Reviewerly/exHarmony huggingface dataset:
Effects of Quantum Technology on the Metaverse

This chapter explores the impact of quantum technology on the metaverse, revolutionizing performance and security. Quantum computing enables faster processing, advanced simulations, and sophisticated AI interactions. Integrating it with the metaverse poses technical challenges and requires addressing ethical considerations. Quantum tech benefits blockchain-based cryptocurrencies, enabling decentralized ownership and secure transactions. Regulatory frameworks are needed. Future internet technologies like 5G, edge computing, and IoT play a vital role, providing high-speed connectivity and enhanced immersion. Overcoming challenges such as limited resources and interdisciplinary expertise is crucial. Addressing ethical concerns and establishing standards and regulations are necessary. This research aims to understand quantum's effects, develop strategies, and foster collaboration for a responsible and equitable integration in the metaverse, shaping a transformative digital future.
SoK: Blockchain Governance

Blockchain systems come with a promise of decentralization that, more often than not, stumbles on a roadblock when key decisions about modifying the software codebase need to be made. In a setting where "code-is-law," modifying the code can be a controversial process, frustrating to system stakeholders, and, most crucially, highly disruptive for the underlying systems. This is attested by the fact that both of the two major cryptocurrencies, Bitcoin and Ethereum, have undergone "hard forks" that resulted in the creation of alternative systems which divided engineering teams, computational resources, and duplicated digital assets creating confusion for the wider community and opportunities for fraudulent activities. The above events, and numerous other similar ones, underscore the importance of Blockchain governance, namely the set of processes that blockchain platforms utilize in order to perform decision-making and converge to a widely accepted direction for the system to evolve. While a rich topic of study in other areas, including social choice theory and electronic voting for public office elections, governance of blockchain platforms is lacking a well established set of methods and practices that are adopted industry wide. Instead, different systems adopt approaches of a variable level of sophistication and degree of integration within the platform and its functionality. This makes the topic of blockchain governance a fertile domain for a thorough systematization that we undertake in this work.
What do we know about online romance fraud studies? A systematic review of the empirical literature (2000 to 2021)

We aimed to identify the critical insights from empirical peer-reviewed studies on online romance fraud published between 2000 and 2021 through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The corpus of studies that met our inclusion criteria comprised twenty-six publication studies employing qualitative (n=13), quantitative (n=11), and mixed (n=2) research methods. Most studies focused on victims, with eight focusing on offenders and fewer investigating public perspectives. All the victim-focused studies relied on data from the Global North, except for one conducted in Malaysia. Five offender-focused studies used online data available in the public domain, and three derived their data from West Africa. Our review highlights offenders' techniques to deceive and manipulate victims, as revealed in these studies, and highlights some limitations of offender- and victim-focused studies. The dominant framework used across the studies was found to be the “Scammers Persuasive Techniques Model.” While this framework provides a helpful way of considering the stages of victim involvement, it also faces some limitations, which we highlight. We stress the importance of considering a greater range of regional and global viewpoints in understanding online romance fraud. Our study reviews the current state of empirical knowledge on romance fraud and identifies certain gaps and biases in the literature. We argue there is a need for further research into online romance fraud to enhance our understanding of it both from the perspective of the offender as well as the experience of the victim. We also highlight the need for a more inclusive and diverse range of data sources and perspectives. Given the scale and impact of online romance fraud, we conclude that its study would benefit from a richer empirical engagement and one that encompasses a regional and global phenomenon.
A Comprehensive Survey on TinyML

Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos

Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing.The applications of this field ranges from generating highlight videos in sports, to intelligent surveillance and gesture recognition.Most activity recognition systems rely on a combination of convolutional neural networks (CNNs) to perform feature extraction from the data and recurrent neural networks (RNNs) to determine the time dependent nature of the data.This paper proposes and designs two transformer neural networks for human activity recognition: a recurrent transformer (ReT), a specialized neural network used to make predictions on sequences of data, as well as a vision transformer (ViT), a transformer optimized for extracting salient features from images, to improve speed and scalability of activity recognition.We have provided an extensive comparison of the proposed transformer neural networks with the contemporary CNN and RNNbased human activity recognition models in terms of speed and accuracy for four publicly available human action datasets.Experimental results reveal that the proposed ViT-ReT framework attains a speedup of 2× over the baseline ResNet50-LSTM approach while attaining nearly the same level of accuracy.Furthermore, results show that the proposed ViT-ReT framework attains significant improvements over the state-of-the-art human action recognition methods in terms of both model accuracy and runtime for each of the datasets used in our experiments, thus verifying the suitability of the proposed ViT-ReT framework for human activity recognition in resource-constrained and real-time environments.
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims to improve the robustness of pre-trained models via contrastive learning. Specifically, we design nine kinds of simple and complex data augmentation operators on the programming language (PL) and natural language (NL) data to construct different variants. Furthermore, we continue to train the existing pre-trained models by masked language modeling (MLM) and contrastive pre-training task on the original samples with their augmented variants to enhance the robustness of the model. The extensive ex-periments demonstrate that ContraBERT can effectively improve the robustness of the existing pre-trained models. Further study also confirms that these robustness-enhanced models provide improvements as compared to original models over four popular downstream tasks.
An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead

The rapid growth of software supply chain attacks has attracted considerable attention to software bill of materials (SBOM). SBOMs are a crucial building block to ensure the transparency of software supply chains that helps improve software supply chain security. Although there are significant efforts from academia and industry to facilitate SBOM development, it is still unclear how practitioners perceive SBOMs and what are the challenges of adopting SBOMs in practice. Furthermore, existing SBOM-related studies tend to be ad-hoc and lack software engineering focuses. To bridge this gap, we conducted the first empirical study to interview and survey SBOM practitioners. We applied a mixed qualitative and quantitative method for gathering data from 17 interviewees and 65 survey respondents from 15 countries across five continents to understand how practitioners perceive the SBOM field. We summarized 26 statements and grouped them into three topics on SBOM's states of practice. Based on the study results, we derived a goal model and highlighted future directions where practitioners can put in their effort.
Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning

With the development of cloud computing, a growing number of applications are migrating to a cloud environment. In the process, the real-time scheduling of workflows has gradually become a technical challenge, due to the dynamic and uncertain nature of cloud environments and the complex dependencies between sub-tasks of the workflow. Although various methods have been reported up to now, these methods have their respective shortcomings, such as heuristic-based methods are hard to find optimal scheduling scheme and metaheuristic-based methods incur high computational overhead, which often lead to the violation of QoS (Quality of Service) requirements and increases service renting costs of executing workflows. Inspired by the successful application of Deep Reinforcement Learning (DRL) in cloud job scheduling, this paper proposes a real-time workflow scheduling method which combines Genetic Algorithm (GA) and DRL, aiming to reduce both execution cost and response time. To be specific, we design a real-time workflow scheduling algorithm named GA-DQN by combining the global search capability of GA and the environment awareness decision-making capability of DRL to divides scheduling process into two stages. First, the execution scheme of workflow in virtual machine is calculated when workflow arrives. Then, a DRL agent uses this scheme as the feature of workflow to assign workflow to a suitable virtual machine. In this way, the use of DRL to sense environment increases the computational efficiency of GA, and the execution scheme obtained by GA helps DRL to obtain the feature of workflow. On this basis of real world workflow, three groups of simulation experience are carried out to compare GA-DQN with four baseline method which consist of three traditional methods and a state-of-the-art method. The comparison results demonstrate that GA-DQN outperforms the other methods in terms of response time, execution cost, and success rate across different workloads and cloud instance configurations.
Study on HGR by Using Machine Learning

The medical field and other research areas fields heavily rely on artificial intelligence (AI) and machine learning (ML). Hand gesture recognition (HGR), which is a straightforward approach for interacting with machines, has drawn the attention of numerous researchers in the context of AI. Utilizing ML technique, HGR is carried out. Both the image technique and the design approach are parts of it. In the image technique, the hand picture is rebuilt utilizing the attributes of the images. However, when using the model-based approach, the image is recreated using many models, including volumetric, geometric, and others. The key difficulty in gesture control continues to be the ML individual's complexity and preparation time. Its accuracy is used to assess the HGR accuracy. Its precision is used to assess the HGR overall system performance. The many techniques and algorithms utilized in gesture control are covered in detail inside this work.
Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast

This study focuses on the utilization of Large Language Models (LLMs) for the rapid development of applications, with a spotlight on LangChain, an open-source software library. LLMs have been rapidly adopted due to their capabilities in a range of tasks, including essay composition, code writing, explanation, and debugging, with OpenAI’s ChatGPT popularizing their usage among millions ofusers. The crux of the study centers around LangChain, designed to expedite the development of bespoke AI applications using LLMs. LangChain has been widely recognized in the AI community for its ability to seamlessly interact with various data sources and applications. The paper provides an examination of LangChain's core features, including its components and chains, acting as modular abstractions and customizable, use-case-specific pipelines, respectively. Through a series of practical examples, the study elucidates the potential of this framework in fostering the swift development of LLM-based applications.
Cross-scene wetland mapping on hyperspectral remote sensing images using adversarial domain adaptation network

Wetlands are one of the most important ecosystems on the Earth, and using hyperspectral remote sensing (RS) technology for fine wetland mapping is important for restoring and protecting the natural resources of coastal wetlands. However, the high cost in collecting labeled samples and inconsistent acquisition conditions across different geographic regions or scenes lead to difficulties in wetland mapping and classification. To mitigate these difficulties, a spatial–spectral weighted adversarial domain adaptation (SSWADA) network is proposed for the cross-scene wetland mapping using hyperspectral image (HSI). The proposed SSWADA employs an idea of weighted adversarial discrimination to align the feature distribution of source and target scenes, where a generator or feature extractor with joint 2D–3D convolution is used to extract spatial–spectral features of HSI, a weighted discriminator is constructed to perform source instance weighting and a multi-classifier structure is designed to improve the classification performance on target samples. Experimental results on four different tasks show that our SSWADA outperforms existing domain adaptation methods for cross-scene wetland mapping.
The role of digital accounting transformation in the banking industry sector: an integrated model

