Reviewerly/exHarmony huggingface dataset:
Face Detection Using the Siamese Network with Reconstruction Supervision

Deep neural networks are important algorithms involved in image classification problems. Among them, Siamese Network is a special type of neural network architecture, whose goal is to calculate the similarity between two given images, which is often applied to distinguish facial similarity. However, how to improve the accuracy of their recognition is still a problem that needs to be solved. To address this issue, this article proposes a new Siamese Network structure, the Reconstructing Supervision Siamese Network. This structure is inspired by the BP neural network and innovatively introduces the idea of BP neural network into the structure of Siamese Network. It has the comprehensive evaluation ability compared to the original graph, further improving the accuracy of the calculated results and improving the classification accuracy. The feasibility and rationality of this method were verified through comparison with traditional Siamese Network. Especially, the added reconstruction supervision process can quickly converge the loss value and demonstrate better accuracy than traditional Siamese Network methods, having an at least 30% improved accuracy. Finally, under the testing of the classic ORL face dataset, this algorithm can be effectively applied to practical face detection and classification problems.
The Metaverse beyond the internet

Just as the evolution of the Internet has transformed the way people live and work, so too the next significant iteration of the Internet, commonly referred to as the Metaverse, which the authors suggest will go beyond the Internet as a sort of successor state to the Internet, will also lead to significant societal change. This paper considers a number of issues that are likely to test the law and its response including in the areas of online wrongs, intellectual property and digital assets.
DILEMMAS OF WORKERS IN PANDEMIC TIMES

This article aims to understand the main difficulties of riverside teachers in the municipality of Porto Walter (Acre), between the pandemic period of 2020 and 2021. The methodology is qualitative in that it was concerned with analyzing and interpreting particularities of riverside teachers, describing their complexities and, therefore, field research is combined with semi-structured interviews. The final considerations point to the great difficulties in bringing quality education to the most remote places in the country. In a country with gigantic territorial dimensions like ours, it is not easy to reach certain regions.
Similar Image Retrieval using Autoencoder. I. Automatic Morphology Classification of Galaxies

Abstract We present the construction of an image similarity retrieval engine for the morphological classification of galaxies using the Convolutional AutoEncoder (CAE). The CAE is trained on 90,370 preprocessed Sloan Digital Sky Survey galaxy images listed in the Galaxy Zoo 2 (GZ2) catalog. The visually similar output images returned by the trained CAE suggest that the encoder efficiently compresses input images into latent features, which are then used to calculate similarity parameters. Our Tool for Searching a similar Galaxy Image based on a Convolutional Autoencoder using Similarity (TSGICAS) leverages this similarity parameter to classify galaxies’ morphological types, enabling the identification of a wider range of classes with high accuracy compared to traditional supervised ML techniques. This approach streamlines the researcher’s work by allowing quick prioritization of the most relevant images from the latent feature database. We investigate the accuracy of our automatic morphological classifications using three galaxy catalogs: GZ2, Extraction de Formes Idéalisées de Galaxies en Imagerie (EFIGI), and Nair &amp; Abraham (NA10). The correlation coefficients between the morphological types of input and retrieved galaxy images were found to be 0.735, 0.811, and 0.815 for GZ2, EFIGI, and NA10 catalogs, respectively. Despite differences in morphology tags between input and retrieved galaxy images, visual inspection showed that the two galaxies were very similar, highlighting TSGICAS’s superior performance in image similarity search. We propose that morphological classifications of galaxies using TSGICAS are fast and efficient, making it a valuable tool for detailed galaxy morphological classifications in other imaging surveys.
Degradation-aware Resilience Strategy for Quantum Key Distribution Co-existence With Classical Communication in Optical Networks

In this paper, we study a degradation-aware network resilience strategy for quantum key distribution (QKD) co-existence with classical communications. The secret key rate (SKR) can be improved by 96.4% on average with the proposed DS-QKD strategy in a wavelength division multiplexing (WDM) optical network with 40 wavelength channels.
Hash Function and Lightweight Encryption Aided Authentication Design for Radio Frequency Watermarking Systems

Radio frequency watermarking systems embed the secret watermark bits into host bits for secure transmissions. However, malicious users may initiate falsifying attacks via broadcasting wireless channels. Meanwhile, the interferences of watermark signals to host signals will degrade the bit error rate (BER) performances. With the aim to enhance both the security and the reliability performances, we propose to utilize the Hash function to generate the unique digital digest of host signals for the integrity check. Then the lightweight encryption and spreading are conducted to compose the watermark signals. At the receiver, reverse operations are performed to recover both host signals and watermark signals. Then the retrieved digest is used to check the integrity and validity of host signals to combat falsifying attacks. Subsequently, we derive the bit error rate (BER) and the information leakage expressions. Simulation results are then provided to validate the proposed design.
News analysis of nursing work in the context of the COVID-19 pandemic

