Reviewerly/exHarmony huggingface dataset:
Block-encoding dense and full-rank kernels using hierarchical matrices: applications in quantum numerical linear algebra

Many quantum algorithms for numerical linear algebra assume black-box access to a block-encoding of the matrix of interest, which is a strong assumption when the matrix is not sparse. Kernel matrices, which arise from discretizing a kernel function <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mi>x</mml:mi><mml:mo>&amp;#x2032;</mml:mo></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math>, have a variety of applications in mathematics and engineering. They are generally dense and full-rank. Classically, the celebrated fast multipole method performs matrix multiplication on kernel matrices of dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> in time almost linear in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> by using the linear algebraic framework of hierarchical matrices. In light of this success, we propose a block-encoding scheme of the hierarchical matrix structure on a quantum computer. When applied to many physical kernel matrices, our method can improve the runtime of solving quantum linear systems of dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>&amp;#x03BA;</mml:mi><mml:mi>polylog</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mi>&amp;#x03B5;</mml:mi></mml:mfrac><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03BA;</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03B5;</mml:mi></mml:math> are the condition number and error bound of the matrix operation. This runtime is near-optimal and, in terms of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>, exponentially improves over prior quantum linear systems algorithms in the case of dense and full-rank kernel matrices. We discuss possible applications of our methodology in solving integral equations and accelerating computations in N-body problems.
Variational approach to the quantum separability problem

We present the variational separability verifier (VSV), which is a variational quantum algorithm that determines the closest separable state (CSS) of an arbitrary quantum state with respect to the Hilbert-Schmidt distance (HSD). We first assess the performance of the VSV by investigating the convergence of the optimization procedure for Greenberger-Horne-Zeilinger states of up to seven qubits, using both state-vector and shot-based simulations. We also numerically determine the CSS of maximally entangled mixed $X$ states, and subsequently use the results of the algorithm to surmise the analytical form of the aforementioned CSS. Our results indicate that current noisy intermediate-scale quantum devices may be useful in addressing the NP-hard full separability problem using the VSV, due to the shallow quantum circuit imposed by employing the destructive swap test to evaluate the HSD. The VSV may also possibly lead to the characterization of multipartite quantum states, once the algorithm is adapted and improved to obtain the closest $k$-separable state of a multipartite entangled state.
Curb Your Enthusiasm: The Fintech Hype Meets Reality in the Remittances Market

Fintech has become one of the most popular topics among policymakers and experts.It usually comes with the qualifier "disruptive".Thus, the hype is easy to understand: fintech would upend the financial system due to its disruptive nature, as it would allow financial services to be completed faster, cheaper, and more efficiently.Indeed, many have predicted that the remittances market was on the verge of being disrupted as remittances are considered too costly while remittance service providers inefficient, opaque, and outdated.Therefore, there seems to be no better setting for assessing the allegedly disruptive effects of fintech.Against that background, this paper investigates how those predictions have fared so far.Contrary to expectations, it found that instead of disrupting incumbents fintechs have increasingly been entangled with them.Therefore, not only there is no evidence of disruption, but it is unlikely to occur in the foreseeable future.Even so, the paper argues that fintechs play an important role in the remittances market.
Ship Recognition for SAR Scene Images under Imbalance Data

Synthetic aperture radar (SAR) ship recognition can obtain location and class information from SAR scene images, which is important in military and civilian fields, and has turned into a very important research focus recently. Limited by data conditions, the current research mainly includes two aspects: ship detection in SAR scene images and ship classification in SAR slice images. These two parts are not yet integrated, but it is necessary to integrate detection and classification in practical applications, although it will cause an imbalance of training samples for different classes. To solve these problems, this paper proposes a ship recognition method on the basis of a deep network to detect and classify ship targets in SAR scene images under imbalance data. First, RetinaNet is used as the backbone network of the method in this paper for the integration of ship detection and classification in SAR scene images. Then, taking into account the issue that there are high similarities among various SAR ship classes, the squeeze-and-excitation (SE) module is introduced for amplifying the difference features as well as reducing the similarity features. Finally, considering the problem of class imbalance in ship target recognition in SAR scene images, a loss function, the central focal loss (CEFL), based on depth feature aggregation is constructed to reduce the differences within classes. Based on the dataset from OpenSARShip and Sentinel-1, the results of the experiment suggest that the the proposed method is feasible and the accuracy of the proposed method is improved by 3.9 percentage points compared with the traditional RetinaNet.
Blockchain Technology for Oil and Gas: Implications and Adoption Framework Using Agile and Lean Supply Chains

Oil and gas (O&amp;G) supply chain management (SCM) is complex because it deals with different geographic locations to manage demand and supply, transportation, inventory, and distribution. Blockchain technology has created an interesting research gap in the SCM domain, and this study is designed to describe the relevancy of blockchain technology for O&amp;G SCM. SCM is based on agile and lean supply chains (SCs). Agile SC focuses on increasing flexibility and responsiveness to gain competitive advantages, and lean SC is based on eliminating waste and processes to improve firm performance. This study is an initial effort to propose a framework that suggests the implication of blockchain for O&amp;G by providing an overview of O&amp;G SCM. Data were collected from SC managers of O&amp;G companies, and we analyzed the impact of agile and lean SCs on firm performance. The results indicate that agile SC is highly important for O&amp;G industries in comparison to lean SC. This study proposes the key requirements of agile SC and how blockchain can uplift agile SC technology with state-of-the-art properties such as data-driven management, information sharing, data privacy, cyber-security, transparency, smart contracts, visibility, traceability, and reliability, which boost SC agility as well as firm performance.
How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models

Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have started to lay down a theoretical foundation for these models, a detailed understanding of the role of the diffusion time T is still lacking. Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution; however, a smaller value of T should be preferred for a better approximation of the score-matching objective and higher computational efficiency. Starting from a variational interpretation of diffusion models, in this work we quantify this trade-off and suggest a new method to improve quality and efficiency of both training and sampling, by adopting smaller diffusion times. Indeed, we show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process. Empirical results support our analysis; for image data, our method is competitive with regard to the state of the art, according to standard sample quality metrics and log-likelihood.
A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.
Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.
Weakly-Supervised Continual Learning for Class-Incremental Segmentation

Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing. Starting from a neural network already trained for semantic segmentation, we propose to modify its label space to swiftly adapt it to new classes under weak supervision. To alleviate the background shift and the catastrophic forgetting problems inherent to this form of continual learning, we compare different regularization terms and leverage a pseudo-label strategy. We experimentally show the relevance of our approach on three public remote sensing datasets. Code is open-source and released in this repository: https://github.com/alteia-ai/ICSS.
Human-Social Robot Interaction, Anthropomorphism And Ontological Boundary Problem In Education

Artificial intelligence and robotics technologies do not manufacture robots only for industrial use, but also for healthcare, marketing, tourism and accommodation industries where social interaction is prevalent. Service robots are social robots that interact directly with individuals and to fulfill the physical, cognitive, emotional and social needs of individuals. Recently, it was observed that the number of studies on the employment of social robots in education has increased. These studies reported positive findings on the employment of social robots in educational settings; however, they also indicated certain problems. One of these problems was the ontological boundary problem due to the anthropomorphic design of these robots. Certain studies on human-social robot interaction demonstrated that the human-machine distinction has blurred, humans started to attribute anthropogenic traits to these robots such as intention, emotion and purpose, while these studies categorized these robots as live or hybrid. Anthropomorphizing the robots and the ambiguity of their ontological category could lead to problems such as excessive attachment, social isolation, and violation of privacy, and perceptions of the individuals about their existence could be altered. The present article aimed to provide information about the studies conducted on the employment of social robots in education, analyze the advantages and disadvantages of human-social robot interaction based on anthropomorphism and ontological boundary problem. Finally, certain recommendations are presented about the employment of social robots in education.
A Perspective on Blockchain-based Cryptocurrency to Boost A Futuristic Digital Economy

The advent of blockchain has transformed the recent technological landscape with more and more applications emerging from every walk of life. In this work, we discuss the adoption of blockchain globally by various countries. We discuss the active application areas of blockchain with special focus on business use cases using cryptocurrency. In particular, we provide a case study on India describing the current scenario of India in terms of the digital economy and the boom it has received in the use of cryptocurrencies. We also discuss how adoption of cryptocurrency can manifest in future in the light of recent annual budget of India. We propose that blockchain can transform the Indian digital economy into one of the world’s strongest digital economies. We also discuss the challenges faced by countries while adopting blockchain followed by the conclusion of the work.
Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method

Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem.The three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example.In blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.
A Framework for Enabling Unpaired Multi-Modal Learning for Deep Cross-Modal Hashing Retrieval

Cross-Modal Hashing (CMH) retrieval methods have garnered increasing attention within the information retrieval research community due to their capability to deal with large amounts of data thanks to the computational efficiency of hash-based methods. To date, the focus of cross-modal hashing methods has been on training with paired data. Paired data refers to samples with one-to-one correspondence across modalities, e.g., image and text pairs where the text sample describes the image. However, real-world applications produce unpaired data that cannot be utilised by most current CMH methods during the training process. Models that can learn from unpaired data are crucial for real-world applications such as cross-modal neural information retrieval where paired data is limited or not available to train the model. This paper provides (1) an overview of the CMH methods when applied to unpaired datasets, (2) proposes a framework that enables pairwise-constrained CMH methods to train with unpaired samples, and (3) evaluates the performance of state-of-the-art CMH methods across different pairing scenarios.
EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.
Antecedents of social media influencers on customer purchase intention: Empirical study in Jordan

The present study examined the impact of social media influencers (SMIs) on consumers’ purchasing decisions, by examining the factors affecting purchase intentions of consumers. Online questionnaire was used to gather data from Facebook users as the study respondents. SMIs are currently a promising marketing technique in influencing purchase intention of customers, but in Jordan, studies on this matter are still lacking. This study therefore presented several key factors associated with SMIs in influencing the purchase intention of customers, in Jordanian context. Accordingly, the key factors affecting customer purchase intention through SMIs were examined. A model was proposed and empirically tested and validated using structural equation model (SEM), with data obtained from 390 Jordanian Facebook users. From the results, significant impact of Information Quality (IQ) and Trustworthiness (TRU) on attitude toward a brand, and consequently on purchase intentions of customers, was affirmed.
The role of cloud computing in supporting decision making: Evidence from banking industry

Current study aimed at examining the influence of cloud computing characteristics and attributes (on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service) on decision making process within commercial banks in Jordan. Quantitative approach was used, and a questionnaire was distributed on a sample of (104) managers within commercial banks in Jordan. SPSS was employed to process and analyze primary data, depending on multiple and linear regression; results of study accepted the main hypothesis, and it appeared that cloud computing attributes support organizational efforts for better decision making process. Study recommended examining the role of cloud computing in the field of accounting, in other meanings; investigate the role of cloud accounting in supporting financial decision making process.