Reviewerly/exHarmony huggingface dataset:
Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs.
Non-Markovian open quantum dynamics in squeezed environments: Coherent-state unraveling

We apply the stochastic Schr\"odinger equation approach to study the non-Markovian dynamics of quantum systems coupled to a squeezed environment. We derive the non-Markovian quantum-state-diffusion equation using coherent-state unraveling and the associated zeroth-order master equation for general models in a microscopic quantum context. Focused on a dissipative optical cavity coupled with a squeezed vacuum, the numerical simulations demonstrate the time evolution of photon numbers in an optical cavity. We observe that the long-time limits of the non-Markovian dynamics are significantly different from those of the Markovian dynamics for various memory factors and squeezing factors. Additionally, non-Markovian dynamics exhibit a distinct pattern of behavior when parameters change. Our work provides a method to study the impact of multiple parameters on the non-Markovian dynamics and long-time limits in a joint manner. The method can be further extended to squeezed finite-temperature environments.
Universal Prototype Transport for Zero-Shot Action Recognition and Localization

This work addresses the problem of recognizing action categories in videos when no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a semantic space, either trained on large-scale seen actions or on objects. While effective, we find that universal action and object mappings are biased to specific regions in the semantic space. These biases lead to a fundamental problem: many unseen action categories are simply never inferred during testing. For example on UCF-101, a quarter of the unseen actions are out of reach with a state-of-the-art universal action model. To that end, this paper introduces universal prototype transport for zero-shot action recognition. The main idea is to re-position the semantic prototypes of unseen actions by matching them to the distribution of all test videos. For universal action models, we propose to match distributions through a hyperspherical optimal transport from unseen action prototypes to the set of all projected test videos. The resulting transport couplings in turn determine the target prototype for each unseen action. Rather than directly using the target prototype as final result, we re-position unseen action prototypes along the geodesic spanned by the original and target prototypes as a form of semantic regularization. For universal object models, we outline a variant that defines target prototypes based on an optimal transport between unseen action prototypes and object prototypes. Empirically, we show that universal prototype transport diminishes the biased selection of unseen action prototypes and boosts both universal action and object models for zero-shot classification and spatio-temporal localization.
A Trustable and Secure Usage-Based Insurance Policy Auction Mechanism and Platform Using Blockchain and Smart Contract Technologies

This study presents an architectural framework for the blockchain-based usage-based insurance (UBI) policy auction mechanism in the internet of vehicles (IoV) applications. The main objective of this study is to analyze and design the specific blockchain architecture and management considerations for the UBI environment. An auction mechanism is developed for the UBI blockchain platform to enhance consumer trust. The study identifies correlations between driving behaviors and associated risks to determine a driver's score. A decentralized bidding algorithm is proposed and implemented on a blockchain platform using elliptic curve cryptography and first-price sealed-bid auctions. Additionally, the model incorporates intelligent contract functionality to prevent unauthorized modifications and ensure that insurance prices align with the prevailing market value. An experimental study evaluates the system's efficacy by expanding the participant pool in the bidding process to identify the winning bidder and is investigated under scenarios where varying numbers of insurance companies submit bids. The experimental results demonstrate that as the number of insurance companies increases exponentially, the temporal overhead incurred by the system exhibits only marginal growth. Moreover, the allocation of bids is accomplished within a significantly abbreviated timeframe. These findings provide evidence that supports the efficiency of the proposed algorithm.
Technology Implementation in Pre-Service Science Teacher Education Based on the Transformative View of TPACK: Effects on Pre-Service Teachers’ TPACK, Behavioral Orientations and Actions in Practice

