Reviewerly/exHarmony huggingface dataset:
Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation

Abstract This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called CrossJustice . We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work.
Image steganography based on least bias generative adversarial network

The main improvement of image steganography is that it can generate dense images with high visual quality. The generated countermeasure network based on least squares is more stable than the conventional generated countermeasure network and converges faster than WGAN. The main work of the least squares generation countermeasure network is to replace the cross-entropy loss function with the least squares loss function. This is considered to improve two problems of the traditional Gan, that is, the image quality generated by the traditional Gan is not high, and the training process is very unstable. The least squares generation countermeasure network attempts to use different distance metrics to build a more stable, convergent and high-quality countermeasure network. In the process of network training, the human visual model is used to evaluate and restrict the visual quality of the secret image, and enhance the concealment of steganography from the perspective of improving the visual quality of the secret image. In the process of steganalysis of the secret image, the secret image is evaluated in combination with the method of image forensics, so as to ensure the visual quality of the image and reduce the trace of image modification, Enhance the concealment of image steganography.
Multi-objective vehicle path planning based on DQN

In the path planning problem based on geographic information system, distance and resource overhead often conflict with each other. For example, the shortest path may not be the most time-saving or convenient one. So, a Multi-objective vehicle path planning method is needed. Therefore, this paper proposes a vehicle path planning algorithm based on improved deep reinforcement learning, which not only solves the problem of single optimization objective restriction, but also reduce the chance of falling into local optimum in traditional path planning algorithm. In this paper, DQN network structure is selected, and neural network is used to store action value function instead of Q table. In the training process, prioritized experience replay strategy is used to accelerate the convergence. The simulation experiment and analysis of the proposed model show that the improved DQN algorithm model can effectively find the path with the optimal value, making the path planning more rational and intelligent.
AlexNet based facial expression classification

In this paper, we use the image data in the dataset-FER2013 as a sample. First, we format the CSV file in the dataset. After finding that there is a large difference in the number of different types of pictures, in order to compare and verify the impact of the number difference on the results, we enhanced the data set through the proposed data augmentation method, so that the data set can meet the experimental needs. After that, by understanding the artificial intelligence learning algorithms at home and abroad, we decided to establish a training model using AlexNet network, and divided the pictures to be identified into three groups with different numbers: original data set, down sampling data set and dataset with data augmentation. In the experiment, we achieves 52.3% accuracy after data enhancement, and 66.9% accuracy using the down sampling dataset.
ANNA: Accelerating Neural Network Accelerator through software-hardware co-design for vertical applications in edge systems

In promising edge systems, AI algorithms and their hardware implementations are often joint optimized as integrated solutions to solve end-to-end design problems. Joint optimization depends on a delicate co-design of software and hardware. According to our knowledge, current co-design methodologies are still coarse-grained. In this paper, we proposed ANNA: Accelerating Neural Network Accelerator through a novel software-hardware co-design methodology. ANNA is a framework composed of three components: ANNA-NAS (Neural Architecture Search), ANNA-ARCH (hardware ARCHitecture) and ANNA-PERF (PERFormance optimizer & evaluator). ANNA-NAS adopts a cell-wise structure and is designed to be hardware aware. It aims at generating a neural network having high inference accuracy and low inference latency. To avoid tremendous time costs, ANNA-NAS synthetically uses differentiable architecture search and early stopping techniques. ANNA-ARCH starts to be designed as long as the architecture search space is defined. Based on the cell-wise structure, ANNA-ARCH specifies its main body which includes Convolution units, Activation Router and Buffer Pool. To well support different neural networks that could be generated by ANNA-NAS, the detailed part of ANNA-ARCH is configurable. ANNA-PERF harmonizes the co-design of ANNA-NAS and ANNA-ARCH. It takes a neural network and a hardware architecture as inputs. After optimizing the mapping strategy between the neural network and hardware accelerator, it feeds back a cycle-accurate latency to ANNA-NAS. Aiming at image classification, we carried out the experiments on ImageNet. Experimental results demonstrate that without loss of much inference accuracy, ANNA wins a significant low inference latency through a harmonious software and hardware co-design.
Procedural Modeling of Interactive Sound Sources in Virtual Reality

