Reviewerly/exHarmony huggingface dataset:
All-Optical Recurrent Neural Network With Reconfigurable Activation Function

Optical Neural Networks (ONNs) can be promising alternatives for conventional electrical neural networks as they offer ultra-fast data processing with low energy consumption. However, lack of suitable nonlinearity is standing in their way of achieving this goal. While this problem can be circumvented in feed-forward neural networks, the performance of the recurrent neural networks (RNNs) depends heavily on their nonlinearity. In this paper, we first propose and numerically demonstrate a novel reconfigurable optical activation function, named ROA, based on adding or subtracting the outputs of two saturable absorbers (SAs). RAO can provide both bounded and unbounded outputs by facilitating an electrically programmable adder/subtractor design. Second, with the help of RAO, which can be altered to resemble Tanh or Sigmoid activation functions, we take a step further and numerically demonstrate OptoRNN, a new design for an all-optical RNN in free-space optics. Moreover, by benefiting from mathematical modeling of the multiplication noise, as well as an altered loss function, we enable vector-matrix multiplication (VMM) six times more parallel than conventional optical VMM method for the linear part of the OptoRNN. Finally, through comprehensive simulation studies, we demonstrate that utilizing the OptoRNN, we can achieve 96.3% train accuracy for sequential-MNIST dataset.
Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligent monitoring system.To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model.The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance.It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function.First, it applies the attention mechanism in the feature extraction.The network can learn the weight of each channel independently and enhance the information dissemination between features.Second, it adopts CIoU loss function to achieve accurate bounding box regression.Third, it utilizes Mish activation function to improve detection accuracy and generalization ability.It builds a safety helmet-wearing detection data set containing more than 10,000 images collected from the Internet for preprocessing.On the self-made helmet wearing test data set, the average accuracy of the helmet detection of the proposed algorithm is 96.7%, which is 1.9% higher than that of the YOLOv5 algorithm.It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.
Optimal Motion Planning in Unknown Workspaces Using Integral Reinforcement Learning

A novel motion planning scheme for optimal navigation in unknown workspaces is proposed in this letter. Based upon the Artificial Harmonic Potential Fields (AHPFs) theory, a robust framework for provably correct (i.e., safe and globally convergent) navigation is enhanced through Integral Reinforcement Learning (IRL) <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> to obtain a provably complete solution for optimal motion planning in unknown workspaces. Our method aims at bridging the gap between the control theoretic framework of mathematical rigor, with the data-driven Reinforcement Learning (RL) paradigm, while preserving the strong traits of each approach. Finally, it is compared against an RRT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\star$</tex-math></inline-formula> method to asses the optimality of the final results in a multiply connected synthetic workspace.
Preparing Students for a Digitized Future

Through this study, a vehicle to enhance existing education curricula was demonstrated, by exposing students to research-informed educational activities and to experience the key digital systems used in different industrial sectors.Digital skills are in high demand across the globe, with over a million digitally skilled people required by 2022 in the UK alone.With digital skills being vital to a country's economy, there is a need to develop the higher education sector to prepare students for a digitised futurea key area of future graduate skills.The overall aim of the project was to train students in cuttingedge digital technologies through two separate week-long boot camps, targeting undergraduate and postgraduate cohorts.To this end, an educational project primed by research-informed teaching was successfully organised at the University of Birmingham in collaboration with the industry, to directly enhance the student's personal development in the digital field, the findings of which are reported in this paper.Researchers and practitioners presented practical applications of various digital technologies during the boot camps.
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission

This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs). Under the assumption that the fronthaul links connecting APs to CS have finite capacity, a rate-splitting transmission at IoT devices (IDs) is proposed which enables hybrid edge and cloud decoding of split uplink messages. The problem of completion time minimization for FL is tackled by optimizing the rate-splitting transmission and fronthaul quantization strategies along with training hyperparameters such as precision and iteration numbers. Numerical results show that the proposed rate-splitting transmission achieves notable gains over benchmark schemes which rely solely on edge or cloud decoding.
An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

