Reviewerly/exHarmony huggingface dataset:
Unpaired Multi-Domain Stain Transfer for Kidney Histopathological Images

As an essential step in the pathological diagnosis, histochemical staining can show specific tissue structure information and, consequently, assist pathologists in making accurate diagnoses. Clinical kidney histopathological analyses usually employ more than one type of staining: H&amp;E, MAS, PAS, PASM, etc. However, due to the interference of colors among multiple stains, it is not easy to perform multiple staining simultaneously on one biological tissue. To address this problem, we propose a network based on unpaired training data to virtually generate multiple types of staining from one staining. Our method can preserve the content of input images while transferring them to multiple target styles accurately. To efficiently control the direction of stain transfer, we propose a style guided normalization (SGN). Furthermore, a multiple style encoding (MSE) is devised to represent the relationship among different staining styles dynamically. An improved one-hot label is also proposed to enhance the generalization ability and extendibility of our method. Vast experiments have demonstrated that our model can achieve superior performance on a tiny dataset. The results exhibit not only good performance but also great visualization and interpretability. Especially, our method also achieves satisfactory results over cross-tissue, cross-staining as well as cross-task. We believe that our method will significantly influence clinical stain transfer and reduce the workload greatly for pathologists. Our code and Supplementary materials are available at https://github.com/linyiyang98/UMDST.
ClueWeb22: 10 Billion Web Documents with Rich Information

ClueWeb22, the newest iteration of the ClueWeb line of datasets, is the result of more than a year of collaboration between industry and academia. Its design is influenced by the research needs of the academic community and the real-world needs of large-scale industry systems. Compared with earlier ClueWeb datasets, the ClueWeb22 corpus is larger, more varied, and has higher-quality documents. Its core is raw HTML, but it includes clean text versions of documents to lower the barrier to entry. Several aspects of ClueWeb22 are available to the research community for the first time at this scale, for example, visual representations of rendered web pages, parsed structured information from the HTML document, and the alignment of document distributions (domains, languages, and topics) to commercial web search.
Robust Multi-watermarking Algorithm for Medical Images Using Patchwork-DCT

The fast development of digital information technology has made great improvement on medical imaging digital information technology. The medical images can not avoid the defect of suffering being attacked or tampered maliciously when they are transmitted, and medical image watermarking algorithms can solve the impact of these attacks or malicious tampering, watermarking algorithms techniques are required in the field of image processing. However, the effectiveness of most proposed watermarking algorithms under geometric attacks for medical images are not promise, such that have poor ability to ensure the safety of the medical images. This paper proposes a robust encrypted multiple watermarking algorithm for medical images based on Patchwork-DCT. First, the Patchwork shearing transform and Discrete Cosine Transform (DCT) are combined to extract robust feature vectors against geometric attacks on medical images. Second, considering with the relatively small capacity of medical images and comparatively high requirements for privacy, zero watermark technology is used to embed and extract the watermark. Finally, the Logistic Map encryption algorithm is implied to enhance the security of the watermark information. The experimental results show that the proposed algorithm can effectively embed and extract watermark information, producing relatively good effect on traditional attacks and geometric attacks. The proposed algorithm can be applied in many fields.
Adaptive Weighted Losses With Distribution Approximation for Efficient Consistency-Based Semi-Supervised Learning

Recent semi-supervised learning (SSL) algorithms such as FixMatch achieve state-of-the-art performance by exploiting consistency regularization and entropy minimization techniques. However, many consistency-based SSL algorithms extract pseudo-labels from unlabeled data through a fixed threshold and ignore the different learning progress of each category, which makes the easy-to-learn categories have more examples contributing to the loss, resulting in a class-imbalance problem and affecting training efficiency. In order to improve the training reliability, we propose adaptive weighted losses (AWL). Through the evaluation of the class-wise learning progress, the loss contribution of the pseudo-labeled data of each category is continuously and dynamically adjusted during the learning process, and the pseudo-label discrimination ability of the model can be steadily improved. Moreover, to improve the training efficiency, we propose a bidirectional distribution approximation (DA) method, which introduces the consistency information of the predictions under the threshold into the loss calculation, and significantly improves the model convergence speed. Through the combination of AWL and DA, our method surpasses the performance of other algorithms on multiple benchmarks with a faster convergence efficiency, especially in the case of labeled data extremely limited. For example, AWL&DA achieves 95.29% test accuracy on the CIFAR-10-40-labels experiment and 92.56% accuracy on a faster experiment setting with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{18}$ </tex-math></inline-formula> iterations.
RD-IWAN: Residual Dense Based Imperceptible Watermark Attack Network

