bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Pemahaman Dan Sanksi Pajak Terhadap Kepatuh an Wajib Pajak UMKM Dengan Pelayanan Fiskus Sebagai Variabel Moderasi
Authors: Marcel Kusuma Gumulya Sonny , Kusumawaty Mia , Handayani Mella , Hari Kurnia Krisna
Published: May 2023

ABSTRAK
 
Penelitian ini meneliti pengaruh pemahaman dan sanksi pajak terhadap kepatuhan WP. Variabel moderasi penelitian ini yaitu pelayanan fiskus. Penelitian menggunakan Pengumpulan sampel simple random sampling. Sampel penelitian didapatkan sebanyak 98 responden UMKM Ilir Timur Palembang. Analisis data memakai Structural Equation Model-Partial Least Square. Hasil penelitian menunjukkan bahwa sanksi pajak berpengaruh positif signifikan terhadap kepatuhan WP. Hasil lain menunjukkan pemahaman pajak tidak berpengaruh signifikan terhadap kepatuhan WP, pelayanan fiskus tidak memoderasi pemahaman pajak dan sanksi pajak
 
Kata Kunci: Pemahaman Pajak, Sanksi Pajak, Kepatuhan WP, Pelayanan Fiskus
 
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ABSTRACT
 
This research analyzes the effect of understanding and tax sanctions on taxpayer compliance. The moderating variable in this study is the tax administration service. The study used simple random sampling to collect samples. The research sample was obtained by 98 respondents from SME Ilir Timur Palembang. Analsys of research data using Structural Equation Model-Partial Least Square. The results of the study show that tax sanctions have a significant positive effect on taxpayer compliance. Other results show that tax understanding has no significant effect on taxpayer compliance, the tax authorities do not moderate tax understanding and tax sanctions 
Keywords: Tax Understanding, Tax Sanction, Taxpayer Complience, Fiscus Service
Analisis Pengaruh Brand Image, Customer Service, Dan E-Wom Terhadap Repurchase Intention Dengan Kepuasan Pelanggan Sebagai Variabel Mediasi
Authors: Nadya Ayu Apritama  , Ihwan Susila
Published: May 2023

ABSTRAK
 
Tujuan penelitian ini yaitu untuk menganalisis pengaruh Brand Image, Customer Service, dan E-wom terhadap Repurchase Intention dengan kepuasan pelanggan sebagai variabel mediasi. Jenis Penelitian ini menggunakan penelitian kuantitif kasual. Pengambilan sampel pada penelitian ini menggunakan teknik non probability sampling, sedangkan teknik sampling yang digunakan pada penelitian ini adalah purposive sampling. Metode pengumpulan data pada penelitian ini dengan kuesioner. Dalam penelitian ini peneliti menggunakan sampel sebanyak 212 responden. Pada tahap analisis peneliti menggunakan Smart PLS 3.0 dengan melihat outer model dan inner model. Hasil penelitian ini yaitu terdapat pengaruh Brand image, Customer Service dan E-wom terhadap Repurchase Intention dan kepuasan pelanggan berperan dalam memediasi antara Brand Image, Customer Service dan E-wom terhadap Repurchase Intention.
 
Keywords: Brand Image, Customer Service, E-Wom, Purchase Intention, Kepuasan Pelanggan, Fast Food
 
ABSTRACT
 
The purpose of this study was to analyze the effect of brand image, customer service, and E-wom on Repurchase Intention with customer satisfaction as a mediating variable. This type of research uses casual quantitative research. Sampling in this study used non-probability sampling techniques, while the sampling technique used in this study was purposive sampling. The data collection method in this study was a questionnaire. In this study, researchers used a sample of 212 respondents. At the analysis stage, researchers used Smart PLS 3.0 by looking at the outer model and inner model. The results of this study are that there is an effect of Brand image, Customer Service and E-wom on Repurchase Intention and customer satisfaction plays a role in mediating between Brand Image, Customer Service and E-wom on Repurchase Intention.
 
Keywords : Brand Image, Customer Service, E-Wom, Purchase Intention, Kepuasan Pelanggan, Fast Food
Pengaruh Likuiditas, Profitabilitas, Ukuran Perusahaan, dan Struktur Modal Terhadap Nilai Perusahaan (Studi Kasus Pada Perusahaan Sub Sektor Pariwisata, Hotel, dan Restoran yang Terdaftar Di Bursa Efek Indonesia (BEI) Tahun 2017-2021)
Authors: Dewi Permana Selly Arsita , Sudiyatno Bambang
Published: May 2023

ABSTRAK

 

