Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of Ivanov–Krouk–Zyablov Cryptosystem

Recently, F. Ivanov, E. Krouk and V. Zyablov proposed new cryptosystem based of Generalized Reed–Solomon (GRS) codes over field extensions. In their approach, the subfield images of GRS codes are masked by a special transform, so that the resulting public codes are not equivalent to subfield images of GRS code but burst errors still can be decoded. In this paper, we show that the complexity of message–recovery attack on this cryptosystem can be reduced due to using burst errors, and the secret key of Ivanov–Krouk–Zyablov cryptosystem can successfully recovered in polynomial time with a linear–algebra based attack and a square–based attack.
Graph-Based Multi-granularity Person Re-identification

The past decade has seen the rapid development of deep learning methods in person re-identification. However, most existing models ignore the sharing and exclusive information between different images, leading to insufficient discriminative information from inter-class images. Therefore, this paper proposes a multi-branch graph convolution model to learn the sharing and exclusive information both at the global and local levels. First, using multi-granularity features as graph nodes and combining cosine similarity to construct local and global graphs to mine the relationships between pedestrian images; then, embedding the inter-local and global relationships into the feature representation of pedestrian images by graph convolution operations; finally, combining identity loss and component segmentation loss as the final loss function for the model training. Experimental results on the Market-1501, CUHK03, and DukeMTMC-reID datasets show that the proposed method can effectively improve the performance of person re-identification.
On Decoding High-Order Interleaved Sum-Rank-Metric Codes

We consider decoding of vertically homogeneous interleaved sum-rank-metric codes with high interleaving order $$s$$ , that are constructed by stacking $$s$$ codewords of a single constituent code. We propose a Metzner–Kapturowski-like decoding algorithm that can correct errors of sum-rank weight $$t \le d-2$$ , where d is the minimum distance of the code, if the interleaving order $$s\ge t$$ and the error matrix fulfills a certain rank condition. The proposed decoding algorithm generalizes the Metzner–Kapturowski(-like) decoders in the Hamming metric and the rank metric and has a computational complexity of $$O\mathopen {}\left( \max \{n^3, n^2s\}\right) \mathclose {}$$ operations in $$\mathbb F_{q^m}$$ , where n is the length of the code. The scheme performs linear-algebraic operations only and thus works for any interleaved linear sum-rank-metric code. We show how the decoder can be used to decode high-order interleaved codes in the skew metric. Apart from error control, the proposed decoder allows to determine the security level of code-based cryptosystems based on interleaved sum-rank metric codes.
Knowledge Embeddings for Explainable Recommendation

The explainable recommendation aims to create models that generate high-quality recommendations and intuitive explanations. The explanations may be ad hoc or derived directly from an explainable model (also called interpretable or transparent in some contexts). Explainable recommendation attempts to address the problem of why: by providing explanations to users or system designers, it assists humans in understanding why certain items are recommended by the algorithm, where humans can be either users or system designers. Explainable recommendations help recommendation systems improve transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction. This work aims to use a knowledge embeddings extraction method to improve the explainability of recommender systems.
A Genetic Programming Encoder for Increasing Autoencoder Interpretability

Autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how the high dimensional features relate to the low-dimensional embedding, which is an issue in applications where explainability is important. There have been attempts to replace both the neural network components in autoencoders with interpretable genetic programming (GP) models. However, for the purposes of interpretable dimensionality reduction, we observe that replacing only the encoder with GP is sufficient. In this work, we propose the Genetic Programming Encoder for Autoencoding (GPE-AE). GPE-AE uses a multi-tree GP individual as an encoder, while retaining the neural network decoder. We demonstrate that GPE-AE is a competitive non-linear dimensionality reduction technique compared to conventional autoencoders and a GP based method that does not use an autoencoder structure. As visualisation is a common goal for dimensionality reduction, we also evaluate the quality of visualisations produced by our method, and highlight the value of functional mappings by demonstrating insights that can be gained from interpreting the GP encoders.
Face Image Completion Method Based on Parsing Features Maps

In this article, we focus on the facial completion task based on the parsing feature map. In recent years, methods based on deep learning have achieved remarkable results in face image completion. However, many methods do not consider the semantic structure information of face images, these methods may lead to unreasonable or discontinuous cases in the return results. To solve this problem, we propose a face image completion network based on face parsing features. The network includes two sub-network: face parsing map prediction network and face image completion network. The face parsing map prediction network predicts the parsing features of the missing regions, and the face image completion network uses these features to guide the face image completion network to fill the missing areas. On the open dataset, CelebA-HQ and Flickr Faces High Quality(FFHQ), the validity of this method is verified by qualitative and quantitative analysis.
Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies

In this paper, we explore the prospect of accelerating tree-based genetic programming (TGP) by way of modern field-programmable gate array (FPGA) devices, which is motivated by the fact that FPGAs can sometimes leverage larger amounts of data/function parallelism, as well as better energy efficiency, when compared to general-purpose CPU/GPU systems. In our preliminary study, we introduce a fixed-depth, tree-based architecture capable of evaluating type-consistent primitives that can be fully unrolled and pipelined. The current primitive constraints preclude arbitrary control structures, but they allow for entire programs to be evaluated every clock cycle. Using a variety of floating-point primitives and random programs, we compare to the recent TensorGP tool executing on a modern 8 nm GPU, and we show that our accelerator implemented on a 14 nm FPGA achieves an average speedup of 43 $$\times $$ . When compared to the popular baseline tool DEAP executing across all cores of a 2-socket, 28-core (56-thread), 14 nm CPU server, our accelerator achieves an average speedup of 4,902 $$\times $$ . Finally, when compared to the recent state-of-the-art tool Operon executing on the same 2-processor CPU system, our accelerator executes about 2.4 $$\times $$ slower on average. Despite not achieving an average speedup over every tool tested, our single-FPGA accelerator is the fastest in several instances, and we describe five future extensions that could allow for a 32–144 $$\times $$ speedup over our current design as well as allow for larger program depths/sizes. Overall, we estimate that a future version of our accelerator will constitute a state-of-the-art GP system for many applications.
Examining the Success of Information Gain, Pearson Correlation, and Symmetric Uncertainty Ranking Methods on 3D Hand Posture Data for Metaverse Systems

Metaverse is a hardware and software interface space that can connect people's social lives as in the real-natural world and provide the feeling of being there at the maximum level. In order for metaverse systems to be efficient, many independent accessories have to work holistically. One of these accessories is wearable gloves called meta gloves and equipped with sensors. Thanks to it, an important stage of metaverse systems is completed with the detection of 3-dimensional (3D) hand postures. In this study, the success of Information Gain, Pearson’s Correlation, and Symmetric Uncertainty ranking methods on 3D hand posture data for metaverse systems were investigated. For this purpose, various preprocessing was performed on the 3D data, and a dataset consisting of 15 features in total was created. The created dataset was ranked by 3 different methods mentioned and the features that the methods determined effectively were classified separately. Obtained results were interpreted with various statistical evaluation criteria. According to the experimental results obtained, it has been seen that the Symmetric Uncertainty ranking algorithm produces successful results for metaverse systems. As a result of the classification made with the active features determined using this method, there has been an increase in statistical performance criteria compared to other methods. In addition, it has been proven that time loss can be avoided in the classification of big data similar to the data used.
QKD Based on Time-Entangled Photons and Its Key-Rate Promise

For secure practical systems, quantum key distribution (QKD) must provide high key rates over long distances. Time-entanglement-based QKD promises to increase the secret key rate and distribution distances compared to other QKD implementations. This article describes the major steps in QKD protocols, focusing on the nascent QKD technology based on high-dimensional time-bin entangled photons. We overview state-of-the-art from the information and coding theory perspective. In particular, we discuss the key rate loss due to single-photon detector imperfections. We hope the open questions posed and discussed in this paper will inspire information and coding theorists to contribute to and impact fledgling quantum applications and influence future quantum communication systems.
Design and Implementation of Employee Recommendation System Based on Neural Graph Collaborative Filtering

Small, medium and micro enterprises are playing an increasingly important role in the growth of the national economy, but nowadays, they have significant difficulties in recruiting and retaining people. In this context, this paper studies the collaborative filtering algorithm based on Graph Neural Networks, according to the traditional recommendation algorithms and deep learning recommendation algorithms. First, this paper analyzes the traditional recommendation algorithm represented by item-based collaborative filtering and the deep learning algorithm represented by Graph Neural Network in the current recommendation system. It analyzes and compares their advantages and disadvantages. Secondly, combined with the characteristics of candidates, an improved algorithm is proposed. Finally, using the data set crawled from the public recruitment website, a recommendation demonstration system based on the collaborative filtering algorithm of Graph Neural Network is designed and implemented. The proposed algorithm has a particular reference value for the research of employee recommendation and recruitment systems, and recommendation systems in other fields can use the idea of combining traditional recommendation algorithms and deep learning recommendation algorithms.
Hierarchical and Multi-Group Data Sharing for Cloud-Assisted Industrial Internet of Things

