Reviewerly/exHarmony huggingface dataset:
Mobile Learning Challenges and Capabilities

Out of a 6 billion-total populace, more than 1.8 billion cell phones are being used. Over the previous decade, there has been a quick improvement in cell phone innovation from straightforward Phones to the most recent age cutting edge telephones which are multifaceted and go about as PDA, phone, camera, minicomputer, and can likewise move various media documents. There is a steady development towards new innovation in cell phone advertise . M-learning is the procurement of any information or ability through utilizing portable innovation, anyplace, whereever. M-learning happens continually, even away from work environments and study halls. Music, radio news, or sports projects are individuals' primary decisions when returning from school or work. After arriving at home, a cell phone is their most ideal method for learning. M-learning doesn't try to supplant conventional adapting yet empower it to utilize new innovation and improve. This paper intends to demonstrate the focal points just as the detriments of M-(Mobile) Learning which by and large fall into: Psychological, Pedagogical, and Technical impediments
Moving Object Detection Using the Genetic Algorithm for Real Times Transportation

Video surveillance, most commonly referred to as closed-circuit TV, is an industry over 30 years old and with a share of modifications in technology. To meet the requirements include: Better image quality, Reduction in costs, Size and scalability etc., video surveillance has experienced a number of technology shifts. For real-time traffic monitoring apps, we implement a process for the identification of objects. The suggested technique is a mixture of a GDSM, an enhanced version of the dynamic saliency map (DSM) and background subtraction. The experimental findings demonstrate the effective detection of moving objects by the suggested technique. Recent advances in vision technologies like distributed intelligent cameras have motivated scientists to create sophisticated apps for computer vision appropriate for embedded platforms. Simple and effective computer vision algorithms are needed in the integrated monitoring system with limited memory and computing resources.
A secure, fast insert and efficient search order preserving encryption scheme for outsourced databases

Order preserving encryption (OPE) schemes have been studied to a great extent in the cryptography literature because of their potential application to database design. For the first time, a scheme called mutable order preserving encoding (mOPE) is introduced to achieve indistinguishability under ordered chosen plaintext attack (IND-OCPA) security. However, even mOPE scheme potentially leaks the distribution of repeated ciphertexts and is less efficient. In this paper, a new scheme is introduced called as a secure and cost efficient order preserving encryption (SCOPE), which is considerably more secure and efficient than mOPE scheme. A new form of strong security notion called as indistinguishability under ordered chosen repeated plaintext distribution attack (IND-OCRPDA) is proposed and we show that SCOPE scheme is IND-OCRPDA secure. Finally, the experimental results show that SCOPE achieves good performance in the context of an encrypted database and have a reasonable overhead which is 3.5× compared to plain data.
Determinants of job satisfaction in the Lebanese construction sector

Job satisfaction is the fulfilment or enjoyment that a person derives from their job. Job satisfaction has been a focal point of social sciences studies since the early 1900s, due to its substantial impact on employees' mental health, as well as its influence on the workplace atmosphere and productivity. This study aims to ascertain the determinants of job satisfaction in the Lebanese construction sector. The primary data was gathered through a quantitative structured questionnaire, while the sample amounted to 106 employees in the construction sector. The results of the study revealed a significant positive relation between payment (salary), financial benefits, promotion, and personal job satisfaction. In addition, this study revealed a non-significant connection between gender, age, package incentives, job security, rank, relationships with co-workers, stress/workfamily conflict, designation (social recognition), and job satisfaction.
WSN Based Flood Estimations and Early Cautioning System using IoT

Wireless Sensor Networks (WSN ) provides virtual layer where knowledge regarding actual world can be retrieved by any computational arrangement as these operate as digital skin. These are irreplaceable possessions used for comprehending ideas of IoT as they are used to gather information about physical phenomenon. IoT offers virtual interpretation through Internet Protocol towards a huge variation of real-life objects from buses to saucer, from building to trees in woods. Its appeal is the universal widespread access to the status and location of anything we may be interested in. The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. WSNs are combined into the “IoT”, where sensor nodes join the Internet vigorously and use it to collaborate and carry out their tasks. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. Weather conditions monitoring is made by gathering quantifiable information regarding prevailing condition of atmospheric procedure to venture how will it progress in that location.
Persons, GLAM institutes and collections: an analysis of entity linking based on the COURAGE registry

