Reviewerly/exHarmony huggingface dataset:
A Study on the Material Imagination of Kim Wan-ha"s Poetry : Focused on the image of “water” in The Road Reaches the Village

김완하는 특유의 서정성과 시 정신을 통해, 꾸준하게 언급의 대상이 되어 온 시인이다. 그러나, 그에 관한 연구는 아직 성실하게 이루어지지 않고 있다. 다수의 평론에서 제출되었지만, 연구는 본격적으로 이루어지지 않은 것이다. 본 논문은 김완하에 첫 번째 시집 『길은 마을에 닿는다』에 나타난 질료적 상상력을 통해, 그에 관한 계기적 고찰을 이루고자 한다. 특히, 그의 시집에는 원형 이미지인 ‘물’이 주요하게 드러나고 있다. ‘물’ 이미지는 김완하에 시적 화자의 감수성과 무의식을 탐색하며 시의 저류에 흐르고, 시적 화자와 적극적인 감응을 일으킨다. 요컨대, ‘물’은 두 가지의 양상을 통해 나타나므로, 시의 면밀하며 구조적인 고찰을 가능하게 한다. ‘소수’의 생명과 회복을 추진하는 ‘물’, ‘소수’의 억압과 고통을 야기하는 ‘물’의 두 축이 그것이다. 이는 ‘강’과 ‘우물’을 통해 드러나 ‘소수’의 생명과 회복을 추동하고, ‘안개’와 ‘장마’와 ‘낚시터’를 통해 드러나 ‘소수’의 고통을 야기한다. Kim Wan-ha is a poet who has been consistently mentioned through his unique lyricism and poetic spirit. However, research on him has not yet been carried out faithfully. A number of criticisms have been presented, but the research has not been done properly. Through the material imagination of Kim Wan-ha"s first collection of poems, The Road Reaches the Village. This paper aims to do a serial consideration on him. In short, “water” appears in two aspects, enabling a close and structural consideration of the poem. Water, which promotes the life and recovery of “minority,” and water, which causes the oppression and pain of “minority,” are the two axes. This is revealed through “river” and “well,” promoting the life and recovery of “minority,” and revealed through “fog,” “rainy season” and “fishing place,” causing oppression and pain of “minority.”
First-order binomial autoregressive processes with Markov-switching coefficients

In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005.
Classification of Credit Card Frauds Using Autoencoded Features

With the advent of online payment and business systems, safety of transactions has become an essential factor. In 2020, Internet Crime Complaint Center (IC3) had received 2,211,396 complaints culminating in a $13.3 billion loss which is approximately equal to cumulative loss from 2016–19. Credit card fraud detection is hindered by concept drift, uneven class distribution, identifying a smaller set of features, and detecting real-time frauds. To overcome the above issues, in this article, we propose an autoencoder-based classification scheme to extract the characteristics such as credit card type, credit grade, credit line, book balance, and other features from a European credit card dataset. Also, the performance of different machine learning algorithms compared for the classification consistency using encoded features. The results obtained are as follows: accuracy of 99.95%, 97.45% precision, 88.26% recall, and 92.36% F1-score.
Classification of orbits in Poincaré maps using machine learning

Poincar\'e plots, also called Poincar\'e maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x,y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.
A Study on the Effectiveness of Nano-LAB Subjects Using Flipped Learning: Focusing on Teacher Efficacy, Meta-cognition, and Intrapersonal Intelligence of Early Childhood Teachers

