Reviewerly/exHarmony huggingface dataset:
Exploring Data Augmentation in Bias Mitigation Against Non-Native-Accented Speech

Automatic speech recognition (ASR) should serve every speaker, not only the majority “standard” speakers of a language. In order to build inclusive ASR, mitigating the bias against speaker groups who speak in a “non-standard” or “diverse” way is crucial. We aim to mitigate the bias against non-native-accented Flemish in a Flemish ASR system. Since this is a low-resource problem, we investigate the optimal type of data augmentation, i.e., speed/pitch perturbation, cross-lingual voice conversion-based methods, and SpecAugment, applied to both native Flemish and non-native-accented Flemish, for bias mitigation. The results showed that specific types of data augmentation applied to both native and non-native-accented speech improve non-native-accented ASR while applying data augmentation to the non-native-accented speech is more conducive to bias reduction. Combining both gave the largest bias reduction for human-machine interaction (HMI) as well as read-type speech.
Image Denoising with CNN-Based Attention

Noise removal is one of the most commonly used processes in computer vision. Noise removal improves the quality of the image, thereby improving the performance of computer vision algorithms and providing user pleasing. In this study, we aim to improve the performance of noise removal by adding an efficient attention module, the Convolutional Block Attention Module (CBAM), to the Fast and Flexible Denoising Network (FFDNet) model with an adjustable noise level map as input. By adding the CBAM module to the convolutions used in FFDNet, the CNN’s representational power was increased and successful results were obtained. The proposed method achieved high PSNRs in quantitative experiments on different datasets, and in qualitative experiments we observed that the denoised images are close to the target images.
Evaluating Self-Supervised Speech Models on a Taiwanese Hokkien Corpus

Taiwanese Hokkien is declining in use and status due to a language shift towards Mandarin in Taiwan. This is partly why it is a low resource language in NLP and speech research today. To ensure that the state of the art in speech processing does not leave Taiwanese Hokkien behind, we contribute a 1.5-hour dataset of Taiwanese Hokkien to MLSUPERB’s hidden set. Evaluating ML-SUPERB’s suite of self-supervised learning (SSL) speech representations on our dataset, we find that model size does not consistently determine performance. In fact, certain smaller models outperform larger ones. Furthermore, linguistic alignment between pretraining data and the target language plays a crucial role.
Leveraging the Multilingual Indonesian Ethnic Languages Dataset In Self-Supervised Models for Low-Resource ASR Task

Indonesia is home to roughly 700 languages, which amounts to about ten percent of the global total, positioning it as the second-most linguistically diverse country after Papua New Guinea. Unfortunately, the availability of language technologies for languages in Indonesia is quite scant. Among those few technologies that have been developed, they mainly focus on the official Indonesian language, while a large number of Indonesian ethnic languages remain uncovered. To accelerate the development of language technology for under-resourced languages, especially for those languages in Indonesia, we contribute a multilingual Indonesian ethnic languages dataset for evaluation by the Multilingual Speech processing Universal PERformance Benchmark (ML-SUPERB). In this study, we also investigate low-resource automatic speech recognition (ASR) tasks in those languages on various self-supervised models, various minimum sets of training data, and various mismatched conditions.
PP-MET: A Real-World Personalized Prompt Based Meeting Transcription System

Speaker-attributed automatic speech recognition (SA-ASR) improves the accuracy and applicability of multi-speaker ASR systems in real-world scenarios by assigning speaker labels to transcribed texts. However, SA-ASR poses unique challenges due to factors such as speaker overlap, speaker variability, background noise, and reverberation. In this study, we propose PP-MeT system, a real-world personalized prompt based meeting transcription system, which consists of a clustering system, target-speaker voice activity detection (TS-VAD), and TS-ASR. Specifically, we utilize target-speaker embedding as a prompt in TS-VAD and TS-ASR modules in our proposed system. In constrast with previous system, we fully leverage pre-trained models for system initialization, thereby bestowing our approach with heightened generalizability and precision. Experiments on M2MeT2.0 Challenge dataset show that our system achieves a cp-CER of 11.27% on the test set, ranking first in both fixed and open training conditions.
Generative Speech Recognition Error Correction With Large Language Models and Task-Activating Prompting

We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction.Our first focus is on instruction prompting to let LLMs perform these task without fine-tuning, for which we evaluate different prompting schemes, both zeroand few-shot in-context learning, and a novel "task activation" prompting method that combines causal instructions and demonstration to increase its context windows.Next, we show that rescoring only by in-context learning with frozen LLMs achieves results that are competitive with rescoring by domain-tuned LMs, using a pretrained first-pass recognition system and rescoring output on two out-of-domain tasks (ATIS and WSJ).By combining prompting techniques with fine-tuning we achieve error rates below the N-best oracle level, showcasing the generalization power of the LLMs.
Local Optimization of MAPF Solutions on Directed Graphs

