Reviewerly/exHarmony huggingface dataset:
Dialect-aware Semi-supervised Learning for End-to-End Multi-dialect Speech Recognition

In this paper, we propose dialect-aware semi- supervised learning for end-to-end automatic speech recognition (ASR) models considering multi-dialect speech. Some multi- domain ASR tasks require a large amount of training data containing additional information (e.g., language or dialect), whereas it is difficult to prepare such data with accurate transcriptions. Semi-supervised learning is a method of using a massive amount of untranscribed data effectively, and it can be applied to multi-domain ASR tasks to relax the missing transcriptions problem. However, semi-supervised learning has usually used generated pseudo-transcriptions only. The problem is that simply combining a multi-domain model with semi- supervised learning makes use of no additional information even though the information can be obtained. Therefore, in this paper, we focus on semi-supervised learning based on a multi-domain model that takes additional domain information into account. Since the accuracy of pseudo-transcriptions can be improved by using the multi-domain model and additional information, our proposed semi-supervised learning is expected to provide a reliable ASR model. In experiments, we performed Japanese multi-dialect ASR as one type of multi-domain ASR. From the results, a model trained with the proposed method yielded the lowest character error rate compared with other models trained with the conventional semi-supervised method.
A Multiframe Super-resolution Pipeline for Sub-image-typed Light Field Data

Due to the trade-off between spatial and angular resolutions in light field cameras, the obtained resolutions of synthesized 2D images are often far less than those captured by conventional digital cameras using the same image sensor. This work proposes a complete digital image processing pipeline for hand-held light field cameras to generate high-resolution all-in-focus 2D images. The flow contains refined disparity estimation, digital refocusing, and super-resolution stages in which the characteristics of light fields are considered. We adopt the efficient first-order primal-dual algorithm as our optimization tool. The results show that the proposed approach gives better image quality when compared to other existing super-resolution methods.
Lattice-based Data Augmentation for Code-switching Speech Recognition

Code-switching is a common phenomenon that occurs within conversations among multilingual speakers. The limited availability of code-switching resources poses some challenges to code-switching speech recognition. Our work addresses both data scarcity and pronunciation variations in word transitions by introducing speech recognition decoding lattice for data augmentation in code-switching speech recognition, specifically in language modeling. Decoding lattices contain both acoustic and textual information that help solve the pronunciation variations problem. We pretrain GPT2, a transformer-based language model, with lattices obtained from the first-pass decoding of code-switching training data. The first-pass decoding is performed by using the baseline speech recognition system with n-gram language model. We successfully reduce around 2 point of word error rate from the previously mentioned baseline and 0.33 point from the baseline that utilizes GPT2 language model. Ablation study also shows an improvement when including acoustic information for code-switching language model pretraining. In addition, we show that despite having a limited amount of word switching variations information, our proposed method achieves a comparable result with previous studies that employ artificial code-switching sentences.
Distance Estimation Between Camera and Vehicles from an Image using YOLO and Machine Learning

We have developed a device that can alert a motorcycle rider when other vehicles enter the rider's blind spot using a camera. This device is necessary for reducing the risk of motorcycling accidents. However, the camera-based approach from a single camera cannot determine the distance between the camera and the vehicles. This paper proposes an algorithm for estimating the distance between a camera and a vehicle from an image based on object detection and machine learning techniques. The algorithm starts with collecting vehicle images of different types and measuring their distances from the camera with a laser measuring device. Each vehicle in an image is then detected by the YOLO algorithm and results in locations bounding boxes. Twelve features are computed from the bounding boxes for machine learning algorithms to predict distance or classify ranges of distance. The experimental results found that when treating the inference as a regression problem or predicting the distance in meters, the lowest error was 0.47 m. for car images and 0.62 m. for motorcycle images. When treating the problem as a classification problem or classifying ranges of distance, the highest accuracy was 96.28% for car images and 94.57% for motorcycle images. Considering the performance with the ease of further development on a small computer in the developing device, it is found that the decision tree algorithm is suitable due to its high accuracy, ease of development, and computational speed.
Smoothed Teager Energy Cepstral Feature for Replay Attack Detection on Voice Assistants

