Reviewerly/exHarmony huggingface dataset:
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice

Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches. For researchers and practitioners who are interested in taking a participatory approach to AI design and development, it remains challenging to assess the extent to which any participatory approach grants substantive agency to stakeholders. This article thus aims to ground what we dub the “participatory turn” in AI design by synthesizing existing theoretical literature on participation and through empirical investigation and critique of its current practices. Specifically, we derive a conceptual framework through synthesis of literature across technology design, political theory, and the social sciences that researchers and practitioners can leverage to evaluate approaches to participation in AI design. Additionally, we articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners. We use these empirical findings to understand the current state of participatory practice and subsequently provide guidance to better align participatory goals and methods in a way that accounts for practical constraints.
A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking

This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues.
Qur’an burning in Norway: stop the Islamisation of Norway (SIAN) and far-right capture of free speech in a Scandinavian context

Fringe political actors’ Qur’an desecrations in Scandinavia have over the past few years resulted in international media attention, diplomatic crises, calls for boycotts and pressures from Muslim states and organisations to criminalize such acts. This article explores the historical genealogies of Qur’an desecration – a transnational phenomenon – that emerged in the wake of the post-9/11 ‘Global War on Terror’. In Norway, public Qur’an burnings have been integral to the strategies of the far-right organization Stop the Islamisation of Norway (SIAN) since 2019. Inspired by Titley’s notion of far-right “free speech capture”, we demonstrate that Qur’an burning functions as a powerful symbolic means to incite hatred against Islam and Muslims in the name of “free speech”. We contend that Qur’an burning in the Norwegian context lies at the intersection between transnational and national flows and modalities of Islamophobia.
Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

Modern hardware design starts with specifications provided in natural language.These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements.Automating this translation could reduce sources of human error from the engineering process.But, it is only recently that artificial intelligence (AI) has demonstrated capabilities for machine-based end-to-end design translations.Commercially-available instruction-tuned Large Language Models (LLMs) such as OpenAI's ChatGPT and Google's Bard claim to be able to produce code in a variety of programming languages; but studies examining them for hardware are still lacking.In this work, we thus explore the challenges faced and opportunities presented when leveraging these recent advances in LLMs for hardware design.Given that these 'conversational' LLMs perform best when used interactively, we perform a case study where a hardware engineer co-architects a novel 8-bit accumulator-based microprocessor architecture with the LLM according to real-world hardware constraints.We then sent the processor to tapeout in a Skywater 130nm shuttle, meaning that this 'Chip-Chat' resulted in what we believe to be the world's first wholly-AI-written HDL for tapeout.
Amazon Web Services (AWS)

This chapter first examines the AWS global infrastructure, which includes availability zones, regions, edge locations, and regional edge caches. It supports over 200 services in numerous categories. Even though the number of services is constantly expanding, this chapter covers the most essential categories of management and monitoring, security, identity, and compliance, networking, autoscaling, elastic load balancing, compute, storage, and databases. In the lab, you will create a fully operational three-tier web architecture that resides and operates on the AWS infrastructure.
A Color Image-Encryption Algorithm Using Extended DNA Coding and Zig-Zag Transform Based on a Fractional-Order Laser System

With the advancement of information technology, the security of digital images has become increasingly important. To ensure the integrity of images, a novel color image-encryption algorithm based on extended DNA coding, Zig-Zag transform, and a fractional-order laser system is proposed in this paper. First, the dynamic characteristics of the fractional-order laser chaotic system (FLCS) were analyzed using a phase diagram and Lyapunov exponent spectra. The chaotic sequences generated by the system were used to design image-encryption algorithms. Second, a modified Zig-Zag confusing method was adopted to confuse the image. Finally, in the diffusion link, the DNA encoding scheme was extended to allow for a greater number of DNA encoding rules, increasing the randomness of the matrix and improving the security of the encryption scheme. The performance of the designed encryption algorithm is analyzed using key space, a histogram, information entropy, correlation coefficients, differential attack, and robustness analysis. The experimental results demonstrate that the algorithm can withstand multiple decryption methods and has strong encryption capability. The proposed novel color image-encryption scheme enables secure communication of digital images.
Improving Existing Segmentators Performance with Zero-Shot Segmentators

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the "oracle" method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.
Enhancing Self-Learning in Higher Education with Virtual and Augmented Reality Role Games: Students’ Perceptions

This study investigates how virtual and augmented reality role games impact self-learning in higher education settings. A qualitative research–action approach that involved creating augmented reality micro-stories to encourage creativity and critical thinking was used. Through role-playing, students collaborated and gained a deeper understanding of the course, improving their self-learning abilities. The findings indicate that incorporating virtual and augmented reality into higher education positively affects self-learning, promoting active student engagement and meaningful learning experiences. Additionally, students perceive these immersive educational methods as bridging the gap between virtual and in-person learning environments, ultimately leading to enhanced educational results.
Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in medical image generation with specific conditions. Moreover, current medical image generation models have their own problems, limiting their usage in various medical image generation tasks. In this paper, we introduce the use of conditional Denoising Diffusion Probabilistic Models (cDDPMs) for medical image generation, which achieve state-of-the-art performance on several medical image generation tasks.
Non-Uniform Motion Aggregation with Graph Convolutional Networks for Skeleton-Based Human Action Recognition