The recent advancements in the web allow users to generate multimedia content, resulting in multimedia information proliferation. Existing search engines provide access to multimedia content via a disjoint assembly of media-specific results called verticals. However, this decentralized assembly of media contents requires manual aggregation and synthesizing efforts at the user&#x2019;s end, hindering the information exploration process and subsequently may cause cognitive overload, hence, demanding innovative tools to discover multimedia content. The researchers have devised numerous state-of-the-art approaches; however, analysis to confirm the efficacy has little emphasis. This study investigates users&#x2019; complex multimedia information-seeking behavior over state-of-the-art web search systems to unveil the user&#x2019;s information-seeking issues. Our research employs between-subjects study and post hoc analysis strategies to analyze participants&#x2019; information-seeking characteristics. The study design adopted statistical hypothesis testing to consolidate previous user behavioral studies, confirm existing strategies, and present recommended practices for future general-purpose web search engines. The participants were assigned Google and an advanced discovery search system using the same multimedia dataset to ensure the obtained results&#x2019; credibility. The primary behavioral parameters include search efforts, multimedia content exploration, search user interface (SUI), information management and presentation, and user cognition. This study uncovers several inadequacies of the search engines in meeting users&#x2019; complex discovery needs, including 29.6% less user engagement, 43% system and searching dissatisfaction, and 32% less knowledge acquisition with 63.9% increased clicking effort on traditional search engines. The results confirmed previous user studies and suggest novel research recommendations statistically significant in multimedia information exploration-related endeavors.
Low-Latency Edge Video Analytics for On-Road Perception of Autonomous Ground Vehicles

To improve the transportation efficiency of advanced manufacturing, cameras have been extensively deployed to enhance the on-road perception of autonomous ground vehicles in smart industrial parks. Considering the informative yet substantial data volume of contents generated by those cameras, we employ vehicle-to-everything links to deliver the captured video frames to neighboring vehicles, road-side units, or base stations, in order to respond to vehicle-control-related video queries. To help vehicles obtain low-latency and high-accuracy on-road information for autonomous driving, an optimization problem is formulated, taking into account the impact of vehicle mobility and diverse resource demands of different video queries. Then, a two-stage algorithm is proposed to determine the frame rate of video cameras, as well as the destination of the associated video frames based on matching theory. Extensive simulation results show that this approach can improve the accuracy by up to 18% and reduce the response delay by 13.2%.
A Tale of Three Videoconferencing Applications: Zoom, Webex, and Meet

Since the outbreak of the COVID-19 pandemic, videoconferencing has become the default mode of communication in our daily lives at homes, workplaces and schools, and it is likely to remain an important part of our lives in the post-pandemic world. Despite its significance, there has not been any systematic study characterizing the user-perceived performance of existing videoconferencing systems other than anecdotal reports. In this paper, we present a detailed measurement study that compares three major videoconferencing systems: Zoom, Webex and Google Meet. Our study is based on 62 hours’ worth of more than 1.1K videoconferencing sessions, which were created with a mix of emulated videoconferencing clients deployed in the cloud, as well as real mobile devices running from a residential network over two separate periods with nine months apart. We find that the existing videoconferencing systems vary in terms of geographic scope and resource provisioning strategies, which in turns determine streaming lag experienced by users. We also observe that streaming rate can change under different conditions (e.g., available bandwidth, number of users in a session, mobile device status), which affects user-perceived streaming quality. Beyond these findings, our measurement methodology enables reproducible benchmark analysis for any types of comparative or longitudinal study on available videoconferencing systems.
Secure Non-interactive Simulation: Feasibility and Rate

