Reviewerly/exHarmony huggingface dataset:
Towards a Semantic Knowledge Base for Competency-Based Training of Airline Pilots

The acquisition and maintenance of non-technical skills by the pilots are fundamental factors for the prevention of aviation accidents. The aviation authorities are promoting that air crew training be carried out through simulator sessions using scenarios specifically designed to develop and assess the global performance of pilots in such skills. When designing custom flight training scenarios, choosing the correct events and conditions from the myriad of possible combinations with respect to their potential utility in training specific competencies is a costly task that depends entirely on highly specialized expert knowledge. In this paper, we present EBTOnto, an OWL DL ontology that allows to formalize this knowledge and other useful data from real cases, laying the foundations for a semantic knowledge base of scenarios for airline pilots training. Previous advances in this matter and possible applications of this system are reviewed. EBTOnto is built on top of a source validated by experts, the Evidence-Based Training Implementation Guide by the International Air Transport Association, and then checked using an automatic reasoner and a database of 37,568 aviation safety incidents, extracted from the widely regarded Aviation Safety Reporting System by the U.S. National Aeronautics and Space Administration. The results suggest that it is possible to classify real aviation scenarios in terms of non-technical competencies and filter useful incident reports for design and enrichment of these training scenarios. EBTOnto opens up new possibilities for interoperability between incident databases and training organizations, and smoothes the path to represent, share and generate custom simulation training scenarios for pilots based on real data.
Soil-sample geochemistry normalised by class membership from machine-learnt clusters of satellite and geophysics data

The primary aim of mineral exploration is to focus the search space from regional scale (1000 s km2) to prospect scale (1–10 km2), identify areas that warrant further investigation, and ultimately drill a target. Mineral deposits typically display geochemical anomalism related to the conditions of their formation, or the weathering and mass transport of sediment away from a mineral deposit. However, this anomalism can be obscured in soil-sample surveys due to background variations in host bedrock chemistry and the transformation and redistribution of original minerals during surface weathering, regolith development, and supergene processes. This contribution uses machine-learnt map products to aid in the interpretation of a regional-scale soil-sample dataset. ASTER (satellite spectral) data were combined with airborne radiometric data using Self-Organising Maps (SOM) to generate a map of regolith type. A map of bedrock geology was previously derived by the authors from airborne geophysical data using Random Forests. These two products are used to classify soil samples with an objective bedrock-geology and regolith-type classification. A z-score normalisation was implemented on the soil-sample geochemical data, using two different matrices (regolith type and bedrock geology) to remove the bias of bedrock geology and regolith type from the soil sample chemistry. The signal that remains likely reflects metasomatic processes, and associated elemental anomalism, potentially linked to mineralisation. These data can then be plotted to provide maps of (pathfinder) element anomalies, thus identifying potential exploration targets. We explore this approach to normalise geochemical data in a dataset of 9,924 soil samples collected over an area of 1,028 km2, from Kerkasha, Eritrea (Nubian Shield). The Kerkasha project is an early-stage exploration project, with a limited understanding of the local geology and mineral deposit types; however, it contains many Au and Cu prospects that have received minimal exploration work.
Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation

Task-agnostic knowledge distillation, a teacher-student fra- mework, has been proved effective for BERT compression. Although achieving promising results on NLP tasks, it requires enormous computational resources. In this paper, we propose Extract Then Distill (ETD), a generic and flexible strategy to reuse the teacher’s parameters for efficient and effective task-agnostic distillation, which can be applied to students of any size. Specifically, we introduce two variants of ETD, ETD-Rand and ETD-Impt, which extract the teacher’s parameters in a random manner and by following an importance metric, respectively. In this way, the student has already acquired some knowledge at the beginning of the distillation process, which makes the distillation process converge faster. We demonstrate the effectiveness of ETD on the GLUE benchmark and SQuAD. The experimental results show that: (1) compared with the baseline without an ETD strategy, ETD can save 70% of computation cost. Moreover, it achieves better results than the baseline when using the same computing resource. (2) ETD is generic and has been proven effective for different distillation methods (e.g., TinyBERT and MiniLM) and students of different sizes. Code is available at https://github.com/huawei-noah/Pretrained-Language-Model.
Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review

Speech summarization techniques take human speech as input and then output an abridged version as text or speech. Speech summarization has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps close to 2 decades of speech summarization literature, spanning from the early machine learning works up to ensemble models, with no restrictions on the language summarized, research method, or paper type. We reviewed a total of 110 papers out of a set of 188 found through a literature search and extracted speech features used, methods, scope, and training corpora. Most studies employ one of four speech summarization architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarization; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. Overall, supervised methods (e.g. Hidden Markov support vector machines, Ranking support vector machines, Conditional random fields) performed better than unsupervised methods. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods. Recent research into unsupervised methods focusses on extending language modelling, for example by combining Uni-gram modelling with deep neural networks. This review does not include recent work in deep learning.
Robust Elliptically Contoured Distribution Based Kalman Filter for INS/DVL Integrated Navigation