Purpose The digital transformation revolution has brought outstanding changes to business organizations, especially in the digital accounting transformation domain. Consequently, the purpose of this study is to explore the important role of digital accounting transformation in improving business performance in the context of the banking industry. Design/methodology/approach Data were collected through a questionnaire from the Jordanian bank sector with a sample of 190 respondents. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data and test the hypotheses. Findings The results have shown that the adoption of digital accounting, adoption of FinTech innovation and technological competition are the major drivers for improving business performance. All direct paths leading to improving business performance were found to be significant in the hypothesized directions, while technological savvy was found to indirectly affect the relationship between (the adoption of digital accounting and FinTech innovation) and improving business performance. Originality/value The current study is differentiated from other studies by developing a theoretical research model to incorporate the adoption of digital accounting, adoption of FinTech innovation, technological competition, technological savvy and business performance in the Jordanian context under the digital transformation revolution. For practitioners, the findings provide policymakers with meaningful insight for organizations looking to adopt these digital technologies for improved business performance.
Harnessing microcomb-based parallel chaos for random number generation and optical decision making

Optical chaos is vital for various applications such as private communication, encryption, anti-interference sensing, and reinforcement learning. Chaotic microcombs have emerged as promising sources for generating massive optical chaos. However, their inter-channel correlation behavior remains elusive, limiting their potential for on-chip parallel chaotic systems with high throughput. In this study, we present massively parallel chaos based on chaotic microcombs and high-nonlinearity AlGaAsOI platforms. We demonstrate the feasibility of generating parallel chaotic signals with inter-channel correlation <0.04 and a high random number generation rate of 3.84 Tbps. We further show the application of our approach by demonstrating a 15-channel integrated random bit generator with a 20 Gbps channel rate using silicon photonic chips. Additionally, we achieved a scalable decision-making accelerator for up to 256-armed bandit problems. Our work opens new possibilities for chaos-based information processing systems using integrated photonics, and potentially can revolutionize the current architecture of communication, sensing and computations.
Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach

We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetric modeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach canmodel large architectural environmentswith far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach's ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs.
Deep learning based efficient ship detection from drone-captured images for maritime surveillance

The use of drones to observe ships is an effective means of maritime surveillance. However, the object scale from drone-captured images changes dramatically, presenting a significant challenge for ship detection. Additionally, the limited computing resources on drones make it difficult to achieve fast detection speed. To address these issues, we propose an efficient deep learning based network, namely the YOLOv5-ODConvNeXt, for ship detection from drone-captured images. YOLOv5-ODConvNeXt is a more accurate and faster network designed to improve the efficiency of maritime surveillance. Based on YOLOv5, we implement Omni-dimensional Convolution (ODConv) in the YOLOv5 backbone to boost the accuracy without increasing the network width and depth. We also replace the original C3 block with a ConvNeXt block in YOLOv5 backbone to accelerate detection speed with only a slight decline in accuracy. We test our model on a self-constructed ship detection dataset containing 3200 images captured by drones or with a drone view. The experimental results show that our model achieves 48.0% AP, exceeding the accuracy of YOLOv5s by 1.2% AP. The detection speed of our network is 8.3 ms per image on an NVIDIA RTX3090 GPU, exceeding the detection speed of YOLOv5s by 13.3%. Our code is available at https://github.com/chengshuxiao/YOLOv5-ODConvNeXt.
Multi-factor Sequential Re-ranking with Perception-Aware Diversification

Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, users tend to browse a large number of items in succession, so the previously viewed items have a significant impact on users' behavior towards the following items. Therefore, traditional methods that mainly focus on improving the accuracy of recommended items are suboptimal for feed recommendations because they may recommend highly similar items. For feed recommendation, it is crucial to consider both the accuracy and diversity of the recommended item sequences in order to satisfy users' evolving interest when consecutively viewing items. To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification~(MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. Specifically, MPAD first extracts users' different scales of interests from their behavior sequences through graph clustering-based aggregations. Then, MPAD proposes two sub-models to respectively evaluate the accuracy and diversity of a given item by capturing users' evolving interest due to the ever-changing context and users' personal perception of diversity from an item sequence perspective. This is consistent with the browsing nature of the feed scenario. Finally, MPAD generates the return list by sequentially selecting optimal items from the candidate set to maximize the joint benefits of accuracy and diversity of the entire list. MPAD has been implemented in Taobao's homepage feed to serve the main traffic and provide services to recommend billions of items to hundreds of millions of users every day.