Reviewerly/exHarmony huggingface dataset:
Reversible data hiding in encrypted image using two-pass pixel value ordering

In recent years, reversible data hiding in encrypted images (RDHEI) has gained popularity as it enables the concealing of confidential information in anonymized host image. Because of this, conventional reversible data hiding (RDH) methods that utilize the spatial correlation of natural host images cannot be employed in RDHEI where the embedding is done in the encrypted image without reserving a room. To address this concern, this paper proposes a new RDHEI method based on popular pixel value ordering (PVO) strategy of the conventional high fidelity RDH. For this, the image pixels are first encrypted in blocks using novel difference-preserving encryption for maintaining the original difference among the pixels of each block. Next, a two-pass PVO (TPPVO) is employed for embedding. The TPPVO first sorts the pixels and then performs the embedding in two passes in such a way that high embedding capacity (EC) can be obtained while also ensuring reversibility. Experimental evaluations also demonstrate that the proposed TPPVO-RDHEI not only enlarges the EC approximately by 0.1130 bpp and 0.01 bpp for the smooth and complex images, respectively in comparison to the best performing related method, but also maintain the visual security while ensuring the complete reversibility.
Automatic Registration Algorithm for SAR and Optical Images Based on Shearlet and Sparse Representation

The registration of synthetic aperture radar (SAR) and optical images is still a challenge task due to the influence of the potential nonlinear intensity differences and severe speckle noise. In this letter, we propose a feature-based SAR and optical images registration algorithm combining shearlet and sparse representation. The work consists of three main components, including the feature points detector, descriptor and matching criterion. Firstly, a new feature points detection method based on the coherence-enhancing diffusion Harris detector (CED-HD) is designed, it integrates coherence-enhancing diffusion function and shearlet-based spatial constraint on the basis of the Harris-Laplace detector, which can obtain highly repeatable feature points while suppressing speckle noise. Secondly, a multilayer complementary joint representation descriptor (MCJRD) based on shallow structural features and deep semantic features is designed. The shallow structural features are obtained jointly by Shearlet and phase congruence, while the deep semantic features are obtained by multilayer convolution sparse representation, which makes the descriptors more discriminable and robust. Finally, a feature matching criterion based on locally constrained sparse representation is designed to better reduce the reconstruction error and improve the matching ability. Experimental results on several real SAR and optical image pairs demonstrate the effectiveness of the proposed algorithm.
EPFNet: Edge-Prototype Fusion Network Toward Few-Shot Semantic Segmentation for Aerial Remote-Sensing Images

Few-shot semantic segmentation is a technique that is receiving increasing attention. The aim of this approach is to enable models to segment objects with a few support images (usually 1, 5, 10, etc.). At present, few-shot semantic segmentation has made great progress in the field of Natural Scene Image (NSI), but these methods cannot be applied directly to the field of Remote Sensing Image (RSI). In order to overcome this challenge, we propose a novel semantic segmentation network structure that integrates prototype information with global edge information to achieve more accurate prototype matching results. In addition, we design a comprehensive weighted loss function to monitor the training process to help overcome the challenges. Results of the performance comparison with state-of-the-art few-shot semantic segmentation methods demonstrate the superiority of the proposed method.
A Lightweight Object Detection and Recognition Method Based on Light Global-Local Module for Remote Sensing Images

Lightweight object detection and recognition models are extremely crucial for in-orbit applications, which is the most critical factor for whether deep learning-based object detection and recognition algorithms can be applied to remote sensing satellites for real-time or near real-time processing. Global information is extremely important for object detection and recognition of remote sensing images. However, due to the high computational cost, the existing CNN-based lightweight models over-emphasize on the extraction of local information, while ignoring the global information. For this reason, we propose a lightweight object detection and recognition model (Lightweight Global-Local Detection, LGLDet) based on the especially light global modeling structure. In LGLDet, light global-local module (LGLM) is proposed to extract the global and local information. The LGLM consists of Point2Patch Non-Local (P2PNL), local branch and skip connection. Specifically, P2PNL is proposed to reduce the computation of global long-range dependency modeling. In addition, the feature fusion part and detection head are also designed in a lightweight way. In the experiments, the proposed method can achieve optimal performance with fewer parameters and lower computational complexity than existing CNN-based lightweight models and transformer-based lightweight models with similar parameters or computational complexity. The code will be released on the site of https://github.com/dyl96/LGLDet.
A Multi-Featured Detection Method for Small Target on Sea Surface Based on GSP

