Reviewerly/exHarmony huggingface dataset:
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image

Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What’s more, accurate segmentation detection of nuclei can be challenging due to the large appearance variation, conjoined and overlapping nuclei, and serious degeneration of histological structures. Supervised methods highly rely on massive annotated samples. The existing two unsupervised methods are prone to failure on degenerated samples. This paper proposes a Mutual-Complementing Framework (MCF) for nuclei detection and segmentation in pathology images. Two branches of MCF are trained in the mutual-complementing manner, where the detection branch complements the pseudo mask of the segmentation branch, while the progressive trained segmentation branch complements the missing nucleus templates through calculating the mask residual between the predicted mask and detected result. In the detection branch, two response map fusion strategies and gradient direction based postprocessing are devised to obtain the optimal detection response. Furthermore, the confidence loss combined with the synthetic samples and self-finetuning is adopted to train the segmentation network with only high confidence areas. Extensive experiments demonstrate that MCF achieves comparable performance with only a few nucleus patches as supervision. Especially, MCF possesses good robustness (only dropping by about 6%) on degenerated samples, which are critical and common cases in clinical diagnosis.
Keeping our rivers clean: Information-theoretic online anomaly detection for streaming business process events

Event log anomaly detection aims at identifying anomalous information in the logs generated by the execution of business processes. While several techniques for detecting trace-level anomalies in event logs in offline settings, i.e., when event logs are processed as a batch, have appeared recently in the literature, such techniques are currently lacking for online settings, i.e., when events are processed as a stream. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to take early corrective actions. Moreover, it is also crucial for creating models that can adapt to concept drift in the process generating the events. This paper describes a novel approach to event log anomaly detection in process event streams: we define a general framework in which different anomaly detection methods can be plugged in and we propose and evaluate our own method based on statistical leverage. The leverage is an information-theoretic measure that has been used extensively in statistics to identify outliers and it has been adapted in this paper to the specific scenario of event streams. The proposed approach has been evaluated on artificial and real event streams and also on artificial event streams characterised by concept drift.
Pilot Evaluations of Two Bluetooth Contact Tracing Approaches on a University Campus: Mixed Methods Study

Many have proposed the use of Bluetooth technology to help scale up contact tracing for COVID-19. However, much remains unknown about the accuracy of this technology in real-world settings, the attitudes of potential users, and the differences between delivery formats (mobile app vs carriable or wearable devices).We pilot tested 2 separate Bluetooth contact tracing technologies on a university campus to evaluate their sensitivity and specificity, and to learn from the experiences of the participants.We used a convergent mixed methods study design, and participants included graduate students and researchers working on a university campus during June and July 2020. We conducted separate 2-week pilot studies for each Bluetooth technology. The first was for a mobile phone app ("app pilot"), and the second was for a small electronic "tag" ("tag pilot"). Participants validated a list of Bluetooth-identified contacts daily and reported additional close contacts not identified by Bluetooth. We used these data to estimate sensitivity and specificity. Participants completed a postparticipation survey regarding appropriateness, usability, acceptability, and adherence, and provided additional feedback via free text. We used tests of proportions to evaluate differences in survey responses between participants from each pilot, paired t tests to measure differences between compatible survey questions, and qualitative analysis to evaluate the survey's free-text responses.Among 25 participants in the app pilot, 53 contact interactions were identified by Bluetooth and an additional 61 by self-report. Among 17 participants in the tag pilot, 171 contact interactions were identified by Bluetooth and an additional 4 by self-report. The tag had significantly higher sensitivity compared with the app (46/49, 94% vs 35/61, 57%; P<.001), as well as higher specificity (120/126, 95% vs 123/141, 87%; P=.02). Most participants felt that Bluetooth contact tracing was appropriate on campus (26/32, 81%), while significantly fewer participants felt that using other technologies, such as GPS or Wi-Fi, was appropriate (17/31, 55%; P=.02). Most participants preferred technology developed and managed by the university rather than a third party (27/32, 84%) and preferred not to have tracing apps on their personal phones (21/32, 66%), due to "concerns with privacy." There were no significant differences in self-reported adherence rates across pilots.Convenient and carriable Bluetooth technology may improve tracing efficiency while alleviating privacy concerns by shifting data collection away from personal devices. With accuracy comparable to, and in this case, superior to, mobile phone apps, such approaches may be suitable for workplace or school settings with the ability to purchase and maintain physical devices.
Proof of Continuous Work for Reliable Data Storage Over Permissionless Blockchain

