Reviewerly/exHarmony huggingface dataset:
Research on formation collision avoidance of aircraft cooperative penetration based on improved potential field method

Aiming at the problem of sudden threat avoidance in the process of aircraft formation cooperative penetration, formation collision avoidance strategies based on improved artificial potential field method are proposed. The effective range of the artificial repulsion potential field and the collision avoidance airspace of the sudden threat source are established. To improve the artificial potential field, the formation velocity correction coefficient and the threat source entry angle correction coefficient are introduced. In addition, the improved artificial repulsion potential energy function is defined. Based on the improved artificial potential filed in three-dimensional space, the formation collision avoidance controller on the horizontal and altitude channel are proposed respectively. Flight control commands such as rudder angle, aileron rudder angle, and elevator yaw angle are given. The simulation results confirm the security and efficiency of the formation collision avoidance strategy.
Joint state estimation for nonlinear state‐space model with unknown time‐variant noise statistics

Summary This paper considers the joint estimation problem of state and unknown measurement noise covariance for nonlinear state‐space models. Using the variational Bayesian inference, the joint posterior distribution of state and measurement noise covariance is approximated by two independent proposal distributions, which is considered as a key idea of the proposed approach. Due to the nonlinearities caused by the system itself, the probability density function (PDF) of state involved in estimation is computationally intractable. Therefore, a set of weighted particles is generated to overlap the empirical density of state, while the PDF of measurement noise covariance is still derived analytically by using the conjugate properties of the Inverse‐Gamma distribution. Two simulation examples are presented to demonstrate the effectiveness of the proposed method, which can provide more satisfied estimation performance for nonlinear systems than the commonly used particle filter when measurement noise covariance is unavailable.
Secret key distillation over realistic satellite-to-satellite free-space channel

In this paper, the satellite-to-satellite secret-key rate lower bounds are determined for two relevant scenarios in which the exclusion zone and Eve’s aperture size are varied.
Diachronic observation of lexical and syntactic patterns in English translations of Taiwan patent texts

In recent years, artificial intelligence-based translation tools for patent documents have undergone rapid development. In 2016, the World Intellectual Property Organization and the European Patent Office successively launched neural machine translation technology. Increasing the volume of English translations in Taiwan's Patent Search System is necessary to strengthen Taiwan's competitive advantages and connectivity with the world. The high-quality bilingual parallel corpora had a decisive influence on the effectiveness of the translation model. Neural machine translation systems for patent texts are trained using human-translated patent texts; thus, exploring the specific pattern of patent translation is vital for improving the future development of neural machine translation for patent texts and providing insight regarding changes in the translations of patent texts over time. In this study, the translation-specific features and patterns of patent translation were identified from accumulated human translations of patent texts over a long-term period. Findings are presented regarding (1) features of English translations, namely of patent abstracts, and the frequency and patterns of simplification, explicitation, normalisation, and levelling-out; (2) specific patterns of translated patent texts and comparable nontranslated patent texts from different periods; and (3) linguistic features of English translations of patent abstracts of the same period.
Scene Text Detection Using Context-Aware Pyramid Feature Extraction

In the past decades, scene text detection has been attached much attention. Many researchers carried out researches on deep learning or neural network. However, the common scene text detection methods are sometimes inadequate because of the various forms and complex characteristics in natural scene images. To overcome those deficiencies, we propose an algorithm combing the Efficient and Accurate Scene Text Detector Model (EAST) with the Context-aware Pyramid Feature Extraction module (CPFE). Built on the base of fully convolutional neural network (FCN), CPFE is integrated to expend receptive field and obtain multi-scale and high-level features from context, which largely overcome the deficiency in scene text detection. Experiments conducted on the ICDAR2015 dataset show that our model outperforms the original EAST model.
Cloud Assisted Privacy Preserving Using Homomorphic Encryption