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.
Program Transfer for Answering Complex Questions over Knowledge Bases

Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer.Learning to induce programs relies on a large number of parallel question-program pairs for the given KB.However, for most KBs, the gold program annotations are usually lacking, making learning difficult.In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations.For program transfer, we design a novel two-stage parsing framework with an efficient ontology-guided pruning strategy.First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions.Second, given the question and sketch, an argument parser searches the detailed arguments from the KB for functions.During the searching, we incorporate the KB ontology to prune the search space.The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework.
Re-purposing Excavation Database Content as Paradata

Although data reusers request information about how research data was created and curated, this information is often non-existent or only briefly covered in data descriptions. The need for such contextual information is particularly critical in fields like archaeology, where old legacy data created during different time periods and through varying methodological framings and fieldwork documentation practices retains its value as an important information source. This article explores the presence of contextual information in archaeological data with a specific focus on data provenance and processing information, i.e., paradata. The purpose of the article is to identify and explicate types of paradata in field observation documentation. The method used is an explorative close reading of field data from an archaeological excavation enriched with geographical metadata. The analysis covers technical and epistemological challenges and opportunities in paradata identification, and discusses the possibility of using identified paradata in data descriptions and for data reliability assessments. Results show that it is possible to identify both knowledge organisation paradata (KOP) relating to data structuring and knowledge-making paradata (KMP) relating to fieldwork methods and interpretative processes. However, while the data contains many traces of the research process, there is an uneven and, in some categories, low level of structure and systematicity that complicates automated metadata and paradata identification and extraction. The results show a need to broaden the understanding of how structure and systematicity are used and how they impact research data in archaeology and in comparable field sciences. The insights into how a dataset’s KOP and KMP can be read is also a methodological contribution to data literacy research and practice development. On a repository level, the results underline the need to include paradata about dataset creation, purpose, terminology, dataset internal and external relations, and eventual data colloquialisms that require explanation to reusers.
M-Mix

Negative pairs, especially hard negatives as combined with common negatives (easy to discriminate), are essential in contrastive learning, which plays a role of avoiding degenerate solutions in the sense of constant representation across different instances. Inspired by recent hard negative mining methods via pairwise mixup operation in vision, we propose M-Mix, which dynamically generates a sequence of hard negatives. Compared with previous methods, M-Mix mainly has three features: 1) adaptively choose samples to mix; 2) simultaneously mix multiple samples; 3) automatically assign different mixing weights to the selected samples. We evaluate our method on two image datasets (CIFAR-10, CIFAR-100), five node classification datasets (PPI, DBLP, Pubmed, etc), five graph classification datasets (IMDB, PTC_MR, etc), and two downstream combinatorial tasks (graph edit distance and node clustering). Results show that it achieves state-of-the-art performance under self-supervised settings. Code is available at: https://github.com/Sherrylone/m-mix.
Make Fairness More Fair

The item fairness issue has become one of the significant concerns with the development of recommender systems in recent years, focusing on whether items' exposures are consistent with their utilities. So the measurement of item unfairness depends on the modeling of item utility, and most previous approaches estimated item utility simply based on user-item interaction logs in recommender systems. The Click-through rate (CTR) is the most popular one. However, we argue that these types of item utilities (named observed utility here) measurements may result in unfair exposures of items. The number of exposure for each item is uneven, and recommendation methods select the exposure audiences (users).
Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.
Modeling enablers for blockchain adoption in the circular economy

Blockchain technology can play a crucial role to facilitate the transition from a linear to a circular economy. This article evaluates those enablers that influence technology adoption in the circular economy. A comprehensive literature review and feedback from experts revealed nineteen critical enablers. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was used to determine the cause-and-effect relationships between them. The findings identified five critical causal enablers: transparency, security, smart contracts, traceability, and enhanced collaboration. Effect enablers were identified as immutability, decentralization, privacy, automation, information sharing, and enhanced regulation.
Artificial intelligence for oral cancer diagnosis: What are the possibilities?

Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advanced staging and a low chance of cure. This condition may be related to physicians' difficulty of making an early diagnosis. With the advancement of information technology, artificial intelligence (AI) holds great promise in terms of assisting in diagnosis. Few machine learning algorithms have been developed for this purpose to date. In this paper, we will discuss the possibilities for diagnosing oral cancer using AI as a tool, as well as the implications for the population. A set of photographic images of oral lesions has been segmented, indicating not only the area of the lesion but also the class of lesion associated with it. Different neural network architectures were trained with the goal of fine segmentation (pixel by pixel), classification of image crops, and classification of whole images based on the presence or absence of a lesion. The accuracy results are acceptable, opening up possibilities not only for identifying lesions but also for classifying the pathology associated with them.
Analysis of Differential Phase Shift Quantum Key Distribution using single-photon detectors

We investigated the performance of differential phase shift quantum key distribution using InGaAs/InP and Silicon-APD (avalanche photo diode) for generating secure keys, secure communication distance, and bit error rates under the various operating conditions.We compare the quantum bit error rate and the secure key generation rate as a function of communication length.Our simulation results show that with frequency conversion at telecommunication wavelength the silicon-APD contributes in enhanced communication rates and higher communication distances than InGaAs/InP APD for optical fiber-based quantum key distribution applications.
Machine Learning and Deep Learning Applications for Solar Radiation Predictions Review: Morocco as a Case of Study

With the increased number of solar power plants, the variation in solar resources causes many problems with grid management and energy systems in general. This is why artificial intelligence (AI) technologies have become useful. AI abilities have been set to use in a variety of situations to handle difficult challenges. The forecast of solar radiation using AI approaches provides a good picture of the integrity of the solar system. This process is simplified by the availability and easy use of different data sources. In reality, there are two famous methods for solar radiation predictions. The first was to use historical solar data, while the second was to integrate other weather parameters. This paper describes the solutions to solar systems and grid management issues using artificial intelligence approaches. It also outlines algorithms for solar radiation prediction by various Machine Learning and Deep Learning techniques, such as ANN, MLP, BPNN, DNN, and LSTM, utilized in various Morocco regions.
Application of artificial intelligence to digital‐rapid on‐site cytopathology evaluation during endoscopic ultrasound‐guided fine needle aspiration: A proof‐of‐concept study

During endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), cytopathology with rapid on-site evaluation (ROSE) can improve diagnostic yield and accuracy. However, ROSE is unavailable in most Asian and European institutions because of the shortage of cytopathologists. Therefore, developing computer-assisted diagnostic tools to replace manual ROSE is crucial. Herein, we reported the validation of an artificial intelligence (AI)-based model (ROSE-AI model) to substitute manual ROSE during EUS-FNA.A total of 467 digitized images from Diff-Quik (D&F)-stained EUS-FNA slides were divided into training (3642 tiles from 367 images) and internal validation (916 tiles from 100 images) datasets. The ROSE-AI model was trained and validated using training and internal validation datasets, respectively. The specificity was emphasized while developing the model. Then, we evaluated the AI model on a 693-image external dataset. We assessed the performance of the AI model to detect cancer cells (CCs) regarding the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).The ROSE-AI model achieved an accuracy of 83.4% in the internal validation dataset and 88.7% in the external test dataset. The sensitivity and PPV were 79.1% and 71.7% in internal validation dataset and 78.0% and 60.7% in external test dataset, respectively.We provided a proof of concept that AI can be used to replace manual ROSE during EUS-FNA. The ROSE-AI model can address the shortage of cytopathologists and make ROSE available in more institutes.
Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications

Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes.
Parallel Repetition of $$(k_1,\dots ,k_{\mu })$$-Special-Sound Multi-round Interactive Proofs

In many occasions, the knowledge error $$\kappa $$ of an interactive proof is not small enough, and thus needs to be reduced. This can be done generically by repeating the interactive proof in parallel. While there have been many works studying the effect of parallel repetition on the soundness error of interactive proofs and arguments, the effect of parallel repetition on the knowledge error has largely remained unstudied. Only recently it was shown that the t-fold parallel repetition of any interactive protocol reduces the knowledge error from $$\kappa $$ down to $$\kappa ^t +\nu $$ for any non-negligible term $$\nu $$ . This generic result is suboptimal in that it does not give the knowledge error $$\kappa ^t$$ that one would expect for typical protocols, and, worse, the knowledge error remains non-negligible. In this work we show that indeed the t-fold parallel repetition of any $$(k_1,\dots ,k_{\mu })$$ -special-sound multi-round public-coin interactive proof optimally reduces the knowledge error from $$\kappa $$ down to $$\kappa ^t$$ . At the core of our results is an alternative, in some sense more fine-grained, measure of quality of a dishonest prover than its success probability, for which we show that it characterizes when knowledge extraction is possible. This new measure then turns out to be very convenient when it comes to analyzing the parallel repetition of such interactive proofs. While parallel repetition reduces the knowledge error, it is easily seen to increase the completeness error. For this reason, we generalize our result to the case of s-out-of-t threshold parallel repetition, where the verifier accepts if s out of t of the parallel instances are accepting. An appropriately chosen threshold s allows both the knowledge error and completeness error to be reduced simultaneously.