Reviewerly/exHarmony huggingface dataset:
Simple proof of security of the multiparty prepare and measure QKD

The majority of research to date has concentrated on the quantum key distribution (QKD) between two parties. In general, the QKD protocols proposed for the multiparty scenario often involve the usage of a maximally entangled state, such as GHZ, which is challenging to implement in practice. This paper examines the prepare and measure version of multiparty communication through quantum key distribution. It is sufficient to have the capability of preparing and measuring a single qubit state. The security of the multiparty prepare and measure QKD is demonstrated by utilizing the well-known techniques of quantum error correction and entanglement purification. We begin by establishing the security of the entangled-based version and progress through a series of reductions to arrive at our equivalent multiparty prepare and measure version of QKD. We establish the security proof of multiparty entangled-based version by combining FTQC and classical statistics on the GHZ basis. Then proved the conditional security of the multiparty prepare-and-measure QKD by the equivalency.
Ranking of Key Components of CNC Machine Tools Based on Complex Network

Complex networks have become a center of interdisciplinary research. The robustness and reliability of complex systems can be determined by analyzing the characteristics of networks. The computer numerical control machine tool (CNCMT) is a typical complex system, and reliability is the key factor that affects the processing quality and performance of the CNCMT. The process of quantitatively determining the key functional components of CNCMT and carrying out reliability growth experiments on them remains difficult. A new importance-ranking method for the key functional components of the CNCMT is proposed in this study. First, the real characteristics of the CNCMT are analyzed, and the complex network of the CNCMT is constructed. Then, we study various indicators of the network and determine that the network has small-world characteristics. Finally, a variety of complex network characteristic indicators are fused, the weight of each indicator is determined based on the analytic hierarchy process (AHP), the importance of each node is quantitatively determined, and the fused indicators are compared with a single indicator.
Instructional Design for a Virtual Teaching-Learning Environment (VTLE): Process, Structure And Validation By Experts

To design and implement quality training actions, teachers follow a conscious and non-routine process, so it is useful to have a model that standardizes and guides this process. Courses designed for virtual environments must respond to models focused on student learning, so a design based on the constructivist approach is proposed that identifies the learning objectives, groups the contents into units, involves the student in learning and evaluation activities; proposes timely feedback and promotes the transfer of knowledge and the development of skills and attitudes. To validate the proposed design, 10 experts were selected. To qualify them, the coefficient of expert competence was calculated and to collect their appreciation of the proposed instructional design, a questionnaire with Likert-type scale questions was applied. The results show that the general assessment of the experts is positive and consistent with the proposal.
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks Through Accuracy Gradient

Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human being. For example, an external attack can modify an image adding noises invisible for a human eye, but a DNN model misclassified the image. A key objective for developing robust DNN models is to use a learning algorithm that is fast but can also give model that is robust against different types of adversarial attacks. Especially for adversarial training, enormously long training times are needed for obtaining high accuracy under many different types of adversarial samples generated using different adversarial attack techniques. This paper aims at accelerating the adversarial training to enable fast development of robust DNN models against adversarial attacks. The general method for improving the training performance is the hyperparameters fine-tuning, where the learning rate is one of the most crucial hyperparameters. By modifying its shape (the value over time) and value during the training, we can obtain a model robust to adversarial attacks faster than standard training. First, we conduct experiments on two different datasets (CIFAR10, CIFAR100), exploring various techniques. Then, this analysis is leveraged to develop a novel fast training methodology, AccelAT, which automatically adjusts the learning rate for different epochs based on the accuracy gradient. The experiments show comparable results with the related works, and in several experiments, the adversarial training of DNNs using our AccelAT framework is conducted up to 2 times faster than the existing techniques. Thus, our findings boost the speed of adversarial training in an era in which security and performance are fundamental optimization objectives in DNN-based applications.
Application of Outlier Detection Model in Korean Language and Culture Communication System Based on Artificial Intelligence

