Reviewerly/exHarmony huggingface dataset:
Experimental Analysis of Trustworthy In-Vehicle Intrusion Detection System Using eXplainable Artificial Intelligence (XAI)

Anomaly-based In-Vehicle Intrusion Detection System (IV-IDS) is one of the protection mechanisms to detect cyber attacks on automotive vehicles. Using artificial intelligence (AI) for anomaly detection to thwart cyber attacks is promising but suffers from generating false alarms and making decisions that are hard to interpret. Consequently, this issue leads to uncertainty and distrust towards such IDS design unless it can explain its behavior, e.g., by using eXplainable AI (XAI). In this paper, we consider the XAI-powered design of such an IV-IDS using CAN bus data from a public dataset, named “Survival”. Novel features are engineered, and a Deep Neural Network (DNN) is trained over the dataset. A visualization-based explanation, “VisExp”, is created to explain the behavior of the AI-based IV-IDS, which is evaluated by experts in a survey, in relation to a rule-based explanation. Our results show that experts’ trust in the AI-based IV-IDS is significantly increased when they are provided with VisExp (more so than the rule-based explanation). These findings confirm the effect, and by extension the need, of explainability in automated systems, and VisExp, being a source of increased explainability, shows promise in helping involved parties gain trust in such systems.
The Effect of Virtual-Reality-Based Restorative Environments on Creativity

This study, based on the theory of restorative environmental, uses virtual reality (VR) technology to construct interactive restorative environments and discusses the influence of the experience of virtual restorative environment on individual creativity. A total of 72 college students were selected as participants in the study. Through psychological scales, three creativity tests, and EEG feedback data, the following conclusions were drawn: (1) The VR restorative environment experience improves individual creativity, especially the creative quality of cohesion; (2) the experience of the VR restorative environment enables participants to experience a desirable sense of presence. Compared with the restorative scene experience without interactive activities, the addition of interactive activities improves the individual sensory fidelity to a greater extent. (3) We cannot simply assume that the experience of the VR restorative environment with interactive activities will make individual creative performance better than non-interactive experience. Interaction with certain difficulty will increase cognitive load, thus disrupting individual creative performance. Garden scenes that can be explored freely and have no interaction can better promote individual creativity. (4) In the environmental experience, participants paid greater attention to natural elements, and the restorative environment they described was very similar to the environment they believed could foster creativity. This study's results provide evidence for the positive effects of the VR restorative environment experience on individuals and contributes to the cognitive exploration of the interaction between restorative environments and individuals in the future.
Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model

This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Characteristic (ROC) curve and overall accuracy. Three benchmark datasets comprised of numerous video frames with various abnormal and normal actions were used to evaluate the performance. Experimental results revealed that the proposed model outperformed other baseline studies on all three datasets and achieved 98% AUC using the ROC curve. Moreover, the proposed model achieved 95.6%, 98%, and 97% AUC on the CUHK Avenue, UCSD Ped-1, and UCSD Ped-2 datasets, respectively.
Evaluating Electronic Customer Relationship Management System Success: The Mediating Role of Customer Satisfaction

This study evaluated the relationships among variables in electronic customer relationship management (e-CRM) success. The purpose of this paper is to examine the effect of technological readiness, privacy, COVID-19, customer pressure, trust, level of service quality, and customer satisfaction. Quantitative research methods were applied in examining the causal associations among the primary variables of the study. We used a sample approach called purposive sampling. Data was obtained from 390 completed questionnaires collected from employees, who took part in CRM technology activities, working in Jordanian firms. Data was analyzed via AMOS software version 22, and hypotheses were examined by applying structural equation modelling (SEM). Results revealed that technological readiness, COVID-19, customer pressure, and customer satisfactions had a positive effect on e-CRM success system. Moreover, the study showed that technological readiness, privacy, and level of service quality positively affected customer satisfaction. Additionally, the mediation of customer satisfaction on the association between trust and success of e-CRM system, and also between level of service quality and e-CRM system success, was demonstrated. Practical implications: In practice, this study can help managers and practitioners in implementing e-CRM systems. Moreover, it can drive further empirical studies on the relations among technological readiness, privacy, COVID-19, customer pressure, trust, and level of service quality, besides customer satisfaction. Only few studies had looked into the variables in the success of e-CRM system, in emerging industries of developing countries, specifically, among technological readiness, privacy, COVID-19, customer pressure, trust, level of service quality, and customer satisfaction. These gaps were addressed in this study. The current model is also relatively constrained, so it may be expanded in further studies. For instance, the construct of culture might be incorporated to the model to make it more thorough in its examination of the topic at issue.
Metaverse and metaverse cryptocurrencies (meta coins): bubbles or future

