Reviewerly/exHarmony huggingface dataset:
Collection metrics

Collection metrics are data gathered about a library’s collection, which are then used to analyze and make conclusions about the collection through statistics. One of the most common library metrics is circulation, which needs to be carefully defined to match what is actually counted by an integrated library system (ILS). Caution is advised at using circulation as the single indication of library activity, as many library services are not tracked through the ILS, but are important gauges of library use. Academic libraries may gather metrics through Counting Online Usage of Networked Electronic Resources (COUNTER) reports, interlibrary loan, Altmetrics, and citation analysis. Group metrics describe the activity of items as a group, while item metrics describe individual items. Different collection decisions can be made using these two types of metrics. Collection audits and diversity audits help libraries improve their data for better clarity and trust in the metrics that are gathered.
MetaTraj

Long-term pedestrian trajectory prediction in crowds is highly valuable for safety driving and social robot navigation. The recent research of trajectory prediction usually focuses on solving the problems of modeling social interactions, physical constraints, and multimodality of futures without considering the generalization of prediction models to other scenes and objects, which is critical for real-world applications. In this paper, we propose a general framework that makes trajectory prediction models able to transfer well across unseen scenes and objects by quickly learning the prior information of trajectories. The trajectory sequences are closely related to the circumstance setting (e.g., exits, roads, buildings, entries etc.) and the objects (e.g., pedestrians, bicycles, vehicles etc.). We argue that trajectory information varying across scenes and objects makes a trained prediction model not perform well over unseen target data. To address it, we introduce MetaTraj that contains carefully designed subtasks and meta-tasks to learn prior information of trajectories related to scenes and objects, which then contributes to accurate long-term future prediction. Both subtasks and meta-tasks are generated from trajectory sequences effortlessly and can be easily integrated into many prediction models. Extensive experiments over several trajectory prediction benchmarks demonstrate that MetaTraj can be applied to multiple prediction models and make them generalize to unseen scenes and objects.
Controllable Text Generation Using Semantic Control Grammar

Controllable text generation is the primary technique for controlling specific attributes such as topic, keywords and obtaining augmented data. This work proposes a novel controllable text generation framework to improve the controllability of generation models. First, we introduce semantic control grammar, a controllable input format to generate sentences that satisfy the constraints. Second, we adopt a generation and rerank method to obtain semantically reranked controlled sentences. Extensive experiments and analyses are conducted on benchmark, natural language understanding, data-to-text generation, and text classification datasets. Through automatic evaluations, we show that our method leads to improvement over strong baselines. The results show that our model can control sentence and word attributes and semantically generate natural sentences. Furthermore, our techniques improve the generation quality of the model.
Skin Medical Image Captioning Using Multi-Label Classification and Siamese Network

Image captioning is a process of automatically generating descriptive sentences for a given image. Text-to-image search is a form of search in which images are retrieved based on matching keywords and image features. We focus on the case in which multiple description sentences are generated for one image. In this study, we used four learning models: (1) a discriminator, which is a binary classifier that distinguishes skin from background; (2) an autoencoder; (3) a multiclass classification model combining the features from the discriminator and autoencoder and producing keyword labels; and (4) a Siamese network learning the textual similarity matching between colloquial description sentences of skin imaging pathology and keywords produced from the multi-class classifier. The experimental results show that the proposed method yields an accuracy of up to 99% for the testing data in terms of colloquial language of skin images. This study enabled users to read the skin. For teaching research on skin diagnosis, the proposed method can significantly relieve the shortage of training personnel and assist hospitals that lack resources for conducting case studies. The results of this study are expected to be feasible and can be applied in actual clinical teaching. For medical education in dermatology, the findings of this study contribute to the practical value of quantitative indicators and assessments for learning outcomes of medical students.
Space truss structures’ optimization using metaheuristic optimization algorithms

Metaheuristic optimization algorithms are popular tools for solving engineering optimization problems. In this chapter, space truss structures are optimized using three population-based metaheuristic algorithms: African Vulture Optimization Algorithm, Artificial Gorilla Troops Optimizer, and Artificial Hummingbird Algorithm. While satisfying several constraints on displacement and stress limits, these three algorithms will minimize the weight of space truss structures. Two well-known space truss benchmarks are used to test the algorithms' capabilities and robustness.
Metaheuristics for optimal feature selection in high-dimensional datasets

