Reviewerly/exHarmony huggingface dataset:
Demonstration of Tunable Three-Body Interactions between Superconducting Qubits

Nonpairwise multiqubit interactions present a useful resource for quantum information processors. Their implementation would facilitate more efficient quantum simulations of molecules and combinatorial optimization problems, and they could simplify error suppression and error correction schemes. Here, we present a superconducting circuit architecture in which a coupling module mediates two-local and three-local interactions between three flux qubits by design. The system Hamiltonian is estimated via multiqubit pulse sequences that implement Ramsey-type interferometry between all neighboring excitation manifolds in the system. The three-local interaction is coherently tunable over several MHz via the coupler flux biases and can be turned off, which is important for applications in quantum annealing, analog quantum simulation, and gate-model quantum computation.
Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning. Deploying federated learning in cloud-edge collaborative architecture is widely considered to be a promising cyber infrastructure in the future. Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still in its infancy and little research has been conducted. This article aims to fill the gap by providing a detailed description of the critical technologies, challenges, and applications of deploying federated learning in cloud-edge collaborative architecture, and providing guidance on future research directions.
Blockchain Enabled SDN Framework for Security Management in 5G Applications

Fifth-generation (5G) wireless networks are now operational to deploy all over the world. The technology of 5G’s objective is to link heterogeneous machines and devices with significant improvements high quality of service (QoS), internet bandwidth, and improved system throughput to enable several upright applications. Despite all these benefits that 5G will provide, still, significant issues need to be resolved, such as decentralization, transparency, and risks associated with data interoperability, network privacy, and security vulnerabilities. Modern networks link an enormous number of devices to the Internet, and in this complicated situation, the use of BC and SDN has been effectively advocated to assure security, privacy, and secrecy. This study offers a blockchain-enabled SDN framework for securing transactions that makes use of Software Defined Network (SDN) and Network Function Virtualization (NFV) to overcome these issues. The proposed framework can address the man-in-the-middle attack between control and data plane in SDN networks. A controller authentication scheme is provided using smart contracts. Smart contracts automatically authenticate the SDN controller to increase the efficiency of controller verification. The communicated data can also be authenticated using smart contracts. The proposed framework can enhance network transparency, data security, and user privacy. Each SDN controller can access verifiable data using the proposed framework.
Equivariant quantum circuits for learning on weighted graphs

Abstract Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm. In quantum machine learning (QML), however, the literature on ansatzes that are motivated by the training data structure is scarce. In this work, we introduce an ansatz for learning tasks on weighted graphs that respects an important graph symmetry, namely equivariance under node permutations. We evaluate the performance of this ansatz on a complex learning task, namely neural combinatorial optimization, where a machine learning model is used to learn a heuristic for a combinatorial optimization problem. We analytically and numerically study the performance of our model, and our results strengthen the notion that symmetry-preserving ansatzes are a key to success in QML.
Underwater-YCC: Underwater Target Detection Optimization Algorithm Based on YOLOv7

Underwater target detection using optical images is a challenging yet promising area that has witnessed significant progress. However, fuzzy distortions and irregular light absorption in the underwater environment often lead to image blur and color bias, particularly for small targets. Consequently, existing methods have yet to yield satisfactory results. To address this issue, we propose the Underwater-YCC optimization algorithm based on You Only Look Once (YOLO) v7 to enhance the accuracy of detecting small targets underwater. Our algorithm utilizes the Convolutional Block Attention Module (CBAM) to obtain fine-grained semantic information by selecting an optimal position through multiple experiments. Furthermore, we employ the Conv2Former as the Neck component of the network for underwater blurred images. Finally, we apply the Wise-IoU, which is effective in improving detection accuracy by assigning multiple weights between high- and low-quality images. Our experiments on the URPC2020 dataset demonstrate that the Underwater-YCC algorithm achieves a mean Average Precision (mAP) of up to 87.16% in complex underwater environments.
Consumer intention to use service robots: a cognitive–affective–conative framework

Purpose Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses. Design/methodology/approach A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses. Findings The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention. Practical implications This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector. Originality/value Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.
To CamelCase or under_score

Naming conventions are generally adopted in an effort to improve program comprehension. Two of the most popular conventions are alternatives for composing multi-word identifiers: the use of underscores and the use of camel casing. While most programmers have a personal opinion as to which style is better, empirical study forms a more appropriate basis for choosing between them. The central hypothesis considered herein is that identifier style affects the speed and accuracy of manipulating programs. An empirical study of 135 programmers and non-programmers was conducted to better understand the impact of identifier style on code readability. The experiment builds on past work of others who study how readers of natural language perform such tasks. Results indicate that camel casing leads to higher accuracy among all subjects regardless of training, and those trained in camel casing are able to recognize identifiers in the camel case style faster than identifiers in the underscore style.
Enhancing Function Approximation Abilities of Neural Networks by Training Derivatives

