIN OUTCOME MEASURE Survey that assessed the use of EHR in practice, types of EHR programs, different elements of EHR employed, and respondents' satisfaction and efficiency with EHR. RESULTS One hundred twenty-seven ANS members responded to the survey with 67 (52.8%) respondents working in academic practice and 60 (47.2%) respondents working in private practice. Epic was the most commonly used EHR with 89 (70.1%) respondents using this system. Among all respondents, 84 (66.1%) respondents reported their efficiency was reduced by EHR use, and there was an even split between respondents who reported they were satisfied versus dissatisfied with their EHR (∼40% each). We found that younger members were more likely to feel EHR increased their efficiency compared with the older members (p = 0.04). In all other analyses, we found no significant difference in efficiency and satisfaction between age groups, practice settings, presence of residents or fellows, or specific EHR used. The main challenges reported by ANS members related to the EHR were increased time burden, poor user interface, lack of customizability, and the focus away from patients. CONCLUSIONS The majority of ANS members felt their efficiency decreased as a result of EHR. These findings provide specific changes to the EHR that would improve efficiency and satisfaction among neurotologists.