Reviewerly/exHarmony huggingface dataset:
Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.
vContact: Private WiFi-Based IoT Contact Tracing With Virus Lifespan

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close contacts</i> . Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vContact as an Android software development kit and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan.
Boosting Co-teaching with Compression Regularization for Label Noise

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance.Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://github.com/yingyichen-cyy/Nested-Co-teaching.
Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

Cloud datacenter (Dc) have become popular in recent years with the rising popularity and high performance of cloud computing. The multi-step of data computation and diverse task dependencies fail in the task, energy consumption, overloading of Virtual Machines (VMs), and violation of the agreement. To overcome these challenges, we propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA) approach for intelligently schedule the tasks over the VMs for multiuser. This MFTGA approach efficiently maps optimal VMs with users according to the service level agreement (SLA). The presented approach comprises four phases namely individual phase, local phase, global phase, and fault tolerance phase. In the individual phase of the MFTGA algorithm, we calculate the local fitness (fl) of each user. Then calculate the global fitness (fg) of multiuser according to the SLA in the global fitness phase. After mapping the optimal VMs with the multiuser, we check the status of task execution in the fault tolerance phase. MFTGA method is used to improve the reliability, latency, and reduce the failure of the task in the cloud computing environment. The proposed MFTGA scheme is compared against the GA and Adoptive Incremental Genetic Algorithm (AIGA). The simulation results validate that the proposed method exhibits better performance than GA and AIGA in terms of execution time, memory utilization, cost, SLA violation, and energy consumption.
CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs do not take full advantage of the graph’s intrinsic structures (e.g., community structure). Moreover, the pooling operations in existing HGPNNs are difficult to be interpreted. In this paper, we propose a new interpretable graph pooling framework — CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process. Specifically, the proposed community pooling mechanism in CommPOOL utilizes an unsupervised approach for capturing the inherent community structure of graphs in an interpretable manner. CommPOOL is a general and flexible framework for hierarchical graph representation learning that can further facilitate various graph-level tasks. Evaluations on five public benchmark datasets and one synthetic dataset demonstrate the superior performance of CommPOOL in graph representation learning for graph classification compared to the state-of-the-art baseline methods, and its effectiveness in capturing and preserving the community structure of graphs.
Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm

Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.
Perceptual Loss for Robust Unsupervised Homography Estimation

Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimizes the distance in the feature space between the warped image from the source viewpoint and the corresponding image from the target viewpoint. Since we use a fixed pre-trained feature extractor and the only learnable component of our frame-work is the homography network, we effectively decouple the homography estimation from representation learning. We use an additional photometric distortion step in the synthetic COCO dataset generation to better represent the illumination variation of the real-world scenarios. We show that biHomE achieves state-of-the-art performance on synthetic COCO dataset, which is also comparable or better compared to supervised approaches. Furthermore, the empirical results demonstrate the robustness of our approach to illumination variation compared to existing methods.
Wasserstein Proximal of GANs

We introduce a new method for training generative adversarial networks by applying the Wasserstein-2 metric proximal on the generators. The approach is based on Wasserstein information geometry. It defines a parametrization invariant natural gradient by pulling back optimal transport structures from probability space to parameter space. We obtain easy-to-implement iterative regularizers for the parameter updates of implicit deep generative models. Our experiments demonstrate that this method improves the speed and stability of training in terms of wall-clock time and Fréchet Inception Distance.
A comprehensive evaluation of deep models and optimizers for Indian sign language recognition

Deep Learning has become popular among researchers for a long time, and still, new deep convolution neural networks come into the picture very frequently. However, it is challenging to select the best amongst such networks due to their dependence on the tuning of optimization hyperparameters, which is a trivial task. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of pre-trained deep models. It is the first comprehensive analysis of pre-trained deep models, gradient-based optimizers and optimization hyperparameters for static Indian sign language recognition. A three-layered CNN model is also proposed and trained from scratch, which attained the best recognition accuracy of 99.0% and 97.6% on numerals and alphabets of a public ISL dataset. Among pre-trained models, ResNet152V2 performed better than other models with a recognition accuracy of 96.2% on numerals and 90.8% on alphabets of the ISL dataset. Our results reinforce the hypothesis for pre-trained deep models that, in general, a pre-trained deep network adequately tuned can yield results way more than the state-of-the-art machine learning techniques without having to train the whole model but only a few top layers for ISL recognition. The effect of hyperparameters like learning rate, batch size and momentum is also analyzed and presented in the paper.
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

The rapid advances in deep generative models over the past years have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. These advances make assessing the authenticity of visual data increasingly difficult and pose a misinformation threat to the trustworthiness of visual content in general. Although recent work has shown strong detection accuracy of such deepfakes, the success largely relies on identifying frequency artifacts in the generated images, which will not yield a sustainable detection approach as generative models continue evolving and closing the gap to real images. In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. The re-synthesis procedure is flexible, allowing us to incorporate a series of visual tasks - we adopt super-resolution, denoising and colorization as the re-synthesis. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN datasets. Source code is available at https://github.com/SSAW14/BeyondtheSpectrum.
Deep Rival Penalized Competitive Learning for low-resolution face recognition

Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e.g., by video surveillance. Existing methods are featured by learning data uncertainty to avoid overfitting the noise, or by adding margins to the angle or cosine space of the normalized softmax loss to penalize the target logit, which enforces intra-class compactness and inter-class discrepancy. In this paper, we propose a deep Rival Penalized Competitive Learning (RPCL) for deep face recognition in low-resolution (LR) images. Inspired by the idea of the RPCL, our method further enforces regulation on the rival logit, which is defined as the largest non-target logit for an input image. Different from existing methods that only consider penalization on the target logit, our method not only strengthens the learning towards the target label, but also enforces a reverse direction, i.e., becoming de-learning, away from the rival label. Comprehensive experiments demonstrate that our method improves the existing state-of-the-art methods to be very robust for LR face recognition.
DMSE: Dynamic Multi-keyword Search Encryption based on inverted index

