Reviewerly/exHarmony huggingface dataset:
Sequential Adversarial Anomaly Detection for One-Class Event Data

We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence in sequential events using the marked point process model. The detector sequentially evaluates the likelihood of a test sequence and compares it with a time-varying threshold, also learned from data through the minimax problem. We demonstrate our proposed method’s good performance using numerical experiments on simulations and proprietary large-scale credit card fraud data sets. The proposed method can generally apply to detecting anomalous sequences. History: W. Nick Street served as the senior editor for this article. Funding: This work is partially supported by the National Science Foundation [Grants CAREER CCF-1650913, DMS-1938106, and DMS-1830210] and grant support from Macy’s Technology. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.2329910.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0026 ).
Enabling anonymized open-data linkage by authorized parties

Nowadays, many entities collect useful information about users, in order to implement the provided service, and publish them as open data. To prevent privacy leakage, data are often anonymized prior to publication. Unfortunately, anonymization strongly hinders data linkage, which can be very useful for analysis purposes instead. In this paper, we deal with the above problem, by proposing a technique that enriches anonymized open data with pseudo-random labels. This way, some authorized parties (i.e., the analysts) are enabled to link data regarding the same user coming from different sources. Instead, for non-authorized people, labels do not carry any information, thus not introducing additional privacy threats with respect to original open data. In other words, our solution allows us to recover linkage capabilities on anonymized open data, thus enabling more powerful data exploitation. Indeed, the linked open data paradigm, involving both the public sector and business, is recognized as one of the most promising approaches for boosting societal growth. To offer a concrete solution, we refer to an existing open-data standard and we implement the protocol through a SAML-based SSO framework adhering to the eIDAS regulation.
Blockchain Driven Supply Chain and Industry 4.0 Technologies

Supply chain networks present new business and financial prospects due to emerging technologies under industry 4.0. In order to advance supply chains toward sustainability, this study will give a general introduction to industry 4.0 (I4.0) and blockchain technology. Blockchain-driven supply chain management (Bscm) fundamentally alters conventional supply chain paradigms by establishing a shared, decentralized public ledger. When connected with Bscm, Industry 4.0 is more effective at constructing a stable and robust supply chain. It can manage multiple Bscm components, including product tracking, payments, database administration, and security. Nevertheless, blockchain and I4.0 technologies present implementation and security difficulties. The solution to these challenges could allow Bscm and I4.0 to improve industrial management systems. This chapter provides an overview of industry 4.0 and related technologies. We focus on the significance of I4.0 technologies in Bscm and their challenges and prospects. Finally, we outline obstacles that may give rise to future areas of study in BSC and I4.0.
Road and Destiny Are Not Same: Leading to and Leading in Blockchain Driven Supply Chain

Blockchain is a technology connecting numerous players of a system in a seamless manner. Being technology-driven, its adoption is inhibited when employees in the organization do not consider them fit for it. To make the employees ready to accept the change, leaders have to adopt transformational leadership. Using transformational leadership, organizations can provide the vision to employees to pursue the adoption of blockchain technology. Additionally, through transformational leadership, organizations can address individual roadblocks hampering adoption of blockchain. Finally, employees may be provided with the required motivation to go for blockchain. Once blockchain has been adopted, organizations need to make themselves more inclusive and employee-centered as blockchain makes information more easily available, the leadership has to become more employee-centered. In this regard, the tenets of servant leadership are of great use.
Blockchain Based Digital Economy and Industry 4.0

Using blockchain-based technologies for supply chain management, this study examines the digital economy and digital industry in terms of their interactions. The backbone of the digital economy is the internet (web 3), the hyperlink distributed between the internet of things and mobile devices. The digital economy can be examined in two ways: information economy and e-economy. In the foundations of the digital economy, there are elements such as the internet, electronic communication, digital automation, digital payments, social media and finally the metaverse. The digital economy is an economic activity resulting from billions of online connections between people, businesses, devices and processes. The digital economy also develops the digital finance structure. Blockchain is a candidate to be the backbone of the digitalization of production and economy, as it can provide a decentralized and reliable infrastructure at this point. Blockchain-based supply chain is rapidly being developed and spread on this platform. Although blockchain was first and effectively applied in the financial fields, the immutable nature of the ledger makes it difficult for identification, qualification, health records and insurance etc. It will come from today. It does not seem possible for sectors that cannot keep up with this rapidly changing process and are not prepared for it to exist in the future.
SIFT-based cortical surface shift estimation for intraoperative image updating

