bluuebunny/crossref_metadata_2025 huggingface dataset:
Deadlock-Free Online Plan Repair in Multi-robot Coordination with Disturbances
Authors: Coskun Adem , O'Kane Jason , Valtorta Marco
Published: May 2021

Multirobot systems are increasingly deployed in environments where they interact with humans. From the perspective of a robot, such interaction could be considered a disturbance that causes a well-planned trajectory to fail. Previous approaches that modify trajectories in the presence of disturbances rearrange the order in which robots pass collision regions and other obstacles, in the laudable attempt to improve the average travel time for all robots. By doing so, however, deadlock may arise. In this paper, we provide a precise definition of deadlock using a graphical representation and prove some of its important properties. We show how to exploit the representation to detect the possibility of deadlock and to characterize conditions under which deadlock may not occur. We provide experiments in simulated environments that illustrate the potential usefulness of our theory of deadlock.
Ensemble-based Semi-Supervised Learning for Hate Speech Detection
Authors: Alsafari Safa
Published: May 2021

Large and accurately labeled textual corpora are vital to developing efficient hate speech classifiers. This paper introduces an ensemble-based semi-supervised learning approach to leverage the availability of abundant social media content. Starting with a reliable hate speech dataset, we train and test diverse classifiers that are then used to label a corpus of one million tweets. Next, we investigate several strategies to select the most confident labels from the obtained pseudo labels. We assess these strategies by re-training all the classifiers with the seed dataset augmented with the trusted pseudo-labeled data. Finally, we demonstrate that our approach improves classification performance over supervised hate speech classification methods.
Single-trial FRPs: A Machine Learning Approach Towards the Study of the Neural Underpinning of Reading Disorders
Authors: Christoforou Christoforos , Papadopoulos Timothy C. , Theodorou Maria
Published: May 2021

Understanding the neural underpinning of reading disorders, such as dyslexia, is a fundamental question in developmental neuroscience. However, identifying and isolating informative neural components elicited during free-naming paradigms (i.e. unprompted and unconstrained naming tasks) has proven a challenging methodological task. These methodological barriers have hindered the study of the neural underpinnings of reading disorders. In this paper, we proposed a machine learning approach for detecting neural components during free-naming, overcoming much of the current methodological challenges. We propose a new neural-based metric to differentiate groups of children with dyslexia (DYS) and their chronological age controls (CAC) in a free-naming task. Our approach combines electroencephalography (EEG) and eye-tracking measures to generate single-trial fixation-related potentials (sFRPs) and formulate an optimization problem to extract naming-related neural components, informative of group differences. Our approach is validated on a real dataset involving children with dyslexia and CAC performing a Rapid-Automatized Naming (RAN) task. Our results demonstrate the validity of the proposed metric as an indicator of the neural-based markers of reading disorders. Importantly, our proposed framework provides a novel approach that can facilitate the study of neural correlates of reading disorders under paradigms current methods are unable to.
Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
Authors: Pillai Nisha , Matuszek Cynthia , Ferraro Francis
Published: May 2021

The success of grounded language acquisition using perceptual data (e.g., in robotics) is affected by the complexity of both the perceptual concepts being learned and the language describing those concepts. We present methods for analyzing this complexity, using both visual features and entropy-based evaluation of sentences. Our work illuminates core, quantifiable statistical differences in how language is used to describe different traits of objects, and the visual representation of those objects. The methods we use provide an additional analytical tool for research in perceptual language learning.
HTN Learning via Transfer Learning of Domain Landmarks
Authors: Pennisi Greg , Fine-Morris Morgan , Floyd Michael W. , Auslander Bryan , Munoz-Avila Hector , Heflin Jeff , Gupta Kalyan Moy
Published: May 2021

Hierarchical Task Network (HTN) planning uses task-subtask relationships to break complex problems into more manageable subtasks, similar to how human problem-solvers plan. However, one limitation of HTN planning is that it requires domain knowledge in the form of planning methods to perform this task decomposition. Recent work has partially alleviated this knowledge engineering requirement by learning HTN methods from traces of observed behavior. Although this greatly reduces the amount of knowledge that must be encoded by a domain expert, it requires a large collection of traces in order to infer important landmark states that are used during trace segmentation and method learning. In this paper we present a novel method for landmark inference that transfers knowledge of landmarks from previously encountered environments to new environments without requiring any traces from the new environment. We evaluate our work in a logistics planning domain and show that our approach performs comparably to the existing landmark inference method but requires far fewer traces.
Selection Strategies for Inductive Reasoning From Conditional Belief Bases and for Belief Change Respecting the Principle of Conditional Preservation
Authors: Beierle Christoph , Kern-Isberner Gabriele
Published: May 2021

