Reviewerly/exHarmony huggingface dataset:
Fundamental Limits of Private User Authentication

Most of the security services in the connected world of cyber-physical systems necessitate authenticating a large number of nodes privately. In this paper, the private authentication problem is considered which consists of a certificate authority, a verifier (or some verifiers), many legitimate users (provers), and an arbitrary number of attackers. Each legitimate user wants to be authenticated (using his personal key) by the verifier(s), while simultaneously staying completely anonymous (even to the verifier). On the other hand, an attacker must fail to be authenticated. We analyze this problem from an information-theoretical perspective and propose a general interactive information-theoretic model for the problem. As a metric to measure the reliability, we consider the normalized total key rate whose maximization has a trade-off with establishing privacy. The problem is considered in two different scenarios: single-server scenario (only one verifier is considered, which all the provers are connected to) and multi-server scenario ($N$ verifiers are assumed, where each verifier is connected to a subset of users). For both scenarios, two regimes are considered: finite size regime (i.e., the variables are elements of a finite field) and asymptotic regime (i.e., the variables are considered to have large enough length). We propose achievable schemes that satisfy the completeness, soundness, and privacy properties in both single-server and multi-server scenarios in all cases. In the finite size regime, the main idea is to generate the authentication keys according to a secret sharing scheme. We show that the proposed scheme in the special case of multi-server authentication in the finite size regime is optimal. In the asymptotic regime, we use a random binning based scheme that relies on the joint typicality to generate the authentication keys.
Semantic-Preserving Adversarial Code Comprehension

Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks. However, they have to compromise on the trade-off between the two aspects and none of them consider improving both sides in an effective and practical way. To fill this gap, we propose Semantic-Preserving Adversarial Code Embeddings (SPACE) to find the worst-case semantic-preserving attacks while forcing the model to predict the correct labels under these worst cases. Experiments and analysis demonstrate that SPACE can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code.
Shannon theory for quantum systems and beyond: information compression for fermions

We address the task of compression of fermionic quantum information. Due to the parity superselection rule, differently from the case of encoding of quantum information in qubit states, part of the information carried by fermionic systems is encoded in their delocalised correlations. As a consequence, reliability of a compression protocol must be assessed in a way that necessarily accounts also for the preservation of correlations. This implies that input/output fidelity is not a satisfactory figure of merit for fermionic compression schemes. We then discuss various aspects regarding the assessment of reliability of an encoding scheme, and show that entanglement fidelity in the fermionic case is capable of evaluating the preservation of correlations, thus revealing itself strictly stronger than input/output fidelity, unlike the qubit case. We then introduce a fermionic version of the source coding theorem showing that, as in the quantum case, the von Neumann entropy is the minimal rate for which a fermionic compression scheme exists, that is reliable according to the entanglement fidelity criterion.
Blockchain Smart Contract-Enabled Secure Energy Trading for Electric Vehicles

In this paper, a blockchain-enabled energy trading method is proposed to deal with the inefficiency and security issues in energy trading for electric vehicles in smart grids. It includes the design of a smart contract and the excitation mechanism of energy sharing. The credit points of each vehicle as a node are considered in the design of the smart contract, which is used to supervise the process of energy trading. A strategy to estimate the credit points of each node and describe the excitation mechanism is illustrated. The connection between the credit points and the probability that a node would be accepted for energy trading has been established. To control the energy trading access, a double auction method is used to choose the part of the nodes participating in energy trading. Only selected nodes with winning bids can supply or request energy from the blockchain-based platform. Then it reaches the conclusion that the higher the credit points they have, the more trading initiative they would have during the energy transaction and transmission. The smart contract design and the excitation mechanism proposed in this paper would reward the vehicles that perform well and punish the beguiling vehicles for regulating the trading process.
Simultaneous two-step phase-shifting lateral shearing interferometry for aspherical surface based on an orthogonal shear displacer