Tujuan dari dilaksanakan kegiatan penelitian ini yakni guna melakukan sebuah analisis dan mengetahui sebuah efek atau pengaruh dari profibilitas, likuiditas, ukuran perusahaan, serta struktur modal di sebuah instansi atau perusahaan sub sektor pariwisata pada sebuah nilai instansi atau perusahaan, hotel serta restoran yang terdaftar pada BEI atau disebut dengan Bursa Efek Indonesia di periode 2017 sampai dengan 2021. Data sekunder dengan berbentuk sebuah laporan keuangan dengan perolehannya didapatkan dari BEI atau Bursa Efek Indonesia merupakan sebuah sumber data dari kegiatan penelitian ini. Didalam kegiatan penelitian ini teknik yang dipakai ialah teknik purposive sampling dengan sampel penelitiannya sejumlah 22 perusahaan. Sedangkan analisis regresi linier berganda ialah teknik analisis yang dipakai didalam kegiatan penelitian ini. Dari kegiatan penelitian ini hasilnya menyatakan bahwasannya dengan cara parsial likuiditas (CR) memberikan sebuah pengaruh baik serta signifikasi pada suatu nilai instansi, dengan cara parsial profitabilitas (ROA) serta struktur modal (DAR) memberikan sebuah pengaruh baik serta tidak tersignifikasi pada suatu nilai instansi, serta dengan cara parsial ukuran perusahaan (SIZE) tidak menimbulkan pengaruh negative serta signifikasi pada suatu nilai instansi.

 

Kata Kunci: Likuiditas, Profitabilitas, Ukuran Perusahaan, dan Struktur Modal dan Nilai Perusahaan

 

ABSTRACT

 

The purpose of carrying out this research activity is to carry out an analysis and find out an effect or influence of profitability, liquidity, company size, and capital structure in an agency or company in the tourism sub-sector on an agency or company value, hotels and restaurants listed on the IDX or known as the Indonesia Stock Exchange in the period 2017 to 2021. Secondary data in the form of a financial report obtained from the IDX or the Indonesian Stock Exchange is a source of data from this research activity. In this research activity, the technique used was a purposive sampling technique with a sample of 22 companies. While multiple linear regression analysis is the analysis technique used. From this research activity the results state that partially liquidity (CR) has a significant positive effect on a firm's value, by means of a partial profitability (ROA) and capital structure (DAR) provides a not significant positive influence on a firm's value, and by means of partial firm size (SIZE) does not have a significant negative effect on firm value.

 

Keywords: Liquidity, Profitability, Company Size, and Capital Structure and Company Value
НАПРЯМИ ІНСТИТУЦІОНАЛЬНИХ ЗМІН В СИСТЕМІ ДЕРЖАВНОГО РЕГУЛЮВАННЯ ПРОЦЕСІВ ДІДЖИТАЛІЗАЦІЇ
Authors: ЧАСТОКОЛЕНКО Ігор
Published: May 2023

У статті акцентовано увагу на процеси цифровізації економіки та їх місце у забезпеченні конкурентоспроможності країни. Виділено позитивні наслідки діджиталізації для розвитку національної економіки. Проведено аналіз останніх досліджень і публікацій стосовно проблем функціонування і розвитку діджиталізації та зроблено висновок про необхідність обґрунтування нових більш адаптованих до сучасних реалій інструментів та засобів державного регулювання розвитку сектора інформаційної економіки України. Визначено напрями інституціональних змін в системі державного регулювання процесів діджиталізації національної економіки України, які згруповано стосовно правового (формування повноцінного законодавства,  усунення правових обмежень, запровадження інституційно-правового механізму з розвитку з цифровізації) та організаційного (створення міжвідомчого органу, запровадження інституційно-організаційного механізму, активізація діджитал-співпраці, запровадження контролю та моніторингу процесів поширення діджиталізації) характеру. Поряд із напрямами ідентифіковано об’єкти врегулювання (інтелектуальний капітал, інфраструктура, освітній та науково-технічний розвиток), які мають визначальне значення при досягненні необхідних інституціональних змін в системі державного регулювання розвитку сектора інформаційної економіки України.
Reflexiones sobre la palabra en un libro de minificción de Westphalen
Authors: Doria Rodas Dany
Published: May 2023

El presente artículo tiene como objetivo analizar las reflexiones sobre la palabra y el lenguaje desarrolladas en Máximas y mínimas de sapiencia pedestre escuchadas al desgaire sin certificación de autenticidad (1982), libro de minificción del escritor peruano Emilio Adolfo Westphalen. Para ello, nos centramos en los textos «Ídolo», «Hojas secas» y «Error de cálculo», que abordan de manera más directa la relación del enunciador con las palabras y con la obra creada, más concretamente del poeta con la poesía, y desde una mirada crítica que evalúa el quehacer poético. Al respecto, cabe precisar que Westphalen, en toda su producción, tiene a la poesía como principal objeto de pensamiento y de representación, razón por la cual las diversas lecturas se han mantenido en el tema poético.           Aunque Máximas y mínimas de sapiencia pedestre escuchadas al desgaire sin certificación de autenticidad constituye un momento esencial en la obra ficcional de Westphalen, especialmente porque supone la adopción de la prosa brevísima como forma escritural predominante para incorporar una serie de recursos que el verso no le permitía, no ha recibido la atención debida de parte de la crítica especializada, concentrada en su mayoría en los dos primeros poemarios de Westphalen. Con la presente aproximación, se pretende también cubrir ese vacío en los estudios sobre la producción literaria de Westphalen.
conjunto textual “Tojjras” de Gamaliel Churata: entre los microrrelatos y los ichik willakuy
Authors: Vásquez Guevara Rony
Published: May 2023

