Reviewerly/exHarmony huggingface dataset:
Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning

Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses.We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset.The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly.Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.
The ‘hijacking’ of the Scandinavian Journal of Information Systems: Implications for the information systems community

Abstract Journal hijacking, which refers to the attempted brand takeover of a journal by a third party, is a nascent threat confronting the information systems (IS) community, as evidenced by cybercriminals having established an online presence, masquerading as the Scandinavian Journal of Information Systems ( SJIS ). The SJIS hijacking damages the journal's reputation, leads to payment and publication scams, involves identity theft among unsuspecting IS researchers, and results in tarnished author reputations. Beyond SJIS , journal hijacking presents a threat, not only to the IS community, but also to science and academic integrity in general if researchers and readers cannot distinguish between fake publications by hijacked journals and real publications by legitimate journals. In this opinion article, we relate the story of the SJIS hijacking from the victims' perspectives. We describe its many aspects, draw attention to the key factors that contribute to the problem, and offer our perspectives on different response strategies in the absence of simple solutions. We hope to create awareness about the problem and stimulate a discussion in the IS community, not least in the face of digital innovations, such as ChatGPT and other artificial intelligence technologies that may inadvertently support paper mills and the production of fake research results.
Morello MicroPython: A Python Interpreter for CHERI

Arm Morello is a prototype system that supports CHERI hardware capabilities for improving runtime security. As Morello becomes more widely available, there is a growing effort to port open source code projects to this novel platform. Although high-level applications generally need minimal code refactoring for CHERI compatibility, low-level systems code bases require significant modification to comply with the stringent memory safety constraints that are dynamically enforced by Morello. In this paper, we describe our work on porting the MicroPython interpreter to Morello with the CheriBSD OS. Our key contribution is to present a set of generic lessons for adapting managed runtime execution environments to CHERI, including (1) a characterization of necessary source code changes, (2) an evaluation of runtime performance of the interpreter on Morello, and (3) a demonstration of pragmatic memory safety bug detection. Although MicroPython is a lightweight interpreter, mostly written in C, we believe that the changes we have implemented and the lessons we have learned are more widely applicable. To the best of our knowledge, this is the first published description of meaningful experience for scripting language runtime engineering with CHERI and Morello.
Blockchains, Internet of Value, and Smart Transactions

The blockchain is a decentralized and distributed digital ledger—an open database with a pattern of sharable and unmodifiable data that are sequenced in chronological order. This new digital infrastructure is validated with mathematical consensus rather than by humans. Practical applications go well beyond the controversial cryptocurrencies, even thanks to smart contracts and the digital scalability of innovative business models. Blockchain technology can be used for e-commerce, for the recording of copyright data, or to track digital access. Thanks to the Internet of Value, transactions do not require intermediaries, exploiting digital networks where different players interact and contribute to value co-creation. The legal nature of the blockchain (public, private, or part of a consortium) and its revenue-driven business model are prerequisites for the appraisal. Utilization value for adopters (regarding lower costs, higher reliability of data, immediacy, etc.) should be considered. The valuation shares similarities with other digital intangibles (database, Internet of Things, big data, etc.) and is primarily founded on the cost savings that derive from the use of blockchains. The valuation of blockchains can be complemented with an estimate of the value that they add to traditional businesses. Blockchains revolutionize the possibilities for individual ownership and market exchanges of any digital asset.
CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

Transformers, due to their ability to learn long-range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning.However, their multi-head attention module only captures global-level feature representations, which is insufficient for medical images.To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images.The proposed CB-HVT comprises five modules to effectively identify lymphocytes.Its (i) channel generation module, uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners.These boosted channels are first concatenated and ranked using an attention mechanism in the (ii) channel exploitation module.A fusion block is then utilized in the (iii) channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network's learning representations.The CB-HVT also employs a proposal network in its (iv) region aware module followed by its (v) detection and segmentation head to accurately identify and distinguish objects, even in the regions with crowded presence and artifacts.We evaluated the proposed CB-HVT on two publicly available datasets for lymphocyte assessment in histopathological images.The results demonstrate that CB-HVT has a good generalization on unseen data therefore, it can serve as a valuable tool for pathologists.
Applying mmWave Radar Sensors to Vocabulary-Level Dynamic Chinese Sign Language Recognition for the Community With Deafness and Hearing Loss

