Reviewerly/exHarmony huggingface dataset:
A Flexible NTT-Based Multiplier for Post-Quantum Cryptography

In this work an NTT-based (Number Theoretic Transform) multiplier for code-based Post-Quantum Cryptography (PQC) is presented, supporting Quasi Cyclic Low/Moderate-Density Parity-Check (QC LDPC/MDPC) codes.The cyclic matrix product, which is the fundamental operation required in this application, is treated as a polynomial product and adapted to the specific case of QC-MDPC codes proposed for Round 3 and 4 in the National Institute of Standards and Technology (NIST) competition for PQC.The multiplier is a fundamental component in both encryption and decryption, and the proposed solution leads to a flexible NTT-based multiplier, which can efficiently handle all types of required products, where the vectors have a length ≈ 10 4 and can be moderately sparse.The proposed architecture is implemented using both Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) technologies and, when compared with the best published results, it features a 10 times reduction of the encryption times with the area increased by 3 times.The proposed multiplier, incorporated in the encryption and decryption stages of a code-based PQC cryptosystem, leads to an improvement over the best published results between 3 to 10 times in terms of LC product (LUT times latency).
DivTheft: An Ensemble Model Stealing Attack by Divide-and-Conquer

Recently, model stealing attacks are widely studied but most of them are focused on stealing a single non-discrete model, e.g., neural networks. For ensemble models, these attacks are either non-executable or suffer from intolerant performance degradation due to the complex model structure (multiple sub-models) and the discreteness possessed by the sub-model (e.g., decision trees). To overcome the bottleneck, this paper proposes a divide-and-conquer strategy called DivTheft to formulate the model stealing attack to common ensemble models by combining active learning (AL). Specifically, based on the boosting learning concept, we divide a hard ensemble model stealing task into multiple simpler ones about single sub-model stealing. Then, we adopt AL to conquer the data-free sub-model stealing task. During the process, the current AL algorithm easily causes the stolen model to be biased because of ignoring the past useful memories. Thus, DivTheft involves a newly designed uncertainty sampling scheme to filter reusable samples from the previously used ones. Experiments show that compared with the prior work, DivTheft can save almost 50% queries while ensuring a competitive agreement rate to the victim model.
A Multiscale Unsupervised Orientation Estimation Method With Transformers for Remote Sensing Image Matching

Estimating the orientations of remote sensing images is a very important step in remote sensing image matching and is now gradually receiving widespread attention. However, due to the inability to explicitly define the standard orientations of feature points, the current methods still produce feature point orientation estimation errors, resulting in reduced matching accuracy. In this letter, we propose a multiscale unsupervised orientation estimation method with transformers, in which we use a multiscale feature extraction module to aggregate rich semantic features and a transformer-based attention mechanism module to address robust feature extraction in weakly textured regions while predicting the orientations of feature points through a carefully designed loss function. We set up image matching experiments on remote sensing images in different scenes for comparison purposes, and the experimental results show that our proposed method achieves substantially improved orientation estimation accuracy and improved image matching performance.
A game-theoretic approach for federated learning: A trade-off among privacy, accuracy and energy

Benefiting from the development of Federated Learning (FL) and distributed communication systems, large-scale intelligent applications become possible. Distributed devices not only provide adequate training data, but also cause privacy leakage and energy consumption. How to optimize the energy consumption in distributed communication systems, while ensuring the privacy of users and model accuracy, has become an urgent challenge. In this paper, we define the FL as a 3-layer architecture including users, agents and server. In order to find a balance among model training accuracy, privacy-preserving effect, and energy consumption, we design the training process of FL as game models. We use an extensive game tree to analyze the key elements that influence the players' decisions in the single game, and then find the incentive mechanism that meet the social norms through the repeated game. The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality, and the proposed incentive mechanism can also promote users to submit high-quality data in FL. Following the multiple rounds of play, the incentive mechanism can help all players find the optimal strategies for energy, privacy, and accuracy of FL in distributed communication systems.
Cyber attacks in healthcare: why they matter and how to defend against them

Professor Gérard Memmi, head of the computer sciences and networks department at Télécom Paris, highlights the potentially severe consequences of cyber attacks in healthcare, with recommendations of how to prevent data theft and reduce the damages of a data breach.
Incoherent Gaussian equivalence of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi></mml:math> -mode Gaussian states

