Reviewerly/exHarmony huggingface dataset:
Hourglass stereo matching network

Although the deep learning-based stereo matching networks have made significant progress, the ability to find corresponding relationships in ill-conditioned regions (weak textures, repeated textures, occluded regions, etc.) still needs to be improved. Aiming at the above problems, an hourglass stereo matching network is proposed, which is mainly composed of an hourglass structure. The first is the hourglass feature extraction module, which uses the information of the global context to obtain more detailed features; then the obtained feature information is aggregated together to construct a cost volume. In the three-dimensional convolution module, multiple hourglass structures are used to refine disparity and use intermediate supervision to standardize the cost volume. It fuses the information again and makes better use of global context information. Finally obtain the disparity map through disparity regression. Through the verification test on the Scene Flow and KITTI dataset, it shows that the proposed method maintains better performance while reducing parameters, significantly reduces the error in ill-conditioned regions, and achieves competitive results.
Multi-Node Data Privacy Audit for Blockchain Integrity

Cross-shard transactions in the blockchain sharding scheme are coordinated and processed by multiple shards. In the sharding scheme using the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, there is a problem that the transaction cannot be verified due to the failure of the shard due to the aggregation of Byzantine nodes in a single bit after sharding. Therefore, to ensure the consistency of data between chips, it is necessary to roll back the partially processed cross-shard transactions, which affects the system’s overall performance. In response to the above problems, a multi-round consensus verification scheme is proposed, which can reduce the rollback probability, support a larger fragmentation scale, and increase the system’s transaction per second (TPS). The advantages and disadvantages of the existing sharding project solutions are briefly described, the probability and rollback probability of cross-sharding transactions are analyzed, and a multi-round consensus verification scheme is proposed. Influence, a reasonable upper limit of the number of rounds is obtained. The comparison experiment with the existing plan shows that the multi-round verification scheme can effectively improve the transaction verification rate, reduce the probability of cross-slice transaction rollback, and improve the overall TPS of the system.
Application of Blockchain in Online Learning

This chapter discusses the opportunities and challenges of applying blockchain technologies in the education sector. Current educational research focuses on using artificial intelligence (AI), blockchain, and other advanced technologies to help educators, educational institutions, and businesses achieve their goals by developing solutions that allow learners and educators to benefit from personalized learning at scale. Specifically, by combining blockchain technology with higher education certificates and diplomas we can make the process of verifying academic credentials more convenient and transparent. Learning records may be stored in a reliable, decentralized system using block-chain technology that can also be used to provide credible digital certificates, exchange learning resources employing digital currencies, and preserve intellectual property through encryption software. According to the findings, using blockchain technology into the development of online education is a promising trend. By permitting legally binding smart-contracts, streamlining credentialing procedures, maintaining the viability and integrity of education data, and boosting the efficacy of educational aggregates, blockchain has the potential to drastically disrupt present business models. For the labor force to be able to quickly apply freshly learned skills and competences to the actual world, credentials that are reliably easy and quick to transfer are critical. At the same time, an education governance model incentivizes education providers to improve management and sustainability.
Comprehensive Analysis of Fundamentals, Innovation, and Key Challenges of Blockchain

Distributed databases are built on top of blockchain, which is based on digital encrypted currency. Decentralization, nontampering, high autonomy, and distributed consensus are only a few of the properties of the blockchain system that allow it to achieve distributed consistency without the need for third-party supervision. Blockchain technology has grown in popularity as a weak trust platform application because of its rapid growth, but it is also vulnerable to assaults on its own system weaknesses. In this chapter, we summarize and analyze some of the key technical principles of blockchain, its advantages and disadvantages, as well as technical vulnerabilities and security attacks in blockchain systems. We also summarize and categorize the different types of technical vulnerabilities and vulnerability attacks. The top three vulnerabilities in the blockchain system are referred to as syntax problems, environment setup mistakes and graphical interface issues. Vulnerability attacks offer a significant security risk to the blockchain. In order to improve future blockchain technology and serve as a point of reference for further development, it is critical that this be taken into account and avoided.
A Novel Approach to Cryptography

