Reviewerly/exHarmony huggingface dataset:
Effect of Linear Prediction Order to Modify Formant Locations for Children Speech Recognition

Children’s speech recognition shows poor performance as compared to adult speech. Large amount of data is required for the neural network models to achieve good performance. A very limited amount of children’s speech data is publicly available. A baseline system was developed using adult speech for training and children’s speech for testing. This kind of system suffers from mismatches between training and testing speech data. To overcome one of the mismatches, which is formant frequency locations between adults and children, in this paper we have explored the effect of linear prediction order to modify the formant frequency locations. The explored method studies for narrowband and wideband speech and found that they gave reductions in word error rate (WER) for GMM-HMM, DNN-HMM, and TDNN acoustic models. The TDNN acoustic model gives the best performance as compared to other acoustic models. The best formant modification factor $$\alpha $$ is 0.1 for linear prediction order 6 for narrowband speech (WER 13.82%), and $$\alpha $$ is 0.1 for linear prediction order 20 for wideband speech (WER 12.19%) for the TDNN acoustic model. Further, we have also compared the method with vocal tract length normalization (VTLN) and speaking rate adaptation (SRA), and it is found that the proposed method gives a better reduction in WERs as compared to VTLN and SRA.
Aggregation Strategies of Wav2vec 2.0 Embeddings for Computational Paralinguistic Tasks

Throughout the history of computational paralinguistics, numerous feature extraction, preprocessing and classification techniques have been used. One of the important challenges in this subfield of speech technology is handling utterances with different duration. Since standard speech processing features (such as filter banks or DNN embeddings) are typically frame-level ones and we would like to classify whole utterances, a set of frame-level features have to be converted into fixed-sized utterance-level features. The choice of this aggregation method is often overlooked, and simple functions like mean and/or standard deviation are used without solid experimental support. In this study we take wav2vec 2.0 deep embeddings, and aggregate them with 11 different functions. We sought to obtain a subset of potentially optimal aggregation functions, because there are no general rules yet that can be applied universally between subtopics. Besides testing both standard and non-traditional aggregation strategies individually, we also combined them to improve the classification performance. By using multiple aggregation functions, we were able to achieve significant improvements on three public paralinguistic corpora.
Analysis of Natural Language Understanding Systems with L2 Learner Specific Synthetic Grammatical Errors Based on Parts-of-Speech

Second language learners often make grammatical mistakes, which can impact the performance of Spoken Language Understanding (SLU) systems. SLU systems consist of two key components: Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). This work focuses on the effects of grammatical errors which were synthetically generated by manipulating Parts of Speech (POS) tokens on the NLU module. Our analysis comprises three main aspects. Firstly, we assess the impact of grammatical errors on the overall performance of NLU systems, specifically in the domains of Intent Detection and Slot Filling. Secondly, we investigate NLU performance concerning POS tags. Lastly, we utilize an Attention-based NLU model to evaluate the significance of different POS. This study evaluates NLU models on ATIS and SNIPS datasets for Intent Detection and Slot Filling tasks with introduced grammatical errors, leading to a significant performance drop as large as 4.7% for Intent Detection and 5.1% for Slot Filling. However, training on datasets with synthetic errors helps mitigate this drop, and the models are shown to be sensitive to increasing levels of corrupted tokens. Additionally, attention analysis reveals Proper Nouns’ higher weights in determining intent, while specific POS corruption, particularly Auxiliary Verbs and Nouns, is more likely to cause intent misclassification.
Self-supervised Speaker Verification Employing Augmentation Mix and Self-augmented Training-Based Clustering

Using clustering algorithms to optimize speaker embedding networks via pseudo-labels is a widely used practice to train self-supervised speaker verification systems. Although pseudo-label-based self-supervised training scheme showed outstanding performance, this latter depends on high-quality pseudo-labels, and recent studies have shown that label noise can remarkably impact downstream performance. In this paper, we propose a general-purpose clustering algorithm called CAMSAT that outperforms all other baselines used to cluster speaker embeddings. Moreover, using the generated pseudo-labels to train our speaker embedding systems allows us to further improve speaker verification performance. CAMSAT is based on two principles: (1) Augmentation Mix (AM) by mixing predictions of augmented samples to provide a complementary supervisory signal for clustering and enforce symmetry within augmentations and (2) Self-Augmented Training (SAT) to enforce representation invariance and maximize the information-theoretic dependency between samples and their predicted pseudo-labels. We provide a thorough comparative analysis of the performance of our clustering method compared to all baselines using a variety of clustering metrics and perform an ablation study to analyze the contribution of each component of our system.
Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models