The detection of floating small targets is a challenging problem for marine surveillance radar. To effectively detect the floating small target in a complex marine environment, this letter proposes an innovative multi-featured detection method by leveraging the graph signal processing (GSP) theory. With GSP, we propose two kinds of graph representations of standardized Doppler power spectrum (SDPS) to capture the correlation of the radar data in the Doppler domain. Then, by exploiting the graph representations, three quantitative graph features, graph Laplacian regularizer, trace of Laplacian matrix, and variance of self-loop weight, are developed to distinguish target returns from sea clutter. Finally, a detector based on the graph features is constructed by the fast convex hull learning algorithm. Experiments conducted on the measured radar datasets and comparisons with existing methods confirm the effectiveness of the proposed method.
High-Level Topology Synthesis Method for Δ-Σ Modulators via Bi-Level Bayesian Optimization

Designing high-performance Δ-Σ modulators is a challenging task, often involving a time-consuming, manual topology search process. We present an automated topology synthesis method for Δ-Σ modulators that significantly improves efficiency in the search for reliable modulator topologies. Our bi-level Bayesian optimization algorithm provides a 41.2%, 24.2%, and 20.2% improvement in FOM compared to random sampling, evolution-based, and general single-level Bayesian optimization methods, respectively. It also achieves a 2.84× speedup compared to single-level BO while achieving the same optimization goal. Our proposed framework allows for better topology-level exploration than existing high-level synthesis software for Δ-Σ modulators, as demonstrated by a case study of a novel high-performance architecture synthesized by our method. The source code and supplementary material are released on GitHub1.
JDMR-Net: Joint Detection and Modulation Recognition Networks for LPI Radar Signals

Low probability of intercept (LPI) radars are widely used in modern electromagnetic environments due to their excellent anti-interception performance. However, this inevitably increases the difficulties in detecting and recognizing LPI radar signals for electronic support systems or radar warning receivers. To address this challenge, this paper proposes a multi-task neural network named JDMR-Net for joint detection and modulation recognition of LPI radar signals. The inherent multi-task learning capability obtains an improved performance through leveraging useful information across tasks. The JDMR-Net receives pulse sequence in I/Q format as input and is computational friendly compared to time-frequency image-based methods. The JDMR-Net consists of a local feature extraction module and a global similarity mining module. The local feature extraction module extracts modulation information within single pulse, while the global similarity mining module determines the similarity relationship among sequential pulses. The JDMR-Net can provide accurate time domain localization of detected pulses, and determine corresponding modulation type simultaneously. Through the multi-task framework, the processing steps of traditional processing chain are compressed efficiently and the two modules are highly parallelizable, making the proposed solution promising for on-line application with raw signal inputs. Extensive experiments on simulated and measured LPI signals demonstrate the effectiveness and robustness of the proposed method in terms of lower detectable signal to noise ratio (SNR) and low computational complexity.
End-to-End Encryption in Resource-Constrained IoT Device

Internet of Things (IoT) technologies will interconnect with a wide range of network devices, regardless of their local network and resource capacities. Ensuring the security, communication, and privacy protection of end-users is a major concern in IoT development. Secure communication is a significant requirement for various applications, especially when communication devices have limited resources. The emergence of IoT also necessitates the use of low-power devices that interconnect with each other for essential processing. These devices are expected to handle large amounts of monitoring and control data while having limited capabilities and resources. The algorithm used for secure encryption should protect vulnerable devices. Conventional encryption methods such as RSA or AES are computationally expensive and require large amounts of memory, which can adversely affect device performance. Simplistic encryption techniques are easily compromised. To address these challenges, an effective and secure lightweight cryptographic process is proposed for computer devices. This process utilizes a symmetrical encryption key block, incorporating a custom proxy network (SP) and a modified Feistel architecture. Security analysis and performance evaluation results demonstrate that the proposed protocol is secure and energy-efficient. The symmetric key encryption scheme is based on sequences in the Feistel cipher, with multiple rounds and sub-keys generated using principles derived from genetic algorithms. This proposed algorithm minimizes processing cycles while providing sufficient security.
WindSR: Improving Spatial Resolution of Satellite Wind Speed Through Super-Resolution