El propósito de esta investigación es postular que algunos textos del conjunto denominado “Tojjras” de Gamaliel Churata exige ser leído como microtextos provenientes de una matriz occidental y/o andina, debido a la influencia de características vanguardistas que convocan su estructura (microrrelato) y elementos andinos que recaen en los temas desarrollados (ichik willakuy). Para alcanzar este objetivo, en primer lugar, se examinan si las “Tojjras” cumplen con los criterios de extensión, ficcionalidad y modo de representación, a efectos de establecer que son microrrelatos (breve, ficcional y narrativo); y, en segundo lugar, se analizan los temas desarrollados, a efectos de determinar si se corresponde con los abordados en los ichik willakuy. En consecuencia, se concluirá que algunas “Tojjras” son ficciones breves que pertenecen a la matriz occidental y andina de la ficción breve, es decir, estos son microrrelatos andinos de la vanguardia.
Augmented Pressure Distal Colostogram.
Authors: Khalaf Ahmed A.
Published: May 2023

Background: Anorectal malformations (ARMs) represent a complex group of congenital anomalies resulting from abnormal development of the hindgut, allantois and Mullerian duct, leading to incomplete or partial urorectal septal malformations .
Patients and methods: This is a prospective study including  sixty-five male patients with high ARMs  who were admitted to the department of  pediatric surgery in Children Welfare Teaching Hospital in Medical City Complex - Baghdad - Iraq ; during the period from April 2009 to November 2011 ; they were subjected to divided descending colostomy in the postnatal period ;Augmented-Pressure Distal Colostogram (APDC) was performed at a later age for demonstration of the level of rectal atresia and the presence of the recto-urinary fistulas which then were confirmed at operation. Objectives: The objective of this study is the accurate demonstration of the anatomy of the level of rectal atresia and any associated recto-urinary fistula by using APDC  for optimal surgical management using  APDC .
Results: Rectourinary fistulas were demonstrated in 52 patients (80%) by APDC  technique and were confirmed at operation in each case. In the remaining 13 patients (20%), this technique failed to demonstrate a fistula, in 8 of them no fistula was identified during operation or thereafter , while in the remaining 5 patients fistulas were detected during operation .
Conclusion: The  APDC  readily demonstrated  the level of the rectal atresia and the associated recto-urinary fistulas in the majority of the patients , providing a road map for definitive surgical repair of ARMs.
Startup Project Development: Travel Schedule Management App “Triplanner”
Authors: Hakim Arif Rahman , Amisesa Bremania Titis , Abundarma Magdalena Rosa , Santoso Adhi Setyo
Published: May 2023

The term "startup" is most often used for companies that do business online. Several business startups in the field of online travel agents (OTA) in Indonesia are already familiar among Indonesian people. Triplanner as a newcomer startup wants to enliven the development of an online-based tourism service business. This is done with the aim of making it easier for tourists to organize their travel schedules easily and economically without the help of conventional travel agents. The travel itinerary feature, which has never been present in Indonesia before, will be the focus of Triplanner to get a positive response from the public.

A travel itinerary is a list of travel activities that includes an estimate of the costs required during the holidays, which includes a list of destinations, a schedule of activities, transportation, and others. In addition to maximizing time, a travel itinerary will also help manage cost expenses during the trip. In this study, we will develop and introduce the features of the Triplanner application to the market. Therefore, we need the right product features to answer the needs of traveling.

The design thinking method will be implemented to capture consumer needs and wants for the product. After the development process, the Triplanner application prototype has been successfully created using Marvel and a landing page using the Wix web builder. Respondents who have tried the prototype suggest that the choice of application features and attraction references can be increased.

The research also discusses business analysis, including marketing for growth, marketing plans, focusing on increasing user enthusiasm for features, and improving product feature services. Our financial scheme would start from bootstrapping, then continue to seed capital, and funding series in forward. This project is estimated to have an ROI of 120% and will be payback within a period of 6 years.
Neoliberalism, Strong State and Democracy
Authors: Gabrisch Hubert
Published: May 2023

The study serves to clarify the term "neoliberalism", which is unclearly defined in academic literature, politics, and everyday life. Its thesis is that neoliberalism is predominantly characterised by its state and constitutional agenda, the basis of which is a theory of the market that departs from classical and neoclassical theory. Methodologically, the study is based on the relationships between economic and political theory. It concludes that, from a neoliberal perspective, state and quasi-state institutions independent of societal interests can best ensure the efficiency and stability of the capitalist system globally, regionally and nationally as "well-meaning dictators".
Psychometric of the interpersonal communication skills scale: A confirmatory factor analysis
Authors: Salahshouri Arash , Fathi Sheida , Jiba Mostafa , Mohamadian Hashem
Published: May 2023

BACKGROUND: In order to improve patient outcomes, healthcare professionals' communication skills need to be evaluated. A valid, culturally appropriate, and endemic scale for assessing interpersonal communication skills among the staff of comprehensive health centers in Iran was examined to achieve such an objective.

METHODS: A convenience sampling was used in November and December 2021 to collect information from 170 voluntary healthcare staff. The questionnaire had 30 items and seven factors. First- and second-order confirmatory factor analysis was used to validate the scale. Model fit was evaluated with GFI, AGFI, CFI, NFI, SRMR, RMSEA, and CN indices during confirmatory factor analysis. Discriminant validity was measured through Fronell-Larcker Criterion. Data were analyzed using Lisrel 8.8 & Smartpls 3.2.8 software.

