Reviewerly/exHarmony huggingface dataset:
Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

There has been an increased interest in the development of models to identify and predict human activities. However, the sparsity of the data gathered from the sensory devices in an ambient living environment creates the challenge of representing activities accurately. Also, such data usually comprise arbitrary lengths of dimensions. Recurrent Neural Networks (RNNs) are one of the widely used algorithms in sequential modelling due to their ability to handle the arbitrary lengths of data. In an attempt to address the above challenges, this paper proposes a method of fuzzy feature representation with Bidirectional Long Short-Term Memory (Bi-LSTM) for human activities modelling and recognition. To obtain optimal feature representation, sensor data are fuzzified and the membership degrees represent the selected features which are then applied to the Bi-LSTM model for activity modelling and recognition. The learning capability of the Bi-LSTM allows the model to learn the temporal relationship in sequential data which is used to identify human activities pattern. The learned pattern is then utilised in the prediction of further activities. The proposed method is tested and evaluated using dataset representing Activity of Daily Living (ADL) for a single user in a smart home environment. The obtained results are also compared with existing approaches that are used for modelling and recognising human activities.
How to construct CSIDH on Edwards curves

CSIDH is an isogeny-based key exchange protocol proposed by Castryck et al. in 2018. It is based on the ideal class group action on Fp-isomorphism classes of Montgomery curves. The original CSIDH algorithm requires a calculation over Fp by representing points as x-coordinate over Montgomery curves. There is a special coordinate on Edwards curves (the w-coordinate) to calculate group operations and isogenies. If we try to calculate the class group action on Edwards curves by using the w-coordinate in a similar way on Montgomery curves, we have to consider points defined over Fp4. Therefore, calculating the class group action on Edwards curves with w-coordinates over only Fp is not a trivial task. In this paper, we prove some theorems about the properties of Edwards curves. We construct the new CSIDH algorithm using these theorems on Edwards curves with w-coordinates over Fp. This algorithm is as fast as (or a little bit faster than) the algorithm proposed by Meyer and Reith. This paper is an extended version of [29]. We added the construction of a technique similar to Elligator on Edwards curves. This technique contributes to the efficiency of the constant-time CSIDH algorithm. We also added the construction of new formulas to compute isogenies in O˜(ℓ) time on Edwards curves. It is based on formulas on Montgomery curves proposed by Bernstein et al. (élu's formulas). In our analysis, these formulas on Edwards curves are a little bit faster than those on Montgomery curves. We finally implemented CSIDH, élu's formulas, and CTIDH [3] (faster constant-time CSIDH) on Edwards curves. Each result shows the efficiency of algorithms on Edwards curves.
A Compound Neural Network for Brain Tumor Segmentation

Brain tumor image segmentation has been a time-consuming and laborious task for decades. Since deep learning has been applied to the image field, there have been many efforts to find automatic segmentation methods. This paper proposes an end-to-end image segmentation system based on neural network algorithms. First we develop a novel Convolutional Neural Network architecture named Hybrid Two-path Convolution that combines coarse and fine features obtained by different paths. Besides, traditional method for up-sampling often leads to insufficient utilization of network parameters. By endues geometric meaning to different channels, our Region-based Unpooling Convolution can effectively improve the detailed feature capture capabilities of the network without wasting any parameters. Due to the imbalance between labels, we design category based loss function as a solution. Experiments on public datasets demonstrate that the proposed system is competitive with the state-of-the-art methods.
Application of Multimedia Animation Engineering Drawing (MMAED) for Vocational High School Students

