Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models' performance by expanding the dataset's size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the \textit{limited} survey data size using data augmentation technique(s). Next, we carry out data unification, followed by clustering for inferencing. We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. One is on features of students to be entrepreneurs, and the second is breast cancer survey data. We compare the results of the inference obtained from the raw survey data and the newly converted data. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data

ARNLI: ARABIC NATURAL LANGUAGE INFERENCE ENTAILMENT AND CONTRADICTION DETECTION

Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a dataset of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created dataset (ArNLI) and on an automatic translation of both PHEME, SICK English datasets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively

HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

To date, facial recognition has been one of the most intriguing, interesting research topics over years. It requires some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms derive from heavy deep learning architectures that cause limitations for development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also calls for large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese celebrity dataset and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. Comparison with another popular algorithm on the dataset, our proposed pipeline not only outweighs the accuracy counterpart, but it also achieves a very speedy time inference for a real-time face recognition application

Czy wodór może być magazynem i nośnikiem energii w budownictwie?

W artykule scharakteryzowano podstawowe warianty wykorzystania wodoru jako magazynu i nośnika energii, a także ogniw paliwowych w energetyce rozproszonej. Przedstawiono możliwości integracji rozwiązań technologii wodorowych i ogniw paliwowych z odnawialnych źródeł energii w systemach niezależnego zasilania dla budownictwa. Wodór wytwarzany w procesie elektrolizy może być magazynowany w skalowalnych zbiornikach wysokociśnieniowych (200–350 barów) oraz w niskociśnieniowych magazynach wodoru, a następnie wykorzystany do produkcji energii elektrycznej z ogniw paliwowych. Interesującą opcją jest również wykorzystanie alternatywnych paliw (np. metanolu) jako nośników wodoru do budowy pomocniczych układów zasilania w budownictwie. Kolejną ważną cechą rozważanych układów rozproszonych jest możliwość uzyskania wariantowego ciepła, zarówno z ogniw paliwowych, jak i w procesach wodorowych

Suwerenność energetyczna w polityce europejskiej i krajowej

Inwazja Rosji na Ukrainę w 2022 r. wzbudziła potrzebę redefinicji bezpieczeństwa energetycznego i suwerenności energetycznej zarówno Unii Europejskiej jako wspólnoty, jak i poszczególnych jej członków. Użycie surowców energetycznych jako broni oznacza, że energia nie może być traktowana wyłącznie jako towar podlegający regułom rynkowym, ale staje się atrybutem suwerenności wspólnoty. W artykule dokonano przeglądu regulacji unijnych i krajowych w obszarze energii, w kontekście wydarzeń od ogłoszenia Zielonego Ładu do początku 2023 r. Przeprowadzono analizę i próbę odpowiedzi na pytanie, czy i pod jakimi warunkami UE może być suwerenna energetycznie

Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints

The School Bus-Driver Problem with Resource Constraints (SBDP-RC) is an optimization problem with many practical applications. In the problem, the number of vehicles is prepared to pick a number of pupils, in which the total resource of all vehicles is less than a predefined value. The aim is to find a tour minimizing the sum of pupils’ waiting times. The problem is NP-hard in the general case. In many cases, reaching a feasible solution becomes an NP-hard problem. To solve the large-sized problem, a metaheuristic approach is a suitable approach. The first phase creates an initial solution by the construction heuristic based on Insertion Heuristic. After that, the post phase improves the solution by the General Variable Neighborhood Search (GVNS) with Random Neighborhood Search combined with Shaking Technique. The hybridization ensures the balance between exploitation and exploration. Therefore, the proposed algorithm can escape from local optimal solutions. The proposed metaheuristic algorithm is tested on a benchmark to show the efficiency of the algorithm. The results show that the algorithm receives good feasible solutions fast. Additionally, in many cases, better solutions can be found in comparison with the previous metaheuristic algorithms