Given a belief base ∆ consisting of a set of conditionals,there are many different ways an agent may inductivelycomplete the knowledge represented by ∆ to a completeepistemic state; two well-known approaches are given by systemP and system Z, and also each ranking model of ∆ induces afull inference relation. C-representations are special rankingmodels that obey the principle of conditional indifference.Inductive reasoning using c-representations can be done withrespect to all c-representations, with respect to a subclass of,e.g., minimal c-representations, or with respect to singlec-representations. In this paper, we present and investigateselection strategies for determining single c-representations tobe used for inductive reasoning from belief bases. We developaxioms for specifying characteristics of selection strategies.We illustrate which desirable properties, like syntaxsplitting, are ensured by the axioms, and develop constructionsfor obtaining selection strategies satisfying the axioms.Furthermore, we also present and study the extension of selectionstrategies to c-revisions that follow the principle ofconditional preservation and that have been employed successfullyin various belief change settings.
Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise
Authors: Xu Nan , Feyisetan Oluwaseyi , Aggarwal Abhinav , Xu Zekun , Teissier Nathanael
Published: May 2021

Deep Neural Networks, despite their success in diverse domains, are provably sensitive to small perturbations which cause the models to return erroneous predictions to minor transformations. Recently, it was proposed that this effect can be addressed in the text domain by optimizing for the worst case loss function over all possible word substitutions within the training examples. However, this approach is prone to weighing semantically unlikely word replacements higher, resulting in accuracy loss. In this paper, we study robustness to adversarial perturbations by using differentially private randomized substitutions while training the model. This approach has two immediate advantages: (1) by ensuring that the word replacement likelihood is weighted by its proximity to the original word in a metric space, we circumvent optimizing for worst case guarantees thereby achieve performance gains; and (2) the calibrated randomness results in training a privacy preserving model, while also guaranteeing robustness against adversarial attacks on the model outputs. Our approach uses a novel density-based differentially private mechanism based on truncated Gumbel noise. This ensures training on substitutions of words in dense and sparse regions of a metric space while maintaining semantic similarity for model robustness. Our experiments on two datasets suggest an improvement of up to 10% on the accuracy metrics.
A Complete Map of Conditional Knowledge Bases in Different Normal Forms and Their Induced System P Inference Relations Over Small Signatures
Authors: Beierle Christoph , Haldimann Jonas , Kutsch Steven
Published: May 2021

Conditional knowledge bases consisting of qualitativeconditionals play a predominant role in knowledge representationand reasoning. In this paper, we develop a full map of allconsistent conditional knowledge bases over a small signature indifferent normal forms. We introduce two new normal formsthat take the induced system P inference relation into account,the system P normal form (SPNF) and the renaming SPNF(ρSPNF) considering additionally renamings of theunderlying signature. For a two-element signature, we systematicallygenerate and compare all consistent knowledge bases in ANF,RANF, SPNF, and their renaming counterparts, as well as allcomplete system P inference relations induced by conditionalknowledge bases.
Alzheimer’s Detection through 3D Convolutional Neural Networks
Authors: Hogan Ryan , Christoforou Christoforos
Published: May 2021

To inform a proper diagnosis and understanding of Alzheimer’s Disease (AD), deep learning has emerged as an alternate approach for detecting physical brain changes within magnetic resonance imaging (MRI). The advancement of deep learning within biomedical imaging, particularly in MRI scans, has proven to be an efficient resource for abnormality detection while utilizing convolutional neural networks (CNN) to perform feature mapping within multilayer perceptrons. In this study, we aim to test the feasibility of using three-dimensional convolutional neural networks to identify neurophysiological degeneration in the entire-brain scans that differentiate between AD patients and controls. In particular, we propose and train a 3D-CNN model to classify between MRI scans of cognitively-healthy individuals and AD patients. We validate our proposed model on a large dataset composed of more than seven hundred MRI scans (half AD). Our results show a validation accuracy of 79% which is at par with the current state-of-the-art. The benefits of our proposed 3D network are that it can assist in the exploration and detection of AD by mapping the complex heterogeneity of the brain, particularly in the limbic system and temporal lobe. The goal of this research is to measure the efficacy and predictability of 3D convolutional networks in detecting the progression of neurodegeneration within MRI brain scans of HC and AD patients.
One game show, two boys, two aces, three prisoners - what’s an AI to do?
Authors: Neufeld Eric , Finnestad Sonje
Published: May 2021

We review a quartet of widely discussed probability puzzles – Monty Hall, the three prisoners, the two boys, and the two aces. Pearl explains why the Monty Hall problem is counterintuitive using a causal diagram. Glenn Shafer uses the puzzle of the two aces to justify reintroducing to probability theory protocols that specify how the information we condition on is obtained. Pearl, in one treatment of the three prisoners, adds to his representation random variables that distinguish actual events and observations. The puzzle of the two boys took a perplexing twist in 2010. We show the puzzles have similar features, and each can be made to give different answers to simple queries corresponding to different presentations of the word problem. We offer a unified treatment that explains this phenomenon in strictly technical terms, as opposed to cognitive or epistemic.
Graph Neural Networks for Modeling Causality in International Trade
Authors: Monken Anderson , Haberkorn Flora , Gopinath Munisamy , Freeman Laura , Batarseh Feras A.
Published: May 2021

