Reviewerly/exHarmony huggingface dataset:
An SVD-based turntable calibration method with multiple reference planes segmented from point clouds

Abstract Turntables play an essential role in various industrial areas due to their ability to improve the degree of freedom of workpieces or tools. In applications where positional accuracy of the objects is required, the accuracy of the system parameters of the turntable is critical for the applications to work properly. This paper proposes a new approach to calibrating the turntable system. First, we propose an optimized RANSAC (random sample consensus) algorithm to obtain high-accuracy reference planes from multiple frames of point clouds grabbed from the reference object placed on a turntable. Second, an singular value decomposition-based two-step method is proposed to calculate the rotation axis vector and the center position of the turntable based on the reference planes extracted by the modified RANSAC algorithm. Finally, several experiments are performed to verify the proposed methods. Compared with the existing methods, our approach gets the analytical results and tries to correct the rotation angles. The experimental results show that the proposed method considerably improves calibration accuracy and efficiency without complex devices.
A Gaussian–Pearson type VII adaptive mixture distribution-based outlier-robust Kalman filter

Abstract Considering that existing robust filtering algorithms rely on the selection of initial values of degree of freedom (DOF) parameters in outlier interference environments and cannot effectively cope with unknown non-stationary heavy-tailed measurement noises (HMN), a Gaussian–Pearson type VII (PTV) adaptive mixture distribution-based outlier-robust Kalman filter (GPTVMAKF) is proposed. In order to determine whether the current measurement is a normal value or an outlier, a judgment factor subject to the Beta-Bernoulli distribution is introduced. PTV distribution is used to model HMN caused by outliers, and two Gamma distributions are used to model the two different DOF parameters, which can make the PTV distribution have the adaptive adjustment ability. By introducing the inverse Wishart distribution as the prior distribution of the measurement noise covariance, which is adaptively estimated to cope with the unknown time-varying measurement noises. The state and parameters are jointly estimated by variational Bayesian. Finally, the simulation experiments verify that the proposed GPTVMAKF can obtain more accurate state estimation than existing filters in the environments with varying degrees of HMN and unknown non-stationary HMN.
CTSARF: A Chinese Text Similarity Analysis Model based on Residual Fusion

Semantic textual similarity analysis is a fundamental task in the field of natural language processing. Both representational and interactive models are extensively employed for this purpose. However, representational models often suffer from issues such as semantic defocus, semantic shift, and low accuracy. In contrast, interactive models can accurately capture the semantic focus of a text and extract ample semantic information, but hindered by high time costs and low retrieval efficiency. To address these challenges, this paper introduces a Chinese Text Similarity Analysis model based on Residual Fusion (CTSARF). This model effectively implements the semantic feature fusion of representational and interactive models through a constant mapping method using residual networks. Therefore CTSARF can achieve both high accuracy and retrieval efficiency simultaneously. In this study, we employ the cosine-based loss function CoSENT and the fine-tuned pre-trained model Mengzi to optimize performance in text similarity analysis tasks. Additionally, we utilize a data augmentation method based on text editing to enhance the robustness of CTSARF when evaluating text similarity on unbalanced datasets. We conducted optimization comparison experiments, benchmark model comparison experiments, and ablation experiments of the CTSARF model on four Chinese datasets. The results demonstrate improvements in accuracy, recall, precision, and F1 scores, thereby highlighting the necessity and superiority of the model fusion method based on residual networks.
Energy-Efficient Single-Core Hardware Acceleration

This chapter deals with designing and implementing single-core specialized hardware accelerators for Machine learning (ML) applications, one abstraction level below the previous chapter. Specifically, a deep neural networks (DNN) hardware accelerator on field programmable gate array (FPGA) for object detection is used as a target application. The advantages that drive the choice of FPGA are the turnaround time of implementation and the availability of complete systems with DRAM, connectivity with a host PC, and several sensor interfaces, enabling real-time demos. The implementation is optimized based on the methodology from the hardware–software co-design concepts and the ZigZag framework to achieve high energy efficiency and throughput on the hardware for the given application.
Hardware–Software Co-optimization Through Design Space Exploration

