Reviewerly/exHarmony huggingface dataset:
Deterministic generation of large-scale hyperentanglement in three degrees of freedom

Entanglement serves as a fundamental resource for quantum information protocols, and hyperentanglement has received an increasing amount of attention for its high-capacity characteristic. Increasing the scale of hyperentanglement, i.e., the number of modes in a hyperentangled system, is crucial for enhancing its capability in quantum information processing. Here, we demonstrate the generation of large-scale continuous-variable (CV) hyperentanglement in three degrees of freedom (DOFs), including azimuthal and radial indices of Laguerre–Gaussian (LG) modes and frequency. In our experiment, 216 pairs of hyperentangled modes are deterministically generated from the four-wave mixing process in an atomic vapor. In addition, we show that the entanglement between coherent LG superposition modes denoted by both azimuthal and radial quantum numbers can also be generated from this system. Such large-scale CV hyperentanglement in three DOFs presents an efficient scheme to significantly increase the information capacity of the CV system. Our results provide a new platform for studying CV quantum information and open the avenue for constructing high-capacity parallel and multiple-DOF CV quantum information protocols.
Hybrid regression model via multivariate adaptive regression spline and online sequential extreme learning machine and its application in vision servo system

To solve the problems of slow convergence speed, poor robustness, and complex calculation of image Jacobian matrix in image-based visual servo system, a hybrid regression model based on multiple adaptive regression spline and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error. In MOS-ELM, MARS is used to evaluate the importance of input features and select specific features as the input features of online sequential extreme learning machine, so as to obtain better generalization performance and increase the stability of regression model. Finally, the method is applied to the speed predictive control of the manipulator end effector controlled by image-based visual servo and the prediction of machine learning data sets. Experimental results show that the algorithm has high prediction accuracy on machine learning data sets and good control performance in image-based visual servo.
A High-Speed CNN Hardware Accelerator with Regular Pruning

The deployment of convolutional neural networks (CNNs) in resource-constrained applications is limited due to its huge amount of parameters and computations. Therefore, the compression of CNN models, such as pruning and quantization, is necessary. In this paper, a hybrid compression strategy is investigated to compress the network. This method divides a CNN model into two parts according to the convolution (CONV) layers and fully-connected (FC) layers, where different pruning methods are applied, respectively. Since the CONV layers are computationally intensive, a hardware-oriented regular pruning (HRP) is proposed. HRP guarantees the weight distribution of the pruned CONV layers is regular, which can promote the high-speed calculation on the parallel architecture. To obtain a high compression rate, non-structured pruning is introduced to the FC layers to eliminate more redundant parameters. The experimental results show that compared to the baseline, the proposed hybrid compression strategy achieves a 31.74 × compress rate improvement with a negligible (0.25%) top-5 accuracy loss for VGG-16 on ILSVRC2012 data set. Furthermore, a hardware accelerator based on the HRP is implemented on Xilinx VCU118 evaluation board. Compared to the state-of-the-art designs, the proposed accelerator reaches a maximum performance of 110.6 frames per second (FPS).
Proactive Privacy-preserving Learning for Cross-modal Retrieval

Deep cross-modal retrieval techniques have recently achieved remarkable performance, which also poses severe threats to data privacy potentially. Nowadays, enormous user-generated contents that convey personal information are released and shared on the Internet. One may abuse a retrieval system to pinpoint sensitive information of a particular Internet user, causing privacy leakage. In this article, we propose a data-centric Proactive Privacy-preserving Cross-modal Learning algorithm that fulfills the protection purpose by employing a generator to transform original data into adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the inside adversarial data can confuse retrieval models under the attacker’s control to make erroneous predictions. We consider the protection under a realistic and challenging setting where the prior knowledge of malicious models is agnostic. To handle this, a surrogate retrieval model is instead introduced, acting as the target to fool. The whole network is trained under a game-theoretical framework, where the generator and the retrieval model persistently evolve to fight against each other. To facilitate the optimization, a Gradient Reversal Layer module is inserted between two models, enabling a one-step learning fashion. Extensive experiments on widely used realistic datasets prove the effectiveness of the proposed method.
New Galois hulls of generalized Reed-Solomon codes

