Miracle-dz/UniFAR-FTUs huggingface dataset:
Principal Component-Based Approach for Profile Optimization Algorithms in DOCSIS 3.1

Data over cable service interface specification (DOCSIS) introduced the possibility of a variable bit-loading over the subcarriers within a channel in its release DOCSIS 3.1. This variable bit-loading will improve the data rates. However, to limit the encoding processing overhead, the concept of profiles was introduced. Each profile defines the modulation per subcarrier for a given channel while the number of allowed profiles is limited. Thus, an efficient profile assignment scheme, which determines the best set of profiles based on the users’ channel conditions, is needed. Although various profile assignment algorithms have been proposed in the literature, realistic evaluation of these schemes has been difficult, as channel quality measurements of real DOCSIS 3.1 systems has not previously been available. In this paper, we exploit DOCSIS 3.1 measurement data to evaluate performance of the proposed algorithms. We propose to employ principal component analysis to derive low-dimensional clustering variables in order to ensure efficient profile optimization. We show how this technique can be employed with different clustering algorithms to improve the spectrum efficiency of the profiles by extracting the most important information of the channels in low-dimensional vectors. This not only reduces the complexity of the clustering, but also ensures better throughput. Moreover, we adapt the clustering algorithms to tailor them to the profile optimization problem. Finally, we present an exhaustive simulation-based performance analysis to compare the different algorithms for various scenarios using extrapolation of the measurements data.
Relative efficiency of unequal cluster sizes in stepped wedge and other trial designs under longitudinal or cross‐sectional sampling

Background: A cluster trial with unequal cluster sizes often has lower precision than one with equal clusters, with a corresponding inflation of the design effect. For parallel group trials, adjustments to the design effect are available under sampling models with a single intracluster correlation. Design effects for equal clusters under more complex scenarios have appeared recently (including stepped wedge trials under cross‐sectional or longitudinal sampling). We investigate the impact of unequal cluster size in these more general settings.
Stargazin Dephosphorylation Mediates Homeostatic Synaptic Downscaling of Excitatory Synapses

Synaptic scaling is a form of homeostatic plasticity that is critical for maintaining neuronal activity within a dynamic range, and which alters synaptic strength through changes in postsynaptic AMPA-type glutamate receptors. Homeostatic scaling down of excitatory synapses has been shown to occur during sleep, and to contribute to synapse remodeling and memory consolidation, but the underlying mechanisms are only partially known. Here, we report that synaptic downscaling in cortical neurons is accompanied by dephosphorylation of the transmembrane AMPA receptor regulatory protein stargazin, and by an increase in its cell surface mobility. The changes in stargazin surface diffusion were paralleled by an increase in the mobility of GluA1-containing AMPA receptors at synaptic sites. In addition, stargazin dephosphorylation was required for the downregulation of surface levels of GluA1-containing AMPA receptors promoted by chronic elevation of neuronal activity, specifically by mediating the interaction with the adaptor proteins AP-2 and AP-3A. Disruption of the stargazin-AP-3A interaction was sufficient to prevent the decrease in cell surface GluA1-AMPA receptor levels associated with chronically enhanced synaptic activity, suggesting that scaling down is accomplished through decreased AMPA receptor recycling and enhanced lysosomal degradation. Thus, synaptic downscaling is associated with both increased stargazin and AMPA receptor cell surface diffusion, as well as with stargazin-mediated AMPA receptor endocytosis and lysosomal degradation.
Identification of down-regulated microRNAs in thyroid cancer and their potential functions.

BACKGROUND The mechanism of microRNAs (miRNAs) in thyroid cancer is still unclear. We identified miRNAs with differential expression in thyroid cancer versus normal tissues. METHODS Microarray datasets were obtained from the GEO and ArrayExpress databases, and from publications found via PubMed, EMBASE, and Web of Science. Differentially expressed miRNAs were identified using the limma package, and their targets predicted using miRWalk. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analyses were performed using these target genes to explore potential carcinogenic mechanisms. Correlations between target gene and miRNA expression levels were examined. Changes in target protein expression were confirmed using data from The Human Protein Atlas and the Cancer Genome Atlas. RESULTS We ultimately included five datasets, and further analyzed the four miRNAs that were down-regulated in at least four datasets (miR-7-2-3p, miR-138-5p, miR-144-5p, miR-486-5p). Predicted targets were enriched in GO terms including extracellular matrix organization, cell surface, and receptor binding, and in KEGG cancer pathways. PPI analysis identified 10 hub genes as key potential targets of these miRNAs. The expression levels of eight target genes were negatively correlated with those of their respective miRNAs. Furthermore, eight predicted target genes in cancer-related pathways showed up-regulated protein and mRNA expression in thyroid cancer. CONCLUSION Low miRNA expression in thyroid cancer might influence tumorigenesis via critical pathways. The genes identified here may act as a starting point for further investigation of the carcinogenic mechanisms of these miRNAs.
Data Plane Offloading on a High-Speed Parallel Processing Architecture

