Reviewerly/exHarmony huggingface dataset:
Deep Learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper

Due to the significant increase in the volume of data shared on the web, Aspect-Based Sentiment Analysis (ABSA) has become essential. This task ensures a detailed sentiment analysis. It identifies firstly the aspect terms (e.g., price, food, etc.) and then classifies their sentiment polarity as positive, negative, or neutral. Many approaches have been used to treat this task including the machine learning-based approach, the rule-based approach, etc. However, with the important increase in the content of the internet, these approaches became relatively unable to analyze this volume of information, resulting in the emergence of the deep learning-based approach which is the subfield of the machine learning-based approach. Recently many researchers used the deep learning-based approach to address the ABSA. This paper provides a summary of the deep learning models that have been developed for ABSA, as well as a survey of studies that have employed these models to address different subtasks of the ABSA task. Finally, we discuss the implications of our work and potential avenues for future research.
Toward an Ontology of Pattern Mining over Data Streams

Pattern Mining over Data Stream (PMDS) is part of the most significant task in data mining. A major challenge is to define a representational framework that unifies PMDS algorithms dealing with different pattern types (frequent itemset, high-utility itemset, uncertain frequent itemset), using different methods (test-and-generate, pattern-growth, hybrid) and different window models (landmark, sliding, decay, tilted) in a uniform fashion. This will help standardize the process and create a better understanding of the algorithm design, provide a base for unification and research opportunities. It also facilitates the variability management and allows the derivation of tools for wide experimentation. In this publication, we propose a reference ontology to formalize the domain knowledge around PMDS. The design process of the ontology followed leading practices in ontology engineering. It is aligned to the most popular data mining and machine learning ontologies and thus, represents a major contribution toward PMDS domain ontologies.
Modeling Graphene Extraction Process Using Generative Diffusion Models

Graphene, a two-dimensional material composed of carbon atoms arranged in a hexagonal lattice, possess a unique array of properties that make it a highly sought-after material for a wide range of applications. Its extraction process, a chemical reaction’s result, is represented as an image that shows areas of the synthesized material. Knowing the initial conditions (oxidizer) the synthesis result could be modeled by generating possible visual outcomes. A novel text2image pipeline to generate experimental images from chemical oxidizers is proposed. Key components of a such pipeline are a textual input encoder and a conditional generative model. In this work, the capabilities of certain text model and generative diffusion model are investigated and some conclusions are drawn providing further suggestions for further full text2image pipeline development
Question-Aware Deep Learning Model for Arabic Machine Reading Comprehension

Machine reading comprehension is one of the most long-standing challenges in artificial intelligence. It is a subtask question-answering system that aims to find a text span in a reading context that answers a question. It is the core of several applications, such as customer service systems and chatbots. However, However, few studies have targeted machine reading comprehension of Arabic text. Besides, The existing models generate a context representation independently from the question. As an analogy to some people’s comprehension styles who believe that by reading the question, they gain a better understanding of the reading passage, we present a question-aware neural machine reading comprehension model for Arabic. Our model extracts a representation of the context by incorporating the question using several bidirectional attention units to achieve various levels of question-centered context understanding. Our experimental evaluation shows that our model outperforms strong machine reading comprehension baselines, including DrQA and QaNET models applied to Arabic, by a significant margin with an F1 score of 59.6%.
Using Blockchain Technology for Ranking the Common Core Cycles’ Students in Algerian Universities

Higher education is regarded as the final phase of the educational system and one of the pillars for the advancement of society. It establishes development strategies for the improvement of society and scientifically qualifies students to work in a variety of societal sectors. However, the North African universities in general and Algerian universities in particular continue to classify their students according to traditional systems in the many universities disciplines. In order to rank its students in the pre-specialization phases, Algerian universities continue to use the LMD system (License - Master - Doctorate). The subjects of the pre-specialty and the outcomes of the specialty of the previous promotions are not taken into account in this method, which is based on the average of classification as an orientation criterion. The fact that the current educational systems in Algeria rely mostly on central storage devices managed by a single authority also makes them more susceptible to the loss of this data in the event that a central server fails or is compromised by hacking operations. The goals of this article are to create a system based on blockchain technology for the automatic classification of students from the second year of the law department at the University of Skikda, Algeria, to the third year using the academy’s history and the results of previous promotions.
3D Facial Reconstruction Based on a Single Image Using CNN