Purpose- Metaverse is one of the blockchain-based digital assets developed in recent years. The concept of metaverse refers to an alternative virtual universe in which individuals are represented by their avatars (virtual representation), they can create applications, socialize, buy and sell goods and services, and share. In addition, there are meta coins, i.e. local crypto coins, used in the metaverse. In this study, the concepts of metaverse and meta coin, which have become popular recently, are explained, and the existence of price bubbles in MANA, the meta coin of the Decentraland digital reality platform, which has the largest market value among meta coins, is investigated. Methodology- The existence of price bubbles is investigated by the newly developed GSADF multiple bubble test. Findings- According to the result of the analysis carried out with the GSADF test, the existence of price bubbles in MANA prices in different periods has been determined. Especially the bubbles that have formed in the recent period have been realized as a longer term. This situation shows the impression that significant speculative movements have occurred in the MANA token recently. Conclusion- According to the findings, MANA prices are open to speculation. It is important for those investing or considering investing in MANA and other meta coins to consider the existence of bubbles in the respective token prices. Keywords: Price bubbles, token, cryptocurrency, metaverse, meta coin. JEL Codes: G11, G14, G15
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper, we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develop PoSH Auto-sklearn, which enables AutoML systems to work well on large datasets under rigid time limits by using a new, simple and meta-feature-free meta-learning technique and by employing a successful bandit strategy for budget allocation. However, PoSH Auto-sklearn introduces even more ways of running AutoML and might make it harder for users to set it up correctly. Therefore, we also go one step further and study the design space of AutoML itself, proposing a solution towards truly hands-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn 2.0. We verify the improvements by these additions in an extensive experimental study on 39 AutoML benchmark datasets. We conclude the paper by comparing to other popular AutoML frameworks and Auto-sklearn 1.0, reducing the relative error by up to a factor of 4.5, and yielding a performance in 10 minutes that is substantially better than what Auto-sklearn 1.0 achieves within an hour.
A Modified Gorilla Troops Optimizer for Global Optimization Problem

The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverback with 50% probability. Finally, the quasi-reflection position of the silverback is generated by QRBL. The optimal solution can be updated by comparing these fitness values. The performance of the proposed MGTO is comprehensively evaluated by 23 classical benchmark functions, 30 CEC2014 benchmark functions, 10 CEC2020 benchmark functions and 7 engineering problems. The experimental results show that MGTO has competitive performance and promising prospects in real-world optimization tasks.
Contrastive Consensus Graph Learning for Multi-View Clustering

Dear Editor, This letter proposes a contrastive consensus graph learning model for multi-view clustering. Graphs are usually built to outline the correlation between multi-model objects in clustering task, and multiview graph clustering aims to learn a consensus graph that integrates the spatial property of each view. Nevertheless, most graph-based models merely consider the overall structure from all views but neglect the local spatial consistency between diverse views, resulting in the lack of global spatial consistency in the learned graph. To overcome this issue, a deep convolutional network is built to explore latent local spatial information from raw affinity graphs. Specifically, we employ a consensus graph constraint to preserve the global consistency between the learned graph and raw graphs. Furthermore, a contrastive reconstruction loss is introduced to achieve the sample-level approximation between reconstructed graphs and raw graphs, which facilitates the network to enhance the consensus graph learning. Experiments on six classical datasets demonstrate that the proposed model outperforms other nine state-of-the-art algorithms.
A mutual neighbor-based clustering method and its medical applications

