Reviewerly/exHarmony huggingface dataset:
SFA: Searching faster architectures for end-to-end automatic speech recognition models

Recently End-to-end (E2E) Automatic Speech Recognition (ASR) has been widely used due to its advantages over the hybrid method. Even though existing E2E ASR models have achieved impressive performance, they usually take a large model size and suffer from a slow inference speed in real-world applications. To obtain faster models for E2E ASR, we propose searching faster architectures with the help of neural architecture search (NAS) in this paper, named SFA. SFA consists of one search space that contains a set of candidate architectures and one search algorithm responsible for searching the optimal architecture from the search space. On one hand, SFA designs a topology-fused search space to integrate different topologies of existing architectures (e.g. Transformer, Conformer) and explore more brand-new ones. On the other hand, combined with the training criterion of E2E ASR, SFA develops a speed-aware differentiable search algorithm to search faster architectures according to target hardware devices. Additionally, a connectionist temporal classification based progressive search algorithm is proposed to reduce the difficulty of the architecture search and obtain better performance. On two commonly-used Mandarin datasets, SFA can effectively improve the inference speed of existing E2E ASR models with comparable performance and achieve at most 2.46×/ 1.98× CPU/GPU speedup than the best human-designed baselines.
Integrating a Blockchain-Based Governance Framework for Responsible AI

This research paper reviews the potential of smart contracts for responsible AI with a focus on frameworks, hardware, energy efficiency, and cyberattacks. Smart contracts are digital agreements that are executed by a blockchain, and they have the potential to revolutionize the way we conduct business by increasing transparency and trust. When it comes to responsible AI systems, smart contracts can play a crucial role in ensuring that the terms and conditions of the contract are fair and transparent as well as that any automated decision-making is explainable and auditable. Furthermore, the energy consumption of blockchain networks has been a matter of concern; this article explores the energy efficiency element of smart contracts. Energy efficiency in smart contracts may be enhanced by the use of techniques such as off-chain processing and sharding. The study emphasises the need for careful auditing and testing of smart contract code in order to protect against cyberattacks along with the use of secure libraries and frameworks to lessen the likelihood of smart contract vulnerabilities.
Transformer architecture based on mutual attention for image-anomaly detection

Image anomaly detection is a popular task in computer graphics, which is widely used in industrial fields. Previous works that address this problem often train CNN-based (e.g. Auto-Encoder, GANs) models to reconstruct covered parts of input images and calculate the difference between the input and the reconstructed image. However, convolutional operations are good at extracting local features making it difficult to identify larger image anomalies. To this end, we propose a transformer architecture based on mutual attention for image anomaly separation. This architecture can capture long-term dependencies and fuse local features with global features to facilitate better image anomaly detection. Our method was extensively evaluated on several benchmarks, and experimental results showed that it improved detection capability by 3.1% and localization capability by 1.0% compared with state-of-the-art reconstruction-based methods.
Unrolling a rain-guided detail recovery network for singleimage deraining

Owing to the rapid development of deep networks, single image deraining tasks have achieved significant progress. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed by existing deraining methods. However, many of them cause a loss of details during deraining, resulting in visual artifacts. To resolve the detail-losing issue, we propose a novel unrolling rain-guided detail recovery network (URDRN) for single image deraining based on the observation that the most degraded areas of the background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learning-based methods trivialize the observation model and simply learn an end-to-end mapping, the proposed URDRN unrolls the single image deraining task into two subproblems: rain extraction and detail recovery. Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks, and then, a rain attention map is generated as an indicator to guide the detail-recovery process. For a detail-recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details. Experiments on several well-known benchmark datasets show that the proposed approach can achieve a competitive performance in comparison with other state-of-the-art methods.
Fog-DeepStream: A new approach combining LSTM and Concept Drift for data stream analytics on Fog computing

Applications and infrastructures designed to support Fog Computing in IoT (Internet of Things) environments generate large volumes of data, usually characterized as open-ended streams, thus making unfeasible the adoption of traditional modeling approaches. Namely, the main limitations with such approaches are: (i) the adoption of offline models might be out of date when new data are available; (ii) the need for high computational power to store and process all data from IoT devices; and (iii) the transmission of all monitored data might overload the network connections from devices to servers. Such limitations are accentuated in complex IoT environments that combine different devices and servers configured in Fog and Cloud Computing. Aiming at dealing with such limitations, we propose a challenging approach, referred to as Fog-DeepStream, which combines three important research areas: Signal Processing, Concept Drift, and Deep Neural Networks. Initially, our approach uses Wavelet Transform to undersample data streams without losing details about their main behavior. Next, we use Concept Drift algorithms to only transmit the data in the environment when a new behavior is detected. These two steps reduce the amount of processed and exchanged data that Deep Neural Networks will later model. Our results emphasize that the contributions of Fog-DeepStream are twofold: we provide an incremental approach to efficiently model data streams in Fog Computing and reduce data transmission between computational resources, thus making it possible to save energy in IoT scenarios.
Estimating Financial Fraud through Transaction-Level Features and Machine Learning

