Reviewerly/exHarmony huggingface dataset:
Deep Bayesian Video Frame Interpolation

We present deep Bayesian video frame interpolation, a novel approach for upsampling a low frame-rate video temporally to its higher frame-rate counterpart. Our approach learns posterior distributions of optical flows and frames to be interpolated, which is optimized via learned gradient descent for fast convergence. Each learned step is a lightweight network manipulating gradients of the log-likelihood of estimated frames and flows. Such gradients, parameterized either explicitly or implicitly, model the fidelity of current estimations when matching real image and flow distributions to explain the input observations. With this approach we show new records on 8 of 10 benchmarks, using an architecture with half the parameters of the state-of-the-art model. Code and models are publicly available at https://github.com/Oceanlib/DBVI .
Conditioned deep feature consistent variational autoencoder for simulating realistic sonar images

Multibeam imaging sonar is one of the primary sensors for underwater navigation with uncrewed underwater vehicles (UUVs) due to the robustness to turbidity and variable lighting conditions that limit the applicability of standard cameras. However, the operating principles and noise models of real sensors make imaging sonar challenging to accurately simulate, and acquiring real images experimentally is difficult and costly. This paper presents an approach for transforming a synthetically generated input image into the textural domain of real sonar images using a variational autoencoder (VAE) with a modified loss function. This allows us to generate realistic sonar images of simulated scenarios emulating the texture of real acoustic images. As a result, large datasets can be created from a relatively small amount of real images, which can be later used in many downstream applications, ranging from evaluating data association algorithms to deep learning. The method was evaluated using an isolated real and simulated dataset that trained a separate convolutional neural network (CNN) to discern between images in the sonar domain and simulated images. The VAE has several advantages over a compared Cycle Consistent Generative Adversarial Network (CycleGAN) approach, including more texturally accurate generated images, and allowing for more variation in generated images.
Video logo detection by Deep-Transfer Active Learning

Brand logo detection is a special aspect of machine vision. However, Video logo detection benchmarks are scarce in the public domain. We exploit the power of a deep convolutional neural network (DCNN) and leverage established datasets related to existing applications to develop a deep-transfer active-learning (DTAL) algorithm to select the most valuable samples so that the smallest number possible needs to be labeled to achieve maximum performance improvements for video object detection model training. By exploiting the possible shared deep feature space between static and video datasets through transfer learning based on highly adaptable DCNN features, DTAL implements diversity-based active learning to select the most informative samples from a sequence of similar image frames for video object detection. We successfully apply the new DTAL algorithm to implement active learning for logo detection from live streaming sports videos as well as pedestrian and face detection from video data. We show that DTAL is a better active-learning method than state-of-the-art deep-learning-based active-learning object detection techniques. We also contribute one of the largest video-based logo resources, the Sports Match Video Logo (SMVL) dataset, to facilitate general logo detection research using transfer- and active-learning applications for video object detection.
Comparision of Segmentation Techniques for Acute Lymphoblastic Leukemia in Leukemia Cancer

Leukemia is a white blood cancer which is commonly seen in the bone marrow and lymphatic system where the production of abnormal cells which are immature blood cells (blast cells) will dominate the normal cell, this may lead the patient to lose proper functioning of the immune system and may not carry their normal functions, Leukemia is classified according to its speed of progression into Acute and Chronic. Acute is fast-growing and needs aggressive, timely treatment. Lymphocytic leukemia affects lymphoid cell that forms lymphatic tissue which makes the immune system. Acute lymphoblastic leukemia is most commonly seen in young children under 20 and also in adults. Segmentation of ALL cell nuclei with help pathologist in a proper diagnosis of ALL cancer. In this paper, a comparative study of Image processing based edge detection and segmentation techniques with different filters were carried out and the results obtained by each technique have been compared with evaluation metrics like PSNR, MAE and achieved MAE 110,62 and 66 and PSNR 33,2.15 and 10.65 respectively
Video Content Understanding Based on Feature Selection and Semantic Alignment Network

