Reviewerly/exHarmony huggingface dataset:
Comprehensive Dashboard for Alzheimer’s Disease Through Machine Learning

In today’s world, personal monitoring of health is given the topmost priority as the number of people becoming health conscious is seeing a spike day by day. Mental health tops the list when the overall health of a person is considered. Being fit mentally helps the population to be physically fit by which one can be of some service to society. Many times, unaddressed mental health problems influence public health in many ways like employment, poverty, local economy, safety, etc. In the proposed work, a comprehensive dashboard is built for Alzheimer’s disease in the form of an interactive web UI. Machine learning algorithms are used in disease prediction and content-based filtering is used to provide precautionary steps for users based on their inputs. This work is based on the OASIS-2 longitudinal dataset, which is a collection of 150 records and covers the subjects of age groups between 60 and 96. The proposed system also provides statistical data based on user input mainly for healthcare professionals through visualization.
Deep Learning Techniques for Explosive Weapons and Arms Detection: A Comprehensive Review

Recent advancements in machine learning (ML) and deep learning (DL) demonstrate the enormous potential to disentangle real-world challenges. The rising number of deaths, injuries caused during the crimes, and armed violence is one of the real-world challenges. An increasing trend in armed violence in populated areas shows a conspicuous impact on civilian lives, physical and mental well-being, physical disabilities, and severe psychological effects. The efforts by law enforcement agencies harp upon the identification of weapons using various real-time information. Manual operations and monitoring of CCTV and visual surveillance sensors impede the prompt identification of miscreants, especially during a hostile stand-off in civil areas. Computer vision algorithms augmented with affordable, widespread visual sensor networks can precisely extend accurate, timely weapon information to law enforcement agencies, speeding up the operation and saving human lives. Existing research works are based on deep learning methods to detect weapons, i.e., revolvers, pistols, knives, shotguns, rifles, automatic rifles, and lethal small arms. The focused review paper explores the datasets, proposed frameworks, and model analysis. The proposed approaches are analyzed considering their methodology, performance, and suitability for real-time employment.
Numerical design optimization of real size complex steel space frame structures using a novel adaptive cuckoo search method

Abstract In this study, real size complex steel space frame structures are numerically designed to achieve optimal design weight. For this aim, standard cuckoo search algorithm is rectified through a newly proposed adaptive method over two fundamental algorithmic parameters of alien egg probability detection (P a ) and step size control ( α ) to overcome incompetency of trapping into local optima. Besides, to strengthen exploitation phase of newly proposed algorithm, it is boosted with greedy selection (GS). So, novel algorithm has more promising exploration and exploitation capabilities. An 8‐story, 1024‐member and a 20‐story, 1860‐member real size complex steel space frame structures are selected as design examples. Also, initially to verify the supremacy of novel algorithm, a well‐known welded beam is optimized as a benchmark structural design problem. Afterwards, the steel space frame structures are optimally designed via novel algorithm. Since ready steel section lists are utilized as selection pool for design variables, discrete programming problem is come into existence. The dead, live, snow, and wind design loads acting on frame structures are calculated in direction of ASCE 7‐05 provisions. Furthermore, the structural design constraints are determined from LRFD‐AISC specifications. Eventually, the newly proposed adaptive cuckoo search algorithm boosted with GS presents outstanding algorithmic performance.
Knowledge Graph Completion via Subgraph Topology Augmentation

Knowledge graph completion (KGC) has achieved widespread success as a key technique to ensure high-quality structured knowledge for downstream tasks (e.g., recommendation systems and question answering). However, within the two primary categories of KGC algorithms, the embedding-based methods lack interpretability and most of them only work in transductive settings, while the rule-based approaches sacrifice expressive power to ensure that the models are interpretable. To address these challenges, we propose KGC-STA, a knowledge graph completion method via subgraph topology augmentation. First, KGC-STA contains two topological augmentations for the enclosing subgraphs, including the missing relation completion for sparse nodes and the removal of redundant nodes. Therefore, the augmented subgraphs can provide more useful information. Then a message-passing layer for multi-relation is designed to efficiently aggregate and learn the surrounding information of nodes in the subgraph for triplet scoring. Experimental results in WN18RR and FB15k-237 show that KGC-STA outperforms other baselines and shows higher effectiveness.
DABP: A Domain Augmentation and Bidirectional Stack-Propagation Model for Task-Oriented NLU

