Reviewerly/exHarmony huggingface dataset:
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer

Rehearsal is a critical component for class-incremental continual learning, yet it requires a substantial memory budget. Our work investigates whether we can significantly reduce this memory budget by leveraging unlabeled data from an agent's environment in a realistic and challenging continual learning paradigm. Specifically, we explore and formalize a novel semi-supervised continual learning (SSCL) setting, where labeled data is scarce yet non-i.i.d. unlabeled data from the agent's environment is plentiful. Importantly, data distributions in the SSCL setting are realistic and therefore reflect object class correlations between, and among, the labeled and unlabeled data distributions. We show that a strategy built on pseudo-labeling, consistency regularization, Out-of- Distribution (OoD) detection, and knowledge distillation reduces forgetting in this setting. Our approach, DistillMatch, increases performance over the state-of-the-art by no less than 8.7% average task accuracy and up to 54.5% average task accuracy in SSCL CIFAR-100 experiments. Moreover, we demonstrate that DistillMatch can save up to 0.23 stored images per processed unlabeled image compared to the next best method which only saves 0.08. Our results suggest that focusing on realistic correlated distributions is a significantly new perspective, which accentuates the importance of leveraging the world's structure as a continual learning strategy. Our code is available at https://github.com/GT-RIPL/DistillMatch-SSCL
Mineral prospecting from biogeochemical and geological information using hyperspectral remote sensing - Feasibility and challenges

Biogeochemistry is an effective and fast approach for orientation studies in mineral exploration. Vegetative cover has been used in a range of geological environments to gain insight into the composition of sub-surface soils and bedrock found at depth; however, there is little to no records of biogeochemistry being coupled within a remote sensing and spectroscopy framework in the domain of mineral exploration. The study uses lab- and airborne hyperspectral remote sensing to localize gold mineralisation using the concept of biogeochemistry. This pilot study uses plant species, Pinus radiata in close proximity to a mineralised shear zone. This study used a 2 m spatial resolution airborne hyperspectral data, captured by AisaFENIX sensor and was complimented with lab-based hyperspectral data of the sampled vegetation and soil/rock exposed along the Hyde-Macraes Shear Zone, within the Otago schist belt in the South Island, New Zealand. The hyperspectral data, and the biogeochemistry data was explored using several statistical approaches to determine significant relationships between the underlying soil and rock substrate and biogeochemistry of the sampled vegetation with a focus on pathfinder elements to gold-mineralisation. Three commonly used multivariate predictive regression models, were tested, including partial least squares regression (PLSR), kernel based PLSR (kPLSR) and random forest regression (RFR) modelling. The results suggest that hyperspectral scanning can discriminate samples from a mineralised zone spectrally. Furthermore, the lab-based hyperspectral data outcompetes the airborne hyperspectral data for vegetation to identify areas of potential mineralisation. The model performances vary depending on the part of the plant analysed, and bark samples are found to be promising to explore underlying geological resources in the future. The regression model that show up to R2cv > 0.7 using bark samples decreases to R2cv ~ 0.2 when trained with needle samples. The airborne data is significantly more complex, which is observed in poorer model performances with R2cv ranging from 0.2 to 0.5.
Surgical Instruction Generation with Transformers

Automatic surgical instruction generation is a prerequisite towards intra-operative context-aware surgical assistance. However, generating instructions from surgical scenes is challenging, as it requires jointly understanding the surgical activity of current view and modelling relationships between visual information and textual description. Inspired by the neural machine translation and imaging captioning tasks in open domain, we introduce a transformer-backboned encoder-decoder network with self-critical reinforcement learning to generate instructions from surgical images. We evaluate the effectiveness of our method on DAISI dataset, which includes 290 procedures from various medical disciplines. Our approach outperforms the existing baseline over all caption evaluation metrics. The results demonstrate the benefits of the encoder-decoder structure backboned by transformer in handling multimodal context.
Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings

Abstract Motivation Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative and/or nonintuitive to pathologists. Results In this article, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step toward realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers. Availability and implementation Relevant code can be found at github.com/CancerDataScience/NuCLS Supplementary information Supplementary data are available at Bioinformatics online.
QSOC: Quantum Service-Oriented Computing