Necessary and sufficient conditions for arbitrary multimode (pure or mixed) Gaussian states to be equivalent under incoherent Gaussian operations are derived. We show that two Gaussian states are incoherent equivalence if and only if they are related by incoherent unitaries. This builds the counterpart of the celebrated result that two pure entangled states are equivalent under local operations and classical communication (LOCC) if and only if they are related by local unitaries. Furthermore, incoherent equivalence of Gaussian states is equivalent to frozen coherence [Phys. Rev. Lett. 114, 210401 (2015)]. Basing this as foundation, we find all measures of coherence are frozen for an initial Gaussian state under strongly incoherent Gaussian operations if and only if the relative entropy measure of coherence is frozen for the state. This gives an entropy-based dynamical condition in which the coherence of an open quantum system is totally unaffected by noise.
An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

For deep learning-based object detection, we present a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios. First of all, multi-scale information is integrated into different feature dimensions to improve the distinction and robustness of features. The design of the detectors increases the variety of detection boxes to accommodate a wider range of detected objects. Secondly, the pooling method is upgraded to obtain more detailed information. At last, we add the Angle cost and assign new weights to different loss functions to accelerate the convergence and improve the accuracy of network detection. In our network, we explore four variants MSS-YOLOv5s, MSS-YOLOv5m, MSS-YOLOv5x, and MSS-YOLOv5l. Experimental results of MSS-Yolov5s show that our technique improves mAP on the PASCAL VOC2007 and PASCAL 2012 datasets by 2.4% and 2.9%, respectively. Meanwhile, it maintains a fast inference speed. At the same time, the other three models have different degrees of performance improvement in terms of balancing speed and precision in challenging detection regions.
Envisioning and Exploring Future Transnational Higher Education and Management Models

This chapter aims to envision future transnational university educational and management models. In the background section, a general review of international universities in China would be outlined followed by an overview of a specific example, international Xi'an Jiaotong-Liverpool University. In the literature review section, an evaluation of relevant literature will demonstrate how the examination of key concepts like knowledge, truth, education, and enlightenment could contribute to deepened understanding of future transnational higher education. In the third section on discussion, a detailed analysis of HeXie education model, HeXie mindset model, and HeXie management model would be addressed followed by an application of these models in XJTLU's management systems. Fourthly, challenges and opportunities would be outlined in terms of student-centered and research-led education, the teaching of research, quality education, future education in the uncertain, ambiguous, complex, and changeable world, and future education, before conclusion is drawn.
Open-Domain Response Generation in Low-Resource Settings using Self-Supervised Pre-Training of Warm-Started Transformers

Learning response generation models constitute the main component of building open-domain dialogue systems. However, training open-domain response generation models requires large amounts of labeled data and pre-trained language generation models that are often nonexistent for low-resource languages. In this article, we propose a framework for training open-domain response generation models in low-resource settings. We consider Dialectal Arabic (DA) as a working example. The framework starts by warm-starting a transformer-based encoder-decoder with pre-trained language model parameters. Next, the resultant encoder-decoder model is adapted to DA by employing self-supervised pre-training on large-scale unlabeled data in the desired dialect. Finally, the model is fine-tuned on a very small labeled dataset for open-domain response generation. The results show significant performance improvements on three spoken Arabic dialects after adopting the framework’s three stages, highlighted by higher BLEU and lower Perplexity scores compared with multiple baseline models. Specifically, our models are capable of generating fluent responses in multiple dialects with an average human-evaluated fluency score above 4. Our data is made publicly available.
PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments

Anonymous single sign-on systems allow users to use a single credential to access multiple services protected by verifiers without revealing their personal information, which is especially important due to privacy regulations such as GDPR. In this paper, we introduce a new strong privacy-preserving single sign-on scheme, dubbed PriSign, based on our proposed attribute-based credential with traceability (ABCT), attribute-based credential with blindness (ABCB), and threshold inner-product functional encryption (TIPFE). Compared with the existing state-of-the-art solutions, PriSign presents three improvements: (1) users can obtain different types of tickets according to the attribute disclosure policies enforced by the ticket issuer to support fine-grained access control; (2) users can hide access tokens and designate a verifier for tokens according to a verifier’s policy jointly issued by multiple policymakers, meaning that non-designated verifiers cannot obtain any information about the tokens; (3) we innovatively use a threshold approach to issue policy keys online in order for verifiers to achieve proxy re-verification services in an unstable cloud environment. We implement PriSign and compare the performance with other ASSO systems in the personal laptop, and the results prove its practicability.
Tire deformation measurement technology based on digital image correlation method

