Reviewerly/exHarmony huggingface dataset:
Implicit Key-Stretching Security of Encryption Schemes

When keys are small or parts thereof leak, key-recovery attacks on symmetric-key primitives still pose a plausible threat. Key stretching is one well-known means to throttle potential adversaries, where stretching a key by s bit means that a key-recovery attack has to perform $$\min \{2^{k-1}, 2^{k-\lambda +s-1}\}$$ operations on average for $$\lambda $$ bit information leakage. However, typical explicit key stretching requires also the defender to pay for the stretch operations. The usual assumption is that a surrounding encryption scheme does not increase the key-recovery security of its internal primitives. This work challenges this assumption by considering the structure of popular encryption schemes. In particular, message lengths may be non-negligible in settings such as full-disk encryption or archiving, where the adversary can obtain only long messages. Surprisingly, the question of whether a surrounding encryption scheme has only a negligible impact on key recovery seems to have remained uninvestigated. Therefore, it is interesting to study if “implicit” key stretching may come for free as an inherent property of popular schemes. We define an encryption scheme as “fully key-stretching-secure” if an adversary that sees plaintext-ciphertext pairs of at least m blocks each must perform at least m primitive calls for testing a key candidate. Using a similar definition of affine modes as Chakraborti et al. in JMC 2018, we systematically explore common encryption schemes with respect to their key-stretching security. In total, we consider five classes of (1) online, (2) SIV-like, (3) parallelizable two-pass (EME-like), (4) sequential two-pass (CMC-like), and (5) three-pass (HCTR-like) encryption schemes. By modeling them as affine modes, we can identify all considered encryption schemes key-stretching-insecure, i.e., one needs only O(1) primitive calls for testing a key candidate. However, for the insecure schemes from types (4) and (5), namely for EME-, CMC-, and HCTR-like schemes, we propose minor tweaks to ensure full key-stretching security.
Recombining Vision Transformer Architecture for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is a challenging task in the image analysis field which requires comprehensive discriminative feature extraction and representation. To get around this problem, previous works focus on designing complex modules, the so-called necks and heads, over simple backbones, while bringing a huge computational burden. In this paper, we bring a new insight: Vision Transformer itself is an all-in-one FGVC framework that consists of basic Backbone for feature extraction, Neck for further feature enhancement and Head for selecting discriminative feature. We delve into the feature extraction and representation pattern of ViT for FGVC and empirically show that simply recombining the original ViT structure to leverage multi-level semantic representation without introducing any other parameters is able to achieve higher performance. Under such insight, we proposed RecViT, a simple recombination and modification of original ViT, which can capture multi-level semantic features and facilitate fine-grained recognition. In RecViT, the deep layers of the original ViT are served as Head, a few middle layers as Neck and shallow layers as Backbone. In addition, we adopt an optional Feature Processing Module to enhance discriminative feature representation at each semantic level and align them for final recognition. With the above simple modifications, RecViT obtains significant improvement in accuracy in FGVC benchmarks: CUB-200-2011, Stanford Cars and Stanford Dogs.
Meter Location System Base on Jetson NX

Analog meter is still widely used due to their mechanical stability and electromagnetic impedance. Relying on humans to read mechanical meters in some industrial scenarios is time-consuming or dangerous, it is difficult for current meter reading robots to operate quickly and maintain high accuracy in edge computing devices. Computer vision-based meter reading systems can solve such dilemmas. We designed an SSD network-based meter image acquisition system that can run in real time in an NVIDIA Jetson NX development board. Moreover, the model can quickly classify meter types and locate meter coordinates in the presence of light changes, complex backgrounds, and camera angle deflection. Tested on NVIDIA Jetson NX using TensorRT acceleration, the inference speed and accuracy reached 9.238 FPS and 53.95 mAP, respectively.
Hybrid Recommendation Algorithm Combining Project Importance and Prediction Score

Aiming at the problem of long tail and poor recommendation quality, a hybrid recommendation algorithm combining item importance and predictive score is proposed. Firstly, the Personal Rank algorithm is used to calculate the item importance. Taking the project score as the edge weight, optimizing its graph structure; Increase the wandering probability of minority projects and high score projects to further capture the potential relationship between users and projects; Generate a set of candidate recommendations by ranking items by importance. Secondly, Slope One algorithm is used to predict the score of the items in the candidate recommendation set. In order to improve the accuracy of prediction, the deviation calculation method was optimized considering the similarity of user scores. The final prediction score was corrected using the credibility weight. Finally, the influence of project importance and predicted score value on recommendation is integrated, and the recommendation is completed by ranking the comprehensive recommendation value. The proposed algorithm was verified and analyzed on the real data set. The experimental results show that the proposed hybrid algorithm has improved the recommendation indexes compared with other literatures, which proves that the proposed algorithm effectively alleviates the long tail problem and improves the recommendation performance.
Unscented Kalman Filter and Its Implementation in Digital Image Correlation

