bluuebunny/crossref_metadata_2025 huggingface dataset:
Українське мовознавство в іменах Ніжинської вищої школи
Authors: Boyko N. I.
Published: May 2023

Статтю присвячено історії розвитку українського мовознавства в Ніжинській вищій школі в контексті столітнього функціонування мовознавчої кафедри (1922–2022 рр.), зокрема: кафедри української мови (до 2019 року), кафедри української мови та методики її навчання (2019–2022 рр.), кафедри української мови, методики її навчання та перекладу (від 2022 року), оглядові наукового доробку членів кафедри та їхньої науково-педагогічної діяльності. Виявлено, що науково-педагогічний колектив кафедри української мови упродовж багатьох років спрямовував свої зусилля на розвиток й утвердження української літературної мови, набуття нею державного статусу. З’ясовано, що лінгвістичні студії членів кафедри висвітлюють широкий спектр актуальних питань українського мовознавства. Домінувальна роль належить науковим працям членів кафедри, присвяченим актуальним проблемам українського мовознавства та української лінгво-дидактики. Ключові позиції посідають студії про українську мову – її державний статус, системну організацію, сучасний стан розвитку, походження та історію, структуру, функціонування, її діалекти, мовну культуру, мову і політику, міжмовні контакти, взаємозв’язки з іншими мовами, історію її досліджень та ін. Виявлено, що найбільшу увагу члени кафедри в останні десятиріччя зосереджували на вивченні українського мовознавства в аспекті лінгвістики тексту, лінгвостилістичних підходів до системних описів різнорівневих мовних одиниць, використовуваних у різночасових і різножанрових творах українських письменників, найчастіше тих, чия життєва або творча біо[1]графія пов’язана з Поліссям, із Чернігівщиною. Значний відсоток наукових праць з українського мовознавства члени кафедри опублікували в часописі «Література та культура Полісся».
1734 - 1735 İran seferlerinin organizasyon ve lojistiğinde Rusçuk örneği
Authors: BİZBİRLİK Alpay , ŞENTÜRK Seçkin
Published: May 2023

ÖZ: Osmanlı Devleti, I. Selim döneminde Çaldıran Muharebesi sonucu Doğu Anadolu coğrafyasını ele geçirmiştir. Osmanlı’nın, XVI. Yüzyılın sonunda Azerbaycan bölgesini ele geçirmesi ile devam eden Osmanlı-İran mücadelesi dahilinde İran tarafının kaybettiği topraklardan vazgeçmemesi sebebiyle sürekli savaşlar yaşanmıştır. Bu süreç içerisinde İran’da Safevi Hanedanı’nın iç karışıklıklar sebebiyle zayıflamış olması ile Osmanlı Devleti’nin ordu ve savaş kabiliyetinin üstünlüğü savaşlardan Osmanlı tarafını galip çıkarmıştır. Ancak XVIII. yüzyılda Osmanlı’da I. Mahmut saltanatının ilk yılları, Patrona Halil’in sebep olduğu isyanın etkileri ile geçmektedir. İran tarafında ise iç karışıklıklara son vererek Safevi hanedanını yönetimden uzaklaştırıp idareyi eline alan Nadir Şah, kaybedilen topraklara tekrar hakim olmak amacıyla harekete geçmiştir. Bu süreç içerisinde yaşanan Osmanlı Devleti’nin 1734-1735 İran Seferi’nin hazırlıklarının Rusçuk şehri özelindeki yapılan hazırlıklar R52 Rusçuk şer’iyye sicil defterindeki belgeler ışığında incelenmiştir.
TOOLKIT FOR ENSURING SYSTEMIC DEVELOPMENT OF HEALTH CARE INSTITUTIONS IN UKRAINE
Authors:   Khmelnytskyi National University, KRAVCHYK Yurii
Published: May 2023

The research emphasizes the objectively existing need, as well as the problem of ensuring effective management of the systemic development of health care institutions in Ukraine, in particular in the segment of private ownership. The purpose of the study is to determine the scientific substantiation of the theoretical, methodological and applied characteristics of the implementation of the toolkit for ensuring the systemic development of health care institutions in Ukraine, which provides for institutional and structural reforms in the field for the sake of organizations and the constant improvement of the quality of medical care for the population in accordance with international standards. It is shown that every enterprise is an open complex dynamic system that functions in a defined external environment. The modern socio-economic environment is characterized by a high rate of change, which can sometimes be predicted with low probability. Maintaining the existing state of the enterprise and moving forward is possible only under conditions of effective adaptation of the enterprise to changes in the external environment Options for ensuring systemic development of health care institutions, namely, internal growth due to more effective use of own resource potential; redistribution of internal resources, focusing them on progressive types of economic activity; termination of the provision of a number of services that require higher quality resources, in their real absence; transfer of certain types of activities, not provided with resources, to other performers on a contractual basis; merger / acquisition with the aim of improving the system of medical care of the population. The advantages and disadvantages and peculiarities of practical implementation of relevant strategic decisions are indicated. The scientific novelty of the research results lies in the further development of methodological and applied principles of strategic management of the development of health care institutions due to the identification of strategic alternatives for the efficiency of activities, as well as tools and means, the implementation of which ensures systemic development. The applied value of the research results lies in the development of various scenarios for the further balanced development of health care institutions.
РОЗВИТОК СИСТЕМИ УПРАВЛІННЯ ГОСПОДАРСЬКОЮ ДІЯЛЬНІСТЮ: ОСОБЛИВОСТІ ЗАКЛАДІВ ОХОРОНИ ЗДОРОВ’Я
Authors: Кравчик Юрій
Published: May 2023

