Reviewerly/exHarmony huggingface dataset:
Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network

Aiming at the influence of different working conditions on recognition accuracy in remote sensing image recognition, this paper adopts hierarchical strategy to construct a network. Firstly, in order to establish the classification relationship between different samples, labeled samples are used for classification. A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) classification network is proposed. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and then identify according to different categories. Through UCI dataset test, the accuracy of LOG-T-SSA-LSSVM network classification is significantly improved compared with that of contrast network. The autoencoder is integrated with Extreme Learning Machine, and the autoencoder is used to realize data compression. The advantages of Extreme Learning Machine (ELM) network, such as less training parameters, fast learning speed, and strong generalization ability, are fully utilized to realize efficient and supervised recognition. Experiments verify that the autoencoder-extreme learning machine (AE-ELM) network has a good recognition effect when the sigmoid activation function is selected and the number of hidden layer neurons are 2000. Finally, after image recognition under different working conditions, it is proved that the recognition accuracy of AE-ELM based on LOG-T-SSA-LSSVM classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network.
Backstepping-based state estimation for a class of stochastic nonlinear systems

The state estimation problem is investigated for a class of continuous-time stochastic nonlinear systems, where a novel filter design method is proposed based on backstepping design and stochastic differential equation. In particular, the structure of the filter is developed following the nonlinear system model, and then the estimation error dynamics can be described by a stochastic differential equation. Motivated by backstepping procedure, the nonlinear dynamics can be converted to an Ornstein–Uhlenbeck process via the control loop design. Thus, the estimation can be achieved once the estimation error is bounded and the variance of the error can be optimized. Since the ideal estimation error is a Brownian motion, the filter parameters can be selected following the Lyapunov stability theory and variance assignment method. Following the same framework, the multivariate stochastic systems can be handled with the block backstepping design. To validate the presented design approach, a numerical example is given as the simulation results to demonstrate the state estimation performance.
Theoretical framework for photon subtraction with non–mode-selective resources

This work establishes a versatile theoretical framework that explicitly describes single-photon subtraction from multimode quantum light in the context of non-Gaussian state generation and manipulation. The treatment focuses on easy-to-implement configurations in which no mode-selective operation is available and evaluates features and advantages of scheme where only simple filtering stages are employed on the experiments. Such configuration, by considerably reducing the experimental overheads, makes experiments involving single photon subtraction easier to be implemented. Obtained theoretical framework allows retrieving, given a multimode input state, optimal conditions required to herald and then to detect non-Gaussian states, providing a practical and powerful toolbox for experiments' design. The application of the proposed approach to the case study of Schr\"odinger kitten preparation starting from a frequency multimode squeezed state illustrates the impact of the derived theoretical tools.
Memory-limited model-based diagnosis

Various model-based diagnosis scenarios require the computation of the most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, and to enable successful diagnosis both on memory-restricted devices and for memory-intensive problem cases, we propose two novel diagnostic search algorithms which build upon tried and tested techniques from the heuristic search domain. The first method, dubbed Recursive Best-First Hitting Set Search (RBF-HS), is based on Korf's well-known Recursive Best-First Search (RBFS) algorithm. We show that RBF-HS can enumerate an arbitrary predefined finite number of fault explanations in best-first order within linear space bounds, without sacrificing the desirable soundness or completeness properties. The second algorithm, called Hybrid Best-First Hitting Set Search (HBF-HS), is a hybrid between RBF-HS and Reiter's seminal HS-Tree. The idea is to find a trade-off between runtime optimization and a restricted space consumption that does not exceed the available memory. Notably, both suggested algorithms are generally applicable to any model-based diagnosis problem, regardless of the used (monotonic) logical language to describe the diagnosed system and of the used reasoning mechanism. We conducted extensive experiments on real-world benchmarks from the knowledge-based systems field, a domain where the features soundness, completeness, the best-first property as well as a general applicability are pivotal and where Reiter's HS-Tree is the predominantly used diagnostic search. The evaluation reveals that, when computing fault explanations minimal-cardinality-first, RBF-HS compared to HS-Tree reduces memory requirements substantially in most cases by up to several orders of magnitude, while also saving runtime in more than a third of the cases. When computing fault explanations most-probable-first, RBF-HS compared to HS-Tree tends to trade memory savings more or less one-to-one for runtime overheads. Whenever runtime overheads were significant, using HBF-HS instead of RBF-HS reduced the runtime to values comparable with HS-Tree while keeping the used memory reasonably bounded.
Crowdfunding Non-fungible Tokens on the Blockchain

Abstract Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs.
Dissecting the stock to flow model for Bitcoin

Purpose Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy. Design/methodology/approach This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness. Findings Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy. Originality/value To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.
Collective aspects of information literacy in developing countries: a Bangladeshi case

