Fast offline Transformer based end to end automatic speech recognition for real world applications

Many real-world applications require to convert speech files into text with limited resources . This paper proposes a method to recognizelarge speech database fast using the Transformer-based end-to-end model . The proposed system can convert speeches into text in less than 3 minutes with 10.73% character error rate which is 27.1% relatively low compared to conventional DNN-HMM based recognition system .…

Adaptive Private Distributed Matrix Multiplication

We consider the problem of designing codes with flexible rate (referred to asrateless codes), for private distributed matrix-matrix multiplication . A masterserver owns two private matrices and hires workernodes to help computing their multiplication . The size of the tasks, hence the rate of the scheme, depends on the number of workers that themaster waits for .…

White Box Analysis over Machine Learning Modeling Performance of Configurable Systems

Performance-influence models can help stakeholders understand how and whereconfiguration options and their interactions influence the performance of asystems . Current black-box techniques combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability . Wepresent Comprex builds similarly accurate performance-influential models to the most accurate and expensive black-boxes approach, but at a reduced cost .…

Game based Pricing and Task Offloading in Mobile Edge Computing Enabled Edge Cloud Systems

Mobile edgecomputing (MEC) provides IoT mobile devices with powerful externalcomputing and storage resources . However, a mechanism addressing distributed task offloading and price competition for the open exchange marketplace has not been established properly . We propose two algorithms, namely iterativeproximal offloading algorithm (IPOA) and iterative Stackelberg game pricing algorithm (ISPA) IPOA solves follower non-cooperative game among IoTMDs and ISPA uses backward induction to deal with the price competition amongOSPs .…

Speaker activity driven neural speech extraction

Target speech extraction, which extracts the speech of a target speaker in amixture given auxiliary speaker clues, has recently received increased interest . Various clues have been investigated such as pre-recorded enrollmentutterances, direction information, or video of the target speaker .…

ZipLine In Network Compression at Line Speed

Network appliances continue to offer novel opportunities to offload processing from computing nodes directly into the data plane . ZipLine is an approach to design and implement (de)compression at linespeed leveraging the Tofino hardware platform which is programmable using theP4_16 language .…

A Ramsey Theorem for Finite Monoids

Given a monoid $M$ it is natural to ask how long a sequence of elements needs to be to ensure the presence of consecutive idempotent factors . We study the behaviour of the Ramsey function $R_M$ by studying the regular $D$-length of the monoid .…

A Perspective Based Understanding of Project Success

An in-depth, longitudinal case study of information systems development in a large manufacturing company was used to investigate howvarious project stakeholders subjectively perceived the project outcome . A conceptual framework is developed for understanding and analyzing evaluations of project success, bothformal and informal .…

A Machine Learning Method for Time Dependent Wave Equations over Unbounded Domains

Time-dependent wave equations represent an important class of partialdifferential equations (PDE) for describing wave propagation phenomena . We present amachine-learning method to solve this equation as an alternative to ABCs . The mapping from the initial conditions to the PDE solution is represented by a neural network, trained using wave packets that areparameterized by their band width and wave numbers .…

Augmented Informative Cooperative Perception

Augmented Informative Cooperative Perception (AICP) is the first fast-filteringsystem which optimizes the informativeness of shared data at vehicles . AICP displays the filtered data to the drivers in augmented reality head-up display . The prototype realizes the informative-optimized perception with only 12.6 milliseconds additional latency .…

Evaluating the Robustness of Collaborative Agents

In order for agents trained by deep reinforcement learning to work alongside humans in realistic settings, we will need to ensure that the agents are\emph{robust}. Since the real world is very diverse, and human behavior oftenchanges in response to agent deployment, the agent will likely encounter novelsituations that have never been seen during training .…

Self Training Pre Trained Language Models for Zero and Few Shot Multi Dialectal Arabic Sequence Labeling

A sufficient amount of annotated data is required to fine-tune language models for downstream tasks . Attaining labeled data can be costly, especially for multiple language varieties/dialects . We propose to self-train pre-trained language models in zero- and few-shot scenarios toimprove the performance on data-scarce dialects using only resources from data-rich ones .…

Whispered and Lombard Neural Speech Synthesis

The resulting synthetic Lombard speech has a significant positive impact on intelligibility gain . We alsoshow that the resulting synthetic . speech . has a positive effect on the . resulting . impact on speech . We can generate high quality speech through the .pre-training/fine-tuning…

