Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Spectral Super-resolution of Hyperspectral Imagery based on

With the wide application of hyperspectral imagery, the resolution required is higher and higher. Without increasing the equipment cost, a spectral super-resolution method based on redundant dictionary is presented in this paper to improve the spectral resolution of hyperspectral imagery. The redundant dictionary based over-complete signal sparse decomposition theory is applied to the super resolution of hyperspectral imagery. By sparse decomposing of the spectrum curve corresponding to each pixel along the direction of spectral dimension, super-resolution is applied to the complete spectral curve of each pixel, which can ensure the consistency of their spectral characteristics during the process of super-resolution restoration, while the spectral resolution of the hyperspectral imagery is improved effectively.
Comparing Naive Bayes and Decision Tree Techniques for Predicting the Risk of Diabetic Retinopathy

Classifying data is a common task in Machine learning. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Diabetic retinopathy the most common diabetic eye disease, is caused by complications that occurs when blood vessels in the retina weakens or distracted. We have applied machine learning methods to predict the early detection of eye disease diabetic retinopathy and found that Decision Tree method to be 90% accurate. The performance was also measured by sensitivity and specificity.
On the net: Using WWW multimedia in the foreign language classroom: Is this for me?

This special issue of Language Learning & Technology looks at evaluating multimedia software. Where does the Internet fit into this picture? When we talk about multimedia in the foreign language (FL) classroom, we are often primarily concerned with the use of authentic materials that make the target language culture seem more real to students. Digitizing the images, sound, and video that represent multimedia can facilitate their distribution. However, digitized multimedia files tend to be quite large, and on the net, large files mean slow going. Can we really do multimedia on the World Wide Web? How is the quality? Does it require a fast connection or can a dial-up connection suffice? In this column we look at several web sites that are intended to help FL teachers learn about using and even making multimedia materials, and then explore a few sites that use online multimedia. Please note that several of these sites were, in fact, created by the authors, who teach a course in multimedia development.
Systems Behaviour Charts for Longitudinal Data Inform Marine Conservation Management

Conservation practitioners need to predict changes in ecological performance, such as species population levels, and the status of threats. Whilst an assessment of ecosystem status can be achieved from complex data analyses, this is less helpful for practical decision-making on the ground where a straightforward, reliable decision-framework is required. Systems Behavior Charts offer a simpler method for reviewing the state of the system. The methods for developing and analyzing System Behavior Charts are applied to two longitudinal datasets from coastal, estuary and freshwater conservation, threatened species head-starting (sea turtles), and mortality of aquatic mammals (manatees) due to human waterway traffic (watercraft collisions). Inferences from observations are made using a series of empirical rules. Results illustrate how Systems Behavior Charts identify changes, declines, recoveries and steady state systems. This insight enables early intervention, identification of actual improvements in ecosystem performance. These insights illustrate how Systems Behavior Charts offer an improved approach to analyzing data sets to support marine and aquatic conservation.
Enhanced indoor positioning utilising Wi-Fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration.
A COMPARISON OF DEFORMABLE IMAGE REGISTRATION ALGORITHMS AND THEIR APPLICABILITY TO 4DCT LUNG IMAGES

OF THE THESIS A Comparison of Deformable Image Registration Algorithms and Their Applicability to 4DCT Lung Images by Brian Francis Loughery Master of Science in Radiological Health Physics San Diego State University, 2012 Lung cancer is the most rampant killer among cancer types, taking more American lives per year than automobile accidents. This staggering annual death toll, along with our citizenry’s unwillingness to cease smoking, has lead to a research emphasis on lung cancer treatment techniques. Real-time treatment of a lung tumor requires accurate tracking of both the Planning Target Volume and the nearby healthy radiosensitive tissues. An imaging modality called 4DCT (Four-Dimensional Computed Tomography) provides accurate tracking by taking a 3D image of the lungs at each phase of the breathing cycle. Once these images are taken, a method is required to link the many phases together and track changes between them. This method is called Image Registration. Standard techniques of Image Registration track these changes using rigid motions. Lungs move in a more complicated fashion than rigid methods can handle. A more detailed form of registration, Deformable Image Registration, is required for 4DCT tracking. Many deformable image registration algorithms exist. One must be chosen. The purpose of this paper is to experimentally compare four well-studied deformable image registration algorithms (Horn-Schunk Optical Flow, Thirion’s Demons, and Yang’s Inverse-Consistent versions of each) as they apply to 4DCT DICOM lung images. These four algorithms were coded in MATLAB with only user-created functionality. The algorithms, codes, and functions are explained in complete detail and alternative methods are provided. The algorithms were applied to ten patient data sets. Standard conditions for parameters were taken from previous recommendations. Tests were performed using Sum of Square Differences measurements to determine each algorithm’s quantitative potential. The algorithms were also timed, and their final resulting images were saved for qualitative analysis. The results demonstrate that the optimal choice for Deformable Image Registration in 4DCT DICOM lung images, especially for small deformations, is the Inverse-Consistent version of Thirion’s Demons algorithm. Inverse-Consistent Demons provided high quality images, a satisfactory computation speed, and the best quantitative potential.
Abstractions for Verifying Isolation Properties in Stateful Networks

