Reviewerly/exHarmony huggingface dataset:
SFTM: Fast matching of web pages using Similarity-based Flexible Tree Matching

Tree matching techniques have been investigated in many fields, including web data mining and extraction, as a key component to analyze the content of web pages. However, when applied to existing web pages, traditional tree matching approaches, covered by algorithms like Tree-Edit Distance (TED) or XyDiff, either fail to scale beyond a few hundred nodes or exhibit a relatively low accuracy. In this article, we therefore propose a novel algorithm, named Similarity-based Flexible Tree Matching (SFTM), which enables high accuracy tree matching on real-life web pages, with practical computation times. We approach tree matching as an optimization problem and leverage node labels and local topology similarity in order to avoid any combinatorial explosion. Our practical evaluation demonstrates that SFTM significantly improves the state of the art in terms of accuracy, while allowing computation times significantly lower than the most accurate solutions. By gaining on these two dimensions, SFTM therefore offers an affordable solution to match complex trees in practice.
Deep Learning enabled Fall Detection exploiting Gait Analysis

Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$434\mathrm{x}2$</tex> videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives.
Hardware-Algorithm Codesign for Fast and Energy Efficient Approximate String Matching on FPGA for Computational Biology

Myers bit-vector algorithm for approximate string matching (ASM) is a dynamic programming based approach that takes advantage of bit-parallel operations. It is one of the fastest algorithms to find the edit distance between two strings. In computational biology, ASM is used at various stages of the computational pipeline, including proteomics and genomics. The computationally intensive nature of the underlying algorithms for ASM operating on the large volume of data necessitates the acceleration of these algorithms. In this paper, we propose a novel ASM architecture based on Myers bit-vector algorithm for parallel searching of multiple query patterns in the biological databases. The proposed parallel architecture uses multiple processing engines and hardware/software codesign for an accelerated and energy-efficient design of ASM algorithm on hardware. In comparison with related literature, the proposed design achieves 22× better performance with a demonstrative energy efficiency of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 500\times 10^{9}$</tex> cell updates per joule.
Attention Temporal Graph Convolutional Network for Tennis Groundstrokes Phases Classification

Action classification makes significant contributions to sport analysis, especially for athletes’ training evaluations. In this study, the identification of two basic tennis groundstrokes were taken into consideration, because these types of movements are performed in every match or training. As the input dataset, the recorded images presenting the registered movements with the optical motion capture system were applied. They show a three dimensional model of the tennis player together with a tennis racket. The Attention Temporal Graph Convolutional Network was used as a classification tool for this sophisticated data. This type of classifier has an advantage that each graph (joint) in the image corresponds to the human topology. The presented approach is the first application for this type of data, according to the authors’ knowledge.In this study, each forehand and backhand shot from input data was divided into the following phases: The preparation and the point of impact together with the racket swinging. Additionally, no shot was also taken into consideration.Due to the fact that the analysed groundstrokes are very fast and dynamically performed, the strict boundaries of these phases are hard to indicate. Fuzzification of the input data was applied in order to reduce this disadvantage. This approach improved the accuracy with respect to the non-fuzzy input. The obtained accuracy with fuzzification was achieved at the level of 86.9% — 93.82%, while without fuzzy inputs at 74.22% – 81.95%.
Implementing Deep Neural Network Based Encoder-Decoder Framework for Image Captioning

This study is concerned with the development of a deep neural network-based framework, including a “convolutional neural network (CNN)” encoder and a “Long Short-Term Memory (LSTM)” decoder in an automatic image captioning application. The proposed model percepts information points in a picture and their relationship to one another in the viewpoint. Firstly, a CNN encoder excels at retaining spatial information and recognizing objects in images by extracting features to produce vocabulary that describes the photos. Secondly, an LSTM network decoder is used for predicting words and creating meaningful sentences from the built keywords. Thus, in the proposed neural network-based system, the VGG-19 model is presented for defining the proposed model as an image feature extractor and sequence processor, and then the LSTM model provides a fixed-length output vector as a final prediction. A variety of images from several open-source datasets, such as Flickr 8k, Flickr 30k, and MS COCO, were explored and used for training as well as testing the proposed model. The experiment was done on Python with Keras and TensorFlow backend. It demonstrated the automatic image captioning and evaluated the performance of the proposed model using the BLEU (BiLingual Evaluation Understudy) metric.
Edge-Aware Extended Star-Tetrix Transforms for CFA-Sampled Raw Camera Image Compression

