Reviewerly/exHarmony huggingface dataset:
Sequential Abruptly Changing Hidden States Estimation using Adaptive Particle Impoverishment Mitigation Scheme

Particle filtering (PF) is a sequential Monte Carlo (SMC) method that infers the states of the hidden parameters of interest from the posterior probability distribution functions (PDFs) given the noisy measurement data obtained from any non-linear systems. Each sample of parameter states (called particle) is randomly drawn, so their likelihoods can be different from each other and particle degeneracy may happen. Resampling eliminates the particles that have low likelihoods and replicates those having high likelihoods. Particle impoverishment is a side-effect where particle diversity is destroyed and all of these particles are likely to have low likelihoods, especially if the true state abnormally evolves. This paper proposes an adaptive scheme that relocates the particles when they are located far away from the maximum likelihood value to mitigate the particle impoverishment. The proposed scheme provides satisfactory effectiveness in discovering the abruptly changing hidden states and satisfactory estimation accuracy.
Vibration Anomaly Indicator in UAVs in presence of Wind

One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed to during a flight. On one hand, external causes such as wind gusts and turbulences or internal vehicle-centric faults such as incorrect sensor mounting or propeller imbalances can cause high vibrations in UAVs. On the other hand, high vibration itself may induce noise in the onboard miniature sensors of the UAV such as its accelerometers, gyroscopes and GPS that can lead to uncertain state estimation causing the multi-rotor to drift from its desired position or even result in loss-of-control. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into vibrations recorded by the autopilot system of a multi-rotor in presence of varying magnitudes of wind. Using data from experimental flights conducted at two separate flight test regions under varying wind conditions, we aim to classify between a nominal and anomalous vibration level for small UAV systems. Further, we analyze other parameters of interest that affect vibrations in UAVs such as UAV air speed and any propeller imbalance signatures. Analysis results from experimental flights demonstrate the effect of wind on vibration magnitude in unmanned aircrafts
Hardware accelerators for solving computationally intensive problems over binary vectors and matrices

The paper selects reusable operations over binary and ternary vectors that are frequently used in many important practical applications (that are explicitly indicated) and suggests methods to implement them in hardware accelerators.Many examples are given.The developed architectures are organized as either combinational or sequential designs.The following steps are elaborated: 1) suggesting methods and architectures based on knowledge and the results of previous publications; 2) modeling in software and estimating the most important characteristics of the future circuits; 3) implementation in hardware, evaluation of the design, and experiments.The presented example demonstrates all these steps.The results of experiments show effectiveness of the proposed accelerators form the point of view of performance and the consumed resources.
Rapid Multi-Objective UAS Mission Planning for Operational Risk Management

Current aircraft operations within the National Airspace System (NAS) rely heavily on the presence of an on-board pilot to safely manage the flight. Integration of Unmanned Aircraft Systems (UAS) into the NAS requires a high confidence that these unmanned aircraft operations can meet or exceed the safety afforded through manned operations. Specifically, these UAS operations must not pose an undue risk to persons, structures, and vehicles on the ground. Minimizing risk to the underlaying area can be accomplished through careful planning during the pre-flight phase with full consideration of potential failure modes and vehicle system responses to those failures. Currently, this time and labor-intensive pre-flight planning is manually performed by an Air Vehicle Operator (AVO). The Autonomous Planning and Replanning (AutoPRep) technology is an Operational Risk Management (ORM) tool that rapidly generates a multi-objective mission plan while also meeting all mission constraints. The multi-objective planning technique emphasizes minimization of risk imposed on persons and property on the ground while balancing path-length, required maneuvering (turning), and required altitude changes. Further, the AutoPRep planning tool considers hazardous weather (AIRMET/SIGMET) and airspace restrictions during mission planning. The AutoPRep tool can significantly reduce the time required to generate mission plans for UAS and is equally applicable to manned aircraft mission planning. This paper describes the AutoPRep planning package, presents several case study examples, and summarizes both pre-flight and in-flight planning evaluations.
Semantic analysis of verb – noun zero derivation in Princeton WordNet

