Reviewerly/exHarmony huggingface dataset:
Explainable Artificial Intelligence for Magnetic Resonance Imaging Aging Brainprints: Grounds and challenges

Marked changes occur in the brain during people&#x2019;s lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the signature of the brain. These are shaped by a great variety of factors, both endogenous and exogenous. Accurate predictions of brain age (BA) can be derived from neuroimaging endophenotypes by using machine and deep learning (DL) techniques. Predictive models leading to accurate estimates while revealing which features contribute the most to final predictions are key to unveiling the mechanisms underlying the evolution of brain aging patterns. Explainable artificial intelligence (XAI) methods are emerging as enabling technology in different fields, and biomedicine is no exception. Within this framework, this article examines BA and presents a comprehensive review of recent advances in the exploitation of explainable machine learning (ML)/DL methods, highlighting the main open issues and providing hints for future directions.
On the importance of building high-quality training datasets for neural code search

The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query and code pairs is demanded to establish a precise mapping from the natural language to the programming language. Due to the limited availability, most widely-used code search datasets are established with compromise, such as using code comments as a replacement of queries. Our empirical study on a famous code search dataset reveals that over one-third of its queries contain noises that make them deviate from natural user queries. Models trained through noisy data are faced with severe performance degradation when applied in real-world scenarios. To improve the dataset quality and make the queries of its samples semantically identical to real user queries is critical for the practical usability of neural code search. In this paper, we propose a data cleaning framework consisting of two subsequent filters: a rule-based syntactic filter and a model-based semantic filter. This is the first framework that applies semantic query cleaning to code search datasets. Experimentally, we evaluated the effectiveness of our framework on two widely-used code search models and three manually-annotated code retrieval benchmarks. Training the popular DeepCS model with the filtered dataset from our framework improves its performance by 19.2% MRR and 21.3% [email protected], on average with the three validation benchmarks.
Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset

The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and Macro-average F1-score of 85.20 percent.
Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems

Abstract The DARPA Explainable Artificial Intelligence (AI) (XAI) Program focused on generating explanations for AI programs that use machine learning techniques. This article highlights progress during the DARPA Program (2017‐2021) relative to research since the 1970s in the field of intelligent tutoring systems (ITSs). ITS researchers learned a great deal about explanation that is directly relevant to XAI. We suggest opportunities for future XAI research deriving from ITS methods, and consider the challenges shared by both ITS and XAI in using AI to assist people in solving difficult problems effectively and efficiently.
Antecedents of blockchain technology application among Malaysian warehouse industry

The purpose of this research is to ascertain the most influential factor that affects the blockchain technology adoption intentions in Malaysian warehouse operations. The unified theory of acceptance and use of technology 2 acts as the underpinning theory and from a comprehensive literature review, five factors have identified to be incorporated in the conceptual framework development. Numerous studies covering the use of a wide range of technologies in the warehouse industry can be found in recent literature. However, it should be noted that, for studies relating to the intentions and application of blockchain technology in the Malaysian warehouse industry, this research would appear to be a pioneering work. Given the countless advantages of blockchain technology adoption, it is crucial that the Malaysian warehouse industry would seriously consider its application as standard business practices. This paper bridges the theoretical gap that will help to identify the opportunities for blockchain technology applications.
Image encryption based on fractional chaotic pseudo-random number generator and DNA encryption method

Nonlinear dynamic systems and chaotic systems have been quite exhaustively researched in the domain of cryptography. However, the possibility of using fractional chaotic systems in the cryptosystem design has been much less explored while it bears advantages such as enlarged keyspace and better resistance to attack compared to classical nonlinear systems. This paper, therefore, proposes a novel structure for the pseudo-random number generator based on 3 different fractional chaotic systems, namely fractional Chen system, Lu system, and fractional generalized double-humped logistic map. Then, the outputs of this fractional chaotic pseudo-random number generator are used as a keystream for an image encryption scheme. The confusion layer of the scheme is conducted by a dynamic DNA encoding and decoding method combined with a 2D cat map for the permutation in the DNA bases level. The diffusion layer is performed through the adoption of a 32 bits discrete logistic map. The performance and security analyses have been conducted for the above-designed cryptosystem, proving that the proposed cryptosystem is practical and efficient, and can be successfully implemented in image encryption.
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection

Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage.In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.Specifically, a stance contrastive learning strategy is employed to better generalize stance features for unseen targets.Further, we build a prototypical graph for each instance to learn the target-based representation, in which the prototypes are deployed as a bridge to share the graph structures between the known targets and the unseen ones.Then a novel target-aware prototypical graph contrastive learning strategy is devised to generalize the reasoning ability of target-based stance representations to the unseen targets.Extensive experiments on three benchmark datasets show that the proposed approach achieves state-ofthe-art performance in the ZSSD task 1 .
CENTRAL BANK DIGITAL CURRENCY: WHAT FACTORS DETERMINE ITS ADOPTION?

This paper attempts to explain the differences in Central Bank Digital Currency (CBDC) adoption across emerging and advanced countries using an ordered probit model. Based on a cross-country dataset, we show that wholesale CBDC is more advanced in countries with developed financial markets and greater cross-border transactions. Retail CBDC is more advanced in countries with lower financial inclusion and a large informal economy. We further show that different factors affect retail CBDC adoption across emerging and advanced countries. However, cross-border transactions are the most crucial factor influencing wholesale CBDC adoption across emerging and advanced countries.
Exploration and Application of College Students’ Management Model during the Epidemic Based on Big Data Technology

In the face of the sudden new crown pneumonia epidemic, “retroversions” across the country are facing difficulties, fighting with their lives on the front line of the fight against the epidemic, and protecting the lives and health of the people across the country with love. As an important position for epidemic prevention work, colleges and universities, in order to maintain the physical and mental health of students, use the grid management model to closely organize the student management work network, and extend the epidemic prevention and control work to the “end” of student management, so as to effectively strengthen the epidemic prevention work position in colleges and universities. In today’s era of big data, internet technology has brought great convenience to people, and people are also subtly enjoying the benefits of the era of big data in production and life. This study mainly discusses the combination of big data technology and efficient managers to deal with the management of students in the era of big data more scientifically and reasonably, and at the same time, colleges and universities do a good job in the practical significance and role of grid work mode for students under the normalization of the antiepidemic situation mode, to drive the overall development of colleges and universities and promote the improvement of college management level.
Understanding of E-Learning Programs using WPM MCDM Method

The Evolution of the Internet All Industry has affected business operations and the development of e-learning has accelerated. Cost of designer courses and learners, Wu, due to time or flexibility, e-learning is an alternative training method by corporations Accepted. E-learning ability assessment is important, and assessment criteria vary. E-learning ability assessment A great deal of relation Attempt has been made; however, a Common size estimate of the model is subjective to simultaneous criteria to the ambiguity of feeling Considers the relationship between. This paper proposed a new novel hybrid that Refers to MCDM-type independent relationships. Longevity, I.e. for 100 years on a much larger scale for bridges fixed traffic Current progress towards needs. To achieve this goal, Long duration of bridge performance Realistic predictions In Bridge Engineering play an important role. In this study, turbulent traffic, Great pressure to carry flows of concrete bridges Long-term deviation, and Dissociation-induced Damage development are being explored. Concrete viscoelastic Behavior and concrete Tensile cracks and plastic softeners taking into account the combined effects Taken, the performance of the bridge an integrated system for evaluating the model has been developed. (WPM-Weighted Product Method).In this method from analysis Colors of WebPages are the first rank, Connection of WebPages is the fifth rank
Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.
Integration of improved YOLOv5 for face mask detector and auto-labeling to generate dataset for fighting against COVID-19

One of the most effective deterrent methods is using face masks to prevent the spread of the virus during the COVID-19 pandemic. Deep learning face mask detection networks have been implemented into COVID-19 monitoring systems to provide effective supervision for public areas. However, previous works have limitations: the challenge of real-time performance (i.e., fast inference and low accuracy) and training datasets. The current study aims to propose a comprehensive solution by creating a new face mask dataset and improving the YOLOv5 baseline to balance accuracy and detection time. Particularly, we improve YOLOv5 by adding coordinate attention (CA) module into the baseline backbone following two different schemes, namely YOLOv5s-CA and YOLOV5s-C3CA. In detail, we train three models with a Kaggle dataset of 853 images consisting of three categories: without a mask “NM," with mask “M,” and incorrectly worn mask “IWM” classes. The experimental results show that our modified YOLOv5 with CA module achieves the highest accuracy mAP@0.5 of 93.9% compared with 87% of baseline and detection time per image of 8.0 ms (125 FPS). In addition, we build an integrated system of improved YOLOv5-CA and auto-labeling module to create a new face mask dataset of 7110 images with more than 3500 labels for three categories from YouTube videos. Our proposed YOLOv5-CA and the state-of-the-art detection models (i.e., YOLOX, YOLOv6, and YOLOv7) are trained on our 7110 images dataset. In our dataset, the YOLOv5-CA performance enhances with mAP@0.5 of 96.8%. The results indicate the enhancement of the improved YOLOv5-CA model compared with several state-of-the-art works.
Virtual reality in digital marketing: research agenda based on bibliometric reflection