Codecs using spectral-spatial transforms efficiently compress raw camera images captured with a color filter array (CFA-sampled raw images) by changing their RGB color space into a decorrelated color space. This study describes two types of spectral-spatial transform, called extended Star-Tetrix transforms (XSTTs), and their edge-aware versions, called edge-aware XSTTs (EXSTTs), with no extra bits (side information) and little extra complexity. They are obtained by (i) extending the Star-Tetrix transform (STT), which is one of the latest spectral-spatial transforms, to a new version of our previously proposed wavelet-based spectral-spatial transform and a simpler version, (ii) considering that each 2-D predict step of the wavelet transform is a combination of two 1-D diagonal or horizontal-vertical transforms, and (iii) weighting the transforms along the edge directions in the images. Compared with XSTTs, the EXSTTs can decorrelate CFA-sampled raw images well: they reduce the difference in energy between the two green components by about 3.38–30.08 % for high-quality camera images and 8.97–14.47 % for mobile phone images. The experiments on JPEG 2000-based lossless and lossy compression of CFA-sampled raw images show better performance than conventional methods. For high-quality camera images, the XSTTs/EXSTTs produce results equal to or better than the conventional methods: especially for images with many edges, the type-I EXSTT improves them by about 0.03–0.19 bpp in average lossless bitrate and the XSTTs improve them by about 0.16–0.96 dB in average Bjøntegaard delta peak signal-to-noise ratio. For mobile phone images, our previous work perform the best, whereas the XSTTs/EXSTTs show similar trends to the case of high-quality camera images.
Vol2Flow: Segment 3D Volumes Using a Sequence of Registration Flows

This work proposes a self-supervised algorithm to segment each arbitrary anatomical structure in a 3D medical image produced under various acquisition conditions, dealing with domain shift problems and generalizability. Furthermore, we advocate an interactive setting in the inference time, where the self-supervised model trained on unlabeled volumes should be directly applicable to segment each test volume given the user-provided single slice annotation. To this end, we learn a novel 3D registration network, namely Vol2Flow, from the perspective of image sequence registration to find 2D displacement fields between all adjacent slices within a 3D medical volume together. Specifically, we present a novel 3D CNN-based architecture that finds a series of registration flows between consecutive slices within a whole volume, resulting in a dense displacement field. A new self-supervised algorithm is proposed to learn the transformations or registration fields between the series of 2D images of a 3D volume. Consequently, we enable gradually propagating the user-provided single slice annotation to other slices of a volume in the inference time. We demonstrate that our model substantially outperforms related methods on various medical image segmentation tasks through several experiments on different medical image segmentation datasets. Code is available at https://github.com/AdelehBitarafan/Vol2Flow .
$ \mathbb{Z}_{p^r}\mathbb{Z}_{p^s}\mathbb{Z}_{p^t} $-additive cyclic codes

&lt;p style='text-indent:20px;'&gt;Let &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; be a prime number and &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ r, s, t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; be positive integers such that &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ r\le s\le t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. A &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \mathbb{Z}_{p^r}\mathbb{Z}_{p^s}\mathbb{Z}_{p^t} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-additive code is a &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ \mathbb{Z}_{p^t} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-submodule of &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ \mathbb{Z}_{p^r}^{\alpha} \times \mathbb{Z}_{p^s}^{\beta} \times \mathbb{Z}_{p^t}^{\gamma} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ \alpha, \beta, \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are positive integers. In this paper, we study &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ \mathbb{Z}_{p^r}\mathbb{Z}_{p^s}\mathbb{Z}_{p^t} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-additive cyclic codes. In fact, we show that these codes can be identified as submodules of the ring &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ R = \mathbb{Z}_{p^r}[x]/\big&amp;lt;x^\alpha-1\big&amp;gt; \times \mathbb{Z}_{p^s}[x]/\big&amp;lt;x^\beta-1\big&amp;gt; \times \mathbb{Z}_{p^t}[x]/\big&amp;lt;x^\gamma-1\big&amp;gt; $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Furthermore, we determine the generator polynomials and minimum generating sets of this kind of codes. Moreover, we investigate their dual codes.&lt;/p&gt;
Experimental Evaluation of Panel-Method-Based Path Planning for eVTOL in A Scaled Urban Environment

