Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative smoothing filtering schemes by using clipping noise-assisted signals for PAPR reduction in OFDM-based carrier aggregation systems

Aggregating a lot of subcarriers will generate high peak-to-average power ratio (PAPR) which makes the overall system performance degradation. Among the existing PAPR reduction methods for orthogonal frequency-division multiplexing (OFDM)-based systems, iterative clipping and filtering (ICF) may be easy to implement, but ICF may cause signal distortion, so that the system cannot approximate the processed signals to the original signals which degrades error rates at receivers. Here, the authors proposed a new method based on ICF with smoothed clipping signals, and find the optimised filter to achieve the minimum error vector magnitude and efficiently reduce PAPR. Owing to the lack of a wide range of continuous bandwidth, the Third Generation Partnership Project proposed carrier aggregation (CA) technology in LTE-advanced systems. CA can aggregate many contiguous or non-contiguous fragment spectrums for supporting larger bandwidth and higher peak data rate. Here, the authors also evaluate the developed PAPR reduction scheme for the use in CA scenarios. The efficacy of the proposed scheme is demonstrated in the simulation results.
Detection of Central Sleep Apnea Based on a Single-Lead ECG

Central sleep apnea (CSA) is a sleep-related disorder in which breathing is either diminished or absent, typically for 10 to 30 seconds, intermittently or in cycles. CSA is usually due to an instability in the body's feedback mechanisms that control respiration. Central sleep apnea can also be an indicator of Arnold-Chiari malformation. Therefore, various attempts have been made to produce a monitoring system for automatic Central sleep apnea scoring to reduce clinical efforts. This paper describes a system that can identify Central sleep apnea by means of a single-lead ECG and a Multilayer Perceptron network (MLP). Results show that a minute-by-minute classification accuracy of over 83% is achievable.
New development: Budgetary accounting in Colombia—arguments for a much-needed reform

ABSTRACT Public financial management reforms are needed in Colombia to integrate the accrual-based accounting system with budgets. This would remove the need to make accounting variations through deficit measurement and would supply better information about the impact of public policies on the country’s financial situation. This article briefly explains why Colombia needs to integrate its accounting and budgeting systems. IMPACT This article argues that policy-makers need to push for a move towards an accrual-based accounting system that is integrated with the budget. The result, compared with traditional cash-based budgeting, would be greater analytical benefits for government deficit measurement and public finance sustainability analysis. In addition, it would provide the macroeconomic statistics needed to improve public financial management.
URSA: Hybrid Block Storage for Cloud-Scale Virtual Disks

This paper presents URSA, a hybrid block store that provides virtual disks for various applications to run efficiently on cloud VMs. Trace analysis shows that the I/O patterns served by block storage have limited locality to exploit. Therefore, instead of using SSDs as a cache layer, URSA proposes an SSD-HDD-hybrid storage structure that directly stores primary replicas on SSDs and replicates backup replicas on HDDs, using journals to bridge the performance gap between SSDs and HDDs. URSA integrates the hybrid structure with designs for high reliability, scalability, and availability. Experiments show that URSA in its hybrid mode achieves almost the same performance as in its SSD-only mode (storing all replicas on SSDs), and outperforms other block stores (Ceph and Sheepdog) even in their SSD-only mode while achieving much higher CPU efficiency (performance per core). We also discuss some practical issues in our deployment.
Accurate Library Recommendation Using Combining Collaborative Filtering and Topic Model for Mobile Development

SUMMARY Background: The applying of third-party libraries is an integral part of many applications. But the libraries choosing is time-consuming even for experienced developers. The automated recommendation system for libraries recommendation is widely researched to help developers to choose libraries. Aim: from software engineering aspect, our research aims to give developers a reliable recommended list of third-party libraries at the early phase of software development lifecycle to help them build their development environment faster; and from technical aspect, our research aims to build a generalizable recommendation system framework which combines collaborative ﬁltering and topic modeling techniques, in order to improve the performance of libraries recommendation signiﬁcantly. Our works on this research: 1) we design a hybrid methodology to combine collaborative ﬁltering and LDA text mining technology; 2) we build a recommendation system framework successfully based on the above hybrid methodology; 3) we make a well-designed experiment to validate the methodology and framework which use the data of 1,013 mobile application projects; 4) we do the evaluation for the result of the experiment. Conclusions: 1) hybrid methodology with collaborative ﬁl-tering and LDA can improve the performance of libraries recommendation signiﬁcantly; 2) based on the hybrid methodology, the framework works very well on the libraries recommendation for helping developers’ libraries choosing. Further research is necessary to improve the performance of the libraries recommendation including: 1) use more accurate NLP technologies improve the correlation analysis; 2) try other similarity calculation methodology for collaborative ﬁltering to rise the accuracy; 3) on this research, we just bring the time-series approach to the framework and make an experiment as comparative trial, the result shows that the performance improves continuously, so in further research we plan to use time-series data-mining as the basic methodology to update the framework. key words:
Energy efficiency clustering based on Gaussian network for wireless sensor network

