Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Glaucoma Screening in Retinal Fundus Images

Abstract Glaucoma is an eye disease which damages the optic nerve that carries information from the eye to the brain. Glaucoma is the second foremost reason of blindness. An efficient hardware based implementation of glaucoma screening is a significant task in the automated retinal image analysis method. This paper presents an automatic glaucoma screening using a TMS320C6416DSK DSP board. The detection procedure consists of two stages. The first stage comprises of image pre-processing and detection of optic nerve head center using circular Hough Transform . In the second stage, the optic disk diameter is calculated and cup is segmented from disk. The proposition between disk and cup is calculated for abnormal image screening. The implemented technique is tested on a publicly available retinal image data sets and the average accuracy achieved is 97.5%. Keywords: fundus image, glaucoma, optic disk, circular hough transform, TMS320c6416 1. Introduction Glaucoma is an irreversible eye syndrome. According to reports in 2010, it is second primary reason of blindness in the world. 2.3% of the peoples got affected from total population. It is predicted that this number will increase to 2.86% in 2020.Studies have been shown that increase in intraocular pressure (IOP) of the eye is one the cause for glaucoma [2]. To maintain healthy vision, eye produces a small amount fluid called aqueous humor the same amount fluid will be thrown out of eye. This balance keeps the IOP in limit. If the balance is not maintained the IOP increase and damage the optic nerve head which make irreversible vision loss. So, the early precise detection and treatment of glaucoma will control the progression of the disease. The ophthalmologist uses the ratio between optic cup and optic disk (CDR) as a parameter to screen the glaucoma patients. The CDR is <0.65 in case of normal eye and for the glaucoma eye the value is lies in between 0.65 to 0.9. Manual glaucoma screening is time consuming and prone to human error. So, An intricate algorithm is needed for mass screening of glaucoma.Figure 1 shows the normal image and glaucoma image.
Contextual Feature Weighting Using Knowledge beyond the Repository Knowledge

Bag of words, bigram, or more complex combinations of words are the most among general and widely used features in text classification. However, in almost all real-world text classification problems, the distribution of the available training dataset for each class often does not match the real distribution of the class concept, which reduces the accuracy of the classifiers. Let W(f) and R(f) be the discriminating power of feature f based on the world knowledge and the repository knowledge, respectively. In an ideal situation, W(f) = R(f) is desirable; however, in most situations, W(f) and R(f) are not equal and sometimes they are quite different, because the repository knowledge and the world knowledge do not have the same statistics about the discriminating power of feature f. In this paper, this phenomenon is called inadequacy of knowledge and we show how this phenomenon could reduce the performance of the text classifiers. To solve this issue, a novel feature weighting method is proposed which combines two bodies of knowledge, world knowledge and repository knowledge, using a particular transformation T. In this method, if both the world knowledge and the repository knowledge indicate a significantly high (resp., low) discriminating power of feature f, the weight of this feature is increased (resp., decreased); otherwise, the weight of the feature will be determined by a linear combination of the two weights. Experimental results show that the performance of classifiers like SVM, KNN and Bayes improves significantly if the proposed feature weighting method is applied on the contextual features such as bigram and unigram. It is shown also that pruning some words from the dataset using the proposed feature weighting method could improve the performance of the text classifier when the feature sets are created using Doc2vec.
Feature-preserving, Adaptive and Anisotropic Smoothing Algorithm for Triangular Mesh Models

Despite the great success in smoothing of triangular mesh, the classical methods mostly require human interactions. In order to reduce the parameter settings, an adaptive and anisotropic smoothing algorithm for removing noise and preserving features of triangular mesh model was proposed. First calculate the expected normal vector of the model triangles, the expected normal vector should be an accurate representation of the desired movement direction and be similar to the original surface normal vector, and then we compute the value of the offset value. We develop an adaptive coefficient scheme which can avoid the parameter settings to obtain the coefficient value for each vertex. Finally, we updated every vertex’s position of the model by formula. Experimental results show that the algorithm can be adaptive to preserve sharp features and avoid shrinkages by comparing with classical methods.
Prior-knowledge Effects on Associative Memory Processing

