HarDNet and Dual-Code Attention Mechanism based model for medical images segmentation

During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

Social Media Content Compilation of Online Banking Scams in the Philippines: A Literature Review

Online banking has been proven to be an effective and convenient way of providing banking services anywhere and has been a necessity since the COVID-19 pandemic has affected the Philippines. Alongside this fact, people have been victims of scams involving these online services. Therefore, this paper aimed to analyze reports of online banking scams reported on social media using content analysis. The 26 filtered posts are labeled with 27 codes generated, and the top six were discussed. Specifically, scam (88.46%) was the most common code, followed by phone calls (57.69%) and impersonation (53.84%). Benefits, verifying personal information, and social engineering all had 46.15% occurrences. It is recommended for potential online banking scams to verify with the bank before giving away information, and to raise awareness on the possible technique's scammers use. © 2023 ACM.

The Plague Confession: Approach to Sophocles' Philoctetes

We propose a reading of the plague in the tragedy of Sophocles Philoctetes, based on the interpretive paradigms of Paul Ricoeur, which emphasizes the role of the mythical model in updating the symbols of origin and especially the presence of evil. We go through the notions associated with dirt as guilt and wound as expiation. From there we offer lines of reflection to think about the non-physical implications of the current pandemic;if the cosmic plane of events connects with an ethical plane, the relationship with present evil demands new community responses.

The Language of Smell: Code Failure

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

IoTeach:Learning Support System for IoT Programming by Integrating Real Devices and Sequential Contents

In recent years, Internet of Things(IoT) has become popular, the requirements for sharing the operation/mechanisms of IoT devices, such as Arduino and M5Stack are increasing. Moreover, owing to the coronavirus pandemic, many educational institutions have adopted online lectures, such as on-demand classes and online classes using video conference systems. For IoT programming education, these methods have challenges, such as a lack of linkage with real-world devices and source codes. In this study, we propose a system called "IoTeach", which supports the learning of IoT programming by attaching a scripting language to sequential contents, such as videos and slides shared on the Web. The IoTeach can link videos and slides with real-world IoT devices and source codes. We describe the concept and implementation of the system in this study. © 2023 Owner/Author.

A modern online interview platform for recruitment system

Video conferencing applications have become an integral part of today's world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code;a video/audio conferencing feature using Peer JS;a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches. © 2021

Practitioner Perspectives on COVID-19's Impact on Computer Science Education Among High Schools Serving Students from Lower and Higher Income Families

Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing.Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students?Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools' Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS.Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators (n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators (n = 185), analyzed the data, and considered how these findings shape research conducted over the last year.Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases.Practitioner perspectives also indicated that schools serving students from lower-income families had (1) a greater decrease in the number of students who received information about CS/CTE pathways;(2) a greater decrease in the number of girls enrolled in CS classes;(3) a greater decrease in the number of students receiving college credit for dual-credit CS courses;(4) a greater decrease in student attendance;and (5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses. © 2022 Association for Computing Machinery.

The Language of Smell: Code Failure

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Novel Approach to Ensure Security and Privacy While Using QR Code Scanning in Business Applications

One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged device-based barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality. © 2022 IEEE.

A Surveillance System Controlling Covid-19 in Office Environments

COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office e nvironment a n i dentification system that uses a deep learning model that prioritizes object recognition for the identification o f e mployees w ho w ear a f ace m ask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. L oss o f Smell after the next component. One person can use one disposable card to check the smell of sniffing. E ach d isposable c ard has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence a nd o ffers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person. © 2022 IEEE.

