Reviewerly/exHarmony huggingface dataset:
Post-Authorship Attribution Using Regularized Deep Neural Network

Post-authorship attribution is a scientific process of using stylometric features to identify the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has useful applications in manifold domains, for instance, in a verification process to proactively detect misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The process assumes that texts can be characterized by sequences of words that agglutinate the functional and content lyrics of a writer. However, defining an appropriate characterization of text to capture the unique writing style of an author is a complex endeavor in the discipline of computational linguistics. Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy of authorship attribution. The vocabularies include idioms, onomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network (RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution. It is based on a convolutional neural network, bidirectional long short-term memory encoder, and distributed highway network. The neural network was used to extract lexical stylometric features that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The feature vector was then supplied as input to the distributed high networks for regularization to minimize the network-generalization error. The regularized feature vector was ultimately passed to the bidirectional decoder to learn the writing style of an author. The feature-classification layer consists of a fully connected network and a SoftMax function to make the prediction. The RDNN method was tested against thirteen state-of-the-art methods using four benchmark experimental datasets to validate its performance. Experimental results have demonstrated the effectiveness of the method when compared to the existing state-of-the-art methods on three datasets while producing comparable results on one dataset.
Decompression induced enrichment and precipitation of gold in the polymetallic sulfide stage of the northwest Jiaodong gold deposits, eastern North China Craton

Mechanisms for the enrichment and re-precipitation of gold in the giant Jiaodong gold deposits (eastern North China Craton) remain poorly constrained. To better understand the mineralization mechanism, we did in situ analyses of S isotopes on sulfides such as pyrite, pyrrhotite, galena and chalcopyrite from the disseminated (altered-rock type) and quartz-vein type gold deposits by femtosecond laser ablation coupled multi-collector inductively coupled plasma mass spectrometry. Pyrites from the altered-rock type gold deposit show δ34S values in the range from 7.4 to 11.3 ‰, which is obviously heavier than the quartz-vein type gold deposits with δ34S = 6.2 ∼ 8.8 ‰. Traditionally, the difference of sulfur isotopic compositions between the two types of gold deposits was attributed to the change in oxygen fugacity. However, we found that, from early to late metallogenic stage, sulfur isotopes of pyrites from the altered rock type gold deposits tend to decrease gradually and pyrrhotites can always be observed in the third stage. Moreover, the S isotopic compositions (δ34S = 7.9 to 8.2 ‰) of the pyrites coexisting with magnetite are comparable with those (δ34S = 6.2 to 8.0 ‰) of the pyrites coexisting with pyrrhotite in the quartz vein type gold deposits. These features indicate that the decrease of sulfur isotopes in pyrites was not caused by increase of oxygen fugacity. We suggest that the S isotopic and fO2 variation could be ascribed to an increase of pH of the ore-forming fluid, which is supported by the typically quartz dissolution and common occurrence of calcite and pyrrhotite in the late metallogenic stage (the third stage) and an overall decrease of aluminum contents of quartz from core to rim. We further proposed that the variation of pH of ore-forming fluids is probably related to a process of decompression due to development and enlargement of fractures filled with ore-forming fluids. Gold enrichment in the main ore-forming stage of the northwest Jiaodong gold deposit probably was realized by multiple phases of fluid pressure fluctuation, which subsequently led to repeatedly dissolution and re-precipitation of Au from pyrites due to decreasing oxygen fugacity and increasing pH values of the ore-forming fluids.
Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces

Recently, the Randomized Kaczmarz algorithm (RK) draws much attention because of its low computational complexity and less requirement on computer memory. Many existing results on analysis focus on the behavior of RK in Euclidean spaces, and typically derive exponential converge rates with the base tending to one, as the condition number of the system increases. The dependence on the condition number largely restricts the application of these estimates. There are also results using relaxation (i.e., small step-sizes tending to zero as the sample size increases) to achieve polynomial convergence rates of RK in Hilbert spaces. In this paper, we prove the weak convergence (with polynomial rates) of RK with constant step-size in Hilbert spaces. As a consequence, the strong convergence of RK in Euclidean spaces is obtained with condition number-free polynomial rates. In the setting with noisy data, we study the relaxation technique and obtain a strong convergence of RK in Hilbert spaces, with the rates arbitrarily close to the minimax optimal ones. We apply the analysis to reproducing kernel-based online gradient descent learning algorithms and improve the state-of-the-art convergence estimate in the literature.
Common Sense Knowledge Infusion for Visual Understanding and Reasoning: Approaches, Challenges, and Applications

