Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Parameterization of Diffeomorphic Image Registration for Cardiac Image Segmentation

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks in analyzing the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, cardiac MRI generates hundreds of images per scan, and manual annotation of them is challenging and time-consuming, and therefore processing these images automatically is of interest. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment cardiac chambers from 2D and 3D images or volumes. To represent actual cardiac deformation, the method parameterizes the transformation using radial and rotational components computed via deep learning, with a set of paired images and segmentation masks used for training. The formulation guarantees transformations that are invertible and prevents mesh folding, which is essential for preserving the topology of the segmentation results. A physically plausible transformation is achieved by employing diffeomorphism in computing the transformations and activation functions that constrain the range of the radial and rotational components. The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.
Rational OpenCog Controlled Agent

In this paper we introduce, ROCCA for Rational OpenCog Controlled Agent, an agent, that, as its name suggests, leverages the OpenCog framework to fulfill goals in uncertain environments. It attempts to act rationally, relying on reasoning for both learning and planning. An experiment in a Minecraft environment is provided as a test case.
A Blockchain-Based Approach for Drug Traceability in Healthcare Supply Chain

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ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI

Intelligent agents are characterized primarily by their far-sighted expedient behavior. We present a working prototype of an intelligent agent (ADAM) based on a novel hierarchical neuro-symbolic architecture (Deep Control) for deep reinforcement learning with a potentially unlimited planning horizon. The control parameters form a hierarchy of formal languages, where higher-level alphabets contain the semantic meanings of lower-level vocabularies.
Digital Speckle Pattern Interferometry for Studying Surface Deformation and Fracture of Materials

In this chapter, a two-stepTwo-step digital speckle pattern interferometry (DSPIDigital speckle pattern interferometry (DSPI)) method with an unknown phase shiftUnknown phase shift of a reference waveReference wave is described. The method implements the recording of two speckle fringe patterns (speckle interferogramsSpeckle interferogram (SI) (SIsSpeckle interferogram (SI))) before deformation of the studied surfaceSurface differing in an arbitrary phase shiftArbitrary phase shift between the reference wavesReference wave and two SIsSpeckle interferogram (SI) after the surface deformationSurface deformations differing in the same arbitrary phase shiftArbitrary phase shift. In this case, the phases of the two initial SIsSpeckle interferogram (SI) recorded before and after surface deformationSurface deformations coincide. As with the phase shifting interferometryPhase shifting interferometry (PSI) methods considered in Chap. 2 , the population Pearson correlation coefficientPopulation Pearson correlation coefficient (PPCC) is used to calculate the unknown phase shiftUnknown phase shift. Computer simulation and experimental verification of this method confirm its reliability to restore in-plane and out-of-plane surface displacementsSurface displacements. Beam and sheet metal specimens, as well as composite specimens containing various defects and damages were studied by using this method performed with the help of 2D and 3D speckle interferometersSpeckle interferometer. The displacement fieldsDisplacement field of the studied specimens were retrieved and the hidden defects were detected. The subtractive synchronized DSPIDigital speckle pattern interferometry (DSPI) method for detection of subsurface defectsSubsurface defect in laminated composites is also represented in this Chapter. Subsurface defectsSubsurface defect were detected using an optical-digital speckle interferometerOptical-digital speckle interferometer (ODSI) (ODSI) and ultrasonic excitationUltrasonic excitation (US excitation) of the studied specimen in the frequency range from 10 to 150 kHz. Experiments performed demonstrate the ability of the ODSIOptical-digital speckle interferometer (ODSI) to detect subsurface delaminations and disbonds in laminated composite panels.
Central Bank Digital Currencies (CBDC) and Socialism: Implications for Economic and Social Change

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Analysis of Weibo User Characteristics and Emotional Tendency in COVID-19 Scenario Based on K-means Clustering Algorithm

By analyzing Sina Weibo content helps to better guide internet public opinion. This paper selects the Weibo trending topic of '11.3 COVID-19 Epidemic in Dalian City' and uses K-means clustering algorithm to construct Weibo User Characteristics Model from three aspects: basic attributes, behavior attributes and value attributes. Then, based on the emotional dictionary, this paper also examines the emotional types of Weibo users and their dissemination trends. The study carries out that the user groups under this topic are mainly divided into hot spot generating users, high-frequency interactive users and ordinary social users. During the outbreak of the epidemic, Weibo users' emotions are mainly positive emotions of 'good' and 'happy', showing a trend from pessimistic to optimistic emotions.
Proposal for a long-distance nonlocality test with entanglement swapping and displacement-based measurements

