bluuebunny/crossref_metadata_2025 huggingface dataset:
Análise da produção científica e de escalas de mensuração do desempenho de internacionalização da universidade
Authors: De Souza Stefani , Barreto Moraes Mário César , Tezza Rafael , Pellizzaro de Lorenzi Cancellier Éverton Luís
Published: October 2022

A internacionalização universitária é um fenômeno multidimensional e, para mensurá-lo, diferentes medidas, quantitativas e qualitativas, são necessárias. Este artigo desenvolve análise das publicações sobre o construto ‘desempenho de internacionalização da universidade’. O objetivo é conhecer e analisar a produção cientítica e as escalas de mensuração do desempenho da internacionalização de universidades. Para tanto, foram realizadas buscas nas bases de dados SciELO, Scopus e Web of Science, bem como no Google Acadêmico, sem delimitação temporal. Ao final, foram analisados sete trabalhos que indicavam aderência ao construto. Verificou-se que os intrumentos mais comuns para medir o construto desempenho de internacionalização das universidades são os indexes, seguidos dos frameworks. Foram identificadas duas tentativas de construção de escalas por meio de indexes formativos – Internationalization Index of Higher Education in Turkey e Community College Internationalization Index. Contudo, não foi identificada uma escala de mensuração do desempenho de internacionalização das universidades consolidada na literatura da área.
Silêncio e narrativa: o foco narrativo e a relação de amor e ódio entre mãe e filha em Uma duas, de Eliane Brum
Authors: Do Prado Priscila Finger , Fechi Ribeiro Rebeca , Da Rosa Savian Julia
Published: October 2022

Em Uma Duas (2011), a narrativa se dá mediante alternância de narradores. As vozes que narram a trama são a voz de Laura, em terceira pessoa, a de Laura, em primeira pessoa, e a de Maria Lucia, em primeira pessoa. Partindo do pressuposto de que o ato de narrar implica uma escolha relacionada com um fim que se pretende atingir, o presente trabalho teve como objetivo: a) analisar o foco narrativo de Uma Duas; b) verificar quais as diferentes soluções narrativas que a autora encontrou para apresentar o pensamento das personagens e c) entender como elas se vinculam dentro da narração. Para isso, se utilizou como referencial teórico o livro Foco Narrativo, de Ligia Chiapinni Leite (1997), dando ênfase para a teoria das visões, de Jean Pouillon e para a tipologia do narrador, de Norman Friedman. Nossa hipótese é a de que a mudança de foco narrativo contribui para que o leitor tenha uma visão mais complexa do conflito entre mãe e filha, já que o silêncio de uma pode ser significado pela narrativa da outra, e vice-versa.
Paisajes en metamorfosis: Alejandro Obregón, lo barroco como modernidad
Authors: Monroy Ortiz Lisbeth Juliana
Published: October 2022

Este trabajo articula una lectura de algunos elementos de la poética del pintor Alejandro Obregón (Barcelona, 1920 - Cartagena, 1992) a partir de las nociones de paisaje, subjetividad y modernidad. El propósito de esta lectura es pensar la poética de Obregón como un acceso para considerar las emergencias de la modernidad en Colombia. Esta lectura dialoga críticamente con los trabajos de Marta Traba sobre la obra del pintor (1994, 2005 y 2016 [1977]). En sus ensayos, la obra de Obregón aparece como el “comienzo de la pintura moderna” en Colombia. Aunque los aportes de Traba son invaluables, en este ensayo reviso dos puntos de su lectura del trabajo de Obregón con el propósito de no reintroducir la jerarquización entre modernidades de centro y periferia, desde la que el arte latinoamericano queda perfilado como un arte epigonal. En contrapartida, desde los aportes teóricos de Ticio Escobar, Henri Meschonnic y Santiago Castro-Gómez, propongo entender las modernidades filosófica, histórica y estética europeas como modelos de racionalidad técnica, epistémica y estética que se auto postulan como centros de poder-saber que irradian sus efectos discursivos sobre las colonias/periferias, produciendo el espejismo de una jerarquía. La idea de lo barroco, tal como ha sido desarrollada por Sarduy y Mabel Moraña (que siguen a Lezama Lima), permite entender las emergencias de la modernidad en América Latina como estrategias de códigofagia que desafían las configuraciones de saber-poder eurocentradas.
A vertigem angélica. 80 anos da geopoética de Iommi & Bó
Authors: Antelo Raul
Published: October 2022

Em 1939, três artistas argentinos, Godofredo Iommi, Efrain Tomas Bó e Juan Raul Young junto a três brasileiros, Gerardo Melo Mourão, Abdias do Nascimiento e Napoleão Lopes Filho, planejavam viajar a Europa. Seus interesses literários conduziram-nos a criar uma irmandade poética e eles resolveram explorar as profundezas do continente para fundar uma cidade utópica. Eles ilustram de que modo a crítica latino-americana, mesmo nos anos 40, não estava disposta a aderir às categorias da filosofia europeia. Não era necessário ignorar as contribuições da cultura ocidental, mas antes induzir uma mescla entre heranças europeias e culturas milenares violentadas pela transculturação.
O desuso dos conceitos de natureza e natureza humana como critério para uma avaliação do estado atual da pesquisa jurídica e da ciência do direito
Authors: Rocha Rodrigo Couto Gondim
Published: October 2022

