Reviewerly/exHarmony huggingface dataset:
Domain and Language Adaptation Using Heterogeneous Datasets for Wav2vec2.0-Based Speech Recognition of Low-Resource Language

We address the effective finetuning of a large-scale pretrained model for automatic speech recognition (ASR) of lowresource languages with only a one-hour matched dataset. The finetuning is composed of domain adaptation and language adaptation, and they are conducted by using heterogeneous datasets, which are matched with either domain or language. For effective adaptation, we incorporate auxiliary tasks of domain identification and language identification with multi-task learning. Moreover, the embedding result of the auxiliary tasks is fused to the encoder output of the pretrained model for ASR. Experimental evaluations on the Khmer ASR using the corpus of ECCC (the Extraordinary Chambers in the Courts of Cambodia) demonstrate that first conducting domain adaption and then language adaption is effective. In addition, multi-tasking with domain identification and fusing the domain ID embedding gives the best performance, which is a CER improvement of 6.47% absolute from the baseline finetuning method.
Hierarchical Graph Learning for Stock Market Prediction Via a Domain-Aware Graph Pooling Operator

The utility of Graph Neural Networks (GNN) for the paradigm of forecasting short-term stock price movements is investigated. In particular, a finance-specific graph pooling operation, referred to as StockPool, is introduced to efficiently coarsen the stock graph. This is achieved by employing domain knowledge to cluster stocks, depending on some task-specific characteristics (e.g. industries, sub-industries, etc.). Unlike fully end-to-end learnable graph pooling strategies (e.g. differentiable pooling, MinCUT pooling, etc.), such a deterministic pooling operator is considerably more computationally efficient and thus scalable to larger stock graphs. Experimentations on the S&P500 stock index demonstrate that the StockPool operator outperforms existing graph pooling strategies on the prediction of price movements. Finally, different graph pooling methods are utilized to create a set of highly uncorrelated GNN models; these are used to construct a graph ensemble model with an improved performance.
PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Accelerators

Running deep learning models on resource-constrained edge devices has drawn significant attention due to its fast response, privacy preservation, and robust operation regardless of Internet connectivity. While these devices already cope with various intelligent tasks, the latest edge devices that are equipped with multiple types of low-power accelerators (i.e., both mobile GPU and NPU) can bring another opportunity; a task that used to be too heavy for an edge device in the single-accelerator world might become viable in the upcoming heterogeneous-accelerator world. To realize the potential in the context of 3D object detection, we identify several technical challenges and propose PointSplit, a novel 3D object detection framework for multi-accelerator edge devices that addresses the problems. Specifically, our PointSplit design includes (1) 2D semantics-aware biased point sampling, (2) parallelized 3D feature extraction, and (3) role-based group-wise quantization. We implement PointSplit on TensorFlow Lite and evaluate it on a customized hardware platform comprising both mobile GPU and EdgeTPU. Experimental results on representative RGB-D datasets, SUN RGB-D and Scannet V2, demonstrate that PointSplit on a multi-accelerator device is 24.7 × faster with similar accuracy compared to the full-precision, 2D-3D fusion-based 3D detector on a GPU-only device.
Gesture Based Virtual Assistant For Deaf-Mutes Using Deep Learning Approach

Roughly 5% of people around the world are deaf-mutes which imply they face difficulties in interacting with normal people, sign language is communication media for them. Gestures made through hands are one of the nonverbal communication modalities used in sign language. In order to communicate among themselves, the speech and hearing impaired utilize sign language as they have an understanding of conventional sign language. However, they are unable to interact with the outside world because most people are not aware of the presence and use of sign language. This technology tries to break down the hurdle between the disabled and everybody else in the world by identifying and converting hand signals into text in English and Kannada languages which are further translated into speech. Convolutional Neural Networks, Tensor flow, Deep Learning, Python text-to-speech module pyttsx3 and OpenCV are among the concepts used. A webcam captures the hand motions and is processed using the OpenCV python library, which is eventually interpreted by the deep learning algorithm called Convolutional Neural Network which generates rational languages. To map these rational languages to their pre-defined datasets we make use of deep learning, and to satisfy this requirement neural networks are used in conjunction with the Tensor Flow Library. Once the gesture is recognized it will be displayed on the viewing screen and instantly converted to speech. This system acts as a two-way virtual assistant that helps in converting sign-to-text and speech and thus assists the disabled in the utilization of modern technologies for communication.
Seismic Tomography of Nabro Caldera, Eritrea: Insights Into the Magmatic and Hydrothermal Systems of a Recently Erupted Volcano

