Reviewerly/exHarmony huggingface dataset:
OneFall-GAN: A one-class GAN framework applied to fall detection

One of the most common health risks for senior citizens is a falling event, and to reduces the risk of death, a fall needs to be quickly reported. Thus, automatic fall detection systems were proposed to mitigate the falling problem, most of them relying on image detection. However, identifying a fall from a sequence of images is challenging because the contextual information present in some activities of daily living (ADL) are similar to fall events, and the recognition model has to deal with very high-level information. Furthermore, the publicly available datasets are generally unbalanced, having only synthetic data of fall events. Thus, approaches that do not need explicit labels to train a model are desirable. As falls are rare events, a possible solution is to use a one-class approach, and Generative Adversarial Networks (GAN) have presented competitive results in this context. This paper proposes OneFall-GAN, a One-Class GAN that uses only ADL for training and can identify a fall as an anomaly on the learned representations. We trained our neural network using the data captured by an RGB camera and pre-processed by a pre-trained Mask R-CNN that can segment and binarize a person in a scene. Thus, our model has robust temporal information used in the training phase and once trained, the discriminator is specialized on classifying ADL events. Consequently, when a fall event is presented, it is capable of classifying them as an anomaly. Using this approach, we are turning a binary classification problem into a one-class approach. Our experimental results show that our OneFall-GAN framework is a competitive alternative for fall classification as an anomaly detection task using only visual data, particularly because it achieved the best performance when compared to current state-of-the-art approaches, reaching an accuracy of 99.02%, 0.97 precision, 1.0 recall on PRECIS HAR and 98.75% accuracy, 0.87 precision, 0.99 recall on UP Fall.
Convolutional neural network based displacement gradients estimation for a full-parameter initial value guess of digital image correlation

The selection of initial value in digital image correlation (DIC) has significant influence on the search efficiency of image subpixel displacement and the algorithmic convergence speed. An accurate and reasonable initial value can reduce the number of iterations of subsequent IC-GN optimization, accelerate the convergence of the results, and avoid the divergence of the algorithm in the iterative process. This paper proposes a full-parameter initial value estimation method based on a regression convolution neural network with multithreaded calculation. The proposed method sequentially uses the integer-pixel estimation based on neighborhood search, the subpixel estimation based on surface fitting and the first-order displacement gradients estimation based on a regressive convolutional neural network to achieve the initial value estimation of inverse compositional Gauss-Newton (IC-GN) iteration. Experimental results show that the iteration times of the proposed method are reduced by about 30% compared with the integer-pixel initial value estimation method. In the process of IC-GN iteration, the computational efficiency of CPU multithreaded calculation is nearly twice higher as that of the single-thread method. It can not only improve the accuracy of the initial value estimation but also has high adaptability, which can adapt to selecting different subset sizes or different speckle patterns. This study provides a reference for the effect of iterative initial value optimization on efficiency and accuracy in DIC.
A Brief Analysis of the Holistically-Nested Edge Detector

This work describes the HED method for edge detection.HED uses a neural network based on a VGG16 backbone, supplemented with some extra layers for merging the results at different scales.The training was performed on an augmented version of the BSDS500 dataset.We perform a brief analysis of the results produced by HED, highlighting its quality but also indicating its limitations.Overall, HED produces state-of-the-art results.
Deep Image Prior for medical image denoising, a study about parameter initialization

Convolutional Neural Networks are widely known and used architectures in image processing contexts, in particular for medical images. These Deep Learning techniques, known for their ability to extract high-level features, almost always require a labeled dataset, a process that can be computationally expensive. Most of the time in the biomedical context, when images are used they are noisy and the ground-truth is unknown. For this reason, and in the context of Green Artificial Intelligence, recently, an unsupervised method that employs Convolutional Neural Networks, or more precisely autoencoders, has appeared in the panorama of Deep Learning. This technique, called Deep Image Prior (DIP) by the authors, can be used in areas such as denoising, superresolution, and inpainting. Starting from these assumptions, this work analyses the robustness of these networks with respect to different types of initialization. First of all, we analyze the different types of parameters: related to the Batch Norm and the Convolutional layers. For the results, we focus on the speed of convergence and the maximum performance obtained. However, this paper aims to apply acquired information on Computer Tomography noised images. In fact, the final purpose is to test the best initializations of the first phase on a phantom image and then on a real Computer Tomography one. In fact, Computer Tomography together with Magnetic Resonance Imaging and Positron Emission Tomography are some of the diagnostic tools currently available to neuroscientists and oncologists. This work shows how initializations affect final performances and, in addition, how they should be used in the medical image reconstruction field. The section on numerical experiments shows results that on the one hand confirm the importance of a good initialization to obtain fast convergence and high performance; on the other hand, it shows how the method is robust to the processing of different image types: natural and medical. Not a single good initialization is discovered, but many of them could be chosen, according to specific necessities of the single problem.
UFKT: Unimportant filters knowledge transfer for CNN pruning