A análise do estado atual da pesquisa jurídica é obstaculizada pela existência de diferentes paradigma de investigação. Os trabalhos informados por paradigma divergentes dificilmente podem ser submetidos a critérios de análise comuns,e é uma exigência de razoabilidade entender que cada trabalho individual merece ser julgado segundo seus próprios pressupostos. Por essa razão, o presente artigo adota a estratégia de via negativa ou apofática, isto é, a estratégia de análise de elementos tipicamente faltosos, e não das qualidades existentes, compartilhadas por diferentes publicações científicas. As dimensões do presente escrito determinam um exame segundo a ausência de dois conceitos considerados imprescindíveis e, por essa razão, empregados durante toda a história do pensamento ético, político e jurídico: natureza e natureza humana. A relevância dos dois conceitos é demonstrada através do exame do seu papel na história das ideias. O exame conduzido a partir de tal ponto de partida determina a crítica do caráter da generalidade da produção acadêmica atual, que corresponde a juízo crítico do estado presente da ciência do direito.
Effects of perinatal disease activity on offspring’s growth in patients with systemic lupus erythematosus
Authors: Wei Yu , Zhu Yun , Tang Jia , Li Jie , Wang Zhiqun , Jin Ziyi , Liang Jun , Hua Bingzhu , Zhao Cheng , Wang Hong , Feng Xuebing
Published: October 2022

Introduction

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disorder with a variable clinical course, ranging from mild to severe forms. It mainly occurs in women, especially those of fertile age. The aim of the study was to systematically analyze the associations of perinatal disease activity with adverse outcomes of Chinese patients with SLE and their off-spring.

Material and methods

Data of prenatal SLE patients and healthy pregnant woman admitted to our hospital during the period October 2001 to January 2018 were retrospectively collected, and the status of offspring of SLE patients was followed up in March 2020. Disease activity was evaluated by
SLE disease activity index 2000 (SLEDAI-2k), and those with scores > 6 were defined as having active disease.

Results

In total, 198 deliveries of 194 SLE patients and 199 deliveries of healthy women were documented. Maternal and fetal adverse outcomes occurred in 74 (37.4%) and 90 (45.5%) deliveries of SLE patients, respectively, which were significantly higher than those of healthy subjects. Among SLE patients, the active group had higher rates of gestational hypertension (p < 0.001), reeclampsia/eclampsia (p < 0.001), low birth weight (p < 0.001), premature birth (p < 0.001) and fetal growth restriction (FGR) (p < 0.01) than the inactive group. Multivariate logistic analysis revealed that perinatal renal activity was associated with gestational hypertension (OR 4.43, p < 0.001), preeclampsia/ eclampsia (OR 9.14, p < 0.001), low birth weight (OR 2.24, p < 0.05) and premature birth (OR 4.20, p < 0.001). Compared with the general population, offspring of SLE patients had relatively high rates of eczema (50/142, 35.2%) and congenital heart disease (6/142, 4.2%), which were irrelevant to perinatal disease activity, but related to specific antibodies.

Conclusions

For perinatal women with SLE, renal activity is associated with a variety of adverse pregnancy outcomes. However, maternal perinatal dis�ease activity does not seem to affect the growth of their offspring
Bioinformatics analysis and experiments identify CD74 as a potential immune target in ovarian carcinoma
Authors: Zhang Yixin , Zhang Xinhui , Zhang Li , Zhao Yuli , Wang Sen , Feng Li
Published: October 2022

Introduction

Ovarian carcinoma (OC) is one of the most common malignancies in women worldwide.Immune checkpoint inhibitors are routinely used to treat OC, but with little clinical success.Here,we aimed to explore novel immune-effective biomarkers for OC management.

Material and methods

Datasets from The Cancer Genome Atlas,Genotype-Tissue Expression, and the Clinical Proteomic Tumor Analysis Consortium were used to identify hub genes significantly associated with CD8+ T effectors and immune checkpoint signatures and to explore the potential oncogenic role of CD74 in OC.The immune checkpoint construct inhibitor score (IMS) was constructed to predict immunotherapy responsiveness and prognosis.CD74 expression was further validated using immunohistochemistry in OC tissues.

Results

The yellow gene module showed a significant correlation with the CD8+ T effector and immune checkpoint profiles.Functional enrichment analysis showed that the yellow gene module was associated with immune response and antigen binding.Thus, IMS can accurately predict immunotherapy responsiveness and prognosis.In addition,CD74 was significantly upregulated at both the mRNA and protein levels in OC, and the high expression of the CD74 protein was related to the activation of various tumor-related pathways. Kaplan-Meier analysis showed that high CD74 expression correlated with poor prognosis in OC.Furthermore,CD74 expression remarkably correlated with inflammatory cytokines,immune cells,the tumor immune microenvironment, and immune checkpoints.Finally,CD74 expression in pericarcinomatous and cancer tissues from OC patients was verified via IHC analysis,which corresponded to the public database results (all p < 0.05).