About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.
Electronic Contact Lensesas a 3P Medicine Tool: the Electrode Lead System Modeling

Electronic contact lenses (ECL) are a high-tech solution for diagnosing, including early, both ophthalmic and systemic diseases. The authors of the article propose the implementation of 3P medicine approaches by integrating electrodes and circuits into the ECL to realize bioelectrical impedance spectroscopy (BIS) of the anterior eye. This helps to analyze the blood circulation parameters of this part of the eye. Electrode lead system modeling integrated into the contact lens was carried out by using the finite element method. It was obtained for the correct operation, the presence of an electrically insulating layer between the biological tissues of the eye and the main material of the contact lens is necessary. The electrode diameter is 0.8 mm, and the optimal distance between the current electrodes should be 6 mm, between the potential – 4 mm. Assessment of the power level required for BIS shows the possibility of technical implementation of this methodology from an energy point of view.
The improvement of YOLOv5s algorithm for real-time detection of vehicle targets

In response to the existing target detection algorithm in urban road vehicle detection, there are problems such as low detection accuracy, poor real-time and robustness, etc., and propose a method of vehicle target detection to improve YOLOv5s. First of all, optimize the YOLOv5s algorithm network structure, and use increasing the detection layer of small targets to improve the accuracy of the detection of small targets; then use CIOU instead of Giou as the border frame regression loss function, solving the border frame that contains each other to compare with length and width. Question; then use the Hardswish activation function on the entire network model for convolution operations. In order to verify the effectiveness of the algorithm in this article, we will test the improved algorithm on the UA-DETRAC dataset. The experimental results show that the accuracy of target detection (Precision) increased by 2.46%, the recall rate (Recall) increased by 5.5%, and the average accuracy (mAP) increased by 2.19% Vehicle goals.
Personnel Factors-Based Safety Risk Assessment Method for Dispatching Automation Master Station System*

The dispatching automation master station system is an important component in the entire operation process of the power grid, and it is of great significance to assess the influencing factors of its safety risks. Frequent failures of this system can lead to significant accidents, resulting in harm to personnel, damage to equipment, and substantial losses for the nation and its people. This article describes the design of a safety risk assessment method for the dispatching automation master station system considering subjective factors of personnel that pose threats to its safety risks. Each major safety risk event involving the system is considered as a node, and a graph neural network method is used to analyze the data information and topology structure of the nodes. By extracting features of the entire automation master station system from both time and space perspectives, this method enables the assessment of safety risks of factors affecting the dispatching automation master station system.
A Monocular Ball Localization Method for Automatic Hitting Robots

The batting robot has to meet the requirements of high-speed continuous motion as well as quickly recognize and localize the table tennis ball and predict the landing point in the working process. The recognition accuracy of the ball in this process is an important factor in measuring the performance of the batting robot. In order to accurately improve the ball localization accuracy, a ball recognition and localization method based on the elliptic projection transform and test field joint correction fitting is proposed. The method uses two monocular cameras of the same model to collect the sphere motion trajectory data at low cost and improves the localization accuracy of the table tennis ball during its motion by combining the elliptic projection transform of the sphere on the phase plane of the monocular camera with the test field data. On this basis, the obtained height information is fitted to the ball motion trajectory to realize the prediction of the table tennis ball's hitting point.
Gesture-Based Human-Robot Interaction Framework for Teleoperation Control of Agricultural Robot

Remote control based on data-driven has been widely studied in human-robot interaction, which converts human body movements into digital signals through somatosensory devices for teleoperation control. It is a convenient and efficient intelligent solution to handle the robot to realize the agricultural task in a complex environment. In this paper, a real-time dynamic recognition framework based on a three-dimensional convolutional neural network is designed for agricultural robot. The deep separable convolution and inverted residual structure are presented, and then a gesture classification network architecture is discussed. Finally, some demonstrations using Unitree agricultural robot are carried out to illustrate the hand gesture application with an efficient response.
Multiple Unmanned Ground Vehicles Coverage Path Planning in Non-Cooperative Environments