Visual understanding involves detecting objects in a scene and investigating rich semantic relationships between the objects, which is required for downstream visual reasoning tasks.The scene graph is widely used for structured scene representation, however, the performance of the scene graph generation for visual reasoning is limited due to challenges posed by imbalanced datasets and insufficient attention toward common sense knowledge infusion.Most of the existing approaches use statistical or language priors for knowledge infusion.Common sense knowledge infusion using heterogeneous knowledge graphs can help in improving the accuracy, robustness, and generalizability of the scene graph generation and enable explainable higher level reasoning by providing rich and diverse background and factual knowledge about the concepts in visual scenes.In this article, we present the background and applications of the scene graph generation and the initial approaches and key challenges in common sense knowledge infusion using heterogeneous knowledge graphs for visual understanding and reasoning.
Entropy-weighted reconstruction adversary and curriculum pseudo labeling for domain adaptation in semantic segmentation

Supervised learning has dominated and gradually evolved into a de facto choice for automated semantic segmentation tasks, in spite of the need for large corpora of pixel-level annotated real-world data. Domain adaptation (DA), especially methods based on adversarial machine learning, can lessen labeling costs by securing that knowledge learned from synthetic (source) data with free annotations is transferable to unlabeled real-world data. However, commonly employed approaches in adversarial-based DA merely consider the correlation alignment between the source and target domains while completely neglecting the uncertainty information introduced from the generator. In this paper, we aim to alleviate this issue by introducing a novel entropy-guided adversarial learning framework with the reconstruction error constraint. Firstly, a perceptual-based color space is employed to transform the synthetic source images into the desired new space to approximate the appearances of the real-world images in the target domain. Secondly, an entropy-weighted adversarial framework is designed to enhance the discriminativeness and transferability of the presented model to the target domain via an autoencoder-based discriminator. In addition, a dynamic pseudo-labeling mechanism is introduced to work in conjunction with the curriculum-based self-training strategy to further improve the domain adaptability of the model. Experimental results on two well-known DA benchmarks demonstrate that the proposed method outperforms existing similar approaches in the task of semantic segmentation.
Detection and tracking of non-cooperative flying obstacles using low SWaP radar and optical sensors: an experimental analysis

Improvements in low altitude, non-cooperative sense and avoid are of major interest for collision hazard mitigation within the UTM/UAM/U-Space framework. In this regard, the sensing architecture must be carefully designed so that its detection and tracking performance is suitable for timely and reliable conflict assessment, while respecting size, weight, power and costs constraints, which are particularly strict for small aerial vehicles. Within this framework, an experimental assessment of non-cooperative sensing solutions based on a lightweight radar and a visual camera, respectively, is presented in this paper. Visual detections are obtained by using a Deep Learning-based neural network, while raw detections produced by the radar are first filtered based on Doppler information to remove ground clutter, and then clustered by means of a centroiding approach. The resulting detection sets are used to generate tentative and firm tracks using customized Kalman filtering techniques. Following a research plan that foresees data gathering with incremental complexity, ground-to-air tests have been carried out using a small UAV as flying intruder, and Carrier-Phase Differential GNSS to get a reference solution and assess visual-based and radar-based detection and tracking performance. Results achieved by standalone radar and visual sensing solutions clearly highlight the potential of sensor fusion strategies to take advantage of their complementary characteristics.
Machine Learning with Echo State Networks for Automated Fault Diagnosis in Small Unmanned Aircraft Systems

Echo State Network (ESN) is one of machine learning methods that can be used to detect anomalies in sensor readings. The method predicts output signals, from which a prediction error can be created. To enable fault-tolerant control, ESN needs to be combined with a robust fault estimation method. Indeed, identifying the source of the faults, whether coming from sensors or actuators, is crucial in safety-critical Unmanned Aircraft Systems (UAS), since it will determine proper control actions when the faults occur. This paper presents a novel method to combine sensor anomaly detection using ESN with actuator fault estimation using adaptive extended Kalman filter (AEKF). Numerical results show the benefit of using the cascaded algorithm in a noisy environment. Furthermore, the presented method is validated using a hexacopter with actuator faults in indoor experiments.
WTA/TLA: A UAV-captured Dataset for Semantic Segmentation of Energy Infrastructure

