Reviewerly/exHarmony huggingface dataset:
Optimal coverage control of stationary and moving agents under effective coverage constraints

This paper addresses the problem of maximizing coverage in a mission space with both stationary and mobile agents such that effective coverage constraints are satisfied, i.e., each point of the mission space must be covered to a predefined level at least once over a given time period. The deployment of the stationary agents may be given in advance or may be obtained by a classical coverage control algorithm. The motion planning of the mobile agents is designed under maximal speed and acceleration constraints. When there is only one mobile agent, it is shown that its path planning and velocity planning can be designed separately. We first obtain an optimal velocity policy and the corresponding optimal coverage performance for a given path, which provides a criterion to prescribe a good path. Then, path planning is generated to meet the effective coverage constraint by connecting a set of “inspection points”. Inspired by the optimal velocity policy, we propose three methods to generate the inspection points and obtain the optimal order of connecting the inspection points by a Traveling Salesman Problem (TSP)-based method. Finally, we extend the one-mobile-agent motion planning scheme to multiple mobile agents by proposing two methods. Simulation examples are included to compare the performance of the three methods for generating inspection points and compare the performance of the proposed methods for multiple mobile agents.
A scalable, efficient, and secured consensus mechanism for Vehicle-to-Vehicle energy trading blockchain

Vehicle-to-Vehicle (V2V) energy trading has emerged as a promising scheme to relieve the load imposed on the grid without intermediaries. The blockchain has always been regarded as the most potential solution for addressing the security and privacy issues of the Internet of Electric Vehicles (IoEV). The consensus mechanism is the core of the blockchain, it determines the security, efficiency, and scalability of the system. However, consensus mechanisms currently employed in V2V energy trading are traditional algorithms, which are unsuitable for the IoEV due to their high computational power and communication overhead. Therefore, we are motivated to propose a Block Alliance Consensus (BAC) mechanism based on Hashgraph. BAC can maintain the high throughput of the Hashgraph, and it solves the problem that the Hashgraph cannot support the dynamic addition and deletion of nodes if it is directly applied to V2V energy trading. We utilize the sharding technique, and each shard generates a consortium blockchain in accordance with the characteristic of the IoEV. The centralized component is retained to ensure macroeconomic control by state and local governments. We design a cryptography-based leader election combined with a reputation incentive mechanism to fairly elect centralized leader committee and motivate honest electric vehicles (EVs). We implement the BAC consensus and V2V energy trading blockchain (ETB) on the Hyperledger Fabric. The performance and practicality of BAC and the V2V ETB are verified through experiments.
Online or remote education? Preferences of Colombian higher education students

While some students had experience receiving online education prior to the COVID-19 pandemic, the emergency remote modality offered a different experience given that higher education faculty had limited time for planning and, most of them, did not have any experience in online education. This research aims to identify the differences between undergraduate students’ perceptions and preferences on emergency remote and online education. Researchers identified a set of online education courses–that pre-existed COVID-19 times–and courses that were transformed into remote education courses–designed as a response of the COVID-19 emergency. Participants of this study are a group of students who, during the same academic semester, participated in both online and (emergency) remote courses. Researchers used a survey to understand students’ perceptions and preferences assessing their experiences in different dimensions (e.g., interaction and evaluation). Researchers used the add-on preferential groups model to identify which of the two modalities was preferred by the students for each dimension. The quantitative analysis was complemented using content analysis of responses to open-ended questions, seeking to gain a better understanding of students’ perceptions and preferences between online and emergency remote education. The results show that students have a positive view of the online and remote modalities; however, each of these modalities offers students advantages for specific dimensions. For example, the remote modality was preferred for having greater interaction among peers and with the instructor. On the other hand, online education provided more flexibility. Students suggested improving assessments’ approaches and instructors’ technological skills for both modalities.
Forged facial video detection framework based on multi-region temporal relationship feature

Face generation and manipulation techniques based on deep learning have enabled the creation of sophisticated forged facial videos, which are indistinguishable by human eyes. However, the illegal use of deep fake technology will have a serious impact on social stability, personal reputation, and even national security. Therefore, the detection technology of fake facial videos is of great significance to protect national security and maintain social order. Although the existing video-based fake face video detection technology has achieved good detection performance on the public fake face video database, there are still the following problems: (1) the existing technology uses a 2D attention mechanism to obtain local region features from face images and lacks a 3D attention mechanism to obtain local area features from face videos; (2) after obtaining local area features, the existing technology is directly used to classify or only model the inter-regional relationship of images without modeling the temporal relationship between regions of the video. This paper proposes a fake facial video detection framework based on multi-region temporal relationship features, including designing a three-dimensional attention mechanism to extract local features of multiple regions of the face from the video. In order to model the time series relationship between different face areas, a time series graph convolution neural network is also introduced to extract the time series relationship features between multiple areas. In order to model the time sequence relationship between different face regions, the convolution neural network of a time sequence diagram is also introduced to extract the characteristics of the time sequence relationship between multiple regions. Through the change characteristics of the time sequence relationship between face regions, the timing inconsistency of the face video is detected so as to determine whether the face has been deeply forged. Through experiments on multiple datasets, the experimental results of the model accuracy test show that the method proposed in the present invention achieves the highest detection accuracy, and the accuracy in the FaceForensics++ (low definition) dataset is 18.19% higher than that of the benchmark method. The experimental results of the generalization ability test show that the method proposed in the present invention achieves the highest generalization performance, and the detection accuracy in the Celeb-DF dataset is 11.92% higher than that of the benchmark method.
E-VarifocalNet: A Lightweight Model to Detect Insulators and Their Defects under Power Grid Surveillance