Conclusions

CD74 may represent a novel immune biomarker to predict the prognosis of OC patients,as well as a potential therapeutic target related to immunotherapy,providing new ideas for the treatment of OC.
THE RELATIONSHIP BETWEEN KNOWLEDGE SHARING AND INNOVATION PERFORMANCE: A CASE OF MANUFACTURING SECTOR OF THE EMERGING ECONOMY
Authors: Fern Yeo Sook , Ashraf Ayesha , Batool Sana
Published: October 2022

This study investigates the relationship between knowledge sharing and innovation performance. Quantitative survey using cross-sectional sampling was used. Sample included 355 different staff members. A questionnaire that was self-administered and was based on previously used metrics was chosen. The percentage of those that responded was 73.9%. A statistical method known as partial least squares structural equation modelling was used in order to examine and evaluate hypotheses that were proposed. The software program SPSS and Smart PLS were used in order to do the analysis on the data that were obtained. Findings reveal that knowledge sharing has a significant effect on innovation performance. In addition to this, data demonstrated a favorable and substantial association between the sharing of knowledge and innovation of both products and processes. The findings of research have significant implications for further developing and enhancing the relation between knowledge sharing and innovative performance.
EPIZOOTIC SITUATION OF ANIMAL EMCAR (BlACKLEG) ON THE TERRITORY OF THE REPUBLIC OF KAZAKHSTAN FOR 2010- 2020
Authors: A. Abutalip , V. Laskavy , B. Aitzhanov , G. Baikadamova , A. Abubekova
Published: October 2022

The article presents the results of epizootological monitoring of emcar in animals based on the analysis of official reporting data of the veterinary service of the Republic of Kazakhstan for 2010-2020 and the results of the authors' own research. The main epizootological indicators of the epizootic process in the country have been established, the incidence areas that affect the unfavorable and tense epizootic situation for this disease have been determined. The focality indicator on emcar, for this period of time, averaged from 1 to 4 animals, which indicates the non-contagiousness of the infection. By studying the occurrence of the disease onthe territory of the Republic of Kazakhstan over the years, the frequency of epizooties has not been established. An analysis of the monthly dynamics of the disease focus showed that blackleg manifests itself in a greater number mainly in October and November. Emcar foci are least or not registered at all in the winter months and early spring (December, January, February and March). For the period from 2010 till 2020 (11 years) on the territory of the Republic of Kazakhstan, a high degree of incidence was observed in 3 regions (West Kazakhstan, East Kazakhstan, Zhambyl), an average degree - in 3 regions (Almaty, Aktobe, Pavlodar), and in other 4 regions (Kostanay, Atyrau, Karaganda, Akmola) - the degree of incidence was low. Based on assessing and retrospective analysis of the established epizootological indicators, epizootological zoning maps were developed showing the balckleg status of each region. According to the compiled map of the epizootic situation, for the period from 2010 to 2020, 71.4% of the territory of 10 regions of the Republic of Kazakhstan turned out to be disadvantaged for animal blackleg. The territory of the remaining 4 regions (28.6%) of the republic (Kyzylorda, North Kazakhstan, Mangystau, Turkestan) is safe from animal emcar. The obtained new scientific data on the dynamics of epizootic situation onemcar on the territory of the Republic of Kazakhstan can be used in drawing up a plan and carrying out preventive measures, development of effective measures to control animal emcar.
EFFECT OF COMMERCIAL BANKS’ CREDIT ALLOCATED TO AGRICULTURAL SECTOR ON ECONOMIC GROWTH IN NIGERIA
Authors: Mathias A. Chuba  , Inedu Hitlar
Published: October 2022

The empirical literatures on the effect of commercial banks’ credit allocated to agricultural sector on economic growth in Nigeria are mixed. The objective of this paper is to evaluate the effect of commercial banks’ credit allocated to agricultural sector on economic growth in Nigeria from 2014q1 to 2020q4 using error correction model (ECM). The results of the investigation revealed that commercial banks’ credit allocated to agricultural sector had a significant positive effect on economic growth in Nigeria. A 1% increase in commercial banks’ credit allocated to agricultural sector led to 23.37% increase in real GDP in Nigeria. The commercial banks’ credit allocated to other sectors did not spur economic growth in Nigeria. The agricultural sector is the backbone of Nigerian economy. The apex bank should ensure that the commercial banks are efficient in the allocation of resources. The Central Bank of Nigeria should direct the commercial banks to allocate the highest percentage of their credit to agricultural sector in order to achieve a sustainable agri-food system and economic growth in Nigeria.

Keywords: Institutions and Economic Growth, Error Correction Model, Nigeria.