Quantum computing is quickly turning from a promise to reality, witnessing the launch of several cloud-based services that provide access to quantum resources, simulators, runtimes, and programming tools, all through the cloud. Unfortunately, however, existing solutions typically implicitly assume intimate knowledge about quantum computing concepts and operators. This vision paper introduces Quantum Service-Oriented Computing (QSOC), a model for building applications using a well-balanced mix of classical and quantum (hybrid) computing approaches. We propose a model-driven methodology that allows application developers, classical service developers, and quantum programmers to build hybrid enterprise applications collaboratively. As a result, quantum programmers and service developers can develop and publish quantum and classical components. At the same time, the application developers can discover, compose, configure and compose pre-built components to build and operate enterprise applications without intimate knowledge on the underlying quantum infrastructure, advocating knowledge reuse and separation of concerns.
Blockchain based Framework for Student Identity and Educational Certificate Verification

With the rise in digitization of documents stored online, it is important to have a document verification process. It involves customized verification and authentication of a document based on the content of the document. Among all the certificates, the educational certificate is one of the most important certificates, especially for students. Unfortunately, it is very easy to fake documents that are hard to identify nowadays and are often considered original. Blockchain has recently emerged as a potential alternative to manual verification of certificates. It provides a distributed ledger that is verifiable with cryptographic mechanisms. Also, it provides a common platform for easily sharing, storing, and accessing documents. The identity of the students can be verified using government authorized identity proofs. This paper proposes the use of such unique identity number and secret phrase provided by the student to further improve the security of the certificate verification system. The student's identity and document are both verified by matching the hashes already present in the Blockchain. Also, in the proposed method the documents are linked to the student to add another layer of verification. The implementation of this proposed platform can be used to issue, receive and verify the certificates.
Poster: Function Delivery Network: Extending Serverless to Heterogeneous Computing

Several of today's cloud applications are spread over heterogeneous connected computing resources and are highly dynamic in their structure and resource requirements. However, serverless computing and Function-as-a-Service (FaaS) platforms are limited to homogeneous clusters and homogeneous functions. We introduce an extension of FaaS to heterogeneous computing and to support heterogeneous functions through a network of distributed heterogeneous target platforms called Function Delivery Network (FDN). A target platform is a combination of a cluster of a homogeneous computing system and a FaaS platform on top of it. FDN provides Function-Delivery-as-a-Service (FDaaS), delivering the function invocations to the right target platform. We showcase the opportunities such as collaborative execution between multiple target platforms and varied target platform's characteristics that the FDN offers in fulfilling two objectives: Service Level Objective (SLO) requirements and energy efficiency when scheduling functions invocations by evaluating over five distributed target platforms.
The soft graphic integer sub-decomposition method for elliptic scalar multiplication

A new version of the ISD method for computing a scalar multiplication on elliptic curve defined over prime field has been proposed. This version is called the soft graphic ISD (SG-ISD) method. The SG-ISD method employed the connected sub-graphs of undirected simple graph, which formed a soft graph (F, A) generated by a set valued function F, to represent the sub-scalars of ISD method. Thus, undirected simple graph can be proved as the SG-ISD version. On the SG-ISD method, the binary representations, which are all edges of the ISD sub-scalars that forms a nonempty set A, has been done directly from the connected sub-graphs. So, every ISD version of a scalar k is proven mathematically as the SG-ISD version of k in an elliptic scalar multiplication kP. New experimental results on the proposed SG-ISD method are presented. The computational complexities of the original ISD and proposed SG-ISD methods are determined mathematically on the basis of the counting operations. These operations are elliptic curve and ﬁnite ﬁeld operations. The comparison results on these computational complexities of the ISD and SG-ISD methods have been discussed. The experiments on the computational complexities reveal that the SG-ISD method is faster than the ISD method. Thus, the SG-ISD method is considered as an eﬃcient algorithm in comparison with the original ISD for cryptographic applications.
Containerization: Cloud Computing based Inspiration Technology for Adoption through Docker and Kubernetes