RESULTS: The predictive power of the model using the Q2-index based on the blindfolding test was equal to % 44. The first-order CFA results indicated that the indices had an acceptable value (χ2= 767.17; DF=375; CFI=0.98; GFI=0.82; AGFI=0.80; NFI=0.97; SRMR=0.22; CN= 127.83; RMSEA=0.068). Also, the fit indices of the second-order measurement model demonstrated the the adequacy and their desirability. (χ2= 797.24; DF=381; CFI=0.98; GFI=0.82; AGFI=0.78; NFI=0.97; SRMR=0.059; CN= 127.33; RMSEA=0.068). In terms of the importance-performance map analysis, the general and listening skills had the highest scores.

CONCLUSION: To develop interpersonal communication skills among healthcare staff, this scale could be useful. The results will need to be compared after further evaluation. It proposes to replicate skills training programs in other populations to determine their effectiveness.
An Analysis of the Continuum Hypothesis
Authors: Powell Andrew
Published: May 2023

This paper analyses the Continuum Hypothesis, that the cardinality of a set of real numbers is either finite, countably infinite or the same as the cardinality of the set of all real numbers. It argues that the Continuum Hypothesis makes sense as a very strong choice principle that is a maximally efficient as a principle for deciding whether a real number is in a set of real numbers, in the sense that it is uniform in deciding membership for every real number in a countable number of steps. The approach taken is to analyze the intended meaning of the Continuum Hypothesis rather than to analyze models of set theory in which CH is true or false and to use those models to support or reject the Continuum Hypothesis.
Another rate view on autocatalytic reactions
Authors: Pekař Miloslav
Published: May 2023

Discussions on the proper conceptualization and definition of autocatalysis are still lively. Several examples of autocatalytic reaction schemes are analyzed for their concentration and rate time profiles using computer modeling. Autocatalytic features should be inherent in a relevant scheme and its real demonstration further depends on “proper parametrization”, i.e., proper values for the rate parameters (their ratios) and relevant concentrations. Flow-through reactors bring a new complication due to the effects of inlet feed on reaction rates. When discussing kinetic features, one should take into account both concentration and rate profiles.
COMPLICATION AND SAFETY OF TRANSPERINEAL PROSTATE BIOPSY USING NOVEL AFFORDABLE VY PROBE (TPPB-VY) IN CLINICAL PRACTICE : A PILOT STUDY
Authors: Ali Ridho Al Haddar  , Pramod Sawkar Vijay , Ferry Safriadi
Published: May 2023

Objective: This study was aimed to know the complication and safety of TPPB-VY in clinical practice. Material & Methods: TPPB were performed in 34 patients between January 2019 and Juni 2021. Ethical approval of this study was sought from the hospital authorities (IRB number: LB.02.01/X.6.5/ 55/2020). Patients underwent systematic 10-12 core TPPB depend on the prostate volume with intravenous light sedation. The patient was included to the study if PSA ≥ 4 ng/ml and/or Digital Rectal Examination (DRE) results suggestive of prostate cancer. Procedures were performed without any antibiotic prophylaxis or an enema before the procedure and post procedure the patients just get an oral fluoroquinolones and low potent analgetics (if needed). The complication include pain level post procedure (using VAS), perineal hematoma, fever and retention post biopsy were recorded. Results: In all, 34 patients were included in the study. The result showed that perineal pain perceived by patients post procedure were 76.47% (26 out of 34 patients) but the VAS score just mild around 0-2. There were 23.53% (8 out of 34 patients) hematuria, 5.88% (2 out of 34 patients) acute urinary retention and 2.9 % (1 out of 34 patients) perineal hematoma. No fever or clinical infection was observed and 82.35% (28 out of 34 patients) patients presented with minor complications (ClavienDindo I). Only one of the patients were hospitalized due to the post-biopsy complication that need intervention. Conclusion: This is the first study TPPB using Novel Affordable VY probe. The results show clinically insignificant complication without infection. TPPB using Novel Affordable VY probe is feasible and safe.
IMPACT OF COVID – 19 ON UROLOGICAL INTERVENTIONS AT TERTIARY REFERRAL HOSPITAL
Authors: Subihardi Lalu Muhammad Editia , Renaldo Johan , Soebadi Mohammad Ayodhia
Published: May 2023

Objective: This study aims to identify and evaluate the impact of COVID-19 on urological interventions at Soetomo general-academic hospital as the tertiary referral center. Material & Methods: This study is a descriptive-retrospective, single-center study that included all confirmed cases of COVID-19 in urological patients from March 1, 2020 until December 31, 2021 at Soetomo general-academic hospital. We investigated these patients' characteristics, focusing on the demography, clinical data, and eventual outcome. Patient's gender, age, primary urological presentation, COVID-19-related symptom, comorbidity, chest x-ray result, and laboratory value were among the involved variables. The patient's outcome was categorized into death, delayed intervention, or intervention as scheduled.  Results: Seventy-seven patients were enrolled, with a mean age of 44.2 years. Fever was found in 42 (54%) patients and respiratory symptoms in 32 (41.5%) patients. An NLR value of >6 was found in 55.8% of patients. A total of 18 patients experienced LUTS (23.4%), 33 patients presented with hematuria (42.9%), and 30 patients had urinary tract infections (39%). Nineteen out of 77 patients (27.3%) died before intervention. On the other hand, surgical intervention in 37 (66%) patients were delayed due to self-isolation, while intervention in 19 (34%) patients was performed as scheduled due to their emergency nature. Conclusion: Urological patients infected with COVID-19 were impacted by the delay of surgical procedures and mortality in the first 22 months of the pandemic.
OUTCOMES ANALYSIS OF TRANSURETHRAL RESECTION OF THE PROSTATE (TURP) AND TRANSURETHRAL VAPORIZATION OF THE PROSTATE (TUVP) IN PATIENT WITH BENIGN PROSTATE HYPERPLASIA: SYSTEMATIC REVIEW
Authors: Kestito Rafael Otniel , Rahaju Anny Setijo , Tarmono  , Purba Abdul K.R.
Published: May 2023