In recent years, 3D facial reconstruction marked its presence in several areas, such as biometric security, computer vision, image synthesis, and video games. Many facial recognition applications require accurate 3D reconstruction; however, this task is complex and requires many calculations. This paper presents a new method based on CNN, which offers an automatic solution for the 3D reconstruction of the face depending on a single 2D image. Our method operates in three steps: the first step is to train a convolutional neural network with a self-made dataset to predict and detect the landmarks of the face and estimate the positions accurately from a single facial image in the image space. The second step involves producing the face’s geometric shape (mesh). Finally, the third step is to do an automatic translation between the 3D space of the object and the 2D image space to determine the texture referrals that correspond to each face polygon; thus, our method produces superior results in terms of accuracy and visual quality, considering all the parameters of the model (shape, expression, reflectance, and lighting) as inputs. Our method is simple, easy to implement, and offers a real-time 3D reconstruction of the face.
Data Clustering Using Tangent Search Algorithm

This article proposes a new algorithm, called Clustering Tangent Search Algorithm (C-TSA) to solve clustering problems. The C-TSA uses Tangent Search Algorithm (TSA) as an optimizer to move a given solution toward a better solution. The main characteristics of this new optimization algorithm are its simplicity, as it requires only a small number of user-defined parameters, and its efficiency in data clustering as demonstrated in the experimental results, where it provides very promising and competitive results on many benchmark datasets. TSA optimizer has been designed with an effective balance between exploration and exploitation. Additionally, it includes a useful local escape mechanism to prevent getting stuck in local optima, and an adaptive step size to improve convergence towards the best solution.
Feature Embedding Representation for Unsupervised Speaker Diarization in Telephone Calls

Speaker diarization aims to segment an audio recording, where different speakers are involved, into speech segments based on the speaker's identity. This study proposes a feature embedding representation for unsupervised speaker diarization in case of telephone conversations. First, the speech waveform is segmented into speech and non-speech segments using a voice activity detection algorithm. Then, a deep autoencoder performs feature embedding representation of the speech segments which are fed to the K-means algorithm to perform the clustering. Indeed, in the case of telephone calls speech data is often unequally shared between the two speakers, and an accurate feature representation helps to overcome limitations related to the varying width across clusters of the K-means algorithm. Experiments carried out on the CALLHOME dataset show the positive impact of the embedding representation in terms of Diarization Error Rate (DER) with an improvement of about 56%.
Off-Line Handwritten Math Formula Layout Analysis by ConvGNN

This work aims to construct an end-to-end system that can accurately analyze the layout of a mathematical formula. To accomplish this, we proposed a deep-learning architecture that makes use of a Convolutional Graph Neural Network (ConvGNN)—a variant of a convolutional neural network that operates directly on graphs to label symbols and their spatial relationships within the context of mathematical formulas, while also factoring in the features of the neighbors and their interrelationships. Testing different models of ConvGNN and comparing with the classical method: a Multi Layers Perceptron (MLP), we confirm the effectiveness of ConvGNN, especially the spectral-based one, as a suitable way to model formula structure that a graph can represent. We demonstrate that our system competes with some related works in several experiments on CROHME, a handwritten math formula standard database. These tests revealed an accuracy of 82% for MLP, 23% for simple ConvGNN, and 92% for spectral-based ConvGNN.
A Semi-supervised Teacher-Student Model Based on MMAN for Brain Tissue Segmentation

In the medical field, deep learning technologies help radiologists gain accurate diagnoses in evaluating both neurological conditions and brain diseases. But these technologies require a huge amount of data to train and validate. The acquisition and labeling of these data are pretty expensive and require medical experts. Therefore, it is necessary to build models that do not require a lot of labeled data like semi-supervised methods. These methods train on few samples of data in a supervised manner and extend this knowledge to the rest of the unlabeled samples. For this reason, we propose a semi-supervised technique called Teacher-Student model using Multi-Modal Aggregation Network (MMAN) and apply it for brain tissue segmentation from Magnetic Resonance Images (MRI). The dataset (MRBrains Challenge) contains a small portion of labeled data and the majority of them are unlabeled. Our proposed method consists of two main steps. The first one aims to exploit the huge unlabeled data to increase the volume of the training set. The second step, it follows the strategy of the Teacher-Student technique in an iterative manner to enhances the performance of our model. The segmentation process will divide the brain into gray matter, white matter and cerebro-spinal fluid. Our approach allows for an improved prediction of brain image segmentation to reach a mean accuracy of 96.21%.
Analysis of The Utilization of The Automatic Exposure Control (AEC) Feature in The Use of Deep Learning Breast Image Technology in Women's Mammogram Screening Examinations at Dharmais Cancer Hospital

