Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.
Evaluation of the effectiveness of the photobiomodulation in the treatment of dentin hypersensitivity after basic therapy. A randomized clinical trial

Background Dentin hypersensitivity (DH) in one of the most common causes of patient discomfort in the general population and its prevalence is higher in patients who have received basic or surgical periodontal treatment. Efficiency of the diode laser with different wavelengths has been studied by several authors, showing an improvement rate of the DH between 60-98%. The aim of the present study was to evaluate the effect of photobiomodulation (PBM) treatment on the reduction of DH after non surgical periodontal treatment. Material and Methods A randomized split mouth clinical trial was performed involving 30 patients (120 teeth) diagnosed with DH after scaling and root planning. Two teeth of the experimental side were treated with the laser and 2 teeth of the control side were treated without activating the laser. The laser treatment parameters for each tooth were 660nm, 200mW, CW, illuminated area 1.15cm2, 173mW/cm2, 60 seconds, 12 J, 10.4J/cm2. Age, gender, smoking, plaque index, gingival recession, probing and VAS (for tactile and thermal stimulation) were registered before the laser treatment, immediate post treatment (after 2 minutes), 2 weeks, 1 month and 2 months after treatment. Results There was significant difference (p<0.01) in discomfort to thermal and mechanical stimulation between the control and diode laser treatment sites at all evaluation periods. The level of discomfort decreased immediately following diode laser therapy, and continued to demonstrate a decrease for the duration of the study. All teeth remained vital after laser treatment, without adverse reactions or complications. Conclusions The PBM can be used to reduce DH without detrimental pulpal effects. Key words:Dental hypersensitivity, laser, diode laser, photobiomodulation.
ParallelCharMax: An Effective Maximal Frequent Itemset Mining Algorithm Based on MapReduce Framework

Nowadays, the explosive growth in data collection in business and scientific areas has required the need to analyze and mine useful knowledge residing in these data. The recourse to data mining techniques seems to be inescapable in order to extract useful and novel patterns/models from large datasets. In this context, frequent itemsets (patterns) play an essential role in many data mining tasks that try to find interesting patterns from datasets. However, conventional approaches for mining frequent itemsets in Big Data era encounter significant challenges when computing power and memory space are limited. This paper proposes an efficient distributed frequent itemset mining algorithm, called ParallelCharMax, that is based on a powerful sequential algorithm, called Charm, and computes the maximal frequent itemsets that are considered perfect summaries of the frequent ones. The proposed algorithm has been implemented using MapReduce framework. The experimental component of the study shows the efficiency and the performance of the proposed algorithm compared with well known algorithms such as MineWithRounds and HMBA.
Language Identification of Bengali-English Code-Mixed Data using Character & Phonetic based LSTM Models

Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language identification at the word level of low resource Bengali-English code-mixed data taken from social media. We employ two methods of word encoding, namely character based and root phone based to train our deep LSTM models. Utilizing these two models we created two ensemble models using stacking and threshold technique which gave 91.78% and 92.35% accuracies respectively on our testing data.
Surveying the sequence diversity of model prebiotic peptides by mass spectrometry

Significance Peptides and proteins are essential for life as we know it, and likely played a critical role in the origins of life as well. In recent years, much progress has been made in understanding plausible routes from amino acids to peptides. However, little is known about the diversity of sequences that could have been produced by abiotic condensation reactions on the prebiotic earth. In this study, multidimensional separations were coupled with mass spectrometry to detect and sequence mixtures of model proto-peptides. It was observed that, starting with a few monomers, proto-peptide diversity increased rapidly following cycling. Experimental proto-peptide sequences were compared with theoretically random sequences, revealing a high sequence diversity of plausible monomer combinations. The rise of peptides with secondary structures and functions would have been a key step in the chemical evolution which led to life. As with modern biology, amino acid sequence would have been a primary determinant of peptide structure and activity in an origins-of-life scenario. It is a commonly held hypothesis that unique functional sequences would have emerged from a diverse soup of proto-peptides, yet there is a lack of experimental data in support of this. Whereas the majority of studies in the field focus on peptides containing only one or two types of amino acids, here we used modern mass spectrometry (MS)-based techniques to separate and sequence de novo proto-peptides containing broader combinations of prebiotically plausible monomers. Using a dry–wet environmental cycling protocol, hundreds of proto-peptide sequences were formed over a mere 4 d of reaction. Sequence homology diagrams were constructed to compare experimental and theoretical sequence spaces of tetrameric proto-peptides. MS-based analyses such as this will be increasingly necessary as origins-of-life researchers move toward systems-level investigations of prebiotic chemistry.
Mono-(2-ethylhexyl) Phthalate Directly Alters the Expression of Leydig Cell Genes and CYP17 Lyase Activity in Cultured Rat Fetal Testis

