Systematic Literature Review of Students' perception of Employability Skills

CONTEXT Over the years, research investigating how engineering education contributes to the employability skills of students has led to the adoption of scenario-, problem- or project-based learning being implemented as effective methods for developing skills. Measuring student perception has emerged as an effective tool to gain insights into how changes to engineering curricula can contribute to various skills and attributes of engineering graduates. The COVID-19 pandemic has, however, disrupted teaching methods, making student engagement challenging. The effectiveness of teaching methods is dependent on the students' engagement level, which in turn translates into developing their employability skills. PURPOSE OR GOAL In order to pave the way for the post-pandemic approach towards improving the employability skills of engineers, it is important to gain a comprehensive understanding of the existing literature in this area of study. Thus, the aim of this study is to conduct a systematic literature review of undergraduate engineering students' perceptions of employability skills. APPROACH OR METHODOLOGY/METHODS Utilising the PRISMA protocol, a systematic review of the existing literature will be performed, looking at student perception of employability skills. The review will look at peer-reviewed research reporting on post-secondary engineering education in the last 20 years. Highly relevant papers will be chosen based on the protocol and reviewed. ACTUAL OUTCOMES Throughout the literature on this topic, a recurring theme is that employability skills are not well-defined, and a range of reference frameworks are used, such as accreditation requirements, 21st century skills and global engineer skills. The review found that the employers perceive that graduating engineers' non-technical skills are inadequate. In response, universities are constantly evolving their curricula and teaching methods to address this gap. Mismatches are identified in terms of the student perceptions of important employability skills and the perceptions of universities and industry employers. Internships, job placements, and problem- and project-based learning have found their place in helping undergraduate students to develop their skills. Suggestions for future work include a comparison with other professional degrees and how engineering education has deviated from these other degrees. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The effect of COVID-19 on engineering student's employability and how long it will persist is currently unknown. This study contributes to the understanding of student perceptions about employability skills before the pandemic to understand the state of play when the COVID-19 disruption to teaching and learning occurred. It adds to the growing body of knowledge on engineering education focussed on employability skills and will help develop this field progress as we emerge from the pandemic. Copyright © Karthikaeyan Chinnakannu Murthy and Tania Machet, 2021.

Emotions in Mental Healthcare and Psychological Interventions: Towards an Inventive Emotions Recognition Framework Using AI

One of the major impacts of COVID-19 in the nations is mental health issues. Constant mental health issues can cause disorders, as well as mortality. The growing demand for mental healthcare treatment and limited healthcare resources across the world has shown the need for an inventive framework solution. Artificial Intelligence (AI), Big Data Science, 5G, and Information Communication Technology (ICT) have proven to be able to bring many great improvements and could be the potential way forward to develop such a framework. AI could be a very effective tool to help the healthcare sector to provide more efficient services to patients with mental health issues through their emotions. This paper presents the initial overview and outcomes of the ongoing research programme to develop a proactive multimodal emotion AI recognition framework that detects emotion from various input data sources for early detection of mental health illnesses, as well as provides the required psychological interventions effectively and promptly when required. The data will be collected from various smart wearables and ad-hoc devices, facial expressions, and speech signals. Then, these data will be interpreted using AI into emotions. These emotions will be utilised using AI-based psychological system, which will provide immediate and customized interventions, as well as transmit critical data to the healthcare provider's central database system for monitoring and supplying the required treatments. © 2022 The authors and IOS Press.

Machine Learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

MLFW: A Database for Face Recognition on Masked Faces

As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces. To figure out the impact of masks on face recognition model, we build a simple but effective tool to generate masked faces from unmasked faces automatically, and construct a new database called Masked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on the masked face generated by our method has good visual consistency with the original face. Moreover, we collect various mask templates, covering most of the common styles appeared in the daily life, to achieve diverse generation effects. Considering realistic scenarios, we design three kinds of combinations of face pairs. The recognition accuracy of SOTA models declines 5%–16% on MLFW database compared with the accuracy on the original images. MLFW database can be viewed and downloaded at http://whdeng.cn/mlfw. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Astro-photography as an effective tool for Outreach and Education: IACT in exposition

