Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Machine and Deep Learning Classifications in Cyber Physical System

Cyber physical system is the most widely used infrastructure for solving different challenges in our day to day lives. In big data environment, taking a correct and rapid decision remains as a big task. Internet of Things or cyber physical systems will be used for transforming the manufacturing industry and other applications to the next level. But, along with these benefits of Cyber physical system, there are numerous problems that one face in their life due to the lack of smart analytical tools that affect the industries, which are not handling the large amount of data generated from the Internet connected devices. Even the skilled people are unable to handle, track or analyze these infrastructures. This creates a necessity for the integration of ML and cyber security in CPS, where it makes the skilled people to track threats on web within a less time period. The proposed research study is performed on the different frameworks used for Cyber-attack detection using learning approach, this proves the importance of machine learning and deep learning in Cyber physical system for detecting the threats in a better way. Security analytics is used by various researchers and also by using it one can prioritize the signals and alerts. The proposed study on different attacks has also highlighted the researchers to be more aware about uncommon attacks that can become very dangerous. Further, the study of various works done in analyzing different attacks are done using various approaches and dataset that is covered along with pros and cons to help in choosing the best approach according to the requirement.
On Understanding Data Scientists

Data is everywhere and in everything we do. Most of the time, usable information is hidden in raw data and because of that, there is an increasing demand for people capable of working creatively with it. To fully understand how we can assist data science workers to become more productive in their jobs, we first need to understand who they are, how they work, what are the skills they hold and lack, and which tools they need. In this paper, we present the results of the analysis of several interviews conducted with data scientists. Our research allowed us to conclude that the heterogeneity between these professionals is still understudied, which makes the development of methodologies and tools more challenging and error prone. The results of this research are particularly useful for both the scientific community and industry to propose adequate solutions for these professionals.
THE EFFECT OF CHITOSAN ON GENE EXPRESSION, SOME MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS OF SWEET BASIL (Ocimum basilicum L.) UNDER SALINITY STRESS

Sweet basil is an important medicinal plant belonging to Lamiaceae family. In this plant, Phenylpropanoid pathway possesses some enzymes involving in generating suitable essential oil constituents. The main purpose of conducting this study was to investigate the effects of chitosan on sweet basil’s growth and physiological parameters as well as gene expression subjected to salinity stress. After employing a foliar-spray of chitosan at 0 (as control) and 0.2 gl–1, the plants were subjected to salinity treatments at 0, 25, 50, 100, and 150 mM NaCl. The results of this research revealed that chitosan, compared to the controls, improved growth parameters under stressed or non-stressed conditions. In this regard, chitosan increased protein and chlorophyll contents as well as the expression of PAL and COVMT genes leading to an increase in phenolic compounds. To sum up, chitosan improved sweet basil tolerance to salinity through influencing the genes involved in the pathway of phenylpropanoid so as to produce secondary metabolites.
Breakdown and streamer behavior in homogeneous synthetic trimethylolpropane triesters insulation oil

A new method, which involves the addition of alkaline Al2O3 (3 wt%) and activated clay (5 wt%) as adsorbents for homogeneous synthetic trimethylolpropane triesters (TME), is reported here to not only improve electrical properties but also to shed light on future volume production. The results show that the acid value and the dielectric loss of TME oil is successfully reduced and meets IEC 61099. The viscosity of TME approaches that of mineral oil (MO) for transformers. In addition, TME oil with antioxidants exhibits fairly good oxidation stability that is evidenced by a notably high oxidation onset temperature of 226°C, which is 39 K and 14 K higher than that of commercial natural ester oil (NEO) (187°C) and MO (212°C), respectively. Furthermore, compared with the AC breakdown voltage (Vb) of NEO (65.3 kV) and MO (70.4 kV), the TME oil has a higher Vb=73.2 kV. Besides, the positive lightning impulse (LI) Vb and streamer behavior of TME at small gaps are comparable to those of MO and NEO. However, the negative LI Vb of TME and NEO are significantly lower than that of MO owing to the combined field variation towards the plane electrode which will facilitate the propagation of the streamer. Therefore, additives without oxygen, fluorine and chlorine should be given priority when they are used to improve the properties of insulation oil.
Attention-Based CNN for Personalized Course Recommendations for MOOC Learners