Existing video understanding models usually use a space-based uniform information extraction method, which is easy to ignore important visual semantics. At the same time, due to the inability to accurately distinguish positive and negative samples, visual semantics and predicted content cannot be accurately aligned, resulting in inaccurate content descriptions. In this paper, we design a method to highlight important features and a new semantic alignment loss function to improve the accuracy of the description. First, the image frame information is mapped into feature vectors, and important features are learned and selected through a selection extraction network based on the relationship between frames, and then extracted through a fully connected layer. Secondly, through the training of negative samples, the decoder can effectively identify the difficult samples, and on this basis, a new semantic alignment loss function is designed to adaptively assign weights to the loss calculated by negative samples to improve the relationship between text and images. Semantic relevance. Experimental results on MSVD, a dataset widely used in this field, show that our method can significantly improve the accuracy of video descriptions, and each indicator is significantly better than existing models.
Interpretable Poisson Optimization-Inspired Deep Network for Single-Photon Counting Image Denoising

Single-photon counting (SPC) imaging is a versatile approach for detecting targets under extremely low-light situations. To increase the quality of SPC images degraded by noise, traditional optimization-based methods seek priors from handcrafted features, which are inadequate to handle different kinds of targets in natural scenes, leading to poor denoising performance. The difficulty of optimal parameter design for good balance between denoising performance and spatial clarity is another obstacle. To address these issues, we develop a novel interpretable Poisson optimization-inspired deep network for SPC image denoising. First, we build a learnable prior that regularizes the Poisson optimization problem for SPC imaging to enhance the denoising performance. We construct a deep network by unfolding the iterative shrinkage-thresholding algorithm to solve the Poisson optimization problem. Therefore, all modules in the network have strong interpretability, enabling good generalization capability in real situations. Second, all parameters are optimized in a data-driven manner in the network. Finally, we conduct both simulated and real experiments to test the effectiveness of the proposed method. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods from aspects of visual effect and quantitative analysis.
Cryptanalysis of the Multi-Power RSA Cryptosystem Variant

We study the Multi-Power variant of the RSA cryptosystem where the modulus is of the form $$N=p^rq^s$$ with $$\gcd (r,s)=1$$ . We present a method to solve the linear equation $$a_1x_1+a_2x_2\equiv 0\pmod {p^uq^v}$$ where $$u<r$$ , $$v<s$$ , and $$a_1$$ , $$a_2$$ are two integers satisfying $$\gcd (a_1a_2,N)=1$$ . We apply the new method to the cryptanalysis of two instances of the Multi-Power RSA. We define a generalization of the CRT-RSA variant of the standard RSA to the Multi-Power RSA, and apply the new method to study its security. The new method is based on Coppersmith's method and lattice reduction techniques.
Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts.
An Improved Object Detection CNN Module for Remote Sensing Images

Convolutional neural network (CNN)-based object detection methods have aroused widespread interest in the remote sensing images field, which usually achieve satisfactory results. However, there still exist some factors that cause the detection performance to degrade, such as scales variability, back-ground complexity and objects tininess. In this work, we propose a CNN module that combines semantic information with fine-grained information and can replace the basic block in the backbone of object detection methods to enhance performance. More specifically, our module include a double branches for extracting semantic information and fine-grained information, and an Efficient channel attention (ECA) module for adjusting weights in channel-wise. Experimental results on DIOR dataset suggest the superiority of our module.
SCFNet: Semantic Condition Constraint Guided Feature Aware Network for Aircraft Detection in SAR Images

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in satellite observation and military decisions. Due to discrete scattering properties, speckle noise interference, and various aircraft types, many existing methods struggle to achieve the desired detection performance. In this article, we propose an innovative semantic condition constraint guided feature aware network (SCFNet) for detecting different aircraft categories in SAR images. First, considering the discrete scattering properties of aircraft, we design a local-global feature aware module (LGA-M) and morphological-semantic feature aware module (MSF-M), which can effectively extract the fine-grained feature information contained in SAR images. Second, to effectively fuse different feature information, we construct a feature fusion pyramid (FFP), which uses different branches and paths to reasonably merge multiple feature information types and suppresses background information interference. Third, according to the structure characteristics of aircraft, the global coordinate attention mechanism (G-CAT) is presented to highlight foreground target features and suppress speckle noise interference. Finally, we construct semantic condition constraints, including constraint condition setting, semantic information calculation, and template matching, to improve aircraft localization and recognition accuracy. Extensive experiments demonstrate that the proposed SCFNet can obtain state-of-the-art performance on the SAR aircraft detection dataset, which achieves AP and F1 Score of 94.83% and 95.58%, respectively. The related implementation codes will be made publicly available at https://github.com/darkseid-arch/AirDetection.
Proposal of Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