The field of cloud computing has been evolving rapidly since inception. Cloud is the virtual pool of resources which can be served to the user through SaaS, PaaS and IaaS flavours. No cloud can be existed without virtualization. With Virtual Machine, the bare metal is virtualized to run multiple Operating System instances. These VMs serve to the users for performing the tasks. All are independent units and user has complete ownership and control to install the required softwares and use as per the wish. The VM solves many problems by optimizing the resources. The developers concern that the code is working fine on the development environment, but fail to work on testing or production environment due to the environment differences, if any. So, the Containerization comes into the picture to address such challenges. In this paper, various aspects of Containerization are explored and highlighted. The Container runtime environment-Docker and Container orchestration tool-Kubernetes are focused and deployed for exploring the possibilities of Containerization adoption, which automate the Container deployment, scaling and load balancing.
A tale of the digital future: analyzing the digitalization of the norwegian education system

Schools are central in sustaining and extending values in society and therefore in shaping future society. While the global trend of education digitalization, commonly is presented as a depoliticized project of improvement, this paper seeks to uncover the values of the digital school as expressed in the strategy for the digitalization of the Norwegian education system. Inspired by Bacchi’s‘WPR approach, the findings reveal that digitalization involves establishing a problem and a solution through discourses or narratives of education, working life, and the future.Given this problem representation and sociotechnical imaginary of the future, the school’s main goal becomes to provide digitally competent future citizen-workers. The educational ideal of The Nordic model, and ideas of humanism and solidarity are not emphasized. Hence, the digital school reflects a neoliberal economy rather than values traditionally associated with the welfare state.
End-to-end Ultrasound Frame to Volume Registration

Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. However, such a multimodal 2D/3D registration problem is very challenging due to several significant obstacles such as dimensional mismatch, large modal appearance difference, and heavy computational load. In this paper, we propose an end-to-end frame-to-volume registration network (FVR-Net), which can efficiently bridge the previous research gaps by aligning a 2D TRUS frame with a 3D TRUS volume without requiring hardware tracking. The proposed FVR-Net utilizes a dual-branch feature extraction module to extract the information from TRUS frame and volume to estimate transformation parameters. To achieve efficient training and inference, we introduce a differentiable 2D slice sampling module which allows gradients backpropagating from an unsupervised image similarity loss for content correspondence learning. Our experiments demonstrate the proposed method's superior efficiency for real-time interventional guidance with highly competitive registration accuracy. Source code of this work is publicly available at https://github.com/DIAL-RPI/FVR-Net.
A Monolithic Hardware Implementation of Kyber: Comparing Apples to Apples in PQC Candidates

With the advent of large-scale quantum computers, factoring and discrete logarithm problems could be solved using the polynomial-time quantum algorithms. To ensure public-key security, a transition to quantum-resistant cryptographic protocols is required. Performance of hardware accelerators targeting different platforms and diverse application goals plays an important role in PQC candidates’ differentiation. Hardware accelerators based on FPGAs and ASICs also provide higher flexibility to create a very low area or ultra-high performance implementations at the high cost of the other. While the hardware/software co-design development of PQC schemes has already received an increasing research effort, a cost analysis of efficient pure hardware implementation is still lacking. On the other hand, since FPGA has various types of hardware resources, evaluating and making the accurate and fair comparison of hardware-based implementations against each other is very challenging. Without a common foundation, apples are compared to oranges. This paper demonstrates a pure hardware architecture for Kyber as one of the finalists in the third round of the NIST post-quantum cryptography standardization process. To enable real, realistic, and comparable evaluations in PQC schemes over hardware platforms, we compare our architecture over the ASIC platform as a common foundation showing that it outperforms the previous works in the literature.
6G technology and taxonomy of attacks on blockchain technology

Blockchain technology is now being used in every aspect of human life. It appears like everyone is racing to develop apps that run on top of the blockchain technology available today. Indeed, this is owing to the fact that it has distinct qualities and a distinctive design. However, it is not well suited to all applications of blockchain technology. Not merely altering the blockchain protocols would make it suitable, but instead redesigning its architecture is required to make it ideal for different applications. This paper describes the overview of blockchain technology thoroughly. It provides a detailed discussion on the prevalent core-oriented and client-oriented attacks on blockchain technology and the vulnerabilities exploited by them. It also presents the possible countermeasure to these attacks. Moreover, it provides insight into the creation of a better version of the blockchain that is more suitable for different types of blockchain applications.
Incorporation of geological constraints and semivariogram scaling law into geostatistical modeling of metal contents in hydrothermal deposits for improved accuracy

Detailed metal distributions in a deposit can be used to determine the generation mechanism and process of the deposit type and improve mining development efficiency. Two geostatistical methods were developed that incorporate geologic information and a semivariogram scaling law to improve the spatial estimation accuracy of metal contents at the fine scale. In the first method, a binary dataset is prepared by assigning 1 to the location of the targeted geologic category in each borehole column and 0 elsewhere. Principal component analysis is implemented to generate a geologic model using principal values and ordinary kriging (OK). By overlapping with a 3D geologic model, OK of the main metal contents is then performed by limiting the content data with the same geologic category as the calculation point. This method is termed kriging with a geological constraint (KGC) and focuses on the correlation between the geologic category and metal content. The next step downscales the KGC model using a semivariogram scaling law to obtain finer spatial resolution (KGC-DS). This law is experimentally derived by repeatedly thinning out the original sample data and approximating the relationship between the mean distance of the data pair and semivariogram parameters (range, nugget effect, and sill) using a power function. This approximation can produce semivariograms in a virtual scenario with more sample data than the original. These two methods were applied to two types of hydrothermal deposits: one of the largest kuroko (volcanogenic massive sulfide) deposits in northern Japan using 77 vertical or sub-vertical drilling data and one porphyry copper deposit in northern Sulawesi, Indonesia using 58 drilling data. In the first case study, the accuracy in the high-content zones increases and the metal content distribution becomes considerably more detailed than the OK result by reducing the smoothing effect. The superposition of the geologic model and high-content zones can be used to trace the ore solution flows and interpret the deposit formation process. For the second case study, oxidation, supergene, and hypogene mineralization zones with high copper contents are clearly detected and the relationship between high-content zones and hydrothermal alteration type is identified. The results of these two case studies therefore demonstrate the effectiveness of KGC and KGC-DS.
Viewpoint-Agnostic Change Captioning with Cycle Consistency

Change captioning is the task of identifying the change and describing it with a concise caption. Despite recent advancements, filtering out insignificant changes still remains as a challenge. Namely, images from different camera perspectives can cause issues; a mere change in viewpoint should be disregarded while still capturing the actual changes. In order to tackle this problem, we present a new Viewpoint-Agnostic change captioning network with Cycle Consistency (VACC) that requires only one image each for the before and after scene, without depending on any other information. We achieve this by devising a new difference encoder module which can encode viewpoint information and model the difference more effectively. In addition, we propose a cycle consistency module that can potentially improve the performance of any change captioning networks in general by matching the composite feature of the generated caption and before image with the after image feature. We evaluate the performance of our proposed model across three datasets for change captioning, including a novel dataset we introduce here that contains images with changes under extreme viewpoint shifts. Through our experiments, we show the excellence of our method with respect to the CIDEr, BLEU-4, METEOR and SPICE scores. Moreover, we demonstrate that attaching our proposed cycle consistency module yields a performance boost for existing change captioning networks, even with varying image encoding mechanisms.
Improving the Explainability of Skin Cancer Diagnosis Using CBIR

Explainability is a key feature for computer-aided diagnosis systems. This property not only helps doctors understand their decisions, but also allows less experienced practitioners to improve their knowledge. Skin cancer diagnosis is a field where explainability is of critical importance, as lesions of different classes often exhibit confounding characteristics. This work proposes a deep neural network (DNN) for skin cancer diagnosis that provides explainability through content-based image retrieval. We explore several state-of-the-art approaches to improve the feature space learned by the DNN, namely contrastive, distillation, and triplet losses. We demonstrate that the combination of these regularization losses with the categorical cross-entropy leads to the best performances on melanoma classification, and results in a hybrid DNN that simultaneously: i) classifies the images; and ii) retrieves similar images justifying the diagnosis. The code is available at https://github.com/catarina-barata/CBIR_Explainability_Skin_Cancer.