Abstract The use of animated multimedia in vocational schools in Indonesia is still not optimal, this is due to the lack of innovative teaching materials, especially multimedia-based instructional materials. This study aims to apply the results of the development of innovative teaching materials based on engineering animated image Multimedia for Vocational High Schools especially in the competence of Mechanical Engineering expertise. Meanwhile, the specific objectives to be achieved in this study are: (1) applying innovative teaching materials in the form of Multimedia Animation Engineering Drawing (MMAED) to improve vocational students’ competence. (2) Measuring the level of vocational students’ competence in engineering image material, especially orthogonal projection material. The method used in this application is using the Experimental method, namely by directly testing the product of Multimedia Animation Engineering Drawing (MMAED) to schools that have mechanical engineering competencies in Indonesia. From the application of Multimedia Animation Engineering Drawing (MMAED), students can increase their competencies in engineering material, especially orthogonal projection material.
Efficient Multi-Sensor Extended Target Tracking using GM-PHD Filter

This work deals with the efficient fusion of multiple disparate sensors, namely THz radar, stereo camera and lidar for autonomous vehicles. In particular, we develop a target tracking algorithm that is object agnostic i.e. we seek to detect any potential object in the scene and track it while also preserving extended target characteristics such as length and width. To this end, we first use conventional clustering and labelling methods in order to generate consistent features from each sensor independently. The features from each sensor are then transformed into a set of bounding boxes located in both range and cross range. The bounding box parameters are then fed into the proposed efficient multi-sensor target tracking algorithm. This is achieved by modifying the Gaussian mixture-PHD filter (GM-PHD) by incorporating a set of class labels that associate a state to a set of sensors. The performance of the proposed method target tracking method is verified using both synthetic and real world data.
Evaluation of a Campus Navigation Application Using an AR Character Guide

In recent years, many studies have been conducted to realize easy route navigation for pedestrians using augmented reality (AR). Many of these studies use methods where the direction or sign is indicated using AR. However, only a few have focused on using AR for route navigation to follow. Our research aims to realize easy route navigation using an AR character agent. We develop a navigation application wherein the user follows an AR character agent. We evaluate it from the behavior of the user. For that purpose, we conduct an experiment at a university festival using the navigation application. We then analyze the experimental data and questionnaire responses. The results show that route navigation by the AR character agent makes route identification easy. However, problems involving many participants feeling uneasy or being lost are encountered. Problems in accuracy caused by errors in the positional information are also observed. In future, a method to indicate directions more accurately must be considered.
Distributed Parameter Optimization Scheduling Strategy and System Design Based on Mesos

This paper implements a distributed parameter optimization system based on Mesos and studies the scheduling strategy of this system. Using the resource interface of Mesos, the system packages a variety of common parameter optimization algorithms and task scheduling strategy into a framework software that can run on Mesos. Aiming at the two-level scheduling mechanism of Mesos, a dynamic scheduling strategy for distributed parameter optimization system in multi-job environment on hybrid deployment cluster is proposed. This paper designs several experiments, and compares the resource scheduling strategy of the architecture software with the FIFO scheduling strategy in the hybrid deployment scenario. This work reduces the difficulty of optimizing distributed parameters in common scenarios such as deep learning in a cluster environment, and improves resource utilization efficiency in multi-task environment.
Software Quality Improvement by Application of a Component-Based Software Meta-Architecture

Designing software is a complex process which requires a lot of knowledge and experience. Every software has a high-level architecture which can be represented as a set of taken design decisions. Software meta-architectures, architectural styles, and reference models are commonly used tools which help to shape the software architectures by delivering sets of already taken, and validated design decisions. The STCBMER is a component-based software meta-architecture founded on a set of simple pieces of architectural knowledge, called architectural principles. The authors of this publication have defined the STCBMER and its principles in previous works. The main aim and the contribution of this paper is to look for a correlation between a component-based software meta-architecture (STCBMER) and software quality attributes.
A Unified Unsupervised Learning Framework for Stereo Matching and Ego-Motion Estimation