У дослідженні актуалізовано увагу до проблематики менеджменту розвитку приватних закладів охорони здоров’я. Метою дослідження визначено ідентифікацію специфіки, напрямів та засобів розвитку приватних закладів охорони здоров’я. Вказано на те, що зміна зовнішнього середовища функціонування вітчизняних закладів охорони здоров’я і її вплив на стан та ефективність господарської діяльності розглядаються як вихідна передумова їх адаптації та стабільного ефективного функціонування. Визначено загальний алгоритм, мету та комплекс заходів з адаптації та розвитку господарської діяльності закладів охорони здоров’я в умовах невизначеності зовнішнього середовища господарювання. Зроблено висновок, що удосконалення системи менеджменту розвитку закладів охорони здоров’я має починатися з покращення її інформаційної та методичної бази. Окремим аспектом цієї проблеми є обґрунтування сукупності конкретних методів виконання техніко-економічного аналізу, зокрема неформалізованих (експертного, морфологічного, рейтингового, фактографічного, моніторингу, логічного моделювання, фундаментального та технічного), а також формалізованих (факторного, кон’юнктурного, математичного, статистичного, дослідження операцій і прийняття рішень, економетричного, економіко-математичного моделювання) методів.
Definición y canon del microrrelato en los manuales españoles de Secundaria y Bachillerato
Authors: Bartolomé Porcar Cristina
Published: May 2023

El microrrelato ha tenido, en las últimas décadas, el respaldo de los autores, la crítica y el mundo académico. Finalmente, ha sido incluido en el temario de la asignatura Lengua castellana y Literatura y, por lo tanto, en los manuales españoles de Enseñanza Secundaria y Bachillerato. En estas páginas aparece explicado tanto el fenómeno del auge de las formas breves a partir de la democracia, como el propio concepto del género. En el presente trabajo se analiza un corpus de libros de texto para comprender las ideas inculcadas a nuestro alumnado tanto desde el punto de vista teórico como histórico. Se ha revisado el grado de incorporación del microrrelato al repertorio de formas, el enfoque empleado para su definición y los modelos y autores destacados. Si bien hay acuerdo en el uso del término “microrrelato”, las propiedades pragmáticas y formales apuntadas son heterogéneas. En cuanto a los ejemplos seleccionados, predominan los relatos sorprendentes y fantásticos (gran parte llegados del otro lado del océano) y muestran todavía un canon de cuentistas en proceso de consagración que precisará futuras revisiones.
Validation of Kala-azar diagnostic tests in a pediatric teaching hospital in Baghdad.
Authors: Al-Janabi Muhi K. , Nasir Nadia A. , Hameed Ammar A.
Published: May 2023

Background: Kala-azar is an important parasitic disease that affects children of all age groups with fatal outcome if left without treatment.
Objectives: Children admitted with Kala-azar were studied for evaluation of Indirect Immunoflorescent Antibody Test and Bone Marrow examination validity.
Results: The majority (94%) of studied children were less than 5 years age group. Males were more than females with a ratio of 1.63:1. Sixty percent of patients were from Diyala governorate and 23% from Baghdad. The main clinical features were prolonged fever and hepatosplenomegaly (100%). Bone marrow examination was positive in 45% of suspected Kala-azar cases. IFAT was positive in 47(78%). IFAT sensitivity was 70% and specificity 15%. All cases were treated with Pentostam (sodium stibogluconate). No resistance to Pentostam treatment was detected. The case mortality rate was 2%.Patients and methods: A cross-sectional study was conducted on 60 children with Kala-azar admitted to Children Welfare Teaching Hospital - Medical City - Baghdad, for the period from the 1st of January 2011 to the 1st of February 2012. Information was obtained for each child including age, sex, residence, signs and symptoms, laboratory investigations and diagnosis was established by serology and/or bone marrow examination. Treatment and outcome were also noted.
Conclusion: The highest sensitivity in laboratory diagnosis among studied children was that obtained with a combination of bone marrow aspirate direct examination and IFAT. Conventional methods for diagnosis of visceral
Leishmaniasis are still indispensable.
A HIGH PREOPERATIVE NEUTROPHIL-LYMPHOCYTE RATIO AS AN INDICATOR OF EARLY RESPONSE OF KIDNEY TRANSPLANT
Authors: Ghinorawa Tanaya , Samudera Gilang , Soeroharjo Indrawarman , Hendri Ahmad Zulfan , Zulfiqqar Andy
Published: May 2023

