Reviewerly/exHarmony huggingface dataset:
Electronic Voting Scheme Based on Blockchain and SM2 Cryptographic Algorithm Zero-Knowledge Proof

Voting is an important basis for making decisions in social life. With the development of society, the efficiency of counting has been improved and the error rate of manual counting has been reduced by using electronic voting technology. However, the problems, such as voter information leakage, ballot reuse, and the authenticity of tallied results still exist. To solve these problems, a blockchain-based electronic voting scheme and a zero-knowledge proof protocol based on the SM2 cryptographic algorithm is proposed in this paper. Before voting, the zero-knowledge proof protocol is used to verify the voting qualification of the voter without disclosing the voter's identity. In the voting stage, a blind signature algorithm based on SM2 is used to verify the validity of the ballot and protect the privacy of the ballot information. In the counting process, the PBFT algorithm is used to ensure that the counting nodes count votes normally and produce accurate and credible counting results. Security analysis and performance analysis show that the proposed scheme meets the security requirements of the electronic voting protocol and has stronger security, privacy, and efficiency than the existing schemes.
A Universal Adversarial Attack on CNN-SAR Image Classification by Feature Dictionary Modeling

Synthetic aperture radar (SAR) image classification with deep learning methods has achieved high accuracy on a variety of scenes. Despite the excellent performance of new methods, the phenomenon that small perturbations in data might lead to a sharp change in the result, raises attention to these black architectures. Increasing number of adversarial attacks on convolutional neural network (CNN) have been proposed, while these methods construct their adversarial examples with the aid of corresponding classifiers. Such condition cannot be realized in actual confrontation. Therefore, we introduce a universal adversarial attack on CNN-SAR image classification. In essence, this method focuses on distinguishing target distribution by feature dictionary modeling, excluding prior knowledge of any classifier. Experiments on simulated data of plane models indicate that this proposed method works well at various typical CNNs.
Application Study of Mobile Technology Assisted Translation Teaching Mode in Applied Universities

Computer Assisted Language Learning (CALL) refers to the use of computer-aided means in language learning. The development and application of wireless communication technology promotes the expansion of CALL to mobile assisted language learning (MALL). With the rapid development of information technology and the wide popularization of mobile communication equipment, the advantages of MALL are gradually revealed. Assisted by modern information tools and apps, new translation teaching recourses can be constructed, and assisted by mobile learning apps and platforms, the translation recourses can be effectively applied. Therefore, combining the advantages of MALL with the learning characteristics of translation major in applied university, it is imperative to construct and practice a new mode for translation teaching. With the assistance of mobile technology, this paper tries to construct and apply the translation teaching mode based on MALL. Furthermore, it aims to innovate translation teaching in applied universities, and improve students’ English language application ability and translation practice level.
Post-Quantum Electronic Identity: Adapting OpenID Connect and OAuth 2.0 to the Post-Quantum Era

The quantum threat affects a multitude of network protocols, frameworks, and systems that use public-key cryptography. OpenID Connect (OIDC) and OAuth 2.0 are no exception, which means they must transition to Post-Quantum Cryptography (PQC). However, the long lifetime of access tokens necessitates this shift, if we consider the possibility of a record-now-decrypt-later attacker retrieving and subsequently impersonating users with these tokens. In this research, we conduct a thorough literature review to identify existing solutions to this problem, suggest modifications to OAuth 2.0 and OIDC and their underlying protocols to make them quantum-safe, then implement and evaluate the effects of PQC on a realistic OIDC study case. Our findings reveal that PQC-based OIDC has the same or better performance in low-latency settings, but the handshake of PQC-based TLS accounts for fifty percent of the overall duration in high-latency scenarios.
The Modeling of Time Series Based Fuzzy Cognitive Maps and Its Application in The Furnace Temperature Prediction

Aiming at the problem that the traditional time series prediction model could not make full use of the relevant information of time series and the interpretability is poor, a time series prediction model based on fuzzy cognitive graph modeling is proposed in this paper and the boiler furnace temperature prediction is taken as an example for experiment. Firstly, this paper divides the fuzzification of time series data of boiler furnace temperature into multiple subsets, and establishes a fuzzy cognitive map model on each subset. Finally, the prediction results of each sub model are fused to obtain the accurate prediction value, prediction interval and semantic interpretation corresponding to the prediction value of furnace temperature.
Image Semantic Segmentation Scheme based on XGBoost combination with Convolution Feature Extraction

