Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of QRS Detection Accuracy to Detector Temporal Resolution

The reliable evaluation of the QRS detection algorithm requires comparability and reproducibility. Although it is commonly accepted to test QRS detection accuracy by standard binary classification parameters, much less attention is paid to the temporal accuracy of the detector. A variety of temporal tolerance values are used in the literature, ranging from 60 ms to 160 ms, which sometimes result in comparison of algorithms with different temporal resolutions. This paper addresses a problem of the dependence of the accuracy of QRS detection algorithms represented by detection error rate, sensitivity, and positive predictive value, on the temporal resolution of the detection defined by Detector Temporal Tolerance (DTT). In this work, the classification statistics achieved for three state-of-the-art low complexity algorithms in a broad range of DTT (from 160 ms to 8 ms) for the entire standard MIT-BIH Arrhythmia Database are compared with the performance of the Pan-Tompkins algorithm. The analysis shows that along with decreasing DTT value, the classification statistics for R-peak detection algorithms deteriorate faster or slower with a rate characteristic for a given algorithm. In addition, the algorithms change their positions in the detection accuracy ranking with changing DTT value. The performed analysis proved that DTT is an integral parameter of QRS complex detection that determines the reproducibility of test results and fair comparative study.
Patterns and Motivations of Code Switching and Code Mixing by Egyptian Facebook Users

The Arabic Language is widely used all over the world. It has been influenced recently by certain factors. Two important constituents of these factors are Code-Switching (CS) and Code-Mixing (CM) from Arabic into other languages within the same discourse. The present study addresses the types and causes of CS and CM on face book. Furthermore, it handles the phenomenon of Franco-Arabic Code (FAC), which spreads rapidly via social media, especially facebook. FrancoArabic, as an apparent example of CS and CM, is a mixture of 'Araby' (the Egyptian word for Arabic) and "Inglizi" (the Egyptian word for English). The targets of this study focus on the socio-linguistic features of this phenomenon used by different generations via different websites and Facebook, in particular. It also tackles the types of CS and CM concerning their relation to computer-mediated communication. The study designs and distributes a questionnaire to different categories of people in various work fields. It utilizes the qualitative and quantitative approaches, leading to the results. It depends on three variables, namely age, gender, and recent careers to compare the participants regarding the use of Franco Arabic Code. The sample of analysis covers 483 FB users. Moreover, the questionnaire comprises two types of questions; the first category is named YES/NO questions while the second one is open-ended questions to give the participants )*( Demonstrator, Department of English, Faculty of Arts, Sohag University. This paper is part of an M.A. thesis entitled : Patterns and Motivations of Code Switching and Code Mixing by Egyptian Facebook Users, Supervised by Prof. Bahaa el Deen M. Mazeed – Faculty of AlAlsun, Sohag University & Dr. Thanaa Al Kady – Faculty of Arts, Sohag University. Patterns and Motivations of Code Switching and Code Mixing by Egyptian Facebook Users 42 more freedom to express their notions and motives. A set of studies handle the phenomenon of Franco-Arabic in different places and from different aspects. However, not a single study tackles it in terms of CS and CM, especially via FB, from the perspective of sociolinguistic CS depending on both qualitative and quantitative approaches.
MUD-Based Behavioral Profiling Security Framework for Software-Defined IoT Networks

The rapid development and deployment of Internet of Things (IoT) devices in modern networks and Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring in IoT networks. We aim to explore whether Industrial IoT (IIoT) devices typically serve a common role in cyber–physical systems, and their communications exhibit predictable patterns that can be defined in MUD profile(s) formally and succinctly. We design a framework that utilizes the concept of digital twins and software-defined networking to improve the security of IIoT environments. The MUD data are profiled, and the actions are evaluated on the network digital twin before they are used in the physical network. The behavioral profiling system is updated in real time, thereby improving the overall system security and compliance to policies in the IoT deployment. Evaluation results show that our solution outperforms existing approaches substantially in terms of attack detection accuracy, predicting security incidents, response time, and resource usage.
Stable Fluorescence of Eu3+ Complex Nanostructures Beneath a Protein Skin for Potential Biometric Recognition

We designed and realized highly fluorescent nanostructures composed of Eu3+ complexes under a protein coating. The nanostructured material, confirmed by photo-induced force microscopy (PiFM), includes a bottom fluorescent layer and an upper protein layer. The bottom fluorescent layer includes Eu3+ that is coordinated by 1,10-phenanthroline (Phen) and oleic acid (O). The complete complexes (OEu3+Phen) formed higher-order structures with diameter 40–150 nm. Distinctive nanoscale striations reminiscent of fingerprints were observed with a high-resolution transmission electron microscope (HRTEM). Stable fluorescence was increased by the addition of Eu3+ coordinated by Phen and 2-thenoyltrifluoroacetone (TTA), and confirmed by fluorescence spectroscopy. A satisfactory result was the observation of red Eu3+ complex emission through a protein coating layer with a fluorescence microscope. Lanthanide nanostructures of these types might ultimately prove useful for biometric applications in the context of human and non-human tissues. The significant innovations of this work include: (1) the structural set-up of the fluorescence image embedded under protein “skin”; and (2) dual confirmations of nanotopography and unique nanofingerprints under PiFM and under TEM, respectively.
T HEORIES IN F LUX : R EIMAGINING T HEORY B UILDING IN THE A GE OF M ACHINE L EARNING 1

