Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing Global Government Health Website Accessibility Errors During COVID-19 and the Necessity of Digital Equity

Background: Many government websites and mobile content are inaccessible for people with vision, hearing, and cognitive disabilities. The COVID-19 pandemic highlighted these disparities when health authority website information, critical in providing resources for curbing the spread of the virus, remained inaccessible for disabled populations. Methods: The Web Content Accessibility Guidelines (WCAG) provide comparatively universally accepted guidelines for website accessibility. We utilized these parameters to examine the number of countries with or without accessible health authority websites. Findings:The resulting data indicate a dearth of countries with websites accessible for persons with disabilities. Fewer than 5% of all countries had health authority websites fully accessible according to the WCAG. Interpretation: Methods of information dissemination must take into consideration individuals with disabilities, particularly in times of global health crises. Government health websites can be considered the most trustworthy sources of preventative healthcare information and therefore must be accessible to all, regardless of ability. Nations should seek to improve website compliance with the WCAG. Funding Statement: The authors report no funding sources. Declaration of Interests: The authors have no other competing interests to declare.
Empty Seat Revelation Using Face Detection and Correlation Matching

The main initiative of this paper is to explain the concept of empty seat revelation system and hence find the number of empty seats left unoccupied in a hall. The efficient empty seat revelation system is achieved using the combination of Viola-Jones algorithm and template based correlation matching. The proposed empty system proves to be highly efficient. This system supports crowd management in the form of updating the number of vacant seats periodically.
The implementation of a software system for detecting a pattern in an ambient light sensor data stream

Ambient light sensors are present in many mid and top tier smart devices in order to give to the user good contrast and color representation. As newer sensors get better resolution it is possible to clearly identify patterns in the light sensor data stream originating from light sources that the user is interacting with. This paper presents the implementation of the software components for a system that receives the ambient light sensor data from a mobile Android Smartphone, saves it to a database and analyses it in order to detect a reference pattern. The pattern is produced by a video stream that is rendered by a display.
A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entail a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean-squared error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. This paper proposes a novel computationally efficient data detection algorithm based on the modified Richardson method. We first propose an antenna-dependent approach for the robust initialization of the Richardson method. It is shown that the proposed initializer outperforms the existing initialization schemes by a large margin. Then, the Chebyshev acceleration technique is proposed to overcome the sensitivity of the Richardson method to relaxation parameter while simultaneously enhancing its convergence rate. We demonstrate that the proposed algorithm mitigates multiuser interference and offers significant performance gains via the iterative cancellation of the bias term by prior estimation. Hence, each step of the iteration routine gives a new and better estimate of the solution. An asymptotic expression for the average convergence rate is also derived in this paper. The numerical results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with a significantly reduced computational complexity.
Bangladeshi Post Office Automation System Using Neural Network

In this paper, bangla handwritten digit recognition based post office automation system has been proposed for Bangladeshi Post offices. Actually, postal automation is not any new concept in developed countries, as they are already practicing this. Though Bangladesh has entered in the digital era, postal systems are not digitalized still now. Proposed system automatically sorts mails according to the post code, thus it can save time and money, reducing the necessity of manual sorting. Envelopes of each mails are scanned and region containing post code information is segmented. Different positional and statistical features including Gray Level Co occurances Matrix (GLCM), Local Binary Pattern (LBP), Histogram of oriented gradients (HOG) feature based bangla handwritten digit recognition system is applied to extract each digit. According to post code, mails are stored in postal zone specific buskets. For recognising purpose, Multi Layer Neural Network is applied. Proposed system gains higher accuracy of 99.71%, thus it outperforms state-of-the-art methods.
Gold Nanoparticles for Photothermal Cancer Therapy

Gold is a multifunctional material that has been utilized in medicinal applications for centuries because it has been recognized for its bacteriostatic, anticorrosive, and antioxidative properties. Modern medicine makes routine, conventional use of gold and has even developed more advanced applications by taking advantage of its ability to be manufactured at the nanoscale and functionalized because of the presence of thiol and amine groups, allowing for the conjugation of various functional groups such as targeted antibodies or drug products. It has been shown that colloidal gold exhibits localized plasmon surface resonance (LPSR), meaning that gold nanoparticles can absorb light at specific wavelengths, resulting in photoacoustic and photothermal properties, making them potentially useful for hyperthermic cancer treatments and medical imaging applications. Modifying gold nanoparticle shape and size can change their LPSR photochemical activities, thereby also altering their photothermal and photoacoustic properties, allowing for the utilization of different wavelengths of light, such as light in the near-infrared spectrum. By manufacturing gold in a nanoscale format, it is possible to passively distribute the material through the body, where it can localize in tumors (which are characterized by leaky blood vessels) and be safely excreted through the urinary system. In this paper, we give a quick review of the structure, applications, recent advancements, and potential future directions for the utilization of gold nanoparticles in cancer therapeutics.
Finite Precision Analysis of FPGA-based Architecture for FBMC Transmultiplexers in Broadband PLC

