Reviewerly/exHarmony huggingface dataset:
The Security of ChaCha20-Poly1305 in the Multi-User Setting

The ChaCha20-Poly1305 AEAD scheme is being increasingly widely deployed in practice. Practitioners need proven security bounds in order to set data limits and rekeying intervals for the scheme. But the formal security analysis of ChaCha20-Poly1305 currently lags behind that of AES-GCM. The only extant analysis (Procter, 2014) contains a flaw and is only for the single-user setting. We rectify this situation. We prove a multi-user security bound on the AEAD security of ChaCha20-Poly1305 and establish the tightness of each term in our bound through matching attacks. We show how our bound differs both qualitatively and quantitatively from the known bounds for AES-GCM, highlighting how subtle design choices lead to distinctive security properties. We translate our bound to the nonce-randomized setting employed in TLS 1.3 and elsewhere, and we additionally improve the corresponding security bounds for GCM. Finally, we provide a simple yet stronger variant of ChaCha20-Poly1305 that addresses the deficiencies highlighted by our analysis.
MPD: Moving Target Defense Through Communication Protocol Dialects

Communication protocol security is among the most significant challenges of the Internet of Things (IoT) due to the wide variety of hardware and software technologies involved. Moving target defense (MTD) has been adopted as an innovative strategy to solve this problem by dynamically changing target system properties and configurations to obfuscate the attack surface. Nevertheless, the existing work of MTD primarily focuses on lower-level properties (e.g., IP addresses or port numbers), and only a limited number of variations can be generated based on these properties. In this paper, we propose a new approach of MTD through communication protocol dialects (MPD) - which dynamically customizes a communication protocol into various protocol dialects and leverages them to create a moving target defense. Specifically, MPD harnesses a dialect generating function to create protocol dialects and then a mapping function to select one specific dialect for each packet during communication. To keep different network entities in synchronization, we also design a self-synchronization mechanism utilizing a pseudo-random number generator with the input of a pre-shared secret key and previously sent packets. We implement a prototype of MPD and evaluate its feasibility on standard network protocol (i.e., File Transfer Protocol) and internet of things protocol (i.e., Message Queuing Telemetry Transport). The results indicate that MPD can create a moving target defense with protocol dialects to effectively address various attacks - including the denial of service attack and malicious packet modifications - with negligible overhead.
Cloud Classification of Ground-based Cloud Images based on Convolutional Neural Network

Cloud is a crucial meteorological factor, and also a key factor affecting solar radiation variables for solar energy, especially the attenuation of different cloud types on solar radiation. However, there are few researches on the cloud classification based on the ground-based cloud image in order to predict solar energy. In this paper, the ground-based cloud images were collected, and then they were classified into five classes based on colour, texture and the attenuation on the theoretical clear-sky solar radiation. The ResNet18 model was trained to classify the ground-based cloud images automatically. The experimental results show that the classification accuracy of the ResNet18 is up to 91%, which is higher than the other two convolutional neural networks (AlexNet and VGG16) and is also much higher than the traditional machine learning classifiers. This work provides a new perspective and important supporting data for solar radiation forecast.
Linguistic Descriptions of Human Motion with Generative Adversarial Seq2Seq Learning

In this paper, we propose a generative model that learns a sequence-to-sequence (Seq2Seq) translation between human whole-body motions and linguistic descriptions by natural language. Our model merges the Seq2Seq model with the training strategy of sequence generative adversarial nets (SeqGAN), which extends a GAN framework to solve the problem that the gradient cannot pass back to the generator network. This model considers a generator, trained using a policy gradient method, as a stochastic parameterized policy. In the policy gradient, we employ a Monte Carlo (MC) search to receive the final reinforcement learning (RL) reward from the discriminator. The proposed generative network is trained on the KIT Motion-Language Dataset, which is one of the few large-scale datasets available and includes 3,911 human motions and 6,278 natural language descriptions. During the experiments, we evaluated the effectiveness of our model by comparing its various configurations and parameter settings. Finally, our model achieves a remarkably high performance, outperforming an existing state-of-the-art method under the same dataset split for fair comparison. In addition, the qualitative results of the motion-to-language translation demonstrate that our model can generate semantically and grammatically correct sentences with detailed linguistic descriptions from human motions.
A Simple and Effective Baseline for Robust Logo Detection