Feature selection is a crucial task in preprocessing high-dimensional datasets. These datasets are largely characterized by a few samples and very many features, traits that hinder the process of effective feature selection. The feature selection process as a combinatorial optimization problem aims at improving the performance of the learning algorithm by removing irrelevant and redundant features. In combinatorial optimization problems, by exploring a large set of feasible solutions, metaheuristics can obtain good solutions with less computational effort in most cases. Thus, in trying to optimally select informative gene subsets, approaches that combine metaheuristics with learning models (wrappers) have received much attention. This chapter presents an overview of the recently proposed wrappers to handle high-dimensional data and analyzes their performance under various conditions.
3D-Yoga: A 3D Yoga Dataset for Visual-Based Hierarchical Sports Action Analysis

Visual-based human action analysis is an important research topic in the field of computer vision, and has great application prospect in sports performance analysis. Currently available 3D action analysis datasets have a number of limitations in sports application, including the lack of special sports actions, distinct class or score labels and variety of samples. Existing researches mainly use various special RGB videos for sports action analysis, but analysis with 2D features is less effective than 3D representation. In this paper, we introduce a new 3D yoga pose dataset (3D-Yoga) with more than 3,792 action samples and 16,668 RGB-D key frames, collected from 22 subjects performing 117 kinds of yoga poses with two RGB-D cameras. We have reconstructed 3D yoga poses with sparse multi-view data and carried out experiments with the proposed cascade two-stream adaptive graph convolutional neural network (Cascade 2S-AGCN) to recognize and assess these poses. Experimental results have shown the advantage of applying our 3D skeleton fusion and hierarchical analysis methods on 3D-Yoga, and the accuracy of Cascade 2S-AGCN outperforms the state-of-the-art methods. The introduction of 3D-Yoga will enable the community to apply, develop and adapt various methods for visual-based sports activity analysis.
CVLNet: Cross-view Semantic Correspondence Learning for Video-Based Camera Localization

This paper tackles the problem of Cross-view Video-based camera Localization (CVL). The task is to localize a query camera by leveraging information from its past observations, i.e., a continuous sequence of images observed at previous time stamps, and matching them to a large overhead-view satellite image. The critical challenge of this task is to learn a powerful global feature descriptor for the sequential ground-view images while considering its domain alignment with reference satellite images. For this purpose, we introduce CVLNet, which first projects the sequential ground-view images into an overhead view by exploring the ground-and-overhead geometric correspondences and then leverages the photo consistency among the projected images to form a global representation. In this way, the cross-view domain differences are bridged. Since the reference satellite images are usually pre-cropped and regularly sampled, there is always a misalignment between the query camera location and its matching satellite image center. Motivated by this, we propose estimating the query camera's relative displacement to a satellite image before similarity matching. In this displacement estimation process, we also consider the uncertainty of the camera location. For example, a camera is unlikely to be on top of trees. To evaluate the performance of the proposed method, we collect satellite images from Google Map for the KITTI dataset and construct a new cross-view video-based localization benchmark dataset, KITTI-CVL. Extensive experiments have demonstrated the effectiveness of video-based localization over single image-based localization and the superiority of each proposed module over other alternatives.
Comparative Analysis of Machine Learning Algorithms to Predict Solar Irradiance

In recent times, there has been a rapid shift from non-renewable to renewable sources of energy. Recently there has been a lot of development in photovoltaic systems that use solar irradiance energy as a source. Solar energy is a clean source of energy that can be tapped to meet the demands of the load/grid. Prediction of the amount of solar irradiance at a location can be beneficial for optimum production. This study compares the accuracy of several machine learning algorithms, including SVM and LSTM, for the prediction of solar irradiance at specified latitudinal and longitudinal coordinates using historic data from NASA POWER. It is observed that solar irradiance is influenced by factors like temperature, precipitation, humidity among others.
A Comparative Study on the Effectiveness of IDM and Card Sorting Method for Autism Specialized School Website

With the advent of new technologies, software and web applications have been an integral part of today’s modern life. These applications are exclusively designed and developed for different themes and purposes. Present day an autism-related web application, being one of the most demanding applications requires effective, efficient and satisfactory services with a package of portability across all platforms. The success of a product significantly depends upon the factor of usability. A clear, concise, and user-interactive design interface is the key to a successful product or application. In the current literature reviewed, no particular design technique has so far been proposed to implement a multi-channel autism application. The objective of our study is to create two websites using Card sorting and Interactive Dialogue Model (IDM) on the same topic, compare between these two websites and perform a comparative study considering some parameters to find out which approach is a better approach for website making. We have developed an autism application using card sorting and IDM. An evaluation was carried out with a number of participants. As an outcome, we have proposed one design technique to produce an effective and easy-to-use web application for autistic children.
A Deep Learning Approach for Recognizing Textual Emotion from Bengali-English Code-Mixed Data