Abstract This chapter addresses the first building block of sonic interactions in virtual environments, i.e., the modeling and synthesis of sound sources. Our main focus is on procedural approaches, which strive to gain recognition in commercial applications and in the overall sound design workflow, firmly grounded in the use of samples and event-based logics. Special emphasis is placed on physics-based sound synthesis methods and their potential for improved interactivity. The chapter starts with a discussion of the categories, functions, and affordances of sounds that we listen to and interact with in real and virtual environments. We then address perceptual and cognitive aspects, with the aim of emphasizing the relevance of sound source modeling with respect to the senses of presence and embodiment of a user in a virtual environment. Next, procedural approaches are presented and compared to sample-based approaches, in terms of models, methods, and computational costs. Finally, we analyze the state of the art in current uses of these approaches for Virtual Reality applications.
Audio Quality Assessment for Virtual Reality

Abstract A variety of methods for audio quality evaluation are available ranging from classic psychoacoustic methods like alternative forced-choice tests to more recent approaches such as quality taxonomies and plausibility. This chapter introduces methods that are deemed to be relevant for audio evaluation in virtual and augmented reality. It details in how far these methods can directly be used for testing in virtual reality or have to be adapted with respect to specific aspects. In addition, it highlights new areas, for example, quality of experience and presence that arise from audiovisual interactions and the mediation of virtual reality. After briefly introducing 3D audio reproduction approaches for virtual reality, the quality that these approaches can achieve is discussed along with the aspects that influence the quality. The concluding section elaborates on current challenges and hot topics in the field of audio quality evaluation and audio reproduction for virtual reality. To bridge the gap between theory and practice useful resources, software and hardware for 3D audio production and research are pointed out.
Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases

Artificial intelligence (AI) models for medical image diagnosis are often trained and validated on curated data. However, in a clinical setting, images that are outliers with respect to the training data, such as those representing rare disease conditions or acquired using a slightly different setup, can lead to wrong decisions. It is not practical to expect clinicians to be trained to discount results for such outlier images. Toward clinical deployment, we have designed a method to train cautious AI that can automatically flag outlier cases.Our method-ClassClust-forms tight clusters of training images using supervised contrastive learning, which helps it identify outliers during testing. We compared ClassClust's ability to detect outliers with three competing methods on four publicly available data sets covering pathology, dermatoscopy, and radiology. We held out certain diseases, artifacts, and types of images from training data and examined the ability of various models to detect these as outliers during testing. We compared the decision accuracy of the models on held-out nonoutlier images also. We visualized the regions of the images that the models used for their decisions.Area under receiver operating characteristic curve for outlier detection was consistently higher using ClassClust compared with the previous methods. Average accuracy on held-out nonoutlier images was also higher, and the visualizations of image regions were more informative using ClassClust.The ability to flag outlier test cases need not be at odds with the ability to accurately classify nonoutliers in AI models. Although the latter capability has received research and regulatory attention, AI models for clinical deployment should possess the former as well.
Harmonic Functions for Three-Dimensional Shape Estimation in Cylindrical Coordinates

With the high resolution of modern sensors such as multilayer LiDARs, estimating the 3D shape in an extended object tracking procedure is possible. In recent years, 3D shapes have been estimated in spherical coordinates using Gaussian processes, spherical double Fourier series or spherical harmonics. However, observations have shown that in many scenarios only a few measurements are obtained from top or bottom surfaces, leading to error-prone estimates in spherical coordinates. Therefore, in this paper we propose to estimate the shape in cylindrical coordinates instead, applying harmonic functions. Specifically, we derive an expansion for 3D shapes in cylindrical coordinates by solving a boundary value problem for the Laplace equation. This shape representation is then integrated in a plain greedy association model and compared to shape estimation procedures in spherical coordinates. Since the shape representation is only integrated in a basic estimator, the results are preliminary and a detailed discussion for future work is presented at the end of the paper.
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR

There have been attempts to detect 3D objects by fusion of stereo camera images and LiDAR sensor data or using LiDAR for pre-training and only monocular images for testing, but there have been less attempts to use only monocular image sequences due to low accuracy. In addition, when depth prediction using only monocular images, only scale-inconsistent depth can be predicted, which is the reason why researchers are reluctant to use monocular images alone.Therefore, we propose a method for predicting absolute depth and detecting 3D objects using only monocular image sequences by enabling end-to-end learning of detection networks and depth prediction networks. As a result, the proposed method surpasses other existing methods in performance on the KITTI 3D dataset. Even when monocular image and 3D LiDAR are used together during training in an attempt to improve performance, ours exhibit is the best performance compared to other methods using the same input. In addition, end-to-end learning not only improves depth prediction performance, but also enables absolute depth prediction, because our network utilizes the fact that the size of a 3D object such as a car is determined by the approximate size.
Vision-based Fall Detection in Aircraft Maintenance Environment with Pose Estimation

Fall-related injuries at the workplace account for a fair percentage of the global accident at work claims according to Health and Safety Executive (HSE). With a significant percentage of these being fatal, industrial and maintenance workshops have great potential for injuries that can be associated with slips, trips, and other types of falls, owing to their characteristic fast-paced workspaces. Typically, the short turnaround time expected for aircraft undergoing maintenance increases the risk of workers falling, and thus makes a good case for the study of more contemporary methods for the detection of work-related falls in the aircraft maintenance environment. Advanced development in human pose estimation using computer vision technology has made it possible to automate real-time detection and classification of human actions by analyzing body part motion and position relative to time. This paper attempts to combine the analysis of body silhouette bounding box with body joint position estimation to detect and categorize in real-time, human motion captured in continuous video feeds into a fall or a non-fall event. We proposed a standard wide-angle camera, installed at a diagonal ceiling position in an aircraft hangar for our visual data input, and a three-dimensional convolutional neural network with Long Short-Term Memory (LSTM) layers using a technique we referred to as Region Key point (Reg-Key) repartitioning for visual pose estimation and fall detection.
Virtual Hairstyle Service Using GANs &amp; Segmentation Mask (Hairstyle Transfer System)

The virtual hair styling service, which now is necessary for cosmetics companies and beauty centers, requires significant improvement efforts. In the existing technologies, the result is unnatural as the hairstyle image is serviced in the form of a ‘composite’ on the face image, image, extracts and synthesizing simple hair images. Because of complicated interactions in illumination, geometrical, and occlusions, that generate pairing among distinct areas of an image, blending features from numerous photos is extremely difficult. To compensate for the shortcomings of the current state of the art, based on GAN-Style, we address and propose an approach to image blending, specifically for the issue of visual hairstyling to increase accuracy and reproducibility, increase user convenience, increase accessibility, and minimize unnaturalness. Based on the extracted real customer image, we provide a virtual hairstyling service (Live Try-On service) that presents a new approach for image blending with maintaining details and mixing spatial features, as well as a new embedding approach-based GAN that can gradually adjust images to fit a segmentation mask, thereby proposing optimal styling and differentiated beauty tech service to users. The visual features from many images, including precise details, can be extracted using our system representation, which also enables image blending and the creation of consistent images. The Flickr-Faces-HQ Dataset (FFHQ) and the CelebA-HQ datasets, which are highly diversified, high quality datasets of human faces images, are both used by our system. In terms of the image evaluation metrics FID, PSNR, and SSIM, our system significantly outperforms the existing state of the art.
Design of Mode-Locked Fibre Laser with Non-Linear Power and Spectrum Width Transfer Functions with a Power Threshold

There is a growing demand for higher computational speed and energy efficiency of machine learning approaches and, in particular, neural networks. Optical implementation of neural networks can address this challenge. Compared to other neuromorphic platforms, fibre-based technologies can unlock a wide bandwidth window and offer flexibility in dimensionality and complexity. Moreover, fibre represents a well-studied, low-cost and low-loss material, widely used for signal processing and transmission. At the same time, mode-locked fibre lasers offer flexibility and control, while the mode-locking effect can be crucial for unlocking ultra-short timescales and providing ultra-fast processing. Here, we propose a mode-locked fibre laser with a non-linear power threshold in both power and spectrum. The advantage of the proposed system is a spectrum width two-branch function dependent on the input signal power. The effect is caused by a transition between two operating regimes and is governed by the input signal power. The proposed design enables receiving a non-linear transfer function in amplitude with a power threshold as an optical analogue of biological neurons with the additional advantage of a non-linear two-branch transfer function in spectrum width. The latter property is similar to the frequency-varied response dependent on stimulus properties in biological neurons. Thus, our work opens new avenues in research into novel types of artificial neurons with a frequency spectrum width variable response and, consequently, spiking neural networks and neural-rate-based coding with potential applications in optical communications and networks with flexible bandwidth, such as 5G and emerging 6G.
A Novel Bio-Inspired Motion Direction Detection Mechanism in Binary and Grayscale Background