Objective: This study aims to evaluate the association of the preoperative Neutrophil-lymphocyte ratio (NLR) and postoperative creatinine levels after kidney transplantation, which is a predictor factor for determining early postoperative transplantation complications. Material & Methods: We conducted a retrospective study of 33 patients enrolled in Sardjito General Hospital in Yogyakarta between 2017 and 2020. In this study, we observed the preoperative NLR and compared it with the postoperative creatinine level. The data were compared and analyzed. Results: This study showed that preoperative NLR in these 33 patients was 4.17 + 2.82. Postoperative creatinine levels were 2.38 + 1.75 mg/dL. Postoperative creatinine levels were higher in patients with low preoperative NLR (p=0.00). Besides, the correlation is weak and had a positive correlation direction (r=0.379). Conclusion: This study showed preoperative NLR and the postoperative serum creatinine level is correlated even weakly. NLR could be considered a predictor factor for determining postoperative transplantation complications, and even it still needs further research.
NEW BASELINE GLOMERULAR FILTRATION RATE ESTIMATION AS A GUIDANCE FOR NEPHRECTOMY STRATEGY IN LOCALIZED RENAL CELL CARCINOMA
Authors: Salman Aziz Muhamad Abi , Mas`udi Achmad Fayyad , Rahaju Anny Setijo , Dewanti Linda
Published: May 2023

Objective: This literature review aims to explain the role of nephrectomy in the treatment of localized RCC and its effects on postoperative renal function, as well as the role of the new baseline glomerular filtration rate (NBGFR) estimation method in the determination of the RCC method. Material & Methods: Articles were obtained through online searches using Pubmed, ScienceDirect, Web of Science, and Wiley Online Library using keywords NBGFR, renal function, nephrectomy, or localized renal cell carcinoma. Results: The development of several diagnostic factors as well as operative factors has made partial nephrectomy (PN) replace radical nephrectomy (RN) which for decades has been the main therapy in localized RCC. Postoperative determination of renal function is a crucial factor in the selection of therapies and several methods of determining NBGFR have been proposed in several previous studies. Conclusion: The NBGFR measurement method can be an accurate guide in the determination of nephrectomy methods in the treatment of localized RCC while use of computer software can help improve accuracy in the implementation of radical nephrectomy in RCC.
ANALYSIS OF HEMODIALYSIS PERIODS, AGE, AND SMOKING TOWARDS THE SEVERITY OF ERECTILE DYSFUNCTION IN PATIENTS WITH CHRONIC RENAL FAILURE ON ROUTINE HEMODIALYSIS
Authors: Hutama Saga Aditya , Daryanto Besut , Medianto  , Gunawan Atma
Published: May 2023

Objective: This study aims to analyze the factors associated such as hemodialysis periods, age, and smoking with ED severity in CKD patients undergoing routine hemodialysis. Material & Methods: A single-center, cross-sectional study was conducted involving a total 101 patients who were on routine hemodialysis who diagnosed with CKD and experienced complaint in sexual function were included. The severity of ED classified based on the International Index of Erectile Function (IIEF-5). The analysis was carried out descriptively and analytically using the ordinal regression method. Results: Hemodialysis duration (p = 0.044) were significantly related to severity of erectile dysfunction in patient with CKD and on routine hemodialysis. Meanwhile, although both older age (p =0.307) and smoking (p = 0.283) showed a positive estimate, they were not statistically significant. Conclusion: The study indicate that prevalence of ED observed in CKD patient with routine hemodialysis remain high. Several factors significantly contribute in ED severity such as shorter hemodialysis duration.
Issue Management in Energy Policy: Focusing on policy changes by period using text mining
Authors:   The Korean Association For Local Administration, Kim Jin Seon
Published: May 2023

This study aims to provide basic data for successful issue management by looking at issues surrounding energy policy macroscopically and collectively and checking changes in issues by period. Text mining techniques, including topic modeling, were used to analyze issues by period and characteristics of each issue, changes in common issues, and connection to the meaning of issues. As a result of the analysis, there were differences in contents, but the “direction” of the policy showed a high rate of appearance at all times, and issues related to nuclear power plants were flowing in two branches along with the direction. In addition, the need for technological innovation is emphasized as a countermeasure, and discussions on regional-led energy transition have begun in earnest in the central government. In addition, it was found that potential issues such as jobs, energy welfare, and conflict exist. In addition, policy suggestions and limitations of the research were described.
Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning
Authors: Hoffmann Maximilian , Bergmann Ralph
Published: May 2023

In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.
TinderAI: Support System for Matching AI Algorithms and Embedded Devices
Authors: Francobaldi Matteo , De Filippo Allegra , Borghesi Andrea , Pizˇurica Nikola , Jovančević Igor , Llewellynn Tim , De Prado Miguel
Published: May 2023

Artificial Intelligence (AI) is becoming increasingly important and pervasive in the modern world. The widespread adoption of AI algorithms is reflected in the extensive range of HW devices on which they can be deployed, from high-performance computing nodes to low-power embedded devices. Given the large set of heterogeneous resources where AI algorithms can be deployed, finding the most suitable device and its con- figuration is challenging, even for experts.