Abstract This study offers insights into the similarities and differences between the zero suffix and overt English suffixes involved in verb-to-noun and noun-to-verb derivation. It is based on morphosemantically related pairs of noun and verb senses released as a Princeton WordNet standoff file, which are annotated with a set of fourteen semantic relations further enriched with information about the affix(es) used in the derivation process. We compare the zero suffix and the overt suffixes with respect to their overall frequency in the dataset and their frequency as per semantic relation. We describe their semantics in terms of the relation between the base and the derived word senses, and of the semantic classes of words involved in affixal and zero derivation. We argue that the zero suffix is highly underspecified, occurring with all semantic relations, even though it manifests some preferences with respect to both the semantic relations expressed and the semantic classes of words it attaches to.
Cross-layer connected network with adaptive attention mechanism for 3D multi-organ and tumor segmentations from CT

3D medical image segmentation is one of the most important and challenging tasks for medical image analysis. To enhance the feature extraction ability of 3D convolutional neural networks (CNN) and improve the segmentation accuracy on small tumors, this paper presents a cross-layer connected network with an adaptive attention mechanism to achieve efficient segmentations for 3D multi-organ and tumor from CT. A cross layer connection is proposed to utilize features more effectively by reusing multiple layers of semantic information while recovering spatial information. The pre-activation Residual Block and Squeeze-and-Excitation (SE) block are introduced to enhance the feature extraction ability. Due to the serious class imbalance problem caused by small tumors, the training process of the 3D CNN model is unstable and small targets are difficult to segment. To relieve the training instability problem, Anti-Aliased Pooling (AAP) is introduced to replace the original maxpooling. Moreover, a region of interest (ROI) adaptive attention mechanism based on the auxiliary loss function is developed to improve the segmentation accuracy on small target tumors by combining the target detection method with semantic segmentation models. Extensive experiments show that the dice coefficient of 3D liver tumor segmentation on the LiTS dataset reaches 0.668 by using our designed network, and the experiments on the BRATS dataset for 3D brain tumor segmentation yield average dice coefficient, sensitivity, and Hausdorff distance of 0.706, 0.730, and 5.972 mm, respectively, which are obviously superior to some existing methods.
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localization

AbstractAbstractLikelihood-free methods are an essential tool for performing inference for implicit models which can be simulated from, but for which the corresponding likelihood is intractable. However, common likelihood-free methods do not scale well to a large number of model parameters. A promising approach to high-dimensional likelihood-free inference involves estimating low-dimensional marginal posteriors by conditioning only on summary statistics believed to be informative for the low-dimensional component, and then combining the low-dimensional approximations in some way. In this paper, we demonstrate that such low-dimensional approximations can be surprisingly poor in practice for seemingly intuitive summary statistic choices. We describe an idealized low-dimensional summary statistic that is, in principle, suitable for marginal estimation. However, a direct approximation of the idealized choice is difficult in practice. We thus suggest an alternative approach to marginal estimation which is easier to implement and automate. Given an initial choice of low-dimensional summary statistic that might only be informative about a marginal posterior location, the new method improves performance by first crudely localising the posterior approximation using all the summary statistics to ensure global identifiability, followed by a second step that hones in on an accurate low-dimensional approximation using the low-dimensional summary statistic. We show that the posterior this approach targets can be represented as a logarithmic pool of posterior distributions based on the low-dimensional and full summary statistics, respectively. The good performance of our method is illustrated in low to moderate dimensional examples. Computer code to implement the methods for the examples of this paper is available at https://github.com/cdrovandi/ABC-marginal-approximations. Supplementary materials including appendices for this article are available online.Keywords: approximate Bayesian computationBayesian synthetic likelihoodcollective cell spreadingg-and-k distributionmarginal adjustmentrobust regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
Car Damage Detection Based on Mask Scoring RCNN