With the development of industrial technology and the improvement of automation level of industrial production, automatic recognition and prediction has gradually become another key issue in automated production. The main purpose of this paper is to elucidate the many advantages of the unscented Kalman filter (UKF) method and to demonstrate its full applicability to many identification and prediction processes in order to explore more possibilities for prediction and state estimation. This study focuses on the new application of unscented Kalman filter in recognition and prediction, explains and discusses its principles and functions, and explains its advantages and functions with an example of digital image correlation method. In the fourth part, an example of the application of unscented Kalman filter in digital image correlation is given, and its specific principle and advantages are explained. The function of unscented Kalman filter in digital image correlation is demonstrated to speed up the operation and avoid errors. Finally, it is concluded that the UKF can be well suited for various prediction and state estimation tasks.
Design and Optimization of Deep Immune Network Algorithm for Smart Grid Fault Data Analysis

In order to solve the problem that the effect of traditional neural network in fault data diagnosis of power system is not obvious, inspired by the application of deep learning image recognition, this paper combines immune algorithm and deep learning, and proposes a new smart grid fault data analysis method based on deep immune network. Deep immune network is an immune-improved deep learning network, which extracts features from data through the multi-layer structure of convolutional neural network, and inputs feature samples into the immune-optimized radial basis network to classify faults. By analogy with the immune process of biological immune system, a fault diagnosis immune model suitable for power grid fault data analysis is proposed and established. By establishing a one-to-one mapping relationship with biological immune system, the model structure was improved and optimized accordingly. Finally, the experiment proves that the fault diagnosis immune model has excellent learning ability and the fault recognition efficiency is improved compared with the traditional fault diagnosis method.
Advanced Feature Extraction Workflow for Few Shot Object Recognition

Abstract Object recognition is well known to have a high importance in various fields. Example applications are anomaly detection and object sorting. Common methods for object recognition in images divide into neural and non-neural approaches: Neural-based concepts, e.g. using deep learning techniques, require a lot of training data and involve a resource intensive learning process. Additionally, when working with a small number of images, the development effort increases. Common non-neural feature detection approaches, such as SIFT, SURF or AKAZE, do not require these steps for preparation. They are computationally less expensive and often more efficient than the neural-based concepts. On the downside, these algorithms usually require grey-scale images as an input. Thus, information about the color of the reference image cannot be considered as a determinant for recognition. Our objective is to achieve an object recognition approach by eliminating the “color blindness” of key point extraction methods by using a combination of SIFT, color histograms and contour detection algorithms. This approach is evaluated in context of object recognition on a conveyor belt. In this scenario, objects can only be recorded while passing the camera’s field of vision. The approach is divided into three stages: In the first step, Otsu’s method is applied among other computer vision algorithms to perform automatic edge detection for object localization. Within the subsequent second stage, SIFT extracts key points out of the previously identified region of interest. In the last step, color histograms of the specified region are created to distinguish between objects that feature a high similarity in the extracted key points. Only one image is sufficient to serve as a template. We are able to show that developing and applying a concept with a combination of SIFT, histograms and edge detection algorithms successfully compensates the color blindness of the SIFT algorithm. Promising results in the conducted proof of concept are achieved without the need for implementing complex and time consuming methods.
A novel approach for obstacle avoidance maneuverer controls to a 49ton autonomous drive heavy-duty truck in GCV

This paper presents a novel obstacle avoidance maneuver for a 49-ton heavy duty truck in a narrow campus environment, which is surrounded by a narrow two-way street. In the beginning of the paper, the autonomous drive system implemented in the GCV (Geely Commercial Vehicle) project are introduced. The proposed planning algorithm combine a state machine and lateral sample method to achieve a balance between real-time functionality and controllability. To simplify the path planning process, a global path from routing module is used as a reference path for vehicle to follow if there’s no obstacle exists. Planning algorithm calculates a feasible local path based on the reference path and avoids obstacles on the reference path. Our test shows that our method is easier to calibration, quicker and safer comparing to an A star search method, which is widely used in autonomous robot domain.
Comparative Assessment of Radio Noise Signal Losses in Radio Frequency and Radiophoton Microwave Transmission Line

