Reviewerly/exHarmony huggingface dataset:
CVCNet: Learning Cost Volume Compression for Efficient Stereo Matching

State-of-the-art deep learning based stereo matching algorithms usually rely on full-size cost volumes for highly accurate disparity estimation. The full-size cost volume processes all possible disparity candidates equally without considering their different matching uncertainties. Consequently, considerable redundant computation is involved on those candidates with very low matching uncertainties, making these methods difficult to be deployed in real-time applications. To tackle this problem, we propose CVCNet featuring an adaptive disparity range prediction module (ADR) and a disparity refinement module (DRM). The ADR adaptively predicts pixel-wise disparity range to discard the “unimportant” disparity candidates. It enables our network to obtain a compressed cost volume. Besides, the DRM improves disparity range prediction and refines the predicted disparity map. With the proposed modules, our CVCNet learns to build a compressed cost volume to achieve efficient disparity estimation. Experimental results on the KITTI and SceneFlow datasets show that our method achieves state-of-the-art performance, and runs at a significant order of magnitude faster speed than existing 3D CNN based methods. Particularly, our method ranks <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {1}\mathrm{st}$</tex-math></inline-formula> on the KITTI 2012 and KITTI 2015 benchmarks among all published methods with running time shorter than 100 ms.
Extraction Method of Target Region for HIFU Therapy Based on Ultrasonic Image Semantic Segmentation

Accurate extraction of the therapeutic target region in the monitoring ultrasonic image is necessary for high intensity focused ultrasound (HIFU) therapy. However, due to the interference of complex background and large amount of noise in the ultrasonic image, traditional image segmentation algorithms have limitations on the target region segmentation of ultrasonic image. In order to extract the target region in ultrasonic image, a semantic segmentation method combining improved DeepLabv3+ network and improved loss function is proposed. On the basis of DeepLabv3+ network, dense atrous spatial pyramid pooling (DenseASPP) enhanced by attention mechanism through series connection of DenseASPP structure and channel attention mechanism is added into the DeepLabv3+ encoder, and the feature map weighting module is added into the DeepLabv3+ decoder to improve the network ability of feature extraction and the segmentation accuracy of the target region edge. In terms of the loss function, the improved loss obtained by combining the Huber loss and the noise-robust Dice loss is used to solve the proportional imbalance problem of multi-category pixel number, while improving the robustness against noise. The ablation experimental results show that the mean intersection over union (MIoU) of segmentation results with improved DeepLabv3+ network and with improved DeepLabv3+ network trained by the improved loss function increase by 0.46 percent and 0.99 percent respectively compared with the original DeepLabv3+ network. Proposed method effectively improves the segmentation accuracy of the target region in the monitoring ultrasonic image for HIFU therapy and has strong robustness against noise.
QSpeech: Low-Qubit Quantum Speech Application Toolkit

Quantum devices with low qubits are common in the Noisy Intermediate-Scale Quantum (NISQ) era. However, Quantum Neural Network (QNN) running on low-qubit quantum devices would be difficult since it is based on Variational Quantum Circuit (VQC), which requires many qubits. Therefore, it is critical to make QNN with VQC run on low-qubit quantum devices. In this study, we propose a novel VQC called the low-qubit VQC. VQC requires numerous qubits based on the input dimension; however, the low-qubit VQC with linear transformation can liberate this condition. Thus, it allows the QNN to run on low-qubit quantum devices for speech applications. Furthermore, as compared to the VQC, our proposed low-qubit VQC can stabilize the training process more. Based on the low-qubit VQC, we implement QSpeech <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our implementation of QSpeech is publicly available at https://github.com/zhenhouhong/QSpeech, a library for quick prototyping of hybrid quantum-classical neural networks in the speech field. It has numerous quantum neural layers and QNN models for speech applications. Experiments on Speech Command Recognition and Text-to-Speech show that our proposed low-qubit VQC outperforms VQC and is more stable.
ORION: Orientation-Sensitive Object Detection

Object detection is of great importance to intelligent stockbreeding applications, but currently, basic tools like models and datasets are still in shortage in specific livestock breeding circumstances. In this paper, we build a cattle object detection dataset with real oriented bounding box (ROBB) annotation. In particular, this dataset is single-category, multiple-instance per frame, and has body-direction-aligned orientation and non-rigid targets. Benchmark models are investigated with our proposed orientation-sensitive IOU algorithm $$COS\text {-}IOU$$ and angle-related loss CosAngleLoss. The combination of these two modules outperforms baseline IOU algorithms and MSE angle loss in a more strict angle-confined criterion. This work is a pioneering exploration in non-rigid oriented object detection with orientation in $$[0,2\pi )$$ , it will shed light on similar issues with single-category, non-rigid, oriented object detection in the stockbreeding and manufacturing industry. Code is available at https://github.com/guowenk/cattle-robb and the dataset will be released to the public later.
Lightweight Real-time Object Detection System Based on Embedded AI Development Kit