Teaching with and about technology is part of science teachers’ 21st century skills. To foster technology-enhanced practice, teachers need to acquire both technological pedagogical content knowledge (TPACK on action) and positive behavioral orientations toward technology exploitation. However, it remains unclear if the gained knowledge is applied in practice (TPACK in action). Therefore, studies are required to investigate the interplay of programs promoting TPACK on action, behavioral orientations, and resulting TPACK in action. This paper presents an approach that explicitly links pre-service science teachers’ pedagogical content knowledge (PCK) with TPACK development in two undergraduate modules, following the transformative view of TPACK. TPACK on action and behavioral orientations are captured through a questionnaire at three points in time. Additionally, lesson plans are analyzed to evaluate the quality of technology use and cognitive engagement, approximating TPACK in action. The results show a significant increase in pre-service science teachers’ (N = 133) self-rated TPACK on action and behavioral orientations between pre- and post-test, with moderate to large effects. Moreover, the analyses of lesson plans reveal a high quality of technology exploitation in the planned lessons, indicating distinctive TPACK in action after attending the modules. This theory-based approach is supported by empirical data, and highly regarded by participants, making it a successful model for course redesign at other universities.
A Decentralized Federated Learning Based on Node Selection and Knowledge Distillation

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. Existing federated learning mainly adopts a central server-based network topology, however, the training process of which is susceptible to the central node. To address this problem, this article proposed a decentralized federated learning method based on node selection and knowledge distillation. Specifically, the central node in this method is variable, and it is selected by the indicator interaction between nodes. Meanwhile, the knowledge distillation mechanism is added to make the student model as close as possible to the teacher’s network and ensure the model’s accuracy. The experiments were conducted on the public MNIST, CIFAR-10, and FEMNIST datasets for both the Independent Identically Distribution (IID) setting and the non-IID setting. Numerical results show that the proposed method can achieve an improved accuracy as compared to the centralized federated learning method, and the computing time is reduced greatly with less accuracy loss as compared to the blockchain decentralized federated learning. Therefore, the proposed method guarantees the model effect while meeting the individual model requirements of each node and reducing the running time.
Deepfake Detection Algorithm Based on Dual-Branch Data Augmentation and Modified Attention Mechanism

Mainstream deepfake detection algorithms generally fail to fully extract forgery traces and have low accuracy when detecting forged images with natural corruptions or human damage. On this basis, a new algorithm based on an adversarial dual-branch data augmentation framework and a modified attention mechanism is proposed in this paper to improve the robustness of detection models. First, this paper combines the traditional random sampling augmentation method with the adversarial sample idea to enhance and expand the forged images in data preprocessing. Then, we obtain training samples with diversity and hardness uniformity. Meanwhile, a new attention mechanism is modified and added to the ResNet50 model. The improved model serves as the backbone, effectively increasing the weight of forged traces in the multi-scale feature maps. The Jensen–Shannon divergence loss and cosine annealing algorithms are introduced into the training process to improve the model’s accuracy and convergence speed. The proposed algorithm is validated on standard and corrupted datasets. The experiments show that the algorithm proposed in this paper significantly improves effectiveness and robustness, with accuracies of 4.16%, 7.37%, and 3.87% better than the baseline model on DeepFakes, FaceSwap, and FaceShifer, respectively. Most importantly, its detection performance on the corrupted datasets DeepFakes-C, FaceSwap-C, and FaceShifer-C is much higher than that of mainstream methods.
Candidate-aware Graph Contrastive Learning for Recommendation

Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system method, where it captures high-order collaborative signals between nodes by performing convolution operations on the user-item interaction graph to predict user preferences for different items. However, in real scenarios, the user-item interaction graph is extremely sparse, which means numerous users only interact with a small number of items, resulting in the inability of GNN in learning high-quality node embeddings. To alleviate this problem, the Graph Contrastive Learning (GCL)-based recommender system method is proposed. GCL improves embedding quality by maximizing the similarity of the positive pair and minimizing the similarity of the negative pair. However, most GCL-based methods use heuristic data augmentation methods, i.e., random node/edge drop and attribute masking, to construct contrastive pairs, resulting in the loss of important information. To solve the problems in GCL-based methods, we propose a novel method, Candidate-aware Graph Contrastive Learning for Recommendation, called CGCL. In CGCL, we explore the relationship between the user and the candidate item in the embedding at different layers and use similar semantic embeddings to construct contrastive pairs. By our proposed CGCL, we construct structural neighbor contrastive learning objects, candidate contrastive learning objects, and candidate structural neighbor contrastive learning objects to obtain high-quality node embeddings. To validate the proposed model, we conducted extensive experiments on three publicly available datasets. Compared with various state-of-the-art DNN-, GNN- and GCL-based methods, our proposed CGCL achieved significant improvements in all indicators.
Towards adaptive and transparent tourism recommendations: A survey

