Miracle-dz/UniFAR-FTUs huggingface dataset:
Coverage and rate analysis in two‐tier heterogeneous networks under suburban and urban scenarios

Massive boost in data traffic demand and inconsistent user's behavior have necessitated modern cellular networks to evolve toward heterogeneous architectural framework consisting of macro and small cells to accommodate ever‐increasing user's density. Literature survey reveals that the deployment of additional small cells can encounter the booming coverage, capacity, and QoS constraints by maintaining the overall operational cost of the network. In this paper, at first, a suitable model based on nonhomogeneous Poisson point process (NHPPP) is designed for heterogeneous wireless network (HetNet) consisting of two‐tiers eNodeBs (Macro and Small Cell), where each of the tiers is differentiated in terms of transmitting power, eNodeB density, and supported data rate. Subsequently, analytical expressions are derived for coverage probability (CP) and average rate (AR) to assess the performance of the HetNet. The contribution of the paper further lies in integrating the K‐means clustering algorithm with NHPPP to find the optimal locations of the small cell eNodeBs for extended coverage and rate improvement. The proposed model is investigated under differently dense scenarios like urban and suburban areas in India. It establishes the requisite of an optimal number of small cells along with the traditional infrastructure to maximize the performance in terms of CP and AR. Finally, the proposed integrated model is compared with the traditional homogeneous Poisson point process (HPPP) and NHPPP for coverage and rate analysis. It is observed that the K‐means clustering algorithm in integration with NHPPP overshadows both HPPP and NHPPP in terms of coverage and rate under both urban and suburban deployment scenarios.
Recording Feature Mappings During Evolution of Cloned Variants

Clone-and-Own and software product lines are often used approaches when developing variational software products. Nevertheless, both approaches have negative points which are inevitable when developing variants. While clone-and-own often is not feasible in terms of synchronizing changes into other variants, software product lines are costly when developing only a small amount of variants. In this thesis, we tackle these problems through the creation of an annotation-based approach. Our approach uses line-based feature mappings to implement variability language independent in any textual documents. Feature mappings are recorded automatically during a source edit with as few developer interactions as possible. As automatically feature mapping is hard without knowing the intentions of developers, they select a feature context and a feature context mode which are then used by in the feature mapping calculation. With the feature mapping assigned, developers can also select patches to be synchronized to variants which also implement the calculated feature mapping. In total, our approach mixes elements from clone-and-own and software product lines and enables developer to work as efficient as possible with an acceptable cost-benefit ratio.
Incorporation family competition into Gaussian and Cauchy mutations to training neural networks using an evolutionary algorithm

The paper presents an evolutionary technique to train neural networks in tasks requiring learning behavior. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations. Different mutations act global and local strategies separately to balance the trade-off between solution quality and convergence speed. The algorithm proposed herein is applied to two different task domains: Boolean functions and artificial ant problem. Experimental results indicate that in all tested problems, the proposed algorithm performs better than other canonical evolutionary algorithms, such as genetic algorithms, evolution strategies, and evolutionary programming. Moreover, essential components such as mutation operators and adaptive rules in the proposed algorithm are thoroughly analyzed.
Design of fuzzy weight controller in single-axis magnetic suspension system

A fuzzy approach is used in a single-axis magnetic suspension system. The dynamic model and electric model are derived, and they are highly nonlinear and naturally unstable. With a linear control approach, it is difficult to get good performance in a large region. The fuzzy control method can meet the requirement of starting performance and stability of the single-axis magnetic suspension system with large controlled region. It has been tested with an experimental apparatus that this method can quicken the response speed of the system, and can improve the abilities of anti-disturbance, rigidity and robustness.
Comparative Efficacy of Clinic-Based and Telerehabilitation Application of Mckenzie Therapy in Chronic Low-Back Pain