Objective: This systematic review aims to compare IPSS score and Uroflowmetry (Qmax) results between Transurethral Resection of The Prostate (monopolar and bipolar TURP) and Transurethral Vaporization of The Prostate (PVP, ThuVARP, Biolitec, TUEVP, BPV). Material & Methods: Articles were obtained through online searches using Medline, Google Scholar, and Cochrane Library using keywords Transurethral Resection of The Prostate (TURP) dan Transurethral Vaporization of The Prostate (TUVP) and IPSS or Qmax. Results: A total of 6 RCTs and 3 Cohort consisted of 1212 BPH patients treated with TURP (B-TURP and B-TURP) or TUVP (TUVP, BPV, Biolitec, ThuVARP, GLVP(PVP)). There is a significant difference result of IPSS score and Qmax at the third month of follow-up after both therapies. Conclusion: The post-operative IPSS score and Qmax results at the third month of follow-up in BPH patients treated with TURP or TUVP presents varied results. TURP has a better post-operative IPSS score compared to Biolitec Laser, but not superior when compared with TUEVP, BPV, ThuVARP, and PVP. Notably, TURP has better post-operative Qmax results than BPV, TUEVP, and ThuVARP, but not when compared with PVP.
Considerações feministas sobre a ciência
Authors: Moreira Camila Bozzo , Cravelin José Felipe
Published: May 2023

As considerações feministas da ciência expõem o meio pelos quais as várias ciências exibem um viés androcêntrico em suas teorias, práticas e pressuposições. Algumas dessas considerações, mas não todas, também questionam sobre em que medida nosso entendimento do que é ser racional, objetivo e científico não carrega em si essas questões de gênero. As análises são abrangentes e diversas, refletindo um conjunto amplo de compromissos dentro da filosofia da ciência e da teoria feminista. É um erro tratar as críticas feministas da ciência como constituinte de um corpo de literatura monolítico; essa atitude conduz a uma caricatura e a uma inevitável repressão de problemas cruciais. Além disso, uma das lições basilares que se obtém por meio de pesquisas cumulativas nessa área é que o papel desempenhado pelo gênero na ciência é deveras complexo e variado. Apresentações populares e referências indiferentes que minimizam essa complexidade e variabilidade deturpam a pesquisa feminista e contribuem para a permanência do abismo infeliz entre esta e a grande parte da filosofia da ciência “mainstream”.
Evolución y carga financiera de las Enfermedades Crónicas no Transmisibles en Colombia: 2010-2021
Authors: Iregui-Bohórquez Ana María , Pinilla-Alarcón Diana Estefanía , Melo-Becerra Ligia Alba , Ramírez-Giraldo María Teresa
Published: May 2023

El objetivo del documento es analizar como la evolución de algunas Enfermedades Crónicas no Transmisibles - ECNT (Cáncer, Diabetes, Enfermedad Cardiovascular, Enfermedad Crónica Respiratoria y Enfermedad Renal Crónica) puede afectar la carga financiera del sector salud en Colombia, debido a que estas enfermedades probablemente aumentarán por el envejecimiento de la población y por factores de riesgo relacionados con hábitos de vida poco saludables. Para el análisis se utilizará información de 2010 a 2021 del Registro Individual de Prestación de Servicios de Salud (RIPS) del Ministerio de Salud para caracterizar las ECNT e información del Manual Tarifario de Salud (Decreto 2423 del 31 de diciembre de 1996) y de la base de suficiencia del Ministerio de Salud para el cálculo de los costos. Los resultados indican que las enfermedades cardiovasculares son las que presentan la mayor tasa de atención por 100.000 habitantes. Además, se encuentra una gran heterogeneidad en las tasas de atención entre regiones y una mayor demanda en la atención por parte de las mujeres. En cuanto a los costos atribuidos a estas enfermedades, se estima que entre 2022 y 2030 éstos aumentarían cerca del 40% en términos reales.
A Study of Compressed Language Models in Social Media Domain
Authors: Zhang Linrui , Copus Belinda
Published: May 2023