Among sub-optimal Multi-Agent Path Finding (MAPF) solvers, rule-based algorithms are particularly appealing since they are complete. Even in crowded scenarios, they allow finding a feasible solution that brings each agent to its target, preventing deadlock situations. However, generally, rule-based algorithms provide solutions that are much longer than the opti-malone. The main contribution of this paper is the introduction of an iterative local search procedure in MAPF. We start from a feasible suboptimal solution and we perform a local search in a neighborhood of this solution, to find a shorter one. Iteratively, we repeat this procedure until the solution cannot be shortened any longer. At the end, we obtain a solution, that is still sub-optimal, but, in general, of much better quality than the initial one. We use dynamic programming for the local search procedure. Under this respect, the fact that our search is local is fundamental to reduce the time complexity of the algorithm. Indeed, if we apply a standard dynamic programming the number of explored states grows exponentially with the number of agents. As we will see, the introduction of a locality constraint allows solving the (local) dynamic programming problem in a time that grows only polynomially with respect to the number of agents.
Tutorial: Neural Network and Autonomous Cyber-Physical Systems Formal Verification for Trustworthy AI and Safe Autonomy

This interactive tutorial describes state-of-the-art methods for formally verifying neural networks and their usage within safety-critical cyber-physical systems (CPS). The inclusion of deep learning models in safety-critical applications requires to formally analyze the behavior of the system, including reasoning about the individual components (e.g., controller robustness), and their interactions and effects in the system as a whole. This tutorial begins with a lecture on this emerging research area, followed by demos of these methods implemented in software tools, specifically the Neural Network Verification (NNV) tool. Examples include systems from aerospace, automotive, and beyond.
Programmable, Latency-Aware and Dynamic Quantum-Secured Optical Network with Key Refresh Rate Negotiation and QKD Sharing

We have demonstrated a dynamic QKD network with a latency-aware and QKD-capacity sensitive algorithm. This scheme negotiates the optimal key refresh period and assigns priorities for preferred channels. Network reconfiguration time is within 49ms.
Compressed Input Data Format of Quantum Annealing Emulator

Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for the hardware emulation since its usable memory size and its bandwidth are limited. As an improved Coordinate (COO) format for a sparse matrix, Compressed Sparse Row (CSR) has been proposed [1]. We have checked CSR on an Ising model but found that the compression efficiency is not high.
Model-as-a-Service (MaaS): A Survey

Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve downstream task performance and emerge with some novel special abilities (e.g., deep learning, complex reasoning, and human alignment) that were not present before. Foundation models are a form of generative artificial intelligence (GenAI), and Model-as-a-Service (MaaS) has emerged as a groundbreaking paradigm that revolutionizes the deployment and utilization of GenAI models. MaaS represents a paradigm shift in how we use AI technologies and provides a scalable and accessible solution for developers and users to leverage pre-trained AI models without the need for extensive infrastructure or expertise in model training. In this paper, the introduction aims to provide a comprehensive overview of MaaS, its significance, and its implications for various industries. We provide a brief review of the development history of “X-as-a-Service” based on cloud computing and present the key technologies involved in MaaS. The development of GenAI models will become more democratized and flourish. We also review recent application studies of MaaS. Finally, we highlight several challenges and future issues in this promising area. MaaS is a new deployment and service paradigm for different AI-based models. We hope this review will inspire future research in the field of MaaS.
Computing Acceleration to Genome-Wide Association Study Based on CPU/FPGA Heterogeneous System

Genome Wide Association Study (GWAS) reveals the influence of single nucleotide polymorphisms (SNP) and other genetic markers on the complex genetic disease traits, making a significant contribution to the prevention and treatment of genetic diseases. Since GWAS needs to calculate large numbers of pairwise tests, it is highly time-consuming and this limits its widespread promotion in many medical applications. To solve the above problem, many studies deploy GWAS on the CPU/GPU platform. By taking advantages of GPUs' high parallelism, the completion time of GWAS can be significantly decreased. However, GPUs have high energy consumption, which increases GWAS's operating cost. Considering FPGA has low power, high parallelism and programmability, this article aims to use FPGA to design a hardware accelerator dedicated to GWAS for the purpose of optimizing both completion time and energy cost of GWAS. First, the CPU/FPGA heterogenous computing system is built, and the computationally-intensive GWAS tasks such as the contingency table creation (CTC) and Kirkwood Superposition Approximation (KSA) screening are offloaded to run on FPGA. Then, a specific FPGA architectures dedicated to CTC and KSA tasks of GWAS are implemented by using a set of architectural design technologies such as hardware and software co-optimization, systolic array, loop unrolling, pipeline and parallelism. To evaluate the performance of FPGA, we compare its completion time, energy cost and performance per Watt with those of CPU and GPU. The results show the FPGA and GPU can respectively achieve 105--fold and 148--fold speedup compared with CPU. Although FPGA's computing performance is a bit lower than that of GPU, it has higher energy efficiency and its performance per Watt is 1.4 times that of GPU.
Trajectory Planning for Automated Vehicles using Quantum Computing