This paper presents the results of experimental studies of variants of construction of intermediate frequency paths of microwave radiometric systems, based on the traditional scheme of construction with the use of copper coaxial lines of transmission of intermediate frequency signals of microwave radiometric receivers and perspective radio photonic fiber-optic line of transmission of microwave signals with internal modulation. The aim of the work is to evaluate the possibility of realization of the radio-photon tract for transmitting signals of intermediate frequency as a part of promising microwave radiometric systems for remote sensing of the atmosphere. The objectives of the work are to analyze the performance of the radio photon transmitters and receivers of Microwave Photonic Systems, Inc. as a part of the intermediate frequency path of the multi-frequency microwave radiometric system of remote sensing of the atmosphere, compared to the coaxial radiofrequency transmission line.
Related-Key Differential Cryptanalysis of GMiMC Used in Post-Quantum Signatures

With the urgency of the threat imposed by quantum computers, there is a strong interest in making the signature schemes quantum resistant. As the promising candidates to ensure post-quantum security, symmetric-key primitives, in particular the recent MPC/FHE/ZK-friendly hash functions or block ciphers, are providing another choice to build efficient and secure signature schemes that do not rely on any assumed hard problems. However, considering the intended use cases, many of these novel ciphers for advanced cryptographic protocols do not claim the related-key security. In this paper, we initiate the study of the ignored related-key security of GMiMC proposed by Albrecht et al. at ESORICS 2019, some versions of which are optimized and designed to be used in post-quantum secure signatures. By investigating the potential threats of related-key attacks for GMiMC intended to be deployed as the underlying building block in post-quantum signature schemes, we then construct two kinds of iterative related-key differentials, from which not only do we explore its security margin against related-key attacks, but also collision attacks on its key space can be performed. For example, for GMiMC instance that beats the smallest signature size obtainable using LowMC, we can find its key collision using only about $$2^{10}$$ key pairs. It worths noting that our current key collision attack is only applicable when the adversarial power is sufficiently strong (e.g., in the so-called multi-user setting), and it does not threaten the one-wayness of GMiMC. Furthermore, from the experiments of our related-key differentials, it can be observed that the differential clustering effect of GMiMC differs in both aspects: the choice of the finite field $$\mathbb {F}$$ being $$\mathbb {F}_p$$ or $$\mathbb {F}_2^n$$ , and the size of the finite field $$\mathbb {F}$$ .
Semantic similarity detection based on knowledge augmentation for short text

The task of semantic similarity detection is crucial to natural language processing. Deep learning-based word embedding with text-matching models has improved the accuracy of spotting semantic similarities in recent years. However, traditional word embedding techniques cannot deal with polysemy sufficiently. In order to improve the quality of the semantic similarity detection for short text, this paper proposed a method based on knowledge augmentation. Here HowNet, as the tool for knowledge augmentation, is introduced integrating pre-trained BERT with fine-tuning and attention mechanisms. Experiments on two Chinese datasets show that the proposed method outperforms a variety of typical text similarity detection methods.
A Proposal-Improved Few-Shot Embedding Model with Contrastive Learning

Few-shot learning is increasingly popular in image classification. The key is to learn the significant features from source classes to match the support and query pairs. In this paper, we redesign the contrastive learning scheme in a few-shot manner with selected proposal boxes generated by Navigator network. The main work of this paper includes: (i) We analyze the limitation of hard sample generating proposed by current few-shot learning methods with contrastive learning and find additional noise introduced in contrastive loss construction. (ii) We propose a novel embedding model with contrastive learning named infoPB which improves hard samples with proposal boxes to improve Noise Contrastive Estimation. (iii) We demonstrate infoPB is effective in few-shot image classification and benefited from Navigator network through the ablation study. (iv) The performance of our method is evaluated thoroughly on typical few-shot image classification tasks. It verifies a new state-of-the-art performance compared with outstanding competitors with their best results on miniImageNet in 5-way, 5-shot, and tieredImageNet in 5-way, 1-shot/5-way, 5-shot.
Impossible Differential Cryptanalysis on Reduced-Round PRINCEcore

The area of lightweight cryptography, i.e., ciphers with particularly low implementation costs, has drawn considerable attention over the last few years. PRINCE is a lightweight block cipher proposed by J. Borghoff et al. at ASIACRYPT 2012. In 2017, Ding et al. constructed a 4-round truncated impossible differential distinguisher. They treat S-boxes as ideal ones that any nonzero input difference could produce any nonzero output difference. Obviously, this is not true for the S-boxes in the real block ciphers. In this paper, after investigating the properties of both the S-box and the linear layer of PRINCE, we construct two types of 5-round impossible differential distinguishers. Then we exhibit two types of key-recovery attacks on 9 out of 12 rounds of PRINCEcore. The corresponding data complexities are $$2^{53.3}$$ and $$2^{56.1}$$ chosen plaintexts, respectively. Our results are the best impossible differential cryptanalysis on PRINCE as far as we know to date, and our attacks meet the security claims of the designers.
The Data Exchange Protocol over Multi-chain Blockchain Using Zero-Knowledge Proof

