Reviewerly/exHarmony huggingface dataset:
Recognition of English speech – using a deep learning algorithm

Abstract The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same.
A Regularity Theory for Static Schrödinger Equations on \(\boldsymbol{\mathbb{R}}\)<sup><i>d</i></sup> in Spectral Barron Spaces

Spectral Barron spaces have received considerable interest recently, as it is the natural function space for approximation theory of two-layer neural networks with a dimension-free convergence rate. In this paper, we study the regularity of solutions to the whole-space static Schrödinger equation in spectral Barron spaces. We prove that if the source of the equation lies in the spectral Barron space and the potential function admitting a nonnegative lower bound decomposes as a positive constant plus a function in , then the solution lies in the spectral Barron space .
A Cost Effective Reliability Aware Scheduler for Task Graphs in Multi-Cloud System

Many scientific workflows can be represented by a Directed Acyclic Graph (DAG) where each node represents a task, and there will be a directed edge between two tasks if and only if there is a dependency relationship between the two i.e. the second one can not be started unless the first one is finished. Due to the increasing computational requirements of these workflows, they are deployed on cloud computing systems. Scheduling of workflows on such systems to achieve certain goals(e.g. minimization of makespan, cost, or maximization of reliability, etc.) remains an active area of research. In this paper, we propose a scheduling algorithm for allocating the nodes of our task graph in a heterogeneous multi -cloud system. The proposed scheduler considers many practical concerns such as pricing mechanisms, discounting schemes, and reliability analysis for task execution. This is a list-based heuristic that allocates tasks based on the expected times for which VMs need to be rented for them. We have analyzed the proposed approach to understand its time requirement. We perform a large number of experiments with real-world workflows: FFT, Ligo, Epigenomics, and Random workflows and observe that the proposed scheduler outperforms the state-of-art approaches up to 12 %, 11 %, and 1.1 % in terms of cost, makespan, and reliability, respectively.
Performance Analysis on Co-Existence of COW-QKD and Classical DWDM Channels Transmission in UK National Quantum Networks

To analyse and evaluate the physical layer performance of a U.K. national quantum network (UKQNtel) utilising COW-QKD, in O band, integrated with 500 Gb/s encrypted data, in C band, over 121 km between BT Labs and the University of Cambridge, a theoretical model encompassing COW-QKD and Raman scattering noise is developed and fitted with real-world experimental data. Different physical mechanisms underpinning the link performance are identified and discussed. The model developed in this work also provides a preliminary technical guidance for future UKQNtel upgrade and expansion, as well as potentially useful insights for quantum-secured networks design prior to practical deployment.
Learning Instance-Level Label Correlation Distribution for Multilabel Classification With Fuzzy Rough Sets

In multilabel learning, research on label correlation provides an effective solution to compress the hypothesis space of classifiers. However, this article focus on the label correlation adapted to overall data, while ignoring the locally targeted information presented by some instances. The lack exploration on the distribution of local label correlation in multilabel instance space undoubtedly limits the in-depth application of label correlation in multilabel learning. Based on the fuzzy rough set theory, the instance-level label correlation distribution is first proposed in this article and applied to design a novel multilabel learner. For each multilabel instance, the local importance of features to label is quantitatively analyzed, by considering the decisive influence of input information on decision making. According to coincidence degree between local feature weight distribution for different labels, the instance-level label correlation is constructed. In order to reflect the internal relationship between label variables objectively, the instance-level label correlation distribution is integrated into the empirical label relevance. On the basis, the label relevance matrix is used to define the constraints of the optimization function in a new form. The relative position of subseparating hyperplanes in input space is quantitatively characterized to reduce the complexity of the multilabel classifier and improve the learning performance. The experiment results on 18 multilabel datasets illustrate the effectiveness of our algorithm. The impact of core parameters on the performance is also dissected.
Egocentric Early Action Prediction via Multimodal Transformer-Based Dual Action Prediction