Deep learning provides a wide range of neural networks (NNs) with varying accuracy and complexity, which can be mapped to various specialized hardware accelerators. This results in a vast optimization space across energy, latency, area, and task accuracy, complicating the assessment of which NNs, respectively, which hardware architectures, are most efficient as it strongly depends on the hardware platform, respectively, the suite of algorithms to support. Finding the most optimal hardware architecture through exhaustive development of all possible architectural combinations would be extremely slow. The ultimate goal is to rapidly and optimally find the hardware supporting a broad suite of NNs and construct NNs efficiently deployed on a comprehensive set of hardware platforms. This chapter, therefore, brings three contributions: (1) a rapid hardware cost assessment methodology across a suite of networks and architectures using our in-house design space exploration framework, ZigZag, (2) deployment of this analysis for a set of architecture on popular networks for the ImageNet task, and (3) derivation of insights from this study on characteristic of optimal architectures for modern NN models and characteristic of optimal network topologies for modern processor architectures.
A Differentiable Acoustic Guitar Model for String-Specific Polyphonic Synthesis

We introduce a differentiable model for acoustic guitar synthesis. The model takes in a MIDI-like conditioning representation, in which the guitar string for each note is specified, and synthesizes polyphonic audio. We employ a Differentiable Digital Signal Processing (DDSP) -style monophonic synthesis module, in which parameters are predicted that drive inharmonic oscillator and filtered noise synthesizer modules. Our monophonic synth is conditioned on a guitar string index, so 6 individual string audio signals are produced, summed, and fed through a trainable reverb to produce the final polyphonic audio. We train our synthesizer using data from the GuitarSet dataset and observe that the output audio is expressive and reflects the timbre and recording environment of the target instrument from the dataset. We conduct a listening test and evaluate the synthesizer’s ability to reconstruct unseen audio, comparing against an off-the-shelf acoustic guitar synthesizer, and a sample-bank synthesizer constructed from excerpts from GuitarSet. Additionally, we demonstrate how synthesized audio can be modified to vary parameters from the performance, including simulating use of a capo and alternate fretboard positioning.
Development of Apparel 360° - An AR based Virtual Trial Room

Virtual trial rooms have gained popularity and demand among retailers over the past few years. It has been considered as a transformative technology in the fashion industry. “Apparel 360°” offers a modern way of shopping, where customers can experience realistic dress fitting using marker-based Augmented Reality (AR) technology. It has been designed and developed with the help of Vuforia and Unity 3D. Users can conveniently access this virtual trial room on their mobile devices. Moreover, users can try-on a wide range of clothing and accessories such as watches without the need of physical inventory. Furthermore, the application allows them to change the apparel’s color and sizes to find the best fit with a simple tap. By just scanning a QR code, the app superimposes and anchors a 3D outfit over the user’s body in real time. It also allows them to move freely and have a 360° view of the dress. Finally, the tool can provide a competitive advantage for businesses as it has several benefits including accessibility, saving time and reducing cost.
A task matching model of photovoltaic storage system under the energy blockchain environment - based on GA-CLOUD-GS algorithm

Photovoltaic storage system (PVSS) has been spawned with the combined application of photovoltaic (PV), energy storage (ES) and energy blockchain (EB), which has also made important contributions to the energy structure adjustment, energy transaction security and ecological environment protection. The establishment of a reasonable task matching mechanism can further improve the operation efficiency and quality of the system under the EB environment, and this paper consequently constructs a task matching model of PVSS based on GA-CLOUD-GS algorithm. Firstly, the decision-making committee is set up and the index system is determined for the to-be-matched task. Secondly, the Genetic Algorithm (GA) comprehensive weighting method is introduced to integrate the subjective and objective weights to achieve the weight optimization. Thirdly, the cloud model is used to obtain the preference order and priority order of the to-be-matched subjects. Fourthly, the Gale-Shapley (GS) algorithm is used to obtain the task matching results. Finally, the operability, the stability and the effectiveness of the model are separately verified through numerical simulation, sensitivity analysis and comparative analysis. Corresponding suggestions for the task matching of PVSS in the EB environment are also put forward according to the research content.
Big Data Mining Algorithm of Internet of Things Based on Artificial Intelligence Technology

This paper focuses on how to improve the processing efficiency, mining efficiency, and result accuracy of IoT big data mining algorithms to meet the growing data volume and complexity through optimal design based on artificial intelligence techniques. We propose methods to build data model trees, detect IoT feature data, and standardize feature data to optimize the performance of traditional algorithms in the face of uneven data distribution, high feature dimensionality, noise and missing values. The simulation experiment results show that the IoT big data mining algorithm based on AI technology can better handle massive data, discover patterns and laws in data, improve the accuracy and efficiency of data mining, and provide more accurate and reliable information and knowledge for IoT decision making and management. It helps to promote the digital transformation and intelligent development of various industries and realize the optimization and upgrading of IoT systems.
Review of 3D Sensing Modalities for 3D Pose Estimation