Feature Adaptation Predictive Coding for Quantized Block Compressive Sensing of COVID-19 X-Ray Images

With the development of remote X-ray detection for Corona Virus Disease 2019 (COVID-19), the quantized block compressive sensing technology plays an important role when remotely acquiring the chest X-ray images of COVID-19 infected people and significantly promoting the portable telemedicine imaging applications. In order to improve the encoding performance of quantized block compressive sensing, a feature adaptation predictive coding (FAPC) method is proposed for the remote transmission of COVID-19 X-ray images. The proposed FAPC method can adaptively calculate the block-wise prediction coefficients according to the main features of COVID-19 X-ray images, and thus provide the optimal prediction candidate from the feature-guided candidate set. The proposed method can implement the high-efficiency encoding of X-ray images, and then swiftly transmit the telemedicine-oriented chest images. The experimental results show that compared with the state-of-the-art predictive coding methods, both rate-distortion and complexity performance of our FAPC method have enough competitive advantages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-party Secure Comparison of Strings Based on Outsourced Computation

Data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Feature Extraction Based Face Mask Detection

Face masks are becoming an essential part of every person's life to protect against COVID-19, air pollution, etc. In public venues like airports, hospitals, malls, and many other locations, face masks are now required. This research introduces a unique method for face mask identification that combines deep learning and machine learning. Thepade Sorted 10-ary Block Trucation Code with RGB colour plane, Thepade Sorted Block Truncation Code with LUV colour plane, Local Binary Pattern method, and Gray Level Co-occurrence Matrics are used for feature extraction. Deep learning techniques are used to train these retrieved characteristics. Accuracy, precision, and f1-score are the performance measures used to evaluate performance. © 2022 IEEE.

QR code beautification by instance segmentation (IS-QR)

Due to the popularity of smartphones, cameras can be seen everywhere. QR codes are widely used daily, and their application is becoming more and more diverse, such as for warehouse management, electronic tickets, mobile payment, etc. As COVID-19 rapidly spread worldwide, people were forced to change their payment habits. Contactless systems, such as electronic tickets, became increasingly used to display information and avoid traditional queues. However, the standard QR code comprises black and white squares in monochrome images, which is not visually appealing. Yet, the easiest way to present a theme in a QR code is an image, which is more eye-catching and easier to understand than text. In this study, we devise an IS-QR method to integrate full-color images with QR codes by instance segmentation, using BlendMask to extract image feature regions and take Human Visual System into account. Discrete wavelet transform and contrast sensitivity were used to lessen the impact of reduced readability of QR codes during printing. Representative image visual quality measures, including PSNR, MSE, SSIM, FSIM, and GMSD, were used to measure the experimental results in order to validate the effectiveness of QR code beautification. The subjective quality evaluation is also performed. Finally, the measurement results indicate that the beautified QR codes generated by the method IS-QR designed in this study perform better than other related studies in terms of visualization and beautification. © 2022 Elsevier Inc.

Enhancing Bandwidth Utilization of IP Telephony over IPv6 Networks

The demand for the telecommunication services, such as IP telephony, has increased dramatically during the COVID-19 pandemic lockdown. IP telephony should be enhanced to provide the expected quality. One of the issues that should be investigated in IP telephony is bandwidth utilization. IP telephony produces very small speech samples attached to a large packet header. The header of the IP telephony consumes a considerable share of the bandwidth allotted to the IP telephony. This wastes the network's bandwidth and influences the IP telephony quality. This paper proposes a mechanism (called Smallerize) that reduces the bandwidth consumed by both the speech sample and the header. This is achieved by assembling numerous IP telephony packets in one header and use the header's fields to carry the speech sample. Several metrics have been used to measure the achievement Smallerize mechanism. The number of calls has been increased by 245.1% compared to the typical mechanism. The bandwidth saving has also reached 68% with the G.28 codec. Therefore, Smallerize is a possible mechanism to enhance bandwidth utilization of the IP telephony. © 2023 CRL Publishing. All rights reserved.

Gaining resilience through transformative services in cause-related events: an interaction ritual chain perspective

PurposeThis study aims to unlock a ritual chain mechanism that promotes socio-mental (or socio-psychological) resilience. This study draws on interaction ritual chains theory and the concept of transformative service to answer the question of how people could be inspired toward an elevated level of group solidarity, emotional energy, morality and, thus, socio-mental resilience. Design/methodology/approachThis study took a qualitative approach resting upon online reviews and observations from an augmented food festival about hot pot delicacies dedicated to medical workers fighting hard amid the early coronavirus outbreak. FindingsThe results of this study point to four primary ritual outcomes (e.g. emotional energy, group solidarity, symbols of relationships and standards of morality) along with a two-tier micro-macro socio-mental resilience sustainability paradigm. Research limitations/implicationsEmpirical findings from this study could help operators to justify their transformative initiatives as means for customers to replenish their depleted physical and mental resources. Originality/valueThis inquiry presents new nuances to interaction ritual chains. This study also extends the transformative role of hospitality services to accentuate a linkage among individuals, communities and the society.

