Reviewerly/exHarmony huggingface dataset:
TL-ADA: Transferable Loss-based Active Domain Adaptation

The field of Active Domain Adaptation (ADA) has been investigating ways to close the performance gap between supervised and unsupervised learning settings. Previous ADA research has primarily focused on query selection, but there has been little examination of how to effectively train newly labeled target samples using both labeled source samples and unlabeled target samples. In this study, we present a novel Transferable Loss-based ADA (TL-ADA) framework. Our approach is inspired by loss-based query selection, which has shown promising results in active learning. However, directly applying loss-based query selection to the ADA scenario leads to a buildup of high-loss samples that do not contribute to the model due to transferability issues and low diversity. To address these challenges, we propose a transferable doubly nested loss, which incorporates target pseudo labels and a domain adversarial loss. Our TL-ADA framework trains the model sequentially, considering both the domain type (source/target) and the availability of labels (labeled/unlabeled). Additionally, we encourage the pseudo labels to have low self-entropy and diverse class distributions to improve their reliability. Experiments on several benchmark datasets demonstrate that our TL-ADA model outperforms previous ADA methods, and in-depth analysis supports the effectiveness of our proposed approach.
An automated online proctoring system using attentive-net to assess student mischievous behavior

In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- 'Attentive system' for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation.
Two-qubit quantum photonic processor manufactured by femtosecond laser writing

We present an experimental implementation of a two-qubit photonic quantum processor fabricated using femtosecond laser writing technology. We employ femtosecond laser writing to create a low-loss reconfigurable photonic chip, implementing precise single-qubit and two-qubit operations. We present a careful characterization of the performance of single- and two-qubit gates. An exemplary application of estimating the ground state energy of an H2 molecule using the variational quantum eigensolver algorithm is demonstrated. Our results highlight the potential of the femtosecond laser writing technology to deliver high quality small-scale quantum photonic processors.
Impact of VR Application in an Academic Context

Traditional learning has faced major changes due to the COVID-19 pandemic, highlighting the necessity for innovative education methods. Virtual reality (VR) technology has the potential to change teaching and learning paradigms by providing a gamified, immersive, and engaging education. The purpose of this study is to evaluate the impact of virtual reality in academic context by using a VR software instrument (called EduAssistant). The system’s features such as virtual amphitheater, search by voice recognition, whiteboard, and a video conference system have fostered a sense of connection and community interaction. The study involved 117 students for VR experience, out of which 97 watched a pre-recorded video and 20 students used the VR headset, and an additional 20 students for traditional learning. The students who used the VR headset achieved a significantly higher mean quiz score of 8.31 compared to 7.55 for the traditional learning group with a two-tailed p-value of 0.0468. Over 80% of the total number of participants were satisfied (4 or 5 out of 5) with the experience and the confidence level when searching through voice recognition was over 90%. The study demonstrates that virtual reality is an excellent approach for changing conventional education. The research results, based on samples, simulations, and surveys, revealed a positive impact of VR and its gamification methods on the students’ cognitive performance, engagement, and learning experience. Immersion provided by a virtual assistant tool helped to promote active and deep learning. Experiments based on EduAssistant features suggest that virtual reality is also an effective strategy for future research related to students with disabilities.
EmpMFF: A Multi-factor Sequence Fusion Framework for Empathetic Response Generation

Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.
Integrated use of KOS and deep learning for data set annotation in tourism domain

Purpose The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system. Design/methodology/approach A method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision. Findings The study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation. Originality/value The paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.
Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model’s predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$ </tex-math></inline-formula> , is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Huiimin5/comwin</uri> .
Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT

Blockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves the cross-institution data sharing ability, there still exist the problems of authentication difficulty and privacy leakage. This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing. This BIoMT system is divided into four layers: perceptual, network, platform, and application. The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger. Utilizing the untampered blockchain ledger can protect the privacy of medical data and system users. Then, an anonymous authentication scheme called the group blind signature (GBS) scheme is designed. This scheme can provide anonymity for the signer as that one member can represent the group to sign without exposing his identity. The blind property also can protect the message from being signed as it is anonymous to the signer. Moreover, this GBS scheme is created with the lattice assumption, which makes it more secure against quantum attacks. In addition, the security proof shows that this GBS scheme can achieve the security properties of dynamical-almost-full anonymity, blindness, traceability, and non-frameability. The comparison analysis and performance evaluation of key size show that this GBS scheme is more efficient than similar schemes in other literature.
Neighborhood-Regularized Self-Training for Learning with Few Labels

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.
Feature Importance Ranking of Translationese Markers in L2 Writing: A Corpus-Based Statistical Analysis Across Disciplines

In recent years, an array of studies has focused on ‘translationese’ (i.e., unique features that manifest in translated texts, causing second language (L2) writings to be similar to translated texts but different from native language (L1) writings). This intriguing linguistic pattern has motivated scholars to investigate potential markers for predicting the divergence of L1 and L2 texts. This study builds on this work, evaluating the feature importance ranking of specific translationese markers, including standardized type-token ratio (STTR), mean sentence length, bottom-frequency words, connectives, and n-grams. A random forest model was used to compare these markers in L1 and L2 academic journal article abstracts, providing a robust quantitative analysis. We further examined the consistency of these markers across different academic disciplines. Our results indicate that bottom-frequency words are the most reliable markers across disciplines, whereas connectives show the least consistency. Interestingly, we identified three-word lexical bundles as discipline-specific markers. These findings present several implications and open new avenues for future research into translationese in L2 writing.
Climate concerns and the future of nonfungible tokens: Leveraging environmental benefits of the Ethereum Merge