Exposure to phthalates in utero alters fetal rat testis gene expression and testosterone production, but much remains to be done to understand the mechanisms underlying the direct action of phthalate within the fetal testis. We aimed to investigate the direct mechanisms of action of mono-(2-ethylhexyl) phthalate (MEHP) on the rat fetal testis, focusing on Leydig cell steroidogenesis in particular. We used an in vitro system based on the culture for three days, with or without MEHP, of rat fetal testes obtained at 14.5 days post-coitum. Exposure to MEHP led to a dose-dependent decrease in testosterone production. Moreover, the production of 5 alpha-dihydrotestosterone (5α-DHT) (−68%) and androstenedione (−54%) was also inhibited by 10 µM MEHP, whereas 17 alpha-hydroxyprogesterone (17α-OHP) production was found to increase (+41%). Testosterone synthesis was rescued by the addition of androstenedione but not by any of the other precursors used. Thus, the hormone data suggested that steroidogenesis was blocked at the level of the 17,20 lyase activity of the P450c17 enzyme (CYP17), converting 17α-OHP to androstenedione. The subsequent gene expression and protein levels supported this hypothesis. In addition to Cyp17a1, microarray analysis showed that several other genes important for testes development were affected by MEHP. These genes included those encoding insulin-like factor 3 (INSL3), which is involved in controlling testicular descent, and Inha, which encodes the alpha subunit of inhibin B. These findings indicate that under in vitro conditions known to support normal differentiation of the fetal rat testis, the exposure to MEHP directly inhibits several important Leydig cell factors involved in testis function and that the Cyp17a1 gene is a specific target to MEHP explaining the MEHP-induced suppression of steroidogenesis observed.
Learning Resource Correlation Mining Based on the Wisdom of Crowds

Mining and setting up semantic relations between learning resources is an important premise to achieve effective learning resource organization, and is also an important basis for deep integration and sharing. To work out the knowledge correlations among the massive volume of learning resources, it is not enough and difficult to implement that only base on machines, because it requires full understanding of the relevant concepts, knowledge relationships, as well as specific domain knowledge, which is hard for machines. This paper studies the common methods of semantic correlation of learning resources, and proposes a knowledge correlation model based on the wisdom of crowds and design a learning Resource Correlation system. The system makes full use of human subjective initiative and elaborately solves the semantic problem of knowledge which is hard to understand for machines. And more, it can effectively aggregate all relation marking and comments submitted by the users to reasonable results.
Neural Correlates of Effective Learning in Experienced Medical Decision-Makers

Accurate associative learning is often hindered by confirmation bias and success-chasing, which together can conspire to produce or solidify false beliefs in the decision-maker. We performed functional magnetic resonance imaging in 35 experienced physicians, while they learned to choose between two treatments in a series of virtual patient encounters. We estimated a learning model for each subject based on their observed behavior and this model divided clearly into high performers and low performers. The high performers showed small, but equal learning rates for both successes (positive outcomes) and failures (no response to the drug). In contrast, low performers showed very large and asymmetric learning rates, learning significantly more from successes than failures; a tendency that led to sub-optimal treatment choices. Consistently with these behavioral findings, high performers showed larger, more sustained BOLD responses to failed vs. successful outcomes in the dorsolateral prefrontal cortex and inferior parietal lobule while low performers displayed the opposite response profile. Furthermore, participants' learning asymmetry correlated with anticipatory activation in the nucleus accumbens at trial onset, well before outcome presentation. Subjects with anticipatory activation in the nucleus accumbens showed more success-chasing during learning. These results suggest that high performers' brains achieve better outcomes by attending to informative failures during training, rather than chasing the reward value of successes. The differential brain activations between high and low performers could potentially be developed into biomarkers to identify efficient learners on novel decision tasks, in medical or other contexts.
Classical molecular dynamics study for defect sink behavior in oxide dispersed strengthened alloys