In complex underwater environment, the outputs of Doppler velocity log (DVL) often contain outliers induced by external disturbance or soft failure, which will degrade the performance of the traditional Kalman filter based INS/DVL integrated navigation system. To address this problem, a novel robust Kalman filter with outlier-contaminated measurement noise modelled as elliptically contoured distributed is proposed. Firstly, an analytical update form of the joint posterior probability density function is derived via introducing an unknown parameter. Then the fixed-point iteration method is exploited to achieve an approximate estimate of the unknown parameter, based on which the required joint posterior probability density function is analytically updated. Finally, simulation results of INS/DVL integrated navigation are used to verify the validity and advantages of the proposed method.
Effects of Category Labeling With Emojis on Likert-Type Scales on the Psychometric Properties of Measurements

Today, emoji have become a popular option for anchoring the categories of Likert-type scales applied to not only adults but also children. The aim of this study was to investigate the effects of category labeling with emoji by comparing the psychometric properties of the emoji- and verbal-anchored versions of the mathematics motivation scale applied to students aged 8–11 years (grades 3, 4, and 5). The participants of the study comprised 658 students. According to the research results, students used the upper categories of the scale more intensively in the emoji-anchored version than in the verbal-anchored one. Parallel to this, the means calculated for the emoji-anchored version were found to be significantly higher. Moreover, the results of the research revealed that the verbal-anchored scale form yielded more reliable and valid measures than the emoji-anchored version.
Geometric Task Networks: Learning Efficient and Explainable Skill Coordination for Object Manipulation

Complex manipulation tasks can contain various execution branches of primitive skills in sequence or in parallel under different scenarios. Manual specifications of such branching conditions and associated skill parameters are not only error-prone due to corner cases, but also quickly untraceable given a large number of objects and skills. On the other hand, learning from demonstration has increasingly shown to be an intuitive and effective way to program such skills for industrial robots. Parameterized skill representations allow generalization over new scenarios, which however makes the planning process much slower thus unsuitable for online applications. In this article, we propose a hierarchical and compositional planning framework that learns a geometric task network (GTN) from exhaustive planners, without any manual inputs. A GTN is a goal-dependent task graph that encapsulates both the transition relations among skill representations and the geometric constraints underlying these transitions. This framework has shown to improve dramatically the offline learning efficiency, the online performance, and the transparency of decision process, by leveraging the task-parameterized models. We demonstrate the approach on a 7-DoF robot arm both in simulation and on hardware solving various manipulation tasks.
Shadow-Catcher: Looking into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing

LiDAR-driven 3D sensing allows new generations of vehicles to achieve advanced levels of situation awareness. However, recent works have demonstrated that physical adversaries can spoof LiDAR return signals and deceive 3D object detectors to erroneously detect "ghost" objects. Existing defenses are either impractical or focus only on vehicles. Unfortunately, it is easier to spoof smaller objects such as pedestrians and cyclists, but harder to defend against and can have worse safety implications. To address this gap, we introduce Shadow-Catcher, a set of new techniques embodied in an end-to-end prototype to detect both large and small ghost object attacks on 3D detectors. We characterize a new semantically meaningful physical invariant (3D shadows) which Shadow-Catcher leverages for validating objects. Our evaluation on the KITTI dataset shows that Shadow-Catcher consistently achieves more than 94% accuracy in identifying anomalous shadows for vehicles, pedestrians, and cyclists, while it remains robust to a novel class of strong "invalidation" attacks targeting the defense system. Shadow-Catcher can achieve real-time detection, requiring only between 0.003 s–0.021 s on average to process an object in a 3D point cloud on commodity hardware and achieves a 2.17x speedup compared to prior work.
FIT BUT: Rational Agents in the Multi-Agent Programming Contest

The 2020 Multi-Agent Programming Contest introduced a modified scenario from last year - Agents Assemble II. Teams of agents compete against each other in completing tasks that consists of assembling blocks into desired structures. In this paper, we describe our strategy, system design, and improvements we made over last year. This paper also contains a description of the tournament matches from our point of view.
A Study on Machine Learning and Deep Learning Methods Using Feature Extraction for Bengali News Document Classification

News is newly received remarkable facts about current phenomenon. Miscellaneous facts are constantly happening in this world. Mass media helps to reach these facts to the common folks widely. As we are pushed forward to modern world, getting a convenient environment, Bengali mass media are also leaning towards digital platforms. In this article, some supervised machine learning approaches and deep learning approaches have been proposed for classifying Bengali news documents. We have used an open dataset for our work which contains more than three hundred thousand (3, 76, 211) Bengali text documents. Removing stop-words, dropping duplicate data, tokenizing, stemming etc have been commonly done as preprocessing steps. Bag-of-Words with TF-IDF and some Word Embedding approaches - Average Word2Vec, Glove & fastText have been used for feature extraction. We have trained our text corpus using supervised machine learning method and Deep learning method. Significantly, among these models, Support Vector Machine with average Word2Vec has achieved 97% accuracy and Bidirectional LSTM has achieved 96% accuracy.
Semantic image segmentation of brain MRI with deep learning.

