Miracle-dz/UniFAR-FTUs huggingface dataset:
A Digital Maturity Model for digital banking revolution for Iranian banks

A wide range of industries is facing a fundamental change: digital transformation. The banking industry is no exception. However, despite such transformation being underway, there is a lack of frameworks and tools to help banking providers navigate such radical change. This article presents a new framework: the digital maturity model for digital banking providers. The model aims to offer a structured view of digital transformation specific to the context and challenges of digital banking. That can be used as a standard to help digital Banking providers benchmark themselves against peers or themselves as they advance their transformation. This article begins with a review of digital banking. A new definition of digital banking was introduced. Digital transformation and digital maturity, and after that, the previous models were investigated. And finally, a new model specific to digital banking in Iran.
Accuracy Analysis of Triage Recommendation Based on CNN, RNN and RCNN Models

It is an universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the lager number of patients, they have to stand in a queue for minutes to consult. In order to solve these problems, we propose to use deep learning models for recommending clinical departments automatically, which help patients to complete self-diagnosis, improve the efficiency of triage, reduce the work intensity of triage nurses. In this study, we construct a Chinese corpus with 20,000 texts of patient symptom descriptions and use word2vec to generated word vector. In addition, we analyze the performance of CNN, RNN and RCNN in clinical departments recommendation. The results of our experiment shows that the accuracy of RCNN is improved markedly than CNN and RNN. Therefore, RCNN is more suitable for the task of clinical departments recommendation.
Effective synthetic data generation for fake user detection

Nowadays, with the pervasiveness of social networks among the people, the possibility of publishing incorrect information has increased more than before. Therefore, detecting fake news and users who publish this incorrect information is of great importance. This paper has proposed a system based on combining context-user and context-network features with the help of a conditional generative adversarial network for balancing the data set to detect users who publish incorrect information in the Persian language on Twitter. Moreover, by conducting numerous experiments, the proposed system in terms of evaluation metrics compared to its competitors, has produced good performance results in detecting fake users.
Comparison of normal and PSE turkey breast meat for chemical composition, pH, color, myoglobin, and drip loss

The objective of this investigation was to determine the differences between normal and PSE (pale, soft, exudative) turkey breast meat in terms of chemical composition, pH, color, myoglobin, and drip loss. Pale and normal skinless, boneless turkey breast fillets were taken from a poultry slaughterhouse in Van/Turkey at about 2-3 h postmortem. Spectrocolorimetry (CIE L*a*b*), pH, protein, ash, dry matter, lipid, myoglobin, and drip loss were measured in normal and pale color turkey breast muscle. The normal breast meat had significantly lower L*, a*, and b* values in comparison with the pale breast meat. Myoglobin concentration and pH were significantly higher in the normal breast meat compared with that of the pale breast meat. Protein content was found to be significantly lower for the pale group compared with the normal group. No significant difference was found in ash, dry matter, and total lipid content between normal and pale breast meat. Drip loss was significantly affected by breast meat color groups at days 1, 2, 3, and 4 postmortem. Pale, soft, exudative turkey breast meat can lead to differences in the chemical composition and shelf life of the product.
FERNANDO: A Software Transient Fault Tolerance Approach for Embedded Systems Based on Redundant Multi-Threading

As semiconductor technology scales, modern microprocessors are more vulnerable to transient faults. Software-level fault tolerance schemes are promising because they can improve reliability effectively without extra hardware. Redundant Multi-threading (RMT) uses off-the-shelf cores as redundancy to achieve error resilience. Latest software RMT fault-tolerance models do not effectively cope with transient faults occurring on multiple components during the application execution, resulting in a large number of silent data corruptions (SDC). To address this challenge, we propose FERNANDO, a software-level RMT runtime fault tolerance scheme which provides enhanced error detection and comprehensive error recovery by Triple-Modular Redundancy (TMR). On an ARM Cortex-A57 like simulated microprocessor, we performed probability model transient fault injection experiments in different components of all cores. The results demonstrate that, compared to the state-of-the-art technique, FERNANDO can reduce the SDC rate by about 86.67 percent and optimize the execution time overhead by about 19.64 percent.
Design of a 50-W Power Amplifier With Two- Octave Bandwidth and High-Efficiency Using a Systematic Optimization Approach