Abstract A novel simultaneous two-step phase-shifting lateral shearing interferometry for aspherical surface based on an orthogonal shear displacer (OSD) is proposed, it is an effective technological measure of aspherical surface measurement, to effectively solve the non-uniformity of light intensity and limited transmission order caused by the beam displacer device. The OSD system is formed by the adoption of two-crystal polarization parallel beam displacers (PBDs), which makes it achieve the orthogonal shearing in the x- and y-directions simultaneously. A quarter-wave plate (QWP) is used to generate the desired phase-shifting, and four beam lateral shearing interference waves are simply generated in OSD orthogonal directions without any bulky and complicated optical components. The phase maps of the aspherical surface can be instantly obtained using the spatial phase-shifting technique with a polarization pixelated mask (or called micro-polarization array: MPA) integrated into CCD. In this study, the proposed method was theoretically described and simulation results were analyzed. The simultaneous two-step phase-shifting lateral shearing interference fringes can be extracted in real-time with the MPA. The experiment results compared with the ZYGO interferometer were performed, and proved that the measurement error is not more than 2%. This interferometry has made it possible to improve the stability and feasibility of aspherical surface testing experiments.
Barriers Faced by Teachers in Acclimatizing to Online Teaching

Centered on the transformation of the norm of education from face-to-face teaching to online platforms, this article explores the intrinsic and extrinsic factors that contribute to the challenges teachers faced in delivering online lessons during the pandemic. The study adapts thematic deductive qualitative analysis approaches by using an open-ended questionnaire where the data were collected via Internet survey. The results portrayed intrinsic factors to exhibit the biggest barriers in comparison to extrinsic factors. Hence, teachers&rsquo; pedagogical needs should be considered, with priority given to teachers&rsquo; technological access by the education authority to improve the quality of the online teaching. This study provides insights for education authorities in addressing the challenges of online teaching, especially in terms of technological access.
Black-box Dataset Ownership Verification via Backdoor Watermarking

Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$, BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at \url{https://github.com/THUYimingLi/DVBW}.
Scalable feature subset selection for big data using parallel hybrid evolutionary algorithm based wrapper under apache spark environment

Owing to the emergence of large datasets, applying current sequential wrapper-based feature subset selection (FSS) algorithms increases the complexity. This limitation motivated us to propose a wrapper for feature subset selection (FSS) based on parallel and distributed hybrid evolutionary algorithms (EAs) under the Apache Spark environment. The hybrid EAs are based on the BDE and Binary Threshold Accepting (BTA), a point-based EA, which is invoked to enhance the search capability and avoid premature convergence of the PB-DE. Thus, we designed the hybrid variants (i) parallel binary differential evolution and threshold accepting (PB-DETA), where DE and TA work in tandem in every iteration, and (ii) parallel binary threshold accepting and differential evolution (PB-TADE), where TA and DE work in tandem in every iteration under the Apache Spark environment. Both PB-DETA and PB-TADE are compared with the baseline, viz., the parallel version of the binary differential evolution (PB-DE). All three proposed approaches use logistic regression (LR) to compute the fitness function, namely, the area under ROC curve (AUC). The effectiveness of the proposed algorithms is tested over the five large datasets of varying feature space dimension, taken from cyber security and biology domains. It is noteworthy that the PB-TADE turned out to be statistically significant compared to PB-DE and PB-DETA. We reported the speedup analysis, average AUC obtained by the most repeated feature subset, feature subset with high AUC and least cardinality.
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators

Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and focus on modeling and correcting its unknown errors in an interpretable manner. This is achieved by a conditional generative model that transforms a given mechanistic operator with unknown errors, arising from a latent space of self-organizing clusters of potential sources of error generation. Once learned, the generative model can be used in place of a fixed forward operator in any traditional optimization-based reconstruction process where, together with the inverse solution, the error in prior mechanistic forward operator can be minimized and the potential source of error uncovered. We apply the presented method to the reconstruction of heart electrical potential from body surface potential. In controlled simulation experiments and in-vivo real data experiments, we demonstrate that the presented method allowed reduction of errors in the physics-based forward operator and thereby delivered inverse reconstruction of heart-surface potential with increased accuracy.
Face and Gender Detection Based on BP Neural Network Algorithm