Detecting insulators and their defects is a key task in real-time power grid surveillance with the rapid development of national smart grid. Traditional surveillance usually relies on maintenance personnel, leading to the issues of inefficiency and unsafety. Thus, with the prosperity of deep learning, we proposed a detection algorithm, named E-VarifocalNet, which is an enhanced version of the basic VarifocalNet method. The proposed E-VarifocalNet is specifically designed for detecting insulators and their defects. We developed a classification loss based on varifocal loss and the number of samples to solve the imbalance problem in object detection. Furthermore, a regression loss based on GIoU loss and Wasserstein distance is designed to gain higher flexibility in the representation of bounding boxes. Additionally, we applied a feature pyramid network based on dilated convolution and heatmap to build global and local semantic relations among pixels so as to enhance the detection accuracy on salient areas. Our dataset containing 2,100 images and 5,217 object instances was collected through real-time drones and an open data platform. Our E-VarifocalNet gets the highest mAP and a low model complexity on our dataset among state-of the-art object detectors, indicating the potential of our algorithm in real-time power grid surveillance applications.
A Conceptual Architecture for Scalable Multi-Application Support in Blockchain-based IoT Environments

Existing proposals that merge IoT with blockchain technologies have been efficient but often limited to a single application scenario. They require specific hardware setups, thus in case of switching to a new application scenario, they may become ineffective and require a new hardware setup. In this paper, we present a new blockchain-based architecture for IoT environments that addresses one of the fundamental challenges in the area which is the capability of supporting multiple application scenarios. The proposed architecture enables transitioning between various application scenarios via smart contracts and configuration files stored on blockchain. The architecture, additionally, addresses scalability concerns of the blockchain-based IoT systems through a cluster-based approach which provides a more flexible and secure solution.
Multi-Prior Based Multi-Scale Condition Network for Single-Image HDR Reconstruction

High Dynamic Range (HDR) imaging aims to reconstruct the natural appearance of real-world scenes by expanding the bit depth of captured images. However, due to the imaging pipeline of off-the-shelf cameras, information loss in over-exposed areas and noise in under-exposed areas pose significant challenges for single-image HDR imaging. As a result, the key to success lies in restoring over-exposed regions and denoising under-exposed regions. In this paper, a multi-prior based multi-scale condition network is proposed to address this issue. (1) Three types of prior knowledge modulate the intermediate features in the reconstruction network from different perspectives, resulting in improved modulation effects. (2) Multi-scale fusion extracts and integrates deep semantic information from various priors. Experiments on the NTIRE HDR challenge dataset demonstrate that the proposed method achieves state-of-the-art quantitative results.
DEMO: STM - A Privacy-Enhanced Solution for Spatio-Temporal Trajectory Management

In this demonstration paper, we present STM: a new system for securing and management of vehicle trajectory data using a generative model that balances privacy and utility. For instance, traditional methods for taxi-demand prediction pose the risk of privacy breaches from both the data and the model. To address this challenge, we deploy Spatiotemporal-GAN to generate synthetic trajectories that meet privacy regulations such as GDPR. We assess the quality of the generated data by constructing several taxi-demand prediction models. Moreover, we evaluate the privacy risk by implementing trajectory user linking attacks against the generated data and membership inference attacks against the prediction model. Our system is designed with rich interactivity and visualization, enabling the audience to use these modules. Overall, our approach demonstrates the potential of generative models in preserving privacy while maintaining data utility in the context of taxi-demand prediction.
RaFID: A Lightweight Approach to Radio Frequency Interference Detection in Time Domain using LSTM and Statistical Analysis

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the generation of an expected signal using DNNs within the time domain. We performed the statistical analysis to compare our generated expected signal with the received signal to detect the existence of interference and determine interference frequency. Experimental results show that signal estimation is accurate, with a mean squared error of 0.012 and an average run-time of 0.5 seconds.
Zero-Correlation-Zone Sonar Sequences

