Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Creativity and Innovation by Integrating Employability Competences into Training Programs for Promoting Graduates Employability

Recently, countries execute strategies that promote employability and improve labor productivity through the cooperation of the university with the business sector in designing and implementing the quality education and accessibility of training opportunities to meet the changeable expectations of employers and society. In this century the employability competencies have a major impact on the basic criteria for employers ' recruitment. Hence, increasing employability qualifications are considered the main goal of Higher Education Institutions to maximize the achievement of their development goal by obtaining innovative ways to develop a workforce that is equipped with continually updated skills and knowledge. This work is currently piloted in Training and Sustainable Education Agency (TSEA), Community College, Imam Abdurrahman bin Faisal University, Dammam, Saudi Arabia where (TSEA) organized an employment initiative enhancing employability skills and other skills that enable improve the innovation capabilities of the graduates. This initiative includes three training programs: the first one is Job Oriented Training Program (JOTR), the second is the Qualified Training Program for Specialized Certification (QTPSC) and the third is the Collaborative Training Program (CTP). Each Training Program has been designed to develop graduates' abilities to satisfy current and future requirements in job market with diverse global developments. This research concluded that integrating employability competences in training proved its crucial role in enhancing creativity and innovation needed to create employment opportunities in various sectors for promoting sustainable development.
M-CELLS AND THEIR ROLE IN INFECTION: A REVIEW

M-cells are located in the epithelia overlying mucosal tissues where they function as the antigen sampling cells of the mucosal immune system. Paradoxically, M-cells are also exploited by a range of pathogens as a route of host invasion. Despite their pivotal roles in immunity and infection, the mechanisms responsible for micro-organism interaction with M-cells and the origin and developmental pathway of these specialised cells has, until recently, remained obscure. Now, the availability of improved methods for identifying M-cells coupled with a variety of modern research tools is permitting more detailed analysis of M-cell origin and function. The majority of evidence now suggests M-cells primarily arise from undifferentiated crypt cells under stimulation from the local lymphoid environment. However, under appropriate antigenic stimulation, enterocytes may also contribute to the M-cell population. The mechanisms by which pathogens target M-cells are now also partially defined and it has become clear that some of the microbial infection mechanisms previously defined in vitro similarly mediate M-cell infection. However, it also appears that M-cell invasion may proceed via additional routes not previously identified by in vitro studies. Furthermore, it is clear that M-cells, which constitute only a minute proportion of the gastro-intestinal epithelium, are not the sole site of microbial translocation from the gut. These findings indicate that many micro-organisms possess multiple alternate routes of intestinal epithelial invasion; reflecting the critical importance of this initial step in microbial infection. In this paper we review the current state of knowledge about the origin of M-cells and their role in pathogen invasion.
A discriminative learning perspective

Over the last decades, a growing body of evidence on the mechanisms governing lexical storage, access, acquisition and processing has questioned traditional models of language architecture and word usage based on the hypothesis of a direct correspondence between modular components of grammar competence (lexicon vs. rules), processing correlates (memory vs. computation) and neuro-anatomical localizations (prefrontal vs. temporo-parietal perisylvian areas of the left hemisphere). In the present chapter, we explore the empirical and theoretical consequences of a distributed, integrative model of the mental lexicon, whereby words are seen as emergent properties of the functional interaction between basic, language-independent processing principles and the language-specific nature and organization of the input. From this perspective, language learning appears to be inextricably related to the way language is processed and internalized by the speakers, and key to an interdisciplinary understanding of such a way, in line with Tomaso Poggio’s suggestion that the development of a cognitive skill is causally and ontogenetically prior to its execution (and sits “on top of it”). In particular, we discuss conditions, potential and prospects of the epistemological continuity between psycholinguistic and computational modelling of word learning, and illustrate the yet largely untapped potential of their integration. We use David Marr’s hierarchy to clarify the complementarity of the two viewpoints. Psycholinguistic models are informative about how speakers learn to use language (interfacing Marr’s levels 1 and 2). When we move from the psycholinguistic analysis of the functional operations involved in language learning to an algorithmic description of how they are computed, computer simulations can help us explore the relation between speakers’ behavior and general learning principles in more detail. In the end, psycho-computational Vito Pirrelli, Claudia Marzi, Marcello Ferro, Franco Alberto Cardillo, Italian National Research Council (CNR), Institute for Computational Linguistics, Pisa, Italy Harald R. Baayen, Eberhard Karls University, Seminar für Sprachwissenschaft/Quantitative Linguistics Sprachwissenschaft/Quantitative Linguistics, Tübingen, Germany Petar Milin, University of Birmingham, Department of Modern Languages, Edgbaston,
DLDroid: Feature Selection based Malware Detection Framework for Android Apps developed during COVID-19

: COVID-19 acted as a window of opportunity for cyber criminals to develop malware-infected apps. During this lockdown period, everyone is sitting at homes and interacting with others mostly through smartphones. With an exponential increase in Android apps and hence in Android malware, it has become really challenging that how to secure user’s privacy. For this purpose, a number of academicians and researchers have proposed various signature-based and machine learning approaches to detect Android malware. Signature-based approaches can detect only known malware whose signature definitions are already present in its database. On the other hand, machine learning approaches, which were proposed in the literature were developed either with irrelevant features or not able to detect malware which are developed during COVID-19 pandemic. To overcome these issues it becomes highly essential to develop an effective and efficient Android malware detection model. Therefore, in this research paper, 11,000 distinct Android apps are collected, that belong to twelve different categories of Android apps. A total of 1844 unique features from these gathered Android apps are extracted and using ten distinct feature selection approaches irrelevant features have been removed. After that, an Android malware detection framework is developed by using significant features as input and Deep Neural Network (DNN) as machine learning technique. The experiment results reveal that the model developed by using rough set analysis as feature selection approach along with DNN can detect 97.9% malware from real-world apps
QUALITY ANALYSIS OF TEA WITH THE HELP OF MACHINE LEARNING ALGORITHMS

- Tea is a beverage that is always in high demand. Taste of tea is one of a major reason behind that. Taste depends on multiple factors like, type of tea-leafs, ingredients in tea, temperature of boiling, etc. Immense flavor of tea can easily analyze by the color, aroma and texture of tea while serving. Basically this flavor originates from tea leafs. There are multiple features that affect the quality of tea leafs like, briskness, appearance, type, color, aroma, chemical components like Caffeine, Poly-phenolic compounds, etc. If we have the details of these features in advance, we can predict and analyze the quality of tea, prepared by those leafs. Machine learning is a sub branch of artificial intelligence that works on existing data and tries to predict the future data. In the proposed research work, quality analysis of tea is determined by the significant effect of ingredients on the taste of tea and check the probability of getting a quality of tea by using machine learning algorithms. Different grades and qualities exist for all plant products and characters such as color, aroma and texture are used as indicators of the product’s quality. Involving all such features, we are using following machine learning algorithms to measure tea quality: Decision tree-to determine the root cause of tea quality, Naïve Bayes to determine probability of getting different tea quality, Random Forest-to produces several decision trees, Support Vector Machine (SVM) and K-means clustering.
Intravascular Ultrasound Imaging for Evaluation of Aortic Elastic Properties: Review of the Literature and a Single-Center Experience

Introduction: Aortic stress is a well-known cardio-vascular risk factor. For years, different methods have been studied in the assesment of aortic elastic properties and large arterial stiffness for risk stratification. We asses the role of intravascular ultrasound imaging for the evaluation of aortic elastic properties. Methods: Intravascular ultrasound imaging of the aorta was performed in 12 patients with transthoracic echocardiography (TTE) and computed tomography (CT) evidence for enlargement of the ascending aorta – diameter ≥ 40.0 mm. Mechanical properties of the aorta were derived from the measured diameters and intra-aortic pressure. Paired samples T-test analysis was performed to determine difference between measurements derived by TTE, CT and IVUS. Results: Mean values of the calculated elastic properties of the ascending aorta were as follows: compliance 0.021 ± 0.02; strain 205 ± 4.3; aortic stiffness index 4.3 ± 0.75; elastic modulus 0.31 ± 0.05. On paired T-test analysis maximum ascending aortic diameter measured by CT aortography and IVUS did not differ significantly (t = -0.19, p=0.985), but a significant difference between IVUS measurements and TTE derived diameters was found (t = 13.118, p = 0.034). On average, IVUS diameters were 4.1 mm larger than the results acquired by TTE (95% CI [14.21, 17.13]). Conclusion: IVUS examination of the ascending aorta provided larger diameters than the ones collected by means of TTE. However, IVUS measurements did not differ significantly from diameters derived by CT aortography.
Predictors of Length of Stay in Intensive Care Unit After Coronary Artery Bypass Grafting: Development a Risk Scoring System

Introduction To determine predictors of length of stay (LOS) in the intensive care unit (ICU) after coronary artery bypass grafting (CABG) and to develop a risk scoring system were the objectives of this study. Methods In this retrospective study, 1202 patients' medical records after CABG were evaluated by a research-made checklist. Tarone-Ware test was used to determine the predictors of patients' LOS in the ICU. Cox regression model was used to determine the risk factors and risk ratios associated with ICU LOS. Results The mean ICU LOS after CABG was 55.27±17.33 hours. Cox regression model showed that having more than two chest tubes (95% confidence interval [CI] 1.005-1.287, Relative Risk [RR]=1.138), occurrence of atelectasis (95% CI 1.000-3.007, RR=1.734), and occurrence of atrial fibrillation after CABG (95% CI 1.428-2.424, RR=1.861) were risk factors associated with longer ICU LOS. The discrimination power of this set of predictors was demonstrated with an area under the receiver operating characteristic curve and it was 0.69. A simple risk scoring system was developed based on three identified predictors that can raise ICU LOS. Conclusion The simple risk scoring system developed based on three identified predictors can help to plan more accurately a patient's LOS in hospital for CABG and can be useful in managing human and financial resources.
An Automated Approach to Assessing an Application Tutorial’s Difficulty