In open cranial procedures, the accuracy of image guidance using preoperative MR (pMR) images can be degraded by intraoperative brain deformation. Intraoperative stereovision (iSV) has been used to acquire 3D surface profile of the exposed cortex at different surgical stages, and surface displacements can be extracted to drive a biomechanical model as sparse data to provide updated MR (uMR) images that match the surgical scene. In previous studies, we have employed an Optical Flow (OF) based registration technique to register iSV surfaces acquired from different surgical stages and estimate cortical surface shift throughout surgery. The technique was efficient and accurate but required manually selected Regions of Interest (ROI) in each image after resection began. In this study, we present a registration technique based on Scale Invariant Feature Transform (SIFT) algorithm and illustrate the methods using an example patient case. Stereovision images of the cortical surface were acquired and reconstructed at different time points during surgery. Both SIFT and OF based registration techniques were used to estimate cortical shift, and extracted displacements were compared against ground truth data. Results show that the overall errors of SIFT and OF based techniques were 0.65±0.53 mm and 2.18±1.35 mm in magnitude, respectively, on the intact cortical surface. The OF-based technique generated inaccurate sparse data near the resection cavity region, whereas SIFT-based technique only generated accurate sparse data. The computational efficiency was &lap;0.5 s and &gap;20 s for SIFT and OF based techniques, respectively. Thus, the SIFT-based registration technique shows promise for OR applications.
Image based registration between full x-ray and spot mammograms: analysis of registration accuracy in subgroups

The most typical type of cancer among women is breast cancer. Despite the crucial role that digital mammography plays in the early identification of breast cancer, many tumors could not be discriminated on mammography, especially in women with dense breast tissue. Contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast is routinely used to find lesions that are invisible on mammography. MRI-guided biopsies must be used to further analyze these lesions. But MRI-guided biopsy is highly priced, time-consuming, and not frequently accessible. In our earlier work, we introduced a novel method using two methods of registration: biomechanical and image-based registration to transfer lesions from MRI to spot mammograms to allow x-ray guided biopsy. In this paper, we focus on enhancing and developing the image-based registration between full and spot mammograms and analyzing a correlation between the accuracy of our method and features such as views, location of lesion, breast area, size of lesion in each modality, and age. Results for 48 patients from the Medical University of Vienna are provided. The median target registration error is 20.9 mm and the standard deviation is 23.9 mm.
Evaluating a PSMNet-based pipeline for intraoperative stereo reconstruction with the da Vinci Xi

Stereo reconstruction is an important tool for generating 3D surface observations of deformable tissues that can be used to non-rigidly update intraoperative image guidance. As compared to traditional image processing-based stereo matching techniques, emerging machine learning approaches aim to deliver shorter processing times, more accurate surface reconstructions, and greater robustness to the suboptimal qualities of intraoperative tissue imaging (e.g., occlusion, reflection, and minimally textured surfaces). This work evaluates the popular PSMNet convolutional neural network as tool for generating disparity maps from the video feed of the da Vinci Xi Surgical System. Reconstruction accuracy and speed were assessed for a series of 44 stereoendoscopic frame pairs showing key structures in a silicone renal phantom. Surface representation accuracy was found to be on the order of 1mm for reconstructions of the kidney and inferior vena cava, and disparity maps were produced in under 2s when inference was performed on a standard modern GPU. These preliminary results suggest that PSMNet and similar trained models may be useful tools for integrating intraoperative stereo reconstruction into advanced navigation platforms and warrant further development of the overall data pipeline and testing with biological tissues in representative surgical conditions.
Stereovision registration using a tracked checkerboard calibration object for a breast surgery image guidance system