The throughput supported by network interfaces can be hardly followed by the packet processing speed in software. The processing rate is bounded due to the overheads imposed by the architecture of the network stack. For this reason, multiple research proposals try to offload packet processing on different types of hardware like GPUs and FPGAs. In this paper we present an architecture which offloads the data plane packet processing on a programmable hardware with parallel processing capability. For this purpose, we use the MPPA (Massively Parallel Processor Array) smart NIC from Kalray which offers the ODP API that can be used for packet processing. Our goal is to build a full mesh non-blocking layer 2 network. We implemented a TRILL protocol on the MPPA processor, which can be used as a basis for fabric network, but some other protocol may be used as well. Our performance evaluation shows that we can process TRILL frames at full-duplex line-rate (up to 40Gbps) for different packet sizes while reducing latency.
Fast Approach for Analysis Windows Computation of Multiwindow Discrete Gabor Transform

By using the biorthogonal analysis approach, an effective algorithm based on factorization for solving the analysis/dual windows in multiwindow discrete Gabor transform (M-DGT) is presented for arbitrary given synthesis windows. The constraint condition matrix of the M-DGT between analysis/dual windows and synthesis windows is proved to be equivalent to a fixed number of independent orthogonal relationship matrixes of discrete Gabor transform (DGT), which can be quickly and efficiently solved by using sub-equation sets. The analysis and comparison of computational complexity of related algorithms have indicated that the proposed algorithm provided a faster computational approach for computing analysis/dual windows as compared with that of the existing algorithms, which can save amount of computation and memory.
Robust Process Mining with Guarantees

Organisations store a lot of data from their business processes nowadays. This execution data is often stored in an event log. In many cases, the inner workings of the process are not known to management, or, for instance, only vaguely resemble their three-year old PowerPoint design. Gaining insights into the process as it is executed using only an event log is the aim of process mining. In this thesis, we address three aspects of process mining: process discovery, conformance checking and enhancement.
Sofosbuvir/Velpatasvir/Voxilaprevir: A Highly Effective Option for Retreatment of Hepatitis C in Difficult-to-treat Patients

Treatment for hepatitis C has escalated rapidly since the advent of direct-acting antivirals. Although there are highly efficacious, pangenotypic regimens available as standard of care, 5–10% of patients do not achieve virological cure. The recently approved fixed-dose combination of sofosbuvir, velpatasvir and voxilaprevir provides an option for retreatment in patients who have failed prior regimens and have characteristics which make them difficult to treat. This review provides a summary of the evidence for use of Vosevi®, a fixed-dose combination pill for treatment of hepatitis C in treatment-experienced patients.
Adaptive HF Signal Detection Algorithm CFAR and Its Verification By Means of SDR Based Digital Ionosonde with USRP Platform

The paper presents the findings of the research into the problem of automatic detection of signal contaminated with nonhomogeneous noise and interferences for the purpose of sounding of ionosphere and HF communication multi-channel. The use of fixed threshold detector is not appropriate in this case. Thus, we studied two modifications (CA-CFAR, OS-CFAR) of adaptive threshold detection also referred as constant false alarm rate detection which utilizes changing threshold where the threshold level is raised and lowered depending on noise and interferences power in a HF communication channel.
The alternative approach of low temperature-long time cooking on bovine semitendinosus meat quality

