Reviewerly/exHarmony huggingface dataset:
A Novel Self-Learning Filters for Automatic Modulation Classification Based on Deep Residual Shrinking Networks

Automatic modulation classification is a critical algorithm for non-cooperative communication systems.This paper addresses the challenging problem of closed-set and openset signal modulation classification in complex channels.We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification.Our approach achieves better performance than existing methods in both closed-set and open-set recognition.In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%.In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness.Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.
An Analysis of Technology Issues in Mobile Augmented Reality

Augmented reality technology is now increasingly modern and developing rapidly, this research is a summary of the popularity of augmented reality technology to date. Augmented reality applications are increasingly used in the fields of education, health, and even the military. Other research explains how to combine augmented reality with other technologies. By conducting a literature review of 1582 documents from 2017 to 2022 related to augmented reality, several combinations using AR technology were obtained. This research is to find challenges and opportunities for augmented reality applications in the future. Some studies illustrate that there are still some shortcomings in augmented reality technology, either in the form of image quality or the design of the technology. It is expected that in the future, the shortcomings found in previous studies can be improved.
Robust detection of marine life with label-free image feature learning and probability calibration

Abstract Advances in in situ marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred in situ class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.
Enhancing Transmission of Voice in Real-Time Applications

In today’s telecommunication world sharing the data becomes very easy. It is a bit-complicated in converting the text documents to voice assistance even proposed a lot of resources. Giving the correct information to the right person in the right way is essential on both a personal and professional level. Numer- ous applications have developed with the purpose of enabling two individu- als to communicate instantly. The major objective of this effort is to address the issues that dysarthria, business meetings, and regular travelers face. To solve this issue, proposing a gadget that will aid in the translation of written language into speech. The majority of these applications include, language translation, signal conversion from text to synthetic voice, and articulators. In this project, proposing the development in a wide range of strategies and algorithms needed to make text to speech a reality (TTS).
ANALISIS BELAJAR SISWA DARI RUMAH SELAMA MASA PANDEMI COVID-19 PADA PEMBELAJARAN MATEMATIKA KELAS II SD NEGERI 22 LUBUKLINGGAU

This study aims to determine how the Mathematics learning process of students from home during the Covid-19 pandemic, to find out whether the obstacles faced by students learning from home and to find out what solutions are given for Class II Mathematics learning. SD Negeri 22 Lubuklinggau can be reached. The research method used is descriptive qualitative, data collection through observation, interviews, questionnaires, and documentation. The data that has been obtained are then analyzed using data reduction, data presentation and drawing conclusions. The subject of this research is the analysis of learning from home during the Covid-19 pandemic, 12 students in class II A and 12 students in class II B, with a total of 24 students. The learning process from the homes of class II students uses an online learning system with the whastapp group application, the obstacles experienced by students in learning Mathematics are difficulties in counting, writing, and doing assignments given by the teacher, other obstacles that are found are disruption of the internet network for students and teachers. It can be concluded that during the covid-19 pandemic the learning process of students from home through online learning, the teacher provides motivation before providing material, students are also asked to attend their attendance, then the teacher explains the subject matter according to the materials on the day. The obstacles faced bt students are that students cannot concentrate on learning from home because of the many disturbances from the sounds around them, children also mostly play when studying at home. As for the solution obtained by the researcher to overcome the obstacles that occur, the teacher is expected to be able to create a fun online learning method so that students do not feel bored in learning at home, the teacher can also make videos when teaching using learning media when learning Mathematics. &#x0D; Keywords: Analysis, Learning from Home, Class II
Design and Research of Educational Teaching Service System Based on Meta-universe Environment

In this paper, we build a dialogue system for English speaking training.In the meta-universe environment, functions such as reading and writing words, reading sentences aloud, multi-round dialogues and dialogue quality evaluation are implemented, and semantic understanding algorithms are applied to the system, and users can practice speaking through dialogues with virtual characters.In this paper, we use speech recognition, virtual character animation calling, pronunciation quality assessment and utterance template matching to implement a multi-round dialogue based on hotel scenarios.The virtual characters contain realistic character movements and animation effects to provide a good learning experience for learners.By analyzing the feedback on the use of the system through a user research method, the results show that students can practice and measure the effectiveness of spoken English by conversing and interacting with avatars in an immersive metaverse environment, which helps them to learn spoken English.
Taxonomy of Open Science: revised and expanded