Most recently, Gao et al. found a nice method to investigate the Euclidean hulls of generalized Reed-Solomon codes in terms of Goppa codes. In this note, we extend the results to general Galois hull. We prove that the Galois hulls of some GRS codes are still GRS codes. We also give some examples on Galois LCD and self-dual MDS codes. Compare with known results, the Galois hulls of GRS codes obtained in this work have flexible parameters.
An Unobtrusive Human Activity Recognition System Using Low Resolution Thermal Sensors, Machine and Deep Learning

Given the aging population, healthcare systems need to be established to deal with health issues such as injurious falls. Wearable devices can be used to detect falls. However, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this study, we developed an unobtrusive monitoring system using infrared technology to unobtrusively detect locations and recognize human activities such as sitting, standing, walking, lying, and falling. We prototyped a system consisting of two 24×32 thermal array sensors and collected data from healthy young volunteers performing ten different scenarios. A supervised deep learning (DL)-based approach classified activities and detected locations from images. The performance of the DL approach was also compared with the machine learning (ML)-based methods. In addition, we fused the data of two sensors and formed a stereo system, which resulted in better performance compared to a single sensor. Furthermore, to detect critical activities such as falling and lying on floor, we performed a binary classification in which one class was falling plus lying on floor and another class was all the remaining activities. Using the DL-based algorithm on the stereo dataset to recognize activities, overall average accuracy and F1-score were achieved as 97.6%, and 0.935, respectively. These scores for location detection were 97.3%, and 0.927, respectively. These scores for binary classification were 97.9%, and 0.945, respectively. Our results suggest the proposed system recognized human activities, detected locations, and detected critical activities namely falling and lying on floor accurately.
Sim-to-Real: Mapless Navigation for USVs Using Deep Reinforcement Learning

In recent years, mapless navigation using deep reinforcement learning algorithms has shown significant advantages in improving robot motion planning capabilities. However, the majority of past works have focused on aerial and ground robotics, with very little attention being paid to unmanned surface vehicle (USV) navigation and ultimate deployment on real platforms. In response, this paper proposes a mapless navigation method based on deep reinforcement learning for USVs. Specifically, we carefully design the observation space, action space, reward function, and neural network for a navigation policy that allows the USV to reach the destination collision-free when equipped with only local sensors. Aiming at the sim-to-real transfer and slow convergence of deep reinforcement learning, this paper proposes a dynamics-free training and consistency strategy and designs domain randomization and adaptive curriculum learning. The method was evaluated using a range of tests applied to simulated and physical environments and was proven to work effectively in a real navigation environment.
A Taxonomy of Blockchain Oracles: The Truth Depends on the Question

Blockchains benefit from guarantees of immutability and reliability due to their high redundancy and distributed nature. They show their value especially when operating between untrusted parties. Their functionality can be extended program-matically by smart contracts, but are limited by high costs of on-chain computation and only being able to truly trust data which is directly included on-chain. To attempt to bridge this limitation, blockchain oracles are introduced as a conceptual solution to act as a trusted source of information within the blockchain. The Oracle Problem emerges as we consider how one can introduce trusted information into a trust-free environment without compromising the validity of the blockchain. Many promising designs for oracle mechanisms have been proposed, but it is not readily apparent how one should assess the applicability of a given mechanism, nor the strengths and features between mechanisms. To be equipped to assess and categorize oracles, we must consider not just the possible answers, but the questions to which these oracles are trying to speak. Categorizing questions by their possible answering populations, we propose a framework for considering oracle questions and the context with which they are posed. We observe that there are limitations to what an oracle can hope to achieve, depending on the nature of the question, while noting the context in which a question exists can change what is viewed as true.
Multi-kernel graph fusion for spectral clustering

Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min–max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence. • The min–max weight strategy improves the quality of the combined kernel and the fusion graph. • We theoretically and experimentally prove the convergence of our optimization method. • The optimization method can easily be applied in all kinds of cases.
Inventory Management Optimization of Green Supply Chain Using IPSO-BPNN Algorithm under the Artificial Intelligence

