Reviewerly/exHarmony huggingface dataset:
Topic Modeling Based on OWA Aggregation to Improve the Semantic Focusing on Relevant Information Extraction Problems

The volume of textual information available has led to the development of many solutions for extracting relevant information from Text Mining. Modeling the topics of texts has been an approach taken by many of these solutions. Topic modeling has been approached in many relevant information extraction solutions and in various ways. However, in all of them, semantic analysis is little exploited in identifying relationships between text terms. This paper presents a topic modeling method based on Ordered Weighted Average (OWA) aggregation as core to improve the semantic focusing on relevant information extraction problems. An OWA operator allow aggregate several values of measures in a single value. In this work OWA is used to aggregate several similarity and distance measures between candidate keyphrases. The measures are combined through a fuzzy aggregation of their values and some weights calculated with the RIM (Regular Increasing Monotone) quantifier. In addition, two other keyphrases extraction and text summarization solutions are proposed to demonstrate the effectiveness of the topical modeling method. The effectiveness of topic modeling is evaluated through the two relevant information extraction solutions proposed. Keyphrases extraction was evaluated with Inspec and 500N-KPCrowd datasets, and text summarization with datasets in English and Spanish was offered in MultiLing 2015. The highest values were reached in most of the metrics used in the experiments carried out with the two solutions. In the keyphrases extraction, significant improvements in precision and f-measure were obtained, while in the summarization, the most significant improvements were in the recall.
Exploring the Role of Monolingual Data in Cross-Attention Pre-training for Neural Machine Translation

Recent advancements in large pre-trained language models have revolutionized the field of natural language processing (NLP). Despite the impressive results achieved in various NLP tasks, their effectiveness in neural machine translation (NMT) remains limited. The main challenge lies in the mismatch between the pre-training objectives of the language model and the translation task, where the language modeling task focuses on reconstructing the language without considering its semantic interaction with other languages. This results in cross-attention weights being randomly initialized and learned from scratch during NMT training. To overcome this issue, one approach is to utilize joint monolingual corpora to pre-train the cross-attention weights, improving the semantic interaction between the source and target languages. In this paper, we perform extensive experiments to analyze the impact of monolingual data on this pre-training approach and demonstrate its effectiveness in enhancing the NMT performance.
ReVQ-VAE: A Vector Quantization-Variational Autoencoder for COVID-19 Chest X-Ray Image Recovery

Nowadays, digital images have a valuable role in our daily life and can be used for a variety of applications like fingerprint recognition, video surveillance, etc. Sometimes, images mainly medical images get infected with attacks due to many reasons such as transmission in a noisy channel. Diagnosing an attacked medical image yields erroneous interpretation. So, to improve it for the right decision, the image must be recovered in advance. Therefore, there is a need for an efficient image recovering technique that helps to deal with medical attacked images. Image recovery is a process to realign the original image from the attacked image. In this paper, we propose an approach for chest X-Ray image recovery based on Vector Quantized Variational Autoencoder (VQ-VAE). The attacked images fed into the model to recover the images and get the original ones. The results of the SSIM show that our proposed model has produced better results than existing models.
Analysis of Different Reinsertion Strategies in Steady State Genetic Algorithm

Usually, it is uncommon to think about the reinsertion of generated offspring in evolutionary computing as a property of the algorithm that can be changed and optimized for a problem. This way reinsertion strategies are mostly overlooked properties of such algorithms. In this paper, we would like to introduce some novel versions and analyze their effectiveness. For this, a test was conducted where ten altered reinsertion strategies (including three commonly used strategies as reference and seven new strategies we came up with) were tested with a Steady State Genetic Algorithm. We tested the strategies with two kinds of population and on five different (three continuous and two discrete) benchmark functions and problems. The results show that there are strategies that work particularly badly, while others work with similar effectiveness. Also, it turns out that the number of the individuals and the nature of the problem can change the relative effectiveness of the strategies previously categorized in the effective group.
Speaker Identification Enhancement Using Emotional Features

