Reviewerly/exHarmony huggingface dataset:
Utilizing Fuzzy AHP in the Evaluation of Barriers to Blockchain Implementation in Reverse Logistics

Digital technologies like blockchain, the Internet of Things, and smart warehouses have been developed due to the fourth industrial revolution, or “Industry 4.0.” Any business’ supply chain includes several stakeholders, including manufacturers, distributors, suppliers, and final consumers. The demand for firms to utilize these technologies to gain competitive advantages has intensified in the modern world due to rising worldwide rivalry. Additionally, the adoption of blockchain technology, in particular, can have a huge impact on a company’s reverse logistics, accelerating processes by decentralizing, tracking, and overseeing the delivery of items to final consumers. The goal of this study is to pinpoint those significant obstacles because several must be overcome for blockchain technology to be successfully implemented in reverse logistics. This study identified 16 impediments to the adoption of blockchain technology after a thorough analysis of the literature and expert opinion. The fuzzy AHP approach was used in this study to rank those barriers as this approach helps to address the complexity and uncertainty associated with decision-making in supply chain management and provides a more robust and reliable ranking of the barriers to blockchain adoption. A case study of Pakistan’s e-commerce industry was carried out. The results show that the high installation cost, stakeholders’ resistance to the blockchain, and the lack of top-management support are the critical success factors in blockchain adoption. From an industrial perspective, the study highlights the need for businesses to carefully evaluate the potential benefits and costs of adopting blockchain technology. It also underscores the importance of addressing the barriers to adoption to ensure successful implementation. By doing so, businesses can enhance their supply chain management and improve their overall competitiveness.
Do quantum circuit Born machines generalize?

Abstract In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distribution. For example, expressive model families such as quantum circuit Born machines (QCBMs) have been almost entirely evaluated on their capability to learn a given target distribution with high accuracy. While this aspect may be ideal for some tasks, it limits the scope of a generative model’s assessment to its ability to memorize data rather than generalize . As a result, there has been little understanding of a model’s generalization performance and the relation between such capability and the resource requirements, e.g. the circuit depth and the amount of training data. In this work, we leverage upon a recently proposed generalization evaluation framework to begin addressing this knowledge gap. We first investigate the QCBM’s learning process of a cardinality-constrained distribution and see an increase in generalization performance while increasing the circuit depth. In the 12-qubit example presented here, we observe that with as few as <?CDATA $30\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>30</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> of the valid data in the training set, the QCBM exhibits the best generalization performance toward generating unseen and valid data. Lastly, we assess the QCBM’s ability to generalize not only to valid samples, but to high-quality bitstrings distributed according to an adequately re-weighted distribution. We see that the QCBM is able to effectively learn the reweighted dataset and generate unseen samples with higher quality than those in the training set. To the best of our knowledge, this is the first work in the literature that presents the QCBM’s generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM’s ability to generalize to high-quality, desired novel samples.
Video Summarization With Spatiotemporal Vision Transformer

Video summarization aims to generate a compact summary of the original video for efficient video browsing. To provide video summaries which are consistent with the human perception and contain important content, supervised learning-based video summarization methods are proposed. These methods aim to learn important content based on continuous frame information of human-created summaries. However, simultaneously considering both of inter-frame correlations among non-adjacent frames and intra-frame attention which attracts the humans for frame importance representations are rarely discussed in recent methods. To address these issues, we propose a novel transformer-based method named spatiotemporal vision transformer (STVT) for video summarization. The STVT is composed of three dominant components including the embedded sequence module, temporal inter-frame attention (TIA) encoder, and spatial intra-frame attention (SIA) encoder. The embedded sequence module generates the embedded sequence by fusing the frame embedding, index embedding and segment class embedding to represent the frames. The temporal inter-frame correlations among non-adjacent frames are learned by the TIA encoder with the multi-head self-attention scheme. Then, the spatial intra-frame attention of each frame is learned by the SIA encoder. Finally, a multi-frame loss is computed to drive the learning of the network in an end-to-end trainable manner. By simultaneously using both inter-frame and intra-frame information, our method outperforms state-of-the-art methods in both of the SumMe and TVSum datasets. The source code of the spatiotemporal vision transformer will be available at https://github.com/nchucvml/STVT.
Speeding Up Learning Quantum States Through Group Equivariant Convolutional Quantum Ansätze

