Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things: Digital Footprints Carry A Device Identity

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet-connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorised access.
Joint reconstructions of growth and expansion histories from stage-IV surveys with minimal assumptions. II. Modified gravity and massive neutrinos

Based on a formalism introduced in our previous work, we reconstruct the phenomenological function $G_{\rm eff}(z)$ describing deviations from General Relativity (GR) in a model-independent manner. In this alternative approach, we model $\mu\equiv G_\mathrm{eff}/G$ as a Gaussian process and use forecasted growth-rate measurements from a stage-IV survey to reconstruct its shape for two different toy models. We follow a two-step procedure: (i) we first reconstruct the background expansion history from Supernovae (SNe) and Baryon Acoustic Oscillation (BAO) measurements; (ii) we then use it to obtain the growth history $f\sigma_8$, that we fit to redshift-space distortions (RSD) measurements to reconstruct $G_\mathrm{eff}$. We find that upcoming surveys such as the Dark Energy Spectroscopic Instrument (DESI) might be capable of detecting deviations from GR, provided the dark energy behavior is accurately determined. We might even be able to constrain the transition redshift from $G\to G_\mathrm{eff}$ for some particular models. We further assess the impact of massive neutrinos on the reconstructions of $G_\mathrm{eff}$ (or $\mu$) assuming the expansion history is given, and only the neutrino mass is free to vary. Given the tight constraints on the neutrino mass, and for the profiles we considered in this work, we recover numerically that the effect of such massive neutrinos does not alter our conclusions. Finally, we stress that incorrectly assuming a $\Lambda$CDM expansion history leads to a degraded reconstruction of $\mu$, and/or a non-negligible bias in the ($\Omega_\mathrm{m,0}$,$\sigma_{8,0}$)-plane.
Optimizing Readability Using Genetic Algorithms

This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of words, syllables, presence or absence of adverbs, and so on). The nature of these factors allows us to implement a genetic learning strategy to replace some existing words with their most suitable synonyms to facilitate optimization. In addition, this research seeks to preserve both the original text's content and form through multi-objective optimization techniques. In this way, neither the text's syntactic structure nor the semantic content of the original message is significantly distorted. An exhaustive study on a substantial number and diversity of texts confirms that our method was able to optimize the degree of readability in all cases without significantly altering their form or meaning. The source code of this approach is available at https://github.com/jorge-martinez-gil/oruga.
Statistical Machine Translation for Indic Languages

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation (SMT) is a very popular and successful architecture used for both low as well as high-resource languages. SMT uses probabilis-tic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a de-tailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. “Distance” reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a
Mechanisms of magnetization reversals in elliptical thin films

The size dependence of reversal mechanisms and the corresponding switching fields of elliptical Permalloy thin films are studied by micromagnetic simulation. From the simulated spin configurations, it is shown that for ellipses with different dimensions there exist three reversal mechanisms: the buckling-instability mode, the vortex-propagation mode, and the mixed mode of buckling-instability and vortex-propagation modes.
Test Reuse based on Adaptive Semantic Matching across Android Mobile Applications

Automatic test generation can help verify and develop the behavior of mobile applications. Test reuse based on semantic similarities between applications of the same category has been utilized to reduce the manual effort of Graphical User Interface (GUI) testing. However, most of the existing studies fail to solve the semantic problem of event matching, which leads to the failure of test reuse. To overcome this challenge, we propose TRASM (Test Reuse based on Adaptive Semantic Matching), a test reuse approach based on adaptive strategies to find a better event matching across android mobile applications. TRASM first performs GUI events deduplication on the initial test set obtained from test generation, and then employs an adaptive strategy to find better event matching, which enables reusing the existing test. Preliminary experiments with comparison to baseline methods on 15 applications demonstrate that TRASM can improve the precision of GUI event matching while reducing the failure of test reuse and the running time required for test reuse.
DRSOM: A Dimension Reduced Second-Order Method

In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only curvature information in a few directions. Consequently, the computational overhead of our method remains comparable to the first-order such as the gradient descent method. Theoretically, we show that the method has a local quadratic convergence and a global convergence rate of $O(\epsilon^{-3/2})$ to satisfy the first-order and second-order conditions if the subspace satisfies a commonly adopted approximated Hessian assumption. We further show that this assumption can be removed if we perform a corrector step using a Krylov-like method periodically at the end stage of the algorithm. The applicability and performance of DRSOM are exhibited by various computational experiments, including $L_2 - L_p$ minimization, CUTEst problems, and sensor network localization.
Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.
Classification and synthesis of permanent magnet bearing configurations