As the deep learning models have been widely used in recent years, there is a high demand for reducing the model size in terms of memory and computation without much compromise in the model performance. Filter pruning is a very widely adopted strategy for model compression. The existing filter pruning methods identify the unimportant filters and prune them without worrying about information loss. They try to recover the same by fine-tuning the remaining filters, limiting their performance. In this paper, we tackle this problem by utilizing the knowledge from unimportant filters before pruning to minimize information loss. First, the proposed method identifies the unimportant and important filters by exploiting the lower and higher importance, respectively, using the L1-norm of filters. Next, the proposed custom UFKT-Reg regularizer (Rufkt) transfers the knowledge from unimportant filters before pruning to remaining filters, notably to a fixed number of important filters. Hence, the proposed method minimizes information loss due to the removal of unimportant filters. The experiments are conducted using the three benchmark datasets, including MNIST, CIFAR-10, and ImageNet. The proposed filter pruning method outperforms many recent state-of-the-art filter pruning methods. An improvement over the baseline in terms of accuracy is observed even after removing 95.15%, 62.28%, and 62.39% of the Floating Point OPerations (FLOPs) from architectures LeNet-5, ResNet-56, and ResNet-110, respectively. After pruning 53.25% of FLOPS from ResNet-50, only 1.02% and 0.47% of drops are observed in top-1 and top-5 accuracies, respectively. The code used in this paper will be publicly available at ( https://github.com/sarvanichinthapalli/UFKT).
Unbounded and lossless compression of multiparameter quantum information

Several tasks in quantum-information processing involve quantum learning. For example, quantum sensing, quantum machine learning, and quantum-computer calibration involve learning and estimating unknown parameters $\mathbit{\ensuremath{\theta}}=({\ensuremath{\theta}}_{1},\ensuremath{\cdots},{\ensuremath{\theta}}_{M})$ from measurements of many copies of a quantum state ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\rho}}}_{\mathbit{\ensuremath{\theta}}}$. This type of metrological information is described by the quantum Fisher information matrix, which bounds the average amount of information learned about $\mathbit{\ensuremath{\theta}}$ per measurement of ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\rho}}}_{\mathbit{\ensuremath{\theta}}}$. In several scenarios, it is advantageous to compress the multiparameter information encoded in ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\rho}}}_{\mathbit{\ensuremath{\theta}}}{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\bigotimes}n}$ into ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\rho}}}_{\mathbit{\ensuremath{\theta}}}^{\text{ps}}\phantom{\rule{0.16em}{0ex}}{}^{\ensuremath{\bigotimes}m}$, where $m\ensuremath{\ll}n$. Here, we present a ``go-go'' theorem proving that $m/n$ can be made arbitrarily small, and that the information compression can happen without loss of information. We also demonstrate how to construct filters that perform this unbounded and lossless information compression. These filters can, for example, reduce arbitrarily the quantum-state intensity on experimental detectors, while retaining all initial information. Finally, we prove that the ability to compress quantum Fisher information is a nonclassical advantage that stems from the negativity of a particular quasiprobability distribution, a quantum extension of a probability distribution.
Bridging the cross-modal gap using adversarial training for speech-to-text translation

The end-to-end speech translation (ST) model usually adopts the encoder-decoder structure, which takes the speech of the source language as input and directly outputs its translation result in the target language. Since the model performs cross-modal translation, it needs to extract semantically rich representations from speech. Compared with text, speech is fine-grained and contains more noise, which puts a great burden on the encoder of the ST model. The modal gap between speech and text usually causes the ST model to perform inferior to the corresponding machine translation (MT) model. To bridge the cross-modal gap, this paper proposes to use adversarial training to relieve the burden on the ST encoder by providing internal supervision signals. In this approach, the encoder in the ST model can extract representations that contain rich semantics, which greatly improves performance. Experiments on the datasets of Augmented Librispeech English-French and MuST-C English-German show the effectiveness of our method. Further analysis indicates that our proposed method can perform well in low-resource conditions compared to strong baselines.
Machine Learning Based Model for Prediction of Loan Approval