Learning to estimate depth and ego-motion from video sequences via deep convolutional networks is attracting significant attention for potentially wide computer vision applications. Most prior work in unsupervised depth learning use monocular video sequences as the input of their networks. However, their results need a scale factor that is computed frame-to-frame to maintain a stable relative scale. In this paper, we propose an unsupervised learning framework for the task of joint depth and ego-motion estimation from stereo sequences. The usage of stereo sequences can provide both spatial (left to right) and temporal (forward to back-ward) photometric warping constrains for supervised learning and allow for an absolute scale factor for the scene depth and camera pose, which is of great significance for vision guidance. Experiments on the KITTI driving dataset reveal that our framework outperforms state-of-the-art results employing unsupervised neural networks.
On $b$ -Symbol Distances of Repeated-Root Constacyclic Codes

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> be a prime, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> be a positive integer, and let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> be an integer satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \leq b &lt; p^{s}$ </tex-math></inline-formula> . In this paper, we obtain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol distances of all repeated-root constacyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{s}$ </tex-math></inline-formula> over finite fields. Using this result, we determine <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol distances of all repeated-root constacyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{s}$ </tex-math></inline-formula> over finite commutative chain rings. We also list all MDS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol repeated-root constacyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{s}$ </tex-math></inline-formula> over finite fields, and all MDS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -symbol repeated-root constacyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{s}$ </tex-math></inline-formula> over finite commutative chain rings in general.
Label Propagation on K-partite Graphs with Heterophily

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks (e.g., social networks) are heterogeneous, they contain different types of vertices (e.g., users, images, and texts) and relations (e.g., friendships and co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a IC-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change overtime, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.
An Adaptive Contrast Threshold SIFT Algorithm Based on Local Extreme Point and Image Texture

SIFT algorithm has good effect for most images in feature matching. But it cannot extract plenty of feature points from blurred images, and the feature points extracted from low-light images have cluster effect seriously. That is because the value of contrast threshold which was used to remove the low responsive-point is fixed. This paper proposes an adaptive contrast threshold SIFT algorithm to improve this situation. The value of contrast threshold is determined by the average value of local extreme points and the texture character of the whole image. Experimental results show that the improved SIFT algorithm can extract plenty of feature points, which are effective, from blurred images and improve the distributed uniformity of the feature points extracted from low-light images.
Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)

Given a sequence of observations for each person in each camera, identifying or re-identifying the same person across different cameras is one of the objectives of video surveillance systems. In the case of video based person re-id, the challenge is to handle the high correlation between temporally adjacent frames. The presence of non-informative frames results in high redundancy which needs to be removed for an efficient re-id. We propose a novel method to handle this challenge using Determinantal Point Process (DPP) to select the most diverse and informative subset of frames from a given sequence. Since subset selection problem is NP-Hard, we propose to use an approximate solution called Lazy Greedy DPP (LGDPP) and further extend it to utilize the temporal information of sequences with our proposed Sequential LGDPP (SLGDPP) for video-based person re-id. The major advantages of the proposed DPP variants are their simplicity and plug and play nature, which make it possible to use them atop any pretrained re-id model followed by a feature fusion module. The effectiveness of proposed frameworks is demonstrated on two popular video re-id benchmark datasets through improvements over state-of-the-art methods and naive baseline sampling methods.
Features of modern Internet Media as a substantive component of training

The article is devoted to the selection and updating of the training content of future media industry specialists. Since the rapid transformation of traditional media dictates the need to clarify and modernize the concepts of the media industry, updating of the substantive component of training is a priority for educational activities. Analyzing the processes of mass media development, the author considers a number of specific features underlying the principles of online media functioning; explores the concept of interaction between online media and the modern consumer; structural and technological transformations affecting the principles of content creation and associated with monetization. The questions discussed in the article will allow participants in the learning process to understand the multidimensionality of the modern mass media and to set guidelines for further research.
Methods for single-cells