Objective This study aimed to elucidate whether innovative sous vide treatment has a significant influence on the beef semitendinosus muscle as compared to common sous vide treatment and traditional cooking. Methods The innovative sous vide treatments were cooked at 45°C and 65°C for 6 h (SV45–65), common sous vide treatment at 45°C and 65°C for 3 h (SV45 and SV65) and traditional cooking at 75°C for 30 min (CON75). Water loss and cooking loss, as well as the physical properties (color and shear force) and chemical properties (protein and collagen solubility) of the treated meat, were investigated. Results The results obtained indicated that the innovative sous vide with double thermal treatment (SV45–65) and cooked with air presence (CON75) resulted to lower a* and higher b* values, respectively. The water loss and cooking loss increased when temperature increased from 45°C to 65°C, and lower water loss was recorded in SV45 and CON75. These samples presented higher water content and revealed strong correlation to protein solubility. Warner-Bratzler shear force (SF) analysis showed the marked interaction between cooking temperature and time. Sample cooked at a high temperature (CON75) and a long period (SV45–65) showed a significantly lower value of SF than sample SV65 (p<0.05). Interestingly, there was no difference in SF values between SV45–65 and CON75. Conclusion The innovative sous vide treatment with double thermal effect appears an attractive cooking method as compared to common sous vide and traditional cooking method, as it has a potential for improving tenderness values of cooked beef semitendinosus muscle.
Leukocyte Immunoglobulin-Like Receptors A2 and A6 are Expressed in Avian Macrophages and Modulate Cytokine Production by Activating Multiple Signaling Pathways

The activating leukocyte immunoglobulin-like receptors (LILRAs) play an important role in innate immunity. However, most of the LILRA members have not been characterized in avian species including chickens. The present study is the first attempt at cloning, structural analysis and functional characterization of two LILRAs (LILRA2 and LILRA6) in chickens. Multiple sequence alignments and construction of a phylogenetic tree of chicken LILRA2 and LILRA6 with mammalian proteins revealed high conservation between chicken LILRA2 and LILRA6 and a close relationship between the chicken and mammalian proteins. The mRNA expression of LILRA2 and LILRA6 was high in chicken HD11 macrophages and the small intestine compared to that in several other tissues and cells tested. To examine the function of LILRA2 and LILRA6 in chicken immunity, LILRA2 and LILRA6 were transfected into HD11 cells. Our findings indicated that LILRA2 and LILRA6 are associated with the phosphorylation of Src kinases and SHP2, which play a regulatory role in immune functions. Moreover, LILRA6 associated with and activated MHC class I, β2-microglobulin and induced the expression of transporters associated with antigen processing but LILRA2 did not. Furthermore, both LILRA2 and LILRA6 activated JAK-STAT, NF-κB, PI3K/AKT and ERK1/2 MAPK signaling pathways and induced Th1-, Th2- and Th17-type cytokines and Toll-like receptors. Collectively, this study indicates that LILRA2 and LILRA6 are essential for macrophage-mediated immune responses and they have the potential to complement the innate and adaptive immune system against pathogens.
Reliability Enhancement without Employing STCs of a 3 × 3 MIMO Communication System Using a Parallel Decoding Technique

In this paper, the performance analysis of a (3 × 3) Multiple Input Multiple Output (MIMO) communication system is performed to achieve spatial diversity using linear minimum mean square estimation (LMMSE) decoding method at the receiver. We propose an efficient algorithm that achieves both spatial diversity and spatial multiplexing simultaneously. We called it parallel processing decoding technique. The proposed technique increases the rate of decoded symbol, improves the reliability of the transmission without employing STCs, and prevents any possible error propagation that may be presented in other sequential decoding methods. Simulation results demonstrate the advantages of using the proposed novel technique.
Information-Driven Multirobot Behavior Adaptation to Emotional Intention in Human–Robot Interaction

To adapt robots’ behavior to human emotional intention, an information-driven multirobot behavior adaptation mechanism is proposed in human–robot interaction (HRI). In the mechanism, optimal policy of behavior is selected by information-driven fuzzy friend-<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> learning (IDFFQ), and facial expression with identification information are used to understand human emotional intention. It aims to make robots be capable of understanding and adapting their behaviors to human emotional intention, in such a way that HRI runs smoothly. Simulation experiments are performed according to a scenario of drinking at a bar. Results show that the proposed IDFFQ reduces 51 learning steps compared to the fuzzy production rule-based friend-<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> learning (FPRFQ), and computational time is about 1/4 of the time consumed in FPRFQ. In Addition, the accuracy of emotion recognition and emotional intention understanding are 80.36% and 85.71%, respectively. The preliminary application experiments are carried out to the developing emotional social robot system, and the basic experimental results are shown in the scenario of drinking at a bar with three emotional robots and 12 volunteers.
Reading Darwin Between the Lines: A Computer-Assisted Analysis of the Concept of Evolution in

