Reviewerly/exHarmony huggingface dataset:
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by 360° panoramic sensors. However, due to the unique imaging process of panoramic cameras, models designed for pinhole images do not directly generalize satisfactorily to 360° panoramic images. In this paper, we put forward a novel network framework——PANO FLOW, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method, which contains radial flow distortion (FDA-R) or equirectangular flow distortion (FDA-E). We further look into the definition and properties of cyclic optical flow for panoramic videos, and hereby propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer 360° optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefits from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset FlowScape based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the fresh established FlowScape benchmarks. Our proposed approach reduces the End-Point-Error (EPE) on FlowScape by 27.3%. On OmniFlowNet, PanoFlow achieves an EPE of 3.17 pixels, a 55.5% error reduction from the best published result (7.12 pixels). We also qualitatively validate our method via an outdoor collection vehicle and a public real-world OmniPhotos dataset, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at PanoFlow.
Facial Age Estimation Models for Embedded Systems: A Comparative Study

Automated age estimation from face images is the process of assigning either an exact age or a specific age range to a facial image.In this paper a comparative study of the current techniques suitable for this task is performed, with an emphasis on lightweight models suitable for embedded implementation.We investigate both the suitable modern deep learning architectures for feature extraction and the variants of framing the problem itself as either classification, regression or soft label classification.The models are evaluated on Audience dataset for age group classification and FG-NET dataset for exact age estimation.To gather in-depth insights into automated age estimation and in contrast to existing studies, we additionally compare the performance of both classification and regression on the same dataset.We propose a novel loss function that combines regression and classification approaches and show that it outperforms other considered approaches.At the same time, with a lightweight backbone, such an architecture is suitable for implementation on embedded devices.
Time series dynamic changing analysis on the mine geological environment in a natural reserve in Tibet based on the satellite remote sensing technology

By combining the offsite interpretation and field verification and the remote sensing data in a natural reserve in Tibet in 2012, 2014, 2016, 2018 and 2020, this article investigates the situation of mine compaction damage and its restoration. Also, using the result of 2012 data as a benchmark, it is focused on analysing the dynamic changes over the following four survey years in terms of compaction damage and restoration, under mining and abandonment mines, and the types of minerals involved. Then, the chart of incremental changing trends is generated. This study reflects the real-time information storage function of satellite remote sensing monitoring technology and its efficiency and advantages in wide-area ecological and geological environment surveys. The results of this study have provided data and technical support for the future management of the mine geological environment in natural reserves in western provinces in China.
Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding

Explainability of deep neural networks has been receiving increasing attention with regard to auditability and trustworthiness purposes. Of the various post-hoc explainability approaches, rule extraction methods assist to understand the logic that underpins their functioning. Whereas the rule-based solutions are directly managed and understood by practitioners, the use of intervals or crisp values in the antecedents that rely on numerical values might not be intuitive enough. In this case, the benefits of a linguistic representation based on fuzzy sets/rules are straightforward, as these semantically meaningful components ease the model understanding. This article proposes fuzzy rule-based explainer systems for deep neural networks. The algorithm learns a compact yet accurate set of fuzzy rules based on features' importance (i.e., attribution values) distilled from the trained networks. These systems can be used for both local and global explainability purposes. The evaluation results of different applications revealed that the fuzzy explainers maintained the fidelity and accuracy of the original deep neural networks while implying lower complexity and better comprehensibility.
Categorical Inference Poisoning: Verifiable Defense Against Black-Box DNN Model Stealing Without Constraining Surrogate Data and Query Times

Deep Neural Network (DNN) models have offered powerful solutions for a wide range of tasks, but the cost to develop such models is nontrivial, which calls for effective model protection. Although black-box distribution can mitigate some threats, model functionality can still be stolen via black-box surrogate attacks. Recent studies have shown that surrogate attacks can be launched in several ways, while the existing defense methods commonly assume attackers with insufficient in-distribution (ID) data and restricted attacking strategies. In this paper, we relax these constraints and assume a practical threat model in which the adversary not only has sufficient ID data and query times but also can adjust the surrogate training data labeled by the victim model. Then, we propose a two-step categorical inference poisoning (CIP) framework, featuring both poisoning for performance degradation (PPD) and poisoning for backdooring (PBD). In the first poisoning step, incoming queries are classified into ID and (out-of-distribution) OOD ones using an energy score (ES) based OOD detector, and the latter are further classified into high ES and low ES ones, which are subsequently passed to a strong and a weak PPD process, respectively. In the second poisoning step, difficult ID queries are detected by a proposed reliability score (RS) measurement and are passed to PBD. In doing so, the first step OOD poisoning leads to substantial performance degradation in surrogate models, the second step ID poisoning further embeds backdoors in them, while both can preserve model fidelity. Extensive experiments confirm that CIP can not only achieve promising performance against state-of-the-art black-box surrogate attacks like KnockoffNets and data-free model extraction (DFME) but also work well against stronger attacks with sufficient ID and deceptive data, better than the existing dynamic adversarial watermarking (DAWN) and deceptive perturbation defense methods. PyTorch code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Hatins/CIP_master.git</uri> .
Discrimination Neural Network Model for Binary Classification Tasks on Tabular Data