Neural network algorithms have proven successful for accurate classifications in many domains such as image recognition and semantic parsing. However, they have long suffered from the lack of ability to measure causality, predict outliers effectively, or provide explainability relevant to the application domain. In this paper we introduce a method that measures causal scenarios during outlier events using neural networks: Artificial Intelligence Network Explanation of Trade (AINET). AINET tailors AI techniques specifically for bilateral trade modeling. Datasets with network-like structures (such as global trade, social networks, or city traffic) can benefit from Graph Neural Networks (GNNs) modeling and structural power. These network-based models (i.e. GNNs) empower policy makers with an understanding of the fast-paced shifts in trade flows around the world due to outlier events such as increased tariffs, natural disasters, embargoes, pandemics, or trade wars. Our work is at the intersection of GNNs' optimization, causality, and their proper application to trade. AINET results are presented with an overall test mean absolute percentage error (MAPE) of 28%, demonstrating the efficacy and potential of harnessing this method.
Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints
Authors: Nouboukpo Adama , Allili Mohand Saïd
Published: May 2021

We propose a new weakly supervised approach for classification and clustering based on mixture models. Ourapproach integrates multi-level pairwise group and classconstraints between samples to learn the underlyinggroup structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes thenumber of classes is known but does not assume anyprior knowledge about the number of mixture components in each class. Therefore, our model : (1) allocatesmultiple mixture components to individual classes, (2)estimates automatically the number of components ofeach class, 3) propagates class labels to unlabelled datain a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasetsshow the robustness and performance of our model overstate-of-the-art methods.
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT
Authors: Yu Mingzhi , Litman Diane
Published: May 2021

Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to develop similar linguistic behaviors in conversation) to rerank the N-best candidates produced by BERT, a state-of-the-art pre-trained language model. Our results show an improvement in R@1 compared to BERT baselines, demonstrating the utility of repairing machine-generated outputs by leveraging a linguistic theory.
Anchored Team Formation Games
Authors: Schlueter Jacob , Addington Christian , Goldsmith Judy
Published: May 2021

We propose Anchored Team Formation Games (ATFGs), a new class of hedonic game inspired by tabletop role playing games.We establish the NP-hardness of determining whether Nash stable coalition structures exist, and provide results for three heuristics for this problem. We highlight costs and benefits of each heuristic and provide evidence that all three are capable of finding Nash stable coalition structures, when they exist, much more quickly than a deterministic algorithm.
Exploring Monte Carlo Negotiation Search with Nontrivial Agreements
Authors: Malaby Elijah Alden , Licato John
Published: May 2021

The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.
A General Two-stage Multi-label Ranking Framework
Authors: Xue Yanbing , Hauskrecht Milos
Published: May 2021

In this paper we develop and study solutions for the multi-label ranking (MLR) problem. Briefly, the goal of multi-label ranking is not only to assign a set of relevant labels to a data instance but also to rank the labels according to their importance. To do so we propose a two-stage model that consists of: (1) a multi-label classification model that first selects an unordered set of labels for a data instance, and, (2) a label ordering model that orders the selected labels post-hoc in order of their importance. The advantage of such a model is that it can represent both the dependencies among labels, as well as, their importance. We evaluate the performance of our framework on both simulated and real-world datasets and show its improved performance compared to the existing multiple-label ranking solutions.
Representing Time Series Data in Intelligent Training Systems
Authors: Hu Shengnan , Xi Zerong , McGowin Greg , Sukthankar Gita , Fiore Stephen M. , Oden Kevin
Published: May 2021

Many of the most popular intelligent training systems, including driving and flight simulators, generate user time series data. This paper presents a comparison of representation options for two different student modeling problems: 1) early failure prediction and 2) classifying student activities. Data for this analysis was gathered from pilots executing simple tasks in a virtual reality flight simulator. We demonstrate that our proposed embedding which uses a combination of dynamic time warping (DTW) and multidimensional scaling (MDS) is valuable for both student modeling tasks. However, Euclidean distance + MDS was found to be a superior embedding for predicting student failure, since DTW can obscure important agility differences between successful and unsuccessful pilots.
Bayesian Networks for software testing
Authors: Atkinson Timothy , Silaghi Marius
Published: May 2021

A software design methodology is proposed that involves development of approximate models based on Bayesian Networks capturing probabilistic representations of expected behavior, which are further used in developing and running tests that can dynamically diagnose bugs and attacks during production. While automation of Software design is still a very remote goal, it can already benefit from AI tools and ideas. One of the main challenges with automating software design methods, for any product with modest complexity, is the mere intractability of enumerating all scenarios of the product usage when also taking into account user intentions.