In this paper, privacy preserving at the cloud side is enabled by adopting Homomorphic Encryption (HE) as a major solution for protecting users' sensitive data. As a Proof Of Concept (POC), we take the case of a university that is adopting the cloud as a practical method for storing and operating over its private data. The main contribution of this application is that different data are stored encrypted at the cloud side. Students' data such as name, gender, date of birth, etc are encrypted using AES and hash functions. Students' indexes and grades are encrypted homomorphically using our modified Domingo Ferrer (DF) encryption scheme [1]. The main importance of the usage of HE in this application is allowing the process over encrypted data at the cloud side such as computing encrypted students' average. Different queries are sent from the university side to the cloud side, after operating and processing over encrypted data all results are shipped back encrypted to the university where the primitive data is recovered. Cloud infrastructure is created using Apache CloudStack and different encryption schemes are implemented under Python using SageMath library.
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention. Deep learning approaches often operate over the entire adjacency matrix - as the input and intermediate network layers are all designed in proportion to the size of the adjacency matrix - leading to intensive computation and large memory requirements as the graph size increases. It is therefore desirable to identify efficient measures to reduce both run-time and memory requirements allowing for the analysis of the largest graphs possible. The use of reduced precision operations within the forward and backward passes of a deep neural network along with novel specialised hardware in modern GPUs could offer promising avenues towards efficiency. In this paper, we provide an in-depth exploration of the use of reduced-precision operations, easily integrable into the highly popular PyTorch framework, and an analysis of the effects of Tensor Cores on graph convolutional neural networks. We perform an extensive experimental evaluation of three GPU architectures and two widely-used graph analysis tasks (vertex classification and link prediction) using well-known benchmark and synthetically generated datasets. Thus allowing us to make important observations on the effects of reduced-precision operations and Tensor Cores on computational and memory usage of graph convolutional neural networks - often neglected in the literature.
The Dawn of the Academic Revolution: An Individual Contribution to Shaping the Future of Universities

A dramatic global socio-economic transformation is challenging the traditional university models. Disruptive technologies are quickly changing the way we live and work, inducing shifts in occupational structures and pushing demand toward new skills and competences. Several factors such as the rise of neoliberalism and knowledge economy, the 2008 global economic crisis, Fourth Industrial Revolution, the 2020 COVID-19 pandemic, the continuing emergence of online learning, the amount of student debt, and employers’ dissatisfaction are generating pressure to reconsider global higher education systems. The key stakeholder groups—governments, education institutions, employers, and learners are seeking for new models that are more learner-oriented. The power struggle between various tensions within the current volatile, uncertain, complex, and ambiguous landscape generates a range of scenarios for the universities’ futures. Regardless of which scenario prevails, it is likely that academia will undergo a significantly dynamic transformation in the near future. Following a literature review shaped by personal experience in higher education, the author analyzes macro factors that may affect higher education in the forthcoming decade, with the aim of supporting strategic planning by universities. By generating a set of potential scenarios—Transformative, Market, and Fortress, the author identifies three possible futures to add to the debate about the direction and intensity of the required higher education transformation.
Separation‐based model for low‐dose CT image denoising

Low-dose computed tomography (LDCT) image often contains mottle noise and streak artefacts, which seriously interfere with clinical diagnosis. In this study, the separation-based (SEPB) method is proposed for mottle noise and streak artefacts suppression and structure preservation. In it, the LDCT image is decomposed into the structural image with residual mottle noise and the streak artefacts image with residual structural details by the image decomposition structural-preserving image smoothing method. The structural image is filtered by the K-singular value decomposition algorithm to remove the residual mottle noise, and the structural details in the streak artefacts image are extracted by the morphological component analysis theory. The extracted structural details are added to the filtered structural image to get the LDCT result image. Meanwhile, in the process of extracting the structural details, the streak artefacts dictionary learned from the streak artefacts image is corrected by the local intuitional fuzzy entropy to remove its structural atoms. The experiments are conducted on the modified Shepp–Logan phantom, the pelvis phantom and the clinical abdominal data to evaluate the proposed SEPB method. Compared to several comparative denoising methods, the experimental results show that the SEPB method has better performance in subjective visual effect and objective indicators.
Structured Feature Derivation for Transfer Learning on Credit Scoring

