Practical and Adaptable Applications of Goal Programming: A Literature Review

Goal programming (GP) is an important optimization technique for handling multiple, and often conflicting, objectives in decision making. This paper undertakes an extensive literature review to synthesize key findings on the diverse real-world applications of GP across domains, its implementation challenges, and emerging directions. The introduction sets the context and objectives of the review. This is followed by an in-depth review of literature analyzing GP applications in areas as varied as agriculture, healthcare, education, energy management, supply chain planning, and macroeconomic policy modeling. The materials and methods provide an overview of the systematic literature review methodology. Key results are presented in terms of major application areas of GP. The discussion highlights the versatility and practical utility of GP, while also identifying limitations. The conclusion outlines promising avenues for enhancing GP modeling approaches to strengthen multi-criteria decision support

A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network

شبكة تبديل الاندفاع البصري (OBS) هي تقنية اتصال بصري من الجيل الجديد. في شبكة OBS ، ترسل عقدة الحافة أولاً حزمة تحكم ، تسمى حزمة رأس الاندفاع (BHP) التي تحتفظ بالموارد اللازمة لدفعة البيانات القادمة (DB). بمجرد اكتمال الحجز ، تبدأ قاعدة البيانات بالتحرك إلى وجهتها من خلال المسار المحجوز. هناك هجوم بارز على شبكة OBS هو هجوم فيضان BHP حيث ترسل عقدة الحافة BHPs لحجز الموارد ، ولكن في الواقع لا ترسل قاعدة البيانات المرتبطة بها. نتيجة لذلك ، يتم إهدار الموارد المحجوزة وعندما يحدث ذلك على نطاق واسع بما فيه الكفاية ، فقد يحدث رفض الخدمة (DoS). في هذه البحث ، نقترح طريقة شبه آلية للتعلم باستخدام خوارزمية الوسائل k ، لاكتشاف العقد الخبيثة في شبكة OBS. تم تدريب النموذج شبه المراقب المقترح والتحقق من صحته باستخدام بيانات كمية صغيرة من مجموعة بيانات مختارة. تُظهر التجارب أن النموذج يمكن أن يصنف العقد إلى فصول تتصرف أو لا تتصرف بدقة 90٪ عند التدريب باستخدام 20٪ فقط من البيانات. عندما يتم تصنيف العقد إلى فصول تتصرف ، لا تتصرف، وربما لا تتصرف ، فإن النموذج يظهر دقة 65.15 ٪ و 71.84 ٪ إذا تم تدريبه بنسبة 20 ٪ و 30 ٪ من البيانات على التوالي. مقارنة مع بعض الأعمال البارزة كشفت أن النموذج المقترح يتفوق عليها في كثير من النواحي.Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm, to detect malicious nodes in an OBS network. The proposed semi-supervised model was trained and validated with small amount data from a selected dataset. Experiments show that the model can classify the nodes into either behaving or not-behaving classes with 90% accuracy when trained with just 20% of data. When the nodes are classified into behaving, not-behaving and potentially not-behaving classes, the model shows 65.15% and 71.84% accuracy if trained with 20% and 30% of data respectively. Comparison with some notable works revealed that the proposed model outperforms them in many respects

A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems

Parametric time-dependent systems are of a crucial importance in modeling real phenomena, often characterized by non-linear behaviors too. Those solutions are typically difficult to generalize in a sufficiently wide parameter space while counting on limited computational resources available. As such, we present a general two-stages deep learning framework able to perform that generalization with low computational effort in time. It consists in a separated training of two pipe-lined predictive models. At first, a certain number of independent neural networks are trained with data-sets taken from different subsets of the parameter space. Successively, a second predictive model is specialized to properly combine the first-stage guesses and compute the right predictions. Promising results are obtained applying the framework to incompressible Navier-Stokes equations in a cavity (Rayleigh-Bernard cavity), obtaining a 97% reduction in the computational time comparing with its numerical resolution for a new value of the Grashof number

