Reviewerly/exHarmony huggingface dataset:
Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.
A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications

When applying deep learning methods in an industrial vision application, they often fall short of the performance shown in a clean and controlled lab environment due to data quality issues. Few would consider the actual labels as a driving factor, yet inaccurate label data can impair model performance significantly. However, being able to mitigate inaccurate or incomplete labels might also be a cost-saver for real-world projects. Here, we survey state-of-the-art deep learning approaches to resolve such missing labels, noisy labels, and partially labeled data in the prospect of an industrial vision application. We systematically present un-, weakly, and semi-supervised approaches from 'A' like anomaly detection to 'Z' like zero-shot classification to resolve these challenges by embracing them.
Environment Agnostic Invariant Risk Minimization for Classification of Sequential Datasets

The generalization of predictive models that follow the standard risk minimization paradigm of machine learning can be hindered by the presence of spurious correlations in the data. Identifying invariant predictors while training on data from multiple environments can influence models to focus on features that have an invariant causal relationship with the target, while reducing the effect of spurious features. Such invariant risk minimization approaches heavily rely on clearly defined environments and data being perfectly segmented into these environments for training. However, in real-world settings, perfect segmentation is challenging to achieve and these environment-aware approaches prove to be sensitive to segmentation errors. In this work, we present an environment-agnostic approach to develop generalizable models for classification tasks in sequential datasets without needing prior knowledge of environments. We show that our approach results in models that can generalize to out-of-distribution data and are not influenced by spurious correlations. We evaluate our approach on real-world sequential datasets from various domains.
Scene Coordinate Regression Network With Global Context-Guided Spatial Feature Transformation for Visual Relocalization

Among visual relocalization from a single RGB image, the scene coordinate regression (SCoRe) based on convolutional neural network (CNN) becomes prevailing, however, it is insufficient to extract invariant features under different viewpoints due to fixed geometric structures of CNN. In this letter, we propose a global context-guided spatial feature transformation (SFT) network to learn invariant feature representation for robustness against viewpoint changes. Specifically, global feature extracted from source feature map is regarded as a dynamic convolutional kernel, which is convolved with source feature map for the prediction of transformation parameters. The predicted parameters are used to transform features of multiple viewpoints to a canonical space with the constraint of maximum likelihood-derived loss, and thus viewpoint invariance is achieved. CoordConv is also employed to further improve the discrimination of features on texture-less or repetitive zones. The proposed SFT network can be easily incorporated into the general SCoRe network. To our best knowledge, features are first decoupled from viewpoints explicitly in SCoRe network by the spatial feature transformation network, which achieves a stable and accurate visual relocalization. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency.
Imbalanced High-Resolution SAR Ship Recognition Method Based on a Lightweight CNN

Convolutional neural network (CNN)-based methods have become the mainstream in radar ship recognition. However, these methods suffer from two common problems. First, the training samples consist largely of common ship types, giving them an overwhelming numerical advantage over rare ship types. As a result, CNN-based recognition algorithms fail to classify rare ship types correctly. Second, huge high-resolution slices result in heavy computational burdens. To solve the first problem, namely, the class imbalance problem, this letter proposes a CNN training method that combines deep metric learning (DML) with gradually balanced sampling. DML obtains the center of each class in the feature space and performs clustering equally. Gradually balanced sampling adopts a smooth transition from instance-aware resampling to class-aware resampling to improve the recognition rate drop caused by traditional resampling methods. As for the second problem, to reduce the computational complexity of high-resolution synthetic aperture radar (SAR) images, a lightweight CNN is also proposed.
Embedding Disentanglement in Graph Convolutional Networks for Recommendation

To improve recommendation performance, many efforts have been made to study how to equip the conventional methods with auxiliary information such as item relations. Meanwhile, a growing body of work has focused on applying graph convolutional networks to recommendation tasks. Thus, it is promising to use graph convolution to model multi-order relations for improving recommendation performance. However, the existing graph convolution-based recommendation methods may suffer from structural design problems: for methods with embedding transformations in graph convolutional layers, the MLP makes the updated embedding dimensions coupled, hurting the embedding expressivity. While for methods based on simplified graph convolution, removing the parameter matrices makes the model attach a same weight to embeddings in different layers, limiting the model expressivity. In this paper, we propose a novel graph convolution-based recommendation method, namely Channel-Independent Graph Convolutional Network (CIGCN). To learn disentangled embeddings, CIGCN uses diagonal parameter matrices as filters in graph convolution, keeping the updated embedding dimensions independent. In addition, with layer-aggregation strategies, the parameters in the diagonal matrices act as trainable weights that attach different importance to the embeddings in each layer and each dimension, enhancing the model expressivity. Results of extensive experiments show the superior performance of our proposed method.
eat2pic: Food-tech Design as a Healthy Nudge with Smart Chopsticks and Canvas