Clustering analysis has been widely used in various real-world applications. Due to the simplicity of K-means, it has become the most popular clustering analysis technique in reality. Unfortunately, the performance of K-means heavily relies on initial centers, which should be specified in prior. Besides, it cannot effectively identify manifold clusters. In this paper, we propose a novel clustering algorithm based on representative data objects derived from mutual neighbors to identify different shaped clusters. Specifically, it first obtains mutual neighbors to estimate the density for each data object, and then identifies representative objects with high densities to represent the whole data. Moreover, a concept of path distance, deriving from a minimum spanning tree, is introduced to measure the similarities of representative objects for manifold structures. Finally, an improved K-means with initial centers and path-based distances is proposed to group the representative objects into clusters. For non-representative objects, their cluster labels are determined by neighborhood information. To verify the effectiveness of the proposed method, we conducted comparison experiments on synthetic data and further applied it to medical scenarios. The results show that our clustering method can effectively identify arbitrary-shaped clusters and disease types in comparing to the state-of-the-art clustering ones.
Roman Urdu Sentiment Analysis Using Transfer Learning

Numerous studies have been conducted to meet the growing need for analytic tools capable of processing increasing amounts of textual data available online, and sentiment analysis has emerged as a frontrunner in this field. Current studies are focused on the English language, while minority languages, such as Roman Urdu, are ignored because of their complex syntax and lexical varieties. In recent years, deep neural networks have become the standard in this field. The entire potential of DL models for text SA has not yet been fully explored, despite their early success. For sentiment analysis, CNN has surpassed in accuracy, although it still has some imperfections. To begin, CNNs need a significant amount of data to train. Second, it presumes that all words have the same impact on the polarity of a statement. To fill these voids, this study proposes a CNN with an attention mechanism and transfer learning to improve SA performance. Compared to state-of-the-art methods, our proposed model appears to have achieved greater classification accuracy in experiments.
The Object Detection, Perspective and Obstacles In Robotic: A Review

A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.
Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR

Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features.
Audio Augmentation for Non-Native Children’s Speech Recognition through Discriminative Learning

Automatic speech recognition (ASR) in children is a rapidly evolving field, as children become more accustomed to interacting with virtual assistants, such as Amazon Echo, Cortana, and other smart speakers, and it has advanced the human-computer interaction in recent generations. Furthermore, non-native children are observed to exhibit a diverse range of reading errors during second language (L2) acquisition, such as lexical disfluency, hesitations, intra-word switching, and word repetitions, which are not yet addressed, resulting in ASR's struggle to recognize non-native children's speech. The main objective of this study is to develop a non-native children's speech recognition system on top of feature-space discriminative models, such as feature-space maximum mutual information (fMMI) and boosted feature-space maximum mutual information (fbMMI). Harnessing the collaborative power of speed perturbation-based data augmentation on the original children's speech corpora yields an effective performance. The corpus focuses on different speaking styles of children, together with read speech and spontaneous speech, in order to investigate the impact of non-native children's L2 speaking proficiency on speech recognition systems. The experiments revealed that feature-space MMI models with steadily increasing speed perturbation factors outperform traditional ASR baseline models.
Short-term day-ahead photovoltaic output forecasting using PCA-SFLA-GRNN algorithm

The work of forecasting solar power is becoming more crucial with directives to regulate the quality of the power and increase the system’s reliability as photovoltaic (PV) sites are being integrated into the architecture of power systems at an increasing rate. This study proposes a metaheuristic model for short-term photovoltaic power forecasting that includes shuffled frog leaping algorithm (SFLA), principal component analysis (PCA), and generalized regression neural network (GRNN). In this model, GRNN is implemented to analyze the input parameters after the dimension reduction process, and its parameters get optimized with the help of the SFLA, which has the advantage of fast convergence speed as well as searching ability, whereas PCA techniques are implemented to diminish the dimension of meteorological conditions. This hybrid model achieves day-ahead short-term forecasting, as shown in an experimental case of a Bhadla Solar Park installed in Gujarat, India. The accuracy of the proposed model obtained a mean absolute error (nMAE) of 2.3325, and a root mean square error (RMSE) of 129.425. Similarly, the error in forecasting obtained by the proposed method results in nMAE = 2.977 and RMSE = 160.92. The output results obtained surpassed all other hybrid models used for comparison in this study.
Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization

E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset's sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer's tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference's hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.
Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education

Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks.