Reviewerly/exHarmony huggingface dataset:
MGML: Momentum group meta-learning for few-shot image classification

At present, image classification covers more and more fields, and it is often difficult to obtain enough data for learning in some specific scenarios, such as medical fields, personalized customization of robots, etc. Few-shot image classification aims to quickly learn new classes of features from few images, and the meta-learning method has become the mainstream due to its good performance. However, the generalization ability of the meta-learning method is still poor and easy to be disturbed by low-quality images. In order to solve the above problems, this paper proposes Momentum Group Meta-Learning (MGML) to achieve a better effect of few-shot learning, which contains Group Meta-Learning module (GML) and Adaptive Momentum Smoothing module (AMS). GML obtains an ensemble model by training multiple episodes in parallel and then grouping them, which can reduce the interference of low-quality samples and improve the stability of meta-learning training. AMS includes the adaptive momentum update rule to further optimally integrate models between different groups, so that the model can memorize experience in more scenarios and enhance the generalization ability. We conduct experiments on miniImageNet and tieredImageNet datasets. The results show that MGML improves the accuracy, stability and cross-domain transfer ability of few-shot image classification, and can be applied to different few-shot learning models.
Blockchain-Based Anti-Counterfeiting Management System for Traceable Luxury Products

In recent years, counterfeit luxury products have become a major concern for consumers worldwide. The reason for the proliferation of counterfeit products is that the manufacturing and distribution process is not transparent to consumers and this information can be easily falsified or altered by others. To solve this problem, this paper proposes the development of a management system using blockchain and smart contract technology to solve the problems of data forgery and data tampering, while tracking the information related to luxury products and ensuring the accuracy and authenticity of the relevant data, to achieve the purpose of luxury product anti-counterfeiting. When using Hyperledger Fabric to deploy the blockchain and execute smart contracts, all information related to the production and logistics process of luxury goods will be uploaded to the blockchain. No human intervention is required to create a complete, traceable, tamper-proof, and trusted repository. Compared to previous work, this paper combines blockchain technology with specific processes in the supply chain, employing a variety of security methods to secure the communication process. Moreover, our proposed solution is more flexible in transmission, with more secure protocols also making data harder to tamper with and falsify, thereby solving the problem of forgery and tracking of luxury products.
RF-DNA

Physical-layer identification aims to identify wireless devices during RF communication by exploiting the imperfections of their radio circuitry, i.e., hardware fingerprint. Previous work proposed several hardware fingerprints for RFIDs (e.g., TIE, ABD, PSD, etc). However, these proposed fingerprints suffer from either unscalability or acquisition inefficiency. This work presents RF-DNA, a new hardware fingerprint composed of millions of Dual Natural Attributes (DNA) organized in a helical structure, where a pair of DNA represents a tag's intrinsic response at some frequency. We take advantage of the frequency agnostic phenomenon that a commercial RFID tag can respond within a wider band than the regulated, to acquire 10X more features than previous fingerprints. At the heart of this work are the context-free acquisition approach to extracting DNA from backscatter signals; and the accurate DNA matching algorithm for verifying a tag's identity. A total of 160,000 RF-DNA instances were collected from 16,000 tags using a customized automatic acquisition system. We subsequently carried out large-scale experiments to test the identification accuracy of RF-DNA and previously proposed fingerprints. Our comprehensive evaluation reveals that RF-DNA can achieve a mean accuracy of 95.98%. In contrast, those of previous fingerprints fall to 60% below when in face of thousands of tags.
Contrastive Label Correlation Enhanced Unified Hashing Encoder for Cross-modal Retrieval

Cross-modal hashing (CMH) has been widely used in multimedia retrieval applications for its low storage cost and fast indexing speed. Thanks to the success of deep learning, cross-modal hashing has made significant progress with high-quality deep features. However, the modal gap is still a crucial bottleneck for existing cross-modal hashing methods: the commonly used convolutional neural network and bag-of-words encoders are customized for single modal prior, limiting the models to learn semantics representation in a cross-modal space. To overcome modality heterogeneity, we propose a shared transformer encoder (UniHash) to unify the cross-modal hashing into the same semantic space. A contrastive label correlation learning (CLC) loss using the category labels as modality bridge is designed together to improve the representation quality. Moreover, we take advantage of the multi-hot label space and propose a negative label generation (NegLG) strategy to get richer and uniformly distributed negative labels for contrast. Extensive experiments on three benchmarks verify the advantage of our proposed method. Besides, the proposed UniHash outperforms state-of-the-art cross-modal hashing methods significantly, establishing a new important baseline for the cross-modal hashing research. Codes are released github.com/idealwhite/Unihash.
Image Encryption Through CA, Chaos and Lucas Sequence Based S-Box