In today’s world, financial institutions (FIs) play a pivotal role in any country’s economic growth and are vital for intermediation between the providers of investable funds, such as depositors, investors and users. FIs focus on developing effective policies for financial fraud risk mitigation however, timely prediction of financial fraud risk helps overcome it effectively and efficiently. Thus, herein, we propose a novel approach for predicting financial fraud using machine learning. We have used transaction-level features of 6,362,620 transactions from a synthetic dataset and have fed them to various machine-learning classifiers. The correlation of different features is also analysed. Furthermore, around 5000 more data samples were generated using a Conditional Generative Adversarial Network for Tabular Data (CTGAN). The evaluation of the proposed predictor showed higher accuracies which outperformed the previously existing machine-learning-based approaches. Among all 27 classifiers, XGBoost outperformed all other classifiers in terms of accuracy score with 0.999 accuracies, however, when evaluated through exhaustive repeated 10-fold cross-validation, the XGBoost still gave an average accuracy score of 0.998. The findings are particularly relevant to financial institutions and are important for regulators and policymakers who aim to develop new and effective policies for risk mitigation against financial fraud.
Development of an Optoelectronic Integrated Sensor for a MEMS Mirror-Based Active Structured Light System

Micro-electro-mechanical system (MEMS) scanning micromirrors are playing an increasingly important role in active structured light systems. However, the initial phase error of the structured light generated by a scanning micromirror seriously affects the accuracy of the corresponding system. This paper reports an optoelectronic integrated sensor with high irradiance responsivity and high linearity that can be used to correct the phase error of the micromirror. The optoelectronic integrated sensor consists of a large-area photodetector (PD) and a receiving circuit, including a post amplifier, an operational amplifier, a bandgap reference, and a reference current circuit. The optoelectronic sensor chip is fabricated in a 180 nm CMOS process. Experimental results show that with a 5 V power supply, the optoelectronic sensor has an irradiance responsivity of 100 mV/(μW/cm2) and a -3 dB bandwidth of 2 kHz. The minimal detectable light power is about 19.4 nW, which satisfies the requirements of many active structured light systems. Through testing, the application of the chip effectively reduces the phase error of the micromirror to 2.5%.
Geochemical Characteristics of Primary Halos and Prospecting Significance of the Qulong Porphyry Copper–Molybdenum Deposit in Tibet

The Qulong porphyry copper deposit in Tibet is located in the Tethis–Himalaya metallogenic domain, one of the three major porphyry metallogenic domains in the world. At present, the mining area is mainly used for surface mining. The depth revealed by the drilling project is less than 2 km. The potential for deep resources is unknown. Based on an analysis of the geochemical characteristics of the primary halos around the No. 16 prospecting line, deep extension is discussed in this paper. Studies show that the metallogenic elements are Cu and Mo; the near-ore halo elements are Co, Au, Ag, and W; the supra-ore halo elements are Pb, Zn, Mn, and As; and the sub-ore halo elements are Sn and Bi. According to Gregorian’s zoning index and the barycenter method, the primary halo zoning of the No. 16 exploration line from shallow to deep is Mn–P–Pb–Ni–Zn–V–As–Hg–Co–Au–Cu–W–Ag–Mo–Sb–Sr–Cd–Sn–Ti–Bi. This sequence has a distinct “reverse” zoning feature, indicating that there may be a blind ore body deep in the mine. The geochemical parameter evaluation index based on the element content contrast coefficient suggests that there may be a hidden ore body in the deep. The relative hydrothermal mineralization in the center position of the section may be located deep below the north side of borehole ZK1601-1 in the middle of the section. The ore body erosion parameter model shows that the bottom of the drilling engineering control is the middle tail of the ore body, and there is a certain amount of extension in the deep part. The ideal superimposed model of the primary halo reflects the ore body trend of the 16th line section. The ore body is inclined to the north as a whole; the ore fluid flows from the deep to the southern side of the north side, and the deep part of the northern side of the ore body has a downward trend.
Oriented Object Detection in Aerial Images Based on the Scaled Smooth L1 Loss Function