The visual system plays a vital role in the daily life of humans, as more than 90 percent of the external information received by the human brain throughout the day comes from the visual system. However, how the human brain processes the received visual information remains a mystery. The information received from the external through the visual system can be divided into three main categories, namely, shape features, color features, and motion features. Of these, motion features are considered the key to deciphering the secrets of the visual system due to their independence and importance. In this paper, we propose a novel bio-inspired motion direction detection mechanism using direction-selective ganglion cells to explore the mystery of motion information extraction and analysis. The mechanism proposed in this paper is divided into two parts: local motion direction detection neurons and global motion direction detection neurons; the former is used to extract motion direction information from the local area, while the latter infers global motion direction from the local motion direction information. This mechanism is more consistent with the biological perception of the human natural visual system than the previously proposed model and has a higher biological plausibility and greater versatility. It is worth mentioning that we have overcome the problem in which the previous motion direction detection model could only be applied in the binary background by introducing the horizontal cells. Through the association formed by horizontal cells and bipolar cells, this model can be applied to recognizing problems of motion direction detection on a grayscale background. To further validate the effectiveness of the proposed model, a series of experiments with objects of different sizes, shapes, and positions are conducted by computer simulation. According to the simulation results, this model has been proven to have high accuracy rates regardless of objects’ sizes, shapes, and positions in all experiments. Furthermore, the proposed model is verified to own more stable accuracy rates and stronger noise immunity by comparing it with the recognized superior classical convolutional neural network in a background of different percentage noise.
Multilingual Handwritten Signature Recognition Based on High-Dimensional Feature Fusion

Handwritten signatures have traditionally been used as a common form of recognition and authentication in tasks such as financial transactions and document authentication. However, there are few studies on minority languages such as Uyghur and Kazakh used in Xinjiang, China, and no available public dataset for these scripts, which are widely used in banking and other fields. Therefore, this paper addresses this problem by constructing a dataset containing Uyghur, Kazakh, and Han languages and presents an automatic handwritten signature recognition approach based on Uyghur, Kazakh, Han, and public datasets. In the paper, a handwritten signature recognition method that combines local maximum occurrence features (LOMO) and histogram of orientated gradients (HOG) features was proposed. LOMO features use a sliding window to represent the local features of the signature image. The high-dimensional features formed by the combination of these methods are dimensionally reduced by principal component analysis (PCA). The classification is performed using k-nearest neighbors (k-NN), and it is compared with the random forest method. The proposed method achieved a recognition rate of 98.4% using a diverse signature database compared with existing methods. It shows that the method was effective and can be applied to large datasets of mixed, multilingual signatures.
On Subsampling Procedures for Support Vector Machines

Herein, theoretical results are presented to provide insights into the effectiveness of subsampling methods in reducing the amount of instances required in the training stage when applying support vector machines (SVMs) for classification in big data scenarios. Our main theorem states that under some conditions, there exists, with high probability, a feasible solution to the SVM problem for a randomly chosen training subsample, with the corresponding classifier as close as desired (in terms of classification error) to the classifier obtained from training with the complete dataset. The main theorem also reflects the curse of dimensionalityin that the assumptions made for the results are much more restrictive in large dimensions; thus, subsampling methods will perform better in lower dimensions. Additionally, we propose an importance sampling and bagging subsampling method that expands the nearest-neighbors ideas presented in previous work. Using different benchmark examples, the method proposed herein presents a faster solution to the SVM problem (without significant loss in accuracy) compared with the available state-of-the-art techniques.