Oxide Dispersed Strengthened (ODS) alloys are considered as a potential candidate for future generation reactors and can even be graded as better radiation resistant materials than the ones already in use. Molecular dynamics (MD) simulation study for defect sink properties of ODS alloys have been investigated at 700K using LAMMPS (Atomic/Molecular massively parallel simulator software. SRIM (stopping range of ions in matter) code was used to see the approximate range of the ions and damage cascade profile. It has been observed that the oxide particle attracts vacancies/interstitials produced in the base matrix near the interface. The modeled structure looked amorphous near the interface but appears crystalline in the rest region. The modeled structure has defect sink property near the interface. The area for vacancy/interstitial cluster shrinks with the time evolution. Initially, the defects are found scattered near the interface but with time evolution, these recombine and annihilate. After the bombardment Fe atoms show better recover than the oxides, thus it could be concluded that Fe has more tendency to recover than oxide. This atomistic investigation would be beneficial to analyze the multiscale behavior determination of the materials being experimented under severe conditions. This defect sink behavior study would help to assess better radiation resistant materials for the current and future generation reactors.
Amniotic membrane stimulates cell migration by modulating transforming growth factor‐β signalling

Keratinocyte migration is a mandatory aspect of wound healing. We have previously shown that amniotic membrane (AM) applied to chronic wounds assists healing through a process resulting in the overexpression of c‐Jun at the wound's leading edge. We have also demonstrated that AM modifies the genetic programme induced by transforming growth factor‐ß (TGF‐ß) in chronic wounds. Here we used a scratch assay of mink lung epithelial cells (Mv1Lu) and a spontaneously immortalized human keratinocyte cell line (HaCaT) cells to examine the influence of AM application on the underlying signalling during scratch closure. AM application induced c‐Jun phosphorylation at the leading edge of scratch wounds in a process dependent on MAPK and JNK signalling. Strikingly, when the TGF‐ß‐dependent Smad‐activation inhibitor SB431542 was used together with AM, migration improvement was partially restrained, whereas the addition of TGF‐ß had a synergistic effect on the AM‐induced cell migration. Moreover, antagonizing TGF‐ß with specific antibodies in both cell lines or knocking out TGF‐ß receptors in Mv1Lu cells had similar effects on cell migration as using SB431542. Furthermore, we found that AM was able to attenuate TGF‐ß‐Smad signalling specifically at the migrating edge; AM treatment abated Smad2 and Smad3 nuclear localization in response to TGF‐ß in a process dependent on mitogen‐activated protein kinase kinase 1 (MEK1) activation but independent of EGF receptor or JNK activation. The involvement of Smad signalling on AM effects on HaCaT keratinocytes was further corroborated by overexpression of either Smad2 or Smad3 and the use of Smad phosphorylation‐specific inhibitors, revealing a differential influence on AM‐induced migration for each Smad. Thus, AM TGF‐ß‐Smad signalling abating is essential for optimal cell migration and wound closure.
iWalk: Intelligent walking stick for visually impaired subjects

Visually impaired people find great difficulty in moving around freely without a human guide, especially in a new terrain. iWalk is an intelligent walking stick specially designed for visually disabled individuals to improve and ease navigation. iWalk uses ultrasonic and water sensors to detect obstacles and water ahead. The water sensor integrated therein activates a distinct buzzer if it detects water. The system comes with a wireless RF remote control that uniquely sounds buzzier when pressed, that helps in locating the stick in case it gets misplaced. The efficiency of the proposed solution has been adequately tested on a dataset composed of randomized locations with obstacles and water. The proposed algorithm achieves an overall efficiency of 90% detection rate for water and ultrasonic sensor and 85.75% for the RF wireless remote control.
Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending

OBJECTIVE To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. DATA SOURCES 2011-2012 Truven MarketScan database. STUDY DESIGN I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. PRINCIPAL FINDINGS My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. CONCLUSIONS The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas.
Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