Objective: review the terminologies and applications of the Open Science taxonomy to build a more comprehensive version that represents the knowledge surrounding the topic theme, in accordance with the current scenario of scholarly communication and the recommendations of the United Nations Educational, Scientific and Cultural Organization (Unesco). Method: his is exploratory research with a deductive approach. The first step was the revision of the taxonomies, with the 12 researchers, who met weekly, for conceptual and epistemological discussions related to Open Science and methodological and procedural definitions for the realization of the study. Results: as a result of the analyses, a taxonomy was developed to be evaluated by the experts. For this, a questionnaire with open questions about each main axis of the taxonomy was sent to 68 specialists. We obtained 21 answers that cooperated with the modeling and exposition of the terms of the new taxonomy proposal. The new taxonomy has 10 main level facets and a total of 96 labels. Conclusions: the perception of the experts brought to light a panorama congruent with Unesco recommendations and the current scenario of Open Science.
Peningkatan Prestasi Belajar Melalui Model Pembelajaran Think Pairs Share (TPS) Pada Mata Pelajaran IPA Kelas IV SDN Tirtosari, Magelang

The purpose of this study was to improve science learning achievement using the Think Pairs Share (TPS) learning model for class IV at SD N Tirtosari. This study uses PTK method. Data collection techniques with interviews, observations, field notes, and tests. Sources of data are teachers and students as research subjects. The data analysis technique is descriptively taken from the average achievement score and the percentage of student completeness. Based on the research, it was concluded that the use of the TPS learning model could improve the learning achievement of fourth grade students at SD N Tirtosari. In the pre-action the average student achievement was 69.28 with a completeness percentage of 42.85%, in cycle I it increased from 5.86 to 75.14 with a completeness percentage of 57.14% because it was not in accordance with the percentage of success then proceed to cycle II . In cycle II there was an average increase of 11.71 so that the average student achievement was 86.85 with a completeness percentage of 100%. From this description it can be seen that there has been an increase from the pre-action stage, cycle I to cycle II.
The effectiveness of E-HRM adoption in the banking industry of Bahrain during the pandemic

The Covid-19 pandemic occurred at the beginning of the year 2020, causing many banking to develop strategies to adapt to sudden change quickly.Human Resource Management (HRM) had a crucial role in these changes since most employees started working remotely.This includes human resources (HR) implementing technology and digitally transforming their practices to meet the requirements of social distancing and remote working.The Purpose of This Study was to evaluate the effectiveness of the E-HR management system.The ever-increasing dependence on records technology in human resource management features has appreciably maximized the effectiveness of all the tactics and practices related to HR management.The HRM practices, HR features can now be carried out within the least time and with the lowest cost.Such technologies make contributions to growing a communication device that is both powerful and efficient.Methodology: For data collection, a mixture of the quantitative and qualitative methods i.e. triangulation is applied through conducting an online survey and distributing it to HR executives and HR employees from multiple banking located in the Kingdom of Bahrain.It involved different surveys and statistical data collection.Major Findings: the adaptation of E-HRM has been approved to be implemented and improved in the banking organizations of Bahrain.Hence, the HRM practice that was e-approached is found the Training and Development, while Compensation is the least.As for the factors that were found of influence to impact E-HRM, Top management support was found of the most influence while Funding was found the least.Moreover, the funding resources in addition to the technical support have been found the top challenges in the digital transformation.it is found that about 89% of people are in favor of digital transformation while 96.4% have experienced it in their workplace.Future research is sought for to provide additional insights of the effectiveness of E-HRM adoption in the banking industry in other countries during the pandemic.
A study to assess the knowledge, practice and investigation of community outbreak of typhoid fever associated with drinking water in UPHC Vadapalani (West)