Emotion detection is a computational approach for finding the distinct emotion or feeling of an individual. Although Bengali is a low-resource language, the amount of Bengali-English codemixed textual data has grown significantly because of the recent widespread use of social media applications among Bengali users. Gradually, the classification of emotions in Bengali-English codemixed data has become a crucial challenge for applications in e-commerce, healthcare, suicidal attempt reduction and crime detection. Nevertheless, the lack of Bengali language processing techniques and Bengali-English dataset have made the emotion recognition more challenging. This research work offers a Deep Learning based approach for classifying emotions from Bengali-English code-mixed data into six basic categories: disgust, sadness, joy, anger, fear, and surprise. Due to the lack of required dataset, a Bengali-English code-mixed corpus consisting of 10,221 sentences is created. In order to identify the best features, this work investigates several word embedding techniques, including Word2Vec, FastText, and Keras Embedding Layer. Different types of of Machine Learning and Deep Learning based algorithms including the proposed technique using Word2Vec and BiLSTM are applied on the developed corpus. In order to find out the best technique, a comparative analysis among all the methods is demonstrated revealing that the BiLSTM with Word2Vec word embedding technique outperforms rest other models achieving the highest accuracy of 76.1%.
Local Dispersion Optimized LoG and Image Fitting for Intensity Inhomogeneous Image Segmentation

Region-based active contour models (ACM) have shown better performance compared to the edge-based models in numerous aspects like noise tolerance, inhomogeneity and background complexity correction, initialization robustness and speed of curve evolution. However, the fusion of edge and region based methods has circumvented the performance of the edge and region based methods working independently. Hence, this paper reveals a new technique to optimize Laplacian of Gaussian (LoG), the popular second order edge detector by local dispersion based edge estimation. The local dispersion computed image assists the optimization process to smooth the region without object boundary and simultaneously enhances the boundaries. The optimized LoG is added with the local image fitting (LIF) energy functional inside a variational framework with suitable scaling parameters. Thus an efficient fusion of region based energy and optimized edge energy performs with greater efficiency in terms of segmentation accuracy, noise tolerance, initial contour placement, iteration time and complex background suppression.
A Hybrid Classification Technique using Belief Rule Based Semi-Supervised Learning

An advancement in the paradigm of machine learning has been acclaimed by the arrival of semi-supervised learning. In real life, it is challenging to get enough labeled samples. On the other hand, unlabeled data are inexpensive because it does not need human expertise. As a result, a lot of research attention has been gained in the semi-supervised field as both labeled and unlabeled data are greatly utilized. Traditional semi-supervised learning may experience information loss and problems with uncertainty. Moreover, the samples cause ambiguity because of unclear descriptions of the information and vague evidence. Therefore, misclassification occurs. In this paper, the ambiguity is dealt by merging the belief rule based methodology with semi-supervised self-training. To further study, the datasets were categorized into three ratios which show that our proposed method works significantly better than the traditional approach. Moreover, this research has included a comparison of results between the proposed and the traditional approach.
Comparative Analysis of Process Scheduling Algorithm using AI models

Process scheduling is an integral part of operating systems. The most widely used scheduling algorithm in operating systems is Round Robin, but the average waiting time in RR is often quite long. The purpose of this study is to propose a new algorithm to minimize waiting time and process starvation by determining the optimal time quantum by predicting CPU burst time. For burst time prediction, we are using the machine learning algorithms like linear regression, decision tree, k-nearest neighbors, and Neural Network Model Multi-Layer Perceptron. Moreover, for 10000 predicted burst time of processes with the same configuration, we have compared the average turnaround time, the average waiting time and the number of context switches of the proposed modified round robin algorithm with Traditional Round Robin, Modified Round Robin, Optimized Round Robin and Self-Adjustment Round Robin. The proposed modified round robin i.e. Absolute Difference Based Time Quantum Round Robin (ADRR) is found to be almost 2 times faster than the other algorithm in terms of process scheduling for the used dataset which contains a huge load of processes.
A Faithful DoG is All you Need

Compared to edge-based models, region-based active contour models (ACM) have demonstrated superior performance in a number of areas, including noise tolerance, back- ground complexity and inhomogeneity correction, initialization resilience, and speed of curve evolution. However, combining both of their credentials with suitable and relevant parameters exhibits promising potential in enhancing segmentation performance. Therefore, this work reports an effective fusion of optimized Difference of Gaussian (DoG) edge estimation, with the region scalable fitting ( RSF) m odel t o c apitalize o n t heir a ttributes. A locally computed edge entropy image is also used as a weight to the energy functional to infuse local edge information in the energy functional. With the integration of relevant edge and region based feature descriptors, the proposed model thereby, outperforms the established ACMs in terms of iteration time, noise tolerance, initial contour convergence, inhomogeneity suppression and segmentation accuracy.
Black and White Image Colorization using Deep Learning