Studies on validation of telerehabilitation as an effective platform to help manage as well as reduce burden of care for Low-Back Pain (LBP) are sparse. This study compared the effects of Telerehabilitation-Based McKenzie Therapy (TBMT) and Clinic-Based McKenzie Therapy (CBMT) among patients with LBP. Forty-seven consenting patients with chronic LBP who demonstrated ‘directional preference’ for McKenzie Extension Protocol (MEP) completed this quasi experimental study. The participants were assigned into either the CBMT or TBMT group using block permuted randomization. Participants in the CBMT and TBMT groups received MEP involving a specific sequence of lumbosacral repeated movements in extension aimed to centralize, decrease, or abolish symptoms, thrice weekly for eight weeks. TBMT is a comparable version of CBMT performed in the home with the assistance of a mobile phone app. Outcomes were assessed at the 4th and 8th weeks of the study in terms of Pain Intensity (PI), Back Extensors Muscles’ Endurance (BEME), Activity Limitation (AL), Participation Restriction (PR), and General Health Status (GHS). Data were analyzed using descriptive and inferential statistics. Alpha level was set at p< 0.05. Within-group comparison across baseline, 4th and 8th weeks indicate that both CBMT and TBMT had significant effects on PI (p=0.001), BEME (p=0.001), AL (p=0.001), PR (p=0.001) and GHS (p=0.001) respectively. However, there were no significant differences (p>0.05) in the treatment effects between TBMT and CBMT, except for ‘vitality’ (p=0.011) scale in the GHS where TBMT led to significantly higher mean score. Mobile-app platform of the McKenzie extension protocol has comparable clinical outcomes with the traditional clinic-based McKenzie Therapy, and thus is an effective supplementary platform for care of patients with low-back pain.
A Study on a Method Protecting a Secure Network against a Hidden Malicious Code in the Image

On the Web, internet sources not only read and view but also are saved in the computer. In addition, we conveniently download the image file which doesn’t know whether it contains a malicious code or not. Moreover, the Internet Image files are the passages which are easy enough to flow in a closed network, but a malicious code, which is secretly inserted into a picture file, can be performed as a malicious offensive code, that is, a hacking code with triggers such as HTML files or executable JavaScript code. So, we suggest an algorithm for safe use with a blocking method of the image file which doesn’t know whether to contain a malicious performance code in a closed network or not.
Visual word representation in the brain

The human visual system is thought to use features of intermediate complexity for scene representation. How the brain computationally represents intermediate features is unclear, however. To study this, we tested the Bag of Words (BoW) model in computer vision against human brain activity. This computational model uses visual word histograms, candidate features of intermediate complexity, to represent visual scenes, and has proven effective in automatic object and scene recognition. We analyzed where in the brain and to what extent human fMRI responses to natural scenes can be accounted for by BoW representations. Voxel-wise application of a distance-based variation partitioning method reveals that BoW representations explain brain activity in visual areas V1, V2 and in particular V4. Area V4 is known to be tuned for features of intermediate complexity, suggesting that the BoW model captures intermediate-level scene representations in the human brain.
High Step-Up DC–DC Converter for Fuel Cell Vehicles Based on Merged Quadratic Boost–Ćuk

Most fuel cell electric vehicles require wide voltage-gain DC–DC converters to increase and equalize the relatively low voltage of fuel cell stacks with DC link bus or energy-storage devices, such as supercapacitors or batteries. This paper proposes two new non-isolated DC–DC converters suitable for such applications, which can be extended to other electric vehicles as well. The proposed converters combine the main characteristics of both quadratic Boost and Ćuk converters, offering high step-up voltage and control simplicity using only one ground referenced active power switch. Additionally, the proposed topologies present reduced voltage stress across the active power switch when compared to other boost converters. Considerations about the design of the proposed converters will also be presented. Experimental results obtained using a laboratory prototype validate the effectiveness and feasibility of the proposed topologies and its suitability for fuel cell electric vehicles.
Quantitative classification of vortical flows based on topological features using graph matching

Vortical flow patterns generated by swimming animals or flow separation (e.g. behind bluff objects such as cylinders) provide important insight to global flow behaviour such as fluid dynamic drag or propulsive performance. The present work introduces a new method for quantitatively comparing and classifying flow fields using a novel graph-theoretic concept, called a weighted Gabriel graph, that employs critical points of the velocity vector field, which identify key flow features such as vortices, as graph vertices. The edges (connections between vertices) and edge weights of the weighted Gabriel graph encode local geometric structure. The resulting graph exhibits robustness to minor changes in the flow fields. Dissimilarity between flow fields is quantified by finding the best match (minimum difference) in weights of matched graph edges under relevant constraints on the properties of the edge vertices, and flows are classified using hierarchical clustering based on computed dissimilarity. Application of this approach to a set of artificially generated, periodic vortical flows demonstrates high classification accuracy, even for large perturbations, and insensitivity to scale variations and number of periods in the periodic flow pattern. The generality of the approach allows for comparison of flows generated by very different means (e.g. different animal species).
The Consumer Digital Video Library

The shortage of high quality video sequences has hampered video quality research for 20 years. Every Video Quality Experts Group (VQEG) test has encountered difficulty in obtaining quality source content. For some validation tests, this caused a multiple–year delay. To address this critical need, the newly launched Consumer Digital Video Library (CDVL) website (www.cdvl.org) accepts and shares contributions of high–quality video sequences. The site is designed to assist video researchers in the development and testing of new algorithms for video processing, coding, and both subjective and objective quality measurement.
Non-invasive measurement of changes in cerebral mitochondrial oxygenation in human volunteers.