We propose a data-driven approach to assist AI adopters and developers in choosing the optimal HW resource. Our approach is based on three key elements: i) fair benchmarking of target AI algorithms on a set of hetero- geneous platforms, ii) creation of ML models to learn the behaviour of these AI algorithms, and iii) support guidelines to help identify the best deployment option for a given AI algorithm. We demonstrate our approach on a specific (and relevant) use case: Deep Neural Net- work (DNN) inference on embedded devices.
Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task
Authors: Rolon-Mérette Damiem , Rolon-Merette Thaddé , Chartier Sylvain
Published: May 2023

Nowadays, artificial neural networks can easily solve the N-bit parity problem. However, each time a different level must be learned, the network must be retrained. This, combined with the exponential increase of learning trials required as N grows, make these models too different from how their biological counterpart solves them. This is because humans learn to recognize patterns, count, and determine if numbers are odd or even.  Once they have learned these tasks, they can have them interact to solve any level without further training. This behavior is akin to performing multiple associations of different tasks. Therefore, it is proposed that by using bidirectional associative memory neural networks, it would be possible to solve the N-bit parity problem in a similar fashion to humans. To achieve this, two networks interacted; one served as a task Identifier and the other as a memory Extractor, giving the desired behavior influenced by the Identifier. Results showed that the model could solve the 2- to 9-bit in linear time once the associations were learned. Moreover, this was possible with 97% fewer inputs and no retraining. In addition, because of the recurrent nature of the model, it could also solve the tasks even under high noise levels.
Prioritizing Answer Sets Based on Conditional Expert Knowledge
Authors: Wilhelm Marco , Thevapalan Andre , Kern-Isberner Gabriele
Published: May 2023

Answer set programming (ASP) and conditional reasoning both are powerful and widely used methodologies from the field of knowledge representation and reasoning (KR) which are capable of formalizing default statements that usually hold but also leave room for exceptions. While ASP convinces with an intuitive rule-based syntax and fast solvers, conditionals come along with a sophisticated preference-based semantics. Here, we combine both approaches by calculating answer sets which we then prioritize based on conditional expert knowledge. We apply our hybrid approach to the task of planning warehouse layouts from the logistics domain which is predestinated for our approach because it involves, on the low-level, many variables and technical framework conditions (like rack positions) and, on the high-level, expert knowledge of the layout designer.
A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries
Authors: Cummins Nicholas , Killen Brad , Ramezani Somayeh Bakhtiari , Rahimi Shahram , Seale Maria , Mittal Sudip
Published: May 2023

Machine learning has shown to be a crucial part of big data analytics; however, it lacks when the data is continuously streaming in from the system and changing too much from the original training data. Online learning is machine learning for streaming data that arrives in a sequential order where the model updates after every data point. While machine learning relies on well-established libraries such as PyTorch and Keras, the libraries for online learning are less well known, but they are here to serve similar purposes of reproducibility and reducing the time from research to production. Here, we compare different libraries for online learning research, specifically supervised learning. We compare them on the axes of developmental experience and benchmark testing as researchers. Our comparison as developers takes maintenance, documentation, and offerings of state-of-the-art algorithms into account. As this is not necessarily free of bias, we also use benchmarks known to online learning to gather power usage, RAM usage, speed, and accuracy of these libraries to get an objective view. Our findings show that Avalanche and River, including River-torch, are among the best libraries in terms of performance and applicability to the research in supervised online learning.
Hybrid Bayesian Networks for the Reliability Analysis of Systems with Continuous Variables
Authors: Portinale Luigi
Published: May 2023

The standard way of dealing with continuous variablesinto reliability models is to discretize (or even binarise)them, resulting in discrete state models. The presentpaper proposes an approach where continuous systemvariables can be directly exploited by resorting to HybridBayesian Networks (HBN), where both continuousand discrete variables can be mixed in a general way.This allows one to: model the inter-dependencies betweendiscrete state components or subsystems, modelthe inter-dependencies between continuous system variables,model the influence of contextual information onsystem variables and components, model the definitionof specific system events or conditions given specificvalues of the system variables. We will show how theabove issues can be captured in a principled way bythe HBN formalism, by making the final analyses moregrounded on the actual values of every system variable.We finally present a case study where the model of agranule storage tank system of a petrochemical plant isconsidered, and we present the results of specific analysesimplemented as inference on the HBN model.
Answering Student Queries with a Supervised Memory Conversational Agent
Authors: Baud Florian , Aussem Alex
Published: May 2023