The ever increasing developments in communication technologies and the rate of multimedia production has put pressure on the scientific community to develop matching multimedia security algorithms. This research work provides a 3-stage image encryption approach. In stage 1, a variant of Rule 30 Cellular Automata (CA) PRNG is employed, a Lucas sequence based S-box in stage 2, and a sin Logistic map variant in stage 3. The performance assessment of the proposed encryption algorithm indicates strong resilience to several kinds of attacks at a low computational cost. A comparison among the proposed image encryption algorithm and its counterparts from the literature is also carried out, showcasing its comparability or even superiority in terms of some of the metrics.
An architecture entropy regularizer for differentiable neural architecture search

Differentiable architecture search (DARTS) is one of the prevailing paradigms of neural architecture search (NAS) due to allowing efficient gradient-based optimization during the search phase. However, its poor stability and generalizability are intolerable. We argue that the crux is the locally optimal architecture parameter caused by a dilemma, which is that the solutions to the Matthew effect and discretization discrepancy are inconsistent. To escape from the dilemma, we propose an architecture entropy to measure the discrepancy of the architecture parameters of different candidate operations and use it as a regularizer to control the learning of architecture parameters. Extensive experiments show that an architecture entropy regularizer with a negative or positive coefficient can effectively solve one side of the contradiction respectively, and the regularizer with a variable coefficient can relieve DARTS from the dilemma. Experimental results demonstrate that our architecture entropy regularizer can significantly improve different differentiable NAS algorithms on different datasets and different search spaces. Furthermore, we also achieve more accurate and more robust results on CIFAR-10 and ImageNet. The code is publicly available at https://github.com/kunjing96/DARTS-AER.
Advancing Computing Education: Assessing CC2020 Dispositions

This Innovative Practice Full Paper addresses the assessment of dispositions which, along with knowledge and skills, form the three legs of competency needed to perform a task in context, as described in recent computing curricular reports, particularly ACM/IEEE Computing Curricula 2020 (CC2020). Here, dispositions in CC2020 express the behavioral characteristics of competence, such as being adaptable, collaborative, or inventive. Instructors have assessed knowledge from the start of computing programs and have paid increased attention to assessing skills in recent decades. However, dispositions and their role within competency is relatively new, with little guidance available for assessing dispositions. Lately, computing instructors have begun to understand the importance of evaluating dispositions during the performance of tasks in the real world or in the context of the industry-based global Skills Framework for the Information Age (SFIA). Hence, this paper develops a criterion-based approach for use by educators in assessing competence based on a reflective portfolio of "real-world" achievements. Building on concepts developed by the UK Institute of Coding and other recent reports, this work demonstrates how this assessment approach relates to industry-based competency frameworks such as SFIA and the European e-Competence Framework (eCF). The paper also explores using the criterion-based approach in a classroom environment to help students focus on particular dispositions. Its main contribution is to advance the competency focus in academic computing programs and future computing curricula.
Fundamental Limitation on the Detectability of Entanglement

Entanglement detection is essential in quantum information science and quantum many-body physics. It has been proved that entanglement exists almost surely for a random quantum state, while the realizations of effective entanglement criteria usually consume exponentially many resources with regard to system size or qubit number, and efficient criteria often perform poorly without prior knowledge. This fact implies a fundamental limitation might exist in the detectability of entanglement. In this work, we formalize this limitation as a fundamental trade-off between the efficiency and effectiveness of entanglement criteria via a systematic method to evaluate the detection capability of entanglement criteria theoretically. For a system coupled to an environment, we prove that any entanglement criterion needs exponentially many observables to detect the entanglement effectively when restricted to single-copy operations. Otherwise, the detection capability of the criterion will decay double exponentially. Furthermore, if multicopy joint measurements are allowed, the effectiveness of entanglement detection can be exponentially improved, which implies a quantum advantage in entanglement detection problems. Our results may shed light on why quantum phenomena are difficult to observe in large noisy systems.Received 10 August 2022Accepted 1 November 2022DOI:https://doi.org/10.1103/PhysRevLett.129.230503© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionQuantum entanglementQuantum Information
Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation

Deep Matrix Factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data. However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a Hypergraph regularized Diverse Deep Matrix Factorization (HDDMF) model for multi-view data representation, to jointly utilize multiview diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multiview learning approaches.
Open another door to education—Applications, challenges and perspectives of the educational metaverse

&lt;p&gt;The metaverse is a new form of the future of the Internet that integrates many new technologies, and its combination with the education sector has great potential. The article firstly points out that metaverse is a concept that is constantly developing and evolving, while the educational metaverse has three core features: interactivity, immersion and multiplicity, and introduces the six underlying supporting technologies and application scenarios of the educational metaverse. The article then presents the current development of the educational metaverse represented by the VR/AR learning environment, starting with case studies from the fields of subject education, informal learning and vocational training. Finally, the article analyses the problems and challenges faced by the educational metaverse and makes suggestions for the initial development of the educational metaverse in terms of mechanism, technology and teaching. The educational meta-universe opens another door to the study of the complexity of educational systems and the laws of educational occurrence and development, while the study of its applications, challenges and perspectives gives direction and impetus to the development of the educational meta-universe.&lt;/p&gt;
Cyber Kill Chain Analysis of Five Major US Data Breaches

