Reviewerly/exHarmony huggingface dataset:
Governance design of blockchain consortia for efficient and transparent procurement and supply chain management

The digital transformation offers a variety of opportunities to increase efficiency in buyer‐supplier relationships as well as whole supply chains and networks. The novel blockchain technology can increase transparency for all partners involved, ensure product provenance, and enable efficient automation of purchasing and supply chain management processes using smart contracts. Blockchain essentially provides a trustless system as the technology itself verifies trust in the network with unalterable transaction records. Concurrently, a higher degree of decentralization can be achieved and a change in decision‐making structures is promoted. A major challenge in managing these new networks is the design and implementation of efficient governance structures. Potential configurations range from hierarchically controlled systems to decentralized autonomous organizations. Which approaches are currently used in real implementations and for what reason has hardly been investigated so far. We use an empirical, qualitative study to analyze blockchain technologyʹs influence on governance structures in industrial consortia and highlight critical steps for selecting a suitable governance configuration for future collaboration efforts.
Chapter 2: Metallogeny of the Neoarchean Malartic Gold Camp, Québec, Canada

Abstract The Malartic gold camp is located in the southern part of the Archean Superior Province and straddles the Larder Lake-Cadillac fault zone that is between the Abitibi and Pontiac subprovinces. It comprises the world-class Canadian Malartic deposit (25.91 Moz, including past production, reserves, and resources), and smaller gold deposits located along faults and shear zones in volcanic and metasedimentary rocks of the Abitibi subprovince. North of the Larder Lake-Cadillac fault zone, the Malartic camp includes 2714 to 2697 Ma volcanic rocks and ≤2687 Ma turbiditic sedimentary rocks overlain by ≤2679 to 2669 Ma polymictic conglomerate and sandstone of the Timiskaming Group. South of the fault, the Pontiac subprovince comprises ≤2685 Ma turbiditic graywacke and mudstone, and minor ultramafic to mafic volcanic rocks and iron formations of the Pontiac Group. These supracrustal rocks were metamorphosed at peak greenschist to lower amphibolite facies conditions at ~2660 to 2658 Ma, during D2 compressive deformation, and are cut by a variety of postvolcanic intrusions ranging from ~2695 to 2640 Ma. The Canadian Malartic deposit encompasses several past underground operations and is currently mined as a low-grade, open-pit operation that accounts for about 80% of the past production and reserves in the camp. It dominantly consists of disseminated-stockwork replacement-style mineralization in greenschist facies sedimentary rocks of the Pontiac Group. The mineralized zones are spatially associated with the Sladen fault and ~2678 Ma subalkaline to alkaline porphyritic quartz monzodiorite and granodiorite. Field relationships and isotopic age data for ore-related vein minerals indicate that gold mineralization in the Canadian Malartic deposit occurred at ~2665 to 2660 Ma and was contemporaneous with syn- to late-D2 peak metamorphism. The smaller deposits in the camp include auriferous disseminated-stockwork zones of the Camflo deposit (1.9 Moz) and quartz ± carbonate-pyrite veins and breccias (0.6 Moz) along faults in chemically and mechanically favorable rocks. The age of these deposits is poorly constrained, but ~2692 Ma postmineral dikes, and ~2625 Ma hydrothermal titanite and rutile from the Camflo deposit highlight a long and complex hydrothermal history. Crosscutting relationships and regional geochronological constraints suggest that an early episode of pre-Timiskaming mineralization occurred at &amp;gt;2692 Ma, shortly after the end of volcanism in the Malartic camp, and postmetamorphic fluid circulation may have contributed to concentration or remobilization of gold until ~2625 Ma. However, the bulk of the gold was concentrated in the Canadian Malartic deposit during the main phase of compressive deformation and peak regional metamorphism.
A New Cuckoo Search Algorithm Using Interval Type-2 Fuzzy Logic for Dynamic Parameter Adaptation

The main objective of this work is to select the best parameters to be dynamically adjusted in the Cuckoo Search via Lévy Flight (CS) Algorithm using interval type 2 fuzzy logic. The main objective is to dynamically change the parameters, our fuzzy will include a Mamdani type system that will include an input and output to dynamically adjust the algorithm and improve the performance of the optimized CS algorithm. In order to demonstrate the performance and results of the algorithm, the comparison is made between the original algorithm, type 1 fuzzy logic and type, tests were performed with benchmark mathematical functions. The results of the simulation shows that the proposed algorithm has good results with respect to using type-1 fuzzy logic in CS or using the original CS algorithm without parameter adaptation. Lévy flights are used in the algorithm with the intention of exploring solutions and randomness solutions in space.
An Improved Chinese Text Classification Ensemble Model