Although many state-of-the-art object detectors have been developed, detecting small and densely packed objects with complicated orientations in remote sensing aerial images remains challenging. For object detection in remote sensing aerial images, different scales, sizes, appearances, and orientations of objects from different categories could most likely enlarge the variance in the detection error. Undoubtedly, the variance in the detection error should have a non-negligible impact on the detection performance. Motivated by the above consideration, in this paper, we tackled this issue, so that we could improve the detection performance and reduce the impact of this variance on the detection performance as much as possible. By proposing a scaled smooth L1 loss function, we developed a new two-stage object detector for remote sensing aerial images, named Faster R-CNN-NeXt with RoI-Transformer. The proposed scaled smooth L1 loss function is used for bounding box regression and makes regression invariant to scale. This property ensures that the bounding box regression is more reliable in detecting small and densely packed objects with complicated orientations and backgrounds, leading to improved detection performance. To learn rotated bounding boxes and produce more accurate object locations, a RoI-Transformer module is employed. This is necessary because horizontal bounding boxes are inadequate for aerial image detection. The ResNeXt backbone is also adopted for the proposed object detector. Experimental results on two popular datasets, DOTA and HRSC2016, show that the variance in the detection error significantly affects detection performance. The proposed object detector is effective and robust, with the optimal scale factor for the scaled smooth L1 loss function being around 2.0. Compared to other promising two-stage oriented methods, our method achieves a mAP of 70.82 on DOTA, with an improvement of at least 1.26 and up to 16.49. On HRSC2016, our method achieves an mAP of 87.1, with an improvement of at least 0.9 and up to 1.4.
Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning

Neural network-based solutions have revolutionized the field of computer vision by achieving outstanding performance in a number of applications. Yet, while these deep learning models outclass previous methods, they still have significant shortcomings relating to generalization and robustness to input disturbances, such as occlusion. Most existing methods that tackle this latter problem use passive neural network architectures that are unable to act on and, thus, influence the observed scene. In this paper, we argue that an active observer agent may be able to achieve superior performance by changing the parameters of the scene, thus, avoiding occlusion by moving to a different position in the scene. To demonstrate this, a reinforcement learning environment is introduced that implements OpenAI Gym’s interface, and allows the creation of synthetic scenes with realistic occlusion. The environment is implemented using differentiable rendering, allowing us to perform direct gradient-based optimization of the camera position. Moreover, two additional methods are also presented, one utilizing self-supervised learning to predict occlusion segments, and optimal camera positions, while the other learns to avoid occlusion using Reinforcement Learning. We present comparative experiments of the proposed methods to demonstrate their efficiency. It was shown, via Bayesian t-tests, that the neural network-based methods credibly outperformed the gradient-based avoidance strategy by avoiding occlusion with an average of 5.0 fewer steps in multi-object scenes.
Adaptive Gaussian Mixture Model for Uncertainty Propagation Using Virtual Sample Generation

Orbit uncertainty propagation plays an important role in the analysis of a space mission. The accuracy and computation expense are two critical essences of uncertainty propagation. Repeated evaluations of the objective model are required to improve the preciseness of prediction, especially for long-term propagation. To balance the computational complexity and accuracy, an adaptive Gaussian mixture model using virtual sample generation (AGMM-VSG) is proposed. First, an unscented transformation and Cubature rule (UT-CR) based splitting method is employed to adaptive update the weights of Gaussian components for nonlinear dynamics. The Gaussian mixture model (GMM) approximation is applied to better approximate the original probability density function. Second, instead of the pure expensive evaluations by conventional GMM methods, virtual samples are generated using a new active-sampling-based Kriging (AS-KRG) method to improve the propagation efficiency. Three cases of uncertain orbital dynamical systems are used to verify the accuracy and efficiency of the proposed manuscript. The likelihood agreement measure (LAM) criterion and the number of expense evaluations prove the performance.
Measuring the impacts of AR HMD on users’ situation awareness during wood frame assembly tasks