Transfer learning from large-scale language models is witnessing incredible growth and popularity in natural language processing (NLP). However, operating these large models always requires a huge amount of computational power and training effort. Many applications leveraging these large models are not very feasible for industrial products since applying them into power-scarce devices, such as mobile phone, is extremely challenging. In this case, model compression, i.e. transform deep and large networks to shallow and small ones, is becoming a popular research trend in NLP community. Currently, there are many techniques available, such as weight pruning and knowledge distillation. The primary concern regarding these techniques is how much of the language understanding capabilities will be retained by the compressed models in a particular domain? In this paper, we conducted a comparative analyses between several popular large-scale language models, such as BERT, RoBERTa, XLNet-Large and their compressed variants, e.g. Distilled BERT, Distilled RoBERTa and etc, and evaluated their performances on three datasets in the social media domain. Experimental results demonstrate that the compressed language models, though consume less computational resources, are able to achieve approximately the same level of language understanding capabilities as the large-scale language models in the social media domain.
SBERTiment: A New Pipeline to Solve Aspect Based Sentiment Analysis in the Zero-Shot Setting
Authors: Muffo Matteo , Cocco Aldo , Negri Edoardo , Bertino Enrico , Sreekumar Devi Veena , Pennesi Giorgio , Lorenzon Riccardo
Published: May 2023

The field of Natural Language Processing is gaining increased attention for the Aspect Based Sentiment Analysis task due to its ability to provide fine-grained information. This paper introduces SBERTiment, a novel approach to perform Aspect Based Sentiment Analysis. The method extracts relevant topics along with their sentiments from the input text by using a 2-step pipeline. In the first step, a token classification model is used to identify the relevant aspect terms and their sentiments. In the second step, a Sentence-BERT embedding model maps each aspect term to a predefined aspect category. Our approach has been tested on benchmark datasets and has achieved scores that are comparable to the best-performing methods. The pipeline is also able to perform zero-shot classification, which means it can extract information in unseen domains without additional training. When evaluated on a dataset with unseen aspect categories, SBERTiment achieved the best score among benchmark approaches.
Modeling organizational level of agents with moise+ to a natural resources management RPG
Authors: Born Míriam , Aguiar Marilton , Adamatti Diana
Published: May 2023

Artificial intelligence and its applications have stood out over the last decade and presented effective solutions in the most diverse sectors and areas of knowledge. For example, in the environmental area, several challenges are faced, mainly in managing natural resources, due to numerous existing conflicts due to increased demand and scarcity of such resources. In this scenario, applications using multi-agent systems show promise. The main objective of this work is the presentation of modeling at the organizational level of agents to study the complexity and the functionalities of characters of a Role-Playing game. The case study of this work is the Gorim, an RPG Game developed in the scenario of the watershed of Lagoa Mirim and Canal Sao Gonc¸alo at ˜the Rio Grande do Sul, Brazil. The organization modeling was developed in the MOISE+ model and implemented to verify it in the JaCaMo platform through hypothetical scenarios. We demonstrated that the results of such scenarios validate the structural, functional, and deontic/normative levels of the Gorim RPG.
Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema
Authors: Mosharrof Adib , Maqbool M.H. , Siddique A.B.
Published: May 2023

Task-oriented dialog systems empower users to accom-plish their goals by facilitating intuitive and expres-sive natural language interactions. State-of-the-art ap-proaches in task-oriented dialog systems formulate theproblem as a conditional sequence generation task andfine-tune pre-trained causal language models in the su-pervised setting. This requires labeled training datafor each new domain or task, and acquiring such datais prohibitively laborious and expensive, thus makingit a bottleneck for scaling systems to a wide rangeof domains. To overcome this challenge, we intro-duce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domainschemas to allow for robust generalization to unseen do-mains and exploits effective summarization of the dia-log history. We employ GPT-2 as a backbone model andintroduce a two-step training process where the goal ofthe first step is to learn the general structure of the dialogdata and the second step optimizes the response gen-eration as well as intermediate outputs, such as dialogstate and system actions. As opposed to state-of-the-artsystems that are trained to fulfill certain intents in thegiven domains and memorize task-specific conversa-tional patterns, ZS-ToD learns generic task-completionskills by comprehending domain semantics via domainschemas and generalizing to unseen domains seam-lessly. We conduct an extensive experimental evaluationon SGD and SGD-X datasets that span up to 20 uniquedomains and ZS-ToD outperforms state-of-the-art sys-tems on key metrics, with an improvement of +17% onjoint goal accuracy and +5 on inform. Additionally,we present a detailed ablation study to demonstrate theeffectiveness of the proposed components and trainingmechanism.
Aggregating Procedure for Fuzzy Cognitive Maps
Authors: Maikel Leon Espinosa
Published: May 2023

In the field of Knowledge Engineering and Representation, a typical struggle encompasses transferring the Subject Matter’s expertise into computational descriptions that could be used to create digital-twin representations of a given real-world scenario. Fuzzy Cognitive Maps (FCMs) have recently gained relevant attention among multiple techniques developed with this aim. However, one issue remains, when numerous of these representations need to be combined into a unique aggregated structure, it is essential to weigh factors (such as quality) into the final form to ensure its veracity, making the process not too straightforward. This paper proposes an aggregation procedure to combine FCMs into one that represents best its contributors. The technique was utilized for solving a real-life problem, and several configurations were explored. The results are compiled and reported in this paper.
Generating more Realistic Packet Loss Patterns for Wireless links using Neural Networks
Authors: Otten Daniel , Hänel Thomas , Römer Tim , Aschenbruck Nils
Published: May 2023