This paper mainly studies the coverage path planning problem of multiple unmanned ground vehicles (UGVs) in non-cooperative environments. Compared to ideal cooperative environments, external uncertain dynamic disturbances may affect the stability of the coverage task execution in non-cooperative environments. The wave-front algorithm is mainly used to perform coverage path planning. The paper improves the problem of wave-front algorithms falling into dead corners by designing a new coverage direction strategy and designs an algorithm framework to improve system openness and adaptability. Besides, the algorithm framework makes it suitable for non-cooperative environments with dynamic window approach (DWA). Experimental results show that the proposed improved wave-front algorithm has optimized the coverage priority, and the planned coverage path has higher efficiency and robustness than traditional wave-front algorithms.
Two Algorithms Based on Iterative Optimization for UAV Path Planning

In this paper, we propose two algorithms based on iterative optimization for UAV path planning. In the first one, a nonlinear programming (NLP) problem of unmanned aerial vehicle (UAV) path planning is transformed into a general quadratically constrained quadratic programming (QCQP) problem by a numerical optimization method and solved through iteration. This method can converge to the optimal solution without giving the initial value. It overcomes the problems that NLP solvers are dependent on the initial guess, slow to converge, and sometimes can't converge to a feasible solution. In the second one, we improve the optimized rapidly exploring random tree algorithm (RRT*) and add UAV flight constraints to it. The improved RRT* algorithm reduces the length of the path and makes the path smoother.
A System Configuration description Language (SCL) Complied File Based Configuration Method for Bridges in Smart Substation

Configuration of smart substation network which composed by bridges and IEDs (Intelligent Electronic Devices), always faces complicated pre-planning and commissioning work and involved with multiple vendors and various configuration file format. BCD (Bridge Configuration Description) file brought in this document is a SCL rule complied file format that composes of only necessary information for substation network bridges to do the IED-learning and establish GOOSE/SV (Generic Object-Oriented Substation Event/Sampled Value) paths, such as GOOSE/SV association and IED definition. All the information in BCD file is extracted from SCD (System Configuration Description) file and IID (Instantiated of IED Description) file without changing to keep the files synchronization. The IID file is specially used to accommodate the information of silent IEDs which cannot be integrated into the SCD file. Compared with the method of using SCD file to configure the network, the proposed BCD file is extremely light weighted, which can implement the same goal. How to use the BCD file to perform IED-learning and generate bridge configuration are provided in this paper to help understand the method application and the principle of substation network auto-routing.
DEHype: Retrofitting Hypervisors for a Resource-Disaggregated Environment

Resource disaggregation has been proposed as a solution for resource under-utilization in data centers. However, host virtualization technologies, which are the basic building blocks for constructing data centers, are implemented without considering the disaggregated resources. In addition, we discover that a RDMA I/O unit plays a significant role in the performance of a disaggregated resource environment. In this study, we propose DEHype, which alleviates the inefficiency of the hypervisors utilized in a disaggregated environment by investigating host virtualization technologies that are suitable for disaggregated memory systems. Specifically, DEHype aims to identify and improve the performance issues associated with virtual machines through KVM/QEMU in a disaggregated resource environment. The results demonstrate the effectiveness of the proposed optimizations in improving the performance of disaggregated memory systems. DEHype achieves up to a 351% improvement over the state-of-the-art disaggregated memory system.
EOD-Net: Enhancing Object Detection in Challenging Weather Conditions Using an Innovative End-to-End Dehazing Network

With the progress of deep learning, significant advancements have been made in solving computer vision tasks such as object detection and semantic segmentation. However, in real-world scenarios like autonomous driving, accurate object predictions become crucial and challenging under adverse weather conditions, including haze and fog. These conditions severely hinder the performance of computer vision algorithms, leading to potential risks and errors. This work proposes an original end-to-end Enhancement for an object detection network (EOD-Net), specifically designed to address dehazing. Our network employs multi-scale feature fusion and residual refinement modules to enhance the quality of dehazed images. We extensively evaluate our method on well-established dehazing benchmark datasets and demonstrate its superiority over several state-of-the-art approaches in terms of quantitative metrics. Additionally, through comprehensive computer simulations, we illustrate that our method significantly improves the accuracy of object detection models in dense haze conditions. This research contributes to the advancement of image dehazing algorithms and their practical applications in computer vision.
Multi-Destination Cooperative Task Offloading in Blockchain-Based Vehicular Edge Computing Networks

