Reviewerly/exHarmony huggingface dataset:
Using Global t-SNE to Preserve Intercluster Data Structure

Abstract The t-distributed stochastic neighbor embedding (t-SNE) method is one of the leading techniques for data visualization and clustering. This method finds lower-dimensional embedding of data points while minimizing distortions in distances between neighboring data points. By construction, t-SNE discards information about large-scale structure of the data. We show that adding a global cost function to the t-SNE cost function makes it possible to cluster the data while preserving global intercluster data structure. We test the new global t-SNE (g-SNE) method on one synthetic and two real data sets on flower shapes and human brain cells. We find that significant and meaningful global structure exists in both the plant and human brain data sets. In all cases, g-SNE outperforms t-SNE and UMAP in preserving the global structure. Topological analysis of the clustering result makes it possible to find an appropriate trade-off of data distribution across scales. We find differences in how data are distributed across scales between the two subjects that were part of the human brain data set. Thus, by striving to produce both accurate clustering and positioning between clusters, the g-SNE method can identify new aspects of data organization across scales.
New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8 th in the challenge.
Light Weight Cryptography for Cloud-Based E-Health Records

Cloud computing is currently a popular research topic among academics. It is an internet-based resource pool with a wide range of resources. The cloud environment is extremely dependable in terms of making resources available to internet users when they are needed. Reliable computing services can be provided without the need for any additional infrastructure, making it a cost-effective alternative. The majority of the businesses relied on cloud computing to host their apps. The health-care unit service is the most important service for the population. It is critical to keep sensitive information about a patient's medical history in a secure location. As a result, the most secure encryption and decryption should be used. The maintenance of Electronic Health Records is one of the advanced technologies in cloud computing. There is very little research and development in the areas of personal health records and electronic health records. The ultimate goal of this work is to offer a system for exchanging personal health information in a safe cloud setting. The medical information about a patient is likewise kept in this EHR by cloud environment in a dispersed manner. The saved information of user can be collected, shared, exchanged, and organised by other users. In addition, this research study demonstrates an effective way for securing an e-health cloud system in a cloud computing environment utilising identity-based cryptographic technique.
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation

In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive performance to the advanced distillation methods. Our core idea is to fully explore the target-domain information from the views of boundaries and features. First, we propose a novel mix-up strategy to generate high-quality target-domain boundaries with ground-truth labels. Different from the source-domain boundaries in previous works, we select the high-confidence target-domain areas and then paste them to the source-domain images. Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels. Consequently, the boundary information of target domain can be effectively captured by learning on the mixed-up samples. Second, we design a multi-level contrastive loss to improve the representation of target-domain data, including pixel-level and prototype-level contrastive learning. By combining two proposed methods, more discriminative features can be extracted and hard object boundaries can be better addressed for the target domain. The experimental results on two commonly adopted benchmarks (i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes) show that our method achieves competitive performance to complicated distillation methods. Notably, for the SYNTHIA-> Cityscapes scenario, our method achieves the state-of-the-art performance with 57.8% mIoU and 64.6% mIoU on 16 classes and 13 classes. Code is available at https://github.com/ljjcoder/EHTDI.
Binocular Image Dehazing via a Plain Network without Disparity Estimation

Heavy haze leads to severely degraded visual quality for images, and thus the performance of high level image-based tasks such as object detection and semantic segmentation is deteriorated. It is necessary and important to design an effective dehazing method for the computer vision system. It is well known that image haze is a function of depth and binocular images can predict the depth. Existing binocular dehazing methods conduct disparity estimation and dehazing jointly to enhance each other. However, a small error in disparity gives rise to a large variation in depth and in the estimation of haze-free images. To alleviate the problem, we propose a plain binocular image dehazing network in this paper, called BidNet, to dehaze both the left and right images simultaneously. BidNet does not explicitly perform disparity estimation that is time-consuming and well-known to be challenging. Instead, we design a stereo transformation module to mine the relationship and correlation between binocular images, making the best of varying information of cross views. Additionally, we design a Stereo Foggy Cityscapes dataset extended from the Foggy Cityscapes dataset for training the proposed BidNet. Extensive experimental results demonstrate that BidNet significantly outperforms the SOTA dehazing methods on the synthetic stereo foggy datasets as well as in real stereo foggy scenes. Experimental results show that jointly dehazing binocular image pairs is mutually beneficial, which is better than only dehazing left images. Furthermore, when applying BidNet to preprocess foggy inputs, large improvements are obtained in the performance of object detection, instance segmentation, semantic segmentation, and stereo-based 3D object detection.
Khail-Net: A Shallow Convolutional Neural Network for Recognizing Sports Activities Using Wearable Inertial Sensors

