Reviewerly/exHarmony huggingface dataset:
Homomorphic encryption: a mathematical survey

If the first thing that comes to mind when you hear the word “encryption” is the Enigma machine, you might think that encryption is complicated and mathematically uninteresting. In fact, many modern encryption systems are quite simple from a mathematical point of view, especially encryption systems that are homomorphic. In these systems, the starting point is a homomorphism that respects some binary operation(s), such as $+$ or $\times$. Depicting this homomorphism with a rectangular commutative diagram, the objects on the top level of the diagram are called ciphertexts, and the objects on the bottom level are called messages or plaintexts. The downward arrows in the diagram are the homomorphism, which we call decryption. The rightward arrows are the operation(s). Decryption commutes with the operations. To the commutative diagram we add one extra ingredient, computational complexity. Specifically, we need for it to be easy (in the sense of polynomial-time) for anyone to compute the rightward arrows in the diagram, but hard to learn how to compute the downward (decryption) arrows except with some special information that we will call a “secret key.” In short, homomorphic encryption is simply a homomorphism that has been “hardened” in the complexity-theoretic sense. Homomorphic encryption allows anyone to compute on encrypted data, without needing (or being able) to decrypt, has many exciting applications. Fully homomorphic encryption (FHE) systems, which allow a rich (functionally complete) set of operations, were finally discovered in 2009. But all of the FHE systems that we have discovered so far follow the same blueprint, and we still wonder whether there are other ways to build FHE. This survey presents homomorphic encryption from a mathematical point of view, illustrating with several examples how to start from a homomorphism and harden it to make it suitable for cryptography, pointing out pitfalls and attacks to avoid, laying out the current blueprint for FHE, and (I hope) serving as an inspiration and useful guide in the development of new approaches to FHE.
Small Sample Image Segmentation By Coupling Convolutions and Transformers

Compared with natural image segmentation, small sample image segmentation tasks, such as medical image segmentation and defect detection, have been less studied. Recent studies made efforts on bringing together Convolutional Neural Networks (CNNs) and Transformers in a serial or interleaved architecture in order to incorporate long-range dependencies into the features extracted using CNNs. In this study, we argue that these architectures limit the capability of the combination of CNNs and Transformers. To this end, we propose a dual-stream small sample image segmentation network, namely, the Interactive Coupling of Convolutions and Transformers Based UNet (ICCT-UNet) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , motivated by the success achieved using the UNet in the scenario of small sample image segmentation. Within this network, a CNN stream is paralleled with a Transformer stream while maintaining feature exchange inside each block through the proposed Window-Based Multi-head Cross-Attention (W-MHCA) mechanism. To derive an overall segmentation, the features learned by both the streams are further fused using a Residual Fusion Module (RFM). Experimental results show that the ICCT-UNet outperforms, or at least performs comparably to, its counterparts on eight sets of medical and defective images. These promising results should be attributed to the effective combination of the local and global features fulfilled by the proposed interactive coupling method.
Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used in the inquiry. This research covers the benefits and drawbacks of several Machine Learning approaches, illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions. The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections. The study evaluates and compares accuracy, precision and recalls of several machine learning models in detecting phishing website URL’s detection.
Cascade Semantic Prompt Alignment Network for Image Captioning

Image captioning (IC) takes an image as input and generates open-form descriptions in the domain of natural language. IC requires the detection of objects, modeling of relations between them, an assessment of the semantics of the scene and representing the extracted knowledge in a language space. Previous detector-based models suffer from limited semantic perception capability due to predefined object detection classes and semantic inconsistency between visual region features and numeric labels of the detector. Inspired by the fact that text prompts in pre-trained multi-modal models contain specific linguistic knowledge rather than discrete labels, and excel at an open-form semantic understanding of visual inputs and their representation in the domain of natural language. We aim to distill and leverage the transferable language knowledge from the pre-trained RegionCLIP model to remedy the detector for generating rich image captioning. In this paper, we propose a novel Cascade Semantic Prompt Alignment Network (CSA-Net) to produce an aligned fine-grained regional semantic-visual space where rich and consistent textual semantic details are automatically incorporated to region features. Specifically, we first align the object semantic prompt and region features to produce semantic grounded object features. Then, we employ these object features and relation semantic prompt to predict the relations between objects. Finally, these enhanced object and relation features are fed into the language decoder, generating rich descriptions. Extensive experiments conducted on the MSCOCO dataset show that our method achieves a new state-of-the-art performance with 145.2% (single model) and 147.0% (ensemble of 4 models) CIDEr scores on the ‘Karpathy’ split, 141.6% (c5) and 144.1% (c40) CIDEr scores on the official online test server. Significantly, CSA-Net outperforms in generating captions with higher quality and diversity, achieving a RefCLIP-S score of 83.2. Moreover, we expand the testbeds to other challenging captioning benchmarks, i.e., nocaps datasets, CSA-Net demonstrates superior zero-shot capability. Source codes released at https://github.com/CrossmodalGroup/CSA-Net.
Multi-view Vision Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?

Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, etc. Inspired by the recent prompt learning in natural language processing, this work proposes a novel Multi-view Vision Fusion Network (MvNet) for few-shot 3D point cloud classification. MvNet investigates the possibility of leveraging the off-the-shelf 2D pre-trained models to achieve the few-shot classification, which can alleviate the over-dependence issue of the existing baseline models towards the large-scale annotated 3D point cloud data. Specifically, MvNet first encodes a 3D point cloud into multi-view image features for a number of different views. Then, a novel multi-view prompt fusion module is developed to fuse information from different views effectively to bridge the gap between 3D point cloud data and 2D pre-trained models. A set of 2D image prompts can then be derived to better describe the suitable prior knowledge for a large-scale pre-trained image model for few-shot 3D point cloud classification. Extensive experiments on ModelNet, ScanObjectNN, and ShapeNet datasets demonstrate that MvNet achieves new state-of-the-art performance for 3D few-shot point cloud image classification. The source code of this work is available at https://github.com/invictus717/MetaTransformer.
Quality of Experience Aware Computation Offloading in MEC-Enabled Blockchain-Based IoT Networks

Blockchain technology enables the Internet of Things (IoT) to build trust among various stakeholders. Offloading the computation-intensive IoT data processing and blockchain mining computations as tasks to Mobile Edge Computing (MEC) and nearby devices in a Device-to-Device (D2D) network is a promising solution for resource constraint IoT devices. In this paper, to decide on the offloading plan, we consider enhancing the users’ Quality of Experience (QoE) parameters reflecting the token-based cost of delay and energy consumption. For this purpose, we formulate the problem as a Mixed Integer Non-Linear Bilevel Programming (MINLBP), which is NP-hard. Therefore, we propose a sub-optimal solution with the help of two-sided matching theory games in two phases: first, we decide to offload IoT data processing tasks using a many-to-one and then make a decision for mining tasks elaborating a many-to-many matching game. We also present algorithms based on the Deferred Acceptance (DA) mechanism to guarantee the stability of the matching results. The simulation results show that the proposed method can perform better in terms of delay and energy cost compared to the existing offloading approaches. Furthermore, it improves the utility of the users deciding to offload their tasks.
Analysis of Material Chemistry Curriculum Content and Professional Curriculum System Setting

PAA 吸水树脂；卫生用品芯体材料；AMPS；NVP
Hyperbolic Graph Learning for Social Recommendation

Social recommendation provides an auxiliary social network structure to enhance recommendation performances. By formulating user-user social network and user-item interaction graph, modern social recommendation architecture is built on learning user and item embeddings into Euclidean space with graph convolution operations. However, the Euclidean space suffers structure distortion when representing the nature power-law distribution of graphs, leading to sub-optimal results for graph based social recommendation. Recently, some studies have explored the alternative of graph embedding learning into hyperbolic space, which can preserve the hierarchy of real-world graphs. However, directly applying current hyperbolic graph embedding models for social recommendation is non-trivial as two challenges: network heterogeneity and social diffusion noise. First, due to the semantic gap existing between social networks and user-item interactions, how to tackle the heterogeneity issue of social recommendation under hyperbolic formulation? Second, explicit modeling of social diffusion easily introduces noise for user preference learning, especially for those active users with amounts of interactions. To tackle the above challenges, in this paper, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hyperbolic Graph Learning based Social Recommendation (HGSR) </i> model. Firstly, we exploit social structure with hyperbolic social embedding pre-training, which could preserve the hierarchical properties of social networks. Secondly, we construct the heterogeneous graph based on user-item interactions and social networks, then treat the pre-trained social embeddings as an additional feature input for user preference learning. Such that, we combine explicit heterogeneous graph learning and implicit feature enhancement for the hyperbolic social recommendation, which can well tackle heterogeneity and social noise issues. We conduct empirical studies on four datasets, and extensive experiments demonstrate the effectiveness of our proposed model compared to state-of-the-art baselines.
Cryptographic Techniques for Data Privacy in Digital Forensics