We analyze an all-optical setup which enables Bell-inequality violation over long distances by exploiting probabilistic entanglement swapping. The setup involves only two-mode squeezers, displacements, beamsplitters, and on/off detectors. We analyze a scenario with dichotomic inputs and outputs, and check the robustness of the Bell inequality violation for up to 6 parties, with respect to phase-, amplitude-, and dark-count noise, as well as loss.
Improving Pre-trained Language Models

Abstract This chapter describes a number of different approaches to improve the performance of Pre-trained Language Models (PLMs), i.e. variants of BERT, autoregressive language models similar to GPT, and sequence-to-sequence models like Transformers. First we may modify the pre-training tasks to learn as much as possible about the syntax and semantics of language. Then we can extend the length of the input sequence to be able to process longer inputs. Multilingual models are simultaneously trained with text in different languages. Most important is the inclusion of further knowledge into the PLM to produce better predictions. It turns out that by increasing the number of parameters, the size of the training data and the computing effort the performance of the models can always be increased. There are a number of different fine-tuning strategies which allow the model to be adapted to special tasks. In addition, models may be instructed by few-shot prompts to solve specific tasks. This is especially rewarding for larger PLMs, which therefore are called Foundation Models.
Knowledge Acquired by Foundation Models

Abstract During pre-training, a Foundation Model is trained on an extensive collection of documents and learns the distribution of words in correct and fluent language. In this chapter, we investigate the knowledge acquired by PLMs and the larger Foundation Models. We first discuss the application of Foundation Models to specific benchmarks to test knowledge in a large number of areas and examine if the models are able to derive correct conclusions from the content. Another group of tests assesses Foundation Models by completing text and by applying specific probing classifiers that consider syntactic knowledge, semantic knowledge, and logical reasoning separately. Finally, we investigate if the benchmarks are reliable and reproducible, i.e. whether they actually test the targeted properties and yield the same performance values when repeated by other researchers.
Advancing reuse of genetic parts: progress and remaining challenges

Issues with data reuse have been recognized in synthetic biology and the broader scientific community. Policies and standards fall short as machine reasoning is not emphasised and enforcement is lacking. We discuss the progress, remaining challenges, and possible solutions.
Analysis of dynamic connectedness relationships between cryptocurrency, NFT and DeFi assets: TVP-VAR approach

The aim of this study is to analyse the dynamic connectedness relationship (DCR) between cryptocurrency, NFT, and DeFi assets. In the study, two cryptocurrencies consisting of Bitcoin and Ethereum, two NFTs consisting of Tezos and The Sandbox, and two DeFi assets consisting of Chainlik and Uniswap were analysed. The results showed that Ethereum cryptocurrency and Chainlink DeFi assets spilled volatility to other crypto assets. The other variables were assets that received volatility, and the volatility spillover relationship between NFT assets is less than other crypto assets.
Region feature smoothness assumption for weakly semi‐supervised crowd counting

Abstract Crowd counting is a hot issue in visual data processing. It also plays an important role in the field of video surveillance, social security, and traffic control. However, most of the existing crowd counting methods always adopt a mount of training data or point‐level annotation to learn the mapping relationships between images and density maps, which would cost much human labor. In this paper, we propose a new weakly semi‐supervised crowd counting method which uses less count‐level data for data training. In particular, we extend the classical smoothness assumption and design a many‐to‐many Region Feature Smoothness Assumption to deal with the uneven density distribution problem within crowd region. Further, we adopt hypergraph representation to explore the complex high‐order relationship for different crowd regions. Besides, we design a multi‐scale dynamic hypergraph convolutional module and hyperedge contrastive loss. Extensive experiments have been conducted on five public datasets. The experimental results show that the proposed method outperforms the state‐of‐the‐art ones.
Digitally mediated code-switching in transnational families in Australia: Fathers and children

The article focuses on Polish-speaking families residing in Australia and their digitally mediated communication practices. Against the backdrop of social changes influencing family roles, a new father–child relationship characterised by presence and active involvement in a child’s life has recently transpired. As the fathers’ role in minority language transmission has not been given much prominence in research, this article offers novel insights into how Polish fathers endeavour to maintain Polish through digitally mediated communication. Research on digital practices in the context of code-switching (CS) has been scant, despite the fact that everyday reality of transnational multilingual families has been permeated by technology. With this article, I aim to touch upon the themes situated at the intersection of transnationalism, family multilingualism, language transmission, as well as new media and their role in contemporary communication practices. Six excerpts containing examples of CS practices among three different families, where instant messaging via Messenger has been adopted to maintain Polish will be scrutinised.
A composite blockchain associated event traceability method for financial activities

The existing blockchain system mostly adopts the equal mining mode. All bookkeepers (entities) record the ledgers on a single main chain, and the data storage is random. Moreover, in complex or classified financial scenarios, the data of the main chain is difficult to realize association or regular storage, resulting in low efficiency of storage and query. At the same time, in the existing blockchain system, event traceability is mostly only found in the source block, and the implicit association between entities cannot be identified, so the query has limitations. To solve these problems, this paper proposes a composite blockchain associated event tracing method. This method firstly constructs the blockchain composite chain storage structure model, proposes the concept of private chain and alliance chain, and realizes the adaptive data association storage in complex or classified scenarios. Secondly, on the basis of obtaining the event source entity block, the auxiliary storage space is established to transfer storage relevant data. A tracing method of associated entity block based on the Apriori algorithm is proposed, and then the obtained traceability entity block is constructed as the source event correlation graph, so as to describe the association relationship between the event entities. Finally, a risk assessment system based on reinforcement learning is proposed to realize the risk assessment of traceability entity. Experiments show that the composite blockchain associated event tracing method proposed in this paper can reduce 60% of the storage overhead, improve 90% of the query accuracy and 50% of the security. can reduce the storage overhead by 60%, improve the query accuracy by 90% and improve security by 50%.
Hybrid heuristic mechanism for occlusion aware facial expression recognition scheme using patch based adaptive CNN with attention mechanism

In computer vision, the recognition of expressions from partially occluded faces is one of the serious problems. By the prior recognizing techniques it can solve the issue by various assumptions. A benchmark-guided branch was proposed for detecting and eliminating the manipulated features from the occluded regions since the human visual system is proficient for eliminating the occlusion and the appropriate focal point was obtained on the non-occluded areas. In recent years deep learning has attained a great place in the recognition of facial reactions Still, the precision of facial expression is affected by the occlusion and large skew. In this research work, a deep structure-based occlusionaware facial expression recognition mechanism is introduced to provide superior recognition results. Firstly, the required image is taken from publically provided online sources and the gathered images are subjected to the face extraction method. The face extraction method is done via the ViolaJones method for the extraction of redundant patterns from the original images. Secondly, the extracted face features are given to the pattern recognition stage, where the Adaptive CNN with Attention Mechanism (ACNN-AM) is introduced. This mechanism automatically forms the occluded region of the face and the focal point was on the most discriminative un-occluded regions. Moreover, the hidden patterns in the Occlusion aware facial expressions are identified through the Hybrid Galactic Swarm Yellow Saddle Goatfish Optimization (HGSYSGO). Finally, the overall effectiveness of the developed occlusion aware facial expression recognition model is examined through the comparative analysis of different existing baseline recognition techniques.
Poisson image restoration via an adaptive Euler’s elastica regularization

Many recent studies have shown that Euler’s elastica regularization performs better than the famous total variation (TV) regularization on keeping image features in smooth regions during the process of denoising. In addition, an adaptive weighted matrix combined with total variation is a key technique for well restoring local features of image. Considering these two factors, in this paper, we propose an adaptive Euler’s elastica model for Poisson image restoration so as to well preserve both image features in smooth regions and local features of image. To solve this non-smooth and non-convex model efficiently, we design an alternating direction method of multipliers. Experiments on both natural and synthetic images illustrate the effectiveness and efficiency of the proposed method over the state-of-the-art methods in Poisson restoration and denoising, respectively. In particular, for Poisson restoration, our proposed method improves the TV method up to 2.46 about PSNR for dealing with the Peppers image with Gaussian blur and noise level σ = 1. In addition, the proposed method for Poisson denoising gets higher PSNR and SSIM values than the TAC method, while costing less CPU time.
A Cryptographic Security Mechanism for Dynamic Groups for Public Cloud Environments

