bluuebunny/crossref_metadata_2025 huggingface dataset:
Religious literacy: Training on the women corpse management in Salam Village, Gebang District
Authors: Sururiyah Siti Khusniyati , Hidayah Nur , Fitri Riski Rahma , Aisyah Annisa Ika Nur
Published: May 2023

The law on the management of corpses is fardhu kifayah (collective obligation). However, the problem is that many people do not have the courage to do so, due to a lack of religious knowledge regarding burial of corpses, as well as fear of the corpse itself. The purpose of this community service is to provide knowledge and understanding of the simulation of handling women's corpse. This training was given twice, namely for PKK women and for members of Fatayat and Muslimat NU as pioneers of religious activities in Salam Village, Gebang District. The implementation method in this activity is Community-Based Participatory which is carried out collaboratively. The training materials provided include procedures for dealing with people in death throes,  procedures for dealing with people who have recently died, procedures for washing the corpse, and ending with purification, procedures for shrouding the corpse, and the last reading the intentions and procedures for praying for a woman's corpse. From the results of the evaluation, the participants showed a positive response and were very enthusiastic about gaining new literacy knowledge about managing women's corpse according to Islamic law. As for the results of the feedback, a social team for the management of women's corpse will be formed in each RT.
Особливості вжитку термінів англомовного походження до жанру фільмів жахів
Authors: Kovtun N. A.
Published: May 2023

У статті здійснено огляд синонімічного зв’язку між україномовним терміном «фільм жахів» та кінематографічними термінами англомовного походження. Предметами для дослідження слугували найуживаніші щодо жанру фільмів жахів в середовищі кіноглядачів та кінокритиків слова-терміни: слешер, зомбі-фільм, саспенс, скример; рідше використовувані – гор, сплеттер, сплетстік та деякі інші. Слова для означення субжанрів (піджанрів) по суті є гіпонімами до основного поняття «фільм жахів», тобто такими, де видові поняття підпорядковуються родовому. Такі лексико-семантичні зв’язки називаються гіперо-гіпонімічними. Ресурсами для пошуку матеріалів автором було обрано «Кінематографічний словник» В. Миславського, кіноплатформи, фільми українського дубляжу, контекстну термінологію сайтів, глядацькі рецензії. Ураховуючи те, що означений словник було видано у 2007 році, то дослідження термінології, наведеної не лише в книзі, а й серед сучасних платформ та рецензій, дозволяє виявити нові тенденції останніх п’ятнадцяти років. Проведені автором статті дослідження «Кінематографічного словника» дозволили зробити висновок про відсутність на той час суто українських відповідників до англомовної термінології, натомість аналіз пізнішого україномовного дубляжу фільмів, контексту сайтів та рецензій показує, що попри стійкість в уживанні доволі тривалого часу англіцизмів, з’являються вже й україномовні терміни. Зокрема, стає поширеним уживання терміну «мрець» замість «зомбі» (досліджено на матеріалах сайтів HDrezka, Netflix, Megogo). У публікації висувається припущення, що в сучасних реаліях української культури використання україномовних відповідників, зокрема у соціальних мережах, може мати вірусний формат, що дозволить новому слову-заміннику закріпитися в мові. У роботі йдеться про цінність роз[1]витку термінологічного арсеналу, важливості підтримки мови шляхом заохочення та створення цікавості серед молоді особливо. Автор роботи припускає, що, імовірно, було б доцільним уведення та поширення наступних україномовних відповідників: слешер-різанина; скример-лякалка, зик, скрик; сплеттер-чвирк, а також пропонується термін «моторош» як синонімічний до терміну «фільм жахів». У статті висловлено думку, що особливе значення має і створення умов для просування українського продукту, а не лише обмеження функціонування запозичених слів. Отримані в публікації результати та висновки дають необхідне термінологічне підґрунтя для подальшого дослідження проблематики саспенсу в кіномистецтві.
Двоєдина задача нової «Просвіти»
Authors: Shkurkо M. P.
Published: May 2023

Стаття присвячена ролі товариств «Просвіта» в сучасних умовах застосування ворогом ідеологічно-психологічних спеціальних операцій в українському інформаційному просторі. Обґрунтовується необхідність вирішення двоєдиної задачі – консолідації відновленої мережі товариств «Просвіта» у всеукраїнській громадській спілці та інтеграції «Просвіт» до владних інститутів на всіх рівнях.
«Щастя порядної людини» або мотив Відходу-і-Повернення в художній літературі
Authors: Plotnikov Ye. , Rolik A.
Published: May 2023

