Reviewerly/exHarmony huggingface dataset:
Dependency Structure-Based Rules Using Root Node Technique for Explicit Aspect Extraction From Online Reviews

Aspect-based sentiment analysis (ABSA) includes aspect extraction and sentiment analysis on those extracted aspects.This paper is mainly focused on the explicit aspect extraction from product based online customer reviews.Many research studies on explicit aspect extractions have adopted dependency rule-based techniques but limited their focus to only nouns and noun phrases as potential aspects.Moreover, extraction of multiple-aspects and multi-word aspects from online reviews are also not addressed by previous research studies.In this paper, we have proposed a Dependency structure-based rules using ROOT Node (DS-RN) technique using spaCy dependency parser to extract nouns, noun phrases, verbs, verb phrases in addition to single word, multiple aspects and multi word aspect extractions from customer review datasets.In our proposed methodology, 30 new dependency-based rules are formulated and implemented on 5 different product-based datasets.This study is based on the pattern analysis of dependency structures of review sentences to develop dependency-based rules for explicit aspect extraction.The proposed approach also incorporated lexicon-based pruning techniques to remove irrelevant aspects and retain correct aspects.The performance results on 5 different product-based customer review datasets demonstrate that our proposed DS-RN approach outperforms all other state-of-the-art baseline works with averaged value of precision as 87%, recall with 97% and 91% as F1-score.
Research on cross-modal hashing method based on diffusion mode

For achieving fast and flexible retrieval across heterogeneous modalities, unsupervised is more flexible and easy to use than supervised methods, of which the unsupervised method GAN is the most popular. However, GAN has been suffering from the problems of lack of diversity in generated samples, debugging difficulties and training instability. A cross-modal hashing method based on a diffusion model is proposed in the paper. Specifically: (1) For the first time, the diffusion model is applied to the field of cross-modal retrieval, targeting three modalities for mutual retrieval. (2) The combination of adversarial network GAN and diffusion model improves the sample quality and sample diversity, and ameliorates the problems of complex GAN debugging and unstable training. The effectiveness of the proposed method is demonstrated through experiments on three datasets and comparison with state-of-the-art methods.
A new method of feature splicing based on wavelet transform for recognition of HRRP with noise

In meta-learning based small-sample HRRP recognition, HRRP data is one-dimensional, and the amount of extractable features is not as much as that of multidimensional data, so it is necessary to splice the one-dimensional data into twodimensional data to improve the recognition rate. This paper strives to reconceptualize the features among HRRP data from a two-dimensional perspective, and proposes a low noise sensitivity feature extraction based on wavelet decomposition and a low-frequency wavelet coefficient splicing method in descending order by scale to make it more applicable to the recognition of small sample targets containing noisy data. The HRRP with noise was decomposed by wavelet packet, and the lowest frequency wavelet coefficient with low noise sensitivity was extracted by wavelet packet sub-band energy and cosine similarity, and then spliced in descending order of scale, combined with the original data to form two-dimensional data, and trained with neural networks. The experiments show that the proposed method has obvious advantages in recognition accuracy, dependence on the number of samples and feature extraction ability.
Classical and Quantum Meet-in-the-Middle Nostradamus Attacks on AES-like Hashing

At EUROCRYPT 2006, Kelsey and Kohno proposed the so-called chosen target forced-prefix (CTFP) preimage attack, where for any challenge prefix P, the attacker can generate a suffix S such that H(P∥S) = y for some hash value y published in advance by the attacker. Consequently, the attacker can pretend to predict some event represented by P she did not know before, and thus this type of attack is also known as the Nostradamus attack. At ASIACRYPT 2022, Benedikt et al. convert Kelsey et al.’s attack to a quantum one, reducing the time complexity from O(√n · 22n/3) to O( 3√n · 23n/7). CTFP preimage attack is less investigated in the literature than (second-)preimage and collision attacks and lacks dedicated methods. In this paper, we propose the first dedicated Nostradamus attack based on the meet-in-the-middle (MITM) attack, and the MITM Nostradamus attack could be up to quadratically accelerated in the quantum setting. According to the recent works on MITM preimage attacks on AES-like hashing, we build an automatic tool to search for optimal MITM Nostradamus attacks and model the tradeoff between the offline and online phases. We apply our method to AES-MMO and Whirlpool, and obtain the first dedicated attack on round-reduced version of these hash functions. Our method and automatic tool are applicable to other AES-like hashings.
تعليم المفردات باستخدام "كاهوت" في المدرسة المتوسطة الإسلامية الحكومية باريتو الجنوبية، كالمنتان الوسطى