Monitoring Programming Styles in Massive Open Online Courses Using Source Embedding

In recent years there has been an exponential growth of distance learning, provided by both public and private institutions. As a matter of fact, the number of students enrolled in courses delivered through the Network, has dramatically grown, also due to the COVID-19 pandemic, which has forced millions of people not to move. Consequently, more and more courses delivered in a remote modality have been attended by a huge number of people, producing an increasing number of Massive Open Online Courses (MOOC)s. These kind of courses are imposing new challenges for teachers, especially for monitoring and assessing the community learning processes. On the one hand, the learning assessment cannot be carried out based solely on closed-ended tests, while, on the other hand, teachers cannot evaluate thousands of open-Answer assignments: They should have at their disposition a set of tools helping them monitor the community learning progress. This paper investigates the possibility of using some of the Source Code Embedding techniques, to give teachers useful information about their learners' programming styles in Massive Open Online Courses. We propose a method to visualize each student's program, included the teacher's one, as a point in a 2-D space, using the doc2vec embeddings technique. Thanks to this representation, teachers can identify in the 2-D space groups of students having similar programming styles and reason on them to start a suitable didactic feedback. Moreover, teachers can reason on the relationship between each point compared to their own point as well, considered as the truth programming style. A first experimentation using Python as the programming language is performed with encouraging results. © 2022 IEEE.

A computational package for measuring Topological Entanglement in Polymers, Proteins and Periodic systems (TEPPP)

Many materials, like polymer melts, solutions, biopolymers and textiles, are composed of entangled filaments. The entanglement in these systems significantly affects their mechanical properties and their function. We introduce the Topological Entanglement in Polymers, Proteins and Periodic systems (TEPPP) software, that enables to measure the topological and geometrical complexity in such systems. In particular, this software enables the computation of the Writhe, the Gauss linking integral and the Jones polynomial of each filament or pair of filaments in the system, whether they are open or closed. In particular, for systems employing Periodic Boundary Conditions (PBC), the software also allows to compute the total pairwise entanglement in PBC, using the Periodic linking number and the Periodic Writhe. For linear (open) chains, TEPPP can calculate all these topological parameters (including the Jones polynomial) without any closure scheme. In addition, TEPPP also enables measuring self and pairwise entanglement at different length-scales along a chain or a pair of chains. With appropriate preprocessing of input files, the code can also be used for star or branched architectures. We provide examples of how the code is used and we present results on the entanglement effect in polymers obtained using this package. We show how TEPPP can be used to measure the topological entanglement of linear polymer chains in a melt, revealing subtle entanglement transitions never seen before. We also used TEPPP to analyze the effect of knotting and its location in diblock copolymer melts, which reveals that knotting localization transition coincides with lamellar-disorder transition in these systems. Finally, we use TEPPP to reveal some of the topological structure of the SARS-CoV-2 Spike protein, which points to interesting structure in a region that contains the S1/S2 cleavage site. Program summary: Program Title: Topological Entanglement in Polymers, Proteins and Periodic systems (TEPPP) software CPC Library link to program files: https://doi.org/10.17632/ygdbpnhpzw.1 Developer's repository link: https://github.com/TEPPP-software Licensing provisions: BSD 3-clause Programming language: C++ Supplementary material: Nature of problem: Measuring single and pairwise entanglement and knotting in systems of linear or ring filaments (open or closed curves) in 3-space or in systems employing Periodic Boundary Conditions (PBC) at different length scales. Solution method: TEPPP can be used to measure topological entanglement complexity in single or multi-chain filament systems in 3-space or in systems employing PBC. Given as input the coordinates of the curves, TEPPP can compute the Gauss linking integral, the Writhe, the Jones polynomial, the Periodic Linking Number and the Periodic Writhe. Also, TEPPP can measure effects of local linking and knotting using scanning methods along the chains. © 2022 Elsevier B.V.