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$ and $S_n$ irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA is able to generate any unitary in any given $S_n$ irrep sector, which may serve as a universal model for a wide array of quantum machine learning problems with the presence of SU($d$) symmetry. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA) and verifies that 4-local SU($d$) symmetric unitaries are sufficient to build generic SU($d$) symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ans\"atze to find the ground state energy of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik's $S_n$ representation theory to quantum physics and machine learning, from which to propose quantum variational ans\"atze that strongly suggests to be classically intractable tailored towards a specific optimization problem.
A formally certified end-to-end implementation of Shor’s factorization algorithm

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors-"bugs." Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof's validity is automatically confirmed-certified-by a "proof assistant." Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor's prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way.
Conceptual framework of hybrid style in fashion image datasets for machine learning

Abstract Fashion image datasets, in which each fashion image has a label indicating its design attributes and styles, have contributed to the achievement of various machine learning techniques in the fashion industry. Computer vision studies have investigated labeling categories (such as fashion items, colors, materials, details, and styles) to create fashion image datasets for supervised learning. Although a considerable number of fashion image datasets has been developed, different style classification criteria exist because of a lack of understanding concerning fashion style. Since fashion styles reflect various design attributes, multiple styles can often be included in a single outfit. Thus, this study aims to build a Hybrid Style Framework to develop a fashion image dataset that can be efficiently applied to supervised learning. We conducted focus group interviews with six fashion experts to determine fashion style categories with which to classify hybrid styles in fashion images. We developed 1,206,931K-fashion image datasets and analyzed the hybrid style convergence. Finally, we applied the datasets to the machine learning model and verified the accuracy of the computer’s ability to recognize style. Overall, this study concludes that the Hybrid Style Framework and developed K-fashion image datasets are helpful, as they can be applied to data-driven fashion services to offer personalized fashion design solutions.
Time series prediction with granular neural networks

Conventional artificial neural networks are inherently equipped with an ambiguous (uncertain) structure which is hard to be quantified and explained. Time series forecasting using neural networks thus becomes a highly challenging task also due to the fact that time series data are always nonlinear and uncertain (because of some disturbances). Considering this, we propose a granular neural network-based time series prediction model connecting the uncertainty of models and data with the concept of information granularity. We aim to provide an explainable time series prediction model to resist the disturbance inner time series data and reduce the vagueness of the model. The functionalities of the granular neural network model are threefold: (1) It reveals the uncertainty of a time series data set through the level of granularity, coverage and specificity and possesses high prediction accuracy; (2) It provides an optimized interval output endowed with enough specificity and sufficient coverage and this interval is more robust than a single value; (3) It owns an interpretable and flexible structure that reflects the uncertainty of the initial numeric neural network and the generalization and robustness of connections while being faced with disturbances. The experimental studies elaborate on each function of our model in detail and show that the developed method performed better than the existing approaches present in the literature when experimenting on several time series data sets.
Blockchain Olive Oil Supply Chain

In Tunisia, one of the major problems in the olive oil industry is marketing. Several factors have an impact such as quality, originality, lobbying, subvention, and Extra Virgin Olive Oil certification. The major issue is still traceability of the production process to ensure that food provenance is always guaranteed. Such fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that should bring visibility to the oil supply chain. It is proposed to guarantee the veracity of the product’s information at the different stages. We use the Ethereum Blockchain to program the smart contract. This smart contract allows us to configure our system to follow the manufacturing process of olive oil from the farmer through the oil factory to the customer. In this paper we present a general-purpose approach for the oil supply chain management, proposing a system that can be configured for productions. The primary purpose is to provide a methodology to facilitate and make more efficient the development of such applications. It is based on general smart contracts and apps interacting with the same smart contracts. We use IoT to configure sensors. These sensors are the data source for the supply chain process.
Adaptive multi-granularity sparse subspace clustering

Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic perspectives and has been widely applied to high-dimensional data. The key to SSC is to learn the sparsest representation and derive an adjacency graph. Theoretically, the adjacency matrix with proper block diagonal structure leads to a desired clustering result. Various generalizations have been made through imposing Laplacian regularization or locally linear embedding to describe the manifold structure based on the nearest neighborhoods of samples. However, a single set of nearest neighborhoods cannot effectively characterize local information. From the perspective of granular computing, the notion of scored nearest neighborhoods is introduced to develop multi-granularity neighborhoods of samples. The multi-granularity representation of samples is integrated with SSC to collaboratively learn the sparse representation, and an adaptive multi-granularity sparse subspace clustering model (AMGSSC) is proposed. The learned adjacency matrix has a consistent block diagonal structure at all granularity levels. Furthermore, the locally linear relationship between samples is embedded in AMGSSC, and an enhanced AMGLSSC is developed to eliminate the over-sparsity of the learned adjacency graph. Experimental results show the superior performance of both models on several clustering criteria compared with state-of-the-art subspace clustering methods.
Quantum Cryptography in Algorithmica