Massive Open Online Courses (MOOCs), which are open for anyone without limitations on time or location, have attracted millions of registered online students. The large number of online courses available raises the question of how appropriate courses can be effectively recommended to interested learners. The recommendation system, widely used in various online applications, is a good solution for reducing decision complexity. In this paper, we propose the method of using attention-based convolutional neural networks (CNN) to obtain a user's profile, predict the user ratings, and recommend the top-n courses. First, we represent the learner behaviors and learning histories into feature vectors. The attention mechanism is then used to improve relevance estimation according to the differences between the estimation scores and the actual scores given by users to train the neural network. Finally, the trained model will recommend courses to learners. At the end of the paper, we introduce the framework of our system.
Distributed implementation of Grafcets through IEC 61499

A Grafcet is a standardized model for describing the behavior of systems which is popular among automation engineers. As the Grafcet standard excludes implementation details, the models are typically translated to automation software. Such software was traditionally programmed in one of the languages specified in IEC 61131-3. Nowadays, automation software is increasingly modelled in IEC 61499 which facilitates designing distributed control systems. In this paper, we define a standardized translation methodology, so that automation engineers can benefit from the advantages of IEC 61499 while continuing to use Grafcet. We discuss the differences between Grafcet and IEC 61499. We translated a Grafcet model into an IEC 61499 application to illustrate the process and derive guidelines for application designers. For the core concepts of Grafcet, we present the corresponding structure in IEC 61499.
Pharmacognostic and phytochemical investigation of Wedelia trilobata leaves

Objective of present work is to study Pharmacognosy of Wedelia trilobata leaves (WTL). It was investigated for morphological and microscopic characters, determination of leaf constants, ash values, extractive values, total phenol, tannin, flavonoid and alkaloid content and preliminary phytochemical study. Microscopical studies indicated the presence of single layer epidermis with cuticle, unicellular covering trichomes and anisocytic stomata. Stomatal number, stomatal index and palisade ratio was 6, 19.3% and 1:5 respectively. Total ash value, acid insoluble ash, water soluble ash and sulphated ash was 6.79%w/w 1.23%w/w, 2.61%w/w and 1.09%w/w respectively. Extractive value in methanol was found to be 7.39%w/w. Total phenol, total tannin, total flavonoid and total alkaloid content was found to be 10.67 mg gallic acid equivalent/g, 7.8 mg gallic acid equivalent/g, 5.8 mg quercetin equivalent/g and 1.22 mg atropine equivalent/g respectively. Preliminary phytochemical investigation showed the presence of alkaloids, glycosides, tannins, triterpenoids, carbohydrates and flavonoids.
MCP-LSTM Network for Sentence-level Sentiment Classification

In this paper, an MCP-LSTM network (Multichannel word vector via Convolution and Pooling to concatenate LSTM) is proposed to generate more complete feature representations for sentence sentiment classification. The proposed model combines LSTM and CNN-multichannel network. It uses two set of word vectors, one is a static word vector and the other is a non-static word vector. The two channel word vectors are concatenated. Then the model performs convolution and max pooling on multichannel word vector, convolution layer produces multiple feature maps and max-pooling layer enables the network to focus on the most important feature. Combining the important feature with long sequence information extracted by LSTM, a more complete feature representation of sentences can be obtained. The experimental results demonstrated that the proposed model improves the accuracy of 1.36%, 0.89% and the AUC values of 1.2%, 1.6% on MPQA and MR datasets respectively.
Chemotherapy induced myocardial dysfunction and heart failure