Deep learning technology is useful for radiology specialists as double reading to help increase the accuracy of image interpretation results. One of the preparations for maximizing the use of this technology is using good-quality images as the source. The Automatic Exposure Control (AEC) feature, which functions to determine exposure factors automatically, is expected to help produce images with good and consistent quality so that deep learning technology can work more effectively. This research aims to determine the quality results of mammogram images taken using the AEC feature and to analyze the use of deep learning technology in evaluating mammogram images. This research method is retrospective by collecting 800 mammogram images randomly and anonymously. Three hundred images were tested, 500 were evaluated, and 250 were analyzed for image quality based on references related to applying AEC and assessing the contrast-to-noise ratio (CNR). Deep learning technology was analyzed by comparing the results of mammogram image evaluation using deep learning and the evaluation results of a radiology specialist. Deep learning technology analysis shows that 98% of mammograms have the same results as the radiology doctor's evaluation, and 2% have different results from the radiology doctor's evaluation where the image has a dense breast type. The image quality results in this research showed that 97.6% of the 250 samples taken using the AEC feature had good image quality, and 2.4% had poor image quality due to inappropriate breast positioning during the examination.
The Stakeholders

Last week, I had the opportunity to listen to the MEng students’ final presentations for their capstone course. In addition to their work scope and methodologies, each team discussed individual challenges that they faced, some of which were common between all teams, such as adapting to new tools and environments, which came to no surprise to me. I was more intrigued, however, by the lessons they learned and recommendations for future work on this project. It was evident that everyone was truly invested in successfully deploying the Ignite platform, as they continued to improve their features as well as deal with error-handling well after the official deployment deadline.
Striving for Agile Practices

True to his promise, Arash has posted a to-do list for each team on Slack, based on the new stories and RC's he has created this past weekend. Likewise, today's standup meeting has been extended to accommodate for planning and grooming such newly created stories since last week's demo meeting went overtime.
A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises

In the collaborative production environment of manufacturing tasks, the evaluation of enterprise technical capability in advance has a direct impact on the high-performance collaboration between the supplier and the demander of production tasks. In order to solve the problem of efficiency and accuracy in the past evaluation methods, this paper applies the adaptive neuro fuzzy inference system (ANFIS) to the field of enterprise technical capability evaluation for the first time, and proposes an improved hybrid intelligent model SFAHP-ANFIS-PSO based on fuzzy theory. The model takes the four key indexes which have the deepest impact on enterprise technical capability evaluation as the input variables of the model, and the input data are evaluated by ANFIS with high accuracy. Spherical fuzzy analytic hierarchy process (SFAHP) calculated the weight of each evaluation index to preprocess the ANFIS model. Particle swarm optimization algorithm (PSO) continuously optimized the model parameters in the training process, shortened the model training time, improved the evaluation efficiency, and further improved the accuracy of the evaluation results. The experimental results show that the SFAHP-ANFIS-PSO model has high convergence speed and accuracy of results, and can be used to evaluate the technical capability of enterprises with high efficiency and high accuracy.
An Effective Pre-trained Visual Encoder for Medical Visual Question Answering

Medical Visual Question Answering (Med-VQA) is a domain-specific task that answers a given clinical question regarding a radiology image. It requires sufficient prior medical knowledge, resulting in additional challenges compared to general VQA tasks. However, the lack of well-annotated large-scale datasets makes it hard to learn sufficient medical knowledge for Med-VQA. To address the challenge, this paper employs a large-scale medical multi-modal dataset to pre-train and fine-tune an effective model, denoted by ROCOGLoRIA. The model can locate semantic-rich regions implied in medical texts and extract local semantic-focusing visual features from the image. We propose to combine the global visual features with the weighted local visual features, for capturing fine-grained semantics in the image. We further incorporate ROCOGLoRIA as the visual encoder into baselines, to investigate whether it benefits Med-VQA. We conduct extensive experiments on three benchmark datasets and the results show that the method using ROCOGLoRIA as a pre-trained visual encoder outperforms strong baselines in the overall accuracy.
Innovative approaches and methods in language and literature education professional training