Security is the most important element of encryption over the Internet because of the value and importance of patient health records (PHR). Users that use keyword searches to obtain access to the database’s PHR are more vulnerable to security issues. Despite the fact that a blockchain-based healthcare system can ensure security, current schemes have significant limitations. Existing methods have solely focused on data storage, with blockchain serving as a storage database. In this study, we created a secure searchable blockchain based on deep learning as a distributed database that uses homomorphic encryption to allow users to securely access data via search. Secure key revocation and update rules will be progressively included in our proposed research. This study uses an IoT dataset to analyze and compare our proposed access control mechanisms to benchmark models. The proposed techniques are implemented in the Hyperledger tool utilizing smart contracts. The proposed strategy is compared to others already in use. In comparison to benchmark models, our proposed solution considerably increases security, anonymity, and user behavior tracking, resulting in a more efficient blockchain-based IoT system.
IoT, AI, and Blockchain

The recent advancement in technology such as Internet of Things (IoT), blockchain, and artificial intelligence (AI) has shown a great influence in industrial and manufacturing applications in terms of connectivity, automation and data driven analysis. The IoT makes each and every component connected. AI allows a highly efficient way of analyzing the acquired data and the blockchain technology makes the transaction of data more secure and transparent. With the involvement of the aforementioned emerging technology a wide variety of industries such as health care, supply chain, food industry and many others are poised to witness a huge transformation in coming decades. Out of all the industries the agriculture and healthcare sectors have experienced very little attention in terms of involvement of emerging technologies. There is a requirement for a concise system which can precisely and perform efficient monitoring, acquisition, transparency, data communication, security, reliability, analyzing of data and smart decision making without human intervention. The classical method of acquisition, storing and communication of data is seamlessly managed by the newly evolved system composed of AI, blockchain and IoT. With the involvement of AI, IoT and blockchain the agriculture and health sectors will become more connected and secure, with data driven smart decisions, and being more transparent and more constructive. The overall result of this association will transform both sectors toward autonomy, having more intelligence and a secure system. The proposed chapter comprised the fundamentals of IoT, AI and blockchain along with the application and implementation of the technology in agriculture and a health care unit. The proposed chapter also demonstrates a comprehensive investigation on the importance of integration of the aforementioned technology and developing an efficient application for agriculture and health sector. The chapter also presents the challenges faced by the individual technology during implementation and their open issues, as well as future aspects of involvement of the technology in both sectors. The sole objective of the chapter is to demonstrate the innovative constructive system build for the agricultural and health care sector with the combine effort of AI, IoT and blockchain which will lead to a secure, automated, transparent, efficient and intelligent decision making support system for both sectors. And we believe that this cognitive transformation in both sectors will benefit the industry and the consumer.
Psyche and Academia: Papers from 21 Years of the Auckland University of Technology Psychotherapy Master’s Programmes

A rugby world cup can be a big deal for rugby fans at any time. I’m not a rugby fan so these things mostly pass me by unnoticed. However, when it’s a women’s side, the Black Ferns, in a sell-out grand final for the first time on home ground at Eden Park in Auckland – as I write – then that exceeds the bubble of the rugby world, reaching into a much wider sphere. Maaori and other wahine being celebrated at national level for their mahi and talent – in whatever field – is a sadly rare occurrence, and so this hyped event brings that into sharp relief. We all get a lift when voices not so often heard or sought out, are brought forward. We all are made stronger when more of us are represented in our experience. We all have a chance to grow when our various ways of being and knowing are admissible. The voices gathered together by Keith and Emma into this book bring a lift to psychotherapy discourse and practice, in their diversity of authorship and method/ology. Our profession is made stronger by the warp and weft of carefully honed enquiries that expand our understanding of subjective experience – and our clinical practice grows when we widen our ways of being and knowing...
Framework for Modeling, Procuring, and Building Systems for Smart City Scenarios Using Blockchain Technology and IoT

Smart technologies have enabled our lives to be more comfortable and tangible day by day. In lieu of that, cities are also turning into “smart cities” through essential implementation of Internet of Things (IoT). This chapter presents a deep insight for modeling, procuring and building the smart city plan using IoT and a security framework using blockchain technology. To plan and develop state-of-the-art infrastructure of a smart city, the major domains on which IoT should consider are individual networks (such as education, transport, hospitals, energy, homes, business, etc.) connecting together with proper security and management. To fulfil the said requirement, it is highly desirable to integrate and manage physical and societal infrastructures ensuring efficient and best utilization of available resources controlled by IoT and secured by blockchain. The IoT system may connect myriad of nodes or machines of various kind capable to communicate with each other to get service whenever required. Use of IoT enables to utilize the wireless sensor networks but every node of the IoT system is considered to be prone to different attacks. The integration of technologies like internet of computing (IoC) and internet of everything (IoE) enabled the things more connected via various communication modes. Blockchain suggestively uses mathematical hashing and encryption tools for data security. By achieving innovative solutions, the citizens of the smart city will be able to collaborate with local governance for various services but not limited to subsidies under government schemes, health and safety, smart assets, education and employment, real estate, waste and sanitation, renewable energy.
Study on space/frequency domain response mechanism of optical synthetic aperture imaging system