oxygenation in human volunteers Tisdall MM, Tachtsidis I, Leung TS, Elwell CE, Smith M. Department of Neuroanaesthesia and Neurocritical Care, The National Hospital for Neurology and Neurosurgery, Queen Square, University College London Hospitals, London, UK; and Department of Medical Physics and Bioengineering, University College London, London, UK. Introduction: Near infrared spectroscopy (NIRS) is a non-invasive technique which can measure changes in the concentration of oxyhemoglobin (D[HbO2]), deoxy-hemoglobin (D[HHb]), and oxidised cytochrome c oxidase (D[oxCCO]). Assuming the total concentration of cytochrome c oxidase (CCO) remains constant during the measurement period then D[oxCCO] represents changes in the CCO redox state and the near infrared CCO signal therefore has potential as a clinical marker of changes in mitochondrial oxygen utilization. Methods: Following ethics committee approval, we induced a reduction in arterial oxygen saturation (SaO2) from baseline to 80% in eight healthy adult volunteers. We measured cerebral D[oxCCO], D[HbO2] and D[HHb] using broadband NIRS, and estimated change in cerebral oxygen delivery (DecDO2) using pulse oximetry and transcranial Doppler ultrasonography. Results: Results are presented as median (interquartile range). At the nadir of hypoxemia estimated cerebral oxygen delivery decreased by 9.2(5.4-12.1) % (p<0.0001). D[oxCCO] decreased by 0.24(0.06-0.28) micromoles/L (p<0.01), total hemoglobin concentration increased by 2.83(2.27-4.46) micromoles/L (p<0.0001) and change in haemoglobin difference concentration (D[Hbdiff]=D[HbO2] D[HHb]) decreased by 12.72(11.32-16.84) micromoles/L (p<0.0001). The time course of these changes for an individual subject and summary data for the whole group are shown in Figures 1 and 2 respectively. DecDO2 correlated with D[oxCCO] (r=0.78, p<0.001), but not with either change in total hemoglobin concentration (D[HbT]=D[HbO2]+D[HHb]), or D[Hbdiff]. The relationship between D[oxCCO], and D[HbO2] and D[HHb] differed between the hypoxemic and recovery phases of the study (p=0.01) suggesting that the D[oxCCO] signal is not merely a crosstalk artefact resulting from the large changes in D[HbO2] and D[HHb]. Conclusions: In the healthy human brain, change in concentration of oxidised cytochrome c oxidase occurs during moderate hypoxemia and is
The Relationship between the Components of Organizational Agility and Improvement of Managers’ Task and Contextual Performance in Telecommunication Company of Tehran

This study was conducted to investigate the relationship between organizational agility and improvement of managers’ task and contextual performance in Telecommunication Company of Tehran. The research population consisted of all 334 managers of Telecommunication Company of Tehran in 2014. A sample size of 181 was determined using Morgan Table and random sampling method. In terms of purpose, this is an applied research. Data collection was conducted using descriptive-correlational and inferential statistics including Pearson correlation coefficient and regression. Organizational agility and improvement of managers’ performance were the independent and dependent variables of the research, respectively. Data collection instruments consisted of a 30-item questionnaire, made by the researcher, to measure dimensions of organizational agility and Paterson’s standard 15-item questionnaire to measure the dimensions of improved management performance. The reliability coefficient of organizational agility and job performance questionnaires based on Cronbach’s alpha test was 0.945 and 0.845, respectively. The validity of the questionnaires was confirmed by the faculty. Data analysis was conducted using SPSS. The results indicated that organizational agility has a significant and positive impact on the improvement of managers’ performance. Keywords: Organizational agility, performance improvement, managers, and Telecommunication Company.
Distributed Ledger Technology for Trust Management Optimisation in M2M

Decentralised environments with a high number of end-users and M2M devices used in several M2M services increase the importance of a secure trust management and comprehensive trust evaluation system to avoid frauds from malicious nodes. Blockchain, as part of the distributed ledger technology (DLT), helps to improve the overall security in a decentralised M2M community in various aspects. This research publication summarises several existing trust management and evaluation approaches, concluding with their benefits and limitations. Besides these, it highlights the advantages of the distributed ledger technology with the focus on blockchain and consensus mechanisms. In this context, most relevant consensus mechanisms are reviewed and an optimisation concept is proposed. Finally, a blockchain-based trust evaluation system is presented which can be used for trust evaluation and computation of decentralised M2M services part of an M2M community.
Survey of RSA Vulnerabilities