In this study, the authors consider the sensors' connection model design and energy optimisation of routing protocol in wireless sensor networks based on the Gaussian network connection model. Accordingly, by the node-symmetric and four different directions to four adjacent nodes of each node in the Gaussian network model, the study recommends a new wireless sensor network connection model, on which the network area will be divided into some virtual square grids. In the new wireless sensor network connection model, they describe each virtual square grid as a node in the Gaussian network, therefrom, they propose a routing method, which is a combination of the shortest path routing protocol in the Gaussian network and clustering protocol to improve the routing efficiency of the wireless sensor network. Some simulations were implemented in NS2, the results show that the proposed routing is very good.
Incentives to Open Access: Perspectives of Health Science Researchers

Technological development has transformed academic publication over the past two decades and new publication models, especially Open Access, have captured an important part of the publishing market, traditionally dominated by the Subscription publication model. Although Health Sciences have been one of the leading fields promoting Open Access, the perspectives of Health Science researchers on the benefits and possibilities of Open Access remain an open question. The present study sought to unveil the perspective of researchers on scientific publication decisions, in terms of the Subscription and Open Access publication model, Gold Road. With this aim, we surveyed Spanish researchers in Health Sciences. Our findings show that the value of publishing in Open Access journals increases as the experience of the researcher increases and the less she/he values the impact factor. Moreover, visibility and dissemination of the results are the main determinants of publication when choosing an Open Access journal as the first option. According to the response of the researchers, the reduction of fees and the increase in financing are important economic incentive measures to promote the Open Access publication model. It is widely accepted that the volume of Open Access publications will increase in the future.
Bismuth‐doped fiber as Q‐switcher in hafnium bismuth erbium co‐doped fiber laser

We demonstrate a compact, all‐fiber Q‐switched hafnium bismuth erbium co‐doped fiber laser based on bismuth‐doped fiber (BDF) as the saturable absorber (SA). The laser cavity was constructed using a homemade hafnium bismuth erbium co‐doped fiber as gain medium. It was found that stable Q‐switched pulses operating at 1531.74 nm can be readily generated when the 980 nm pump power is raised above the threshold of 96 mW. By increasing the pump power to the maximum power of 198 mW, the repetition rate was tunable from 27.2 to 59.1 kHz while the pulse width reduces from 36.3 to 6.8 μs. At the maximum pump power, the Q‐switched laser produced a pulse energy of 10.1 nJ. The finding provides evidence of the possibility of BDF as an effective SA for Q‐switched laser operating in 1.5 μm region.
Simulation integration platforms for cyber-physical systems

Simulation-based analysis is essential in the model-based design process of Cyber-Physical Systems (CPS). Since heterogeneity is inherent to CPS, virtual prototyping of CPS designs and the simulation of their behavior in various environments typically involve a number of physical and computation/communication domains interacting with each other. Affordability of the model-based design process makes the use of existing domain-specific modeling and simulation tools all but mandatory. However, this pressure establishes the requirement for integrating the domain-specific models and simulators into a semantically consistent and efficient system-of-system simulation. The focus of the paper is the interoperability of popular integration platforms supporting heterogeneous multi-model simulations. We examine the relationship among three existing platforms: the High-Level Architecture (HLA)-based CPS Wind Tunnel (CPSWT), MOSAIK, and the Functional Mockup Unit (FMU). We discuss approaches to establish interoperability and present results of ongoing work in the context of an example.
Integrated e-Business System Architecture for Small and Medium Enterprises

Small and Medium Enterprises (SMEs) are organizations that have a tendency to focus only on their business process because they have limitation in terms of resource, both human and financial. E-business is the recommended solution for SMEs to support their business process. E-Business system is a suggested solution for SMEs to support their business processes, where SMEs can use applications in accordance with their organizational characteristics. This system can support business growth in SMEs. But SMEs have very diverse characteristics, based on size, industry sectors, tendency to adopt IT, etc. E-Business solution for SMEs is not a one-size-fits-all solution but need to pay attention to the characteristics of SMEs as the end-users. By adopting the Design Science Research Methodology, this paper proposes an architecture for integrated e-Business System that can be implemented based on the characteristics of SMEs. So that it is expected, SMEs can get e-Business solutions that are in accordance with their characteristics.
Leadership in Chinese higher education and the influence of sociocultural constructs