Objective: To analyze the news published in the Brazilian press about nursing work during the COVID-19 pandemic period, between February 2020 and August 2021. Methods: This is a documentary, descriptive study with a qualitative approach. Elaborated from information collected on news sites, through clipping, following the stages of monitoring, collection, analysis and archiving of mentions of work in nursing made by the Brazilian media. The sample totaled 432 news. Data analysis was performed using the Thematic Content Analysis proposed by Laurence Bardin. Results The year 2020 stood out among the analyzed news (59.96%), whose characters involved were nursing professionals, female (36.57%), technical (38.89%) and higher (38. 43%). There was a predominance of news in the Southeast (45.83%) and Midwest (17.36%) regions. Six categories were identified for data analysis: deaths among workers (40.74%), precarious working conditions (28.94%), vaccination (8.56%), contamination of workers (8.33%), suspected of ethical infraction (8.10%) and COVID-19 cure (5.32%). Conclusion: The COVID-19 pandemic in Brazil brought nursing work and workers to the center of journalistic debate. Death, precarious working conditions and contamination of nursing workers, reported in the press, are related to the precariousness of nursing work in this study. It is conjectured that the journalistic debate on nursing work in the COVID-19 pandemic helped to strengthen political guidelines, such as the one that led to the enactment of Law 14,434/2022, which deals with the national wage floor for these professionals.
Scholarly Communication at the Dawn of Exponential Revolution

It is clear that we are entering a new phase of technological growth: Not an industrial revolution of the type that we have seen before but an exponential revolution awaits with all the challenges and wonders that entails. An abundance of data coupled with sophisticated tools for both capturing and analysing data has transformed research over the last half century. And yet, scholarly communication is still reliant on 17th Century technology. In this seminar we examine why this is the case and what the future of scholarly communication could look like.
The status of science in the age of advanced automation

As human abilities to automate mathematical work, text generation, and image creation achieve heightened progress, once-speculative questions regarding the wholesale automation of scientific disciplines, and adaptation thereto, become pragmatic. Such questions prompt organizations that produce knowledge goods to develop strategies for research, curation, and distribution that avoid predictable obsolescence, sustain trust, and deploy new practices of human cognitive labor that create novel value in excess of that deliverable by machine-driven science. In his talk, James Douglas Boyd will discuss strategies for navigating a new economic regime in the sciences; thoughts on the future role of pure research in an increasingly technological society; and implications for the 'decentralized science' (DeSci) movement.
Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm

With the increasing popularity of 5G communication technology, the high speed and low latency of 5G network provide information technology support for the development of the UAV industry.Nowadays, the material distribution mode of "delivery vehicle + drone" is widely used in many tasks such as disaster relief and cargo transportation.At present, in order to improve the transportation efficiency and reduce operating costs in this mode, it is particularly important to reasonably plan the distribution scheme of drones and vehicles.Based on the given location, route and distribution demand, the optimal distribution scheme is solved, the Dijkstra algorithm is used to calculate the shortest distance between any two points in the network diagram and the corresponding path to build a fully connected network, and then the genetic algorithm is used to solve the TSP problem with the shortest distance of the vehicle as the objective function, so as to approximate a Hamilton loop, and finally find the actual driving path of the vehicle.
The age and geochemical evolution of granite magmatism in Olkhon region from Caledonian syncollisional ore-free granite to the rare metal granite and pegmatite of Middle Paleozoic intraplate setting.

The detailed description of two granite complexes in the Olkhon subterrane is given. The Early Paleozoic Sharanur complex was formed by granitization of gneisses of the Olkhon series. It includes migmatites, granite-gneisses, granites and pegmatites of normal alkalinity; they belong to the type of syncollisional granites. The Middle Paleozoic Aya granite complex includes mother Aya massif of amazonite-bearing granites and several types of rare-metal pegmatites. They have elevated alkalinity, low of Ba, Sr, and high LILE and HFSE elements contents. The Aya pegmatites lie in northwest cracks of stretching and associated with the rise of the territory under the influence of the North Asian plume. These cracks and pegmatites mark the beginning of a new intraplate geodynamic setting. Two geochemical types are distinguished among the pegmatites of this complex. These are amazonite pegmatites of Li-F type with Ta mineralization and complex type pegmatite with Be-Rb-Nb-Ta and Li-F mineralization (the Ilixin vein). The Tashkiney pegmatite vein is similar to Ilixin, but lies in the gneisses of the Olkhon series. It shows high concentrations of Be, Nb, Ta, as well as W, Sn, but lacks Li and F, due to a greater depth and higher temperature of the melt crystallization of this pegmatite.
Insider Attack Model Against HSM-Based Architecture