Face recognition technology has a wide range of applications in real life, and many applications can be developed on the basis of face recognition, such as gender recognition, age recognition, face comparison, and beauty image decoration. In this paper, based on the computer vision library OpenCV in the python language, the face is recognized, and the mature BP neural network algorithm is used to learn the photos in the database, and finally the gender detection of the face is successfully realized.
Research on Multimodal Summarization Based on Sequence to Sequence Model

With the rapid development of information technology in the 21st century, the data on the Internet is growing exponentially, which makes it difficult for users to receive useful information from an endless stream of information sources. Therefore, as a kind of data compression task, automatic summarization technology has gradually become a research hotspot in academia and industry. This paper mainly introduces the multimodal summarization method from sequence to sequence model, then introduces the current multimodal summarization evaluation methods and finally gives some conclusions and thoughts.
Image Defogging Algorithm Based on Deblurgan Network

The reduced visibility in foggy days impairs the quality of captured images and videos to varying degrees, leading to limited applications of these images in the field of computer vision. To solve this problem, direct recovery of fog-free images based on an improved Deblurgan network is proposed. We add the expanded convolution (Dialted Conv) module in the generator to expand the perceptual field to extract richer semantic information, and add the spatial attention mechanism module at specified locations to facilitate the elimination of residual fog; the discriminator uses the traditional PatchGAN for chunk determination to improve the discriminative accuracy; the loss function adds BCE loss to improve the discriminative accuracy of the discriminator and the pixel-level detail retention of the generator. In the synthetic fogged dataset RESIDE, the subjective visual comparison with dark channel, DehazeNet, AOD-Net, and defogging effect shows that the defogging effect of this network model and the detail information and color contrast of defogged images are improved; meanwhile, the objective evaluation indexes Peak Signalto Nise Rtioo a, PSNR and Structure Smilaritiy (SSIM) were also improved respectively.
Exploration and Practice of School-enterprise Collaborative Education

In recent years, school-enterprise collaborative education has become the subject of research in higher vocational colleges. The article explores the significance of school-enterprise collaborative education and the construction of school-enterprise collaborative education mechanism. At the same time, through years of practice, it has proposed the establishment of a "gradual" talent training class system, a "one hospital one case" curriculum system, a "production and teaching sharing" joint team, a school-enterprise complementary practice base, and an off-campus craftsman spirit practice base, practical teaching 6S professional standardization and breeding system and other enterprise college talent training system.
Length Approach on English Majors’ Writing Anxiety and Writing Performance under Pigai.org

With the help of Pigai.org, based on "Length Approach", this present study is to explore the changes of writing anxiety and writing performance between experimental class and control class before and after the experiment, so as to confirm the influence of "Length Approach" on the first-year English majors in writing teaching.After nearly three months' writing training experiment, the following conclusions are drawn: (1) the "Length Approach" can effectively relieve the writing anxiety of first-year English majors, and it is mainly realized by reducing the cognitive anxiety in writing anxiety; (2) the "Length Approach" can also improve the writing performance of first-year English majors to a certain extent.(3) Pigai.org is an appropriate choice and effective platform for English majors to practice English writing and receive feedback.This study provides some enlightenment for the application of "Length Approach" and Pigai.org in English major's writing teaching.
Comprehensive Review of Learnable and Adaptive Recommendation Systems

Due to the dynamic and heterogeneous nature of the Web, it is increasingly difficult for users to choose amongst large amounts of data. For this reason, the modelling of users and access to personalised information becomes essential. Recommendation systems stand out as systems that aim to provide the most appropriate and efficient services by offering personalised recommendations to users. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences are presented.
A Face Recognition Algorithm Based on Improved Resnet

