Reviewerly/exHarmony huggingface dataset:
Adversarial Network Traffic: Towards Evaluating the Robustness of Deep-Learning-Based Network Traffic Classification

Network traffic classification is used in various applications such as network traffic management, policy enforcement, and intrusion detection systems. Although most applications encrypt their network traffic and some of them dynamically change their port numbers, Machine Learning (ML) and especially Deep Learning (DL)-based classifiers have shown impressive performance in network traffic classification. In this article, we evaluate the robustness of DL-based network traffic classifiers against Adversarial Network Traffic (ANT). ANT causes DL-based network traffic classifiers to predict incorrectly using Universal Adversarial Perturbation (UAP) generating methods. Since there is no need to buffer network traffic before sending ANT, it is generated live. We partition the input space of the DL-based network traffic classification into three categories: packet classification, flow content classification, and flow time series classification. To generate ANT, we propose three new attacks injecting UAP into network traffic. AdvPad attack injects a UAP into the content of packets to evaluate the robustness of packet classifiers. AdvPay attack injects a UAP into the payload of a dummy packet to evaluate the robustness of flow content classifiers. AdvBurst attack injects a specific number of dummy packets with crafted statistical features based on a UAP into a selected burst of a flow to evaluate the robustness of flow time series classifiers. The results indicate injecting a little UAP into network traffic, highly decreases the performance of DL-based network traffic classifiers in all categories.
Gini-Impurity Index Analysis

In the past few decades, DPA-based side-channel attack strategies, such as DPA and CPA, have shown strong ability to analyze the security of the cryptographic implementations. However, the unpredictability of the leakage model and the correspondence between leakage behavior of the target device and the hypothetical leakage value make it less-effective without prior knowledge. Therefore, in this paper, we present a novel generic side-channel analysis method called Gini-impurity Index Analysis (GIA), utilizing Gini-impurity Index as the distinguisher, which can perform well even without any leakage model and is not sensitive to the existing methods' restrictions about the leakage behavior. Firstly, we introduce the basic idea of GIA. According to the proposed GIA attack strategy, the Gini-impurity index for each key hypothesis should be calculated, determined by the clustered power consumption and the classified subsets based on the key dependent target function. Secondly, we verify the feasibility and evaluate the efficiency of GIA with different target functions by the practical experimental results against AES-128 implemented on an AT89S52 microcontroller. We present one possible multivariate extension of GIA and find the advantage of GIA on leakage information utilization. Thirdly, we present the results of comparisons. On the one hand, we compare GIA with three widely-used distinguishers under simulated traces in various leakage scenarios and practical traces with Hamming-weight-related leakage. Results confirm that GIA can always perform well with different leakage models in most situations. On the other hand, we analyze the relationship between GIA and Mutual Information Analysis (MIA). Theoretical and experimental results confirm that these two methods can obtain similar attack results. However, the guessing entropy of GIA is lower than MIA by up to 21%, and the averaged computational time overhead of GIA is lower than MIA by up to 13.3%, indicating that GIA is more efficient than MIA. Compared to traditional MIA, GIA is easier to operate and more flexible with noise. Therefore, GIA is an efficient and useful alternative to these existed strategies.
Rethinking Channel Dimensions for Efficient Model Design

Designing an efficient model within the limited computational cost is challenging. We argue the accuracy of a lightweight model has been further limited by the design convention: a stage-wise configuration of the channel dimensions, which looks like a piecewise linear function of the network stage. In this paper, we study an effective channel dimension configuration towards better performance than the convention. To this end, we empirically study how to design a single layer properly by analyzing the rank of the output feature. We then investigate the channel configuration of a model by searching network architectures concerning the channel configuration under the computational cost restriction. Based on the investigation, we propose a simple yet effective channel configuration that can be parameterized by the layer index. As a result, our proposed model following the channel parameterization achieves remarkable performance on ImageNet classification and transfer learning tasks including COCO object detection, COCO instance segmentation, and fine-grained classifications. Code and ImageNet pretrained models are available at https: //github.com/clovaai/rexnet.
A feature selection method via analysis of relevance, redundancy, and interaction