A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen.Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character twice and three times respectively, compared to the one-time appearance in the single paradigm. To compare the named paradigms, three subjects participated in experiments using all three paradigms.Offline results demonstrate an average character detection accuracy of 97% for the single and double protocols, and 80% for the Triple paradigm. In addition, average ITR is calculated to be 5.45, 7.62 and 7.90 bit/min for the single, Dual and Triple paradigms respectively. Results identify the Dual RSVP paradigm as the most suitable approach that provides the best balance between ITR and character detection accuracy.The novel speller system (the Dual paradigm) suggested in this paper demonstrates improved performance compared to existing methods, and overcomes the gaze dependency issue.Overall, our novel method is a reliable alternative that both removes limitations for users suffering from impaired oculomotor control and improves performance.
質問意図の明確化に着目した機械読解による質問応答手法の提案

The performance of reading comprehension, which is a question answering technique, by deep neural networks is now comparable to that of humans. However, there are still problems with the reading comprehension when given ambiguous questions. In this work, we propose a novel task called Specific Question Generation (SQG). SQG specifically revises the input question and suggests several specific question (SQ) candidates so that users can choose the SQ that is closest to their intent and obtain a highly accurate answer from the reading comprehension. We also propose a Specific Question Generation Model (SQGM) for facilitating the SQG. This model is based on an encoder-decoder model and uses two copy mechanisms (question copy and passage copy). The key idea here is that the missing information in the user-input question is described in the passage. Experimental results with public reading comprehension datasets demonstrated that our model generated specific questions that can improve reading comprehension accuracy.
Study on construction of loss of component cooling water system initiating event fault tree for fire event PSA

In this study, the construction of the loss of component cooling water system (LOCCWS) initiating event (IE) fault tree (FT) for an actual fire event probabilistic safety assessment (PSA) model of the Korean reference nuclear power plant considering only IE initiators was validated. The quantification results of the LOCCWS accident sequences obtained using an LOCCWS IE FT model with only initiators are similar to that with initiators and enabling events. This confirmed that the LOCCWS IE FT for an actual fire event PSA model could be constructed by considering only IE initiators. In addition, the same LOCCWS accident sequences were quantified assuming that fire triggering only the LOCCWS IE leads to reactor shutdown. Compared with the quantification result obtained based on the assumption that any fire included in the fire event PSA leads to reactor shutdown, the core damage frequency (CDF) can be reduced. Thus, it can be concluded that there is a possibility of underestimation of CDF when the LOCCWS IE FT model with only initiators is used and the assumption that fire triggering only the LOCCWS IE results in reactor shutdown is employed for the quantification of LOCCWS accident sequences.
Stimulating Multiple Intelligences in Infant Education From an Augmented Didactic Itinerary

This chapter investigates the contributions made by augmented reality (AR) to develop multiple intelligences in infant education, along with the opportunities offered by augmented didactic itineraries (ADI) as a suitable formula for the global activation of different intelligences by means of AR resources. The methodological guidelines followed to prepare an ADI are explained, and the case study method is adopted for the purpose of describing a model aimed at infant education schoolchildren—used in teacher training—in which are specified the activities included, together with the AR resources and applications that it comprises. More precisely, this ADI takes children's literature as a starting point and especially activates naturalistic, spatial, and bodily kinesthetic intelligence. Likewise, its training potential is analyzed from a didactic, digital and creative dimension so that it can serve as a model for future teachers to design their own aids.
Dense Correspondence of 3d Facial Point Clouds Via Neural Network Fitting

3D face dense correspondence is an important and challenging problem in 3D face analysis. Previous methods usually solve this problem by applying transformations to align different 3D faces and are thus constrained by the employed specific transformations. In this paper, instead, we approach the problem as a surface fitting and re-sampling problem. Modelling the point cloud of a 3D face as a surface, we use a neural network to fit the surface, and evaluate the obtained network at pre-specified sampling points that are defined based on prior knowledge of face structure. We evaluate the proposed method on the BU-3DFE database and prove its effectiveness both qualitatively and quantitatively.