Three-dimensional (3D) pose estimation is an essential process in all human interactive systems. The capturing of 3D sensing modalities consists of time-of-flight sensors, structured light sensors and stereo sensors. These data can subsequently be processed for 3D human pose using deep learning approaches. These approaches can be classified into direct 3D pose estimation and 2D-to-3D pose estimation. The 3D sensing modalities and the respectively processed algorithms are highlighted to show the advantages and disadvantages. The production of the 3D human pose can be directly processed using the volumetric-based method, point cloud-based method and graph theory-based method. These methods are recently drawn huge attention in the field to increase depth processing and fast computation on pose estimation in 3D space.
Research on ship path planning technology based on improved RRT algorithm

With the rapid development of economic globalization, waterway transportation plays an increasingly important role in international trade, and therefore the development of ship technology has become very urgent. To guarantee the safety and economy of ship navigation, one of the focal points of researchers’ research is how to plan a path that is both safe and reliable and smooth and gentle with a short range. Therefore, this study focuses on this scientific problem and proposes an improved fast extended random tree algorithm. The traditional fast extended random tree algorithm often suffers from the problem of planning a tortuous path because of its excessive randomness. To solve this problem, we consider the maneuvering performance requirements of the ship based on the traditional algorithm and introduce a sector sampling strategy to avoid large-angle steering, thus making the planned paths smoother and more feasible. To improve the search efficiency, we also design a target biasing strategy to improve the random point sampling mechanism by setting different sampling probabilities according to the distance from the random sampling point to the target point. Finally, we also propose an adaptive dynamic extension length strategy, which takes the extension length as a variable, and the extension length changes with the environment when the random tree is extended. Through simulation experiments, we demonstrate that the algorithm can reduce the path length while improving the search efficiency, and improve the path smoothness, which is more suitable for the actual navigation needs of ships’ vessels.
Improved Sparrow Search Algorithm Based on Multi Strategy Fusion

The Integrated Strategy Sparrow Search Algorithm (ISSA) has been developed as an advanced optimization algorithm to enhance solution accuracy, stability, and to address local optima issues associated with the conventional Sparrow Search Algorithm (SSA). The ISSA algorithm incorporates various strategies, including a chaotic lens strategy, the Golden Sinusoidal Algorithm (GSA), and an adaptive control parameter $\eta$ based on gamma distribution, to augment population diversity, search coverage, exploration, and development capabilities. The performance of the ISSA algorithm has been evaluated using 12 benchmark functions, which demonstrate that the ISSA algorithm outperforms the basic SSA algorithm, other improved algorithms, as well as other swarm intelligence optimization algorithms.
Image Blind Deblurring using Luminance Minimization Prior

We propose a novel image prior for image blind deblurring. Observing that the luminance component of YUV colour space has very low intensity value in the clear images. Based on this properties, we propose a novel luminance minimization prior for blind image deblurring. In particular, a L0-regularized term is applied to enforce the sparsity of the luminance component. Then an efficient algorithm is presented for solving the proposed formulation, which obtains the clear image. To validate the proposed approach, we conduct extensive experiments on deblurring images. The results show the effectiveness of the proposed method for image blind deblurring.
Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning

In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware. In TECO, we first introduce a two-stage importance evaluation framework, which efficiently and comprehensively evaluates each pruning unit according to both the local importance inside each dimension and the global importance across different dimensions. Based on the evaluation framework, we present a heuristic pruning algorithm to progressively prune the three dimensions of CNNs towards the optimal trade-off between accuracy and efficiency. Experiments on multiple benchmarks validate the advantages of TECO over existing state-of-the-art (SOTA) approaches. The code and pre-trained models are available anonymously at https://github.com/ntuliuteam/Teco.
Haze Removal And Ship Tracking In Hazy Weather Based On Deep Learning