Objectives The purpose of this study is to find out the effectiveness of Nano-LAB subjects using flipped learning for teacher efficacy, meta-cognition, and intrapersonal intelligence of early childhood teachers. Methods Seven students who took the Nano-LAB class among the Department of Early Childhood Education at A University in Chungcheong-do were the subjects of this study. Pre-tests and follow-up tests were conducted to measure the variable of teacher efficacy, meta-cognition, and intrapersonal intelligence. We tested the normality with Kolmogorov-Smirnova test (K-S) and Shapiro-Wilk test (S-W) using SPSS 23.0, and analyzed the difference between the pre-average score and post-average score with Wilcoxon as statistical significance. Results The main results are as follows. First, although most of the research variables satisfied the normality, there was a statistically significant difference between the sub-variable of teacher efficacy, personal teacher efficacy K-S post-score and meta-cognition K-S pre-score and the sub-variable, planning S-W post-score. Second, for the difference between the pre-average score and the post-average score in the view of the statistical significance, there was a statistically significant difference between teacher efficacy and the sub-variable, personal teacher efficacy, while there was no difference in general teacher efficacy. While statistically significant differences were found in meta-cognition and the sub-variables such as planning and inspection, no differences were found in regulation. There were statistically significant differences in intrapersonal intelligence and the sub-variables such as self-understanding, self-design, self-regulation, and emotional utilization, but there was no difference in self-reflection. Conclusions The results of this study can be uses as basic data to provide directions to conduct flipped learning more effectively when operating subjects other than regular curriculum. In addition, we discussed the ways to lead competency-oriented education.
Low-Latency Motion Transfer with Electromagnetic Actuation for Joint Action

Joint action, performing a task together by multiple people, can be beneficial in transferring the embodied skill, establishing physical relationships, engaging multiple people, and completing the task more effectively. To facilitate real-time joint bodily action, we introduce a low-latency motion transfer system (total latency: 22.9 ± 2.0ms, finger attraction latency: 13.7 ± 2.1ms) using electromagnetic actuation, which allows them to share their instantaneous actions, such as pressing the button with their fingers. Our system can change the type of motion transfer at both the direction of the transfer between users (mono- or bi-direction) and the input logic (”AND” or ”OR”) based on the users’ input. We will discuss its potential as a computer-assisted joint action and the possibility that the computer system orchestrates the joint action between the users or the user and the computer.
Promoting communicative interactions in virtual environments: a methodological approach

The rapid transition from face-to-face to digital learning has created new opportunities for interaction. However, the low participation of students in virtual learning environments has promoted research that seeks to know the causes and propose alternatives to increase communicative interactions in E-Learning platforms. This article presents the results of a research developed through a qualitative perspective with a hermeneutic methodological approach, where texts and virtual learning environments of the Academic UVirtual platform of the University of Medellín -Colombia were analyzed. Thus, a methodology is proposed to promote communicative interactions in virtual environments mediated by ICT, from higher education institutions. This methodology will make it possible to distinguish characteristics of communicative interactions, facilitate tasks aimed at promoting critical and collaborative thinking, self-learning, permanent training, and flexibility; implement enriched virtual environments enabling dynamic and proactive use in teaching-learning processes, to promote communicative interactions and permanent collaboration of teachers and students on the ELearning platform.
Theoretical Study of Global Solar Radiation on Horizontal Area for Determination of Direct and Diffuse Solar Radiation

This study aimed to compare global solar radiation on the horizontal area between two models of Chabane Foued and M.Capderou. The model of Chabane has been interested in pollution factors such as TL (turbidity), BE (Angstraon), and the chemical components of the air such as WV, O3, CH4, CO, CO2, and the especial part the new pollution factor such as hbeam, kbeam, and kdiffuse, which all influenced onto solar radiation, and the model of Capderou has been used the atmospheric disturbance to calculate the direct and diffuse components of radiation received on a plane, while the constituents of the atmosphere (absorption and diffusion) can be expressed by disturbance factors, which is very necessary to determine irradiation In the clear sky. The results reveal a significant difference between the two models with approximated curves. The difference between the models probably returns to the nature of the geographic site which the authors used and injected into the models.
Whole-Slide Cytometric Feature Mapping for Distinguishing Tumor Genomic Subtypes in Head and Neck Squamous Cell Carcinoma Whole-Slide Images

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin–stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC. Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin–stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.
Reversible Programming: A Case Study of Two String-Matching Algorithms

