Miracle-dz/UniFAR-FTUs huggingface dataset:
End to end Bangla handwritten and scene text detection using convolutional neural network

Handwritten text detection from a natural image has a large set of difficulties. A systematic approach that can automatically recognise text from handwriting, printed books, road signs and also classifies text and nontext blocks from natural image has many significant applications. For instance, visual assistance for visually impaired people, image understanding, classification of text in image, implementing autonomous navigation system. Recent development of deep learning approach has strong capabilities to extract high level feature from a kernel(patch) of an Image. In this thesis we will demonstrate an alternate approach that integrates a multilayer convolutional neural network (CNN) with supervised feature learning . This approach allows a higher recall rate for the text in an image and thus increases the overall performances of the system. And we have used these methodologies to create a learning model using synthetic and real-world data that is capable to process bangla and english handwritten and scene text in natural image.
A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users’ privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.
A New Method of Ship Sketch Matching in Digital Ship Images using MLBP features with DWT

: The problem of matching a ship sketch to a gallery of mug shot images is addressed in this paper. Previous research in ship matching offered solutions to matching highly accurate sketches that were drawn while looking at the subject. Ship sketches differ from viewed sketches in that they are drawn by a sketch artist using the description provided by who is typically unfamiliar with the subject. To solve the problem of matching ship sketches, use a robust framework called Local Feature-Based Discriminant Analysis (LFDA). In LFDA, both sketches and photos are represented using Multi Scale Local Binary Patterns (MLBP). Multi Scale Local Binary Patterns (MLBP) has also been applied at dense grids (dense MLBP) which have been shown to lead to better performance for tasks such as object categorization, texture classification, image alignment and biometrics. A discriminant projection method is then used on the feature-based representation for minimum distance matching and applies the LFDA method to match a dataset of ship sketches against a mug shot gallery containing images. Though these methods address the image matching, they do not take in to account noise caused by the quality of paper, charcoal pencil, scanning noise etc. This paper presents a method where in pre-processing of the image to address such noise is introduced using discrete wavelet transforms (DWT). This when used along with MLBP gave superior results
Large‐eddy simulations of neutrally stratified airflow over the complex terrain around Hong Kong International Airport with a three‐runway system

Situated just north of the mountainous Lantau Island, Hong Kong International Airport (HKIA) can experience terrain‐induced airflow disturbances under certain meteorological conditions, such as during a tropical cyclone. For approaching aircraft, such disturbances can lead to wind shear, jeopardizing flight safety. In this numerical study, large‐eddy simulations of turbulent airflow over Lantau Island are performed under neutrally stratified conditions with a view to understanding how terrain‐induced disturbances affect the headwind and crosswind experienced by aircraft landing at HKIA. Analysis of 1 min output data shows that, consistent with previous studies, high‐speed transient flow disturbances can emerge from gaps in the mountains of Lantau Island, producing spatiotemporal variations in wind speed and direction along the glide paths. An attempt is made to quantify the relative likelihood of wind shear by analysing how frequently wind‐speed fluctuations due to terrain interaction exceed predefined thresholds within a prescribed time window. The results show that wind shear is most likely to occur (1) on the eastern approach to the south runway (25L) in a southerly background wind; and (2) on the western approach to the south runway (07R) in a southeasterly background wind. These findings are consistent with the present operations at HKIA, where the north runway, which has a lower likelihood of wind shear, is used as the predominant landing runway. The simulations also show that the proposed third runway, which is currently under construction, will see an even lower likelihood of wind shear because it is the farthest of the three runways from Lantau Island. The study provides new insight into the unsteady flow behaviour at an airport surrounded by complex terrain, contributing to aviation safety.
Research Productivity of Teaching Faculty Members in Nigerian Federal Universities: An Investigative Study

This study assessed the level of research productivity of teaching faculty members in Nigerian federal universities. The findings of the study show that the research productivity of the teaching faculty members in Nigerian federal universities is high in journal publications, technical reports, conference papers, working papers, and occasional papers. The research productivity is higher in Northeast (M=22.53; SD=25.73), and Southwest (M=21.74; SD=87.28), and North Central (M=20.69; SD=31.24) Nigeria. Also, the mean score of information resources availability (M=2.41; SD=0.90) indicates that information resources are readily available to teaching faculty members in Nigerian federal universities. The barriers to research productivity by teaching faculty members in the universities include low Internet bandwidth (M=3.793; SD=1.162) and financial constraint (M=3.543; SD=1.257). Besides, the study has shown the strengths and weaknesses of the teaching faculty members in Nigerian universities in terms of their research output.
Robots in Nigerian academic libraries: Investigating infrastructural readiness and potential for library services