This paper describes a discussion-bot that provides answers to students’ questions about the Data Science master program at the University of Lyon 1. Based on a seq2seq architecture combined with a supervised memory module, the bot identifies the questioner’s interest and encodes relevant information from the past conversation to provide personalized answers. A dialogue generator based on hand-crafted dialogues was built to train our model on these synthetic dialogues. The agent and its memory are adaptable to another context by modifying the intention database of the generator. The model was deployed and the results show that the discussion-bot meets most students’ learning requests. We discuss further directions that might be taken to increase the model's effectiveness.
Mitigating Age Biases in Resume Screening AI Models
Authors: Harris Christopher
Published: May 2023

As populations age, an increasing number of workers beyond the traditional retirement age are opting to continue working. Nevertheless, discrimination against older job seekers seeking new employment opportunities remains widespread. To address this issue, we enlisted a pool of crowdworkers to assess the resumes of IT job candidates and guess each candidate's age, race, and gender. Using this crowdsourced data, we trained an AI model and applied bias correction techniques from IBM's AI 360 and Microsoft's Fairlearn toolkits to correct for biases based on race, gender, and age. We analyzed the effectiveness of these tools in mitigating different types of bias in job hiring algorithms, explored why age may be more challenging to eliminate than other forms of bias, and discussed additional approaches to enhance fairness. Our results indicate that implicit age bias, or ageism, is prevalent in hiring decisions and more pervasive than other well-documented forms of bias, such as race and gender biases.
Bayesian Network Conflict Detection for Normative Monitoring of Black-Box Systems
Authors: Onnes Annet , Dastani Mehdi , Renooij Silja
Published: May 2023

Bayesian networks are interpretable probabilistic models that can be constructed from both data and domain knowledge. They are applied in various domains and for different tasks, including that of anomaly detection, for which an easy to compute measure of data conflict exists. In this paper we consider the use of Bayesian networks to monitor input-output pairs of a black-box AI system, to establish whether the output is acceptable in the current context in which the AI system operates. A Bayesian network-based prescriptive, or normative, model is assumed that includes context variables relevant for deciding what is or is not acceptable. We analyse and adjust the conflict measure to make it applicable to our new type of monitoring setting.
Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach
Authors: Abbas Asim , Khalid Mutahira , Chalarca Sebastian , Keshtkar Fazel , Bukhari Syed Ahmad Chan
Published: May 2023

An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically
Towards a Temporal Probabilistic Argumentation Framework
Authors: Bistarelli Stefano , David Victor , Santini Francesco , Taticchi Carlo
Published: May 2023

In recent years, the notion of time has been studied in different ways in Dung-style Argumentation Frameworks. For example, time intervals of availability have been added to arguments and relations. As a result, the output of Dung semantics varies over time. In this paper, we consider the situation in which arguments hold with a certain probability distribution during a given interval. To model the uncertain character of events, we propose different notions of temporal conflict between arguments according to the type of availabilities intersection (partial, inclusive, or total). Then, we refine these notions of conflict by a defeat relation, using criterion functions that evaluate an attack’s significance according to the probability over time.
Preliminary results of a conceptual graph query language
Authors: Genest David , Legeay Marc , Loiseau Stéphane
Published: May 2023

The contribution of this article is to bring the interesting features of SPARQL to the conceptual graph model while preserving its distinctive features and especially the graphical ones.Therefore this article presents a general conceptual graph query language, called CGQL.This article also provides an experimental comparison between CGQL and SPARQL engines: it shows the interest of our implementation for querying knowledge.
Causanom: Anomaly Detection With Flexible Causal Graphs
Authors: Strelnikoff Sasha , Jammalamadaka Aruna , Lu Tsai-Ching
Published: May 2023

Causality-based anomaly detection methods provide at least two significant theoretical benefits over purely statistical methods: 1. Improved robustness to non-anomalous out-of-distribution data, which implies a reduction in false-alarms; 2. A potential for failure localization due to the topological ordering of the causal graph. Recent studies have considered the utilization of causality-based methods for time series anomaly detection, however, these methods require the causal graph to be fixed; resultingly, such methods are not robust to incorrectly estimated causal graphs and are not able to natively model counterfactual scenarios. To address these limitations, we introduce Causanom: a graph-based encoder-decoder neural network for time series anomaly detection. Causanom utilizes a node conditional data-stream representation in conjunction with a weighted graph aggregation function in order to efficiently capture heterogeneous node dynamics whilst allowing for a flexible graphical structure. We show that Causanom can be trained along with auxiliary constraints in order to tune the causal graph and improve performance. Additionally, we show that Causanom can be used to produce counterfactual data, which we leverage to identify violated causal relationships. Using real and synthetic time series data respectively, we show that Causanom performs at least as well as state-of-the-art baselines in the anomaly detection task and outperforms existing methods in a causal attribution task.
Visualization of Learning Process in Feature Space
Authors: Inoue Tomohiro , Murata Noboru , Sugiura Taiki
Published: May 2023

