Reviewerly/exHarmony huggingface dataset:
Me-home: Medical Test Kit for Users at Home

Fast and effective diagnosis is essential for doctors and patients to move forward with the proper treatment. Mobile based point of care testing has emerged as a feasible option for quickly identifying the disease, cutting waiting times, and hastening the beginning of therapies as the healthcare industry looks for solutions that are more patient-focused. In view of this, this paper presents analysis and an android mobile application for the users to perform medical tests like blood group typing, Haemoglobin level estimation, Urine analysis, Wound healing time estimation at home by applying image processing techniques on the images captured using mobile camera.
A discussion on mass function generation for EORELM-based apple grading

Apple grading has become an important research topic in recent years. In order to improve the performance of automatic apple grading and sorting system, in this paper, evidential ordinal extreme learning machine is applied to model the relation between apple features and grades. Based on the ordinal extreme learning machine, evidence ordinal extreme learning machine can further deal with the epistemic uncertainty of grade labels modelled by mass functions. Considering the uncertainty of labels and the ranking relationship between labels of apple training example, the triangle mass function and Gaussian mass functions with different standard deviation σ values are designed and detailed. In addition, three evidential coding schemes are compared, each of which outputs a different coding vector with a given mass function. Experiments on Red Fuji apples show the grading performances of the single-model multi-output EORELM models with different mass function generation approaches combined with distinct coding schemes. Compared with traditional ELM model and ordinal ELM model, EORELM can obtain better grading performance.
Genetic Programming for Optimizing Behavioral Rules of Agents Mimicking Human Behavior Patterns

Genetic Programming (GP) is one of the effective methods to automatically generate a structure of behavior rule of agent such as robots. In optimization of a behavior rule of an agent to achieve a task, it is important to generate robust rules that work well in an environment involving slight errors. This paper shows a new approach for generating a flexible behavior rule of agent achieving task in an inaccurate environment. In our approach, we focus on the flexibility of humans’ behavior to apply learned knowledge to similar patterns. Here we extend the Santa Fe Trail problem which is one of artificial ant problems, and introduce a degree of imitation of human operations to the objective function. Through the numerical experiments, we show that GP with a new objective function can generate rules that work well in an environment with errors.
Fast degree of aggregation filter for reducing impulse noise in satellite images

The interest of this paper is the reduction of impulse noise in satellite images. The vector median filters are by far the most popular filters for reducing impulse noise in images. A recent variant of this class of filters is the degree of aggregation filter that operates by associating the degree of aggregation as the weight of a pixel in representing the signal component in the image. This approach can lead to improved filtering as pixels that are more similar obtain higher weights and vice versa. However the problem in this approach is that filtering should be performed over all the pixels in a sequential fashion thus leading to high computational complexity. This article improves on this filter and proposes a fast degree of association algorithm. The efficacy of the proposal is measured using a combined measure of time and accuracy.
Quantification of Pediatric Brain Development with X-ray CT Images using 3D-CNN

Brain development in children between the ages of 0 and 3 is extremely rapid. In the diagnosis of the pediatric brain, quantitative methods for evaluating brain growth during this period are needed. We propose a method for predicting the developmental age of the brain using deep learning. In the proposed method, at first, the cranial region is extracted from CT images, and then the posture and position are calibrated. We propose a new neural network model that uses a 3D convolutional neural network (3D-CNN) to extract features from CT images and estimate the developmental age of the brain in all coupled layers. 204 pediatric patients (0 to 47 months) with no neurological abnormalities were used for study and evaluation. The root mean square error (RMSE) between the predicted age and the patient’s actual age was 6.45 months with a correlation coefficient of 0.89. In addition, the output of the attention map showed a high degree of attention to the anterior region. This result is consistent with medical findings that anterior regions of the pediatric brain are particularly developed.
Prediction of encoding bitrate for each CRF value using video features and deep learning

In this paper, we quantify elements representing video features and we propose the bitrate prediction of compressed encoding video using deep learning. Particularly, to overcome disadvantage that we cannot predict bitrate of compression video by using Constant Rate Factor (CRF), we use deep learning. We can find element of video feature with relationship of bitrate when we compress the video, and we can confirm its possibility to find relationship through various deep learning techniques.
Application of Neuro-Symbolic Reasoning in Natural Language Processing

