Reviewerly/exHarmony huggingface dataset:
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching

Recent studies have shown that cascade cost volume can play a vital role in deep stereo matching to achieve high resolution depth map with efficient hardware usage. However, how to construct good cascade volume as well as effective sampling for them are still under in-depth study. Previous cascade-based methods usually perform uniform sampling in a predicted disparity range based on variance, which easily misses the ground truth disparity and decreases disparity map accuracy. In this paper, we propose an uncertainty adaptive sampling network (UASNet) featuring two modules: an uncertainty distribution-guided range prediction (URP) model and an uncertainty-based disparity sampler (UDS) module. The URP explores the more discriminative uncertainty distribution to handle the complex matching ambiguities and to improve disparity range prediction. The UDS adaptively adjusts sampling interval to localize disparity with improved accuracy. With the proposed modules, our UASNet learns to construct cascade cost volume and predict full-resolution disparity map directly. Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods. Our method also achieves top performance on both SceneFlow dataset and KITTI benchmark.
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging

Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI). Histology WSIs can have billions of pixels, which create enormous computational and annotation challenges. Typically, such images are divided into a set of patches (a bag of instances), where only bag-level class labels are provided. Deep learning based MIL methods calculate instance features using convolutional neural network (CNN). Our proposed approach is also deep learning based, with the following two contributions: Firstly, we propose to explicitly account for dependencies between instances during training by embedding self-attention Transformer blocks to capture dependencies between instances. For example, a tumor grade may depend on the presence of several particular patterns at different locations in WSI, which requires to account for dependencies between patches. Secondly, we propose an instance-wise loss function based on instance pseudo-labels. We compare the proposed algorithm to multiple baseline methods, evaluate it on the PANDA challenge dataset, the largest publicly available WSI dataset with over 11K images, and demonstrate state-of-the-art results.
Deepfake Video Detection by Combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)

Nowadays, people are facing an emerging problem called deepfake videos. These videos were created using deep learning technology. Some are created just for fun, while others are trying to manipulate your opinions, cause threats to your privacy, reputation, and so on. Sometimes, deepfake videos created using the latest algorithms can be hard to distinguish with the naked eye. That's why we need better algorithms to detect deepfake. The system we are going to present is based on a combination of CNN and RNN, as research shows that using CNN and RNN combined achieve better results. We are going to use a pre-trained CNN model called Resnext50. Using this, we save the time of training the model from scratch. The proposed system uses Resnext pretrained model for Feature Extraction and these extracted features are used to train the Long short-term memory (LSTM). Using CNN and RNN combined, we capture the inter frames as well as intra frames features which will be used to detect if the video is real or fake. We evaluated our method using a large collection of deepfake videos gathered from a variety of distribution sources. We demonstrate how our system may obtain competitive results while utilizing a simplistic architecture.
Semantic Consistent Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation

Unsupervised domain adaptation (UDA) for cross-modality medical image segmentation has shown great progress by domain-invariant feature learning or image appearance translation. Feature-level adaptation based methods learn good domain-invariant features in classification tasks but usually cannot detect domain shift at the pixel level and are not able to achieve good results in dense semantic segmentation tasks. Image appearance adaptation based methods translate images into different styles with good appearance, but semantic consistency is hard to maintain and results in poor cross-modality segmentation. In this paper, we propose intra- and cross-modality semantic consistency (ICMSC) for UDA and our key insight is that the segmentation of synthesised images in different styles should be consistent. Specifically, our model consists of an image translation module and a domain-specific segmentation module. The image translation module is a standard CycleGAN, while the segmentation module contains two domain-specific segmentation networks. The intra-modality semantic consistency (IMSC) forces the reconstructed image after a cycle to be segmented in the same way as the original input image, while the cross-modality semantic consistency (CMSC) encourages the synthesised images after translation to be segmented exactly the same as before translation. Comprehensive experiments on two different datasets (cardiac and hip) demonstrate that our proposed method outperforms other UDA state-of-the-art methods by a large margin.
A Hopf physical reservoir computer

Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler-Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.
End-to-End Video Instance Segmentation via Spatial-Temporal Graph Neural Networks

Video instance segmentation is a challenging task that extends image instance segmentation to the video domain. Existing methods either rely only on single-frame information for the detection and segmentation subproblems or handle tracking as a separate post-processing step, which limit their capability to fully leverage and share useful spatial-temporal information for all the subproblems. In this paper, we propose a novel graph-neural-network (GNN) based method to handle the aforementioned limitation. Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations are used for tracking. Both inter and intra-frame information is effectively propagated and shared via graph updates and all the subproblems (i.e. detection, segmentation and tracking) are jointly optimized in an unified framework. The performance of our method shows great improvement on the YoutubeVIS validation dataset compared to existing methods and achieves 36.5% AP with a ResNet-50 backbone, operating at 22 FPS.
In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.
Superpixel-Guided Iterative Learning from Noisy Labels for Medical Image Segmentation

Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring high-quality annotations. Most existing methods neglect the pixel correlation and structural prior in segmentation, often producing noisy predictions around object boundaries. To address this, we adopt a superpixel representation and develop a robust iterative learning strategy that combines noise-aware training of segmentation network and noisy label refinement, both guided by the superpixels. This design enables us to exploit the structural constraints in segmentation labels and effectively mitigate the impact of label noise in learning. Experiments on two benchmarks show that our method outperforms recent state-of-the-art approaches, and achieves superior robustness in a wide range of label noises. Code is available at https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg.
Stacked Homography Transformations for Multi-View Pedestrian Detection

Multi-view pedestrian detection aims to predict a bird’s eye view (BEV) occupancy map from multiple camera views. This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views. In this paper, we propose a novel Stacked HOmography Transformations (SHOT) approach, which is motivated by approximating projections in 3D world coordinates via a stack of homographies. We first construct a stack of transformations for projecting views to the ground plane at different height levels. Then we design a soft selection module so that the network learns to predict the likelihood of the stack of transformations. Moreover, we provide an in-depth theoretical analysis on constructing SHOT and how well SHOT approximates projections in 3D world coordinates. SHOT is empirically verified to be capable of estimating accurate correspondences from individual views to the BEV map, leading to new state-of-the-art performance on standard evaluation benchmarks.
Combining neural networks for modulation recognition

This paper proposes a combined Convolutional Neural Network (CNN) scheme appropriate for automatic modulation recognition. The suggested network includes a deep network involving multiscale convolutions with an attention module for recognizing 24 modulation modes and a shallow one network for recognizing two modulation modes, aiming to reduce the misjudgment caused by the deeper network. We evaluate our proposed network on the DeepSig dataset achieving an accuracy of 98.7% that exceeds the performance of current state-of-the-art (SOTA) methods exploiting this dataset. The network achieved almost 100% accuracy on modulation modes of the order less than or equal to 64 and under high SNR. The accuracy attained by our method on high-order modulation modes exceeds the one of ResNet, which currently is the SOTA method.
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discrete labels. However, in many tasks (e.g., medical diagnosis), the labels are discrete and successively distributed. The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space. Target for this, the partially ordered set (poset) is defined for constraining the latent vector Instead of the typically i.i.d. Gaussian latent prior, in this work, a recursively conditional Gaussian (RCG) set is adapted for ordered constraint modeling, which admits a tractable joint distribution prior Furthermore, we are able to control the density of content vector that violates the poset constraints by a simple "three-sigma rule". We explicitly disentangle the cross-domain images into a shared ordinal prior induced ordinal content space and two separate source/target ordinal-unrelated spaces, and the self-training is worked on the shared space exclusively for ordinal-aware domain alignment. Extensive experiments on UDA medical diagnoses and facial age estimation demonstrate its effectiveness.
Scrum VR: Virtual Reality Serious Video Game to Learn Scrum

Education is crucial for the growth of society, and the usage of effective learning methods is key to transmit knowledge to young students. Some initiatives present Virtual Reality technologies as a promising medium to provide active, effective, and innovative teaching. In turn, the use of this technology seems to be very attractive to students, making it possible to acquire knowledge through it. On the other hand, agile methodologies have taken an essential role within information technologies and they are key in Software Engineering education. This paper combines both areas and presents prior research about Virtual Reality experiences with educational purposes and introduces a serious VR video game that aims to promote the learning of agile methodologies in Software Engineering education, specifically the Scrum methodology. This application tries to bring students closer to their first days of work within a software development team that uses the Scrum methodology. Two evaluation processes performed with university teachers and students indicate that the developed video game meets the proposed objectives and looks promising.
EMDQ-SLAM: Real-Time High-Resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos

We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue surfaces in real-time and preserve high-resolution color textures. EMDQ-SLAM uses the expectation maximization and dual quaternion (EMDQ) algorithm combined with SURF features to track the camera motion and estimate tissue deformation between video frames. To overcome the problem of accumulative errors over time, we have integrated a g2o-based graph optimization method that combines the EMDQ mismatch removal and as-rigid-as-possible (ARAP) smoothing methods. Finally, the multi-band blending (MBB) algorithm has been used to obtain high resolution color textures with real-time performance. Experimental results demonstrate that our method outperforms two state-of-the-art non-rigid SLAM methods: MISSLAM and DefSLAM. Quantitative evaluation shows an average error in the range of 0.8-2.2 mm for different cases.
An IoT implementation to ATM safety system