Researchers employ methodologies that rely on contemporary technologies to study phenomenon. Recent advances in artificial intelligence (AI), particularly machine learning (ML), have intensified the speed, and our abilities, to create and deploy new knowledge for constructing theories (Abbasi et al. 2016). The availability of big data and ML tools is no longer the sole domain of academics. Business processes generate large amounts of data and now practitioners also deploy MLenabled methodologies to create new knowledge through working theories.
MATH-SHU 236 Compressive Sensing

In many applications in science and technology, we face the task of recovering/inferring a signal from its measured data. This task is often reduced to solving a linear equation: y = Ax+ where A ∈ Rm×n, x ∈ R, and y ∈ R. Here the matrix A is given, usually called the design matrix/measurement matrix; y is the observed data; x is the unknown signal; is the noise. In other words, we design a sequence of vectors {ai}i=1 ∈ R and
Supplementary material to "High-resolution modeling the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source-receptor model (GNAQPMS-SM)"

Many efforts have been devoted to quantifying the impact of intercontinental transport on global air quality by using global chemical transport models with horizontal resolutions of hundreds of kilometers in recent decades. In this study, a global online air quality source-receptor model (GNAQPMS-SM) is designed to effectively compute the contributions of various 15 regions to ambient pollutant concentrations. The newly developed model is able to quantify source-receptor (S-R) relationships in one simulation without introducing errors by nonlinear chemistry, which largely reduces the computation costs compared to the brute force method. We calculate the surface and planetary boundary layer (PBL) S-R relationships in 19 regions over the whole globe for ozone, black carbon (BC) and non-sea-salt sulphate (nss-sulphate) by conducting a high-resolution (0.5°×0.5°) simulation for the year 2018. The model exhibits a realistic capacity in reproducing the spatial distributions and 20 seasonal variations of tropospheric ozone, carbon monoxide, and aerosols at global and regional scales (Europe, North America and East Asia). The correlation coefficient (R) and normalized mean bias (NMB) for seasonal ozone at global background and urban-rural sites ranged from 0.49 to 0.87 and -2% to 14.97%, respectively. For aerosols, the R and NMB in Europe, North America and East Asia mostly exceed 0.6 and are within ±15%. These statistical parameters based on this global simulation can match those of regional models in key regions. The simulated tropospheric nitrogen dioxide and aerosol optical depths are 25 generally in agreement with satellite observations. The model overestimates ozone mixing ratios in the upper troposphere and stratosphere in the tropics, mid-latitude and polar regions of the Southern Hemisphere due to the use of a simplified stratospheric ozone scheme and/or biases in estimated stratosphere-troposphere exchange dynamics. We find that O3 in the surface layer can travel a long distance and contributes a nonnegligible fraction to downwind regions. Nonlocal source transport explains approximately 35-60% of surface O3 in East Asia, South Asia, Europe and North America. The O3 exported 30 from Europe can also be transported across the Arctic Ocean to the North Pacific and contributes nearly 5-7.5% to the North Pacific. BC, as a primary aerosol, is directly linked to local emissions, and each BC source region mainly contributes to itself https://doi.org/10.5194/gmd-2021-201 Preprint. Discussion started: 6 July 2021 c © Author(s) 2021. CC BY 4.0 License.
A Novel Model-Based Robust Super-Twisting Sliding Mode Control of PKMs: Design and Real-Time Experiments

In this paper, a new robust model-based super-twisting algorithm is proposed as a control solution for parallel kinematic manipulators (PKMs). The conventional super-twisting algorithm for robot manipulators has the structure of a computed-torque control which can be sensitive to measurement noise. This issue may deteriorate the dynamic performance of the manipulator and reduce its robustness towards changes in the operating conditions. The proposed approach, relying on the desired trajectory, is more computationally efficient and more robust. It includes a feedforward dynamic compensator, the super-twisting feedback control, and a feedback stabilizing term. As a validation, real-time experiments have been conducted on a 5-DOF redundantly actuated PKM. Several scenarios have been tested including nominal case and the robustness towards speed variations. The relevance of the proposed control solution is proved through the improvement of the tracking performance at different dynamic operating conditions.
Effect of Foliar Application With some Micronutrients on ‘Florida Prince’ Cultivar Peach Tree

