Reviewerly/exHarmony huggingface dataset:
MLNav: Learning to Safely Navigate on Martian Terrains

We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety constraints.In particular, the dominant computational cost in such safety-critical settings is running a model-based safety checker on the proposed paths.Our learned search heuristic can simultaneously predict the feasibility for all path options in a single run, and the model-based safety checker is only invoked on the top-scoring paths.We validate in high-fidelity simulations using both real Martian terrain data collected by the Perseverance rover, as well as a suite of challenging synthetic terrains.Our experiments show that: (i) compared to the baseline ENav path planner on board the Perserverance rover, MLNav can provide a significant improvement in multiple key metrics, such as a 10x reduction in collision checks when navigating real Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav can successfully navigate highly challenging terrains where the baseline ENav fails to find a feasible path before timing out.
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step. Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.
Self-Attention Networks and Adaptive Support Vector Machine for aspect-level sentiment classification

Aspect-level sentiment classification aims to integrating the context to predict the sentiment polarity of aspect-specific in a text, which has been quite useful and popular, e.g., opinion survey and products’ recommending in e-commerce. Many recent studies exploit a Long Short-Term Memory (LSTM) networks to perform aspect-level sentiment classification, but the limitation of long-term dependencies is not solved well, so that the semantic correlations between each two words of the text are ignored. In addition, traditional classification model adopts SoftMax function based on probability statistics as classifier, but ignores the words’ features in the semantic space. Support Vector Machine (SVM) can fully use the information of characteristics, and it is appropriate to make classification in the high-dimensional space, however, which just considers the maximum distance between different classes and ignores the similarities between different features of the same classes. To address these defects, we propose the two-stage novel architecture named Self Attention Networks and Adaptive SVM (SAN-ASVM) for aspect-level sentiment classification. In the first stage, in order to overcome the long-term dependencies, Multi-Heads Self Attention (MHSA) mechanism is applied to extract the semantic relationships between each two words; furthermore, 1-hop attention mechanism is designed to pay more attention on some important words related to aspect-specific. In the second stage, ASVM is designed to substitute the SoftMax function to perform sentiment classification, which can effectively make multi-classifications in high-dimensional space. Extensive experiments on SemEval2014, SemEval2016 and Twitter datasets are conducted, and compared experiments prove that SAN-ASVM model can obtain better performance.
Calibration of an underwater binocular vision system based on the refraction model

Underwater measurement based on stereo vision attaches great importance to camera calibration. However, it is challenging to perform accurate calibration due to the significant refraction presented at the interfaces of air and water. To solve this problem, a calibration method for an underwater binocular vision system based on the optimized refractive model is proposed. First, conventional calibration is performed to obtain basic initial camera parameters using checkerboard images collected in the air. Then, an evolutionary multi-objective function is established according to Snell's law, the refractive light propagation path, and checkerboard geometric relationship. Finally, precise camera parameters and involved refraction parameters are both obtained for underwater target positioning and size measurement by the non-dominated sorting genetic algorithm of the reference point. A group of experiments is performed, and the validity and effectiveness of the proposed calibration algorithm is demonstrated.
Virtualization Over Multiprocessor Systems-on-Chip: An Enabling Paradigm for the Industrial Internet of Things

The next-generation Industrial Internet of Things (IIoT) requires smart devices featuring rich connectivity, local intelligence, and autonomous behavior. We review representative solutions, highlighting aspects that are the most relevant for integration in IIoT solutions.
PersianQuAD: The Native Question Answering Dataset for the Persian Language