Speaker recognition is a broad field that encompasses many different tasks related to identifying speakers in audio recordings. Two specific sub-tasks that are often studied are speaker segmentation and speaker identification. These tasks typically involve analyzing acoustic features of the audio to determine who is speaking. However, one limitation of traditional speaker identification methods is that they can struggle when dealing with emotional conversations, as the acoustic features can change due to the emotions being expressed. To address this limitation, focuses on studying the effect of emotion on speaker identification by combining features of both the emotions and speakers. This approach has shown to improve identification accuracy, increasing it from 72% using speaker features alone to 75% when both emotion and speaker features are used.
DE-Forest – Optimized Decision Tree Ensemble

Classifier ensembles are still in the spotlight due to their proven applications for many practical problems. One of the most interesting approaches is methods proposing the construction of such models based on learned classifiers using randomly selected attributes. Among the essential algorithms in this area is Random Forest, which uses decision trees as base classifiers. Although these methods obtain outstanding quality for many tasks, the random selection of attributes for individual base classifiers used above does not guarantee the optimal ensemble selection. In this paper, we proposed DE-Forest - an ensemble classifier learning method using evolutionary techniques to select non-random and optimized subsets. Differential Evolution was chosen as the optimization algorithm, selecting the best set of attributes to learn the individual base classifiers. The quality of the proposed method has been evaluated based on computer experiments performed on many benchmark datasets. The obtained results are promising and show the superiority of the proposed method over the benchmark solutions.
A Convolutional Autoencoder Approach for Weakly Supervised Anomaly Video Detection

Weakly-supervised video anomaly detection uses video-level labels to avoid annotating all frames or segments in the training video. This problem is typically considered as a multiple instance learning problem, the training process aims to learn how to score both abnormal segments and normal segments, and the score of abnormal segments is higher than the score of normal segments. The features are extracted from videos before the training or testing process. Although many models have been proposed and obtained good results, improving the performance of the problem remains a challenge. This study proposes a convolutional autoencoder based approach to reconstruct features, with an assumption that the reconstructed features contain important information early bound to the objective of normal or abnormal video classification (minimizing the cost function). The work is validated on the ShanghaiTech Campus dataset and has produced results that outperform state-of-the-art methods.
An Architecture for Enabling Collective Intelligence in IoT Networks

Proliferation of the Internet of Things (IoT) has fundamentally changed how different application environments are being used. IoT networks are prone to malicious attacks similar to other networks. Additionally, physical tampering, injection and capturing of the nodes are more probable in IoT networks. Therefore, conventional security practices require substantial re-engineering for IoT networks. Here we present an architecture that enables collective intelligence for IoT networks via smart network nodes and blockchain technology. In this architecture, various security related functionalities are distributed to network nodes to detect tampered, captured and injected devices, recognize their movements and prevent networks’ use as an attack surface. Nodes interact with signaling, security information and data traffic. Security information aids to distribute cyber-security functionalities across the IoT network based on the device and/or application type. Every node in the proposed IoT network does not need to have all the cyber-security functionalities, but the network as a whole needs these functionalities.
Data Augmentation Techniques for Facial Image Generation: A Brief Literature Review

Image processing has gained notoriety over the last few years in performing various tasks through deep learning (DL) algorithms, such as face recognition and identity verification. Unfortunately, most of them require a large set of images for training, usually manually labeled, which is a costly task both in time and effort, not to mention being prone to human error. Data Augmentation (DA) techniques have been used to mitigate this situation, as they generate images by applying variations to real image sets. This chapter presents a brief literature review on various DA methods dedicated to image generation. The technique that has presented outstanding results in the task of generating artificial images is Generative Adversarial Networks (GANs). Some recent research papers in which GANs have been used for the generation of artificial images are presented. General aspects of GANs, such as their definition, architecture, training, and challenges, are described. Additionally, the implementation of a GAN architecture for the generation of artificial face images from a public set of images is presented. The need for a great computational capacity to generate images with great sharpness and realism is highlighted.
Learning from Imbalanced Data Streams Using Rotation-Based Ensemble Classifiers