It is an important task to connect encyclopaedic knowledge graphs by finding and linking the same entity nodes. Various available automated linking solutions cannot be applied in situations where data is sparse, private or a high degree of correctness is expected. Wikidata has grown into a leading linking hub collecting entity identifiers from various registries and repositories. To get a picture of connectability, we analysed the linking methods and results between the COURAGE registry and Wikidata, VIAF, ISNI and ULAN. This paper describes our investigations and solutions while mapping and enriching entities in Wikidata. Each possible mapped pair of entities received a numeric score of reliability. Using this score-based matching method, we tried to minimise the need for human decisions, hence we introduced the term human decision window for the mappings where neither acceptance nor refusal can be made automatically and safely. Furthermore, Wikidata has been enriched with related COURAGE entities and bi-directional links between mapped persons, organisations, collections, and collection items. We also describe the findings on coverage and quality of mapping among the above mentioned authority databases.
Segmentation of Ultrasound Abdominal Images to extract Region of Interest

Ultrasound imaging is one of the safest techniques for disease diagnosis which can be used in any part of the body. One of the major reason for using ultrasound images is the cost when compared with MRI, PET etc. Further, it is free from any radiation exposure and is an efficient technique for initial diagnosis. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney and hence conducting study on this segmented image becomes easy with an efficient segmentation technique. Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated in this paper. One of the major drawback of ultrasound image is that due to the complicated internal organs of the abdominal region, extraction of only kidney region is very challenging. This paper proposes a new technique where the collected abdominal ultrasound image is cleaned, to remove unwanted noise produced due to various interferences. After applying the filtering technique, kidney region is segmented. This extracted kidney image is subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study. The method is experimented with a reasonable number of dataset and applied the statistical performance test to check for the accuracy.
Kratos: a solution for data privacy, literacy, and student agency in a data driven school ecosystem

Growing digitisation has made data ownership an important focus for institutions and students. Broadly, there are three issues, which require urgent attention to obtain data privacy, literacy, and utilisation. First, schools globally lack necessary frameworks for data interoperability, while maintaining privacy and control. Second, the scale, source, and nature of school data make its interoperability impractical, resulting in an inability to assess the true impact of educational technologies on learning. Third, while data supports teaching, an increasingly data-driven decision-making suggests that student participation in curriculum design becomes secondary. Finding a balance between data-driven decision-making and student voice is critical for an efficient school ecosystem. In this paper, we introduce a proof-of-concept for Kratos: an immutable decentralised data management system that enables privacy, applied data literacy while empowering students with a user interface for data governance and active participation in school processes.
Development of Low-Cost Auto Robot for Plastic Floating Garbage Collection using IoT

Nowadays, there is still much plastic waste floating in the sea and rivers, causing a limited number of officials to take care of this waste is not thoroughly. In this paper propose to develop the low-cost auto robot from waste materials based on Internet of Things for grabbing and collecting the floating garbage in the closed of water pool. The sensors and motors were mixed to control the robot on water surface and collect the plastic floating waste which is detected by sensors. To classified the floating garbage, the Fast Approximation Nearest Neighbor algorithm was applied to system in the web platform. The activities for garbage collection were submitted to social media such as LINE notify application. As a result, the auto robot system has the accuracy value at 94.4% and 98.8% for stationary mode and cover mode respectively.
Understanding human aspects for an effective information security management implementation

In today's world, information security is a trending as well as a crucial topic for both individuals and organisations. Cyber attacks cause financial loss for businesses with data breaches and production loss. Data breaches can result in loss of reputation, reduced customer loyalty, and fines. Also due to cyber attacks, business continuity is affected so that organisations cannot provide continuous production. Therefore, organisations should reduce cyber risks by managing their information security. For this purpose, they may use ISO/IEC 27001 information security management standard. ISO/IEC 27001:2013 includes 114 controls that are in both technical and organisational level. However, in the practice of security management, individuals' information security behaviour could be underestimated. Herein, technology alone cannot guarantee the safety of information assets in organisations, thereby a range of human aspects should be taken into consideration. In this study, the importance of security behaviour with respect to ISO/IEC 27001 information security management implementation is presented. The present study extensively analyses the data collected from a survey of 630 people. The results of reliability measures and confirmatory factor analysis support the scale of the study.
A safe semi-supervised graph convolution network

In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version (S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.
Impact of Age, Gender, and Experience on Task Performance in Service Sector: An Empirical Study

Purpose: The prior purpose of this study is to find the impact of various constructs on the job performance of the employees. The study is influenced by the service sector employees. The Study related to the Job Performance of Service Sector Employees in India. Job Performance has two variables; task and contextual performance. Task performance reflects a representative's essential occupation obligations; while relevant execution alludes to conduct that influences the more extensive hierarchical, social, and mental climate in which task execution happens. Methodology/Approach/Design: The Primary data were collected with the help of a structured questionnaire. Although some of the secondary sources were also consulted to find out the directions of the research. Data were collected from 83 lower-level employees (both and female) of all ages and experiences working in the service sector. Data were analyzed based on age, gender, and experience to know the actual performance on their job. Stata was used to analyze the data. Findings: The inferences drawn from the current study reveal the scenario of the level of the job performance of employees working service sector. The study is focusing on task performance, as the results of the study are significantly positive to the job performance of service sector employees. The study reveals that the adult youth have a high level of task performance; whereas the older youth have a low level of task performance. Research Limitations: The study conclusion is based on three constructs. So, the findings may not be generalized for the entire service sector. Most of the respondents in this study were either from no formal education or from low education level because the data were collected from lower lever employees where in some of the departments no formal education is much required. Therefore, there may be variations in their responses. Practical Implications: As the primary aim of this research is to find out the impact of various constructs on task performance, which can be important for practice, theory, and subsequent research. Originality/Value: This research is grounded on the data collected from primary sources; findings have been supported by various secondary sources available, especially in the service sector.
Effect of Virtual Simulation Practice for Nursing Students: Focusing on Virtual Presence and Virtual Patient Learning System Evaluation (VPLSE)

Purpose: Since the COVID-19 pandemic, virtual simulation practice has been increasingly activated as an alternative to clinical practice in nursing colleges. This study aimed to provide basic data by confirming changes in self-efficacy and nursing knowledge in the virtual simulations of nursing students, and identifying virtual presence, virtual patient learning system evaluation (VPLSE), and practical satisfaction. Methods: This was a single-group pre-post quasi-experimental study. The subjects were 28 third-grade nursing students. Results: Self-efficacy and nursing knowledge increased significantly (p<.001). Virtual presence had a significant positive correlation with VPLSE) (p=.002) and practice satisfaction (p=.011). There was also a significant positive correlation between virtual simulation learning evaluation and practice satisfaction (p<.001). Conclusion: Based on these results, virtual simulation practice can be used with clinical practice as an educational method to improve nursing students' self-efficacy and nursing knowledge in nursing education. Virtual presence was confirmed as a significant variable to improve practice satisfaction and VPLSE. It is necessary to develop a virtual simulation program that can improve virtual presence through collaboration with virtual reality technology experts.
DH-576-02 EKG AS A NON-FUNGIBLE TOKEN: IMPLICATIONS OF BLOCKCHAIN TECHNOLOGY FOR THE ELECTROPHYSIOLOGIST, THE PATIENT, AND HEALTH SYSTEMS