Developing Question Answering systems (QA) is one of the main goals in Artificial Intelligence. With the advent of Deep Learning (DL) techniques, QA systems have witnessed significant advances. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the QA task; most of them are exclusively in English. In order to address the need for a high-quality QA dataset in the Persian language, we present PersianQuAD, the native QA dataset for the Persian language. We create PersianQuAD in four steps: 1) Wikipedia article selection, 2) question-answer collection, 3) three-candidates test set preparation, and 4) Data Quality Monitoring. PersianQuAD consists of approximately 20,000 questions and answers made by native annotators on a set of Persian Wikipedia articles. The answer to each question is a segment of the corresponding article text. To better understand PersianQuAD and ensure its representativeness, we analyze PersianQuAD and show it contains questions of varying types and difficulties. We also present three versions of a deep learning-based QA system trained with PersianQuAD. Our best system achieves an F1 score of 82.97% which is comparable to that of QA systems on English SQuAD, made by the Stanford University. This shows that PersianQuAD performs well for training deep-learning-based QA systems. Human performance on PersianQuAD is significantly better (96.49%), demonstrating that PersianQuAD is challenging enough and there is still plenty of room for future improvement. PersianQuAD and all QA models implemented in this paper are freely available.
Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

In recent years, computational approaches for automatically extracting the voice of the customer from user generated content have been proposed. These studies have tackled the task of obtaining current customer needs, however, there is a lack of methods that predict future needs (i.e. needs that may become popular in the marketplace). Therefore, this study presents a multi-document keyphrase extraction algorithm which predicts future customer needs from users’ social media posts on Reddit. Key to our approach is a novel document filtering method (discovering potentially relevant social media content) and a keyphrase ranking method, which promotes terms with rising frequency likely to be future product needs. In order to evaluate the approach, a case study of “toothpaste” needs is reviewed and a novel evaluation approach using ground truth automatically extracted from a collection of future specifications of new-to-market products is proposed. In our evaluation, we show that the approach is significantly better than simple baselines at identifying customer needs on social media before they trend in the marketplace. We also show that our approach can capture important customer needs identified by a large multinational company with lead times of up to 25 months ahead of them trending in the marketplace. The findings of this research could provide many benefits to businesses such as gaining early access into markets ahead of their competitors and giving early notice to manufacturers/engineers/developers before a need for a product is in demand.
Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.
Alpine Skiing Tracking Method Based on Deep Learning and Correlation Filter

In outdoor sports such as alpine skiing, athletes hope to obtain information such as movement trajectory and speed to achieve scientific training.The portable sensor has a sense of restraint and affects the athlete's performance.Cameras have difficulty capturing the whole race in an outdoor environment.For alpine skiing needs, we designed an alpine skiing information acquisition system, using unmanned aerial vehicle (UAV) and ground cameras to obtain information on the whole path of the race.The environment of the alpine skiing field, the high-speed curvilinear movement of athletes and the flight characteristics of UAVs cause challenges in the detection and tracking of athletes by UAVs.To realize the detection and tracking of alpine skiing by UAVs, we propose a detection algorithm by combining neural networks and correlation filters.A tracking confidence score based on the motion distance of the interval frame is defined.When the tracking confidence of the neural network detector is less than the threshold, the correlation filter is used to expand the recognition range and realize redetection.In addition, we created and annotated a fresh alpine skiing dataset.We compare our algorithm with four advanced algorithms on the UAV123 dataset.Three methods are used to evaluate the performance of our method on the alpine skiing sequence dataset.From the simulation results, our algorithm outperforms the comparison methods in terms of accuracy and robustness.Therefore, our algorithm has application value in the scientific training of alpine skiers.
SplitFlyer Air: A Modular Quadcopter That Disassembles Into Two Bicopters Mid-Air

