Reviewerly/exHarmony huggingface dataset:
Low-Dimensional Vector Representation Learning for Text Visualization Using Task-Oriented Dialogue Dataset

Text visualization is a complex technique that helps in data understanding and insight, and may lead to loss of information. Through the proposed low-dimensional vector representation learning method, deep learning and visualization through low-dimensional vector space construction were simultaneously performed. This method can transform a task-oriented dialogue dataset into low-dimensional coordinates, and based on this, a deep learning vector space can be constructed. The low-dimensional vector representation deep learning model found the intent of a sentence within a dataset and predicted the sentence components well in 3 out of 5 datasets. In addition, by checking the prediction results in the low-dimensional vector space, it was possible to improve the understanding of the data, such as identifying the structure or errors in the data.
Techniques for Continuous Touch-Based Authentication

The field of continuous touch-based authentication has been rapidly developing over the last decade, creating a fragmented and difficult-to-navigate area for researchers and application developers alike. In this study, we perform a systematic literature analysis of 30 studies on the techniques used for feature extraction, classification, and aggregation in continuous touch-based authentication systems as well as the performance metrics reported by each study. Based on our findings, we design a set of experiments to compare the performance of the most frequently used techniques in the field under clearly defined conditions. In addition, we introduce two new techniques for continuous touch-based authentication: an expanded feature set (consisting of 149 unique features) and a multi-algorithm ensemble-based classifier. The comparison includes 13 feature sets, 11 classifiers, and 5 aggregation methods. In total, 204 model configurations are examined and we show that our novel techniques outperform the current state-of-the-art in each category. The results are also validated across three different publicly available datasets. Our best performing model achieves 4.8% EER using 16 consecutive strokes. Finally, we discuss the findings of our investigation with the aim of making the field more understandable and accessible for researchers and practitioners.
Use of Rosetta Stone Application as Online Learning Media in Learning German for Tenth-Graders of Language Studies Major at SMA Laboratorium UM

The research aims to depict students' activities using Rosetta Stone as the online learning media in learning German at SMA Laboratorium UM. The study was also looking for students' responses towards the use of the application. The research design used in this research was descriptive qualitative. The participants of the research consist of 22 students of tenth-graders majoring in language studies. The researcher obtained the data from observation and interviews. The researcher did the investigation to learn the activity of using the application in online learning German. The researcher interviewed the students to get responses from the students using the application as their online learning media. The result revealed that the application gave new exciting experiences in learning language, mainly German. The research had shown that the students actively engaged and gave a positive attitude towards the learning and teaching process, with the result that fostering learning independence. Students were able to access the materials in the application, and they understood the materials given comprehensively. Following the learning activity, the goal of the research was well achieved.
Machine-learning number fields

We show that standard machine-learning algorithms may be trained to predict certain invariants of algebraic number fields to high accuracy. A random-forest classifier that is trained on finitely many Dedekind zeta coefficients is able to distinguish between real quadratic fields with class number 1 and 2, to 0.96 precision. Furthermore, the classifier is able to extrapolate to fields with discriminant outside the range of the training data. When trained on the coefficients of defining polynomials for Galois extensions of degrees 2, 6, and 8, a logistic regression classifier can distinguish between Galois groups and predict the ranks of unit groups with precision >0.97.
Early detection of a fall using Wi-Fi and deep learning

There have been millions of dramatic falls in the elderly population, the leading cause of traumatic injury and death. While the problem is more serious, the number of elderly is continuously increasing. To relieve this issue, early detection of a fall is very helpful for the elderly who are concerned about falling or a health problem when alone and can automatically alert medical responders. In this paper, we present a fall detection system using Wi-Fi channel state informations (CSIs). Wi-Fi technology is selected since it is ubiquitous allowing our system to work everywhere even in the private area like in a bathroom and to be easily implemented with low cost. The CSI provides information not only about the environment, but also human movements. To interpret an event as fall or non-fall, we employ several classification algorithms as well as deep learning models, which can provide very accurate results up to 97.7%.
Petrogenesis of Late Ordovician intrusive rocks in the West Kunlun orogenic belt: Constraints on the subduction process of the Proto-Tethys Ocean