Previous studies on brain MRI image segmentation, such as threshold method, boundary detection method, and region method did not achieve good performance in complex scenes. Based on the deep learning segmentation technology, this study constructed a neural network model by using the algorithm of atrous convolution combined with conditional random field (CRF) to segment the thalamus, caudate nucleus, and lenticular nucleus in brain MRI, which laid a good foundation for MRI diagnosis of brain diseases.A total of 1 200 MRI-Flair images of the brain were randomly selected, and 3 anatomical structures of thalamus, caudate nucleus, and lenticular nucleus were manually labeled, of which 1 000 were used as training data sets and 200 were used as test data sets. The neural network model was established by using deep convolutional neural networks (DCNN) combined with CRF algorithm. The training data set was input into the model, and the parameterized neural network model was obtained after iteration for 30 000 times. The test data set was used to evaluate, test, and output the predicted image.The model optimization results showed that the new brain MRI segmentation model DeepXAG had the highest accuracy. Therefore, DeepXAG was selected as the segmentation algorithm. The mean intersection over union (mIOU) of the DeepXAG model was 72.3%, which was significantly higher than other classical segmentation algorithms (CRF-RNN1, FCN-8s2, DPN3, RefineNet4, and PSPNet5).The DeepXAG algorithm has good accuracy and robustness in segmenting the anatomical structure of brain MRI images.目的: 既往脑部MRI图像分割法，如阈值法、边界检测法、区域法等，在复杂场景下分割效果不理想。本研究在深度学习分割技术的基础上，应用空洞卷积算法结合条件随机场(conditional random field，CRF)算法构造神经网络模型，对脑部MRI中的丘脑、尾状核和豆状核3种解剖结构进行分割，为脑部疾病MRI诊断打好基础。方法: 随机选取1 200张脑部MRI-Flair图像，人工标记出丘脑、尾状核和豆状核3种解剖结构，其中1 000张作为训练数据集，200张作为测试数据集。采用深度卷积神经网络(deep convolutional neural networks，DCNN)结合CRF算法建立神经网络模型，将训练数据集输入模型，迭代30 000次后得到参数化的神经网络模型。利用测试数据集评估、测试神经网络模型，并输出预测图像。结果: 模型优化结果表明新构造的脑部MRI分割模型——DeepXAG的精确度最高，因此选用DeepXAG作为本研究的分割算法。DeepXAG模型的均交并比(mean intersection over union，mIOU)达到72.5%，明显高于其他经典分割算法(CRF-RNN、FCN-8s、DPN、RefineNet及PSPNet)。结论: DeepXAG模型分割脑部MRI的解剖结构具有较高的精确度及良好的鲁棒性。.
General Frameworks for Anomaly Detection Explainability: Comparative Study

Since their inception, AutoEncoders have been very important in representational learning. They have achieved ground-breaking results in the realm of automated unsupervised anomaly detection for various critical applications. However, anomaly detection through AutoEncoders suffers from lack of transparency when it comes to decision making based on the outputs of the AutoEncoder network, especially for image-based models. Though the residual reconstruction error map from the AutoEncoder helps explaining anomalies to a certain extent, it is not a good indicator of the implicitly learnt attributes by the model. A human interpretable explanation of why an instance is anomalous not only enables the experts to fine-tune the model but also establishes and increases trust by non-expert users of the model. Convolutional AutoEncoders in particular suffer the most as there are only limited studies that focus on transparency and explainability. In this paper, aiming to bridge this gap, we explore the feasibility and compare the performances of several State-of-the-Art Explainable Artificial Intelligence (XAI) frameworks on Convolutional AutoEncoders. The paper also aims at providing the basis for future developments of reliable and trustworthy AutoEncoders for visual anomaly detection.
Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models