This leads to an intractability of generating exact specifications and exhaustive tests.

We show how approximate models of the design can exploit AI techniques to represent the system and to derive meaningful tests, warning when the environment is not behaving as designed, detecting bugs and attacks. The representation can use Bayesian Networks that are rather simple, enabling usage by novice practitioners. We validate the methodologies with on two different applications: a device driver for Wi-Fi Direct, and a website, MindBlog.com. In the Wi-Fi Direct use case, we successfully built a test ensuring the connection is fair and contrasted it experimentally to earlier work where we created a robust Bayes network based on expert knowledge. In the MindBlog.com use case, we show that the procedure is flexible and can detect when the developers found a bug and were attempting to debug their application yielding anomalous behavior.
MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare
Authors: Kumar Nitish , Chaurasiya Deepak , Singh Alok , Asthana Siddhartha , Agarwal Kushagra , Arora Ankur
Published: May 2021

Every year, health insurance fraud costs taxpayers billions of dollars and puts patient’s health and welfare at risk. Existing solutions to detect fraudulent providers (hospitals, physicians, etc.) aim to find unusual pattern at claim level features but fail to harness provider-provider and provider-patient interaction information. We propose a novel framework, Med-Dynamic meta learning (MeDML), that extends the capability of traditional fraud detection by learning patterns from 1) patient-provider interaction using temporal and geo-spatial characteristics 2) provider's treatment using encounter data (e.g. medical codes, mix of attended patients) and 3) referral using underlying provider-provider relationships based on common patient visits within 30 days. To the best of our knowledge, MeDML is first framework that can model fraud using multi-aspect representation of provider.MeDML also encapsulates provider's phantom billing index, which identifies excessive and unnecessary services provided to patients, by segmenting frequently co-occurring diagnosis and procedures in non-fraudulent provider's claims. It uses a novel framework to aggregate the learned representations capturing their task-specific relative importance via attention mechanism. We test the dynamically generated meta embedding using various downstream models and show that it outperforms all baseline algorithms for provider fraud prediction task.
ADHD Prediction via Time Series Ensemble fed Driving Simulator Data
Authors: Grethlein David , Sladek Aleksanteri , Ontañón Santiago
Published: May 2021

In this paper, we identify the on-road scenarios within a simulated driving environment where a group of clinical trial participants (n= 30) with and without Attention Deficit Hyper-activity Disorder (ADHD) drive perceivably different fromone another. We partition the simulated routes into smaller non-overlapping sections in order to determine which sections elicit behaviors that are predictive of ADHD. Then, we develop section-specific classifiers, which are used as voters in bagging ensemble classifiers. Our results show gains in classifying ADHD (increase in 5-fold average evaluation accuracy) over our previous efforts, as well as providing explainable evidence that driving behaviors indicative of ADHD tend to be exhibited in turns and curves.
Expanding the Reach of Structured EHR Data with Clinical Notes
Authors: Bilaloglu Seda , Major Vincent J , Grover Himanshu , Metzger Isabel , Aphinyanaphongs Yindalon
Published: May 2021

Appropriate treatment decisions and end-of-life planning for patients with serious, life-limiting disease rely on accurate prognosis estimates. Many existing methods rely on structured electronic health record data which may limit generalizability across sites and restrict performance for patients with less documented history. Clinical notes may help to 'level the playing field'. We use History and Physical (H&P) notes written within 16 hours of hospitalization to predict 60-day, all-cause mortality. We test several neural network approaches and observe little improvement over a CNN by adding bi-directional recurrence or convolutional attention. The CNN was prospectively validated against an existing system using structured data. The CNN reports higher discrimination (86.0% vs. 80.6%) and average precision (31.4% vs. 17.9%). The CNN identifies fewer patients at high-risk but 91% were under-estimated by the existing method (high-risk: 80 vs. 131 with overlap of 7). Patients of both groups do die in the following months suggesting the two approaches identify different patient phenotypes which supplement one another. The CNN model better adapts to a new hospital where many patients have little or no structured history incapacitating the existing system (high-risk: 27 vs. 1).
Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions
Authors: Ribeiro Luiz Alberto Pereira Afonso , Garcia Ana Cristina Bicharra , Dos Santos Paulo Sérgio Medeiros
Published: May 2021