Egocentric early action prediction, which aims to recognize the on-going action in the video captured in the first-person view as early as possible before the action is fully executed, is a new yet challenging task due to the limited partial video input. Pioneer studies focused on solving this task with LSTMs as the backbone and simply compiling the observed video segment and unobserved video segment into a single vector, which hence suffer from two key limitations: lack the non-sequential relation modeling with the video snippet sequence and the correlation modeling between the observed and unobserved video segment. To address these two limitations, in this paper, we propose a novel multimodal TransfoRmer-based duAl aCtion prEdiction (mTRACE) model for the task of egocentric early action prediction, which consists of two key modules: the early (observed) segment action prediction module and the future (unobserved) segment action prediction module. Both modules take Transformer encoders as the backbone for encoding all the potential relations among the input video snippets, and involve several single-modal and multi-modal classifiers for comprehensive supervision. Different from previous work, each of the two modules outputs two multi-modal feature vectors: one for encoding the current input video segment, and the other one for predicting the missing video segment. For optimization, we design a two-stage training scheme, including the mutual enhancement stage and end-to-end aggregation stage. The former stage alternatively optimizes the two action prediction modules, where the correlation between the observed and unobserved video segment is modeled with a consistency regularizer, while the latter seamlessly aggregates the two modules to fully utilize the capacity of the two modules. Extensive experiments have demonstrated the superiority of our proposed model. We have released the codes and the corresponding parameters to benefit other researchers at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://trace729.wixsite.com/trace</uri> .
I Can't See That! Considering the Readability of Small Objects in Virtual Environments

Though virtual reality has repeatedly seen usability improvements through higher fidelity headsets, interacting with small objects has remained an issue due to a reduction in visual acuity. Given the current uptake of virtual reality platforms and the range of real world applications that they may be used for, it is worth considering how such interactions can be accounted for. We propose three techniques for improving the usability of small objects in virtual environments: i) expanding them in place, ii) showing a zoomed-in twin above the original object, and iii) showing a large readout of the object's current state. We conducted a study comparing each technique's usability, induced presence, and effect on short-term knowledge retention in a VR training scenario that simulated the common geoscience exercise of measuring strike and dip. Participant feedback highlighted the need for this research, however simply scaling the area of interest may not be enough to improve the usability of information-bearing objects, while displaying this information in large text format can make tasks faster to complete at the cost of reducing the user's ability to transfer knowledge they've learned to the real world. We discuss these results and their implications for the design of future virtual reality experiences.
I3N: Intra- and Inter-representation Interaction Network for Change Captioning

Change captioning aims to describe the disagreement of image pairs with a linguistic sentence. Compared with single image captioning, change captioning requires not only understanding the fine-grained information of each image, but also determining whether change occurs and further representing the differences of image pairs. Although much progress has been made, it remains a severe challenge of the precise difference representation in the distraction of viewpoint change, especially that of tiny difference. In this paper, we propose a novel Intra- and Inter-representation Interaction Network (I3N) to learn the fine difference representation and be immune to viewpoint change. In the Intra-representation Interaction stage, we design Geometry-Semantic Interaction Refining (GSIR) to explore the positional and semantic interactions of intra-image, which can be a prior knowledge of enduring viewpoint change and reinforce the cognition of semantic change. In the Inter-representation Interaction stage, to endow the model with the capability of pinpointing the latent difference in viewpoint change, Hierarchical Representation Interaction (HRI) models difference from coarse to fine representations through the Semantic Matcher and Change Amplifier module. The proposed approach outperforms the state-of-the-art methods with an encouraging performance on the existing change captioning benchmarks. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yueshengbin/I3N</uri> .
ABRF: Adaptive BitRate-FEC Joint Control for Real-Time Video Streaming

Adaptive Forward Error Correction (AFEC) algorithms are proposed to achieve efficient Forward Error Correction (FEC) in Real-Time Communication (RTC). However, current AFEC approaches suffer two key limitations. 1) They do not consider the squeezing effect of redundancy on source bitrate while the squeezing usually happens in real RTC applications with limited bandwidth. 2) They estimate the future packet loss simply, ignoring some critical features of packet loss like randomness and multi-pattern. These drawbacks stop them from providing better Quality of Experience (QoE) in RTC services. We propose ABRF, a general QoE-oriented Adaptive BitRate-FEC joint control algorithm. ABRF makes predictions on the network loss pattern in the coming time and jointly calculates the optimal bitrate-FEC decision based on a QoE model for real-time video streaming. Moreover, ABRF is equipped with a fast adaptation method which helps it generalize across diverse network environments. In terms of Video Multi-method Assessment Fusion (VMAF), experimental results tell that ABRF decreases VMAF degradation caused by packet loss by 68%-95% compared with other AFEC algorithms in real-time video streaming in real-world Internet.
A Survey of Class Activation Mapping for the Interpretability of Convolution Neural Networks