Стаття присвячена дослідженню природи та специфіки дії мотиву Відходу-і-Повернення в житті та творчості М. Гоголя. Творча особистість, йдучи, випадаючи зі свого соціального оточення, перетворена, повертається потім у те саме оточення; повертається, наділена новими здібностями та новими силами. Відхід дозволяє особистості реалізувати свої індивіду[1]альні потенції, які не могли бути висловлені, придушені пресом соціальних зобов’язань. Відхід дає можливість, і, можливо, і є необхідною умовою духовного перетворення; але в той же час перетворення позбавлене мети і сенсу, якщо воно не стає прелюдією до повернення перевтіленої особистості у суспільство, з якого вона пішла. Повернення є сутність всього руху, як і його остаточна мета. Мотив Відходу-і-Повернення може виявлятися у різних формах. У найпростішій їх розрив, яким закінчується глава практичної діяльності, відбувається раз і назавжди, після чого все життя творчої особистості наповнюється літературною діяльністю. У житті Гоголя ця модель складніша. Замість одного розриву там спостерігаються кілька, і періоди практичної та літературної діяльності переплітаються. На початку своєї біографії Гоголь мав намір вплинути на своїх співвітчизників через пряму дію на них своєю волею. Вимушений відхід змушував його шукати нову форму активності, інший канал своєї енергії. Позбавлений можливості безпосередньо впливати на своїх співвітчизників, він знайшов шлях для опосередкованого впливу на людей інших поколінь через літературні твори. Творчість Гоголя жива і донині і саме в цьому полягає духовне значення ритму Відходу-і-Повернення. Питання про дію мотиву Відходу-і-Повернення у творчому просторі творів Миколи Гоголя може, на наш погляд, виявитися досить продуктивним, що, природно, вимагає подальших, більш детальних досліджень. У цій статті ми зупинимося лише на одній сюжетній лінії, яка вперше артикулюється в повісті «Ніс» (1836), а потім отримує розвиток у поемі «Мертві душі» (1842), а саме щастя порядної людини.
Implementasi Pengelolaan Dana Pagu DIPA di MTs Negeri 2 Musi Banyuasin
Authors: Ariswindra Jaya  , Harapan Edi , Mahasir
Published: May 2023

ABSTRAK
 
Penelitian ini memiliki tujuan untuk mendeskripsikan: 1) pengelolaan dana pagu DIPA di MTs Negeri 2 Musi Banyuasin; dan 2) faktor penghambat dan pendukung dalam pengelolaan dana pagu DIPA di MTs Negeri 2 Musi Banyuasin. Penelitian ini menggunakan metode deskriptif kualitatif dengan teknik pengumpulan data: Observasi, wawancara, dan dokumentasi. Selanjutnya data yang dikumpulkan di analisis dengan teknik: reduksi data, penyajian data dan verifikasi data. Penelitian ini menghasilkan kesimpulan bahwa: 1) pengelolaan dana pagu DIPA di MTs Negeri 2 Musi Banyuasin melalui empat tahapan fungsi manajemen yaitu Planning, Organizing, Actuating, dan Controlling telah berjalan dengan baik. 2) Faktor penghambat dalam pengelolaan dana pagu DIPA di MTs Negeri 2 Musi Banyuasin yaitu: pemadaman listrik, sinyal kurang stabil dan maintenance server yang dilakukan Kementerian Keuangan Republik Indonesia Sedangkan faktor pendukung yaitu: infrastruktur yang lengkap, sinyal yang stabil dan sumber daya manusia yang mendukung.
 
Kata Kunci : Implementasi, Pengelolaan Dana, Pagu DIPA
 
 
ABSTRACT
 
This study aims to describe: 1) the management of DIPA ceiling funds in MTs Negeri 2 Musi Banyuasin; and 2) inhibiting and supporting factors in the management of DIPA ceiling funds in MTs Negeri 2 Musi Banyuasin. This study used qualitative descriptive methods with data collection techniques: Observation, interviews, and documentation. Furthermore, the data collected is analyzed with techniques: data reduction, data presentation and data verification. This study concluded that: 1) the management of DIPA ceiling funds in MTs Negeri 2 Musi Banyuasin through four stages of management functions, namely Planning, Organizing, Actuating, and Controlling has gone well. 2) Inhibiting factors in the management of DIPA ceiling funds in MTs Negeri 2 Musi Banyuasin are: power outages, unstable signals and server maintenance carried out by the Ministry of Finance of the Republic of Indonesia While the supporting factors are: complete infrastructure, stable signals and supporting human resources.
 
Keywords : Implementation, Fund Management, DIPA Ceiling
Perbedaan Uang Saku Mahasiswa STEBIS IGM Pada Saat Covid 19 dan New Normal
Authors: Fadilla Fadilla , Erdiansyah Erdiansyah
Published: May 2023

ABSTRAK

 

Pada saat Pandemi Covid 19 sistem pembelajaran di STEBIS IGM dilakukan secara online. Hal ini memberikan keuntungan bagi para orang tua karena mereka tidak perlu menyiapkan uang lebih untuk membiayai transport dan jajan ketika berada dikampus. Para orang tua hanya perlu memberikan uang membeli kuota internet. Penelitian ini bertujuan untuk mengetahui adakah perbedaan uang saku yang diberikan oleh mahasiswa STEBIS IGM pada saat pandemi dan new Normal. Metode penelitian yang digunakan adalah metode paired t-tes. Adapun sample yang digunakan sebanyak 55 mahasiswa STEBIS IGM. Proses pengambilan sampel dilakukan dengan metode simple ramdom sampling. Hasil penelitian menunjukkan nilai signifikansi 0,00 yang berarti terdapat perbedaan uang saku yang diterima mahasiswa STEBIS IGM pada saat pandemi Covid 19 dan New Normal. Jumlah uang yang diterima saat Covid lebih sedikit dari pada saat New Normal.