The objectives of this study is to measure the effectiveness of using kahoot media in vocabulary learning in Junior High School Islamic South Barito, Cental Kalimantan in eighth grade.The research method used in this study is a quantitative approach with an experimental method with the type of Quasi Experiment Design. The data collection techniques in this study used tests (pretest and post-test), observation and questionnaires. And Data analysis using Independent Sample T-test. The results of this study can be concluded that The use of kahoot media is effective for vocabulary learning in Junior High School Islamic South Barito. Based on the T test = 8,0. The result is greater than the result in a significant 1% = 2.39012 and 5% = 1.67065 . This means that Ha is accepted and H0 is rejected. And the results of this test are accepted.
Romance, Suspense, and Translations

Translations were usually valued slightly less than Dutch originals. Participants attributed the lowest mean scores for literary quality to E.L. James’ Fifty Shades of Grey. The Dutch publisher marketed the translations of the Fifty Shades trilogy as Literary novels. To find out if this label indeed suits the novels best, the chapter applies cluster analysis using Burrows’ Delta and principal components analysis (PCA). The same methods are used to explore five novels by Swedish author Henning Mankell, to see whether the style of his Literary novels differs from his Suspense novels. Finally, keyword analysis is applied to get an indication of the fluency of translations, to see whether the results may explain the differences in ratings of originals and translations.
Literary Novels Written by Men

Literary novels written by male authors received higher scores for literary quality than those by female authors. This chapter focuses on the literary novels written by men, both translations and Dutch originals. Cluster analysis using Burrows’ Delta, principal components analysis (PCA), and keyword analysis are applied to explore differences in word usage and word frequencies between highly rated and lowly rated novels to search for an explanation of the ratings. The translation of Julian Barnes’ The Sense of an Ending, which received the highest mean score for literary quality, gets special attention. Dutch novels analysed in more detail are by Mart Smeets, Herman Koch, Kluun, and Arnon Grunberg, whose novels linguistically were close to Romance novels but received much higher ratings.
Style, Gender, and Genre

This chapter sums up which textual features beyond words were analysed and how they may play a role in readers’ evaluation of literary quality. It discusses sentence structures, parts of speech (PoS), bigrams, and cliché expressions. It describes how topic modelling was used to analyse thematic differences and how predictive modelling was applied to explore which textual and contextual features most influenced the ratings for literary quality. Mean sentence length, in number of words per sentence, proved to be a very good indicator. Next, Dutch Literary novels by women and men are compared in several computational ways and the influence of the author’s gender on readers’ perceptions is described in more detail. Finally, the feedback loop of genre is discussed.
Multimedia Imaging System of Data Collection and Antenna Alignment for Unmanned Aerial Vehicles Based Internet of Things

Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path.
ANALISIS LAPORAN GRAFIK BARBER JOHNSON DALAM MENUNJANG PELAPORAN YANG EFEKTIF DAN EFISIEN DI RSUD KANJURUHAN KABUPATEN MALANG