The growth of the car industry is now closely linked to the rise of the number of car accidents. As a result, insurance companies must deal with several claims at the same time while also addressing claims leakage. To resolve these issues, we propose a car damage detection system based on Mask Scoring RCNN. The experiment first makes a dataset by collecting car damage pictures of different types and on different angels for pre-processing then use Mask scoring RCNN for training. It is envisaged that this method would assist insurance in correctly classifying the damage and reducing the time spent on damage detection. The test results demonstrate that the proposed system has better masking accuracy in the case of complex images, allowing the car damage detection duty to be completed swiftly and easily.
Enhanced covertness class discriminative universal adversarial perturbations

The main aim of class discriminative universal adversarial perturbations (CD-UAPs) is that the adversary can flexibly control the targeted class and influence remaining classes limitedly. CD-UAPs generated by the existing attack strategies suffer from a high fooling ratio of non-targeted source classes under non-targeted and targeted attacks, and face the increasing risk of discovery. In this paper, we propose a training framework for generating enhanced covertness CD-UAPs. It trains the targeted source class set and the non-targeted source classes set alternately to update the perturbation and introduces logit pairing to mitigate the influence of perturbation on the non-targeted source classes set. Further, we extend CD-UAPs on the targeted (one-targeted) attack to the multi-targeted attack, which perturbs a targeted source class to multiple targeted sink classes that seriously threaten the current scenario. It can not only provide the adversary with freedom of precise attack but reduce the risk of being detected. This attack poses a strong threat to security-sensitive applications. Extensive experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets show our method can generate more deceptive perturbations and enhance the covertness of CD-UAPs. For example, our method improves the absolute fooling ratio gaps of ResNet-20 and VGG-16 by 9.46% and 6.94% compared with the baseline method, respectively. We achieve the multi-targeted attack with a high fooling ratio on the GTSRB dataset. The average absolute target fooling ratio gaps of ResNet-20 and VGG-16 are 81.89% and 76.33%, respectively.
A novel KG-based recommendation model via relation-aware attentional GCN

Leveraging knowledge graphs (KGs) to enhance recommender systems has gained considerable attention, with researchers obtaining user preferences by aggregating entity pairs with explicit relations in KGs via graph convolutional networks (GCNs). Existing approaches currently overlook many entity pairs without relations, which, however, may have potentially useful information. To address this issue, we propose a novel relation-aware attentional GCN (RAAGCN) with the following improvements over vanilla GCNs: (1) it aggregates all entity pairs with and without explicit relations and (2) it distinguishes the importance of different relational context information. Based on the proposed RAAGCN, we further propose a user preference and item attractiveness capturing model (UPIACM) for KG-based recommendation. In the UPIACM, the user preference is decomposed into interest and rating preferences. The interest preference is the user’s interest taste toward the items with specific features, while the rating preference reflects the intention of rating high or low. Additionally, our model accounts for item attractiveness, which reflects an item’s popularity among users. Additionally, we incorporate a gated filtering mechanism to further improve our model’s performance. Through extensive experiments, we show that the proposed UPIACM outperforms state-of-the-art baseline methods.
A dual-channel network for cross-domain one-shot semantic segmentation via adversarial learning

The purpose of one-shot semantic segmentation is to classify all pixels in a query image with a labeled support sample. Its current approaches divide data into two disjoint parts, a training set and a testing set. When the training and testing data are from different domains, the migration of the trained model to the testing data for prediction tends to perform less efficiently. Therefore, through the introduction of a distribution alignment module, a new dual-channel network (DCNet) for one-shot segmentation is proposed in this paper. The purpose of this introduction is to alleviate the performance degradation caused by distribution differences between the datasets. Furthermore, a soft pseudo-mask based prototype refinement module with Gumbel-Softmax is designed to enhance the expressive ability of prototypes and improve segmentation performance. Experiments are made on four datasets with large distribution differences, proving that our approach outperforms the compared methods by a large margin.
A systematization of voter registration security