Purpose The purpose of this paper is to report on the exploration of women farmers' information literacy (IL) in the context of rural Bangladesh within the context of an ICT-based women empowerment project. Design/methodology/approach This study uses thematic analysis of qualitative data collected during a culturally sensitive workshop on IL with a group of project participants. Findings The findings showed that women understood their information needs and where to find information, which indicates that participants had some basic IL skills. However, the online environment presented challenges for them to evaluate the quality of the information and its relevance to their daily activities. The cultural complexity of IL was observed with the rural women’s information practices affected by family patterns, community and religion, amongst other social factors. Collective practices are made highly evident by the women’s natural tendency to share phones and information and by the way information is maintained. While the collective practices are very useful in utilising information for daily needs, the downside is that women are potentially vulnerable to threats in an online environment when sharing confidential information. Originality/value This article shows that in the context of rural Bangladesh, women farmers’ information-seeking behaviour and practices of sharing and creating information are influenced by sociocultural characteristics. It describes how the women’s situational context of collectivity and power relations influence their ways of handling information.
Incorporating Code Structure and Quality in Deep Code Search

Developers usually search for reusable code snippets to improve software development efficiency. Existing code search methods, including methods based on full-text or deep learning, have two disadvantages: (1) ignoring structural information of code snippets, such as conditional statements and loop statements, and (2) ignoring quality information of code snippets, such as naming clarity and logical correctness. These disadvantages limit the performance of existing code search methods. In this paper, we propose a novel code search method named Structure and Quality based Deep Code Search (SQ-DeepCS). SQ-DeepCS introduces a code representation method called program slice to represent structual information as well as API usage of code snippets. Meanwhile, SQ-DeepCS introduces a novel deep neural network named Method-Description-Joint Embedding Neural Network (MD-JEnn) to weight the quality of code snippets. To evaluate the proposed methods, we train MD-JEnn and evaluate SQ-DeepCS by searching for code snippets with respect to the top-rated questions from Stack Overflow. We use four evaluation indicators to measure the effectiveness of SQ-DeepCS: FRank, SuccessRate@k, PrecisionRate@k, and Mean Reciprocal Rank (MRR). The experimental results show that our approach can provide better results than existing techniques when searching for relevant code snippets.
3D U-Net with trans-coder for brain tumor segmentation

3D Medical image segmentation is a basic and key task in computer-aided diagnosis, and glioma has a high degree of non-uniformity and irregular shape in multimodal MRI images. Therefore, accurate and reliable segmentation of brain tumors is still a challenging work in medical image analysis. U-Net has become the de facto standard in various medical image segmentation tasks and has achieved great success. However, due to the inherent locality of convolution operations, U-Net is generally limited in terms of explicitly modeling long-term dependencies. Although this problem is solved in Transformer, it has extreme complexity in terms of calculation and space when processing high-resolution 3D feature maps. In this paper, we propose the Trans-coder, which is embedded in the end of the U-Net encoder to improve the segmentation performance while reducing the amount of calculation. The Trans-coder takes the feature map from U-Net as the input sequence, and extracts the relative position information of the feature map, so as to get more detailed information of the image, and input it into the decoder to obtain good segmentation performance. At the same time, a variational autoencoder is used for regularization to prevent over-fitting problems. our method achieves superior performances to certain competing methods on the Multimodal Brain Tumor Segmentation Challenge (Brats) dataset.
Toward Quantifying Decentralization of Blockchain Networks With Relay Nodes

In this paper, we present a methodology for quantifying the decentralization degree of a blockchain network. To accomplish this, we use two well-known graph models of Erdös-Rény and Barabási–Albert in order to study the blockchain network topology. We then quantify the decentralization degree using the clustering coefficient of our network models. We validate our approach through extensive simulations and analyze the decentralization degree with respect to network parameters such as the number of connections per node and the peer selection algorithm. Our results expose the trade-off between the average shortest path and the decentralization degree. Furthermore, we observe the impact of the average shortest path on the network speed and traffic overhead. Finally, we demonstrate that the presence of hub-like nodes such as relay gateways negatively impacts the decentralization degree of blockchain networks.
Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks

Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node's Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by η ≥ 0dB, our proposed new CAMC scheme's correct classification probability Pcc could reach up close to 100%. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime.
Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study

To solve the problem of low precision of pearl shape parameters’ measurement caused by the mutual contact of batches of pearls and the error of shape sorting, a method of contacting pearls’ segmentation based on the pit detection was proposed. Multiple pearl images were obtained by backlit imaging, the quality of the pearl images was improved through appropriate preprocessing, and the contacted pearl area was extracted by calculating the area ratio of the connected domains. Then, the contour feature of the contact area was obtained by edge tracking to establish the mathematical model of the angles between the edge contour points. By judging the angle with a threshold of 60° as the candidate concave point, a concave point matching algorithm was introduced to get the true concave point, and the Euclidean distance was adopted as a metric function to achieve the segmentation of the tangent pearls. The pearl shape parameters’ model was established through the pearl contour image information, and the shape classification standard was constructed according to the national standard. Experimental results showed that the proposed method produced a better segmentation performance than the popular watershed algorithm and morphological algorithm. The segmentation accuracy was above 95%, the average loss rate was within 4%, and the sorting accuracy based on the shape information was 94%.
A comparison of dataset search behaviour of internal versus search engine referred sessions