We construct a classical oracle relative to which P = NP yet single-copy secure pseudorandom quantum states exist. In the language of Impagliazzo's five worlds, this is a construction of pseudorandom states in "Algorithmica," and hence shows that in a black-box setting, quantum cryptography based on pseudorandom states is possible even if one-way functions do not exist. As a consequence, we demonstrate that there exists a property of a cryptographic hash function that simultaneously (1) suffices to construct pseudorandom states, (2) holds for a random oracle, and (3) is independent of P vs. NP in the black-box setting. We also introduce a conjecture that would generalize our results to multi-copy secure pseudorandom states.
Broadening horizons in the diachronic and sociolinguistic study of Philippine English with the Twitter Corpus of Philippine Englishes (TCOPE)

Abstract This paper presents the Twitter Corpus of Philippine Englishes (TCOPE): a dataset of 27 million tweets amounting to 135 million words collected from 29 cities across the Philippines. It provides an overview of the dataset, and then shows how it can be employed to examine Philippine English (PhilE) and its relationship with extralinguistic factors (e.g. ethno-geographic region, time, sex). The focus is on the patterns of variation involving four PhilE features: (1) irregular past tense morpheme - t , (2) double comparatives, (3) subjunctive were , and (4) phrasal verb base from . My analyses corroborate previous work and further demonstrate structured heterogeneity within PhilE, indicating that it is a multifaceted and dynamic variety. TCOPE has shown itself to be useful for exploring both the “general” features of contemporary PhilE and the different forms of variation within it. It contributes to a deeper understanding of Philippine English(es) over time and in different social contexts.
Current challenges in adopting machine learning to critical care and emergency medicine

Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very powerful in deciphering hidden patterns in complex critical care and emergency medicine data, various factors including data, feature generation, model design, performance assessment, and limited implementation could affect the utility of the research. In this short review, a series of current challenges of adopting ML models to clinical research will be discussed.
Digital rock resolution enhancement and detail recovery with multi attention neural network

High-quality digital rock images are essential for subsequent high-precision numerical simulations. But limited by the imaging capability of computed tomography (CT), high resolution digital rock images with wide imaging field of view (FOV) cannot be acquired simultaneously. To cope with this constraint, we propose a novel Multi Attention Super-Resolution Neural Network (MASR) that enhances the resolution of images with wide FOV. Considering that textures and edges are more crucial in digital rocks, MASR introduces the component attention mechanism of Component Divide-and-Conquer Super-Resolution (CDCSR) model. By redesigning the hourglass network with spatial and channel attention mechanisms, proposing a spatial attention-based mask module, and optimizing the component attention mask calculation process, MASR delivers higher information utilization with fewer parameters and faster training than CDCSR. In addition, we optimize the depth of MASR to trade off speed and super-resolution quality. Furthermore, we retrain several state-of-the-art models. Through quantitative evaluations and qualitative visualizations, it is verified that MASR can recover sharper edges while removing noise, and obtain digital rock images with superior quality and reliability. The pixel-wise relative errors of MASR reconstructions are reduced by 15%–26% over bicubic interpolation method. Our codes and pretrained model are publicly available at https://github.com/MHDXing/MASR-for-Digital-Rock-Images.
Robust image steganography against lossy JPEG compression based on embedding domain selection and adaptive error correction

Transmitting images for communication on social networks has become routine, which is helpful for covert communication. The traditional steganography algorithm is unable to successfully convey secret information since the social network channel will perform lossy operations on images, such as JPEG compression. Previous studies tried to solve this problem by enhancing the robustness or making the cover adapt to the channel processing. In this study, we proposed a robust image steganography method against lossy JPEG compression based on embedding domain selection and adaptive error correction. To improve anti-steganalysis performance, the embedding domain is selected adaptively. To increase robustness and lessen the impact on anti-steganalysis performance, the error correction capacity of the error correction code is adaptively adjusted to eliminate redundancy. The experimental results show that the proposed method achieves better anti-steganalysis and robustness.
A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods are discussed. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks. Like the part 1 paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.
Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.