The article explores various innovative teaching technologies for the Ukrainian language and the appropriateness of their use to enhance students' knowledge quality and develop their intellectual and linguistic abilities. Presently, the demand for specialists in the labor market and their competitiveness largely depend on competent speech (oral and written), the ability to communicate effectively, knowledge of language influence techniques, and persuasion. It is noted that societal needs are changing, which require a teacher to be an innovator and researcher capable of critical thinking. However, there is a limited number of developed innovative technologies focused on fostering the individuality of future professionals. The article examines innovative educational approaches that are advisable to prepare future professionals, their impact on the professional training of language teachers, and the dilemmas that arise in current educational conditions. It is proven that experts analyze the development of various directions in contemporary education and science and also contribute to future changes in the reform of future teacher training systems. There is a continuous exchange of ideas, experiences, and knowledge on many modern education and science issues. Higher education teachers use the progressive experience of national and foreign scientists and introduce effective, innovative technologies and teaching methods to improve the education of future language teachers at the current stage of education and science development. The discussed content of innovative technologies and their role in the methodological training of present-day language teachers are oriented toward forming and improving professional competencies. The described technologies can be used in teaching various subjects within the philological cycle. It will help develop a conscious readiness of future language teachers to implement innovative approaches in the school practices of secondary education institutions. The use of innovative technologies helps address educational and developmental tasks, such as activating students' mental activities and implementing individualized learning. These will help to save learning time for further use, ensure controllability and predictability of results, utilize the best practices from worldwide educational experiences, and create conditions for the practical application of the potential abilities of future language teachers. Innovative technologies effectively implement important didactic principles of linguistic education, such as scientific nature, visuality, accessibility, and autonomy
Innovative practices in teaching social sciences and humanities as the basis of modern pedagogical discourse

Innovation in education is of paramount importance, as it facilitates the continual enhancement of educational content value and quality. This article aims to present the implementation of an "innovative environment" within university education as a significant element of pedagogical discourse. It remains pertinent to explore strategies for incorporating innovative practices into the teaching of social sciences and humanities within the realm of higher education. The primary objective of this study is to ascertain the effectiveness of innovative practices in the instruction of social sciences and humanities, while also determining students' perceptions regarding the necessity of integrating innovative practices, resources, and instructional techniques within the modern educational process. Given the nature of the study's objective, an integrated approach to methodology is required. The primary methodology employed in this study is the pedagogical experiment. Additionally, the research utilized methods such as questioning, observation, and the descriptive approach. The principal hypothesis posits that incorporating disciplines with social and humanitarian content that actively employ innovative practices is imperative for the education and training of professionals across various fields. The inclusion of such practices is expected to enhance the quality of education by fostering motivation to learn, facilitating career advancement, and improving the overall efficiency of the educational process, among other outcomes. The findings of this study provide evidence supporting the value of innovative practices in teaching humanities disciplines, serving as a foundation for pedagogical discourse within university education. Furthermore, the integration of innovative approaches in the instruction of social sciences and humanities establishes the groundwork for future reflection and the development of pedagogical tools, facilitating the successful application of acquired knowledge in practical contexts.
A Chinese Named Entity Recognition Method Based on Textual Information Perception Fusion

Named entity recognition (NER) aims to identify the entities with specific meanings from text, which is an important basic tool in many fields such as information extraction, question answering, and machine translation. In real-world, to perform named entity recognition tasks conveniently and quickly, it requires NER methods can quickly and accurately identify entities from the input text after being trained by a small training set for subsequent operations. The existing Chinese NER methods are usually based on BERT + LSTM + CRF models. However, when the training set is small, the recognition accuracy of these methods is relatively low. To solve this problem, this paper proposes a named entity recognition method based on Textual Information Perception Fusion (TIPF). It fully extracts the global features of the Chinese text through the Textual Information Memory Perception module, and fully fuses the global features and local features of the Chinese text using the Textual Information Adaptive Fusion module, so that the Chinese NER can be realized quickly and accurately. The proposed method is tested on several public datasets. Experimental results show that, compared with the existing methods, TIPF can achieve a higher recognition accuracy after training with a smaller training set.