JEL Classification: E51, O43, Q14, C24
Chatbot System for Mental Health in Bahasa Malaysia
Authors: Ismael Muhammad Imran , Nik Hashim Nik Nur Wahidah , Mohd Shah Nur Syahirah , Mohd Munir Nur Syuhada
Published: October 2022

Chatbot has been the driving force of modern communication for business, customer service and even mental healthcare. At the same time, there are not many research and project regarding mental health chatbots in Bahasa Malaysia. This project focuses on developing a chatbot application for mental healthcare in Bahasa Malaysia. This chatbot system is integrated with artificial intelligence and natural language processing. This chatbot utilize the feedforward neural network model to train the datasets. Apart from the backend of the application, Kivy and KivyMD are used to create the app's graphical user interface.
Obturator nerve endometriosis: A systematic review of the literature
Authors: Kale A , Aboalhasan Y , Gündoğdu E.C. , Usta T , Oral E
Published: October 2022

Background: Symptomatic obturator nerve endometriosis is a rare condition. In this paper, we aim to review and discuss the characteristics of obturator nerve endometriosis in light of current literature.

Methods: An electronic search was conducted using the PubMed/Medline database.

Results: Symptomatic obturator nerve endometriosis is rare; only 8 cases have been reported in the literature. Symptoms including difficulty walking, weak thigh adduction and pain in the inner thigh, which are all related to obturator nerve function, could be seen in the case of the entrapment of the nerve by endometrial nodules. A history of recurrent symptoms during menstrual cycles and physical examination, combined with appropriate radiologic imaging, led to a suspicion of obturator nerve involvement.

Conclusion: Early diagnosis and surgical treatment of obturator nerve endometriosis is essential to minimise the nerve damage caused by recurrent cycles of bleeding and fibrosis, which are characteristics of endometriosis. The laparoscopic minimally invasive technique is feasible for the surgery of obturator nerve endometriosis. It offers the advantage of precise discrimination of vital structures and excellent access to deep anatomic sites.

What is new? Obturator nerve endometriosis may be a severe cause of chronic pelvic pain in women of reproductive age. Treatment may be achieved surgically and in experienced hands, laparoscopic surgery would be the preferred choice.
Corporate Governance Codes in Pakistan: A Review
Authors: Asghar Tayyaba Noor , Mortimer Professor Tom , Bilal Muhammad
Published: October 2022

This article examines the current corporate governance laws and rules in Pakistan. Strong corporate governance is crucial for market stability and investor confidence in any nation, developed or underdeveloped. In Pakistan, a Corporate Governance Code was first established in 2002. Later updates occurred in 2012, 2017 and finally in 2019. Although corporate governance practices and concepts are well established across most of the world, Pakistan's corporate governance literature is still limited, and the current rule, as it was enacted in September 2019, hasn't received any commentary or analysis. This research aims to bridge this gap by revealing the evolution of Pakistan’s corporate governance and contributing to the continuing discussion on the effect of the fresh legislation using a comparative methodology and a documentary analysis of the changes.
The Reception of Romanian Literature from the Republic of Moldova in Ukraine: Plurigenural Anthologies
Authors:   ", Apetri Dumitru ,   Bogdan Petriceicu-Hasdeu",   Institute of Romanian Philology, Republic of Moldova
Published: October 2022

In the present article, the author highlights an important aspect of the RomanianUkrainian literary dialogue: the editing by Ukrainians in the years from 1953 to 1977 of four plurigenural anthological collections from the literary creation and partly from the folklore of the Republic of Moldova. These editions are seen by the researcher as a significant contribution to the above mentioned dialogue. But the dominance of the ideological dictatorship in that period left its negative mark on several works included in anthologies – a fact that determined a value discernment of the material in each editorial unit. This action is accomplished in the hope that the dialogue discussed will be reopened in the upcoming years. Therefore, the author’s comments pursued several purposes: listing the writings that have devalued in the meantime showing the causes of this phenomenon; indication of the selection criteria and directions for the development of new volumes such as plurigenural anthologies.
PELATIHAN PENGOLAHAN DATA PENELITIAN BISNIS PADA MAHASISWA SEKOLAH TINGGI ILMU EKONOMI WIYATAMANDALA DENGAN MENGGUNAKAN APLIKASI SPSS (STATISTICAL PRODUCT AND SERVICE SOLUTIONS)
Authors: Nurhalim Andres Dharma , Rohmanuddin Rohmanuddin
Published: October 2022

In data processing activities, it is known that there are still many students who do not understand the SPSS program in processing the data used to compose their final project. This is experienced by students of the management and accounting study program at the Wiyatamandala School of Economics where there are still many who do not understand how to operate the SPSS program. Therefore, it is necessary to introduce technology-based learning media related to the SPSS program through training. This training is part of community service activities that aim to provide additional knowledge, improve skills, train students' creative thinking and make it easier to determine the statistical tests used in compiling the final project. After the training was completed, the students seemed very enthusiastic, many of them even asked questions. Students also gave a good appreciation of the training activities and hoped that more activities related to data analysis would be held in the future. This training activity gives more value to students, especially in completing their final project
PELATIHAN SPSS PADA MAHASISWA TINGKAT AKHIR JURUSAN AKUNTANSI DAN MANAJEMEN SEKOLAH TINGGI ILMU EKONOMI WIYATAMANDALA
Authors: Nurhalim Andres Dharma , Puspita Shandy
Published: October 2022