A wideband and highly efficient power amplifier (PA) is designed using a simple and systematic approach. This method employs an optimization process to set the input impedance of the matching networks in optimal regions on the Smith chart, obtained using conventional load-pull/source-pull simulations, in both the fundamental and second harmonics. The developed design method led to a GaN PA whose measurements showed an output power of 46.8–48.1 dBm and a drain efficiency of 57%–71% in a two-octave bandwidth from 0.8 to 3.2 GHz.
Community Detection Algorithm Based on Single Target PSO

With the development of information technology, complex networks have become more common in people's lives, and methods based on modularity optimization have attracted more and more attention. Because the traditional particle swarm algorithm is used to solve continuous optimization problems, the community structure detection problem is a discrete optimization problem based on graphs. We applied a new coding strategy and a particle update strategy to overcome this problem. In the update strategy, we introduced a method based on neighbor update to ensure that the update of the particles is guided to a certain extent by following the neighborhood information, which is in line with the characteristics of real complex networks. In addition, the expanded module density function is used for optimization to overcome the resolution limitation problem of the traditional module density function and to ensure that the community structure of complex networks is found at different resolutions.
Radio Environment Map Construction with Spatially Distributed Sensors

In this paper, we propose an analysis framework based on spectrum data gathered by spatially distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage Bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling.
Wideband Filtering Differential Phase Shifter With Enhanced Harmonic Suppression

This letter proposes a wideband filtering differential phase shifter with enhanced harmonic suppression. Through the original transmission line model, the numerical relationship between the prescribed amplitude/phase response and the initial design parameters can be readily established. Then the simple transformation from the transmission line to its T-type lowpass network is utilized to suppress the harmonics efficiently with almost the same in-band phase shift response. For demonstration, a prototype is designed and fabricated, the proposed filtering phase shifter features enhanced harmonic suppression up to $3. 5f_{0}$ and an enhanced suppression level of 33.5 dB.
Lower Bounds on Bandwidth Requirements of Regenerating Code Parameter Scaling in Distributed Storage Systems

In a fault-tolerant Distributed Storage System (DSS) that depends on regenerating codes, there may be a variety of motivations for the system or the user to switch from one set of code parameters (<inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>) to another. For example, the user may change their demand on reliability or the system may want to change the configuration due to implementation challenges, cost considerations and issues related to availability/ accessibility of geographically-distributed nodes. This can be managed by a well-designed DSS capable of dynamically scaling the parameters. In this letter, we present lower bounds on the bandwidth requirements for moving from a regenerating code configuration to another. In case of functional repair, our lower bounds are achievable, which helps the system designers identify the minimum-cost scaling strategy.
Discontinuous User-Energy Distribution for Dynamically Ordered Successive Interference Cancellation

In this letter, we consider many users sharing the same codebook and employing random signatures to modulate preamble plus payload data. Successive interference cancellation (SIC) is performed at the receiver side, whereby all user-strengths are estimated by preamble cross-correlations to set the user-decoding order, after which SIC operates. Our contribution in the infinite-user regime is twofold. Firstly, the innovative application of variational calculus to derive the asymptotic user-energy distribution that maximizes SIC’s performance. Contrarily to exponentially decaying distributions obtained under perfectly ordered SIC, we derive stationary points with piecewise constant structures as potential locally-optimal solutions for the spectral efficiency maximization of estimation-based SIC. Secondly, the proposal of a suboptimal algorithm to compute locally-optimal solutions with high performance/complexity ratio.
Smart Agriculture Based on IoT and Machine Learning

The IoT advancements have majorly influenced in redefining the agricultural field. A reliable remote monitoring system is the need of the hour. In this paper, two objectives are addressed. Firstly, an app based solution is presented which helps in displaying the current sensor values that efficiently aid in remotely administrating the field. Secondly, an IoT based prototype system for surveillance is proposed that embeds the concept of multi-class classification technique using Machine and Deep Learning for the labels clear farm, horse, cow, wild elephant and wild boar. Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) were analysed for this purpose and the best model was chosen based on accuracy metric.
Research on Abnormal Output Current Drop of Solar Array of a Low Earth Orbit Satellite