This exploration is aimed at reducing the waste of resources in the supply chain inventory management and provide better services for green supply chain management. It mainly proposes a backpropagation neural network (BPNN) model based on improved particle swarm optimization (IPSO) (IPSO-BPNN) and applies it to inventory management prediction. First, the important technologies of green supply chain and intelligent supply chain are analyzed from the perspective of the ecological environment. Next, the particle swarm optimization (PSO) algorithm is optimized based on the adaptive improvement of the learning factor and the addition of the speed mutation operator. Then, it is applied to the learning and training of BPNN. Finally, the simulation experiment of the combination model is conducted. The application fields of the combination model are analyzed. The results show that a single BPNN model will produce large errors in the training process. The final error of BPNN using the traditional PSO algorithm is 0.0259, while the error of BPNN optimized by IPSO is 0.0163. The optimized combination model has higher accuracy, better performance, and the lowest error rate. The classification error rate of its training set and test set is 1.51 and 2.16, respectively. The mean square error of the training set is 0.0163 and that of the test set is 0.0229. Under 6 ~ 12 different hidden nodes, the daily measurement model error and monthly measurement model error are both low when the number of nodes is 11. Moreover, the training set is always better than the test set. Finally, the network structure of the combination model is determined as the structure of 6-11-1. This prediction module will provide purchase volume suggestions and inventory volume suggestions to provide a feasible direction for the green development of inventory management.
Label-Guided Generative Adversarial Network for Realistic Image Synthesis

Generating photo-realistic images from labels (e.g., semantic labels or sketch labels) is much more challenging than the general image-to-image translation task, mainly due to the large differences between extremely sparse labels and detail rich images. We propose a general framework Lab2Pix to tackle this issue from two aspects: 1) how to extract useful information from the input; and 2) how to efficiently bridge the gap between the labels and images. Specifically, we propose a Double-Guided Normalization (DG-Norm) to use the input label for semantically guiding activations in normalization layers, and use global features with large receptive fields for differentiating the activations within the same semantic region. To efficiently generate the images, we further propose Label Guided Spatial Co-Attention (LSCA) to encourage the learning of incremental visual information using limited model parameters while storing the well-synthesized part in lower-level features. Accordingly, Hierarchical Perceptual Discriminators with Foreground Enhancement Masks are proposed to toughly work against the generator thus encouraging realistic image generation and a sharp enhancement loss is further introduced for high-quality sharp image generation. We instantiate our Lab2Pix for the task of label-to-image in both unpaired (Lab2Pix-V1) and paired settings (Lab2Pix-V2). Extensive experiments conducted on various datasets demonstrate that our method significantly outperforms state-of-the-art methods quantitatively and qualitatively in both settings.
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits the effectiveness and efficiency in the application to gigapixel histopathology images. In this paper, we propose a kernel attention Transformer (KAT) for histopathology WSI classification. The information transmission of the tokens is achieved by cross-attention between the tokens and a set of kernels related to a set of positional anchors on the WSI. Compared to the common Transformer structure, the proposed KAT can better describe the hierarchical context information of the local regions of the WSI and meanwhile maintains a lower computational complexity. The proposed method was evaluated on a gastric dataset with 2040 WSIs and an endometrial dataset with 2560 WSIs, and was compared with 6 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI classification and is superior to the state-of-the-art methods. The code is available at https://github.com/zhengyushan/kat .
An overview of machine learning algorithms for detecting phishing attacks on electronic messaging services

Phishing attacks have become today one of the most common security breaches performed on different communication channels. Their goal is to direct users to malicious websites or to infect a user's computer in an effort to acquire personal or sensitive data for later misuse. Phishing is often the first step in the process of cybercrime, and in order to be able to recognize potential attacks and adequately protect users, it is necessary to understand the underlying principles of attack strategies. Therefore, applying machine learning for training a system that would recognize phishing messages would be essential for increasing the level of security from cyberattacks. The aim of this paper is to analyze the diverse types of phishing messages and to provide an overview of machine learning techniques used for the detection of phishing (and spam) e-mails, hereby mainly focusing on regression and classification algorithms. In addition to the mentioned techniques, an analysis of datasets that are used for training of systems for detecting phishing attacks (and spam) is presented with regard to their size, language and accuracy scores.
Algorithms for Artificial Intelligence