With the continuous expansion of the Internet in China, network communication models and network services have become more and more complex, and with the increase of data types and diversification of data sources, network operation and maintenance projects have become more and more difficult. Passive statistical testing techniques for outliers are widely used, and many shortcomings have been pointed out, so there is an urgent need for improvement. After the research on the abnormal test of the text, it is determined that each method uses the visualization tool to simulate the data through the classification algorithm of the existing abnormal test and to identify the possibility of the data of each method and the PR diagram. The accuracy is counterintuitive. At present, Hallyu has a greater influence on the Internet, mainly in the fields of fashion and culture in Asian countries. Therefore, Korean language teaching has received widespread attention from the society, and combining cultural education with language teaching is an important way to improve the efficiency of language teaching. In the actual Korean language teaching work, a variety of Korean language materials are essential educational and teaching resources. The simulation results show that a large amount of training can make the DNN generate a better auto-encoder. In addition to strengthening the template for simulation training in deep learning, multiple supervisions of the data can be performed to reduce errors and improve efficiency.
Fault Diagnosis of Balancing Machine Based on ISSA-ELM

Balancing machine is a general equipment for dynamic balance verification of rotating parts, whether it breaks down or does not determine the accuracy of dynamic balance verification. In order to solve the problem of insufficient fault diagnosis accuracy of balancing machine, a fault diagnosis method of balancing machine based on the Improved Sparrow Search Algorithm (ISSA) optimized Extreme Learning Machine (ELM) was proposed. Firstly, iterative chaos mapping and Fuch chaos mapping were introduced to initialize the population and increase the population diversity. Secondly, the adaptive dynamic factor and Levy flight strategy were also introduced to update the individual positions and improve the model convergence speed. Finally, the fault feature vector was input to the ISSA-ELM model with the fault type as the output. The experiment showed that the fault diagnosis accuracy of ISSA-ELM is as high as 99.17%, which is 1.67%, 2.50%, 7.50%, and 17.50% higher than that of SSA-ELM, HHO-ELM, PSO-ELM, and ELM, respectively, further improving the prediction accuracy of the operation state of the balancing machine.
Automated Satellite Fault Detection using Machine Learning

The US Space Force (USSF) requires pervasive and game-changing technologies to improve its concept of operations. One recognized need is for increased satellite survivability through local situational awareness and fault attribution which lead to faster decision-making. The following paper discusses the process the USSF uses to identify technology needs within its enterprise and identification of solutions to those technology needs. We then provide a specific instance of a potential technology solution in the form of machine learning-based fault detection. The solution presented uses machine learning models trained on actual satellite telemetry to predict future anomalies encountered by the satellite. The detailed proof-of-concept methodology highlights a case study in how new technologies can be shown to apply to identified USSF needs, which in turn allows for rapid decision-making for future investments. The specific fault detection proof-of-concept was successful in providing fault alerts up to two weeks in advance of when those faults were recorded by the satellite operators, with minimal false alarms. This early measure of success enabled continued investment and further advancement of the concept towards a deployable software solution for fault management.
Graph-based Management and Mining of Blockchain Data

The mainstream adoption of blockchains led to the preparation of many decentralized applications and web platforms, including Web 3.0, a peer-to-peer internet with no single authority. The data stored in blockchain can be considered as big data -- massive-volume, dynamic, and heterogeneous. Due to highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in blockchains. Recently, several research works performed graph analysis on the publicly available blockchain data to reveal insights into its business transactions and for critical downstream tasks, e.g., cryptocurrency price prediction, phishing scams and counterfeit token detection. In this tutorial, we discuss relevant literature on blockchain data structures, storage, categories, data extraction and graphs construction, graph mining, topological data analysis, and machine learning methods used, target applications, and the new insights revealed by them, aiming towards providing a clear view of unified graph-data models for UTXO and account-based blockchains. We also emphasize future research directions.
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks. Under partial observation, existing node- or subgraph-level message-passing produces suboptimal representations. In this paper, we formulate a novel task of learning representations of partially observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax (PSI) framework and generalize existing InfoMax models, including DGI, InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the mutual information between the partial subgraph's summary and various substructures from nodes to full subgraphs. In addition, we suggest a novel two-stage model with k-hop PSI, which reconstructs the representation of the full subgraph and improves its expressiveness from different local-global structures. Under training and evaluation protocols designed for this problem, we conduct experiments on three real-world datasets and demonstrate that PSI models outperform baselines.
On Smoothed Explanations