Banks are vital to financial management and controlling the economy of a country. Banks and financial institution distribute loans and these loans act as the core business part of almost every banks. The profits are earned from the loans distributed by the banks. The prime goal is to invest their assets in safe hands. The success of bank depends on the decision-making capability to evaluate risk of lending loan to the customer. Checking manually individual consumer&#x2019;s credibility for the loan approval is difficult, time consuming and risky. Thus, the banks aim to minimize the credit risks of defaulting. In this study we have applied logistic regression as a tool to predict whether an applicant is eligible for the loan or not. The data is collected from the Kaggle for studying and prediction.
On Algorand Transaction Fees: Challenges and Mechanism Design

Algorand is a public proof-of-stake (PoS) blockchain with a throughput of 750 MB of transactions per hour, 125 times more than Bitcoin. While the throughput of Algorand depends on the participation of most of its nodes, rational nodes may behave selfishly and not cooperate with others. To encourage nodes to participate in the consensus protocol, Algorand rewards nodes in each round. However, currently Algorand does not pay transaction fees to participating nodes, rather storing it for future use. In this paper, we show that this current approach of Algorand motivates selfish block proposers to increase their profits by creating empty blocks. Such selfish behavior reduces the throughput of Algorand. Therefore, the price of Algo will decrease in the long run. Because of this price reduction, nodes will leave Algorand, compromising its security. Moreover, lack of an appropriate mechanism to pay fees to participants causes additional issues, such as lack of transparency, centralization, and inability of nodes to prioritize transactions. To overcome this challenge, we design a perfectly competitive market and propose an algorithm for computing optimal transaction fees and block size in Algorand We also propose an algorithm that reduces the cost of Algorand, without compromising its security. We further simulate the Algorand network and show how the optimal transaction fee and block size can be calculated in practice.
Improvement in Automatic Speech Recognition of South Asian Accent Using Transfer Learning of DeepSpeech2

Automatic speech recognition (ASR) has ensured a convenient and fast mode of communication between humans and computers. It has become more accurate over the passage of time. However, in majority of ASR systems, the models have been trained using native English accents. While they serve best for native English speakers, their accuracy drops drastically for non-native English accents. Our proposed model covers this limitation for non-native English accents. We fine-tuned the DeepSpeech2 model, pretrained on the native English accent dataset by LibriSpeech. We retrain the model on a subset of the common voice dataset having only South Asian accents using the proposed novel loss function. We experimented with three different layer configurations of model to learn the best features for South Asian accents. Three evaluation parameters, word error rate (WER), match error rate (MER), and word information loss (WIL) were used. The results show that DeepSpeech2 can perform significantly well for South Asian accents if the weights of initial convolutional layers are retained while updating weights of deeper layers in the model (i.e., RNN and fully connected layers). Our model gave WER of 18.08%, which is the minimum error achieved for non-native English accents in comparison with the original model.
Link of Transformers in CV and NLP: A Brief Survey

Transformer, a new type of neural network based on the self-attention mechanism, has revolutionized the field of natural language processing. Since Transformer has a more powerful representation ability than Convolutional Neural Networks (CNN), researchers are trying to apply it to computer vision tasks. In the past, most of the review papers summarized Transformers in the field of computer vision and natural language processing, respectively, which isolates the relationship between the two fields and fails to show the correlations and differences between the two fields. In order to better demonstrate the mutual promotion between the two fields, this paper will combine the development of Transformer in the two fields to provide a comprehensive review. Furthermore, we will review these Transformer models by their application scenarios and analyze their advantages and disadvantages. Finally, we discuss the challenges and some future research directions for Transformer.
Regional-union based federated learning for wireless traffic prediction in 5G-Advanced/6G network

With the development of artificial intelligence, wireless traffic prediction plays an important role in the intelligence of wireless communication, such as load balancing and energy saving of base stations. Most of the existing prediction methods adopt the centralized training method, which requires the data to be centralized, which will cause data security and privacy issues. We introduce a framework named region-union based federated learning(FedRU) to solve this problem. The central node trains the shared model according to the regional model, and each region trains its own regional model. The shared model not only takes into account the global user characteristics, but also ensures the regional data characteristics to the greatest extent. We verify performance on real-world datasets through comparative experiments, and the results show that after only 27 rounds of communication, the accuracy rate can reach 0.80, and finally after 50 rounds of communication, the accuracy rate can reach 0.86.
The Impact of Network Impairments on the QoE of WebRTC applications: A Subjective study