Regarding the problem that the increasing number of layers of CNN (convolutional neural network) leads to the decline of accuracy, an improved loss function algorithm based on the Resnet-50 model is proposed. The Softmax loss function lacks constraints on the distance within the same class and between different classes. Replacing the Softmax layer with improved Arcface loss enables the neural network to learn more distinguishing features. Experiments on LFW and AgeDB data sets show that the algorithm can not only learn deep-face characteristics but also efficiently improve the accuracy of face recognition compared with ordinary CNN. In the meantime, the improved Resnet also obtains a higher discerning rate under the conditions of occlusions, illumination, expression, Age.
Classifying textual data: shallow, deep and ensemble methods

This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming calls to the customer care of an Italian phone company. We will show that deep learning outperforms many classical (shallow) strategies but the combination of shallow and deep learning methods in a unique ensemble classifier may improve the robustness and the accuracy of "single" classification methods.
Research on Multi-Channel Information Tracking Method

This project provides a trajectory tracking method, device and computer storage medium, including associating the bottom database features of each target object with at least one trajectory feature to construct the identity association information of each target object; According to at least one track feature of each target object, each detection feature obtained from different target videos is identified, and the target object corresponding to each detection feature is determined; According to the identity association information of each target object and the target object of each detection feature, all detection features of the object to be queried are obtained by using any one of the track code and the base code of the object to be queried, and then the trajectory tracking results of the object to be queried are generated. According to this, the project can support the track tracking of multi-channel monitoring video, And it can effectively improve the accuracy and integrity of trajectory tracking results.
Digitalization of Judicial Electronic Archives using the Rational Unified Process Method with An Application Development Framework Using CodeIgniter (Case Study of Pengadilan Tinggi Agama Palembang)

The archiving process at the Pengadilan Tinggi Agama Palembang was still manual, where archive officers must prepare storage areas, sort and classify archives, put files into folders, and create archival data. The increasing number of archives and piling up in storage space makes searching for archives difficult and takes a long time in the process of searching for the required archives. The purpose of this research is to improve the existing system by designing a web-based archiving digitization application. In this research, the system development stage uses the Rational Unified Process (RUP) method with an application development framework using CodeIgniter. Based on the test results using the blackbox testing method, the system can display information on the decision archives graphically, the archive search process quickly, downloads the decision archive documents digitally, digital documents that have been separated by archive type and archive year, so it can be concluded that the archival information system with the application of information technology can facilitate work in processing, storing, reporting to obtain information and can then be used for decision making.
Analysis on the Investment Environment and Investment Problems of Manufacturing Industry in Anhui Province

Anhui has gradually changed from a traditional agricultural province to a new manufacturing province in recent years, and has made proud achievements. This paper attempts to take the investment situation of Anhui manufacturing industry as the starting point to analyze the hard environment and soft environment of Anhui manufacturing investment. The results show that Anhui has the conditions to promote manufacturing investment in natural resources, human resources, infrastructure and scientific and technological innovation. However, after further understanding the development status of Anhui manufacturing investment, some problems have also been found, such as the reduction of investment scale, slow recovery speed, unreasonable source of funds, large investment gap between regions, and low efficiency of resource utilization. In order to solve the above problems, this paper puts forward countermeasures from the aspects of investment intensity, investment confidence, financing channels, regional gap, investment efficiency, etc., in order to speed up the construction of a strong manufacturing province in Anhui.
Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022

Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV. However, ASV systems and CMs are generally developed and optimized independently without considering their inter-relationship. In this paper, we propose a new spoofing-aware ASV back-end module that efficiently computes a combined ASV score based on speaker similarity and CM score. In addition to the learnable fusion function of the two scores, the proposed back-end module has two types of attention components, scaled-dot and feed-forward self-attention, so that intra-relationship information of multiple enrollment utterances can also be learned at the same time. Moreover, a new effective trials-sampling strategy is designed for simulating new spoofing-aware verification scenarios introduced in the Spoof-Aware Speaker Verification (SASV) challenge 2022.