Reviewerly/exHarmony huggingface dataset:
Spoken Language Identification Using Deep Learning

The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.
Quantum circuit architecture search for variational quantum algorithms

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.
Virtual agents and flow experience: An empirical examination of AI-powered chatbots

The aspects that could shape customers' virtual experiences with chatbot applications are poorly understood. Therefore, this study aims to empirically examine the main factors that shape customers' virtual flow experiences with AI-powered chatbots. The conceptual model was based on flow theory and the technology interactivity model. This model was extended to include the impact of both readability and transparency. The data were collected using an online questionnaire survey posted to 500 customers of courier, package delivery, and express mail services. The statistical results largely supported the role of readability, transparency, personalisation, responsiveness, and ubiquitous connectivity in shaping the virtual flow experience with chatbots, which in turn has a significant impact on both communication quality and satisfaction. This study opens new horizons for researchers and practitioners to consider dimensions other than satisfaction and intention to use, to facilitate and accelerate the pace of success of chatbot applications. However, several areas have not been fully addressed in the current study which could be worth considering in future research, as discussed in the related subsection.
Efficient density and cluster based incremental outlier detection in data streams

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN) to detect outliers efficiently in data streams. The iLDCBOF has many advanced advantages compared to previously reported iLOF-based studies: (1) it is based on a newly-developed core k-nearest neighbor (CkNN) concept to reliably and scalably detect outliers from data streams and prevent the clustering of outliers; 2) it uses a newly-developed algorithm that automatically adjusts the value of the k (number of neighbors) parameter for different real-time applications; and 3) it uses the Mahalanobis distance metric, so its performance is not affected even for large amounts of data. The iLDCBOF method is well suited for different data stream applications because it requires no distribution assumptions, it is parameterless (determined automatically), and it is easy to implement. ROC-AUC and statistical test analysis results from extensive experiments performed on 16 different real-world datasets showed that the iLDCBOF method significantly outperformed benchmark methods.
A new complex evidence theory

Dempster-Shafer evidence theory is widely used in the field of information fusion since it satisfies weaker conditions than probability theory. Nevertheless, the description space of the current evidence theory is only real space, and it cannot effectively describe and process the uncertain information in the face of multidimensional characteristic data and periodic data with phase angle changes. Thus, in this paper, evidence theory is extended to the complex Dempster-Shafer evidence theory. The mass function that is used to describe the uncertain information extends from the real space to the complex space, named as complex mass function. The modulus of the complex mass function indicates the degree of support for the proposition. Moreover, other basic concepts that are used to describe uncertainty information are also defined and discussed. To perfect the complex evidence theory, the complex Dempster rule of combination is supplemented. The complex Dempster rule of combination is an extension of Dempster rule of combination, which satisfies the commutative and associative laws just as Dempster rule of combination does, and it can degenerate into Dempster rule of combination. This paper also proposes a method to generate complex mass function and apply it to target recognition. The recognized results show that compared with the mass function, the target recognition rate is larger by using the complex mass function.
A first bibliometric literature review on Metaverse

The term “Metaverse” first appeared in Neal Stephenson’s 1992 science fiction novel Snow Crash. It was defined as a virtual environment shared through the Internet in which avatars represent persons in three dimensions and users can build what they wish. Since the publication of Stephenson’s novel, technology advances have enabled the creation of several Metaverses for popular usage, such as virtual world platforms like World of Warcraft and Second Life. This paper aims to review studies on the Metaverse using bibliometric analysis. The findings include a summary of the most important scientific articles andjournals in the subject area, as well as the most prolific and prominent authors, organisations, and nations. Furthermore, keyword co-occurrence analysis revealed the core research clusters and their sub-themes, allowing for more pertinent debates and viewpoints on vital areas for future research. Finally, the implications for theory and practice have been addressed, offering a comprehensive overview of expected Metaverse impacts on industry and society.
Nonstationary fuzzy neural network based on FCMnet clustering and a modified CG method with Armijo-type rule