Natural language understanding (NLU) is the key part of task-oriented dialogue systems. Nowadays, most existing task-oriented NLU models use pre-trained models (PTMs) for semantic encoding, but those PTMs often perform poorly on specific task-oriented dialogue data due to small data volume and lack of domain-specific knowledge. Besides that, most joint modeling models of slot filling and intention detection only use a joint loss function, or only provides a one-way semantic connection, which fails to achieve the interaction of information between the two tasks at a deep level. In this paper, we propose a Domain Augmentation and Bidirectional Stack Propagation (DABP) model for NLU. In the proposed model, we use the masked language model (MLM) task and the proposed part-of-speech tagging task to enhance PTMs with domain-specific knowledge include both implicit and explicit. Besides that, we propose a bidirectional stack-propagation mechanism to propagate the information between the two tasks. Experimental results show that the proposed model can achieve better performance than the state-of-the-art models on the ATIS and SNIPS datasets.
Robust Approach for Person Identification Using Three-Triangle Concept

Managing a large volume of data in today’s fast-paced environment is a challenging feat. As with the increasing number of data breaches, there are concerns about data security and authentic use. This landed us on the significance of metrics relating to a person’s personal identity. The personal attributes cannot be stolen and are very simple to deploy. This study uses an individual's facial features to permit his/her presence in a specific location, in this case, university premises. The interesting aspect of this system is that it is subjected to use in association with a passive infrared sensor (PIR) to detect human presence. The improved face recognition system employed in the work poses a new algorithm that incorporates a combined approach for extracting ear and geometrical features of the face using the three-triangle concept. By keeping a student database along with the participation record in classrooms, the implementation of a data handling system using a cost-effective, interwoven recognition technology with KNN as the classifier is demonstrated. As a result, an innovative framework for the potential consumers is devised with reduced memory size and error rate.
Generate Artificial Human Faces with Deep Convolutional Generative Adversarial Network (DCGAN) Machine Learning Model

Generative Models have received a lot of interest in the field of unsupervised learning thanks to a cutting-edge framework called Generative Adversarial Networks (GANs) because of their impressive data-generating skills. Numerous GAN models have been published, and numerous applications in computer vision and machine learning have emerged in a variety of domains. Backpropagation and a competitive process called Generative Network G and Discriminative Network D, in which G generates artificial images and D classifies them into real or artificial image categories, are combined in GANs. As the training progresses, G acquires realistic picture-generating abilities to deceive D. This work has trained Deep Convolutional Generative Adversarial Networks (DCGAN), a GAN-based convolutional architecture to develop a generative model capable of producing precise images of human faces. The CelebFaces Attributes Dataset was utilized to train the DCGAN model (CelebA). The outcomes demonstrate that the model is capable of producing human faces from unlabeled data and random noise. The trained DCGAN model has been quantitatively evaluated using the Structural Similarities Index (SSIM), which calculates the structural and contextual similarity between two images, and a Peak Signal-to-Noise Ratio (PSNR), which measures how well an image can be represented compared to the amount of noise that can degrade the quality of that representation.
Detection of BotNet Using Extreme Learning Machine Tuned by Enhanced Sine Cosine Algorithm

The rise of the internet of things (IoT) popularity with smart home systems and improvements in the technologies of embedded devices raised interest in such solutions. However, with the increase in the utilization of these technologies, the security risks increased as well. Connecting a large number of devices to the same network is risky, because the security of any network is only as strong as its weakest part, and in the case of IoT the weakest link can be a small home appliance. This research aims to improve the means for detecting and preventing attacks on such networks via a hybrid method between machine learning and population-based metaheuristics. The proposed study introduces an enhanced version of the recently emerged sine cosine algorithm (SCA) that was used for tuning extreme learning machine (ELM). The hybrid method was validated against the UNSW-NB15 dataset that consists of features that describe regular (normal) and botnet traffic, where the latter refers to the distributed denial of service (DDoS) attack. The hybrid framework’s performance metrics were compared with several other classic machine learning models, as well as with other ELM solutions tuned by other population-based metaheuristics. Based on experimental data, the conclusion that the proposed method on average obtains the best performance can be derived.
COVID-19 Disease Detection Using Explainable AI

With the advent of technologies like Artificial Intelligence, Machine Learning and Deep Learning, there has been unfathomable progress across all the fields. This progress has produced equally important results in the medical field, where these technologies are being used for diagnosis, identifying healthcare requirements and providing the necessary solutions with greater accuracy. These technologies have particularly shown highly accurate results in medical imaging, and the results are also extremely sensitive to the minute details while processing the information. Although we have achieved such overwhelming headway, when it comes to the layman, these results become extremely difficult to analyse and always require professionals for help. The entire process of making predictions using these technologies is covered under a black box, which needs to be made comprehensible for everyone. To make this information understandable, there is another concept arising known as Explainable AI.
Cloud Services Management Using LSTM-RNN

Cloud computing is a general term for anything that involves the provision of online hosting services. It is available on-demand, through the internet, through the use of resources—applications, services, data storage, hardware upgrades, network capabilities, and other resources. The main purpose of cloud computing is to permit users to take full benefits of all of these techniques, without the necessity for in-depth information or expertise with their developers. The objective of the cloud is to reduce expenses and allow the consumers to concentrate on their parent trade in place of being hindered by means of IT boundaries. There are so many models for Workload Prediction in cloud data centers but most of them are static in nature. In this paper, we have proposed the LSTM model which is quite dynamic in nature and capable to remember previous time step information and this is how it learns.
Short-Text Conceptualization Based on Hyper-Graph Learning and Multiple Prior Knowledge

Short-text conceptualization is a notable task and popular issue in current social network analysis and natural language processing. This line of work usually views the data as a heterogeneous semantic network connecting terms (in short-text) and corresponding concepts (in prior knowledge base), with complex relationships (e.g., term-correlation, concept-correlation and subordination, etc.,). Therefore, this paper introduces hyper-graph learning strategy for solving this problem, because of its ability for modeling complex relationships. Overall, this paper proposes a novel short-text conceptualization model based on hyper-graph convolutional network. Especially, this model is capable to make the signals (i.e., terms and concepts) to be sufficiently interacted, by leveraging three prior knowledge for modeling heterogeneous correlations among terms and concepts, including: subordination prior knowledge, concept correlation prior knowledge and term correlation prior knowledge. The experimental results demonstrate that the proposed work achieves higher accuracy in short-text conceptualization task when compared with the current state-of-the-art algorithms.
Leverage Heterogeneous Graph Neural Networks for Short-Text Conceptualization

The conceptualization of short-texts is playing an increasingly important role in text comprehension, social media processing and other applications. Generally, this problem could be modeled as a heterogeneous semantic network connecting words (in short-text) and corresponding concepts (in knowledge base). As a crucial step in short-text conceptualization task, learning interactive relations among words and concepts has been explored through numerous methods. One intuitive method is to place them in a graph based neural network with a more complex structure to capture inter-concept/word relationships. Hence, this paper presents a heterogeneous graph-based neural network (HGNN) for short-text conceptualization, which includes semantic nodes with various granularity levels, mainly consisting of basic- semantic nodes (e.g., words) and supernodes (e.g., concepts). The proposed model could provide a flexible and natural modeling tool to model such complex relationships and capture more expressive and discriminative concepts, by leveraging mutually reinforcing strategy on heterogeneous correlations. On the other word, it is a beneficial attempt to introduce different types of semantic nodes into graph based neural networks for short-text conceptualization task, and we conduct comprehensive qualitative analysis to investigate its benefits.
Predicting Aging Related Bugs with Automated Feature Selection Techniques in Cloud Oriented Softwares

Aging related bugs (ARBs) are accumulated errors in the long running software due to memory leakage or unreleased files and locks which result in performance degradation and depletion of re-sources. It is a challenging task to reproduce ARBs in the software during software testing. The prediction of Aging related bugs in software will greatly contribute for software quality assurance team to enhance their testing efforts. In this study, the work is presented on predicting aging related bugs in cloud oriented software with the help of source code metrics and machine learning techniques. High dimensionality and class imbalance are the two technical challenges found in prediction of aging related bugs. This study investigated the application of extracting aging related bug reports from commit content of thousands of bug reports using automated keyword abstraction and the five different feature selection techniques (Random Forest, Logistic Regression, Gradient Boosting Machine (GBM), Extra Tree Classifier, Recursive Feature elimination (RFE)) for dimensionality reduction and SMOTE to deal with the class imbalance problem in our proposed machine learning based approach. Experimental results convey that when SMOTE is applied with feature selection technique then accuracy, recall and precision are among the best performance measures to evaluate prediction. Bagging and Logistic Regression perform better than other machine learning techniques for cloud oriented software in predicting ARBs.
Quality–diversity optimization of decision trees for interpretable reinforcement learning