Forming new associations is a fundamental process in building our knowledge system and creating episodic memories. How prior-knowledge influences acquisition of novel associations has not been thoroughly investigated. Based on recent cognitive neuroscience literature on multiple-component memory processing, I hypothesize that prior-knowledge can trigger additional evaluative, semantic, or episodic processes, supported by the ventral medial prefrontal cortex (vmPFC), anterior temporal pole regions (aTPL), and hippocampus (HPC), respectively, to facilitate new associative learning. To test this hypothesis, I first developed a face-house associative memory task in Experiment 1, in which famous faces were used to elicit multiplecomponent prior-knowledge, such as social emotional, semantic, and episodic memories. Using this task, I found that recollection-based, not familiarity-based, associative memory was indeed facilitated by this type of prior-knowledge. Then, I used this task in two fMRI studies to examine how prior-knowledge could affect brain activity during associative encoding (Experiment 2) and post-encoding rests (Experiment 3). Consistent with my hypotheses, in Experiment 2, I found that the vmPFC and HPC, as well as the parahippocampal place area (PPA) and fusiform face area (FFA), showed stronger activation when famous, compared to nonfamous, faces were involved. Importantly, the vmPFC, aTPL, and HPC also exhibited stronger activation when famous faces elicited stronger prior emotions and memories. Experiment 3 showed that HPC iii connectivity with FFA and vmPFC during rest was stronger following encoding of associations with famous than non-famous faces. Post-encoding connectivity between the HPC and FFA, and between the aTPL and PPA/FFA, also predicted later associative, but not item, memory, only in the famous condition. These results indicate that when prior-knowledge is involved, the HPC, vmPFC, and aTPL, which support prior episodic, social-evaluative, and semantic memories, respectively, continue to interact with each other and the posterior perceptual brain regions (e.g., the PPA and FFA) during the post-encoding rest to facilitate off-line processing of the newly formed memory and enhance it. Taken together, these findings support my hypotheses that priorknowledge reconfigures memory processing among different brain systems, by triggering additional evaluative, semantic, or episodic processes, to facilitate new associative memory formation during both encoding phase and post-encoding rests.
The Research of the Recommendation Algorithm in Online Learning

Recommendation algorithm is a kind of method in information filtering and has been widely applied on Internet. Collaborative filtering is widely used in the recommendation systems and has turned out to be successful. With the growth of the resources, it is difficult for users to find learning resources that suit for themselves. The recommendation algorithm is required to analyze the behavior of the users and then recommend object of learning. Online judge is a kind of the online learning. People can evaluate their programming ability through online judge. The performance of the different recommendation algorithms is analyzed in this paper and it is proposed that the item-based collaborative filtering recommendation algorithms should be applied into the online learning system. Based on the algorithm, we propose that by pre-processing the data and using user data that have solve large number of problems, we can get a better recommendation result.
Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications.
Text-based Medical Image Retrieval using Convolutional Neural Network and Specific Medical Features

With the proliferation of digital imaging data in hospitals, the amount of medical images is increasing rapidly. Thus, the need for efficient retrieval systems, to find relevant information from large medical datasets, becomes high. The Convolutional Neural Network (CNN)-based models have been proved to be effective in several areas including, for example, medical image retrieval. Moreover, the Text-Based Image Retrieval (TBIR) was successful in retrieving images with textual description. However, in TBIR, all queries and documents are processed without taking into account the influence of certain medical terminologies (Specific Medical Features (SMF)) on the retrieval performance. In this paper, we propose a re-ranking method using the CNN and the SMF for text-medical image retrieval. First, images (documents) and queries are indexed to specific medical image features. Second, the Word2vec tool is used to construct feature vectors for both documents and queries. These vectors are then integrated into a neural network process and a matching function is used to re-rank documents obtained initially by a classical retrieval model. To evaluate our approach, several experiments are carried out with Medical ImageCLEF datasets from 2009 to 2012. Results show that our proposed approach significantly enhances image retrieval performance compared to several state of the art models.
Development of Cost Optimized Horizontal Gel Electrophoresis Running Unit for Developing Countries (Nepal)