Voice Controlled Systems (VCS) are easy and convenient to use for smart applications. However, it brings security issue for replay and hidden voice commands. To that effect, recently developed ReMASC database focuses on replay spoof attacks targeted for different acoustic environments along with single and multi-channel microphone array with different recording devices. This paper presents Spoof Speech Detection (SSD) system using smoothed Teager Energy Cepstral Coefficients (STECC) and Gaussian Mixture Model (GMM) as classifier. The smooth Teager energy profile resolves the spurious peaks and spikes of subband filtered signal. Teager Energy Operator (TEO) also has noise suppression capability that further helps to detect the replay signal. We performed experiments on ReMASC database. The experiments are classified into three tasks, namely, mismatch training, environment-independent, and environment-dependent. In addition, we also focused on the impact of different recording devices with single input channel. For environment-dependent task, STECC feature set performed better than other features. With impact of different devices, proposed STECC outperforms with a difference of 10 % compared to the other systems.
Exploring Speaker Age Estimation on Different Self-Supervised Learning Models

Self-supervised learning (SSL) has played an important role in various tasks in the field of speech and audio processing. However, there is limited research on adapting these SSL models to predict the speaker's age and gender using speech signals. In this paper, we investigate seven SSL models, namely PASE+, NPC, wav2vec 2.0, XLSR, HuBERT, WavLM, and data2vec in the joint age estimation and gender classification task on the TIMIT corpus. Additionally, we also study the effect of using different hidden encoder layers within these models on the age estimation result. Furthermore, we evaluate how the performance of different SSL models varies in predicting the speaker's age under simulated noisy conditions. The simulated noisy speech is created by mixing the clean utterance from the TIMIT test set with random noises from the Music and Noise category of the MUSAN corpus on multiple levels of signal-to-noise ratio (SNR). Our findings confirm that a recent SSL model, namely WavLM can obtain better and more robust speech representation than wav2vec 2.0 SSL model used in the current state-of-the-art (SOTA) approach by achieving a 3.6% and 11.32% mean average error (MAE) reduction on the clean and 5dB SNR TIMIT test set.
Attacks on Image Sensors

This paper provides a taxonomy of security vulnerabilities of smart image sensor systems. Image sensors form an important class of sensors. Many image sensors include computation units that can provide traditional algorithms such as image or video compression along with machine learning tasks such as classification. Some attacks rely on the physics and optics of imaging. Other attacks take advantage of the complex logic and software required to perform imaging systems.
Hardware Architecture of Graph Neural Network-Enabled Motion Planner (Invited Paper)

Motion planning aims to find a collision-free trajectory from the start to goal configurations of a robot. As a key cognition task for all the autonomous machines, motion planning is fundamentally required in various real-world robotic applications, such as 2-D/3-D autonomous navigation of unmanned mobile and aerial vehicles and high degree-of-freedom (DoF) autonomous manipulation of industry/medical robot arms and graspers.
Quantum Machine Learning Applications in High-Energy Physics

Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.
INDENT

The performance and energy efficiency potential of heterogeneous architectures has fueled domain-specific systems-on-chip (DSSoCs) that integrate general-purpose and domain-specialized hardware accelerators. Decision trees (DTs) perform high-quality, low-latency task scheduling to utilize the massive parallelism and heterogeneity in DSSoCs effectively. However, offline trained DT scheduling policies can quickly become ineffective when applications or hardware configurations change. There is a critical need for runtime techniques to train DTs incrementally without sacrificing accuracy since current training approaches have large memory and computational power requirements. To address this need, we propose INDENT, an incremental online DT framework to update the scheduling policy and adapt it to unseen scenarios. INDENT updates DT schedulers at runtime using only 1--8% of the original training data embedded during training. Thorough evaluations with hardware platforms and DSSoC simulators demonstrate that INDENT performs within 5% of a DT trained from scratch using the entire dataset and outperforms current state-of-the-art approaches.
MCQA

In response to the rapid development of quantum processors, quantum software must be advanced by considering the actual hardware limitations. Among the various design automation problems in quantum computing, qubit allocation modifies the input circuit to match the hardware topology constraints. In this work, we present an effective heuristic approach for qubit allocation that considers not only the hardware topology but also other constraints for near-fault-tolerant quantum computing (near-FTQC). We propose a practical methodology to find an effective initial mapping to reduce both the number of gates and circuit latency. We then perform dynamic scheduling to maximize the number of gates executed in parallel in the main mapping phase. Our experimental results with a Surface-17 processor confirmed a substantial reduction in the number of gates, latency, and runtime by 58%, 28%, and 99%, respectively, compared with the previous method [18]. Moreover, our mapping method is scalable and has a linear time complexity with respect to the number of gates.
AntiSIFA-CAD