The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant data to facilitate investigations. However, the cost of this readiness often directly impacts the privacy of innocent third parties and suspects if the collected information is irrelevant. Approaches that have been suggested for privacy-preserving digital forensics focus on the use of policy, non-cryptography-based, and cryptography-based solutions. Cryptographic techniques have been proposed to address issues of data privacy during data analysis. As the utilization of some of these cryptographic techniques continues to increase, it is important to evaluate their applicability and challenges in relation to digital forensics processes. This study provides digital forensics investigators and researchers with a roadmap to understanding the data privacy challenges in digital forensics and examines the various privacy techniques that can be utilized to tackle these challenges. Specifically, we review the cryptographic techniques applied for privacy protection in digital forensics and categorize them within the context of whether they support trusted third parties, multiple investigators, and multi-keyword searches. We highlight some of the drawbacks of utilizing cryptography-based methods in privacy-preserving digital forensics and suggest potential solutions to the identified shortcomings. In addition, we propose a conceptual privacy-preserving digital forensics (PPDF) model that is based on the use of cryptographic techniques and analyze the model within the context of the above-mentioned factors. An evaluation of the model is provided through a consideration of identified factors that may affect an investigation. Lastly, we provide an analysis of how existing principles for preserving privacy in digital forensics are addressed in our PPDF model. Our evaluation shows that the model aligns with many of the existing privacy principles recommended for privacy protection in digital forensics.
A Single-pixel Imaging based Stereo Deflectometry for 3D Reconstruction of Free-form Transparent Objects with Parasitic Reflections

Deflectometry with structured light for high-precision measurement of optical specular surfaces has attracted considerable attention. However, conventional deflectometry schemes often encounter challenges when they deal with lens-like transparent objects due to parasitic reflections arising from the rear side of objects. To address this issue, we propose a novel stereo deflectometry method that replaces traditional phase-shift patterns with Fourier-based patterns inspired by single-pixel imaging. This method enables the acquisition of Fourier spectra of light response coefficients for each camera pixel. Specifically, through inverse discrete Fourier transform and the Fourier slice theorem, we obtain the light response coefficients of the light source to the camera pixel. Subsequently, a geometry optical analysis method is proposed to separate the light response coefficients components associated with the front surface given the lack of epipolar constraint between the camera and LCD screen. An iterative algorithm integrated with Particle swarm optimization is proposed to accelerate the reconstruction process and obtain the surface normal field of the upper surface of the lens-like transparent object. The wavefront reconstruction algorithm for gradient integration is used on the normal vector field to enables high-precision reconstruction of the object's surface shape. Experiments on actual optical components demonstrate that the proposed method can accurately reconstruct the front surface of transparent lens by eliminating the interference from parasitic reflection, and an error of less than 200 nm is obtained.
The Application of Physical Education in Middle School Physics Teaching

Interdisciplinary practice; Physics teaching; Curriculum resources
Jamming Detection in Low-BER Mobile Indoor Scenarios via Deep Learning

The current state of the art on jamming detection relies on link-layer metrics. A few examples are the bit-error rate (BER), the packet delivery ratio, the throughput, and the signal-to-noise ratio (SNR). As a result, these techniques can only detect jamming ex-post, i.e., once the attack has already taken down the communication link. These solutions are unfit for mobile devices, e.g., drones, which might lose the connection to the remote controller, being unable to predict the attack. Our solution is rooted in the idea that a drone unknowingly flying toward a jammed area is experiencing an increasing effect of the jamming, e.g., in terms of BER and SNR. Therefore, drones might use the abovementioned phenomenon to detect jamming before the increase of the BER and the decrease of the SNR completely disrupt the communication link. Such an approach would allow drones and their pilots to make informed decisions and maintain complete control of navigation, enhancing security and safety. This paper proposes Bloodhound+, a solution for jamming detection on mobile devices in low-BER regimes. Our approach analyzes raw physical-layer information (I-Q samples) acquired from the wireless channel. We assemble this information into grayscale images and use sparse autoencoders to detect image anomalies caused by jamming attacks. To test our solution against a broad set of configurations, we acquired a large dataset of indoor measurements using multiple hardware, jamming strategies, and communication parameters. Our results indicate that Bloodhound+ can detect indoor jamming up to 20 meters from the jamming source at the minimum available relative jamming power, with a minimum accuracy of 99.7%. Our solution is also robust to various sampling rates adopted by the jammer and to the type of signal used for jamming.
A Dynamic and Parallel Two-Stage Lossless Data Compression Method for Smart Grid