In modern times, breast cancer (BC) ranks first among malignant tumors in all developed countries of the world and in recent years there has been a progressive increase in this indicator. Although anti-cancer therapies have developed rapidly to increase patient survival, there has also been an increase in side-related morbidity and mortality. Cardiovascular diseases are particularly common among these and thus the incidence of disease and death has increased in patients recovering from this disease. Cardio-oncology has taken special actuality in recent years. Additional research is needed for many aspects of the developing cardiovascular system related to chemotherapy. The development of cardiotoxicity associated with anticancer therapy may be either a direct result of its effects or indirectly related to the progression of cardiovascular disease.
Real Time Drowsiness Detection using Viola Jones & KLT

Technologies are developing day by day, sector for road transport implement new technologies for the welfare of people all over the world, even though the number of road accidents had increased day by day. Studies say that there are many reasons for these accidents but one of the main reasons is the mistake made by the driver, due to drowsiness or any other reason. The proposed methodology helps to detect whether the driver is drowsy or not. The violajones algorithm is used to detect the face and eyes. After detection, using Kanade Lucas Tomasi (KLT) the detected part is tracked. The eyeballs are detected using hough transform. Two conditions are set to analyze the drowsiness if the conditions are satisfied the system will raise the alarm to alert the driver. The alarm got stopped only if the driver awake from the drowsy situation. The proposed system was simulated using MATLAB 2017a. The result was analysed by testing 55 samples in real-time. The accuracy of the proposed system is 96.3%. The accuracy was analysed using the parameters like True Positive, True Negative, False Positive, False Negative. The method helps to reduce the number of road accidents and the proposed system does not need any physical contact with the driver so it is quite easy to implement
Split-Ring Resonator-Loaded Baffles for Decoupling of Dual-Polarized Base Station Array

Baffles loaded by split-ring resonator (SRR) are proposed to reduce the mutual coupling for ±45° dual-polarized multiple-input–multiple-output (MIMO) antenna. Each baffle consists of two rectangular SRR printed on one side of a 1 mm FR4 substrate. The final decoupling structure contains four baffles forming a barrier wall that reduces the coupling over the 5G band of 3.4–3.8 GHz. Dual-polarized cross-dipole array with SRR loading baffles is simulated and measured to demonstrate their effectiveness in the coupling reduction. The results show that the mutual coupling can be effectively reduced below −25 dB over the entire bandwidth while maintaining a compact array.
An EM Imaging Method Based on Plane-Wave Spectrum and Transmission Line Model

There is a great demand and challenge for the nondestructive detection of an unknown electromagnetic interference (EMI) radiation source inside the printed circuit board (PCB). In this article, an electromagnetic imaging method based on the plane-wave spectrum and transmission line model is proposed. By scanning the radiated field outside the PCB and calibrating the electric or magnetic probe with a reference source, the field inside the PCB can be evaluated, and the image of the unknown radiation source inside the PCB can thus be predicted. The proposed method is validated by simulation and measurement examples. Correlation coefficients between the predicted field and the reference are larger than 0.9, which reveals the good accuracy of the proposed method. This method will be greatly applicable in determining the EMI sources inside electronic devices.
Broadband Enhancement of the Spontaneous Emission by a Split-Ring Nanoantenna: Impact of Azimuthally Propagating Surface Plasmon Polaritons

—We propose a split-ring optical nanoantenna which can achieve enhancement factors of total and radiative emission rates up to high values of 7411 and 1941, respectively, with a tunable bandwidth of enhanced emission (Purcell factor over 1000) up to 221 nm at visible wavelengths. To clarify the underlying physical mechanism, we build up a semi-analytical model by considering an intuitive multiple-scattering process of surface plasmon polaritons (SPPs) that propagate azimuthally along the curved antenna arms. The SPP model provides an intuitive picture that under two phase matching conditions, the SPPs form paired and tunable whispering-gallery-like resonances and are then coupled to the emitter in the nano-gap or scattered into free space, which results in an enhancement of the total or radiative emission rate, respectively. Based on the SPP model, we further build up a semi-analytical model for the quasi-normal modes (QNMs) supported by the antenna, which provides a frequency analyticity and indicates that the broadband enhancement of the spontaneous emission rate results from an excitation of paired symmetric and anti-symmetric QNMs with different eigenfrequencies. The proposed models clarify the impact of azimuthally propagating SPPs on the antenna radiation and may be extended to other optical antennas with curved arms.
DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