With the development of Industrial Internet of Things (IIoT) and 5G, massive data are easily collected and transmitted in cloud. Therefore, it is critical to guarantee the security of data sharing. In IIoT applications, the users of a group are in hierarchical structure and they intend to access data by external groups, which requires fine-grained access control, data authenticity and data retrieval. However, existing approaches rarely provide such solutions to satisfy these requirements simultaneously. In this paper, we propose an efficient hierarchical and multi-group data sharing framework (HMGDSF) in cloud-assisted IIoT. Apart from fine-grained data access control for hierarchical users with key leakage resilience, HMGDSF achieves user anonymity with traceability and keyword-based data retrieval. Moreover, the approach supports data authenticity and integrity verification for multi-group data sharing by integrating group signature mechanism. We provide proof for the security of framework and demonstrate its efficiency and practicability by extensive evaluations.
Processing Time Performance Analysis of Scheduling Algorithms for Virtual Machines Placement in Cloud Computing Environment

In recent years, a large range of applications have been migrated from traditional computing environments to cloud systems. On the other hand, organizations with existing infrastructure investments leverage the server consolidation in the cloud to serve the upcoming load from various clients around the world. Server consolidation techniques in cloud computing are applied at infrastructure levels and spilled into three main steps; load detection, selection and placement of migrated virtual machines. The placement process can be divided to initial placement and task scheduling while allowing fair load and better resource utilization. The challenge is executing several tasks with the available shared computing resources. Further, the cloud provider should apply a good task strategy depending on the customer’s need. To maintain the best performance of the cloud system, the optimal resource utilization and the shortest completion time for task execution are the essential keys. The heuristics and metaheuristics methods are applied to achieve this goal. In this work, some of the proposed scheduling algorithms will be compared through CloudSim as a simulation tool and through processing time as a performance metric. More specifically, the comparison is made by proposing various simulation scenarios, using the processing time to classify algorithms according to time-shared and space-shared. The evaluation results show that SJF (Short Job First) and FCFS (First Come First Serve) outperforms other baseline algorithms according to different simulations set up.
Deep Graph Embeddings for Content Based-Book Recommendations

The artificial intelligence community has done much theorizing about how AI can help with the problem of successful information search in the vast reservoir of knowledge available on the Internet. Deep learning advances in speech recognition, image processing, and natural language processing have gotten much attention recently. Meanwhile, in several recent studies, deep learning is helpful in recommendation systems and information retrieval. Recommendation systems provide individualized recommendations as a solution to this problem. Among the most common method for predicting these recommendations is content-based. This study employs this method to develop a system that provides more precise book recommendations.
Memetic Semantic Genetic Programming for Symbolic Regression

This paper describes a new memetic semantic algorithm for symbolic regression (SR). While memetic computation offers a way to encode domain knowledge into a population-based process, semantic-based algorithms allow one to improve them locally to achieve a desired output. Hence, combining memetic and semantic enables us to (a) enhance the exploration and exploitation features of genetic programming (GP) and (b) discover short symbolic expressions that are easy to understand and interpret without losing the expressivity characteristics of symbolic regression. Experimental results show that our proposed memetic semantic algorithm can outperform traditional evolutionary and non-evolutionary methods on several real-world symbolic regression problems, paving a new direction to handle both the bloating and generalization endeavors of genetic programming.
Regression-decision-tree based parameter optimization of measurement-device-independent quantum key distribution