Breast cancer is a leading cause of death among women in the United States, and Breast Conserving Surgery (BCS) is a common treatment option for women with early-stage breast cancer. The goal of BCS is to localize and remove the tumor with negative margins, and incomplete tumor excision can lead to reoperation and recurrence. To aid with intraoperative tumor localization, a BCS image guidance system that features an optical tracking camera (NDI Polaris Vicra) and stereocameras (FLIR Grasshopper stereocamera pair) for tracking surgical tools and surgical scene monitoring, respectively, is being investigated. However, sensor data collected from both stereovision systems are not inherently co-registered. Thus, this paper proposes utilizing a tracked checkerboard calibration object paired with a custom 3DSlicer module for optical tracker and stereo camera co-registration. The module features an easy-to-use user interface to streamline calibration. The calibration process was validated with a tissue-mimicking breast phantom embedded with beads to represent surface fiducials and undergoing deformations (n=5 mock phantom tissue states). Across five independent calibration trials, the average Fiducial Registration Error (FRE) was calculated to be 0.68mm ± 0.11mm while the average Target Registration Error (TRE) was calculated to be 0.35mm ± 0.14mm. With respect to the challenge test conditions using the mock breast phantom under differing deformations, TRE values were on average determined to be 2.58mm ± 0.26mm over all breast phantom states. Thus, a tracked checkerboard tool paired with the custom 3DSlicer module provided the ability to localize and track points of interest for accurate registration of two different optical tracking systems.
A semantic segmentation method for laparoscopic images using semantically similar groups

In this paper, we present a segmentation method for laparoscopic images using semantically similar groups for multi-class semantic segmentation. Accurate semantic segmentation is a key problem for computer assisted surgeries. Common segmentation models do not explicitly learn similarities between classes. We propose a model that, in addition to learning to segment an image into classes, also learns to segment it into human-defined semantically similar groups. We modify the LinkNet34 architecture by adding a second decoder with an auxiliary task of segmenting the image into these groups. The feature maps of the second decoder are merged into the final decoder. We validate our method against our base model LinkNet34 and a larger LinkNet50. We find that our proposed modification increased the performance both with mean Dice (average +1.5%) and mean Intersection over Union metrics (average +2.8%) on two laparoscopic datasets.
Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery

After breast-conserving surgery, positive margins occur when breast cancer cells are found on the resection margin, leading to a higher chance of recurrence and the need for repeat surgery. The NaviKnife is an electromagnetic tracking-based surgical navigation system that helps to provide visual and spatial feedback to the surgeon. In this study, we conduct a gross evaluation of this navigation system with respect to resection margins. The trajectory of the surgical cautery relative to ultrasound-visible tumor will be visualized, and its distance and location from the tumor will be compared with pathology reports. Six breast-conserving surgery cases that resulted in positive margins were performed using the NaviKnife system. Trackers were placed on the surgical tools and their positions in three-dimensional space were recorded throughout the procedure. The closest distance between the cautery and the tumor throughout the procedure was measured. The trajectory of the cautery when it came closest to the tumor model was plotted in 3D Slicer and compared with pathology reports. In two of the six cases, the side at which the cautery came the closest to the tumor model coincided with the side at which positive margins were found from pathology reports. Our method shows that positive margins occur mainly in areas that are not visible from ultrasound imaging. Our system will need to be used in combination with intraoperative tissue characterization methods to effectively predict the occurrence and location of positive margins.
Self-supervised registration and segmentation on ossicles with a single ground truth label

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually annotated ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method.
Full-field digital mammogram retrieval using Fourier feature based auto-encoder: application in breast cancer screening training

Using manually selected cases for breast screener training is time-consuming and prohibitively costly. However, retrieving medical images of breasts via computer vision methods that reflect diagnostically relevant visual features is challenging because the overall appearance variability of whole breasts is high compared to often subtle lesions of interest. Our work aims to develop an automatic and low-costing tool for retrieving and recommending similar full-field digital mammograms (FFDMs) of breast cancer by providing a query image. This tool will help to identify poor reader performance in real-life screening that allows interventions to change practice according to error cases, such as reviewing practice or further training. The core element of this tool is automatic content-based image retrieval. In this paper, we propose an unsupervised method for training an artificial intelligence (AI) model to rank FFDMs according to their similarity in visual features. The proposed method consists of a patch-based pre-processing step, an auto-encoder with Fourier feature mapping and a K-nearest neighbour (K-NN)-based similarity ranking method. To evaluate our method, we used 2500 images (30% abnormal images) from the regular assessment database of UK radiologists over the last ten years. We compared our method with a numerical-data-based method. The results have shown the advantages of our proposed method in retrieving similar cases by considering much richer image information (including but not limited to the breast density, lesion size and location, etc.) without any labour-intensive data labelling.
A progress in diagnostic performances of Vietnamese doctors in reading mammograms with different Level of breast density via the VIETRAD program