Commonsense reasoning is a crucial part of human intelligence. It has been a goal in the study of artificial intelligence to develop machines mimicking commonsense reasoning at par with humans. The deep learning models have advanced in many natural language processing tasks. However, they lack logical reasoning ability. Symbolic artificial intelligence has the potential reasoning ability wherein logic is used to reason efficiently. This led to the rise of neuro-symbolic reasoning, which is an integration of symbolic logic with deep learning. In this paper, we have experimented with the BERT, RoBERTa and GPT-J language models for question answering task to check their reasoning ability and found that they lack the reasoning ability. This paper compares the performance of the BERT, RoBERTa and GPT-J question-answering models with the neuro-symbolic reasoning model using logical neural networks. It is found that the neuro-symbolic reasoning model has the reasoning ability like humans, which infers that the neuro-symbolic reasoning model can effectively handle logical reasoning and is a key to the future of artificial intelligence.
A Code Transformation Approach To Enhance Code Cardinality Understanding

Small and Medium-sized Enterprises (SMEs) have been recognized as universal and they have a vital effect on developing countries economies. However, to develop or maintain software systems for such SMEs, special structures should be considered for each type. In addition, to maintain existing software systems, software engineers face a big challenge while trying to understand their code first. To overcome such difficulties, several researchers have developed tools to support software maintenance engineers in understanding software code. They have set up different code interpretation rules to understand code structure such as classes, methods, attributes, and the relationships between classes. However, these tools lack the focus on transforming code cardinality. This paper proposes a code transformation approach to enhance the understandability of code structure with a focus on code cardinality. Therefore, a semi-automated approach that relies on five main transformation rules is introduced. To evaluate the effectiveness of this approach, two case studies were conducted using different project codes. The results show that compared to traditional approaches the proposed approach has decreased the time and effort consumed by developers to understand the investigated code.
Combining Source and Vocal Tract Information for Text Dependent Speaker Verification

In the recent trend, text-dependent speaker verification (TDSV) system becomes a challenging task for practical implementation and deployment of the speech biometric based online attendance system as an application. The collected speech utterances contain the environmental conditions and background noise, due to this the system performance drops drastically. To improve the system performance, accurate begin and end points detection needs to be done using Energy based end point detection (EPD) method. Then focuses on considering a recently proposed discrete cosine transform of the integrated linear prediction residual (DCTILPR) excitation source feature along with a vocal tract Mel-Frequency Cepstral Coefficients (MFCC) features. Two parallel systems are developed using both the DCTILPR and MFCCs features for the stated Dynamic Time Warping (DTW) based speaker verification system used for experimental analysis conducted on the IITG database for recording the attendance over a six month period is shown. The widely used MFCC features are considered as the baseline system in the TDSV system. The DCTILPR and MFCC features individual performance, and their score level combination shows a significant improvement over the baseline results as compared to the standalone MFCC features.
Soccer Players Real Location Determination using Perspective Transformation

Recently, the use of Multi-Object Tracking(MOT) technology has become vital for sports analysis using video. The main advantages over GPS are mainly cost and non-interference with the players. However, to estimate the same real location comparable to the GPS, camera calibration and play area dimensions detection are necessary. This work, proposes a deep learning method for player, ball and field markers detection and the use of the perspective transform to determine the real location of the players in the field irrespective of the camera position. Our results show that, this technology could be useful and cheap for both amateur and professional teams.
Methodology to Evaluate the Performance of Hadoop MapReduce on a Hyper-V Cluster using SAN Storage

Deploying Hadoop MapReduce applications in a virtualized environment is adopted by some cloud computing providers for better resource utilization. However, the virtualization overhead can negatively affect the performance of applications when executed on virtual machines rather than physical servers. In that regard, this paper introduces a methodology to match the software and hardware specifications of virtual and physical Hadoop clusters to allow accurate measurement of virtualization overhead as well as enable the efficient execution of MapReduce applications on both clusters. The methodology considers configuring non uniform memory access in the utilized powerful servers. It also factors in multipath aggregation that facilitates load balancing and failover protection when the servers access SAN storage over a local Ethernet network. The WordCount application with workloads that reached 750 GB was executed on both clusters to evaluate the effects of virtualization on application performance. The results showed that the average elapsed time of a MapReduce application on a specific virtual cluster can be 19.2% higher than that on a physical cluster, mainly due to I/O throughput degradation over the virtual cluster in that case.
Detection and Classification of Ships using a self-attention Residual Network