Skeleton-based human action recognition aims to recognize human actions from given skeleton sequences. The literature utilizes fixed-stride sampling and uniform aggregations, which are independent of the input data and do not focus on representative motion frames. In this paper, to overcome the challenge of the fixed uniform aggregation strategy being unable to focus on discriminative motion information, a novel non-uniform motion aggregation embedded with a graph convolutional network (NMA-GCN) is proposed for skeleton-based human action recognition. Based on the skeleton quality and motion-salient regions, NMA is able to focus on the discriminative motion information of human motion-salient regions. Finally, the aggregated skeleton sequences are embedded with the GCN backbone for skeleton-based human action recognition. Experiments were conducted on three large benchmarks: NTU RGB+D, NTU RGB+D 120, and FineGym. The results show that our method achieves 93.4% (Xsub) and 98.2% (Xview) on NTU RGB+D dataset, 87.0% (Xsub) and 90.0% (Xset) on the NTU RGB+D 120 dataset, and 90.3% on FineGym dataset. Ablation studies and evaluations across various GCN-based backbones further support the effectiveness and generalization of NMA-GCN.
Can ChatGPT Replace Traditional KBQA Models? An In-Depth Analysis of the Question Answering Performance of the GPT LLM Family

ChatGPT is a powerful large language model (LLM) that covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge. Therefore, there is growing interest in exploring whether ChatGPT can replace traditional knowledge-based question answering (KBQA) models. Although there have been some works analyzing the question answering performance of ChatGPT, there is still a lack of large-scale, comprehensive testing of various types of complex questions to analyze the limitations of the model. In this paper, we present a framework that follows the black-box testing specifications of CheckList proposed by [38]. We evaluate ChatGPT and its family of LLMs on eight real-world KB-based complex question answering datasets, which include six English datasets and two multilingual datasets. The total number of test cases is approximately 190,000. In addition to the GPT family of LLMs, we also evaluate the well-known FLAN-T5 to identify commonalities between the GPT family and other LLMs. The dataset and code are available at https://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-GPT-family.git .
BrightsightNet: A lightweight progressive low-light image enhancement network and its application in “Rainbow” maglev train

To address the low-light image (LLI) problem in train driving scenarios, this paper proposes a progressive and lightweight network called BrightsightNet for LLI enhancement. First, to overcome the problem of insufficient local exposure level, two structurally identical light curve parameter estimation sub-networks are used for light enhancement in turn. Second, for real-time inference, an efficient feature extraction operator is proposed that combines depth-separable convolution and attention mechanism. Third, the overall network uses encoder–decoder architecture. For the encoder, the output features of the three layers are fused through skip connections to form an information-rich feature map. For the decoder, a hierarchical decoding approach is used to predict the light curve parameters through the three convolution layers sequentially. Experimental results show that BrightsightNet achieves a user study score (USR) of 4.43 on the proposed dataset, outperforming Zero-DCE++, SCI, RetinexDIP, and RUAS by 0.51, 0.86, 0.64, and 1.39, respectively. Moreover, BrightsightNet has parameters of only 2.6K and a single inference time of 0.052 s, which is an innovative and practical solution for low-light image enhancement in train driving scenarios, contributing to safer and more reliable train operations.
Separate and Locate: Rethink the Text in Text-based Visual Question Answering

Text-based Visual Question Answering (TextVQA) aims at answering questions about the text in images. Most works in this field focus on designing network structures or pre-training tasks. All these methods list the OCR texts in reading order (from left to right and top to bottom) to form a sequence, which is treated as a natural language ''sentence''. However, they ignore the fact that most OCR words in the TextVQA task do not have a semantical contextual relationship. In addition, these approaches use 1-D position embedding to construct the spatial relation between OCR tokens sequentially, which is not reasonable. The 1-D position embedding can only represent the left-right sequence relationship between words in a sentence, but not the complex spatial position relationship. To tackle these problems, we propose a novel method named Separate and Locate (SaL) that explores text contextual cues and designs spatial position embedding to construct spatial relations between OCR texts. Specifically, we propose a Text Semantic Separate (TSS) module that helps the model recognize whether words have semantic contextual relations. Then, we introduce a Spatial Circle Position (SCP) module that helps the model better construct and reason the spatial position relationships between OCR texts. Our SaL model outperforms the baseline model by 4.44% and 3.96% accuracy on TextVQA and ST-VQA datasets. Compared with the pre-training state-of-the-art method pre-trained on 64 million pre-training samples, our method, without any pre-training tasks, still achieves 2.68% and 2.52% accuracy improvement on TextVQA and ST-VQA. Our code and models will be released at https://github.com/fangbufang/SaL.
Arabic Toxic Tweet Classification: Leveraging the AraBERT Model