 

Kata Kunci: Uang Saku, Covid 19, New Normal

 

 

ABSTRACT

 

During the Covid 19 Pandemic the learning system at STEBIS IGM was carried out online. This provides an advantage for parents because they do not need to prepare extra money to pay for transportation and snacks while on campus. Parents only need to give money to buy internet quota. This study aims to find out whether there is a difference in the pocket money given by STEBIS IGM students during the pandemic and the new normal. The research method used is paired t-test method. The sample used was 55 STEBIS IGM students. The sampling process was carried out by simple random sampling method. The results of the study showed a significance value of 0.00, which meant that there was a difference in the pocket money received by STEBIS IGM students during the Covid 19 pandemic and New Normal. The amount of money received during Covid was less than during New Normal.

 

Keywords: Pocket Money, Covid 19, New Normal
PEMANFAATAN BUAH NIPAH SEBAGAI BAHAN BAKU PEMBUATAN BIOETANOL
Authors: Eddy Syaiful , Pranata Gelen , Setiawan Andi Arif
Published: May 2023

Bioetanol merupakan bahan bakar hayati (biofuel) yang mempunyai prospek baik sebagai bahan bakar alternatif yang dapat diperbaharui, ramah lingkungan dan sangat menguntungkan secara ekonomi terutama bagi petani. Bioetanol tersebut dapat dihasilkan dari tumbuhan nipah. Tumbuhan nipah sangat melimpah di Indonesia, karena pada umumnya tumbuh subur di pantai, salah satunya berada di Hutan Lindung Air Telang (HLAT), Kabupaten Banyuasin, Provinsi Sumatera Selatan. Penelitian ini bertujuan mengkaji pemanfaatan buah nipah sebagai bahan baku bioetanol dan menganalisis volume dan kadar alkohol dari buah nipah. Sempel buah nipah di ambil di HLAT, lalu di haluskan dengan cara di blender dan ditambahkan enzim alfa amilase, selanjutnya di fermentasi selama 8, 9, 10, 11 hari. Hasil dari penelitian ini menunjukkan bahwa kadar alkohol tertinggi terdapat pada hari ke 8 dengan rata-rata sebesar 3.75%, pada hari ke 9, 10 dan 11 kadar alkohol menurun masing-masing dengan rata-rata 3.375%, 3% dan 2.625%. Hasil penelitian ini memberikan implikasi bahwa bioethanol dapat diperoleh dari daging buah nipah melalui proses ekstraksi dan fermentasi. Penelitian ini dapat dilanjutkan dengan menggunakan metode-metode lainnya untuk mendapatkan bioethanol dari buah nipah.
Publicación, difusión y legitimación de la microficción en Facebook: las páginas de Rogelio Ramos Signes e Ildiko Nassr
Authors: Quispe Gloria Carmen
Published: May 2023

Las redes sociales modificaron las formas de vinculación y comunicación, y con el tiempo, se convirtieron en canales efectivos de circulación de la literatura. Así comenzaron a publicarse, desde perfiles personales y/o fanpages, crónicas, fragmentos de novelas, cuentos y microficciones, que encontraron muy pronto curiosos y/o especializados lectores. Estas formas alternativas de circulación de textos literarios promovieron la conformación de redes de escritores y facilitaron la cercanía y la interacción con los lectores.

Importante es destacar que la difusión de los textos literarios en las redes intensificó la relación entre texto e imagen. Ambos lenguajes interpelan a los lectores-seguidores y conforman una estructura de sentido. A veces palabra e imagen aparecen de manera simultánea y no pueden percibirse por separado, y otras, la imagen complementa o adiciona información visual a las palabras o son las palabras las que resultan del impacto y la mirada que transmite la imagen (Kibedi Varga 2000).

En este trabajo, además de problematizar la publicación y difusión de microficción en la red social Facebook, los procesos de legitimación y su valoración literaria, nos interesa analizar las minificciones publicadas por el tucumano Rogelio Ramos Signes y la jujeña Ildiko Nassr. Ambos autores en sus publicaciones ponen en diálogo palabra e imagen.
On the Bell Experiment and Quantum Foundation
Authors: Helland Inge Svein
Published: May 2023

The Bell experiment is discussed in light of a new approach towards the foundation of quantum mechanics. It is concluded from the basic model that the mind of any observer must be limited in some way: In certain contexts, he is simply not able to keep enough variables in his mind when making decisions. This has consequences for Bell's theorem, but it also seems to have wider consequences.
EXPERIENCE OF BILATERAL SYNCHRONOUS PERCUTANEOUS NEPHROLITHOLAPAXY (PCNL)
Authors: Nugroho Prasetyo , Satyagraha Paksi , Daryanto Besut
Published: May 2023

