Reviewerly/exHarmony huggingface dataset:
Training Object Detectors from Scratch: An Empirical Study in the Era of Vision Transformer

Abstract Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performance of self-attention mechanism in the language field, transformers tailored for visual data have drawn significant attention and triumphed over CNNs in various vision tasks. These vision transformers heavily rely on large-scale pre-training to achieve competitive accuracy, which not only hinders the freedom of architectural design in downstream tasks like object detection, but also causes learning bias and domain mismatch in the fine-tuning stages. To this end, we aim to get rid of the “pre-train and fine-tune” paradigm of vision transformer and train transformer based object detector from scratch. Some earlier works in the CNNs era have successfully trained CNNs based detectors without pre-training, unfortunately, their findings do not generalize well when the backbone is switched from CNNs to a vision transformer. Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch. In particular, we expect those insights to help other researchers and practitioners, and inspire more interesting research in other fields, such as remote sensing, visual-linguistic pre-training, etc. One of the key findings is that both architectural changes and more epochs play critical roles in training vision transformer based detectors from scratch. Experiments on the MS COCO dataset demonstrate that vision transformer based detectors trained from scratch can also achieve similar performance to their counterparts with ImageNet pre-training.
Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.
Applying human-centered design to the construction of a cirrhosis management clinical decision support system

Electronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx.We conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts.Workshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists).This is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.
Improved RRT* Algorithm for Disinfecting Robot Path Planning

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.
Enhancing Prediction Precision and Reliability in Cervical, Lung, and Breast Cancer Diagnosis

This study covers a wide range of cancers, focusing on cervical, lung, and breast cancer. Developing fast, accurate, and interpretable machine learning models for early diagnosis is critical to reducing the multifactorial mortality associated with these cancer types. Using a two-stage hybrid feature selection method, this study evaluates classification models using specific cervical, lung, and breast cancer data obtained from the UCI Machine Learning Repository. The cervical cancer dataset contains 36 features, the lung cancer dataset contains 16 features, and the breast cancer dataset contains 31 features. In the first stage, a random forest architecture is used for feature selection to identify features 5,7, and 7 that show a strong correlation with their cancer while reducing the difference between them. In Stage 2. Logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), and decision making (DT) were used to identify cervical cancer, lung, and breast cancer patients for five selections.
Managing and Monitoring Patient's Healthcare Using AI and IoT Technologies

This book chapter explores the transformative integration of AI and IoT technologies in the realm of healthcare, focusing on their collective impact on patient monitoring. The chapter delves into the emerging trends and challenges associated with the utilization of AI and IoT in the digital healthcare landscape, emphasizing their role in real-time patient monitoring, data analytics, and personalized healthcare solutions. Through an examination of case studies and current applications, the chapter highlights the potential of these technologies to revolutionize patient care by enabling continuous and remote monitoring, early detection of health issues, and data-driven decision-making for healthcare professionals. Furthermore, the chapter addresses the ethical considerations and challenges that accompany the implementation of AI and IoT in healthcare settings. Overall, the chapter provides a comprehensive overview of the innovative ways AI and IoT technologies are reshaping patient monitoring practices, offering insights into the future of digital healthcare.
An Integrated Approach to Next-Generation Telemedicine and Health Advice Systems Through AI Applications in Disease Diagnosis

This chapter explores the transformative potential of integrating knowledge engineering and artificial intelligence in healthcare, focusing on constructing a knowledge-based clinical decision support system (KBCDS). The primary objective is to design an AI-enabled health portal to enhance accessibility to medical advice globally. The study investigates the application of AI in diagnosing COVID-19 and pneumonia, aiming to improve diagnostic accuracy and speed, reducing the burden on healthcare systems, and saving lives. The research is based on extensive literature study, delving into the depths of knowledge engineering, AI applications in healthcare, and medical ontology. The findings underscore the transformative potential of the integrated approach, highlighting its impact on healthcare disparities globally. The AI-enabled health portal proves to be a reliable source of medical advice, demonstrating that the fusion of knowledge engineering and AI technologies empowers medical professionals and significantly enhances healthcare accessibility and diagnostic capabilities.
Fuzzy Thresholding-Based Brain Image Segmentation Using Multi-Threshold Level Set Model

Region of interest with reference to medical image is a challenging task. Clustering or grouping data objects can be used to isolate certain area of interest called image segmentation from human brain MRI scans is considered here. Together with the combination of Multilevel Otsu's thresholding and Level set approach, the most widely used fuzzy-based clustering like fuzzy C means (FCM) thresholding are taken into consideration. Here, the proper thresholding is determined using FCM thresholding. This threshold value can also be used to modify the Multilevel Otsu' method's threshold. The level set technique is then used to this segmented output image, yielding a more precise boundary level estimation. To improve brain MRI image segmentation, the proposed FTMLS system integrates the three aforementioned methods.
The Digital Competence of Students Preparing to become Primary School Teachers - Perspectives for Development

