Reviewerly/exHarmony huggingface dataset:
A Blockchain-Assisted Verifiable Outsourced Attribute-Based Signcryption Scheme for EHRs Sharing in the Cloud

The sharing of electronic health records (EHRs) has shown great advantages in the accurate treatment of patients and the development of medical institutions.However, it is easy to cause some security problems in the process of medical data sharing.Generally, after a patient's EHRs are generated by different medical institutions, they are outsourced to the cloud server (CS) by the authorized medical institutions for storage, which causes the patient to lose control of EHRs.Moreover, malicious medical institutions and semi-trusted cloud servers may collude to tamper with EHRs to seek benefits, which threatens the integrity of EHRs.Therefore, we propose a blockchain-assisted verifiable outsourced attribute-based signcryption scheme (BVOABSC) which realizes the secure sharing of EHRs in a multi-authority cloud storge environment.Firstly, we use the attribute-based signcryption algorithm to realize the confidentiality and unforgeability of the EHRs and protect the privacy of the signer.Secondly, it greatly reduces the computational burden of users by using verifiable outsourcing computation mechanism.Most of the designcryption calculation is performed by the cloud server, and the correctness of the generated partial designcryption ciphertext is verified by users.Furthermore, we use blockchain technology to protect outsourced EHRs from tampering by illegal users.Specifically, each operation on outsourced EHRs is stored as a transaction on the public blockchain, which ensures that EHRs cannot be modified.At the same time, the auditor can verify the integrity of the outsourced EHRs by checking the corresponding transactions.In addition, the smart contract created by the patient can solve the problems in cloud storage, such as tampering EHRs and returning incorrect results.Finally, security analysis and performance evaluation show that the proposed BVOABSC scheme satisfies stronger security and higher efficiency than similar schemes.
CircleNet: Anchor-Free Glomerulus Detection with Circle Representation

Object detection networks are powerful in computer vision, but not necessarily optimized for biomedical object detection. In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. Different from the traditional bounding box based detection method, the bounding circle (1) reduces the degrees of freedom of detection representation, (2) is naturally rotation invariant, (3) and optimized for ball-shaped objects. The key innovation to enable this representation is the anchor-free framework with the circle detection head. We evaluate CircleNet in the context of detection of glomerulus. CircleNet increases average precision of the glomerulus detection from 0.598 to 0.647. Another key advantage is that CircleNet achieves better rotation consistency compared with bounding box representations.
A framework for optimizing extended belief rule base systems with improved Ball trees

Decision support systems imposed a challenge for efficiently reasoning through utilizing stored unordered rules. The Extended Belief Rule-Base (EBRB) system, renowned for its capability of modeling data with vagueness, incompleteness, uncertainty and nonlinear features, requires to traverse over all the rules for the execution of reasoning and consequently suffers reduced inference accuracy and efficiency. To improve the performance, we propose a framework of storing and retrieving belief rules based on employing an optimized structure called improved Ball tree. The framework first constructs a Ball tree index according to the distance between different rules in the metric space, via using the k-means++ algorithm rather than traditionally employed k-centers. Then, through our proposed dynamic threshold radius adjusting method, our algorithm finds an appropriate dataset threshold and accordingly activates more related rules. Moreover, the number of retrieved and activated irrelevant rules is significantly reduced, resulting in consequently improved reasoning accuracy and efficiency. Lastly, three sets of experiments were carried out to validate our algorithm in comparison with the previous EBRB systems.
SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays

Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario. In addition, obtaining annotations for X-rays is very time consuming and requires extensive training of radiologists. Hence, training anomaly detection in a fully unsupervised or self-supervised fashion would be advantageous, allowing a significant reduction of time spent on the report by radiologists. In this paper, we present SALAD, an end-to-end deep self-supervised methodology for anomaly detection on X-Ray images. The proposed method is based on an optimization strategy in which a deep neural network is encouraged to represent prototypical local patterns of the normal data in the embedding space. During training, we record the prototypical patterns of normal training samples via a memory bank. Our anomaly score is then derived by measuring similarity to a weighted combination of normal prototypical patterns within a memory bank without using any anomalous patterns. We present extensive experiments on the challenging NIH Chest X-rays and MURA dataset, which indicate that our algorithm improves state-of-the-art methods by a wide margin.
Benchmarking the Robustness of Semantic Segmentation Models with Respect to Common Corruptions

Abstract When designing a semantic segmentation model for a real-world application, such as autonomous driving, it is crucial to understand the robustness of the network with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on many established neural network architectures. We utilize almost 400,000 images generated from the Cityscapes dataset, PASCAL VOC 2012, and ADE20K. Based on the benchmark study, we gain several new insights. Firstly, many networks perform well with respect to real-world image corruptions, such as a realistic PSF blur. Secondly, some architecture properties significantly affect robustness, such as a Dense Prediction Cell, designed to maximize performance on clean data only. Thirdly, the generalization capability of semantic segmentation models depends strongly on the type of image corruption. Models generalize well for image noise and image blur, however, not with respect to digitally corrupted data or weather corruptions.
Survey of Various AI Chatbots Based on Technology Used