Aiming at the problem that the existing image semantic segmentation schemes based on convolutional neural network require a large number of training data sets and high training cost, this paper presents a novel scheme for image semantic segmentation based on extreme gradient boosting (XGBoost), a scalable tree boosting system and the combination of convolution feature extraction (CFE). The scheme consists of three stages. First, convolution operation is performed on a small amount of RGB pixel information of the image processed by Gaussian filtering to acquire more multi -dimensional image feature information. Then, some preprocessing techniques of image feature information, including feature normalization and image label pixel number uniformity are performed to match the model. Finally, the preprocessed image feature information and corresponding labels are input into XGBoost to train and predict the data of the test set. Through simulation and practical tests, compared with other commonly used few data classification algorithms like k-Nearest-Neighbor (kNN) and Support-Vector-Machine (SVM), this scheme can achieve high precision pixel-level image semantic segmentation under the condition of lower training cost.
Enabling Hidden Frequency Keyword-Based Auditing on Distributed Architectures for a Smart Government

Public auditing checks the integrity of outsourced data via random sampling and verifying sample data blocks. In practice, however, users do not pay attention to the entire data set but focus on the integrity of only the part of the data containing keywords of interest. Therefore, the keyword-based auditing paradigm is proposed; it depends entirely on the subjective choice or access habits, which makes it possible for malicious storage servers to analyze the auditing frequency, or reduce redundant backups. For government data, auditing frequency privacy leakage or corruption of any file could be catastrophic. In this paper, we propose a hidden frequency keyword-based auditing scheme for a smart government named HFKA, which is compatible with distributed storage architecture. HFKA leverages a Bloom filter, which adjusts the false positive rate to consider auditing files corresponding to specified keywords and auditing random files obtained via fuzzy matching. To obtain privacy-preserving fuzzy matching, HFKA constructs an index table embedded with update times to retrieve a wide range of files to be audited. This approach is secure against the replay attack and supports the index table update through structure iteration instead of recalculation. HFKA provides storage robustness, privacy protection of hidden frequencies, and data security. Additionally, HFKA can reduce audit computation overhead by 32.6% compared to the probabilistic public auditing.
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

Artificial Intelligence, particularly through recent advancements in deep learning (DL), has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. For certain high-stake domains, in addition to desirable performance metrics, a high level of interpretability is often required in order for AI to be reliably utilized. Unfortunately, the black box nature of DL models prevents researchers from providing explicative descriptions for a DL model’s reasoning process and decisions. In this work, we propose a novel framework utilizing Adversarial Inverse Reinforcement Learning that can provide global explanations for decisions made by a Reinforcement Learning model and capture intuitive tendencies that the model follows by summarizing the model’s decision-making process.
Cross-Modal Data Augmentation for Tasks of Different Modalities

Data augmentation has become one of the keys to alleviating the over-fitting of models on training data and improving the generalization capabilities on testing data. Most existing data augmentation methods only focus on one modality, which is incapable when facing multiple data modalities. Some prior works try to interpolate with random coefficients in the latent space to generate new samples, which can generically work for any data modality. However, these works ignore the extra information conveyed by multimodality data. In fact, the extra information in one modality can provide semantic directions to generate more meaningful samples in another modality. This paper proposes Cross-modal Data Augmentation (CMDA), a simple yet effective data augmentation method to alleviate the over-fitting issue and improve the generalization performance. We evaluate CMDA on unsupervised and supervised tasks of different modalities, on which CMDA consistently and significantly outperforms baselines. For instance, CMDA improves the unsupervised anomaly detection baseline in vision modality from the AUROC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$76.46\%, 73.07\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$64.36\%$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$83.25\%, 76.22\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$70.57\%$</tex-math></inline-formula> on three different datasets, respectively. Besides, extensive experiments demonstrate that CMDA is applicable to various neural network architectures. Furthermore, prior methods that interpolate in the latent space need to work with downstream tasks to construct the latent space. In contrast, CMDA can work with or without downstream tasks, which makes the applicability of CMDA more extensive. Our source code is publicly available for non-commercial or research use at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Anfeather/CMDA</uri> .
Cyberology: Cyber–Physical–Social-Thinking Spaces-Based Discipline and Interdiscipline Hierarchy for Metaverse (General Cyberspace)