Online step-by-step text and video tutorials play an integral role in learning feature-rich software applications. However, when searching, users can find it difficult to assess whether a tutorial is designed for their level of software expertise. Novice users can struggle when a tutorial is out of their reach, whereas more advanced users can end up wasting time with overly simple, first-principles instruction. To assist users in selecting tutorials, we investigate the feasibility of using machine-learning techniques to automatically assess a tutorial’s difficulty. Using Photoshop as our primary testbed, we develop a set of distinguishable tutorial features, and use these features to train a classifier that can label a tutorial as either Beginner or Advanced with 85% accuracy. To illustrate a potential application, we developed a tutorial browsing interface called TutVis. Our initial user evaluation provides insight into TutVis’s ability to support users in a range of tutorial selection scenarios.
Digital Weapons in Social Media Manipulation Campaigns

In the last few years, social media have been weaponized to spread false information and affect users’ opinions. Bots represent one of the main actors associated with manipulation campaigns and interference operations on social media. Un-covering the strategies behind bots’ activities is of paramount importance to develop computational tools for their detection. In this paper, we propose to explore bots’ behavior by inspecting the digital weapons (i.e., sharing activities) they utilize to spread content and interact with humans in the run-up to the 2018 US Midterm Election. We observe that bots strategically mimicked the human temporal activity (since several months before the election) and balanced their interaction among human and bot population. Human response to bots’ deceptive activity is alarming: One in three retweets performed by humans comes from a bot-generated content.
Predicting lexical skills from oral reading with acoustic measures

Literacy assessment is an important activity for education administrators across the globe. Typically achieved in a school setting by testing a child's oral reading, it is intensive in human resources. While automatic speech recognition (ASR) is a potential solution to the problem, it tends to be computationally expensive for hand-held devices apart from needing language and accent-specific speech for training. In this work, we propose a system to predict the word-decoding skills of a student based on simple acoustic features derived from the recording. We first identify a meaningful categorization of word-decoding skills by analyzing a manually transcribed data set of children's oral reading recordings. Next the automatic prediction of the category is attempted with the proposed acoustic features. Pause statistics, syllable rate and spectral and intensity dynamics are found to be reliable indicators of specific types of oral reading deficits, providing useful feedback by discriminating the different characteristics of beginning readers. This computationally simple and language-agnostic approach is found to provide a performance close to that obtained using a language dependent ASR that required considerable tuning of its parameters.
Optimal Sequential Maximization One Interview is Enough!

Maximum selection under probabilistic queries (probabilistic maximization) is a fundamental algorithmic problem arising in numerous theoretical and practical contexts. We derive the ﬁrst query-optimal sequential algorithm for probabilistic-maximization. Departing from previous assumptions, the algorithm and performance guarantees apply even for inﬁnitely many items, hence in particular do not require a-priori knowledge of the number of items. The algo-rithm has linear query complexity, and is optimal also in the streaming setting. To derive these results we consider a probabilistic setting where several candidates for a position are asked multiple questions with the goal of ﬁnding who has the highest probability of answering interview questions correctly. Previous work minimized the total number of questions asked by alternating back and forth between the best performing candidates, in a sense, inviting them to multiple interviews. We show that the same order-wise selection accuracy can be achieved by querying the candidates sequentially, never returning to a previously queried candidate. Hence one interview is enough!
END-TO-END DEEP CONVOLUTIONAL NEURAL NETWORK WITH MULTI-SCALE STRUCTURE FOR WEAKLY LABELED SOUND EVENT DETECTION

In this paper, an end-to-end sound event detection algorithm that detects and classiﬁes the sound events from the waveform itself. The proposed model consists of multi-scale time frames and networks to handle both short and long signal characteristics; the frame slides 0.1 second to provide sufﬁciently ﬁne resolution. The element network for each time frame data consists of several one-dimensional convolutional neural networks (1D-CNNs) with deeply stacked structure. The results of element networks are averaged and gated by sound activity detection. The decision is made by performing the double thresholding, and the results are enhanced by class-wise minimum gap/length compensation. To evaluate our proposed network, the simulation was performed with development data from DCASE 2019 Task 4, and the results show that the proposed algo-rithm has a macro-averaged f1-score of 31.7% for the development dataset of DCASE 2019 and 30.2% for the evaluation dataset of DCASE 2018.
The Wondrous World of Carbon Nanotubes