Modern networks achieve robustness and scalability by maintaining states on their nodes. These nodes are referred to as middleboxes and are essential for network functionality. However, the presence of middleboxes drastically complicates the task of network verification. Previous work showed that the problem is undecidable in general and EXPSPACE-complete when abstracting away the order of packet arrival. We describe a new algorithm for conservatively checking isolation properties of stateful networks. The asymptotic complexity of the algorithm is polynomial in the size of the network, albeit being exponential in the maximal number of queries of the local state that a middlebox can do, which is often small. Our algorithm is sound, i.e., it can never miss a violation of safety but may fail to verify some properties. The algorithm performs on-the fly abstract interpretation by (1) abstracting away the order of packet processing and the number of times each packet arrives, (2) abstracting away correlations between states of different middleboxes and channel contents, and (3) representing middlebox states by their effect on each packet separately, rather than taking into account the entire state space. We show that the abstractions do not lose precision when middleboxes may reset in any state. This is encouraging since many real middleboxes reset, e.g., after some session timeout is reached or due to hardware failure.
Generative Systems in Information Visualization

The author presents various approaches particularly in the field of visual arts and sound visualization based on hi-tech artificial agents and audiovisual systems. The number of digital artists and designers who tend to computational creativity has rapidly grown in recent years and their artworks and generative visuals that manifest the new cultural paradigm “Form Follows Data” meet with a wide interest. The author describes and presents a collection of tools and programming environments used for creating visual representations of sound as well as live coding visualizations which fall under so called generative art movement. Concepts of interactive audiovisual systems, sound-reactive programming software, and immersive environments refer to synergy of sound, visuals, and gestures. Another purpose is to point to applications of generative systems and agent-based frameworks in social and cognitive sciences to study environmental and social systems and their interactions.
Digital field fragment classification and object clusterization using the fast search method

The method of fast classification of digital field fragments and clusterization of analogous objects has been developed using the stack algorithm of the information transmission theory. The algorithm operation speed is studied as a function of the resemblance measure. The computational complexity is evaluated. The method is tried in the recognition of planar patterns. It has also been used for processing a two-dimensional digital field comprised of a set of time sequences. The algorithm allows the real-time building of the dictionary tree for a large number of time series and clusterization of input vectors for a particular resemblance measure.
Clinical Utility of Next-generation Sequencing in the Management of Myeloproliferative Neoplasms: A Single-Center Experience

Abstract Although next-generation sequencing (NGS) has helped characterize the complex genomic landscape of myeloid malignancies, its clinical utility remains undefined. This has resulted in variable funding for NGS testing, limiting its accessibility. At our center, targeted sequencing (TAR-SEQ) using a 54-gene NGS myeloid panel is offered to all new patients referred for myeloid malignancies, as part of a prospective observational study. Here, we evaluated the diagnostic, prognostic, and potential therapeutic utility of clinical grade TAR-SEQ in the routine workflow of 179 patients with myeloproliferative neoplasms (MPN). Of 13 patients with triple negative (TN) MPN, who lacked driver mutations in JAK2, CALR, and MPL, TAR-SEQ confirmed clonal hematopoiesis in 8 patients. In patients with intermediate-risk myelofibrosis (MF), TAR-SEQ helped optimize clinical decisions in hematopoietic cell transplant (HCT)-eligible patients through identifying a high molecular risk (HMR) mutation profile. The presence of an HMR profile favored HCT in 9 patients with intermediate-1 risk MF. Absence of an HMR profile resulted in a delayed HCT strategy in 10 patients with intermediate-2 risk MF, 7 of which were stable at the last follow-up. Finally, TAR-SEQ identified patients with various targetable mutations in IDH1/2 (4%), spliceosome genes (28%), and EZH2 (7%). Some of these patients can be potential candidates for future targeted therapy trials. In conclusion, we have demonstrated that TAR-SEQ improves the characterization of TN MPN, can be integrated in clinical practice as an additional tool to refine decision making in HCT, and has the potential to identify candidates for future targeted therapy trials.
An Experience-Centered Framework for Designing Non-Task-Oriented Embodied Interaction Environments