Social media platforms have become the primary means of communication and information sharing, facilitating interactive exchanges among users. Unfortunately, these platforms also witness the dissemination of inappropriate and toxic content, including hate speech and insults. While significant efforts have been made to classify toxic content in the English language, the same level of attention has not been given to Arabic texts. This study addresses this gap by constructing a standardized Arabic dataset specifically designed for toxic tweet classification. The dataset is annotated automatically using Google’s Perspective API and the expertise of three native Arabic speakers and linguists. To evaluate the performance of different models, we conduct a series of experiments using seven models: long short-term memory (LSTM), bidirectional LSTM, a convolutional neural network, a gated recurrent unit (GRU), bidirectional GRU, multilingual bidirectional encoder representations from transformers, and AraBERT. Additionally, we employ word embedding techniques. Our experimental findings demonstrate that the fine-tuned AraBERT model surpasses the performance of other models, achieving an impressive accuracy of 0.9960. Notably, this accuracy value outperforms similar approaches reported in recent literature. This study represents a significant advancement in Arabic toxic tweet classification, shedding light on the importance of addressing toxicity in social media platforms while considering diverse languages and cultures.
Evolved opposition-based Mountain Gazelle Optimizer to solve optimization problems

A recently established swarm-based algorithm, namely, Mountain Gazelle Optimizer (MGO) which draws inspiration from social structure and hierarchy of wild mountain gazelles is competitive for solving optimization problems. However, the MGO has some drawbacks: when dealing with higher dimensions, early iterations could become stuck in suboptimal search area. It would be difficult for the MGO to abandon the local optimal solution if the early best solutions neglect the relevant search space. Therefore, to overcome these limitations, this paper offers an Evolved Opposition-based Learning (EOBL) mechanism which helps the algorithm to jump out of the local optima while accelerating the convergence speed. This novel mechanism is incorporating with MGO to propose Evolved Opposition-based Mountain Gazelle Optimizer (EOBMGO). The experiments are conducted with CEC2005 and CEC2019 benchmark functions, along with seven engineering challenges to examine the performance of the proposed EOBMGO. Furthermore, the statistical tests, like the t-test and Wilcoxon rank-sum test, are verified and demonstrate that the proposed EOBMGO outperforms the existing top-performing algorithms. The outcomes indicated that the proposed technique may be seen as an efficient and successful approach for complex optimization challenges.
Federated singular value decomposition for high-dimensional data

Abstract Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors corresponding to the samples are never exchanged and the vectors associated with the features are only transmitted to an aggregator for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.
Empowering the Metaverse with Generative AI: Survey and Future Directions

This paper aims to motivate the development of the metaverse by highlighting the potential of artificial-intelligence-generated content (AIGC) for the metaverse. We present the first literature review on AIGC in the metaverse with state-of-the-art research classified into 5 key application areas (avatars and Non-player Characters (NPCs), content creation, virtual world generation, automatic digital twin, and personalization). Having noticed a notable gap in research through our review, we propose ways in which state-of-the-art generative AI can be applied to the metaverse. Additionally, we offer a roadmap for future research with related ethical implications.
Motion Pattern-Based Scene Classification Using Adaptive Synthetic Oversampling and Fully Connected Deep Neural Network

Analyzing crowded environments has become an increasingly researched topic in computer vision community, largely due to its myriad practical applications, including enhanced video surveillance systems and the estimation of crowd density in specific settings. This paper presents a comprehensive method for progressing the study of crowd dynamics and behavioral analysis, specifically focusing on the classification of movement patterns. We introduce a specialized neural network-based classifier explicitly designed for the accurate categorization of various crowd scenes. This classifier fills a unique niche in the existing literature by offering robust and adaptive classification capabilities. To optimize the performance of our model, we conduct an in-depth analysis of loss functions commonly employed in multi-class classification tasks. Our study encompasses four widely used loss functions: Focal Loss, Huber Loss, Cross-Entropy Loss, and Multi-Margin Loss. Based on empirical findings, we introduce a Joint Loss function that combines the strengths of Cross-Entropy and Multi-Margin Loss, outperforming existing methods across key performance metrics such as accuracy, precision, recall, and F1-score. Furthermore, we address the critical challenge of class imbalance in motion patterns within crowd scenes. To this end, we perform a comprehensive comparative study of two leading oversampling techniques: the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Our results indicate that ADASYN is superior at enhancing classification performance. This approach not only mitigates the issue of class imbalance but also provides robust empirical validation for our proposed method. Finally, we subject our model to a rigorous evaluation using the Collective Motion Database, facilitating a comprehensive comparison with existing state-of-the-art techniques. This evaluation confirms the effectiveness of our model and aligns it with established paradigms in the field.