Power-Line Communications (PLC) systems are becoming more and more relevant in certain environments since they take advantage of the infrastructure already installed to provide broadband data access. Previous proposals have developed different medium access techniques based on a Multi-Carrier Modulation (MCM) for the transmission of information, such as Filter-Bank Multi-Carrier (FBMC). These transmultiplexers present a receiver, where an equalizer is involved to compensate the effects coming from the PLC channel. Due to their high computational load, the definition of suitable hardware architectures for the real-time implementation of these proposals is still an ongoing issue. Currently, Field-Programmable Gate Array (FPGA) devices support implementations based on floating- or a fixed-point representation. This work presents an analysis between a fixed- and a floating-point architecture for the real-time implementation of a FBMC receiver, including an Adaptive SMFB/CMFB Equalizer for Transmultiplexers (ASCET), applied to broadband PLC. The study deals with every block from the FBMC scheme, analyzing which representation (fixed- or single floating-point) is more suitable in terms of performance and resource consumption. For that purpose, the Signal-Noise Ratio (SNR) and Root-Mean-Square Error (RMSE) for the whole system have been estimated. The final architecture has been particularized for a Zynq® UltraScale+™ device, taking into account the internal DSP cells and memory blocks.
Security in Modern Smart Cities: An Information Technology Perspective

In a big and modern city, the security system is not a single-layered single module system. Instead, it consists of many layers with autonomous subsystems which are capable of interfacing reciprocally in an efficient and coordinated way horizontally and vertically. The design of such indigenous subsystems and ensuring tight interaction between them while taking into account high usability and resource utilization is not a simple task. In this regard, this article provides a comprehensive overview of the security systems in smart cities. We begin by introducing trends and key stakeholders in smart cities. Next, we provide examples of mass surveillance systems in developed countries like the USA, UK, and China. Finally, we highlight some of the major challenges in the practical realization of smart cities. The concepts and security aspects discussed in this study can be beneficial in designing security systems for future smart cities.
Efficient Mental Arithmetic Task Classification using Wavelet Domain Statistical Features and SVM Classifier

Functional Near Infrared Spectroscopy (fNIRS) has been emerged as a potential technique in the research of BCI. In this paper, we proposed a discrete wavelet transform based feature extraction technique to classify mental arithmetic tasks from fNIRS data. In order to investigate the change in brain activities during mental arithmetic task, recorded data are windowed in several frames. DWT has been employed on different channels of each frame and then a number of statistical features are extracted from both the approximate and the detail coefficients of data in order to distinguish the mental arithmetic task and the rest condition. Six-fold cross validation is performed using SVM classifier to examine the effectiveness of DWT based features. Efficacy of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin data from different selected channel combinations are also examined. It is observed that proposed algorithm provides a satisfactory accuracy of 93.26% using DWT based features extracted from 104 channels.
Efficient Hybrid Scheme for Cluster Head Election in Mobile Ad-hoc Networks

Achieving reliable routing is the primary concern in the Mobile Ad-hoc Network (MANET) due to dynamic topology and lack of centralized coordination. Among all protocols, the Cluster Based Routing Protocol (CBRP) is proved as an energy efficient routing protocol. In CBRP, selecting a suitable Cluster Head (CH) leads to enhanced network life time. Electing CH by considering security is one of the challenging tasks in CBRP. If elected node is malicious then attack such as routing disruption leads to data transmission failure. In order to avoid such attacks, a trustable node needs to be elected as CH for reliable routing. Existing CH election algorithms consider either trust or weight of the node as a factor to elect CH. Hence this paper proposes a Stable Trust-based Clustering Algorithm (STCA) in which both trust and weight of the node are considered for CH election. The proposed STCA allows only trustable nodes for weight calculation. This reduces computation cost in CH election. The proposed algorithm is simulated in NS3 and found to give better results in terms of number of clusters formed and lifetime of the CH.
Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree

Chronic Kidney Disease (CKD) is one of the deadliest diseases that slowly damages human kidney. The disease remains undetected in its early stage and the patients can only realize the severity of the disease when it gets advanced. Hence, detecting such disease at earlier stage is a key challenge now. Data mining is a branch of Artificial Intelligence that is widely used to derive interesting patterns from a large volume of medical data. While various data mining techniques used by Experts, boosting and rule extraction techniques have rarely been applied in analyzing Kidney diseases. Boosting is a method of ensemble technique that enhances the prediction power of a data mining model. AdaBoost and LogitBoost are used here for comparing the performance of classification. Ant-Miner is also a data mining algorithm that applies Ant Colony Optimization technique. Ant-Miner along with Decision tree have been used in the paper to derive rules. The aim of this paper is two-fold: analyzing the performance of boosting algorithms for detecting CKD and deriving rules illustrating relationship among the attributes of CKD. The best information retrieved by both classification and rule generation techniques are promising and can be adopted by the Medical Scientists for their research purpose.
Disentangling the Influence of Environment, Host Specificity and Thallus Differentiation on Bacterial Communities in Siphonous Green Seaweeds

Siphonous green seaweeds, such as Caulerpa, are among the most morphologically complex algae with differentiated algal structures (morphological niches). Caulerpa is also host to a rich diversity of bacterial endo- and epibionts. The degree to which these bacterial communities are species-, or even niche-specific remains largely unknown. To address this, we investigated the diversity of bacteria associated to different morphological niches of both native and invasive species of Caulerpa from different geographic locations along the Turkish coastline of the Aegean sea. Associated bacteria were identified using the 16S rDNA marker gene for three morphological niches, such as the endobiome, epibiome, and rhizobiome. Bacterial community structure was explored and deterministic factors behind bacterial variation were investigated. Of the total variation, only 21.5% could be explained. Pronounced differences in bacterial community composition were observed and variation was partly explained by a combination of host species, biogeography and nutrient levels. The majority of the explained bacterial variation within the algal holobiont was attributed to the micro-environments established by distinct morphological niches. This study further supports the hypothesis that the bacterial assembly is largely stochastic in nature and bacterial community structure is most likely linked to functional genes rather than taxonomy.
Kreon® (Creon®) vs. Lipancrea®: In Vitro Comparison of Two Encapsulated Pancreatin Preparations

Kreon® (Creon®) and Lipancrea® are pancreatic enzyme supplements indicated in the treatment of exocrine pancreatic insufficiency. In order to determine their interchangeability, an in vitro comparison of their physical properties and enzymatic activity was carried out. Capsule fill weight and particle size were also determined in order to establish their physical properties. Amylase, lipase and protease activities, lipase release at different pHs and the dissolution time of pellets were assessed for enzymatic analysis. The length range of Kreon® and Lipancrea® pellets was 1.1–2.2 mm and 1.5–2.8 mm, respectively. Protease activity was below the label claim for Lipancrea® and above for Kreon® presentations. Lipase and amylase activity were equal to or higher than the label claim in both preparations. In dissolution experiments simulating the stomach passage, significant release of lipase activity was observed for Lipancrea® (% actual activity: 41% for Lipancrea® 8000; 21% for Lipancrea® 16000) after 60 min at pH 5.0. No release of lipase activity was observed for Kreon® at that particular pH. Enzyme release for Lipancrea® at pH 6.0 was generally slower than for Kreon® and seemed to be influenced by the preceding incubation at lower pH. More than 85% of Kreon® and Lipancrea® dissolved in a pH 6.0 phosphate buffer within 20 min. Despite the similarities of the enzyme content on the respective labels, Kreon® and Lipancrea® differ in pellet size, enzymatic activity and release. This may impact their therapeutic efficacy and, therefore, may limit their interchangeability.
Efficient Simulation of Faults in Networked Cyber-Physical Systems

Functional safety is a major concern in today’s networked cyber-physical systems such as connected machines, autonomous vehicles, and intelligent environments. Simulation is a well-known methodology for the assessment of functional safety. Simulation models of networked cyber-physical systems are very heterogeneous relying on digital hardware executing software, analog hardware, and network domains. Current functional safety assessment is mainly focused on digital hardware failures while minor or no attention is devoted to the faults that are conveyed by the interconnecting network. The paper discusses how software errors, digital failures and communication issues can affect the information exchange between network nodes in networked cyber-physical systems. Then the resulting faults are described from the network perspective and simulated at high abstraction level to speed up analysis.
Method to Model Input Voltage Ripple in Multi-Domain Fully Integrated Voltage Regulators

The on-chip power domains of the latest generation of high performance server microprocessors are generated using integrated switched mode dc-dc converters also known as Fully Integrated Voltage regulators (FIVR). The multi-domain FIVRs in the chip share a common input power supply and the input voltage ripple can be quite high when all of them switch together. The modeling of the input voltage ripple in multi-domain FIVRs using circuit simulation tools such as SPICE is a tedious task. This paper proposes a simple method to accurately model the steady state input voltage ripple using the Harmonic Domain (HD) method.
Innovations for Supporting Communication: Opportunities and Challenges for People with Complex Communication Needs

