Reviewerly/exHarmony huggingface dataset:
Bi-regularization enhanced azimuthal mode analysis method for the aero-engine fan

Azimuthal mode analysis is regarded as an effective tool for aero-engine flow field investigation, where numerous microphones are evenly mounted as a ring to provide sufficient spatial resolution for azimuthal pressure acquisition. The current compressive sampling method enables azimuthal modes to be obtained from much fewer microphones, with dominant and non-dominant modes separately estimated by L1-norm regularization and spatial Fourier transform or least square method. Despite the effort of measurement system simplification, this classical compressive sampling method regularized by L1-norm inherently introduces accuracy loss in amplitude estimation for both dominant and non-dominant modes. With the aim of accuracy promotion and further measurement reduction, the Bi-regularization enhanced azimuthal mode analysis (BRAMA) method is devised to investigate the azimuthal modes of the aero-engine fan via limited acoustic measurements. The BRAMA method substitutes L1-norm regularization with Lp-norm (0<p<1) regularization to reach more accurate reconstruction of dominant modes. Meanwhile, the Tikhonov regularization based on L2-norm is introduced to estimate non-dominant modes. The effectiveness of the BRAMA method is verified in two cases, by operating a 2.5-stage aero-engine fan test rig at 50% and 90% of the nominal speed, respectively. Firstly, acoustic pressure signals are pre-processed to extract the dominant tonal component, which are used as the input of dominant mode estimation. Next, comparative realizations by L1-norm and Lp-norm are conducted to detect the dominant modes, meanwhile the amplitude accuracy with respect to p value and measurement number is discussed in both cases. Finally, non-dominant modes are estimated by using Tikhonov regularization in comparison to the classical approaches. Experimental results of both cases indicate that the BRAMA method outperforms the classical compressive sampling approach in accuracy improving, measurements reducing and interference proof capability.
Smart Healthcare System for Biomedical and Health Care Applications using Aadhaar and Blockchain

Bitcoin cryptocurrency was proposed in 2008 and its open-source implementation was released in 2009. Blockchain was underlying technology in the implementation of bitcoin. Blockchain is decentralized which facilitate transactions across a peer-to-peer network. Researchers have been trying to explore its applications in non-financial domains. Healthcare is one such industry in which blockchain can play significant role. Blockchain technology has the potential to strengthen the healthcare industry in a variety of ways, health monitoring devices, clinical trial data, sharing and storing of patient medical records, insurance related information storage and claim settlement. The proposed system is an attempt to use Blockchain technology to address issues about healthcare data privacy, security, and ownership.
Blockchain in Logistics and Supply Chain Monitoring

Supply chain management (SCM) is a system to manage the flow of goods and services, and from transforming the raw into finished products, it has challenges that are needed to be achieved like good quality services to the consumer, reducing labor cost, etc. Industries need to digitize real assets and make distributed, immutable transactions possible to trace assets from manufacture to supply. To overcome the lack of transparency and traceability of the products in the enterprise resource planning system in supply chain (SC) and logistics issues, there is a solid need to employ a method that can efficiently track assets from production to supply decentralized, immutable records of all transactions. A blockchain (BC) is a decentralized software network that follows a digital ledger to exchange entities digitally and a way through which it makes secure transactions. Thus, this chapter proposes integrating BC in logistics and SC monitoring by giving a template on how Python and Flask can be used for BC with the SCM system to improve traceability without involving any intermediary.
Research on Multiparty Payment Technology Based on Blockchain and Smart Contract Mechanism

As a peer-to-peer “P2P” distributed ledger, the blockchain has the advantages of decentralization, no trust, open autonomy, and nontampering. Therefore, many users are willing to conduct transactions in blockchain cryptocurrency systems such as Bitcoin and Ethereum. However, the throughput of traditional blockchain is extremely low, and the transaction is so delayed. The payment channel network is the most promising solution to expand the blockchain for widespread use. Achieving secure instant payment on the payment channel can significantly increase transaction throughput and reduce transaction delays. When the payment channel is closed, the balance in the channel will be returned to an account on the blockchain. In this paper, we discuss the design and the implementation of a multiparty payment channel network based on smart contracts. Where a two-party payment channel is designed based on blockchain and smart contracts, a new multiparty payment channel is established on the basis of the payment channel. A detailed definition and description are given, and the creation, update, and closing functions of the multiparty payment channel are designed. Moreover, we design a multiparty payment channel smart contract, deploy it to the local private blockchain, and conduct simulation and testing. The delay time of different transaction methods is counted, and the network topology type, transaction amount, and other factors are studied. The impact of transaction success rate and the gas consumption of different transaction methods are analyzed through multiple sets of experimental statistics.
Quantum Neurobiology

Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.
Machine Learning and Deep Learning as New Tools for Business Analytics

Data scientists need to develop accurate and effective tools and techniques to handle a huge amount of data. Therefore, machine learning and deep learning algorithms have come to the life, especially with the impressive advances in both hardware and software fields. Many impressive existing services are helping us in our daily lives, such as Google Assistant, Uber, Alexa, and Siri, which are based on big data, machine learning, and deep learning. Although intelligent algorithms and big data have been adopted in many modern business intelligence and analytics applications, in this chapter, the authors aim to present the basics of machine learning and deep learning concepts and their utilization in the field of business intelligence. As concrete examples, with the high spread of the COVID-19 pandemic, Tesla and Amazon achieved the biggest revenue ever, where many other companies suffer. These high revenues could be due to the strategic decisions of their leaders, which are based especially on artificial intelligence.
Integrating data, expert opinion and fuzzy logic in the development of an index of wetland condition

Wetlands face an intensifying level of degradation, and management to protect their extent and character is paramount. To support wetland management in south-east Australia, we developed a wetland condition assessment tool for palustrine and lacustrine wetlands. Through extensive consultation with end users during its development, the tool, the Index of Wetland Condition (IWC), considers user needs and skills, as well as attempts to assess the complex nature of wetland systems and their inherent variability, both spatially and temporally. The IWC is structured as a hierarchical index with 13 indicators nested under six characteristics (subindices) that influence wetland function: wetland catchment, physical form, wetland soils, water properties, hydrology and biota. The contribution of each to the overall index (scored along a condition gradient) was estimated from quantitative biological and physicochemical data from 24 wetlands using a fuzzy cognitive map approach. The IWC development framework will be particularly useful in jurisdictions globally where reference condition information is limited or lacking for indicators, or where there are substantial practical constraints that limit the selection of indicators, such as management staff capability or capacity. Uptake of the IWC with wetland practitioners in south-east Australia has been substantial.
Linear and non-linear ARDL estimation of financial innovation and economic growth in Ghana

Purpose This study aimed to explore the effect of Financial Innovation (FI) on economic growth in Ghana, with a dataset spanning 1960–2019, adopting a broader conceptualization of FI as the ratio of broad money to narrow money. Design/methodology/approach The study employs a non-linear autoregressive distributed lag (ARDL) time series econometric model to estimate data from the World Bank (1960–2019). Findings There is no evidence that FI significantly impacts economic growth. This could be due to the early and strict regulation of the financial technology (FIN-TECH) sector and the general inconclusiveness of the impact of financial development on economic growth. Practical implications Policymakers must empirically explore the impact of early and strict regulation on the transformational impact of FI. Originality/value The paper is among the first to apply a broader conceptualization of FI in estimating the impact of FI on economic growth.
Secure File Storage on Cloud using Hybrid Cryptography

In recent years, Cloud Computing has played a vital role in the field of computing. It has revolutionized how computing is used in the industry from first setting up the infrastructure and then using it to just spinning up the resources as needed from different cloud vendors. It is also used in different industries for various services and storage of data. The data stored on the cloud can be retrieved as per the user's request but the concern of many users is the security of their data. This Security issue that most of the users are concerned about can be resolved by the techniques such as steganography and cryptography. Cryptographic techniques such as DES and AES are used in order to provide Security to the data but using a single technique sometimes doesn't provide high-level security. So here we have focused on introducing a hybrid cryptographic mechanism that involves multiple techniques to encrypt and decrypt the data. In this proposed system 3DES (Triple Data Encryption Standard) and Blowfish algorithms are used to provide security. Here the encryption is divided into three parts. Each part is encrypted with different encryption algorithms and decrypted using the different keys when required. This system of encryption and decryption guarantees better security of data to the users by storing encrypted data on a single cloud server, using 3DES and Blowfish.
Practical Black Box Model Inversion Attacks Against Neural Nets