Typhoid fever is a unique human systemic infection known to medical science, since the days of Hippocrates though its clear differentiation from typhus fever was made only by the end of the 18th Century. Though the advent of new antibiotics especially the chloramphenicol has dramatized the final outcome of the disease, the clinical pattern by which enteric fever can present is still diverse. Typhoid fever is still an important public health problem or endemic in many parts of India and in many other developing countries. It is difficult to estimate its real global impact due to problems related to clinical and laboratory diagnosis and since most of the cases are being treated outside the hospital and there is no reliable information about the incidence in our country. So the present study aims to assess the Knowledge, Practice and Investigation of Community Outbreak of Typhoid Fever associated with drinking water in UPHC Vadapalani (West). A qualitative approach with Descriptive research design was adopted to conduct the study among 60 Typhoid Fever patients who were selected by Non- probability purposive sampling technique. Semi-structured interview method was used to collect the demographic data and also using a semi-structured questionnaire to assess the knowledge, practice and investigation of community outbreak of typhoid fever associated with drinking water in UPHC Vadapalani (west).The results of the study shows that among 60 samples proves that 46(76.67%) had moderately adequate knowledge, 8(13.33%) had inadequate knowledge and 6(10%) had adequate knowledge regarding typhoid fever associated with drinking water and that the mean score of knowledge among samples was 4.38 with standard deviation of 1.54. The maximum score was 8.0 and the minimum score was 0. Assessment of Knowledge, Practice and Investigation of Community Outbreak of Typhoid Fever associated with drinking water is effective method to prevent complication during patient affected with typhoid fever and also it helps the Typhoid Fever patient to improve to the knowledge and to provide the better quality of life to Typhoid Fever patients.
Protection of COVID-19 images using multiple elliptic curve cryptography

COVID-19 pandemic has made the medical industry around the world under unprecedented and growing pressure. To preserve the integrity and protection of medical data for transmission and for secure diagnosis, this work suggests an encryption method for the protection of privacy of COVID-19 patient’s data by securing medical images based on elliptic curve cryptography. This is done through amalgamating three different types of stages by using elliptic curve cryptography. First, the coordinates of pixels of medical images are scrambled using elliptic curve and encrypt the coordinates of pixels, which help in decreasing the correlation between the image’s pixels. The second stage empowers the first stage by encrypting the group of pixels from previous outputs irrespective whether the pixel value lies on the curve or not based on the multiplication modulo operation of elliptic curve cryptography. Finally, the group of pixels from the previous output is encrypted by XORing with the key generated from ECC DHKE (Elliptic Curve Cryptography Diffie–Hellman Key Exchange). The NIST test suite examines and verifies the statistical characteristics of the binary sequences for 210 bits and 216 bits that are created by cryptographic random or pseudorandom number generators. The outcomes of the experiments indicate how the suggested approach satisfies the security requirements of COVID-19 images of patients in terms of high effectiveness in encryption quality, resilient against many statistical analyses and attacks and also performs better than some recent schemes. This work also evaluated comparisons of quantitative, differential, time consumptions and computational complexity with several recent schemes.
Generative Stylization using Non-linear Style Mixing via Layer-wise Transformers

Stylization은 주어진 이미지를 원하는 특정 스타일로 변환하는 기술이다. 초기의 연구에서는 그래디언트를이용한 연구가 진행되었으나, 최근에는 고품질의 얼굴 이미지 생성이 가능한 생성 모델의 발전으로, 생성망을 활용한 stylization 기술이 발전되어왔다. 이 중 JoJoGAN은 GAN inversion을 통해 찾아낸 참조 스타일의 잠재 벡터와 임의의 노이즈의 가중합 통해 fine-tuning이 가능한 학습 과정을 소개했으며, 한 장의 참조 이미지만을 사용해 우수한 스타일 변환이 가능함을 보였다. 하지만 기존 가중합 기반 스타일 결합은 단순 선형 결합 형태로써 스타일 코드를 표현하는 매니폴드가 제한되는 문제점을 갖는다. 따라서 해당 잠재공간을 활용한 stylization 결과물은 미세한 표현을 효과적으로 반영하지 못함과 동시에 다양성에서 한계를 갖는다. 이러한 문제점을 해결하고자 본 논문에서는 두 가지 방법을 제안한다. 첫 번째로 다양성과 생성 품질을 높이기 위한 비선형적 스타일 결합으로써 스타일 잠재 공간을 폭 넓게 구성하는 방법이다. 이는 확장된 스타일 공간에서의 다양한 샘플링을 가능하게 함으로써 결과물의 품질 및 다양성을 향상시킨다. 두 번째로 스타일 결합에 있어서 공간적 장기 의존성을 파악해 의미 있는 스타일 특징의 조합을 만들어낼 수 있도록 계층별 Transformer 구조를 활용했다. 즉, coarse, middle, fine 계층에 대해 별도로 독립적인 신경망을 두어 스타일 코드를 인코딩한다. 제안하는 방법을 통해 전체적 외형은 원본의 외형을 따르도록 하고 미세영역에 대해 참조 스타일을 반영하도록 유도함으로써, 정량적/정성적/주관적 실험 결과를 통해 개선된 stylization 성능을 확인했다. Stylization is the method of transforming a given image into a specific desired style. In early studies, studies using gradients were conducted, but recently, stylization using generative networks have been developed along with the development of a generative model capable of generating high-quality facial images. Among them, JoJoGAN introduced a learning process that enables fine-tuning through the weighted sum of the reference style latent vector and random noise found through GAN inversion and showed that excellent style transformis possible using only one reference image. However, the existing weighted sum-based style combination has a problem in that the manifold expressing the style code is limited as a simple linear combination form. Therefore, the result of stylization using the potential space does not reflect the fine expression effectively and at the same time has a limit in diversity. To solve this problem, this paper proposes two methods. First, it is a method to broadly compose a style latent space by combining non-linear stylization to increase the diversity and quality of creation. This allows for different sampling in an expanded style space, improving the quality and variety of the output. Second, we used a hierarchical Transformer structure to identify spatial long-term dependencies in style combinations to generate meaningful combinations of style features. In other words, style codes are encoded by separate independent neural networks for coarse, medium and fine layers. In the proposed method, improved stylization performance was confirmed through quantitative/qualitative/subjective experiments by inducing the overall appearance to follow the original appearance and to reflect the reference style for fine details.
PACMNET V1, N1, June 2023 Editorial

We are very excited to introduce the first issue of the ACM Proceedings of the ACM on Networking (PACMNET) journal. The Proceedings of the ACM series presents the highest-quality research conducted in diverse areas of computer science, as represented by the ACM Special Interest Groups (SIGs). PACMNET focuses on contributions presenting significant and novel research results on emerging computer networks and their applications. PACMNET was created to collect top-quality scholarly articles in the area of computer networking. PACMNET encourages submissions that present new technologies, novel experimentation, creative use of networking technologies, and new insights made possible using analysis. The journal is strongly supported by the ACM Special Interest Group on Communications and Computer Networks (SIGCOMM) and involves top-level researchers in its Editorial Board.bWe started working on PACMNET in 2019, with the idea of having a new journal that guarantees both a fast publication time and a very rigorous and constructive review process. The creation of PACMNET was done with the enthusiastic support of the ACM SIGCOMM community, and it will be especially attractive for networking researchers from regions where conference publications are no valued.
Perancangan Sistem Informasi Pengelolaan Praktikum Berbasis Website

Practicum is one of the supporting activities in the learning process as well as routine and mandatory activities that students must follow. Practicum management at the Untirta Industrial Engineering Laboratory is still carried out conventionally, so it is inflexible, inefficient, and the information provided is not detailed and transparent. The design of information systems in practicum management is carried out to overcome these problems. This study aims to obtain a practicum management information system design at the Untirta Industrial Engineering Laboratory. The method used in this study is the Framework for the Applications of System Thinking (FAST) method. The results of this study indicate that the practicum management information system design at the Untirta Industrial Engineering Laboratory is in the form of a context diagram that describes the process and scope of the system. Data flow diagrams provide an overview of data flow in processes within the system and entity relationship diagrams describe the relationships that exist in entities. Blackbox testing has been carried out on 17 page groups and shows valid results and functions properly. The usability test resulted in a system usability scale value of 86.75 which indicates that the designed information system is acceptable with grade B and an excellent rating.
Enhance Knowledge Graph Embedding by Mixup

Knowledge graphs have important applications for many computational tasks, such as personalized recommendations, information search, and natural language processing. Knowledge graph embedding, which is to learn representations of nodes and relations, is very critical to facilitate these applications and thus has been studied extensively in the literature. Most existing knowledge graphs (e.g., Freebase) have a data scarcity issue, i.e., the number of observed triplets is much less than that of all possible pairs of nodes. While the data augmentation technique has been widely applied to addressing data scarcity in other domains (e.g., image data), there are few prior studies exploring it for knowledge graph embedding probably because the discrete data structure of knowledge graphs prohibits the employment of most data augmentation methods. To fill this research gap, this paper introduces a novel data augmentation framework, namely knowledge graph mixup (KG Mixup), to enhance knowledge graph embedding. Based on the proposed framework, we develop two specific methods: vanilla mixup and influence mixup. Both approaches generate virtual mixup triplets and incorporate them into the learning process through a new mixup loss function. While vanilla mixup generates virtual triplets based on a uniform distribution, the influence mixup approach employs the influence function to guide the generation of mixup samples. Experiments with multiple datasets have shown that both approaches significantly outperform knowledge graph embedding models trained by the ordinary training framework.
Wiser: Increasing Throughput in Payment Channel Networks with Transaction Aggregation

Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting technique is used to mitigate liquidity demands.