In this paper, we propose a combinatory attack model for insider attacks against HSM-based security architectures. Our model is based on the study of attack vectors in the security architecture and the conduction of all possible attacks on those vectors. It shows that these typical architectures are vulnerable to private key theft and replacement, as well as data theft, alteration, swapping, nullification, and deletion. Results show that each of those attacks was successfully conducted on an HSM-based security architecture with relative ease. They prove the essential need for a security architecture that also considers insider threats.
Local Style Awareness of Font Images

When we compare fonts, we often pay attention to styles of local parts, such as serifs and curvatures. This paper proposes an attention mechanism to find important local parts. The local parts with larger attention are then considered important. The proposed mechanism can be trained in a quasi-self-supervised manner that requires no manual annotation other than knowing that a set of character images are from the same font, such as Helvetica. After confirming that the trained attention mechanism can find style-relevant local parts, we utilize the resulting attention for local style-aware font generation. Specifically, we design a new reconstruction loss function to put more weight on the local parts with larger attention for generating character images with more accurate style realization. This loss function has the merit of applicability to various font generation models. Our experimental results show that the proposed loss function improves the quality of generated character images by several few-shot font generation models.
GOing FAIR &amp; DOing FAIR

Since their introduction in 2016, the FAIR Guiding Principles have been widely recognised and have inspired countless initiatives to make data and services more Findable, Accessible, Interoperable and Reusable by machines. The GO FAIR Foundation was created in 2019 as an independent and non-profit organization to support FAIRifcation efforts which includes explicit interpretations of the FAIR Principles and a few other essential criteria when “going FAIR”. Although FAIR implementations can assume a broad range of architectures and technology choices, recently a critical mass of stakeholders has converged on a potential FAIR Digital Object specification that could make “doing FAIR” a more standardised and streamlined proposition. In this presentation, Erik will recount the brief but explosive history of FAIR and the current interest around FDOs as Universal Machine Actionable Containers (UMACs).
Perbandingan Kata Kunci Abstrak Skripsi dengan Subjek Menggunakan Dalil ZIPF pada Skripsi Prodi Teknologi Informasi Politeknik Caltex Riau Tahun 2018

This research aims to find out the comparison of keywords in thesis abstracts with subjects using zipf law in the thesis of students of IT program of Politeknik Caltex Riau 2018. The research method used in this study is a quantitative method with a comparative approach. Population and sample in this study is the entire thesis of 82 students in 2018. Based on data from the results of subjects indexing with zipf law and keywords in thesis abstracts have a relatively high level of relevance compared to relevant and irrelevant marginal. The relevant level (the resulting subject is entirely and precisely the same) of the comparison result is 79% or 65 theses, for marginal relevant (the resulting subject has similarities) of 17% or 14 theses, and for irrelevant (the resulting subject has absolutely no similarity) by 4% or three theses.
CCCC Secretary’s Report, 2018–2019

Preview this article: CCCC Secretary’s Report, 2018–2019, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/ccc/71/2/collegecompositionandcommunication30429-1.gif
Investigating Pre-trained Audio Encoders in the Low-Resource Condition

Pre-trained speech encoders have been central to pushing stateof-the-art results across various speech understanding and generation tasks.Nonetheless, the capabilities of these encoders in low-resource settings are yet to be thoroughly explored.To address this, we conduct a comprehensive set of experiments using a representative set of 3 state-of-the-art encoders (Wav2vec2, WavLM, Whisper) in the low-resource setting across 7 speech understanding and generation tasks.We provide various quantitative and qualitative analyses on task performance, convergence speed, and representational properties of the encoders.We observe a connection between the pre-training protocols of these encoders and the way in which they capture information in their internal layers.In particular, we observe the Whisper encoder exhibits the greatest low-resource capabilities on contentdriven tasks in terms of performance and convergence speed.
Modern education: development in the face of new challenges and new responses

Today the whole country and the whole world live in a new geopolitical reality and a situation of a number of challenges. That existential war waged by the collective West against Russia has shown that the future of any country is determined not only by its military and economic power, but also by its values and meanings. In order to win such a war, the superiority of neither weapons nor the economy is needed, only the influence on the enemy, carried out through education, culture, and the media. The article discusses a number of challenges that directly affect the state of the national education system.
Model of Critical Success Factors for Agile Information Technology Project in Indonesia using Analytic Hierarchy Process (AHP)