Feature selection aims at selecting important features that can enhance learning performance in data mining, pattern recognition, and machine learning. Filter feature selection methods offer computational efficiency and feature evaluation criteria, while feature interaction information, which may greatly help increase classification accuracy, is often ignored. In this work, we instead propose a novel feature selection algorithm that uses the “maximum of the maximum” criterion to select highly relevant features and their maximally interactive features. Extensive experiments are performed to evaluate the performance of the proposed method with regard to the number of selected features and classification accuracy on thirty UCI datasets. The results demonstrate that the proposed algorithm not only efficiently selects the relevant features and the interactive features, but also enables classifiers to achieve classification accuracy that is better than, or comparably well to, ten representative competing feature selection algorithms.
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data. This paper focuses on a popular pipeline known as self-learning, where we point out a weakness named lazy mimicking that refers to the inertia that a model retains the prediction from itself and thus resists updates. To alleviate this issue, we propose the Asynchronous Teacher-Student Optimization (ATSO) algorithm that (i) breaks up continual learning from teacher to student and (ii) partitions the unlabeled training data into two subsets and alternately uses one subset to fine-tune the model which updates the labels on the other. We show the ability of ATSO on medical and natural image segmentation. In both scenarios, our method reports competitive performance, on par with the state-of-the-arts, in either using partial labeled data in the same dataset or transferring the trained model to an unlabeled dataset.
Simplified Phasmatodea population evolution algorithm for optimization

Abstract This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.
CAT: Cross Attention in Vision Transformer

Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image patches for Transformer after the tokenization of the image is vast(e.g., ViT), which bottlenecks model training and inference. In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps to capture global information. Both operations have less computation than standard self-attention in Transformer. Based on that, we build a hierarchical network called Cross Attention Transformer(CAT) for other vision tasks. Our model achieves 82.8% on ImageNet-1K, and improves the performance of other methods on COCO and ADE20K, illustrating that our network has the potential to serve as general backbones. The code and models are avalible at https://github.com/linhezheng19/CAT.
Secure and efficient data storage and sharing scheme for <scp>blockchain‐based mobile‐edge</scp> computing

Abstract With the rapid development of Internet of Things (IoT) technology, IoT devices have been widely used to collect physiological health data and provide diversified services to the terminal users. However, traditional data storage and sharing scheme cloud computing based in IoT face many challenges. For example, IoT devices are usually resource‐constrained (storage, computing power, battery capacity, etc.), data signed by IoT devices to ensure data integrity and authenticity will consume a lot of computing resources of IoT devices. At the same time, there is the challenge of high latency and unsafe data storage and sharing. To overcome these challenges, we propose a secure and efficient data storage and sharing scheme for blockchain‐based mobile‐edge computing. In our scheme, we construct the unique signature private key in a region into multiple key shares. IoT devices only need to submit the data and the random key shares allocated to the edge node. Edge node uses the recovered signature private key to realize data signature and homomorphic encryption. At the same time, the edge node will process timely data and return to the user. For data that need to be uploaded to the cloud for analysis, we use backup uploads to avoid data floods. Through experiments, it was found that our scheme can not only realize low‐latency message response for the terminal users, but also realize anonymous identity verification while ensuring data integrity and authenticity. The key shares of the signature private key are stored in different blocks of the blockchain to improve fault tolerance. The content extraction signature algorithm ensures that the key shares stored in different blocks are publicly verifiable. Safety analysis and performance analysis verify the feasibility and effectiveness of our scheme.
Bottom-Up Shift and Reasoning for Referring Image Segmentation