Nonstationary fuzzy inference systems (NFISs) model the variation in opinions of individual experts and expert groups. They have the capability similar to type-2 fuzzy systems in some regards with less computational costs. This paper presents a nonstationary fuzzy neural network (NFNN) model by combining NFISs and neural networks (NNs). The proposed model synthesizes the logical inference and language expression abilities of NFIS with the learning mechanism of NNs. Hence, it makes NNs more translatable and also achieves self-learning of fuzzy rules. Besides, a fuzzy c-means network (FCMnet) clustering and a modified conjugate gradient method with constrained Armijo-type rule (MCGA) are proposed to initialize and train NFNN, respectively. The proposed FCMnet first combines the fuzzy c-means (FCM) algorithm with NNs. Thus, it not only improves the performance of FCM, but also makes NNs more interpretable. For MCGA, we construct a specific conjugate coefficient to ensure the sufficient descent property and propose a constrained Armijo-type rule to search a suitable learning rate in each iteration. By adopting these two techniques, MCGA achieves a fast convergence speed. In addition, both weak and strong convergence results are proven rigorously. Experiments on 14 datasets are carried out to illustrate the competitive performance of our models.
A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.
An analysis of children’ interaction with an AI chatbot and its impact on their interest in reading

Educators have indicated that social approaches to reading such as book talk activities are helpful for promoting students' interest in reading. However, it is not possible for teachers to interact with all students to talk about the books they have read as they have different language proficiency levels and different topics of interest. This study thus aimed to understand the affordances of a chatbot built with artificial intelligence techniques as a book talk companion, and to explore the role of the interaction in students' engagement and interest in reading. Adopting AI techniques, the chatbot in this study had basic understanding of 157 books. While students could choose any of the books to read and interact with the chatbot, the chatbot provided book talk and social affective cues to facilitate the book talk. Multiple data sources from 68 students participating in a 6-week reading activity were collected and analyzed. It was found that students perceived a high level of social connection with the chatbot. In particular, students talking with the chatbot maintained a stable level of situational interest in the value dimension, while the interest of those who did not participate in the book talk with the chatbot faded significantly. Students' perceptions of the social connection with the chatbot were closely related to their engagement in the reading activity and correlated with both their triggered-situational interest and maintained situational interest. The results provide insights into how a chatbot with AI techniques can create a positive reading experience to sustain students’ interest in learning.
Immersive virtual reality for science learning: Design, implementation, and evaluation

The advanced visualisation and interactive capabilities make immersive virtual reality (IVR) attractive for educators to investigate its educational benefits. This research reviewed 64 studies published in 2016–2020 to understand how science educators designed, implemented, and evaluated IVR-based learning. The immersive design features (sensory, actional, narrative, and social) originally suggested by Dede provided the framework for the analysis of IVR designs. Educators commonly adopted IVR to better aid visualisation of abstract concepts and enhance learning experience. IVR applications tended to have sensory and actional features, leaving out narrative and social features. Learning theories did not appear to play a strong role in the design, implementation, and evaluation of IVR-based learning. Participants generally reported their IVR experiences as positive on engagement and motivation but the learning outcomes were mixed. No particular immersive design features were identified to result in better learning outcomes. Careful consideration of the immersive design features in alignment with the rationales for adopting IVR and evaluation methods may contribute to more productive investigations of the educational benefits of IVR to improve science teaching and learning.
Game Theory-Based Authentication Framework to Secure Internet of Vehicles with Blockchain

The Internet of Vehicles (IoV) is a new paradigm for vehicular networks. Using diverse access methods, IoV enables vehicles to connect with their surroundings. However, without data security, IoV settings might be hazardous. Because of the IoV's openness and self-organization, they are prone to malevolent attack. To overcome this problem, this paper proposes a revolutionary blockchain-enabled game theory-based authentication mechanism for securing IoVs. Here, a three layer multi-trusted authorization solution is provided in which authentication of vehicles can be performed from initial entry to movement into different trusted authorities' areas without any delay by the use of Physical Unclonable Functions (PUFs) in the beginning and later through duel gaming, and a dynamic Proof-of-Work (dPoW) consensus mechanism. Formal and informal security analyses justify the framework's credibility in more depth with mathematical proofs. A rigorous comparative study demonstrates that the suggested framework achieves greater security and functionality characteristics and provides lower transaction and computation overhead than many of the available solutions so far. However, these solutions never considered the prime concerns of physical cloning and side-channel attacks. However, the framework in this paper is capable of handling them along with all the other security attacks the previous work can handle. Finally, the suggested framework has been subjected to a blockchain implementation to demonstrate its efficacy with duel gaming to achieve authentication in addition to its capability of using lower burdened blockchain at the physical layer, which current blockchain-based authentication models for IoVs do not support.
GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021

In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality. However, in challenging scenarios like drone videos, they still suffer from problems, such as small objects, camera movements and view changes. In this paper, we propose a new multiple object tracker, which employs Global Information And some Optimizing strategies, named GIAOTracker It consists of three stages, i.e., online tracking, global link and post-processing. Given detections in every frame, the first stage generates reliable track- lets using information of camera motion, object motion and object appearance. Then they are associated into trajectories by exploiting global clues and refined through four post-processing methods. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset and wins the 2nd place in the VisDrone2021 MOT Challenge.
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this problem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.
Development of a hybrid computational intelligent model for daily global solar radiation prediction

Providing an accurate and reliable solar radiation prediction is highly significant for optimal design and management of thermal and solar photovoltaic systems. It is massively essential for producing renewable and clean energy production. In the current research, an exploration of newly developed computation intelligent models based on the hybridization of Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) models for building robust predictive models for daily scale solar radiation. The proposed hybrid models are examined at four meteorological stations (i.e., Boromo, Dori, Gaoua, and Po) located in the Burkina Faso region, Sub-Sahara Africa. The prediction matrix is constructed using several related climate parameters. Based on the statistical and graphical evaluation, the proposed models demonstrated an outstanding predictability performance. The computational experiments showed that the proposed framework consistently enhanced prediction accuracy by 17.2%, 4.9%, 39.8%, and 44.5%, at Boromo, Dori, Gaoua, and Po stations using the Root Mean Squared Error over the testing phase against the existing methods proposed in the literature. The resulting method has the potential to be used as a surrogate tool for solar radiation prediction on the stations. The findings of this study contribute to a growing body of literature towards advancing the study of machine learning methods for predicting renewable energy production.
Volumetric memory network for interactive medical image segmentation

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet clinically acceptable accuracy, thus typically require further refinement. To this end, we propose a novel Volumetric Memory Network, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner. Provided by user hints on an arbitrary slice, a 2D interaction network is firstly employed to produce an initial 2D segmentation for the chosen slice. Then, the VMN propagates the initial segmentation mask bidirectionally to all slices of the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To facilitate smooth human-in-the-loop segmentation, a quality assessment module is introduced to suggest the next slice for interaction based on the segmentation quality of each slice produced in the previous round. Our VMN demonstrates two distinctive features: First, the memory-augmented network design offers our model the ability to quickly encode past segmentation information, which will be retrieved later for the segmentation of other slices; Second, the quality assessment module enables the model to directly estimate the quality of each segmentation prediction, which allows for an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribble, bounding box, extreme clicking). Extensive experiments have been conducted on three public medical image segmentation datasets (i.e., MSD, KiTS19, CVC-ClinicDB), and the results clearly confirm the superiority of our approach in comparison with state-of-the-art segmentation models. The code is made publicly available at https://github.com/0liliulei/Mem3D.
Virtual Worlds (Metaverse): From Skepticism, to Fear, to Immersive Opportunities

The advancement of human–computer interfaces and computational power enables the creation of believable virtual worlds. These were once limited to the gaming community but are now used for business purposes, including industrial applications, health, education.
Topic modeling algorithms and applications: A survey

Topic modeling is used in information retrieval to infer the hidden themes in a collection of documents and thus provides an automatic means to organize, understand and summarize large collections of textual information. Topic models also offer an interpretable representation of documents used in several downstream Natural Language Processing (NLP) tasks. Modeling techniques vary from probabilistic graphical models to the more recent neural models. This paper surveys topic models from four aspects. The first aspect categorizes different topic modeling techniques into four categories: algebraic, fuzzy, probabilistic, and neural. We review the wide variety of available models from each category, highlight differences and similarities between models and model categories using a unified perspective, investigate these models’ characteristics and limitations, and discuss their proper use cases. The second aspect illustrates six criteria for proper evaluation of topic models, from modeling quality to interpretability, stability, efficiency, and beyond. Topic modeling has found applications in various disciplines, owing to its interpretability. We examine these applications along with some popular software tools which provide an implementation of some models. The fourth aspect reviews available datasets and benchmarks. Using two benchmark datasets, we conducted experiments to compare seven topic models along the proposed metrics. The discussion highlights the differences between the models and their relative suitability for various applications. It notes the relationship between evaluation metrics and proposes four key aspects to help decide which model to use for an application. Our discussion also shows that the research trends move towards developing and tuning neural topic models and leveraging the power of pre-trained language models. Finally, it highlights research gaps in developing unified benchmarks and evaluation metrics.