Reviewerly/exHarmony huggingface dataset:
Multimodal Data Augmentation for Image Captioning using Diffusion Models

Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation method, leveraging a recent text-to-image model called Stable Diffusion, to expand the training set via high-quality generation of image-caption pairs. Extensive experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods, and particularly a significant boost when having fewer training instances. In addition, models trained on our augmented datasets also outperform prior unpaired image captioning methods by a large margin. Finally, further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data based on quality assessment.
Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation

Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems. Existing fairness-aware approaches ignore accounting for rich user and item attributes and thus cannot capture the impact of attributes on affecting recommendation fairness. These real-world attributes severely cause unfair recommendations by favoring items with popular attributes, leading to item exposure unfairness in recommendations. Moreover, existing approaches mostly mitigate unfairness for static recommendation models, e.g., collaborative filtering. Static models can not handle dynamic user interactions with the system that reflect users’ preferences shift through time. Thus, static models are limited in their ability to adapt to user behavior shifts to gain long-run user satisfaction. As user and item attributes are largely involved in modern recommenders and user interactions are naturally dynamic, it is essential to develop a novel method that eliminates unfairness caused by attributes meanwhile embrace the dynamic modeling of user behavior shifts. In this paper, we propose Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation (Fair-HINpolicy) , which uses recent advances in context-aware off-policy learning to produce fairness-aware recommendations with rich attributes from a Heterogeneous Information Network. In particular, we formulate the off-policy learning as a Constrained Markov Decision Process (CMDP) by dynamically constraining the fairness of item exposure at each iteration. We also design an attentive action sampling to reduce the search space for off-policy learning. Our solution adaptively receives HIN-augmented corrections for counterfactual risk minimization, and ultimately yields an effective policy that maximizes long-term user satisfaction. We extensively evaluate our method through simulations on large-scale real-world datasets, obtaining favorable results compared with state-of-the-art methods.
Exploring Transformational Head Teachers' Practices of Digitalization in the Primary School

Digitalization in education is crucial because it transforms conventional learning strategies and offers new options for both students and teachers. This research aims to look into how transformational headteachers are practicing digitalization in primary schools in Bangladesh. This study follows a mixed method that includes both qualitative and quantitative approaches. At first transformational leadership self-assessment scale, which McShane et al. developed based on the research of Podsakoff et al., was used to measure transformational head teachers. Then 16 head teachers, 16 teachers, and 16 FGDs were conducted to get in-depth views. This study indicates that primary school head teachers who demonstrate transformational leadership have the vision to digitalize their schools because they are conscious of the digitalization impact, but due to financial barriers, resource shortage, and infrastructural constraints, lack of skills among them and class teachers, head teachers cannot practice digitalization in their school, which is the barrier for ensuring quality education.
The Impact of a Lack of Information and Communication Technologies at Rural Schools

The purpose of this chapter is to present a literature review on issues related to the integration of information and communication technologies (ICTs) in especially South African rural schools. Against the background of the impact of digitalization in a changing educational environment, the chapter looks at some of the theories and models that had been developed in that specific field, based on empirical findings from various authors. The review also outlines some of the literature that had touched on this topic, as well as the respective findings from such studies and the conclusions reached on the impact of a lack of ICTs at rural schools.
HR-ASPP: An improved semantic segmentation model of cervical nucleus images with accurate spatial localization and better shape feature extraction based on Deeplabv3+