Arc fault detection is important technology to guarantee the safety of power systems and is therefore essential for producing practical power systems for real-world applications. However, fuses and arc fault detection devices (AFDD) struggle to detect series arc faults in DC systems, because the series arc fault induces small current variation between the normal and abnormal conditions. In addition, switching noise from the grid-connected inverter makes detecting arc fault conditions even more difficult. This paper proposes arc fault detection algorithm based on the relative comparison of current variability in terms of frequency spectrum and time series. The operational principle of the proposed algorithm is analyzed to detect the arc fault condition. In addition, the investigation of arc fault impedance using the small-signal modeling can obtain the resonant frequency of arc fault condition at low frequency range. From the impedance model, the frequency analysis range can be designed to avoid the switching noise of inverter. The performance of proposed arc fault detection algorithm is verified with a 3.8 kW grid-connected PV system and arc fault generator.
Quantum Engineering Enables Broadband and Robust Terahertz Surface Plasmon-Polaritons Coupler

The in-plane terahertz (THz) surface plasmon polaritons (SPPs) coupler is a key element of THz information transmission and processing. However, existing parallel coupler based on two SPPs waveguides is not robust against perturbations of geometric parameters (arising due to fabrication imperfections) and are limited by the single frequency of operation. In this work, we propose a robust and broadband integrated THz coupler based on the in-plane SPPs waveguides, conducted with the quantum engineering – Stimulated Raman Adiabatic Passage (STIRAP). Our coupler consists of two asymmetric specific curved corrugated metallic structures working as the input and output SPPs waveguides, and one straight corrugated metallic structure functioning as the middle SPPs waveguide. From the theoretical and simulated results, we demonstrate that the SPPs can be efficiently transferred from the input to the output waveguides. Our device is robust against the perturbations of geometric parameters, and meanwhile it manifests broadband performance (from 0.5 THz to 0.8 THz) with a high transmission rate over 70${\%}$. This work represents a step forward for significant improvements in THz sensing, THz imaging, on-chip THz signal processing, and other next-generation THz photonic device.
Image processing based Latent Fingerprint Forensics - A Survey

Latent fingerprints are the prints not visible to the naked eye and are impressions left at crime scenes unintentionally. Feature extraction is a substantial step towards latent fingerprints forensics. The factors affecting the image segmentation and quality enhancement of latent fingerprints can be the variation in the quality of image, continuous noise barrier, fingerprint lifting mechanism, overlapping of fingerprints. The intricacies of correct decision-making, policies impact the manual effort to be reduced to an automated system. The Frame of reference is challenged in pre-feature extraction and post-feature extraction phases of latent fingerprint analysis leading to erroneous decision making, influencing image segmentation. An intelligent automated system runs on controlling and learning the variation in quantitative and qualitative factors. Whereas segmentation perceives the quality of decision making as secondary to the need to address the parameters affecting the process, use of efficient and reliable estimation is considered a sufficient argument to impetus automation and thereby giving freedom to the rate of error in the output.
Mechanistic and Translational Advances Using iPSC-Derived Blood Cells

Human induced pluripotent stem cell (iPSC)-based model systems can be used to produce blood cells for the study of both hematologic and non-hematologic disorders. This commentary discusses recent advances that have utilized iPSC-derived red blood cells, megakaryocytes, myeloid cells, and lymphoid cells to model hematopoietic disorders. In addition, we review recent studies that have defined how microglial cells differentiated from iPSC-derived monocytes impact neurodegenerative disease. Related translational insights highlight the utility of iPSC models for studying pathologic anemia, bleeding, thrombosis, autoimmunity, immunodeficiency, blood cancers, and neurodegenerative disease such as Alzheimer’s.
Chatbot-based Interview Simulator: A Feasible Approach to Train Novice Requirements Engineers