Referring image segmentation aims to segment the referent that is the corresponding object or stuff referred by a natural language expression in an image. Its main challenge lies in how to effectively and efficiently differentiate between the referent and other objects of the same category as the referent. In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules. Specifically, BUS progressively locates the referent along hierarchical reasoning steps implied by the expression. At each step, it locates the corresponding visual region by disambiguating between similar regions, where the disambiguation bases on the relationships between regions. By the explainable visual reasoning, BUS explicitly aligns linguistic components with visual regions so that it can identify all the mentioned entities in the expression. BIAR fuses multi-level features via a two-way attentive message passing, which captures the visual details relevant to the referent to refine segmentation results. Experimental results demonstrate that the proposed method consisting of BUS and BIAR modules, can not only consistently surpass all existing state-of-the-art algorithms across common benchmark datasets but also visualize interpretable reasoning steps for stepwise segmentation. Code is available at https://github.com/incredibleXM/BUSNet.
Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.
Blockchain-Based Solutions in Achieving SDGs after COVID-19

In this paper, we attempt to explore the extent to which the hard won development gains over the last several years could be reversed due to the unfolding COVID-19 global pandemic, how we can reboot the global response to accelerate the SDGs in times of uncertainties, and most importantly how to turn the recovery into an opportunity to build back better and more resilient economies. To do so, we examine the case of blockchain as one of the emerging innovative work-streams in development practices that could lead the way forward and pave the path for new developmental narratives as we all navigate the uncharted territories of the new digital age. This paper provides useful insights about the underlying dynamics underpinning the adoption of blockchain backed-solutions for sustainable development, and it showcases some of the promising use-cases being developed through trial-and-error experiments by its early adopters. The paper offers a deep dive into a burgeoning development practice in search of disrupting business-as-usual to solve increasingly complex development challenges by mainstreaming innovations such as blockchain-enabled solutions to rethink the ways in which development solutions are being delivered across the SDG spectrum. This work points to the significant potential of blockchain technology as a game changer in solving some of the most pressing issues hindering the global recovery post Covid-19 to transition towards greener and more inclusive economies. Nevertheless, we also stress that the hype-cycle behind the "let's blockchain it" trend does not mean that blockchain-backed solutions are necessarily superior to other alternatives which might be less costly and less technical in nature. Development practitioners prototyping and implementing blockchain-based solutions for sustainable development can utilize these insights and discussions to make informed decisions in their journey to harness the disruptive potential of blockchain alone or in tandem with other emerging technologies in the new world of business as unusual.
Learning Process-consistent Knowledge Tracing

Knowledge tracing (KT), which aims to trace students' changing knowledge state during their learning process, has improved students' learning efficiency in online learning systems. Recently, KT has attracted much research attention due to its critical significance in education. However, most of the existing KT methods pursue high accuracy of student performance prediction but neglect the consistency of students' changing knowledge state with their learning process. In this paper, we explore a new paradigm for the KT task and propose a novel model named Learning Process-consistent Knowledge Tracing (LPKT), which monitors students' knowledge state through directly modeling their learning process. Specifically, we first formalize the basic learning cell as the tuple exercise---answer time---answer. Then, we deeply measure the learning gain as well as its diversity from the difference of the present and previous learning cells, their interval time, and students' related knowledge state. We also design a learning gate to distinguish students' absorptive capacity of knowledge. Besides, we design a forgetting gate to model the decline of students' knowledge over time, which is based on their previous knowledge state, present learning gains, and the interval time. Extensive experimental results on three public datasets demonstrate that LPKT could obtain more reasonable knowledge state in line with the learning process. Moreover, LPKT also outperforms state-of-the-art KT methods on student performance prediction. Our work indicates a potential future research direction for KT, which is of both high interpretability and accuracy.
Deep Animation Video Interpolation in the Wild