The use of big data and information fusion in electronichealth records (EHR) allowed the identification of adversedrug reactions(ADR) through the integration of heteroge-neous sources such as clinical notes (CN), medication pre-scriptions, and pathological examinations. This heterogene-ity of data sources entails the need to address redundancy,conflict, and uncertainty caused by the high dimensionalitypresent in EHR. The use of multisensor information fusion(MSIF) presents an ideal scenario to deal with uncertainty,especially when adding resources of the theory of evidence,also called Dempster–Shafer Theory (DST). In that scenariothere is a challenge which is to specify the attribution of be-lief through the mass function, from the datasets, named basicprobability assignment (BPA). The objective of the presentwork is to create a form of BPA generation using analy-sis of data regarding causal and time relationships betweensources, entities and sensors, not only through correlation, butby causal inference.
Observing the Unobserved: A Newspaper Based Dengue Surveillance System for the Low-Income Regions of Bangladesh
Authors: Tasnim Nazia , Shihab Md. Istiak Hossain , Rahman Moqsadur , Saurav Jillur Rahman , Islam Sheikh Rabiul , Amin Mohammad Ruhul
Published: May 2021

Dengue is one of the emerging diseases of this century, which established itself as both endemic and epidemic - particularly in the tropical and subtropical-regions. Because of its high morbidity and mortality rates, Dengue is a significant economic and health burden for middle to lower-income countries. The lack of a stable, cost-effective, and suitable surveillance system has made the identification of dengue zones and designing potential control programs very challenging. As a result, it is not feasible to assess the effect of the intervention actions properly. Therefore, most of the prevention and mitigation efforts by the associated health officials are failing. In this work, we chose Bangladesh, a developing country from the South-East Asia region with its occasional history of dengue outbreaks and with a high out-of-pocket medical expenditure, as a use case. We use some well known data-mining techniques on the local newspapers, written in Bengali, to unearth valuable insights and develop a dengue news surveillance system. We categorize dengue-news and detect the spatio-temporal trends among crucial variables. Our technique provides an f-score of 91.45\% and very closely follows the ground truth of reported cases. Additionally, we identify the under-reported regions of the country effectively while establishing a meaningful relationship between complex socio-economic factors and reporting of dengue.
Visualization of Anomalies using Graph-Based Anomaly Detection
Authors: Paudel Ramesh , Tharp Lauren , Kaiser Dulce , Eberle William , Gannod Gerald
Published: May 2021

Network protocol analyzers such asWireshark are valuable for analyzing network traffic but pose a challenge in that it can be difficult to determine which behaviors are out of the ordinary due to the volume of data that must be analyzed. Network anomaly detection systems can provide vital insights to security analysts to supplement protocol analyzers, but this feedback can be difficult to interpret due to the complexity of the algorithms used and the lack of context to determine the reasoning for which an event was labeled as anomalous. We present an approach for visualizing anomalies using a graph-based anomaly detection methodology that aims to provide visual context to network traffic. We demonstrate the approach using network traffic flows as an approach for aiding in the investigation and triage of anomalous network events. The simplicity of a visual representation supports fast analysis of anomalous traffic to identify true positives from false positives and prevent further potential damage.
Topic Modeling for Makerspace Artifact Analysis
Authors: Wilson David , Okerlund Johanna , Exley Dominique
Published: May 2021

As the making phenomenon becomes more prevalent, diverse, and vast, it becomes increasingly challenging to identify general temporal or spatial trends in types of making endeavors. Identifying trends in what participants are making is important to makerspace leaders who seek to understand the impact of the making phenomenon on the world or who are interested in broadening participation within their own maker contexts. This paper shows how topic modeling by means of LDA can be used to analyze maker artifacts, and illustrates how these types of insights can be used to make inferences about the making phenomenon, as well as to inform efforts to broaden participation.
Study on the Mechanism of Cancer/Testicular Antigen FSIP1 in Regulating Autophagy and Promoting Survival of Lung Cancer Cells
Authors: Zhu Xiaoping , Song Yingping , Wang Wei , Yang Weiqi
Published: May 2021