It is well known that the metaverse, also named general cyberspace (GC), is virtual-real fusion spaces, consisting of a virtual space, namely, cyberspace and virtual-real spaces, namely, cyber-enabled physical, social, and thinking (cyber-enabled) spaces. This article discusses the open issues of the metaverse in terms of science and technology and proposes a new discipline and interdiscipline hierarchy for the metaverse (GC), namely, cyberology first. Then, it explores various relevant standards of discipline classification and a discipline and interdiscipline hierarchy based on physical, social, and thinking spaces, and investigates the cyberspace and cyber-enabled spaces. On the basis of the above research, this article enriches the contents of cyberology in two terms: 1) the disciplines in cyberspace and 2) the interdisciplines in cyber-enabled spaces. Finally, this article gives predictions of cyberology on the future development of the metaverse from the aspects of cyber–physical space, cyber–social space and cyber-thinking space.
Identification of students’ faces in a video stream using the Viola-Jones method

This article describes and analyzes the development of a person identification system for automating attendance control. The system allows you to automatically register the time of arrival and departure of students in real time. Based on the joint application of the Viola-Jones method and the nearest neighbor method using histograms of centrally symmetric local binary patterns, an algorithm for real-time face recognition in video sequences is proposed. An algorithm is described and software for face recognition in the Python programming language is developed using the Keras open source library. An example of work is given and the results of program testing are presented. It is shown that the use of the proposed algorithm makes it possible to effectively recognize faces in video sequences.
Kolmogorov-Smirnov and ROC curve metrics for binary classification performance assessment are equivalent

Binary classification problems are very common in decision making. In decision support systems, classifiers which produce a propensity score for mapping the multidimensional space into a scalar give the human decider the power to control the operating point by just setting a threshold to separate the two classes. These classifiers, usually called rankers, have their discriminating power measured along the continuous range of the score by the Area Under the ROC curve (AUC_ROC) in most application fields. Only the domain of finances uses the single point metric maximum Kolmogorov-Smirnov (KS) distance. This paper presents the uncommon Area Under the KS curve (AUC_KS) for performance assessment and proves that AUC_ROC =0.5+AUC_KS. This equivalence allows for a simpler calculation of the AUC_ROC and brings about new perspectives for ROC averaging in ensembles of classifiers or n-fold cross-validation. The equivalence proof is geometrically inspired on rotating the KS curve counter-clockwise via a simple matrix transformation. Having the independent variable (normalized score) on the abscissa of the KS curve simplifies the calculation of the average AUC_KS and its errors in ensembles, thus yielding their ROC counterparts calculations by simple matrix multiplication. This research gives insights on probabilistic interpretations of classifiers assessment and integrates the existing body of knowledge of the information theoretical ROC approach with the proposed statistical approach based on the thoroughly known KS distribution.
Performance Evaluation of Automated Essay Scoring Online System for Competency Assessment of Community Academy

The essay scoring system has been done manually so far, which has become an obstacle in the learning evaluation process. The use of the Automated Essay Scoring (AES) system is an alternative to developing automated scoring applications. The purpose of this research paper is to evaluate the performance of the AES online system on the assessment of vocational education competencies in the IT Multimedia field at the Community Academy in Blitar and Pacitan, East Java. The method of this study is consisting of pre-processing text, similarity calculation with cosine similarity and correlation method to measure performance of AES with human rater. This study compares the performance of the assessment system using a human rater and the AES online system. The results show that the correlation value is 0.903569186 with excellent criteria and the MAE (Mean Absolute Error) value is 0.133547009 which indicates a fairly small error value.
A New Signature-Based Blockchain Paradigm: Foreseeable Impact on Healthcare Applications

Blockchain is a promising technology that has the potential to revolutionize the healthcare sector's conventional IT infrastructure from centralized, isolated, and small-scale systems to decentralized, distributed, and highly scalable systems, resulting in a remarkable improvement in the quality of service, thereby eliminating expensive administrative inefficiencies. One of the most important challenges of the healthcare system is to maintain consistent patient data as well as unified files amongst numerous applications and programs. So, any attack or loss of data integrity and repudiation can lead to a wide range of potentially catastrophic repercussions, including inappropriate treatment, data theft, etc. As a result, a model is required that not only allows convenient data storage, access, and retrieval but also enables a transparent and traceable environment for verifying the integrity and authenticity of patient data. The work presented in this article is an integration of digital signature and blockchain technology. Here, a digital signature technique based on Hash-based Message Authentication Code (HMAC) is used which is an efficient method to achieve non-repudiation and mitigate Sybil attack. Furthermore, the integrity of data is ensured by the underlying blockchain technology. This advancement will enable the healthcare sector to achieve scalable improvement over the present system.
BigCloneBench: A Retrospective and Roadmap