Credit scoring is a fast growing risk management methodology, where probability of default of customer borrowers are assessed by machine learning based on historical data. However, when the customer borrowers are in a different domain from the existing customers, the predictions are not executed properly because of domain adaptation problem. In this paper, we propose a transfer learning framework to predict probability of default extracting useful knowledge from an existing domain and divert it to a target domain by deriving latent feature vectors by Bayesian network structured learning. An experimental result showed the proposed method predicted the probability of default of customer borrowers more accurately than existing methods in a multi-domain space.
Toward a Building an Ontology of Artefact

The paper considers approaches to ontologies using in task of requirements tracing. It is stated that for successful requirements management it is necessary to reveal the image of rational activity object, not its individual properties. In this approach requirement is defined as an aspect projection of some object ontology onto considered subject area in the system purpose context. A model of the activity object ontology, as a system of functionally and logically interrelated concepts, quantities and methods, is developed. Proposed model combines an ontology of quantities and measures and an ontology of means, “technologies” of assessment. Such model makes it possible to describe processes occurring in the system in more detail, and in combination with other ontologies (ontology of an activity life cycle stages, properties ontology, certain project ontology constructed by project documentation), it allows to obtain tools for more efficient requirements revealing, understanding of causal relationship in their changing and management.
Multivariate Density Estimation with Deep Neural Mixture Models

Abstract Albeit worryingly underrated in the recent literature on machine learning in general (and, on deep learning in particular), multivariate density estimation is a fundamental task in many applications, at least implicitly, and still an open issue. With a few exceptions, deep neural networks (DNNs) have seldom been applied to density estimation, mostly due to the unsupervised nature of the estimation task, and (especially) due to the need for constrained training algorithms that ended up realizing proper probabilistic models that satisfy Kolmogorov’s axioms. Moreover, in spite of the well-known improvement in terms of modeling capabilities yielded by mixture models over plain single-density statistical estimators, no proper mixtures of multivariate DNN-based component densities have been investigated so far. The paper fills this gap by extending our previous work on neural mixture densities (NMMs) to multivariate DNN mixtures. A maximum-likelihood (ML) algorithm for estimating Deep NMMs (DNMMs) is handed out, which satisfies numerically a combination of hard and soft constraints aimed at ensuring satisfaction of Kolmogorov’s axioms. The class of probability density functions that can be modeled to any degree of precision via DNMMs is formally defined. A procedure for the automatic selection of the DNMM architecture, as well as of the hyperparameters for its ML training algorithm, is presented (exploiting the probabilistic nature of the DNMM). Experimental results on univariate and multivariate data are reported on, corroborating the effectiveness of the approach and its superiority to the most popular statistical estimation techniques.
A FAIR evaluation of public datasets for stress detection systems

Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle. These findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community.
GLO-YOLO

Renal pathology is the gold standard in renal clinical diagnosis. The glomerulus is the most important region of interest in the renal biopsy microscope slides. However, the development of renal pathology is limited by the doctor's laborious, time-consuming and repetitive detection of glomeruli. In this paper, we established GLO-YOLO to detect and slice glomerulus in whole slide image (WSI) of human renal biopsy microscope slides. In the training phase, GLO-YOLO improved the YOLO-v3 to improve the speed and efficiency of training. In the testing phase, we proposed dynamic scale evaluation, which can help models find the most appropriate evaluation scale for each high-resolution WSI. We collected and digitalized 1204 renal biopsy microscope slides to WSIs contained more than 42000 annotated glomeruli. The experimental results show the validity of dynamic-scale evaluation and improved YOLO-v3. GLO-YOLO has obtained state-of-the-art performance and outperformed the recently published approaches. Furthermore, GLO-YOLO can deploy in the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by detecting glomeruli in high-resolution WSIs very quickly and effectively.
Knee Point Identification Based on Voronoi Diagram

Finding preferred solutions is important for DMs to take the next step in solving Multi-objective optimisation problems (MOPs). When no specific preferences are available, knee point(s) are typically considered to be the most preferred solutions in multi-criterion decision-making since their smallest trade-off loss at all objectives. Knee point(s), including concave, convex and edge knee point(s), can reflect some geometry characteristics of the given non-dominated solutions because of its unique location. However, most of contemporary research for knee point identification (KPI) is only designed for convex knee point(s). Based on Voronoi diagram which can effectively reflect the distribution of the given set, we propose a method to identify all three types of knee points from a geometry view. In order to validate our method, we compare the performance of our method with other three state of the art approaches on benchmark problems for knee point identification. Experimental results fully show the effectiveness and competitiveness of our proposed KPI method based on Voronoi diagram (KPIVD) for identifying three types of knee points.
Sensing of the Self, Society, and the Environment