Lightweight object detection focuses on the lightweight and real-time performance of the model, aiming to reduce the size of the model as much as possible without significantly reducing the detection accuracy, so that it can be deployed in practical application scenarios. This system improves the MobileNet-SSD object detection algorithm and uses standard datasets for training and testing. Through channel pruning, the parameters of the model are greatly reduced while the accuracy remains unchanged, and the model compression ratio is 1.17:1, which reduces the model’s occupation of the device memory capacity, and obtains about 44% improvement in detection speed. Finally, the trained model is deployed on the embedded artificial intelligence development kit EAIDK-610 to process and detect the collected video content in real time. This system can be extended to practical detection tasks with limited computing resources in various specific occasions.
Towards a Better Representation of Research Objects in Interdisciplinary Research

In the Cluster of Excellence “Matters of Activity”, researchers from humanities, design, natural and social sciences, and medicine work together, producing diverse outcomes which all are relevant for reporting to the funding agencies and society. Here, we describe the development of a researcher-oriented CRIS embracing not only text publications but also other research outcomes on equal footing. We illustrate the flexibility of the system by introducing a new output category Research Prototypes. We discuss the challenges and benefits of a local CRIS solution for a small, non-permanent organization, in which researchers from several organizations are working on specific research topics.
A Novel Visual Assisted Technique for Video Summarization Using Attention-Based Key-Frames Selection

The advancements in digital videos led to new demands and novel research opportunities. Many tools and methods are developed over time for efficient video browsing. One of the widespread methods is to extract keyframes to generate summary of the video that present the complete message of the video. The video summarization uses visual attention model, in which noteworthy frames are extracted as a keyframe based on human attention model. It is a workable solution for video summarization, however high computational cost limits its application in practical scenario. In this paper, we propose a novel keyframe extraction (KFE) method based on improved visual attention model. The computational cost and expensive calculations are minimised by utilising dynamic visual highlights that are based on the temporal gradient as an alternative of the conventional optical flow procedures. Secondly, introducing an effective method by means of a discrete cosine conversion is utilised for the static visual salience. Moreover, static and dynamic visual attention system of measurements are combined through a nonlinear weighted fusion method. The practical outcomes show that proposed method is efficient and high standard for the video summarization.
Data Librarians: Changes in Role and Job Duties Over 25 Years

The role and the job duties of librarians may evolve owing to social changes and users’ needs. It is important to know academic libraries have adapted to their needs. Data librarians have held a key position in academic libraries. In this study, we investigated the adaptation in their job duties over 25 years. We conducted content analyses using descriptions of job advertisements to examine changes in the role and job duties of data librarians to clarify libraries’ adaptation. All job advertisements for data librarians posted in the IFLA - Library and Information Science Jobs Mailing List from 1995 to 2022 were collected, and 36 job advertisements were obtained after the preprocessing procedures. Based on job titles, data librarians for specific subjects were needed in the early days, and research data librarians appeared in recent years. Job descriptions indicate that data librarians were required to have knowledge of data about various subjects and use statistical analysis software to process data from 2000–2004; however, knowledge of research data was required from 2015–2022. There have been many other changes in work performed by data librarians over the past 25 years, and libraries have regularly adapted their job descriptions.
Research on UAV State Estimation Method Based on Variable Structure Multiple Model

In order to provide accurate UAV state estimation information for UAV monitoring and control, the UAV state estimation method based on variable structure multi model is studied. Firstly, the state model set is established for the motion forms of UAV, such as smooth flight, lateral turning maneuver and longitudinal jumping maneuver, and the measurement model is established based on the radar measurement principle; Then, based on the variable structure multi model framework, a model set adaptive strategy is proposed, which can solve the problems of too many threshold parameters and complex adaptive strategy; Finally, the simulation scenarios of two radar tracking UAVs are built, and the superiority of the proposed method in the accuracy of state estimation is verified by mathematical simulation.
Dehazing Network Based on U-Net Structure and Residual Block