Prediction of accurate wind speed is necessary for a variety of applications such as energy production, agriculture, climate modeling, and weather forecasting. Various satellites orbiting the earth measure the wind speed, which is particularly useful as they provide measurements of wind speed over large areas and in remote locations that might be difficult to measure using other methods. However, satellite-based wind speed measurements have relatively low spatial resolution compared to other methods, such as ground-based radar. In this research, we develop WindSR and a lightweight tiny-WindSR to improve the resolution of satellite wind speed data by four times from the NASA’s GEOS-5 Nature Run dataset. WindSR has SRResNet-based architecture consisting of several Residual-in-Residual Dense Blocks to compute features from low spatial resolution (28 km) wind speed for upscaling. We train WindSR with more than 20,000 pairs of low-resolution (28 km) and corresponding high-resolution (7 km) wind speed data and evaluate its performance on the validation set consisting of 2,102 wind speed images. Experimental results show that WindSR outperforms classical upsampling algorithms, such as Bicubic interpolation and Lanczos interpolation by 17.89% and general-purpose super-resolution GANs such as BSRGAN and SwinIR by up to 11.35% on the RMSE metric.
CUPVC: A Constraint-Based Unsupervised Prosody Transfer for Improving Telephone Banking Services

Low efficiency in telephone banking services reduces customer satisfaction. Therefore, some recent studies have concentrated on applying voice conversion models to improve telephone banking services. However, building such a model raises three huge challenges, as practical telephone banking services require natural and high-quality conversations. These challenges include the lack of parallel speech data, difficulty in generating natural speech, and difficulty in modeling long speech. To tackle such challenges, we propose a novel unsupervised prosody transfer for improving customer satisfaction in telephone conversations relying on grounded theoretical foundations. Our model consists of a solo-encoding disentanglement module and a forge module. (i) The disentanglement module uses three unique constraints to effectively reduce manual feature engineering and training costs and decompose extremely long speech without parallel data. (ii) The forge module hammers at converting the source prosody to the target one and guarantees correct fine-grained alignments, thereby generating natural speech. Finally, extensive experiments are conducted on large-scale telephone recordings from XWbank in China and suggest that our model can achieve promising outcomes. Moreover, we open-source our codes and unique datasets on GitHub.
Audio Compression on Multimedia Compression Techniques

Recently, the study of audio compression has become more prominent. Today's apps make use of advancements in audio signal processing, including advanced audio coding (AAC), perceptual audio coding techniques (MP3 encoding), internet radio, and other lossless audio coding systems. In this essay, we provide a summary and contrast the algorithms Huffman and Arithmetic. The method is used with a comparison of the two algorithms, Huffman and Arithmetic, on raw WAV files. The fundamental objective of the steganography technique is to increase the security of the transmitted data. Unauthorized users are unable to access or misuse the steganographic file. Audio steganography is applicable to non-technical areas as well in order to protect the privacy and confidentiality of the data. This paper also includes a review of recent work on audio steganography. For database applications like storage and transfer, audio compression is essential. This essay discusses how compression techniques are applied to audio using various transform coding and focuses on the advantages of transform coding in compared to current methods. By using the transform coding technique, numerous attempts have been made to completely eradicate or minimize audio noise, and the study has produced a number of fruitful results. Examining current fractal coding techniques for digital multimedia compression is the aim of the fractional compression study. It discusses strategies for reducing encoding time, which is considered to be the primary challenge in fractal compression, as well as suggested fractal coding techniques in the audio and image domains. In order to use the most widely used communication platforms, large audio file sizes must be transferred via digital audio; this poses substantial challenges for storage and preservation. This article makes good use of audio.It is advised to use a mixed transform coding scheme as a compressive method. The audio file size was greatly decreased while maintaining high quality, and the compression results are promising. The cascaded prediction approach was improved and reported in Audio Compression utilizing OLS+ and CDCCR Method. Comparing three primary predictor block types with varying levels of complexity included two complicated prediction tech- niques with backward adaptation, namely extended Active Level Classification. Extended Ordinary Least Square (OLS+) and the Extended ALCM+ Model
Transductive clustering optimization learning for few-shot image classification

Few-shot image classification aims to perform image classification on new categories with only a small amount of labeled training data. However, it is difficult to complete such a task under the existing conditions. Therefore, the current few-shot learning methods are built on the paradigm of transfer learning, where the core idea is to learn a priori that can solve unknown new tasks. The idea of meta-learning is fast learning, which is similar to the idea of few-shot learning. Therefore, conventional few-shot learning methods generally adopt a meta-training approach with episodic training to construct knowledge prior. However, research work indicates that an embedding model with powerful feature representations is simpler and more effective than the existing sophisticated few-shot learning methods. Inspired by this insight, we propose a few-shot image classification method based on the transductive clustering optimization learning. First, memory block is introduced to store the internal feature structure of each category, namely comprehensive representation of various features. Second, during the training process, sample features are compared with the memory content to further expand the differences between different categories and improve the performance of feature extractor. Finally, in the transductive few-shot setting, a clustering optimization module has been introduced to select appropriate samples for clustering to alleviate the fundamental problem of sample scarcity. A large number of experimental results show that the proposed few-shot image classification method based on the transductive clustering optimization learning effectively improves the classification accuracy under various training conditions.
Papillary thyroid carcinoma whole-slide images as a basis for deep learning