Data breaches are a major concern for both US and global corporations. With more companies allowing their employees to be working remote, providing them a secure work environment has been a priority for employers. The Interpol 2020 report on cyber breaches mentions that the number of cyber-attacks has multiplied in the last year. The IBM Data Breach Report of 2021 notes that data breach costs rose from USD 3.86 million to USD 4.24 million, while the average cost was USD 1.07 Mil higher in breaches where remote work was a factor in causing the breach. Given this environment of increased cyber breaches, it is important to learn from previous major data breaches to understand the root cause which led to the compromise of information security and the steps which could have effectively prevented the same. This paper evaluates five major data breaches in US history using Lockheed's Cyber Kill Chain Analysis, since the details of these breaches have never been documented for research and also proposes an eight-step cyber-attack prevention plan.
An Initial Exploration of Visual Cues in Head-Mounted Display Augmented Reality for Book Searching

Augmented reality (AR) is today becoming more widely utilized as it allows for interacting with the virtual objects. In this study, we propose an head-mounted display (HMD) AR system supporting book searching with visual cues. The visual cue is represented as a light-green blob hinting users for task completion, which significantly strengthens the overall performance. The system is implemented by using Microsoft HoloLens 2. The proof-of-concept version of the proposed solution is demonstrated in a pilot user study (n=8) comprising an experimental group (with visual cues, n=4) and a control group (without visual cues, n=4), followed by quantitative analysis of task completion time (TCT) and NASA task load index (TLX). The results show that our proposed HMD AR solution improved the task performance and had cognitive benefits for book searching tasks.
Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach

In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic. This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity. We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system’s sensitivity while producing more stable predictions. We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data.
Abstractive text summarization of low-resourced languages using deep learning

Humans must be able to cope with the huge amounts of information produced by the information technology revolution. As a result, automatic text summarization is being employed in a range of industries to assist individuals in identifying the most important information. For text summarization, two approaches are mainly considered: text summarization by the extractive and abstractive methods. The extractive summarisation approach selects chunks of sentences like source documents, while the abstractive approach can generate a summary based on mined keywords. For low-resourced languages, e.g., Urdu, extractive summarization uses various models and algorithms. However, the study of abstractive summarization in Urdu is still a challenging task. Because there are so many literary works in Urdu, producing abstractive summaries demands extensive research.This article proposed a deep learning model for the Urdu language by using the Urdu 1 Million news dataset and compared its performance with the two widely used methods based on machine learning, such as support vector machine (SVM) and logistic regression (LR). The results show that the suggested deep learning model performs better than the other two approaches. The summaries produced by extractive summaries are processed using the encoder-decoder paradigm to create an abstractive summary.With the help of Urdu language specialists, the system-generated summaries were validated, showing the proposed model's improvement and accuracy.
DAGAD: Data Augmentation for Graph Anomaly Detection

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.
Weight-guided class complementing for long-tailed image recognition

Real-world data are often long-tailed distributed and have plenty classes. This characteristic leads to a significant performance drop for various models. One reason behind that is the gradient shift caused by unsampled classes in each training iteration. In this paper, we propose a Weight-Guided Class Complementing framework to address this issue. Specifically, this framework first complements the unsampled classes in each training iteration by using a dynamic updated data slot. Then, considering the over-fitting issue caused by class complementing, we utilize the classifier weights as learned knowledge and encourage the model to discover more class specific characteristics. Finally, we design a weight refining scheme to deal with the long-tailed bias existing in classifier weights. Experimental results show that our framework can be implemented upon different existing approaches effectively, achieving consistent improvements on various benchmarks with new state-of-the-art performances. Codes will be released.
Cross-Domain Video Anomaly Detection without Target Domain Adaptation

Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain. However, this requires laborious model-tuning by the end-user who may prefer to have a system that works "out-of-the-box." To address such practical scenarios, we identify a novel target domain (inference-time) VAD task where no target domain training data are available. To this end, we propose a new ‘Zero-shot Cross-domain Video Anomaly Detection (zxVAD)’ framework that includes a future-frame prediction generative model setup. Different from prior future-frame prediction models, our model uses a novel Normalcy Classifier module to learn the features of normal event videos by learning how such features are different "relatively" to features in pseudo-abnormal examples. A novel Untrained Convolutional Neural Network based Anomaly Synthesis module crafts these pseudo-abnormal examples by adding foreign objects in normal video frames with no extra training cost. With our novel relative normalcy feature learning strategy, zxVAD generalizes and learns to distinguish between normal and abnormal frames in a new target domain without adaptation during inference. Through evaluations on common datasets, we show that zxVAD outperforms the state-of-the-art (SOTA), regardless of whether task-relevant (i.e., VAD) source training data are available or not. Lastly, zxVAD also beats the SOTA methods in inference-time efficiency metrics including the model size, total parameters, GPU energy consumption, and GMACs.