Time-series data can be generated by wearable sensors such as accelerometers, gyroscopes, and magnetometers. This data may be used to classify various everyday life activities using machine learning or deep learning models. Athletics, education, child monitoring, ambient assisted living, and other applications benefit from human activity recognition. Human activity recognition includes sports activity recognition. A typical sports activity is any action that is often employed in a variety of sports. Walking, jogging, sprinting, and leaping are basic sports motions. A unique sports action exists exclusively in one sport, such as a badminton smash. We proposed a shallow convolution neural network for sports activity recognition. It has just 1251 trainable parameters. The test accuracy attained is 98.387%. The average F1 score, recall, and precision are 98.0, 98.7, and 98.0%, respectively. We have also trained the model on a benchmark human activity recognition dataset from WISDM lab for performance evaluation and comparison of the model.
Thermal and Visible Image Registration Using Deep Homography

Fusing thermal and visible images is a recurring challenge in computer vision, especially when the images of the two modalities are not well registered. This registration problem is traditionally solved by matching descriptors and depends on the richness and discriminating power of the representation. Ensuring that detected features are dense and uniformly distributed is not necessarily guaranteed. More recently, machine learning methods addressed the issue of visible to visible matching, but few address the multi-modality setting. In this paper, we propose to address the special case of thermal-visible image registration with small baseline parallax correction. Our deep homography model is evaluated on an open thermal and visible dataset with two training settings, unsupervised and supervised. Results demonstrate the feasibility of the approach, and performances comparison to state-of-the-art models is evaluated.
Blockchain Simulators: A Systematic Mapping Study

Recently, distributed ledger technologies like blockchain have been proliferating and have attracted interest from the academic community, government, and industry. A wide range of blockchain solutions has been introduced, such as Bitcoin, Ethereum, and Hyperledger technologies in the literature. However, tools for evaluating these solutions and their applications are still lacking, limiting the exploration of their potentiality and associated challenges/limitations. That is, experimenting with real blockchain networks usually requires a solid budget; and thus, sophisticated blockchain simulators can facilitate designing and evaluating solutions before the actual implementation stage. The quality of such simulators depends on several factors such as usability, reliability, provided capabilities, and supported features. This paper aims to provide a systemic mapping review of blockchain simulators focusing on these quality factors. This paper also sheds light on the configuration parameters (inputs) and produced metrics (outputs) supported by each simulator. Furthermore, it investigates which metrics supported by each simulator are scientifically validated/evaluated. Moreover, code quality comparison is carried out to assess the source code of the covered simulators. The results reveal that no simulator fully covers the wide operational range of features and capabilities of existing blockchain technologies. However, several promising efforts exist in the domain of blockchain simulation with interesting and useful features. Finally, we discuss the subject of blockchain simulation and provide our insight into the matter.
ECFRNet: Effective corner feature representations network for image corner detection

Interest points (corners and blobs) play an important role in computer vision tasks such as image matching, image retrieval, and 3D reconstruction. Existing deep learning based interest point detection methods mainly focus on the interest point detection with high repeatability under image affine transformations while neglecting the importance of the characteristics of interest points. This will affect the detection and localization accuracy of interest points. In this paper, we design an effective corner feature representations network based on the characteristics of corners. The designed network has the ability to effectively learn corner feature information from images. A novel loss function is proposed to minimize the localization error between the corner positions of the original image block and the transformed image blocks. Furthermore, a novel corner detection architecture is proposed. The criteria on detection accuracy, localization accuracy, average repeatability, region repeatability, and image matching score are used to evaluate the proposed method against fourteen state-of-the-art methods. The experimental results show that the proposed performs significantly better than the state-of-the-arts.
Image inpainting via spatial projections

Image inpainting is now-a-days sought after due to its wide variety of applications in the reconstruction of the corrupted image, occlusion removal, reflection removal, etc. Existing image inpainting approaches utilize different types of attention mechanisms to inpaint the image and produce visibly admirable results. These methods are more concerned at weighing the feature maps of the hole region with some weight from the non-hole region. But, due to the lack of spatial contextual correlation in the attention maps, the inpainted image may suffer from the inconsistencies among hole and non-hole regions. Transformer-based inpainting methods give significant results by capturing the relationship between the patches with a compromise of high computational complexity. In this context, we propose a novel spatial projection layer (SPL) without any attention mechanism to project the spatial contextual information in the hole region from non-hole regions for producing a spatially plausible inpainted image. The SPL is proposed mainly to focus on the non-hole spatial information in the high-level feature maps for filling the hole regions efficiently. Also, while training the network, we propose the use of edge loss with a Canny edge operator for image inpainting to focus on the relevant edges instead of noise contents. Analysis with the extensive experiments, ablation, and user study on the proposed architecture demonstrates the superiority over existing state-of-the-art methods for image inpainting. The code is available at: https://github.com/shrutiphutke/spatial_projection_inpainting.
Deep-Learned Perceptual Quality Control for Intelligent Video Communication

With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a challenge due to the limited network bandwidth and instability, which further affects the quality of video service closely linked with consumer electronic video display. To address this challenge, we propose a deep-learned perceptual quality control approach, which can significantly improve the video quality and visual experience at the same bandwidth. The proposed scheme mainly involves saliency region extraction, perceptual-based bits allocation, and video enhancement. Firstly, we exploit a multi-scale deep convolutional network module to predict the static saliency map that semantically highlights the salient regions. Secondly, we develop a recurrent neural network model to extract the dynamic saliency regions. Finally, a three-level rate allocation scheme is developed based on the resulted saliency guidance, which is more reasonable by taking into account the visual characteristics of human eyes. Experimental results on a large dataset show that our method achieves an average gain of 1.5dB on the salient regions without introducing an extra bandwidth burden, which significantly improves the visual experience and paves the way to intelligent video communication.
Solar Irradiance Forecasting with Transformer Model

Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems. Here, it is extended by combining features based on their spatiotemporal properties in solar irradiance prediction. The results were predicted for arbitrary long-time horizons since the prediction is always 1 day ahead, which can be included at the end along the timestep axis of the input data and the first timestep representing the oldest timestep removed. A maximum worst-case mean absolute percentage error of 3.45% for the one-day-ahead prediction was obtained, which gave better results than the directly competing methods.
Deep learning approach to solve image retrieval issues associated with IOT sensors

Deep learning is particularly well suited for that kind of circumstance, but ensuring confidentiality and safety has turned into a major problem in IoT management. A principal component analysis (PCA) is included in this specific research to identify and extract features more effectively. Additionally, the primary purpose of this study project is to compile an in-depth survey mostly on various IoT installations, and concerns regarding security and privacy with something like a rapid percentage of detection. The achievement of a higher detection frequency in IoT image classification is another main objective of this research work. Mostly on IoT datasets, the CNN methodology was trained and validated for effectiveness by using a wide range of approaches. Investigating the use of deep learning with IoT capturing images might be the initial phase. Furthermore, the value of such a deep learning approach is mostly assessed for improving the suitability of image identification with continuous testing reliability whenever it pertains to IoT image registration. An image identification approach that provides a range of acceptable criteria summarizes the study findings on such use of deep learning inside the IoT platform.
Designing Mobile MR Workspaces

Virtual Reality (VR) is increasingly used in everyday contexts for a variety of tasks. We particularly look at the confined space for passengers inside cars, where head-mounted displays (HMDs) could complement the prevalent use of mobile devices for work. In a field study (N=19), we tested three mobile workspace setups along the reality-virtuality continuum (Mounted Tablet, Augmented Focus Bubble, and Virtual Office) and let users re-position the virtual keyboard and display while typing on a physical keyboard in a parked car. The results revealed that using HMDs lowered users' awareness of their real surroundings but increased their perceived workload with a performance impairment of text entry rate compared to just using a tablet. Letting users customize their workspace layout improved their perceived performance and decreased pitch-axis head movements for switching between the virtual display and keyboard. This paper discusses challenges and strategies for future work regarding dynamic incorporation of productivity tools, adaptive mixed reality (MR) work environment designs, and optimizing upper thresholds of physical discomfort in mobile MR workspaces.
Multi-Valued Logics as an Advanced Basis for Artificial Intelligence (As an Example of Applied Philosophy)

It is shown that the creation of artificial intelligence systems, gradually approaching human intelligence, cannot be limited by binary logic and the classical interpretation of the category of truth. The nature of human thinking is variable, which requires the use of flexible algorithmic platforms that operate with multivalued logic. It is shown that the existing approaches to the interpretation of the essence of intelligence, going back to the Turing test, are not consistent. More adequate is a criterion built on the ability of the intellect to lie, including creative particulars. It is shown that using multi-valued logic is a promising tool for constructing the algorithmic basis of artificial intelligence. It is shown that the use of just such logic is of interest from the point of view of studying self-organization processes in a telecommunication environment, resulting in the appearance of “spontaneous intelligence”, too. An analogue of such intelligence, for example, is the phenomenon of bureaucracy. It is shown that the question of the essence of the intellect is a prerequisite for the further development of non-trivial logical systems since the functioning of the intellect cannot be reduced to the operations of classical formal logic.
Development of inductive sensors for a robotic interface based on noninvasive tongue control

Tongue based robotic interfaces have shown the potential to control assistive robotic devices developed for individuals with severe disabilities due to spinal cord injury. However, current tongue-robotic interfaces require invasive methods such as piercing to attach an activation unit (AU) to the tongue. A noninvasive tongue interface concept, which used a frame integrated AU instead of a tongue attached AU, was previously proposed. However, there is a need for the development of compact one-piece sensor printed circuit boards (PCBs) to enable activation of all inductive sensors. In this study, we developed and tested four designs of compact one-piece sensor PCBs incorporating inductive sensors for the design of a noninvasive tongue-robotic interface. We measured electrical parameters of the developed sensors to detect activation and compared them with a sensor of the current version of the inductive tongue-computer interface (ITCI) by moving AUs with different contact surfaces at the surface of the sensors. Results showed that, the newly developed inductive sensors had higher and wider activation than the sensor of ITCI and the AU with a flat contact surface had 3.5 - 4 times higher activation than the AU with a spherical contact surface. A higher sensor activation can result in a higher signal to noise ratio and thus a higher AU tracking resolution.