WebRTC-based applications allow for real-time communications that are subject to network impairments affecting the end user's Quality of Experience (QoE). In this paper, we conducted subjective tests involving 20 people to investigate the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. A dedicated system was implemented to introduce controlled network impairments (delay, jitter, and packet loss) to impair the communication between the parties. In addition, test participants had to rate the perceived QoE for the audio, the video, and the overall service, as well as the three emotional dimensions, i.e., valence, arousal, and dominance. Extensive results were obtained regarding the impact of the network impairments on the multimedia quality, the emotional dimensions, and the communication feasibility.
A Secured Peer-to-Peer Messaging System Based on Blockchain

Nowadays, the messaging system is one of the most popular mobile applications, and therefore the authentication between clients is essential. Various kinds of such mobile applications are using encryption-based security protocols, but they are facing many security threat issues. It clearly defines the necessity for a trustful security procedure. Therefore, a blockchain-based messaging system could be an alternative to this problem. That is why, we have developed a secured peer-to-peer messaging system supported by blockchain. This proposed mechanism provides data security among the users. In a blockchain-based framework, all the information can be verified and controlled automatically and all the transactions are recorded that have been created already. In our paper, we have explained how the users can communicate through a blockchain-based messaging system that can maintain a secured network. We explored why blockchain would improve communication security in this post, and we proposed a model architecture for blockchain-based messaging that retains the performance and security of data stored on the blockchain. Our proposed architecture is completely decentralized and enables users to send and receive messages in an acceptable and secure manner.
Fastly converged transfer learning using neuron-pruning nonlinear equalizer for intra data center networking

We introduce a neuron-connections pruning neural network equalizer (NNE) into transfer learning of intra data center networking. Accelerated convergence of NNE during transfer learning can be obtained.
Autonomous navigation of a tracked unmanned ground vehicle*

This work proposes a complete autonomous navigation system for a tracked vehicle. The system enables a complete autonomous execution of waypoint and patrolling tasks selected by the user. It also enables user-vehicle shared autonomy, switching between the user teleoperation and the vehicle autonomous operation. Our navigation system uses the model predictive control scheme based on a navigation function. We propose the navigation function which takes into account changing environments, any-shape footprint, and non-holonomic motion of the tracked vehicle. Besides the waypoint and patrolling tasks, we implemented a fail-safe scenario in case of the user-vehicle communication loss, in which the vehicle returns autonomously to the previously visited goal where the communication was stable. The efficiency of the proposed system is validated by experimental results on the Komodo tracked vehicle.
No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism

With the rapid development of deep learning, no-reference image quality assessment (NR-IQA) based on convolutional neural network (CNN) plays an important role in image processing. Currently, most CNN-based NR-IQA methods focus primarily on the global features of images while ignoring detail-rich local features and channel dependencies. In fact, there are subtle differences in detail between distorted and reference images, as well as differences in the contribution of different channels to IQA. Furthermore, multi-scale feature extraction can be used to fuse the detailed information from images with different resolutions, and the combination of global and local features is critical in extracting image features. As a result, in this paper, a multi-scale residual CNN with an attention mechanism (MsRCANet) is proposed for NR-IQA. Specifically, a multi-scale residual block is first used to extract features from distorted images. Then, the residual learning with active weighted mapping strategy and channel attention mechanism is used to further process image features to obtain more abundant information. Finally, the fusion strategy and full connection layer are used to evaluate image quality. The experimental results on four synthetic databases and three in-the-wild IQA databases, as well as cross-database validation results, show that the proposed method has good generalization ability and can be compared with the most advanced methods.
Reliability investigation of the instrument transfer function calibration technique based on binary pseudo-random array standards

The reliability of the instrument transfer function (ITF) calibration technique based on binary pseudo-random array (BPRA) standards is investigated and demonstrated in application to interferometric microscopes. We demonstrate the linearity of the calibration (that is, independence of the ITF calibration on the standards root-mean-square roughness) via comparison of the ITF measurements with a number of artifacts with the etched depth varying from 30 nm to 120 nm. We also show that the calibration does not depend on the surface reflectivity, at least in the range between ~36% and ~80%. The criteria for selection of the geometrical parameters of the BPRA standard design appropriate for a particular interferometric microscope arrangement (including optical magnification), as well as the data acquisition and analysis procedures for different applications are also discussed.