Objectives . Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a system of similar anatomical region in the histopathological image. Authors discuss the second selection of signs for the marking of histological images, methodological approaches to dissect whole-slide images, how to prepare raw data for a future analysis. The influence of the representative size of the fragment of the full-to-suction image of papillary thyroid cancer on the accuracy of the classification of trained neural network EfficientNetB0 is conducted. The analysis of the resulting results is carried out, the weaknesses of the use of fragments of images of different representative size and the cause of the unsatisfactory accuracy of the classification on large increase are evaluated. Materials and methods . Histopathological whole-slide imaged of 129 patients were used. Histological micropreparations containing elements of a tumor and surrounding tissue were scanned in the Aperio AT2 (Leica Biosystems, Germany) apparatus with maximum resolution. The marking was carried out in the ASAP software package. To choose the optimal representative size of the fragment the problem of classification was solved using the pre-study neural network EfficientNetB0. Results . A methodology for preparing a database of histopathological images of papillary thyroid cancer was proposed. Experiments were conducted to determine the optimal representative size of the image fragment. The best result of the accuracy of determining the class of test sample showed the size of a representative fragment as 394.32×394.32 microns. Conclusion . The analysis of the influence of the representative sizes of fragments of histopathological images showed the problems in solving the classification tasks because of cutting and staining images specifics, morphological complex and textured differences in the images of the same class. At the same time, it was determined that the task of preparing a set of data for training neural network to solve the problem of finding invasion of vessels in a histopathological image is not trivial and it requires additional stages of data preparation.
Families’ discursive practices in data discussions about migration histories

Large-scale data and data visualizations are ubiquitous now in the stories that shape our society. In particular, these stories influence youth and families’ communication and understanding of scientific, social, and personal issues. Consequently, we need to better understand how youth and families can engage and learn with the tools that generate such narratives. This study reports on a qualitative analysis of 13 data discussions with families that used data visualization tools to explore georeferenced data connected to their family migration histories. We developed the Co-Construct, Situate, Advance a Theory (CSAT) model, from family cases, which details the discursive and interactional processes by which families tell personal stories grounded in data displays. Extending earlier studies of storytelling as theory-building in family conversations, our model provides an understanding of families’ discursive practices around data exploration. We discuss implications for family learning with data technologies.
Second Grader and Mathematics: Mastering the Subject Within the Framework of the Requirements of the Current Standard

The main features of teaching mathematics according to the updated standard are: the development of spatial thinking, logical and algorithmic thinking; formation of the ability to work with subject terminology and actively use it in solving educational problems; the formation of mathematical functional literacy, taking into account the age capabilities of children. The article discusses the organization of teaching second-graders, focused on the implementation of the requirements of the current standard for mathematical preparation: analysis of a mathematical text; the use of ready-made and independently compiled models to discuss the problem; commenting on the process of solving, constructing (images), oral calculations; the use of different methods for performing the measurement action.
Improving the Efficiency of Quantum Circuits for Information Set Decoding

Code-based cryptosystems are a promising option for Post-Quantum Cryptography, as neither classical nor quantum algorithms provide polynomial time solvers for their underlying hard problem. Indeed, to provide sound alternatives to lattice-based cryptosystems, U.S. National Institute of Standards and Technology (NIST) advanced all round 3 code-based cryptosystems to round 4 of its Post-Quantum standardization initiative. We present a complete implementation of a quantum circuit based on the Information Set Decoding (ISD) strategy, the best known one against code-based cryptosystems, providing quantitative measures for the security margin achieved with respect to the quantum-accelerated key recovery on AES, targeting both the current state-of-the-art approach and the NIST estimates. Our work improves the state-of-the-art, reducing the circuit depth by 2 19 to 2 30 for all the parameters of the NIST selected cryptosystems, mainly due to an improved quantum Gauss–Jordan elimination circuit with respect to previous proposals. We show how our Prange’s-based quantum ISD circuit reduces the security margin with respect to its classical counterpart. Finally, we address the concern brought forward in the latest NIST report on the parameters choice for the McEliece cryptosystem, showing that its parameter choice yields a computational effort slightly below the required target level.