Reviewerly/exHarmony huggingface dataset:
Node-Feature Convolution for Graph Convolutional Networks

Graph convolutional network (GCN) is an effective neural network model for graph representation learning. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node features within a node feature vector are considered equally important. Several extensions have been proposed to tackle the limitations respectively. This paper focuses on tackling all the proposed limitations. Specifically, we propose a new node-feature convolutional (NFC) layer for GCN. The NFC layer first constructs a feature map using features selected and ordered from a fixed number of neighbors. It then performs a convolution operation on this feature map to learn the node representation. In this way, we can learn the usefulness of both individual nodes and individual features from a fixed-size neighborhood. Experiments on three benchmark datasets show that NFC-GCN consistently outperforms state-of-the-art methods in node classification.
Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

In recent years, drones have been widely used in various applications, from entertainment, agriculture, their use in photo and video services, military applications and so on. The risk of accidents while using a drone is quite high. To meet this risk, the most important solution is to use a device that helps and simplifies the control of a drone; in addition, the training of drone pilots is very important. To train the drone pilots, both physical and virtual environments can be used, but the probability of an accident is higher for beginners, so the safest method is to train in a virtual environment. The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods.
Ability boosted knowledge tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services, which estimates and traces the degree of learner’s mastery of concepts based on students’ responses to learning resources. The existing mainstream KT models, only attribute learners’ feedback to the degree of knowledge mastery and leave the influence of mental ability factors out of consideration. Although ability is an essential component of the problem-solving process, these knowledge-centered models cause a contradiction between data fitting and rationalization of the model decision-making process, making it difficult to achieve high precision and readability simultaneously. In this paper, an innovative KT model, ability boosted knowledge tracing (ABKT)1 is proposed, which introduces the ability factor into learning feedback attribution to enable the model to analyze the learning process from two perspectives, knowledge and ability, simultaneously. Based on constructive learning theory, continuous matrix factorization (CMF) model is proposed to simulate the knowledge internalization process, following the initiative growth and stationarity principles. In addition, the linear graph latent ability (LGLA) model is proposed to construct learner and item latent ability features, from graph-structured learner interaction data. Then, the knowledge and ability dual-tracing framework is constructed to integrate the knowledge and ability modules. Experimental results on four public databases indicate that the proposed methods perform better than state-of-the-art knowledge tracing algorithms in terms of prediction accuracy in quantitative assessments, displaying some advantages in model interpretability and intelligibility.
Immersive Speculative Enactments: Bringing Future Scenarios and Technology to Life Using Virtual Reality

In this paper we present Immersive Speculative Enactments (ISEs), a novel concept that extends conventional Speculative Enactments to Virtual Reality. Through ISEs, participants are immersed in a speculative world depicted by the designers and can engage with it in its truest envisioned form. We explore this concept via four scenarios with increasing technological uncertainty: a glimpse in the daily life of the parent of a newborn baby; a Mixed Reality experience supporting hybrid classrooms; two wearable devices that present a pet’s emotional state and needs; and an enactment on the effect of communication delay across interplanetary distances. We discuss the concept of ISEs and contrast them to other forms of speculation, provide guidelines on how to design them, as well as reflecting on the challenges, limitations, and potential associated with the role of ISEs in the HCI discourse.
Synthetic Data Generation to Speed-Up the Object Recognition Pipeline

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of men at sea, lead us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features to be applied to various contexts.
DevSecOps Metrics

DevSecOps is an emerging paradigm that breaks the Security Team Silo into the DevOps Methodology and adds security practices to the Software Development Cycle (SDL). Security practices in SDL are important to avoid data breaches, guarantee compliance with the law and is an obligation to protect customers data. This study aims to identify metrics teams can use to measure the effectiveness of DevSecOps methodology implementation inside organizations. To that end, we performed a Multivocal Literature Review (MLR), where we reviewed a selection of grey literature. Several metrics purposed by professionals to monitor DevSecOps were identified and listed.
Convolutional Neural Networks