In this paper, the problem of learning from imbalanced data streams is considered. To solve this problem, an approach is presented based on the processing of data chunks, which are formed using over-sampling and under-sampling. The final classification output is determined using an ensemble approach, which is supported by the rotation technique to introduce more diversification into the pool of base classifiers and increase the final performance of the system. The proposed approach is called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). It is validated experimentally using several selected benchmarks, and some results are presented and discussed. The paper concludes with a discussion of future research directions.
Two-Dimensional Pheromone in Ant Colony Optimization

Ant Colony Optimization (ACO) is an acclaimed method for solving combinatorial problems proposed by Marco Dorigo in 1992 and has since been enhanced and hybridized many times. This paper proposes a novel modification of the algorithm, based on the introduction of a two-dimensional pheromone into a single-criteria ACO. The complex structure of the pheromone is supposed to increase ants’ awareness when choosing the next edge of the graph, helping them achieve better results than in the original algorithm. The proposed modification is general and thus can be applied to any ACO-type algorithm. We show the results based on a representative instance of TSPLIB and discuss them in order to support our claims regarding the efficiency and efficacy of the proposed approach.
A Context-Aware Approach for Improving Dialog Act Detection in a Multilingual Conversational Platform

This paper proposes a method to exploit and integrate dialogue context information into two neural models for dialogue act detection, including recurrent networks and transformers networks. The proposed models are evaluated on two standard benchmark datasets for English and Vietnamese. Extensive experimental results show that our method achieves a significant better performance in comparison to baseline results. Our proposed method has been deployed as a core component of a commercial conversational platform, effectively serving millions of clients in multiple markets.
Complexity-Based Code Embeddings

This paper presents a generic method for transforming the source code of various algorithms to numerical embeddings, by dynamically analysing the behaviour of computer programs against different inputs and by tailoring multiple generic complexity functions for the analysed metrics. The used algorithms embeddings are based on r-Complexity [7]. Using the proposed code embeddings, we present an implementation of the XGBoost algorithm that achieves an average $$90\%$$ F1-score on a multi-label dataset with 11 classes, built using real-world code snippets submitted for programming competitions on the Codeforces platform.
MEP: A Comprehensive Medicines Extraction System on Prescriptions

Accurately identifying drug names from prescription images is essential for effectively processing and managing patient medical information. In order to enhance our previous approach, we have introduced a new version named Medicines Extraction on Prescription (MEP). This innovative approach employs heuristic rules and a Temporal Convolutional Network model to extract and classify proprietary medicines from prescription images captured by smartphones. Our model not only achieves a precision score of up to 0.94 on experimental datasets, but it also boasts impressive processing speed. The total drug name recognition and average extraction time is only 6.67 s per prescription, significantly faster than the 17.81 s average processing time of our previous model.
Big Data Analysis and Modeling Method of College Student Employment Management Based on Deep Learning Model

In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The results are as follows: By comparing the application evaluation of linear regression method, BP neural network and DBN model, this paper finds that DBN model has better accuracy and lower error and has better advantages in the application of college students' employment data management characteristics. In addition, the development of social economy and the number of college graduates are the key factors for the employment rate of college students. Therefore, this paper suggests that through the use of big data technology, college will build a data platform for college students' employment management and provide a carrier for college students to obtain professional information and employment management information.
Intelligent Informatization Construction of University Libraries in the Environment of Multimedia Big Data