When the tracking algorithm is applied to a hazy maritime image with some small ships, the algorithm performance is negatively affected. To address the above problems, a framework for ship tracking in hazy weather using deep learning methods is proposed in this study. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm with a small target detection layer added is used to detect ships in dehazed marine images, and the Deep SORT algorithm is used to track ships. Then, multi-target tracking evaluation metrics are used to evaluate the tracking effectiveness of ships in videos taken in hazy environments and ships in videos after removal of haze. The experiments show that the PSNR of the 0.96, indicating that the framework can effectively improve the quality of hazy maritime images. The evaluation metrics for multi-objective tracking also show that for hazy videos with small ships, the ship tracking algorithm is more robust when applied after haze removal.
Multi-CNN Voting Method for Improved Arabic Handwritten Digits Classification

Handwritten Arabic recognition poses a unique set of challenges due to the intricate nature of the script, its diverse styles, and the inherent complexity of the Arabic language. With the increasing need for automated recognition systems in various domains, such as document analysis, postal services, and historical document preservation, finding the best system for accurate and efficient recognition becomes crucial. The primary objective of this paper is to propose a method for enhancing the performance of handwritten Arabic written classification tasks by incorporating ensemble learning that employs multiple CNN classical networks, including Inception, Lenet-5, AlexNet, Dense, MobileV2, and ResNet. The final r esults a re t hen c hosen using a voting approach. To validate the effectiveness of our proposed method, we evaluated it on the MADBase dataset and compared it with the latest works in the field. The results demonstrate that our proposed method achieved better accuracy, Fl-score, precision, and recall, with 99.31, 99.31, 99.31, and 99.30, respectively, outperforming existing research. This proposed method can be adapted and utilized for various classification tasks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
How to Boost Deep Neural Networks for Computer Vision

As the range of neural network applications has exploded, various model compression techniques have been developed to increase the accuracy of neural networks under the resource constraints given by the hardware platform and the performance constraints required by users. In this perspective paper, the current status and future prospects of individual techniques are briefly summarized. And it presents the importance of understanding the characteristics of the hardware platform and the systematic methodology of applying these techniques harmoniously.
Stalker: A Framework to Analyze Fragility of Cryptographic Libraries under Hardware Fault Models

For embedded devices, the uncertainty of target physical environments is always a great challenge. With constrained resources and common overloaded uses, they can be more exposed to hardware faults. Other than stability and ordinary security issues, there exist some subtle phenomenons that lead to potential cryptanalysis or secret leakage. In this paper, we present STALKER, a framework to analyze the fragility of libraries under hardware fault models. Compared with existing tools, our framework targets faulty execution outputs, and can flexibly work on different libraries, architectures and support different search schemes. We find dozens of security-sensitive bits that may cause critical issues and provide detailed analysis.
The Impact of SBVR Adoption on Booking Intention in Ciputra Hotel Jakarta

The hospitality business has completely changed in recent times. This is due to the emergence of smart technology, which has resulted in a better experience for customers while increasing the operational efficiency of the industry. One of the advanced technologies currently being used by these service companies is Virtual Reality (VR). Using cognitive evaluation theory, the aim of this research is to examine what motivates customers to adopt Smartphone-Based Virtual Reality Systems (SBVR) at Ciputra Hotel Jakarta. This study focuses on customers who have never used or experienced the SBVR application before. This study adopted a quantitative approach and used a convenient sample involving 165 participants. The findings reveal that consumer attitudes toward the SBVR application at Ciputra Hotel Jakarta prove positive when the application offers a pleasurable experience (perceived enjoyment) and effective data or functional benefits (perceived usefulness). Moreover, it was revealed that attitude has a beneficial impact on intention. It is hypothesized that consumers who have a positive view of SBVR applications are more likely to intend to use them. Based on principles from the psychology and intelligent technology literature, this study enhances our understanding of how customers react to the implementation of SBVR in Hospitality companies.
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs

In recent years, large-scale pre-trained speech language models (SLMs) have demonstrated remarkable advancements in various generative speech modeling applications, such as text-to-speech synthesis, voice conversion, and speech enhancement. These applications typically involve mapping text or speech inputs to pretrained SLM representations, from which target speech is decoded. This paper introduces a new approach, SLMGAN, to leverage SLM representations for discriminative tasks within the generative adversarial network (GAN) framework, specifically for voice conversion. Building upon StarGANv2-VC, we add our novel SLM-based WavLM discriminators on top of the mel-based discriminators along with our newly designed SLM feature matching loss function, resulting in an unsupervised zero-shot voice conversion system that does not require text labels during training. Subjective evaluation results show that SLMGAN outperforms existing state-of-the-art zero-shot voice conversion models in terms of naturalness and achieves comparable similarity, highlighting the potential of SLM-based discriminators for related applications.