We propose a novel sensemaking taxonomy, and a working wearable computing system within that taxonomy, to help people sense and understand the world around them. This unified system and taxonomy organizes the world of sensors and sensory experience into the following: (A) Self-and-Technology; (B) Self-and-Society; and (C) Self-and-Environment. This wearable system, which we named "Vironment™", embodies a suite of functionalities which demonstrate each of the three presented levels. The taxonomy helps in understanding the line between self, society, and the world around us, while Vironment extends the wearer's sensing capabilities in these three areas.
Towards Closing the Security Gap of Tweak-aNd-Tweak (TNT)

Tweakable block ciphers (TBCs) have been established as a valuable replacement for many applications of classical block ciphers. While several dedicated TBCs have been proposed in the previous years, generic constructions that build a TBC from a classical block cipher are still highly useful, for example, to reuse an existing implementation. However, most generic constructions need an additional call to either the block cipher or a universal hash function to process the tweak, which limited their efficiency. To address this deficit, Bao et al. proposed Tweak-aNd-Tweak (TNT) at EUROCRYPT'20. Their construction chains three calls to independent keyed permutations and adds the unmodified tweak to the state in between the calls. They further suggested an efficient instantiation TNT-AES that was based on round-reduced AES for each of the permutations. Their work could prove 2n/3-bit security for their construction, where n is the block size in bits. Though, in the absence of an upper bound, their analysis had to consider all possible attack vectors with up to $$2^n$$ time, data, and memory. Still, closing the gap between both bounds remained a highly interesting research question. In this work, we show that a variant of Mennink's distinguisher on CLRW2 with $$O(\sqrt{n} 2^{3n/4})$$ data and $$O(2^{3n/2})$$ time from TCC'18 also applies to TNT. We reduce its time complexity to $$O(\sqrt{n} 2^{3n/4})$$ , show the existence of a second similar distinguisher, and demonstrate how to transform the distinguisher to a key-recovery attack on from an impossible differential. From a constructive point of view, we adapt the rigorous STPRP analysis of CLRW2 by Jha and Nandi to show $$O(2^{3n/4})$$ TPRP security for TNT. Thus, we move towards closing the gap between the previous proof and attacks for TNT as well as its proposed instance.
<scp>ColAtt‐Net</scp>: In Reducing the Ambiguity of Pedestrian Orientations on Attribute‐Aware Semantic Segmentation Task

Abstract Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute‐aware semantic segmentation has been introduced. Since the task requires to handle pixel‐wise object class estimation with its attributes such as a pedestrian's body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object‐level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt‐Net that is an attribute‐aware semantic segmentation model augmented by a column‐wise mask branch to predict the pedestrians' orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car‐mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column‐wise attribute branch, is to map the recognition of pedestrian's orientations that are distributed horizontally. This method successfully enhances the performance of attribute‐aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian's orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
MATCH

Matchmaking is a core enabling element in peer-to-peer markets. To date, matchmaking is predominantly performed by proprietary algorithms, fully controlled by market operators. This raises fairness concerns as market operators effectively can hide, prioritize, or delay the orders of specific users. Blockchain technology has been proposed as an alternative for fair matchmaking without a trusted operator but is still vulnerable to specific fairness attacks.
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow

Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i.e. using a single sensor. Towards this end, we exploit the latest achievements in single image depth estimation, optical flow, and sparse-to-dense interpolation and propose a monocular combination approach (MonoComb) to compute dense scene flow. MonoComb uses optical flow to relate reconstructed 3D positions over time and interpolates occluded areas. This way, existing monocular methods are outperformed in dynamic foreground regions which leads to the second best result among the competitors on the challenging KITTI 2015 scene flow benchmark.