To facilitate human–computer interaction (HCL) for the community with deafness and hearing loss (D&HL), this article explored the feasibility of recognizing a vocabulary of dynamic Chinese sign language (CSL) based on millimeter-wave (mmWave) radar sensors within the scope of data science. Fundamental problems that challenge its applications on computers and other electronic devices were addressed from multidisciplinary aspects including how to capture signs with an mmWave radar sensor, how to characterize signs using mmWave, and how to recognize signs based on the extracted features. Accordingly, this article proposed tentative solutions for the key concerns on this topic. A case study was later given by constructing a lightweight attentive augmented convolutional neural network (CNN) to classify 15 Chinese sign words based on radar spectrograms. The network achieved a 98.82% weight reduction and a 3.7% accuracy improvement over the original ResNet-18. Furthermore, a deep convolutional generated adversarial network for data augmentation was used to alleviate the conflicts between large numbers of parameters and small data sample sizes. This article shows the keenness of the authors to witness more practical developments in this new area with interdisciplinary collaboration and evolutionary process.
Efficient Algorithm for Binary Quadratic Problem by Column Generation and Quantum Annealing

We propose an efficient algorithm that combines column generation and quantum annealing to solve binary quadratic problems.Binary quadratic problems are difficult to solve because they are NP-hard.An attempt to solve binary quadratic problems efficiently by column generation has been studied, but it demands successively solving quadratic unconstrained binary optimization problems.We solve the bottleneck by using quantum annealing or simulated annealing.Our results demonstrate a good approximate solution obtained in 2.7 to 1000 times shorter computational time to use column generation and quantum annealing to solve binary quadratic problems than the existing fast solver.
Unveiling the Tricks: Automated Detection of Dark Patterns in Mobile Applications

Mobile apps bring us many conveniences, such as online shopping and communication, but some use malicious designs called dark patterns to trick users into doing things that are not in their best interest. Many works have been done to summarize the taxonomy of these patterns and some have tried to mitigate the problems through various techniques. However, these techniques are either time-consuming, not generalisable or limited to specific patterns. To address these issues, we propose UIGuard, a knowledge-driven system that utilizes computer vision and natural language pattern matching to automatically detect a wide range of dark patterns in mobile UIs. Our system relieves the need for manually creating rules for each new UI/app and covers more types with superior performance. In detail, we integrated existing taxonomies into a consistent one, conducted a characteristic analysis and distilled knowledge from real-world examples and the taxonomy. Our UIGuard consists of two components, Property Extraction and Knowledge-Driven Dark Pattern Checker. We collected the first dark pattern dataset, which contains 4,999 benign UIs and 1,353 malicious UIs of 1,660 instances spanning 1,023 mobile apps. Our system achieves a superior performance in detecting dark patterns (micro averages: 0.82 in precision, 0.77 in recall, 0.79 in F1 score). A user study involving 58 participants further showed that UIGuard significantly increases users' knowledge of dark patterns. We demonstrated potential use cases of our work, which can benefit different stakeholders, and serve as a training tool for raising awareness of dark patterns.
CriTrainer: An Adaptive Training Tool for Critical Paper Reading

