Reviewerly/exHarmony huggingface dataset:
A System for Management of Adaptable Mobile Applications for People with Intellectual Disabilities

One of the trends in the development of modern mobile applications is adaptability, which makes it possible to increase the accessibility of mobile applications for various categories of users. The article is devoted to the study of approaches to the development of adaptable mobile applications and software development tools for such applications. The possibilities of using CALS-technologies to support various stages of the MA life cycle are considered. A technology for the development of MA, the interface of which can be adapted at the maintenance stage, and ways of adapting the interfaces of the developed MA for the end users are proposed. A system has been developed that combines the typical capabilities of mobile application management systems at the stages of implementation and maintenance, and expands them by adding functions that provide the ability to adapt the MA user interface. MA development is based on a multi-module architecture, which makes it possible to use MA components for reuse in other MAs. A template-based technology for adapting the MA interface and a method for adapting the interface using a built-in configuration panel (CP) have been developed. A method of using the system for developing adaptable MA is described. It is shown that the proposed technology makes it possible to increase accessibility of MA and applicability for different people with intellectual disabilities by customizing the interface for each user in accordance with his/her capabilities and limitations and significantly reduce the overall development time of MA through the use of a unified multi-module architecture and the inclusion into the MA project of ready-made modules, as well as the generation of CPs for adapting the MA interface.
Printed Text Recognition for Lexical Lists in Chinese-International Phonetic Alphabet (IPA) Glossing

This study presents a dataset serving as a benchmark for the recognition of printed text in lexical lists using Chinese-IPA glossing. The paper provides an overview of the baseline model, transcription model, and PyLaia engines employed in the research. Furthermore, it elucidates the specific need for digitizing the aforementioned lexical lists, outlines the methodology employed for training the baseline model for layout analysis, and describes the training process of the transcription model using the ground truth data generated on Transkribus. This comprehensive approach encompasses both the images of the lexical list content and their corresponding transcriptions as input. Additionally, the study highlights the limitations of the model and identifies avenues for future development. By making this dataset openly accessible, it can be utilized by researchers seeking to digitize lexical lists using Chinese-IPA glossing. Moreover, since the model can recognize both Chinese characters and IPA symbols, it has the potential to contribute to linguistic analysis of languages documented in Chinese-IPA glossing.
A CycleGAN-Based Method for Translating Recordings of Interjections

This article is dedicated to a new method for translating audio recordings of interjections between two domains. The original speaker's voice is transformed to the target one within a given voice pair, and vice versa. Basing on an overview conducted, mel-frequency cepstral coefficients are selected as main features of signal. The introduced method is implemented and approbated on the grounds of a book-reading training dataset and sample interjections for a number of voice pairs, including human-robotic. Recommendations are given on the method applicability and on dataset recording. Obtained results testify that we found the solution to the problem of overcoming constraints of existing speech-synthesizing software, namely that of the limitedness of interjections forms and that of the poor intonations variety. The method is to be applied in order to fill reactions databases for dialogue systems designed for affective communication, such as the F-2 interlocutor robot. The introduced method will enable spoken reactions corpora developers to record interjections of interest and to translate them to the selected synthetic voice.
Artificial Intelligence: The Legal Implications of Intellectual Property Rights for AI-generated Inventions

The advancement of science and technology, which led to the birth of Artificial Intelligence, has evolved to the point of advanced autonomy, which has recently triggered the quest to make Artificial intelligence “subjects of intellectual property rights”. Intellectual property law has been faced with the challenges of ascribing ownership/authorship rights to Artificial Intelligence, which proves to be an inventor due to the emphasis on the creativity of the human mind in intellectual property law. This article, therefore, addressed the ongoing legal debate as to whether inventions made by machines can enjoy intellectual property rights and aimed at addressing the implications of ascribing intellectual property rights to Artificial Intelligence. The article concluded by stating that intellectual property law would face reoccurring threats and difficulties upon recognizing Artificial Intelligence as subject of intellectual property rights. It was recommended, among other things, that inventors of AI, rather than AI, should be ascribed with intellectual property rights over inventions generated by Artificial Intelligence.
Near-lossless Compression of Tc-99 m DMSA Scan Images Using Discrete Cosine Transformation