Purpose This study unravels the intellectual structure of virtual reality (VR) in digital marketing (DM) research, identifies core research gaps and presents future research avenues. The study also conducts a performance analysis of publications in the field and identifies the most important stakeholders of the stated topic. Design/methodology/approach The Web of Science database was used to retrieve the publications that were relevant to the topic between 2012 and 2022. Biblioshiny, a shiny app for the Bibliometrix R package, is used to conduct a bibliometric analysis by decoding the results into several visual representations. Findings This report includes the most prolific contributors, keyword analysis results, productive nations, authors and connections, as well as the most often cited publications on VR in DM. In DM research, numerous perspectives on VR were looked at, explored and revealed. Practical implications The findings provide a new perspective and understanding of the issue for researchers in order to improve their research insights in the field. This study will also benefit marketing practitioners in ensuring the sustainability and innovativeness of technology used to run their DM campaigns. Originality/value This research provides the first bibliometric analysis of the citation works and productivity in the field of VR in DM using Biblioshiny, identifies core research gaps and provides future research agenda which can be useful to advance the research understanding in this context.
Teach Your Robot Your Language! Trainable Neural Parser for Modeling Human Sentence Processing: Examples for 15 Languages

We present a recurrent neural network (RNN) that performs thematic role assignment and can be used for human-robot interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g., predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this article, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in 15 languages, namely English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay, and Mandarin Chinese. Moreover, in the corpora, we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that: 1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature and 2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language-independent aspect makes it a good candidate for modeling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture.
A Model of Spatial Directness in Interactive Visualization

We discuss the concept of directness in the context of spatial interaction with visualization. In particular, we propose a model that allows practitioners to analyze and describe the spatial directness of interaction techniques, ultimately to be able to better understand interaction issues that may affect usability. To reach these goals, we distinguish between different types of directness. Each type of directness depends on a particular mapping between different spaces, for which we consider the data space, the visualization space, the output space, the user space, the manipulation space, and the interaction space. In addition to the introduction of the model itself, we also show how to apply it to several real-world interaction scenarios in visualization, and thus discuss the resulting types of spatial directness, without recommending either more direct or more indirect interaction techniques. In particular, we will demonstrate descriptive and evaluative usage of the proposed model, and also briefly discuss its generative usage.
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

Abstract. Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring. In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.
Intelligent hybrid cuckoo search and β-hill climbing algorithm

One of the major problems that is usually associated with any optimization algorithm including the Cuckoo Search (CS) algorithm is the premature convergence to suboptimal solutions. This problem normally occurs when the optimization operators of CS are not able to maintain the diversity of the solutions over multiple generations. One possible solution to the problem of premature convergence of CS is to hybridize it with other search techniques to reduce the likelihood of premature convergence. However, the hybrid CS algorithms normally require more computations than the original CS algorithm. The β-hill climbing algorithm, a variation of the Hill climbing algorithm, is capable of reaching better solutions in a shorter time than many popular local search algorithms. This paper proposes a new hybrid CS algorithm (CSBHC) that intelligently combines the CS algorithm with the β-hill climbing algorithm. In order to balance between the computational time and effectiveness of CSBHC, the β-hill climbing algorithm is called at each iteration of CSBHC based on an exponentially decreasing probability (i.e., the probability function used in Simulated Annealing). The proposed algorithm was evaluated and compared to popular hybrid CS algorithms using 16 standard benchmark functions. The experimental results suggest that the proposed algorithm produces more accurate results in a shorter running time compared to the original CS and other approaches.