Automated inspection of energy infrastructure with Unmanned Aerial Vehicles (UAVs) is becoming increasingly important, exhibiting significant advantages over manual inspection, including improved scalability, cost/time effectiveness, and risks reduction. Although recent technological advancements enabled the collection of an abundance of vision data from UAVs’ sensors, significant efforts are still required from experts to interpret manually the collected data and assess the condition of energy infrastructure. Thus, semantic understanding of vision data collected from UAVs during inspection is a critical prerequisite for performing autonomous robotic tasks. However, the lack of labeled data introduces challenges and limitations in evaluating the performance of semantic prediction algorithms. To this end, we release two novel semantic datasets (WTA and TLA) of aerial images captured from power transmission networks and wind turbine farms, collected during real inspection scenarios with UAVs. We also propose modifications to existing state-of-the-art semantic segmentation CNNs to achieve improved trade-off between accuracy and computational complexity. Qualitative and quantitative experiments demonstrate both the challenging properties of the provided dataset and the effectiveness of the proposed networks in this domain.The dataset is available at: https://github.com/gzamps/wta_tla_dataset.
Observability based Path Planning for Multi-Agent Systems to aid Relative Pose Estimation

When multiple coordinating vehicles are involved in missions like landing, docking, refueling, etc., knowledge of relative pose between vehicles is crucial. The absence of GPS in such scenarios is detrimental to the mission. Although vision-based techniques can be used to identify and localize one vehicle for the other, they can be computationally intensive and fail in dark or featureless environments. In this paper, a range-based relative navigation framework is proposed for missions like landing and refueling, where localization accuracy is enhanced in the presence of additional vehicles that help by communicating their sensor measurements. Satisfying the observability criteria of the system is crucial for localization accuracy. Therefore, a trajectory optimization technique coupled with Model Predictive Control is proposed to solve for trajectories of the additional vehicles to maximize the system’s observability. Using simulations, it is shown that having an individual controller for each additional vehicle is as effective as having a centralized control at a fraction of computation cost. Simulation results also show that it is efficient for each additional vehicle to maximize observability with respect to the "ego-vehicle" when all vehicles are in sensing range of each other.
A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance

As the base of the whole economy and society, internet security of finance directly affects the overall development of the country. With the development of the Internet, it is essential to effectively extract the relation between financial entities from internet financial intelligence and build a financial security knowledge graph, which lays the foundation for monitoring of internet security of finance. For relation extraction of Chinese internet financial intelligence, the existing models are all based on single-modal text semantics ignoring the role of Chinese pictographic semantics, while the shape and structure of Chinese characters contains useful semantics. In addition, the pictographic semantic fusion method of Chinese text also needs to be improved for better performance. To solve these shortcomings, we propose a Chinese Multimodal Relation Extraction model (CMRE), which improves the relation extraction ability on the Chinese internet financial intelligence. In CMRE, we extract pictographic semantics based on Chinese character shape and structure. Furthermore, we design a novel multi-modal semantic fusion module based on improved Transformer to effectively fuse the text and pictographic semantics. Additionally, we design experiments on the Chinese literature dataset(Sanwen) to test the relation extraction capability of CMRE. Finally, we employ CMRE to extract relations between financial entities on the internet financial intelligence dataset(FinRE) to compare with other baseline models.
A Robust Framework for Adaptive Selection of Filter Ensembles to Detect Adversarial Inputs

Existing defense strategies against adversarial attacks (AAs) on AI/ML are primarily focused on examining the input data streams using a wide variety of filtering techniques. For instance, input filters are used to remove noisy, misleading, and out-of-class inputs along with a variety of attacks on learning systems. However, a single filter may not be able to detect all types of AAs. To address this issue, in the current work, we propose a robust, transferable, distribution-independent, and cross-domain supported framework for selecting Adaptive Filter Ensembles (AFEs) to minimize the impact of data poisoning on learning systems. The optimal filter ensembles are determined through a Multi-Objective Bi-Level Programming Problem (MOBLPP) that provides a subset of diverse filter sequences, each exhibiting fair detection accuracy. The proposed framework of AFE is trained to model the pristine data distribution to identify the corrupted inputs and converges to the optimal AFE without vanishing gradients and mode collapses irrespective of input data distributions. We presented preliminary experiments to show the proposed defense outperforms the existing defenses in terms of robustness and accuracy.
Event‐based Dynamic Graph Drawing without the Agonizing Pain