Objective — To investigate the molecular mechanism of FSIP1 gene expression and autophagy inhibition in patients with lung adenocarcinoma. Methods — TTP expression in lung adenocarcinoma H1299, H1975 and normal lung forming cells was detected by QRT-PCR and Western Bloting. Quantitative analysis and Western Bloting were performed by transfecting pCMV-FSIPI at 24h, 48h and 72h respectively to analyze the expression of autophagy related factors like P62, LC3II/LCI and BecIinl. The transient transfection of overexpressed and empty plasmid was performed by adding 10 mg/ml of actinomycin D. After the termination of this transcription, RNA extraction was performed at different time points to detect the expression of Beclin1m RNA at different time points. After adding TNF-a by transfection plasmid, lung adenocarcinoma cells were divided into different groups, including no-load group, FSIPI group, no-load + TNF-A group, and FSIPI + TNF-a group. The gene expression of P50, c-Rel and NF-KBp65 in the nucleus was analyzed by immunofluorescence and Western Bloting. Lung adenocarcinoma was divided into Ikba-mut no-load group, FSIPI no-load group, 1Ikba-mut group, FSIPI group and FSIPI+ Ikba-mut group. FSIPI expression and autophagy gene expression were detected by quantitative analysis and Western Bloting. Results — In lung adenocarcinoma cells, FSIP1 RNA and protein levels were RELATIVELY low, and autophagy Becline and LC 3II/I had lower RNA and protein levels after overexpression of FSIPI compared with the no-load group. Transcription is terminated by the addition of actinomycin D. There was no significant difference in the expression of the autophagy-related gene BEC LINEM between FSIP1 and the no-load group. After overexpression of FSIPI, according to Western Bloting results, nuclear C-Rel and P65 proteins were less than those in the no-load +TNF-a group and the no-load group, whileP50 protein did not change significantly. Combined with immunofluorescence studies, it was found that the expressions of c-Rel and P65 were significantly decreased after FSIP1 overexpression compared with the no-load group, while the expression of P50 was not significantly changed. Conclusion — FSIP1 is usually low expressed in lung adenocarcinoma. Overexpression of FSIP1 can effectively inhibit the nuclear metastasis of C-Rel and NF-KBp65, thus inhibiting autophagy of the cells.
Proposta De Implementação Da Ferramenta 5w2h Na Gestão Dos Processos De Atendimento Ao Cliente: Estudo De Caso Da Loja Claro Do Amazonas Shopping
Authors: Alfaia Samuel Pinheiro , Souza Josiane Santana de , Almeida Victor da Silva , Roberto José Carlos Alves
Published: May 2021

Este artigo tem a finalidade de apresentar os resultados da análise realizada por meio de diagnóstico organizacional executado na loja Claro, situada no Amazonas Shopping, bem como expor proposta de implementação por meio de dados fundamentados, visando a solução dos problemas obtidos através da análise. Podemos destacar a utilização da ferramenta da qualidade 5W2H no planejamento de estratégia com o intuito de potencializar os serviços de vendas e soluções de problemas de clientes executados por esta empresa de telefonia. A execução deste artigo tomou como base uma pesquisa bibliográfica de abordagem qualitativa. Mediante análise dos dados desta pesquisa, o grupo obteve uma visão com mais do que se faz necessário para melhoria nos processos de serviços prestados no estabelecimento proposto no estudo.
Lei Geral De Proteção De Dados Pessoais - LGPD
Authors: Machado Daniel Dias
Published: May 2021

Tendo em vista que a vigência da Lei Geral de Proteção de dados, em 2020, veio a limitar a circulação de informações pessoais dos indivíduos, causando um impacto negativo sobre os mecanismos existentes para Prevenção a Lavagem de Dinheiro, o presente artigo tem como objetivo tratar sobre a nova Lei de proteção de dados pessoais, a qual visa proteger os dados pessoais das pessoas físicas e jurídicas, a fim de descobrir se haverá dificuldades a serem enfrentadas pelos mecanismos de prevenção à lavagem de dinheiro frente às adequações exigidas pela Lei Geral de Proteção de Dados. Tal pesquisa se mostra relevante no sentido de se mensurar as consequências práticas que advirão dessa maior proteção a dados pessoais, como a segurança jurídica. Isso porque na prática significa um grande avanço, não só dentro do sistema legal e jurídico, mas também dentro do sistema social que abrange o cidadão. Sendo assim, trata-se de uma pesquisa bibliográfica que constatou a existência de mecanismos de proteção dos dados eficientes e a possibilidade de uma maior ampliação dos mesmos nos próximos anos.
ВЕКТОРИ РОЗВИТКУ МЕДИЧНОГО СТРАХУВАННЯ В УКРАЇНІ
Authors: Водолазська Оксана , Сіроштан Тетяна
Published: May 2021

Стаття присвячена актуальним питанням розвитку медичного страхування в Україні. Розглянуто економічну сутність медичного страхування, підкреслено актуальність її дослідження та різноплановість. Визначено структуру медичного страхування з урахуванням форм та видів. Доведено, що страхування здоров’я ‒ вагома частина соціальної інфраструктури держави, що виступає насамперед допоміжною складовою медичної сфери держави. Проаналізовано сучасний стан страхової медицини на страховому ринку України. Досліджено проблеми страхового захисту здоров’я, які перешкоджають його розвитку. Окреслено перспективи впровадження медичного страхування на загальнодержавному рівні та поетапність заходів, які треба виконати для його ефективного функціонування. Зазначено, що медичне страхування в даний час набирає популярності серед споживачів.
ФОРЕНЗИК ЯК ФОРМА ПОПЕРЕДЖЕННЯ ФІНАНСОВИХ ПРАВОПОРУШЕНЬ
Authors: Мельничук Ірина
Published: May 2021

Стаття присвячена питанням попередження правопорушень у сфері застосування фінансів. Сутність таких правопорушень полягає у шахрайстві, яке класифікується у вітчизняному законодавстві як незаконне використання бюджетних коштів, шахрайство під час проведення приватизації державного майна, шахрайство у сфері земельних відносин та інші. Такі порушення спричиняють значні збитки для бізнесу та державного сектору. Тому виникає потреба попередити такі правопорушення. Для цього варто застосовувати дієву зарубіжну практику, де значного поширення набули послуги форензику. Незважаючи на вартість таких послуг, вони є досить дієвими та апробуються вітчизняними суб’єктами господарювання, про що свідчать результати проведених досліджень компанією Kreston GCG в Україні. Тому важливо розвивати такі форми контролю, що дозволить мінімізувати випадки шахрайств та фінансових порушень.
МІЖДИСЦИПЛІНАРНИЙ ПІДХІД ДО ФОРМУВАННЯ УНІВЕРСАЛЬНИХ КОМПЕТЕНЦІЙ У ПРОЦЕСІ ВИВЧЕННЯ ДИСЦИПЛІН ОСВІТНЬОЇ ПРОГРАМИ «МІЖНАРОДНИЙ БІЗНЕС»
Authors: Ступницький Олексій , Приятельчук Олена
Published: May 2021

У статті розглядаються проблеми впровадження та використання міждисциплінарного підходу у професійній підготовці майбутніх фахівців за спеціальністю «міжнародний бізнес». Досліджуються міждисциплінарні зв’язки, які забезпечують міждисциплінарний підхід у сфері освіти, наукова база, а також система навчання як комплекс фундаментальних і таксономічних дисциплін. Здійснений опис та надана характеристика специфіки майбутньої професійної діяльності спеціалістів у сфері міжнародного бізнесу, світової економіки і міжнародних економічних відносин. Використання міждисциплінарного підходу у професійній підготовці майбутніх спеціалістів розглядається як частина процесу формування їх професійної компетентності шляхом реалізації зв’язків із дисциплінами гуманітарної, соціально-економічної та природно-наукової підготовки. Зі спрямованістю на ефективне навчання розглянуті міждисциплінарні зв’язки, погодженість учбових програм, детерміновані дидактичними цілями та змістом. При компетентносному підході міждисциплінарні зв’язки дозволяють переносити знання, вміння та навички із одної сфери науки і професійної діяльності до інших. Міждисциплінарний підхід спрямований на зв'язок дисциплін, коли згладжуються протиріччя в опануванні ідей, методів і прийомів дослідження між науками та відбувається комплексне використання у професійній діяльності теорії і практики, отриманих на основі вивчених дисциплін. Розглянуті як суттєві переваги, так і проблеми, що виникають при використанні міждисциплінарного підходу. Доведено, що вміння та навички майбутніх бакалаврів і магістрів спеціальності «міжнародний бізнес» формуються під впливом вивчення і взаємозв’язку з іншими дисциплінами. Обґрунтована необхідність посилення ролі природничої і методологічної складових у викладанні дисциплін спеціальності «міжнародний бізнес», а також активізації використання різних методів для поліпшення сприйняття і засвоєння різних аспектів функціонування та розвитку світової економіки як цілісної системи.
СИНЕРГЕТИЧНИЙ ПОТЕНЦІАЛ РОЗВИТКУ ТУРИСТИЧНО-РЕКРЕАЦІЙНОЇ СОЦІО-ЕКОЛОГО-ЕКОНОМІЧНОЇ СИСТЕМИ
Authors: Хитра Олена
Published: May 2021

У статті обґрунтовуються ознаки синергетичності туристично-рекреаційної системи, що пов’язана когерентними і коеволюційними взаємозв’язками з іншими галузями економіки, елементами суспільного життя, природним середовищем. Залежність цієї системи, з одного боку, від потреб людини, а з іншого – від рівня науково-технічного прогресу, тенденцій розвитку світового ринку, державного регулювання тощо, обумовлює численні флуктуації і велику кількість біфуркаційних гілок еволюції. Проходження через каскад точок біфуркації супроводжується якісними перетвореннями, які змінюють цикли рекреаційної діяльності, рекреаційну місткість та освоєність різних територій. Еволюція туристично-рекреаційних районів відбувається за принципами самоорганізації та синергетичної циклічності і супроводжується наростанням ентропії, для подолання якої пропонується застосовувати інструменти рекреаційного природокористування, збереження культурної самобутності народів і підвищення якості туристичного продукту.
MATHEMATICAL MODELING OF OPTIMAL HEIGHTS OF EXTERNAL GEODESIC SIGNS
Authors: Voronkov O. , Baistryk O. , Danylyuk A.
Published: May 2021