Adversarial machine learning is a set of malicious techniques that aim to exploit machine learning’s underlying mathematics. Model inversion is a particular type of adversarial machine learning attack where an adversary attempts to reconstruct the target model’s private training data. Specifically, given black box access to a target classifier, the attacker aims to recreate a particular class sample with just the ability to query the model. Traditionally, these attacks have depended on the target classifier returning a confidence vector. The process of model inversion iteratively creates an image to maximize the target model’s confidence of a particular class. Our technique allows the attack to be performed with only a one-hot-encoded confidence vector from the target. The approach begins with performing model extraction, e.g. training a local model to mimic the behavior of a target model. Then we perform inversion on the local model within our control. Through this combination, we introduce the first model inversion attack that can be performed in a true black box setting; i.e. without knowledge of the target model’s architecture, and by only using outputted class labels. This is possible due to transferability properties inherent in our model extraction approach known as Jacobian Dataset Augmentation. Throughout this work, we will train shallow Artificial Neural Nets (ANNs) to mimic deeper ANNs, and CNNs. These shallow local models allow us to extend Fredrikson et al.’s inversion attack to invert more complex models than previously thought possible.
INGCF: An Improved Recommendation Algorithm Based on NGCF

Strengthening the representation and learning of user vector and item vector is the key of recommendation system. Neural Graph Collaborative Filtering (NGCF) has the problem of insufficient feature extraction of user vector and item vector. In addition, it uses the linear combination of the final embedding vectors of user and item to calculate the inner product, which is difficult to accurately obtain the user-item prediction score. In this article, we propose a graph neural network collaborative filtering algorithm based on NGCF, named INGCF. This new algorithm uses a 4-layer IndRNN layer and a feature extraction layer constructed by a self-attention mechanism to enhance the feature extraction capabilities of NGCF. And INGCF optimizes the user-item prediction score method to capture the complex structure of user-item interaction data to improve the accuracy of score calculations. We compare several indicators of INGCF, NGCF, PinSage and GCN by using same data sets and perform ablation experiments. The experimental results show that the INGCF algorithm has a better recommendation effect.
A Deep Learning Framework for Video-Based Vehicle Counting

Traffic surveillance can be used to monitor and collect the traffic condition data of road networks, which plays an important role in a wide range of applications in intelligent transportation systems (ITSs). Accurately and rapidly detecting and counting vehicles in traffic videos is one of the main problems of traffic surveillance. Traditional video-based vehicle detection methods, such as background subtraction, frame difference, and optical flow have some limitations in accuracy or efficiency. In this paper, deep learning is applied for vehicle counting in traffic videos. First, to solve the problem of the lack of annotated data, a method for vehicle detection based on transfer learning is proposed. Then, based on vehicle detection, a vehicle counting method based on fusing the virtual detection area and vehicle tracking is proposed. Finally, due to possible situations of missing detection and false detection, a missing alarm suppression module and a false alarm suppression module are designed to improve the accuracy of vehicle counting. The results show that the proposed deep learning vehicle counting framework can achieve lane-level vehicle counting without enough annotated data, and the accuracy of vehicle counting can reach up to 99%. In terms of computational efficiency, this method has high real-time performance and can be used for real-time vehicle counting.
Multi-level Attentive Adversarial Learning with Temporal Dilation for Unsupervised Video Domain Adaptation

Most existing works on unsupervised video domain adaptation attempt to mitigate the distribution gap across domains in frame and video levels. Such two-level distribution alignment approach may suffer from the problems of insufficient alignment for complex video data and misalignment along the temporal dimension. To address these issues, we develop a novel framework of Multi-level Attentive Adversarial Learning with Temporal Dilation (MA<sup>2</sup>L- TD). Given frame-level features as input, multi-level temporal features are generated and multiple domain discriminators are individually trained by adversarial learning for them. For better distribution alignment, level-wise attention weights are calculated by the degree of domain confusion in each level. To mitigate the negative effect of misalignment, features are aggregated with the attention mechanism determined by individual domain discriminators. Moreover, temporal dilation is designed for sequential non-repeatability to balance the computational efficiency and the possible number of levels. Extensive experimental results show that our proposed method outperforms the state of the art on four benchmark datasets.<sup>1</sup>
Computing minimum-volume enclosing ellipsoids for large datasets