Research data processing becomes a frightening specter for students because of limited knowledge about data processing applications and how to process the data itself. The purpose of this service activity is to increase students' understanding, knowledge and skills in using applications/software for quantitative data processing, namely SPSS. The SPSS used in this service activity uses version 25 which is a software program that aims to analyze data and perform statistical calculations both parametric and non-parametric. This service activity uses lecture, discussion and direct practice/practice methods. The target audience for this activity is final year students who are preparing a thesis
WORKSHOP DIGITAL MARKETING DAN PENYUSUNAN LAPORAN KEUANGAN BAGI SISWA SMKN 51 JAKARTA
Authors: Indriastuti Mila , Akbari Deni Adha , Widarey Atsna Virayani Yusriy , Maulana Galang , Munawati Afdila , Chassman Piedra Volada Maynard , Fahrini Anisa Dwi
Published: October 2022

In facts, two thirds of Gen-Z or millennials in Asia Pacific have a dream of becoming an entrepreneur. Therefore, the focus of the world of education today is to provide skills for the younger generation to become entrepreneurs, especially for vocational high schools. Digital marketing skills and the preparation of financial reports are the keys to success in winning business competition. Therefore, the community service is focused on providing digital marketing workshops and preparing financial reports both manually and digitally. The object is students majoring in accounting at SMKN 51 Jakarta. The results of the workshop conducted by the PKM 27 Uhamka team proved to have a significant impact on students' abilities in entrepreneurship using digital marketing technology and preparing financial reports. There was an increase in the score of more than 80% in the post-test carried out by SMKN 51 Jakarta students.
PELATIHAN MICROSOFT EXCEL UNTUK ADMINISTRASI BISNIS DI BANK JASA JAKARTA
Authors: Wahjudi Januar
Published: October 2022

This community service aims to provide information technology training, in order to improve the ability of Human Resources at Bank Jasa Jakarta, especially the use of Microsoft Excel in completing daily tasks. The benefits of this service in the form of training are expected that participants will experience an increase in the knowledge and skills needed in utilizing spreadsheet applications, especially Microsoft Excel in completing work, compiling reports, and compiling presentations in daily work routines. This community service in the form of training is carried out using presentation methods and guided practice step by step, where each participant carries out hands-on practice by being given materials in the form of electronic books, presentation materials for training guides, and worksheet files according to the training guidelines. Data on the development of the training participants were obtained from the initial questionnaire and evaluation questionnaire. From the data obtained information that the training has been carried out well, and the training materials have been well understood by the participants and participants have increased knowledge of the use of spreadsheet applications and expect advanced training topics
Optimal wireless rate and power control in the presence of jammers using reinforcement learning
Authors: Raji Fadlullah , Miao Lei
Published: October 2022

Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from other nodes, quality of the wireless channel, battery status, etc. The reward function is normalized and does not involve unit conversion. It can be used to train three different types of agents: throughput-critical, energy-critical, and throughput and energy balanced. Using the NS-3 network simulation software, we implement and train these agents in an 802.11ac network with the presence of a jammer. We then test the agents with two jamming nodes interfering with the packets received at the receiver. We compare the performance of our RL optimal policies with the popular Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and slightly higher energy when using the throughput and energy balanced reward function. Although our discussion is focused on 802.11ac networks, our method is readily applicable to other types of wireless networks.
Experimental evaluation of multi-PHY 6TiSCH networks
Authors: Lekic Milica , Gardasevic Gordana , Mladen Milan
Published: October 2022

The architectural design of Wireless Sensor Networks (WSNs) for the Industrial Internet of Things (IIoT) applications requires the careful planning and selection of an appropriate operational strategy. Harmonization of standards is crucial to ensure easier certification and commercialization of IIoT solutions. The ongoing research activities are directed toward designing agile, reliable, and secure transmission technologies and protocols. Recently, Time Slotted Channel Hopping (TSCH) standardization bodies have started to consider support for multiple physical layers thus accommodating a wide range of applications. This paper presents the results of the extensive experimental measurement campaign to study the performance of the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) network while supporting multiple physical layers (PHYs). For measurement purposes, all experiments were performed on OpenMote-B hardware. These devices are equipped with an Atmel AT86RF215 dual radio transceiver implementing IEEE 802.15.4g. The performance evaluation is provided for the following metrics: network formation time, Packet Delivery Ratio (PDR), latency, and duty cycle. Results are encouraging, particularly in terms of high PDR for all tested PHYs. Performance evaluation indicates the importance of proper node positioning, link quality estimation and careful selection of network parameters. Moreover, collected experimental results create a dataset that provides insights into the tested PHYs' performance and their potential for indoor 6TiSCH networking.
Low-precision deep-learning-based automatic modulation recognition system
Authors: Kumar Satish , Agarwal Aakash , Varshney Neeraj , Mahapatra Rajarshi
Published: October 2022