The study used the lens of Robotics and related strands such as Artificial Intelligence (AI) in the libraries to view how ready the selected Nigerian universities libraries vis-a-vis their librarians’ potentials of the technologies. The study addressed the following questions: What is the level of readiness of university libraries in Nigeria in terms of digital infrastructure, policy framework and human capacity for the adoption and use of robots in providing library and information services? What services are currently available in the university libraries that can potentially be adopted and rendered through robots? What are the attitudes of librarians towards the potential use of robots in providing and accessing library and information services?
Aging and technology perspectives of web-based chronic disease self-management

Many people suffer from chronic disease; however, older adults are at greatest risk of chronic conditions. Although social workers regularly engage with chronically ill older adults, they are not noticeably involved with the research and development of chronic disease management. As such, with recent movements toward health information technology, the efficacy of technology-based chronic disease management is not well established for older adults. Informed by theories of self-management, human development, and technology design, this research investigated lifespan differences of web-based chronic disease self-management. Using a sequential mixed methods design, a secondary data analysis of a diabetes specific web-based self-management intervention (n=462) was performed, followed by qualitative focus groups with 40 older intervention participants, and then mixed for overall interpretation. Results indicated that social workers must take a leadership role in the evaluation and implementation of web-based self-management for older adults to address identified lifespan differences.
Genetic Algorithm with Baker’s SUS Selection for Shift Scheduling of the Security Officers at Rawa Buntu Train Station, Indonesia

The increase of railway passengers in Indonesia should be balanced with security and convenience, especially at departure and train arrival stations. In ensuring passenger safety, PT Kereta Api Indonesia (KAI) provides some security officers at each railway station, including the Rawa Buntu Station. The Station Head and the Security Commander are responsible for drafting a guard schedule for 30 security officers at the station. The security schedule is divided into three shifts and eight guard locations. Manually arranged schedules often result in unbalanced work hours, clashing schedules and allowing emptiness of security officers. It is getting harder with additional rules that whenever a security guard gets a night shift, the next day must be off. In this research, we proposed a shift scheduling method based on a genetic algorithm using Stochastic Universal Sampling (SUS) selection method, double point crossover, random mutation and using generational model (elitism) for its generation update process. This research produces an application that can assist in the preparation of automatic shift scheduling of the security officers. Based on the test results, the application can arrange the schedule with the suitability level of 70% and the average time needed to generate schedule is 72.48 seconds. With this research, the process of arranging the schedule of security officers at the Rawa Buntu Station to be faster.
Future technologies for very high throughput satellite systems

In this paper, we investigate the possible satellite's role and technical solutions required for providing broadband services complementing the terrestrial fifth generation wireless standards. First, we review the satellite networks use cases, services, and system requirements. Then we investigate the satellite potential purpose for broadband service provision and the associated challenges with focus on the system and space/ground technological aspects. Different architectural satellite network solutions are illustrated jointly with the key system design trade‐offs. Particular emphasis is dedicated to the payload architecture. Finally, we illustrate the space and ground segment technologies enabling the successful satellite contribution to broadband services provision.
Visualizing project information from the perspective of a client

The importance for businesses to adapt to the digital age should not be understated. The demand for businesses to provide its customers and clients with engaging, immersive, collaborative, and interactive products is increasing as well. User experience is a term that encapsulates these demands very well. This statement also holds true in less obvious aspects of business-customer relationships, namely in the field of project management and the relationship between the client and the provider. The question then becomes: How do you visualize and present information regarding the process and progress of a project to a client in a user friendly and interactive manner? User-centered design was the approach used to create the design solution. Five clients were interviewed, and the results of their interviews rendered in a prototype which they then tested and had evaluated. The prototype incorporate three task visualizations, supports communication and approval of design proposals, highlights when the user is required to make decisions, among other things. This design solution was deemed to adequately fulfill the users’ needs and goals, but it also had room for improvements.
KNOWLEDGE, ATTITUDE AND PERCEPTION OF PHYSICIANS TOWARDS ADVERSE DRUG REACTION (ADR) REPORTING: A PHARMACOEPIDEMIOLOGICAL STUDY