In machine learning, the structure of feature space is an important factor that determines the performance of a model. Therefore, we can deepen our understanding of learning algorithms if we can visualize changes in the structure of feature space during the learning process. However, visualizing such changes is difficult because it requires dimensionality reduction while maintaining consistency with the data structure in high-dimensional space and in the temporal direction. In this study, we visualized feature changes during the learning process by capturing them as changes in the positional relationship between target features and time-invariant reference coordinates with a log-bilinear model.
A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Authors: Pokhrel Prativa , Lazar Alina
Published: May 2023

The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. Therefore, finding the optimal values of these hyperparameters is integral in improving the prediction accuracy of an ML algorithm and the model selection. However, manually searching for the best configuration is a tedious task, and many AutoML (Automated Machine Learning) frameworks have been proposed recently to help practitioners solve this problem. Hyperparameters are the values or configurations that control the algorithm’s behavior while building the model. Hyperparameter optimization (HPO) is the guided process of finding the best combination of hyperparameters that delivers the best performance on the data and task at hand in a reasonable amount of time. In this work, we compare the performance of two frequently used AutoML HPO frameworks, Optuna and HyperOpt, on popular OpenML tabular datasets to identify the best framework for tabular data. The results of the experiments show that Optuna performs better than HyperOpt, whereas HyperOpt is the fastest for hyperparameter optimization.
Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets
Authors: Brue Jacob , Shymanski Joseph , Karaoglu Selim , Sen Sandip
Published: May 2023

The long-running Automated Negotiating Agents Competition (ANAC) is comprised of various agent-agent and human-agent negotiation leagues. One such competition is the Automated Negotiation League (ANL) which involves repeated, bilateral negotiation over multiple issues. Researchers have investigated a tournament setting for this scenario involving a small, fixed number of agents. We are interested in automated agents participating in large and open marketplaces containing many instances of well-known agent types of varying sophistication. We experiment with four representative negotiation behaviors as agent types: Hardliner, Boulware, Conceder, and Tit-for-Tat. We simulate open markets with varying negotiation domain sizes, agent type distributions, and negotiation time available to evaluate the relative performances of different negotiation strategies. We analyze and report relative performances of the strategies on relevant performance metrics. We also extend this analysis using a head-to-head matrix.
Towards binary encoding in Bidirectional Associative Memories
Authors: Rolon-Merette Thaddé , Rolon-Merette Damiem , Chartier Sylvain
Published: May 2023

Bidirectional Associative Memories (BAMs) are Artificial Neural Networks frequently utilized in cognitive modeling. While bipolar encoding is commonly used in BAMs for optimal performance, binary encoding presents interesting properties. As such, this study introduces a novel transmission function for binary encoding and compares its performance to the conventional bipolar transmission function. To evaluate, an auto-association learning task and a noisy recall task were implemented. Results revealed that despite longer learning times, binary encoding preserves or enhances the properties observed in binary encoding. Findings are promising from a cognitive perspective, as they open the possibility of building intricate models of human cognition.
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Authors: Neiman David , Rubinstein Zachary , Smith Stephen
Published: May 2023

Roadway congestion leads to wasted time and money and environmental damage. Since adding more roadway capacity is often not possible in urban environments, it is becoming more important to use existing road networks more efficiently. Toward this goal, recent research in real-time, schedule-driven intersection control has shown an ability to significantly reduce the delays incurred at signalized intersections. Such approaches operate by building a predictive model of when locally sensed approaching traffic is expected to arrive at a given intersection, and then using the model to generate a signal timing plan (a phase schedule) that minimizes the cumulative delay of this traffic as it moves through the intersection. In this paper, we consider whether further reduction in delay could be gained by applying this predictive model to dynamically reroute some portion of vehicles willing to share their destinations along less congested paths. We developed an algorithm that simulates the current traffic state forward at each vehicle decision point, based on knowledge of other vehicles’ current routes and traffic signals’ control algorithms, and evaluated it using the SUMO microscopic simulator on different road networks (one as a simple synthetic example and the other taken from the real world) using different traffic signal control algorithms (fixed-timing plans and schedule-driven intersection control). Experiments carried out on combinations of networks and traffic signal control algorithms show that our rerouting protocol reduces delay for both vehicles participating in route guidance (adopters) and those that do not (non-adopters) and that the reduction in delay generally increases as the proportion of adopters does.
GABSA-PT: Graph Neural Networks for Aspect-level Sentiment Analysis in Portuguese Language
Authors: Gomes Gabriel , Corrêa Ulisses , Freitas Larissa
Published: May 2023