西昆仑造山带发育有大量的早古生代岩浆岩, 它们的形成与原特提斯洋俯冲-碰撞密切相关, 然而, 对于这些岩浆岩的岩石成因和形成的构造背景仍然存在较大的争议。本文对西昆仑造山带早古生代阿喀孜二长岩和赛图拉石英闪长岩岩体开展了详细的矿物学、锆石U-Pb年代学、全岩地球化学和Sr-Nd-Hf-O同位素研究, 以探讨这些侵入岩的岩浆源区与成因, 揭示原特提斯洋在这一时期的构造演化过程。这两个岩体的LA-ICP-MS锆石U-Pb年龄分别为456±2Ma和452±2Ma, 指示它们形成于晚奥陶世。阿喀孜二长岩具有富碱(K<sub>2</sub>O+Na<sub>2</sub>O=10.5%~11.8%)、富钾(K<sub>2</sub>O/Na<sub>2</sub>O=1.20~1.44)特征, 相对低MgO(0.89%~0.97%)、TiO<sub>2</sub>(0.42%~0.45 %)含量, 富集Rb、Th、U等大离子亲石元素, 且具有较高的全岩锆饱和温度(<i>T</i><sub>Zr</sub>=814~862℃), 属于钾质碱性岩石。该岩体具有较高的(<sup>87</sup>Sr/<sup>86</sup>Sr)<sub>i</sub>值(0.72057~0.72186), 负的全岩<i>ε</i><sub>Nd</sub>(<i>t</i>)值(-5.1~-4.5)和负的锆石<i>ε</i><sub>Hf</sub>(<i>t</i>)值(-7.6~-3.4), 以及较高的锆石<i>δ</i><sup>18</sup>O值(5.39‰~7.94‰), 表明其源区可能为伸展环境下古老基性下地壳部分熔融。赛图拉石英闪长岩SiO<sub>2</sub>含量为56.8%~58.6%, 全碱(Na<sub>2</sub>O+K<sub>2</sub>O)含量为5.75%~6.39%, 具有较低的铝饱和指数(A/CNK=0.85~0.89), 相对高的MgO含量(2.93%~3.41%)以及Mg<sup>#</sup>值(~45), 为高钾钙碱性系列岩石。它们富集大离子亲石元素(Rb、Ba、Th)和亏损高场强元素(Nb、Ta、Ti), 且具有微弱的Eu异常(<i>δ</i>Eu= 0.72~0.77), 类似于俯冲带岩浆岩地球化学特征。这些石英闪长岩具有相对高的(<sup>87</sup>Sr/<sup>86</sup>Sr)<sub>i</sub>比值(0.71317~0.71322), 负的全岩<i>ε</i><sub>Nd</sub>(<i>t</i>)值(-5.6~-5.1)和负的锆石<i>ε</i><sub>Hf</sub>(<i>t</i>)值(-7.8~-2.9), 与同时期来源于富集地幔的基性岩同位素特征相似, 表明赛图拉石英闪长岩岩浆可能是由俯冲板片流体交代的富集地幔楔部分熔融形成, 并在岩浆演化的过程中经历了橄榄石、辉石、角闪石、磷灰石和Fe-Ti氧化物等矿物的分离结晶作用。结合同位素和数字模拟计算(Rhyolite-MELTS)结果表明, 赛图拉石英闪长岩可能由塔里木克拉通南缘玄武质岩浆的分离结晶而成。综合前人研究, 本文提出晚奥陶世阿喀孜二长岩和赛图拉石英闪长岩形成于原特提斯洋向北俯冲相关的活动大陆边缘环境, 该时期原特提斯洋板片回撤, 诱发软流圈地幔的上涌, 从而产生了大量的弧岩浆岩。
EAU-Net: A New Edge-Attention Based U-Net for Nationality Identification