In this paper, we examine the use of a deep multi-layer perceptron architecture to classify received signals as one of seven common waveforms, single carrier (SC), single-carrier frequency division multiple access (SC-FDMA), orthogonal frequency division multiplexing (OFDM), linear frequency modulation (LFM), amplitude modulation (AM), frequency modulation (FM), and phase-coded pulse modulation used in communication and radar networks. Synchronization of the signals is not needed as we assume there is an unknown and uncompensated time and frequency offset. The classifier is open-set meaning it assumes unknown waveforms may appear. Isolation forest (IF) models acting as binary classifiers are used for each known signal class to perform detection of possible unknown signals. This is accomplished using the 32-length feature vector from a dense layer as input to the IF models. The classifier and IF models work together to monitor the spectrum and identify waveforms along with detecting unknown waveforms. Results showed the classifier had 100% classification rate above 0 dB with an accuracy of 83.2% and 94.7% at -10 dB and -5 dB, respectively, with signal impairments present. Results for the IF models showed an overall accuracy of 98% when detecting known and unknown signals with signal impairments present. IF models were able to reject all unknown signals while signals similar to known signals were able to pass through 2% of the time due to the contamination rate used during training. Overall, the entire system can classify correctly in an open-set mode with 98% accuracy at SNR greater than 0 dB.
Hypergraphs, Local Reasoning, and Weakly Aggregative Modal Logic

This paper connects the following three apparently unrelated topics: an epistemic framework fighting logical omniscience, a class of generalized graphs without the arities of relations, and a family of non-normal modal logics rejecting the aggregative axiom. Through neighborhood frames as their meeting point, we show that, among many completeness results obtained in this paper, the limit of a family of weakly aggregative logics is both exactly the modal logic of hypergraphs and also the epistemic logic of local reasoning with veracity and positive introspection. The logics studied are shown to be decidable based on a filtration construction.
Interpretable Anomaly Detection Using A Generalized Markov Jump Particle Filter

When performing anomaly detection on an autonomous vehicle’s sensory data, it is fundamental to infer the cause of the found anomalies. This paper proposes a method for learning prediction models and detecting anomalies by decomposing the evolution of an agent’s state into its different motion-related parameters. A filter is introduced based on Generalized Filtering to increase the interpretability of the results with respect to previous methods. The proposed anomaly detection method is tested on data from a real vehicle. We also consider the case in which multiple models are learned, how to extract the salient discriminatory features of each, and use the proposed anomaly detection method to perform behavior classification.
A Novel Multi-objective Lion Swarm Intelligent Optimization Based on Cloud Model for Manufacturing Scheduling Problem

Abstract With the continuous upgrading of industrial manufacturing, various artificial intelligence technologies have gradually been applied to the field of industrial production, including swarm intelligence optimization algorithms. Aiming at the flow shop scheduling problem (FSP) and job shop scheduling problem (JSP) in industrial production, which are NP-hard problems, we use a multi-objective optimization method to solve them. We proposed a novel multi-objective optimization named multi-objective lion swarm optimization based on cloud model mutation (CMOLSO). This new optimization algorithm, which is based on the Lion Swarm Optimization (LSO), introduces the concept of cloud model and cloud generator algorithm. The introduction of the cloud model mechanism can expand the search range of CMOLSO in high-dimensional multi-objective problems, make it avoid falling into local extremes, and improve its optimization accuracy. Compared with the traditional multi-objective optimization, the new algorithm CMOLSO achieves better performance, and it can effectively solve the scheduling problems in practice.
Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition

Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon contextual representation learning (CRL), bidirectional encoder, self-attention, and sequential conditional random field (CRF). The CaBiLSTM model incorporates task-oriented CRL based on joint character-level and word-level representations. It takes character-level input to learn the character representations. Afterwards, the character representations are transformed into word features, and the bidirectional encoder learns the word representations. The output of the final encoder is fed into the self-attention through a hidden layer before decoding. Finally, we employ the CRF for the prediction of label sequences. The baselines and the proposed CaBiLSTM model are compared by exploiting pretrained Sindhi GloVe (SdGloVe), Sindhi fastText (SdfastText), task-oriented, and CRL-based word representations on the recently proposed SiNER dataset. Our proposed CaBiLSTM model achieved a high F1-score of 91.25% on the SiNER dataset with CRL without relying on additional handmade features, such as hand-crafted rules, gazetteers, or dictionaries.
iNAS: Integral NAS for Device-Aware Salient Object Detection

Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations. A powerful backbone could still achieve sub-optimal performance with a weak saliency head and vice versa. Moreover, the balance between model performance and inference latency poses a great challenge to model design, especially when considering different deployment scenarios. Considering all components in an integral neural architecture search (iNAS) space, we propose a flexible device-aware search scheme that only trains the SOD model once and quickly finds high-performance but low-latency models on multiple devices. An evolution search with latency-group sampling (LGS) is proposed to explore the entire latency area of our enlarged search space. Models searched by iNAS achieve similar performance with SOTA methods but reduce the 3.8×, 3.3×, 2.6×, 1.9× latency on Huawei Nova6 SE, Intel Core CPU, the Jetson Nano, and Nvidia Titan Xp. The code is released at https://mmcheng.net/inas/.