In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to develop computational models that can automatically interpolate the in-between animation frames. However, existing video interpolation methods fail to produce satisfying results on animation data. Compared to natural videos, animation videos possess two unique characteristics that make frame interpolation difficult: 1) cartoons comprise lines and smooth color pieces. The smooth areas lack textures and make it difficult to estimate accurate motions on animation videos. 2) cartoons express stories via exaggeration. Some of the motions are non-linear and extremely large. In this work, we formally define and study the animation video interpolation problem for the first time. To address the aforementioned challenges, we propose an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner. Specifically, 1) Segment-Guided Matching resolves the "lack of textures" challenge by exploiting global matching among color pieces that are piece-wise coherent. 2) Recurrent Flow Refinement resolves the "non-linear and extremely large motion" challenge by recur-rent predictions using a transformer-like architecture. To facilitate comprehensive training and evaluations, we build a large-scale animation triplet dataset, ATD-12K, which comprises 12,000 triplets with rich annotations. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art interpolation methods for animation videos. Notably, AnimeInterp shows favorable perceptual quality and robustness for animation scenarios in the wild. The proposed dataset and code are available at https://github.com/lisiyao21/AnimeInterp/.
Adversarial Robustness under Long-Tailed Distribution

Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robustness towards more realistic scenarios, in this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. In particular, we first reveal the negative impacts induced by imbalanced data on both recognition performance and adversarial robustness, uncovering the intrinsic challenges of this problem. We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework. Several valuable observations are obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of robust accuracy exists under unreliable evaluation, and 3) boundary error limits the promotion of robustness. Inspired by these observations, we propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant classifier and data re-balancing via both margin engineering at training stage and boundary adjustment during inference. Extensive experiments demonstrate the superiority of our approach over other state-of-the-art defense methods. To our best knowledge, we are the first to tackle adversarial robustness under long-tailed distributions, which we believe would be a significant step towards real-world robustness. Our code is available at: https://github.com/wutong16/Adversarial_Long-Tail.
Does volatility connectedness across major cryptocurrencies behave the same at different frequencies? A portfolio risk analysis

This study investigates dynamic frequency connectedness for volatility differences among eight popular cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, Nem and Stellar). It employs the methodologies of Diebold and Yilmaz (2014; 2016) and Baruník and Křehlík (2018). Furthermore, an analysis of diversification benefits and downside risk reductions is carried out. The results demonstrated dynamic spillovers, which intensified after 2017. Furthermore, Bitcoin, Ethereum, and Litecoin are net transmitters of risk, which can be a contagion source; Dash, Ripple, Monero, Stellar, and Nem are net receivers of risk. Moreover, the short-term risk spillover is more pronounced than the medium- and long-term risk spillovers, which also increased after 2017. The directional spillovers among cryptocurrencies is sensitive to frequencies. Finally, adding a cryptocurrency to a benchmark Bitcoin portfolio provides diversification benefits and downside risk reductions. In contrast, adding Bitcoin to a cryptocurrency portfolio do not offers diversification opportunities.
Exploring Stable Coefficients on Joint Sub-Bands for Robust Video Watermarking in DT CWT Domain

Video watermarking on the dual tree-complex wavelet (DT CWT) domain is shown to be effective to offer high robustness. Existing DT CWT video watermarking schemes tend to use all the coefficients on the high-pass sub-bands of the DT CWT domain for watermark embedding and detection, which lack of investigating the correlations among different sub-bands and fail to explore the stable coefficients for robust watermarking. In this paper, we propose a novel DT CWT video watermarking scheme by exploring the stable coefficients on joint sub-bands. We first extract a set of candidate coefficients by applying block singular value decomposition (SVD) on the DT CWT domain. Then, we simulate the watermark embedding by modifying the candidate coefficients on each sub-band, from which we identity two pairs of strongly correlated sub-bands termed as the joint sub-bands. The watermark is eventually embedded by modifying the candidate coefficients of the joint sub-bands on a level which is adaptively chosen according to the video resolution. During the watermark detection, we identify and extract a set of stable coefficients from the candidate coefficients of the joint sub-bands to verify the ownership of the video. Extensive experiments demonstrate the advantage of our propose scheme over the latest DT CWT based schemes, which also performs better than the existing non-DT CWT transformed domain video watermarking schemes.