Reviewerly/exHarmony huggingface dataset:
Efficient 2D Mapping for Reversible Data Hiding

After the prediction, how to modify the 2D prediction error histogram (PEH) for reversible data hiding (RDH) is an important issue. In this letter, we propose an efficient 2D mapping strategy by considering the error pairs on two diagonal lines of the 2D PEH. The proposed 2D mapping can quickly adapt to the frequencies of the error pairs for different mapping area sizes. As a bonus, a satisfactory embedding performance can be achieved since in each group of two symmetrical error pairs the one with larger frequencies has priority to select more mapping ways. Experimental results have shown that it is low time-consuming and achieves better embedding performance in comparison with several classical and advanced 2D mappings.
Cross-modal transformer with language query for referring image segmentation

Referring image segmentation (RIS) aims to predict a segmentation mask for a target specified by a natural language expression. However, the existing methods failed to implement deep interaction between vision and language is needed in RIS, resulting inaccurate segmentation. To address the problem, a cross-modal transformer (CMT) with language queries for referring image segmentation is proposed. First, a cross-modal encoder of CMT is designed for intra-modal and inter-modal interaction, capturing context-aware visual features. Secondly, to generate compact visual-aware language queries, a language-query encoder (LQ) embeds key visual cues into linguistic features. In particular, the combination of the cross-modal encoder and language query encoder realizes the mutual guidance of vision and language. Finally, the cross-modal decoder of CMT is constructed to learn multimodal features of the referent from the context-aware visual features and visual-aware language queries. In addition, a semantics-guided detail enhancement (SDE) module is constructed to fuse the semantic-rich multimodal features with detail-rich low-level visual features, which supplements the spatial details of the predicted segmentation masks. Extensive experiments on four referring image segmentation datasets demonstrate the effectiveness of the proposed method.
Group fairness in non-monotone submodular maximization

Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items.
Convolutional neural network-based image watermarking using discrete wavelet transform

With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most common ways to maintain security. Watermarking is proving and declaring ownership of an image by adding a digital watermark to the original image. Watermarks can be either text or an image placed overtly or covertly in an image and are expected to be challenging to remove. This paper proposes a combination of convolutional neural networks (CNNs) and wavelet transforms to obtain a watermarking network for embedding and extracting watermarks. The network is independent of the host image resolution, can accept all kinds of watermarks, and has only 11 layers while keeping performance. Performance is measured by two terms; the similarity between the extracted watermark and the original one and the similarity between the host image and the watermarked one.
Introduction to FastAPI

The recent surge in the popularity of Python as a programming language is mainly due to its libraries used in the field of data science applications. However, Python is also extensively used for web application development, thanks to the abundance of its web application frameworks.
Impact of delightful somatosensory augmented reality experience on online consumer stickiness intention

Purpose Drawing on embodied cognition theory, this study examined the impact of midair, gesture-based somatosensory augmented reality (AR) experience on consumer delight and stickiness intention. The mediating effects of three psychological states for body schema (i.e. natural symbol sets, vivid memory and human touch) on the relationships between somatosensory AR and consumer delight/stickiness intention are determined. By filling gaps in the research, we hope to provide guidance on how to drive delightful somatosensory AR marketing. Design/methodology/approach Two experiments were conducted (Study 1 and Study 2) to test the research model and hypotheses. These experiments compared the effects of the “presence” (midair, gesture-based) and “absence” (mouse-based traditional website) conditions in somatosensory AR on consumer body schema and the creation of a delightful virtual shopping experience (i.e. consumer delight and stickiness intention). Findings The consumer delight and stickiness intention created in the presence condition was much higher than those in the absence condition. Consumers appeared to prefer engaging in a midair gesture-based somatosensory AR experience and exploring an augmented metaverse reality to interacting with a mouse-based traditional website. We also found that giving online consumers more somatosensory activities and kinesthetic experiences effectively inspired three psychological states of body schema in online consumers. Originality/value The results contribute to the AR experience and somatosensory marketing literature by revealing the role of natural symbol sets, vivid memory and the sense of human touch. This research breaks through the long-developed research paradigm on consumer delight, which has been limited to traditional entities and web contexts. We also extend embodied cognition theory to the study of somatosensory AR marketing.
Traceable Asynchronous Workflows in Video Retrieval with vitrivr-VR

Virtual reality (VR) interfaces allow for entirely new modes of user interaction with systems and interfaces. Much like in physical workspaces, documents, tools, and interfaces can be used, put aside, and used again later. Such asynchronous workflows are a great advantage of virtual environments, as they enable users to perform multiple tasks in an interleaved manner. However, VR interfaces also face new challenges, such as text input without physical keyboards, and the analysis of such asynchronous workflows. In this paper we present the version of vitrivr-VR participating in the Video Browser Showdown (VBS) 2023. We describe the current state of our system, with a focus on improvements in text input methods and logging of asynchronous workflows.
Functionality of neural dynamics induced by long-tailed synaptic distribution in reservoir computing