In this paper, we introduce a food-tech design as a healthy nudge called eat2pic that encourages people to slow down their eating pace and encourage healthier eating habits. The eat2pic system is composed of a sensor-equipped chopstick (one of a pair) and components that use context-aware digital canvases. The eat2pic system achieves (1) automatic tracking of what and how fast the user consumed each mouthful of food, (2) real-time visual feedback that indicates good or bad eating behavior. The key concept of eat2pic involves extending the relationship between humans and paintings in order to establish a closed-loop in which daily eating behavior is reflected in pictures on the canvas near us and feedback from the canvas leads our lifestyles towards healthier diets. The eat2pic system provides a novel experience in which users can come to see eating as a playful task that involves coloring landscape pictures, and not simply taking nutrition into their bodies.
Introducing Uncertainty into Explainable AI Methods

Learning from uncertain or incomplete data is one of the major challenges in building artificial intelligence systems. However, the research in this area is more focused on the impact of uncertainty on the algorithms performance or robustness, rather than on human understanding of the model and the explainability of the system. In this paper we present our work in the field of knowledge discovery from uncertain data and show its potential usage for the purpose of improving system interpretability by generating Local Uncertain Explanations (LUX) for machine learning models. We present a method that allows to propagate uncertainty of data into the explanation model, providing more insight into the certainty of the decision making process and certainty of explanations of these decisions. We demonstrate the method on synthetic, reproducible dataset and compare it to the most popular explanation frameworks.
Predicting Elections Results using Social Media Activity A Case Study: USA Presidential Election 2020

Utilizing social media to ascertain its user's opinions over an entity, more specifically Twitter to forecast Trends is a popular field of research. Employing Twitter for election campaigns, to monitor predict election results, capturing the say of both voters candidates in real-time has escalated over the years. Users share their views, preferences on Twitter voluntarily and are publicly accessible. `Tweets' are quick, brief real-time user updates and can be extracted through Twitter API. Downloading replies on the most recent Tweets of the candidates can serve our purpose. Location parameters can help in obtaining more accurate credible results. in this paper, we have compared four methods from machine learning and deep learning domains named as “textblob”, “naive bayes method “support vector machine” and “BERT based deep learning approach” for sentiment analysis. we found that BERT based approach is superior to others. To demonstrate the performance of these methodologies we have taken a recent USA presidential election 2020 as a case study.
A Sports Training Video Classification Model Based on Deep Learning

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.
Reversible data hiding in encrypted color images using cross-channel correlations

In recent years, the increasing requirements in cloud storage and cloud computing have made it necessary to encrypt digital images for privacy protection. Meanwhile, many reversible data hiding (RDH) algorithms in the encrypted domain have been proposed. However, most of these algorithms are for gray-level images, and the intrinsic cross-channel correlations of color images cannot be utilized to improve the embedding capacity. In this paper, we propose a novel data hiding method for encrypted color images. In the encryption stage, the homomorphic property of encryption is achieved by basic modular addition. During the data hiding process, the cross-channel correlations between R, G and B channels are generated in encrypted domain, and data hiding is performed by the difference histogram shifting. Analysis and experiments demonstrate that the proposed method is secure and the RDH performance is superior.
Deep Matching Prior: Test-Time Optimization for Dense Correspondence

Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model in general. To overcome this, recent learning-based methods have attempted to learn a good matching prior within a model itself on large training data. The performance improvement was apparent, but the need for sufficient training data and intensive learning hinders their applicability. Moreover, using the fixed model at test time does not account for the fact that a pair of images may require their own prior, thus providing limited performance and poor generalization to unseen images.In this paper, we show that an image pair-specific prior can be captured by solely optimizing the untrained matching networks on an input pair of images. Tailored for such test-time optimization for dense correspondence, we present a residual matching network and a confidence-aware contrastive loss to guarantee a meaningful convergence. Experiments demonstrate that our framework, dubbed Deep Matching Prior (DMP), is competitive, or even outperforms, against the latest learning-based methods on several benchmarks for geometric matching and semantic matching, even though it requires neither large training data nor intensive learning. With the networks pre-trained, DMP attains state-of-the-art performance on all benchmarks.
Optimization of a Controlled- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>Z</mml:mi></mml:math> Gate with Data-Driven Gradient-Ascent Pulse Engineering in a Superconducting-Qubit System

