Reviewerly/exHarmony huggingface dataset:
A study of the para-text system based on the theory of intertextuality

This paper examines the intertextual relationship between the main text and the para-text that completes the text of an academic thesis among text types classified according to the standard of style and its system. All text systems are composed of main text and para-text, and their boundaries are spatially separated. Para-text also has the characteristic of being structured in stages in the text system. Para-text is composed in stages in the main text and functions suitable for the purpose of the text are exhibited. As a result, the text is completed as a single whole. In this paper, the intertextual relationship between the main text and the para-text is largely divided into two categories: ‘repetition’ and ‘transformation’. In addition, specific sub-ranges were presented in addition to the two categories. According to this standard, the intertextual relationship between the para-text and the main text of an academic thesis is specified.
Estimation of Gender Using Convolutional Neural Network

Gender prediction is one of the key challenges in computer vision. It is easier to decide the gender by finding the presence of beard and moustache. For the non-beard and moustache images, gender prediction is always a challenging task. One of the approaches that is helpful in this case is the prediction by convolutional neural networks. At first 1173 male and 1134 female images are used in the training set. 80% of the training set images are used for training and 20% for validation purposes. Different models were trained by choosing different epochs. In order to estimate the efficiency of the classifiers, a testing set of 531 male images and 405 female images (which are not present in the training set) are used. The best gender efficiency achieved is 92.4% at 200 epochs. It is found that either increase in epoch or blocks neither directly nor inversely affects the gender prediction efficiency. The combination of classifiers also failed to get better results than 92.4%.
3D photonic integration for scalable neural network computing

Scalability is essential for computing, yet classical 2D integration of neural networks faces fundamental challenges in this regard. Using 3D printing via two photon polymerization-based direct laser writing, we overcome this challenge and create low loss waveguides and demonstrate dense as well as convolutional network topologies that scale linear in size. Air-clad high-confinement waveguides allow for high-density multimode photonic integration. Leveraging the writing laser’s power as a degree of freedom in a (3+1)D printing technique, we also achieve precise control over refractive index contrast, which enables single mode propagation and low-loss evanescent couplers for next generation 3D integrated photonic circuits.
ORGANISATIONAL PROVISION OF TEAM PROJECT SOLUTION AT THE FACULTY OF CIVIL ENGINEERING, TU KOSICE

R. Baskova , Y. Nechyporchuk, E. Panulinova , J. ČabalaTechnical University of Kosice, Faculty of Civil Engineering (SLOVAKIA)
Observations on a 158km-long quantum internet testbed connecting room-temperature quantum memories

First-generation long-distance quantum repeater networks require quantum memories capable of interfacing with telecom photons to perform quantum-interference-mediated entanglement generation operations. The ability to demonstrate these interconnections using real-life fiber connections in a long-distance setting is paramount to realize a scalable quantum internet.
Countermeasures against Unintentional Infringement of Legal Interests by Artificial Intelligence

Purpose: In recent years, the keyword that penetrates our society is the 4th Industrial Revolution. The 4th In-dustrial Revolution can be expressed in many ways, but the most important keyword is “connection”, and the key phenomenon from the perspective of this connection is the development of AI. The 4th Industrial Revolution and the impact of artificial intelligence are more than we think, and each academic division in society is discussing how to respond to it, and criminal law is no exception. In particular, fierce debate continues as to whether the criminal law will recognize the criminal subjectivity of AI, and whether punishment for artificial intelligence will be possible. With the development of AI, infringement of legal interests, whether intentional or unintentional, can become a reality. When users and developers intention-ally use artificial intelligence for crimes, AI becomes a human tool. The question is how to deal criminally with cases where legal interests are violated by intellectual activities or source code errors of AI, although not intended by users or developers. However, the premise to think about this is that AI technology is not complete. In other words, in the event of unintentional infringement of legal interests by AI, punishing humans based on this can have a contraction effect on development, resulting in a regression of AI technology with clear social utility. Method: Therefore, this study examines how the criminal law should respond in the event of infringement of legal interests while human factors by AI is excluded. To this end, we first look at the current status of criminal law responding to AI. Here, we will first examine whether a specific operation of AI will be included in an act that is the scope of criminal law, and, if so, whether to recognize criminal identity to AI that caused such a specific operation. Results: Through this process, the specific operation of AI can be subject to criminal law, and furthermore, if there is an infringement of legal interests by AI, it is confirmed that there is room for a crime. However, it is as-sumed that the action of AI is only unintentional infringement in which human elements are excluded. AI infringes on legal interests by human factors because it has the same meaning as human tools. Conclusion: Since the ongoing development of AI technology can hinder the development of artificial intelligence technology with clear social effectiveness, it argued that the criminal law should be supplemented after administrative procedures.
A New Learned Construction Of Indonesian Networks (Online) At Elementary School Sutomo 1 Medan

A current online learning applied, done to suppress Covid-19 spread rates. An online learning system is implemented through a Personal Computer (PC) or laptop connected to an internet network connection. This study uses a descriptive qualitative approach. The purpose of this study was to describe the construction of online learning in the field of Indonesian language studies at SD Sutomo 1, Medan. The research subjects were taken as many as 9 people, namely 3 Indonesian language teachers, 3 parents, and 3 students who attend SD Sutomo 1. The object of research is teaching material in online learning which includes writing, reading, and listening. The data determination technique used the Simple Random Sampling method. The research method is a literature study. The data analysis technique used is by collecting data from interviews, observations, documentation, and other sources. The result of this study is that online learning is not as good as face-to-face due to the lack of direct interaction between teachers and students. The conclusion of this research isSUTOMO 1 Elementary School chose to continue using the 2013 national curriculum because the school wanted to continue to provide maximum learning services to students even through online learning.
Decidability of Intuitionistic Sentential Logic with Identity via Sequent Calculus

The aim of our paper is twofold: firstly we present a sequent calculus for an intuitionistic non-Fregean logic ISCI, which is based on the calculus presented in [3] and, secondly, we discuss the problem of decidability of ISCI via the obtained system.The original calculus from [3] did not provide the decidability result for ISCI.There are two problems to be solved in order to obtain this result: the so-called loops characteristic for intuitionistic logic and the lack of the subformula property due to the form of the identity-dedicated rules.We discuss possible routes to overcome these problems: we consider a weaker version of the subformula property, guarded by the complexity of formulas which can be included within it; we also present a proof-search procedure such that when it fails, then there exists a countermodel (in Kripke semantics for ISCI).
Review on Computational Models for Prostate Segmentation from Ultrasound Medical Images

The segmentation method plays an important character to obtain region of interest from Prostate Ultrasound (US) image. Segmentation of the prostate ultrasound image is a tough task, and the challenges significantly differ from one imaging modality to another. The partition tool is utilized to segregate an image into multiple slices for easy evaluation. The design of automatic segmentation is a challenging task to obtain a required portion of the image, due to auditory intervention and artifacts like shadow acquire in prostate US image. This research article reviews the methods so far developed for prostate segmentation using ultrasound images. The main objective of this review work is to contemplate the key similarities and contrasts among the various methods, highlighting their strengths and weaknesses to assist in the choice of an appropriate prostate ultrasound image segmentation methodology. The prime task of the analysts working in the field of image processing and analysis is to build up a technique for effective and better image segmentation. This paper focuses on the different techniques that are broadly used for prostate image segmentation.
Augmenting VR Ski Training using Time Distortion

Virtual reality-based sports simulators are widely developed, which makes training in a virtual environment possible. On the other hand, methods using temporal features are also introduced to realize an adaptive training. In this paper, we study the effect of time distortion on alpine ski training to find out how modifying the temporal space can affect sports training. Experiments are conducted to investigate how a fast/slow and a static/dynamic time distortion-based training, respectively, can impact the performance of users.
Investigating the multimedia pointing techniques in the tabletop-centric cross-device interaction

Abstract Group collaboration needs the support of a digital interaction system composed of a tabletop-centric shared display and individual operational physical cursors and mobile devices. We examine the usability of a fundamental function of the system, i.e., the pointing techniques. Direct pointing refers to the selection with a cursor placed on the target object, and proximity pointing requires the cursor nearby the target object. The empirical evidence from sixty-two participants shows that the direct pointing technique, although proved as the best choice in single device interaction, is inferior to proximity pointing in cross-device interaction in completion time and operation correctness. We also examine how the display for confirmation on mobile devices fits for pointing technique, discuss how users habituated to single device interaction adapt to cross-device interaction, and suggest guidelines for the design practice.
GSEnet: feature extraction of gene expression data and its application to Leukemia classification

Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.
Preliminary evaluation of an IVR user experience design model using eye-tracking attention measurements

The present study drafts a simplified IVR user experience design model to guideline a preliminary evaluation of attention variance for semantically distinct elements. 27 participants (11 females) freely explored an interactive multi-user virtual setting, whilst equipped with full-body motion support and eye-tracking which procured attention duration measurements. Initial results confirm significant element attention discrepancy and provide the first indication toward a more detailed categorical organization of experience components for follow-up experimentation.
Internal Temporal Structure of the Biblical Hebrew Verb: A Case Study of Lexical Aspect in the Verb yd'

Traditional Biblical Hebrew (BH) grammars have often defined the temporal information of BH verbs only in terms of tense or grammatical aspect, which can be identified by the finite verbal forms. This paper argues that lexical aspect, which is also known as situation aspect or Aktionsart, is an essential semantic factor for determining the internal temporal structure of BH predicates. As a trial cut, this paper analyses lexical aspect in the verb yd' in the qatal, qotel, and wayyiqtol forms.
Overcoming the Semiotic Boundary through Translation Strategies

The possibilities of implementing the intermediary function of translation from one language to another in a communicative act are considered in the article. Its functioning can be detected both in cross-cultural communication, and when expanding the meaning of a communicative act, i.e. when including actors of different languages in it. At the same time, in each act of communication, the desynchronization of the semiotic systems of each participant is preserved. Double contingencies and polysemy also serve as obstacles to the work of translator, in the translation mechanism. However, the achievement of mutual understanding seems to be possible due to the various strategies of interpretation of meanings in the act of translation. These strategies are described in the most detail in B.Latour's research devoted to the analysis of the structure of the scientific laboratory. Considering the works of L. Pasteur, Latour concludes that communicative processes expand and go beyond the language. This makes it possible to present the interactions of actors and actors as having a network character. Latour gives the translation a geometric meaning that reflects the specifics of redirecting meanings to achieve mutual understanding. It is here that a special translation function opens up – the possibility of crossing semiotic boundaries and ensuring social relationships. In this regard, translation can be understood as a necessary mechanism aimed at overcoming the boundaries of various semiotic systems. These borders are presented as a special border space where processes of connecting meanings are discovered and the uniqueness of each semiotic system is revealed.
Implementasi Data Mining dengan Metode Apriori Dalam Menentukan Pola Pemilihan Pemeriksaan Kimia

Businesses and organizations that want to survive need to develop effective business strategies. Reviewing the company's transaction data gradually leads to sales data. So it is very if not re-analyzed. The examination products offered at Prodia Laboratories vary, and the exploration of the packages offered influences the public to choose these packages. The Apriori method is a method that can help companies find out which test inspections are the most sold and know the relationship between inspections with one another, and with the help of the RapidMiner software the types of tests or tests that come out at the same time can be known. The a priori algorithm is a type of association rule in data mining (data mining). One of the stages of Association Analysis that has attracted the attention of many researchers to produce efficient algorithms is high-frequency pattern mining. The importance or not of an association rule can be understood with two benchmarks, namely the support value and the confidence value. Support (support value) is the proportion of the combination of these items in the database, while confidence (value) is the strength of the relationship between items in the association rules. From the results of calculations using the Apriori method to determine purchasing patterns on the type of chemical/fat check by determining a minimum support of 35% and 75% confidence, resulting in 3 association rules, namely first, if you buy Total Cholesterol, you will buy LDL Cholesterol with 100% support and 100% confidence, secondly. If Total Cholesterol, then I will buy Triglycerides with 100% support and 100% confidence, buy all three. If I buy LDL Cholesterol, I will buy Triglycerides with 100% support and 100% confidence
Open-source computer systems initiative: The motivation, essence, challenges, and methodology

The global community faces many pressing and uncertain challenges like pandemics and global climate change. Information technology (IT) infrastructure has become the enabler to addressing those challenges. Unfortunately, IT decoupling has distracted and weakened the international community’s ability to handle those challenges. This article initiates an open-source computer system (OSCS) initiative to tackle the challenges of IT decoupling. The OSCS movement is where open-source software converges with open-source hardware. Its essential is to utilize the inherent characteristics of a class of representative workloads and propose innovative abstraction and methodology to co-explore the software and hardware design spaces of high-end computer systems, attaining peak performance, security, and other fundamental dimensions. I discuss its four challenges, including the system complexity, the tradeoff between universal and ideal systems, guaranteeing quality of computation results and performance under different conditions, e.g., best-case, worst-case, or average-case, and balancing legal, patent, and license issues. Inspired by the philosophy of building large systems out of smaller functions, I propose the funclet abstraction and methodology to tackle the first challenge. The funclet abstraction is a well-defined, evolvable, reusable, independently deployable, and testable functionality with modest complexity. Each funclet interoperates with other funclets through standard bus interfaces or interconnections. Four funclet building blocks: chiplet, HWlet, envlet, and servlet at the chip, hardware, environment management, and service layers form the four-layer funclet architecture. The advantages of the funclet abstraction and architecture are discussed. The project’s website is publicly available from https://www.opensourcecomputer.org or https://www.computercouncil.org.
Out-Of-Distribution As A Target Class in Semi-Supervised Learning

A key limitation of supervised learning is the ability to handle data from unknown distributions. Often, such methods fail when presented with samples from a source not represented in the training data. This work proposes an effective way of controlling the behavior of a neural network in the presence of out-of-distribution examples. For this, the training dataset is supplemented with extraneous data assigned to an additional out-of-distribution class. The extraneous data may come from a different dataset or be even noise. By applying a Gaussian mixture model on the latent representation, and by taking advantage of the ability of these models to generalize well, the method described thereafter performs well. Training the model on a segregated dataset helps the model to distinguish out-of-distribution data, including the ones the model were never confronted to during training.
Naming the Identified Feature Implementation Blocks from Software Source Code

Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall.
A Generic Method to Estimate Camera Extrinsic Parameters

In this paper, an approach to self-calibrate an outward-looking camera from camera images is presented. Ego lane boundaries are detected in the image frame. A straight line is fitted to each detected boundary. Vanishing point in image space is computed as the intersection of the fitted straight lines. A closed-form solution is obtained for camera pitch and yaw angles using vanishing points coordinates. A particle filter is initialized using pitch and yaw angles from closed-form solution and the rest of the camera extrinsic parameters (viz., roll, and translation parameters) obtained from approximate measurements.
Data from connected cars for the public cause

Sensors of connected cars generate a large body of data. Although intended to enhance vehicle functions, these data can be used for many more purposes. In literature, much attention has gone to use of data for added commercial services. The data can however also be used for government services, such as for planning, organizing and execution of road inspections and maintenance and for informing government (mobility) policy. The manufacturer (hereinafter OEM) finds itself in a gatekeeper position in relation to the data. In relation to commercial added services, the gatekeeper position of OEMs fuels concerns about adverse economic or welfare implications. Does the gatekeeper function raise similar (or even very different) concerns about the use of data for government purposes? This paper identifies potential concerns in this field and considers starting points for regulatory responses to the concerns. A forecast of concerns and responses is complicated by the rapid pace of technical developments and the continuously changing roles of actors in the field.