Advances in the development of Augmented Reality (AR) hardware and software allow for novel opportunities to positively influence the traditional construction industry. Recent research studied the feasibility of various AR devices for supporting construction assembly tasks, however, there is limited work examining the usability of AR head-mounted display (HMD) in relation to construction workers’ cognitive skills such as situation awareness. This study evaluated three information display types (paper blueprint, tag-along image and conformal model) during wood frame assembly of three different scales (small, medium, and large) to investigate how AR HMDs impact user’s situation awareness with respect to mental workload. Eighteen construction engineering students were recruited in a within-subjects experiment. The findings showed no significant difference in both the mental workload and the situation awareness for all three display types, suggesting that AR displays did not generate significant excessive mental burden or distractions on users in comparison to traditional paper-based information. Specifically comparing the AR displays, the results revealed that user’s mental workload was affected by field of view (FOV) restriction in AR HMD, whereas situation awareness wasn’t impacted.
A Quantum-Classical Hybrid Solution for Deep Anomaly Detection

Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notably, image AD is one of the most representative tasks in current deep AD research. ML's interaction with quantum computing is giving rise to a heated topic named quantum machine learning (QML), which enjoys great prospects according to recent academic research. This paper attempts to address the image AD problem in a deep manner with a novel QML solution. Specifically, we design a quantum-classical hybrid DNN (QHDNN) that aims to learn directly from normal raw images to train a normality model and then exclude images that do not conform to this model as anomalies during its inference. To enable the QHDNN to perform satisfactorily in deep image AD, we explore multiple quantum layer architectures and design a VQC-based QHDNN solution. Extensive experiments were conducted on commonly used benchmarks to test the proposed QML solution, whose results demonstrate the feasibility of addressing deep image AD with QML. Importantly, the experimental results show that our quantum-classical hybrid solution can even yield superior performance to that of its classical counterpart when they share the same number of learnable parameters.
Role Knowledge Prompting for Document-Level Event Argument Extraction

Document-level event argument extraction (DEAE) aims to identify the arguments corresponding to the roles of a given event type in a document. However, arguments scattering and arguments and roles overlapping make DEAE face great challenges. In this paper, we propose a novel DEAE model called Role Knowledge Prompting for Document-Level Event Argument Extraction (RKDE), which enhances the interaction between templates and roles through a role knowledge guidance mechanism to precisely prompt pretrained language models (PLMs) for argument extraction. Specifically, it not only facilitates PLMs to understand deep semantics but also generates all the arguments simultaneously. The experimental results show that our model achieved decent performance on two public DEAE datasets, with 3.2% and 1.4% F1 improvement on Arg-C, and to some extent, it addressed the overlapping arguments and roles.
Automatic Text summarization in Gujarati language

Automatic text summarization is an essential part of Natural language processing(NLP), a subpart of the Artificial Intelligence domain. Widespread usage of text summarization is due to the massive usage of the internet in every aspect of life. In this research article, we perform a statistical text summarization technique on Gujarati text which is one of the resource-poor South Asian languages. We have performed TF-IDF, LSA, and LDA methods on our custom dataset. We evaluated our summary using the rouge score using 10%,20%, and 30% compression ratios. We have used Rouge-1, Rouge-2, Rouge-w, and Rouge-l to measure the accuracy, and LDA gets the highest rouge score among other methods. All the results are formed in table format with an individual rouge score and an average rouge score of all the methods. This article aims to analyze the performance of the unsupervised method in automatic text summarization methods of Gujarati language without any pre-processing technique. Sentences are selected using a concept-based method based on outside information [4], [5]. The title matching in the main text is covered by the topic-based idea. When the title words are identical, the phrase receives a good grade. If not, the sentence in question won’t be included in the summary [6], [7]. Depending on the topic, the cluster-based method organises comparable sentences. In this procedure, cluster counts must be specified [8] –[11]. The similarity notion is the foundation of the graph-based method. It compares the similarity of all the words and determines the best phrases using those results. Numerous studies on graph-based approaches have been conducted [12] through [16].
AI based Automatic Vehicle Detection from Unmanned Aerial Vehicles (UAV) using YOLOv5 Model

Unmanned aerial vehicle (UAV) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. The challenge arises in keeping minimal computational complexity allowing the system to be real-time as well. Applications of vehicle detection from UAVs include traffic parameter estimation, violation detection, number plate reading, and parking lot monitoring. The one stage detection model, YOLOv5 is used in this research work to develop a deep neural model-based vehicle detection system on highways from UAVs. In our system, several improvised strategies are put forth that are appropriate for small vehicle recognition under an aerial view angle which can accomplish real-time detection and high accuracy by incorporating an optimal pooling approach and dense topology method. Tilting the orientation of aerial photographs can improve the system's effectiveness. Metrics like hit rate, accuracy, and precision values are used to assess the performance of the proposed hybrid model, and performance is compared to that of other state-of-the-art algorithms.