The computerized colorization of black and white images began in the 1970's., after which image colorization has come a long way. RGB images are widely used in many fields to get additional information regarding the image that cannot be obtained from B&W images. Some areas where RGB images might be used in determining the chemical composition of a material, chromatographic spectroscopy and studying the biology of plants. In this system we approach the image colorization problem using deep learning techniques. We achieve this using Conditional Generative Adversarial Networks (cGAN) which will be trained on the COCO dataset. This is a fully automated model and requires no human assistance to achieve artifact-free quality images. The recent achievements in Generative models have inspired this system. We present two models in this system, of which the second model produces better results with only one-fourth of the training time of the first model.
A Comparative Analysis of Machine Learning techniques on Breast Cancer diagnosis using WEKA

Breast cancer is one of the most common malignancies affecting women worldwide, with many fatalities yearly. The risk of death suffered by breast cancer is increasing exponentially. Due to a surge of development of research in the medical field, providing more timely and possible early detection of disease has become a time-demanding option. By far, radiologists have manually checked cancer images and diagnosed them. Research has shown that a considerable number of ultrasound images are created every individual day. However, the number of radiologists is limited, so they cannot provide service on time. However, they often misclassify breast lesions, resulting in a high false-positive rate. An automatic system for detecting disease assists radiologists in disease diagnosis and provides reliable, productive, and reduces the risk of death. In this paper, we compare six machine learning models, namely (i) Support Vector Machine (SVM), (ii) Naive Bayes (NB), (iii) Logistic Regression (LR), (iv) Decision Tree (DT), (v) Random Forest (RF), and (vi) k-Nearest Neighbors (k-NN) on two different datasets (i) the Wisconsin Breast Cancer Dataset (WBCD) and (ii) the Breast Cancer Coimbra Dataset (BCCD). This study aims to create different classification models to analyze the obtained results and compare them to predict breast cancer. We use several performance metrics to select the best classification model among them. Our comparative analysis shows that SVM models can achieve better performance metrics, and thus the model of this research possesses relevant to use in clinical applications.
An Efficient Approach to Automatic Tag Prediction from Movie Plot Synopses using Transformer-based Language Model

Automated tag prediction is one of the critical techniques to enhance the retrieval of similar movies in recommendation systems. Additionally, it serves the purpose of drawing a preview of the content the viewers hope to watch. However, the quotidian architectures employed for this task encompass sequence-to-sequence models like Neural Networks (NN), Long Short Term Memory (LSTM), etc., which can not learn the overall context. On the contrary, Transformer-based Language models are tailored toward understanding the broad context since the tokens generated are learned parallelly. Therefore, our study proposes an Encoder-based Transformer Language model to predict movie tags from movie plot synopsis. We use the Movie Plot Synopses with Tags (MPST) dataset and employ lightweight Vanilla Neural Networks (VNN) and RoBERTa language models as our pipeline for leveraging contextual knowledge to predict movie tags from an extensive movie synopses corpus. The MPST dataset has an uneven distribution of tags, and reducing the tag space ensued the model’s efficacy by achieving a 38.34% F1 score and 21.14% Recall, which outperformed previous studies for automated movie tag prediction.
Medical Text Extraction and Classification from Prescription Images

The right to health is one of the fundamental human rights. Every state is obliged to provide healthcare facilities to its population. In Bangladesh, the government is working hard to provide a better healthcare system, though the country needs to go a long way to have a unified healthcare system. There is a lack of a proper referral system in the country, and proper diagnosis is hindered due to a patient’s lack of medical history. In this paper, we propose a system that helps the patient to create a medical history from images of the prescriptions. Our system extracts and classifies data from an unstructured Bangladeshi medical prescription that can be used to create a repository of medical history. The proposed method works in four phases: phase I text localization and extraction from the images of prescriptions, phase II - classification of the extracted images, phase III - image to text conversion using OCR, and phase IV - classification of the text in four categories symptoms, medicines, diagnostic tests, and others. For image classification, we use a very deep convolutional network, VGG-16 and for text classification, we use the Bidirectional Encoder Representations from Transformers (BERT) model. Performance evaluation of the proposed system is very promising and the system can be used in any country like Bangladesh to facilitate better treatment.