Objective: This case report will present bilateral synchronous percutaneous nephrolitholapaxy (PCNL) that was performed in Saiful Anwar General Hospital Malang for the first time. Case(s) Presentation: 49 years old male with flank pain on both sides continuously. Non contrast abdominal Computed Tomography (CT) scan showed bilateral pyelum stone and bilateral hydronephrosis. From blood analysis, the creatinine serum was 1.5 mg/dL and ureum serum was level 69.8 mg/dL. Discussion: Bilateral synchronous PCNL was performed in prone position, and stone fragmentation was done with pneumatic lithotripter, the operation last for about 2 hours with minimal amount of bleeding.  After 3 months of follow-up, urology ultrasound and abdominal X-ray shows no residual stone, and normal ureum-creatinine level (0.77mg/dL; 43.7mg/dL). Conclusion: Bilateral synchronous PCNL is a safe choice to be done in order to treat bilateral pyelum stone, it can decrease length of stay , total cost therapy, and morbidity of the patient.
Manual de prevención del suicidio en el contexto educativo: profesores
Authors: Méndez Bustos Pablo , Figueroa Saavedra Carla
Published: May 2023

En esta guía, serán abordados en profundidad factores de riesgo tales como el acoso escolar, autolesiones y consumo de drogas. Además, se incluye un protocolo de acción preventivo donde se especifican fuentes de ayuda comunitaria y actividades reflexivas para estudiantes.
A estética musical por trás de Vom Musikalisch-Schönen
Authors: Oliveira Sidnei de
Published: May 2023

Eduard Hanslick, renomado crítico e esteta musical do século XIX, é conhecido, sobretudo, pela sua obra intitulada Vom Musikalisch-Schönen (Do belo musical). Este breve artigo propõe uma leitura a partir das principais influências, ideias e teorias que o esteta teve para elaboração de sua principal teoria em relação à música, especialmente a forma de como o ouvinte desta arte se relaciona com ela.
Além de apresentar tais nomes que foram relevantes para Hanslick, é possível notar a competência e responsabilidade dos autores contemporâneos de Hanslick quando o tema, em suas obras, artigos e folhetins, percorria diferentes áreas do conhecimento.
Brentano acerca do psicologismo e o background da fenomenologia
Authors: Brito Evandro Oliveira de
Published: May 2023

Em um texto intitulado Vom Psychologismus, que só foi publicado como um apêndice da 2ª edição do Psychologie vom empirischen Standpunkt em 1911, Franz Brentano posicionou-se sobre a controvérsia acerca do psicologismo (Psychologismusstreit), recusando expressamente o rótulo de psicologista que lhe fora atribuído por Edmund Husserl, um dos nomes mais influentes no que viria a ser conhecido como a Escola de Brentano. O desenvolvimento deste trabalho, que visa analisar o Psychologismusstreit exclusivamente a partir da perspectiva brentaniana, está dividido em três momentos. No primeiro momento, apresento a resposta textual de Brentano a Husserl. No segundo momento, eu recorro aos resultados recentes das investigações de Porta sobre o status do método psicológico em filosofia no século XIX e explicito como este método se apresentou no contexto da formulação da teoria do conhecimento brentaniana, tal como foi desenvolvida entre 1874 e 1891 (PES e PD). No terceiro e último momento, tomando como base as suposições da teoria do conhecimento de Brentano formuladas em sua Psicologia descritiva, apresento duas razões sustentadas por Brentano para recusar o rótulo de psicologismo epistemológico supostamente atribuído a sua teoria do conhecimento: a) Husserl estava equivocado acerca da sua definição de verdade; b) Husserl também estava equivocado acerca da sua descrição psíquica do ato de julgar e, portanto, era incapaz de compreender a relação entre psicologia, lógica e teoria do conhecimento. Deixo aberta a questão sobre a plausibilidade da tese brentaniana, dadas as formulações e reformulações posteriores recorrentes da fenomenologia Husserliana, bem como o desenvolvimento do Psychologismusstreit.
Lápides pioneiras: os epitáfios, as lajes e o mármore como elementos constitutivos e sociais no Cemitério Campo da Esperança, em Brasília
Authors: Bastos Sócrates Alves
Published: May 2023

O Cemitério Campo da Esperança, a principal necrópole de Brasília, guarda em si especificidades que fazem dele um caso sui-generis dentro dos cemitérios do país. Fundado junto com a Capital tem formato espiralado, lápides muito baixas e foi concebido originalmente como cemitério jardim. O cemitério alberga dentro de sua área dois subcemitérios autônomos, um israelita e o outro islâmico, e áreas de sepultamento exclusivas para pioneiros e autoridades. É a partir dessa estratificação geoespacial e, por conseguinte, social que se desdobra o estudo de caso de seis lápides escolhidas e suas inscrições epigráficas e epitáfios, em sepulturas analisadas entre a Praça das Autoridades e a Quadra dos Pioneiros. A partir dos epitáfios analisados se pode observar uma narrativa, quase hegemônica, do imaginário do pioneiro, do desbravador dos sertões do Brasil Central, além dos silêncios, das plegárias por indulgências no porvir post-mortem. Este artigo é o ensaio de uma pesquisa sobre epitáfios, memória e narrativas migratórias para sertões dos antigos limites do Estado de Goiás nas primeiras décadas da Capital Federal.
Code Generation for Collectible Card Games with Complex APIs
Authors: Licato John , Fields Logan , Hollis Brayden
Published: May 2023