Aspect-based Sentiment Analysis is a Natural Language Processing task that aims to extract aspects from an opinionated text and identify the sentiment expressed by the opinion holder towards these aspects. Graph-based text representation has been shown to bring benefits to this task. While studies have demonstrated the effectiveness of this representation for ABSA using Graph Neural Networks in English, more work currently needs to be done evaluating this methodology for Portuguese language. In this article, we adapt a GNN known as DualGCN for sentiment classification using Brazilian Portuguese data. The model created using this approach achieved satisfactory results with a balanced accuracy of 75%
Predicting the Effectiveness of Blockchain Bug Bounty Programs
Authors: Marcavage Ed , Mason Jake , Zhong Chen
Published: May 2023

Bug bounty programs have proven to be an effective means for organizations to incentivize ethical hackers to report security vulnerabilities in their software. As the use of blockchain-based applications has grown, bug bounty programs have been established to identify vulnerabilities in these applications, such as smart contracts. However, bug bounty programs face unique challenges in encouraging ethical hackers. In this study, we collected data from about 200 bug bounty programs related to blockchain software from multiple bug bounty platforms. We analyzed the content of these programs and examined the involvement of ethical hackers, with the aim of examining the effectiveness of the current bug bounty programs for blockchain software. Additionally, we extracted various features from the content and format of the bug bounty programs and utilized them to construct a regression model that predicts the effectiveness of a program in drawing in ethical hackers. Our work is a fundamental step towards developing effective strategies for incentivizing ethical hackers in the blockchain domain.
MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN
Authors: Perekadan Vishal , Banerjee Chaity , Mukherjee Tathagata , Pasiliao Eduardo , Kulhandjian Hovannes , Kulhandjian Michel
Published: May 2023

In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data.  Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolutions) to learn spatio-temporal features from the signal for the problem of modulation recognition. By tweaking the convolutional filters to learn the helical symmetry of the data, we can design a shallow network for automatic modulation recognition (AMR). We present the results of our experiments with raw I/Q signal data collected in an uncalibrated radio frequency (RF) environment using several different modulation schemes. We show that with our methods and implementation, we can achieve around 99 % accuracy for automatic modulation recognition, for a variety of practical modulation techniques without the need for explicit feature engineering.
SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension
Authors: Chapagain Jeevan , Risha Zak , Banjade Rabin , Oli Priti , Tamang Lasang , Brusilovsky Peter , RUs Vasile
Published: May 2023

The ability to automatically assess learners' activities is the key to user modeling and personalization in adaptive educational systems.The work presented in this paper opens an opportunity to expand the scope of automated assessment from traditional programming problems to code comprehension tasks where students are requested to explain the critical steps of a program. The ability to automatically assess these self-explanations offers a unique opportunity to understand the current state of student knowledge, recognize possible misconceptions, and provide feedback. Annotated datasets are needed to train Artificial Intelligence/Machine Learning approaches for the automated assessment of student explanations. To answer this need, we present a novel corpus called SelfCode which consists of 1,770 sentence pairs of student and expert self-explanations of Java code examples, along with semantic similarity judgments provided by experts. We also present a baseline automated assessment model that relies on textual features. The corpus is available at the GitHub repository (https://github.com/jeevanchaps/SelfCode).
Comparing Statistical Models for Retrieval based Question-answering Dialogue: BERT vs Relevance Models
Authors: Pal Debaditya , Leuski Anton , Traum David
Published: May 2023

In this paper, we compare the performance of four models in a retrieval based question answering dialogue task on two moderately sized corpora (~ 10,000 utterances). One model is a statistical model and uses cross language relevance while the others are deep neural networks utilizing the BERT architecture along with different retrieval methods. The statistical model has previously outperformed LSTM based neural networks in a similar task whereas BERT has been proven to perform well on a variety of NLP tasks, achieving state-of-the-art results in many of them. Results show that the statistical cross language relevance model outperforms the BERT based architectures in learning question-answer mappings. BERT achieves better results by mapping new questions to existing questions.
Teaching Artificial Intelligence (AI) with AI for AI applications
Authors: Brawner Keith , Wang Ning , Nye Ben
Published: May 2023

The emergence of widely-used artificial intelligence (AI) has created a critical need for AI expertise, not just as a research area but for workers in the wide variety of careers and roles that AI disrupts.  While AI is still an area of research for new processing, application, and development – it continues to partially automate, augment, or replace many of the tasks which are performed through active use of human hands.  While recently publicized items such as ChatGPT and MidJourney have made press in their adjustment to writing and image generation technology, the basic workflow of copyeditors and digital artists was completely transformed, inside of the year, to a combination of partially automated or fully automated AI tasks.  While some blame AI as part of the “problem”, it is naturally part of the “solution” – AI tools to help workers develop AI competencies.  The paper describes an array of strategies which the DoD and its ICT UARC are using to address the fundamental problem of quickly upskilling the DoD workforce of over 2 million adult learners.
Tradeoffs within Representational Behaviors: Multi-Objective Optimization Strategies for Simulated Military Units
Authors: Kewley Rob , Argenta Chris , McGoarty Chris
Published: May 2023