ABSTRACT Leadership has been increasingly distributed away from formal leaders towards more shared arrangements in Western educational contexts, wherein the vast majority of the existing research literature has been based. There is a dearth of literature on whether leadership strategies that originate in the West could be applied successfully to Asian contexts, especially in relation to China. The need to expand the literature beyond the Western context prompted the authors to conduct this study, which explores how the distinctive aspects of Chinese culture might influence educational leadership. Drawing upon empirical evidence from 42 semi-structured interviews with faculty members in a Chinese university, this study suggests that the development of leadership towards a more distributed or shared model is heavily influenced by cultural aspects such as harmony and socialism, but that even in organisations in which institutional leadership is hierarchical, it is possible for departmental leadership to be less so.
Careful-Packing: A Practical and Scalable Anti-Tampering Software Protection enforced by Trusted Computing

Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional software layers. To illustrate our approach we implemented a secure monitor which collects user activities, such as keyboard and mouse events for insider attack detection. We show how our solution provides a strong anti-tampering guarantee with a low overhead: around 10 lines of code added to the entire application, an average execution time overhead of 5.7% and only 300KB of memory allocated for the trusted module.
A Model for Sibilant Distortion Detection in Children

The distortion of sibilant sounds is a common type of speech sound disorder in European Portuguese speaking children. Speech and language pathologists (SLP) use different types of speech production tasks to assess these distortions. One of these tasks consists of the sustained production of isolated sibilants. Using these sound productions, SLPs usually rely on auditory perceptual evaluation to assess the sibilant distortions. Here we propose to use an isolated sibilant machine learning model to help SLPs assess these distortions. Our model uses Mel frequency cepstral coefficients of the isolated sibilant phones from 145 children, and was trained using support vector machines. The analysis of the false negatives detected by the model can give insight into whether the child has a sibilant production distortion. We were able to confirm that there exists a relation between the model classification results and the distortion assessment of professional SLPs. Approximately 66% of the distortion cases identified by the model are confirmed by an SLP as having some sort of distortion or are perceived as being the production of a different sound.
Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning

As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction. key words: extreme multi-label classification, k-nearest neighbor graph, tree-based classifier
D2TLS: delegation-based DTLS for cloud-based IoT services

The Internet of Things (IoT) becomes proliferated due to the advances in embedded devices, wireless communications, and cloud technologies. However, the security problem in the Internet will be worsened in IoT services considering the constrained resources of IoT devices. We propose a delegation-based DTLS framework (D2TLS) for cloud-based IoT services. D2TLS aims to achieve mutual authentication and to lower the burden of setting up secure connections significantly while keeping the private keys of the IoT devices secret. Leveraging the session resumption in the DTLS standard and introducing a security agent, D2TLS achieves these goals while requiring the modifications only on the client side. That is, the cloud and PKI systems need not change to deploy D2TLS. Numerical results show that D2TLS can achieve better performance in terms of delay and energy consumption than the current DTLS protocol in standalone mode.
Identification and Characterisation of cis-Regulatory Elements Upstream of the Human Receptor Tyrosine Kinase Gene MERTK

BACKGROUND: MERTK encodes a receptor tyrosine kinase that regulates immune homeostasis via phagocytosis of apoptotic cells and cytokine-mediated immunosuppression. MERTK is highly expressed in the central nervous system (CNS), specifically in myeloid derived innate immune cells and its dysregulation is implicated in CNS pathologies including the autoimmune disease multiple sclerosis (MS). OBJECTIVE: While the cell types and tissues that express MERTK have been well described, the genetic elements that define the gene’s promoter and regulate specific transcription domains remain unknown. The primary objective of this study was to define and characterise the human MERTK promoter region. METHODS: We cloned and characterized the 5’ upstream region of MERTK to identify cis-acting DNA elements that promote gene transcription in luciferase reporter assays. In addition, promoter regions were tested for sensitivity to the anti-inflammatory glucocorticoid dexamethasone. RESULTS: This study identified identified both proximal and distal-acting DNA elements that promote transcription. The strongest promoter activity was identified in an ∼850 bp region situated 3 kb upstream of the MERTK transcription start site. Serial deletions of this putative enhancer revealed that the entire region is essential for expression activity. Using in silico analysis, we identified several candidate transcription factor binding sites. Despite a well-established upregulation of MERTK in response to anti-inflammatory glucocorticoids, no DNA region within the 5 kb putative promoter was found to directly respond to dexamethasone treatment. CONCLUSIONS: Elucidating the genetic mechanisms that regulate MERTK expression gives insights into gene regulation during homeostasis and disease, providing potential targets for therapeutic modulation of MERTK transcription.
Analysis of SSD Internal Cache Problem in a Key-Value Store System