This study was carried out during the two successive seasons 2015 and 2016 on five years old ‘Florida prince’ peach trees (Prunus persica L.) budded on ‘Nemagard’ peach rootstock, grown in sandy soil under drip irrigation system in a private orchard located at El-Nubaria region, El-Behira Governorate, Egypt. The aims of this research was to investigate the effect of some micronutrients foliar as Boric acid (H3BO3), Zinc sulphate (ZnSO4) and FeEDTA on yield, fruit quality and elemental composition of ‘Florida prince’ peach trees. The followed experimental design was randomized complete block design with five replicates. Results indicated that all the foliar application treatments, significantly increased the yield (kg /tree) and fruit weight (g) as compared with control. The highest yield and fruit weight were recorded with 0.2% Fe-EDTA foliar application treatment (99.85 and 104.40 kg/tree, for two seasons) as compared with the control (36.87 and 37.38 kg/tree, respectively). Moreover, all Boric acid, Zinc sulphate and Fe-EDTA foliar treatments increased length (cm), diameter (cm), firmness (Ib/inch 2 ), total soluble solids (%), acidity (%), and total sugars (%) as compared with control ones during both seasons. On the other hand, A gradual elemental increases in Boron, Iron and Zinc in leaves (%) were recorded with the micronutrient treatments as compared with
Silver/chitosan/ascorbic acid nanocomposites ameliorate diabetic nephropathy in the model of type 1 diabetes

Aims: The present study aimed to evaluate anti-diabetic properties of AgNPs/chitosan/ascorbic acid nanocomposites (Ag-NCs) in streptozotocin-induced diabetic rats. Main methods: Eighteen male Wistar albino rats were divided into three main groups (6 rats/group); control, diabetic and Ag-NCs groups. Control group: after a single dose of citrate buffer (0.1 mol/l, i.p), the rats orally received 1 ml distilled water daily for four weeks. The diabetic model was induced by a single dose of streptozotocin (60 mg/kg, i.p) for type 1diabetes. Diabetic groups were treated orally with and Ag-NCs (0.25mg/Kg body weight) daily for four weeks. Key findings: AgNPs/chitosan/ascorbic acid nanocomposite group showed a reduction in the concentrations of glucose, NO, MDA, creatinine, urea and uric acid. At the same time, it appeared a general increase in insulin, CAT, and SOD activities and GSH concentration. The histopathological investigation illustrated a clear improvement in renal architecture. Significance: The suggested mechanism of action for Ag-NCs in decreasing diabetic nephropathy includes two pathways; the hypoglycemic activity and the antioxidant role of Ag-NCs
Acute Stress Disorder, Posttraumatic Stress Disorder and Complex PTSD AUSTRALIAN GUIDELINES FOR THE TREATMENT OF General considerations when working with children and adolescents

Although most principles that underpin good clinical practice apply equally across various age groups, some differences will inevitably apply when working with children and adolescents. The following considerations should inform every aspect of the way in which clinicians think about, assess, and treat posttraumatic mental health problems in children and adolescents. Many of these points will be elaborated on in the following sections.
High dose gamma irradiation effects on properties of active layers in ZnO thin film transistors

Solution-based bottom-gate zinc oxide thin film transistors (TFTs) were fabricated, remaining functional and demonstrating stability under extreme gamma irradiation conditions. Unpassivated TFTs were fabricated on samples with different number of ZnO layers grown via sol-gel spin coating technique. The devices were characterized before and after exposure to a cumulative dose of 220 MRad (air) of gamma irradiation. Atomic force microscopy (AFM), x-ray diffraction (XRD), and photoluminescence (PL) were employed to characterize the TFT active layers. Thickness measurements and optical images suggest the removal of the channel surface, conceivably due to cumulative effect of displacement damage near the ZnO surface. Device electrical characteristics were extracted from current–voltage measurements. The impact of displacement damage on the degradation/enhancement of device characteristics as a consequence of surface/bulk effects is discussed.
Resonator circuits: a neural network for e � ciently solving factorization problems

For the brain to make sense of knowledge, it must not only be able to represent features about sensory inputs, but also it must be able to store and manipulate this information within data structures. Connectionist theories have long been criticized because it is hard to imagine how complex, hierarchical structures could be represented and processed by neural networks. The connectionist literature proposed “vector-symbolic architectures” (VSA) to solve this problem. Such models deﬁne operations between vectors that are dimensionality-preserving . These deﬁne an algebra that can be used for building hierarchical representations and computing with data structures (4; 1; 3). In the VSA literature, however, deep hierarchical relationships for solving computational problems are rarely utilized. This is because the problem of simultaneous inference, or factorization, arises. Previous solutions required enumerating an unwieldy combinatoric search to solve the inference problem. In this work, we detail an algorithm that makes computation with compound distributed representations signiﬁcantly more practical, because it can e � ciently solve factorization problems without directly enumerating all combinations of components. Rather, the principle of superposition allows a set of recurrent neural networks, called a “resonator circuit”, to iteratively search through the space to resolve factors in a compound representation. The resonator circuit is demonstrated in a scene understanding task, where the system is asked to identify and localize simple objects.
Enhancing Malware Classification with Symbolic Features