Due to the great importance of geodetic networks for the formation of a unified coordinate system on the territory of Ukraine, external geodetic signs have been established, which need to be restored and further developed. At the design stage, the calculation of the heights of geodetic signs is performed on topographic maps. The cost of erection of geodetic signs on average is 50 - 60% of the total cost of creating a geodetic network, so there is a need to pay close attention to the choice of places to build signs that provide their optimal height. The article presents a methodical approach to determining the heights of external geodetic signs, based on the mathematical apparatus used for modeling and solving optimization problems. The principle of construction of the optimization model of the problem during the design of external geodetic signs in the conditions when their direct visibility should be provided is considered. The article considers in detail the types and structures of external geodetic signs, identifies the features of their location and construction. The resulting optimization model includes objective function, which is a quadratic form, and line restriction. This model is a model of quadratic programming, that belongs to a class of nonlinear programming models, but have their particular case and the simplest of nonlinear. This is because property quadratic model, which consists in the fact that since the problem of quadratic programming set of feasible solutions is convex, then, if the objective function is concave, any local maximum is global, and if the objective function is convex, then any local minimum is also global. The necessity of solving the problem of optimizing the heights of geodetic signs is substantiated, which is still connected with the financial costs for their construction and reconstruction. It is concluded that the approach to determining the heights of external geodetic signs presented in the article, which uses a mathematical apparatus for solving optimization problems, is an effective and efficient approach, and allows to numerically justify the minimum required and sufficient height of external geodetic signs. Using the present approach to the determination of geodetic heights external signs to optimize the financial costs of their construction, which is essential.
SYNTHESIS OF THERMAL DIAGNOSTIC EXPERT COMPONENTS WITH AN ARTIFICIAL NEURON
Authors: Yesaulov S. , Babicheva О. , Akinshyn D.
Published: May 2021

The article notes the growing popularity of digital programmable technology in diagnostic monitoring systems of electromechanical equipment (EME) for various purposes due to the ability to monitor the technical condition of operating devices in real time. The main reasons that restrain the use of DMS with artificial neural networks in the municipal sphere are considered. It has been noted the directions of improvement of popular means of thermal parameters monitoring and hardware solutions to increase the initial data validity used in the possible EMO fault identification. The purpose of this work was to study and develop components for the formation of initial information, including artificial neurons, which make it possible to increase the reliability of possible fault identification accompanied by heating of individual parts of the operated electromechanical equipment. Based on the adopted algorithm for approximating the initial data arrays, the priority of using the logistic function for modeling the rate of temperature change in the EME was justified. It have been proposed the electronic model structure of an artificial neuron (AN) and an algorithm for generating information output signal, depending on the rate of change of a controlled parameter at a technological object. It have been presented the electronic modeling results in the Simulink environment and the physical implementation of the AN electronic model, which confirmed the suitability of the proposed device in the diagnostic thermal expert of the EME technical condition during its operation in real time. Electronic experiments with AN made it possible to obtain a calibration characteristic for a practical assessment of the tendency for the development of non-standardized thermal events that may cause possible faults in certain parts of the equipment. It have been considered possible options for using AN in local thermal diagnostic tools for the analysis and assessment of events indicating the feasibility of performing unscheduled maintenance or preceding possible and unknown electromechanical equipment faults. It has been presented the results of experiments and simulation of thermal processes, confirming the expandability of the functional diagnostic devices properties with neural network systems, which popularity is constantly growing.
SYSTEMATIZATION OF APPROACHES TO THE SOLUTION OF THE PROBLEM OF ENERGY SAVING IN THE CONSTRUCTION INDUSTRY
Authors: Shapoval S.
Published: May 2021

The information on existing energy-efficient building materials and constructions on the basis of the accumulated experience of operation is analyzed; collected and systematized according to certain priorities requirements for building materials and structures, the order of their design and operation; the classification of energy saving methods is formulated, substantiated and offered for use; recommended a number of technical solutions to achieve energy savings in the construction of residential and public buildings. Possibilities of using highly effective foreign additives in concretes prepared with the use of local materials are considered, features of preservation of properties of concrete mix during transportation, time after laying of concrete in a timbering in various temperature conditions (summer, winter and off-season), use of vacuum processing are studied and investigated. which is aimed at further improvement of organizational and technological solutions for the construction of load-bearing structures of monolithic buildings. The recommendations on regulation of technological parameters of erection of responsible bearing designs, reduction of terms of construction, labor intensity and cost of works are reduced. Imperfect design solutions, outdated standards, defects in construction and operation lead to excessive heat loss in buildings. Architectural and construction systems of the new generation provide ample opportunities for organizing the interior of residential buildings, hygienic quality, safety and comfort of housing, as well as increasing energy efficiency and reducing material consumption of construction through technical re-equipment of existing housing and creating new production technologies.