Abstract Understanding the crustal structure and the storage and movement of fluids beneath a volcano is necessary for characterizing volcanic hazard, geothermal prospects and potential mineral resources. This study uses local earthquake traveltime tomography to image the seismic velocity structure beneath Nabro, an off‐rift volcano located within the central part of the Danakil microplate near the Ethiopia‐Eritrea border. Nabro underwent its first historically documented eruption in June 2011, thereby providing an opportunity to analyze its post‐eruptive state by mapping subsurface fluid distributions. We use a catalog of earthquakes detected on a temporary seismic array using machine learning methods to simultaneously relocate the seismicity and invert for the three‐dimensional P‐ and S‐wave velocity structures ( V P , V S ) and the ratio between them ( V P / V S ). Overall, our model shows higher than average P‐ and S‐wave velocities, suggesting the presence of high‐strength, solidified intrusive magmatic rocks in the crust. We identify an aseismic region of low V P , low V S , and high V P / V S ratio at depths of 6–10 km b.s.l., interpreted as the primary melt storage region that fed the 2011 eruption. Above this is a zone of high V S , low V P , and low V P / V S ratio, representing an intrusive complex of fractured rocks partially saturated with over‐pressurized gases. Our observations identify the persistence of magma in the subsurface following the eruption, and track the degassing of this melt through the crust to the surface. The presence of volatiles and high temperatures within the shallow crust indicate that Nabro is a viable candidate for geothermal exploration.
Privacy-Preserving Occupancy Estimation

In this paper, we introduce an audio-based framework for occupancy estimation, including a new public dataset, and evaluate occupancy in a ‘cocktail party’ scenario where the party is simulated by mixing audio to produce speech with overlapping talkers (1-10 people). To estimate the number of speakers in an audio clip, we explored five different types of speech signal features and trained several versions of our model using convolutional neural networks (CNNs). Further, we adapted the framework to be privacy-preserving by making random perturbations of audio frames in order to conceal speech content and speaker identity. We show that some of our privacy-preserving features perform better at occupancy estimation than original waveforms. We analyse privacy further using two adversarial tasks: speaker recognition and speech recognition. Our privacy-preserving models can estimate the number of speakers in the simulated cocktail party clips within 1-2 persons based on a mean-square error (MSE) of 0.9-1.6 and we achieve up to 34.9% classification accuracy while preserving speech content privacy. However, it is still possible for an attacker to identify individual speakers, which motivates further work in this area.
Improving the out-of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough

Counterfactually-Augmented Data (CAD) has the potential to improve language models’ Out-Of-Distribution (OOD) generalization capability, as CAD induces language models to exploit causal features and exclude spurious correlations. However, the empirical results of OOD generalization on CAD are not as efficient as expected. In this paper, we attribute the inefficiency to Myopia Phenomenon caused by CAD: language models only focus on causal features that are edited in the augmentation and exclude other non-edited causal features. As a result, the potential of CAD is not fully exploited. Based on the structural properties of CAD, we design two additional constraints to help language models extract more complete causal features contained in CAD, thus improving the OOD generalization capability. We evaluate our method on two tasks: Sentiment Analysis and Natural Language Inference, and the experimental results demonstrate that our method could unlock CAD’s potential and improve language models’ OOD generalization capability.
Quantum Low-Density Parity-Check Codes for Modular Architectures

In efforts to scale the size of quantum computers, modularity plays a central role across most quantum computing technologies. In the light of fault tolerance, this necessitates designing quantum error-correcting codes that are compatible with the connectivity arising from the architectural layouts. In this paper, we aim to bridge this gap by giving a novel way to view and construct quantum LDPC codes tailored for modular architectures. We demonstrate that if the intra- and inter-modular qubit connectivity can be viewed as corresponding to some classical or quantum LDPC codes, then their hypergraph product code fully respects the architectural connectivity constraints. Finally, we show that relaxed connectivity constraints that allow twists of connections between modules pave a way to construct codes with better parameters.
Rethinking Population-assisted Off-policy Reinforcement Learning