Identifying crime or individuals is one of the key tasks toward smart and safe city development when different nationals are involved. In this regard, identifying Nationality/Ethnicity through handwriting has received special attention. But due to freestyle and unconstrained writing, identifying nationality is challenging. This work considers words written by people of 10 nationals namely, India, Malaysia, Myanmar, Bangladesh, Iran, Pakistan, Sri Lanka, Cambodia, Palestine, and China, for identification. To extract invariant features, such as the distribution of edge patterns despite of the adverse effect of different writing styles, paper, pen, and ink, we explore a new Edge-Attention based U-Net (EAU-Net), which generates edge points for each input word image written by different nationals. Inspired by the success of the Convolutional Neural Network for classification, we explore CNN for the classification of 10 classes by considering candidate points given by EAU-Net as input. The proposed method is tested on our newly developed dataset of 10 classes, a standard dataset of 5 classes to demonstrate the effectiveness in classifying different nationalities. Furthermore, the efficacy of the proposed method is shown by testing on IAM dataset for gender identification. The results of the proposed and existing methods show that the proposed method outperforms the existing methods for both nationality and gender identification.
Semi-Extractive Text Summarization Approach to Extract Research Objective of Academic Literature

The task of literature review is an important for all research areas.The volume of academic literature published in the scientific journals, books and conference proceedings is increasing rapidly.It has become very difficult for researchers to analyze huge amount of published research papers and draw conclusions.Therefore, an automatic system able to extract the research objectives that recap the meaning and the definition of research from the article published is needed.The main challenge is to deal with the highly multiplicity of authors' writing style used in forming the objective sentences.A semi-extractive approach for precisely extracting research objective from the research abstract is proposed.The extraction process is performed in two tasks, search for pre-collected Key Expressions and verify the findings using pre-built General Key Phrases.An extensive evaluation has been conducted to find out how well it is performing using special dataset that includes diversified writing styles.The results showed that the method is a promising and able to extract objective sentence accurately.
Harnessing Multi-Semantic Hypergraph for Few-Shot Learning

Recently, Graph-based Few Shot Learning (FSL) methods exhibit good generalization by mining relations among the few examples with Graph Neural Networks. However, most Graph-based FSL methods consider only binary relations and ignore the multi-semantic information of the global context knowledge. We propose a framework of Multi-Semantic Hypergraph for FSL (MSH-FSL) to explore complex latent high-order multi-semantic relations among the few examples. Specifically, we first build up a novel Multi-Semantic Hypergraph by identifying associated examples with various semantic features from different receptive fields. With the constructed hypergraph, we then develop the Hyergraph Neural Network along with a novel multi-generation hypergraph message passing so as to better leverage the complex latent semantic relations among examples. Finally, after a number of generations, the hyper-node representations embedded in the learned hypergraph become more accurate for obtaining few-shot predictions. In the 5-way 1-shot task on miniImagenet dataset, the multi-semantic hypergraph outperforms the single-semantic graph by 3.1%, and with the proposed semantic-distribution message passing, the improvement can further reach 6.5%.
Automated RESTful API Service Discovery with Various Interface Features

In recent years, the rapid increase in the number of RESTful API services has made it more difficult for developers to select functionally suitable and callable ones from massive service information, which brings new challenges for service discovery. Most existing RESTful API discovery methods only use the function description texts, ignoring the information contained in the input and output interfaces, making the callability of the discovery results unguaranteed. This paper proposes an automated RESTful API service discovery framework considering both the description texts and interfaces. Based on the three newly-noticed RESTful API features of interface design and parameter matching, this paper presents an interface preprocessing and matching strategy. Interface preprocessing consists of interface transformation and identifier expansion to deal with parameter abbreviations. About the matching strategy, required and optional input parameters are matched with different importance, and one-to-many parameter matching relationships are allowed in this scheme. Experiments show that the proposed method is more suitable for automated RESTful API service discovery with various interface features. Better results are reflected in three metrics.
WISE: Whitebox Image Stylization by Example-Based Learning

Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering. In contrast to deep learning-based methods, these heuristics-based filtering techniques can operate on high-resolution images, are interpretable, and can be parameterized according to various design aspects. However, adapting or extending these techniques to produce new styles is often a tedious and error-prone task that requires expert knowledge. We propose a new paradigm to alleviate this problem: implementing algorithmic image filtering techniques as differentiable operations that can learn parametrizations aligned to certain reference styles. To this end, we present WISE, an example-based image-processing system that can handle a multitude of stylization techniques, such as watercolor, oil or cartoon stylization, within a common framework. By training parameter prediction networks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation. We demonstrate that jointly training an XDoG filter and a CNN for postprocessing can achieve comparable results to a state-of-the-art GAN-based method. https://github.com/winfried-loetzsch/wise .
Multi-level Kapur’s thresholding using whale optimization and social group optimization for brain MRI image segmentation

Image thresholding is the vital part for segmentation of a region in image. A fresh technique of multilevel thresholding based on the whale optimizer and social group optimizer are in fact recommended in this particular paper. New Optimization techniques inspired are actually affected as a result of the interpersonal as well as the backpacking behaviour of all of the whales. The process of multi-level thresholding problem is applied through the Kapur’s entropy and metaheuristic algorithms. The suggested strategy is tried on brain MRI test image set. The suggested methods are next in contrast with particle swarm optimization (PSO) and differential evaluation (DE) constructed multilevel thresholding techniques and other optimization techniques in the literature. The segmentation really calculated with evaluation measures like Sensitivity, Specificity, and Dice Score (DS).
The Metaverse

Facebook’s Mark Zuckerberg has a knack for making great strategic decisions. Just some include the acquisition of Instagram and the pivot to mobile.
PoSC: Combined Score for Consensus in Intemet-of-Things Applications

The properties of decentralization, security, and immutability have made the blockchain technology beneficial for various Internet of Things (IoT) applications. Consensus algorithms enable the peers in IoT-based networks to reach a common agreement on the current state of the network stored in a distributed ledger. The consensus protocols such as Proof of Work (PoW) and Proof of Stake (PoS) face centrality failure problem in terms of high concentration of computational power and the amount of tokens respectively. Thus, the scope exists to enhance the existing consensus protocols and address the centralization problem maintaining low complexity and resource consumption. We propose a novel consensus protocol based on Proof of Work (PoW) and Proof of Stake (PoS). We name our proposed consensus protocol as Proof of SCore (PoSC). PoSC provides fairness in decentralized network by calculating the combination score from all the nodes. This combination score relates to the stake and reputation score. This score helps the IoT nodes to maintain trust and ensure security. We have also performed the experimental and logical interpretation of PoSC. We compare the performance of PoSC with the existing consensus protocols including PoW, PoS, Byzantine Fault Tolerance, Raft, and Paxos. Our proposed PoSC protocol is efficient in terms of reduced memory consumption by 35%, reduced time consumption by 30%, and energy consumption reduction by 25% as compared to the mentioned existing protocols on average. Moreover, PoSC avoids centrality failure suffered by PoW and PoS. Thus, PoSC is suitable for resource constrained applications of IoTs.
The role of critical thinking development technology in the development of students' intellectual ability

This article discusses the role of critical thinking development technology in the formation of students' intellectual abilities. Purposeful formation of intellectual abilities of students allows establishing cause-and-effect relations between phenomena and laws of development of events that is necessary for the student for mastering scientific knowledge and foreign languages. The aim of the study is to reveal the essence of critical thinking of students in the learning process and to develop and test the technology of critical thinking as a tool for intellectual development of students. The study applied methods such as analysis of psychological, pedagogical, scientific, methodological and educational literature on the topic of research; monitoring the progress of the educational process; questioning; rating control; testing; conversations with teachers and students. The article proposes a solution to the problem of formation of intellectual qualities of students on the basis of technology of critical thinking.
The Regulation of Cloud Computing: Getting It Right

Cloud computing brings important benefits to millions of consumers and businesses and it is expected to play a key role in facilitating the uptake of emerging technologies and applications, including artificial intelligence, blockchain, and high-performance computing. Despite its potential to deliver cost and time-efficient services, the majority of businesses in the EU have still not implemented cloud computing. This illustrates the need for a more widespread adoption of the technology. Yet, recent regulatory initiatives may obstruct the uptake of cloud services. This is arguably because such initiatives do not reflect a proper understanding of the market, which our paper intends to provide. To that end, the paper examines what cloud computing is and how it works. It subsequently discusses the EU’s attempts to regulate cloud computing, which has been included in the scope of tools that are expected to play a significant role in completing the digital single market, including the Digital Markets Act, the Digital Services Act, and the Data Act proposal. Our analysis demonstrates that that the logic of these instruments and the obligations they establish do not fit the characteristics and workings of cloud computing. The paper concludes by noting that future regulation must mirror the specificities of the cloud, which has a value chain and traits that differ significantly from other digital services, most notably online platforms.
Popularity Forecasting for Emerging Research Topics at Its Early Stage of Evolution