With the development of deep learning technology, the dehazing method based on convolutional neural network has also developed rapidly. However, it still faces some problems such as incomplete dehazing and difficulty in detail restoration. Aiming at the problems, we propose a dehazing network based on U-Net structure and residual module. In the down sampling process, we introduce the residual block of attention mechanism, which effectively improves the feature extraction and expression ability of the network. In the middle part of the network, the smooth dilation convolution residual block is used to expand the receptive field of the network and improve the restoration effect of small items. At the same time, we introduce a feature fusion mechanism based on attention mechanism, which can reduce the loss of information and weight the down-sampling features into intermediate features. In the process of up sampling, the method of adding elements instead of feature stitching is used to reduce the number of parameters and realize the function of jump connection. Finally, dense convolution residual blocks are added to the jump connection between input and output to further improve the performance. The experimental results show that the PSNR of this method can reach 32.893, which is better than the existing methods.
Summary and Discussion

In this book, we have provided a research landscape for normalization techniques, covering methods, analyses and applications. We believe that our work can provide valuable guidelines for selecting normalization techniques to use in training DNNs. With the help of these guidelines, it will be possible to design new normalization methods tailored to specific tasks (by the choice of NAP) or improve the trade-off between efficiency and performance (by the choice of NOP). We leave the following open problems for discussion.
Fair Outsourcing Paid in Fiat Money Using Blockchain

Seeking outsourcing from cloud service providers is common for resource-constrained users to complete complex computing. Conventional cloud computing outsourcing solutions focus on guiding users to verify the returned results, which is unfair since malicious users can refuse to pay by falsely claiming that the results are wrong. To address this problem, fair payment schemes, both blockchain-free and blockchain-based, have been proposed. However, the former is usually inefficient and the latter only supports payment through cryptocurrencies. The use of cryptocurrencies faces hurdles as it exposes cloud service providers to a significant risk of sharp currency price fluctuations, as well as vulnerability to government bans due to regulatory concerns. In contrast, fiat money payment is not only more in line with the business norm, but also naturally avoids the above troubles. Motivated by this, a blockchain-based fair outsourcing scheme to support payment in fiat money is proposed in this paper. The proposed scheme has broad compatibility and, as an example, is subsequently instantiated with a conventional outsourcing solution of eigen-decomposition. The performance of the scheme in fairness, privacy, and efficiency is verified by both theoretical and experimental evaluations.
Design Justice for Robot Design and Policy Making

Technology, such as robots, can entrench existing inequities and create new ones. Leveraging design justice, par-ticipatory design, and design fictions, we propose new ways for designing robots and policies around social robots to incorporate more voices and values into design processes. This work critically examines design justice in the context of human-robot interaction (HRI) and suggests a framework to engage multiple stakeholders in participatory design of robots and policies grounded in design justice. Overall, we promote discussion around how we can design more equitable robot technology design and policy design in HRI.
State Estimation Algorithm for Nonlinear System with Unknown Parameter Based on Minimum Perturbation Principle

For the state estimation problem of nonlinear systems with unknown parameters, based on the principle of minimum perturbation, the state equation with unknown parameters and both the state equation and measurement equation with unknown parameters are considered, respectively. By converting to a constrained optimization problem, and using the alternate estimation of the system state and parameters, we design the filters in two cases, and give the calculation form when the Jacobian matrix of the system is singular. In order to prove the effectiveness of the proposed algorithm, a nonlinear model with unknown parameters is used, and the simulation results show the superiority of the proposed algorithm.
LoBERTa: A Composition Named Entity Recognition Method Based on Longformer and DeBERTa Model

More and more NLP processing models have been proposed, the more popular ones being Longformer and models such as DeBERTa and RoBERTa. The aim of this paper is to propose a better way of processing to recognize elements and text in articles, i.e., automatic segmented text processing. In this paper, the LoBERTa model is proposed to address this limitation. It is a fusion of Longformer and DeBERTa models using a weighted arithmetic average method. And to solve the problem of overfitting, LoBERTa adds a Dropout layer at the head position of the model. Two models, longformer-base-4096 and DeBERTta-large, were trained and then post-processed to place restrictions on the minimum length and minimum confidence of each entity, sieve out predictions smaller than a threshold, and finally perform CV-10Fold and a simple weighted fusion. Three different models were used to classify and evaluate a text dataset of essays written by students in grades 6–12. In the Longformer Baseline model and DeBERTa-large model combined with NER to process the dataset, the experimental results suffer from limitations such as overfitting and low accuracy. The Public LB of the model was 67.8% when using Longformer Baseline and set to 5-Fold, and the Public LB of the DeBERTa-large model alone resulted in 70.5%. The fusion of the two models resulted in 71.2% for LoBERTa, and the final accuracy was 71.8% when trying to repair the labels and replacing 5fold with 10-fold. LoBERTa further improves the accuracy of text processing when compared to using it alone.
Task Adaptive Modeling for Few-shot Action Recognition