This paper gave an improved ensemble model for Chinese text classification based on CNN and RNN structure. We first changed the bidirectional LSTM structure in the TextRCNN model to a bidirectional GRU structure and added a learning rate reduction mechanism into it. Then TextCNN model was also added into a learning rate reduction mechanism. Finally, our ensemble model was constructed through equal weight voting between the improved TextRCNN and improved TextCNN models. Experimental results showed that compared with TextCNN [7], TextRCNN [8], LSTM+attention [15], DPCNN [5] and FastText [6], the classification error rate of our model on SINA_P News dataset dropped by 8.36%, 13.2%, 20.23%, 7.33% and 29.17% respectively; dropped by 8.28%, 3.98%, 10.68% and 5.86% and rose by 3.8% on THUC_P news dataset. Hence, our ensemble model has higher classification accuracy than most other text classification models and is a competitive method.
Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-Specific Pruning

Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained images. However, quantitative analysis on multi-source Ki67 images is yet a challenging task in practice due to cross-domain distribution differences, which result from imaging variation, staining styles and lesion types. Many recent studies have made some efforts on domain generalization (DG), whereas there are still some noteworthy limitations. Specifically in the case of Ki67 images, learning invariant representation is at the mercy of the insufficient number of domains and the cell categories mismatching in different domains. In this paper, we propose a novel method to improve DG by searching the domain-agnostic subnetwork in a domain merging scenario. Partial model parameters are iteratively pruned according to the domain gap, which is caused by the data converting from a single domain into merged domains during training. In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains. Furthermore, an appropriate implementation is attained by applying the pruning method to different parts of the framework. Compared with known DG methods, our method yields excellent performance in multiclass nucleus recognition of Ki67 IHC images, especially in the lost category cases. Moreover, our competitive results are also evaluated on the public dataset over the state-of-the-art DG methods.
MRI-Trans-GAN: 3D MRI Cross-Modality Translation

Multi-modality magnetic resonance imaging (MRI) data prove more effective than a single modality in diagnosis, yet data collection for paired MRI images is a challenge. It is usually time consuming and may raise privacy concern. In this study, we explore cross-modality translation between MRI T1-weighted and T2-weighted images with 3D dual conditional generative adversarial networks (CGANs), and aim to synthesize missing modality of MRI images to mitigate the issue of insufficient data. To that end, a novel 3D-method, MRI translation generative adversarial network (MRI-Trans-GAN) is established. First, different from existing 3D big-patch methods, MRI-Trans-GAN is based on modified 3D patches with larger size in sagittal and coronal (frontal) axes and smaller size in vertical axis, i.e., 128×128×3 voxels. In addition, the tensor shape in vertical axis is fixed in forward process, and as a result, our model uses less memory than 3D big-patch methods. Moreover, it considers voxels relationship among adjacent slice other 2D-methods ignore. Second, we investigate loss functions and leverage wasserstein distance with gradient penalty and spectral normalization over traditional adversarial losses for better image quality and fidelity. Third, extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art. It shows that our method renders better quality in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM).
SecretMatch

Inventory matching is a process by which a broker or bank pairs buyers and sellers, without revealing their respective orders in a public exchange. Banks often undertake to match their clients, so that these clients can trade securities without incurring adverse price movements. If a bank finds matches between clients, it may execute them at reduced rates; if no matches are found, the clients must trade in a public market, which introduces costs for both parties. This problem is distinct from that solved by dark pools or public exchanges, which implement Continuous Double Auctions (CDAs). CDAs incorporate both price and volume. Inventory matching incorporates volume alone, and extracts price from an external source (such as a public market).
Inverse Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems with Missing Measurements and Linearly Correlated White Noises

For systems with missing measurements and linearly correlated white noises, by introducing the fictitious noises to compensate the multiplicative noise terms, the original system can be converted into one with only noise variances. The steady-state Kalman filter is designed by inverse covariance intersection (ICI) fusion. It overcomes the disadvantage that the covariance intersection (CI) fuser has larger conservativeness. The accuracy of the ICI filter is higher than CI filter and that of local filter. A simulation example is given to verify the accuracy relations.
A framework for randomized time-splitting in linear-quadratic optimal control