Simulations of wireless network connections are essential forthe development of new technologies because they are farmore scalable than real-world experiments and reproducible.Modeling packet loss realistically provides a highly abstractyet powerful tool for the simulation of wirelesses links. Typi-cally, simple statistical models or replaying of recorded tracesare used for the simulation. For a proper parametrization ofsimple statistical models, recorded traces are required, too.Both approaches have drawbacks: replaying traces is limitedto the length of the traces, a repetition may lead to unwantedeffects in the simulation. The statistical models solve this, butthe resulting packet loss patterns significantly differ from realones. In this paper, we propose using a neural network in-stead. It takes the same kind of input, i.e., a real-world trace,but it can generate longer traces with more realistic loss pat-terns. We share pre-trained neural networks for multiple linksin office and industry scenarios with the community for usein future research.
Query Transformation for Processing Streams in Decision-making Agents
Authors: Schiff Simon , Leemhuis Mena , Özcep Özgür , Möller Ralf
Published: May 2023

An agent in pursuit of a task repeatedly perceives its environment through sensors, updates its state based on observations, and then decides which action to take, given the current state of the environment. Observations have in common that they are made at a given time point and thus referred to as temporal data. Usually, such temporal data is provided as stream data if the agent continuously receives the data, or it is provided as historic data if the stream data is stored in, for instance, a database the agent has access to. DBMSs are especially designed to process static data (i.e. non-temporal data) given a declarative query language such as SQL. However, if the aim is to exploit temporal data as required in time series analysis, SQL has its limits because it does not provide useful abstractions such as a window operator. Hence high-level declarative stream query languages, equipped with time-based window operators were designed. A challenge of those abstractions is the additional overhead of the algorithms that automatically transform high-level queries into low-level queries executable over DBMSs. If not handled properly those transformation algorithms may result in low-level queries with processing times too long for agents to make decisions. We describe a robust and optimized transformation algorithm for a high-level declarative stream query language and show that it leads to low-level queries with feasible processing times on real-world data.
Making Time Series Embeddings More Interpretable in Deep Learning
Authors: Schwenke Leonid , Atzmueller Martin
Published: May 2023

With the success of language models in deep learning, multiple new time series embeddings have been proposed. However, the interpretability of those representations is often still lacking compared to word embeddings. This paper tackles this issue, aiming to present some criteria for making time series embeddings applied in deep learning models more interpretable using higher-level features in symbolic form. For that, we investigate two different approaches for extracting symbolic approximation representations regarding the frequency and the trend information, i.e. the Symbolic Fourier Approximation (SFA) and the Symbolic Aggregate approXimation (SAX). In particular, we analyze and discuss the impact of applying the different representation approaches. Furthermore, in our experimentation, we apply a state-of-the-art Transformer model to demonstrate the efficacy of the proposed approach regarding explainability in a comprehensive evaluation using a large set of time series datasets.
Comparative Study Between Vision Transformer and EfficientNet on Marsh Grass Classification
Authors: Testagrose Conrad , Shabbir Mehlam , Weaver Braden , Liu Xudong
Published: May 2023

Due to rapidly changing ecosystems, effective environmental protection often calls for the monitoring of the vegetation for any environmental changes. Vegetation monitoring is essential in assessing the changes and impacts to environmentally valuable ecosystems such as marshlands. While vegetation monitoring of marsh grasses is crucial to the maintenance and protection of marshlands, it is a tedious and time-consuming task that involves careful examination of individual pixels within large resolution images. In this study we compare the use of Vision Transformers (ViT) and two different EfficientNet models on automated marsh grass identification using the GTMNERR Marsh Grass Species data set. Our results show that the use of a ViT allowed for an increase in the accuracy of marsh grass identification. The Vision Transformer was also able to better distinguish between the 6 classes in the data set and provided competitive training time to the smaller of the two EfficientNet models tested in this study.
ML-based Plant Stress Detection from IoT-sensed Reduced Electromes
Authors: De Oliveira Jr Marcos , Sedrez Gregory , H. Cavalheiro Gerson Geraldo
Published: May 2023

The recognition of patterns in the electrical activities of plants (electromes, in time series format) has gained prominence in recent years. The use of Internet of Things (IoT) devices and Machine Learning (ML) techniques has automated and enhanced data collection and classification, helping researchers identify behaviors and classify them to detect plant stress. However, processing this information means dealing with large amounts of data, which is a major challenge from a computer science perspective. Thus, in this work, we propose an approach for reduction and classification of time series representing plant electromes to balance the trade-off between reduction and data quality, without compromising the classification task. We investigated the use of three time series approximation techniques (PAA, SAX, and MCB) in combination with ML algorithms, such as ANN, KNN, and SVM, in order to find the most suitable approach for this scope. The results validated the proposed approach, with the best performance obtained with the PAA+SAX techniques combined with the SVM algorithm, achieving good data reduction and improving stress detection, without compromising data quality. The main challenges in these tasks and future research directions are also discussed.
Heart Murmur Classification in Phonocardiogram Representations Using Convolutional Neural Networks
Authors: Shabbir Mehlam , Liu Xudong , Nasseri Mona , Helgeson Scott
Published: May 2023