The contact between the tire and the road surface causes tread deformation, and using traditional methods to measure tire tread deformation is difficult and time-consuming. Therefore, based on the principle of the digital image correlation method, this paper develops a binocular vision measurement system and realizes the measurement of tire tread deformation through the test bench for tire ground pressure distribution measurement. The related programs of the measurement system, including camera calibration, image matching, and strain fitting, are proposed. The deformation measurement accuracy of the test bench is validated by the sprayed speckle rubber sheet test, and the relative error of the theoretical value and measurement value is within 2%. The tire tread deformation test under static loading conditions was carried out using the test bench, and the tread strain distribution in the contact area was obtained. The analysis of the test results shows that the strain distribution in the contact patch is axisymmetric under static loading conditions. The longitudinal (tire rolling direction) deformation of the tread at the leading edge and the trailing edge area of the contact patch has the largest strain, while the deformation of the shoulder area on both sides of the contact patch is relatively small. For lateral (tire axial direction) deformation, the leading edge and the trailing edge areas of the contact patch are less deformed, and the strain distribution is approximately axisymmetric. The shoulder area of the contact surface has a larger deformation. The tire’s outer shoulder area has a larger deformation than the inside shoulder area, which may be due to the asymmetric tread structure of the tire. For the vertical deformation of the tread, the central area of the tread has the smallest strain, while the surrounding area has a much larger vertical strain.
Lightweight similarity checking for English literatures in mobile edge computing

With the advent of information age, mobile devices have become one of the major convenient equipment that aids people's daily office activities such as academic research, one of whose major tasks is to check the repetition rate or similarity among different English literatures. Traditional literature similarity checking solutions in cloud paradigm often call for intensive computational cost and long waiting time. To tackle this issue, in this paper, we modify the traditional literature similarity checking solution in cloud paradigm to make it suitable for the light-weight mobile edge environment. Furthermore, we put forward a lightweight similarity checking approach SCMEC for English literatures in mobile edge computing environment. To validate the advantages of SCMEC , we have designed massive experiments on a dataset. The reported experimental results show that SCMEC can deliver a satisfactory similarity checking result of literatures compared to other existing approaches.
Qualia Role-Based Quantity Relation Extraction for Solving Algebra Story Problems

A qualia role-based entity-dependency graph (EDG) is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese.Traditional neural solvers use end-to-end models to translate problem texts into math expressions, which lack quantity relation acquisition in sophisticated scenarios.To address the problem, the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects.Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems.Experimental result shows that the proposed method achieved an average accuracy of 82.2% on quantity relation extraction compared to 74.5% of baseline method.Another prompt learning result shows a 5% increase obtained in problem solving by injecting the extracted quantity relations into the baseline neural solvers.
Privacy-preserving reachability query over graphs with result verifiability

Reachability query is one of the most popular and fundamental operations, which answers whether two given nodes are reachable in a graph. With the continuous increase of graph scale, data owners would like to upload graphs to the cloud server to enjoy the nice computing and storage service. How to realize privacy-preserving reachability query over graphs has become an important problem. Although there have already been some existed schemes for this problem, all of them only support the honest-but-curious model. If the cloud server returns the invalid query result, existing schemes will not work any longer. In this paper, we initiate the first study on realizing the verifiable privacy-preserving reachability query over graphs, and propose three step-by-step schemes based on the 2-hop labeling index and the MAC technology. The first proposed scheme can generate the verification tag for each pair of nodes in the index. But it leads to high communication and computation overhead. In order to address this problem, we propose the second scheme by constructing a special matrix to store the verification tag for each two nodes and their common nodes in the graph. It only returns one verification tag to the user, which requires less communication and computation overhead. Finally, we optimize the second scheme and propose the third scheme to further reduce the computation complexity. We make use of the symmetric-key primitives to secure the index, ensuring the necessary privacy with the ability of querying. The security analysis and extensive experiments show that our proposed schemes are secure and efficient.
Characterizing architecture related posts and their usefulness in Stack Overflow

Stack Overflow (SO) has won the intention from software engineers (e.g., architects) to learn, practice, and utilize development knowledge, such as Architectural Knowledge (AK). But little is known about AK communicated in SO, which is a type of high-level but important knowledge in development. This study aims to investigate the AK in SO posts in terms of their categories and characteristics as well as their usefulness from the point of view of SO users. We conducted an exploratory study by qualitatively analyzing a statistically representative sample of 968 Architecture Related Posts (ARPs) from SO. The main findings are: (1) architecture related questions can be classified into 9 core categories, in which "architecture configuration" is the most common category, followed by the "architecture decision" category, and (2) architecture related questions that provide clear descriptions together with architectural diagrams increase their likelihood of getting more than one answer, while poorly structured architecture questions tend to only get one answer. Our findings suggest that future research can focus on enabling automated approaches and tools that could facilitate the search and (re)use of AK in SO. SO users can refer to our proposed guidelines to compose architecture related questions with the likelihood of getting more responses in SO.
Anomaly detection framework for unmanned vending machines