Large pre-trained language models (LMs) such as GPT-3 Codex are able to generate code remarkably well given prompts of natural language text. But if we want to use such LMs to generate code compatible with a specific API or library (e.g., an API which provides the environments in which certain rules, laws, or orders are to be carried out), the amount of computational and data resources required to fine-tune such models can be cost prohibitive to most organizations. Given these practical limitations, is it possible to utilize these massive code-generation LMs to write code compatible with a given API? We develop an algorithm that selects code examples using a smaller LM trained to predict which features of an API are likely to be used in the resulting code, which is a simpler problem than actually generating the code. The selected examples are then used to build a prompt for the larger LM, which in turn generates the final code. We demonstrate our results on a benchmark dataset derived from the collectible card game "Magic: the Gathering," and obtain state-of-the-art results.
On the Comparison of Markov Chains-based Models in Process Mining for Healthcare: A Case Study
Authors: Vallati Mauro , Orini Stefania , Lorusso Mariagrazia , Savino Mariachiara , Gatta Roberto , Filosto Massimiliano
Published: May 2023

In the last decade, Process Mining has become a significant field to help healthcare process experts understand and gain relevant insights about the processes they execute. One of the most challenging questions in Process Mining, and particularly in healthcare, typically is: how good are the discovered models? Previous studies have suggested approaches for comparing the (few) available discovery algorithms and measure their quality. However, a general and clear comparison framework is missing, and none of the analyzed algorithms exploits Markov Chains-based Models.

In this paper, we propose and discuss effective ways for assessing both quality and performance of discovered models. This is done by focusing on a case study, where the pMiner tool is used for generating Markov Chains-based models, on a large set of real Clinical Guidelines and workflows.
Evaluating Fairness in Predictive Policing Using Domain Knowledge
Authors: Downey Ava , Islam Sheikh Rabiul , Sarker Md Kamruzzaman
Published: May 2023

As an increasing number of Artificial Intelligence (AI) systems are ingrained in our day-to-day lives, it is crucial that they are fair and trustworthy. Unfortunately, this is often not the case for predictive policing systems, where there is evidence of bias towards age as well as race and sex leading to many people being mistakenly labeled as likely to be involved in a crime. In a system that already is under criticism for its unjust treatment of minority groups, it is crucial to find ways to mitigate this negative trend. In this work, we explored and evaluated the infusion of domain knowledge in the predictive policing system to minimize the prevailing fairness issues. The experimental results demonstrate an increase in fairness across all of the metrics for all of the protected classes bringing more trust into the predictive policing system by reducing the unfair policing of people.
Leveraging Graph Networks to Model Environments in Reinforcement Learning
Authors: Chadalapaka Viswanath , Ustun Volkan , Liu Lixing
Published: May 2023

This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.
MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks
Authors: Welch Lucas Wayne , Liu Xudong
Published: May 2023

Marsh ecosystems are some of our most important, serving many crucial ecological functions. They are also rapidly changing, and it is vital for scientists to track these changes. This includes monitoring the health of marshes via estimating ground coverage by various grass species, a task that requires human labor to look at marsh images and manually estimate the coverage. Clearly, this task can be quite formidable. To automate this standard yet laborsome process, we developa web-based system, called MarshCover, that automates the process of estimating vegetation density in marsh images using convolutional neural networks (CNNs). MarshCover, to the best of our knowledge, is the first such tool available to biologists that uses CNNs for marsh vegetation estimations. In order to select effective CNN models for our MarshCover server, we conduct extensive empirical analyses of three distinct CNNs, i.e., LeNet-5, AlexNet and VGG-16, to compare their performances on a public marsh image dataset. To this end, we address two classification problems for this paper: a binary classification problem classifying points as vegetated and unvegetated, and a multiclass classification problem that classifies points into either an unvegetated class or one of five different species classes. Our experiments identify the VGG16 model as the best classifier to embed in MarshCover for both the binary classification problem and the full classification problem with a two model classifier (called two-shot). These two classifiers had accuracies on test data of 90.76% and 84% respectively. MarshCover is publicly available online.
Multiple View Summarization Framework for Social Media
Authors: Li Chih-yuan , Chun Soon , Geller James
Published: May 2023