Convolution Neural Network (CNN)-based deep learning models have recently been employed in Automated Modulation Classification (AMC) systems, with excellent results. However, hardware deployment of these CNN-based AMC models is very difficult due to their large size, floating point weights and activations, and real-time processing requirements in hardware such as Field Programmable Gate Arrays (FPGAs). In this study, we designed CNN-based AMC techniques for complex-valued temporal radio signal domains and made them less complex with a small memory footprint for FPGA implementation. This work mainly focuses on quantized CNN, low precision mathematics, and quantization-aware CNN training to overcome the problem of larger model sizes, floating-point weights, and activations. Low precision weights, activations, and quantized CNN, on the other hand, have a considerable impact on the accuracy of the model. Thus, we propose an iterative pruning-based training mechanism to maintain the overall accuracy above a certain threshold while decreasing the model size for hardware implementation. The proposed schemes are 21.55 times less complex and achieve at least 1.6% higher accuracy than the baseline. Moreover, results show that our convolution layer-based Quantized Modulation Classification Network (QMCNet) with pruning has 92.01% less multiply-accumulate bit operations (bit_operations), 61.39% less activation bits, and 87.58% less weight bits than the 8 bit quantized baseline model whereas the quantized and pruned Residual-Unit based model (RUNet) has 95.36% less bit_operations, 29.97% less activation bits and 98.22% less weight bits than the 8 bit quantized baseline model.
Neural network compression with feedback magnitude pruning for automatic modulation classification
Authors: Krzyston Jakob , Bhattacharjea Rajib , Stark Andrew
Published: October 2022

In the past few years, there have been numerous demonstrations of neural networks outperforming traditional signal processing methods in communications, notably for Automatic Modulation Classification (AMC). Despite the increase in accuracy, these algorithms are notoriously infeasible for integrating into edge computing applications. In this work, we propose an enhanced version of a simple neural network pruning technique, Iterative Magnitude Pruning (IMP), called Feedback Magnitude Pruning (FMP) and demonstrate its effectiveness for the "Lightning-Fast Modulation Classification with Hardware-Effficient Neural Network" 2021 AI for Good: Machine Learning in 5G Challenge hosted by the International Telecommunications Union (ITU) and Xilinx. IMP achieved a compression ratio of 9.313, while our proposed FMP achieved a compression ratio of 831 and normalized cost of 0.0419. Our FMP result was awarded second place, demonstrating the compression and classification accuracy benefits of pruning with feedback.
BacalhauNet: A tiny CNN for lightning-fast modulation classification
Authors: Rosa Jose , Granhao Daniel , Carvalho Guilherme , Gonçalves Tiago , Figueiredo Monica , Bento Luis Conde , Paulino Nuno , Pessoa Luis M.
Published: October 2022

Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI/ML in 5G Challenge 2021, achieving 61.73× compression over the challenge baseline and being over 2.6× better than the second best submission. The source code of this work is publicly available at github.com/ITU-AI- ML-in-5G-Challenge/ITU-ML5G-PS-007-BacalhauNet.
Network anomaly detection based on keyword embedding log
Authors: Song Yong , Yan Zhiwei , Qin Yukun , Xie Yuchen , Ye Xiaozhou , Ouyang Ye
Published: October 2022

Log anomaly detection is an important and challenging task in the field of Artificial Intelligence for IT Operations (AIOps). Logs that record important runtime information are widely used for troubleshooting purposes. There have been many studies that use log data to construct deep learning methods for detecting system anomalies, which are usually based on log parsing. However, they ignore the effect of keywords that are promising for system status analysis. Here, we propose KELog (Keyword Embedding Log), a novel log anomaly detection approach that utilizes keyword information. We build a keyword library by keyword information extraction and fuse them into log representations. In this way, KELog can raise the reliability of anomaly detection. The experimental results on a real-world log dataset of a communications operator show that the F1 score of our proposed KELog method achieves a maximum increase of 0.341 compared with the commonly used machine learning algorithms (PCA, SVM, Invaiant Mining) and a maximum increase of 0.039 compared with deep learning algorithms (DeepLog, LogBERT) respectively. In 2021, ITU launched the second ITU AI/ML in 5G Challenge. We used KELog to participate in the thematic track of the Artificial Intelligence Innovation and Application Competition in the China Division, and won first place with a full F1 score.
Non-coherent modulation with random phase configurations in RIS-empowered cellular MIMO systems
Authors: Chen.Hu Kun , Alexandropoulos G. C. , Armada Ana Garcia
Published: October 2022