Fault Attacks (FA) have gained a lot of attention from both industry and academia due to their practicality, and wide applicability to different domains of computing. In the context of symmetric-key cryptography, designing countermeasures against FA is still an open problem. Recently proposed attacks such as Statistical Ineffective Fault Analysis (SIFA) has shown that merely adding redundancy or infection-based countermeasure to detect the fault doesn't work and a proper combination of masking and error correction/detection is required. In this work, we show that masking which is mathematically established as a good countermeasure against a certain class of SIFA faults, in practice may fall short if low-level details during physical design layout development are not taken care of. We initiate this study by demonstrating a successful SIFA attack on a post placed-and-routed masked crypto design for ASIC platform. Eventually, we propose a fully automated approach along with a proper choice of placement constraints which can be realized easily for any commercial CAD tools to successfully get rid of this vulnerability during the physical layout development process. Our experimental validation of our tool flow over masked implementation on PRESENT cipher establishes our claim.
Personalized Heterogeneity-Aware Federated Search Towards Better Accuracy and Energy Efficiency

Federated learning (FL), a new distributed technology, allows us to train the global model on the edge and embedded devices without local data sharing. However, due to the wide distribution of different types of devices, FL faces severe heterogeneity issues. The accuracy and efficiency of FL deployment at the edge are severely impacted by heterogeneous data and heterogeneous systems. In this paper, we perform joint FL model personalization for heterogeneous systems and heterogeneous data to address the challenges posed by heterogeneities. We begin by using model inference efficiency as a starting point to personalize network scale on each node. Furthermore, it can be used to guide the efficient FL training process, which can help to ease the problem of straggler devices and improve FL's energy efficiency. During FL training, federated search is then used to acquire highly accurate personalized network structures. By taking into account the unique characteristics of FL deployment at edge devices, the personalized network structures obtained by our federated search framework with a lightweight search controller can achieve competitive accuracy with state-of-the-art (SOTA) methods, while reducing inference and training energy consumption by up to 3.57× and 1.82×, respectively.
HyperEF

This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical framework for low-resistance-diameter decomposition of simple graphs, HyperEF aims at decomposing large hypergraphs into multiple node clusters with only a few inter-cluster hyperedges. The key component in HyperEF is a nearly-linear time algorithm for estimating hyperedge effective resistances, which allows incorporating the latest diffusion-based non-linear quadratic operators defined on hypergraphs. To achieve good runtime scalability, HyperEF searches within the Krylov subspace (or approximate eigensubspace) for identifying the nearly-optimal vectors for approximating the hyperedge effective resistances. In addition, a node weight propagation scheme for multilevel spectral hypergraph decomposition has been introduced for achieving even greater node coarsening ratios. When compared with state-of-the-art hypergraph partitioning (clustering) methods, extensive experiment results on real-world VLSI designs show that HyperEF can more effectively coarsen (decompose) hypergraphs without losing key structural (spectral) properties of the original hypergraphs, while achieving over 70× runtime speedups over hMetis and 20× speedups over HyperSF.
Usage-Based RTL Subsetting for Hardware Accelerators

Recent years have witnessed increasing use of domain-specific accelerators in computing platforms to provide power-performance efficiency for emerging applications. To increase their applicability within the domain, these accelerators tend to support a large set of functions, e.g. Nvidia's open-source Deep Learning Accelerator, NVDLA, supports five distinct groups of functions [17]. However, an individual use case of an accelerator may utilize only a subset of these functions. The unused functions lead to unnecessary overhead of silicon area, power, and hardware verification/hardware-software co-verification complexity. This motivates our research question: Given an RTL design for an accelerator and a subset of functions of interest, can we automatically extract a subset of the RTL that is sufficient for these functions and sequentially equivalent to the original RTL? We call this the Usage-based RTL Subsetting problem, referred to as the RTL subsetting problem in short. We first formally define this problem and show that it can be formulated as a program synthesis problem, which can be solved by performing expensive hyperproperty checks. To overcome the high cost, we propose multiple levels of sound over-approximations to construct an effective algorithm based on relatively less expensive temporal property checking and taint analysis for information flow checking. We demonstrate the acceptable computation cost and the quality of the results of our algorithm through several case studies of accelerators from different domains. The applicability of our proposed algorithm can be seen in its ability to subset the large NVDLA accelerator (with over 50,000 registers and 1,600,000 gates) for the group of convolution functions, where the subset reduces the total number of registers by 18.6% and the total number of gates by 37.1%.
Two Enhanced Feistel Steps for Medical Image Encryption