In this study, previously proposed panel method based path planning for electric vertical take off and landing vehicles in urban environments is tested in a high fidelity simulation environment and with real-life drones in an indoor flight arena. Panel method is a numerical tool, borrowed form fluid dynamics domain, that can generate collision free paths for multiple vehicles in environments with arbitrarily shaped obstacles while guaranteeing obstacle avoidance and convergence to global minima with little computational load. In this study, panel method based path planning is further improved with introduction of novel safety source element that enables a safety perimeter around obstacles without losing convergence guarantee. Furthermore, path planning capability of panel method for electric vertical take off and landing vehicles in urban environments is demonstrated with hardware experiments in a scaled urban environment. Experiment results indicate that panel method is a promising tool for path planning in urban environments.
Suitability of the Single Transferable Vote as a Replacement for Largest Remainder Proportional Representation

There are two main approaches to achieving proportional representation in elections: the single transferable vote and methods based on party lists. This paper discusses ways to use the single transferable vote while using some of the main features used with the largest remainder method, such as the electoral threshold. The investigation has shown that the Weighted Inclusive Gregory method is a suitable replacement for the largest remainder method when it is desirable to avoid wasted votes and to handle independent candidates in a straightforward way, but it is also desirable to keep the results as close to the ones achieved under the largest remainder method as possible. The investigation also led to the development of an algorithm for using the single transferable vote when preference lists are based on party lists, exploiting commonalities and symmetries between the patterns of preferences given in the votes. It has been shown that such an algorithm makes the calculations faster than the use of ordinary implementations of the single transferable vote when the numbers of seats and candidates are high, as commonly happens when methods based on party lists are used.
Ethnography of/and Virtual Reality

This chapter explores the relationship between ethnography and virtual reality (VR) both in terms of practices and promises. I take as a starting point that both pursuits are animated by claims of immersion and appeal to offering an experience of “being there.” To consider how anthropologists ought to think about the re-emergence of VR, I draw on fieldwork conducted in Los Angeles with members of the community producing high-end, cinematic VR (as distinct from socially networked virtual worlds). The chapter examines how both ethnographers and the VR community define immersion, the ways in which immersion differs between these two domains, and how anthropological ideas might contribute toward a critique of immersion of use to VR creators. Through this approach, an ethnography of virtual reality revealingly places in conversation ethnography and virtual reality.
Solutions for Data Sharing and Storage: A Comparative Analysis of Data Repositories

Research data management is an essential process in scientific research activities. It includes monitoring data from the moment it is created until it is deposited in a repository so that later it can be accessed and reused by others. Sharing and reuse are the last steps in this process. It is essential to ensure that the data stored in digital repositories is well preserved in the long term and that its adequate interpretation and future reuse is guaranteed. Following this debate, questions arise related to the interoperability of systems and the suitability of platforms. In this study, we study how data management platforms can solve the problems associated with description, preservation, and access in digital media, making their usefulness evident. We identify some of the most relevant repository platforms in the scope of research data management, offering the scientific community an aggregating view of the various solutions and their main characteristics, thus aiming at a better understanding of them for their appropriate choice.
On Dimensions of Plausibility for Narrative Information Access to Digital Libraries

Designing keyword-based access paths is a common practice in digital libraries. They are easy to use and accepted by users and come with moderate costs for content providers. However, users usually have to break down the search into pieces if they search for stories of interest that are more complex than searching for a few keywords. After searching for every piece one by one, information must then be reassembled manually. In previous work we recommended narrative information access, i.e., users can precisely state their information needs as graph patterns called narratives. Then a system takes a narrative and searches for evidence for each of its parts. If the whole query, i.e., every part, can be bound against data, the narrative is considered plausible and, thus, the query is answered. But is it as easy as that? In this work we perform case studies to analyze the process of making a given narrative plausible. Therefore, we summarize conceptual problems and challenges to face. Moreover, we contribute a set of dimensions that must be considered when realizing narrative information access in digital libraries.
Formation of Academic Leadership in the Context of Trends in the Development of Modern Russian Higher Education

