Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalent association with the bacterial cell envelope of prokaryotic expansins revealed by bioinformatics analysis

Expansins are a group of proteins from diverse organisms from bacteria to plants. Although expansins show structural conservation, their biological roles seem to differ among kingdoms. In plants, these proteins remodel the cell wall during plant growth and other processes. Contrarily, determination of bacterial expansin activity has proven difficult, although genetic evidence of bacterial mutants indicates that expansins participate in bacteria–plant interactions. Nevertheless, a large proportion of expansin genes are found in the genomes of free‐living bacteria, suggesting roles that are independent of the interaction with living plants. Here, we analyzed all available sequences of prokaryotic expansins for correlations between surface electric charge, extra protein modules, and sequence motifs for association with the bacteria exterior after export. Additionally, information on the fate of protein after translocation across the membrane also points to bacterial cell association of expansins through six different mechanisms, such as attachment of a lipid molecule for membrane anchoring in diderm species or covalent linking to the peptidoglycan layer in monoderms such as the Bacilliales. Our results have implications for expansin function in the context of bacteria–plant interactions and also for free‐living species in which expansins might affect cell–cell or cell–substrate interaction properties and indicate the need to re‐examine the roles currently considered for these proteins.
An insight into microscopy and analytical techniques for morphological, structural, chemical, and thermal characterization of cellulose

Cellulose obtained from plants is a bio‐polysaccharide and the most abundant organic polymer on earth that has immense household and industrial applications. Hence, the characterization of cellulose is important for determining its appropriate applications. In this article, we review the characterization of cellulose morphology, surface topography using microscopic techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Other physicochemical characteristics like crystallinity, chemical composition, and thermal properties are studied using techniques including X‐ray diffraction, Fourier transform infrared, Raman spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, and thermogravimetric analysis. This review may contribute to the development of using cellulose as a low‐cost raw material with anticipated physicochemical properties.
A Ground-Based Comparison of the Muscle Atrophy Research and Exercise System (MARES) and a Standard Isokinetic Dynamometer

PURPOSE: 1) To compare the test-to-test reliability of Muscle Atrophy Research and Exercise System (MARES) with a standard laboratory isokinetic dynamometer (ISOK DYN) and; 2) to determine if measures of peak torque and total work differ between devices. METHODS: Ten subjects (6M, 4F) completed two trials on both MARES and an ISOK DYN in a counterbalanced order. Peak torque values at 60 deg & 180 deg / s were obtained from five maximal repetitions of knee extension (KE) and knee flexion (KF). Total work at 180 deg / s was determined from the area under the torque vs. displacement curve during twenty maximal repetitions of KE and KF. Reliability of measures within devices was interpreted from the intraclass correlation coefficient (ICC) and compared between devices using the ratio of the within-device standard deviations. Indicators of agreement for the two devices were evaluated from: 1) a calculation of concordance (rho) and; 2) the correlation between the mean of measures versus the delta difference between measures (m u vs delta). RESULTS: For all outcome measures ICCs were high for both the ISOK DYN (0.95-0.99) and MARES (0.90-0.99). However, ratios of the within-device standard deviation were 1.3 to 4.3 times higher on MARES. On average, a wide range (3.3 to 1054 Nm) of differences existed between the values obtained. Only KE peak torque measured at 60 deg & 180 deg / s showed similarities between devices (rho = 0.91 & 0.87; Pearson's r for m u vs delta = -0.22 & -0.37, respectively). CONCLUSION: Although MARES was designed for use in microgravity it was quite reliable during ground-based testing. However, MARES was consistently more variable than an ISOK DYN. Future longitudinal studies evaluating a change in isokinetic peak torque or total work should be limited within one device.
Privacy-Friendly De-authentication with BLUFADE: Blurred Face Detection

Ideally, secure user sessions should start and end with authentication and de-authentication phases, respectively. While the user must pass the former to start a secure session, the latter’s importance is often ignored or underestimated. Dangling or unattended sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, the research community focused on automated de-authentication systems. Unfortunately, no single approach offers security, privacy, and usability. For instance, although facial recognition-based methods might be a good fit for security and usability, they violate user privacy by constantly recording the user and the surrounding environment.In this work, we propose BLUFADE, a fast, secure, and transparent de-authentication system that takes advantage of blurred faces to preserve user privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection continuously. To assess BLUFADE‘s practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The former was used to train and evaluate the deauthentication system performances, the latter to assess the privacy and to increase variance in training data. We show that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Furthermore, we demonstrate that BLUFADE effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements.
ActiSight: Wearer Foreground Extraction Using a Practical RGB-Thermal Wearable