This article introduces the solution of the third place team green hand for ACM Multimedia 2021 Security AI Challenger Phase 7: Robust defense competition for e-commerce logo detection. In this work, we use the DetectoRS model and 4 strategies, including resampling, equalization loss v2, data augmentation and weighted boxes fusion. It aims to solve the three main problems in the competition, including small target detection, long-tail distribution and adversarial examples. The final results show that our model achieves an evaluation score of 0.654611 in this semi-final, which ranks first in semi-final and third place in final among all 36,489 teams.
Generalizing to New Domains by Mapping Natural Language to Lifted LTL

Recent work on using natural language to specify commands to robots has grounded that language to LTL. However, mapping natural language task specifications to LTL task specifications using language models require probability distributions over finite vocabulary. Existing state-of-the-art methods have extended this finite vocabulary to include unseen terms from the input sequence to improve output generalization. However, novel out-of-vocabulary atomic propositions cannot be generated using these methods. To overcome this, we introduce an intermediate contextual query representation which can be learned from single positive task specification examples, associating a contextual query with an LTL template. We demonstrate that this intermediate representation allows for generalization over unseen object references, assuming accurate groundings are available. We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references. We demonstrate that the grounded LTL our method outputs can be used for planning in a simulated OO-MDP environment. Finally, we discuss some common failure modes encountered when translating natural language task specifications to grounded LTL.
Partial Tubal Nuclear Norm Regularized Multi-view Learning

Multi-view clustering and multi-view dimension reduction explore ubiquitous and complementary information between multiple features to enhance the clustering, recognition performance. However, multi-view clustering and multi-view dimension reduction are treated independently, ignoring the underlying correlations between them. In addition, previous methods mainly focus on using the tensor nuclear norm for low-rank representation to explore the high correlation of multi-view features, which often causes the estimation bias of the tensor rank. To overcome these limitations, we propose the partial tubal nuclear norm regularized multi-view learning (PTN2ML) method, in which the partial tubal nuclear norm as a non-convex surrogate of the tensor tubal multi-rank, only minimizes the partial sum of the smaller tubal singular values to preserve the low-rank property of the self-representation tensor. PTN2ML pursues the latent representation from the projection space rather than from the input space to reveal the structural consensus and suppress the disturbance of noisy data. The proposed method can be efficiently optimized by the alternating direction method of multipliers. Extensive experiments, including multi-view clustering and multi-view dimension reduction substantiate the superiority of the proposed methods beyond state-of-the-arts.
Assessing Cognitive Load via Pupillometry

A fierce search is called for a reliable, non-intrusive, and real-time capable method for assessing a person’s experienced cognitive load. Software systems capable of adapting their complexity to the mental demand of their users would be beneficial in a variety of domains. The only disclosed algorithm that seems to reliably detect cognitive load in pupillometry signals—the index of pupillary activity (IPA)—has not yet been sufficiently validated. We take a first step in validating the IPA by applying it to a working memory experiment with finely granulated levels of difficulty, and comparing the results to traditional pupillometry metrics analyzed in cognitive research. Our findings confirm the significant positive correlation between task difficulty and IPA the authors stated.
Handwriting Analysis Using Deep Learning Approach for the Detection of Personality Traits

Handwriting is one of many that makes a person distinctive in their identity, and it is the method of identifying the writer's physical characteristics. The computerization is becoming more advanced in different fields and thus, handwriting analysis is gaining more importance these days. For recognizing handwriting, deep learning has been commonly used. In this paper, we have contrived Resnet50 comparison with CNN to predict personality traits. Hence, handwriting is one of the easiest ways to collect information on physical characteristics, such as mental health, emotional state, physical strength and many more.
FLOPs-efficient filter pruning via transfer scale for neural network acceleration