In the cerebral cortex, excitatory postsynaptic potentials (EPSPs) exhibit a long-tailed distribution. Although EPSPs induce rich neural activity, their contributions to brain function remain unclear. Therefore, this study evaluated the effect of the dynamics induced by long-tailed synaptic weights by constructing a reservoir computing (RC) model and comparing the memory capacity and predictive accuracy for nonlinear time-series between RCs, with and without strong weights. The results revealed that strong weights significantly enhance the RC performance through gamma-band dynamic neural activity. This mechanism may support the cognitive processes in the actual brain network.
Unconstrained Facial Expression Recognition With No-Reference De-Elements Learning

Most unconstrained facial expression recognition (FER) methods take original facial images as inputs to learn discriminative features by well-designed loss functions, which cannot reflect important visual information in faces. Although existing methods have explored the visual information of constrained facial expressions, there is no explicit modeling of what visual information is important for unconstrained FER. To find out valuable information of unconstrained facial expressions, we pose a new problem of no-reference de-elements learning: we decompose any unconstrained facial image into the facial expression element and a neutral face without the reference of corresponding neutral faces. Importantly, the element provides visualization results to understand important facial expression information and improves the discriminative power of features. Moreover, we propose a simple yet effective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> e- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> lements <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work (DENet) to learn the element and introduce appropriate constraints to overcome no ground truth of corresponding neutral faces during the de-elements learning. We extensively evaluate the proposed method on in-the-wild FER datasets including RAF-DB, AffectNet, SFEW and FERPlus. The comparable results show that our method is promising to improve classification performance and achieves equivalent performance compared with state-of-the-art methods. Also, we demonstrate the strong generalization performance on realistic occlusion and pose variation datasets and the cross-dataset evaluation.
Identification and Tracking of Takeout Delivery Motorcycles Using Low-Channel Roadside LiDAR

In recent years, food takeout services have grown rapidly in large cities. Likewise, the number of delivery motorcycles has grown with the rise of the takeout delivery market. Therefore, identifying and tracking motorcycles is essential as primary steps toward preventing and predicting delivery riders’ crashes using trajectory data. First, kinematic features were proposed to combine with shape and density information as input indicators of random forests (RFs) to classify the traffic objects. Then, a multifeature fusion method was proposed to track the traffic object by constructing a mathematics matrix to present the adjacent point cloud frames. Furthermore, an improved Hungarian algorithm was developed to track the same object in the mathematics matrix. The experiment results showed that the average recognition accuracy of the proposed method is 99.59%, the average tracking accuracy is 92.53%, the tracking accuracy for delivery motorcycles is up to 98%, and the tracking speed stability is 98%. This study contributes to developing proactive strategies to reduce crashes related to takeout delivery motorcycles.
MediaPipe based Gesture Recognition System for English Letters

Gesture recognition is widely used in communication, virtual reality, smart cockpit, smart home, etc. This paper proposes an application system based on MediaPipe for intelligent recognition of 26 letter gestures in English. The system uses images captured by a monocular camera, reads images using OpenCV, creates a hand detection model, uses MediaPipe to identify 21 key points of the hand, constructs a gesture recognition module for English letters, and recognizes letters through rough gesture recognition algorithm and fine gesture recognition algorithm. The experimental results show that the recognition accuracy of all English letters in this system is more than 81%, and the recall rate is more than 83%, which can be applied to gesture recognition related applications of English letters.
SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition

Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills.
A Metamodeling Approach for Structuring and Organizing Cloud Forensics Domain

Metamodeling is a technique that can be utilized in general to integrate and define models from various cloud computing research areas. Metamodeling method is applicable in a wide variety of application fields, particularly in the standardization process. In addition, the primary focus of this process is on identifying general ideas present in a variety of problem domains and their relations, as well as on resolving issues pertaining to the complexity, interoperability, and heterogeneity of diverse domains. Numerous models and frameworks have been proposed for cloud forensics (CF) in the literature. However, these models and frameworks are specific and developed to solve a particular cloud incident scenario. Therefore, cloud forensics is a complex, heterogeneous and ambiguous domain among domain forensic practitioners cloud computing. This study's findings led to the development of a unified meta-model that structures and organizes cloud forensics investigation domain information, making it easier for domain experts to manage, share, and reuse such knowledge. Standardization is a challenging task, owing to the relevance of information such as models, framework, and issues related to models. The proposed cloud forensics metamodels allow field practitioners to derive/instantiate a unified model for cloud forensics.
Vision Paper: Do We Need to Change Some Things?