An algorithm is provided for calculating the minimum-volume enclosing ellipsoid (MVEE) for a large dataset stored in a separate database, for which the existing algorithms run out of memory or become prohibitively slow. The focus is on tall datasets, i.e., those consisting of huge numbers of data points of moderate dimensionality. The proposed Big Index Batching algorithm works in an optimization-deletion-adaptation cycle that consists of: using an existing algorithm by applying it on a smaller batch of data; pruning the vector of the indices of the data points by removing the points that are guaranteed to not lie on the boundary of the MVEE; and efficiently adapting the choice of the batch. The algorithm is provably convergent, and simple to describe and implement. The reading of tall data from the database is very time consuming, therefore the amount of reading during an MVEE computation should be as small as possible. It is shown on examples that Big Index Batching tends to find the MVEE after reading all data points just two or three times. As a consequence, the proposed algorithm usually converges to the MVEE reasonably fast. Its usefulness in robust statistics and anomaly detection is demonstrated by finding the potential outliers in a large dataset by using so-called ellipsoidal trimming.
The Impact of Blockchain Technology on the Smart City Industry

In recent decades, the Smart City Industry has been actively developing around the world due to the economic benefits of the globalization of product design and development. Information exchange is an important characteristic of the global activities of the Smart City Industry. The Internet of Things (IoT) enhances information sharing for the Smart City manufacturing industry. Despite this success, the adoption of IoT applications to automate business processes faces many challenges due to security concerns. IoT technology helps collect real-time information about the Smart City Industry using tags and radio frequency identification (RFID) sensors. Blockchain technology with IoT can enable broader categories of business applications to improve integration and improve the efficiency of business transactions. This article provides an overview of blockchain integration with IoT technology, highlighting the advantages and disadvantages of integration. This article provides a classification of security threat models that are taken into account by blockchain protocols in IoT networks.
1-Round Distributed Key Generation With Efficient Reconstruction Using Decentralized CP-ABE

Distributed key generation (DKG) is widely used in multi-party computation and decentralized applications. DKG has two phases, namely sharing and reconstruction. Most of the prior DKG protocols need at least 2 rounds for the sharing phase, in case some party raises a dispute. The existing 1-round DKG protocol [Fouque <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , PKC’01], built based on a publicly verifiable secret sharing (PVSS) scheme, assumes a static adversary model and its reconstruction phase requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> communication complexity. Motivated by the observation that a ciphertext-policy attribute-based encryption (CP-ABE) scheme hides secret sharing (SS) in ciphertext, we utilize decentralized CP-ABE to achieve the first adaptively secure 1-round DKG protocol. Firstly, a CP-ABE scheme enables the ciphertexts in DKG to be externally decrypted, making our protocol superior to the PVSS-based DKG protocol in reconstruction. The communication and computation complexities are both lowered to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> thanks to the constant-sized decryption key and the proposed batch decryption. The use of CP-ABE also makes our DKG protocol storage-friendly, i.e., the parties store no ciphertext after the sharing phase. Secondly, we add non-interactive zero-knowledge (NIZK) proofs to make the CP-ABE ciphertext publicly verifiable by leveraging the sigma protocol and the Fiat-Shamir heuristic. Thirdly, we demonstrate our protocol’s feasibility by presenting a proof-of-concept implementation over Ethereum, which is used as a public channel and a trustworthy computation platform. The implementation is a non-trivial task due to Ethereum’s incompatibility with the bilinear mapping group.
Safeguarding the Intelligence of Neural Networks with Built-in Light-weight Integrity MArks (LIMA)

As Deep Neural Networks (DNNs) are widely adopted in many real-world applications, their integrity becomes critical. Unfortunately, DNN models are not resilient to fault injection attacks. In particular, recent work has shown that Bit-Flip Attack (BFA) can completely destroy the intelligence of DNNs with a few carefully injected bit-flips. To defend against this threat, we propose Light-weight Integrity MArks (LIMA) framework which protects the integrity of the most significant bits (MSBs) of DNN weights - the main target of BFA. Such protection is enabled by embedding specific property into a trained DNN model&#x0027;s weights before deploying it in hardware. LIMA outperforms existing BFA countermeasures as it requires no retraining, imposes no storage overhead, offers full-coverage of all DNN layers, and can be easily verified with Multiply-Accumulate (MAC) operations to detect BFA. Our comprehensive study demonstrates 100% effectiveness in detecting chains of bit-flips and near-zero accuracy loss for embedding LIMA. The eresults also show that even when the attacker has complete knowledge of the proposed defense plan, attacking DNNs with built-in LIMA is extremely difficult, if not completely impossible.