Abstract Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event‐based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to retain its own real‐valued time coordinate. While existing work has demonstrated clear advantages for this approach, they come at a running time cost. We investigate the problem of accelerating event‐based layout to make it more competitive with existing layout techniques. In this paper, we describe the design, implementation and experimental evaluation of MultiDynNoS , the first multi‐level event‐based graph layout algorithm. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP and FM 3 , which we couple with an event‐based graph drawing algorithm. We also propose two extensions to the core algorithm: AutoTau and Bend Transfer . We perform two experiments: first, we compare MultiDynNoS variants to existing state‐of‐the‐art dynamic graph layout approaches; second, we investigate the impact of each of the proposed algorithm extensions. MultiDynNoS proves to be competitive with existing approaches, and the proposed extensions achieve their design goals and contribute in opening new research directions.
An Improved KNN Classifier for 3D Face Recognition Based on SURF Descriptors

In this article, we propose a three-dimensional (3D) face recognition approach for depth data captured by Kinect based on a combination of speeded up robust features (SURF) and k-nearest neighbor (KNN) algorithms. First, the shape index maps of the preprocessed 3D faces of the training gallery are computed, then the SURF feature vectors are extracted and used to form the dictionary. In the recognition process, we propose an improved KNN classifier to find the best match. The evaluation was performed using CurtinFaces and KinectFaceDB data sets, achieving rank-1 recognition rates of 96.78% and 94.23%, respectively, when using two samples per person for training.
A closer look at referring expressions for video object segmentation

Abstract The task of Language-guided Video Object Segmentation (LVOS) aims at generating binary masks for an object referred by a linguistic expression. When this expression unambiguously describes an object in the scene, it is named referring expression (RE). Our work argues that existing benchmarks used for LVOS are mainly composed of trivial cases, in which referents can be identified with simple phrases. Our analysis relies on a new categorization of the referring expressions in the DAVIS-2017 and Actor-Action datasets into trivial and non-trivial REs, where the non-trivial REs are further annotated with seven RE semantic categories. We leverage these data to analyze the performance of RefVOS, a novel neural network that obtains competitive results for the task of language-guided image segmentation and state of the art results for LVOS. Our study indicates that the major challenges for the task are related to understanding motion and static actions.
Open-set domain adaptation by deconfounding domain gaps

Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps (DDP) during class separation and domain adaptation in OSDA. The mechanism of DDP is to transfer domain-related styles and contexts from the target domain to the source domain. It enables the model to recognize a class as known (or unknown) because of the class semantics rather than the confusion caused by spurious styles or contexts. In addition, we propose a module of ensembling multiple transformations (EMT) to produce calibrated recognition scores, i.e., reliable normality scores, for the samples in the target domain. Extensive experiments on two standard benchmarks verify that our proposed method outperforms a wide range of OSDA methods, because of its advanced ability of correctly recognizing unknown classes.
A joint learning framework for Gaussian processes regression and graph learning

In the traditional Gaussian process regression (GPR), covariance matrix of outputs is dominated by a given kernel function, that generally depends on pairwise distance or correlation between sample inputs. Nevertheless, this kind of models hardly utilize high-order statistical properties or globally topological information among sample inputs, undermining their prediction capability. To remedy this defect, we propose in this paper a novel GPR framework combining the MLE of Gaussian processes with graph learning. In our model, sample inputs are modeled by a weighted graph, whose topology is directly inferred from sample inputs based on either the smoothness assumption or the self-representative property. Such global information can be viewed as a kind of knowledge a prior, guiding the process of learning hyper-parameters of the chosen kernel function and the construction of covariance matrix of GPR model outputs. In practice, hyper-parameters of the GPR model and adjacency matrix of the graph can be trained by the alternating optimization. Theoretical analyses regarding solutions to graph learning are also presented to reduce computational complexity. Experimental results demonstrate that the proposed framework can achieve competitive performance in terms of prediction accuracies and computational efficiency, compared to state-of-the-art GPR algorithms.
A salp swarm algorithm based on Harris Eagle foraging strategy

For the operation of the salp swarm algorithm(SSA), the leaders fall into local optimum, which leads to the population falling into local optimum problem. In this paper, a salp swarm algorithm based on Harris Eagle foraging strategy is proposed(ISSAHF). Harris hawk optimization algorithms of four types of feeding mechanism are integrated into salp location update process optimization algorithm follower. To strengthen the exploitation of potential areas, complemented by a multi-point leadership crossover strategy to maintain a balance between exploitation and exploration. The performance of the proposed ISSAHF was compared, and Wilcoxon's statistical analysis was performed on 20 benchmark functions including unimodal, multimodal, and partial CEC2014. Finally, ISSAHF is applied to three practical engineering optimization problems to evaluate its optimization performance further.