With the popularity of cloud storage, increasing people are willing to upload their files to cloud services. They will encrypt these files before uploading to protect the privacy of files. However, encryption makes effective search very difficult and inefficient. In this paper, we design a dynamic multi-keyword searchable encryption scheme on the encrypted cloud files, called DMSE. Firstly, the outsourced files in DMSE are pre-classified and preprocessed according to their privacy to achieve classified search. Secondly, based on the inverted index, DMSE realizes multiple keywords search, which protects the privacy of outsourced files and users and greatly improves the search efficiency. In addition, our DMSE extends the inverted index to the multi-DO/multi-DU scenario, which is the distinguishable difference between DMSE and other existing multi-keyword search schemes. Thirdly, our DMSE can further support the update of keywords to cope with the mismatch dilemma between the first extracted keywords and outsourced files, thus improving the practicality of DMSE scheme. Finally, comprehensive performance evaluation shows our DMSE scheme is effective and feasible in practical application.
<i>DeepRepair:</i> Style-Guided Repairing for Deep Neural Networks in the Real-World Operational Environment

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise, etc. Hence, it poses a rather important problem for the DNNs’ real-world applications: how to repair the deployed DNNs for correcting the failure samples under the deployed operational environment while not harming their capability of handling normal or clean data with limited failure samples we can collect. In this article, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">style-guided data augmentation for repairing DNN in the operational environment</i> , which learns and introduces the unknown failure patterns within the failure samples into the training data via the style transfer. Moreover, we further propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustering-based failure data generation</i> for much more effective style-guided data augmentation. We conduct a large-scale evaluation with 15 degradation factors that may happen in the real world and compare with four state-of-the-art data augmentation methods and two DNN repairing methods. Our technique successfully repairs three convolutional neural networks and two recurrent neural networks with averaging 62.88% and 39.02% accuracy enhancements on the 15 failure patterns, respectively, achieving higher repairing performance than state-of-the-art repairing methods on the most failure patterns with even better accuracy on clean datasets.
DualRing: Generic Construction of Ring Signatures with Efficient Instantiations

We introduce a novel generic ring signature construction, called DualRing, which can be built from several canonical identification schemes (such as Schnorr identification). DualRing differs from the classical ring signatures by its formation of two rings: a ring of commitments and a ring of challenges. It has a structural difference from the common ring signature approaches based on accumulators or zero-knowledge proofs of the signer index. Comparatively, DualRing has a number of unique advantages. Considering the DL-based setting by using Schnorr identification scheme, our DualRing structure allows the signature size to be compressed into logarithmic size via an argument of knowledge system such as Bulletproofs. We further improve on the Bulletproofs argument system to eliminate about half of the computation while maintaining the same proof size. We call this Sum Argument and it can be of independent interest. This DL-based construction, named DualRing-EC, using Schnorr identification with Sum Argument has the shortest ring signature size in the literature without using trusted setup. Considering the lattice-based setting, we instantiate DualRing by a canonical identification based on M-LWE and M-SIS. In practice, we achieve the shortest lattice-based ring signature, named DualRing-LB, when the ring size is between 4 and 2000. DualRing-LB is also 5 $$\times $$ faster in signing and verification than the fastest lattice-based scheme by Esgin et al. (CRYPTO’19).
Smart contracts in tourism industry: a model with blockchain integration for post pandemic economy

This research aims to fill the gap in the existing methods related to the use of the Internet of Things (IoT) devices with smart contracts without a need for intermediaries for the reservations and services secured by Blockchain for the post-pandemic economy. Although there is a limited number of studies in the literature, which are mainly based on the reviews of the integration of smart contracts with the IoT devices, the questions about how to apply these methods in the tourism industry remain unanswered. Therefore, this present study contributes to the existing literature by creating a contract with a reservation system integrated with hotel services. The proposed method creates a booking system with a unique smart contract between customers and hotels. These contracts will include all kinds of services a customer may need during the stay and will be secured by a Blockchain structure. Besides, it might help eliminate commission fees and reception costs with the decentralized proof mechanism in the proposed method which combines smart services, IoT devices, decentralized proof methods, and interplanetary file systems with Blockchain-based smart contracts.
Poison Ink: Robust and Invisible Backdoor Attack

Recent research shows deep neural networks are vulnerable to different types of attacks, such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among them, backdoor attacks are the most cunning and can occur in almost every stage of the deep learning pipeline. Backdoor attacks have attracted lots of interest from both academia and industry. However, most existing backdoor attack methods are visible or fragile to some effortless pre-processing such as common data transformations. To address these limitations, we propose a robust and invisible backdoor attack called "Poison Ink". Concretely, we first leverage the image structures as target poisoning areas and fill them with poison ink (information) to generate the trigger pattern. As the image structure can keep its semantic meaning during the data transformation, such a trigger pattern is inherently robust to data transformations. Then we leverage a deep injection network to embed such input-aware trigger pattern into the cover image to achieve stealthiness. Compared to existing popular backdoor attack methods, Poison Ink outperforms both in stealthiness and robustness. Through extensive experiments, we demonstrate that Poison Ink is not only general to different datasets and network architectures but also flexible for different attack scenarios. Besides, it also has very strong resistance against many state-of-the-art defense techniques.
QED: A Framework and Dataset for Explanations in Question Answering

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.