As a novel computing paradigm, the Vehicular Edge Computing (VEC) network provides an additional avenue for onboard task execution for connected and automated vehicles (CAVs) in unmanned systems. In a VEC network, CAVs can offload tasks to different external servers for execution and obtain results. However, existing task offloading studies only focus on the one-to-one offloading of onboard tasks to edge servers, ignoring the requirements of parallel task execution and security. In this paper, we propose a blockchain-based multi-destination cooperative task offloading architecture in a VEC network. Based on this architecture, we analyze the task offloading and blockchain models and formulate a multi-destination cooperative task offloading optimization problem. Finally, we compare the performance of different multi-destination task offloading methods in simulations and demonstrate the effectiveness of the proposed architecture and optimization.
Interactive and light approach for near duplicate retrieval in multiple contexts

The increasing use of images in various industrial contexts, such as marketing, media, research, education, and documentation, has led to a constant growth in the size of image databases. As a result, the task of image retrieval has become even more challenging. To address this issue, we present a study on image retrieval in an industrial context, conducted in collaboration between a research laboratory and a company that is centered on Digital Asset Management (DAM). We propose an interactive and lightweight workflow for near-duplicate retrieval, using deep feature extraction and re-ranking techniques. Our first contribution focus on an unsupervised indexing approach for near-duplicate detection which achieves state-of-the-art performance on benchmark datasets. Then, we study the joint impact of different similarity metrics and deep convolutional neural network models on retrieval performance. We then create a new re-ranking distribution based on the chosen embedding and perform binary classification to refine near-duplicate detection. Lastly, we enable efficient user interactivity in selecting and visualizing near-duplicates, with respect to individual user preferences and context requirements.
Cyber Attack Detection and Location in Distributed Multi-Sensor Unmanned Systems

With the breakthrough and development of communication technology, security of unmanned autonomous systems has become an essential problem researchers concern. This paper focuses on the problem of attack detection and location in distributed multi-sensor systems and some specific measures are provided in detail. In terms of false data injection attack in sensors and communication channel, we firstly implement information fusion employing Dempster-Shafer theory after selecting the form of data fusion. Next, based on the designed threshold, we detect whether there exists false data injection attack in this system, and determine the specific location of the attack through comparing fused results. Finally, a numerical example of general automatic platoon of vehicles is given to verify our method.
Improved RRT* Algorithm Based on Node Density Diffusion Strategy

Although the optimal rapidly-exploring random tree (RRT*) algorithm has probability completeness, it cannot guarantee the feasible path solution within the specified time, especially in an obstacle environment with complex geometric characteristics. This paper proposes Density Quick-RRT*(DQ-RRT*), a modified RRT* algorithm in which sampling space is dynamically adjusted according to the node density of the random tree. DQ-RRT* algorithm has a sampling preference for the unexplored region and achieves the initial feasibility path solution faster than RRT*. For the path optimality problem, the proposed algorithm is combined with the Quick-RRT* algorithm to achieve fast path optimization based on the parent node reconnection strategy. The simulation results show that the random sampling strategy proposed in this paper can get a faster initial feasible path solution, and can be combined with other graph pruning algorithms to further improvement
UAV Path Planning Method Based on Modified Beluga Whale Optimization

With more and more widely use of Unmanned Aerial Vehicle (UAV), UAV path planning in 3D environment has become a significant and complicated task. This paper presents a new method for UAV path planning, which is based on a modified version of the Beluga Whale Optimization (BWO) algorithm. The proposed method introduces Sine-cosine algorithm to improve the exploration process of BWO, and then this Sine-cosine modified BWO (SBWO) is used to plan the flight path for UAV. The simulation results show that SBWO has obvious advantages in convergence speed. Besides, the use of SBWO can get better results in UAV 3D path planning, which shortens the shortest path by 5.6% and the execution time by 40.3 % on average compared with other algorithms, fully demonstrating its effectiveness.