Inspired by the successes of stochastic algorithms in the training of deep neural networks and the simulation of interacting particle systems, we propose and analyze a framework for randomized time-splitting in linear-quadratic optimal control. In our proposed framework, the linear dynamics of the original problem is replaced by a randomized dynamics. To obtain the randomized dynamics, the system matrix is split into simpler submatrices and the time interval of interest is split into subintervals. The randomized dynamics is then found by selecting randomly one or more submatrices in each subinterval. We show that the dynamics, the minimal values of the cost functional, and the optimal control obtained with the proposed randomized time-splitting method converge in expectation to their analogues in the original problem when the time grid is refined. The derived convergence rates are validated in several numerical experiments. Our numerical results also indicate that the proposed method can lead to a reduction in computational cost for the simulation and optimal control of large-scale linear dynamical systems.
Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain

Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today’s world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.
Blockchain and IoT Integration for Society 5.0

The integration of Blockchain and Internet of Things (IoT) will have many implications in the Society 5.0. Blockchain technology has the potential to deal with issues that are related to data ownership, data integrity and data market monopolies. Public blockchains support the implementation of the data democratisation vision where everybody has access to data and there are no gatekeepers that make use of isolated data silos. Smart contracts can be used to provide contract transparency and allow citizens to manage their own data and the deriving economic value. This paper describes a novel blockchain-based security protocol that has been applied to a bicycle rental case study. This has been designed and implemented with the Society 5.0 vision in mind. Users store their own rental data by using a public blockchain. This eliminates the need of a centralised authority, provides data immutability and allows users to agree on transparent smart contract to manage their insurance, their payments and their own rental data. The smart lock protocol has been implemented in a real industrial product that uses the Ethereum public blockchain.
Assignment-Space-based Multi-Object Tracking and Segmentation

Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge. MOTS methods formulate tracking locally, i.e., frame-by-frame, leading to sub-optimal results. Classical global methods on tracking operate directly on object detections, which leads to a combinatorial growth in the detection space. In contrast, we formulate a global method for MOTS over the space of assignments rather than detections: First, we find all top-k assignments of objects detected and segmented between any two consecutive frames and develop a structured prediction formulation to score assignment sequences across any number of consecutive frames. We use dynamic programming to find the global optimizer of this formulation in polynomial time. Second, we connect objects which reappear after having been out of view for some time. For this we formulate an assignment problem. On the challenging KITTI-MOTS and MOTSChallenge datasets, this achieves state-of-the-art results among methods which don’t use depth data.
Integration of Blockchain and AI in EHR sharing: A survey

In the past decade, the digitalization of the health-care industry and electronic healthcare records (EHR) rises rapidly. The concept of smart health gradually gains momentum as information technology advances. Smart healthcare transforms and improves traditional medical systems in efficiency, service and personalization by integrating technologies like the internet of things (IoT), big data, cloud computing, and artificial intelligence (AI). On the other hand, smart healthcare systems are highly prone to security breaches and various malicious attacks. Blockchain technology has recently emerged as a promising solution for improving data management, access control, and integrity inside healthcare systems. This paper presents a survey of research on the latest blockchain solutions combined with AI technologies for improving and innovating new technical standards for the healthcare ecosystem related to medical diagnostics and electronic health records sharing, with a special focus on medical imaging.
Privacy-Preserving Neural Network Inference Framework via Homomorphic Encryption and SGX

Edge computing is a promising paradigm that pushes computing, storage, and energy to the networks' edge. It utilizes the data nearby the users to provide real-time, energy-efficient, and reliable services. Neural network inference in edge computing is a powerful tool for various applications. However, edge server will collect more personal sensitive information of users inevitably. It is the most basic requirement for users to ensure their security and privacy while obtaining accurate inference results. Homomorphic encryption (HE) technology is confidential computing that directly performs mathematical computing on encrypted data. But it only can carry out limited addition and multiplication operation with very low efficiency. Intel software guard extension (SGX) can provide a trusted isolation space in the CPU to ensure the confidentiality and integrity of code and data executed. But several defects are hard to overcome due to hardware design limitations when applying SGX in inference services. This paper proposes a hybrid framework utilizing SGX to accelerate the HE-based convolutional neural network (CNN) inference, eliminating the approximation operations in HE to improve inference accuracy in theory. Besides, SGX is also taken as a built-in trusted third party to distribute keys, thereby improving our framework's scalability and flexibility. We have quantified the various CNN operations in the respective cases of HE and SGX to provide the foresight practice. Taking the connected and autonomous vehicles as a case study in edge computing, we implemented this hybrid framework in CNN to verify its feasibility and advantage.
Improve Semantic Segmentation of Remote sensing Images with K-Mean Pixel Clustering: A semantic segmentation post-processing method based on k-means clustering