The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation.Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05.At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively.Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.
Lord Rama Artificial Intelligence

This book is authored under the guidance of Lord Rama (GOD). This book "Lord Rama Artificial Intelligence" is a collection of five different chapters. First chapter shows "Lord Rama Devotees Algorithm" which comes under Devotees-Inspired Metaheuristic Optimization Algorithms. Chapter 2 defines a new Artificial Intelligence field titled "Out of the Box Artificial Intelligence". Chapter 3 and Chapter 4 describes two new Artificial Intelligence fields "The Interesting and Complete Artificial Intelligence" and "Artificial Intelligence Plus Plus" respectively. Ten Human-Inspired Metaheuristic Optimization Algorithms are shown in the last chapter of this book. Lord Rama Devotees Algorithm, Artificial Cartoon Popeye Algorithm, Artificial Cartoon Chhota Bheem Algorithm, Artificial Cartoon Jerry Algorithm, Artificial Cartoon Happy Kid Algorithm, Artificial Friendship Algorithm, Artificial Brotherhood Algorithm, Artificial Mother and Son Algorithm, Artificial Husband and Wife Algorithm, Human Bhagavad Gita Particle Swarm Optimization (HBGPSO), Human Poverty Particle Swarm Optimization (HPPSO), Human Dedication Particle Swarm Optimization (HuDePSO), Human Selection Particle Swarm Optimization (HuSePSO), Human Safety Particle Swarm Optimization (HuSaPSO), Human Kindness Particle Swarm Optimization (HKPSO), Human Relaxation Particle Swarm Optimization (HRPSO), Multiple Strategy Human Particle Swarm Optimization (MSHPSO), Human Thinking Particle Swarm Optimization (HTPSO), Human Disease Particle Swarm Optimization (HDPSO) are Hybrid PSO algorithms that are shown in this book. For the sake of simplicity, PSO algorithm is modified in this book to create several new Hybrid algorithms. An ideal method is to create all algorithms from scratch rather than modifying PSO algorithm.
Federated Learning-inspired User Personality Prediction Using Sentiment Analysis and Topic Preference

Users are accustomed to posting their opinions on social platforms, and these text data can be used to analyze users preferences and personality traits. To predict the personality type of users, a user personality prediction model based on sentiment analysis and theme preference is proposed. This work employs latent Dirichlet allocation (LDA) to extract different topics of user comments and long-short term memory (LSTM) for text sentiment analysis. Then, the Big Five personality theory is introduced and combined with the text sentiment analysis method. Based on the data of the social event “28 years of commutation life”, the experimental results show that the accuracy of the sentiment classification on the validation set is 92%. Combined with the Big Five personality theory, the largest proportion of users’ emotions is “anger”, accounting for 23%. There are more users with extroverted personalities and neurotic personalities. These conclusions provide information in regard to predicting and controlling the direction of public opinion.
Visual Grounding With Joint Multimodal Representation and Interaction

This paper tackles the challenging yet significant task of grounding a natural language query to the corresponding region onto an image. The main challenge in visual grounding is to model the correspondence between visual context and semantic concept referred by the language expression, that is, multi-modal fusion. Nevertheless, there is an inherent deficiency in the current fusion module designs, which makes visual and linguistic feature embeddings cannot be unified into the same semantic space. To address the issue, we present a novel and effective visual grounding framework based on joint multi-modal representation and interaction (JMRI). Specifically, we propose to perform image-text alignment in a multi-modal embedding space learned by a large-scale foundation model, so as to obtain semantically unified joint representations. Furthermore, the transformer-based deep interactor is designed to capture intra-modal and inter-modal correlations, rendering our model to highlight the localization-relevant cues for accurate reasoning. By freezing the pre-trained vision-language foundation model and updating the other modules, we achieve the best performance with the lowest training cost. Extensive experimental results on five benchmark datasets with quantitative and qualitative analysis show that the proposed method performs favorably against the state-of-the-arts.
No-Reference Virtual Reality Image Quality Evaluator Using Global and Local Natural Scene Statistics

