Reviewerly/exHarmony huggingface dataset:
Deep Boosting Robustness of DNN-Based Image Watermarking via Dbmark

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DB MARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(LL)$</tex> sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression.
Sparse Transformer-Based Algorithm for Long-Short Temporal Association Action Recognition

In order to recognize actions with a long time span and model the global timing information of videos, this paper combines 3D Convolutional Neural Networks(3DCNN) and Transformer to propose a sparse Transformer-based long-short temporal association action recognition algorithm. The algorithm uses a pre-trained model to extract clip features, embeds a video feature clustering module to reduce the potential noise of the input features, and uses a Transformer long-short temporal association module based on sparse self-attentiveness which introduces a sparse mask matrix masking operations on the similarity matrix to suppress smaller attention weights, selectively retain important long-short temporal information, and improve the model's attention concentration on global contextual information. Experimental results show the model can achieve the Top-1 accuracy of 97.41% on UCF101 and 78.79% on HMDB51 with small number of parameters and computational complexity.
A Survey of Screen Shooting Resilient Image Watermarking Algorithms

Image watermarking is an important copyright protection scheme which has been developed for over 30 years. Screen Shooting Resilient Image Watermarking (SSRIW) is a technique that after the watermarked image is displayed on a computer or projection screen, and be captured from this screen by a camera, from which the watermark can still be accurately extracted. That has very broad and practical application value. Several researchers have conducted some academic research and made a series of progress in this field, but as far as we know, there is no systematic analysis of SSRIW in literatures. This paper focuses on a comprehensive review and analysis of SSRIW, including algorithms based on traditional methods and deep learning, points out the main difficulties in SSRIW research, and puts forward our suggestions for the future development and the research focus which can be continued of this technique.
Research on the Extraction, Storage and Innovative Design Application of Cultural Relic Genes

The reverse transcription genetic engineering in the field of biology is closely related to the extraction of cultural genes contained in relics. This process is essential for building a genetic database of cultural artifacts, which can then be used for displaying and disseminating their knowledge and innovative application. The paper proposes a genetic classification system based on dominant genes such as material, pattern, vessel type, and technique, and recessive genes such as history, use, story, and meaning. The study also constructs a genetic storage system that complies with international metadata standards for artifacts. Furthermore, the paper illustrates the complete process of innovative application of genetic recombination by using specific artifacts as examples. This method not only provides a reference for other traditional cultural gene extraction, storage, and design applications but also contributes to the inheritance and application of outstanding Chinese cultural genes.
A Cascaded Wearable Product Feature Tag Extraction Method Based on Sentiment Prior

Nowadays wearable products have a large application space in many places. Extracting product feature tags from user comments and mining product advantages and disadvantages found by users during use can effectively improve the design of wearable products and then promote product sales. The current methods can extract tags from sentences through methods like keyword extraction, semantic similarity or multi-label classification. However, these methods have problems such as inaccurate tag extraction and a need of large amounts of labeled data. To address these issues, this paper proposes a hierarchical feature tag extraction algorithm based on aspect-level sentiment prior. Specifically, the user comments are first with aspect-level sentiment recognition to extract opinion words as prior information, and then the semantic similarity model is applied to get the final feature tags. The experimental results show that the proposed method improves the accuracy of product feature tag extraction from user comments in the field of wearable products.
A Research on Applications of Blockchain Consensus Protocols in Digital Copyright Protection

Blockchain technology has attracted great attention in the field of digital copyright protection and provided a new method for effective protection of digital copyright. The consensus protocols, a crucial component of blockchain technology, enables nodes to agree on data in accordance with specified rules, ensuring the effectiveness and security of the copyright protection procedure. Firstly, the existing state of digital copyright protection and the consensus protocols are introduced, followed by the proposal of a blockchain framework suited to digital copyright protection. Secondly, a more in-depth study was conducted on consensus protocols in the field of digital copyright protection, which is divided into permissioned consensus and permissionless consensus. Finally, future consensus protocols research directions are discussed.
Research on Intelligent Control of Stage Costume Storage in Smart Theaters