In our epoch, images are a powerful way to convey a message to a large audience. Through the use of amazing astronomical photographs, science can be communicated effectively at different levels, to a very diverse audience of all ages. In fact, astrophotography combines aesthetic appeal with the illustration of the science behind astronomical phenomena. This is the aim of the exhibit "A che Punto è la NOTTE - A scientific exhibition of astrophotography” organized by us in Italy, in October 2020, with the partnership of the cultural association PhysicalPub. Many different authors, both single individuals and professional or amateur observatories, were asked to send their best pictures. The 54 astronomical images chosen by a scientific committee, categorised in three different topics (night landscape, deep sky, instrumentation), were seen by more than 2000 visitors and 11 school groups (despite the difficult period due to the COVID pandemic). A free audio-guide, available on-line through a web-application developed on purpose, delivered scientific explanations of images for self-guided tours. Conferences and guided tours were also organized. The highlight of the exhibit were four mirrors from the MAGIC telescope and an ASTRI scale-model that allowed an in-depth description of how an Imaging Atmospheric Cherenkov Telescope (IACT) works, introducing the science of VHE cosmic radiation. We will summarize the main difficulties in organizing this event and the feedback we had from the visitors. The exhibit is still available online, visiting the website mostrascientifica.it or via the web audio-guide (english and italian) at guida.mostrascientifica.it. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

The Usefulness of Telemedicine in Perinatal Mental Health Services During and after COVID-19 Pandemic. Detailed Experience of the Team of "Together" BabyMother-Father Unit in Budapest

Due to the nature of the perinatal period, it affects generations who are more at home in the electronic space, hence some form of telemedicine can be used in a number of areas. The "Together" Baby-Mother-Father Integrated Program has been running since 2004. Both the condition of those affected and the current epidemic makes it difficult for patients and their families to access adequate perinatal specialist care. At the beginning of the epidemic, the switch to telemedicinal psychiatric care has been rapid and focused mainly on the use of Phone, Skype, Viber and Email. To our findings the advantages include, easier access to care, and more frequent contacts. The home environment is accessible and the families are more involved. Also, care does not compete with the scarce resources of time and space. Some of the possible disadvantages are, that more work on intimacy is needed, and the treatability of certain diseases is questionable (e.g., psychoses). Care is less documentable with the current regulations, and funding has not yet been adapted to the changes. The telemedicinal care and support network in Hungary - among many - contains an online medical system (EESZT) including e-prescription. Online- psychotherapy, consultation, peer-group platforms. There is a non-stop hotline for patients, etc. In 2021 the total number of our cases increased by 34%, but realistically the visit number was also higher, due to the amount of shorter telephone and e-mail interactions. Depression and bipolar disorder were among the highest proportion by the patients present.

Development of Startups During the Covid-19 Pandemic

Currently, both domestic and global economies are facing a crisis associated with a new pandemic such as the coronavirus SARS CoV-2 (COVID-19). Economic leaders are addressing and looking for effective tools to deal with this crisis and start the economy as soon as possible, while mitigating the effects of the crisis as much as possible. In view of these facts, new startups in various sectors of the economy will play an important role in economic growth. At the same time, the world is facing another crisis - the oil crisis, which began with a price war between Russia and other oil-producing countries, followed by a decline in fuel demand due to reduced traffic. In this situation, in which the world economy finds itself, it is possible to assume that new technologies in the form of startups will be among the key ones in starting the economy. This article should highlight how startups can currently help the economy recover and what new risks the current crisis has brought to them. The start-up scene has long been characterized by a high degree of flexibility and the ability to adapt quickly to a new situation. The last year has been very challenging for many industries from a business perspective, e-commerce and the digital environment in general have often seen tens of percent growth. According to experts, startups, which operate in the mentioned segments, have also successfully dealt with the crisis. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Smart Learning Environments: Overview of Effective Tools, Methods, and Models

