Reviewerly/exHarmony huggingface dataset:
Linguistic Data Consortium

The Linguistic Data Consortium (LDC) creates, stores, and distributes language resources, in particular language corpora, for educational, governmental, and commercial purposes. At this time, the number of corpora available on the LDC platform exceeds 900, and new ones are added on a monthly basis. Since its founding in 1992, LDC has had thousands of members such as universities, libraries, government research laboratories, and corporations. While there is a plethora of language resources available to researchers these days, LDC continues to stand out by offering an unparalleled selection of carefully curated corpora combined with a variety of linguistic tools.
Asymptotically Optimal Circuit Depth for Quantum State Preparation and General Unitary Synthesis

The Quantum State Preparation problem aims to prepare an n-qubit quantum state |ψv=k=02n-1vk|k from the initial state |0n, for a given unit vector v=(v0,v1,v2,v2n-1)TC2n with ||v||2=1. The problem is of fundamental importance in quantum algorithm design, Hamiltonian simulation and quantum machine learning, yet its circuit depth complexity remains open when ancillary qubits are available. In this paper, we study quantum circuits when there are m ancillary qubits available. We construct, for any m, circuits that can prepare |ψvin depth Õ(2nm+n+n) and size O(2n), achieving the optimal value for both measures simultaneously. These results also imply a depth complexity of (4nm+n) for quantum circuits implementing a general n-qubit unitary for any m≤O(2n/n) number of ancillary qubits. This resolves the depth complexity for circuits without ancillary qubits. And for circuits with exponentially many ancillary qubits, our result quadratically improves the currently best upper bound of O(4n) to ˜(2n). Our circuits are deterministic, prepare the state and carry out the unitary precisely, utilize the ancillary qubits tightly and the depths are optimal in a wide parameter regime. The results can be viewed as (optimal) time-space trade-off bounds, which is not only theoretically interesting, but also practically relevant in the current trend that the number of qubits starts to take off, by showing a way to use a large number of qubits to compensate the short qubit lifetime.
BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images

Breast cancer is considered the most commonly diagnosed cancer globally and falls second to lung cancer. For the early detection of breast tumors in women, breast cancer analysis using Ultrasound, Mammography, and MRI modalities as the initial screening process. Due to the random variation, irregular shapes, and blurred boundaries of tumor regions, the accurate segmentation of breast tumors is still a tricky task. The existing convolutional neural networks (CNNs) inherit their limitation by extracting global context information and, in most cases, proved less efficient in obtaining satisfactory results. As a solution, we proposed the BTS-ST network, a novel solution for breast tumor segmentation and classification that Swin-Transformer (ST) inspires. The BTS-ST network incorporates Swin-Transformer into traditional CNNs-based U-Net to improve global modeling capabilities. To improve the feature representation capability of irregularly shaped tumors, we first introduced a Spatial Interaction block (SIB), encoding spatial knowledge in the Swin Transformer block by developing pixel-level correlation. The segmentation accuracy of small-scale tumor regions is increased by building a Feature Compression block (FCB) to prevent information loss and compress smaller-scale features in patch token down sampling of Swin-Transformer. Finally, a Relationship Aggregation block (RAB) is developed as a bridge between dual encoders to combine global dependencies from Swin-Transformer into the features from CNN hierarchically. Extensive experiments are performed on breast tumor segmentation and classification tasks using multimodality Ultrasound, Mammogram, and MRI-based datasets. The results demonstrate that our proposed solution is comparatively better than other state-of-the-art methods.
An evolutionary explainable deep learning approach for Alzheimer's MRI classification