U nder the trend of Internet+, modern theaters have transitioned from traditional models to intelligent buildings, and have entered a new stage of constructing an intelligent performance ecosystem from different dimensions with a systematic approach. Based on this, stage costumes, as an indispensable part of the performance ecosystem, are forced to break the traditional storage mode and upgrade to intelligent storage from multiple dimensions such as technology, art, communication, industry, and management. On one hand, it improves the efficiency of stage costume storage management, standardizes execution, and reduces management costs, driving the informatization, systematization, and refinement of management. On the other hand, it enables stage costumes to become an organic component of advanced intelligent theater digital management, including cloud design, cloud production, and cloud storage of theater costumes.
On Adaptive Client/Miner Selection for Efficient Blockchain-Based Decentralized Federated Learning

This study presents a fast and accurate blockchain-based decentralized federated learning (BC-DFL) based on an adaptive client/miner selection algorithm. BC-DFL is a learning method that manages the machine learning models on a blockchain. Although the blockchain can improve the security of model sharing and realize reward management, its mining process extensively increases computation and communication load. The training accuracy could also be degraded if each client’s data distribution follows non-independent and identically distributed (Non-IID) conditions. In the proposed method, a client selection allows parallel processing of local training and mining on the network, reducing round time. In addition, using a client selection algorithm based on the estimation of the label distribution, the accuracy degradation caused by Non-IID is suppressed. Numerical results demonstrate that the proposed method can improve training time and accuracy performances compared to related frameworks.
Using the PMO to enforce and standardize the attention of software project managers to needs of software project teams

Software project teams (SPTs) are critical stakeholders. Their contribution in successful delivery of software projects is unquestionable. However, the empirical evidence of their importance seems to exist on paper only, as software project managers (SPMs) and scholars in the project management field ignore their individual needs, and as a result, SPTs remain the most neglected stakeholder group in the software industry. In endeavoring to address the neglect of SPTs by SPMs, the authors of this study developed a model aimed at assisting SPMs to pay due consideration to the needs of this important stakeholder group. At the heart of the model’s functionality is the Project Management Office (PMO), which intends to enforce and standardize the gathering and addressing of software project team needs and interests by SPMs. The aim of the research study is to investigate how the functions of the PMO can be applied to operationalize the enforcement and standardization of the overall function of the model. Since the study is practical-oriented, the pragmatic interpretive approach was considered a suitable methodology. Through the interpretative methodology, several appropriate functions of the PMO, such as «Project management methodology, standards, and tooling», «Monitoring and controlling project performance», «Human resource management» and «Development of project management competencies» as established from project management literature were utilized to achieve the study’s purpose. Even though the interpretation process was guided by literature, the inference was also influenced, to a certain extent, by the researcher opinion as «interested observer». Therefore, the approach presents a limitation to the study. Future studies should include the validation of the feasibility of the study’s claim in a real-world project setup.
Breaking the Bento Box: Accelerating Visual Momentum in Data-flow Analysis

The bento-box user interface and tool integration paradigm dominates integrated development environments (IDEs). In this paradigm, tools project different information about a system in disjoint panes (boxes) of a window while integrating updates between them as needed. Although popular and functional, the bento-box paradigm has its drawbacks; previous research has shown that expert developers experience disorientation as they work in these environments. In this paper, we explore how context can be preserved for developers within the bento-box paradigm by introducing and experimenting with a tool named ReachHover. This tool supports the answering of common data-flow reachability questions, which have been previously shown to be difficult for developers to answer. To ensure ReachHover supported practical reachability questions of interest to developers, we conducted, and report on, a formative survey of 72 practicing developers about the type and frequency of reachability questions they encounter in their work. We then conducted, and report on, a controlled user study in which 20 practicing developers used ReachHover, finding that participants who used ReachHover answered questions involving visiting multiple files more correctly than those who used standard tooling, and that those developers better maintained context while determining their answers. These findings demonstrate the potential of introducing context-preserving user interfaces for tools within the standard bento-box paradigm of development environments, opening up new avenues for improved tool expression and adoption.
Confidential Consortium Framework: Secure Multiparty Applications with Confidentiality, Integrity, and High Availability

Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that building modern CIA systems is more challenging than ever. In response, we present the Confidential Consortium Framework (CCF), a general-purpose foundation for developing secure stateful CIA applications. CCF combines centralized compute with decentralized trust, supporting deployment on untrusted cloud infrastructure and transparent governance by mutually untrusted parties. CCF leverages hardware-based trusted execution environments for remotely verifiable confidentiality and code integrity. This is coupled with state machine replication backed by an auditable immutable ledger for data integrity and high availability. CCF enables each service to bring its own application logic, custom multiparty governance model, and deployment scenario, decoupling the operators of nodes from the consortium that governs them. CCF is open-source and available now at https://github.com/microsoft/CCF.
Abnormal Behaviour Detection of Self-Adaptive Agents in Traffic Environments

Open collectives consisting of self-adaptive and semi-autonomous systems are typically difficult to control, especially if a self-improving integration behaviour is to be achieved at runtime. Therefore, a variety of metrics for external observation have been presented for this context, which can also be applied to a system-wide monitoring and behaviour assessment of semi-autonomous systems. However, this lacks an analysis of the behaviour in real systems. In this paper, we investigate to what extent metrics for the external observation of self-adaptive and self-organising properties of such systems can be used in real applications. We do this by analysing the behaviour of two examples from different traffic systems and thus detecting abnormal behaviour at runtime, which in turn should serve as a basis for subsequent reintegration decisions.
Siamese Representation Learning for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) technology tries to use image-level annotations as supervision to learn object-level detectors, which can effectively reduce the labor and time cost of constructing object detection systems. Existing WSOL methods usually adopt a computational framework based on class activation mapping, which only supports a single input scale and lacks constraints in feature learning. This makes the existing WSOL methods unable to learn different scale features of the same object category, resulting in the model cannot capture the complete object region in complex scenarios, and making it hard to locate the object instances of multiple scales at the same time. This paper proposes a novel WSOL framework based on siamese representation learning, which includes a self-supervised semantic mask (SSSM) module and a cross-scale semantic consistency constraint (CsSCC) module. The framework enhances the perception ability of the model to the object features at different scales by jointly learning different scale transformations of input samples. Extensive experiments on typical WSOL datasets demonstrate the advantage of our method in improving multi-scale localization ability and improving localization integrity under the weak supervision setting.
Design and Implementation of Error Control System Experiment Platform Based on MATLAB App Designer

In order to solve the incomprehensibility of error control coding principle, and avoid the negative effects brought by the use of traditional experimental equipment, such as the aging and loss of experimental equipment, the experimenter needs to spend a lot of time on the connection of equipment and many other problems. In this paper, an error control system experiment platform is designed by using MATLAB App Designer with many controls, friendly interface and strong interaction. The state of each coding stage of these three typical error control systems is shown in detail by a dynamic process. The coding and decoding principles of various error control systems are displayed intuitively through user interaction, so that users can intuitively observe how various error control systems complete error control through mathematical relations. Users can set different parameters to compare and analyze the performance differences of different error control codes. The system has good man-machine interaction.
Automatic Modulation Recognition Method for Adaptive Noise Reduction

Experiments on modulation automatic recognition combined with previous deep convolutional neural network models show that most feature extraction models have low recognition accuracy under a low signal-to-noise ratio, and the problems of more model parameters and high computational complexity are ignored while pursuing high recognition accuracy, so the research focuses on adaptive noise reduction and balancing model accuracy and computational complexity. A lightweight convolutional neural network model based on adaptive noise reduction is proposed, and a phase accumulation method based on forward propagation is proposed to enhance the feature representation of signal samples in the training concentration and an adaptive noise reduction module based on attention mechanism to reduce the noise of signal samples, and time-frequency features are extracted serially by a convolutional neural network (CNN) and gated recurrent unit (GRU), and the recognition accuracy of the method is maintained at 0dB signal-to-noise ratio on the benchmark data set, and the recognition rate is more than 87.82%, and the overall recognition accuracy is high, the model parameters are small-scale.
A CNN-GRU based Approach for Strong In-Band Interference Mitigation in Wireless Communication Systems