Wearable cameras provide an informative view of wearer activities, context, and interactions. Video obtained from wearable cameras is useful for life-logging, human activity recognition, visual confirmation, and other tasks widely utilized in mobile computing today. Extracting foreground information related to the wearer and separating irrelevant background pixels is the fundamental operation underlying these tasks. However, current wearer foreground extraction methods that depend on image data alone are slow, energy-inefficient, and even inaccurate in some cases, making many tasks–like activity recognition–challenging to implement in the absence of significant computational resources. To fill this gap, we built ActiSight, a wearable RGB-Thermal video camera that uses thermal information to make wearer segmentation practical for body-worn video. Using ActiSight, we collected a total of 59 hours of video from 6 participants, capturing a wide variety of activities in a natural setting. We show that wearer foreground extracted with ActiSight achieves a high dice similarity score while significantly lowering execution time and energy cost when compared with an RGB-only approach.
Retinal Blood Vessel Segmentation using Random Forest with Gabor and Canny Edge Features

Recent developments in machine learning increases the researcher’s interest in processing the medical images in diagnosis. The medical field requires precise diagnosis to detect the disease. This paper proposed a fusion of features that are extracted from canny edge detector and Gabor feature extractors. These features dimension is huge while combining the features of canny edge detector and Gabor extractor. The Principal Component Analysis applied on the extracted features to reduce the dimension to increase the computational speed. The ensemble method Random Forest is applied on the features to classify the vessel’s existence in fundus image. The results have been compared with Decision Tree algorithm. The experiments have proved the Random Forest performed better result with 99.86% accuracy and F1 score 0.997.
Bridging Natruralistic Stimuli, Eye Movement And Brain Activity Via Cca And Locality Preserving Projection

Naturalistic stimuli provide more life-like multimodal inputs and promise to investigate brain activity and cognition. Many studies have succeeded in bridging multimedia features and semantics derived from brain responses. In this naturalistic stimulus paradigm, eye movement was widely used as a behavior measurement, which was also demonstrated to closely related to cognitive processes of individuals. However, there lacks an integrative work to bridge these three modalities ranging from low-level multimedia feature, high-level brain semantics, to human behavior. In this work, we adopted the canonical correlation analysis (CCA) method with the locality preserving projection (LPP) to correlate functional MRI based brain responses to multimedia features under the guidance of eye movement types. Canonical correlation was estimated between brain responses and multimedia features while the LPP constraint is used to preserve the temporal relationship by maximizing the discrimination between time points which have different eye movement types. Brain regions identified by our method exhibit a gradient on the cortex, and both selected brain regions and multimedia features have higher distinguishing ability among eye movement types than other unselected ones, suggestive of the validity of the method.
A lane detection system to assistant drivers for better driving using gradient decent algorithm comparison with KNN and SVM

The aim of this study is to detect and identify the different lane signals using Gradient Descent algorithm and correlate with the KNN and Innovative SVM. A total of 370 samples were collected from different road signals available in kaggle. These samples were divided into a training dataset of 185 (50%) and a test dataset 185 (50%). Accuracy values were calculated to quantify the performance of the Gradient descent algorithm comparison with KNN and Innovative SVM. The study parameters are alpha=0.03, G power=0.8. On performing an independent samples T-test on the two groups considered. The Gradient descent algorithm has a 85% accuracy rate, KNN has an 88% accuracy rate and Innovative SVM has a 96% accuracy rate. Finally, Innovative SVM appears significantly better than Gradient descent and KNN. The statistical significance value is 0.001 (p<0.05). From the analysis of the experimental results, the Support Vector Machine algorithm (SVM) gives better results than the KNN (K Nearest Neighbour) and Gradient Descent for the detection of the various lane traffic signals.
Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function

The segmentation of arteries in invasive coronary angiography is necessary to build quantitative models and eventually improve the diagnosis of cardiovascular diseases. Standard segmentation algorithms suffer due to the lack of fully annotated datasets and tend to return disconnected vessels. Thus, we explore a semi-supervised segmentation framework to address these issues. Specifically, we use a student model and a teacher model as the main framework with Nested U-Nets (UNet++) as their backbones. The student model learns by minimizing a segmentation loss between the output and the ground truth, and a consistency loss guided by the uncertainty information. Additionally, a special loss function based on elastic interaction is used to improve the connectivity of arterial branches. We demonstrate the effectiveness of our proposed techniques over 42 labeled and 60 unlabeled samples and find relative improvement of 5.59% for Dice score and 69.99% for Betti number compared to a U-Net.
Clustering Approach to Anonymize Online Social Network Data