Abstract Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non‐curated nature of the crowd‐originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state‐of‐art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream‐based scenario, and identifies future research opportunities.
Eyeballing internal auditors’ and the firms’ intention to adopt Metaverse technologies: case study in Indonesia

Purpose This paper aims to give broad insights into what components can significantly influence the adoption of Metaverse from the perspective of internal auditors and their firms in Indonesia. Design/methodology/approach This research used primary data from questionnaires. Relying on the empirical view of 202 internal auditors in Indonesia, this research’s framework is executed using structural equation model. Company’s intention in adopting the Metaverse technology intervenes in the influence of perception of external control and usefulness on the internal auditor’s intention to adopt that technology. Findings The perception of external control and perceived usefulness of Metaverse significantly influenced the adoption intention of Metaverse by internal auditors and their firms in Indonesia. This paper is helpful for practitioners who would like to know what factors are needed to make the internal auditors in Indonesia able to adopt Metaverse. Research limitations/implications Results might be varied from country to country as each country has different technology development. Therefore, upcoming research can compare similar studies in another country. This paper can contribute to further empirical development for the theory of acceptance model of the third version. Many researchers use the theory to study advanced technology adoption intention. Practical implications The paper is also essential for future research and could enhance companies' knowledge about staying updated in the market with the advanced technology that keeps developing. Originality/value This paper contributes to an integrated view of the intention of internal auditors and firms in Indonesia to adopt Metaverse. To the best of the authors’ knowledge, this topic is relatively new in Indonesia.
SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval

Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different sparse retrievers running in a unified, common environment. This hinders practitioners from fairly comparing different sparse models and obtaining realistic evaluation results. Another missing piece is, that a majority of prior work evaluates sparse retrieval models on in-domain retrieval, i.e. on a single dataset: MS MARCO. However, a key requirement in practical retrieval systems requires models that can generalize well to unseen out-of-domain, i.e. zero-shot retrieval tasks. In this work, we provide SPRINT, a unified python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. The toolkit currently includes five built-in models: uniCOIL, DeepImpact, SPARTA, TILDEv2 and SPLADEv2. Users can also easily add customized models by defining their term weighting method. Using our toolkit, we establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. Our results demonstrate that SPLADEv2 achieves the best average score of 0.470 [email protected] on BEIR amongst all neural sparse retrievers. In this work, we further uncover the reasons behind its performance gain. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document which is often crucial for its performance gains, i.e. a limitation among its other sparse counterparts. We provide our SPRINT toolkit, models, and data used in our experiments publicly here: https://github.com/thakur-nandan/sprint.
The design of distributed filtering based on lattice rule

For the problem of multiple micro-target tracking and positioning, the high-dynamic and communication coupling are two cases in actual motion. The dynamic evolution and measurement among them are depicted by a nonlinear system. This paper proposes a new distributed lattice Kalman filter (DLKF) to cope with the two cases simultaneously. The prediction is based on the Cranley-Patterson shift and Korobov lattice rule to generate the low-difference sample points. The update stage is based on the weighted average consistency to fusion its innovation and the information of its neighbor. The stability of this distributed filter is proved by constructing the upper and lower bound of the Lyapunov functional. The finite steps consensus iteration strategies for the update fusion are also investigated. To evaluate the efficiency of the DLKF, it is compared with unscented Kalman filtering and its distributed form. The simulation results show that DLKF uses significantly fewer sampling points than other quasi-Monte Carlo (QMC) filters while maintaining the estimation accuracy. The computational complexity is significantly lower, which is the key factor for application to multiple micro-targets.
Profiling side-channel attacks based on CNN model fusion