Cervical cancer, the fourth most common fatal cancer in women, has a considerably increased cure rate if identified and treated at early stages. Computer-aided diagnosis technology is the key for scaling up cervical cancer screening. Computer-processed images of nuclei can help doctors better analyze and diagnose the extent of cancer lesions. However, the lesion characteristics of cervical cancer are mainly reflected in the cell nuclei, which will be significantly larger, with distorted boundary shapes and deepened colors. What's worse is that the nuclei of diseased cells may be hidden in cell clusters, making it difficult for existing medical image segmentation models to achieve satisfactory results. In this paper, based on Deeplabv3+, we propose a HR-ASPP model with accurate spatial localization and better shape feature extraction to segment cervical nuclei. Firstly, HR-ASPP uses HRNet as the backbone network, which has good feature extraction ability for small targets. The structure of parallel multi-resolution and repeated multi-scale fusion makes the target information avoid losing in the deep network, so as to better extract the location information of cell nuclei. Secondly, we devise a lighter weight deformable convolution. The proposed HR-ASPP replaces part of the convolution in HRNet with a lighter weight deformable convolution to learn the edge shape of aberrant nuclei better while reducing the network computation. In addition, HR-ASPP incorporate CARAFE, which is able to aggregate information within a large receptive field and dynamically adapt to the content of a particular instance. The HR-ASPP method proposed in this paper achieves about 50.64%, 84.98% intersection over union (IoU) respectively on our dataset and the public datasets-ISBI. The results indicate that the proposed method achieves the best performance when compared with state-of-the-art approaches for pathologic image segmentation.
Coloring and fusing architectural sketches by combining a Y‐shaped generative adversarial network and a denoising diffusion implicit model

Abstract Sketch‐based architectural design tasks require considerable time and effort. However, developing an artificial intelligence agent to color and fuse architectural sketches is challenging due to the imaginative nature of the task and technical limitations. Here, we first introduce Y‐shaped generative adversarial networks (GANs), which can color sketches on the basis of temporarily specified reference images by embedding an attention mechanism model that can match spatial and color information and by maintaining a type of triangular equilibrium state. Then, we develop practicable methods for coloring and fusing the given sketches simultaneously using a combination of our Y‐shaped GANs and the denoising diffusion implicit model, yielding results with high quality and veracity. A novel interpolation process is developed as a necessary trick for realizing the fusion function. The testing results indicate that this method works better than the existing methods for the tasks of coloring and fusing architectural sketches.
Design anthropological foresighting: Reframing automated futures

This article introduces design anthropological foresighting. It does so through the example of automated futures and demonstrates how this approach can be applied to investigate, understand and reframe dominant assumptions about how people will live with emerging technologies and energy systems in possible futures. In doing so we: outline the theoretical and methodological principles of design anthropological foresighting, and the implications this has for understanding automated futures; exemplify our approach through a discussion of research into digital energy futures; and demonstrate our reframing practice. We propose that design anthropological foresighting adds new layers of empirical and analytical knowledge to foresighting practice and invite researchers to join us in developing it further.
Not So New Kid on the Block: Accounting and Valuation Aspects of Non-Fungible Tokens (NFTs)

Aggregated trading volume in February 2023 across the leading six NFT marketplaces totalled USD 1.89 billion. This reflects a continuing positive trajectory, marked by a 91.9% month-on-month (MoM) growth from January 2023, where NFT trading volume amounted to USD 987.9 million. This study conducts a systematic review and textual analysis of industry and academic articles on NFTs primarily related to Accounting, Finance, and Information Systems where the NFT is treated as a tradable digital asset. The sample period spans 2012 to 30 June 2023, using an initial set of 5549 and a final set of 146 articles. In addition, the authors develop an NFT valuation framework, using Scopus bibliometrics data and public domain materials, that can aid in the fair valuation of NFTs and understanding their accounting implications. We further examine the accounting implications of NFTs in terms of international accounting standards, fair value recognition, taxation, auditing, and the metaverse. NFTs have the potential to become a cross-technology and cross-field topic, attracting interest from auditors, accountants, financial institutions, accounting professional bodies, regulators, governments, and investors.
Practical quantum imaging with undetected photons

Infrared (IR) imaging is invaluable across many scientific disciplines, from material analysis to diagnostic medicine. However, applications are often limited by detector cost, resolution and sensitivity, noise caused by the thermal IR background, and the cost, portability and tunability of infrared sources. Here, we describe a compact, portable, and low-cost system that is able to image objects at IR wavelengths without an IR source or IR detector. This imaging with undetected photons (IUP) approach uses quantum interference and correlations between entangled photon pairs to transfer image information from the IR to a wavelength which can be detected with a standard silicon camera. We also demonstrate a rapid analysis approach to acquire both phase and transmission image information. These developments provide an important step towards making IUP a commercially viable technique.
Education Prototyping: a Methodological Device for Technical Democracy