A method to increase the precision of feedforward networks is proposed. It requires prior knowledge of a target's function derivatives of several orders and uses this information in gradient-based training. Forward pass calculates not only the values of the output layer of a network but also their derivatives. The deviations of those derivatives from the target ones are used in an extended cost function, and then, the backward pass calculates the gradient of the extended cost with respect to weights, which is then used by a weights update algorithm. The most accurate approximation is obtained when the training starts with all available derivatives that are then step by step excluded from the extended cost function, starting with the highest orders up until only values are trained. Despite a substantial increase in arithmetic operations per pattern (compared with the conventional training), the method allows to obtain 140-1000 times more accurate approximation for simple cases if the total number of operations is equal. This precision also happens to be out of reach for the regular cost function. The method works well for solving differential equations with neural networks. The cost function is the deviation of the equation's residual from zero, and it can be extended by differentiating the equation itself so no prior information is required. This extension allows to solve 2-D nonlinear partial differential equation 13 times more accurately using seven times fewer grid points. The GPU-efficient algorithm for calculating the gradient of the extended cost function is proposed.
Opening the Black Box: Hierarchical Sampling Optimization for Hand Pose Estimation

Hand pose estimation, formulated as an inverse problem, is typically optimized by an energy function over pose parameters using a `black box' image generation procedure, knowing little about either the relationships between the parameters or the form of the energy function. In this paper, we show significant improvement upon such black box optimization by exploiting high-level knowledge of the parameter structure and using a local surrogate energy function. Our new framework, hierarchical sampling optimization (HSO), consists of a sequence of discriminative predictors organized into a kinematic hierarchy. Each predictor is conditioned on its ancestors, and generates a set of samples over a subset of the pose parameters, with only one selected by the highly-efficient surrogate energy. The selected partial poses are concatenated to generate a full-pose hypothesis. Repeating the same process, several hypotheses are generated and the full energy function selects the best result. Under the same kinematic hierarchy, two methods based on decision forest and convolutional neural network are proposed to generate the samples and two optimization methods are studied when optimizing these samples. Experimental evaluations on three publicly available datasets show that our method is particularly impressive in low-compute scenarios where it significantly outperforms all other state-of-the-art methods.
End-to-end Billet Identification Number Recognition System

In steel industry, product number recognition is necessary for factory automation. Before final production, the billet identification number (BIN) should be checked to prevent mixing billets of different material. There are two types of BINs, namely, paint-type and sticker-type BINs. In addition, the BIN comprises seven to nine alphanumeric characters except the letters I and O. The BIN may be rotated in various directions. Therefore, for proper recognition and accident prevention, end-to-end BIN recognition system that uses the deep learning is proposed. Specifically, interpretation and sticker extraction modules are developed. Furthermore, the fully convolutional network (FCN) with deconvolution layer is used and optimized. To increase the BIN recognition accuracy, the FCN was simulated for various structures and was transferred from the pre-trained model. The BIN is identified by the trained FCN model and interpretation module. If the BIN is sticker-type, it is inferred after the sticker region is extracted by the sticker extraction module. The accuracy of the proposed system was shown to be approximately 99.59% in an eight-day period.
Patient Cluster Divergence Based Healthcare Insurance Fraudster Detection

Healthcare insurance frauds are causing millions of dollars in loss for public healthcare funds around the world.Healthcare fraud detection methods can help us to avoid the loss of medical healthcare insurance fund and to improve medical quality.The existing fraudster detection methods always consider people who violate normal behavior patterns as fraudsters.However, fraudsters can evade these monitors by camouflage, by adding normal behaviors so that they look ''normal.''Our focus is to spot healthcare insurance patient fraudsters in the presence of camouflage.Although camouflage may hinder fraudster detection to some extent, we find that camouflage behaviors always sustain in a short period when the fraudster is conducting fraud.In other words, camouflage behaviors will not last long.Hence, if we can consider the cluster divergence of each patients' hospital admission graph during a long time, we can detect healthcare insurance fraudsters free of the interference of fraudsters' camouflage behaviors.In this paper, we propose the patient cluster divergence-based healthcare insurance fraudster detection (PCDHIFD), which can get rid of the disturbance of camouflage in fraud detection.Extensive experiment results show that our PCDHIFD outperforms the comparison approaches in terms of f -measure by over 15%.
RX_myKarve carving framework for reassembling complex fragmentations of JPEG images

