Reviewerly/exHarmony huggingface dataset:
Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the caption generation lingual subsystem, this paper tested three different pre-trained language embedding models: Glove (Global Vectors for Word Representation), BERT (Bidirectional Encoder Representations from Transformers), and TaCL (Token-aware Contrastive Learning), to select from them the most accurate pre-trained language embedding model. Our experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN (Shifted window) and PVT (Pyramid Vision Transformer) as an image feature extractor, combined with the TaCL language embedding model is the best result among the other combinations.
Automated Leukemia Screening and Sub-types Classification Using Deep Learning

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are also utilized while examining the patient for leukemia. However, all these methods are labor-intensive, slow, inaccurate, and require a lot of human experience and dedication. Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations. They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells. However, these systems are more efficient, reliable, and fast than previous manual diagnosing methods. However, more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity. In this paper, we have proposed a robust automated system to diagnose leukemia and its sub-types. We have classified ALL into its sub-types based on FAB classification, i.e., L1, L2, and L3 types with better performance. We have achieved 96.06% accuracy for subtypes classification, which is better when compared with the state-of-the-art methodologies.
Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.
Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the category. Due to the text classification model, we can effectively reduce the search space for finding the answers to a given question. Therefore, the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference time. Furthermore, to improve the performance of finding similar sentences in each category, we present an ensemble embedding model for sentences, improving the performance compared to the individual embedding models. Using real-world QA data sets, we evaluate the performance of the proposed QA matching model. As a result, the accuracy of our final ensemble embedding model based on the text classification model is 81.18%, which outperforms the existing models by 9.81%∼14.16% point. Moreover, in terms of the model inference speed, our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.
Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.
Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’ opinions. To gain fame or profit, generally, spam reviews are written to demote or promote certain targeted products or services. This practice is called review spamming. During the last few years, various techniques have been recommended to solve the problem of spam reviews. Previous spam detection study focuses on English reviews, with a lesser interest in other languages. Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced. Thus, this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit (SRD-OSGRU) on Arabic Opinion Text. The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes: spam and truthful. Initially, the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format. Next, unigram and bigram feature extractors are utilized. The SGRU model is employed in this study to identify and classify Arabic spam reviews. Since the trial-and-error adjustment of hyperparameters is a tedious process, a white shark optimizer (WSO) is utilized, boosting the detection efficiency of the SGRU model. The experimental validation of the SRD-OSGRU model is assessed under two datasets, namely DOSC dataset. An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.
Generative Networks and the AutoEncoder

Neural networks were invented to classify signals. However, networks can also be used to generate signals. This chapter introduces generative networks, and shows that discriminative networks can be combined with generative networks to produce an autoencoder. Autoencoders can be trained with self-supervised learning to provide a compact code for signals. This code can be used to reconstruct clean copies of noisy signals. With a simple modification to the loss function using information theory, an autoencoder can be used for unsupervised discovery of categories in data, providing the basis for self-supervised learning that is at the heart of Transformers. To explain this modification, this chapter reviews basic concepts from information theory including entropy, cross entropy and the Kullback-Leiblier (KL) divergence. The chapter concludes with brief presentations of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Learning Objectives: This chapter provides students with an introduction to generative neural networks and autoencoders, covering fundamental concepts from information theory as well as well as applications such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Mastering the material in this chapter will enable students to understand how a neural network can be trained to generate signals, and how an auto-encoder can be used for unsupervised and self-supervised learning. Students will acquire an understanding of fundamental concepts from information theory such as entropy and sparsity. Students will be able to explain how generative networks can be combined with discriminative networks to construct generative adversarial networks.
THPoseLite, a Lightweight Neural Network for Detecting Pose in Thermal Images