The use of expensive lab techniques has left many high schools and even university students unacquainted with the basic experimental procedures and protocols in developing country including Nepal. Horizontal gel electrophoresis is one of the expensive protocols, which every student in the laboratory may not get an equal chance to access individually. However, this technique, being indispensable and inevitable in molecular biology principles, is of abounding importance for students to be familiar with. Thus, realizing its importance, we present an extremely simple and inexpensive design of gel-electrophoresis unit, which emulates electrophoresis analysis with the use of nichrome and aluminum wires as a substitute for platinum wires, together with daily used plastic materials. Because of these factors, the approximate cost of unit design is significantly reduced to an amount of 10 USD. The efficiency of the substitute wires was confirmed and it resulted in satisfactory data characterized by good resolution of the DNA fragments. The inexpensive nature, good results and simplicity of the device make it an ideal unit for teaching and learning in developing countries.
ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue

A Monte Carlo method for photon transport has gained wide popularity in biomedical optics for studying light behaviour in tissue. Nowadays, typical computation times range from a few minutes to hours. Although various implementations of the Monte Carlo algorithm exist, there is only a limited number of free software available. In addition, these packages may require substantial learning efforts. To address these issues, we present a new Monte Carlo software with a user-friendly interface. The simulation geometry is defined using an unstructured (triangular or tetrahedral) mesh. The program solves the photon fluence in the computation domain and the exitance at the domain boundary. It is capable of simulating complex measurement geometries with spatially varying optical parameter distributions and supports several types of light sources as well as intensity modulated light. Furthermore, attention is given to ease of use and fast problem set up with a MATLAB (The MathWorks Inc., Natick, MA) interface. The simulation code is written in C++ and parallelized using OpenMP. The simulation code has been validated against analytical and numerical solutions of radiative transfer equation and other Monte Carlo software in good agreement. The software is available for download from the homepage https://inverselight.github. io/ValoMC/ and the source code from GitHub https://github.com/InverseLight/ValoMC.
Performance Improvement of Sensorless Induction Motor Drive with Fuzzy Logic Controller

In this paper, the conventional PI controller used in the rotor flux based model reference adaptive system (MRAS) is replaced with a novel Fuzzy logic based adaptation mechanism. Such adaptive system works satisfactorily in both steady state and dynamic modes of operation. A detailed study between the conventional and proposed schemes is done in closed loop sensorless mode of operation. MATLAB/SIMULINK software is used for the Computer simulation study. Better dynamic performance is observed using proposed Fuzzy logic controller based speed estimator.
Virtual Interactive Tablet to Support Vocational Training in Immersive Environment

This paper presents a tool designed to assist a virtual reality learner in a vocational training context. The benefits of VR are spreading outside the video-games and research field, leading the vocational education institutions to consider using this technology for training purposes. By simulating emblematic professional situations, teachers can train students in good conditions regarding safety, logistics and financial resources. In the French vocational training system, VR is just at its beginnings and the lack of experiments with this training context highlighted the need for new tools allowing teachers to use VR with their students. The work presented in this paper is part of a global project aiming to create, design and assess new VR tools and methodologies for this specific context. In order to guide, inform and assist the user, we are presenting a generic tool that can ease VR sessions by proposing embedded tools within easy reach of the immersed user, such as a camera, an inventory, a printer or stock management. This tool is a virtual tablet that can be grabbed by the immersed user allowing her/him to interact efficiently with the virtual environment.
Combining Two-level Data Structures and Line Space Precomputations to Accelerate Indirect Illumination

We present a method for combining two-level data structures and directional precomputation based on the Line Space (LS). While previous work has shown that LS precomputation significantly improves ray traversal performance of typical spatial data structures, it suffers from high memory consumption and low image quality due to internal approximations. Our method combines this technique with two-level BVHs, where the LS is integrated within second-level object BVHs. The advantages are, among others, optimizations in terms of approximation accuracy and required memory. In addition, we propose a method to use an accurate BVH in combination with the fast approximation-based LS for path tracing in order to further reduce image errors of the LS while still benefitting from its gain in performance.
Visual storytelling for improving the comprehension and utility in disseminating information systems research: Evidence from a quasi‐experiment