In this paper, we will develop a new method for color image encryption, based on an improved Feistel scheme designed from chaotic maps. After having vectorized the image, we subdivide the vector into a block of even size, we will apply two rounds of improved Feistel, which implements a chaotic permutation and a linear transformation provided by a not necessarily invertible square matrix. finally, a diffusion function is performed to overcome the differential attack. the effectiveness of our approach is proven by security test, namely correlation, histogram, entropy and differential parameters.\begin{equation*}{\mathbf{Notation}}\left\{ {{{\mathbf{G}}_{\mathbf{t}}}{{\mathbf{ = }}^{\mathbf{Z}}}{{\mathbf{/}}_{{\mathbf{tZ}}}}} \right.{\mathbf{ring}}\end{equation*}
Trios: A Framework for Interactive 3D Photo Stylization on Mobile Devices

For decades, Image-based Artistic Rendering (IB-AR) has been successfully employed to simulate the appeal of traditional artistic styles for enhanced visual communication. Recently, 3D photography has emerged as a new medium that provides an immersive dimension compared to 2D photos. The possibility to change the viewpoint, depicting parallax effects is mesmerizing. We present Trios, an interactive mobile app that combines the vividness of IB-AR with immersive 3D photos. Trios implements an end-to-end pipeline for the acquisition of data, generation of a 3D photo in the form of a Layered Depth Image (LDI), and its artistic rendering. The app allows the user to either capture the input data or load existing data from the device. As part of the generation step, users can set the number of layers used for representation of 3D photos. Finally, with different artistic filters and their parameterization users can stylize either an individual semantic layer or all layers simultaneously. The complete pipeline runs at interactive frame rates and the final output is obtained as a compact video, which can easily be shared. Thus, it serves as a unique interactive tool for digital artists interested in creating immersive artistic content.
Blockchain technology: A bibliographic study

At the rate of breakthroughs and new technologies, the world is always changing and evolving. The "Economist" magazine's report on the blockchain at the end of 2015 helped the technology gain popularity. A virtual currency based on blockchain technology, Bitcoin, is becoming more and more popular in the media. This article seeks to educate readers by popularizing and demystifying how the blockchain works. Many industries, including the financial industry, starting with the banking system, could see their game rules altered by this technology. The goal of this new trust model is to eliminate the current trust intermediary and replace it with a decentralized, shared system. A brand-new era is beginning. In this work, we are interested in the use of Blockchain in different fields.
IS3TA - A mixed reality tool for exposure-based therapies

In vivo exposure is the most common treatment for phobias, although it has several drawbacks that can be mitigated by adopting technological alternatives such as virtual reality or augmented reality. Augmented reality provides some advantages over virtual reality, including fewer modelling costs and a higher level of realism. As a result, the goal is to develop an alternate treatment to exposure in vivo using augmented reality with Hololens 2. The proposed approach allows the patient to interact with the phobic elements while simultaneously giving the psychologist complete control over them, allowing each person to have a unique and personalized experience based on their phobias. Implementation and preliminary analysis results are presented.
A CPHD Filter Based on EM Star-Convex Random Hypersurface Model for Multiple Extended Targets

Aiming at the problem of tracking multiple extended targets with irregular shapes in complex scenes, this paper presents a Cardinalized Probability Hypothesis Density (CPHD) filter built on EM iteration and Star-Convex Random Hypersurface Model (SRHM). Firstly, based on the theory of Finite Set Statistics (FISST), the Bayesian filtering framework for multiple extended targets is established by using the CPHD filter. Then, SRHM is adopted to describe the measurement source distribution of the star-convex extended target, and Unscented Transform is used to embed the CPHD filtering process. In addition, the incorporation of EM iteration significantly alleviates the dependence of the SRHM-CPHD filter on the RHM prior. The simulation results show that the tracking performance of the proposed EMSRHM-CPHD filter is better than that of the SRHM-CPHD filter, and the parameter estimation of the extended targets is more accurate.
A Comparative Study on RepVGG and ResNet for Monocular 3D Object Detection

In this work, a comparative analysis is made on two cutting-edge deep learning backbones, i.e., ResNet and RepVGG, in the scenario of monocular 3D object detection. RepVGG, as a more recent variant of VGG, is claimed enjoy a favorable accuracy-speed balance compared to other state-of-the-art ones, especially ResNet. We build on a very recent 3D object detection model KM3D as the test bed, to investigate the performance differences of ResNet and RepVGG in terms of detection speed and accuracy. Experiment results on the popular 3D detection dataset KITTI demonstrate that the backbone RepVGG has not convincingly surpassed the backbone ResNet. Undoubtedly, such an empirical finding inspires one research on more robust candidate backbones than RepVGG.