Deep Neural Networks (DNN) are prominent Machine Learning (ML) algorithms widely used, especially in medical tasks. Among them, Convolutional Neural Networks (CNN) are well-known for image-based tasks and have shown excellent performance. In contrast to this remarkable performance, one of their most fundamental drawbacks is their inability to clarify the cause of their outputs. Moreover, each ML algorithm needs to present an explanation of its output to the users to increase its reliability. Occlusion Map is a method used for this purpose and aims to find regions of an image that have a significant impact on determining the network's output, which does this through an iterative process of occluding different regions of images. In this study, we used Magnetic Resonance Imaging (MRI) scans from Alzheimer's Disease Neuroimaging Initiative (ADNI) and trained a 3D-CNN model to diagnose Alzheimer's Disease (AD) patients from cognitively normal (CN) subjects. We tried to combine a genetic algorithm-based Occlusion Map method with a set of Backpropagation-based explainability methods, and ultimately, we found a brain mask for AD patients. Also, by comparing the extracted brain regions with the studies in this field, we found that the extracted regions are significantly effective in diagnosing AD from the perspective of Alzheimer's specialists. Our model achieved an accuracy of 87% in 5-fold cross-validation, which is an acceptable accuracy compared to similar studies. We considered a 3D-CNN model with 96% validation accuracy (on unmasked data that includes all 96 distinct brain regions of the Harvard-Oxford brain atlas), which we used in the genetic algorithm phase to produce a suitable brain mask. Finally, using lrp_z_plus_fast explainability method, we achieved 93% validation accuracy with only 29 brain regions.
Banking 4.0: Artificial Intelligence (AI) in Banking Industry &amp; Consumer’s Perspective

The simulation of human intelligence in machines, called Artificial intelligence, has risen, and plays an important role in the new banking era. The present study aims to discuss the consumer’s perspective on artificial intelligence’s adoption in Asian countries. The questionnaire was developed and distributed to collect data from five Asian countries (Pakistan, China, Iran, Saudi Arabia, and Thailand). The total useable responses were 799. The results showed that the factors (awareness, attitude, subjective norms, perceived usefulness, and knowledge of artificial intelligence technology) had a significant and positive relationship with the intention to adopt AI in the banking sector. However, perceived risk shows a negative but significant relationship with the intentions to adopt AI. Overall, the findings of this study will be a worthy insight for making strategic decision-making in the banking industry. This will enable the banking management to build a strategy to increase the trust of consumers, which will help them to overcome risks and give them confidence in using digital technology while making transactions. The banking sector also focuses on innovative AI technologies to improve customer services as well as overall growth by generating more revenue.
Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

With increasingly stringent in requirements on the reliability and safety of hydraulic systems, data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems may have multiple failure modes, and accurately diagnosing compound failures in multi-component systems is a daunting task. In this paper, a method of multi-output classification by combining linear discriminant analysis (LDA) with the hybrid kernel extreme learning machine (HKELM) is proposed to diagnose compound faults in hydraulic systems. Data selection based on LDA is used in place of expert knowledge to screen out sensitive channels of each component from multi-channel signals. The multi-output strategy is embedded into the HKELM, which can simultaneously output the fault status of multiple components to diagnose the health of the system. An improved Hamming loss is also proposed to evaluate the total error in the multi-output classification because it has greater applicative relevance than classification accuracy. The results of experiments show that the proposed method can diagnose composite faults in multi-component systems with an accuracy higher than 99.5% and an error of only 0.20% on a dataset of hydraulic systems. As a shallow feed-forward network model, it can be used for real-time fault diagnosis due to its efficiency.
A novel healthy and time-aware food recommender system using attributed community detection

Food recommendation systems aim to provide recommendations according to a user’s diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, which restricts their ability to generate healthy recommendations. This paper develops a novel health-aware food recommendation system that explicitly accounts for food ingredients, food categories, and the factor of time, predicting the user’s preference through time-aware collaborative filtering and a food ingredient content-based model. Based on the user's predicted preferences and the health factor of each food, our model provides final recommendations to the target user. The performance of our model was compared to several state-of-the-art recommender systems in terms of five distinct metrics: Precision, Recall, F1, AUC, and NDCG. Experimental analysis of datasets extracted from the websites Allrecipes.com and Food.com demonstrated that our proposed food recommender system performs well compared to previous food recommendation models.
Requirements engineering for artificial intelligence systems: A systematic mapping study