Background: Reporting adverse drug reactions (ADRs) spontaneously is considered as a cornerstone of pharmacovigilance. However, its success depends on co-operative and motivated health care professionals. Under-reporting of the adverse drug reactions (ADRs) by the prescribers is a common problem. Objective: The present study is aimed at assessing the knowledge, attitude and perception of physicians from various fields in Hyderabad, towards adverse drug reactions reporting, to get an in-sight into the causes of under-reporting of ADRs and to suggest possible ways of improving this method of reporting. Method: The study was cross-sectional and questionnaire-based involving only medical doctors working in different fields. The completion of the questionnaire by respondents was taken as their consent to participate in the study. A total of 120 predesigned KAP (Knowledge, Attitude and Perception) questionnaires consisting of 25 questions were distributed to doctors with minimum qualification of MBBS. Microsoft Excel worksheet (Microsoft Office 2007) was used for statistical analysis. Result: Only 94 out of 120 respondents filled and returned the questionnaire within the stipulated time frame giving a response rate of about 78.33%. 88 [93.61%] doctors feel that ADR reporting and monitoring system would benefit the patients. Busy schedule, lack of knowledge about the exact authority to report ADRs, unavailability of ADR reporting forms, lack of incentives were some of the reasons for under-reporting of ADRs. Conclusion: It was observed that the knowledge of ADRs and how to report them are inadequate among doctors. More awareness should be created on the ADR reporting system.
On Living a Long, Healthy, and Happy Life, Full of Love, and with no Regrets, until Our Last Breath

Professor Jadad is a physician, educator, researcher, and public advocate, whose mission is to help improving health and wellness for all, through human networks fueled by innovative uses of information and communication technologies. He has been called a ‘human Internet’, as his research and innovation work seeks to identify and connect the best minds, the best knowledge, and the best tools across traditional boundaries to eliminate unnecessary suffering. Such work focuses on a radical ‘glocal’ innovation model designed to improve the capacity of humans to imagine, to create, and to promote new and better approaches for living, healing, working, and learning across the world. Powered by social networks and other leading-edge telecommunication tools, his projects attempt to anticipate and respond to major public health threats (e.g., multiple chronic conditions, pandemics) through strong and sustainable international collaboration, and to enable the public (particularly young people) to shape the health system and society. Alejandro Jadad holds various positions at the University of Toronto and the University Health Network, all related to the creation and optimization of human health. He is the Canada Research Chair in eHealth Innovation; founder of the Centre for Global eHealth Innovation; Senior Scientist at the Centre for Health, Wellness and Cancer Survivorship (ELLICSR); and Professor at the Departments of Anesthesia, Faculty of Medicine, and at the Dalla Lana Faculty of Public Health. The interview was conducted by Professor Claus Vogele.
Optimal Transaction Throughput in Proof-of-Work Based Blockchain Networks

As of today, Bitcoin suffers with restrictive transaction throughput of 3–7 transactions per sec and the transaction confirmation takes several min as bitcoin blockchain was designed with a block creation time of 10 min and each block is restricted with less blocksize for fast transmission. In this paper, we obtained the optimal transaction throughput for a Proof-of-Work (PoW) based longest chain rule blockchain network (called bitcoin protocol). This is done by modeling the delay diameter (D) and double spending attack in a Erd o ¨ s-R e ´ nyi random network topology as constraints. Through numerical results, it is shown that the throughput can be significatly improved without compromising the fairness of the network.
Some challenges of postoperative pain treatment