The world is facing a formidable climate predicament due to elevated greenhouse gas (GHG) emissions from fossil fuels. The preceding decade has also witnessed a dramatic surge in blockchain-based applications, constituting yet another substantial energy consumer. Nonfungible tokens (NFTs) are one such application traded on Ethereum (ETH) marketplaces that have raised concerns about their climate impacts. The transition of ETH from proof of work (PoW) to proof of stake (PoS) is a step toward reducing the carbon footprint of the NFT sector. However, this alone will not address the climate impacts of the growing blockchain industry. Our analysis indicates that NFTs can cause yearly GHG emissions of up to 18% of the peak under the energy-intensive PoW algorithm. This results in a significant carbon debt of 4.56 Mt CO 2 -eq by the end of this decade, equivalent to CO 2 emissions from a 600-MW coal-fired power plant in 1 y which would meet residential power demand in North Dakota. To mitigate the climate impact, we propose technological solutions to sustainably power the NFT sector using unutilized renewable energy sources in the United States. We find that 15% utilization of curtailed solar and wind power in Texas or 50 MW of potential hydropower from existing nonpowered dams can support the exponential growth of NFT transactions. In summary, the NFT sector has the potential to generate significant GHG emissions, and measures are necessary to mitigate its climate impact. The proposed technological solutions and policy support can help promote climate-friendly development in the blockchain industry.
Locally stable sets with minimum cardinality

The nonlocal set has received wide attention over recent years. Shortly before, Li and Wang arXiv:2202.09034 proposed the concept of a locally stable set: The only possible orthogonality preserving measurement on each subsystem is trivial. Locally stable sets present stronger nonlocality than those sets that are just locally indistinguishable. In this work, we focus on the constructions of locally stable sets in multipartite quantum systems. First, two lemmas are put forward to prove that an orthogonality-preserving local measurement must be trivial. Then we present the constructions of locally stable sets with minimum cardinality in bipartite quantum systems ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}\phantom{\rule{4pt}{0ex}}(d\ensuremath{\ge}3)$ and ${\mathbb{C}}^{{d}_{1}}\ensuremath{\bigotimes}{\mathbb{C}}^{{d}_{2}}\phantom{\rule{4pt}{0ex}}(3\ensuremath{\le}{d}_{1}\ensuremath{\le}{d}_{2})$. Moreover, for the multipartite quantum systems ${({\mathbb{C}}^{d})}^{\ensuremath{\bigotimes}n}\phantom{\rule{4pt}{0ex}}(d\ensuremath{\ge}2)$ and ${\ensuremath{\bigotimes}}_{i=1}^{n}{\mathbb{C}}^{{d}_{i}}\phantom{\rule{4pt}{0ex}}(3\ensuremath{\le}{d}_{1}\ensuremath{\le}{d}_{2}\ensuremath{\le}\ensuremath{\cdots}\ensuremath{\le}{d}_{n})$, we also obtain $d+1$ and ${d}_{n}+1$ locally stable orthogonal states, respectively. Fortunately, our constructions reach the lower bound of the cardinality on the locally stable sets, which provides a positive and complete answer to an open problem raised in arXiv:2202.09034.
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We combine this similarity learning with multiple existing object detectors to build Quasi-Dense Tracking (QDTrack), which does not require displacement regression or motion priors. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. In addition, we show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input, enabling a competitive tracking performance without training on videos or using tracking supervision. We conduct extensive experiments on a wide variety of popular MOT benchmarks. We find that, despite its simplicity, QDTrack rivals the performance of state-of-the-art tracking methods on all benchmarks and sets a new state-of-the-art on the large-scale BDD100K MOT benchmark, while introducing negligible computational overhead to the detector.
Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

Abstract In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&amp;E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep‐learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep‐learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention‐based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well‐established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology‐based advanced analytics to meaningfully impact clinical care, specific measures of ‘added benefit’ to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre‐clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM) for mammography image classification. The SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, with an accuracy of 94.10% and a sensitivity of 94.30%. A 10-fold cross-validation was performed to ensure the robustness of the results, and the mean and standard deviation of various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all performance indicators, indicating its superior performance. This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images. The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis. This may have significant implications for reducing breast cancer mortality rates.
Evidence of questionable research practices in clinical prediction models

Abstract Background Clinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the discriminatory ability of a clinical prediction model. The AUC is often interpreted relative to thresholds, with “good” or “excellent” models defined at 0.7, 0.8 or 0.9. These thresholds may create targets that result in “hacking”, where researchers are motivated to re-analyse their data until they achieve a “good” result. Methods We extracted AUC values from PubMed abstracts to look for evidence of hacking. We used histograms of the AUC values in bins of size 0.01 and compared the observed distribution to a smooth distribution from a spline. Results The distribution of 306,888 AUC values showed clear excesses above the thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds. Conclusions The AUCs for some models are over-inflated, which risks exposing patients to sub-optimal clinical decision-making. Greater modelling transparency is needed, including published protocols, and data and code sharing.