A convolutional neural network (CNN) is essentially a neural network that employs the convolution operation (instead of a fully connected layer) as one of its layers. CNNs are an incredibly successful technology that has been applied to problems where in the input data on which predictions are to be made has a known grid-like topology, like a time series (a 1-D grid) or an image (a 2-D grid). CNNs ushered deep learning into modern times, solving one of the most crucial computational problems in the digital era of computer vision. With the popularity of CNNs, a surge in the research for deep learning was witnessed that continues today.
Distributed denial of service attacks detection in cloud computing using extreme learning machine

Cloud computing has become popular due to its on-demand, pay-as-you-use and ubiquitous features. This technology suffers from various security risks. Distributed denial of service (DDoS) attack is one of these security risks. It is used to disrupt the services provided by cloud computing. In DDoS attack, the cloud server is overwhelmed with fake requests by the attacker. This makes long response time for legitimate users or shut down of the service completely. In this work, a DDoS attack detection model based on extreme learning machine (ELM) has been proposed. Experiments show that proposed model can be trained in a very short period of time and provides high detection accuracy.
Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.
Harmony search algorithm and related variants: A systematic review

Harmony search (HS) algorithm is a novel metaheuristic algorithm, inspired by the phenomenon that musicians repeatedly adjust the pitch of each instrument to finally achieve a beautiful harmony state. Due to its simple concept, few adjustable parameters, easy implementation, and the ability to balance exploitation and exploration during search, HS has been widely applied to deal with various optimization problems in real life. However, HS has shortcomings such as low optimization accuracy and premature convergence. Therefore, various HS variants have been proposed. In response to this trend, this article systematically reviews HS and its variants from various aspects. First, the basic HS principle is described. Secondly, how the improvement of HS affects the performance of the algorithm is discussed. Finally, the characteristics of HS variants and their applications are analyzed. It can be seen from the literature that the improvement of HS is mainly focused on parameter improvement and mixing with other metaheuristic algorithms. Some HS even use both techniques to improve the algorithm simultaneously. HS is mainly used to solve optimization problems in engineering, which provides future research directions for improving HS.
Fuzzy Logic Based Logical Query Answering on Knowledge Graphs

Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering via dense similarity search. However, most logical operators designed in previous studies do not satisfy the axiomatic system of classical logic, limiting their performance. Moreover, these logical operators are parameterized and thus require many complex FOL queries as training data, which are often arduous to collect or even inaccessible in most real-world KGs. We thus present FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a principled and learning-free manner, where only entity and relation embeddings require learning. FuzzQE can further benefit from labeled complex logical queries for training. Extensive experiments on two benchmark datasets demonstrate that FuzzQE provides significantly better performance in answering FOL queries compared to state-of-the-art methods. In addition, FuzzQE trained with only KG link prediction can achieve comparable performance to those trained with extra complex query data.
AST-trans

Code summarization aims to generate brief natural language descriptions for source codes. The state-of-the-art approaches follow a transformer-based encoder-decoder architecture. As the source code is highly structured and follows strict grammars, its Abstract Syntax Tree (AST) is widely used for encoding structural information. However, ASTs are much longer than the corresponding source code. Existing approaches ignore the size constraint and simply feed the whole linearized AST into the encoders. We argue that such a simple process makes it difficult to extract the truly useful dependency relations from the overlong input sequence. It also incurs significant computational overhead since each node needs to apply self-attention to all other nodes in the AST. To encode the AST more effectively and efficiently, we propose AST-Trans in this paper which exploits two types of node relationships in the AST: ancestor-descendant and sibling relationships. It applies the tree-structured attention to dynamically allocate weights for relevant nodes and exclude irrelevant nodes based on these two relationships. We further propose an efficient implementation to support fast parallel computation for tree-structure attention. On the two code summarization datasets, experimental results show that AST-Trans significantly outperforms the state-of-the-arts while being times more efficient than standard transformers1.
Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different.Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations.DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm.This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images.Methods: The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets.An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019.There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model.Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model.Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data.Results: The characteristics of high nuclear grade (P<0.001),necrosis (P=0.006),estrogen receptor negative (ER -; P=0.003), progesterone receptor negative (PR -; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001)all showed significant correlations with MIC.The Inception-v3 model achieved the best performance (P<0.05) in MIC identification.The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765.Conclusions: Deep learning can be used to identify MIC of breast DCIS from ultrasound images.Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.
Performance of Deep Learning Pickers in Routine Network Processing Applications