Third-generation cryptocurrencies with smart-contract capability have allowed for the creation of non-fungible tokens, or NFTs. While NFTs of multi-million-dollar art have been popularized recently in the media, there are several potential use-cases for NFTs that could be relevant to electrophysiology and medicine as a whole. The security, immutability, decentralization, anonymity, and permanence of blockchain technology may be ideal for the secure storage of electrophysiologic media such as EKGs, ablation maps, or Holter data.
A Successful Model of Microsoft Teams Online Learning Platform in Vocational High School

An excellent online learning platform requires a success model in its utilization. During the pandemic, a new adjustment (new normal) was needed in the educational field. It was initiated by providing numerous online learning platforms. Therefore, there are only a few successful model evaluations. Thus, this research aims to (1) determine the effect of Microsoft (MS) Teams and (2) develop a successful model as an online learning platform in Vocational High Schools (SMK). This research used a quantitative approach with the PLS-SEM technique in the data analysis. The collection of data was also carried out via the use of a questionnaire, with a total sample of 430 participants (85 teachers and 345 students) using MS Teams at Vocational High Schools. The results showed that four factors were significantly affecting the success of MS Teams, and these includes the information &amp; system qualities, use, and user's satisfaction. However, these findings were based on the factors which were insignificant to the success of the MS Teams. It was also reported that the factors that supported the MS Teams' operation included the Internet and software specifications. Moreover, additional observations also indicated that one of the supporting factors (SF2) was considered invalid.
Color Vision Deficiency Survey in Anatomic Pathology

Abstract Objectives To learn what color vision–deficient pathologists and cytotechnologists consider their most significant problems and advantages as well as any accommodations. Methods An anonymous online survey developed for practicing pathologists and cytotechnologists regarding their experiences with stains was sent to the members of 4 national societies. Results We received 377 responses. Twenty-three people, all men, identified themselves as color vision deficient, with 22 reporting red-green color vision deficiency and 1 reporting uncertain type. Eight pathologists and cytotechnologists indicated that they thought that their color vision deficiency conferred advantages to them, including a greater appreciation of morphology, with less confusion resulting from variations in stain quality or intensity. Nineteen pathologists and cytotechnologists thought that their color vision deficiency conferred disadvantages; the most common disadvantages stated were the identification of eosinophils and acid-fast bacilli. Other difficulties included interpretation of RBCs and nucleoli and sometimes Alcian blue, Brown and Brenn, Congo red, crystal violet, Fite, Giemsa, mucicarmine, periodic acid–Schiff, and fluorescence in situ hybridization stains. Only 2 of the color vision–deficient pathologists and cytotechnologists found digital slides more difficult than glass slides. Conclusions Color vision–deficient pathologists and cytotechnologists report that they have developed approaches to viewing slides that do not compromise their interpretations. Digital pathology may provide several approaches for aiding color vision–deficient pathologists with the interpretation of certain stains.
Secure Content-Based Image Retrieval with Copyright Protection within Cloud Computing Environment

Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.
The Hong-Ou-Mandel effect is really odd

We show that any odd parity state entering one input port of a 50:50 beam splitter, with an arbitrary state entering the other input port, produces zero coincidence counts. This result extends the HOM effect.
Network-side 5G mmWave Channel Signatures for Pandemic Contact Tracing

Contact tracing is a key mechanism to help contain the COVID-19 pandemic and other pandemics in the future. In this work, we propose using 5G channel signatures &#x2013; specifically, mm-Wave channel signatures &#x2013; to perform contact tracing and infer early sources of the infection. Our network-side approach is motivated by the density of mm-Wave base stations, coupled with the large amount of data about mobile device signals already being collected by cellular operators. We model the contact tracing problem as a graph mining problem, and develop machine learning models to estimate contacts between UEs based on 5G channel signatures such as received power. These contacts are also used to infer the original sources of the infection. Simulations of our proposed method using the ns-3 5G mmWave module suggest that contact can be inferred with a recall of 85% and specificity of 94%. Our infection sources estimation method can accurately rank the most likely infection sources, with the true infection sources lying in the top 25% of the ranked list on average. These methods represent a first step towards network-based contact tracing, and can complement other contact tracing methods to help reduce the spread of disease.