The categorization and detection of ships is crucial in maritime applications such as marine surveillance, traffic monitoring etc., which are extremely crucial for ensuring national security. There has been extensive amount of research happening in this area since the last decade. The acceleration of computational power and increasing number of satellite images collected over the years paved the way for such an extensive research. However, high-resolution images of ships that capture the texture details for an effective categorization are still very limited. In the recent years, researchers collected images over various sources to create datasets which are made publicly available for use. In our work, we use the ShipRSImageNet dataset which was recently released. Many state-of-the-art object detection methods were employed on this dataset for different levels of ship detection. But due to the complexity of the dataset, the state-of-the-art models are not capable enough to perform a full categorization of the ships in these dataset. In this work, a residual attention based convolutional neural network (CNN) model is employed for feature extraction, which can be fed in to the state-of-the-art object detection models for the extraction of the features. Usually, other backbone networks which can be employed are complex and consists of multiple layers. Due to the need for a large number of training parameters, these models and approaches have heavy-weight designs. Therefore, they need more storage and costlier training processes.
A Novel Image Upscaling Method Using High Order Error Sharpening

A novel sophisticated interpolation scheme for converting low resolution images to higher resolution images is presented. Because of prediction, some blurring is introduced in the edges of the high resolution image while performing up-sampling. As a result, one post-processing scheme is used to eliminate the blurring artifact. The proposed interpolation scheme makes two major contributions. First, the high frequency degradation due to interpolation is identified. The degraded edge is sharpened using a high order laplacian filter in the next step. For various natural images, the proposed interpolation scheme is tested. Experiment results show that the proposed interpolation scheme outperforms the other state-of-the-art interpolation methods.
Improving Oral Cancer Detection Using Pretrained Model

Oral Cancer is a kind of cancer, if found early enough, has a great chance of survival. Artificial Intelligence techniques is helping in a great way by automating the cancer prediction at a faster pace and low cost. Deep learning algorithms are extremely helpful in automating the oral cancer detection. These algorithms are helpful in extracting the features and classifying the images. The Convolutional Neural Network is used in this paper to compare with pretrained DenseNet201, DenseNet169 and DenseNet121 model. The results of the DenseNet201 has shown a good improvement in detection accuracy with 85% and significantly reduces the loss whereas the DenseNet169 provides excellent training accuracy of 98.96%. DenseNet201 also performs well at recall and F1-score 93.93% and 93.22%. The pretrained model also improves computational efficiency by using predefined weights.
On Some Fuzzy Clustering Algorithms with Dimensionality Reduction

Certain fuzzy clustering algorithms involve dimensionality reduction techniques, such as principal component analysis (PCA), probabilistic principal component analysis (PPCA), and t-factor analysis (t-FA). Other fuzzification techniques have been applied to fuzzy clustering without dimensionality reduction. In this study, eleven fuzzy clustering algorithms are proposed based on five dimensionality reduction methods: PCA, PPCA, t-distribution-based PPCA, FA, and t-FA; and three fuzzification techniques: Bezdek-type, Kullback-Leibler divergence-regularization, and q-divergence-regularization. Based on numerical experiments using an artificial dataset, it is shown that some of the proposed methods outperforms the conventional methods on clustering accuracy.
Efficient Arabic Query Auto-Completion for Question Answering at a University

In this paper we describe an implementation of an Arabic query auto-completion system for student question-answering at a University. University students make many inquiries concerning academic life: about majors, concentrations, dates, instructors, courses, rooms, exams and more. Auto-completion (AC) has recently been part of many user interfaces, such as search bars on web pages, social media sites and mobile applications. We investigate multiple approaches to completion candidate generation and ranking and the role Arabic NLP may play in that. After experimenting with other options, we collected the data used in our system directly from students at the University. This data can be expanded to account for more types of queries and ways to express information needs. We describe our dataset, give an evaluation of individual system components and of the system results in general. The goal of this work is to improve the answer search experience by reducing the time of entering the query and biasing the completed query towards unambiguous and easily answerable questions. We divided the implementation into several stages and report on the results for each stage separately. Despite the difficulties of Arabic NLP, the results of our system were encouraging and compared well with other QAC systems described in the literature. Finally, we give an informal evaluation of overall results and the improvement resulting from using QAC for our QA system.
Improving Imbalanced Dialogue Act Classification Using Cost-Sensitive Learning