This paper presents a method for automated vehicle trajectory planning using quantum computing. The use of quantum computing for the trajectory planning problem was studied because the current classical methods for solving the problem are computationally expensive. Simulation tests with the presented method were performed in more complex test cases than what has previously been tested with this method. Simulation tests show, that the method scales also for more complex problems. The method presented here can theoretically outperform classical methods in complexity in certain conditions, although it is not guaranteed in every condition.
STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup

Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. When trained using such data, models tend to assign higher probabilities to normal cases, leading to biased performance. Common oversampling techniques such as SMOTE rely on local information and can introduce marginalization issues. This paper investigates the potential of using Mixup augmentation that combines two training examples along with their corresponding labels to generate new data points as a generic vicinal distribution. To this end, we propose STEM, which combines SMOTEENN and Mixup at the instance level. This integration enables us to effectively leverage the entire distribution of minority classes, thereby mitigating both between-class and within-class imbalances. We focus on the breast cancer problem, where imbalanced datasets are prevalent. The results demonstrate the effectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in the Digital Database for Screening Mammography and Wisconsin Breast Cancer (Diagnostics) datasets, respectively. Moreover, this method shows promising potential when applied with an ensemble of machine learning (ML) classifiers.
Two-Stage Adaptive Gradient Quantization in Federated Learning

As the training tasks become more complex, the models become lager with more parameters, and the limited communication resources are still the main bottleneck of federated learning. Some studies have proposed many methods to reduce the communication consumption in federated learning. However, in previous studies, they ignored the impact of different gradient distributions on quantization strategies. Therefore, our study proposes an adaptive gradient compression method(AGCML) which realizes quantization process from two stages, gradient division and gradient quantization. In the first stage, gradient are divided into two parts according to the importance of the gradient. And in the second stage, we select different quantization strategies to quantize the two parts. Our experiments demonstrate that the method we proposed achieve higher model accuracy and compression rate.
Revolutionizing Concurrent Crawling: A Novel Approach to Enhance PHP-Python Integration using AMQP, Selenium, Celery, and RabbitMQ

This research proposes a practical solution for seamlessly integrating PHP with Python in web development, focusing on achieving efficient web crawling. The problem is that many PHP applications need to call the Python application for machine learning or crawling work. With default functions from PHP, such as PHP-exec, the Python program could be executed but cannot be maintained smoothly if the program is a lengthy task in the background. By leveraging the AMQP (Advanced Message Queuing Protocol) library and the Selenium Crawler, Celery, and RabbitMQ, we establish interoperability between PHP and Python. In our approach, PHP acts as the front end, initiating web crawling tasks by doing some action in the web component. These requests are queued with a message broker application such as RabbitMQ, the message broker. RabbitMQ connected with Celery for the seamless scheduling and execution of tasks. This research enables effective web crawling and concurrent data scraping by seamlessly integrating the Selenium Crawler with Celery. Results from extracted data from the crawler are saved to the database to give a certain status of whether the data collection process is done or pending. Through experimentation, we validate the effectiveness of our seamlessly integrated approach by making a variation of worker and concurrent connection. The testing scenario shows that increasing workers only increase a small amount of memory. This result indicates that workers could help maintain the response time if there is some user, but need some consideration based on the number of users and availability of memory and CPU.
BioDwelling: A participatory approach to living with living material

BioDwelling is an arts-led research project that brings ethical concerns of culture, gender and multispecies relationality from the feminist technosciences into direct conversation with the emerging field of biotechnological architecture (bio-architecture). Working within a multi-disciplinary bio-architecture research group, we develop a practice-led methodology to facilitate the exploration of questions that arise when we begin to engineer more-than-human dwelling spaces. In this article we give a brief overview of the work of the Hub for Biotechnology in the Built Environment (HBBE) and the Responsible Interactions research theme, which outlines the project context. We go on to describe the BioDwelling methodology and introduce three arts-led interventions that draw on feminist science and cultural theory to invite reflections on the ethical implications of working with living materials to build (or grow?) spaces in which humans intend to live. We conclude with a summary of the project to date, reflections on methods and possible next steps.
Suzanne Ciani: The diva of the diode