With the advent of the big data internet era, the corresponding multimedia technology is also developing. At the same time, as an important place for students to improve their cognitive level and expand their knowledge coverage, the construction of knowledge and information in university libraries will have an important impact on the growth of students and the development of the library itself. Big data not only brings specific challenges to the intelligent information construction of libraries, but also provides opportunities for the further development of intelligent information construction. Therefore, this paper aims to analyze the intelligent informatization construction of university libraries in the multimedia big data environment, make suggestions for the further development of library intelligent informatization construction, and provide reference materials for library intelligent informatization construction.
Extended Collaborative Filtering Recommendation System with Adaptive KNN and SVD

In recent years, recommendation systems have gained significant importance due to the vast amount of digital content available on various online platforms. Collaborative filtering is a widely adopted approach in recommendation systems, leveraging user-item interactions to make personalized predictions. However, traditional collaborative filtering methods face challenges such as the cold-start problem and data sparsity. To address these issues, researchers have proposed advanced techniques, including Adaptive KNN-Based and SVD-Based Extended Collaborative Filtering. This paper provides a comprehensive review of these two recommendation systems, discussing their underlying principles, advantages, and limitations. Furthermore, we explore recent research advancements and real-world applications, providing insights into the potential future developments in this field.
Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition

Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors’ data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.
Dynamic Resource Allocation Strategy of Multi-Objective Fuzzy Optimization Based on Markov Decision Process

With the limited amount of national defense resources, the dynamic allocation of resources in overall project construction is conducive to improving the efficiency of project construction. In this regard, this paper constructs a dynamic resource allocation model of multi-objective and multi-stage fuzzy optimization based on the Markov decision process (MDP). On this basis, this paper further adopts the Q-learning algorithm to seek strategy optimization. At last, this paper determines the resource allocation strategy that can optimize the total benefit of construction projects through the example analysis. Relevant results reveal that the dynamic resource allocation model of multi-objective and multi-stage fuzzy optimization based on MDP can achieve the effect of optimizing the project construction objectives and maximizing the construction output.
Unsupervised Domain Adaptation for Remote-Sensing Vehicle Detection Using Domain-Specific Channel Recalibration

Vehicle detection methods based on deep learning have achieved remarkable results on remote sensing images. However, the performance of the detector degrades when the test images are distinct from the training images. Domain adaptive vehicle detection is a promising approach to bridging the domain gap. Existing methods usually adopt fully shared networks, but ignore the problem that features from different domains may be incompatible within a single model. In this letter, we present a novel domain adaptive vehicle detection method based on patch-wise domain-specific channel recalibration (PDSCR). The PDSCR module routes the feature to the corresponding network branch and extracts the channel dependence using separate parameters. In this way, our method can explicitly capture domain-specific information for each domain. Furthermore, we propose a dynamic weighted prototype alignment (DWPA) to avoid the negative effects of false pseudo-labels, especially in the early stage of training. Experimental results of adaptation from our synthetic dataset to three real vehicle detection datasets demonstrate the effectiveness of our method. Code and our synthetic data will be available at https://weix-liu.github.io/.
Inter span learning for document-level relation extraction

The entity-relationship extraction model has a significant influence in relation extraction. The existing model cannot effectively identify the entity-relationship triples in overlapping relationships. It also has the problem of long-distance dependencies between entities. In this paper, an inter span learning for document-level relation extraction model is proposed. Firstly, the model converts input of the BERT pre-training model into word vectors. Secondly, it divides the word vectors to form span sequences by random initial span and uses convolutional neural networks to extract entity information in the span sequences. Dividing the word vector into span sequences can divide the entity pairs that may have overlapping relationships into the same span sequence, partially solving the overlapping relationship problem. Thirdly, the model uses inter span learning to obtain entity information in different span sequences. It fuses entity type features and uses Softmax regression to achieve entity recognition. Aiming at solving the problem of long-distance dependence between entities, inter span learning can fuse the information in different span sequences. Finally, it fuses text information and relationship type features, and uses Linear Layer to classify relationships. Experiments demonstrate that the model improves the F1-score of the DocRED dataset by 2.74% when compared to the baseline model.