The Reconfigurable Intelligent Surface (RIS) constitutes one of the prominent technologies for the 6th Generation (6G) of wireless communications. It is envisioned to enhance signal coverage in cases where obstacles block direct communication from Base Stations (BSs), and when high carrier frequencies are used that are sensitive to attenuation losses. In the literature, the exploitation of RISs based on traditional coherent demodulation, relies on the availability of accurate Channel State Information (CSI). Given that CSI estimation, a multi-antenna BS or a dedicated orchestration controller jointly computes the pre-coder/combiner and the RIS configuration. The latter tasks require a significant amount of time and resources, which may not be affordable when the channel is time-varying or the CSI is not accurate enough. In this paper, we consider the uplink between a single-antenna user and a multi-antenna BS, and present a novel RIS-empowered Orthogonal Frequency Division Multiplexing (OFDM) communication system, which is based on differential phase shift keying combined with random phase configurations at the RIS, thus, avoiding the channel estimation and any complex optimization processes. This feature renders our RIS-enabled system operation proposal suitable for high noise and/or mobility scenarios. Considering both an idealistic and a realistic channel model, analytical expressions for the Signal-to-Interference and Noise Ratio (SINR) and the Symbol Error Probability (SEP) of the proposed non-coherent RIS-empowered communication system are presented. Our extensive computer simulation results verify the accuracy of the presented analysis and showcase the proposed system's performance superiority over coherent demodulation in different mobility and spatial correlation scenarios.
4G/5G Cell-level multi-indicator forecasting based on dense-MLP
Authors: Yin Jiacheng , Li Wenwen , Wang Xidong , Ye Xiaozhou , Ouyang Ye
Published: October 2022

With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated. It is necessary for operators to solve the congestion in advance and effectively to guarantee the quality of user experience. Cell-level multi-indicator forecasting is the foundation task for proactive complex network optimization. In this paper, we propose the 4G/5G Cell-level multi-indicator forecasting method based on the dense-Multi-Layer Perceptron (MLP) neural network, which adds additional fully-connected layers between non-adjacent layers in an MLP network. The model forecasted the following week's traffic indicators of 13000 cells according to the six-month historical indicators of 65000 cells in the 4G&5G network, which got the highest weighted MAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in 5G Challenge 2021. Furthermore, the proposed model has been integrated into the AsiaInfo 4G/5G energy-saving system and deployed in Jiangsu Province of China.
Decision tree-based radio link failure prediction for 5G communication reliability
Authors: Sivakumar Nethraa , Srinivasan Pooja , Viswanath Nikhil , N Venkateswaran
Published: October 2022

Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. Network disruption may occur due to unexpected variations in environmental conditions such as weather, wind, and natural or man-made surroundings, and the influence of the defect is quite severe. Prediction of such undesirable events at a low cost can boost 5G communication reliability, massive network capacity, and decreased latency. This research work makes use of novel preprocessing and feature engineering techniques, followed by a trained decision tree model to predict the occurrence of Radio Link Failure (RLF). This system is designed to predict RLF for not just the next day, but also any of the next 5 days. This prediction supports reliance and increasing demand for good Internet connectivity. In order to achieve accurate RLF prediction, comprehensive data has been used which undergoes preprocessing. To account for the influence of surrounding weather conditions on radio links, the proposed system makes use of information from the past i.e., previous RLFs, and the information from the future i.e., the weather forecast from the weather station around the radio link station. The decision tree model was trained with the integration of feature engineering. A macro-averaged F1-score of 70% and 77% were obtained for RLF prediction for the next day and RLF prediction for the next 5 days, respectively. The results show improvement in performance after the incorporation of feature engineering in the pipeline. Further, an additional metric termed G-Mean is introduced in the paper. Owing to the high imbalance in the dataset, this metric was found to provide a more realistic representation of the results. The G-Mean score was found to be 98.69% and 92.89% for RLF prediction for the next day and RLF prediction for the next 5 days, respectively.
Using manufacturer usage descriptions for IoT network security: An experimental investigation of smart home network devices
Authors: Darazam Milad Kazemi , Angin Pelin , Acarturk Cengiz
Published: October 2022

The Internet of Things (IoT) has shown significant growth in the past decades. Recently, IoT networks have been subject to cybersecurity threats on multiple fronts. A lack of improvement in IoT infrastructures' cybersecurity may result in challenges with a broad impact, such as DDoS attacks that target global DNS services. This paper reviews existing solutions to mitigate attacks on and from IoT networks. As a specific mitigation approach, we propose the use of a standardized whitelisting method, namely Manufacturer Usage Description (MUD), which provides enhanced explainability over machine learning-based approaches and is complementary to them. For evaluating the use of MUD in IoT networks, we report a case study, which we conducted through traffic analysis of two IoT devices by detecting recognizable and distinctive traffic patterns, which demonstrate the feasibility of MUD-based intrusion prevention.
Research on network cloud equipment anomaly and root cause analysis
Authors: Zou Dandan , Ding Jianbing , Wang Xidong , Ye Xiaozhou , Ouyang Ye
Published: October 2022