Recent advances in deep learning have brought a new era for many fields, including object identification, image classification, and image compression. These tasks mostly build models based on the convolutional neural network (CNN). The trained models provide “input-output” end-to-end solutions with excellent automatic feature extraction performance. On the other hand, due to the distributed feature coding and the growing complexity of the model structure, users cannot comprehend the model’s internal knowledge representation and the potential reasons for a particular decision. However, applying CNN models in high-risk areas requires a thorough understanding of the reason behind their decisions in order to earn users’ trust. Therefore, CNN’s interpretation capabilities have gradually gained attention. This survey reviews the literature on class activation mapping (CAM), a straightforward technique for interpreting deep neural networks. We summarize the traditional CAM method and related methods based on the traditional model and then discuss new frontiers. This review can provide researchers in the field of CAM with guidance and, hopefully, shed light on future research regarding the interpretability of CNNs.
Fast and Secure Routing Algorithms for Quantum Key Distribution Networks

We consider the problem of secure packet routing at the maximum achievable rate in Quantum Key Distribution (QKD) networks.Assume that a QKD protocol generates symmetric private key pairs for secure communication over each link in a network.The quantum key generation process is modeled using a stochastic counting process.Packets are first encrypted with the quantum keys available for each hop and then transmitted on a point-to-point basis over the links.A fundamental problem in this setting is the design of a secure and capacity-achieving routing policy that takes into account the time-varying availability of the encryption keys and finite link capacities.In this paper, we propose a new secure throughput-optimal policy called Tandem Queue Decomposition (TQD).The TQD policy is derived by combining the QKD process with the Universal Max Weight routing policy, proposed earlier by Sinha and Modiano.We show that the TQD policy solves the problem of secure and efficient packet routing for a broad class of traffic, including unicast, broadcast, multicast, and anycast.The proposed decomposition reduces the problem to the generalized network flow problem without the key availability constraints over a transformed network.The proof of the throughput-optimality of the TQD policy uses the Lyapunov stability theory for analyzing the interdependent packet queueing process and the key-storage dynamics.Finally, we demonstrate the competitiveness of the TQD policy over the existing algorithms by numerically comparing them on a simulator that we build on top of the state-of-the-art OMNeT++ network simulator platform.
Achievable Refined Asymptotics for Successive Refinement Using Gaussian Codebooks

We study the mismatched successive refinement problem where one uses Gaussian codebooks to compress an arbitrary memoryless source with successive minimum Euclidean distance encoding under the quadratic distortion measure. Specifically, we derive achievable refined asymptotics under both the joint excess-distortion probability (JEP) and the separate excess-distortion probabilities (SEP) criteria. For both second-order and moderate deviations asymptotics, we consider two types of codebooks: the spherical codebook where each codeword is drawn independently and uniformly from the surface of a sphere and the i.i.d. Gaussian codebook where each component of each codeword is drawn independently from a Gaussian distribution. We establish the achievable second-order rate-region under JEP and we show that under SEP any memoryless source satisfying mild moment conditions is strongly successively refinable. When specialized to a Gaussian memoryless source (GMS), our results provide an alternative achievability proof with specific code design. We show that under JEP and SEP, the same moderate deviations constant is achievable. For large deviations asymptotics, we only consider the i.i.d. Gaussian codebook since the i.i.d. Gaussian codebook has better performance than the spherical codebook in this regime for the one layer mismatched rate-distortion problem (Zhou et al., 2019). We derive achievable exponents of both JEP and SEP and specialize our results to a GMS, which appears to be a novel result of independent interest.
Recent deep learning models for dementia as point-of-care testing: Potential for early detection

Deep learning has been intensively researched over the last decade, yielding several new models for natural language processing, images, speech and time series processing that have dramatically improved performance. This wave of technological developments in deep learning is also spreading to medicine. The effective use of deep learning in medicine is concentrated in diagnostic imaging-related applications, but deep learning has the potential to lead to early detection and prevention of diseases. Physical aspects of disease that went unnoticed can now be used in diagnosis with deep learning. In particular, deep learning models for the early detection of dementia have been proposed to predict cognitive function based on various information such as blood test results, speech, and the appearance of the face, where the effects of dementia can be seen. Deep learning is a useful diagnostic tool, as it has the potential to detect diseases early based on trivial aspects before clear signs of disease appear. The ability to easily make a simple diagnosis based on information such as blood test results, voice, pictures of the body, and lifestyle is a method suited to point-of-cate testing, which requires immediate testing at the desired time and place. Over the past few years, the process of predicting disease can now be visualized using deep learning, providing insights into new methods of diagnosis.
Managing Data Protection and Privacy on Cloud