Heart murmurs are sounds made by rapid blood flow in the heart. Abnormal heart murmurs can be a sign of serious heart conditions such as arrhythmia and cardiovascular diseases. Therefore, heart murmur classification is crucial for early detection of such conditions. To this end, we study the heart murmur classification problem training selected convolutional neural network (CNN) models (such as VGGNet and ResNet) using various signal representations (such as spectrogram, mel-frequency cepstral coefficient (MFCC), and shorttime Fourier transform (STFT)) of the phonocardiograms in the public PASCAL CHSC dataset. Our preliminary results show that ResNet outperforms VGGNet across all metrics and representations, consistent with the recent published works we can find in literature. Unlike some of these works, however, we see MFCC and STFT in general more effective with higher test accuracies than spectrogram across all CNN models. Looking forward, we propose to study other effective models (such as InceptionV3 and Vision Transformer) to predict heart murmur conditions in phonocardiogram representations including spectrogram, MFCC and STFT, as well as others like Wigner Ville distribution.
Biogeography-based optimization for feature selection
Authors: Gholami Mandana , Mouhoub Malek , Sadaoui Samira
Published: May 2023

Data clustering has many applications in medical sciences, banking, and data mining. K-means is the most popular data clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has some limitations, which may affect its effectiveness, such as all the features having the same degree of importance. To address these limitations and improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) algorithm to select the most relevant features of datasets. Our primary idea is to reduce the intra-cluster distance while increasing the distance between clusters.
Player Identification for Collectible Card Games with Dynamic Game States
Authors: Fields Logan , Licato John
Published: May 2023

Collectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allow for flexibility in play styles, thus facilitating behavioral analysis at the individual level rather than the aggregate level. When presented with the same options and the same amount of resources, a player's selection of cards and their choice of moves gives insight into their unique play style and decision-making tendencies. As such, we use the virtual collectible card game Legends of Code and Magic to determine whether we can identify a player from their actions and, conversely, predict the future actions of a known player. Our main contributions to this task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, as well as the first use of large transformer-based language models to address this problem.
Identifying Protein-Protein Interaction using Tree-Transformers and Heterogeneous Graph Neural Network
Authors: Singha Roy Sudipta , Mercer Robert
Published: May 2023

For a better understanding of the underlying biological mechanisms, it is crucial to identify the reciprocity between proteins. Often, extracting such interactions between proteins from biomedical articles faces challenges due to the complex sentence structure of the textual information sources. Most of the prominent previous works have applied additional hand-crafted features for the protein-protein interaction task. In this work, we have utilized two tree-structured attention-based neural network models along with a heterogeneous graph approach to perform this task. We suggest that the proposed model preserves the syntactic as well as the semantic information of the text. The experimental results demonstrate that even without using any additional feature extraction techniques, this model achieves significant performance boosts when applied on the five standard benchmark corpora compared to the previous works.
A Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution
Authors: Ivanov Lubomir
Published: May 2023

We present some early results from a research project aimed at exploring the usefulness of abstractness/concreteness as stylistic features for authorship attribution. We conjecture that authors use abstract/concrete words and phrases in suf- ficiently unique ways, so that machine learning classifiers can learn to distinguish the individual authors’ writing styles. Our approach is based on using the abstractness rat- ings of words and phrases from texts with established au- thorship to generate training vectors for different machine learning classifiers. The combined word/phrase ratings are extracted from two separate abstractness dictionaries – an approach that yields stronger results than using single ab- stractness dictionaries. The paper describes the details of our methodology and compares the results to those obtained using traditional authorship attribution stylistic features. The limitations of our current methodology and directions for further research are outlined at the end of the paper.
Unsupervised Keyword Extraction for Hashtag Recommendation in Social Media
Authors: Jafari Behafarid Mohammad , Luo Xiao , Jafari Ali
Published: May 2023

Hashtag recommendation aims to suggest hashtags to users to annotate and describe the key information in the text, or categorize their posts. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This paper investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation. To do so, well-known unsupervised keyword extraction methods are applied to three real-world datasets including a new dataset containing texts of user-generated posts on a social learning platform. Experimental evaluations demonstrate that statistical methods performs newer methods including graph-based and embedding-based approaches in generating hashtags for long text, whereas the embedding-based approaches works better on generating hashtags for short texts. As a consequence, it can be concluded that unsupervised keyword extraction models can be adapted for hashtag recommendation when the social platform is new or there is no existing data to develop dedicated supervised learning models.
Weight-based multi-stream model for Multi-Modal Video Question Answering
Authors: Rajesh Mohith , Sridhar Sanjiv , Kulkarni Chinmay , Shah Aaditya , S Natarajan
Published: May 2023

There has been a tremendous success in individual domains of Computer Vision, Natural Language Processing, and Knowledge Representation. Videos are a rich source of information with the multi-modal data forms of images, audio, and optionally subtitles blended. Current research is going on in combining these individual domains which have given rise to topics such as image captioning, visual question answering, and video question answering. Video Question Answering is a model which combines research topics like object detection and recognition, temporal information processing, visual attention, and natural language processing. In this paper, we propose a model with Attention Mechanism for Video Question Answering that assigns varying weights to the many pieces of information the video encompasses. The model combines the question with 3 streams i.e., video's frames, subtitles, and objects to get the most probable answer. The model also receives the set of answer candidates as input and predicts one of them as the most probable answer since it has been trained and tested on the TVQA dataset.