Embodied Interaction faces designers with the challenge of thinking about users and interaction from different viewpoints with respect to traditional technologies. This task is even more complex when designing non-task oriented systems. We propose a framework to guide researchers in thinking and designing non-task-oriented Embodied Interaction Environments or, in other words, embodied experiences that users can enjoy for its own sake and not as means for accomplishing a task or achieving an extrinsic goal. The framework is grounded on experience-centered design approaches and will present four qualities ((1) Spatial, Corporeal and Material Consistency, (2) Contingent Enhancement, (3) Mindful Embodied Engagement and (4) Situated Reflexivity) aimed at providing critical lenses, strategies and techniques to guide the design and research processes. Finally, we will discuss how designers can implement the proposed framework in different stages of the design process and paths for future research.
Text Categorization and Classifications Based on Weka

As the volume of information available on the Internet and corporate increases, there is growing interest in developing tools to help people better find, filter, and manage these electronic resources. Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. This article is an attempt to touch base with this vital research field in Data Mining. Our interest in this article comes from my interest in doing research in NLP related to analyzing biomedical documents like Discharge summaries. The Data Collection of this project includes collecting several discharge summaries from the i2b2 as well as other text-based dataset documents that are notable for text categorization (like the 20-Newsgroups). The Data Preprocessing stage is a significant part of this project as it includes several NLP filters as well as converting the text format of the collected documents to ARFF as our investigation found that the Weka framework is the most suitable Data Mining framework for text analysis. The third stage involves experimenting with several machine learning algorithms for text categorization and classification. In this direction we found the SMO (Sequential Minimal Optimization) is the best classifiers that provide the highest accuracy even when the size of sample training size decreases or with the increase of the mix of different type of text documents included in the sample.
Ontology-based terminology management for transitive translations focusing on NEs

I demonstrate that there are two types of transitive translations of Named Entities (NEs), both of which should be handled in the process of Cross Lingual Information Retrieval (CLIR). An official transitive translation is defined as a translation made through an official English translation often provided from an authorized local entity. A lexical translation is a direct lexical translation from a source language to a target language. However, a lexical translation also requires a transitive translation when a language pair is a rare combination having inadequate language resources. Hence I define it as a lexical transitive translation. I assess the inconsistency level of the official- and lexical transitive translations of NEs and propose an ontology-based CLIR solution referred to as triangulated terminology management.
High Voltage Gain Quasi-Switched Boost Inverters With Low Input Current Ripple

Two high voltage gain quasi-switched boost inverters (HG-qSBIs) are introduced in this paper. The proposed HG-qSBIs has the following characteristics: 1) continuous input current with low ripple; 2) reduced voltage stress on the capacitor, switch, and diodes; 3) shoot-through immunity; 4) achieved high voltage gain with single-stage conversion; and 5) improve the output voltage capability with using high modulation index. A novel pulse-width modulationcontrol technique is proposed for the introduced HG-qSBI. Operating principle, circuit analysis, and passive component design guideline of the HG-qSBI are addressed. Comparison analysis between the introduced HG-qSBI and other Z-source-based high voltage gain inverters is presented. A prototype is made to test the introduced HG-qSBI. Simulation and experimental verifications are shown to prove the accuracy of the theoretical analysis.
COVID-19 and the risk of CNS demyelinating diseases: A systematic review

Background Viral infections are a proposed possible cause of inflammatory central nervous system (CNS) demyelinating diseases, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). During the past 2 years, CNS demyelinating events associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported, but causality is unclear. Objective To investigate the relationship between CNS demyelinating disease development and exacerbation with antecedent and/or concurrent SARS-CoV-2 infection. Methods A systematic literature review of all publications describing either a new diagnosis or relapse of CNS demyelinating diseases (MS, NMOSD, MOGAD) in association with SARS-CoV-2 infection was performed utilizing PRISMA guidelines. Descriptive statistics were used for data analysis, using a case analysis approach. Results Sixty-seven articles met the inclusion criteria for the study. Most of the reported cases of NMOSD (n = 13, 72.2% of reported cases) and MOGAD (n = 27, 96.5% of reported cases) were of new disease onset, presenting with typical clinical and radiographic features of these conditions, respectively. In contrast, reported MS cases varied amongst newly diagnosed cases (n = 10, 10.5% of reported cases), relapses (n = 63, 66.4%) and pseudo-relapses (n = 22, 23.2%). The median duration between COVID-19 infection and demyelinating event onset was 11.5 days (range 0–90 days) in NMOSD, 6 days (range−7 to +45 days) in MOGAD, and 13.5 days (range−21 to +180 days) in MS. Most cases received high-dose corticosteroids with a good clinical outcome. Conclusion Based upon available literature, the rate of CNS demyelinating events occurring in the setting of preceding or concurrent SARS-CoV-2 infection is relatively low considering the prevalence of SARS-CoV-2 infection. The clinical outcomes of new onset or relapsing MS, NMOSD, or MOGAD associated with antecedent or concurrent infection were mostly favorable. Larger prospective epidemiological studies are needed to better delineate the impact of COVID-19 on CNS demyelinating diseases.
Fuzzy-Based Medical Image Processing

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.