Nowadays, smart environments (SEs) enable the monitoring of people with physical disabilities by incorporating activity recognition. Thermal cameras are being incorporated as they preserve privacy. Some deep learning (DL) solutions use the pose of the users because it removes external noise. Although there are robust DL solutions in the visible spectrum (VS), they fail in the thermal domain. Thus, we propose thermal human pose lite (THPoseLite), a convolutional neural network (CNN) based on MobileNetV2 that extracts pose from thermal images (TIs). In a novel way, an auto-labeling approach has been developed. It includes a background removal using an optical flow estimator. It also integrates Blazepose [a pose estimator for VS images (VSIs)] to obtain the poses in the preprocessed TIs. Results show that the preprocessing increases the percentage of detected poses by Blazepose from 19.55% to 76.85%. This allows the recording of human pose estimation (HPE) data sets in the VS without requiring VS cameras or manually annotating data sets. Furthermore, THPoseLite has been embedded in an Internet of Things (IoT) device incorporating an edge tensor processing unit (TPU) accelerator, which can process TIs recorded at 9 frames per second (FPS) in real time (12.28 FPS). It requires fewer than 6W of energy to run. It has been achieved using model quantization, decreasing the accuracy in estimating the poses by only 1%. The mean-squared error of MobileNetV2 in test images is 35.48, obtaining accurate poses in 21% of the images that Blazepose is not able to detect any pose.
Data Engineering for the Factory of the Future, Multimedia Applications and Cyber-Physical Systems

This chapter is the companion chapter to “Part 1: State-of-the-Art Time-Series Data Formats Performance Evaluation.” In this chapter, algorithms for converting data from one format to other formats are presented. To implement the algorithms, existing open-source Python libraries are used extensively, and where needed, new Python routines for converting data formats are developed. It is envisaged that the algorithms and Python libraries and routines that are freely provided in this chapter will be useful for data engineers, data scientists, and for industrial IoT, cyber-physical systems (CPS), multimedia, and big data practitioners who are on the quest to use different types of data formats that are compatible with memory-constrained factory floor IoT devices. It will also be useful for Delta Lake and big data engineers, who are on the quest for delivering robust bronze, silver, and gold data lakes in the cloud.
A Detailed Review on Quantum Computing Technologies

Modern computing approaches like quantum computing are based on the remarkable occurrences of quantum mechanics. It is a combination of information theory, computer science, mathematics, and physics. The study of quantum computing includes the design and development of computer-based technologies based on quantum theory, which explains the nature and behaviour of energy and matter at atomic and sub-atomic levels. Sub-atomic particles such as electrons and photons are used in quantum computing. These particles exist in two different states (i.e., 1 and 0) based on quantum bits or qubits. Compared to conventional computers, quantum computing offers higher computing capacity, lower energy utilization, and high speed. This article reviews the state-of-the-art quantum computing technologies and different ways in which the qubits can exponentially increase the efficiency of quantum computers and sort various possibilities and identify potential solutions to different challenges.
Enhancing the elderly disease rehabilitation based on semantic understanding and knowledge graph

Recent research shows that intake of the corresponding nutrients can enhancing disease rehabilitation in the elderly. The traditional medical question and answer (Q&A) system can provide convenient query services for the elderly. However, the traditional medical Q&A system also has defects, for instance, the traditional medical Q&A system is not very targeted to the elderly, along with this cannot meet the storage needs of massive data. In this paper, a semantic-based word separation method was proposed, which uses Chinese semantic segmentation to obtain keyword information. Meanwhile, we use knowledge inference to perform answer inference, and leverage Cypher statement to query the answers. In the evaluation experiment, we invited 50 volunteers (25 female and 25 male) who living in a community to be divided into two groups for the experiment. The experimental group made dietary adjustments based on the Q&A system and track record their blood pressure values. As suggested by the experimental results, the answers given by the system in this paper have a positive feedback effect on enhancing the rehabilitation of elderly diseases.
THE ESSENCE OF CREATIVITY IN TEACHING FOREIGN LANGUAGE

The development of creative thinking is one of the most important tasks in the modern world. The striving to realize oneself, to show one's capabilities is the guiding principle that manifests itself in all forms of human life – the desire for development, expansion, improvement, maturity, the tendency to express and manifest all the abilities of the body. The aim of this study is determined by the research of creative abilities' development problem in modern science from the point of Kazakhstani, Russian, and foreign scholars. The history and current state of the problem of creativity development and the essence and structure of creativity are studied on the basis of a wide range of scientific sources. Interpretations of creative abilities of researchers from different countries are proposed and substantiated. The authors carry out a critical analysis of the components of creative abilities. Key words: creativity, education, creation, creative abilities, creative competence, creative abilities components
Exploiting Character-Word Fusion to Enhance Chinese Named Entity Recognition Combined with Multi-head Attention Mechanism