Abstract The potential of applying a ‘technical democracy’ (Callon et al. 2009) to the context of sociotechnical controversies in education is the focus of this paper. This process reflects an emergent 'thought collective' (Fleck 1979) whose common interests, yet diverse expertise, are articulated through provisional objects and infrastructure for collective and experimental knowledge production. The technique of ‘prototyping’ was then deployed for a design experiment to: first, slow down, or suspend, existing power relations of co-evolving technologies and methodologies and, second, to accelerate, or expand, new possibilities and configurations for democratisation. Education prototyping is then introduced, with the intent to co-produce pluralistic spaces that expose challenges and test possibilities. Key aspects include the following: (i) prototyping dynamics: problematization and prefiguration; and, (ii) prototyping practices: spanning the temporal, methodological, relational, material, and spatial. These aspects were tested in the context of a research project exploring automated essay scoring in Australian schools. While always situated and partial, we argue that prototyping offers a unique device to interrupt and experiment with the politics of collaboratively researching increasingly networked and commercialised technologies across education and society.
Benchmarking quantum logic operations relative to thresholds for fault tolerance

Abstract Contemporary methods for benchmarking noisy quantum processors typically measure average error rates or process infidelities. However, thresholds for fault-tolerant quantum error correction are given in terms of worst-case error rates—defined via the diamond norm—which can differ from average error rates by orders of magnitude. One method for resolving this discrepancy is to randomize the physical implementation of quantum gates, using techniques like randomized compiling (RC). In this work, we use gate set tomography to perform precision characterization of a set of two-qubit logic gates to study RC on a superconducting quantum processor. We find that, under RC, gate errors are accurately described by a stochastic Pauli noise model without coherent errors, and that spatially correlated coherent errors and non-Markovian errors are strongly suppressed. We further show that the average and worst-case error rates are equal for randomly compiled gates, and measure a maximum worst-case error of 0.0197(3) for our gate set. Our results show that randomized benchmarks are a viable route to both verifying that a quantum processor’s error rates are below a fault-tolerance threshold, and to bounding the failure rates of near-term algorithms, if—and only if—gates are implemented via randomization methods which tailor noise.
Quantised data-driven iterative learning bipartite consensus control for unknown heterogeneous linear MASs with varying trial lengths

This paper aims to realise the robust output bipartite consensus for unknown heterogeneous linear time-varying multiagent systems (MASs) subject to varying trial lengths, unknown measurement disturbances and data quantisation. To this end, inspired by the idea of quantised control, a quantised data-driven adaptive iterative learning bipartite consensus (AILBC) method is proposed. Specifically, to address the problem of varying trial lengths, a distributed auxiliary output prediction system is constructed based on the agents' input-output (I/O) dynamic relationship. An adaptive update protocol is developed to estimate the measurement disturbances and unknown parameters of I/O dynamic relationship. Subsequently, a quantised distributed data-driven iterative learning control (ILC) approach based on the quantised output information is proposed for MASs to achieve robust bipartite consensus tracking, with an attempt to relax the need of explicit model information. The bipartite consensus tracking errors are ultimately bounded through rigorous analysis, and this result is further extended to switching topologies. Finally, numerical simulations are conducted to verify the validity of the AILBC method.
<scp>cyto‐Knet</scp>: An instance segmentation approach for multiple myeloma plasma cells using conditional kernels

Abstract Multiple myeloma disrupts normal blood cell production, requiring early detection due to the increased risk of bone metastases. Although various artificial intelligence (AI) methods have been developed to assist pathologists, they often lack comprehensive metrics to measure both detection and segmentation errors. This study presents a hybrid framework that combines deep learning and heuristic techniques to achieve accurate instance segmentation of individual plasma cells in Giemsa‐stained cytology images. Our proposed network, called cyto‐Knet, incorporates an innovative color‐balancing method as a preprocessing step to standardize the appearance of cytological images. Our network leverages a 4‐class segmentation strategy with conditional kernels to enhance segmentation performance and accuracy. Additionally, a marker‐based watershed algorithm is employed in the postprocessing step to address the challenge of merged objects. Extensive validation at both pixel and object‐based levels demonstrates superior performance compared with state‐of‐the‐art techniques. Our method achieves pixel‐based metrics (precision, recall, and F1‐score) of approximately 0.90. The object‐based evaluation reveals an average Dice coefficient of 0.9130 and an aggregated Jaccard index of 0.8237. Importantly, our solution is designed for integration into an end‐to‐end system for diagnosis support and can be easily extended to other applications.
An Unbiased Quantum Random Number Generator Based on Boson Sampling