Collecting action recognition datasets is time-consuming and labor-intensive. To solve this problem, a few-shot action recognition task that uses episode training to learn the model appears. However, due to the randomness of few-shot learning task sampling, there are great differences between each task, and the characteristics of classes are also diverse. Most of the current methods simply use the same processing flow for action recognition, ignoring the correlation between tasks. To solve this problem, we propose a task adaptive network for few-shot action recognition, which utilizes the dependency of support set and query set categories. Our method mainly includes two key points: Firstly, we add an attention module after the feature extraction module, which can use the attention mechanism to focus the obtained feature representation on more important local information. Secondly, we design a task adaptive module, which uses the support set samples to strengthen all samples of the current task. The module strengthens the common features within each class of the support set and expands the query set to highlight the differences between classes. We have conducted a large number of experiments on two commonly used action recognition data sets: HMDB51 and UCF101. The results of experiments show that our method has strong competitiveness and performs well in the field of few-shot action recognition.
Teaching Reform of Piano Accompaniment Art Direction Based on Data Mining Technology

Network technology has promoted the development of piano teaching in higher vocational colleges, and brought new opportunities for piano teaching methods and talent training in higher vocational colleges. It has become the main force of piano teaching in higher vocational colleges. The network has played an important role and piano. The value of teaching, this article aims to study the reform of piano accompaniment art teaching based on data mining. This article first puts forward the question of the teaching reform of accompaniment art. Based on the investigation, it discusses the current situation of insufficient basic theoretical research, loose learning system, single teaching method, and mismatch between quality talent training and market demand in piano accompaniment teaching in colleges and universities in my country. Researched the history of the development of piano accompaniment at home and abroad, and investigated the problems and reasons existing in the teaching of piano accompaniment art in my country. Then, we conducted a questionnaire survey on the piano major of the music department of the two universities to obtain the data. According to the data obtained by the questionnaire survey in this article, the data obtained from the references indicate that the popularity rate of online courses is getting higher and higher. More than 90% of the students said they have taken online courses or heard of the concept of online courses, and the element of loving the future will often appear in the classroom. So far, less than 10% of the students have never heard of online courses or attended online courses.
A Vehicle Detection Method Based on Improved YOLOV5s

Along with the advancement of artificial intelligence technology, the development of autonomous driving has been expanding. Vehicle detection is an indispensable part of autonomous driving technology. To achieve high accuracy and high speed, this paper proposes a vehicle detection method based on improved YOLOV5s. Firstly, the Convolutional Block Attention Module (CBAM) is introduced, which enhances the ability of the network to extract vehicle features and improves the detection accuracy significantly. Secondly, some ordinary convolutions are replaced by the Ghost Module, which is a lightweight convolutional module in Backbone in order to reduce the computational cost and the number of parameters significantly. Finally, experimental results show that the proposed method improves the accuracy by 2 % over the original YOLOV5s, reduces the parameter quantity to 82.97% of the original model, and achieves the target detection of vehicles effectively.
A Glimpse into Statistical Relational AI: The Power of Indistinguishability

Statistical relational artificial intelligence, StaRAI for short, focuses on combining reasoning in uncertain environments with reasoning about individuals and relations in those environments. An important concept in StaRAI is indistinguishability, where groups of individuals behave indistinguishably in relation to each other in an environment. This indistinguishability manifests itself in symmetries in a propositional model and can be encoded compactly using logical constructs in relational models. Lifted inference then exploits indistinguishability for efficiency gains. This article showcases how to encode indistinguishability in models using logical constructs and highlights various ways of using indistinguishability during probabilistic inference.
A Comprehensive Analysis Of Normalization Approaches For Privacy Protection In Data Mining

Data Mining is a fundamental method of extracting large volumes of data sets by unfamiliar patterns. These extracted data can be shared between enterprises to improve their corporate benefits. For the data mining process, sharing such confidential data is very important. It is very important to safeguard such information against unwanted exposure that leads to privacy leakage. In recent days, privacy in various data mining applications has become very important. In order to overcome such problem privacy, data mining techniques are preserved. It provides accurate data mining results without scarifying the original data values and ensures both accuracy and privacy. Data protection is achieved through the analysis of the data using normalization techniques in this proposed work. The approach proposed in comparison the effects of min-max, decimal scaling, and Z-Score normalization techniques. Experimental effects confirmed that the Min-max Standardization technology archived the most precision with minimum data loss.