When pandemic rose in 2020, people were fighting against COVID-19 virus and organizations had accelerated their digitization and cloud adoption rapidly (De et al. in Int J Inf Manag 55:102171, 2020 [1]) to meet the online based business during the lockdown. This chaos helped fraudsters and attackers taking advantage of the momentary lack of security controls and oversight. Federal Investigation Bureau (FBI) Internet Crime Compliant Center (IC3) 2020 reported highest number of complaints in 2020 (791 k + ) compared to prior five years (298 k + in 2016), with peak losses reported ($4.2 Billion in 2020 compared to $1.5 Billion in 2016) (Internet Crime Complaint Center in Internet crime report. Federal Bureau of Investigation, Washington, D.C., 2020 [2]). Majority of these incidents were connected to financial fraud, identity fraud, and phishing for personally identifiable information (PII). Considering the severity and impact of personal data exposure over cloud and hybrid environment, this paper provides a brief overview of prior research and discuss technical solutions to protect data across heterogeneous environments and ensure privacy regulations.
A Detailed Survey on Sentimental Analysis on Social Media

Public opinion or point of view matters a lot as it plays vital role in shaping country’s image all around the world, may it be on certain products, decisions or their own feelings/expressions. At this time, people are more expressive on social media, more rather than in person, they express or share their daily tasks their feelings, opinions some good or bad incidents, and many more on social media, and we can collect this data and process into classifiable form. The data can be collected through API and then transformed into classifiable form. To classify data, we will be foreseeing three algorithms which are Naïve Bayes, maximum entropy, and lexicon-based. The main reason of gathering this data is to process and generate positive and negative notions of certain demography or specific person. This data can be useful in many ways such as to check if a person is in good mental health condition or suffering through depression through his/her positive negative posts on social media. Multi-national companies can collect insights on their products based on peoples review and opinion on social media. MNC and e-commerce stores can also product recommendation system based on those reviews. In short, sentiment analysis plays a very vital role in our economy as well as health care.
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning

The central idea of contrastive learning is to discriminate between different instances and force different views from the same instance to share the same representation. To avoid trivial solutions, augmentation plays an important role in generating different views, among which random cropping is shown to be effective for the model to learn a generalized and robust representation. Commonly used random crop operation keeps the distribution of the difference between two views unchanged along the training process. In this work, we show that adaptively controlling the disparity between two augmented views along the training process enhances the quality of the learned representations. Specifically, we present a parametric cubic cropping operation, ParamCrop, for video contrastive learning, which automatically crops a 3D cubic by differentiable 3D affine transformations. ParamCrop is trained simultaneously with the video backbone using an adversarial objective, so that it learns to increase the contrastive loss and thus gradually reduces the shared contents between two cropped views. Experiments show that this adaptive and gradual increase in the disparity yielded by ParamCrop is beneficial to learning a strong and generalized representation for downstream tasks, which is shown to be effective on multiple contrastive learning frameworks and video backbones.
A Novel Automatic Content Generation and Optimization Framework

With the rapid growth of IoT multimedia devices, more and more content is being delivered in multimedia forms which generally requires more effort and resources from users to create and could be challenging to streamline. In this article, we propose Text2Animation (T2A), a framework that helps to generate complex multimedia content, and animation, from the simple textual script input. By leveraging recent advances in computational cinematography and video understanding, the purposed workflow further reduces associated knowledge requirements dramatically for cinematography. By jointly incorporating the fidelity and aesthetic models, T2A jointly considers the comprehensiveness of the visual presentation of the input script and the compliance of generated video with given cinematography specifications. The virtual camera placement in a 3-D environment is mapped into an optimization problem that can be resolved by using dynamic programming to achieve the lowest computational complexity. Experimental results show that T2A can reduce the manual animation production process by around 74%, and the new optimization framework can improve the perceptual quality of the output video by up to 35%. More video footage can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/watch?v=MMTJbmWL3gs</uri> .