With the rapid proliferation of virtual reality (VR) technologies, the usage of VR in multimedia, education, and social media platforms has increased due to realistic and immersive 3D viewing experiences. In particular, VR refers to a computer-generated synthetic environment where the users can experience 180 × 360° spherical VR content through head-mounted displays (HMD). Due to the 180 × 360° viewing range, the quality assessment (QA) of VR images becomes quite difficult compared to conventional 2D image quality assessment (IQA) models. To alleviate this problem, in this paper, we propose a supervised no-reference (NR) VR IQA model based on global and local natural scene statistics (NSS) of a VR image. The global features are computed based on joint dependencies between adjacent pixels of equirectangular projection (ERP) VR image using generalized Gaussian distributions (GGD). Specifically, we compute the model parameters of GGDs at multiple scales and show that the model parameters are distortion discriminable. Further, we compute local features based on statistical properties of spatial and spectral entropy maps of cube map projection (CMP) faces of a VR image. Since the local feature extraction is carried out at the CMP face level, we compute the average of the face-level features to obtain the overall local feature set of a VR image. Finally, global and local feature sets are combined and given to support vector regressor (SVR) to map the quality-aware feature set to VR image quality with labels as human assessment scores. The performance of the proposed VR IQA model is verified on three omnidirectional VR IQA datasets, such as CVIQD, OIQA, ODIQA, and one stereoscopic VR IQA dataset, namely, LIVE S3D-VR. Experimental results show that the predicted scores of the proposed VR IQA model correlate very well with the subjective scores of the aforementioned VR IQA datasets and achieved state-of-the-art performance numbers compared to the existing 2D and 3D IQA models.
A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

With the development of artificial intelligence technology, an increasing number of human action recognition (HAR) methods are being applied to tennis training action analysis. The HAR methods based on skeletal points have been extensively researched and applied due to their superior action expression capabilities. In order to enhance the HAR ability of tennis players and effectively capture the detailed features in training actions, this paper proposes a tennis training action analysis model based on graph convolutional neural networks. Firstly, this paper establishes the limb vectors of humans in three-dimensional spatial coordinates and extracts the features of tennis error techniques based on the distances between the skeletal joints of five parts of the human body. Secondly, the data’s time frames are segmented to extract attention and improve the model’s ability to capture detailed features. Additionally, the attention mechanism is introduced to embed the position information into the attention map, enhancing the model’s generalization ability. Experiments conducted on several action datasets demonstrate that the proposed model in this paper achieves higher HAR accuracy and better recognition results compared to most current methods.
4-bit CNN Quantization Method With Compact LUT-Based Multiplier Implementation on FPGA

To address the challenge of deploying Convolutional Neural Networks (CNNs) on edge devices with limited resources, this paper presents an effective 4-bit quantization scheme for CNN and proposes a DSP-free multiplier solution for deploying quantized neural networks on Field-Programmable Gate Array (FPGA) devices. Specifically, we first introduce a Threshold-Aware Quantization (TAQ) method with a mixed rounding strategy to compress the scale of the model while maintaining the accuracy of the original full-precision model. Experimental results demonstrate that the proposed quantization method retains a high classification accuracy for 4-bit quantized CNN models. Additionally, we propose a Compact Lookup-based Multiplier (CLM) design that replaces numerical multiplication with a lookup table of precomputed 4-bit multiplication results, leveraging LUT6 resources instead of scarce DSP blocks to improve the scalability of FPGA to implement multiplication-intensive CNN algorithms. The proposed 4-bit CLM only consumes 13 LUT6 resources, surpassing existing LUT-based multipliers in terms of resource consumption. The proposed CNN quantization and CLM multiplier scheme effectively save FPGA resource consumption for FPGA implementation on image classification tasks, providing strong support for deep learning algorithms in unmanned systems, industrial inspection, and other relevant vision and measurement scenarios running on DSP-constrained edge devices.
Problem Relaxation Methods for Quantum Minimum Fill-in Algorithm

