Reviewerly/exHarmony huggingface dataset:
Path Planning of Unmanned Helicopter in Complex Environment Based on Heuristic Deep Q-Network

Unmanned helicopters (UH) can evade radar detection by flying at ultralow altitudes, so as to conduct raids on targets. Path planning is one of the key technologies to realize UH’s autonomous completion of raid missions. Since the probability of UH being detected by radar varies with height, how to accurately identify the radar coverage area to avoid crossing has become a difficult problem in UH path planning. Aiming at this problem, a heuristic deep Q-network (H-DQN) algorithm is proposed. First, as part of the comprehensive reward function, a heuristic reward function is designed. The function can generate dynamic rewards in real time according to the environmental information, so as to guide the UH to move closer to the target and at the same time promote the convergence of the algorithm. Second, in order to smooth the flight path, a smoothing reward function is proposed. This function can evaluate the pros and cons of UH’s actions, so as to prompt UH to choose a smoother path for flight. Finally, the heuristic reward function, the smooth reward function, the collision penalty, and the completion reward are weighted and summed to obtain the heuristic comprehensive reward function. Simulation experiments show that the H-DQN algorithm can help UH to effectively avoid the radar coverage area and successfully complete the raid mission.
Comparative analysis of frameworks using TypeScript to build server applications

The subject of the research was a comparative analysis of programming frameworks that are intended for building applications. NestJS (version 8.1.1), FoalTS (version 2.5.0) and Ts.ED (version 6.69.1) were put together. An experiment was prepared based on scenarios that focused on the response time of applications to requests. Each of them had the same set of functionalities. NestJS turned out to be the most efficient of the compared skeletons. It achieved the best results. The worst results in each scenario were achieved by Ts.ED, especially with higher loads. The biggest differences in the comparison can be seen in studies conducted according to a scenario based on GET-type requests, and in particular with more objects in response.
Mirror, mirror, on the wall: During pandemics, how can self-perception research in people with eating disorders happen at all?

Abstract Background ‘Classic’ embodiment illusions (e.g., the feeling of owning another person’s hand) involves a three-way interaction between visual, proprioceptive, and tactile stimuli. These illusions have been studied in eating disorder (ED) populations given the potential implications for better understanding and improving body image concerns. Recently, several studies have employed ‘tactile-reduced’ methods mainly relying on the integration of visual and proprioceptive information to induce embodiment illusions in non-ED populations. To date, there has been no substantial review of these tactile-reduced approaches to consider their potential research and clinical utility in ED populations. Method This review sought to examine and integrate studies across three areas. Firstly, those that employed tactile-reduced embodiment techniques in non-ED populations. Secondly, those that used classic embodiment techniques to compare whether ED and non-ED populations differ in their susceptibility to embodiment. Thirdly, studies that investigated whether experiencing classic or tactile-reduced embodiment techniques can improve image-related concerns. Results For the first aim five studies were identified, all of which found that tactile-reduced methods consistently induced embodiment illusions in non-ED individuals. For the second aim, seven studies comparing ED and non-ED samples were found. ED patients were more susceptible to embodiment than non-ED samples in four studies, less susceptible in one study, and equally susceptible in two studies. Finally, for aim three, amongst the seven studies that used classic or tactile-reduced embodiment methods in ED populations, six reported improvements in self-perception (i.e., reduced body size overestimation, decreased body dissatisfaction and fear of gaining weight). Conclusions Along with the classic approach, tactile-reduced embodiment approaches have implications for ED research and clinical practice, particularly for situations when face-to-face contact with people are restricted. Suggestions are provided for future researchers who wish to ensure best practice for planning embodiment research involving classic and tactile-reduced approaches.
Towards continuous consistency axiom