Quite a number of configurations allow to get passive permanent magnet thrust or radial bearings. However, most of existing devices are based on the same two or three structures. In many cases, a different geometrical structure keeping the same magnetic and mechanical characteristics would allow a simpler and cheaper realisation. We present here a synthesis of passive bearings allowing original structures.
CMOS photonics for optical manipulation of particles and biosensing

We will discuss the latest progress in our work on using complementary metal-oxide semiconductor (CMOS) photonics for optical manipulation of dielectric microparticles and submicrometer particles in a microfluidic channel. Specifically, we will review optical trapping and routing of particles using silicon nitride waveguide-based directional couplers and multimode-interference (MMI)-based couplers. Our experiments reveal that microparticles can be directionally coupled from one waveguide to another waveguide via evanescent light coupling over submicrometer gap spacing. We also observe that microparticles can be preferentially transported to the larger field-intensity output-port of a 1×2 MMI optical power splitter. We thus envision that these photonic components, along with other photonic components that have previously been demonstrated with functionalities of optical manipulation of particles in fluids, constitute basic building blocks of CMOS optofluidic "particle circuits" for particle manipulation and biosensing.
Expression of Three Clones of PD-L1 in Lung Cancer: A Single-center Experience

Abstract Background/Aim: Programmed cell death ligand 1 (PD-L1) is an immune checkpoint protein involved in immune evasion of malignant tumors. Confirmation of PD-L1 expression in non-small cell lung cancer (NSCLC) is necessary for the determination of immunotherapy using immune checkpoint inhibitors (ICIs). PDL-1 expression is currently analyzed by immunohistochemistry and is the only available biomarker that can guide the treatment of NSCLC using ICIs. The present study was conducted to compare the expression of three different commercial clones of PD-L1 in order for immunohistochemistry (IHC) for these clones to become more reliable for surgical pathologists. Materials and Methods: This study examined the expression of PD-L1 in 76 cases of resected lung cancer using IHC. Three clones were examined: SP263, SP142, and 22C3PharmDx, which are commercially approved for quantifying PD-L1 expression in lung cancer. Results: Of the 76 patients whose samples were evaluated for PD-L1 using the IHC 22C3pharmDx assay, 19 (25.0%) had a tumor proportion score (TPS) of ≥50% and 41 (53.9%) had a PD-L1 TPS of ≥1%. Furthermore, using the SP263, 48.7% had a TPS of ≥1% and 18.4% of >50%. The SP142 assay was used to evaluate tumor cells (TCs) and immune cells (ICs). Twenty (26.3%) cases were positive for TCs and 25 (32.9%) were reactive for ICs. Conclusion: These three commercial PD-L1 clones are comparable for detecting primary targets for anti-tumor immunotherapies. Careful evaluation by a pathologist is necessary to minimize misinterpretation errors.
Evaluation of TET Family Gene Expression and 5-Hydroxymethylcytosine as Potential Epigenetic Markers in Non-small Cell Lung Cancer

Abstract Background/Aim: DNA methylation is the most studied epigenetic modification in cancer. Ten-eleven translocation enzymes (TET) catalyze the oxidation of 5-methylcytosine (5-mC) to 5-hydroxymethylcytosine (5-hmC) in the DNA. In the current research, we aimed to evaluate the role of 5-hmC and TET enzymes in non-small cell lung cancer (NSCLC) patients and their possible association with outcomes. Patients and Methods: ELISA was used to measure the 5-hmC levels in genomic DNA and qRT-PCR was used to evaluate TET1, TET2, and TET3 mRNAs expression levels in NSCLC tissues and their paired normal controls. Results: The levels of 5-hmC were significantly lower in NSCLC tissues than in normal tissues, with a mean ±SD of 0.28±0.37 vs. 1.84±0.58, respectively (t=22.77, p<0.0001), and this reduction was correlated with adverse clinical features. In addition, all TET genes were significantly down-regulated in NSCLC tissues in comparison to their matched normal tissues. The mean±SD level of TET1-mRNA was 38.48±16.38 in NSCLC vs. 80.65±11.25 in normal tissues (t=21.33, p<0.0001), TET2-mRNA level in NSCLC was 5.25±2.78 vs. 9.52±1.01 in normal tissues (t=14.48, p<0.0001), and TET3-mRNA level in NSCLC was 5.21±2.8 vs. 9.51±0.86 in normal tissues (t=14.75, p<0.0001). Downregulation of TET genes was correlated with poor clinical features. Conclusion: 5-HmC levels as well as TET1, TET2, and TET3 mRNA levels were reduced in NSCLC tissues. The reduced levels of 5-hmC and TET mRNAs were associated with adverse clinical features, suggesting that the level of 5-hmC may serve as a valuable prognostic biomarker for NSCLC.
Lichens as an Indicator of Sea-Level Rise