Model pruning is a useful technique to reduce the computational cost of convolutional neural networks. In this paper, we first propose a simple but effective filter level pruning criterion, which assesses the importance of a filter by exploring the transfer scale (TS) of its feature maps in the next layer. The principle is that for a trained CNN model, an important filter should have strong connections with the next layer, otherwise the transfer scale of its feature map will be low and hence removing it will have little influence on the network. Besides, we observe that filters from the computationally-intensive layers are more sensitive to pruning, which makes it difficult to further compress the floating-point operations (FLOPs) of the model without reducing accuracy. To solve this problem, we propose a FLOPs-efficient group Lasso approach for TS to guide the network to use fewer filters in the computationally-intensive layers, which leads to better FLOPs compression performance after pruning. We refer to the proposed method as FETS. Compared with the state-of-the-art methods, our FETS achieves similar or better accuracy, but with significantly larger FLOPs compression ratio. In particular, with VGG-16, ResNet-56 and DenseNet-40 on CIFAR-10, we achieve similar or better accuracies than other methods, with only 48%, 64% and 58% of the FLOPs. With ResNet-50 on ImageNet, we also achieve a relative FLOPs reduction of 30%.
CNewSum: A Large-Scale Summarization Dataset with Human-Annotated Adequacy and Deducibility Level

Automatic text summarization aims to produce a brief but crucial summary for the input documents. Both extractive and abstractive methods have witnessed great success in English datasets in recent years. However, there has been a minimal exploration of text summarization in other languages, limited by the lack of large-scale datasets. In this paper, we present a large-scale Chinese news summarization dataset CNewSum, which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which encourages document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set includes adequacy and deducibility annotations for the summaries. The adequacy level measures the degree of summary information covered by the document, and the deducibility indicates the reasoning ability the model needs to generate the summary. These annotations help researchers target their model performance bottleneck. We examine recent methods on CNewSum and will release our dataset after the anonymous period to provide a solid testbed for automatic Chinese summarization research.
Occlusion Contrasts for Self-Supervised Facial Age Estimation

In this paper, we propose an Occlusion Contrast(OCCO) approach for self-supervised facial partial occluded age estimation. Unlike the conventional facial age estimation approaches which utilize fully-visible faces as input data that does not generalize well for occlusion images, our approach aims to ignore the occlusion and only focus on the non-occluded facial areas so that we can improve the occluded facial age estimation accuracy. To achieve this, we utilize self-supervised contrastive learning to learn non-occluded feature representation, since contrastive learning makes the distances between the anchor and positive samples as close as possible in embedded space, while simultaneously pushing apart the negative samples. Furthermore, our OCCO incorporates with ordinal relationship of different ages, which is modeled by the deep label distribution learning. Considering that face aging datasets usually undergo a label imbalance problem, we employ the cost-sensitive strategy to constrain the learning of classifier. Extensive experimental results on two face aging datasets show that our OCCO not only achieve satisfactory performance over the masked faces but also comparable to the state-of-the-art age estimation methods for raw facial images.
Multi-Scale HARRIS-PIIFD Features for Registration of Visible and Infrared Images

This paper aims at providing visible and infrared images registered in geometric space for image fusion. Focusing on the characteristics and differences of visible and infrared images, a feature-based registration algorithm is implemented. The key technologies include image scale-space for implementing multi -scale properties, Harris corner detection for keypoints extraction, and partial intensity invariant feature descriptor (PIIFD) for keypoints description. Eventually, a multi-scale Harris- Piifd image registration algorithm framework is proposed. The experimental results of four sets of representative real data show that the algorithm has excellent, stable performance in visible and infrared image registration, and can achieve accurate spatial alignment, which has strong practical application value and certain generalization ability.
A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