Bitcoin first proposed the Nakamoto consensus that applies proof of work into the blockchain structure to build a trustless append-only ledger. The Nakamoto consensus solves the distributed consistency problem in the public network but wastes too much computing power. Instead of consuming computing resources, many improved consensus schemes address this problem by leveraging miners’ storage resources. However, these schemes fail to let miners store data constantly and usually rely on a dealer to assign data, which is hard to build a reliable decentralized storage system. In this article, we first design a variant consensus algorithm named Proof of Continuous Work (PoCW) with a storage-related incentive mechanism. Miners can accumulate mining advantage by continuously submitting proofs of storage. Then, we present a hash ring-based data allocation algorithm using the blockchain’s state. Combined with both of them, we build a reliable blockchain-based storage system without relying on any third parties. The theoretical analysis and simulation results demonstrate that the proposed system has higher reliability than those existing systems, and we also give practical suggestions about system parameters. Finally, we discuss additional benefits that our system brings.
Image Synthesis from Layout with Locality-Aware Mask Adaption

This paper is concerned with synthesizing images conditioned on a layout (a set of bounding boxes with object categories). Existing works construct a layout-mask-image pipeline. Object masks are generated separately and mapped to bounding boxes to form a whole semantic segmentation mask (layout-to-mask), with which a new image is generated (mask-to-image). However, overlapped boxes in layouts result in overlapped object masks, which reduces the mask clarity and causes confusion in image generation. We hypothesize the importance of generating clean and semantically clear semantic masks. The hypothesis is supported by the finding that the performance of state-of-the-art LostGAN decreases when input masks are tainted. Motivated by this hypothesis, we propose Locality-Aware Mask Adaption (LAMA) module to adapt overlapped or nearby object masks in the generation. Experimental results show our proposed model with LAMA outperforms existing approaches regarding visual fidelity and alignment with input layouts. On COCO-stuff in 256×256, our method improves the state-of-the-art FID score from 41.65 to 31.12 and the SceneFID from 22.00 to 18.64.
Robust Segmentation Models Using an Uncertainty Slice Sampling-Based Annotation Workflow

Semantic segmentation neural networks require pixel-level annotations in large quantities to achieve a good performance. In the medical domain, such annotations are expensive because they are time-consuming and require expert knowledge. Active learning optimizes the annotation effort by devising strategies to select cases for labeling that are the most informative to the model. In this work, we propose an uncertainty slice sampling (USS) strategy for the semantic segmentation of 3D medical volumes that selects 2D image slices for annotation and we compare it with various other strategies. We demonstrate the efficiency of USS on a CT liver segmentation task using multisite data. After five iterations, the training data resulting from USS consisted of 2410 slices (4&#x0025; of all slices in the data pool) compared to 8121(13&#x0025;), 8641(14&#x0025;), and 3730(6&#x0025;) slices for uncertainty volume (UVS), random volume (RVS), and random slice (RSS) sampling, respectively. Despite being trained on the smallest amount of data, the model based on the USS strategy evaluated on 234 test volumes significantly outperformed models trained according to the UVS, RVS, and RSS strategies and achieved a mean Dice index of 0.964, a relative volume error of 4.2&#x0025;, a mean surface distance of 1.35mm, and a Hausdorff distance of 23.4mm. This was only slightly inferior to 0.967, 3.8&#x0025;, 1.18mm, and 22.9mm achieved by a model trained on all available data. Our robustness analysis using the 5<sup>th</sup> percentile of Dice and the 95<sup>th</sup> percentile of the remaining metrics demonstrated that USS not only resulted in the most robust model compared to other strategies, but also outperformed the model trained on all data according to the 5<sup>th</sup> percentile of Dice (0.946 vs. 0.945) and the 95<sup>th</sup> percentile of mean surface distance (1.92mm vs. 2.03mm).
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Prehensile object rearrangement in cluttered and confined spaces has broad applications but is also challenging. For instance, rearranging products in a grocery shelf means that the robot cannot directly access all objects and has limited free space. This is harder than tabletop rearrangement where objects are easily accessible with top-down grasps, which simplifies robot-object interactions. This work focuses on problems where such interactions are critical for completing tasks. It proposes a new efficient and complete solver under general constraints for monotone instances, which can be solved by moving each object at most once. The monotone solver reasons about robot-object constraints and uses them to effectively prune the search space. The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast. Furthermore, this work contributes an effective pre-processing tool to significantly speed up online motion planning queries for rearrangement in confined spaces. Experiments further demonstrate that the proposed monotone solver, equipped with the pre-processing tool, results in 57.3% faster computation and 3 times higher success rate than state-of-the-art methods. Similarly, the resulting global planner is computationally more efficient and has a higher success rate, while producing high-quality solutions for non-monotone instances (i.e., only 1.3 additional actions are needed on average). Videos of demonstrating solutions on a real robotic system and codes can be found at https://github.com/Rui1223/uniform_object_rearrangement.
Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network