In this paper, we propose reconstructive reservoir computing (RRC) to detect anomaly in time-series signals. In the RRC, an echo state network (ESN) learns to reconstruct normal input signals fed to its input terminals. Since it fails to reconstruct abnormal signals, RRC can detect anomaly based on its reconstruction error. It is shown for the first time that an ESN reconstructs time-series signals effectively for anomaly detection while we already know that it can realize anomaly detection by forecasting. Experiments demonstrate that the RRC works for anomaly detection effectively. Though forecasting errors are used in conventional methods for anomaly detection working for time-series signals, we find experimentally that the reconstruction method has an advantage in its larger margin between normal and abnormal errors. We also find that a smaller leaking rate enhances the ability of anomaly detection. In general, reservoir computing has merits of fast training and, consequently, less energy consumption. We can also make reservoir computing work with the use of physical phenomena. Utilizing reservoir computing is really meaningful for these aspects which layered or other types of recurrent neural networks do not have. Anomaly detection by RRC will be an important application of micro physical-reservoir devices in the near future.
MPC-Based Privacy-Preserving Serverless Federated Learning

Federated learning (FL) enables multiple users to collaboratively train a global model by keeping their data sets local. Since a single server is used, traditional FL faces the single point of failure problem. An available approach is to adopt a serverless architecture. However, most of existing serverless FL schemes fail to protect gradient privacy, except for a few schemes that adopt differential privacy (DP) where the global model accuracy will decrease. To address these problems, we propose a privacy-preserving serverless FL scheme based on secure multiparty computation (MPC). Combining multiple cryptographic primitives (e.g., key agreement and symmetric encryption), our scheme protects gradient privacy in FL and it is accuracy-lossless. By secret sharing, our scheme supports users to quit an FL task in each round during training.
Agreement or Disagreement in Noise-tolerant Mutual Learning?

Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network. However, the dual network always tends to convergent which would weaken the dual-network mechanism to resist the noisy labels. In this paper, we proposed a noise-tolerant framework named MLC in an end-to-end manner. It adjusts the dual-network with divergent regularization to ensure the effectiveness of the mechanism. In addition, we correct the label distribution according to the agreement between dual-networks. The proposed method can utilize the noisy data to improve the accuracy, generalization, and robustness of the network. We test the proposed method on the simulate noisy dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The experimental result shows that our method outperforms the previous state-of-the-art method. Besides, our method is network-free thus it is applicable to many tasks. Our code can be found at https://github.com/JiarunLiu/MLC.
TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision

Nowadays, deep neural networks outperform humans in many tasks. However, if the input distribution drifts away from the one used in training, their performance drops significantly. Recently published research has shown that adapting the model parameters to the test sample can mitigate this performance degradation. In this paper, we therefore propose a novel modification of the self-supervised training algorithm SwAV that adds the ability to adapt to single test samples. Using the provided prototypes of SwAV and our derived test-time loss, we align the representation of unseen test samples with the self-supervised learned prototypes. We show the success of our method on the common benchmark dataset CIFAR10-C.
Achievable Thrust Expansion Control at Current Saturation of Variable-Pitch Propeller for Drones

On drones, which have become popular in recent years, the installation of variable pitch propellers is being considered and researched for industrial applications such as large drones and flying mobility. A variable-pitch propeller not only provides higher responsiveness than control by rotational speed alone, but also expands the thrust that can be achieved by optimizing the combination of rotational speed and pitch angle. In this paper, it is experimentally verified that, in the current saturation region of a variable-pitch propeller motor, the control method using simple frequency-separated command values for rotational speed and pitch angle causes an inverse response in transient conditions, and we attempt to explain this response by a linearized model including unstable zero. Basing on the model, we proposed a control method in which the pitch angle is controlled by a first-order filter while the maximum current is applied. The proposed method enables control design with a trade-off between initial undershoot and settling time with one parameter. The effectiveness of the proposed method is verified by simulation and experiment.
Deep Incremental Learning for Personalized Human Activity Recognition on Edge Devices