Social Media provide voluminous posts about current topics and events. When a user desires to investigate a popular topic, it is not feasible as there are many posts. Besides, posts show different biases, viewpoints, perspectives, and emotions. Thus, providing summaries of large post sets with different viewpoints is necessary. We develop a multiple view summa-rization framework to generate different view-based summar-ies of Twitter posts. Users can apply different methods to generate summaries: 1) Entity-centered, 2) Social feature-based, 3) Event-based summarization, using all triple embed-dings and 4) Sentiment-based summarization to generate summaries of positive or negative views of tweets. These summarization methods are compared with BertSum, SBert, T5, and Bart-Large-CNN with a gold standard dataset. Our results, based on Rouge scores, were better than these pub-lished extractive and abstractive summarization models.
A Survey of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems.
Authors: Oluwadare Sunkanmi , ElSayed Zag
Published: May 2023

Intrusion detection systems (IDS) are systems that are used to monitor networks for malicious events, abnormal activities, and policy violations. They are systems that are capable of detecting and classifying network attacks based on behaviors or signatures of previously known attacks based on markers. However, since network attacks are constantly evolving and it is almost impossible to infuse all possible combinations and signatures of the attacks, the effectiveness of Machine Learning based IDS is often challenged and called into play as a result of novel attacks generated, known as Zero-day attacks. This has facilitated the need to have intelligent-based IDS that could detect anomalies without relying on a detailed signature repository. In this paper, we present a literature-based survey of popular deep learning algorithms and evaluated their capabilities, strengths, limitations, and resource requirements for detecting anomalies and Zero-Day attacks. Based on our evaluation, we propose Long Short-Term (LSTM) networks and Autoencoder networks as the best algorithms for further analysis in intrusion detection.
BERTimbau in Action: An Investigation of its Abilities in Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection.
Authors: Da Rocha Junqueira Julia , Da Silva Felix , Costa Wesley , Carvalho Rodrigo , Bender Alexandre , Correa Ulisses , Freitas Larissa
Published: May 2023

Social Media has revolutionized how individuals, groups, and communities interact. This immense quantity of unstructured data holds valuable information expressed in informal language. However, automatically extracting this information using Natural Language Processing requires adaptations of traditional methods or the development of new strategies capable of extracting information tackling web-prone language. BERT, a Deep Learning methodology proposed by Google in 2018, brought transfer learning to Natural Language Processing. In this work, we used a BERT model for the Portuguese language called BERTimbau to create models for Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection. We experimented with the two BERTimbau models, base and large. Finally, we compared the results obtained in each task. Experiments with BERTimbau based models obtained improved results, F-Measure of 0.88 and 0.89 in Sentiment Analysis and Hate Speech Detection tasks, respectively, compared to classical Machine Learning approaches.
Harnessing Bullying Traces to Enhance Bullying Participant Role Identification in Multi-Party Chats
Authors: Ollagnier Anaïs , Cabrio Elena , Villata Serena
Published: May 2023

As online content continues to grow, so does the spread of online hate, especially on social media. Most research efforts conducted on the task of bullying participant role identification are directed towards social networks such as Twitter and Instagram. However, private instant messaging platforms and channels were pinpointed in recent studies as the most prominent grounds for cyberbullying, especially among teens. Since data collection from major social media platforms is strictly limited, very few studies have investigated this task in a multi-party setting. However, the recent release of resources mimicking online aggression situations that may occur among teens on private instant messaging platforms contributes to filling this gap. In this study, we introduce a full pipeline aiming at automating the identification of bullying participant roles (bully and victim) in multi-party chats. Leveraging pre-trained language models and different learning frameworks, we perform hateful content classification of exchanged messages according to a binary scheme (online hate or no online hate). Then, - from these bullying traces - bullying behavioural cues (repetition and intention to harm) are derived and formalised into a role scoring function. As a result, the proposed pipeline identifies the bully and the victim among chat participants. Evaluated against state-of-the-art methods, the proposed pipeline achieves better performances considering all the datasets and roles to predict. In addition, the error analysis confirms that deriving bullying behavioural cues is beneficial to the task of participant role identification.
Impact of Artificial Intelligence Regulations on Organizational Risks
Authors: Payton Misti Jo
Published: May 2023

The data science industry has grown exponentially due to the surge in consumer consumption of technology-related services and products. Following closely is the data science specialty of Artificial Intelligence (AI) algorithm development and use, which holds significant potential for an organization’s bottom line. By combining these new AI capabilities with the treasure troves of new and legacy consumer data, organizations can automate decision-making to entice new consumers to their products and services. These AI capabilities come with a plethora of potential uses and misuses. Organizations must take stock of the precedence of laws and regulations related to consumer privacy and ensure careful consideration of AI risk management to balance the potential financial implications of AI algorithm development and use, good and bad.
On Enhancing Security for Division Homomorphism with ElGamal
Authors: Silaghi Marius , Alsulami Ameerah
Published: May 2023