Digital learning environments have undergone a zigzagging evolution over the contemporary history of intelligent learning environments. In the pre-COVID-19 phase, e-learning struggled to establish itself in traditional training systems, but since the pandemic outbreak of March 2020, distance learning has become the only possible way to use the training actions. Today’s debate following this enormous experimentation has produced tools, methods, and models that need a further rethink for the post-COVID-19 phase. A possible evolution of full online education is a hybrid version of learning environments in which online and in-person, tangible and digital, alternate in time, space/place, media technology, learning design, and content coexist. These five categories guide the structuring of intelligent environments and adapt to the needs of students, teachers, and the social context in which they are inserted. Although the design follows recursive patterns, it is extremely flexible and adaptable. Furthermore, these digital environments make it possible to convey specific self-regulated learning methods and to develop specific motivational methods aimed at self-determination. The models of hybrid learning environments differ in the purposes to be pursued or the type of users to be reached. The surveys and experiences gained in the sector of innovative teaching methodologies find their most important field of application in hybrid environments. The purpose of this chapter is to summarize the future applications of the results that emerged from the experiments conducted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach

During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. In this paper, we present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification. This approach allows us to experiment with different combinations of face detection and face mask classification modules. More specifically, we experimented with PyramidKey and RetinaFace as face detectors while maintaining a lightweight backbone for the face mask classification module. Moreover, we also provide a relabeled annotation of the test set of the AIZOO dataset, where we rectified the incorrect labels for some face images. The evaluation results on the AIZOO and Moxa 3K datasets show that the proposed face mask detection pipeline surpassed the state-of-the-art methods. The proposed pipeline also yielded a higher mAP on the relabeled test set of the AIZOO dataset than the original test set. Since we trained the proposed model using in-the-wild face images, we can successfully deploy our model to monitor the mask-wearing rate using public CCTV images. © 2022 IEEE.

A new method for detecting COVID-19 combining deep learning with improved histogram equalization and median filter

The new coronavirus (COVID-2019) epidemic outbreak has devastating impacts on people's daily lives and public healthcare systems. The chest X-ray image is an effective tool for diagnosing new coronavirus diseases. This paper proposes a new method to identify the new coronavirus from chest X-ray images to assist radiologists in fast and accurate image reading. We first enhance the contrast of X-ray images by using adaptive histogram equalization and eliminating image noise by using a median filter. Then, the X-ray image is fed to a sophisticated deep neural network (FAC-DPN-SENet) proposed by us to train a classifier, which is used to classify an X-ray image as usual or COVID-2019 or other pneumonia. Applying our method to a standard dataset, we achieve a classification accuracy of 93%, which is significantly better performance than several other state-of-the-art models, such as ResNet and DenseNet. This shows that the proposed method can be used as an effective tool to detect COVID-2019. © 2021 IEEE.

3D dilated and residual convolutional neural network for COVID-19 detection from the chest computed tomography

Chest Computed Tomography (CT) is regarded as one of the most effective tools in diagnosing COVID-19 due to its high sensitivity and ease of use. However, analysis of CT images may be time-consuming for the clinicians, which highly influence their performance. Artificial-intelligence-based methods can help automating the process of interpreting chest CT images and diagnosis of COVID-19 in suspicious patients. In this paper, we propose a 3D deep convolutional neural network for classifying chest CT images into COVID-19-infected and normal classes. Dilated convolution and residual connections are employed to increase the model's performance by enlarging the receptive field of the kernels and direct propagation of the information. The accuracy, precision, sensitivity, specificity, and F1-score achieved by our model are 0.99, 0.98, 1.0, 0.979, and 0.99, respectively. The high sensitivity value of the model demonstrates its efficiency in detecting/identifying all the infected patients correctly, which allows early quarantine and the start of the treatment process. © 2021 IEEE.

The Proliferation of Twitter Accounts in a Higher Education Institution

Social media networks in higher education have become effective tools. Students, instructors, staff, and society rely on social media to support educational activities, spreading information and news, and responding to user inquiries. Twitter, in particular, is considered one of the most influential social media tools in the education process. This has resulted in the emergence of many Twitter accounts affiliated with the same higher education institution. The purpose of this study was to identify the magnitude of this phenomenon and user attitudes toward it. The study was carried out at Imam Abdulrahman Bin Faisal University and included a digital exploration of all accounts that were released during the past decade and an online survey in which a sample of followers (1,200) and a group of account managers (116) participated. The results showed that multiple accounts did represent the higher education institution. Additionally,the study revealed that the COVID-19 pandemic increased the emergence of new accounts and the abandoning of existing accounts. Furthermore, users confirmed their confidence in these accounts for information and support;however, they believe that the proliferation of Twitter accounts is distracting and overwhelming. Finally, this paper reveals some recommendations and opportunities for future studies related to the subject. © 2022. All Rights Reserved.