Since BigCloneBench was released in 2014, it has been widely used for evaluating the modern clone detection tools. In this position paper, we outline our observations over the years and our thoughts for next steps. We conclude that BigCloneBench has been an accurate and useful vehicle for evaluating the recall of contemporary clone detection tools and could be further improved for evaluating semantic clone detectors. We observe that its dataset has been used in training innovative machine-learning based approaches to Type-4 clone detection, although it was not specifically designed for this purpose and may not be optimal. We call for more research in task-specific clone benchmarks, including datasets built with ML training in mind.
Writer Retrieval using Compact Convolutional Transformers and NetMVLAD

This paper presents a method for writer retrieval where embeddings of patches extracted at SIFT keypoint locations are learned by a Compact Convolutional Transformer (CCT), a modified attention-based transformer architecture including convolutions, followed by a NetMVLAD layer and Generalized Max Pooling (GMP) to obtain global page descriptors. We introduce the application of CCTs for writer retrieval and show that they outperform Convolutional Neural Networks (CNNs) used in current State-of-the-Art methods for writer retrieval, namely ResNet18, while at the same time only have one-third of the number of parameters. Additionally, we propose Net-MVLAD, an extension of NetVLAD with multiple vocabularies, to encode information with different vocabulary sizes improving the original NetVLAD. An evaluation of the performance of CCTs compared to ResNet18 is provided on the ICDAR2013 Competition on Writer Identification dataset (ICDAR2013) and CVL dataset. The effect of multiple vocabularies applied within the NetVLAD layer is shown. CCT7 pretrained on CIFAR-100 combined with NetMVLAD achieves 89.3% Mean Average Precision (mAP) on the ICDAR2013 dataset and 96.5% on the CVL dataset.
Fast Loading of a Trapped Ion Quantum Computer Using a 2D Magneto-Optical Trap

We demonstrate fast loading of our trapped ion quantum computer using a compact 2D Magneto-Optical Trap (MOT). We characterize the MOT loading system by direct measurements of the cold atomic beam produced thereby. We load both ytterbium and barium ions two orders of magnitude faster than using a conventional oven loading method with negligible impact on vacuum performance in the trapped ion region. These improvements to loading address one of the concerns in scaling our QCCD architecture up to large Qubit numbers.
Joint use of Bivariate Empirical Mode Decomposition and Convolutional Neural Networks for Automatic Modulation Recognition

The AMR (Automatic Modulation Recognition) domain has recently shown an increase of interest, particularly as an application for monitoring the physical layer of wireless transmissions. In this work, it is proposed to study the combination of BIEMD (BivariatelEmpirical Mode Decomposition) and CNN (Convolutional Neural Network) in order to improve the modulated signals recognition rates. On the one hand, B/EMD is able to decompose signals into a finite number of IMFs (Intrinsic Mode Functions) whose features can be analyzed. On the other hand, CNNs are already used in the scientific literature to classify modulations. Adopting a publicly available data base containing numerous modulated signals under varying channel conditions, it is possible to decompose the complex signals using B/EMD and inserting them into a CNN. It is shown that the shape of the data has an important impact on the classification. Furthermore, the performance is compared and it is shown that employing the IMFs provided by a BEMD (Bivariate Empirical Mode Decomposition) instead of the original IQ signal improves the overall accuracy of the recognition by 2% in the frame of the parameters used in this paper. More than that, it is also able to increase the recognition rates up to a maximum of 4.4% for hizher sianal to noise ratio values.
SoK: Confidential Quartet - Comparison of Platforms for Virtualization-Based Confidential Computing

Confidential computing allows processing sensitive workloads in securely isolated spaces. Following earlier adoption of process-based approaches to isolation, vendors are now enabling hardware and firmware support for virtualization-based confidential computing on several server platforms. Due to variations in the technology stack, threat model, implementation and functionality, the available solutions offer somewhat different capabilities, trade-offs and security guarantees. In this paper we review, compare and contextualize four virtualization-based confidential computing technologies for enterprise server platforms - AMD SEV, ARM CCA, IBM PEF and Intel TDX.
NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces

When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.