5 TIIVISTELMÄ . 7 ABBREVIATIONS. 11 LIST OF ORIGINAL PUBLICATIONS . 13 INTRODUCTION. 14 REVIEW OF THE LITERATURE. 16 1. Mechanisms of postsurgical pain . 16 1.1 Pathophysiology of acute postsurgical pain . 18 1.2 Pathophysiology of persistent postsurgical pain.19 2. Pharmacological treatment of acute postsurgical pain . 20 2.1 Paracetamol.21 2.2 Nonsteroidal anti-inflammatory drugs . 22 2.3 COX-2 inhibitors.24 2.4 Opioid analgesics . 26 2.5 Regional anaesthesia . 26 3. Cyclo-oxygenase inhibitors and renal function . 28 3.1 Clinical implications of COX inhibitors for renal function.28 3.2 Novel biomarkers of renal function . 29 4. Persistent postsurgical pain.30 4.1 Epidemiology of persistent pain in different types of surgery.31 4.2 Risk factors of persistent postsurgical pain . 32 AIMS OF THE STUDY. 36 PATIENTS AND METHODS. 37 1. Patients.37 2. Anaesthesia and fluid treatment . 39 3. Pain assessment, pain treatment and premedication . 39 4. Adverse events and laboratory samples . 40
Adaptive learning in a numerical methods course for engineers: Evaluation in blended and flipped classrooms

As use of adaptive learning technology in STEM courses gains traction, studies evaluating its impacts are important to undertake. Adaptive e‐learning platforms provide personalized, flexible learning via monitoring of student progress and performance and subsequent provision of an individualized learning path containing various resources. In this study, adaptive technology was utilized in blended and flipped versions of a numerical methods course. A particular challenge with flipped instruction is preclass preparation, in which videos with the same instruction for all students are often assigned. Therefore, to diversify preclass learning, the instructor developed adaptive lessons via an NSF grant and rigorously assessed outcomes in this flipped class with adaptive learning. In addition, to fully evaluate the lessons and respond to calls from the literature, the lessons were implemented and evaluated in a blended version of the course, which was lecture‐based with available online resources. Data from previous semesters of flipped and blended instruction (without adaptive learning were available), enabling a comparison of four instructional methods. The comparisons consisted of direct assessment (i.e., exam questions) and affective assessment via a survey (i.e., perceptions of the classroom environment). An analysis was performed for students collectively and for underrepresented minority students in engineering. Focus groups enabled a comparison of student perspectives of using adaptive technology in blended versus flipped classrooms as well as by demographic. Upon combining these outcomes, including a notable Cohen's d = 0.34 for open‐ended‐response performance, the flipped classroom with adaptive learning may be the best method for this STEM course.
Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Diabetic Retinopathy (DR) is a diabetic complication that affects the eyes and may lead to blurred vision or even blindness. The diagnosis of DR through eye fundus images is traditionally performed by ophthalmologists who inspect for the presence and significance of many subtle features, a process which is cumbersome and time-consuming. As there are many undiagnosed and untreated cases of DR, DR screening of all diabetic patients is a huge challenge. Some previous works have applied deep convolutional neural networks(CNNs) to detect DR automatically. However, these methods employed very deep CNNs which require extensive computational resources. In this paper, we proposed a computationally efficient classification system based on efficient CNNs. Our results show that the proposed method achieves or surpasses state-of-the-art methods on two commonly used DR datasets.
Byte from BEACH. No: 2014;2 Debunking the myth that general practice is '6 minute medicine'

For the last decade we have published the average consultation length for MBS/DVA claimable encounters with GPs for the past 12 year.1,2 The average has remained consistent at about 15 minutes, with the median being 1314 minutes in all years. All these reports are freely available through links in the “Publications” section of our web site, http://sydney.edu. au/medicine/fmrc/. We have also published journal articles on consultation length, how it varies among groups of GPs ( e.g. males and females, younger and older), and identified the predictors of consultation length.3-6
Secured Offline Data Synchronization Using Speech Recognition and Artificial Intelligence

- Offline data synchronization is a technique used to design an application that works without an internet connection. In the proposed work, offline synchronization methods for android have been enhanced along with the security and prioritization in the data file. The synchronization process has to be first validated and hence a speech recognition mechanism has also been applied in the proposed solution. Speech authentication systems along with unique feature selection and classification techniques are applied for providing the security. In addition to security, the proposed framework presents a set of cache memory which is updated when the user is online in order to optimize the synchronization process. The priority algorithm works according to user preferences. The files which are not in use for a long time are removed from the cache of offline user and the files which are getting used by the user often, get migrated to the cache memory automatically. The proposed job does not just emphasize user access, but also provides a safe way to manage the data. The proposed job also manages memory well so that the user does not find any hysteresis.