Secure auctions and machine learning in cloud increasingly employs multi-party and homomorphic encryption support.A modification to Elgamal public key cryptosystem was shown to enable homomorphic division using an encoding of plaintext as fractions with numerator and denominator encrypted separately. However we notice that unlike for other homomorphic cryptography schemes, the obtained division homomorphism allows for the retrieval of the input secrets from the result of the division. Since this cancels the benefit of the encryption, we propose the introduction of a masking operation based on random factors and discuss its success with operations in Zp and Q.
CyberReco:~Cybersecurity Workforce Readiness Recommender System
Authors: Lasisi Ramoni
Published: May 2023

Using the United States National Initiative for Cybersecurity Education framework as a guide, we propose CyberReco - Cybersecurity Workforce Readiness Recommender System, an AI/ML model that attempts to address readiness gaps needed to prepare users lacking in some cybersecurity knowledge, skills, and activities to be workforce-ready. CyberReco is built using natural language processing. We present a hierarchical-based framework that is composed of four components including, text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and a recommender component.
DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM
Authors: Tahabi Fattah Muhammad , Luo Xiao
Published: May 2023

Most studies in graph theory assume that graphs are static, but in reality, graph structures and features change over time, leading to the concept of dynamic graphs, which is an under-researched area. Contemporary research in dynamic graph representation learning typically treats different snapshots of the graph as separate entities, disregarding the benefits of incorporating temporal information. While some techniques try to solve this problem using recurrent neural network-based solutions, these approaches still face the challenge of the vanishing or exploding gradient problem and complicated training procedures. To address these issues, we propose DynamicG2B, a BiLSTM-based graph neural architecture that computes node representations guided by attention using neighborhood aggregation. Our method applies relevant attention weights at different time steps to classify nodes in a supervised manner, utilizing dynamic edges and node feature information. Our evaluation of two benchmark datasets shows that DynamicG2B outperforms seven state-of-the-art baseline models in node classification in dynamic graphs. Additionally, our analysis of attention weights opens up opportunities for further research into exploring the importance of relationships among graph nodes.
Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization
Authors: Jain Bhanu , Huber Manfred , Elmasri Ramez
Published: May 2023

Increasing utilization of machine learning based decision support systems emphasizes the need for resulting predictions to be both accurate and fair to all stakeholders. In this work we present a novel approach to increase a Neural Network model's fairness during training. We introduce a family of fairness enhancing regularization components that we use in conjunction with the traditional binary-cross-entropy based accuracy loss. These loss functions are based on Bias Parity Score (BPS), a score that helps quantify bias in the models with a single number. In the current work we investigate the behavior and effect of these regularization components on bias. We deploy them in the context of a recidivism prediction task as well as on a census-based adult income dataset. The results demonstrate that with a good choice of fairness loss function we can reduce the trained model's bias without deteriorating accuracy even in unbalanced datasets.
Visual Episodic Memory-based Exploration
Authors: Vice Jack , Ruiz-Sanchez Natalie , Douglas Pamela K , Sukthankar Gita
Published: May 2023

In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is episodic memory which enables both the recollection of events from the past and the projection of subjective future. This paper explores the use of visual episodic memory as a source of intrinsic motivation for robotic exploration problems. Using a convolutional recurrent neural network autoencoder, the agent learns an efficient representation for spatiotemporal features such that accurate sequence prediction can only happen once spatiotemporal features have been learned. Structural similarity between ground truth and autoencoder generated images is used as an intrinsic motivation signal to guide exploration. Our proposed episodic memory model also implicitly accounts for the agent's actions, motivating the robot to seek new interactive experiences rather than just areas that are visually dissimilar. When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.
Using Deep Learning to Detect Islamophobia on Reddit
Authors: Aldreabi Esraa , Lee Justin M. , Blackburn Jeremy
Published: May 2023

Islamophobia, a negative predilection towards the Muslim community, is present on social media platforms. In addition to causing harm to victims, it also hurts the reputation of social media platforms that claim to provide a safe online environment for all users. The volume of social media content is impossible to be manually reviewed, thus, it is important to find automated solutions to combat hate speech on social media platforms. Machine learning approaches have been used in the literature as a way to automate hate speech detection. In this paper, we use deep learning techniques to detect Islamophobia over Reddit and topic modeling to analyze the content and reveal topics from comments identified as Islamophobic. Some topics we identified include the Islamic dress code, religious practices, marriage, and politics. To detect Islamophobia, we used deep learning models. The highest performance was achieved with BERTbase+CNN, with an F1-Score of 0.92.
How Does a Minority Opinion Spread?
Authors: Baccino Luca , Villata Serena
Published: May 2023

In this paper, we propose a discrete opinion model in which two beliefs models compete. One opinion is endorsed bythe vast majority of agents, but this majority remains silent and rarely expresses its opinion. Conversely, the other beliefmodel is supported by a small minority that is very committed and does not hesitate to be loud. We observe that a very smallnumber of individuals is necessary for the minority opinion to become the majority. However, the presence of so-called ”inflexible” and ”super propagators” agents can counteract this phenomenon and prevent the loud minority from propagatingits view.
Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat
Authors: Gannod Mateo , Masto Nicholas , Owusu Collins , Highway Cory , Brown Katherine , Blake-Bradshaw Abigail , Feddersen Jamie , Hagy Heath , Talbert Douglas , Cohen Bradley
Published: May 2023