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is essential to prevent the further spread of the disease and reduce its mortality. Chest Computed tomography (CT) is an effective tool for the early diagnosis of lung diseases including pneumonia. However, detecting COVID-19 from CT is demanding and prone to human errors as some early-stage patients may have negative findings on images. Recently, many deep learning methods have achieved impressive performance in this regard. Despite their effectiveness, most of these methods underestimate the rich spatial information preserved in the 3D structure or suffer from the propagation of errors. To address this problem, we propose a Dual-Attention Residual Network (DARNet) to automatically identify COVID-19 from other common pneumonia (CP) and healthy people using 3D chest CT images. Specifically, we design a dual-attention module consisting of channel-wise attention and depth-wise attention mechanisms. The former is utilized to enhance channel independence, while the latter is developed to recalibrate the depth-level features. Then, we integrate them in a unified manner to extract and refine the features at different levels to further improve the diagnostic performance. We evaluate DARNet on a large public CT dataset and obtain superior performance. Besides, the ablation study and visualization analysis prove the effectiveness and interpretability of the proposed method. © 2021 IEEE.

Leveraging Drug-Target Interaction Data for the Translation of Computational Models into Clinically Actionable Interventions

Computational modeling is an effective tool for studying complex disease. However, solutions to many models are purely mathematical and cannot immediately provide clinical insights. To overcome this barrier, we propose a series of quantitative scoring metrics that can be used in combination with drug-target interaction data to identify solutions that are readily clinically actionable. Furthermore, we introduce methods for the prediction and ranking of pharmaceutical interventions that closely align with these high-scoring solutions, with an emphasis on robustness across multiple solutions. We demonstrate these methods on a previously-described model of COVID-19 induced cytokine storm. These scoring methods ultimately identify multiple pharmaceutical candidates that have been shown to be effective in reducing mortality rates in COVID-19 patients. © 2021 IEEE.

The Use of Virtual Reality to Drive Innovations. VRE-IP Experiment

In the context of widespread digitalization of businesses and academia due to COVID-19, the new effective tool has become necessary to drive innovations. This paper observes the international scientific experiment VRE-IP that tested the hypothesis of the advantages of using virtual reality for teamwork, creativity, and innovation. VRE-IP experiment was based on design thinking methodology and supported SAP innovation process. The analysis of the trends of using virtual reality for educational and business development goals, and VRE-IP experiment revealed that employing virtual reality technology to organize workshops, distant meetings, plenary reports, and other tasks is exceptionally efficient, improves the engagement rate and creativity and contributes to the process of innovative ideas stimulation. The experiment also demonstrated the need for building a hybrid model for teamwork. The combination of traditional teamwork instruments and virtual technologies is proposed as the most sustainable and systematic model for online collaboration at the current stage of technological development. © 2022, Springer Nature Switzerland AG.

Contactless IoT Doorbell for Covid-safe Household

The Covid-19 pandemic has proven to be the most disastrous pandemic in the history. Millions of people have lost their lives sending nations into lockdown and economic slowdowns. Given the fact that no specific anti-viral treatment is yet suggested for treating Covid-19 infection, 'Social distancing' is probably the most effective tool so far in stopping the virus spread. This paper has proposed an IoT based doorbell which alerts the house owner about arrival of a visitor having fever and who could be a Covid-19 patient. The system uses NodeMCU and MLX90614 non-contact infrared temperature sensor. FireBase online database is used to log all the readings of the system and a companion mobile App is also provided. The system was extensively tested using an experimental set up under various conditions. The system works with 99% average accuracy of body temperature measurement. © 2021 IEEE.

A Two-Phase ASP Encoding for Solving Rehabilitation Scheduling

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g., patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a solution to rehabilitation scheduling based on Answer Set Programming (ASP), which proved to be an effective tool for solving practical scheduling problems. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution. © 2021, Springer Nature Switzerland AG.