Introduction: Although the interview is the most important and widely used requirements elicitation technique, novice engineers do not receive adequate training in Requirements Engineering (RE) courses.Objectives: Develop an AI-based interview simulator for helping novice requirements engineers in gaining interview skills.Methods: The research is based on the Design Science Methodology for Information Systems. The simulator is the outcome of six cycles; in each cycle, a proof of concept with additional features is created. Each cycle finishes with evaluation and improvement suggestions.Results: The simulator has been tested with students and results have been promising. The interview simulator understands context-free questions, retrieving the right information related to RE concepts such as goals, tasks, users, benefits, and constraints. The simulator also answers questions based on the context, makes summaries of the conversation, responds to meta-questions, and adds ambiguity and incompleteness to the conversation.Conclusions: The results have been promising. The simulator has been tested with degree and master level students. They were able to create a requirements specification using the simulator, and the feedback has been generally positive. The simulator will be tested in a real RE course in the academic year 2020–2021. Once it proves effective in the classroom, it will be opened to the RE community for free use and improvement.
Assessment of eight kola trees clones (Cola nitida (Vent.) Schott and Endl.) for their ability to cutting from terminal and sub-terminal branch

The objective of this study was to determine the influence of branch type on the cutting ability of eight kola clones (Cola nitida). To this end, the clones were tested in a split-plot experimental design with the factors studied being the clone and the nature of the branch. After seven weeks of experimentation, survival rates were evaluated for each clone and for each type of cutting. The results showed that the clones were significantly different for the survival rates obtained (p = 0.00). The terminal branches were more suitable for cutting (70.62% survival) than the sub-terminal branches (33.95%). The age of cutting therefore influenced the success of cutting in the clones studied. At the end this work, we note that clones 305, 323, 330, 903A3 and 911A2 are promising for kola tree cutting from terminal branch and that clone 910A1 cut easily with both terminal and sub-terminal branch.
Low Complexity LDPC Error Correction Code for Modified Anderson PUF to Improve its Uniformity

This paper introduced a new LDPC error correction algorithm to Modified Anderson PUF key generation for hardware security. Most of the time PUFs are easily affected by the environmental conditions. To avoid those effects, introducing a new error correction scheme called Low-density parity checker error correction (LDPC). LDPC code helps to find the position of the error and also error has been corrected, using the Bit flipping algorithm at the decoder end. For better PUF key generation than the previously used BCH error correction algorithm, LDPC helps to perform the responses with good uniformity and less complexity. LDPC error code for Modified Anderson PUF can be used for many applications like WiMax, DVB, DAB, UMTS, multimedia Applications.
High Sensitivity Current Transformer with low Settling Time, for Magnified AC Current Measurements in Pulsed Applications

In solid-state pulsed power systems based on switching converters often a low amplitude, high frequency ripple current is superimposed on the high flat-top pulse current. Despite its low amplitude (usually measured in ppm) the ripple current could have a major impact on the performance of the system and therefore needs to be quantified/reduced. However, measuring such currents is challenging, due to the limited resolution of the current probe, especially when the DC offset current of the pulse is in the kA range. Therefore a suitable AC current probe able to perform magnified ripple current measurements with high fidelity is proposed in this paper. The probe is based on the principle of current transformers and filters the DC offset of the pulsed current, enabling to measure the ripple with a very high resolution. The design trade-offs of the conventional CT for such an application are explained, and an adaptive burden resistance topology is proposed in order to achieve simultaneously a low settling time, which is essential for measuring short pulses, and the needed high sensitivity, while maintaining a relatively high bandwidth.