Current quantum annealing and quantum-inspired annealing devices have many usage limitations and are difficult to apply to real-scale problems. In particular, a major hardware limitation is the limited number of available variables (qubits). This paper proposes a problem relaxation method for the Quantum Minimum Fill-in (QMF) algorithm. QMF finds the ordering of matrix rows and columns that minimizes the incidence of fill-ins that occur when a direct solver is used to solve linear equations with sparse matrices. In general, the first few steps in the forward elimination of sparse matrices add the most fill-ins. Therefore, QMF was relaxed to order the rows and columns in only the first few steps. The results obtained using the Fixstars Amplify Annealing Engine, a quantum-inspired annealing device, show that the problem relaxation can be computed with 20%-60% of the qubits for the original problem and that relaxation can be applied to larger problems. Furthermore, it is found that more solutions that satisfy the constraints can be obtained with problem relaxation and that the number of fill-ins is reduced. These results confirm that the proposed problem relaxation is effective for QMF.
Segmentation-Guided Semantic-Aware Self-Supervised Denoising for SAR Image

Synthetic aperture radar (SAR) images often suffer from speckle noise, which can degrade their visual quality and affect downstream applications. Recently, supervised and self-supervised methods have been proposed by virtue of deep learning with synthetic “noisy-clean” image pairs or only real noisy images as training data, respectively. Among these methods, by avoiding artifact problems in real SAR image denoising, self-supervised methods solve the domain gap problem of supervised methods and hence have attracted significant attention. However, existing self-supervised denoising methods essentially rely on pixel information of images and ignore corresponding semantic information, which makes them challenging to remove speckle noise while retaining detailed features. To this end, we propose a segmentation-guided semantic-aware self-supervised denoising method for SAR images, namely SARDeSeg, where a segmentation network is incorporated with a denoising network and guides it to learn and be aware of the semantic information of the input noisy SAR images. Additionally, a wavelet transform-based connector is introduced to efficiently transmit semantic information between the denoising network and the segmentation network, together with an edge-aware smoothing loss to improve speckle noise suppression while preserving edge features. Experimental results demonstrate that the proposed SARDeSeg outperforms state-of-the-art denoising methods for SAR images, particularly in preserving detailed edge features.
IRL-Net: Inpainted Region Localization Network via Spatial Attention

Identifying manipulated regions in images is a challenging task due to the existence of very accurate image inpainting techniques leaving almost unnoticeable traces in tampered regions. These image inpainting methods can be used for multiple purposes (e.g., removing objects, reconstructing corrupted areas, eliminating various types of distortion, etc.) makes creating forensic detectors for image manipulation an extremely difficult and time-consuming procedure. The aim of this paper is to localize the tampered regions manipulated by image inpainting methods. To do this, we propose a novel CNN-based deep learning model called IRL-Net which includes three main modules: Enhancement, Encoder, and Decoder modules. To evaluate our method, three image inpainting methods have been used to reconstruct the missed regions in two face and scene image datasets. We perform both qualitative and quantitative evaluations on the generated datasets. Experimental results demonstrate that our method outperforms previous learning-based manipulated region detection methods and generates realistic and semantically plausible images. We also provide the implementation of the proposed approach to support reproducible research via https://github.com/amiretefaghi/IRL-Net.
Multilevel Spatial Feature-Based Manifold Metric Learning for Domain Adaptation in Remote Sensing Image Classification

Domain adaptation purposes to transfer well-labelled domain knowledge to poor-labelled domain. However, many domain adaptation methods focus too much on spectral features and simple spatial features, causing failure to eliminate noise that is an important risk in remote sensing image classification. Degenerated features are also a potential risk to feature transformation in domain adaptation. In addition, it is also a typical problem that reduces the difference in probability distribution between different domains for domain adaptation. To alleviate these issues, we propose a multilevel spatial features-based manifold metric learning (MSF-MML) method for domain adaptation in remote sensing image classification. Firstly, it applies and iterates a spatial information filtering to extract multilevel spatial features by calculating the average of sample pixels and neighboring pixels, effectively obtaining more stable feature information. Then, it utilizes the Grassmann manifold space to alleviate the problem of degenerated features of remote sensing data in domain adaptation. Finally, it exploits maximum mean discrepancy to construct metric constraints that reduces discrepancy in domains probability distributions. The effectiveness of MSF-MML was demonstrated on three real-world datasets.