The implementation of blockchain technology is becoming popular among cyber-physical systems. However, the current solutions suffer from scalability and privacy issues. In this position paper, we leverage zero-knowledge proof and multichain technology to propose an efficient system for data transferring across different components. Each component may maintain a private chain storing its data, and the system acts as a relayer between different chains, in which multiple private chains are efficient for appending new data. Only encrypted data is transferred from a source chain to a destination chain. The relayer handles data transferring in two phases: send and receive, and the relayer keeps a Merkle tree of all sent data. In fact, it only transfers the data if the receiver can submit a valid zero-knowledge proof that proves the ownership of the data. The zero-knowledge proof discloses nothing but the statement is true; therefore it protects anonymity for the data owners. This system is secure and satisfies relevant properties such as ledger indistinguishability, transaction non-malleability, and matchability.
Training Node Screening in Decentralized Trusted Federated Learning

The emergence of federated learning has to some extent solved the current problems of privacy protection of terminal data and the processing technology of massive data. However, its centralized architecture still has problems such as limited access and high establishment cost, so the trend of decentralization is inevitable. Although decentralized federated learning architecture circumvents the drawbacks of centralized structure, it also loses the convenience of third-party supervision. Therefore, to address the problem of missing supervision mechanisms for worker node training behavior in decentralized federated learning architecture, this paper proposes a backdoor-based supervision mechanism for arithmetic node training behavior. The mechanism can be applied to general classification tasks. Experiments revealed that this mechanism can accurately assess the training behavior of worker nodes while maintaining the accuracy of the original task. In addition, this paper proposes a rotation scheme for the watermarked datasets involved and gives a corresponding replacement prediction method, which further ensures that the training behavior of arithmetic nodes can be quantified completely by predicting and replacing the watermarked datasets, aiding the arithmetic party training operation of the behavior monitoring mechanism.
muxGNN: Multiplex Graph Neural Network for Heterogeneous Graphs

Graph neural networks (GNNs) have become effective learning techniques for many downstream network mining tasks including node and graph classification, link prediction, and network reconstruction. However, most GNN methods have been developed for homogeneous networks with only a single type of node and edge. In this work we present muxGNN, a multiplex graph neural network for heterogeneous graphs. To model heterogeneity, we represent graphs as multiplex networks consisting of a set of relation layer graphs and a coupling graph that links node instantiations across multiple relations. We parameterize relation-specific representations of nodes and design a novel coupling attention mechanism that models the importance of multi-relational contexts for different types of nodes and edges in heterogeneous graphs. We further develop two complementary coupling structures: node invariant coupling suitable for node- and graph-level tasks, and node equivariant coupling suitable for link-level tasks. Extensive experiments conducted on six real-world datasets for link prediction in both transductive and inductive contexts and graph classification demonstrate the superior performance of muxGNN over state-of-the-art heterogeneous GNNs. In addition, we show that muxGNN's coupling attention discovers interpretable connections between different relations in heterogeneous networks.
Pre-Training and Fine-Tuning

In this chapter, we focus on modern parameter-based methods: the pre-training and fine-tuning approach. We will also step into deep transfer learning starting from this chapter. In next chapters, the deep transfer learning methods focus on how to design better network architectures and loss functions based on the pre-trained network. Thus, this chapter can be seen as the foundations of the next chapters. Pre-training and fine-tuning belongs to the category of parameter/model-based transfer learning methods that perform knowledge transfer by sharing some important information of the model structures. The basic assumption is that there exists some common information between source and target structures that can be shared.
Federated Learning for Heterogeneous Mobile Edge Device: A Client Selection Game

In the federated learning (FL) paradigm, edge devices use local datasets to participate in machine learning model training, and servers are responsible for aggregating and maintaining public models. FL cannot only solve the bandwidth limitation problem of centralized training, but also protect data privacy. However, it is difficult for heterogeneous edge devices to obtain optimal learning performance due to limited computing and communication resources. Specifically, in each round of the global aggregation process by the FL, clients in a ‘strong group’ have a greater chance to contribute their own local training results, while those clients in a ‘weak group’ have a low opportunity to participate, resulting in a negative impact on the final training result. In this paper, we consider a federated learning multi-client selection (FL-MCS) problem, which is an NP-hard problem. To find the optimal solution, we model the FL global aggregation process for clients participation as a potential game. In this game, each client will selfishly decide whether to participate in the FL global aggregation process based on its efforts and rewards. By the potential game, we prove that the competition among clients eventually reaches a stationary state, i.e. the Nash equilibrium point. We also design a distributed heuristic FL multi-client selection algorithm to achieve the maximum reward for the client in a finite number of iterations. Extensive numerical experiments prove the effectiveness of the algorithm.