Migratory waterfowl (i.e., ducks, geese, and swans) management relies on landscape bioenergetic models to inform on-the-ground habitat conditions and conservation practices. Therefore, conservation planners rely on accurate predictions of wetland habitats for waterfowl at regional scales. Unharvested flooded corn is a popular management tool on public and private lands that greatly increases landscape-level energy compared to other wetlands; thus, landscape bioenergetic models are particularly sensitive to these habitat features. Despite their importance to conservation planning and implementation, the abundance and distribution of unharvested flooded corn fields across North America is unknown. Furthermore, training data is difficult to collect and accurate predictions are challenging given their unique attributes and discreteness at landscape-level lens. Advances in multispectral imagery and deep learning algorithms may enable continuous and autonomous detection of these habitat features. Therefore, we conducted modeling experiments using training data of unharvested flooded corn fields in West Tennessee and multispectral imagery collected from Sentinel-2 satellite missions. We performed several experiments using individual band combination composites and/or vegetation indices to identify optimal bands using MRUNET architectures. We subsequently used 3 ensemble models of important individual networks. We found the use of multispectral bands was necessary and although the CIR composite and OSAVI index improved precision, the 12-band composite increased recall, the metric we were most interested in. Moreover, all ensembles exhibited poor performance. Here, we present results of our initial modeling experiments and suggest future modeling exercises including temporal image and vegetation index stacking using multi-modal and/or recurrent neural network architectures.
Group Bias and the Complexity/Accuracy Tradeoff in Machine Learning-Based Trauma Triage Models
Authors: Phillips Katherine , Brown Katherine , Talbert Steve , Talbert Doug
Published: May 2023

Trauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy tradeoff, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I Trauma Center’s registry cases (n=50,644), this study describes, uses, and derives observations from a methodology to more thoroughly examine this tradeoff. This or similar methods can provide the insight needed for practitioners to balance understandability with accuracy. Additionally, this study incorporates an evaluation of group-based fairness into this tradeoff analysis to provide an additional dimension of insight into model selection. The experiments allow us to draw several conclusions regarding the machine learning models in the domain of trauma triage and demonstrate the value of our tradeoff analysis to provide insight into choices regarding model complexity, model accuracy, and model fairness.
ARCHIMEDES: Active Reasoning Conducted with Heterogeneous Information Monitors for Evading Dangers and Extended Surveillance
Authors: Schoenbeck Simon , Kuter Ugur
Published: May 2023

ARCHIMEDES is a suite of algorithms for planning and task allocation to monitor and surveil a geometrically defined area with a group of heterogenous agents. We focus on maximizing the information gained over a fleet of such agents. The multi-step process allows for our force-based system to find a solution faster by generating reasonable starting points for agents. We present our proposed algorithms, compare them to the most relevant related work via a walkthrough discussion, and describe experimental results from ARCHIMEDES in many mathematically abstract scenarios.
IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception
Authors: Bao Joseph , Kantaciourglu Murat , Vorobeychik Yevgeniy , Kamhoua Charles
Published: May 2023

Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn their behavior. However, most of the existing IoT honeypots, even the high interaction ones, are easily detected by an attacker who can observe honeypot traffic due to lack of real network traffic originating from the honeypot. This implies that, to build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows.

To achieve this goal, we propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator.We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices.Through an extensive experimental evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT traffic generation tool significantly outperforms state of the art sequence and packet generators in remaining indistinguishable from real traffic even to an adaptive attacker.
k-medianoids Clustering Algorithm
Authors: Cha James , Cha Teryn , Cha Sung-Hyuk
Published: May 2023

One of the simplest and popular clustering method is the simple k-means clustering algorithm. One of the drawbacks of the method is its sensitivity to outliers. To overcome this problem, the k-medians clustering algorithm is used. Another limitation of the simple k-means clustering algorithm is the Euclidean space assumption. The k-medoids has been used to overcome this assumption. Here a combined method called the k-medianoids clustering algorithm is proposed. A medianoid is a kind of median that does not require the Euclidean space assumption and is formally defined. The proposed method is demonstrated using nucleotide sequences.
Interpreting Predictive Models through Causality: A Query-Driven Methodology
Authors: Hadj Ali Mahdi , Le Biannic Yann , Wuillemin Pierre-Henri
Published: May 2023

Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the prediction of a pre-trained model. Despite these advancements, practitioners still seek to gain causal insights into the underlying data-generating mechanisms. To this end, some works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. These efforts have provided answers to various queries, but relying on a single pre-trained model may result in quantification problems. In this paper, we argue that each causal query requires its own reasoning; thus, a single predictive model is not suited for all questions. Instead, we propose a new framework that prioritizes the query of interest and then derives a query-driven methodology accordingly to the structure of the causal model. It results in a tailored predictive model adapted to the query and an adapted interpretability technique. Specifically, it provides a numerical estimate of causal effects, which allows for accurate answers to explanatory questions when the causal structure is known.