Abstract It is proven that Boson sampling is a much promising model of optical quantum computation, which is applied to designing quantum computer successfully, such as “Jiuzhang” . However, the meaningful randomness of Boson sampling results has not been utilized or exploited. In this research, Boson sampling is applied to design a quantum random number generator (QRNG) by fully exploiting the randomness of Boson sampling results, and its prototype system is constructed with the programmable silicon photonic processor, which can generate unbiased random sequences and overcome the shortcomings of the existing discrete QRNGs such as source‐restricted, high demand for the photon number resolution capability of detector and slow self‐detection generator speed. Boson sampling is implemented as a random entropy source, and random bit strings with satisfactory randomness and uniformity can be obtained after post‐processing the sampling results. It is the first approach for applying the randomness of Boson sampling results to develop a practical prototype system for actual tasks, and the experiment results demonstrate that the designed Boson sampling‐based QRNG prototype system passes 15 tests of the NIST SP 800‐22 statistical test component, which proves that Boson sampling has great potential for practical applications with desirable performance besides quantum advantage.
Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval

The rapid development of deep learning and artificial intelligence has transformed our approach to solving scientific problems across various domains, including computer vision, natural language processing, and automatic content generation. Information retrieval (IR) has also experienced significant advancements, with natural language understanding and multimodal content analysis enabling accurate information retrieval. However, the widespread adoption of neural networks has also influenced the focus of IR problem-solving, which nowadays predominantly relies on evaluating the similarity of dense vectors derived from the latent spaces of deep neural networks. Nevertheless, the challenges of conducting similarity searches on large-scale databases with billions of vectors persist. Traditional IR approaches use inverted indices and vector space models, which work well with sparse vectors. In this paper, we propose Vec2Doc, a novel method that converts dense vectors into sparse integer vectors, allowing for the use of inverted indices. Preliminary experimental evaluation shows a promising solution for large-scale vector-based IR problems.
Non-volatile materials for programmable photonics

Programmable photonics play a crucial role in many emerging applications, from optical accelerators for machine learning to quantum information technologies. Conventionally, photonic systems are tuned by mechanisms such as the thermo-optic effect, free carrier dispersion, the electro-optic effect, or micro-mechanical movement. Although these physical effects allow either fast (&amp;gt;100 GHz) or large contrast (&amp;gt;60 dB) switching, their high static power consumption is not optimal for programmability, which requires only infrequent switching and has a long static time. Non-volatile materials, such as phase-change materials, ferroelectrics, vanadium dioxide, and memristive metal oxide materials, can offer an ideal solution thanks to their reversible switching and non-volatile behavior, enabling a truly “set-and-forget” programmable unit with no static power consumption. In recent years, we have indeed witnessed the fast adoption of non-volatile materials in programmable photonic systems, including photonic integrated circuits and free-space meta-optics. Here, we review the recent progress in the field of programmable photonics, based on non-volatile materials. We first discuss the material’s properties, operating mechanisms, and then their potential applications in programmable photonics. Finally, we provide an outlook for future research directions. The review serves as a reference for choosing the ideal material system to realize non-volatile operation for various photonic applications.
That's What I Said: Fully-Controllable Talking Face Generation

The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while eliminating identity information. To disentangle identity and motion, we introduce an orthogonality constraint between the two different latent spaces. From this, our method can generate natural-looking talking faces with fully controllable facial attributes and accurate lip synchronisation. Extensive experiments demonstrate that our method achieves state-of-the-art results in terms of both visual quality and lip-sync score. To the best of our knowledge, we are the first to develop a talking face generation framework that can accurately manifest full target facial motions including lip, head pose, and eye movements in the generated video without any additional supervision beyond RGB video with audio.
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning

Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit various complex downstream tasks, including cross-modal image-text retrieval and image classification. Despite its promising prospect, the security issue of cross-modal pre-trained encoder has not been fully explored yet, especially when the pre-trained encoder is publicly available for commercial use.