In traditional software systems, Requirements Engineering (RE) activities are well-established and researched. However, building Artificial Intelligence (AI) based software with limited or no insight into the system’s inner workings poses significant new challenges to RE. Existing literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). This paper investigates current approaches for specifying requirements for AI systems, identifies available frameworks, methodologies, tools, and techniques used to model requirements, and finds existing challenges and limitations. We performed a systematic mapping study to find papers on current RE4AI approaches. We identified 43 primary studies and analyzed the existing methodologies, models, tools, and techniques used to specify and model requirements in real-world scenarios. We found several challenges and limitations of existing RE4AI practices. The findings highlighted that current RE applications were not adequately adaptable for building AI systems and emphasized the need to provide new techniques and tools to support RE4AI. Our results showed that most of the empirical studies on RE4AI focused on autonomous, self-driving vehicles and managing data requirements, and areas such as ethics, trust, and explainability need further research.
Korean Sign Language Recognition Using Transformer-Based Deep Neural Network

Sign language recognition (SLR) is one of the crucial applications of the hand gesture recognition and computer vision research domain. There are many researchers who have been working to develop a hand gesture-based SLR application for English, Turkey, Arabic, and other sign languages. However, few studies have been conducted on Korean sign language classification because few KSL datasets are publicly available. In addition, the existing Korean sign language recognition work still faces challenges in being conducted efficiently because light illumination and background complexity are the major problems in this field. In the last decade, researchers successfully applied a vision-based transformer for recognizing sign language by extracting long-range dependency within the image. Moreover, there is a significant gap between the CNN and transformer in terms of the performance and efficiency of the model. In addition, we have not found a combination of CNN and transformer-based Korean sign language recognition models yet. To overcome the challenges, we proposed a convolution and transformer-based multi-branch network aiming to take advantage of the long-range dependencies computation of the transformer and local feature calculation of the CNN for sign language recognition. We extracted initial features with the grained model and then parallelly extracted features from the transformer and CNN. After concatenating the local and long-range dependencies features, a new classification module was applied for the classification. We evaluated the proposed model with a KSL benchmark dataset and our lab dataset, where our model achieved 89.00% accuracy for 77 label KSL dataset and 98.30% accuracy for the lab dataset. The higher performance proves that the proposed model can achieve a generalized property with considerably less computational cost.
An enhanced approach for sentiment analysis based on meta-ensemble deep learning

Abstract Sentiment analysis, commonly known as “opinion mining,” aims to identify sentiment polarities in opinion texts. Recent years have seen a significant increase in the acceptance of sentiment analysis by academics, businesses, governments, and several other organizations. Numerous deep-learning efforts have been developed to effectively handle more challenging sentiment analysis problems. However, the main difficulty with deep learning approaches is that they require a lot of experience and hard work to tune the optimal hyperparameters, making it a tedious and time-consuming task. Several recent research efforts have attempted to solve this difficulty by combining the power of ensemble learning and deep learning. Many of these efforts have concentrated on simple ensemble techniques, which have some drawbacks. Therefore, this paper makes the following contributions: First, we propose a meta-ensemble deep learning approach to improve the performance of sentiment analysis. In this approach, we train and fuse baseline deep learning models using three levels of meta-learners. Second, we propose the benchmark dataset “Arabic-Egyptian Corpus 2” as an extension of a previous corpus. The corpus size has been increased by 10,000 annotated tweets written in colloquial Arabic on various topics. Third, we conduct several experiments on six benchmark datasets of sentiment analysis in different languages and dialects to evaluate the performance of the proposed meta-ensemble deep learning approach. The experimental results reveal that the meta-ensemble approach effectively outperforms the baseline deep learning models. Also, the experiments reveal that meta-learning improves performance further when the probability class distributions are used to train the meta-learners.
Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

The virtual-to-real paradigm, i.e., training models on virtual data and then applying them to solve real-world problems, has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine learning. To summarize the advances in recent years, this survey comprehensively reviews the literature, from the viewport of parallel intelligence. First, an extended parallel learning framework is proposed to cover main domains including computer vision, natural language processing, robotics, and autonomous driving. Second, a multi-dimensional taxonomy is designed to organize the literature in a hierarchical structure. Third, the related virtual-to-real works are analyzed and compared according to the three principles of parallel learning known as description, prediction, and prescription, which cover the methods for constructing virtual worlds, generating labeled data, domain transferring, model training and testing, as well as optimizing the strategies to guide the task-oriented data generator for better learning performance. Key issues remained in virtual-to-real are discussed. Furthermore, the future research directions from the viewpoint of parallel learning are suggested.
Framework for Implementation of Personality Inventory Model on Natural Language Processing with Personality Traits Analysis