It is shown for the first time in this paper, that Kleinberg’s (2002) (self-contradictory) axiomatic system for distance-based clustering fails (that is one of the data transforming axioms, consistency axiom, turns out to be identity transformation) in fixed-dimensional Euclidean space due to the consistency axiom limitations and that its replacement with inner-consistency or outer consistency does not help if continuous data transformations are required. Therefore we formulate a new, sound axiomatic framework for cluster analysis in the fixed dimensional Euclidean space, suitable for k-means like algorithms. The system incorporates centric consistency axiom and motion consistency axiom which induce clustering preserving transformations useful e.g. for deriving new labelled sets for testing clustering procedures. It is suitable for continuous data transformations so that labelled data with small perturbations can be derived. Unlike Kleinberg’s consistency, the new axioms do not lead the data outside of Euclidean space nor cause increase in data dimensionality. Our cluster preserving transformations have linear complexity in data transformation and checking. They are in practice less restrictive, less rigid than Kleinberg’s consistency as they do not enforce inter-cluster distance increase and inner cluster distance decrease when performing clustering preserving transformation.
Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism

Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency.
Interactive Media Edpuzzle and Its Implementation in Teaching Vocabulary in New Normal Era

The aim of this study was to describe the use of interactive media EdPuzzle in teaching vocabulary. Many students had difficulties finding the right vocabulary in conveying ideas because the learning media used by the teacher was not varied. In new normal era, the teacher must be good at choosing interactive media for teaching vocabulary. One of interactive media was EdPuzzle, because it could make the learning process to become interesting. This study was quantitative descriptive. The research subject was the 8th-I grade of students at SMPN 2 Taman which consisted of 33 students. In this study, the researchers used an observation guide and questionnaire to collect the data which were then analyzed descriptive quantitatively and in percentage terms. The result of observational data analysis showed that the interactive media EdPuzzle used in teaching vocabulary made students more active and the class become fun. The results of the questionnaire analysis showed that 54% of students agreed that teaching vocabulary using interactive media Edpuzzle helped them understand the text. The conclusion was that interactive media EdPuzzle could be used in teaching vocabulary, because students felt more enjoyable, interested, and excited, also easier to memorize the English vocabulary during learning process.
Self-elimination of phase noise in coherence scanning interferometry for nano-scale measurement

Coherence scanning interferometry (CSI) has been a powerful non-contact technique. In this work, a novel surface recovery algorithm based on phase noise self-correction is proposed for nano-scale measurements. Through spectrum analysis, the surface height is coarsely retrieved from the linear relationship between phase and angular wavenumber. Afterwards, the phase noise is modeled, and a correction array is designed to compensate the noises in surface height. A coefficient is defined to assess the accuracy of correction array, then the surface height is automatically corrected with the coefficient minimized. In the experiments, three step height standards are tested to prove the measurement accuracy, and additional comparisons with a commercial white light profiler and an AFM are provided as well to testify the measurement noise level. At last, a nano-scale grids matrix is measured, where the nano-topography is accurately recovered. All investigations have verified the effectiveness of the proposed method in nano-scale measurements.
Reading material selection for bibliotherapy based on blood type in young adult groups

Blood type as biological information is still considered a prophecy and pseudoscience that still needs to be proven. It is the easiest and cheapest among other genetic identification tools.This study aimed to map reading material selections based on blood type personality. This study was a quantitative approach through cross-sectional survey. Identification was obtained from data in identity cards and laboratory blood type tests. The study population was 100 UPI LIS students with 80 samples of young adults aged 18-22 through random sampling with stratification. The samples were: 9 respondents with AB blood type and 25 with A blood type. Respondents of O and B blood types each followed the selection of the expected sample was 20 people. Each homogeneous sample filled out a questionnaire on reading material selection aspects. Results showed that 55.6% of the AB blood type chose non-fiction books such as 'how-to' related to hobbies, and 52% of A blood type tended to select non-fiction books that support their tasks and work. Also, 81.8% of B blood type chose fiction books and adventure stories opening up fantasy horizons, and 80% of O blood type chose books that did not always have to be brought to the big screen/filmed; however, they were recommended and told. In conclusion, this blood type personality model can identify young adult clients' profiles to develop bibliotherapy service programs in different types of libraries and make it easier for librarians and bibliotherapists to recommend reading materials suitable for the benefit of preventive-curative bibliotherapy.
Privacy-Preserve Content-Based Image Retrieval Using Aggregated Local Features

Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.
Learning Interpretable, High-Performing Policies for Autonomous Driving

Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles.While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).AD requires interpretable and verifiable control policies that maintain high performance.We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, RL approaches to produce highperforming, interpretable policies.The key to our approach is a procedure for allowing direct optimization in a sparse decisiontree-like representation.We validate ICCTs against baselines across six domains, showing that ICCTs are capable of learning interpretable policy representations that parity or outperform baselines by up to 33% in AD scenarios while achieving a 300x-600x reduction in the number of policy parameters against deep learning baselines.Furthermore, we demonstrate the interpretability and utility of our ICCTs through a 14-car physical robot demonstration.i x k -b i > 0).As each leaf node is represented as a probability mass function over output classes in prior work, leaf nodes, l, must be modified to produce
Adversarial Data Augmentation for Task-Specific Knowledge Distillation of Pre-trained Transformers

Deep and large pre-trained language models (e.g., BERT, GPT-3) are state-of-the-art for various natural language processing tasks. However, the huge size of these models brings challenges to fine-tuning and online deployment due to latency and cost constraints. Existing knowledge distillation methods reduce the model size, but they may encounter difficulties transferring knowledge from the teacher model to the student model due to the limited data from the downstream tasks. In this work, we propose AD^2, a novel and effective data augmentation approach to improving the task-specific knowledge transfer when compressing large pre-trained transformer models. Different from prior methods, AD^2 performs distillation by using an enhanced training set that contains both original inputs and adversarially perturbed samples that mimic the output distribution from the teacher. Experimental results show that this method allows better transfer of knowledge from the teacher to the student during distillation, producing student models that retain 99.6\% accuracy of the teacher model while outperforming existing task-specific knowledge distillation baselines by 1.2 points on average over a variety of natural language understanding tasks. Moreover, compared with alternative data augmentation methods, such as text-editing-based approaches, AD^2 is up to 28 times faster while achieving comparable or higher accuracy. In addition, when AD^2 is combined with more advanced task-agnostic distillation, we can advance the state-of-the-art performance even more. On top of the encouraging performance, this paper also provides thorough ablation studies and analysis. The discovered interplay between KD and adversarial data augmentation for compressing pre-trained Transformers may further inspire more advanced KD algorithms for compressing even larger scale models.
Entropy Estimation via Normalizing Flow

Entropy estimation is an important problem in information theory and statistical science. Many popular entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for high dimensional problems. In this work we propose a transformbased method for high dimensional entropy estimation, which consists of the following two main ingredients. First by modifying the k-NN based entropy estimator, we propose a new estimator which enjoys small estimation bias for samples that are close to a uniform distribution. Second we design a normalizing flow based mapping that pushes samples toward a uniform distribution, and the relation between the entropy of the original samples and the transformed ones is also derived. As a result the entropy of a given set of samples is estimated by first transforming them toward a uniform distribution and then applying the proposed estimator to the transformed samples. Numerical experiments demonstrate the effectiveness of the method for high dimensional entropy estimation problems.
Transformer-Based Unsupervised Learning for Early Detection of Sepsis (Student Abstract)

A 6-hour early detection of sepsis leads to a significant increase in the chance of surviving it. Previous sepsis early detection studies have focused on improving the performance of supervised learning algorithms while ignoring the potential correlation in data mining, and there was no reliable method to deal with the problem of incomplete data. In this paper, we proposed the Denoising Transformer AutoEncoder (DTAE) for the first time combining transformer and unsupervised learning. DTAE can learn the correlation of the features required for early detection of sepsis without the label. This method can effectively solve the problems of data sparsity and noise and discover the potential correlation of features by adding DTAE enhancement module without modifying the existing algorithms. Finally, the experimental results show that the proposed method improves the existing algorithms and achieves the best results of early detection.
Promoting Single-Modal Optical Flow Network for Diverse Cross-Modal Flow Estimation