Explanation methods highlight the importance of the input features in taking a predictive decision, and represent a solution to increase the transparency and trustworthiness in machine learning and deep neural networks (DNNs). However, explanation methods can be easily manipulated generating misleading explanations particularly under visually imperceptible adversarial perturbations. Recent work has identified the decision surface geometry of DNNs as the main cause of this phenomenon. To make explanation methods more robust against adversarially crafted perturbations, recent research has promoted several smoothing approaches. These approaches smooth either the explanation map or the decision surface.
SASNet: Stage-aware Sequential Matching for Online Travel Recommendation

Sequential matching, which aims to predict the item a user will next interact with in the sequential context of the user's historical behaviors, is widely adopted in recommender systems. Existing works mainly characterize the sequential context as the dependencies of user interactions, which is less effective for online travel recommendation where users' behaviors are highly correlated with theirstages in the travel life cycle. Specifically, users on an online travel platform (OTP) usually go through different stages (e.g., exploring a destination, planning an itinerary), and make several correlated interactions (e.g., booking a flight, reserving a hotel, renting a car) at each stage. In this paper, we propose to capture the deep sequential context by modeling the evolving of user stages, and develop a novel stage-aware deep sequential matching network (SASNet) that incorporates inter-stage and intra-stage dependencies over stage-augmented interaction sequence for more accurate and interpretable recommendation. Extensive experiments on real-world datasets validate the superiority of our model for both online travel recommendation and general next-item recommendation. Our model has been successfully deployed at Fliggy, one of the most popular OTPs in China, and shows good performance in serving online traffic.
Fusing Geometric and Scene Information for Cross-View Geo-Localization

Cross-view geo-localization is to match scene images (e.g. ground-view images) with geo-tagged aerial images, which is crucial to a wide range of applications such as autonomous driving and street view navigation. Existing methods can neither address the perspective difference well nor effectively capture the scene information. In this work, we propose a Geometric and Scene Information Fusion (GSIF) model for more accurate cross-view geo-localization. GSIF first learns the geometric information of scene images and aerial images via log-polar transformation and spatial-attention aggregation to alleviate the perspective difference. Then, it mines the scene information of scene images via Sky View Factor (SVF) extraction. Finally, both geometric information and scene information are fused for image matching, and a balanced loss function is introduced to boost the matching accuracy. Experimental results on two real datasets show that our model can significantly outperforms the existing methods.
One Rating to Rule Them All?

Two general approaches are common for evaluating automatically generated labels in topic modeling: direct human assessment; or performance metrics that can be calculated without, but still correlate with, human assessment. However, both approaches implicitly assume that the quality of a topic label is single-dimensional. In contrast, this paper provides evidence that human assessments about the quality of topic labels consist of multiple latent dimensions. This evidence comes from human assessments of four simple labeling techniques. For each label, study participants responded to several items asking them to assess each label according to a variety of different criteria. Exploratory factor analysis shows that these human assessments of labeling quality have a two-factor latent structure. Subsequent analysis demonstrates that this multi-item, two-factor assessment can reveal nuances that would be missed using either a single-item human assessment of perceived label quality or established performance metrics. The paper concludes by suggesting future directions for the development of human-centered approaches to evaluating NLP and ML systems more broadly.
Unsupervised Representation Learning on Attributed Multiplex Network

Embedding learning in multiplex networks has drawn increasing attention in recent years and achieved outstanding performance in many downstream tasks. However, most existing network embedding methods either only focus on the structured information of graphs, rely on the human-annotated data, or mainly rely on multi-layer GCNs to encode graphs at the risk of learning ill-posed spectral filters. Moreover, it is also challenging in multiplex network embedding to learn consensus embeddings for nodes across the multiple views by the inter-relationship among graphs. In this study, we propose a novel and flexible unsupervised network embedding method for attributed multiplex networks to generate more precise node embeddings by simplified Bernstein encoders and alternate contrastive learning between local and global. Specifically, we design a graph encoder based on simplified Bernstein polynomials to learn node embeddings of a specific graph view. During the learning of each specific view, local and global contrastive learning are alternately applied to update the view-specific embedding and the consensus embedding simultaneously. Furthermore, the proposed model can be easily extended as a semi-supervised model by adding additional semi-supervised cost or as an attention-based model to attentively integrate embeddings from multiple graphs. Experiments on three publicly available real-world datasets show that the proposed method achieves significant improvements on downstream tasks over state-of-the-art baselines, while being faster or competitive in terms of runtime compared to the previous studies.
AutoMARS: Searching to Compress Multi-Modality Recommendation Systems