Cryptographic algorithms are vital components ensuring the privacy and security of computer systems. They have constantly improved and evolved over the years following new developments, attacks, breaks, and lessons learned. A recent example is that of quantum-resistant cryptography, which has gained a lot of attention in the last decade and is leading to new algorithms being standardized today. These algorithms, however, present a real challenge: they come with strikingly different size and performance characteristics than their classical counterparts. At the same time, common foundational aspects of our transport protocols have lagged behind as the Internet remains a very diverse space in which different use-cases and parts of the world have different needs. This vision paper motivates more research and possible standards updates related to the upcoming quantum-resistant cryptography migration. It stresses the importance of amplification reflection attacks and congestion control concerns in transport protocols and presents research and standardization takeaways for assessing the impact and the efficacy of potential countermeasures. It emphasizes the need to go beyond the standardization of key encapsulation mechanisms in order to address the numerous protocols and deployments of public-key encryption while avoiding pitfalls. Finally, it motivates the critical need for research in anonymous credentials and blind signatures at the core of numerous deployments and standardization efforts aimed at providing privacy-preserving trust signals.
Mineral prospectivity mapping for forecasting gold deposits in the Central Kolyma region (Magadan region, Russia)

A geostatistical predictive and prospecting model of gold mineralization (map of potential resource prospects) for the area of the Central Kolyma region (within the Inyali-Debinsky synclinorium) was built using machine learning methods. Multispectral satellite images, materials of medium- and small-scale geological mapping, data of medium-scale geophysical and lithochemical surveys were used as source data. Based on this data, an exhaustive set of indicators related to the search criteria was calculated. To build a forecast, the Random Forest method was used using random negative samples and averaging the result. The obtained forecast maps allow localizing potential gold ore objects with a spatial resolution of 2 km, which makes it possible to select promising areas for licensing. In addition, the possibilities of using machine learning methods for metallogenic zoning of the territory are shown.
Cloud Container Orchestration Architectures, Models and Methods: a Systematic Mapping Study

In this study we explore and systematically review the latest research on container orchestration architectures, models and methods and their application in cloud environments. The aim of our systematic mapping study is to identify, classify the existing research papers and thus give organized knowledge of this evolving area. We tried to establish a systematic methodology that ensures the validity of our secondary study and construct a classification scheme that helps derive useful information from the existing body of research. We’ve gained insights about specific technology related issues like container orchestrators and container runtimes but also knowledge about more generalizable aspects like research strategies and approaches used in the primary studies. The results gave us insights about the current state and maturity of the research area and helped us identify research gaps. We were able to formulate recommendations for future research.
A Data Sharing Scheme Based on Federated Learning in IoV

As the functions of connected vehicles become more complicated, the amount and type of data they generate during driving are increasing. Sharing the data among connected vehicles can improve users' driving experience and reduce traffic pressure. However, the leakage of users' private information caused by data sharing may harm the interest of vehicle users and even endanger their lives. Meanwhile, since the quality of data collected by vehicles varies, high-quality data sharing means users get more reliable services, while low-quality data can reduce service reliability, drive experience, or cause traffic accidents. Therefore, developing an effective and highly reliable privacy protection scheme for shared data becomes a pressing problem to be solved. At the same time, since data sharing requires low latency, it is also a challenge to introduce a privacy protection mechanism without significant delay. In this paper, we propose a decentralized federated learning-based data sharing scheme that provides strong privacy protection for data and improves the system's robustness. In particular, we decouple the data request process from the data sharing process to improve the sharing efficiency. Then we propose a TOP-K-based nodes selection scheme to improve the accuracy of the trained models and ensure data reliability. The security analysis shows that the scheme can resist attacks and achieve secure data sharing. Finally, the scheme's effectiveness and the model prediction's efficiency are verified through experiments. The results show that the scheme has high accuracy, efficiency, and security.
Merging public health and automated approaches to address online hate speech

The COVID-19 pandemic sparked a rise in misinformation from various media sources, which contributed to the heightened severity of hate speech. The upsurgence of hate speech online has devastatingly translated to real-life hate crimes, which saw an increase of 32% in 2020 in the United States alone (U.S. Department of Justice 2022). In this paper, I explore the current effects of hate speech and why hate speech should be widely recognized as a public health issue. I also discuss current artificial intelligence (AI) and machine learning (ML) strategies to mitigate hate speech along with the ethical concerns with using these technologies. Future considerations to improve AI/ML are also examined. Through analyzing these two contrasting methodologies (public health versus AI/ML), I argue that these two approaches applied by themselves are not efficient or sustainable. Therefore, I propose a third approach that combines both AI/ML and public health. With this proposed approach, the reactive side of AI/ML and the preventative nature of public health measures are united to develop an effective manner of addressing hate speech.
User Onboarding in Virtual Reality: An Investigation of Current Practices

Explaining to novice users how to interact in immersive VR applications may be challenging. This is in particular due to the fact that the learners are isolated from the real world, and they are asked to manipulate hardware and software objects they are not used to. Consequently, the onboarding phase, which consists in teaching the user how to interact with the application is particularly crucial. In this paper, we aim at giving a better understanding of current VR onboarding methods, their benefits and challenges. We performed 21 VR tutorial ergonomic reviews and 15 interviews with VR experts with experience in VR onboarding. Building on the results, we propose a conceptual framework for VR onboarding and discuss important research directions to explore the design of future efficient onboarding solutions adapted to VR.