With the rapid development of smart grid (SG) technologies, massive system data has been generated for SG for grid operation status monitoring and fault warning, this massive integrated data has brought great challenges for data transmission and storage. Thus, the breakthrough of data compression technology has become more and more important for data processing, this proposal, a lossless data compression method is proposed. The proposed method improves the Lempel-Ziv-Welch (LZW) algorithm. Specifically, the proposed method employs a parallel search approach to search the index of the dictionary globally by dividing the dictionary into several small dictionaries of different sizes and bit widths. In addition, the dynamic variable-length coding method is used as the output code which can offer flexible bit widths instead of fixed values, and the optimal dictionary partition combination and size parameters are selected to construct dictionaries. At last, the improved algorithm is cascaded with Huffman algorithm to form the proposed data compression algorithm. The data compression efficiency has been successfully verified by comparing with conventional Huffman and LZW data compression algorithms through data simulation, and it is observed to offer a better compression ratio than those methods with only LZW algorithm or Huffman algorithm, and save storage space effectively. More than this, the proposed method has realized lossless compression for power system data and guaranteed the integrity of the data, which will have better applicability for dealing with any similar information data.
A Memory-efficient Hybrid Parallel Framework for Deep Neural Network Training

With the increasing volumes of data samples and deep neural network (DNN) models, efficiently scaling the training of DNN models has become a significant challenge for server clusters with AI accelerators in terms of memory and computing efficiency. Existing parallelism schemes can be broadly classified into three categories: data parallelism (splitting data samples), model parallelism (splitting model parameters), and pipeline model parallelism (splitting model layers). Hybrid approaches split data and models, offering a comprehensive solution for parallel training. However, these methods encounter limitations in efficiently scaling larger models across more computing nodes, as they incur substantial memory constraints that affect training efficiency and overall throughput. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIPPIE</i> , a hybrid parallel training framework designed to enhance memory efficiency and scalability of large DNN training. First, to evaluate the optimization effect more reasonably, we propose an index of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memory Efficiency</i> (ME) to quantify the tradeoff between throughput and memory overhead. Second, driven by the informed ME optimization objective, we automatically partition the pipeline to balance the throughput and memory. Third, we optimize the model training process via a novel hybrid parallel scheduler that improves the throughput and scalability by informed pipeline scheduling and communication scheduling with gradient-hidden optimization. Experiments on various models show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIPPIE</i> achieves above 90% scaling efficiency on a 16-GPU platform. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIPPIE</i> increases throughput by up to 80%, while saving 57% of memory overhead and achieving 4.18× memory-efficiency improvement.
TempoLearn Network: Leveraging Spatio-temporal Learning for Traffic Accident Detection

Recognizing traffic accident events in driving videos is a challenging task and has emerged as a crucial area of interest in autonomous driving applications in recent years. To ensure safe driving alongside human drivers and anticipation of their behaviors, methods to efficiently and accurately detect traffic accidents from a first-person viewpoint must be developed. This paper proposes a novel model, named the TempoLearn network, which leverages spatio-temporal learning to detect traffic accidents. The proposed approach incorporates temporal convolutions, given their effectiveness in identifying abnormalities, and a dilation factor for achieving large receptive fields. The TempoLearn network has two key components: accident localization, for predicting when the accident occurs in a video, and accident classification based on the localization results. To evaluate the performance of the proposed network, we conduct experiments using a traffic accident dashcam video benchmark dataset, i.e., the detection of traffic anomaly (DoTA) dataset, which is currently the largest and most complex traffic accident dataset. The proposed network achieves excellent performance on the DoTA dataset, and the accident localization score, measured in terms of AUC, is 16.5% higher than that of the existing state-of-the-art model. Moreover, we demonstrate the effectiveness of the TempoLearn network through experiments conducted on another benchmark dataset, i.e., the car crash dataset (CCD).