Background: Hospital reporting is a health service activity in a hospital that has the task of collecting data, processing data, and presenting data into information.Objective : The purpose of hospital reporting is to produce data or information quickly, precisely, andaccurately.Methods : This study uses a quantitative method with a descriptive approach. This study aims to analyze the use of the Barber Jhonson Graph report at Kanjuruan Hospital, Malang Regency, after analyzing that in the evaluation of the inpatient indicator report, the results of the Barber Jhonson graph did not refer to the results of the Barber Jhonson graph, due to lack of knowledge of the reporting section in making Barber Jhonson Graph.Results : Then from the analysis of Barber Johnson's data sources, namely the recapitulation of the inpatient census data and also the daily inpatient census, the calculation of the length of stay did not meet the specified standard. If the patient enters and leaves on the same day, the value is 1, but the calculation of the SIMRs at Kanuruan Hospital, the calculation of the length of time treated by patients entering and leaving on the same day is 0. If the calculation of the length of stay is not immediately justified, the data on the inpatient indicator report has not been said to be efficient. and also in using the results of the inpatient indicator report it is less effective if it does not display the Barber Johnson Graph. The results of the calculation of the inpatient census recapitulation data parameters, namely, BOR, ALOS, TOI, and BTO.Conclusion : The four parameters found BOR below the ideal number. Therefore, the suggestion that the researcher gives is the need for good team coordination between the medical recorder as part of data collection and management as well as the IT team as a supporter of data presentation. So as to produce reports of hospitalization indicators that are accurate and with integrity.Keywords : Barber Johnson, Inpatient, Hospital
One‐to‐Many Spectral Upsampling of Reflectances and Transmittances

Spectral rendering is essential for the production of physically-plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems.While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one-to-one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one-to-many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism -- the change of color with depth, and metamerism.
Extended Reality (XR) as a Communication Medium: Special Issue Guest Editorial

August 10 2023 Extended Reality (XR) as a Communication Medium: Special Issue Guest Editorial Spyros Vosinakis, Spyros Vosinakis Department of Product & Systems Design Engineering University of the Aegean Konstantinoupoleos 2, Syros, 84100 Greece * Corresponding Author: spyrosv@aegean.gr Search for other works by this author on: This Site Google Scholar Vlasios Kasapakis, Vlasios Kasapakis Department of Cultural Technology & Communication University of the Aegean University Hill, Mytilene, 81100 Greece Search for other works by this author on: This Site Google Scholar Damianos Gavalas Damianos Gavalas Department of Product & Systems Design Engineering University of the Aegean Konstantinoupoleos 2, Syros, 84100 Greece Search for other works by this author on: This Site Google Scholar Author and Article Information Spyros Vosinakis Department of Product & Systems Design Engineering University of the Aegean Konstantinoupoleos 2, Syros, 84100 Greece Vlasios Kasapakis Department of Cultural Technology & Communication University of the Aegean University Hill, Mytilene, 81100 Greece Damianos Gavalas Department of Product & Systems Design Engineering University of the Aegean Konstantinoupoleos 2, Syros, 84100 Greece * Corresponding Author: spyrosv@aegean.gr Online ISSN: 1531-3263 Print ISSN: 1054-7460 © 2023 by the Massachusetts Institute of Technology2023Massachusetts Institute of Technology PRESENCE: Virtual and Augmented Reality 1–4. https://doi.org/10.1162/pres_e_00388 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Spyros Vosinakis, Vlasios Kasapakis, Damianos Gavalas; Extended Reality (XR) as a Communication Medium: Special Issue Guest Editorial. PRESENCE: Virtual and Augmented Reality 2023; doi: https://doi.org/10.1162/pres_e_00388 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 JournalsPRESENCE: Virtual and Augmented Reality Search Advanced Search © 2023 by the Massachusetts Institute of Technology2023Massachusetts Institute of Technology Article PDF first page preview Close Modal You do not currently have access to this content.
A VM-Agnostic and Backwards Compatible Protected Modifier for Dynamically-Typed Languages

In object-oriented languages, method visibility modifiers hold a key role in separating internal methods from the public API. Protected visibility modifiers offer a way to hide methods from external objects while authorizing internal use and overriding in subclasses. While present in main statically-typed languages, visibility modifiers are not as common or mature in dynamically-typed languages. In this article, we present ProtDyn, a self-send-based visibility model calculated at compile time for dynamically-typed languages relying on name-mangling and syntactic differentiation of self vs non self sends. We present #Pharo, a ProtDyn implementation of this model that is backwards compatible with existing programs, and its port to Python. Using these implementations we study the performance impact of ProtDyn on the method lookup, in the presence of global lookup caches and polymorphic inline caches. We show that our name mangling and double method registration technique has a very low impact on performance and keeps the benefits from the global lookup cache and polymorphic inline cache. We also show that the memory overhead on a real use case is between 2% and 13% in the worst-case scenario. Protected modifier semantics enforces encapsulation such as private but allow developers to still extend the class in subclasses. ProtDyn offers a VM-agnostic and backwards-compatible design to introduce protected semantics in dynamically-typed languages.
Dištančné vzdelávanie v technike