While off-policy reinforcement learning (RL) algorithms are sample efficient due to gradient-based updates and data reuse in the replay buffer, they struggle with convergence to local optima due to limited exploration. On the other hand, population-based algorithms offer a natural exploration strategy, but their heuristic black-box operators are inefficient. Recent algorithms have integrated these two methods, connecting them through a shared replay buffer. However, the effect of using diverse data from population optimization iterations on off-policy RL algorithms has not been thoroughly investigated. In this paper, we first analyze the use of off-policy RL algorithms in combination with population-based algorithms, showing that the use of population data could introduce an overlooked error and harm performance. To test this, we propose a uniform and scalable training design and conduct experiments on our tailored framework in robot locomotion tasks from the OpenAI gym. Our results substantiate that using population data in off-policy RL can cause instability during training and even degrade performance. To remedy this issue, we further propose a double replay buffer design that provides more on-policy data and show its effectiveness through experiments. Our results offer practical insights for training these hybrid methods.
Privacy in process mining: Logs conversion to images using machine learning algorithm

In today’s digital age, log files are crucial. However, the conversion of text log files into images has only recently been developed. The security of log files is a major source of concern, and the security of the systems in which the logs are stored determines the safety of the log file in process mining. This calls for the first conversion of a text log file into an image file. Thus, this research aims to convert the log files into images in a mugshot database and detect illegal activity and criminals from the converted images employing a novel Convolutional Neural Network (CNN). The developed model has three stages: pre-processing, feature extraction, and detection and matching. The pre-processing was performed by min-max normalization, and in feature extraction, the deep learning method was used. Moreover, in the detection phase, CNN is employed for detecting illegal activities, and the matching process is performed for detecting illegal activities from converted images and criminals in the mugshot database. The model’s performance is evaluated in terms of precision, F1-score, recall, and accuracy values of 99.6%, 98.5%, 98.7%, and 99.8%, respectively. A further comparison has been performed to show the effectiveness of the suggested model over other methods.
Feature-Alignment-Based Cross-Platform Question Answering Expert Recommendation

Community question answering (CQA), with its flexible user interaction characteristics, is gradually becoming a new knowledge-sharing platform that allows people to acquire knowledge and share experiences. The number of questions is rapidly increasing with the open registration of communities and the massive influx of users, which makes it impossible to match many questions to suitable question answering experts (noted as experts) in a timely manner. Therefore, it is of great importance to perform expert recommendation in CQA. Existing expert recommendation algorithms only use data from a single platform, which is not ideal for new CQA platforms with sparse historical interaction and a small number of questions and users. Considering that many mature CQA platforms (source platforms) have rich historical interaction data and a large amount of questions and experts, this paper will fully mine the information and transfer it to new platforms with sparse data (target platform), which can effectively alleviate the data sparsity problem. However, the feature composition of questions and experts in different platforms is inconsistent, so the data from the source platform cannot be directly transferred for training in the target platform. Therefore, this paper proposes feature-alignment-based cross-platform question answering expert recommendation (FA-CPQAER), which can align expert and question features while transferring data. First, we use the rating predictor composed by the BP network for expert recommendation within the domains, and then the feature matching of questions and experts between two domains by similarity calculation is achieved for the purpose of using the information in the source platform to assist expert recommendation in the target platform. Meanwhile, we train a stacked denoising autoencoder (SDAE) in both domains, which can map user and question features to the same dimension and align the data distributions. Extensive experiments are conducted on two real CQA datasets, Toutiao and Zhihu datasets, and the results show that compared to the other advanced expert recommendation algorithms, this paper’s method achieves better results in the evaluation metrics of MAE, RMSE, Accuracy, and Recall, which fully demonstrates the effectiveness of the method in this paper to solve the data sparsity problem in expert recommendation.
Radar Spectrum Image Classification Based on Deep Learning

With the continuous development and progress of science and technology, the increasingly complex electromagnetic environment and the research and development of new radar systems have led to the emergence of various radar signals. Traditional methods of radar emitter identification cannot meet the needs of current practical applications. For the purpose of classification and recognition of radar emitter signals, this paper proposes an improved EfficientNetv2-s classification method based on deep learning for more precise classification and recognition of radar radiation source signals. Using 16 different types of radar signal parameters from the signal parameter setting table, the proposed method generates random data sets consisting of spectrum images with varying amplitude. The proposed method replaces two-dimensional convolution in EfficientNetV2 with one-dimensional convolution. Additionally, the channel attention mechanism of the EfficientNetv2-s is optimized and modified to obtain attention weights without dimensional reduction, resulting in superior accuracy. Compared with other deep-learning image-classification methods, the test results of this method have better classification accuracy on the test set: the top1 accuracy reaches 98.12%, which is 0.17~3.12% higher than other methods. Furthermore, the proposed method has lower complexity compared to most methods.
Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy

With the increasing requirements for the security of medical data, military data, and other data transmission, data hiding technology has gradually developed from only protecting the security of secret data to all transmission data. As a necessary technical means, reversible data hiding in encrypted images (RDH-EIs) provides superior performance in terms of security. To simultaneously improve the effectiveness of RDH-EIs, this work proposes a mixed multi-bit layer embedding strategy in encrypted images. The cover image is processed into two categories: available hidden blocks (AHBs) and unavailable hidden blocks (UHBs) at the sender. Then, all data are embedded in the multi-bit layer of the encrypted pixels in AHBs through two embedding strategies to obtain the transmission image. At the receiver, the user can extract the needed data separably according to different keys to achieve error-free extraction of the secret data and lossless recovery of the cover image. The experimental results show that the proposed scheme has the advantages of superior embedding capacity and high decryption quality over the current state-of-the-art works.
Use of Cloud-Based Virtual Reality in Chinese Glove Puppetry to Preserve Intangible Cultural Heritage

Chinese traditional glove puppetry is a folk art with a long history. It is worth inheriting and safeguarding this distinguished intangible cultural traditional art using virtual reality. With this background, this study integrates the digital resources of glove puppetry from the perspective of satisfying users’ performance needs. In this study, a multi-user, cloud-based virtual reality glove puppetry system was developed that enhances the classic works of glove puppetry. Each user has a unique perception of the virtual environment and can interact remotely. The system involves human–computer and human–human interactions. This study also describes the design and control of glove puppets. The virtual reality system provides a unique entertainment experience to users of all ages. Through a questionnaire administered to 30 subjects after the user play, this study investigated the operation and experience of this system. According to the research findings, the proposed cloud-based VR system is not only easy to use, but also helps to preserve traditional intangible culture. Our research has high theoretical value and can help preserve traditional glove puppetry. Our cloud-based virtual reality system offers a new application for disseminating and preserving intangible cultural heritage.
A sparse expansion for deep Gaussian processes

In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity, as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP) model as the composition of multiple hierarchical expansion of TMGPs. The proposed DTMGP model has the following properties: (i) the outputs of each activation function are deterministic while the weights are chosen independently from standard Gaussian distribution; (ii) in training or prediction, only O(polylog(M)) (out of M) activation functions have non-zero outputs, which significantly boosts the computational efficiency. Our numerical experiments on synthetic models and real datasets show the superior computational efficiency of DTMGP over existing DGP models.
Random-time quantum measurements

The analysis of a continuous measurement record $z(t)$ poses a fundamental challenge in quantum measurement theory. Different approaches were used in the past as records can, e.g., exhibit predominantly Gaussian noise, telegraph noise, or clicks at random times. The last case may appear as photon clicks in an optical spin-noise measurement at very low probe laser power. Here we show that such random-time quantum measurements can, similarly to the first two cases, be analyzed in terms of higher-order temporal correlations of the detector output $z(t)$ and be related to the Liouvillian of the measured quantum system. Our analysis in terms of up to fourth-order spectra (quantum polyspectra) shows that this type of spectra reveals the same valuable information as previously studied higher-order spectra in the case of the usual continuous quantum measurements. Surprisingly, broadband system dynamics is revealed even for deliberately low average measurement rates. Many applications are envisioned in high-resolution spectroscopy, single-photon microscopy, circuit quantum electrodynamics, quantum sensing, and quantum measurements, in general.
PID control and simulation of moving mass quadcopter UAV

Abstract Based on the quadrotor drones we see today, in this paper, a mass center control mechanism installed on the body is proposed to control the flying attitude of the UAV. The moving mass control technology changes the position of the center of mass by moving the position of the mass block of the system. The technology is used in a wide range of aircraft, from drones to gravity satellites. The moving mass center mechanism mentioned in this paper is composed of three sliding blocks that can slide along the spatial axes and are controlled by a linear motor. The linear motor controls the slider movement to generate the torque along different channel directions, to realize the attitude change of the UAV. In this paper, through the kinematics analysis of the quadrotor UAV, combined with PID control and simulation, finally realized the modification of the UAV’s flight attitude with moving mass technology. The simulation results show that the quadrotor UAV can complete the attitude conversion stably with the help of the mass center control mechanism.