In this paper, we define (m,n,r) zero-correlation-zone (ZCZ) sonar sequences and present some of their properties. We prove an upper bound on r for (m,n,r) ZCZ sonar sequences and propose a new and simple construction for (m,n,r) ZCZ sonar sequences with m = r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> − 1 and any positive integer n. We also propose two constructions for (m,n,2) ZCZ-DD sonar sequences for some m and n which are some variations of well-known sonar sequence constructions. We report a lot of exhaustive search results and some interesting open problems.
Bounds on the Essential Covering Radius of Constrained Systems

Motivated by applications for error-correcting constrained codes, we study the essential covering radius of constrained systems. In a recent work, the essential covering radius was suggested as new fundamental parameter of constrained systems that characterizes the error-correction capabilities of the quantized-constraint concatenation (QCC) scheme. We provide general efficiently computable upper-bounds on the essential covering radius using Markov chains and sliding-block codes, which in some cases, we show to be tight.
Constructive Combinatorics in Elementary School Mathematics

Abstract The paper describes in detail a class of educational problems from an elementary school course of mathematics and computer science. This course has been implemented over the past decades by a team led by Academician of the RAS A.L. Semenov. In the problems, it is necessary to find, build, or list all objects that satisfy a certain system of conditions. The student conducts these activities in a visual world of basic objects of discrete mathematics and computer science: strings (finite sequences of symbols), bags (multisets), tables, trees, and statements containing quantifiers. The connections of these problems with problems in computational combinatorics (counting the number of options), with search problems in the theory of computational complexity, with “big ideas,” and general cognitive strategies for their formation in education are considered. The content of education in our approach is more adequate to the context of the modern world.
A Practical Upper Bound for the Worst-Case Attribution Deviations

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks; however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{2}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{\infty}$</tex> -norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.
CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning

The recently proposed FixMatch and FlexMatch have achieved remarkable results in the field of semi-supervised learning. But these two methods go to two extremes as FixMatch and FlexMatch use a pre-defined constant threshold for all classes and an adaptive threshold for each category, respectively. By only investigating consistency regularization, they also suffer from unstable results and indiscriminative feature representation, especially under the situation of few labeled samples. In this paper, we propose a novel CHMatch method, which can learn robust adaptive thresholds for instance-level prediction matching as well as discriminative features by contrastive hierarchical matching. We first present a memory-bank based robust threshold learning strategy to select highly-confident samples. In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning. CHMatch achieves very stable and superior results on several commonly-used benchmarks. For example, CHMatch achieves 8.44% and 9.02% error rate reduction over FlexMatch on CIFAR-100 under WRN-28-2 with only 4 and 25 labeled samples per class, respectively <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project address: https://github.com/sailist/CHMatch.
1000 FPS HDR Video with a Spike-RGB Hybrid Camera

Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging. The increasing frame rate is usually guaranteed by using shorter exposure time so that the captured video is severely interfered by noise. Alternating exposures can alleviate the noise issue but sacrifice frame rate due to involving long-exposure frames. The neuromorphic spiking camera records high-speed scenes of high dynamic range without colors using a completely different sensing mechanism and visual representation. We introduce a hybrid camera system composed of a spiking and an alternating-exposure RGB camera to capture HFR&HDR scenes with high fidelity. Our insight is to bring each camera's superiority into full play. The spike frames, with accurate fast motion information encoded, are firstly reconstructed for motion representation, from which the spike-based optical flows guide the recovery of missing temporal information for long-exposure RGB images while retaining their reliable color appearances. With the strong temporal constraint estimated from spike trains, both missing and distorted colors cross RGB frames are recovered to generate time-consistent and HFR color frames. We collect a new Spike-RGB dataset that contains 300 sequences of synthetic data and 20 groups of real-world data to demonstrate 1000 FPS HDR videos outperforming HDR video reconstruction methods and commercial high-speed cameras.
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval

Dominant pre-training works for image-text retrieval adopt “dual-encoder” architecture to enable high efficiency, where two encoders are used to extract image and text representations and contrastive learning is employed for global alignment. However, coarse-grained global alignment ignores detailed semantic associations between image and text. In this work, we propose a novel proxy task, named Visual-Language Error Modeling (ViLEM), to inject detailed image-text association into “dual-encoder” model by “proofreading” each word in the text against the corresponding image. Specifically, we first edit the image-paired text to automatically generate diverse plausible negative texts with pre-trained language models. ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information. Further-more, we propose a multi-granularity interaction framework to perform ViLEM via interacting text features with both global and local image features, which associates local text semantics with both high-level visual context and multi-level local visual information. Our method surpasses state-of-the-art “dual-encoder” methods by a large margin on the image-text retrieval task and significantly improves discriminativeness to local textual semantics. Our model can also generalize well to video-text retrieval.
Adaptive Graph Convolutional Subspace Clustering

Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that, by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We present the codes of AGCSC and the evaluated algorithms on https://github.com/weilyshmtu/AGCSC. outperforms some related methods as well as some deep models.