The phrase "personality" refers to an individual's distinct mode of thought, action, and behaviour Personality is a collection of feelings, thoughts, and aspirations that may be seen in the way people interact with one another. Behavioural features that separate one person from another and may be clearly seen when interacting with individuals in one's immediate surroundings and social group are included in this category of traits. To improve good healthy discourse, a variety of ways for evaluating candidate personalities based on the meaning of their textual message have been developed. According to the research, the textual content of interview responses to conventional interview questions is an effective measure for predicting a person's personality attribute. Nowadays, personality prediction has garnered considerable interest. It analyses user activity and displays their ideas, feelings, and so on. Historically, defining a personality trait was a laborious process. Thus, automated prediction is required for a big number of users. Different algorithms, data sources, and feature sets are used in various techniques. As a way to gauge someone's personality, personality prediction has evolved into an important topic of research in both psychology and computer science. Candidate personality traits may be classified using a word embedding model, which is the subject of this article.
Machine learning in microseismic monitoring

The confluence of our ability to handle big data, significant increases in instrumentation density and quality, and rapid advances in machine learning (ML) algorithms have placed Earth Sciences at the threshold of dramatic progress. ML techniques have been attracting increased attention within the seismic community, and, in particular, in microseismic monitoring where they are now being considered a game-changer due to their real-time processing potential. In our review of the recent developments in microseismic monitoring and characterisation, we find a strong trend in utilising ML methods for enhancing the passive seismic data quality, detecting microseismic events, and locating their hypocenters. Moreover, they are being adopted for advanced event characterisation of induced seismicity, such as source mechanism determination, cluster analysis and forecasting, as well as seismic velocity inversion. These advancements, based on ML, include by-products often ignored in classical methods, like uncertainty analysis and data statistics. In our assessment of future trends in ML utilisation, we also see a strong push toward its application on distributed acoustic sensing (DAS) data and real-time monitoring to handle the large amount of data acquired in these cases.
E-Voting Meets Blockchain: A Survey

The utilization of electronic voting systems for the election of public offices is becoming widespread globally. This trend can be attributed to the benefits provided by these systems, including remote voting capabilities and accelerated vote counting. Furthermore, electronic voting systems offer improved privacy and enhanced protection against voting bias. Blockchain technology enhances the robustness of the voting process through its immutable vote storage mechanism, thereby reducing the threat of vote tampering and safeguarding the legitimacy of elections. This technology has been adopted by countries such as Germany, Russia, Estonia, and Switzerland for use in their e-voting systems. This study provides a comprehensive overview of the blockchain-based e-voting systems currently being implemented by various countries and companies and proposed for academic research. Additionally, this study analyzes the challenges faced by blockchain e-voting systems and identifies areas for future research to enhance the trustworthiness of such systems.
Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm

This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions.
The Need of Integrating Digital Education in Higher Education: Challenges and Opportunities

Although it existed in a few different forms earlier, digital education is essentially a modern invention. It is the digitalization of a segment of the educational system. This article attempts to offer insightful thoughts on the future potential and difficulties of information and communication technology (ICT) and digital education as they relate to adopting the most recent technological advancements in the digital era and extensive online open courses. With the development of internet technology, we have observed a significant shift in how we communicate and collaborate among academics. The digital revolution encouraged unrestricted access to information on a global scale. Today’s classrooms are equipped with a wealth of ICT tools, and almost all instructors have made significant progress in integrating digital technology to improve students’ access to information and cooperative learning opportunities. The higher education system must seek to utilize the power of ICT to be competitive and provide high-quality education as a consequence of digital transformation, disruptive technological innovations, and accelerated change. To accomplish these ambitions, this paper describes some challenges that higher education encounters, as well as technological resources and methodologies they have used in the current scenario to transform higher education to adopt digital transformation. The paper aims to synthesize considerable insights that can be applied to the digitalization of higher education in the current and near future.