Abstract Picking arrival times of P and S phases is a fundamental and time-consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal-to-noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival-time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well-documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large-amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.
Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods

Artificial intelligence (AI) promises breakthroughs in space operations, from mission design planning to satellite data processing and navigation systems. Advances in AI and space transportation have enabled AI technologies in spacecraft tracking control and synchronization. This study assesses and evaluates three alternative spacecraft tracking control and synchronization (TCS) approaches, including non-AI TCS methods, AI TCS methods, and combined TCS methods. The study proposes a hybrid model, including a new model for defining weight coefficients and interval type-2 fuzzy sets based combined compromised solution (IT2FSs-CoCoSo) to solve the spacecraft TCS problem. A new methodology is used to calculate the weight coefficients of criteria, while IT2FSs-CoCoSo is applied to rank the prioritization of TCS methods. A comparative analysis is conducted to demonstrate the performance of the proposed hybrid model. We present a case study to illustrate the applicability and exhibit the efficacy of the proposed method for prioritizing the alternative TCS approaches based on ten different sub-criteria, grouped under three main aspects, including complexity aspects, operational aspects, and efficiency aspects. AI and non-AI methods combined are the most advantageous alternative, whereas non-AI methods are the least advantageous, according to the findings of this study.
Joint Specifics and Consistency Hash Learning for Large-Scale Cross-Modal Retrieval

With the dramatic increase in the amount of multimedia data, cross-modal similarity retrieval has become one of the most popular yet challenging problems. Hashing offers a promising solution for large-scale cross-modal data searching by embedding the high-dimensional data into the low-dimensional similarity preserving Hamming space. However, most existing cross-modal hashing usually seeks a semantic representation shared by multiple modalities, which cannot fully preserve and fuse the discriminative modal-specific features and heterogeneous similarity for cross-modal similarity searching. In this paper, we propose a joint specifics and consistency hash learning method for cross-modal retrieval. Specifically, we introduce an asymmetric learning framework to fully exploit the label information for discriminative hash code learning, where 1) each individual modality can be better converted into a meaningful subspace with specific information, 2) multiple subspaces are semantically connected to capture consistent information, and 3) the integration complexity of different subspaces is overcome so that the learned collaborative binary codes can merge the specifics with consistency. Then, we introduce an alternatively iterative optimization to tackle the specifics and consistency hashing learning problem, making it scalable for large-scale cross-modal retrieval. Extensive experiments on five widely used benchmark databases clearly demonstrate the effectiveness and efficiency of our proposed method on both one-cross-one and one-cross-two retrieval tasks.
Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning

The multi-label customer reviews classification task aims to identify the different thoughts of customers about the product they are purchasing. Due to the impact of the COVID-19 pandemic, customers have become more prone to shopping online. As a consequence, the amount of text data on e-commerce is continuously increasing, which enables new studies to be carried out and important findings to be obtained with more detailed analysis. Nowadays, e-commerce customer reviews are analyzed by both researchers and sector experts, and are subject to many sentiment analysis studies. Herein, an analysis of customer reviews is carried out in order to obtain more in-depth thoughts about the product, rather than engaging in emotion-based analysis. Initially, we form a new customer reviews dataset made up of reviews by Turkish consumers in order to perform the proposed analysis. The created dataset contains more than 50,000 reviews in three different categories, and each review has multiple labels according to the comments made by the customers. Later, we applied machine learning methods employed for multi-label classification to the dataset. Finally, we compared and analyzed the results we obtained using a diverse set of statistical metrics. As a result of our experimental studies, we found the Micro Precision 0.9157, Micro Recall 0.8837, Micro F1 Score 0.8925, and Hamming Loss 0.0278 to be the most successful approaches.