Optical synthetic aperture (OSA) imaging system is an effective method for astronomical telescopes to realize superresolution imaging, which has important applications in astronomical observation and space remote sensing. The timedivision multiplexing sub-aperture array is a potentially powerful way to construct the larger array configuration by changing the basic array configuration several times with less apertures. In this paper, according to the theory of incoherent optical imaging, the physical mechanism of space/frequency domain response of OSA system is discussed. From the basic pinhole-aperture array to the circular-aperture array, the evolution of the point spread function (PSF) and the modulation transfer function (MTF) is analyzed. The inherent laws and imaging differences between the time-division multiplexing sub-aperture array and the traditional synthetic aperture array are revealed. Also, it is shown that under the specific array condition, the reconstructed image obtained by the time-division multiplexing synthetic aperture system can approach the imaging quality and resolution given by the traditional synthetic aperture array. It provides a new perspective for the array configuration design of OSA imaging system.
An attention-based residual neural network for deformable image registration

Deformable image registration is a basic image processing task, especially widely used in medical image processing and analysis. Different from rigid registration, its purpose is to find the optimal nonlinear transformation between two images and establish corresponding relationship, so as to achieve image consistency. In recent years, deformable registration methods based on deep learning have been studied a lot. Compared with traditional methods, they show great advantages in registration performance. This paper proposes an attention-based residual neural network for deformable image registration, which utilizes the U-Net encoder-decoder structure to design a convolutional neural network to predict the deformation field, and uses the residual module and attention mechanism enhances the ability of the model to extract features, and finally uses the spatial transformation function to obtain the registered image, and the entire network is trained in an unsupervised manner. We conducted experiments on the MNIST dataset and 2D brain magnetic resonance images (MRI) respectively. The experimental results show that the deformable registration network proposed in this paper has good performance and shows good results in registration accuracy.
Simplified scheme for passive decoy-state reference-frame-independent quantum key distribution

Reference-frame-independent quantum key distribution (RFI-QKD) has been proven to be intrinsically robust against slowly varying reference frames under realistic environment. In this paper, we propose a simplified scheme for the passive decoy-state RFI-QKD considering the afterpulsing effect. We investigated the system performance with the weak coherent source (WCS) and parametric down conversion (PDC) source, respectively. Numerical simulation results show that the simplified scheme can achieve comparable secure key rate with the active decoy-state scheme. Our scheme is advantageous to simplify the system implementation and reduces the time cost. Besides, it is proven that the afterpulsing effect plays dominant role in the system. Our work could provide a useful solution for RFI-QKD in practical situations.
MSA: an end-to-end scene text spotter with mask-supervised-attention

End-to-end(E2E) scene text recognition (the joint detection and recognition of natural text images) is developing rapidly, and the joint optimization strategy for image sequence alignment has become a research hotspot. Those existing methods are either difficult to train or costly for character annotations. In this paper, a novel end-to-end scene text recognition framework is proposed, Based on the Swin-Transformer (Swin-T) FPN backbone network, the model adopts the instance segmentation method to obtain the text mask and binarizes it to directly locate its polygon boundaries. Meanwhile, to solve the problem of low fitting efficiency of the text sequence recognition module, we designed a self-monitoring Mask-Supervised Attention (MSA) mechanism to accelerate the fitting speed and fitting accuracy of the recognition module, thereby improving the joint performance for E2E text recognition. The results show that in the E2E text recognition task, the F-measure performance of the proposed model achieves not only 2.3%, 2.7% and 11.9% improvement on ICDAR 2015 on strong, weak and generic lexicons, but also 4.8%, 9.9% improvement on Total-text on full and none lexicons compared with other typical models.
A Real-Time Visage Expression Detection Using Convolutional Neural Network (RTVED)