Abstract Voter registration is an essential part of almost any election process, and its security is a critical component of election security. Yet, despite notable compromises of voter registration systems, relatively little academic work has been devoted to securing voter registration systems, compared to research on other aspects of election security. In this paper, we present a systematic treatment of voter registration system security. We propose the first rigorous definitional framework for voter registration systems, describing the entities and core functionalities inherent in most voter registration systems, the jurisdictional policies that constrain specific implementations, and key security properties. Our definitions are configurable based on jurisdiction-specific parameters and policies. We provide a template for the structured presentation of detailed jurisdictional policy information, via a series of tables, and illustrate its application with detailed case studies of the voter registration systems of three US states and Panama. Throughout our research, with the aim of realism and practical applicability, we consulted current and former US election officials, civil society, and nonprofits in the elections space. We conclude with a list of critical questions regarding voter registration security.
Software evolutionary architecture: Automated planning for functional changes

Software systems often evolve over time due to frequent changes in user requirements. The refactoring of the architectural design due to numerous functional changes significantly impacts the software system. Evolutionary architecture is a design principle that supports the implementation of frequent changes. One key aspect of an evolutionary architecture is the definition of fitness functions to ensure that the changes align with the intended goals. However, planning the incremental evolution of the architectural design remains a challenge. This paper presents an approach to automatically generate evolution plans for refactoring the architectural design in support of new functionalities. Formal modelling has been applied to allow for the verification of functional properties against the design. By utilising the generated evolution plan, we can determine a safe path for evolving the software system with minimal risk of failure. We evaluated the rigour and effectiveness of the evolution plan generated by our approach for six software systems. Our experimental results demonstrate the effectiveness of the proposed approach in generating evolution plans. Additionally, we were able to identify the most suitable planning strategy that minimises system interruptions in the generated evolution plan.
Capturing Multivariate Time Series Interactions to Detect High-Risk Instability During Approach

With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be used to gain insight on future autonomous systems and make model translation to different airframes less difficult. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead-of-time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.
Scalable maximal subgraph mining with backbone-preserving graph convolutions

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing approaches for identifying maximal subgraphs typically rely on traversing a graph lattice. However, in practice, these approaches are limited to relatively small subgraphs due to the exponential growth of the search space and the NP-completeness of the underlying subgraph isomorphism test. In this work, we propose SCAMA, an approach that addresses these limitations by adopting a divide-and-conquer strategy for efficient mining of maximal subgraphs. Our approach involves initially partitioning a graph database into equivalence classes using bootstrapped backbones, which are tree-shaped frequent subgraphs. We then introduce a learning process based on a novel graph convolutional network (GCN) to extract maximal backbones for each equivalence class. A critical insight of our approach is that by estimating each maximal backbone directly in the embedding space, we can avoid the exponential traversal of the graph lattice. From the extracted maximal backbones, we construct the maximal frequent subgraphs. Furthermore, we outline how SCAMA can be extended to perform top-k largest frequent subgraph mining and how the discovered patterns facilitate graph classification. Our experimental results demonstrate the effectiveness of SCAMA in identifying almost perfectly maximal frequent subgraphs, while exhibiting approximately 10 times faster performance compared to the best baseline technique.
Tri-objective optimization-based cascade ensemble pruning for deep forest

Deep forest is a new multi-layer ensemble model, where the high time costs and storage requirements inhibit its large-scale application. However, current deep forest pruning methods used to alleviate these drawbacks do not consider its cascade coupling characteristics. Therefore, we propose a tri-objective optimization-based cascade ensemble pruning (TOOCEP) algorithm for it. Concretely, we first present a tri-objective optimization-based single-layer pruning (TOOSLP) method to prune its single-layer by simultaneously optimizing three objectives, namely accuracy, independent diversity, and coupled diversity. Particularly, the coupled diversity is designed for deep forest to deal with the coupling relationships between its adjacent layers. Then, we perform TOOSLP in a cascade framework to prune the deep forest layer-by-layer. Experimental results on 15 UCI datasets show that TOOCEP outperforms several state-of-the-art methods in accuracy and pruned rate, which significantly reduces the storage space and accelerate the prediction speed of deep forest.