A Multilayer Perceptron (MLP) is a feedforward neural network model consisting of one or more hidden layers between the input and output layers. MLPs have been successfully applied to solve a wide range of problems in the fields of neuroscience, computational linguistics, and parallel distributed processing. While MLPs are highly successful in solving problems which are not linearly separable, two of the biggest challenges in their development and application are the local-minima problem and the problem of slow convergence under big data challenge. In order to tackle these problems, this study proposes a Hybrid Chaotic Biogeography-Based Optimization (HCBBO) algorithm for training MLPs for big data analysis and processing. Four benchmark datasets are employed to investigate the effectiveness of HCBBO in training MLPs. The accuracy of the results and the convergence of HCBBO are compared to three well-known heuristic algorithms: (a) Biogeography-Based Optimization (BBO), (b) Particle Swarm Optimization (PSO), and (c) Genetic Algorithms (GA). The experimental results show that training MLPs by using HCBBO is better than the other three heuristic learning approaches for big data processing.
Atom probe tomography of metallic nanostructures

This article focuses on four topics that demonstrate the importance of atom probe tomography for obtaining nanostructural information that provides deep insights into the structures of metallic alloys, leading to a better understanding of their properties. First, we discuss the microstructure–coercivity relationship of Nd-Fe-B permanent magnets, essential for developing a higher coercivity magnet. Second, we address equilibrium segregation at grain boundaries with the aim of manipulating their interfacial structure, energies, compositions, and properties, thereby enabling beneficial material behavior. Third, recent progress in the search to extend the performance and practicality of the next generation of advanced high-strength steels is discussed. Finally, a study of the temporal evolution of a Ni-Al-Cr alloy through the stages of nucleation, growth, and coarsening (Ostwald ripening) and its relationship with the predictions of a model for quasi-stationary coarsening is described. This information is critical for understanding high-temperature mechanical properties of the material.
Overview of Various Sorts of Polymer Nanocomposite Reinforced with Layered Silicate

ABSTRACT Polymer/layered silicate nanocomposites represent an important class of materials in which polymer is hardened by homogeneously dispersed inorganic particles. The most widely used nanoclay is bentonite which mainly consists of montmorillonite. Nanoclays are known to form composites with epoxy resin, polystyrene, poly(methyl metacraylate), polyvinyl chloride, and ternary blends. An outline of advancement in polymer-layered silicate nanocomposite is presented with different preparation methods such as in situ polymerization, solution-induced intercalation, and melt processing. The property enhancement comprises increased strength, decreased flammability, higher modulus, thermal stability, and barrier properties. Thermal, mechanical, and nonflammability property improvement has resulted in automotive and industrial applications. GRAPHICAL ABSTRACT
Dynamic prioritization and cell fixation placement algorithm based on simulated annealing

Placement on the field programmable gate arrays (FPGAs) is one of the most complex and time-consuming parts of the computer-aided design (CAD) algorithms. Nowadays, the simulated annealing (SA) is considered as an effective approach for designing placement algorithms on the FPGAs. In order to design a scalable and high-speed FPGA placer, in this paper, we propose a new dynamic prioritization and cell fixation placement algorithm called PFSA. The main advantages of the proposed algorithm are (a) its scalability and flexibility, (b) dynamic prioritization, (c) low wirelength, and (d) high speed. The proposed algorithm comprises two phases. The first one is the "computing priority of cells" and the second one is the "placing cells based on the priority". The experimental results indicate that the speed of placement in our proposed algorithm is approximately twice than the previous placement algorithms while the accuracy is the same.
Fault Localization Models in Debugging

Debugging is considered as a rigorous but important feature of software engineering process. Since more than a decade, the software engineering research community is exploring different techniques for removal of faults from programs but it is quite difficult to overcome all the faults of software programs. Thus, it is still remains as a real challenge for software debugging and maintenance community. In this paper, we briefly introduced software anomalies and faults classification and then explained different fault localization models using theory of diagnosis. Furthermore, we compared and contrasted between value based and dependencies based models in accordance with different real misbehaviours and presented some insight information for the debugging process. Moreover, we discussed the results of both models and manifested the shortcomings as well as advantages of these models in terms of debugging and maintenance.