In this paper, we analyze how the journaling mechanism on a key-value store has an impact of the performance of a Solid State Drive (SSD). Journaling is a widely used technique in a modern database management system to provide high reliability and fast recovery from system failures. However, journaling mechanism writes the same data twice to both the journal area and data area and this write amplification causes performance degradation by increasing the storage traffic. Eliminating the impact of the write amplification is very challenging. This observation motivated us to change the approach for solving the performance degradation. Instead of handling the problem in system level, we focused on internal SSD level. For the purpose, we analyze the performance bottleneck of an SSD when journaling mechanism is used for a key-value store. A key-value store system model is proposed for the analysis. This paper reveals that frequent write operation to journal area can cause serious SSD performance degradation by increasing the overhead of cache line eviction.
SMChain: a scalable blockchain protocol for secure metering systems in distributed industrial plants

Metering is a critical process in large-scale distributed industrial plants, which enables multiple plants to collaborate to offer mutual services without outside interference. When distributed plants measure the data from a shared common source, e.g., flow metering in an oil pipeline, trustworthiness and immutability must be guaranteed among them. In this paper, we propose a hierarchical and scalable blockchain-based secure metering system, SMChain, to provide strong security, trustworthy guarantee, and immutable services. SMChain adopts a two-layer blockchain structure, consisting of independent local blockchains stored at individual plants and one state blockchain stored in the cloud. To deal with the scalability issues within each plant, we propose a novel scalable Byzantine Fault Tolerance (BFT) consensus protocol based on (k, n)-threshold signature scheme to deal with the Byzantine faults and reduce the intra-plant communication complexity from O(n2) to O(n). For the state blockchain, we use a cloud-based service to synchronize and integrate the local blockchains into one state blockchain, which can further be distributed back to each plant.
Classification of Focal Liver Lesions Using Deep Learning with Fine-Tuning

Liver cancer is one of the leading causes of death worldwide. Computer-aided diagnoses play an important role in liver lesion diagnoses (classification). Recently, several deep-learning-based computer-aided diagnosis systems have been proposed for the classification of liver lesions. The effectiveness of these systems has been demonstrated; however, the main challenge in deep-learning-based medical image classification is the lack of annotated training samples. In this paper, we demonstrate that transfer learning and fine-tuning can significantly improve the accuracy of liver lesion classification, especially for small training samples. We used the residual convolutional neural network (ResNet), which is a state-of-the-art network, as our baseline network for focal liver lesion classification using multi-phase CT images. Fine-tuning significantly improved the classification accuracy from 83.7% to 91.2%. This classification accuracy (91.2%) is higher than that of state-of-the-art methods.
Mental Stress Evaluation of Car Driver in Different Road Complexity Using Heart Rate Variability (HRV) Analysis

Controlling driver stress level is going popular research and put it very important factor to reduce risk of road accident. The aim of the paper is to analysis the impact of road complexity on driver stress level based on physiological factor of Heart Rate Variability (HRV). The first step of the research is literature study on human stress, Heart Rate Variability (HRV), Electrocardiograph (ECG), and NASA TLX mental work load. The driver will use ECG to monitor and then recorded at every heart rate change at any time from three different road conditions of city road, rural road, and motorways. The collected sampling data are 26 male drivers with the average age of 21 years old and average driving experience of 4.08 years. Mental stress evaluation of driver was assessed by frustration level (F) in NASA TLX questioner (subjective measurenment) and HRV in time domain analysis mRR (objective measurenment). The statistic test demontrated that there are not signifficant different mental stress level for driver between mRR and F - NASA TLX. The city road produced avarage F - NASA TLX = 3.92 and mRR = 612.40ms, rural road produced avarage F - NASA TLX = 3.46 and mRR = 621.26 ms, and motorway produced avarage F - NASA TLX = 2.50 and mRR = 820.20 ms. In sort, the mRR of HRV data can be used to monitor the mental stress level of driver in real time as consequence it baneficely implemented in car alert safety system.