In connection with the current situation of distance education, a project was implemented within the operational program Interreg V-A Slovak Republic - Czech Republic 2014-2020 to develop knowledge outside the teaching process under the title "Support of distance methods in technical education", application code NFP304010AYI2. The project is aimed at creating distance learning materials for improving the quality of education of students and academic staff, by introducing technical webinars and online lectures for education in technical fields with a high proportion of examples of practical engineering production. An important activity of the project is the solution of CASE STUDIES - technical tasks from industrial practice, which lead to mutual transfer of experience between students and teachers and also experts from practice. The focus of the project thus leads to contact of students with important employers from the Moravian-Silesian and Žilina regions and thus helps their future employment on the labor market
Digital Phonics Literacy Media to Support the Initial Reading Skills of First-Grade Students in Multicultural Bilingual Schools

Literacy in Indonesia still needs to be improved, and the scope of literacy of students in Indonesia still needs to be narrower, including reading activities not understanding. This research aims to create Indonesian digital phonics literacy media for the beginning reading ability of first-grade students in multicultural elementary schools. The research was developed with the ADDIE model. The research subjects were experts, grade I elementary school teachers, and bilingual learners in multicultural schools. Data collection methods were observation, interview, test, and questionnaire. Data analysis was done with qualitative and quantitative descriptions. The analysis shows that Indonesian digital phonics literacy media consists of digital storybooks, posters, and phonic songs packaged in websites with Google site applications. Media, material, and language validity in Indonesian digital phonics literacy media is very high. Digital phonics literacy media shows a very practical category with a practicality level of 80%-100%, a teacher response score of 89.38%, and a response to the collaboration of parents and students of 88.75%. The effectiveness of the media shows a large category with a significant contribution to students' beginning reading skills. The effect size coefficient of the test shown is 1.53 and 1.86 on the results of the observation analysis of the beginning reading ability of bilingual learners in multicultural schools.
Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation

The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used.We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network.The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An F-score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation.An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.
CAT: A Collaborative Annotation Tool for Chinese Genealogy Textual Documents

The annotation for Chinese genealogy textual documents is helpful for constructing genealogy knowledge graph, training effective machine learning models for knowledge extraction, etc. However, this kind of documents is difficult to annotate. The primary reason is that the texts are written in both classical and vernacular Chinese. These texts also contain numerous ancient characters and are usually without punctuation. Understanding genealogy texts requires sufficient expertise. When multiple users labeling the same text, conflicts may occur. Existing annotation tools are inappropriate for this work. In this paper, we propose a novel interactive labeling tool, which provides text segmenting, entity and relationship tagging etc. With the annotated information, it is convenient to construct knowledge graph from textual documents, which can be used to analyze Chinese genealogy texts. Furthermore, we introduce a weak supervised mechanism with Hidden Markov Model for collaborative annotating with crowdsourcing. The practice shows that our approach is effective for collaborative annotation. It also facilitates the construction of knowledge graph and obtains more high-quality data sets. At present, this annotation tool has been applied into service.
Weakly Supervised Object Detection with Pyramid Squeeze Attention and Candidate Box Screening Algorithm

Weakly supervised object detection is mainly applicable when there is no instance-level category annotation. Due to the lack of annotations, it is difficult for weakly supervised methods to accurately predict the location of objects. Existing methods tend to converge to the most salient local regions of objects. To address this problem, we propose a weakly supervised object detection framework. In our framework, we add the Pyramid Squeeze Attention Module to the backbone feature extraction network to improve the localization ability of the network, thus enabling the network to consider the global features of objects. To further improve detection accuracy and obtain better candidate boxes from weakly supervised object detection networks as a pseudo-GT for regression and classification, we propose a Candidate Box Screening Algorithm (CBSA). Extensive experiments are run on the PASCAL VOC 2007 dataset under a single model. The method achieves a detection accuracy of 51.7% on this dataset. Improved detection accuracy compared to recent weakly supervised object detection methods.