In this work, the purpose is to implement parts of seven Real-Time Visage Expression Detection using Convolutional Neural network (RTVED), Data Augmentation to get high accuracy, and focus on the Methodology of Real-Time Visage Expression Detection using CNN. In this paper, considered a convention Convolutional Neural Network model and utilized it to prepare and test diverse look pictures with the Keras, TensorFlow, and deep learning library. Real-Time human visage Expressions Detection has two sections, recognizer Validation, and data preparation, and a training model for data preparation and training. The recognizer contains a visage Expression detector and a visage Expression recognizer. The visage expression detector extricates facial pictures from the camera and the visage Expression recognizer recognizes the extracted pictures. The Data Training model utilizes the Convolutional Neural Network to prepare data and the recognizer likewise utilizes Convolutional Neural Network to identify the emotional condition through their visage Expressions. The framework perceives the six widespread emotions as anger, disgust, happiness, sadness, surprise neutral, and contempt.
Large-scale digital forensic investigation for Windows registry on Apache Spark

In this study, we investigate large-scale digital forensic investigation on Apache Spark using a Windows registry. Because the Windows registry depends on the system on which it operates, the existing forensic methods on the Windows registry have been targeted on the Windows registry in a single system. However, it is a critical issue to analyze large-scale registry data collected from several Windows systems because it allows us to detect suspiciously changed data by comparing the Windows registry in multiple systems. To this end, we devise distributed algorithms to analyze large-scale registry data collected from multiple Windows systems on the Apache Spark framework. First, we define three main scenarios in which we classify the existing registry forensic studies into them. Second, we propose an algorithm to load the Windows registry into the Hadoop distributed file system (HDFS) for subsequent forensics. Third, we propose a distributed algorithm for each defined forensic scenario using Apache Spark operations. Through extensive experiments using eight nodes in an actual distributed environment, we demonstrate that the proposed method can perform forensics efficiently on large-scale registry data. Specifically, we perform forensics on 1.52 GB of Windows registry data collected from four computers and show that the proposed algorithms can reduce the processing time by up to approximately 3.31 times, as we increase the number of CPUs from 1 to 8 and the number of worker nodes from 2 to 8. Because the distributed algorithms on Apache Spark require the inherent network and MapReduce overheads, this improvement of the processing performance verifies the efficiency and scalability of the proposed algorithms.
Emati: a recommender system for biomedical literature based on supervised learning

The scientific literature continues to grow at an ever-increasing rate. Considering that thousands of new articles are published every week, it is obvious how challenging it is to keep up with newly published literature on a regular basis. Using a recommender system that improves the user experience in the online environment can be a solution to this problem. In the present study, we aimed to develop a web-based article recommender service, called Emati. Since the data are text-based by nature and we wanted our system to be independent of the number of users, a content-based approach has been adopted in this study. A supervised machine learning model has been proposed to generate article recommendations. Two different supervised learning approaches, namely the naïve Bayes model with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and the state-of-the-art language model bidirectional encoder representations from transformers (BERT), have been implemented. In the first one, a list of documents is converted into TF-IDF-weighted features and fed into a classifier to distinguish relevant articles from irrelevant ones. Multinomial naïve Bayes algorithm is used as a classifier since, along with the class label, it also gives the probability that the input belongs to this class. The second approach is based on fine-tuning the pretrained state-of-the-art language model BERT for the text classification task. Emati provides a weekly updated list of article recommendations and presents it to the user, sorted by probability scores. New article recommendations are also sent to users' email addresses on a weekly basis. Additionally, Emati has a personalized search feature to search online services' (such as PubMed and arXiv) content and have the results sorted by the user's classifier. Database URL: https://emati.biotec.tu-dresden.de.
Optimizing Geometric Constellations for Phase Noise Channels Using Deep Learning

We optimize 16 and 64QAM constellations and detectors via end-to-end learning for residual laser phase noise. At the biterror-rate (BER) of 1e-3, the required signal-to-noise ratio (SNR) is shown to reduce about 0.9 and 1 dB for 16 and 64-QAM, respectively.
PRIDE: A Privacy-Preserving Decentralised Key Management System

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC using procedures that are not scalable and prone to operational mistakes. A single system would simplify the KMAC distribution between KMCs; nevertheless, it is difficult to place the responsibility for such a system for the whole European area within one central organization. A single system could also expose relationships between KMCs, revealing information, such as plans to use an alternative route or serve a new region, jeopardizing competitive advantage. This paper proposes a scalable and decentralised key management system that allows KMC to share cryptographic keys using transactions while keeping relationships anonymous. Using non-interactive proofs of knowledge and assigning each entity a private and public key, private key owners can issue valid transactions while all system actors can validate them. Our performance analysis shows that the proposed system is scalable when a proof of concept is implemented with settings close to the expected railway landscape in 2030.