Since their discovery in 1991 by Iijima, carbon nanotubes have been of great interest, both from a fundamental point of view and for future applications. The most eye-catching features of these structures are their electronic, mechanical, optical and chemical characteristics, which open a way to future applications. These properties can even be measured on single nanotubes. For commercial application, large quantities of purified nanotubes are needed. Different types of carbon nanotubes can be produced in various ways. The most common techniques used nowadays are: arc discharge, laser ablation, chemical vapour deposition and flame synthesis. Purification of the tubes can be divided into a couple of main techniques: oxidation, acid treatment, annealing, sonication, filtering and functionalisation techniques. Economically feasible large-scale production and purification techniques still have to be developed. Fundamental and practical nanotube researches have shown possible applications in the fields of energy storage, molecular electronics, nanomechanic devices, and composite materials. Real applications are still under development. This report provides an overview of current nanotube technology, with a special focus on synthesis and purification, energy storage in nanotubes and molecular electronics. Four types of energy storage known in carbon nanotubes are: electrochemical hydrogen storage, gas phase intercalation, electrochemical lithium storage and charge storage in supercapacitors. Modelling aspects and the characteristics of the storage are studied. CNTs have also proven to be good field emitters and single molecule transistors. The characteristics of and the production techniques for these devices are briefly presented.
Black-Box Variational Inference as Distilled Langevin Dynamics:

Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased. In this paper, we analyze gradientbased MCMC and VI procedures and find theoretical and empirical evidence that these procedures are not as different as one might think. In particular, a close examination of the FokkerPlanck equation that governs the Langevin dynamics (LD) MCMC procedure reveals that LD implicitly follows a gradient flow that corresponds to a variational inference procedure based on optimizing a nonparametric normalizing flow. This result suggests that the transient bias of LD (due to the Markov chain not having burned in) may track that of VI (due to the optimizer not having converged), up to differences due to VI’s asymptotic bias and parameterization. Empirically, we find that the transient biases of these algorithms (and their momentum-accelerated counterparts) do evolve similarly. This suggests that practitioners with a limited time budget may get more accurate results by running an MCMC procedure (even if it’s far from burned in) than a VI procedure, as long as the variance of the MCMC estimator can be dealt with (e.g., by running many parallel chains).
Efficacy and Safety of Neuromuscular Blockade in Overweight Patients Undergoing Nasopharyngeal Surgery

Background Adequate muscle relaxation and rapid recovery of neuromuscular function are essential in the perioperative period. We therefore compared various anesthetic regimens of neuromuscular blockers and antagonists administered to overweight patients undergoing nasopharyngeal surgery. Material/Methods This prospective, randomized, double-blind study was conducted in overweight patients undergoing nasopharyngeal surgery. We randomly assigned 102 patients into 3 groups (each n=34) treated with various muscle relaxant agents and antagonists: rocuronium and sugammadex (Group RS), rocuronium and neostigmine (Group RN), and cisatracurium and neostigmine (Group CN). Then, we compared the efficacy and safety indexes of the 3 groups. Results Onset times of muscular relaxation in Group RS and Group RN (110 s and 120 s) were shorter than in Group CN (183 s). Time from administration of antagonist to recovery of the TOF ratio to 0.9 was shorter in Group RS (3.3 min) than in other groups (20.7 min and 19.1 min, respectively). The incidence of postoperative residual curarization (PORC) was significantly lower in Group RS (5.9%) than in the other 2 groups (both 41.2%). The hemodynamic parameter changes before extubation were significantly higher in Group RN and Group CN than in Group RS. The postoperative pain scores were lowest in Group RS. Conclusions For overweight patients undergoing nasopharyngeal surgery, the use of rocuronium with sugammadex had the shortest onset time of neuromuscular relaxation, accelerated the reversion of neuromuscular blockade, effectively reduced the occurrence of PORC, relieved postoperative pain, and maintained hemodynamic stability before extubation. The combination of rocuronium and sugammadex may be the best anesthetic regimen for overweight patients undergoing nasopharyngeal surgery.
FEEDBACK IN A COMPUTER-BASED LEARNING ENVIRONMENT ABOUT QUADRATIC FUNCTIONS: RESEARCH DESIGN AND PILOT STUDY

Even though feedback is an essential part of computer-based learning environments (CBLEs), it is still not clear what effect different types of feedback have on students math achievement and on their abilities to assess their own achievement. To approach this problem we are currently planning a quasi-experimental study with grade eight or nine students using a CBLE called “Discover Quadratic Functions”. This paper gives insight both into existing research concerning the design of CBLE as well as concerning different types of feedback and into the theoretical principals that guided the CBLE design. Additionally, results of a qualitative pilot study as well as the design of the up-coming main study are presented.