Abstract In the current Artificial Intelligence (AI) landscape, addressing explainability and interpretability in Machine Learning (ML) is of critical importance. In fact, the vast majority of works on AI focus on Deep Neural Networks (DNNs), which are not interpretable, as they are extremely hard to inspect and understand for humans. This is a crucial disadvantage of these methods, which hinders their trustability in high-stakes scenarios. On the other hand, interpretable models are considerably easier to inspect, which allows humans to test them exhaustively, and thus trust them. While the fields of eXplainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI) are progressing in supervised settings, the field of Interpretable Reinforcement Learning (IRL) is falling behind. Several approaches leveraging Decision Trees (DTs) for IRL have been proposed in recent years. However, all of them use goal-directed optimization methods, which may have limited exploration capabilities. In this work, we extend a previous study on the applicability of Quality–Diversity (QD) algorithms to the optimization of DTs for IRL. We test the methods on two well-known Reinforcement Learning (RL) benchmark tasks from OpenAI Gym, comparing their results in terms of score and “illumination” patterns. We show that using QD algorithms is an effective way to explore the search space of IRL models. Moreover, we find that, in the context of DTs for IRL, QD approaches based on MAP-Elites (ME) and its variant Covariance Matrix Adaptation MAP-Elites (CMA-ME) can significantly improve convergence speed over the goal-directed approaches.
Implementation of WSN in the Smart Hanger to Facilitate MRO Operations on Aircraft Fuselage Using Machine Learning

New technology techniques have lately been adopted to make visual examinations of the outer aircraft fuselage easier. Automated image identifiers are now capturing high-resolution images of the aircraft for offline analysis inside the tarmac. Initiating the concept of smart hanger for optimization of MRO operations, it is noted that the properly trained dataset may result in accurate outcomes. Some acquisitions were annotated to give the vast dataset needed to train machine learning systems, particularly for important fault identification and defect detection. The high imbalance (i.e., unequal distribution between classes) of this dataset is an inherent problem: the rarest and most valued examples constitute a small number of elements among thousands of annotated objects. Just when enough data is there for each of the desired class, deep learning approaches have proven to be very effective, and also systems with lesser complexity like Support Vector Machine needs lesser data and dedicated methods of few-shot learning (Prototypical Network, matching network etc.) that can easily learn through limited illustrations. On the basis of the applicative scenario, several techniques are contrasted for the proposed system. Preliminary findings reveal that there is existence of empirical frontiers regarding dataset volume training, indicating associated strategy may be preferred. On the basis of these findings, a way for combining diverse approaches to produce the greatest outcomes in categorical accuracy, with a focus on underrepresented categories is proposed.
Unmanned Arial Vehicle as a Tool for Facemask Detection

Facemask detection has become a significant concern for many organizations in today’s technological era. There are numerous ways for detecting facemasks already available. This proposed system tracks a person until he/she wears the mask. The system implementation focuses on the use of deep learning and computer vision. Facemask detection convection systems have current limitations since they were built for non-mobile equipment. This study aims to create a facemask detection system in a quadcopter-style unmanned aerial vehicle. While some quadcopters are fit for recognizing and following faces and shapes, many components are used for particular diversions. The training accuracy of the proposed system observed is 93%, while the validation accuracy is 94%. This research outcome identifies a person’s identity in crowed if he/she is not wearing a facemask. This helps in the spread of covid-19 in the crowd because of infected persons. From time to time based on government instructions, it is mandatory to use facemasks in public areas to protect people from coronavirus. With the emergence of novel variants (such as Omicron), careful adherence to Covid guidelines is required, necessitating the creation of this study.
Comparative Analysis of U-Net-Based Architectures for Medical Image Segmentation

The semantic segmentation techniques which are based on Deep Learning (DL) come up with an advanced performance for years. In particular, such techniques are used successfully in areas such as the classification of medical images, their segmentation, and identification. The deep learning technology, U-Net, is the most popular of these applications. U-Net is used for image segmentation in the field of biomedicine. It is a convolutional neural network. Several U-Net-based networks have been proposed in past such as U-Net++, 3D U-Net, multi-res U-Net, Laddernet, etc. Some of such models will be studied, compared, and analyzed in this paper based on various parameters and datasets used for these architectures. Various imaging modalities like CT Scan, MRI, X-Ray, etc., are used in medical imaging. Every architecture uses a different modality.
Fear of Monsters: Toward an Understanding of the Threat of the Computational Monster Read Through the Theoretical Lens of Game-Play

This article analyzes the configuration of fear generated by the computational monster in computer games. We view the monster as a computational entity, which we approach through our theory of game-play coupled with the concepts of loss aversion and endowment effect. Of particular interest is player perception of the threat posed by monsters as they perturb the experience of progression and the sensation of control within the game. We scrutinize this aspect from a situational as well as an existential perspective. Furthermore, we advance an analytical scheme of the threat of the computational monster, which is radically different from the traditional academic approach with its emphasis on the representation of monsters. Overall, we argue that the threat players perceive when facing monsters in computer games springs more from the computational nature of monsters—how they upset progression and the feeling of control—and less from the representation of the monster(s).