With the development of 5G communication technology, a cloud computing system has become a trend. However, with the expansion of the scale of deployment and the increase in framework complexity, ensuring the security and stability of cloud-based systems has become a serious challenge. In a real business environment, existing algorithms are powerless in the face of current problems, such as complex types of abnormal logs, inaccurate time information, and the lack of key information. This paper proposes network cloud equipment anomaly detection and a root cause analysis scheme based on large-scale logs in distributed cluster systems. The scheme uses unsupervised integrated learning, keyword search, and root cause generalization to analyze logs, accurately find anomalies and locate root causes. The F1 score in log anomaly detection is 0.962, and the accuracy in root cause location of anomalies is 0.849. In the ITU AI/ML in 5G Challenge 2021, the solution got the highest final score 93.904 in the China Mobile problem statement of network cloud equipment anomaly and root cause analysis. Furthermore, the scheme has been deployed on China Mobile's 5G network management system and achieves the detection and location of anomalies under intelligent operation.
A probabilistic refined policy for topology-independent medium access control in ad hoc network environments
Authors: Dragonas Vasileios , Tsoumanis Georgios , Oikonomou Konstantinos
Published: October 2022

Modern network environments are starting to engulf billions of different interconnected devices that support a wide range of applications. Depending on the case, these environments range from static (e.g., wireless sensors) to highly dynamic (e.g., vehicular networks) with respect to topology changes and have different constraints for throughput, time delay, energy consumption etc. Supporting such applications in a topology-varying ad hoc environment is a challenging task. Thus, TDMA-based MAC policies are revisited here and a new policy, i.e., the refined policy is proposed, which builds and improves on the topology-independent policies that appear in the literature. In particular, an individual access probability is introduced that is distributively calculated by each node to access time slots that may result to collisions, but if not then unused network resources will be utilized, thus, increasing throughput. The key idea under the refined policy is to identify and refrain from transmitting during slots that collisions are likely to appear. An analytical expression for the individual access probability is also derived here. It is also shown through simulation experiments that energy consumption is also reduced in addition to throughput incremented under the proposed policy.
RFNet: Fast and efficient neural network for modulation classification of radio frequency signals
Authors: Chegini Mohammad , Shiri Pouya , Baniasadi Amirali
Published: October 2022

Automatic Modulation Classification (AMC) is a well-known problem in the Radio Frequency (RF) domain. Solving this problem requires determining the modulation of an RF signal. Once the modulation is determined, the signal could be demodulated making it possible to analyse the signal for various purposes. Deep Neural Networks (DNNs) have recently proven to be successful in solving this problem efficiently. However, since deep networks consist of several layers resulting in a high number of trainable parameters, the hardware implementations of these solutions are resource-demanding. In order to address this challenge, we propose an efficient deep neural network referred to as RFNet to tackle the AMC problem efficiently. This network introduces the novel Multiscale Convolutional (MSC) layer to extract robust features in different resolutions. In addition, the network takes advantage of several Separable Convolution Blocks (SCB). These blocks employ pointwise and depth-wise convolutions to reduce network complexity. We further introduce RFNet+ and RFNet++ as extensions of RFNet with fewer number of parameters. These variants include fewer floating-point operations and hence a lower hardware implementation cost. Experimental results using the challenging RadioML 2018 dataset show that RFNet-32++ achieves an average classification accuracy of 56.09% over all Signal-to-Noise Ratios (SNRs) and an accuracy of 92.21% in+20dB SNR using only 3.1K parameters. The small number of parameters makes the RFNet family a promising solution for future AMC systems.
Promoting Open Science through bibliometrics
Authors: Bracco Laetitia
Published: October 2022

In order to assess the progress of Open Science in France, the French Ministry of Higher Education, Research and Innovation published the French Open Science Monitor in 2019. Even if this tool has a bias, for only the publications with a DOI can be considered, thus promoting article-dominant research communities, its indicators are trustworthy and reliable. The University of Lorraine was the very first institution to reuse the National Monitor in order to create a new version at the scale of one university in 2020. Since its release, the Lorraine Open Science Monitor has been reused by many other institutions. In 2022, the French Open Science Monitor further evolved, enabling new insights on open science. The Lorraine Open Science Monitor has also evolved since it began. This paper details how the initial code for the Lorraine Open Science Monitor was developed and disseminated. It then outlines plans for development in the next few years.
Curriculum Design of Preservice Teacher Education for Indigenization of Elementary School Science in the Philippines
Authors:   College of Education, University of the Philippines, Diliman, Quezon City, Philippines, Fajardo Amelia
Published: October 2022

This paper presents proposed changes in the pre-service teacher education program for the development of skills for indigenization of elementary school science. The proposal emphasizes the need for the development of more specific competence for teaching science to indigenous people or diverse culture. There should be conscious effort to develop awareness of one’s attitude towards ethnocultural groups in the foundation courses (sociology, philosophy and psychology). Moreover, there’s also a need to develop skills in designing instruction, developing instructional materials using local materials to make science teaching relevant to the indigenous people and learners of diverse culture. It is also important that field experience in schools with indigenous people and diverse culture be integrated in the program.
Social Impact Bonds: An Innovative Mechanism of Funding for Social Problems in Developing Countries
Authors: Roy Rabindra Nath , Sinha Rabindra Nath
Published: October 2022

In todayâ€™s complex world the government resources and charitable donations are insufficient to address the worldâ€™s social problems. An impact investment offers a new alternative for channeling private capital for social benefit .Social Impact Bonds (SIB) an innovative financial instrument which facilitates fund flows to address major social problems. It is a public-private partnership that drives government resources toward social programmes.