OSNs (Online Social Networks) link billions of people to offline activities. OSNs have recently undergone significant development with increasing users every day. These networks are now an essential partner for individuals, but their online companions now play an unquestionably important role with expanding internet users. Users connect with other users who have overlapping personal trajectories after establishing confidence. OSN and other comparable programs extract massive amounts of data from individuals. As a result, data owners profit from this data, including personal information, sharing it with ethical third parties such as marketers. Unauthorized users can utilize data for unethical goals, resulting in various attacks and illicit uses of data. In this case, it is necessary to anonymize users' data before sharing it with others. Clustering users and anonymizing data set in a way that will not isolate individuals from the group. By failing to form tighter and k-anonymized clusters, one can track a person and perform illegal activities such as theft, robbery, and persona profiling. Anonymization protects personal information from prying eyes. However, data loss occurs as privacy increases, decreasing data utility. Hence, balancing privacy and utility is an open research issue.
SPA-RESUNET: Strip Pooling Attention Resunet for Multi-Class Segmentation of Vertebrae and Intervertebral Discs

Automated multi-class segmentation of vertebrae and intervertebral discs for volumetric magnetic resonance image(MRI) can help the diagnosis and the treatment of many spinal diseases. However, existing methods based on fully convolutional network mostly stacked local convolution and pooling operations, and thus failed to capture the long-range dependencies for spine segmentation. In this paper, we propose a novel and computation-efficient residual U-Net with strip-pooling attention mechanism (SPA-ResUNet) for the multi-class segmentation of vertebrae and intervertebral discs. Due to the effectiveness of skip connections in U-Net, the strip pooling attention module is adopted in the skip connections to learn the long-range dependencies for improving the segmentation performance of the top structures and sacral crest of spine. Our SPA-ResUNet was implemented on the MRSpineSeg Challenge dataset. Experiments show that our SPA-ResUNet achieves impressive performance with mean Dice similarity coefficients of 88.14% and 87.88% in all 19 spinal structures on two test datasets, respectively. The visual segmentation results also show that our method achieves superior segmentation in top structures and sacral crest of spine. The proposed SPA-ResUNet has high potential in aiding the diagnosis and the treatment of spinal diseases.
Performance Evaluation of Uplink Shared Channel for Cooperative Relay based Narrow Band Internet of Things Network

Low Power Wide Area Network (LPWAN) is one of the fastest growing network techniques provides efficient communciations for smart cities, e-Health, industry 4.0 and other applications. LPWAN enables long-rang communciations for M2M and cellular IoT networks. Narrowband-IoT (NB-IoT) is a type of LPWAN developed by 3GPP to connect a wide stream of IoT services and devices. NB-IoT systems rely on the mechanism of repeating the same signal every specified period of time in order to improve radio coverage better than it is in LTE systems. Repetition process is used to enhance the coverage of NB-IoT and for upgrade throughput as well. However, increasing the repetition of the signal significantly may give a negative result relative to the bandwidth limits. A cooperative relay (CoR) can be used beside repetition mechanism to helps reduce bandwidth stress. Moreover, the use of CoR for NB-IoT in physical uplink shared channel with repetitions will enhance the throughput. This paper will evaluate the performance of the CoR to enhance physical uplink shared channel in NB-IoT. The NB-IoT system model is simulated bu MATLAB to demonstrate the use of Cooperative relay (CoR) scheme in NPUSCH for NB-IoT for performance evaluation and comparison of using CoR scheme by considering metrics like data rate, throughput, and delay. The results conclude that in using CoR in NB-IoT gives high performance in overall NoT network throughput.
Lightseg: Efficient Yet Effective Medical Image Segmentation

While recent development in deep learning-based medical image segmentation has been fascinating, effectiveness mostly comes with the expense of expensive computing resources. In search of more affordable and convenient solutions, we propose a lightweight and faster yet effective medical image segmentation approach namely LightSeg. LightSeg leverages separable convolutional layers to decrease the model parameters and an attention mechanism to maintain segmentation quality. Our experimental evaluations on two different backbone networks (U-Net and ResU-Net) in segmenting the lungs from two publicly available chest X-ray datasets demonstrate the robustness of LightSeg while substantially reducing the network parameters.
Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals using Modified Convolutional Neural Network Technique compared with Support Vector Machine

The aim of this research work is to appraise the accuracy of Support Vector Machine (SVM) and Modified Convolutional Neural Network Technique (MCNNT) by replacing the hierarchical data processing for Smart Surveillance System. Materials and Methods: With MCNNT our novel object detection framework utilizes hierarchical data models of data processing, it is made up of layers that are completely interconnected to each node to control the complexities of object detection and using model image dataset calculated the data with sample size of 20 per group using p-value as 0.05. Result: The acquired mean accuracy of MCNNT (96.16%) obtained greater than SVM (94.40%). There is statistically significant deviation between obtained accuracies of two algorithms and for confidence interval (CI) 95% independent sample test was performed. Conclusion: Based on obtained results MCNNT acquired better accuracy than SVM of object detection.
Design and Development of IoT Gateway Software System