Artificial Intelligence is the ability to make machines intelligent. The development in AI made machines capable to initiate the simulation of natural intelligence and are able to take decisions where humans do better. Natural Language Processing, Machine Learning are some of the major branches of AI. A Chatbot is an AI software uses Natural Language Processing Techniques to simulate a chat between a system and a user. Natural Language Processing help Chatbots to understand natural language more clearly and generate an intelligent response. Since the introduction of first Chatbot ELIZA in 1966, different versions with variety of technology and approach have been developed. In this paper, we describe the generic work flow of Chatbots and represents their comparison on the basis of technology/approach used with some significant parameters. To make natural language conversation more efficient, a Chatbot must analyze and understand the user input correctly for a relevant response. They are currently being used in various important domains like science, education, health care etc. Chatbots have a potential to improve human interaction with machines.
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning

Enabling robots to understand instructions provided via spoken natural language would facilitate interaction between robots and people in a variety of settings in homes and workplaces. However, natural language instructions are often missing information that would be obvious to a human based on environmental context and common sense, and hence does not need to be explicitly stated. In this paper, we introduce Language-Model-based Commonsense Reasoning (LMCR), a new method which enables a robot to listen to a natural language instruction from a human, observe the environment around it, and automatically fill in information missing from the instruction using environmental context and a new commonsense reasoning approach. Our approach first converts an instruction provided as unconstrained natural language into a form that a robot can understand by parsing it into verb frames. Our approach then fills in missing information in the instruction by observing objects in its vicinity and leveraging commonsense reasoning. To learn commonsense reasoning automatically, our approach distills knowledge from large unstructured textual corpora by training a language model. Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.
Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term prediction, linked to the environment and task constraints. In this work we investigate encoding short-term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization. Experiments on real motion data demonstrate that our framework improves the prediction with respect to state-of-the-art motion prediction methods, as it accounts to beforehand unseen environmental structures. Moreover we demonstrate on an example, how this framework can be used to plan robot trajectories that are optimized to coordinate with a human partner.
School's Back

Social virtual reality (social VR) is an emerging technology that has the potential to support unique social experiences for groups of older adults. In this paper we explore the use of social VR to support group reminiscence, an activity that has been shown to have a positive impact on the lives of older adults. We developed School Days, a bespoke social VR application that enables groups of geographically dispersed older adults to meet in a virtual environment to reminisce about their school experiences. We conducted a user study over the course of 5 months with 16 participants aged 70--81 to evaluate how School Days supported reminiscence. In this paper, we focus on how the use of reminiscence scaffolding features in School Days impacted on the older adults' ability to participate more fully in the reminiscence activities. Our results illustrate the value of social VR for connecting older adults over distance, and contribute new knowledge of how virtual environments can be designed to scaffold reminiscence; how techniques such as 3D conversation starters and individual artefacts can be used to scaffold reminiscence; and how pre-recorded holographic stories (Avacasts) can be used to introduce new perspectives and prompt self-reflection. We contribute five design reflections aimed at guiding the design of future reminiscence tools in social VR.
Post-Quantum TLS on Embedded Systems: Integrating and Evaluating Kyber and SPHINCS+ with mbed TLS

We present our integration of post-quantum cryptography (PQC), more specifically of the post-quantum KEM scheme Kyber for key establishment and the post-quantum signature scheme SPHINCS+, into the embedded TLS library mbed TLS. We measure the performance of these post-quantum primitives on four different embedded platforms with three different ARM processors and an Xtensa LX6 processor. Furthermore, we compare the performance of our experimental PQC cipher suite to a classical TLS variant using elliptic curve cryptography (ECC). Post-quantum key establishment and signature schemes have been either integrated into TLS or ported to embedded devices before. However, to the best of our knowledge, we are the first to combine TLS, post-quantum schemes, and embedded systems and to measure and evaluate the performance of post-quantum TLS on embedded platforms. Our results show that post-quantum key establishment with Kyber performs well in TLS on embedded devices compared to ECC variants. The use of SPHINCS+ signatures comes with certain challenges in terms of signature size and signing time, which mainly affects the use of embedded systems as PQC-TLS server but does not necessarily prevent embedded systems to act as PQC-TLS clients.
Venturing Crowdfunding using Smart Contracts in Blockchain