The experimental optimization of a two-qubit controlled-Z (CZ) gate is realized following two different data-driven gradient ascent pulse engineering (GRAPE) protocols in the aim of optimizing the gate operator and the output quantum state, respectively. For both GRAPE protocols, the key computation of gradients utilizes mixed information of the input Z-control pulse and the experimental measurement. With an imperfect initial pulse in a flattop waveform, our experimental implementation shows that the CZ gate is quickly improved and the gate fidelities subject to the two optimized pulses are around 99%. Our experimental study confirms the applicability of the data-driven GRAPE protocols in the problem of the gate optimization.
Supply chain finance based on smart contract

With the emergence and development of blockchain technology, due to its innovation in the underlying technology, many potential innovations have been created in the financial aspects of different industries, and even disruptive changes have occurred. Especially mature applications in supply chain finance are more extensive, and at the same time, the technology also effectively promotes the development of finance-related technology. We proposed a blockchain-based framework and used an accessory technology, namely smart contracts. The credit mechanism can be reformed to promote the flow of credit value and make it highly coupled with financial scenarios to obtain the feasibility of supply chain process design.
P2DPI: Practical and Privacy-Preserving Deep Packet Inspection

The amount of encrypted Internet traffic almost doubles every year thanks to the wide adoption of end-to-end traffic encryption solutions such as IPSec, TLS and SSH. Despite all the benefits of user privacy the end-to-end encryption provides, the encrypted internet traffic blinds intrusion detection system (IDS) and makes detecting malicious traffic hugely difficult. The resulting conflict between the user's privacy and security has demanded solutions for deep packet inspection (DPI) over encrypted traffic. The approach of those solutions proposed to date is still restricted in that they require intensive computations during connection setup or detection. For example, BlindBox, introduced by Sherry et al. (SIGCOMM 2015) enables inspection over the TLS-encrypted traffic without compromising users' privacy, but its usage is limited due to a significant delay on establishing an inspected channel. PrivDPI, proposed more recently by Ning et al. (ACM CCS 2019), improves the overall efficiency of BlindBox and makes the inspection scenario more viable. Despite the improvement, we show in this paper that the user privacy of Ning et al.'s PrivDPI can be compromised entirely by the rule generator without involving any other parties, including the middlebox. Having observed the difficulties of realizing efficiency and security in the previous work, we propose a new DPI system for encrypted traffic, named "Practical and Privacy-Preserving Deep Packet Inspection (P2DPI)''. P2DPI enjoys the same level of security and privacy that BlindBox provides. At the same time, P2DPI offers fast setup and encryption and outperforms PrivDPI. Our results are supported by formal security analysis. We implemented our P2DPI and comparable PrivDPI and performed extensive experimentation for performance analysis and comparison.
Domain Shift Preservation for Zero-Shot Domain Adaptation

In learning-based image processing a model that is learned in one domain often performs poorly in another since the image samples originate from different sources and thus have different distributions. Domain adaptation techniques alleviate the problem of domain shift by learning transferable knowledge from the source domain to the target domain. Zero-shot domain adaptation (ZSDA) refers to a category of challenging tasks in which no target-domain sample for the task of interest is accessible for training. To address this challenge, we propose a simple but effective method that is based on the strategy of domain shift preservation across tasks. First, we learn the shift between the source domain and the target domain from an irrelevant task for which sufficient data samples from both domains are available. Then, we transfer the domain shift to the task of interest under the hypothesis that different tasks may share the domain shift for a specified pair of domains. Via this strategy, we can learn a model for the unseen target domain of the task of interest. Our method uses two coupled generative adversarial networks (CoGANs) to capture the joint distribution of data samples in dual-domains and another generative adversarial network (GAN) to explicitly model the domain shift. The experimental results on image classification and semantic segmentation demonstrate the satisfactory performance of our method in transferring various kinds of domain shifts across tasks.
Zeroth-order algorithms for stochastic distributed nonconvex optimization

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(p/(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p/(nT)) convergence rate when the global cost function additionally satisfies the Polyak–Łojasiewicz (P–Ł) condition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms. It consequently enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relatively bounded second moment assumptions and the P–Ł condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms.
A Framework for Modeling Cyber Attack Techniques from Security Vulnerability Descriptions

Attack graphs are one of the main techniques used to automate the cybersecurity risk assessment process. In order to derive a relevant attack graph, up-to-date information on known cyber attack techniques should be represented as interaction rules. However, designing and creating new interaction rules is a time consuming task performed manually by security experts. We present a novel, end-to-end, automated framework for modeling new attack techniques from the textual description of security vulnerabilities. Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is then integrated within the MulVal attack graph tool. The proposed framework implements a novel data science pipeline that includes a dedicated cybersecurity linguistic model trained on the NVD repository, a recurrent neural network model used for attack entity extraction, a logistic regression model used for completing the missing information, and a transition probability matrix for automatically generating new interaction rule. We evaluated the performance of each of the individual algorithms, as well as the complete framework, and demonstrated its effectiveness.