The world of synthesizers and synthesists is historically male-dominated. Women in synthesis tend to be obscured by males, and their contribution suffers from erasure. This article considers Suzanne Ciani within the contexts of art, technology, science and culture and her work in composition, performance and media. In particular, her National Endowment for the Arts report of 1976 is a groundbreaking document detailing her composition and performance process using synthesizers. Her composition approach has parallels with techniques drawn from serialism, performed and improvised live using machines. Her performances utilize quadraphonic techniques for spatialized performance as part of the composition and performance. She was the first woman to score a major Hollywood film in 1981. Her work in media and technology highlights a space that women have always worked in but have frequently not been acknowledged as doing so. Ciani’s story reveals tenacity in the face of bias and rejection. It is a tale of someone who always has to claim her credentials, always legitimate her mode of expression, and always counter the assumption that somewhere in her work, there is a man.
Security Driven Cost-Effective Deadline Aware Workflow Allocation Strategy in Cloud Computing Environment

In cloud computing, a pay-per-use framework enables dynamic scalability in response to the demands of workflow applications. Workflow allocation is an essential issue due to the trade-off between obtaining high computing performance and offering the appropriate level of security protection. This paper integrates security services into the workflow allocation to enhance the security measures in the cloud system. Therefore, we propose a multi-constraints workflow allocation strategy for heterogeneous tasks in cloud computing. Our proposed strategy is to minimize the risk probability and the execution cost while meeting the budget, and deadline constraints. The findings show that our approach consistently outperforms the existing algorithms in the proposed system, which uses multi-constraints.
Program Synthesis with Generative Pre-trained Transformers and Grammar-Guided Genetic Programming Grammar

Grammar-Guided Genetic Programming (G3P) is widely recognised as one of the most successful approaches to program synthesis. Using a set of input/output tests, G3P evolves programs that fit a defined BNF grammar and that are capable of solving a wide range of program synthesis problems. However, G3P’s inability to scale to more complex problems has limited its applicability. Recently, Generative Pre-trained Transformers (GPTs) have shown promise in revolutionizing program synthesis by generating code based on natural language prompts. However, challenges such as ensuring correctness and safety still need to be addressed as some GPT-generated programs might not work while others might include security vulnerabilities or blacklisted library calls. In this work, we proposed to combine GPT (in our case ChatGPT) with a G3P system, forcing any synthesised program to fit the BNF grammar-thus offering an opportunity to evolve/fix incorrect programs and reducing security threats. In our work, we leverage GPT-generated programs in G3P’s initial population. However, since GPT-generated programs have an arbitrary structure, the initial work that we undertake is to devise a technique that maps such programs to a predefined BNF grammar before seeding the code into G3P’s initial population. By seeding the grammar-mapped code into the population of our G3P system, we were able to successfully improve some of the desired programs using a well-known program synthesis benchmark. However, in its default configuration, G3P is not successful in fixing some incorrect GPT-generated programs–even when they are close to a correct program. We analysed the performance of our approach in depth and discussed its limitations and possible future improvements.
Many-objective Grammar-guided Genetic Programming with Code Similarity Measurement for Program Synthesis

The approach known as Grammar-Guided Genetic Programming (G3P) is widely acknowledged as a highly effective method for program synthesis, which involves automatically generating code based on high-level formal specifications. Given the increasing quantity and scale of open software repositories and generative artificial intelligence techniques, there exists a significant range of methods for retrieving or generating source code using textual problem descriptions. Therefore, in light of the prevailing circumstances, it becomes imperative to introduce G3P into alternative means of user intent, with a specific focus on textual depictions. In our previous work, we assessed the potential for G3P to evolve programs based on bi-objectives that combine the similarity to the target program using four different similarity measures and the traditional input/output error rate. The result showed that such an approach improved the success rate for generating correct solutions for some of the considered problems. Nevertheless, it is noteworthy that despite the inclusion of various similarity measures, there is no single measure that uniformly improves the success rate of G3P across all problems. Instead, certain similarity measures exhibit effectiveness in addressing specific problems while demonstrating limited efficacy in others. In this paper, we would like to expand the bi-objective framework with different similarity measures to a many-objective framework to enhance the general performance of the algorithm to more range of problems. Our experiments show that compared to the bi-objective G3P(BOG3P), the Many-objective G3P(MaOG3P) approach could achieve the best result of all BOG3P algorithms with different similarity measures.