Motivated by the flight ability of severely underactuated rotorcraft, we introduce a transformable quadcopter—SplitFlyer Air. The novel vehicle consists of two bicopter modules, each equipped with only two propellers and capable of position-controlled flight. Despite the notable difference in their flight regimes, the robots, in both the bicopter and quadcopter configurations, can be modeled and controlled by a single framework. In the flight experiments, an undocking mechanism with preloaded elastic energy was designed and employed to assist SplitFlyer Air to disassemble autonomously. The catapult-inspired mechanism provides initial yaw rates for the bicopters, accelerating them to reach their hovering states without losing stability or substantial altitude. Thanks to the developed controller and devised mechanism, the transformation flights can be reliably achieved. The ability to disassemble mid-air shows promise for search and rescue missions or other swarm applications as it allows the flock size to adaptively grow on demand.
Handling Imbalanced Data: Uncertainty-Guided Virtual Adversarial Training With Batch Nuclear-Norm Optimization for Semi-Supervised Medical Image Classification

In manyclinical settings, a lot of medical image datasets suffer from imbalance problems, which makes predictions of trained models to be biased toward majority classes. Semi-supervised Learning (SSL) algorithms trained with such imbalanced datasets become more problematic since pseudo-supervision of unlabeled data are generated from the model's biased predictions. To address these issues, in this work, we propose a novel semi-supervised deep learning method, i.e., uncertainty-guided virtual adversarial training (VAT) with batch nuclear-norm (BNN) optimization, for large-scale medical image classification. To effectively exploit useful information from both labeled and unlabeled data, we leverage VAT and BNN optimization to harness the underlying knowledge, which helps to improve discriminability, diversity and generalization of the trained models. More concretely, our network is trained by minimizing a combination of four types of losses, including a supervised cross-entropy loss, a BNN loss defined on the output matrix of labeled data batch (lBNN loss), a negative BNN loss defined on the output matrix of unlabeled data batch (uBNN loss), and a VAT loss on both labeled and unlabeled data. We additionally propose to use uncertainty estimation to filter out unlabeled samples near the decision boundary when computing the VAT loss. We conduct comprehensive experiments to evaluate the performance of our method on two publicly available datasets and one in-house collected dataset. The experimental results demonstrated that our method achieved better results than state-of-the-art SSL methods.
Validating Simulations of User Query Variants

System-oriented IR evaluations are limited to rather abstract understandings of real user behavior. As a solution, simulating user interactions provides a cost-efficient way to support system-oriented experiments with more realistic directives when no interaction logs are available. While there are several user models for simulated clicks or result list interactions, very few attempts have been made towards query simulations, and it has not been investigated if these can reproduce properties of real queries. In this work, we validate simulated user query variants with the help of TREC test collections in reference to real user queries that were made for the corresponding topics. Besides, we introduce a simple yet effective method that gives better reproductions of real queries than the established methods. Our evaluation framework validates the simulations regarding the retrieval performance, reproducibility of topic score distributions, shared task utility, effort and effect, and query term similarity when compared with real user query variants. While the retrieval effectiveness and statistical properties of the topic score distributions as well as economic aspects are close to that of real queries, it is still challenging to simulate exact term matches and later query reformulations.
An Efficient Cascaded Model for Ship Segmentation in Aerial Images

In this work, we propose a new method for real-time ship segmentation during maritime surveillance missions using aircrafts with onboard video cameras.We propose a cascade model with a detection stage followed by a segmentation stage.The detection stage selects candidate regions (bounding boxes) likely to contain ships.These bounding boxes are passed to the segmentation stage, where the ship segmentation mask is then obtained.By focusing the segmentation effort only in the image regions recommended by the previous detection stage, it is possible to improve the overall image processing time and, simultaneously, to obtain a better segmentation score than the one obtained by monolithic segmentation models that are trained in an end-to-end way.Additionally, we test the viability of using a Conditional Random Field model as final boundary refinement stage: although such model can improve the segmentation results when a full segmentation approach is used, our experiments did not show any significant improvements when using our proposed cascade model.We trained the detection and segmentation models with aerial ship images from publicly available maritime datasets.We tested the cascade model on the Airbus ship detection challenge, showing real-time performance and accurate maritime ship segmentation, comparable to state-of-the-art results.
Hybrid Niching-Based Differential Evolution With Two Archives for Nonlinear Equation System