Crowd funding is an online cash raising technique that started as a path for the people to contribute limited quantity of money to enable innovative individuals to fund the venture. Using crowdfunding, people can put resources into pioneering businesses through a middle medium or platform. The issue with the current crowd funding technique is that, third party medium don't give the assurance of the money investor contributed for the project and investor don't have control over the cash they contributed. This paper proposes the blockchain based crowd funding by using which the platform can give a private, secure and decentralized path for crowdfunding. The main objective of this paper is to let investors contribute to any project effectively by creating smart contracts through which the contributors can have a control over the invested money and also both the project creators and investors can effectively make and reserve funding for the project.
Extracting and Classifying Requirements from Software Engineering Contracts

In this paper, we present our work on extracting and classifying requirements from large software engineering contracts. Typically, the process of requirements elicitation begins after a contractual agreement is signed by all participants. Our interactions with the legal compliance team in a large vendor organization reveal that business contracts can help in the identification of high-level requirements relevant to the success of software engineering projects. We posit that requirements engineering as a discipline has an even wider scope than software engineering of which it is traditionally considered to be a sub-discipline. This is because software engineering-specific requirements are but a part of the success story of any large project. The requirements that emerge from contracts are obligatory in nature, whether or not they pertain to core software development. Therefore, it is important that these are extracted and classified for the benefit of software engineers and other stakeholders responsible for a project. We discuss the results of an exploratory study and a range of experiments from the use of regular expressions to Bidirectional Encoder Representations from Transformers for automating the extraction and classification of requirements from software engineering contracts. With Bidirectional Encoder Representations from Transformers, we obtained a high f-score of greater than eighty four percent for classification of requirements.
Grover on Korean Block Ciphers

The Grover search algorithm reduces the security level of symmetric key cryptography with n-bit security level to O(2n/2). In order to evaluate the Grover search algorithm, the target block cipher should be efficiently implemented in quantum circuits. Recently, many research works evaluated required quantum resources of AES block ciphers by optimizing the expensive substitute layer. However, few works were devoted to the lightweight block ciphers, even though it is an active research area, nowadays. In this paper, we present optimized implementations of every Korean made lightweight block ciphers for quantum computers, which include HIGHT, CHAM, and LEA, and NSA made lightweight block ciphers, namely SPECK. Primitive operations for block ciphers, including addition, rotation, and exclusive-or, are finely optimized to achieve the optimal quantum circuit, in terms of qubits, Toffoli gate, CNOT gate, and X gate. To the best of our knowledge, this is the first implementation of ARX-based Korean lightweight block ciphers in quantum circuits.
Image Reconstruction of Static and Dynamic Scenes Through Anisoplanatic Turbulence

Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere. While methods exist for removing such turbulent distortions, many are limited to static sequences which cannot be extended to dynamic scenes. In addition, the physics of the turbulence is often not integrated into the image reconstruction algorithms, making the physics foundations of the methods weak. In this article, we present a unified method for atmospheric turbulence mitigation in both static, and dynamic sequences. We are able to achieve better results compared to existing methods by utilizing (i) a novel space-time non-local averaging method to construct a reliable reference frame, (ii) a geometric consistency, and a sharpness metric to generate the lucky frame, (iii) a physics-constrained prior model of the point spread function for blind deconvolution. Experimental results based on synthetic and real long-range turbulence sequences validate the performance of the proposed method.
Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks

Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification tasks. However, the cost and time for acquiring high quality task-specific large annotated training data are subject to intra- and inter-observer variability, thus challenging the adoption of such tools. To address these challenges, we propose a classification framework via co-representation learning to maximize the learning capability of deep neural networks while using a reduced amount of training data. The framework captures the class-label information and the local spatial distribution information by jointly optimizing a categorical cross-entropy objective and a deep metric learning objective respectively. A deep metric learning objective is incorporated to enhance the classification, especially in the low training data regime. Further, a neighborhood-aware multiple similarity sampling strategy, and a soft-multi-pair objective that optimizes interactions between multiple informative sample pairs, is proposed to accelerate deep metric learning. We evaluate the proposed framework on five benchmark datasets from three digital pathology tasks, i.e., nuclei classification, mitosis detection, and tissue type classification. For all the datasets, our framework achieves state-of-the-art performance when using approximately only 50% of the training data. On using complete training data, the proposed framework outperforms the state-of-the-art on all the five datasets.
Generalized Few-shot Semantic Segmentation

Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few- Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS- Seg), to analyze the generalization ability of simultaneously segmenting the novel categories with very few examples and the base categories with sufficient examples. It is the first study showing that previous representative state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained setting of FS-Seg. To make GFS-Seg tractable, we set up a GFS-Seg baseline that achieves decent performance without structural change on the original model. Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image. Both two contributions are experimentally manifested for their substantial practical merit. Extensive experiments on Pascal-Voc and COCO also show that CAPL generalizes well to FS-Seg by achieving competitive performance. Code is available at https://github.com/dvlab-research/GFS-Seg.