Artificial intelligence and machine learning use a wide variety of algorithms. More than 60 years of research have shown that intelligent behavior requires a variety of methods. We discuss the major classes of algorithms and their uses.
Bounded error modeling using interval neural networks with parameter optimization

Aiming at the issue of interval modeling of uncertain systems, this article proposes a data-based interval neural network (INN) optimization modeling method that combines interval analysis with a neural network and particle swarm optimization (PSO) algorithm, adopts an INN model with interval weights and interval thresholds, and constructs an interval multiobjective PSO (IMOPSO) algorithm to evolve the network, thereby modeling an uncertain system under an unknown-but-bounded error (UBBE) condition. Considering the cases of known and unknown error bounds in UBBE, two optimization objectives, i.e., interval coverage and interval width, are constructed to optimize the parameters of INN, aiming to improve the accuracy and reliability of prediction. The proposed method effectively solves many constraints such as the model structure demanded and error bounds known in UBBE modeling, and provides a new method of data-based interval system modeling. The method was applied to model linear and nonlinear systems, and simulation results showed that the established INN models have good prediction effects and dynamic characteristics, which demonstrate the effectiveness of the method.
iCGPN: Interaction-centric graph parsing network for human-object interaction detection

Human-Object Interaction (HOI) detection aims to infer different interactions, which occur between humans and related objects of images. HOI is usually represented by a triplet human,action,object and can be modeled as a graph. Thus, with global structural information of images, graph-based methods can detect interactions. However, in existing graph networks, although different fully-connected graphs are built, all detected bounding boxes are regarded as graph nodes equally or different types of nodes according to the category, thereby the dominant role of humans in HOI is ignored. In addition, object node representations mainly focus on appearance features, contributing little to HOI inference. To address these issues, a novel graph-based HOI detection model, named interaction-centric graph parsing network (iCGPN), models one human node as a central node, and other nodes as semantic nodes. Firstly, for each detected human instance, a human-centric fully-connected graph is constructed to learn related HOIs. Secondly, in order to reflect the difference between central nodes and semantic nodes, we design different feature representations and model different edge relationships. Through introducing the attention mechanism, global information related to human-object interaction is explored to enrich the semantic node representation, in which spatial layout, relative locations and object categories information are also combined. Finally, a multi-relation graph convolutional network is applied to update the node feature and infer the HOI. Furthermore, a multi-IOU random shift scheme is proposed to augment the data of the training set to fit the object detection deviation and enhance the generalization ability of our network. Extensive experimental results show that iCGPN achieves very competitive results in comparison with state-of-the-arraph-based methods on the V-COCO and HICO-DET datasets, which demonstrate the effectiveness of the proposed method.
An improved target-oriented path planning algorithm for wheeled mobile robots

This paper proposes a target posture-oriented path planning method, where an improved A* algorithm and an optimized dynamic windows approach (DWA) are used at the global and local planning aspects, respectively. The method enables wheeled mobile robots (WMRs) to plan an optimal path under different posture situations which can shorten the time of achieving navigation task in certain scene. On the hand of global path planning, target posture and orientation information are added into A* algorithm to obtain appropriate path. At the level of local path planning, based on DWA, evaluation functions are optimized to make the robot movement adapting to curve global path and dynamic obstacle. By introducing rotation index to evaluate experiment results, the tests verify that WMRs can reasonably plan more efficient path compared with traditional path planning method, by satisfying various posture demand with the aid of the proposed target posture-oriented A* algorithm, and quickly avoid dynamic obstacles via the optimized DWA. Therefore, the proposed algorithm can be effectively applied to WMRs to execute various target posture demands navigation tasks.
Scalable and Privacy-Focused Company-Centric Supply Chain Management

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products.