Generating representative behaviors for opposing and adjacent forces is critical for training military units in a simulated environment. Humans are often required in-the-loop because automated systems struggle to provide intelligent and adaptive behaviors that represent appropriate military doctrine and can be explained in the After Action Review. Automated solutions often fail to maintain enough diversity to be replayed in successive iterations of the simulated mission without becoming easily predictable. This paper describes the decision-making approach for automated hierarchical planning agents designed to overcome these challenges. Our Mission Command Agents (MCAs) combine constraint optimization for multi-agent goal reasoning and state-space planning for route planning to generate Courses of Action (COAs) for simulated units. We detail extensions in both steps to produce a set of COAs on the Pareto front for multiple practical (e.g., distance), doctrinal (e.g., cover and concealment), and behavioral (e.g., risk-tolerance or aggressiveness) objectives.

                        We compare COAs generated using this new approach to results of our previous MCA that uses a more traditional approach of aggregating objectives or preferences into a single weighted utility function and optimizing with respect to that joint utility to find multiple optimal and near-optimal solutions. In contrast to previously emitted behaviors, which often reflected many small differences in utility or positioning, analyzing the trade-offs among multiple objectives produces more meaningfully diverse solutions. In an experiential training context, this diversity supports replaying scenarios with automated units that have distinct preferences and priorities. We summarize our results with evaluation metrics showing a set of COAs can appropriately represent doctrinal constraints with diverse, reasonable, and explainable trade-offs between objectives.
SISTEM MONITORING OUTPUT SOLAR PANEL MENGGUNAKAN LABVIEW
Authors: Abdurahman Abdurahman , Kusnadi Heri , Utomo Luki
Published: May 2023

Penyediaan listrik yang ramah lingkungan sangat baik sekali untuk mengurangi polusi, salah satunya adalah pembangkit listrik dengan memanfaatkan sinar matahari yang dikenal dengan panel surya. Kesulitan timbul pada saat  pengukuran arus yang dihasilkan secara real time. Tujuan penelitian ini adalah merancang dan mengimplementasikan sebuah sistem monitoring arus dan tegangan yang dihasilkan panel surya. Output dari panel PV yaitu parameter arus dan tegangan masing-masing diukur oleh sensor dalam bentuk sinyal analog lalu diumpankan pada mikrokontroller yang selanjutnya sinyal tersebut diubah menjadi bentuk digital dan diolah menggunakan software LabView menjadi bentuk informasi grafis dalam tampilan Graphics User Interface (GUI). Penelitian dilakukan dengan menggunakan 2x100 Wp Panel PV  dimana output daya yang dihasilkan antara 40,6 - 142,6 watt dengan waktu pengambilan data selama 7 jam. Hasilnya arus dan tegangan yang dihasilkan dapat dimonitor secara langsung.Kata kunci : solar panel, labview, monitoring, real time
SIMULASI PERBAIKAN PROFIL TEGANGAN DAN RUGI-RUGI DAYA PADA SISTEM KELISTRIKAN GEDUNG SMA NEGERI 1 SIANTAN
Authors: Mustar Mustar
Published: May 2023

Tegangan yang tidak standar dapat menyebabkan peralatan listrik tidak dapat bekerja dengan optimal, bahkan dapat menyebabkan kerusakan permanen pada peralatan listrik itu sendiri. Oleh karena itu upaya untuk mengatasi permasalahan jatuh tegangan dan rugi-rugi daya dilakukan dengan Simulasi I: mengelompokkan beban lebih seimbang pada setiap fasa, Simulasi II: mengubah diameter penampang dan Simulasi III: kombinasi antara keduanya yaitu mengelompokkan beban sekaligus merubah diameter penampang.  Pada penelitian ini ketiga simulasi tersebut menggunakan program ETAP 16.0.0, dimana simulasi yang dijalankan ULF (Unbalanced Load Flow) untuk menghitung perbaikan jatuh tegangan dan rugi-rugi daya. Dari ketiga simulasi yang dilakukan diperoleh hasil Simulasi III lebih baik dari Simulasi I dan II. Dengan mengelompokkan beban fasa R=13.300 Watt, fasa S=12.110 Watt, fasa T=12.721 Watt dan mengubah diameter penampang di Panel Utama fasa R=10 mm; ruang Laboratorium Komputer 1=6 mm; Ruang Guru=4 mm, didapatkan  perbaikan jatuh tegangan dari 184 Volt menjadi 202 Volt pada ruang laboratorium komputer 1 dan ruang guru dari 194 Volt menjadi 202 Volt, sedangkan rugi-rugi daya dapat ditekan hingga 40,91% pada fasa R-0 (dari 2,070 kW menjadi 0,847 kW).Kata kunci : profil tegangan; jatuh tegangan; rugi-rugi daya; ETAP