Automated Teller Machine (ATM) cash robbery is very common in many countries including India. The cash robbery by damaging the ATM and breaking the cash vault is the prime technique to loot cash from ATM. Remote locations of the ATM, less or no security guards. Negligence and inadequate safety measures are the main reasons for ATM cash robbery in India. Many gangs perform ATM robbery only because it is easy and comparatively safer compared to the bank robbery. The amount lost in the ATM robbery is the hard-earned money of the public and its loot indirectly hits the public as lesser money in the hand of banking leads to lesser money in the hands of the public. ATM robbers have become smarter and darer after several successful loots. Much faster and stronger tools are being used today like gas cutters, welding, and pneumatic breakers. Even JCBs are used to lift the whole ATM itself to be break opened later. It is evident that the safety of all the ATMs cannot be guaranteed with manual means. With the appropriate use of technology, it is practically possible to save the ATMs. One thing is observant that in all types of ATM robbery the ATM box is damaged for sure in one or another way. Our proposed ideology is based on this concept to detect the damage exerted on the ATM boxes. To report the damage, this research work utilizes the Internet of Things (IoT) technology, which has worldwide coverage. It is currently the most efficient communication technology. To report the incidence to the authorities, IoT is the best possible solution as it uses internet technology which is already present in the ATMs. The embedded system is combined with IoT to perform the task. The ESP8266 based IoT system connected with a vibration sensor and ATMEGA 328 P PU microcontroller is utilized for ATM robbery detection and alert to authorities. Our paper performs a detailed analysis of the issue and concludes the most appropriate solution. Also, this research work replicates the overall system and verify its possibility to be implemented for real-time operations.
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncertainties are poorly calibrated, computationally expensive, or based on heuristics. We present a new method that views image embeddings as stochastic features rather than deterministic features. Our two main contributions are (1) a likelihood that matches the triplet constraint and that evaluates the probability of an anchor being closer to a positive than a negative; and (2) a prior over the feature space that justifies the conventional l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization. To ensure computational efficiency, we derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates and matches the predictive performance of current state-of-the-art methods.
Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO

Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians.We present custom deep learning models generated in DeePathology™ STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions.We describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide.Deep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity.Reduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images.
SemiHand: Semi-supervised Hand Pose Estimation with Consistency

We present SemiHand, a semi-supervised framework for 3D hand pose estimation from monocular images. We pre-train the model on labelled synthetic data and fine-tune it on unlabelled real-world data by pseudo-labeling with consistency training. By design, we introduce data augmentation of differing difficulties, consistency regularizer, label correction and sample selection for RGB-based 3D hand pose estimation. In particular, by approximating the hand masks from hand poses, we propose a cross-modal consistency and leverage semantic predictions to guide the predicted poses. Meanwhile, we introduce pose registration as label correction to guarantee the biomechanical feasibility of hand bone lengths. Experiments show that our method achieves a favorable improvement on real-world datasets after fine-tuning.
A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data

Today's business ecosystem has become very competitive. Customer satisfaction has become a major focus for business growth. Business organizations are spending a lot of money and human resources on various strategies to understand and fulfill their customer's needs. But, because of defective manual analysis on multifarious needs of customers, many organizations are failing to achieve customer satisfaction. As a result, they are losing customer's loyalty and spending extra money on marketing. We can solve the problems by implementing Sentiment Analysis. It is a combined technique of Natural Language Processing (NLP) and Machine Learning (ML). Sentiment Analysis is broadly used to extract insights from wider public opinion behind certain topics, products, and services. We can do it from any online available data. In this paper, we have introduced two NLP techniques (Bag-of-Words and TF-IDF) and various ML classification algorithms (Support Vector Machine, Logistic Regression, Multinomial Naive Bayes, Random Forest) to find an effective approach for Sentiment Analysis on a large, imbalanced, and multi-classed dataset. Our best approaches provide 77% accuracy using Support Vector Machine and Logistic Regression with Bag-of-Words technique.
Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection

In this paper, we propose a Frequency-Aware Spatiotemporal Transformer (FAST) for video inpainting detection, which aims to simultaneously mine the traces of video in-painting from spatial, temporal, and frequency domains. Unlike existing deep video inpainting detection methods that usually rely on hand-designed attention modules and memory mechanism, our proposed FAST have innate global self-attention mechanisms to capture the long-range relations. While existing video inpainting methods usually exploit the spatial and temporal connections in a video, our method employs a spatiotemporal transformer framework to detect the spatial connections between patches and temporal dependency between frames. As the inpainted videos usually lack high frequency details, our proposed FAST synchronously exploits the frequency domain information with a specifically designed decoder. Extensive experimental results demonstrate that our approach achieves very competitive performance and generalizes well.