Rivest et al. patented (US) RSA. RSA forms the basis of most public encryption systems. It describes a public key encryption algorithm and certification process, which protects user data over networks. The patent expired in September 2000 and now is available for general use. According to Marketsandmarkets.com, the global network encryption market size is expected to grow from USD 2.9 billion in 2018 to USD 4.6 billion by 2023, at a compound annual growth rate (CAGR) of 9.8%. Major growth drivers for the market include increasing adoption of optical transmission, an increasing demand to meet various regulatory compliances and a growing focus on shielding organizations from network security breaches. In short, RSA forms the basis of almost all public encryption systems. This, however, is not without risk. This chapter explores some of these vulnerabilities in a mathematical context and provides the reader with an appreciation of the strength of RSA.
Evaluation of synthetic vision information system (SVIS) displays based on pilot performance

While Synthetic Vision Information Systems (SVIS) are still in the research and development stages, it is possible that we will see them become an integral part of the commercial flight deck. The goal of this study was to evaluate an SVIS display against a conventional glass cockpit display. The study was conducted in a fixed base laboratory flight simulator located in the Operator Performance Laboratory (OPL) at the University of Iowa. Twelve pilot evaluators hand-flew fairly difficult approaches into the Eagle County Colorado (KEGE) airport. Specially designed questionnaires were used in various phases of the flights to estimate workload, situation awareness, and user preference. Information relating to situation awareness was collected using the Situational Awareness Global Assessment Technique (SAGAT) and workload estimates were obtained using the NASA Task Loading Index (TLX) technique. Eye movement data was collected using a modified IScan ETL 500 system. The resulting eye movement data was used to conduct a link analysis (an analysis of eye movement transitions between areas of interest on the displays) between and within the PFD and ND. Flight Technical Error (FTE) performance was also determined.
Direction and Position Reconstruction on Mobile Robots

The control of autonomous mobile robot motion are still a big challenge due to the uncertainties of the complex environment, noisy sensor signals on robot and failure of mechanical design. Mobile robot needs to determine its location accurately for performing different task such as trajectory tracking and mapping. In this paper, we propose two different rule-based methods for reconstructing robot position and the direction during the motion. We initially introduce the four wheel mobile robot that we have developed for this study by giving the detail information about mechanical, electronic and software design steps and components. The proposed methods are used to estimate position and direction error and then reconstruct its motion while moving in the forward direction. The proposed methods are tested on a real mobile robot.
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories

End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
Increasing the Efficiency of High-Recall Information Retrieval

The goal of high-recall information retrieval (HRIR) is to find all, or nearly all, relevant documents while maintaining reasonable assessment effort. Achieving high recall is a key problem in the use of applications such as electronic discovery, systematic review, and construction of test collections for information retrieval tasks. State-of-the-art HRIR systems commonly rely on iterative relevance feedback in which human assessors continually assess machine learning-selected documents. The relevance of the assessed documents is then fed back to the machine learning model to improve its ability to select the next set of potentially relevant documents for assessment. In many instances, thousands of human assessments might be required to achieve high recall. These assessments represent the main cost of such HRIR applications. Therefore, their effectiveness in achieving high recall is limited by their reliance on human input when assessing the relevance of documents. In this thesis, we test different methods in order to improve the effectiveness and efficiency of finding relevant documents using state-of-the-art HRIR system. With regard to the effectiveness, we try to build a machine-learned model that retrieves relevant documents more accurately. For efficiency, we try to help human assessors make relevance assessments more easily and quickly via our HRIR system. Furthermore, we try to establish a stopping criteria for the assessment process so as to avoid excessive assessment. In particular, we hypothesize that total assessment effort to achieve high recall can be reduced by using shorter document excerpts (e.g., extractive summaries) in place of full documents for the assessment of relevance and using a high-recall retrieval system based on continuous active learning (CAL). In order to test this hypothesis, we implemented a high-recall retrieval system based on state-of-the-art implementation of CAL. This high-recall retrieval system could display either full documents or short document excerpts for relevance assessment. A search engine was also integrated into our system to provide assessors the option of conducting interactive search and judging. We conducted a simulation study, and separately, a 50-person controlled user study to test our hypothesis. The results of the simulation study show that judging even a single extracted sentence for relevance feedback may be adequate for CAL to achieve high recall. The results of the controlled user study confirmed that human assessors were able to find a significantly larger number of relevant documents within limited time when they used the system with paragraph-length document excerpts as opposed to full documents. In addition, we found that allowing participants to compose and execute their own search queries did not improve their ability to find relevant documents and, by some measures, impaired performance. Moreover, integrating sampling methods with active learning can yield accurate estimates of the number of relevant documents, and thus avoid excessive assessments.