Dialogue acts represent the content or intention of the speaker’s utterance, and are classified into different types, which are called dialogue act labels. Since dialogue acts are helpful in understanding the dialogue content and generating sentences, many studies have been conducted on the classification of dialogue acts. In particular, the dialogue act classification of a response is a problem in predicting dialogue act labels of response sentences and is effective for generating response sentences if the labels are correctly classified. However, the dataset used in this study is unbalanced, with the utter and understand labels accounting for more than 70 % of the nine dialogue act labels. Therefore, the majority labels are predicted more often and the minority labels are not predicted at all. This paper aims to solve the problem of minority labels not being predicted. Increasing the loss value when minority labels are misclassified, lets the model learn to capture the features of minority labels. We believe that cost-sensitive learning is effective for classifying imbalanced datasets because it allows us to adjust the loss value in case of misclassification. The cost-sensitive learning used in this experiment can be roughly divided into two types depending on the setting of the cost value. The first is a method that sets a cost value for each class, and the second is a method that sets a higher cost value when a minority label is misclassified as a majority label. Using these methods, we have improved the Recall and F1 compared to the conventional methods and reduced the misclassification rate of minority labels as majority labels. We confirmed that the cost-sensitive learning method is effective for imbalanced datasets. However, the cost-sensitive learning method did not improve accuracy.
An Overview on Detecting Digital Image Splicing

In today's society, digital images increasingly predominate as a source of information. However, they are easily modifiable with accessible image editing software. An image forgery technique that is frequently used is splicing. It involves combining two or more separate images to produce a merged image that differs greatly from the source image. Image splicing detection is crucial to digital forensics; hence it has recently drawn more attention. We present a comprehensive analysis of the research on several image splicing detection technologies. In the literature, a variety of methods utilizing machine and deep learning to detect image splicing have been suggested. The investigation carried out in this paper may assist the researcher in better comprehending the benefits and uses of the image splicing detection technologies already in use and in the development of more effective algorithms for detection.
A Behavioural Analysis of US Election 2020 Using Deep Learning

Technology has evolved into a pivotal driving force for global communication. Twitter, Facebook, and Instagram are the most essential channels for expressing opinions on the daily developments that occur in and around the world. In this work, the tweets of US Election 2020 have been collected from twitter. After pre-processing the tweets, Sentimental Analysis using textblob and Behaviour Analysis using text2emotion are applied. The generation of feature vector is done using n-grams (unigram + bigram) for Machine Learning and neural networks keras for Deep Learning. Finally, the performance of the model is evaluated by using Deep Learning algorithms and then compared with Machine Learning algorithm. The model has achieved an accuracy of 84% using Bi-LSTM.
Quality Enhancement of Abstractive Text Summaries with Content Selection

Abstractive text summarization focuses on generating summaries by preserving the vital information from the source document. The content selector module proposed in this paper takes the source document's top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> sentences and feeds them into the abstractive text summarization model to produce quality summaries. When combined with the content selector module, the abstractive text summarization model produces summaries that are semantically equivalent to the reference summaries. We use the ROUGE evaluation metric and human evaluation to assess the abstractive text summarization model's summaries with and without the content selector module. We found that the abstractive text summarization models using the content selector module provide summaries on par with those generated by humans.
The Influence of E-Iearning, M-learning, and D-learning on the Student Performance: Moderating Role of Institutional Support

Student performance was affected due to the covid-19 pandemic; therefore, electronic (E), mobile (M), and distance (D) learning are the significant solution for this issue and needs researchers' focus. This study examines the impact of E learning, M learning, and D-Iearning on student performance in educational institutions in the UAE. Besides, this study investigates the moderating impact of institutional support on E-Iearning, M-learning, D-Iearning, and student performance in educational institutions in the UAE. The data were collected from students using questionnaires utilizing smart-PLS to check the data reliability and relationships among variables. The results indicated that E-Iearning, M-Iearning, and D-Iearning have a positive impact on student performance in educational institutions in the UAE. The results also revealed that the institutional support significantly moderates E-Iearning, M-learning, D-Iearning, and student performance in educational institutions in the UAE. The findings of this study help policymakers in establishing the policies related to the improvement of student performance using E learning, M-learning, and D-Iearning.
Low-rank kernel decomposition for scalable manifold modeling

The purpose of this study is to develop a method for scalable manifold modeling. A popular method using Gaussian process requires a computational cost of the cubic order for data size, which does not afford to apply large-scale datasets. We aim to achieve the linear order in computational cost using unsupervised kernel regression and its sparse approximation instead of using the Gaussian process regression. For this purpose, we introduce two types of sparse approximations; one is the discretization of the latent space by a grid of inducing points, and the other is a sparse matrix decomposition of the local and global kernel matrices. We evaluated the computation time of the proposed method by using an artificial dataset, and the results showed that the proposed method achieved the linear order in computation time.