String matching is a fundamental problem in algorithm.This study examines the development and construction of two reversible string-matching algorithms: a naive string-matching algorithm and the Rabin-Karp algorithm.The algorithms are used to introduce reversible computing concepts, beginning from basic reversible programming techniques to more advanced considerations about the injectivization of the polynomial hash-update function employed by the Rabin-Karp algorithm.The results are two clean input-preserving reversible algorithms that require no additional space and have the same asymptotic time complexity as their classic irreversible originals.This study aims to contribute to the body of reversible algorithms and to the discipline of reversible programming.
Alexa, Do What I Want To. Implementing a Voice Spoofing Attack Tool for Virtual Voice Assistants

Deepfakes are created by using artificial intelligence algorithms to generate realistic images, videos or even sound of people that do not actually exist. These fake images, videos or sound can be used to create fake news stories, or to impersonate someone for malicious purposes. As these deepfakes are becoming increasingly realistic and difficult to detect, they pose a serious threat to the security and integrity of our digital control and information. In this work, spoofing techniques have been used to try to impersonate another person before Amazon Alexa or other Virtual Voice Assistants (VVA), and verify that unauthorized activities could be done. In order to do this, we use Coqui YourTTS to clone another person’s voice using a Telegram bot, which will create audios that tricks Alexa and its use of voice profiles that allow the identification of people.
Is Artificial Intelligence the Ideal Partner for Blockchain and Crypto Currencies?

For several years everyone, companies and individuals have been more and more interested in new technologies concerning the blockchain, crypto currencies and also in the development of artificial intelligence. Blockchain is a technology that should transform our societies in depth, by providing a decentralized and ultra-secure system, and constitute the crypto currencies basis. Artificial intelligence is a new technology created to facilitate daily lives. But its application remains very technical and little known to the public. Both artificial intelligence and blockchain are technologies that are helping to change the world, but their applications are currently very technical and little known to the public. The present article tries to study the combination between blockchain technology and Artificial Intelligence and how this combination, can be so beneficial for the optimization of crypto-currency trading. The contribution of our work is to try to improve the notion of the use of these two new technologies, while trying to reveal the impact that can be generated on the financial, economic and social level. Our work will be organized as follows: In the first section we will define the blockchain concepts, and the artificial intelligence. In the second section, we will present the implication and effects between artificial intelligence and blockchain in the first place and in a second part, we will present the impact on cryptocurrencies. And finally we will present our conclusion. Certainly our work has limits, which can be surpassed by empirical work to affirm or not our basic hypothesis.
Data Association Tools for Target Identification in Distributed Multi-target Tracking Systems

Distributed tracking systems have several benefits over centralized setups such as faster processing time and greater robustness to failures. However, the practical deployment of a distributed multi-camera multi-target tracking system poses other important challenges. In this work, we address two of these practical problems. The first one is the spatial and temporal identification of the targets in the network, i.e., the data association problem. To solve it, we propose to build intelligent and adaptive local appearance models of each target that only store the most relevant information. The second problem is the intensive use of bandwidth caused by the periodic communications that each camera requires for the cooperative tracking and the data association of all the targets. In the paper, we manage the bandwidth usage with an event-triggered mechanism that controls how much information is sent. The main novelty of our mechanism is to account for the scene density, coupling it with the data association module and enhancing it. We integrate the new modules into an existing distributed multi-person multi-camera tracking system and demonstrate their benefits on different public benchmarks of increasing difficulty.
WPEviRC: A Multi-rules-based Classifier for Evidential Databases Without Class Label Ambiguities

Rule-based classifiers use a collection of high-quality rules to classify new data instances. They can be categorized according to the adopted classification strategy: Classifiers based on a single rule, and classifiers based on multiple rules. Many works were proposed in this field. However, most of them do not handle imperfect data. In this study, we focus on the issue of multi-rules-based classification for evidential data, i.e., data where imperfection is modeled via the belief functions theory. In this respect, we introduce a new algorithm called PWEviRC. This latter involves a two-level pruning technique to remove redundant and noisy rules. Finally, it applies the Dempster rule of combination to fuse the selected rules and make the final decision. To evaluate the proposed method, we carried out extensive experiments on several benchmark data sets. The performance study showed interesting results in comparison to existing methods.
HybriDC: A Resource-Efficient CPU-FPGA Heterogeneous Acceleration System for Lossless Data Compression