Digital forensic aims to provide an assistance for making decisions about a crime by looking at a file content which usually involves image files such as GIF, BMP, JPEG and etc. JPEG is a very popular image file format. It has less structured contents than other images which makes its recovery possible in the absence of some file system metadata. However, an essential problem of which is fragmented JPEG file intertwined with non-JPEG files and/or Bifragmented in the scan area. This paper proposes RX_myKarve as a new file carving framework for solving a number of forensic recovery problems including fragmentation. The RX_myKarve basic design includes a structure-based and content-based carving approaches. It adopts machine learning and evolutionary algorithms in its main components of identification validation and reassembling. The identification and validation techniques encompass an Extreme Learning Machine (ELM) for identifying and filtering the image data in the scan area. The reassembling technique encompasses a genetic algorithm to reconstruct the data from fragmented pieces to a complete image. The main contribution of the paper lies on the reassembling of fragmented image file clusters in the scan area. The RX_myKarve is tested and evaluated by using the Digital Forensic Research Workshop (DFRWS) 2006 and 2007 forensic challenge datasets. The results show that the RX_myKarve is able to carve and fully recover all the giving cases of the DFRWS-2006 dataset, which are 19 images, and all the relevant cases of the DFRWS-2007 dataset, which are 18 images. This improvement in file carving is mostly attributed to the novel identification and reassembling techniques.
FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method

Objective The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI). Materials and methods FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST). Results With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours. Conclusions FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.
Efficient Image Retrieval through Hybrid Feature Set and Neural Network

Images are an important part of daily life.Any person cannot easily control the huge repository of digitally existing images.Extensive scanning of the image database is very much essential to search a particular image from the huge repository.In some cases, this procedure becomes very exhaustive also.As a result, if a count of ten thousand, lakhs or considerably more images are included in the image database, then it may be transformed into a tedious and never-ending process.Content-based image retrieval (CBIR) is a technique, which is used for retrieving an image.This type of image retrieval procedure is centered on the real content of the image.This paper proposed a model of the hybrid feature set of Haar wavelets and Gabor features and analyzed with different existing models image retrieval.Contentbased image retrieval using hybrid feature set of Haar wavelets and Gabor features superiors on other models.
Multi-level Network Construction Based on Intelligent Big Data Analysis

In this work, we present a hybrid miner-network analyzer (HMNA) system includes three main stages; the first stage called preparing and preprocessing stage that includes building initial network from citation file and Find Keywords through apply Rake and cleaning. The second stage involves building classification model including parameters detection and apply LDA for find topics, final stage, add the topics of document into initial network to construction multi-layer network, where each level represent community related of that topic. We can summarize the main points of HMNA system as: (i) It deals with real, complex, huge database of papers ‘citation’. (ii) The preprocessing stage involves retrieve keywords from corpus using Rake after add constructions on it and cleaning without using any feature selection method. (iii) It building digital corpus that combines with dictionary to clustering the clean text into multi groups based on LDA model. (iv) It reconstructed the initial network by add the labeling of topic results from above step to it. (v) It builds multi communities (multi-level network), each level in that network represent single communities, (vi) It computes the main characteristics measures for each level or communities to determine the similarity in it structure with cluster in citation network.
An Adaptive Nonlocal Gaussian Prior for Hyperspectral Image Denoising

Nonlocal similar patches are effectively used in the Gaussian prior denoising model. However, it is difficult to learn an accurate Gaussian model for hyperspectral image (HSI) with noisy and limited similar patches, which will result in unstable Gaussian parameters (mean and covariance). In this letter, several techniques are proposed to overcome the noisy and small sample problems for HSI denoising. For Gaussian parameters, we propose the adaptive weighted mean of nonlocal similar patches and use a positive semidefinite constraint on the covariance parameter. In addition, an iterative manner is used to achieve more accurate parameters. The proposed method can achieve more robust Gaussian model for HSI denoising. Experiments on a HSI demonstrate the effectiveness of the proposed algorithm compared with the traditional methods for HSI denoising.
Passquerade

Entering text passwords on mobile devices is a significant challenge. Current systems either display passwords in plain text: making them visible to bystanders, or replace characters with asterisks shortly after they are typed: making editing them harder. This work presents a novel approach to mask text passwords by distorting them using graphical filters. Distorted passwords are difficult to observe by attackers because they cannot mentally reverse the distortions. Yet passwords remain readable by their owners because humans can recognize visually distorted versions of content they saw before. We present results of an online questionnaire and a user study where we compared Color-halftone, Crystallize, Blurring, and Mosaic filters to Plain text and Asterisks when 1) entering, 2) editing, and 3) shoulder surfing one-word passwords, random character passwords, and passphrases. Rigorous analysis shows that Color-halftone and Crystallize filters significantly improve editing speed, editing accuracy and observation resistance compared to current approaches.
A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks

Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classification. However, most of these make assumptions about patient orientation and imaging direction to simplify the problem and/or work with the full 3D images. As such, they perform poorly when these assumptions are not met. In this paper, we propose a method of classification for 3D organ images that is rotation and translation invariant. To this end, we extract a representative two-dimensional (2D) slice along the plane of best symmetry from the 3D image. We then use this slice to represent the 3D image and use a 20-layer deep convolutional neural network (DCNN) to perform the classification task. We show experimentally, using multi-modal data, that our method is comparable to existing methods when the assumptions of patient orientation and viewing direction are met. Notably, it shows similarly high accuracy even when these assumptions are violated, where other methods fail. We also explore how this method can be used with other DCNN models as well as conventional classification approaches.