Dataset discovery is a first step for data-centric tasks, from data storytelling to labelling for supervised machine learning. Previous qualitative research suggests that people use two types of search affordances to find the data they need: they either go to a data portal that probably contains the data and search there; or they start on a regular web search engine, which sometimes returns results that are datasets. For the first type of search, prior works have analysed logs from different data portals to understand basic tenets of search behaviour such as query length or topics. In this paper, we advance the state of the art in dataset search behaviour with a comprehensive transaction log analysis study (n = 236441 sessions) of an international open data portal, in which we compare sessions straight on a data portal (internal searches) against sessions that land on a dataset or SERP (search engine result page) through a referral from a web search engine (external). Using dataset downloads as a proxy for successful searches, we find a statistically significant, though weak relationship between the use of keyword search and session type and between the use of search facets and session type (moderate). We also discover and discuss behavioural patterns and user profiles across session types.
Supersensitivity of Kerr phase estimation with two-mode squeezed vacuum states

We analytically investigate the sensitivity of Kerr nonlinear phase estimation in a Mach-Zehnder interferometer with two-mode squeezed vacuum states. We find that such a metrological scheme could access a sensitivity scaling over the Boixo et al.'s generalized sensitivity limit [S. Boixo et al., Phys. Rev. Lett. 98, 090401 (2007)], which is saturable with celebrated NOON states. We also show that parity detection is a quasioptimal measurement which can nearly saturate the quantum Cram\'er-Rao bound in the aforementioned situation. Moreover, we further clarify the supersensitive performance observed in the above scheme is due to the restriction of Boixo et al.'s generalized sensitivity limit (BGSL) to probe states with fixed photon numbers. To conquer this problem, we generalize the BGSL into the case with probe states of a fluctuating number of photons, to which our scheme belongs.
Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy

Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate selection, unstable solutions, weak generalization ability, and an ability to easily fall into local minimum points, the extreme learning machine (ELM) was applied to sulfur hexafluoride (SF6) concentration inversion research. To solve the problems of high dimensionality, collinearity, and noise of the spectral data input to the ELM network, a genetic algorithm was used to obtain fewer but critical spectral data. This was used as an input variable to achieve a genetic algorithm joint extreme learning machine (GA-ELM) whose performance was compared with the genetic algorithm joint backpropagation (GA-BP) neural network algorithm to verify its effectiveness. The experiment used 60 groups of SF6 gas samples with different concentrations, made via a self-developed Fourier transform infrared spectroscopy instrument. The SF6 gas samples were placed in an open optical path to obtain infrared interference signals, and then spectral restoration was performed. Fifty groups were randomly selected as training samples, and 10 groups were used as test samples. The BP neural network and ELM algorithms were used to invert the SF6 gas concentration of the mixed absorbance spectrum, and the results of the two algorithms were compared. The sample mean square error decreased from 248.6917 to 63.0359; the coefficient of determination increased from 0.9941 to 0.9984; and the single running time decreased from 0.0773 to 0.0042 s. Comparing the optimized GA-ELM algorithm with traditional algorithms such as ELM and partial least squares, the GA-ELM algorithm had higher prediction accuracy and operating efficiency and better stability and generalization performance in the quantitative analysis of small samples of gas under complex noise backgrounds.
Bonanza Mine: an Ultra-Low-Voltage Energy-Efficient Bitcoin Mining ASIC

Bitcoin is the leading blockchain-based cryptocurrency used to facilitate peer-to-peer transactions without relying on a centralized clearing house [1]. The conjoined process of transaction validation and currency minting, known as mining, employs the compute-intensive SHA256 double hash as proof-of-work. The one-way property of SHA256 necessitates a brute-force search by sweeping a 32b random input value called nonce. The 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sup> nonce space search results in energy-intensive pool operations distributed on high-throughput mining systems, executing parallel nonce searches with candidate Merkle roots. Energy-efficient custom ASICs are required for cost-effective mining, where energy costs dominate operational expenses, and the number of hash engines integrated on a single die govern platform cost and peak mining throughput [2]. In this paper, we present BonanzaMine, an energy-efficient mining ASIC fabricated in 7nm CMOS (Fig. 21.3.7), featuring: (i) bitcoin-optimized look-ahead message digest datapath resulting in 33% C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dyn</inf> reduction compared to conventional SHA256 digest datapath; (ii) a half-frequency scheduler datapath, reducing sequential and clock power by 33%; (iii) 3-phase latch-based design with stretchable non-overlapping clocks, eliminating min-delay paths; (iv) robust ultra-low-voltage operation at 355mV using board-level voltage-stacking; and (v) mining throughput of 137GHash/s at an energy efficiency of 55J/THash.