A natural solution to increase the efficiency of secure computation will be to non-interactively and securely transform diverse inexpensive-to-generate correlated randomness, like, joint samples from noise sources, into correlations useful for secure computation protocols. Motivated by this general application for secure computation, our work introduces the notion of secure non-interactive simulation (SNIS). Parties receive samples of correlated randomness, and they, without any interaction, securely convert them into samples from another correlated randomness. Our work presents a simulation-based security definition for SNIS and initiates the study of the feasibility and efficiency of SNIS. We also study SNIS among fundamental correlated randomnesses like random samples from the binary symmetric and binary erasure channels, represented by $$\mathsf {BSS}$$ and $$\mathsf {BES}$$ , respectively. We show the impossibility of interconversion between $$\mathsf {BSS}$$ and $$\mathsf {BES}$$ samples. Next, we prove that a SNIS of a $$\mathsf {BES} (\varepsilon ')$$ sample (a $$\mathsf {BES}$$ with noise characteristic $$\varepsilon '$$ ) from $$\mathsf {BES} (\varepsilon )$$ is feasible if and only if $$(1-\varepsilon ') = (1-\varepsilon )^k$$ , for some $$k\in \mathbb {N}$$ . In this context, we prove that all SNIS constructions must be linear. Furthermore, if $$(1-\varepsilon ') = (1-\varepsilon )^k$$ , then the rate of simulating multiple independent $$\mathsf {BES} (\varepsilon ')$$ samples is at most 1/k, which is also achievable using (block) linear constructions. Finally, we show that a SNIS of a $$\mathsf {BSS} (\varepsilon ')$$ sample from $$\mathsf {BSS} (\varepsilon )$$ samples is feasible if and only if $$(1-2\varepsilon ')=(1-2\varepsilon )^k$$ , for some $$k\in \mathbb {N}$$ . Interestingly, there are linear as well as non-linear SNIS constructions. When $$(1-2\varepsilon ')=(1-2\varepsilon )^k$$ , we prove that the rate of a perfectly secure SNIS is at most 1/k, which is achievable using linear and non-linear constructions. Our technical approach algebraizes the definition of SNIS and proceeds via Fourier analysis. Our work develops general analysis methodologies for Boolean functions, explicitly incorporating cryptographic security constraints. Our work also proves strong forms of statistical-to-perfect security transformations: one can error-correct a statistically secure SNIS to make it perfectly secure. We show a connection of our research with homogeneous Boolean functions and distance-invariant codes, which may be of independent interest.
Test-Case Generation for Data Flow Testing of Smart Contracts Based on Improved Genetic Algorithm

Smart contracts are commonly deployed for safety-critical applications, the quality assurance of which has been a vital factor. Test cases are standard means to ensure the correctness of data flows in smart contracts. To more efficiently generate test cases with high coverage, we propose an improved genetic algorithm-based test-case generation approach for smart contract data flow testing. Our approach introduces the theory of particle swarm optimization into the genetic algorithm, which reduces the influence brought by the randomness of genetic operations and enhances its capability to find global optima. A set of 30 real smart contracts deployed on Ethereum and GitHub is collected to perform the experimental study, on which our approach is compared with three baseline approaches. The experimental results show that, in most cases, the coverage of the test cases generated by our approach is significantly higher than the baseline approaches with relatively lower numbers of iterations and lower execution time.
Weighted Nuclear Norms of Transformed Tensors for Nonlocal Hyperspectral Image Denoising

Nonlocal low-rank (LR) hyperspectral image (HSI) denoising approaches have gained a lot of attention because of their capacity to fully take advantage of spectral correlation and nonlocal self-similarity (NLSS). Most of the existing LR-tensor-based approaches use tensor singular value decomposition (t-SVD). However, fixed discrete Fourier transform-based t-SVD may compromise the low rank structure. Additionally, these approaches restricts flexibility in dealing with HSI data because it treats the singular values of each frontal slice equally. To overcome these issues, we propose a method using weighted nuclear norms of transformed tensors (WNNTT) for nonlocal HSI denoising. Our approach exploits the low-rankness in both the spectral and NLSS dimensions. We also compare our proposed method with other t-SVD based LR-tensor regularization methods under the same framework. Our experiments show that our WNNTT approach outperforms several state-of-the-art nonlocal t-SVD based methods on open HSI data.
Self-Supervised Pre-Training for Attention-Based Encoder-Decoder ASR Model