The deep learning algorithm analyzes the power consumption, electromagnetic, and other side-channel information leaked by hardware devices, which has powerful attack capability. However, there is also the problem of high difficulty in hyperparameter tuning. To this end, we propose a side-channel attack scheme using convolutional neural network (CNN) model fusion. First, N sets of hyperparameters were randomly selected in the search space, and the CNN model was trained using the side-channel dataset to obtain N base models. Secondly, the high-dimensional features in the middle layer of the base model were merged to increase the amount of effective information and enhance the generalization ability of the neural network model, and a new fusion model was constructed by training. Finally, the fusion model was used to predict the output probability of DPA Contest v4, ASCAD, AES_HD, and AES_RD public side-channel datasets, analyze the guessing entropy of each key to recover the key, and evaluate the attack efficiency of the scheme. The experimental results show that the CNN model fusion method can effectively improve the side-channel attack capability and reduce the difficulty of hyperparameter tuning.
An efficient heterogeneous signcryption scheme for internet of things

In today’s world, Internet of Things (IoT) has penetrated every aspect of us. The security of IoT is challenging because of the open nature of wireless communication and limited resources of sensor nodes (SNs). In this paper, we propose an efficient heterogeneous signcryption scheme from IoT to an Internet server, in which the server is in a public key infrastructure (PKI) environment, while the IoT is in a certificateless cryptosystem (CLC) environment. The proposed scheme is briefly referred to as CP-EHSC. CLC can settle the drawbacks of key escrow in identity-based cryptography (IBC) and public key certificate management in PKI. At the same time, PKI is suitable for the server since it has been widely adopted on the Internet. Our proposed scheme can provide security proof in the random oracle model (ROM). Performance analysis shows that the proposed scheme not only achieves great optimization in calculation cost and communication consumption but also makes great progress in total energy consumption. In addition, we illustrate the practical applicability of our proposed scheme by providing a realistic use case scenario.
Physical ZKP protocols for Nurimisaki and Kurodoko

Proving to someone else the knowledge of a secret without revealing any of its information is an interesting feature in cryptography. The best solution to solve this problem is a Zero-Knowledge Proof (ZKP) protocol. Nurimisaki is a Nikoli puzzle. The goal of this game is to draw a kind of abstract painting ("Nuri") that represents a sea with some capes ("Misaki") of an island (represented by white cells). For this, the player has to fulfill cells of a grid in black (representing the sea) in order to draw some capes while respecting some simple rules. One of the specificity of the rules of this game is that every "Misaki" cell can only have one white neighbor. Kurodoko is also a Nikoli puzzle where some cells need to be blackened in order to ensure that each numbered cell is surrounded by the same number of white cells in the four directions (north, east, south, and west). Both of these puzzles, Nurimisaki and Kurodoko, share a common connectivity constraint. Using a deck of cards, we propose a physical ZKP protocol for each of the two puzzles above. Our protocols prove that a player knows a solution of (1) a Nurimisaki grid, or (2) a Kurodoko grid, without revealing any information about the solution.
A Mobile Social App for Better Life of Poor People Based on Perceived Similarity and Trust Using Supply Chain Management

As an attempt to collaborate the digital world with the physical one, the authors maintain track of the journey of dairy products, clothes, watches, bags, etc.; any type of material from producer to consumer. The authors can use the strength of internet technology and its extensive network, by which individuals can assist one other with only a click from their phone, to satisfy the growing need for advances in donation facilities and services. The suggested system for stuff donation aims to give needy people an online platform for giving clothes, books, or any unneeded things. Users can sign up for this system by providing the required information. Once registered, users can add a post either for giving donations or for requesting a donation. Using their app, the authors can supply all the items outside of the country. Today, supply chain management is one of the most important usable ways for NGO to earn competitive benefits.