Tracking human daily activities is a useful functionality supported in many applications, especially with the pervasive use of wearable devices. State-of-the-art human activity recognition (HAR) uses machine or deep learning techniques to identify activities based on sensor readings. However, these models represent patterns from standardized experiment setups, with limited diversity when it comes to the individuals involved in data collection. This leads to limited success of HAR in real deployment scenarios, where individuals perform the same activity in different ways. Training models from scratch on real-time data streams is challenging due to the computational complexity of machine and deep learning architectures. In this article, we propose an incremental learning model for HAR that tweaks a deep learning model pretrained on a standardized HAR dataset and incrementally trains on newly generated individuals personalized data on their personal devices. The proposed solution promotes the preservation of data privacy, improves the model performance in terms of accuracy and efficiency without having to retrain from scratch, and tweaks the model according to personalized activity patterns. Extensive experiments show improvement of the base model’s accuracy up to 19% after incrementally training the model on filtered users’ datasets for the standing, walking, and running activities.
Is Deep Learning a Valid Approach for Inferring Subjective Self-Disclosure in Human-Robot Interactions?

One limitation of social robots has been the ability of the models they operate on to infer meaningful social information about people's subjective perceptions, specifically from non-invasive behavioral cues. Accordingly, our paper aims to demonstrate how different deep learning architectures trained on data from human-robot, human-human, and human-agent interactions can help artificial agents to extract meaning, in terms of people's subjective perceptions, in speech-based interactions. Here we focus on identifying people's perceptions of their subjective self-disclosure (i.e., to what extent one perceives to be sharing personal information with an agent). We approached this problem in a data-first manner, prioritizing high quality data over complex model architectures. In this context, we aimed to examine the extent to which relatively simple deep neural networks could extract non-lexical features related to this kind of subjective self perception. We show that five standard neural network architectures and one novel architecture, which we call a Hopfield Convolutional Neural Network, are all able to extract meaningful features from speech data relating to subjective self-disclosure.
Concept drift detector based on centroid distance analysis

The interest in data stream mining is continuously growing due to the increasing volume of data arriving at high speed produced by various systems. Processing and classification to gain valuable information from this kind of data require special treatment. The main problem is the non-stationary nature of data streams, which reveals in concept drifts - changes in sample characteristics describing classes. There are many approaches to mitigate the adverse effects of this phenomenon. However, one of the most popular techniques is drift detection. This work will propose a new drift detection approach based on the distance analysis between the subsequent data chunk centroids. The original solution in this methodology is to gather and preserve information about the data characteristics starting from the previous drift detection or data stream beginning. Two variants of the proposed method were compared with state-of-the-art concept drift detection algorithms. The evaluation was made on a comprehensive set of synthetic streams with different concept drift types and selected real streams. The evaluated results subjected to statistical analysis showed that the proposed concept drift detection algorithm could improve the classification of tested classifiers with a statistically significant advantage over the compared drift detection methods.
A Business Intelligence Tool for Explaining Similarity

Agile Business often requires to identify similar objects (firms, providers, end users, products) between an older business domain and a newer one. Data-driven tools for aggregating similar resources are nowadays often used in Business Intelligence applications, and a large majority of them involve Machine Learning techniques based on similarity metrics. However effective, the mathematics such tools are based on does not lend itself to human-readable explanations of their results, leaving a manager using them in a “take it as is”-or-not dilemma. To increase trust in such tools, we propose and implement a general method to explain the similarity of a given group of RDF resources. Our tool is based on the theory of Least Common Subsumers (LCS), and can be applied to every domain requiring the comparison of RDF resources, including business organizations. Given a set of RDF resources found to be similar by Data-driven tools, we first compute the LCS of the resources, which is a generic RDF resource describing the features shared by the group recursively—i.e., at any depth in feature paths. Subsequently, we translate the LCS in English common language. Being agnostic to the aggregation criteria, our implementation can be pipelined with every other aggregation tool. To prove this, we cascade an implementation of our method to (i) the comparison of contracting processes in Public Procurement (using TheyBuyForYou), and (ii) the comparison and clustering of drugs (using k-Means) in Drugbank. For both applications, we present a fairly readable description of the commonalities of the cluster given as input.