While Darwin’s first major work, On the Origin of Species by means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life, is generally considered as the birth document of the theory of evolution, studies on and around this book often overlook the fact that the word evolution itself is rarely used by the author and thus complicates any attempt to properly analyze the concept. This specific issue will be addressed via a computer-assisted analysis of the concept of evolution in the Origin of Species: by means of automatic classification, categorization, and annotation strategies used in text mining, a new and automated approach for “reading Darwin between the lines” is here proposed, aiming to give a full account of the said concept regardless of any proper designation.
Integrating Behavioural Security Factors for Enhanced Protection of Organisational Information Technology Assets

Recently organisations are beginning to realise that strategic application of controls and the application of security behavioural principles in organisational processes and procedures is a prerequisite to developing a cybersecurity infrastructure that will support economic development in line with organisations value orientation. In this study, we propose a model that attempts to examine how organisational cultural value orientation and employee security behaviour complement each other in fostering organisational compliance to practices and procedure for the protection of information technology assets. Drawing from the theory of planned behaviour, triandis' model and competing value framework, a theoretical model that fuses the constituents of the aforementioned was proposed while relevant hypotheses emanating from relevant and related literatures were also presented. The study's implication for continuing research and how this model will be tested empirically are discussed.
We Like, We Post: A Joint User-Post Approach for Facebook Post Stance Labeling

Web post and user stance labeling is challenging not only because of the informality and variation in language on the Web but also because of the lack of labeled data on fast-emerging new topics—even the labeled data we do have are usually heavily skewed. In this paper, we propose a joint user-post approach for stance labeling to mitigate the latter two difficulties. In labeling post stance, the proposed approach considers post content as well as posting and liking behavior, which involves users. Sentiment analysis is applied to posts to acquire their initial stance, and then the post and user stance are updated iteratively with correlated posting-related actions. The whole process works with limited labeled data, which solves the first problem. We use real interaction between authors and readers for stance labeling. Experimental results show that the proposed approach not only substantially improves content-based post stance labeling, but also yields better performance for the minor stance class, which solves the second problem.
A hybrid approach to select features and classify diseases based on medical data

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms
FPGA-Based Real-Time Super-Resolution System for Ultra High Definition Videos

The market benefits from a barrage of Ultra High Definition (Ultra-HD) displays, yet most extant cameras are barely equipped with Full-HD video capturing. In order to upgrade existing videos without extra storage costs, we propose an FPGA-based super-resolution system that enables real-time Ultra-HD upscaling in high quality. Our super-resolution system crops each frame into blocks, measures their total variation values, and dispatches them accordingly to a neural network or an interpolation module for upscaling. This approach balances the FPGA resource utilization, the attainable frame rate, and the image quality. Evaluations demonstrate that the proposed system achieves superior performance in both throughput and reconstruction quality, comparing to current approaches.
Using Sparse Matrices to Prevent Information Leakage in Cloud Computing

Cloud computing represents the promise of outsourcing of scientific computing such as matrix multiplication. However, this can introduce new vulnerabilities such as information leakage. Cloud server intentionally or unintentionally may reveal sensitive input matrices of the client as well as multiplication results to unauthorised entities. In this paper, we propose two protocols that use sparse matrices to prevent information leakage in outsourcing matrix multiplications to cloud computing without encryption. The protocols are considered lightweight compared to other comparable approaches. We also provide a running example to demonstrate how the protocols ensure no information leakage of client data.
A Realistic Spatial-Distribution-Based Connectivity-Aware Routing Protocol in Multilevel Scenarios of Urban VANETs

In multilevel scenarios, conventional routing protocols choose the next forwarding street based on a metric called connectivity considering the number or density of vehicles. However, they ignore that the uneven spatial distribution of vehicles could also significantly change the connectivity of single-level or multilevel streets. This letter proposes a spatial-distribution-based connectivity-aware routing protocol. It divides a street into multiple segments and gathers the vehicle number and position distribution at each segment to assess the connectivity quality of segments. Then, the proposed protocol evaluates the connectivity of the whole street, in which the shadowing of overpasses would also be taken into account if the street is multilevel. The experimental results show that the proposed protocol outperforms conventional protocols in both end-to-end delay and packet delivery ratio.
A Spatiotemporal Extent Pattern based on Semantic Trajectories

While the concept is certainly not new to ontology modeling, we have not been able to identify a published pattern on the fundamental notion of spatiotemporal extent, which captures the dimensions of things as they move through space and time. We present such a pattern, based on an already published pattern for semantic trajectories.