Individuals with complex communication needs have benefited greatly from technological innovations over the past two decades, as well as from social movements that have shifted focus from disability to functioning and participation in society. Three strands of technological innovation are reviewed in this paper: (1) innovations in the tools that have become available, specifically tablet technologies; (2) innovations in access methods (eye gaze technologies and brain-computer interfaces); and (3) innovations in output, specifically speech technologies. The opportunities these innovations offer are explored, as are some of the challenges that they imply, not only for individuals with complex communication needs, but also for families, professionals, and researchers.
An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition

This research proposed a multi-modal person authentication system developed by feature level fusion of iris recognition and retina recognition. The reasons for choosing iris and retina as biometric characteristics are they provide the highest level of uniqueness, performance, universality, and circumvention. The ‘curse-of-dimensionality’ problem introduced in feature level fusion which was the main limitation of the prior works in this field, was minimized to a great extent by applying Principal Component Analysis (PCA) on the augmented feature template. To validate this approach, iris and retina images obtained from ‘IITD’ and ‘DRIVE’ datasets respectively are used. The recognition rate for the proposed multi-modal biometric system was 98.37% whereas it is 96.74% and 94.56% for iris recognition and retina recognition respectively.
Seq2seq Fingerprint with Byte-Pair Encoding for Predicting Changes in Protein Stability upon Single Point Mutation

The engineering of stable proteins is crucial for various industrial purposes. Several machine learning methods have been developed to predict changes in the stability of proteins corresponding to single point mutations. To improve the prediction accuracy, we propose a new unsupervised descriptor for protein sequences, which is based on a sequence-to-sequence (seq2seq) neural network model combined with a sequence-compression method called byte-pair encoding (BPE). Our results demonstrate that BPE can encode a protein sequence into a sequence of shorter length, thereby enabling efficient training of the seq2seq model. Furthermore, we implement a basic predictor using the proposed descriptor, and our experimental results demonstrate that the predictor achieves state-of-the-art accuracy in tests for proteins that are not included in the training data.
Modified VS3-NET for Reading Comprehension and Question Answering with No-Answers

Span-based machine reading comprehension of question answering (MRQA) is basically composed of an encoding module for the question and passage respectively, and a question-passage matching module to locate the answer in the passage using an attention mechanism. Recently, the newly published SQuAD 2.0 is a span-based MRQA for dealing with unanswerable questions. In this paper, we solve three issues of current practices in SQuAD 2.0: (1) modules that encode slowly, (2) type-unaware questions encoding module with unanswerable questions, and (3) a non-compositional matching module. We propose a modified VS3-NET model. This modified model is based on co-attention with contextual embedding using simple recurrent unit (SRU) instead of other recurrent neural network (RNN) to speed up training and test while retaining the recurrency of the model. Moreover, it uses variational inference to infer the question type with the unanswerable questions, uses a hierarchical matching and scoring module, and uses a gated module for features and a context vector. Experiments show that the modified VS3-NET provides outstanding improvements over the base model performance and produces performances competitive with the state-of-the-art models on SQuAD v2.0.
An Efficient Design of 4 - to - 2 Encoder and Priority Encoder Based on 3-dot QCA Architecture

In the field of electronic devices, transistor based CMOS technology has been a significant invention. But in the context of modern technology, CMOS has been facing some limitations, which can be overcome by the Quantum dot Cellular Automata (QCA) based nanotechnology. This promising technology allows us to design digital logic circuits, which consumes less power and can create the low-density device. In this paper, we have proposed the design of 4 - to - 2 encoder and priority encoder by using 3-dot based QCA architecture. This design has been simulated and then compared with the existing designs, which indicates that it has less number of cells and less consumed area. In near future, QCA based design can bring revolutionary change in the field of electronic devices.
Embedded Emotion Recognition within Cyber-Physical Systems using Physiological Signals

Cyber-Physical Systems (CPSs) are systems designed as a network of different interacting elements, which integrate computational and physical capabilities. The human-machine interaction plays a significant role in CPSs, especially in applications where people are an active element. In this context, emotion recognition is a relevant aspect to achieve a more efficient, collaborative, and resilient machine performance in collaboration with humans. On this basis, this paper proposes an embedded machine learning approach for emotion recognition fully implemented in an ultra low-power System-on-Chip (SoC) with limited resources. To this end, the intelligence system considers a reduced set of raw physiological signals within an approximate computing focus.