The article is based on the study of current problems of higher education based on the appeal to the ideas of the strategic importance of the reorientation of higher professional education in Russia to the formation of students’ creative skills and abilities, about the paradigm shift radically changing the culture of education itself as the fundamental basis of the educational space, the concept of regionalization of state policy in the field of higher education. The article highlights the leading trends in the diversification of state policy in the field of higher education and key scenarios for the development of higher education in the context of global and local challenges of our time. Among the leading trends that determine the specifics of the development of higher education, including in Russian conditions, there are professionalization, digitalization and regionalization. It is shown that the prospects of state policy in the educational sphere can be associated with the implementation of the “third mission” of the university, aimed at creating the necessary public goods, the forced formation of the institutional environment of its own region. The strategy of achieving academic leadership as a special state of the educational and scientific-innovative sphere, including in the regional space, is highlighted as the main mechanism for the development of state policy in the field of higher education.
The Effect of Limited Face-to-Face Learning in New Normal Era towards Learning Motivation of Elementary School Students

The purpose of this research is to measure the implementation effect of limited face-to-face learning in the new normal era towards elementary school student learning motivation. This research used an ex- post facto approach. The research population is fifth-grade students in Gugus Alalak, Banjarmasin, Indonesia total number of 296 students. The research sample was taken using a simple random sampling technique with a total of 183 fifth-grade students in the Alalak cluster, Banjarmasin, Indonesia. The data collection used are questionnaires given to 183 fifth grade students and interviews with 6 teachers in fifth grade. The data analysis technique used is descriptive statistical analysis and inferential analysis. Research findings show that the online learning process and change to limited face-to-face learning turn out to have an effect on students' learning motivation at school, in limited face-to-face learning students also become more motivated in learning.
Analysis of digital literacy type and influence factor in MZ Generation

Objectives The purpose of this study was to classify latent profiles of adult learners aged 20 to 30' digital literacy through the latent profile analysis(LPA) and to explore what factors affect this classification.&#x0D; Methods For this study, The latent profile analysis was conducted using the data of ‘Korean Media Panel Data’ for the 12th year (2021). To inquire into factors affecting the classification, we were selected as influence variables: ‘life satisfaction and mental health’, ‘self-esteem’, and ‘SNS and OTT consumption behavior’.&#x0D; Results The results of this study showed as follows: First, digital literacy types were classified into three types: ‘low level’, ‘overall high level’, and ‘high level(ability to utilize SNS)’. Second, the results of examining the factors to explain differences in groups show as follows: First, there were significant differences in ‘life satisfaction and mental health’ and ‘SNS and OTT consumption behavior’. Next, there were no significant differences in ‘self-esteem’. In terms of ‘life satisfaction and mental health’, the group with ‘a high level(ability to utilize SNS)’ is higher than the other groups. On the other hand, in the ‘SNS and OTT consumption behavior’, the group of ‘low level’ is higher than the other groups. Online is another learning space and it is necessary to distinguish between wise use and consumption.&#x0D; Conclusions This study was to search the characteristics of digital literacy of learners aged 20 to 30. Based on the results of this study, we suggested that it is necessary to deeply examine the characteristics of learners from a high level to a low level through a focus group interview, content analysis, and so on.
Motion Capture Technology of Digital Animation Art Based on Machine Learning

With the progress of society and the increasing development of science and technology, digital animation technology is also constantly innovating. Motion capture technology is widely used in film, television, media, and other fields and has the characteristics of the times. The use of motion capture technology in animation production can greatly improve the accuracy and professionalism of animation performances. It plays an important role in the development of animated character activities, especially in the creation of martial arts and professional dance performances. It uses motion capture technology to create animated character performances and composite effects. In this paper, four feature selection algorithms such as document frequency (DF), information gain (IG), mutual information (MI), and the chi-square test (CHI) were used, and they were used to capture action features and feature selection application steps based on the support vector machine (SVM) algorithm and the naive Bayes (NB) algorithm. The results of the experimental study showed that the TF-CHI algorithm in the improved algorithm had an accuracy rate of 89%, and the feature selection accuracy it made was better than the traditional algorithm. Therefore, the effectiveness of the animation motion capture technology based on the machine learning motion improvement algorithm has been confirmed.