This article describes the phenomenon of the abnormal output current drop of a solar array (SA) of a low Earth orbit satellite. All possible failure causes were analyzed in detail and the most likely reason was determined to be an open-circuit in solar cell interconnectors. The failure mechanisms were comprehensively analyzed from three aspects: solar cell replacement process, erosion of atomic oxygen, and thermal fatigue. The analysis results were verified by corresponding ground tests. For the on-orbit failure, a solution to slow down the output current drop of SA was given and verified to be effective by ground remote control operations on the satellite on orbit. The improvement measures for the development of SAs to avoid the output current drop are also proposed.
Map Updating Revisited for Navigation Map : A mathematical way to perform map updating for autonomous mobile robot

Simultaneous localization and mapping, SLAM can product a Map for autonomous robots and self-driving vehicle in navigation. In the actual environment, the scene changes frequently, which makes the old map no long reliable. Therefore, it is necessary to update such a map by an efficient and safely way. In this paper, we review the existing map updating, long-term localization methods and discuss about the challenges in this situation. We present a Map updating method in mathematical way which can update accurately. Our proposed method are tested in five indoor dataset and demonstrated feasibility.
Evaluating Pretrained Transformer-based Models for COVID-19 Fake News Detection

The expeditious growth of technology with social media as a platform for communication has led to a proliferous increase in the spread of misinformation and fake news. The ongoing COVID-19 widespread has pushed us to review posts on various social media platforms to stop people from being subjected to false and perilous posts. Detecting fake news in social media has been the need of an hour. The proposed research work has approached it with various Transformer and recurrent models with several contextual word embedding models. Furthermore, the effectiveness of the proposed model is evaluated by using a different loss function instead of the conventional loss function, Binary cross Entropy. The fake news detection is considered as a sequence classification task, one of the downstream tasks of natural language processing. It has been observed that using domain-specific language models along with custom loss function has achieved the highest weighted average F1-score.
Surface Defect Detection Methods Based on Deep Learning: a Brief Review

Surface defect detection techniques based on deep learning have been widely used in various industrial scenarios. This paper reviews the latest works on deep learning-based surface defect detection methods. They are classified into three categories: full-supervised learning model method, unsupervised learning model method, and other methods. The typical methods are further subdivided and compared. The advantages and disadvantages of these methods and their application scenarios are summarized. This paper analyzes three key issues in surface defect detection and introduces common data sets for industrial surface defects. Finally, the future development trend of surface defect detection is predicted.
Single-Trial Decoding of Motion Direction During Visual Attention From Local Field Potential Signals

Brain-Computer Interface (BCI) based on Local Field Potential (LFP) has recently been developed to restore communication or behavioral functions. LFP provides comprehensive information, due to its stability, robustness, and reach frequency content within the cognitive process. It has been demonstrated that spatial attention can be decoded from brain activity in the visual cortical areas. However, whether motion direction can be decoded from the LFP signal in the primate visual cortex remains uninvestigated, as well as how decoding performance may be influenced by spatial attention. In this paper, these issues were examined by recording LFP from the middle temporal area (MT) of macaque, employing machine learning algorithms. The animal was trained to report a brief direction change in a target stimulus which moved in various directions during a visual attention task. It was found that the LFP-gamma power was able to provide significant information to reliably decode motion direction, compared with other frequency bands, on a single-trial basis. Moreover, the results show that spatial attention leads to enhancements in motion direction discrimination performance. The highest decoding performance was achieved in the high-gamma frequencies (60–120Hz) when targets were presented inside the receptive field in opposite directions. Using a feature selection approach, performance was improved by optimally selecting features where the highest level of participation was observed in the gamma-band. Generally, the results suggest that in the MT area, LFP signals exhibit appreciable information about visual features like motion direction, which could thus be utilized as a control signal for cognitive BCI systems.
A Survey: Machine Learning Based Security Analytics Approaches and Applications of Blockchain in Network Security

Contemporarily, two emerging techniques, blockchain, and machine learning are driving dramatic rapid growth in the field of network security. This paper describes a literature review of machine learning approaches for network security analytics and summarizes some applications of blockchain in the field of network security. We first illustrate three types of network security data, including network traffic, software binary, and security logs. Then we discuss the application of machine learning and deep learning approaches to analyze these data. We cover a broad array of attack types, including malware, spam, insider threats, network intrusions. We also summarize some applications and potential development direction of blockchain technology.
Automatic Selection and Insertion of HLS Directives Via a Source-to-Source Compiler