Previous research has revealed that Vietnamese radiologists had lower diagnostic efficacy in interpreting mammograms than radiologists from Western countries. This study investigated the improvement in diagnostic performances of Vietnamese doctors in breast cancer detection via VIETRAD (VIEtnam: Transformation of Radiological Detection) program. Data of 33 participants who completed three training sessions containing normal and cancer mammographic cases from Australia and Vietnam were assessed in sensitivity, specificity, ROC and JAFROC. Results show that Vietnamese doctors have improved their diagnostic accuracy in identifying normal and cancer cases on mammograms across different levels of breast density.
Evaluating generative stochastic image models using task-based image quality measures

Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several applications in medical imaging that include unconditional medical image synthesis, image translation, and optimization of imaging systems. However, the extent to which a GAN learns image statistics that are relevant to a diagnostic task is unknown. In this work, canonical stochastic image models (SIMs) that simulate realistic mammographic textures are employed to evaluate GAN-based SIMs with respect to detection, detection-localization, and detection-estimation tasks. It is shown that the specific GAN architecture considered has higher propensity to generate statistics that confound the observers performing the three considered tasks. This work highlights the need for continued development of objective metrics for evaluating GANs.
Challenges and solutions to processing and visualizing eye tracking data from digital pathology studies

Digital Pathology (DP) reporting workstations permit eye tracking experiments which can aid our understanding of reading strategies and medical errors in pathology. However, eye tracking with DP slides is complex due to the nature of the slide viewing process: slide panning and zooming. Eye tracking technology records gaze coordinates to a screen surface, but these coordinates do not account for the ever-changing on-screen content (due to slide navigation), and therefore it is essential to track pathologists’ slide navigation to determine where on the slide the pathologist has viewed and what features were fixated. Additionally, visualising the resulting eye tracking data proves challenging due to the zooming component. Other eye tracking studies in DP have accounted for slide navigation by employing custom slide viewers that output slide movements as a data stream with the eye tracking data which are co-registered for analysis. We are conducting a DP eye tracking study using a commercial slide viewer which has been adopted at selected UK hospital sites, but slide movement data cannot be outputted as a data stream in this context. Therefore, we’re developing a software platform using computer vision techniques that can be applied to the recorded screen capture of the DP workstation which is synchronised with the eye tracking data. The developed algorithm could be adapted for use with other commercial slide viewers for future studies. Here, we explore how studies have addressed these issues and we discuss our approach.
A Reference Architecture for Datacenter Scheduler Programming Abstractions: Design and Experiments (Work In Progress Paper)

Datacenters are the backbone of our digital society, used by the industry, academic researchers, public institutions, etc. To manage resources, data centers make use of sophisticated schedulers. Each scheduler offers a different set of capabilities and users make use of them through the APIs they offer. However, there is not a clear understanding of what programming abstractions they offer, nor why they offer some and not others. Consequently, it is difficult to understand the differences between them and the performance costs that are imposed by their APIs. In this work, we study the programming abstractions offered by industrial schedulers, their shortcomings, and the performance costs of the shortcomings. We propose a general reference architecture for scheduler programming abstractions. Specifically, we analyze the programming abstractions of five popular industrial schedulers, we analyze the differences in their APIs, we identify the missing abstractions, and finally, we carry out an exemplary experiment to demonstrate that schedulers sacrifice performance by under-implementing programming abstractions. In the experiments, we demonstrate that an API extension can improve task runtime by up to 23%. This work allows schedulers to identify their shortcomings and points of improvement in their APIs, but most importantly, provides a reference architecture for existing and future schedulers.
Graph-Optimizer: Towards Predictable Large-Scale Graph Processing Workloads

Graph and hardware-specific optimisations lead to orders of magnitude improvements in performance, energy, and cost over conventional graph processing methods. Typical big data platforms, such as Apache MapReduce and Apache Spark, rely on generic primitives, exhibiting poor performance and high financial and environmental costs. Even optimised basic graph operations (BGOs) lack the tools to combine them towards real-world applications. Furthermore, graph topology and dynamics (i.e., changing the number and content of vertices and edges) lead to high variability in computational needs. Primitive predictive models demonstrate they can enable algorithm selection and advanced auto-scaling techniques to ensure better performance, but no such models exist for energy consumption.
Challenges and Future Directions in Efficiency Benchmarking (Vision Paper)

SPEC benchmarks are crucial contributors behind the improvement of server efficiency since 2007, given their role in making the power consumption and efficiency of servers transparent for government regulators, customers, and the manufacturers themselves.