The accurate modelling and forecasting of the popularity of emerging topics can benefit researchers by allocating resources and efforts on promising research directions. While existing forecasting approaches enjoy various levels of success, most suffer from at least one of the following three limitations: a limited scope due to having to mine topic terms from only a few documents, low generalizability due to assigning arbitrary binary classifications on topics to be either “emerging” or not, or using an emerging topic or field of study’s historical features as inputs to forecast its future popularity while disregarding the existing effect of a “cold start”. In this paper we propose a forecasting algorithm that address all three limitations in three steps. Firstly, we leverage the field of study taxonomy present in most academic databases to obtain a neighborhood of trending fields within the discipline of the field of study of interest. Then, dynamic time warping is used to measure the similarity of each neighbour’s trending pattern compared to the trending pattern of the field of study of interest. Lastly, we conduct multivariate forecasting using a LSTM model while utilizing the historical popularity scores of similar trending neighbours as input. Experimental results on 5 emerging fields of study showcases the “cold start” phenomenon as well as the proposed algorithm reducing RMSE, MAE, and MAPE by half for 4 emerging topics. This validates the claim of the limitations for existing methods and provides insight on the dependency structure of emerging topics with their historical features.
Experimental Study on Mooring Tension of Porous-Slope Type Floating Breakwater

Floating breakwater is an alternative structure to fixed breakwater because of its effectiveness in coastal areas. Under certain conditions, floating breakwater is more flexible in design and more accessible in installation. The fundamental aspect of floating breakwater design is the mooring system to keep in position and determine the system's performance. This study uses physical modeling to determine the magnitude of maximum mooring stress on a mooring line. Moreover, several slope-porous floating breakwaters and pontoon types are examined. The floating breakwater model is constructed from polylactic acid, while the mooring line uses polyethylene. The physical experiment and testing are carried out in a wave flume with regular waves under several variations of wave height, wave period, slope angle, and porosity. It is cultivated that the mooring stress on the seaward position is always greater than on the leeward position since the seaward side will receive the first wave force, and the mooring line will dampen the wave force on the seaward side. However, the pontoon receives higher maximum mooring stress than the other models due to incoming wave forces that arrive on a vertical surface perpendicular to the direction of the waves. On the other hand, floating breakwaters with 5% porosity provide lower mooring stress than non-porous breakwaters. The greater the porosity is, the smaller the mooring forces are. The floating breakwater with 5% porosity can reduce the mooring stress by 16.5% (45 deg) and 13.5% (60 deg). At a slope of 45 deg, the maximum mooring stress received by the floating breakwater will increase by 8.5% and 5.3%, respectively, at 0% and 5% porosity.
Reversible Data Hiding in Encrypted Images Based on Binary Symmetric Channel Model and Polar Code

For vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), an essential problem is how to address potential errors in data extraction and image recovery. This problem significantly limits the capacities of the existing VRAE RDHEI methods. To solve the problem while losing as little capacity as possible, in this paper, a novel method is proposed that uses the ideas of noisy channel model and channel code. By designing the data hiding mechanism appropriately, the embedding and extraction of data in the proposed method can be equivalent to the input and output of a virtual binary symmetric channel (BSC) model, so that the errors in data extraction are equivalent to the bit transitions in BSC. Based on the virtual BSC model, polar code is used to encode the secret data in the data hider's side. With the help of polar code, the receiver can decode the extracted bits containing errors to obtain correct secret data, then recover the error-free original image based on the corrected secret data. The experimental results proved that, compared with the existing VRAE methods, the proposed method can significantly improve the capacity and the quality of the decrypted images under the premise of complete reversibility.