With the advent of the sixth generation (6G), the scarcity of spectrum resources and interference issues are receiving increasing attention. In order to address the issue of decreased communication reliability caused by the presence of interference signals, various methods have been proposed in previous research. However, these traditional methods often involve manual parameter selection, lacking automation and intelligence. And in the face of strong in-band interference, they are unable to effectively achieve interference mitigation. Research has shown that deep learning can be used for signal processing. But most of them are used for handling frequency modulated continuous wave (FMCW) radars and rarely applied to address strong in-band interference issue in wireless communication systems. Therefore, we leverage the advantages of convolutional neural network (CNN) and recurrent neural network (RNN) with gated recurrent unit (GRU) in processing sequential data, and propose a fusion network to achieve strong in-band interference mitigation in wireless communication systems. Through experiments, it has been demonstrated that our proposed approach exhibits better suppression performance in strong interference scenarios, reducing the bit error rate by over 75 percent. Additionally, in the presence of various types of interference, our approach is still capable of effectively reducing the bit error rate and enhancing the reliability of the communication system.
Facial Landmark based BMI Analysis for Pervasive Health Informatics

Body Mass Index (BMI), calculated based on the ratio between a person's height and weight, is a widely used metric for body weight or fatness. In this paper, we investigate the potential of face image-based BMI estimation using an RGB camera. We proposed a simple yet highly reproducible image processing framework that converts an input face image into a BMI value or obesity class (underweight, normal and overweight). In this framework, we explored the options of using 2D or 3D facial landmark models, view angle correction in 2D and 3D, different choices for facial feature extraction (landmark distances or coordinates), and different prediction models (regression or classification) based on shallow machine learning techniques. Our framework was thoroughly validated on two public datasets. The insights of this measurement are discussed, as well as the challenges and limitations, to increase the understanding for future improvement of camera-based BMI estimation. The source code of this study is available at https://github.com/hxfj/Facial-Landmark-based-BMI-Analysis.git.Clinical relevance- This contributes to simpler and more effective daily health management.
Semantic Communications and Networking

Until fifth-generation communications, researchers focused on transmitting the data fast and accurately for many users. For this, many studies have been shown around the relay system, with achievable rate maximization considering the co-interference. These are the first Shannon communication level, which focuses on bit transmission. Spurred by the advance of deep learning, the fusion of artificial intelligence (AI) and communications or networks emerged. In the second and third level of the Shannon-Weaver model, contextual meaning and reasoning are the objectives of transmission. Fortunately, AI has shown tremendous empirical results for contextual reasoning. In the physical (PHY) layer, joint source-channel coding (JSCC) is studied broadly. The variational autoencoder shows a strong background in research. In this chapter, we introduce the concepts of semantic communication (SC) and semantic networking. In addition, we introduce the performance enhancements such as loss of information, rate maximization, and robustness of noisy channels. Moreover, we provide the performance gain cited from research papers.
Techno-optimism: Framing data and digital infrastructure for public acceptance in Ghana

This paper examines public discourse on data and digital infrastructure in Ghana to understand how dominant actors are framing the conversation to galvanise support for the implementation of the technology in the country and its consequences. Using publicly available information on social media, this paper finds that the discourse is led by the government, contending with consistent optimism and iterations from an abstract idea of data positivism that data and digital infrastructure are prerequisites for the country's development. The government claims that building digital infrastructure would accelerate the digitalisation of the country to increase data flow. Hence, going digital with its immense data outcome would then become a means to fighting corruption, ensuring smooth and efficient governance and ensuring economic development. By carefully selecting and presenting these benefits of the technology to the public, the discourse goes unchallenged becoming the sole truth to which citizens have to believe.