The analysis of the European Digital Competence Framework for Citizens, outlined the need to study: the digital competence of students - future primary teachers; their attitudes towards the study of required, elective and optional subjects developing digital competence. If students preparing to become primary teachers are offered sufficient and varied required, elective and optional subjects to develop these competences, they will be able to successfully apply digital competences in their future teaching practice. In May 2023, a survey was conducted with 200 students preparing to become primary teachers at the Pedagogical Faculty of Plovdiv University "Paisii Hilendarski", which would establish their attitudes on the issues raised. The results of this study showed: the students' high assessment of the usefulness of the studied compulsory disciplines for the development of digital competences, as well as an increased interest in the offered elective and optional disciplines with the same focus; the importance of primary teacher candidates having a good level of digital competences. The obtained results support the process of updating the current curricula in the disciplines developing digital competences and developing new training courses adapted to the needs of students for an even more complete development of their digital competences.
An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network

Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.
Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department

This paper proposed two models of data warehouse schema for the fire department of DKI Jakarta, where the 1st model contains six tables consisting of 3 fact and 3-dimensional tables, and the 2nd model only contains three fact tables. The 2nd model denormalises the 1st model, where the number of tables is less than the 1st model, where at the end of the day, the 2nd model will reduce the join table process, which increases the SQL performances. These two models have been recognised as fact constellation schema with more than one fact table and sharing dimension and sub-dimension tables. The database resources were collected from http://data.jakarta.go.id under the Fire and Rescue Service Agency. Those two data warehouse schema models were developed based on a report sector list, a report on Hydrants list, and vehicle register reports. This paper proposes to support Automatic Identification Systems (AIS) research, particularly implementing the data warehouse concept.
The Barriers and Readiness to Deal With Digital Transformation in Higher Education

During the recovery from the COVID-19 pandemic, stakeholders in higher education institutions have been encouraged to adapt to the changes caused by digital transformation. This study employs a case study approach to investigate the barriers and readiness of students, educators, and higher education institutions for digital transformation. Data were collected through questionnaires and interviews with stakeholders in a higher education institution in Indonesia. The data from the questionnaire were analysed using qualitative descriptive analysis, while the transcripts of the interview data were run into the ATLAS.ti Qualitative Data Analysis application and analysed thematically. Codes developed from digital transformation barriers were suggested by Volgesang and later interpolated during data analysis into readiness and strategy codes. The emerging themes related to students and lecturers, and universities obstacles, readiness and strategies in dealing with digital transformation could then be identified. The findings are crucial for higher education institutions to thrive in the digitally evolving environment. Understanding how the university's stakeholders perceive the transformation would help anticipate a more knowledgeable and organized method to handle the intricacies and difficulties associated with such a profound change.
Survey Analysis of University Teachers in Ukraine Regarding the Use of Google Workspace for Education

The article highlights the results of a survey of teachers at higher education institutions of Ukraine regarding the possibilities provided by Google Workspace for Education tools in the organization of the educational process. A questionnaire was used to gather data for this purpose, in which 167 respondents from different regions of the country took part. The questionnaire focused on the frequency of using specific educational applications, such as learning management systems, collaborative tools, and educational games. Most often, Google applications are used in blended learning (72% of respondents). To organize and conduct lectures/practical classes, teachers quite actively use Google Slides (79.4%/73.8% of respondents), Google Meet (69.2%/65.4% of respondents), YouTube (60.7%/56.1% of respondents). It was found that the vast majority of teachers (91%) positively evaluate the impact of Google Workspace for education tools on the effectiveness of teaching methods. In the future, it is planned to research students’ opinions regarding the use of Google Workspace for education while studying at university.
Quantum Computing in The Cloud - A Systematic Literature Review

Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.
Measuring digital skills in community adult learning settings – implications for Australian policy development

Social and economic participation is increasingly dependent on the proficient use of digital technologies in everyday life. Digital skills, alongside literacy and numeracy, are now foundational skills that all Australians should have. Yet, how policy makers, scholars, and adult educators can effectively identify people’s current and desired digital skills is not well established. Existing frameworks tend to be instrumentalist, listing generic abilities that all citizens need for success. Digital abilities, however, are context-specific and attempts to generalise need to be responsive to local circumstances. We present a theory-driven Digital Ability Self-Assessment Tool, developed for a government agency and piloted in a community organisation. The Tool takes a distinctive socio-material approach to overcome shortcomings of other digital ability assessment tools, which often focus on normative measures of skills attainment. Overall, we argue that authentic digital skills policy and programme development must account for the contexts in which digital technologies are used.
A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation

Abstract For semantic segmentation tasks, it is expensive to get pixel-level annotations on real images. Domain adaptation eliminates this process by transferring networks trained on synthetic images to real-world images. As one of the mainstream approaches to domain adaptation, most of the self-training based domain adaptive methods focus on how to select high confidence pseudo-labels, i.e., to obtain domain invariant knowledge indirectly. A more direct means to explicitly align the data of the source and target domains globally and locally is lacking. Meanwhile, the target features obtained by traditional self-training methods are relatively scattered and cannot be aggregated in a relatively compact space. We offer an approach that utilizes data augmentation and contrastive learning in this paper to perform more effective knowledge migration with the basis of self-training. Specifically, the style migration and image mixing modules are first introduced for data augmentation to cope with the problem of large domain gaps in the source and target domains. To assure the aggregation of features from the same class and the discriminability of features from other classes during the training process, we propose a multi-scale pixel-level contrastive learning module. What’s more, a cross-scale contrastive learning module is proposed to help each level of the model gain the capability to obtain more information on the basis of its own original task. Experiments show that our final trained model can effectively classify the images from target domain.
Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.