Lossless data compression is a crucial and computing-intensive application in data-centric scenarios. To reduce the CPU overhead, FPGA-based accelerators have been proposed to offload compression workloads. However, most existing schemes have the problem of an imbalanced resource utilization and a poor practicability. In this paper, we propose HybriDC, an adaptive resource-efficient CPU-FPGA heterogeneous acceleration system for lossless data compression. Leveraging complementary advantages of the heterogeneous architecture, HybriDC provides a universal end-to-end compression acceleration framework with application compatibility and performance scalability. To optimize the hardware compression kernel design, we build a performance-resource model of the compression algorithm taking into account the design goal, compression performance, available resources, etc. According to the deduced resource-balanced design principle, the compression algorithm parameters are fine-tuned, which reduces 32% of the block RAM usage of the LZ4 kernel. In the parallel compression kernel implementation, a memory-efficient parallel hash table with an extra checksum is proposed, which supports parallel processing and improves the compression ratio without extra memory. We develop an LZ4-based HybriDC system prototype and evaluate it in detail. Our LZ4 compression kernel achieves state-of-the-art memory efficiency, 2.5-4× better than existing designs with comparable compression ratios. The evaluation of total resource utilization and end-to-end throughput demonstrates the excellent scalability of HybriDC. In power efficiency, the four-kernel HybriDC prototype achieves a threefold advantage over the standard LZ4 algorithm.
Metaverse

Metaverse is an emerging concept in the world of technology which is being anticipated to become the next biggest market in have near future. It can be defined as the next generation social media in an improvised and modernised form. Big tech giants viz. Microsoft, Facebook, along with other big companies have already invested millions of dollars in the development metaverse believing that it will make its way to the world market of education, technology, and economy. The world's largest counties such as China, India, USA, UK, Korea, Japan have heavily invested in companies which are working towards developing metaverse. It is to be noted that metaverse comes along with several drawbacks viz data privacy, social harassment and bullying, detachment with the actual world, and too much dependence on technology that will eventually result in extreme digitization.
Credit card fraud detection based on ensemble learning

With the development of the Internet industry and the rise of mobile payment, the risk prevention of credit card fraud has become more difficult. With the continuous expansion of the number of cards issued, the total amount of credit, and the transaction volume, the problem of credit card fraud has become increasingly prominent. This seriously affects the normal operation of financial institutions such as banks, threatens the property safety of users, and even directly threatens the normal operation of society. In this paper, after standardizing the data set, the SMOTE oversampling method is used for sample expansion and synthesis, so that the black and white samples are balanced, and the integrated algorithm is used to train a high-performance credit card fraud detection model. Through experimental analysis, the accuracy of the model is over 99.4%, and compared with the conventional classification model, the model has superior performance.
Gunnar Jansson – Interview and reflection

In the interview conducted by Agnes Szokolszky in 2001 Gunnar Jansson recounts how his research career began and how he became a research assistant, then research associate of Gunnar Johansson in the 1950s. He describes the field experiments they conducted in traffic psychology and his change of focus on motion perception that came with his Ph.D. work with Gunnar Johansson who pursued a noncognitivist approach to motion perception. In the interview, Gunnar Jansson describes the research directions he pursued and gives an insider's view of Johansson's personality and an overview of students and collaborators around him. He describes the relationship between Johansson and James Gibson and shares his thoughts on ecological psychology. In his current reflection on Dr. Jansson's interview, Dankert Vedeler, a former colleague puts Jansson's work into perspective.
IBM sets quantum computing record

A quantum program that required over 1700 operations is the largest ever run