Abstract. Coastal areas around the globe are threatened with sea-level rise (SLR). Many of these areas support coastal forests, and determining the threat is complicated by wind patterns and sea floor and shoreline variability, beyond simple elevational concerns. Lichens can be used as indicators of SLR at site specific locations. Developing vegetative indicators of SLR could assist land managers, municipalities, and homeowners in evaluating the risk of building homes, roads, and other structures in some locations. The absence of salt sensitive lichen species can be an indicator of SLR potential. We base this on lichen distribution patterns within two Florida state parks – one a barrier island, the other on the nearby mainland. Rapidly colonizing lichen taxa with asexual reproductive structures are poor indicators of SLR, because of their ability to quickly recolonize following storm events and other disturbances. Knowledge of lichen distribution patterns in combination with reproductive traits help facilitate the development of lichen indicator guidelines. We have learned that Coccocarpia palmicola, Dirinaria picta, and Heterodermia albicans do not appear to be salt-tolerant but have abundant asexual propagules and recolonize rapidly. Sites lacking salt sensitive lichens such as Bulbothrix laevigatula, Cryptothecia rubrocincta, Leptogium cyanescens and Parmotrema rampoddense could indicate areas that are at high risk to SLR at a fine scale. Such areas should be avoided for the construction of homes, roads, and other structures. Lichens make good indicator species and could help guide management of coastal areas in Florida and much of the U.S. southeast Coastal Plain.
Kolmogorov's ideas in the theory of operations on sets

CONTENTS Foreword Introduction § 1. Introduction to the theory of operations on sets § 2. The Kolmogorov hierarchy § 3. The R-transform § 4. Inductive analysis. Indices § 5. R-sets: construction of the hierarchy § 6. The nature of R-sets § 7. Properties of R-sets § 8. Kolmogorov's problem on the lengths of Borel hierarchies Conclusion References
Fronto-parietal single-trial brain connectivity benefits successful memory recognition

Abstract Successful recognition has been known to produce distinct patterns of neural activity. Many studies have used spectral power or event-related potentials of single recognition-specific regions as classification features. However, this does not accurately reflect the mechanisms behind recognition, in that recognition requires multiple brain regions to work together. Hence, classification accuracy of subsequent memory performance could be improved by using functional connectivity within memory-related brain networks instead of using local brain activity as classifiers. In this study, we examined electroencephalography (EEG) signals while performing a word recognition memory task. Recorded EEG signals were collected using a 32-channel cap. Connectivity measures related to the left hemispheric fronto-parietal connectivity (P3 and F3) were found to contribute to the accurate recognition of previously studied memory items. Classification of subsequent memory outcome using connectivity features revealed that the classifier with support vector machine achieved the highest classification accuracy of 86.79 ± 5.93% (mean ± standard deviation) by using theta (3–8 Hz) connectivity during successful recognition trials. The results strongly suggest that highly accurate classification of subsequent memory outcome can be achieved by using single-trial functional connectivity.
Maintaining the Magic: Adapting Practice Pedagogies Across Course Modalities

ABSTRACT Social work programs are facing the need to gain further clarity about how to leverage instructional strategies specific to the course modality (i.e., traditional, hybrid, online hybrid, and fully online). At its heart, this challenge is an analogical transfer problem: practice instructors must adapt what they do in one modality to new modalities through modifying how they guide students to master learning objectives. In doing so, instructors can maintain the essence of what makes a practice course work. The discussion is subsequently shaped around common modalities that programs offer (1) traditional courses; (2) hybrid courses (in-person/online); (3) online hybrid courses (synchronous/asynchronous online); and (4) fully asynchronous online courses. This paper presents an organizing framework that explains pedagogical choices by structures (i.e., space, time, and people) and processes (i.e., interactivity). It then provides examples of how practice instructors can navigate the unique strengths and limitations that simultaneously facilitate and constrain interactions between students and instructors, their peers, and the instructional content. As instructors grapple with the inevitable shifts within the academy, they require specific tools to help them generalize existing skills. The proposed framework is an important first step to help shape and evaluate best practices across and within modalities.
Career Choices of BSW Graduates of an Older Adults Concentration: An Analysis of Twenty Cohorts

ABSTRACT Most of the studies exploring social work education’s impact on working with older adults (OA) examine intentions among students. This study goes beyond expressed intention and focuses upon the actual post-graduation career choices of 20 cohorts (1996–2016) of BSW graduates of an OA concentration at an Israeli school of social work. The 203 study participants comprise 60% of all the concentration`s graduates. Findings showed that 47% of the graduates never worked with OA after graduation, 25% reported that they had but this was no longer the case, and 28% are currently working with OA. Graduates distanced themselves from the aging field primarily because they found it uninteresting or unappealing and viewed social work with OA negatively. However, most of those currently working with older adults associated this work with positive feelings toward OA and work with them, favorable workplace conditions, and a commitment to advancing the field of social work with OA. Social work educators now need to rethink how to better expose students to work with OA during the concentration in ways that increase their positive experience and attitudes toward working with this population and that can increase the likelihood of a career choice to work with them.
Design of a protein-targeted DNA aptamer using atomistic simulation

Abstract The concentrations of specific macromolecular species can be quantified using diagnostic tools that rely on molecular recognition by nucleic acid aptamers. One such approach involves the formation of osmium tetroxide 2,2'-bipyridine protein adducts, followed by electrochemical detection of analytes that bind specifically to electrode-tethered aptamers. In conjunction with a 27-mer DNA aptamer that binds specifically to exosite II on human alpha thrombin, this technique permits, in theory, a highly sensitive diagnostic tool for the quantification of serum thrombin levels. However, thrombin's aptamer binding site is lined by two tryptophan residues and the conjugation of bulky osmium groups to these residues weakens aptamer binding by an estimated 4 to 12 kcal/mol, undermining detection sensitivity. Therefore, we have rationally modified this DNA aptamer to strengthen its thrombin binding in the presence of conjugated osmium. Specifically, aptamers carrying long hydrophobic thymine derivatives in place of guanine 21 have binding affinities for osmium-conjugated thrombin that are enhanced by 10 to 15 kcal/mol, suggesting that these modified aptamers may be effective in a highly sensitive electrochemical sensor for the quantification of low concentrations of thrombin. Our approach of using molecular simulation to subtly re-engineer a DNA aptamer may be generally applicable for the optimization of other macromolecular binding interfaces. Communicated by Ramaswamy H. Sarma
Manifestation of the Relationship Between Molecular Relaxation Processes and Vibrational Dynamics of Polymers at Terahertz Frequencies in Their IR Spectra

Abstract The temperature dependence of low-frequency IR spectra of glassy polymers in the range ν = 0.24–4 THz (8–135 cm−1) at temperatures from 90 to 400 K has been studied. Their analysis made it possible to detect absorptions corresponding to both individual and correlated torsional vibrations, which couple to the manifestation of relaxation dynamics. The temperature evolution of IR spectra at terahertz frequencies shows 3 universal features: low-temperature, corresponding to the anharmonicity of torsional-vibrational motion, high-temperature, due to the contribution of the α-relaxation (glass transition), and intermediate, associated with unfreezing of the conformational mobility of chains during the β-transition. The results obtained were shown to be applicable to polymers with both hydrogen and van der Waals bonds between the macromolecules.
Search for a low mass standard model Higgs Boson with the ATLAS detector at the LHC

In 2011, the ATLAS experiment collected 4.9 fb−1 of proton-proton collision data at √s = 7 TeV provided by the Large Hadron Collider. This data set allowed the search for a Standard Model Higgs Boson up to 1 TeV. In this paper, the focus is put on the searches of a low mass Higgs in the range (110-150) GeV. Using the 2011 ATLAS dataset, the combination of all the channels showed that a SM Higgs boson was restricted to live in the region (116.6-119.4) GeV and (122.1-129.2) GeV. Importantly, an excess of events has been observed around 126 GeV with a statistical significance of 2.9 standard deviations. The present document describes the final results published by ATLAS based on the 2011 dataset alone.