The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in segmenting complex medical images. We present a two-stage framework based on Mask RCNN to automatically segment overlapping cells. In stage one, candidate cytoplasm bounding boxes are proposed. In stage two, pixel-to-pixel alignment is used to refine the boundary and category classification is also presented. The performance of the proposed method is evaluated on publicly available datasets from ISBI 2014 and 2015. The experimental results demonstrate that our method outperforms other state-of-the-art approaches with DSC 0.92 and FPRp 0.0008 at the DSC threshold of 0.8. Those results indicate that our Mask RCNN-based segmentation method could be effective in cytological analysis.
EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization

Abstract Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub‐field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state‐of‐the‐art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up‐to‐date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions.
Section Patterns: Efficiently Solving Narrow Passage Problems in Multilevel Motion Planning

Sampling-based planning methods often become inefficient due to narrow passages. Narrow passages induce a higher runtime, because the chance to sample them becomes vanishingly small. In recent work, we showed that narrow passages can be approached by relaxing the problem using admissible lower dimensional projections of the state space. Those relaxations often increase the volume of narrow passages under projection. Solving the relaxed problem is often efficient and produces an admissible heuristic we can exploit. However, given a base path, i.e., a solution to a relaxed problem, there are currently no tailored methods to efficiently exploit the base path. To efficiently exploit the base path and thereby its admissible heuristic, we develop section patterns, which are solution strategies to efficiently exploit base paths in particular around narrow passages. To coordinate section patterns, we develop the pattern dance algorithm, which efficiently coordinates section patterns to reactively traverse narrow passages. We combine the pattern dance algorithm with previously developed multilevel planning algorithms and benchmark them on challenging planning problems like the Bugtrap, the double L-shape, an egress problem, and on four pregrasp scenarios for a 37 degrees-of-freedom shadow hand mounted on a KUKA LWR robot. Our results confirm that section patterns are useful to efficiently solve high-dimensional narrow passage motion planning problems.
Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction

Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results.
Harmonization with Flow-Based Causal Inference

Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well. This paper leverages a recently proposed normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM), in order to achieve harmonization of such data. A causal model is used to model observed effects (brain magnetic resonance imaging data) that result from known confounders (site, gender and age) and exogenous noise variables. Our formulation exploits the bijection induced by flow for the purpose of harmonization. We infer the posterior of exogenous variables, intervene on observations, and draw samples from the resultant SCM to obtain counterfactuals. This approach is evaluated extensively on multiple, large, real-world medical datasets and displayed better cross-domain generalization compared to state-of-the-art algorithms. Further experiments that evaluate the quality of confounder-independent data generated by our model using regression and classification tasks are provided.
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs

As Knowledge Graphs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques. RDF2Vec is an unsupervised technique that can create task-agnostic numerical representations of the nodes in a KG by extending successful language modeling techniques. The original work proposed the Weisfeiler-Lehman kernel to improve the quality of the representations. However, in this work, we show that the Weisfeiler-Lehman kernel does little to improve walk embeddings in the context of a single Knowledge Graph. As an alternative, we examined five alternative strategies to extract information complementary to basic random walks and compare them on several benchmark datasets to show that research within this field is still relevant for node classification tasks.
Computing the Original eBWT Faster, Simpler, and with Less Memory

Mantaci et al. [TCS 2007] defined the \(\mathrm {eBWT}\) to extend the definition of the \(\mathrm {BWT}\) to a collection of strings. However, since this introduction, it has been used more generally to describe any \(\mathrm {BWT}\) of a collection of strings, and the fundamental property of the original definition (i.e., the independence from the input order) is frequently disregarded. In this paper, we propose a simple linear-time algorithm for the construction of the original \(\mathrm {eBWT}\), which does not require the preprocessing of Bannai et al. [CPM 2021]. As a byproduct, we obtain the first linear-time algorithm for computing the \(\mathrm {BWT}\) of a single string that uses neither an end-of-string symbol nor Lyndon rotations. We combine our new \(\mathrm {eBWT}\) construction with a variation of prefix-free parsing to allow for scalable construction of the \(\mathrm {eBWT}\). We evaluate our algorithm (pfpebwt) on sets of human chromosomes 19, Salmonella, and SARS-CoV2 genomes, and demonstrate that it is the fastest method for all collections, with a maximum speedup of 7.6\(\times \) on the second best method. The peak memory is at most 2\(\times \) larger than the second best method. Comparing with methods that are also, as our algorithm, able to report suffix array samples, we obtain a 57.1\(\times \) improvement in peak memory. The source code is publicly available at https://github.com/davidecenzato/PFP-eBWT.
Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift

In nonstationary environments, data distributions can change over time. This phenomenon is known as concept drift, and the related models need to adapt if they are to remain accurate. With gradient boosting (GB) ensemble models, selecting which weak learners to keep/prune to maintain model accuracy under concept drift is nontrivial research. Unlike existing models such as AdaBoost, which can directly compare weak learners' performance by their accuracy (a metric between [0, 1]), in GB, weak learners' performance is measured with different scales. To address the performance measurement scaling issue, we propose a novel criterion to evaluate weak learners in GB models, called the loss improvement ratio (LIR). Based on LIR, we develop two pruning strategies: 1) naive pruning (NP), which simply deletes all learners with increasing loss and 2) statistical pruning (SP), which removes learners if their loss increase meets a significance threshold. We also devise a scheme to dynamically switch between NP and SP to achieve the best performance. We implement the scheme as a concept drift learning algorithm, called evolving gradient boost (LIR-eGB). On average, LIR-eGB delivered the best performance against state-of-the-art methods on both stationary and nonstationary data.
Security Concerns in IoT Systems and Its Blockchain Solutions

IoT is an ecosystem that deals with electronic objects and performs various operations such as interaction among themselves, controlling operations, monitoring operations that may or may not involve human beings. The evolution of IoT made a significant contribution to the world, and as a result, IoT is omnipresent. Because of this omnipresent attribute, IoT will be a family of 75 billion (approx.) connected devices by the year 2025, and as this time will pass, this number of connected devices will grow exponentially. This will cause enormous data generation, which will be sent and received through the Internet. This will surely cause diverse security, privacy, and interoperability issues that will influence the lives of the people in one or the other way. Dealing with these issues is extremely important since if carelessness will be there, then this data can put a negative impact on the country's economy. The conventional approaches that have been a primary solution in the IoT spectrum are outdated and require modern and smart solutions. This is so because the orthodox solutions of IoT are somehow responsible for the birth of diverse concerns of IoT. This paper focuses on these issues and challenges of IoT and highlights the importance of solving these issues by a decentralized peer-to-peer (P2P) network technology blockchain.