Named Entity Recognition (NER) is a fundamental subtask in natural language processing. The specific challenge of Chinese NER, as opposed to English NER, lies primarily in word segmentation ambiguity. To tackle the concerns, a novel weighted character-word fusion model (CWMA-LSTM) is developed in conjunction with the multi-head attention mechanism. Specifically, character and word representations are encoded using the pre-trained model. By matching the lexicon, the weighted lexical features are included in the character sequence. The feature vectors are subsequently fed into the Long Short-Term Memory (LSTM) for the extraction of contextual semantic information. The multi-head attention is utilized to improve the implicit awareness of the sentence. Experiments on benchmark datasets show that CWMA-LSTM exceeds other state-of-the-art methods, and validates the efficacy of weighted character-word fusion.
AN ENHANCED DIFFERENTIAL EVOLUTION ALGORITHM WITH ADAPTIVE WEIGHT BOUNDS FOR EFFICIENT TRAINING OF NEURAL NETWORKS

Artificial neural networks are essential intelligent tools for various learning tasks. Training them is challenging due to the nature of the data set, many training weights, and their dependency, which gives rise to a complicated high-dimensional error function for minimization. Thus, global optimization methods have become an alternative approach. Many variants of differential evolution (DE) have been applied as training methods to approximate the weights of a neural network. However, empirical studies show that they suffer from generally fixed weight bounds. In this research, we propose an enhanced differential evolution algorithm with adaptive weight bound adjustment (DEAW) for the efficient training of neural networks. The DEAW algorithm uses small initial weight bounds and adaptive adjustment in the mutation process. It gradually extends the bounds when a component of a mutant vector reaches its limits. We also experiment with using several scales of an activation function with the DEAW algorithm. Then, we apply the proposed method with its suitable setting to solve function approximation problems. DEAW can achieve satisfactory results compared to exact solutions.
APPLICATION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE IN SOFTWARE BUG CLASSIFICATION

Fault management is an expensive process and analyzing data manually requires a lot of resources. Modern software bug tracking systems may be armed with automated bug report assignment functionality that facilitates bug classification or bug assignment to proper development group. For supporting decision systems, it would be beneficial to introduce information related to explainability. The purpose of this work is to evaluate the use of explainable artificial intelligence (XAI) in processes related to software development and bug classification based on bug reports created by either software testers or software users. The research was conducted on two different datasets. The first one is related to classification of security vs non-security bug reports. It comes from a telecommunication company which develops software and hardware solutions for mobile operators. The second dataset contains a list of software bugs taken from an opensource project. In this dataset the task is to classify issues with one of following labels crash, memory, performance, and security. Studies on XAI-related algorithms show that there are no major differences in the results of the algorithms used when comparing them with others. Therefore, not only the users can obtain results with possible explanations or experts can verify model or its part before introducing into production, but also it does not provide degradation of accuracy. Studies showed that it could be put into practice, but it has not been done so far.
ANALYSIS OF APPLICATION PERFORMANCE TESTING USING LOAD TESTING AND STRESS TESTING METHODS IN API SERVICE

Testing an application is an important thing to do in software development. However, there are some tests that are sometimes missed after application development has been completed, such as performance testing. Performance testing is a type of test to ensure software will work properly under the expected workload. When performance testing is important to ensure that the functional needs of the system are running well. To ensure that the business objectives of the system are satisfactory and according to user needs can be done using Black Box Method. After ensuring the final results meet the specifications of the system needs, performance testing can be carried out. In performance testing, a load and stress testing methods are used to test the system, so as to be able to validate system performance are correct and determine the operating capacity of the system, with load limit at the break threshold and above the break threshold. This test is carried out to help determine how software can act well when accessed by several users together with a lot of data. This study conduct analyze the behavior of the system in the server environment that currently running and then optimize the configuration of the service and server to achieve concurrent users with 500 users at a time with jmeter as a performance test tools.