Off grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems

This paper proposes an off-grid channel estimation scheme for orthogonaltime-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework . To avoid channel spreading caused by fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler(DD) domain, we estimate the original DD domain channel response rather thanthe effective DD channel response as commonly adopted in the literature .…

Unsupervised heart abnormality detection based on phonocardiogram analysis with Beta Variational Auto Encoders

Heart Sound (also known as phonocardiogram (PCG) analysis) analysis is a popular way to detect cardiovascular diseases (CVDs) Most PCG analysis uses supervised way, which demands both normal and abnormal samples . This paper proposes amethod of unsupervised PCG . analysis that uses beta variational auto-encoder ($\beta-\text{VAE) to model the normal PCG signals .…

A Multiple Classifier Approach for Concatenate Designed Neural Networks

This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet . We use the L2 normalization method to obtain the classifier score instead of the Softmax normalization . The proposed classifiers are able to improve accuracy in the experimental cases significantly, and show that the method has better performance than the original models, but also produces faster convergence .…

Intelligent Reflecting Surfaces for Compute and Forward

Using Intelligent Reflecting Surfaces (IRS) can enhance the computing capability of a wireless network scenario . The results confirm the usefulness of IRS technology for future networks — such as 6G — with massive computation requirements, authors say . They say IRS technology can be useful for future wireless networks such as the likes of 6G .…

Piano Skills Assessment

Can a computer determine a piano player’s skill level? Is it preferable to visual analysis of the player’s performance or should we trust our ears over our eyes? Since current CNNs have difficulty processing long video videos, how can shorter clips be sampled to best reflect the playersskill level?…

Optimal Energy Shaping via Neural Approximators

A promising feature of passivity theory,alongside stability, has traditionally been claimed to be intuitive performancetuning along the execution of a given task . We introduce optimal energy shaping as an enhancement of classicalpassivity-based control methods . Once a task-dependent performance metric is defined, an optimalsolution is systematically obtained through an iterative procedure relying onneural networks and gradient-based optimization .…

A Nature Inspired Feature Selection Approach based on Hypercomplex Information

Nature-inspired optimization can mitigatethis problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions . New mathematicalrepresentations, such as quaternions and octonions, are being used to handle higher-dimensional spaces . The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to somestate-of-the-art approaches .…

On a Class of Time Varying Gaussian ISI Channels

This paper studies a class of stochastic and time-varying Gaussianintersymbol interference (ISI) channels . The $i^{th$ channel tap during timeslot $t$ is uniformly distributed over an interval of centre $c_i$ and radius $r_i$. The proposed lower bound is achieved by a joint-typicalityity decoder that is tuned to a set of candidates for the channel matrix .…

Uncertainty Quantification of Bifurcations in Random Ordinary Differential Equations

We are concerned with random ordinary differential equations (RODEs) Ourmain question of interest is how uncertainties in system parameters propagatethrough the possibly highly nonlinear dynamical system and affect the system’sbifurcation behavior . We come up with a methodology to determine theprobability of the occurrence of different types of bifurcations based on the probability distribution of the input parameters .…

Practical Face Reconstruction via Differentiable Ray Tracing

The proposed method models sceneillumination via a novel, parameterized virtual light stage, which introduces a coarse-to-fineoptimization formulation for face reconstruction . Our method can not only handle unconstrained illumination and self-shadows conditions, but also estimates diffuse and specular albedos . With consistent faceattributes reconstruction, our method leads to several style — illumination,albedo, self-shadow — edit and transfer applications, as discussed in thepaper .…

Automating Gamification Personalization To the User and Beyond

Personalized gamification explores knowledge about the users to tailorgamification designs to improve one-size-fits-all gamification . The tailoring process should simultaneously consider user and contextual characteristics, which leads to several instances to tailor . The main implications are that demographics, game-related characteristics, geographic location, and geographic location to be done, should be considered in defining gamification designs, as well as the interaction between different kinds of information (user and contextualcharacteristics) can be considered .…

Noise Is Useful Exploiting Data Diversity for Edge Intelligence

Edge intelligence requires to fast access distributed data samples generated by edge devices . The challenge is using limited radio resource to acquire massive data samples for training machine learning models at edge server . In this article, we propose a new communication-efficient edge intelligence schemewhere the most useful data samples are selected to train the model .…