Malware is a constant threat for the security of devices and users. Successful and automatic malware detection is a critical necessity [1]. Existing malware detection solutions cannot accurately characterize the behavior of a malware and, thereby, they rely on other indicators, e.g., digital signatures. Nevertheless, behavior-based detection is an active field of research since it can deal with zero-day malware. Although many proposals leveraging machine learning (ML) classifiers have been put forward, finding proper behavioral features is still an open problem. Existing solutions typically consider either static or dynamic software features. Static refers to the program syntax while dynamic refers to features observed at runtime. However, both of them suffer from limitations which impact on the effectiveness of the ML classification. Here we follow a different approach. We used symbolic execution to model features that denote the malware behavior in a more precise way. To this aim, we introduce a novel feature specification language called Symbolic Feature Specification Language (SFSL). Each rule precisely models a specific malicious behavior that has been documented in past malware samples. Then, we apply local, bounded symbolic exploration to establish whether a binary under analysis matches the defined rules. Eventually, the result of the rule matching process is used to generate vectors of features for a ML classifier. Our current experiments with different ML classifiers show that this technique can lead to actual improvements of the classification accuracy. Moreover, since behavioral features do not depend on the program syntax, our methodology can even detect threats in new malware samples.
Capturing Skill State in Curriculum Learning for Human Skill Acquisition∗

Humans learn complex motor skills with practice and training. Though the learning process is not fully understood, several theories from motor learning, neuroscience, education, and game design suggest that curriculum-based training may be the key to efficient skill acquisition. However, designing such a curriculum and understanding its effects on learning are challenging problems. In this paper, we define the Human-skill Curriculum Markov Decision Process (H-CMDP) to systematize the design of training protocols. We also identify a vocabulary of performance features to enable the approximation for a human’s skill level across a variety of cognitive and motor tasks. A novel task domain is introduced as a testbed to evaluate the effectiveness of our approach. Human subject experiments show that (1) participants can learn to improve their performance in tasks within this domain, (2) the learning is quantifiable via our performance features, and (3) the domain is flexible enough to create distinct levels of difficulty. The long-term goal of this work is to systematize the process of curriculum-based training toward the design of protocols for robot-mediated rehabilitation.
Present Status of Aquatic Resource and Its Catch of Mogra River in Bangladesh

Bangladesh is very rich in aquatic fauna with a biodiversity. The present study, conducted during 2015 to 2019, recorded a total number of 131 species (104 fish, 09 prawn, 01 snail, 04 crabs, and 13 turtles) belonging to 26 families were identified from the Mogra River and its flood plain. About ten types of fishing gears, different crafts, hook and line were found oper - ative in the river. Increasing rates of using current jal (16.0-26.40%) and Kapuri jal (11.0-16.70%) were identified as detrimental gears destroying different species. The fish productivity was decreased dramatically from 170.63±10.81mt to 134.75±8.02 mt with a decreasing percentage of 6.26 to 21.03% within five years. Three important aquatic species turtiles (Cyclem - ys oldhami, Melanocheelys trjuuga and Morenia petersi) became rare and 17 commercially important aquatic species were at the edge of extinction (critically endangered, CR). From the study, 67 species were recorded in the endangered (EN) category, 20 species vulnerable status (VU), 11 spe- cies lower risk (LR), 07 species Least concern (LC) and 04 Data deficient (DF). To save the existing aquatic species in the studied riverine ecosystem and ensure better livelihood of the fishes, a team of local management com -mittee, similar to the Hilsa fisheries management technology is needed.
A Pattern Approach to Comprehensible and Pleasant Human-Robot Interaction

HRI designers are faced with the task of creating robots that are easy and pleasant to use for the users. The growing body of research in human–robot interaction (HRI) is still mainly focused on technical aspects of the interaction. It lacks defined guidelines that describe how behavioral expressions for social robots need to be designed to promote high usability and positive user experience. To achieve this goal, we propose to apply the concept of design patterns to HRI. We present a design process that provides step-by-step guidance and methods for HRI designers to generate high quality behavioral patterns for social robots that can be used for different robots and use cases. To document the resulting patterns, we developed a documentation format that provides a clear, standardized structure to note down all relevant aspects of a pattern so that others can understand its design recommendations and apply them to their own robot and use cases. In the present paper, we demonstrate our pattern approach based on an example and describe how we arrived at a pattern language of 40 behavioral patterns that found the basis for future social robot design and related research activities.