The parameter configuration of quantum key distribution (QKD) has a great effect on the communication effect, and in the practical application of the QKD network in the future, it is necessary to quickly realize the parameter configuration optimization of the asymmetric channel measurement-device-independent QKD according to the communication state, so as to ensure the good communication effect of the mobile users, which is an inevitable requirement for real-time quantum communication. Aiming at the problem that the traditional QKD parameter optimization configuration scheme cannot guarantee real-time, in this paper we propose to apply the supervised machine learning algorithm to the QKD parameter optimization configuration, and predict the optimal parameters of TF-QKD and MDI-QKD under different conditions through the machine learning model. First, we delineate the range of system parameters and evenly spaced (linear or logarithmic) values through experimental experience, and then use the traditional local search algorithm (LSA) to obtain the optimal parameters and take them as the optimal parameters in this work. Finally, we train various machine learning models based on the above data and compare their performances. We compare the supervised regression learning models such as neural network, K-nearest neighbors, random forest, gradient tree boosting and classification and regression tree (CART), and the results show that the CART decision tree model has the best performance in the regression evaluation index, and the average value of the key rate (of the prediction parameters) and the optimal key rate ratio is about 0.995, which can meet the communication needs in the actual environment. At the same time, the CART decision tree model shows good environmental robustness in the residual analysis of asymmetric QKD protocol. In addition, compared with the traditional scheme, the new scheme based on CART decision tree greatly improves the real-time performance of computing, shortening the single prediction time of the optimal parameters of different environments to the microsecond level, which well meets the real-time communication needs of the communicator in the movable state. This work mainly focuses on the parameter optimization of discrete variable QKD (DV-QKD). In recent years, the continuous variable QKD (CV-QKD) has developed also rapidly. At the end of the paper, we briefly introduce academic attempts of applying machine learning to the parameter optimization of CV-QKD system, and discuss the applicability of the scheme in CV-QKD system.
Graph Neural Network with Item Life Cycle for Social Recommendation

Recommendation system is a strategy to provide users with better decisions and help them make decisions. The sequence based recommendation system is to predict the probability of users clicking on the next item, or to score something, and help users make decisions. However, for these prediction work, most models or works in recent years only connect many models, ignoring the order of user behavior, and the location information is not taken into account.Secondly, most of the modeling is based on the interests of users, which just ignores the changes of the life cycle of the item itself. In addition, in the previous work, the model based on graph neural network obtains the potential information of users by aggregating the information of their neighbors’ nodes. However, these neighbors may not interact with users in reality and have no impact on the changes of users’ preferences. Therefore, we take the real friends of users into account and speculate the impact of friends on the changes of users’ preferences through social networks, And we also take into account the short-term and long-term interests of users, and model users at the same time. At the same time, for the item, its own life cycle is changing, and the possible changes of similar items are also similar. Aiming at this point, we capture the short-term and long-term changes of the item life cycle, and jointly predict the short-term and long-term information. A large number of experiments on real data sets show that several methods are compared to verify the effectiveness of our model.
Monocular depth estimation using synthetic data with domain-separated feature alignment

Depth estimation is a research hotspot in today's computer vision tasks, and its depth information is of great significance for many applications such as autonomous driving, 3D reconstruction, and object tracking. Although supervised depth estimation based on deep learning has high prediction accuracy, it is difficult to obtain a large amount of labeled data. Therefore, self-supervised monocular depth estimation methods that do not require labeled data have become the mainstream of research. Self-supervised learning utilizes the constraints of objects on spatial geometry, reducing the need for labeled data, but the problems of dynamic objects, shooting angles, and visibility weaken the effect of self-supervision. Using the accurate depth information of synthetic datasets to assist the self-supervised training of real datasets can improve the accuracy of self-supervised depth estimation, but most methods do not consider the distribution difference between synthetic data and real data, which affects the estimation effect. Aiming at this problem, this paper proposes a domain-separated Monocular Depth Estimation (DsMDE) algorithm based on domain separation network, which uses orthogonal loss to separate the public and private features of each domain, and then uses the maximum mean difference to The common features are aligned to reduce the difference between the synthetic domain and the real domain. The experimental results show that the DsMDE method proposed in this paper improves the depth estimation effect, and the depth estimation accuracy is better than that of the mainstream algorithms.
An online auction mechanism for double time-varying constraints in vehicular edge computing

Real-time resource allocation has become a hot topic in vehicular edge computing environment. However, vehicle equipment with limited computing capability cannot meet the Quality of service of computation intensive and delay sensitive vehicle applications. Moreover, in most studies, the deployment constraints in edge computing and users' resource demand are static, which is inconsistent with real scenarios. In this paper, an online auction mechanism with double time-varying constraints is proposed to solve the resource allocation problem in a vehicular edge computing environment. Specifically, we first formulate the resource allocation problem between edge computing servers (ECSs) and vehicles as a novel integer programming model with time-varying deployment constraint and time-varying resource demand. Then, we design an online auction mechanism based on heuristics. In the resource allocation algorithm, a matching model and dominant-resource-proportion strategy are adopted to improve the resource utilization and social welfare. Simultaneously, a payment pricing algorithm based on the dichotomy is proposed. Finally, we prove that the online mechanism is individually rational and truthful. The experimental results demonstrate that our online mechanism has higher social welfare and resource utilization than existing research.