Unmanned aerial vehicles (UAVs) have recently attracted widespread attention in civil and commercial applications. For example, UAVs (or drone) technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time. However, due to the open atmosphere, drones can easily be lost or captured by attackers when reporting information to the crowd management center. In addition, the attackers may initiate malicious detection to disrupt the crowd-sensing communication network. Therefore, security and privacy are one of the most significant challenges faced by drones or the Internet of Drones (IoD) that supports the Internet of Things (IoT). In the literature, we can find some authenticated key agreement (AKA) schemes to protect access control between entities involved in the IoD environment. However, the AKA scheme involves many vulnerabilities in terms of security and privacy. In this paper, we propose an enhanced AKA solution for crowd monitoring applications that require secure communication between drones and controlling entities. Our scheme supports key security features, including anti-forgery attacks, and confirms user privacy. The security characteristics of our scheme are analyzed by NS2 simulation and verified by a random oracle model. Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.
LiteEdge: Lightweight Semantic Edge Detection Network

Scene parsing is a critical component for understanding complex scenes in applications such as autonomous driving. Semantic segmentation networks are typically reported for scene parsing but semantic edge networks have also become of interest because of the sparseness of the segmented maps. This work presents an end-to-end trained lightweight deep semantic edge detection architecture called LiteEdge suitable for edge deployment. By utilizing hierarchical supervision and a new weighted multi-label loss function to balance different edge classes during training, LiteEdge predicts with high accuracy category-wise binary edges. Our LiteEdge network with only ≈ 3M parameters, has a semantic edge prediction accuracy of 52.9% mean maximum F (MF) score on the Cityscapes dataset. This accuracy was evaluated on the network trained to produce a low resolution edge map. The network can be quantized to 6-bit weights and 8-bit activations and shows only a 2% drop in the mean MF score. This quantization leads to a memory footprint savings of 6X for an edge device.
Self-Supervised Learning for Monocular Depth Estimation on Minimally Invasive Surgery Scenes

Self-supervised learning algorithms that compute depth map from monocular videos have achieved remarkable performance on urban scenes and have been applied extensively. These techniques still face significant challenges, however, when applied directly to endoscopic videos because of the brightness variations from frame to frame and inadequate representation learning during the training phase. Inspired by the optical flow for motion alignment between adjacent frames, we design a AFNet with structural stability loss and residual-based smoothness loss to learn the appearance flow across adjacent frames, which handles the brightness inconsistency issue efficaciously. In addition, we propose a novel self-attention mechanism named feature scaling module to alleviate the inadequate representation learning problem. In a comparison study to the current state-of-the-art self-supervised methods explored for urban videos on the SCARED dataset, the developed model surpasses existing methods by a large margin.
Towards Streaming Egocentric Action Anticipation

Egocentric action anticipation consists in predicting the future actions a camera wearer will likely perform based on past video observations. While in a real-world system it is fundamental to produce such predictions before the action begins, past works have not generally paid attention to model runtime during evaluation. Indeed, current evaluation schemes assume that predictions can be made offline, and hence that computational resources are not limited. In this paper, we propose a "streaming" evaluation protocol which explicitly considers model runtime for performance assessment, assuming that predictions will be available only after the current video segment is processed, which depends on the processing time of a method. Following the proposed evaluation scheme, we benchmark different state-of-the-art approaches for egocentric action anticipation on two popular datasets. Our analysis shows that models with a smaller runtime tend to outperform heavier models in the considered streaming scenario, thus changing the rankings observed in standard offline evaluations. Based on this observation, we propose a lightweight action anticipation model consisting in a simple feed-forward 3D CNN, which we propose to optimize using knowledge distillation techniques and a custom loss. The results show that the proposed approach outperforms prior art in the streaming scenario, also in combination with other lightweight models.
Self-Supervised Motion Retargeting with Safety Guarantee

In this paper, we present self-supervised shared latent embedding (S<sup>3</sup>LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos.
Dynamic-Aware Autonomous Exploration in Populated Environments

Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking passages, narrow paths, exits or entrances to other areas yet to be explored. In this work, we formulate a novel exploration strategy capable of explicitly handling dynamic obstacles, thus leading to complete and reliable exploration outcomes in populated environments. We introduce the concept of dynamic frontiers to represent unknown regions at the boundaries with dynamic obstacles together with a cost function which allows the robot to make informed decisions about when to revisit such frontiers. We evaluate the proposed strategy in challenging simulated environments and show that it outperforms a state-of-the-art baseline in these populated scenarios.