Learning to read scientific papers critically, which requires first grasping their main ideas and then raising critical thoughts, is important yet challenging for novice researchers. The traditional ways to develop critical paper reading (CPR) skills, e.g., checking general tutorials or taking reading courses, often can not provide individuals with adaptive and accessible support. In this paper, we first derive user requirements of a CPR training tool based on literature and a survey study (N=52). Then, we develop CriTrainer , an interactive tool for CPR training. It leverages text summarization techniques to train readers' skills in grasping the paper's main ideas. It further utilizes template-based generated questions to help them learn how to raise critical thoughts. A mixed-design study (N=24) shows that compared to a baseline tool with general CPR guidance, students trained by CriTrainer perform better in independently raising critical thinking questions on a new paper. We conclude with design considerations for CPR training tools.
Aerial Image Object Detection with Vision Transformer Detector (ViTDet)

The past few years have seen an increased interest in aerial image object detection due to its critical value to large-scale geoscientific research like environmental studies, urban planning, and intelligence monitoring. However, the task is very challenging due to the bird’s-eye view perspective, complex backgrounds, large and various image sizes, and the scarcity of well-annotated datasets. Recent advances in computer vision have shown promise tackling the challenge. Specifically, Vision Transformer Detector (ViTDet) was proposed to extract multi-scale features for object detection. The empirical study shows that ViTDet’s simple design achieves good performance on natural scene images and can be easily embedded into any detector architecture. To date, ViTDet’s potential benefit to challenging aerial image object detection has not been explored. As such, we carried out experiments to evaluate the effectiveness of ViTDet for aerial image object detection on three well-known datasets: Airbus Aircraft, RarePlanes, and Dataset of Object DeTection in Aerial images (DOTA). Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on object detection by a large margin (up to 17% on average precision) and that it achieves the competitive performance for oriented bounding box (OBB) object detection.
DAMP-YOLO: A Lightweight Network Based on Deformable Features and Aggregation for Meter Reading Recognition

With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantities. Most meter readings are still manually taken on-site, and some are even taken in high-risk locations such as hazardous chemical storage. However, existing methods often fail to provide real-time detections or result in misreadings due to the complex nature of natural environments. Thus, we propose a lightweight network called DAMP-YOLO. It combines the deformable CSP bottleneck (DCB) module, aggregated triplet attention (ATA) mechanism, meter data augmentation (MDA), and network pruning (NP) with the YOLOv8 model. In the meter reading recognition dataset, the model parameters decreased by 30.64% while mAP50:95 rose from 87.92% to 88.82%, with a short inference time of 129.6 ms for the Jetson TX1 intelligent car. In the VOC dataset, our model demonstrated improved performance, with mAP50:95 increasing from 41.03% to 45.64%. The experimental results show that the proposed model is competitive for general object detection tasks and possesses exceptional feature extraction capabilities. Additionally, we have devised and implemented a pipeline on the Jetson TX1 intelligent vehicle, facilitating real-time meter reading recognition in situations where manual interventions are inconvenient and hazardous, thereby confirming its feasibility for practical applications.
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems

.It is often desirable to summarize a probability measure on a space \(X\) in terms of a mode, or MAP estimator, i.e., a point of maximum probability. Such points can be rigorously defined using masses of metric balls in the small-radius limit. However, the theory is not entirely straightforward: the literature contains multiple notions of mode and various examples of pathological measures that have no mode in any sense. Since the masses of balls induce natural orderings on the points of \(X\), this article aims to shed light on some of the problems in nonparametric MAP estimation by taking an order-theoretic perspective, which appears to be a new one in the inverse problems community. This point of view opens up attractive proof strategies based upon the Cantor and Kuratowski intersection theorems; it also reveals that many of the pathologies arise from the distinction between greatest and maximal elements of an order, and from the existence of incomparable elements of \(X\), which we show can be dense in \(X\), even for an absolutely continuous measure on \(X = \mathbb{R}\).KeywordsBayesian inverse problemslocal behavior of measuresmaximum a posteriori estimationmodes of probability measuresorders on metric spacesMSC codes06F9928A7528C1560B0562F1062R20
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI

While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not assess XAI methods holistically in the sense that they do not treat explanations' effects on humans as a complex user experience. To tackle this challenge, we propose to adapt the User-Centric Evaluation Framework used in recommender systems: we integrate explanation aspects, summarise explanation properties, indicate relations between them, and categorise metrics that measure these properties. With this comprehensive evaluation framework, we hope to contribute to the human-centred standardisation of XAI evaluation.
Evaluating Self-attention Interpretability Through Human-Grounded Experimental Protocol

Attention mechanisms have played a crucial role in the development of complex architectures such as Transformers in natural language processing. However, Transformers remain hard to interpret and are considered as black-boxes. In this paper we assess how attention coefficients from Transformers help in providing classifier interpretability when properly aggregated. A fast and easy-to-implement way of aggregating attention is proposed to build local feature importance. A human-grounded experiment is conducted to evaluate and compare this approach to other usual interpretability methods. The experimental protocol relies on the capacity of an interpretability method to provide explanation in line with human reasoning. Experiment design includes measuring reaction times and correct response rates by human subjects. Attention performs comparably to usual interpretability methods and significantly better than a random baseline regarding average participant reaction time and accuracy. Moreover, data analysis highlights that high probability prediction induces great explanation relevance. This work shows how self-attention can be aggregated and used to explain Transformer classifiers. The low computational cost of attention compared to other interpretability methods and its availability by design within Transformer classifiers make it particularly beneficial. Finally, the quality of its explanation depends strongly on the certainty of the classifier’s prediction related to it.
Knowledge Injection to Counter Large Language Model (LLM) Hallucination

A shortfall of Large Language Model (LLM) content generation is hallucination, i.e., including false information in the output. This is especially risky for enterprise use cases that require reliable, fact-based, controllable text generation at scale. To mitigate this, we utilize a technique called Knowledge Injection (KI), where contextual data about the entities relevant to a text-generation task is mapped from a knowledge graph to text space for inclusion in an LLM prompt. Using the task of responding to online customer reviews of retail locations as an example, we have found that KI increases the count of correct assertions included in generated text. In a qualitative review, fine-tuned bloom-560m with KI outperformed a non-fine-tuned text-davinci-003 model from OpenAI, though text-davinci-003 has 300 times more parameters. Thus, the KI method can increase enterprise users' confidence leveraging LLMs to replace tedious manual text generation and enable better performance from smaller, cheaper models.
Makers, Not Users: Inscriptions of Design in the Development of Postdigital Technology Education

Abstract This paper directs attention to a recent government-led school development initiative in Denmark, where a broad concept and practice of design became central to imagining and reworking technology education for the postdigital condition, in which schoolchildren were imagined to become makers, not users of technological society. Using an analytical apparatus inspired by Science and Technology Studies (STS), the paper examines how and what understandings of design were propagated in the trial of technology comprehension in the public school. The paper examines two design models as didactic-pedagogical translation of design into the epistemic practices of the public school, articulated in the intellectual traditions of Bildung and Didaktik . Examining the knowledges that make knowledges, the paper contributes an elucidation of the features, capacities, and limitations of the concept and practice of design, as it is mobilised to make knowledge and subjectivities about agency and change for schooling in the postdigital educational future.
Spatial analysis of physical reservoir computers

Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly-energy-efficient devices capable of solving machine-learning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, the chosen dynamical system must have two desirable properties: nonlinearity and memory. We present task-agnostic spatial measures to locally measure both of these properties and exemplify them for a specific physical reservoir based upon magnetic skyrmion textures. In contrast to typical reservoir-computing metrics, these metrics can be resolved spatially and in parallel from a single input signal, allowing for efficient parameter searches to design efficient and high-performance reservoirs. Additionally, we show the natural trade-off between memory capacity and nonlinearity in our reservoir's behavior, both locally and globally. Finally, by balancing the memory and nonlinearity in a reservoir, we can improve its performance for specific tasks.