Adapting kk-means algorithms for outliers

This paper shows how to adapt several simple and classical sampling-based algorithms for the $k$-means problem to the setting with outliers. Recently, Bhaskara et al. (NeurIPS 2019) showed how to adapt the classical $k$-means++ algorithm to the setting with outliers. However, their algorithm needs to output $O(\log (k) \cdot z)$ outliers, where $z$ is the number of true outliers, to match the $O(\log k)$-approximation guarantee of $k$-means++. In this paper, we build on their ideas and show how to adapt several sequential and distributed $k$-means algorithms to the setting with outliers, but with substantially stronger theoretical guarantees: our algorithms output $(1+\varepsilon)z$ outliers while achieving an $O(1 / \varepsilon)$-approximation to the objective function. In the sequential world, we achieve this by adapting a recent algorithm of Lattanzi and Sohler (ICML 2019). In the distributed setting, we adapt a simple algorithm of Guha et al. (IEEE Trans. Know. and Data Engineering 2003) and the popular $k$-means$\|$ of Bahmani et al. (PVLDB 2012). A theoretical application of our techniques is an algorithm with running time $\tilde{O}(nk^2/z)$ that achieves an $O(1)$-approximation to the objective function while outputting $O(z)$ outliers, assuming $k \ll z \ll n$. This is complemented with a matching lower bound of $\Omega(nk^2/z)$ for this problem in the oracle model

INCÊNDIOS NA AMAZÔNIA A PARTIR DE UMA PERSPECTIVA DE CIRCULAÇÃO DE TWEETS

Forest fires are one of the main threats to biodiversity in the Amazon region. Official data revealed record numbers in the volume of fire outbreaks in the year 2022. This worrying scenario reverberates not only in political and governmental issues of environmental preservation, but also in social networks, where society exposes and debates its views and opinions. This paper presents an exploratory study on a set of tweets in Portuguese related to the fires in the Amazon. Computational solutions are used to generate results that allow the thematic identification of the content conveyed by Twitter users on the subject. The results revealed a polarization on the issue, going beyond environmental problems and going against political and affective issues.Los incendios forestales son una de las principales amenazas para la biodiversidad en la región amazónica. Datos oficiales revelaron cifras récord en el volumen de focos de incendios en el año 2022. Este preocupante escenario repercute no solo en temas políticos y gubernamentales de preservación ambiental, sino también en las redes sociales, donde la sociedad expone y debate sus puntos de vista y opiniones. Este artículo presenta un estudio exploratorio sobre un conjunto de tuits en portugués relacionados con los incendios en la Amazonía. Se utilizan soluciones computacionales para generar resultados que permitan la identificación temática de los contenidos transmitidos por los usuarios de Twitter sobre el tema. Los resultados revelaron una polarización sobre el tema, yendo más allá de los problemas ambientales y yendo en contra de lo político y lo afectivo.Os incêndios florestais são uma das principais ameaças à biodiversidade da região amazônica. Dados oficiais revelaram números recordes no volume de focos de incêndios no ano de 2022. Esse cenário preocupante se reverbera não apenas nas questões políticas e governamentais de preservação ambiental, mas também nas redes sociais, onde a sociedade expõe e debate suas visões e opiniões. Neste artigo é apresentado um estudo exploratório sobre um conjunto de tweets em língua portuguesa relacionados aos incêndios na Amazônia. São utilizadas soluções computacionais para a geração de resultados que possibilitaram a identificação temática dos conteúdos veiculados pelos usuários do Twitter sobre o assunto. Os resultados revelaram uma polarização sobre a questão, extrapolando os problemas ambientais e indo de encontro a questões políticas e afetivas

Improving Distance-Join Query Processing with Voronoi-Diagram based Partitioning in SpatialHadoop

SpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial datasets across several machines and improve spatial query processing performance compared to traditional Hadoop systems. SpatialHadoop supports several spatial operations (e.g., Nearest Neighbor search, range query, spatial intersection join, etc.) and seven spatial partitioning techniques (Grid, Quadtree, STR, STR+, -d tree, Z-curve and Hilbert-curve). Distance-Join Queries (DJQs), like the Nearest Neighbors Join Query (NNJQ) and Closest Pairs Query (CPQ), are common operations used in numerous spatial applications. DJQs are costly operations, since they combine spatial joins with distance-based search. Data partitioning improves the management of large datasets and speeds up query performance. Therefore, performing DJQs efficiently with new partitioning methods in SpatialHadoop is a challenging task. In this paper, a new data partitioning technique based on Voronoi-Diagrams is designed and implemented in SpatialHadoop. Moreover, improved NNJQ and CPQ MapReduce algorithms, using the new partitioning mechanism, are also designed and developed for SpatialHadoop. Finally, the results of an extensive set of experiments with real-world datasets are presented, demonstrating that the new partitioning technique and the improved DJQ MapReduce algorithms are efficient, scalable and robust in SpatialHadoop