In recent years, optical flow methods develop rapidly, achieving unprecedented high performance. Most of the methods only consider single-modal optical flow under the well-known brightness-constancy assumption. However, in many application systems, images of different modalities need to be aligned, which demands to estimate cross-modal flow between the cross-modal image pairs. A lot of cross-modal matching methods are designed for some specific cross-modal scenarios. We argue that the prior knowledge of the advanced optical flow models can be transferred to the cross-modal flow estimation, which may be a simple but unified solution for diverse cross-modal matching tasks. To verify our hypothesis, we design a self-supervised framework to promote the single-modal optical flow networks for diverse corss-modal flow estimation. Moreover, we add a Cross-Modal-Adapter block as a plugin to the state-of-the-art optical flow model RAFT for better performance in cross-modal scenarios. Our proposed Modality Promotion Framework and Cross-Modal Adapter have multiple advantages compared to the existing methods. The experiments demonstrate that our method is effective on multiple datasets of different cross-modal scenarios.
Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds

Deep neural networks are extremely vulnerable to malicious input data. As 3D data is increasingly used in vision tasks such as robots, autonomous driving and drones, the internal robustness of the classification models for 3D point cloud has received widespread attention. In this paper, we propose a novel method named SPGA (Shape Prior Guided Attack) to generate adversarial point cloud examples. We use shape prior information to make perturbations sparser and thus achieve imperceptible attacks. In particular, we propose a Spatially Logical Block (SLB) to apply adversarial points through sliding in the oriented bounding box. Moreover, we design an algorithm called FOFA for this type of task, which further refines the adversarial attack in the process of breaking down complicated problems into sub-problems. Compared with the methods of global perturbation, our attack method consumes significantly fewer computations, making it more efficient. Most importantly of all, SPGA can generate examples with a higher attack success rate (even in a defensive situation), less perturbation budget and stronger transferability.
Bridging LTLf Inference to GNN Inference for Learning LTLf Formulae

Learning linear temporal logic on finite traces (LTLf) formulae aims to learn a target formula that characterizes the high-level behavior of a system from observation traces in planning. Existing approaches to learning LTLf formulae, however, can hardly learn accurate LTLf formulae from noisy data. It is challenging to design an efficient search mechanism in the large search space in form of arbitrary LTLf formulae while alleviating the wrong search bias resulting from noisy data. In this paper, we tackle this problem by bridging LTLf inference to GNN inference. Our key theoretical contribution is showing that GNN inference can simulate LTLf inference to distinguish traces. Based on our theoretical result, we design a GNN-based approach, GLTLf, which combines GNN inference and parameter interpretation to seek the target formula in the large search space. Thanks to the non-deterministic learning process of GNNs, GLTLf is able to cope with noise. We evaluate GLTLf on various datasets with noise. Our experimental results confirm the effectiveness of GNN inference in learning LTLf formulae and show that GLTLf is superior to the state-of-the-art approaches.
Enforcement Heuristics for Argumentation with Deep Reinforcement Learning

In this paper, we present a learning-based approach to the symbolic reasoning problem of dynamic argumentation, where the knowledge about attacks between arguments is incomplete or evolving. Specifically, we employ deep reinforcement learning to learn which attack relations between arguments should be added or deleted in order to enforce the acceptability of (a set of) arguments. We show that our Graph Neural Network (GNN) architecture EGNN can learn a near optimal enforcement heuristic for all common argument-fixed enforcement problems, including problems for which no other (symbolic) solvers exist. We demonstrate that EGNN outperforms other GNN baselines and on enforcement problems with high computational complexity performs better than state-of-the-art symbolic solvers with respect to efficiency. Thus, we show our neuro-symbolic approach is able to learn heuristics without the expert knowledge of a human designer and offers a valid alternative to symbolic solvers. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation.