Reviewerly/exHarmony huggingface dataset:
Issue Analysis using Topic Modeling: Focusing on Hatred Articles in National Daily Newspapers

This study aims to explore the possibility of using topic modeling in terms of issue analysis in tracking and detecting social issues. Among the topic modeling methods, LDA(Latent Dirichlet Allocation) based topic analysis and STM(Structural Topic Modeling) need to be used separately according to the research approach. STM is suitable for Confirmatory Data Analysis for hypothesis test, and LDA is suitable for Exploratory Data Analysis, such as issue analysis on news media. Dynamic data analysis may be possible if document categories are classified using meta-information in documents in LDA based topic modeling. In order to empirically examine, an LDA based topic analysis was conducted on hatred articles reported by 10 major national daily newspapers in Korea. From the results, all newspapers mainly dealt with hatred crimes against women in 2021, and hatred politics of politicians showed an upward trend in 2022. The direction of news reporting on the hatred issue did not show much difference among newspapers, and the characteristics of news reporting due to political bias were not large. This indicates that the issue of hatred has not yet emerged as an important issue in news coverage. In conclusion, this study demonstrates through empirical analysis that LDA based topic modeling can be fully utilized for research activities that track and detect issues.
Increasing the reliability of seismic classification: A comparison of strategies to deal with class size imbalanced datasets.

Recent employment of large seismic arrays and distributed&amp;#160;fibre&amp;#160;optic&amp;#160;sensing&amp;#160;cables leads to an overwhelming amount of seismic data. As a consequence, the need for reliable automatic processing and analysis techniques increases. Therefore, the number of machine learning applications for detection and classification of seismic signal augments too.A challenge however, is that seismic datasets are highly class imbalanced, i.e. certain seismic classes are dominant while others are underrepresented. Unfortunately, a skewed dataset may lead to biases in the model and thus to higher uncertainties in the model predictions. In the machine learning literature, several strategies are described to mitigate this problem. In presented work we explore and compare those approaches.For our application, we use catalogues and seismic continuous recordings of the RD network in France [RESIF, 2018]. Using a simple 3-layered convolutional neural network (CNN) we aim to differentiate between six seismic classes, which are based on hand-picked catalogues.&amp;#160;The training set we obtained is highly skewed with earthquakes as the majority class, containing 77% of the samples. &amp;#160;The remaining classes (quarry blasts, marine explosions, suspected induced events, noise and earthquakes with unquantifiable magnitude) represent 2.1 - 7.5% of the dataset.We compare four strategies to deal with an imbalanced datasets for a multi-class classification problem. The first strategy is to resample the dataset (i.e. reduction of the majority class). Another approach is the adaptation of the loss function by weighting the classes when penalizing the loss (i.e. increasing the weight of the minority classes). Those class weights can be adjusted either w.r.t. the reciprocal of class frequency [inspired by King and Zeng, 2001] or w.r.t. the effective number of samples [Cui et al., 2019]. Lastly, we have explored the use of a focal loss function [Lin et al., 2020].Using balanced accuracy as a metric while minimizing the loss, we found that in our case adjusting the class weights in the loss function according to the reciprocal of the class frequency provides the best results.&amp;#160;References:- RESIF, 2018: https://doi.org/10.15778/RESIF.RD- King, G., &amp; Zeng, L. (2001). Logistic regression in rare events data.&amp;#160;Political analysis,&amp;#160;9(2), 137-163.- Lin et al. (2020), Focal Loss for Dense Object Detection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 42, NO. 2, FEBRUARY 2020- Cui et al. (2019), Class-Balanced Loss Based on Effective Number of Samples, https://doi.org/10.48550/arXiv.1901.05555
Optimalisasi Jaringan Komputer Tanpa Harddisk (Diskless) Pada Laboratorium Jaringan AKN Pacitan

Diskless technology provides convenience for users in terms of managing large numbers of computers. In the process, a group of computers run the system without using a hard disk, with the PXE method every computer connected to the network can run the system by receiving a cloned image provided by the service provider computer (server). The ability of the network to transfer data and the server to serve clients has a huge impact on the process. Optimization from the network side is done by increasing the network's ability to transmit data from 100 Mbps to 1000 Mbps, besides that improvements are also made from the server side where the use of a core i-7 processor, 8 GB memory, and SSD can provide increased performance. The time required to enter the system is 5 minutes, 20 minutes faster than the previous experiment, which was 25 minutes.&#x0D;
Spectral Galerkin Approximation

In this chapter, we describe a spectral Petrov–Galerkin method for solving the subdiffusion model ( 1.1 ). Compared with time-stepping schemes introduced in Chaps. 3 – 5 , spectral methods with specially constructed basis functions can compensate the weakly singular behavior of solutions near $$t=0$$ , and can approximate solutions of the time-fractional diffusion model accurately.
Exploration and Aspects on Augmented Reality

Augmented Reality (AR) is a collaborative experience of a physical world environment along with 3D virtual objects. Augmented Reality allows the user to interact with the virtual objects created in the real world. A device must be capable of calculating the position and orientation of virtual objects with respect to other physical objects for displaying AR content. This is one of the emerging technologies in today's world. Augmented Reality has become popular in various domains. Various applications of AR include prototyping, Sales and marketing, Gaming, health care, etc. A device that generates AR will combine the real world and virtual objects and provide concluded meanings for the user. The combination of AR with other technologies such as Computer Vision, Artificial Intelligence, IoT, Natural Language Processing takes the technology to another level. The evolution of mobile apps helped in the spread of AR among people. The main objective of this paper is to review various aspects of Augmented Reality, its applications along with advantages and challenges faced by it.
On Generating SHACL Shapes from Collective Collection of Plant Trait Data

Collective data collection has become common in various domains, including biodiversity science. Multiple individuals work on the same biological samples or specimens using various scientific tools to measure different characteristics. Moreover, the measurements are typically regulated by different data collection procedures and protocols. Integrating and guaranteeing the quality of the data has become a significant issue. One solution is to adopt the RDF (Resource Description Framework) data model in combination with a language for validating RDF graphs such as SHACL (Shapes Constraint Language). The RDF data model provides flexibility in accommodating multiple data schemas, while SHACL uses a set of conditions so called shapes, to validate the RDF data graphs. The remaining challenge is an effective method to define SHACL shapes that can be used to validate any given RDF data.
NoSQL Databases: Modern Data Systems for Big Data Analytics

Because of the massive utilization of the world wide web and the drastic use of electronic gadgets to access the online world, there is an exponential growth in the information produced by these hardware gadgets. The data produced by different sources, such as smart transportation, healthcare, and e-commerce, are large, complex, and heterogeneous. Therefore, storing and querying this data, coined "Big Data," is challenging. This paper compares relational databases with a few of the popular NoSQL databases. The performance of various databases in executing join queries, filter queries, and aggregate queries on large datasets are compared on a single node and multinode clusters. The experimental results demonstrate the suitability of NoSQL databases for Big Data Analytics and for supporting large userbase interactive web applications.
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned From Image Pairs

Video generation has achieved rapid progress benefiting from high-quality renderings provided by powerful image generators. We regard the video synthesis task as generating a sequence of images sharing the same contents but varying in motions. However, most previous video synthesis frameworks based on pre-trained image generators treat content and motion generation separately, leading to unrealistic generated videos. Therefore, we design a novel framework to build the motion space, aiming to achieve content consistency and fast convergence for video generation. We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators. Firstly, we propose an image pair generator named MotionStyleGAN to generate image pairs sharing the same contents and producing various motions. Then we manage to acquire motion codes to edit one image in the generated image pairs and keep the other unchanged. The motion codes help us edit images within the motion space since the edited image shares the same contents with the other unchanged one in image pairs. Finally, we introduce a latent code generator to produce latent code sequences using motion codes for video generation. Our approach achieves state-of-the-art performance on the most complex video dataset ever used for unconditional video generation evaluation, UCF101.
The Java Framework Construction of the Intelligent Information System of University Scientific Research

This article analyzes the current situation of scientific research management informatization construction in universities based on the knowledge gained from the research literature and Java language, where the utilization rate of university scientific research management informatization is not high, and it can only be limited to traditional Java and word forms for simple data statistics and scientific research. A comprehensive analysis of management methods and other shortcomings was carried out. Further, the design and implementation of the university scientific research management information platform were explored, and the establishment of a networked database of information resources was proposed, and the university scientific research management information system was developed, which increased the efficiency by 6.5%.
An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains

Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.
A Study on Research of Korean Textbooks in the Internet Era: Basic Research for the Development of Korean Teaching Materials’ Class

Objectives This study was written for the purpose of examining the research trend of Korean textbooks according to technological development for ‘content development’ of Korean textbook theory class for the Internet age.&#x0D; Methods This study conducted keywords search on RISS and reviewed 270 studies. This study was divided into three categories. First, research on multimedia textbooks was discussed. Second, research on ‘digital textbooks’, which is opposed to the concept of paper textbooks, was analyzed. Finally, Metaverse Korean education research was reviewed.&#x0D; Results First, since multimedia is already widely used as a textbook and a tool, research on it is very active and it is expected to expand further in the future. Second, although the digitization research of Korean textbooks is currently in a leap stage, it is expected that innovations will occur in the future. Third, in the Metaverse study applied to Korean language education, there was no study related to the development of textbooks.&#x0D; Conclusions The contents to be covered in the ‘Korean Textbook Theory Class’ can be summarized as follows. First, the perception of Korean textbooks in the Internet era is being reborn as a multi-dimensional material from a paper book. Second, we need a discourse on what needs to be discussed for the development of multimedia or digital textbooks. Third, it is necessary to insert program education so that some materials can be developed by instructors themselves. Since the definition of textbooks has been expanded, the ‘tools education course of textbook development’ for prospective teachers should be included as part of the textbook theory.
Research on Service Design on the Basis of Huafu Experience

By utilizing the service design as the predominant core, this paper conducted a detailed analysis of the relevant concepts of the Huafu experience, as well as two examples of "Yungang" and "The Return of Millennium Tang Music" of the HEMU brand show.At the same time, it has been pointed out that the former will allow the audience to become an aesthetic experiencer after visual enjoyment by effectively breaking through the noumenon of Huafu which integrates historical religious culture with modern beauty.Correspondingly, the latter utilizes the music as a carrier and enables the audience to emerge as the enjoyer and potential consumers of the aesthetic experience of Huafu through the multi-dimensional experience.The purpose of this paper is to combine these cases to reflect the service design in the Huafu experience.
Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.
Frequency-Driven Approach for Extractive Text Summarization

Due to Digital Revolution, most books and newspaper articles are now available online. Particularly for kids and students, prolonged screen time might be bad for eyesight and attention span. As a result, summarizing algorithms are required to provide long web content in an easily digestible style. The proposed methodology is using term frequency and inverse document frequency driven model, in which the document summary is generated based on each word in a corpus. According to the preferred method, each sentence is rated according to its tf-idf score, and the document summary is produced in a fixed ratio to the original text. Expert summaries froma data set are used for measuring precision and recall using the proposed approach’s ROUGE model. towards the development of such a framework is presented.
The Use of Technology in Sign Language Testing: Results of a Pre-Pandemic Survey

This study is a follow-up to previous research conducted in 2012 on computer-assisted language testing (CALT) that applied a survey approach to investigate the use of technology in sign language testing worldwide. The goal of the current study was to replicate the 2012 study and to obtain updated information on the use of technology in sign language testing. Another goal was to broaden the scope of the previous study by also considering the use of automatic sign language recognition and production (by avatars) in applied testing scenarios through technologies that make use of artificial intelligence (AI). Thirty-two sign language testing professionals participated in the current study. The results of the survey confirm the findings from 2012, but also raise new issues for future research (trends) related to sign language testing, such as the use of automatic sign language recognition for automatic scoring and the problem of acquiring funding for developing and maintaining web-based sign language testing platforms.
SpecFlow: an end-to-end framework for specular reflection restoration in endoscopy videos using flow-guided video completion (Conference Presentation)

Specular reflections (SR) commonly found in endoscopy videos can severely disrupt a surgeon’s observation and judgment, but existing methods to inpaint SR regions can result in false clinical interpretations. Therefore, we propose an end-to-end pipeline termed SpecFlow to detect and restore SR regions from endoscopy videos. Our proposed SpecFlow consists of two phases: detection using a reduced U-net model and a novel restoration method using optical flow-guided color propagation. Our detection pipeline achieves a competitive 82.8% Dice score with only 14ms of computational time (near real-time), and our restoration pipeline successfully incorporates temporal information for more accurate restorations.
About This Issue

March 03 2023 About This Issue Douglas Keislar Douglas Keislar Search for other works by this author on: This Site Google Scholar Author and Article Information Douglas Keislar Online ISSN: 1531-5169 Print ISSN: 0148-9267 © 2023 Massachusetts Institute of Technology2023Massachusetts Institute of Technology Computer Music Journal 1–2. https://doi.org/10.1162/comj_e_00626 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Douglas Keislar; About This Issue. Computer Music Journal 2023; doi: https://doi.org/10.1162/comj_e_00626 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsComputer Music Journal Search Advanced Search This content is only available as a PDF. © 2023 Massachusetts Institute of Technology2023Massachusetts Institute of Technology Article PDF first page preview Close Modal You do not currently have access to this content.
Sinhalese

Sinhalese (Sinhala) is the language of the greater part of the population of Sri Lanka (more than 9 million people). Sundered from the rest of the IA languages since the middle of the first millennium BC, the language has developed along independent, if broadly similar lines. As a result, Sinhalese exhibits so many peculiarities that its place among the NIA languages remains unclear, and it has been variously connected, by different researchers, with the Eastern, the Southern and the Western groups. The modern language began to take its present shape around the beginning of the second millennium of our era. A rich corpus of ancient literature has been preserved. The classical literary form of Sinhalese – codified as early as the thirteenth century – differs strikingly from the colloquial. The nineteenth century saw the beginnings of an intensified process of borrowing from Sanskrit. Dravidian influence is unmistakable. The Mahl dialect of Sinhalese is spoken in the Maidive Islands.
Improved evidence for chaos during peeling of pressure sensitive adhesive tape from a metal substrate

Electrical currents produced when a pressure sensitive adhesive is peeled from a copper substrate provide a sensitive probe of the dynamics of interfacial failure. This current is produced when charged adhesive pulls away from the metal. Time-series analysis of the current fluctuations shows that the peel dynamics displays fractal behavior, with a correlation dimension of about 2.3. The small size of this dimension suggests that the dynamical behavior of the peel front could be described with a relatively simple model (3–5 degrees of freedom). Estimates of the Lyapunov exponent, λ, indicate a large, positive exponent (≈ 100 bits/s), showing that fluctuations along the peel front become inherently unpredictable on time scales of tens of ms (λ −1). The presence of a positive Lyapunov exponent demonstrates conclusively that the current fluctuations (and thus adhesive detachment) exhibit deterministic chaos.