Unmanned vending machines (UVMs) based on computer vision have shown huge commercial potential in unmanned retail. Anomaly detection is the problem of recognizing the falling-over and occlusion anomalies in UVMs. However, considering various anomaly features with different feature complexities, the existing methods are not sufficiently effective. In this paper, we propose an unmanned retail anomaly detection method based on deep convolutional neural networks (CNNs) called the complexity-classification anomaly detection (ClassAD) framework. The ClassAD method consists of two modules: a complexity rating module and a classification module. These two modules jointly input images of different feature complexity into different-capacity networks, which makes full use of the feature extraction ability of CNNs. ClassAD includes a decomposition feature enhancement approach to enhance inference speed. Furthermore, we introduce complexity-class loss and multi-instance loss in ClassAD for accuracy and learning stability. Experiments show that ClassAD yields promising results compared with state-of-the-art methods in both effectiveness and efficiency. In addition, we propose a UVM-Anomaly dataset for anomaly detection in UVMs.
High-precision projection moiré measurement method for the large depth of field with significant distortion

The projection moiré method is a high-precision optical topography measurement method. Because the reception system has the complex characteristic of two-stage imaging, the nonlinearity of phase-height mapping is severe, and imaging errors are substantial. Therefore, many existing solutions consider measurement scenes with the small depth of field and distortion. In this paper, we propose a two-stage imaging model, which can accurately describe the imaging process of the reception system under various distortion conditions. Furthermore, we derive a strongly nonlinear moiré model based on the imaging model, which fully represents the nonlinear effects caused by the depth of field and distortion. Several experiments are conducted for measurement scenarios with varying depths of field and distortion using various imaging models and nonlinear moiré models. The outcomes demonstrate that the proposed imaging and moiré model can reduce the corresponding imaging and model errors and improve the measurement accuracy and practicality in different environments.
EFFECTIVE SOLUTIONS FOR COMMON PROBLEMS OF ARTIFICIAL POTENTIAL FIELD BASED PATH PLANNING ALGORITHMS FOR MOBILE ROBOTS

Abstract&#x0D; Autonomous Path Planning (APP) capability is one of the main factors determining the autonomous level of a mobile robot. Although different methods are used for APP in the literature, the path planning approach based on Artificial Potential Fields (APF) has a very common usage area with its modeling ease and computational performance. APF-based APP, which is a grid-based path planning approach, is usually performed by combining a repulsive and attractive component that models many basic motions with a certain equation and calculating the gradient of this potential field to obtain the vector field. In this study, the basic models used for APF-based APP are examined, and how they are realized and how the resultant potential field is produced are mentioned. Although APF-based APP approaches have advantages, they also have problems such as local minimum, obstacles positioned too close, oscillation, and targets positioned too close to obstacles. Within the scope of the study, these problems were defined one by one and the approaches suggested in the literature for the solution of these problems were mentioned in detail. As a result, it has been seen that to obtain an effective APF-based APP solution, it is necessary to generate a convolutional vector field, limit the fundamental potential fields with exponential functions, use virtual potential fields and perform models with harmonic functions.
Power System Anomaly Detection Via Ensemble of Encoder and Decoder Networks

Hacking and false data injection from adversaries can threaten power grids' normal operations and cause significant economic loss. Anomaly detection in power grids aims to detect and discriminate anomalies caused by the cyberattack against the power system which is essential for keeping power grids working properly and efficiently. Different types of methods have been applied for anomaly detection such as statistical methods and machine learning-based methods. For machine learning-based methods, we usually need to model the distribution of normal data. In this work, we propose a novel anomaly detection method by modeling the data distribution of normal samples via multiple encoders and decoders. Specifically, the proposed method maps input samples into a latent space and then reconstructs output samples from latent vectors. The extra encoder finally maps reconstructed samples into the latent representations. During the training phase, parameters are optimized by minimizing reconstruction loss and encoding loss. Furthermore, training samples are re-weighted to focus more on missed correlations among features of normal data. Experiment results on network intrusion and power system datasets demonstrate the effectiveness of our proposed method, where our model consistently outperforms all baselines.