High-level Synthesis (HLS) is of paramount importance to leverage the use of FPGA-based accelerators by software developers. To achieve efficient FPGA implementations, code restructuring and source code annotating with HLS directives are necessary. However, this is still a manual process conducted by experienced developers. This paper proposes a step on a framework to automatically optimize C code via directives, using a source-to-source compiler on a stage before HLS. This optimization is primarily applied by strategies that select, configure, and insert directives on the code input to the Vivado HLS tool to synthesize more latency-efficient FPGA hardware. Those strategies rely on very simple but effective heuristics, which use a small set of properties extracted from the control/dataflow graphs generated from the input source code. We evaluate the framework using a variety of source codes. The experiments show that it can achieve efficient results while maintaining a low resource usage in most cases. Our experiments also compare the framework results to code optimized manually with directives, and they show that for most benchmarks used, it achieves similar results.
A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset

ECG beats have a key role in the reduction of fatality rate arising from cardiovascular diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the important information from patient cardiac conditions to the specialist. However, the accuracy and speed of arrhythmia diagnosis are challenging in ECG classification systems, and the existence of noise, instability nature, and imbalance in heartbeats challenged these systems. Accurate and on-time diagnosis of CVDs is a vital and important factor. So it has a significant effect on the treatment and recovery of patients. In this study, with the aim of accurate diagnosis of CVDs types, according to arrhythmia in ECG heartbeats, we implement an automatic ECG heartbeats classification by using discrete wavelet transformation on db2 mother wavelet and SMOTE oversampling algorithm as pre-processing level, and a classifier that consists of Convolutional neural network and BLSTM network. Then evaluate the proposed system on MIT-BIH imbalanced dataset, according to AAMI standards. The evaluations results show this approach with 50 epoch training achieved 99.78% accuracy for category F, 98.85% accuracy for category N, 99.43% accuracy for category S, 99.49% accuracy for category V, 99.87% accuracy for category Q. The source code is available at https://gitlab.com/arminshoughi/cnnlstmecg-classification. Our proposed classification system can be used as a tool for the automatic diagnosis of arrhythmia for CVDs specialists with the aim of primary screening of patients with heart arrhythmia.
Studies on Activity Guided Fractionation of Pomegranate Peel Extracts and Its Effect on Antidiabetic and Cardiovascular Protection Properties

Pomegranate peel (PP) was sequentially extracted with hexane, ethyl acetate, methanol, methanol/water and water (C1–C5, respectively) and the extracts were assessed for its antioxidant and antidiabetic potential. Extracts, C2 and C3, which exhibited maximum free radical scavenging and α-glucosidase inhibitory activity, were further fractionated using hexane, ethyl acetate and methanol (C2–F1, F2, F3; C3–F4, F5, F6). Fractions, F1 and F5, of methanol extract showed highest 2,2-diphenyl picrylhydrazyl scavenging activity (IC50 – 3.69 and 4.39 µg/mL, respectively); F5 exhibited significantly high 2,2′-azinobis 3-ethylbenzothiazoline 6-sulfonic acid, superoxide and hydroxyl radical scavenging activity (IC50 – 3.6, 20.89 and 10.7 µg/mL) and α-glucosidase inhibition (IC50 – 0.0453 µg/mL). F5 demonstrated promising inhibition against LDL oxidation (IC50 – 16.2 µg/mL) and ACE inhibition properties (IC50 – 0.77 µg/mL). These findings suggest the ability of PP extracts to manage type 2 diabetes and associated complications. Practical Applications Natural antioxidants have been recommended as an aid for prevention and management of diabetes and associated complications. Pomegranate peel (PP) is reported to be a rich source of dietary antioxidants. We evaluated the effect of activity guided fractionation of PP on antioxidant and antidiabetic potential of extracts prepared by sequential extraction followed by its fractionation. The results indicated that on fractionation of crude extracts, the antioxidant and antidiabetic activity of some of fractions were significantly improved. The antidiabetic effect of PP active fraction may be related to α-glucosidase inhibition and enhanced glucose uptake. The active fraction was found to be effective in reducing cardiac problems associated with diabetes by inhibiting LDL oxidation and ACE. The study points out the potential of PP as nutraceuticals for the prevention and management of type 2 diabetes and associated complications.