For the classification task, neural network-based approaches attempt to distinguish between two distributions by determining the joint distribution of input variables for each class.However, the most challenging task is still to classify the observations in the overlapping region of two classes.In this work, we propose a new discrimination neural network (DiscNN) architecture to address this issue.Our DiscNN learns to embed the initial input into more informative representations with better discriminability between the two distributions based on the cosine embedding loss.We also train our proposed model using the few-shot learning method to extract better-generalized representations from the initial input.We applied the DiscNN model to 35 tabular datasets from the OpenML-CC18 benchmark for a binary classification task.Our model showed superior performances on 28 datasets of them.In addition, we also performed experiments on 95 imbalanced datasets from the KEEL repository.The experiment results showed that the DiscNN outperformed the state-of-the-art models, including CatBoost, LightGBM, TabNet, VIME and Scarf, by around 0.23% AUC, 0.20% G-mean, and 1.06% F1 score.
Studies on construction of real time kinematic inspection field based on geographic information and remote sensing technology

Continuously Operating Reference Station (CORS) is an integrated service system constructed based on multi-base station network Real Time Kinematic (RTK) technology. CORS system is composed of several fixed and continuously operating GNSS reference stations which form a network automatically providing different types of GNSS observations such as carrier phase and pseudorange, various correction numbers, status information and other GNSS services through computers, data communication and Internet (LAN/WAN) technology. This article selects an appropriate location and sets up a reference station to receive Beidou, GPS, GLONASS satellite data basing on CORS system, establishes an All-Terrain inspection field, designs a set of mobile positioning terminals, and develops a set of real time inspection software for the calibration of navigation products. Experimental verification results shows that the calibration system designed by this article is applicable to calibrate navigation products and worthy of extensive promotion.
Metaverse-Based Instructional Settings: Matters That Matter

Teaching in the metaverse can present challenges. Most are unrelated to technology.
Measuring Cryptocurrency Mining in Public Cloud Services: A Security Perspective

Cryptocurrencies, arguably the most prominent application of blockchain systems, have been on the rise with wide mainstream acceptance. A central entity in cryptocurrencies is “mining pools”, groups of cooperating cryptocurrency miners who agree to share block rewards in proportion to their contributed mining hash power. Despite the many promised benefits of cryptocurrencies, they are equally utilized for malicious activities, e.g., ransomware payments, stealthy command, and control, etc. Thus, understanding the interplay between cryptocurrencies, particularly the mining pools, and other essential infrastructures for profiling and characterization is necessary. In this paper, we initiate the study of the interplay between mining pools and public clouds by analyzing their communication association through passive domain name system (pDNS) traces. We observe that 24 cloud providers have some association with mining pools as observed from the pDNS query traces, where popular public cloud providers, namely Amazon and Google, have almost 48% of such an association. Moreover, we found that the cloud provider presence and cloud provider-to-mining pool association exhibit a heavy-tailed distribution, emphasizing an intrinsic preferential attachment model with both mining pools and cloud providers. We measure the security risk and exposure of the cloud providers, as that might aid in understanding the intent of the mining. Among the top two cloud providers, we found almost 35% and 30% of their associated endpoints are positively detected to be associated with malicious activities, per the virustotal.com scan. Finally, we found that the mining pools presented in our dataset are predominantly used for mining Metaverse currencies, highlighting a shift in cryptocurrency use and demonstrating the prevalence of mining using public clouds.
Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%.
Universum-Inspired Supervised Contrastive Learning

Mixup is an efficient data augmentation method which generates additional samples through respective convex combinations of original data points and labels. Although being theoretically dependent on data properties, Mixup is reported to perform well as a regularizer and calibrator contributing reliable robustness and generalization to neural network training. In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum. We find that in the framework of supervised contrastive learning, universum-style Mixup produces surprisingly high-quality hard negatives, greatly relieving the need for a large batch size in contrastive learning. With these findings, we propose Universum-inspired Contrastive learning (UniCon), which incorporates Mixup strategy to generate universum data as g-negatives and pushes them apart from anchor samples of the target classes. Our approach not only improves Mixup with hard labels, but also innovates a novel measure to generate universum data. With a linear classifier on the learned representations, on Resnet-50, our method achieves 81.68% top-1 accuracy on CIFAR-100, surpassing the state of art by a significant margin of 5% with a much smaller batch size.
Protection against attacks using social engineering tools and methods