Since the start of human civilisation, storytelling has served as an effective medium for disseminating important knowledge within families, communities, and organisations. We make a case for the use of visual storytelling, namely, video stories, to supplement traditional scholarly articles in the Information Systems (IS) discipline, thereby exploring its potential to improve actual and perceived comprehension, perceived utility, satisfaction as well as intentions to cite, share, and accept research. Drawing on cognitive learning theory, the cognitive theory of multimedia learning and the literature on deep processing, we develop our research model, which is based on the model by Jiang and Benbasat (2007). We test our model in experimental settings with 269 research‐oriented students and academics who were randomised into four conditions: (1) reading a text‐based article, (2) reading the script for a video about the article, (3) viewing the video story of the article, and (4) viewing the video story followed by reading the article. Results showed that the article's script was significantly perceived to be the least useful in disseminating research content. The video story and text‐based article were perceived to be equally useful, and supplementing the text‐based article with a video story was perceived to be the most useful. Moreover, the video story and text‐based article supplemented by a video story were of roughly equal effectiveness; yet, the video script was the most effective, and the text‐based article was least effective relative to other formats in disseminating scholarly knowledge. Last, we discuss how to further improve the design of video stories by referring to the critical narrative theory, which has the potential to significantly promote the dissemination of IS scholarly knowledge.
A Biomechanical Model for the Estimation of Dynamic Interactions at the Transfemoral Socket Interface

For the analysis of the human gait with prosthesis and the design of prosthetic devices, the knowledge of fundamental dynamic interactions at the user-prosthesis interface is required. A direct measurement of these interactions, including forces, torques, and powers, is complex and necessitates additional sensors in the prosthesis. In this work, a biomechanical model for the estimation of dynamic interactions at the transfermoral interface in sagittal plane is presented. By applying an inverse dynamic simulation, the normal force, torque and power at the interface are computed for different gait scenarios and lengths of the residual limb. The biomechanical model consists of multi-body system dynamics models of the legs and the trunk. The sound leg is modeled by three rigid bodies for thigh, shank, and foot as well as three rotatory joints representing the hip, knee, and ankle joint. The model of the leg with prosthesis is obtained by partitioning the rigid body of the thigh into a residual and a prothetic limb and linking both partitions by virtual joints representing the transfemoral interface. The parameters of the residual limb are identified by fitting the inertial specifications to a detailed geometric model of the human thigh. The simulation results provide essential information for gait analysis and prosthesis design. The power characteristics allow to evaluate the effort that has to be expended by the prosthesis user, while the torque characteristics show the influence of different prosthesis lengths on the loads of the residual limb. The normal force characteristics describe the impact of force peaks introduced by ground reactions and are a measure for the pressure distribution at the prosthesis stem. The presented biomachnical model is a first approach towards a comprehensive simulation environment for the human gait with prosthesis and will be further enhanced in accuracy and applicability.
Contribution-Based Multi-Stream Feature Distance Fusion Method With  ${k}$ -Distribution Re-Ranking for Person Re-Identification

Person re-identification (re-ID) is an important and challenging topic in video surveillance and public security. Re-ID aims to retrieve persons from different cameras. Despite the developments in recent years, re-ID still faces many challenges due to different camera views, changeable person posture, complex background, and occlusion. To exploit more discriminative image similarity descriptor, we propose a novel method in this paper. First, we design a body partition extraction network to extract three body regions with efficient alignment. Second, we propose a multi-stream contribution framework to fuse feature distance with different contributions and generate the final image similarity descriptor. In addition, we combine re-ID and semantic segmentation. A mask feature is introduced to the proposed framework and we design a contribution feedback module to generate contribution coefficients dynamically. Third, in order to improve re-ID performance, we propose a fragment learning method to optimize the contribution feedback module. Fourth and last, we propose a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-distribution re-ranking strategy to further improve performance. Our method achieves competitive results on two popular datasets, CUHK03, and Market1501, with rank-1 accuracy of 93.5% and 85.7%. The proposed re-ranking method achieves 2.3% and 2.8% performance boost. The data demonstrate the effectiveness of the proposed multi-stream contribution framework and the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-distribution re-ranking strategy.