In this research and implementation, commercially available embedded development boards are used to implement the software functions of the IoT gateway, and the goal is to enable users to view the information of industrial-grade sensors through web pages. The development board uses LinkIt™ Smart 7688 Duo, developed by MediaTek. The retail price of this board is US$30 about, more cheaper than that of Raspberry Pi (about US$60~150). The proposal scheme is economical, and provides a customizing solution for System Integrators, who want to introduce IOT service to their customers. The main functions developed additionally into the IoT gateway are those for the mechanisms of Modbus and MQTT. The Web operation interfaces are also developed corresponding to the two functions. The function of Modbus over TCP is to provide the IOT manager station for reading/writing sensor systems through the IOT gateway, and that of MQTT lets the IOT gateway automatically read sensor systems and then send data to the IOT manager station. The physical bus between the IOT gateway and sensors is RS485, which is connected to 7688 Duo with an USB-to-485 adaptor. This development skill on the IOT gateway can provide a fully customizing solution for those who develop the IOT management system, and thus they can give an economical and full IOT service for their customers.
Detection of Defective Steel Surface with Image Segmentation

The detection of defects is an important part of steel manufacturing. Conventional ways of detecting defects in steel include ultrasonic testing, magnetic powder testing, osmosis testing, eddy current testing and X-ray testing. In this study, we used deep learning and computer vision to identify surface defects on steel. The study used U-Net, Mask R-CNN and Xception-V1which are deep neural network models used to detect specific parts of an image or image segmentation. The Xception- V1model performed well with complex defects whereas Mask-RCNN gave better results in rare defects. U-Net performed well with every type of defect and was a versatile model. The accuracy of U-Net model came out to be 80.21%. Images with no defects resulted in higher percentage of false predictions. It can be concluded that the existing models, while are capable of identifying different defects, they are not very good at predicting if there is no defect in this particular case.
AiEEG: Personalized Seizure Prediction Through Partially-Reconfigurable Deep Neural Networks

With more than 65M people affected by epilepsy worldwide, early prediction and response to seizure onsets have become more important than ever. Cutting-edge research in implantable medical devices (IMDs) has shown that deep neural networks (DNNs) applied to intracranial electroencephalogram (iEEG) data can predict seizures up to an hour before onset. However, offloading of iEEG data to the edge/cloud is highly prohibitive, due to the sheer size of the generated data. Existing work either focuses on the DNN training phase only, or does not consider the severe energy/space limitations of IMDs. Moreover, the technical aspects of patient personalization, which allows for patient-specific hyper-parameter tuning, still remain unaddressed. In this paper, we propose a platform called AiEEG for in vivo early seizure prediction, whose DNN hardware circuitry can be reconfigured remotely without surgery. We prototype AiEEG on a system on chip (SoC) platform and demonstrate its end-to-end capabilities in seizure prediction with a population of 30 epileptic patients, with iEEG signals coming from a real-world dataset. Extensive experimental results shows that (i) our embedded and personalized DNN has an area under the curve (AUC) averaging at 0.97 and as low as zero false positives per hour (FPH) for over half the patients, an improvement of about 3.5x with respect to a non-personalized prediction method and the best for a dataset of this size when compared to the state-of-the-art; (ii) our AiEEG platform consumes 4.2x less energy than a cloud-based approach, leading to a 4x battery lifetime improvement; (iii) we are able to remotely fine-tune the DNN through partial reconfiguration as needed in about 10s.
Mitochondria Instance Segmentation in Electron Microscopy Image Volumes using 3D Deep Learning Networks

Segmentation of mitochondria in electron mi-croscopy (EM) images is a challenging task due to complex shapes of mitochondria and other sub-cellular structures, background clutter, weak boundaries, low contrast, low signal-to-noise ratio, touching mitochondria, and large data size. For robust and accu-rate segmentation of individual mitochondria within an electron microscopy image volume, we propose a 3D deep convolutional neural network. The proposed network extends the classical U-Net semantic segmentation network with a convolutional long-short term memory (3D CLSTM U-NET). This extension allows better integration of 3D image features at different scales and abstraction levels. The proposed network generates two outputs, one corresponding to mitochondrial regions, and the other to mitochondrial boundaries. These region and boundary cues are used by a watershed segmentation module for identification of individual mitochondria. Our experiments on both animal and human datasets from MitoEM challenge showed promising results. The proposed pipeline achieved dice scores of 0.94 and 0.91 for the rat and human datasets respectively, and mAPs scores of 0.73 and 0.65.