Cloud computing has emerged as a significant technology domain, primarily due to the emergence of big data, machine learning, and quantum computing applications. While earlier, cloud computing services were focused mainly on providing storage and some infrastructures/platforms for applications, the need to advance computational power analysis of massive datasets. It has made cloud computing almost inevitable from most client-based applications, mobile applications, or web applications. The allied challenge to protect data shared from and to cloud-based platforms has cropped up with the necessity to access public clouds. While conventional cryptographic algorithms have been used for securing and authenticating cloud data, advancements in cryptanalysis and access to faster computation have led to possible threats to the traditional security of cloud mechanisms. This has led to extensive research in homomorphic encryption pertaining to cloud security. In this paper, a security mechanism is designed targeted towards dynamic groups using public clouds. Cloud security mechanisms generally face a significant challenge in terms of overhead, throughput, and execution time to encrypt data from dynamic groups with frequent member addition and removal. A two-stage homomorphic encryption process is proposed for data security in this paper. The performance of the proposed system is evaluated in terms of the salient cryptographic metrics, which are the avalanche effect, throughput, and execution time. A comparative analysis with conventional cryptographic algorithms shows that the proposed system outperforms them regarding the cryptographic performance metrics.
A heterogeneous multi-relations-based method for knowledge-intensive service recommendation

Recommendation systems (RSs) have been widely used in consumer product trading platforms due to their excellent capability to assist users in finding relevant items, which have also attracted tremendous attention in knowledge-intensive service (KIS) transaction platforms. While numerous RSs have been applied in KIS platforms for task recommendation and worker recommendation, an RS specialized for KIS recommendation to meet employers’ potential needs is still missing. To this end, the heterogeneous multi-relations, including knowledge-service relations, activity-service relations, and worker-service relations, are investigated firstly to bridge the knowledge gap, and a novel methodology for KIS recommendation is proposed based on the heterogeneous multi-relations construction and fusion. This method develops a heterogeneous information network (HIN) to structure high-level interactions among employers, services, activities, and workers, and designs KIS-oriented meta-paths to obtain valuable heterogeneous multi-relations. Furthermore, these valuable relations are aggregated and fused hierarchically through metapath2vec and attention mechanism to learn representations of employers and services and further to predict the targeted employer's potential KIS needs. Finally, the transaction data from ZBJ.com - the largest KIS trading platform in China, is used to validate the effectiveness and high accuracy of the proposed method in KIS recommendation. It is also found that as the service-service knowledge similarity increase, the proposed model has an inverted U-shape performance, in which the composite relation of activity-service and service-service knowledge similarity is the main factor for KIS node representation learning.
Makeup Extraction of 3D Representation via Illumination‐Aware Image Decomposition

Abstract Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real‐world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression‐based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well‐aligned in the UV space, from which we build a large‐scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination‐aware makeup interpolation/removal without a reference image.
Improving Healthcare Facility Safety with Electronic Monitoring by a Machine Learning Framework Based on the Internet of Things

Hacks, unauthorised access, and other problems have increased the risk to the healthcare system dependent on data analytics in recent years. When a system is kept in its factory settings, it provides an easier target for hackers who wish to get access to the server and steal data. In order to protect the privacy of patients, we use an innovative encryption approach called the Whale-based Random Forest (WbRF) Scheme in this research. Furthermore, ciphertext is made by layering micro-electronic sensors and employing Identity-based Encryption (IBE) on plaintext. The purpose of this surveillance is to ensure the model’s continued health while keeping a vigilant eye out for threats. Therefore the framework is programmed into the Python tool, and the system is trained on more than 200 patient datasets. Medical records for patients can be encrypted and stored safely in the cloud using nano-electronic jargon, in the end. The generated model is subjected to various attacks in order to determine how secure and effective it really is. Energy consumption, execution time, encryption time, latency, accuracy, and decryption time are compared between the created framework and conventional methods.