Nonlinear equation systems (NESs) are ubiquitous, and solving them is an important yet challenging task in numerical computation. Most of the NESs usually contain multiple roots. To locate multiple roots, the use of evolutionary algorithms attracts more attention recently. For the purpose of finding different roots within a limited computational budget in a single run simultaneously, in this article, a hybrid niching-based differential evolution with two archives, referred to as HNDE/2A, is proposed. It can be featured as: 1) two niching techniques, i.e., crowding and speciation, are combined to balance the diversity and convergence; 2) a root archive is used to save the found roots during the run. Additionally, if a root is saved into the root archive, it will be reinitialized immediately to further promote the population diversity; and 3) an inferior offspring archive is presented to utilize the useful information of the inferior offspring. To evaluate the performance of our proposal, 30 problems are chosen as the test suite. Moreover, HNDE/2A is also used to solve two real-world problems. Compared with other algorithms, HNDE/2A yields better results in terms of the root ratio and the success rate with a less computational budget.
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predictions associated to adversarial examples lying outside of the training data distribution. We explore this intuition in a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the constrained classifier learns to fulfill the domain knowledge over the marginal distribution, and can naturally reject samples with incoherent predictions. Even though our method does not exploit any knowledge of attacks during training, our experimental analysis surprisingly unveils that domain-knowledge constraints can help detect adversarial examples effectively, especially if such constraints are not known to the attacker. We show how to implement an adaptive attack exploiting knowledge of the constraints and, in a specifically-designed setting, we provide experimental comparisons with popular state-of-the-art attacks. We believe that our approach may provide a significant step towards designing more robust multi-label classifiers.
Bayesian homodyne and heterodyne tomography

Continuous-variable (CV) photonic states are of increasing interest in quantum information science, bolstered by features such as deterministic resource state generation and error correction via bosonic codes. Data-efficient characterization methods will prove critical in the fine-tuning and maturation of such CV quantum technology. Although Bayesian inference offers appealing properties-including uncertainty quantification and optimality in mean-squared error-Bayesian methods have yet to be demonstrated for the tomography of arbitrary CV states. Here we introduce a complete Bayesian quantum state tomography workflow capable of inferring generic CV states measured by homodyne or heterodyne detection, with no assumption of Gaussianity. As examples, we demonstrate our approach on experimental coherent, thermal, and cat state data, obtaining excellent agreement between our Bayesian estimates and theoretical predictions. Our approach lays the groundwork for Bayesian estimation of highly complex CV quantum states in emerging quantum photonic platforms, such as quantum communications networks and sensors.
Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?

Statistical language models on source code have successfully assisted software engineering tasks. However, developers can create or pick arbitrary identifiers when writing source code. Freely chosen identifiers lead to the notorious out-of-vocabulary (OOV) problem that negatively affects model performance. Recently, Karampatsis et al. showed that using the Byte Pair Encoding (BPE) algorithm to address the OOV problem can improve the language models&#x0027; predictive performance on source code. However, a drawback of BPE is that it cannot split the identifiers in a way that preserves the meaningful semantics. Prior researchers also show that splitting compound identifiers into sub-words that reflect the semantics can benefit software development tools. These two facts motivate us to explore whether identifier splitting techniques can be utilized to augment the BPE algorithm and boost the performance of open-vocabulary language models considered in Karampatsis et al.&#x0027;s work. This paper proposes to split identifiers in both constructing vocabulary and processing model inputs procedures, thus exploiting three different settings of applying identifier splitting to language models for the code completion task. We contrast models&#x0027; performance under these settings and find that simply inserting identifier splitting into the pipeline hurts the model performance, while a hybrid strategy combining identifier splitting and the BPE algorithm can outperform the original open-vocabulary models on predicting identifiers by 3.68&#x0025; of recall and 6.32&#x0025; of Mean Reciprocal Rank. The results also show that the hybrid strategy can improve the entropy of language models by 2.02&#x0025;.