End-to-end (E2E) models, including the attention-based encoder-decoder (AED) models, have achieved promising performance on the automatic speech recognition (ASR) task. However, the supervised training process of the E2E model needs a large amount of speech-text paired data. In contrast, self-supervised pre-training can pre-train the model on the unlabeled data and then fine-tune it on the limited labeled data to realize better performance. Most of the previous self-supervised pre-training methods focus on learning hidden representations from speech but ignore how to utilize the unpaired text. As a result, previous works often pre-train an acoustic encoder and then fine-tune it as a classification based ASR model, such as Connectionist Temporal Classification (CTC) based model, rather than an AED model. In this paper, we propose a self-supervised pre-training method for the AED model (SP-AED). The SP-AED method contains acoustic pre-training for the encoder, linguistic pre-training for the decoder, and an adaptive combination fine-tuning for the whole system. We first design a linguistic pre-training method for decoder by utilizing the text-only data. The decoder will be pre-trained as a noise-condition language model to learn the prior distribution of the text. Then, we pre-train the AED encoder with the wav2vec2.0 method with some modifications. Finally, we combine the pre-trained encoder and decoder and fine-tune them on the limited labeled data. We design an adaptive combination method during fine-tuning by modifying the decoder’s input and output to prevent catastrophic forgetting. Experiments prove that compared with the random initialized models, the SP-AED pre-trained models can realize up to 17% relative improvement. And with similar model size or computational cost, we can get comparable results to other classification-based models on both English and Chinese corpus.
Multilevel Spatial-Temporal Feature Aggregation for Video Object Detection

Video object detection (VOD) focuses on detecting objects for each frame in a video, which is a challenging task due to appearance deterioration in certain video frames. Recent works usually distill crucial information from multiple support frames to improve the reference features, but they only perform at frame level or proposal level that cannot integrate spatial-temporal features sufficiently. To deal with this challenge, we treat VOD as a spatial-temporal hierarchical features interacting process and introduce a Multi-level Spatial-Temporal (MST) feature aggregation framework to fully exploit frame-level, proposal-level, and instance-level information in a unified framework. Specifically, MST first measures context similarity in pixel space to enhance all frame-level features rather than only update reference features. The proposal-level feature aggregation then models object relation to augment reference object proposals. Furthermore, to filter out irrelevant information from other classes and backgrounds, we introduce an instance ID constraint to boost instance-level features by leveraging support object proposal features that belong to the same object. Besides, we propose a Deformable Feature Alignment (DAlign) module before MST to achieve a more accurate pixel-level spatial alignment for better feature aggregation. Extensive experiments are conducted on ImageNet VID and UAVDT datasets that demonstrate the superiority of our method over state-of-the-art (SOTA) methods. Our method achieves 83.3% and 62.1% with ResNet-101 on two datasets, outperforming SOTA MEGA by 0.4% and 2.7%.
Twitter Sentiment Analysis Based on Neural Network Techniques

Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for today’s businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up “tweets” to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Naïve Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques.
The development of a chatbot using Convolutional Neural Networks

Chatbots are artificial intelligence systems that comprehend the intent, context, and sentiment of the user, interact properly with them leading to an increased development of their creation, the past few years. In this study, Convolutional Neural Networks (CNNs) are applied as the classifier and some specific tools for tokenization are used for the creation of a chatbot. Taking into account that it is difficult to apply any algorithm in text, we use a technique called “Word Embedding”, which converts a text into numbers in order to run text processing. Specifically, “Word2Vec” Word Embedding technique was applied. AlexNet, LeNet5, ResNet and VGGNet CNN architectures were utilised. These architectures were compared for their accuracy, f1 score, training time and execution time. The results obtained highlighted that there were significant differences in the performance of the architectures applied. The most preferable architecture of our study was LeNet-5 having the best accuracy compared to other architectures, the fastest training time, and the least losses whereas it was not very accurate on smaller datasets such as our Test Set. Limitations and directions for future research are also presented.
Comparative Analysis of Deep Learning Techniques for Facemask Detection

Pandemic caused owing to widespread of corona-virus has changed our lives upside down. Covering the face area particularly nose and mouth is the prime need of the hour. Any negligence of not wearing the mask or incorrectly wearing the mask can be hazardous. This necessitates the need of understanding the real importance of wearing the mask appropriately in order to avoid the spread of Covid 19. Knowing the present population of the country, manual monitoring of the individuals is quite difficult. So, this research puts forward the use of deep learning techniques for automatic facemask detection using techniques such as capsule network, ResNet50, Mobile-Net architecture, and Convolution Neural Network. The techniques are validated on the merged dataset taken from MaskedFace-Net dataset and Kaggle (publicly available) based on the performance measures namely accuracy, precision, recall and F1-score. Amongst all, the results showed that capsule neural network achieved superlative performance with the accuracy of around 99% in comparison to other aforesaid deep learning techniques.