Objective. In the modern world, innovative technologies are actively developing, communications are moving into the Internet space. Nevertheless, new technologies turned out to be in demand in the segment of organizations, individuals, cyber fraudsters and hackers. The purpose of the study is to analyze the essence and structure of modern social engineering, including reverse social engineering in the context of the digitalization of society. Method. Adapting the study to the current moment, it is clarified that technologies, developments and manipulative sociological tactics are picked up not just by hackers, but also by followers of radical ideas. Result. The optimal definition of the term "social engineering" is proposed - the identification and exploitation of incompetence, insufficient professional level or negligence of employees of an organization or individuals to obtain unauthorized access to confidential data; a set of technologies based on the use of the psychological specifics of a person. Conclusion. The novelty of the study lies in an in-depth study of the classification of types of attacks using social engineering methods and recommendations for countering and preventing them.
Bug localization based on syntactical and semantic information of source code

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree (AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement level. Secondly, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore, the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank (MRR), mean average precision (MAP) and Top N Rank.
Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction. The code and four of the datasets used in this work are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Jess-jpg-txt/sln-learning</uri> .
Digital exchange attributes and the risk of closure

Presently there are over 500 cryptocurrency exchanges worldwide and more than 19,700 different cryptocurrencies. Despite the fact that cryptocurrency trading is possible via private peer-to-peer transactions, more than 90% of trading occurs on organised exchanges which provide convenience and liquidity. However, centralised cryptocurrency exchanges are regarded as high-value targets by criminals and are often victims of cyberattacks. In this paper, we investigate the risk of cryptocurrency exchange closures and develop predictive models to forecast which markets will close down and which ones will remain active using publicly available data. Our models perform well and reach a high level of classification accuracy. Exchange trading volume, the availability of public information on exchange staff, exchange lifetime, and several cybersecurity features are identified as key attributes in predicting exchange closures. Nevertheless, our models do not account for all sources of risk, e.g. potential fraud and mismanagement of client funds committed by the exchanges themselves, and market participants are encouraged to carefully consider where and how they store their digital assets.
Metaverse from Philosophy

When Facebook changed its name to Meta, the upsurge of the metaverse was violently set off. Every company starts claiming they're working on the Metaverse, and everybody talks about the Metaverse. There isn't any formal definition of the Metaverse. This article first introduces the relationship between human beings and the universe, then uses philosophical methods to explore the definitions of the universe and the metaverse, and then tells several philosophical stories related to the metaverse to map the definition of the metaverse.
Time distancing to avoid network microbursts from drones’ high-definition video streams

Maintenance of social infrastructure, such as utility poles and bridges, is a major challenge in the era of declining labor force. While remote maintenance with drones is expected to solve this problem, it requires high-quality images for error-free decisions by a remote maintainer and low-latency to prevent many retries of remote operations since they directly affect maintenance efficiency. For that purpose, low-compression video with low encoding delay and small distortion is a leading candidate. However, we found that such traffic with high-burstiness causes a microburst, which is a short-time congestion experienced at the aggregation point even though the network capacity seems to be sufficient. We also propose a congestion avoidance method by designating appropriate timing to the encoders without losing image quality and report latency reduction in 5G-NR simulations.
A Novel Model for IoT Blockchain Assurance-Based Compliance to COVID Quarantine

IoT technology is emerging as a fully developed automation that could be integrated in various web applications, which will be present in upcoming generations of the World Wide Web. Blockchain, like IoT, is a burgeoning field whereby every system associated in the blockchain incorporates a disseminated ledger that improves safety and consistency. Due to the blockchain network abilities to accomplish smart contracts and consensus, unauthorized users are unable to undertake any fault transactions. The IoT and blockchain can be aggregated to improve application performance dynamically at run time. However, controlling and monitoring the machines linked to sensors in an IoT background and mining the blockchain will always be a technical challenge to the researchers. With this context, this paper enables to review the fundamentals of IoT, blockchain field, and its topographies. In this paper, design architecture, namely, IoT Blockchain Assurance-Based Compliance to COVID Quarantine, is proposed and concluded up with novel architectural framework that improves the efficiency of data safety and data transparency. Unlicensed users are not permitted to conduct any erroneous transactions within the blockchain network, which has the capability to engage in smooth contracts and agreement, thus extending the safekeeping between clinicians and chronically ill patients. This methodology was created with immobile elderly chronically ill patients in mind who are suffering from COVID that require on-the-spot treatment and continuous monitoring by a doctor in mind. This paper is designed to analyze the performance of proposed IoT Blockchain Assurance-Based Compliance to COVID Quarantine with Ethereum private blockchain network beneath a genesis block and the results are conferred.