Web applications utilize Recommendation Systems (RS) to address the problem of consumer over-choices. Recent works have taken advantage of multi-modality or multi-view, input information (such as user interaction, images, texts, rating scores) to boost recommendation system performance compared with using single-modality information. However, the use of multi-modality input demands much higher computational cost and storage capacity. On the other hand, the real-world RS services usually have strict budgets on both time and space for a good customer experience. As a result, the model efficiency of multi-modality recommendation systems has gained increasing importance. While unfortunately, to the best of our knowledge, there is no existing study of a generic compression framework for multi-modality RS. In this paper, we investigate, for the first time, how to compress a multi-modality recommendation system with a fixed budget. Assuming that input information from different modalities are of unequal importance, a good compression algorithm should learn to automatically allocate different resource budgets to each input, based on their importance in maximally preserving recommendation efficacy. To this end, we leverage the tools of neural architecture search (NAS) and distillation and propose Auto Multi-modAlity Recommendation System (AutoMARS), a unified modality-aware model compression framework dedicated to multi-modality recommendation systems. We demonstrate the effectiveness and generality of AutoMARS by testing it on three different Amazon datasets of various sparsity. AutoMARS demonstrates superior multi-modality compression performance than previous state-of-the-art compression methods. For example on the Amazon Beauty dataset, we achieve on average a 20% higher accuracy over previous state-of-the-art methods, while enjoying 65% reduction over baselines. Codes are available at: https://github.com/VITA-Group/AutoMARS.
A Model-Centric Explainer for Graph Neural Network based Node Classification

Graph Neural Networks (GNNs) learn node representations by aggregating a node's feature vector with its neighbors. They perform well across a variety of graph tasks. However, to enhance the reliability and trustworthiness of these models during use in critical scenarios, it is of essence to look into the decision making mechanisms of these models rather than treating them as black boxes. Our model-centric method gives insight into the kind of information learnt by GNNs about node neighborhoods during the task of node classification. We propose a neighborhood generator as an explainer that generates optimal neighborhoods to maximize a particular class prediction of the trained GNN model. We formulate neighborhood generation as a reinforcement learning problem and use a policy gradient method to train our generator using feedback from the trained GNN-based node classifier. Our method provides intelligible explanations of learning mechanisms of GNN models on synthetic as well as real-world datasets and even highlights certain shortcomings of these models.
Towards Principled User-side Recommender Systems

Traditionally, recommendation algorithms have been designed for service developers. However, recently, a new paradigm called user-side recommender systems has been proposed and they enable web service users to construct their own recommender systems without access to trade-secret data. This approach opens the door to user-defined fair systems even if the official recommender system of the service is not fair. While existing methods for user-side recommender systems have addressed the challenging problem of building recommender systems without using log data, they rely on heuristic approaches, and it is still unclear whether constructing user-side recommender systems is a well-defined problem from theoretical point of view. In this paper, we provide theoretical justification of user-side recommender systems. Specifically, we see that hidden item features can be recovered from the information available to the user, making the construction of user-side recommender system well-defined. However, this theoretically grounded approach is not efficient. To realize practical yet theoretically sound recommender systems, we propose three desirable properties of user-side recommender systems and propose an effective and efficient user-side recommender system, Consul, based on these foundations. We prove that Consul satisfies all three properties, whereas existing user-side recommender systems lack at least one of them. In the experiments, we empirically validate the theory of feature recovery via numerical experiments. We also show that our proposed method achieves an excellent trade-off between effectiveness and efficiency and demonstrate via case studies that the proposed method can retrieve information that the provider's official recommender system cannot.