Semantic image segmentation has been used to detect objects and label pixels in images. It has been applied to high-resolution remote sensing images to detect different types of terrains and landforms. However, the accuracy of the existing methods is not always satisfactory. Here we propose a semantic segmentation post-processing method using K-mean clustering. Our method aggregates the predictions from network training algorithms such as Unet and HrNet [1], and then performs postprocessing using K-Mean clustering iteratively [2] [3]. The accuracy of our method improves as the number of iterations increases. Source code is at https://github.com/carlsummer/SSK.
Learning Rare Category Classifiers on a Tight Labeling Budget

Many real-world ML deployments face the challenge of training a rare category model with a small labeling budget. In these settings, there is often access to large amounts of unlabeled data, therefore it is attractive to consider semi-supervised or active learning approaches to reduce human labeling effort. However, prior approaches make two assumptions that do not often hold in practice; (a) one has access to a modest amount of labeled data to bootstrap learning and (b) every image belongs to a common category of interest. In this paper, we consider the scenario where we start with as-little-as five labeled positives of a rare category and a large amount of unlabeled data of which 99.9% of it is negatives. We propose an active semi-supervised method for building accurate models in this challenging setting. Our method leverages two key ideas: (a) Utilize human and machine effort where they are most effective; human labels are used to identify "needle-in-a-haystack" positives, while machine-generated pseudo-labels are used to identify negatives. (b) Adapt recently proposed representation learning techniques for handling extremely imbalanced human labeled data to iteratively train models with noisy machine labeled data. We compare our approach with prior active learning and semi-supervised approaches, demonstrating significant improvements in accuracy per unit labeling effort, particularly on a tight labeling budget.
Performance analysis and optimization of decision tree classifiers on embedded devices

Decision trees (DTs) offer a popular implementation choice for machine learning classifiers since they are highly interpretable and easy to use. Resource management decision overheads must be minimal in embedded systems to meet latency targets and deadline constraints. While the literature has preferred hardware architectures for DTs to meet latency targets, they are not suitable for ultra-low latency applications due to their data movement overheads despite the parallelism they offer. Therefore, we propose software optimization techniques for decision trees. The proposed DTs achieve lower than 50 ns latencies for depth 12, making them highly suitable for classification in embedded resource management.
Effects of system- and media-driven immersive capabilities on presence and affective experience

Abstract Virtual reality (VR) is receiving widespread attention as a delivery tool for exposure therapies. The advantage offered by VR over traditional technology is a greater sense of presence and immersion, which magnifies user effects and enhances the effectiveness of exposure-based interventions. The current study systematically examined the basic factors involved in generating presence in VR as compared to standard technology, namely (1) system-driven factors that are exclusive to VR devices while controlling general factors such as field of view and image quality; (2) media-driven factors of the virtual environment eliciting motivational salience through different levels of arousal and valence (relaxing, exciting and fear evoking stimuli); and (3) the effects of presence on magnifying affective response. Participants ( N = 14) watched 3 different emotionally salient videos (1 × fear evoking, 1 × relaxing and 1 × exciting) in both viewing modes (VR and Projector). Subjective scores of user experience were collected as well as objective EEG markers of presence (frontal alpha power, theta/beta ratio). Subjective and objective presence was significantly greater in the VR condition. There was no difference in subjective or objective presence for stimulus type, suggesting presence is not moderated by arousal, but may be reliant on activation of motivational systems. Finally, presence did not magnify feelings of relaxation or excitement, but did significantly magnify users’ experience of fear when viewing fear evoking stimuli. This is in line with previous literature showing strong links between presence and generation of fear, which is vital in the efficacy of exposure therapies.