The implementation of Agile shows rapid development. For business continuity, organizations apply Agile in building innovative products according to market conditions The implementation of Agile shows rapid development. For business continuity, organizations apply Agile in building innovative products according to market conditions. Agile implementation in Indonesian IT projects faces problems include cultural constraints, lack of education/training, unclear guidelines, limited Agile mindset understanding, and insufficient management support. This study aims to solve these problems by exploring success factors of Agile projects with the analytic hierarchy process (AHP) model to help organizations in making Agile implementation strategies for their projects. A qualitative approach is used as a research method to confirm CSF by experts and practitioners, while a quantitative process is used to perform AHP calculations. The research identified critical success factors for Agile project implementation, including management support, competent project teams, organization-specific guidelines for processes and procedures, adaptive organizational culture, Agile training and coaching for team members, and a clear product roadmap. This research makes significant contributions to both the academic and practical domains. In academia, it serves as a valuable reference for critical success factors (CSFs) in the Agile field. In practical terms, it offers valuable insights for organizations in Indonesia, aiding them in effectively planning and implementing Agile methodologies
Design and implementation of a fast interrupt system based on RISC-V

Interrupt technology is a key technology for processors to respond to external events and plays an important role in the embedded field. The open source RISC-V architecture defines a complete set of interrupt mechanisms with hardware definitions that do not support interrupt nesting in embedded systems that only support machine mode, and software support for interrupt nesting adds additional time overhead. This paper is based on the interrupt mechanism defined by RISC-V, using a state machine to control the global interrupt enablement of the processor to support interrupt nesting, and to save the site and restore the site through hardware stacking and out-stacking to avoid the time consumption caused by the software level; a dedicated entry point for interrupt service program jumps further speeds up the response. In this paper, we design and implement a fast interrupt system using Hummingbird E203, an open source kernel defined by standard RISC-V, as an experimental platform. Simulation verification and comprehensive implementation on FPGA platform show that compared to the interrupt handling process of Hummingbird E203, using a small amount of hardware resource consumption, the response speed is improved by 1/4, providing better real-time performance and flexibility in embedded applications.
The Utilizing Of Video Maker As A Media In Writing Greeting Card At Junior High School 1 Mantiat Pari

Media is one of the essential things in the teaching-learning process. One of them is video maker. Video maker is one of the applications that can be used as a media in teaching writing. The research aim is to see the utilizing of video maker as a media in writing greeting cards. There were sixteen students as a sample, especially eighth grades students. The researcher gave them ten items’ of questionnaires. The findings of this research can be divided into two categories. The first was video maker gave the students motivation in learning English. They were in statement one (84,44%) statement two (94,44%), statement four (88,89%), and statement six (93,33%). All of the statements have category very high. It meant video maker gives the students motivation to learn writing. The second was the students interested in using video maker as a media in writing. They were in the third statement (81,11%) and the category was very high, the fifth statement (83,33%) and the sort was very high, the eighth statement (76,67%) and the sort was high, the ninth statement (68,89%) and the sort was high, the tenth statement (86,67%) and the sort was very high. There was no questionnaire in medium, low or very low levels. Based on the result, it can be concluded that the utilization of video maker as a media in writing at SMPN-1 Mantiat Pari was positive. The students gave a positive response to it.
Graph Extraction for Assisting Crash Simulation Data Analysis

In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs. Such graph representations of CAE data can improve design guidelines and support recommendation systems by enabling the comparison of simulations, highlighting unexplored experimental designs, and correlating different designs. We focus on the load-path in crashworthiness analysis, a complex sub-discipline in vehicle design. The load-path is the sequence of parts that absorb most of the energy caused by the impact. To detect the load-path, we generate a directed weighted graph from the CAE data. The vertices represent the vehicle’s parts, and the edges are an abstraction of the connectivity of the parts. The edge direction follows the temporal occurrence of the collision, where the edge weights reflect aspects of the energy absorption. We introduce and assess three methods for graph extraction and an additional method for further updating each graph with the sequences of absorption. Based on longest-path calculations, we introduce an automated detection of the load-path, which we analyse for the different graph extraction methods and weights. Finally, we show how our method for the detection of load-paths helps in the classification and labelling of CAE simulations.
Language Competence Based on B-Learning for English Language Teaching in University Students

The present research aimed to determine the effect of B-learning on linguistics competence in English language in Peruvian university students. The sample was composed of 180 undergraduate students from Peruvian universities. In the learning sessions, the B-learning methodology was used for the development of the 4 skills and for the communicative competence, the pre and postest was used. The results show the effectiveness of the B-learning methodology in the development of communicative competence in English language in Peruvian university students who showed a statistically significant advance in their learning and use of the language.