Reviewerly/exHarmony huggingface dataset:
Transfer Learning: A New Promising Techniques

Transfer Learning[1] is a machine learning technique that involves utilizing knowledge learned from one task to improve performance on another related task. This approach has been widely adopted in various fields such as computer vision, natural language processing, and speech recognition. The goal of this paper is to provide an overview of transfer learning and its recent developments. Transfer learning is particularly useful in situations where there is limited labeled data available for the target task. In these cases, the model can leverage knowledge learned from a related task with a larger amount of labeled data. This allows the model to overcome the problem of overfitting and improve performance on the target task.
Inflation uncertainty, macroeconomic instability and the efficiency of financial institutions

Purpose The study examines the effect of macroeconomic risk, inflation uncertainty and instability associated with key macroeconomic indicators on the efficiency of financial institutions among economies in sub-Saharan Africa (SSA). Design/methodology/approach Data for the empirical inquiry were compiled from 35 SSA economies from 1996 to 2019. The empirical estimates were carried out using pooled ordinary least squares (POLS) with Driscoll and Kraay’s (1998) standard errors. Findings Reported empirical estimates show that macroeconomic risk and exchange rate volatility constrain the efficiency of financial institutions. Further results suggest that inflation uncertainty has a significant influence on the efficiency of financial institutions among economies in the subregion. Additionally, reviewed empirical estimates show that institutional quality positively moderates the nexus between inflation uncertainty and financial institution efficiency. At the same time, political instability is found to worsen the adverse effect of macroeconomic risk on the efficiency of financial institutions. Practical implications For policymakers and governments, improved institutional structures are recommended to ensure the operational efficiency of financial institutions, especially during an inflationary period. For decision-makers among financial institutions, the study recommends policies that have the potential to make their institutions less vulnerable to macroeconomic risk and exchange rate fluctuations. Originality/value The approach adopted in this study differs significantly from related studies in that the study examines and reviews interactions and relationships not readily found in the reviewed literature.
Predicting physical fatigue in athletes in rope skipping training using ECG signals

Physical fatigue is a crucial factor that leads to a decrease in performance, especially in athletes. This study proposes effective physiological indicators and methods to predict physical fatigue. We designed a ”training-cognitive task” experiment using specialized electrocardiogram (ECG) acquisition devices (Shimmer3) to collect ECG data during the training process. The participants were required to complete a 2-back cognitive task before and after one-hour of rope skipping training. After the experiment, we analyzed the heart rate variability (HRV) signals of the subjects using the time domain, frequency domain, and nonlinear dynamics. Pearson’s correlation coefficient analysis and paired t-tests were applied to the measured indicators. Subsequently, selected ECG indicators with statistical significance, the particle swarm optimization-support vector regression (PSO-SVR), and extreme learning machine (ELM) were used to build regression models that predicted subjective and objective physical fatigue. Moreover, we have compared the performance of Gaussian process regression (GPR), random forest (RF), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost) based machine learning model for prediction. The results indicated that PSO-SVR had a better performance in predicting subjective physical fatigue, with mean absolute percentage error (MAPE) of 11.53%, and ELM achieved competitive outcomes for objective physical fatigue prediction with MAPE of 19.77%. This study provides a reliable experimental basis for identifying effective physical fatigue.
User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model

Identity recognition is influenced at all educational levels by biometric technology. The invention of facial recognition technology has added new efficiencies to the traditional method of tracking student examination attendance. This study aims to determine whether biometric recognition technologies could be utilized to enhance undergraduate examination attendance systems. The study examined the perceptions of first-year college students regarding the system’s use of face recognition technologies. Based on the proposed framework, experimental results were obtained by developing and deploying unimodal and multimodal face recognition methods. Using a quasi-practical design with sample groups, undergraduate students’ perceptions of traditional and biometric examination attendance were compared. Adopting the Theory for Reasoned Action and the Technology Acceptance Model, a questionnaire was distributed and analyzed to determine perception factors. The findings reveal that perceived ease of use, and trust and security significantly impact perceived usefulness. It was discovered that perceived usefulness significantly affects behavioral intention to use a system. According to the research findings, multimodal biometric recognition receives significantly more positive ratings than unimodal biometric recognition. This study proposes that universities utilize biometric technology, particularly facial recognition, to assess users’ acceptance of the system.
Improving transferable adversarial attack via feature-momentum

Transferable adversarial attackusing adversarial perturbations made on known models to attack unknown modelshas made significant progress in recent years. The feature-level adversarial approach, in particular, is one of the most common solutions and can improve transferability by disrupting intermediate features, regardless of the task-specific loss objectives. Once the intermediate features are disrupted, the subsequent prediction will naturally go wrong. To accomplish this, the existing methods often start an attack by creating a guidance map on features that shows the importance level of each feature element, and then they use an iterative strategy to disrupt the features based on the guidance map. However, the drawback of existing methods is that the guidance map is always fixed in iterations, which can not consistently reflect the importance of feature elements, limiting the performance of the attack consequently. In this paper, we describe a new method called Feature-Momentum Adversarial Attack (FMAA) to enhance transferability. The key idea is that we estimate a guidance map dynamically at each iteration using a momentum-style approach to effectively disturb the features. Extensive experiments demonstrate that our method significantly outperforms other state-of-the-art methods by a large margin on different target models.
Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing

In recent years, data has become one of the most important resources in the digital economy. Unlike traditional resources, the digital nature of data makes it difficult to value and contract. Therefore, establishing an efficient and standard data-transaction market system would be beneficial for lowering cost and improving productivity among the parties in this industry. Although numerous studies have been dedicated to the issue of complying with data regulations and other data-transaction issues such as privacy and pricing, little work has been done to provide a comprehensive review of these studies in the fields of machine learning and data science. To provide a complete and up-to-date understanding of this topic, this review covers the three key issues of data transaction: data rights, data pricing, and privacy computing. By connecting these topics, this paper provides a big picture of a data ecosystem in which data is generated by data subjects such as individuals, research agencies, and governments, while data processors acquire data for innovational or operational purposes, and benefits are allocated according to the data’s respective ownership via an appropriate price. With the long-term goal of making artificial intelligence (AI) beneficial to human society, AI algorithms will then be assessed by data protection regulations (i.e., privacy protection regulations) to help build trustworthy AI systems for daily life.
Talking human face generation: A survey

Talking human face generation aims at synthesizing a natural human face that talks in correspondence to the given text or audio series. Implementing the recently developed Deep Learning(DL) methods such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN)s, Neural Rendering Fields (NeRF) for data generation, and talking human face generation has attracted significant research interest from academia and industry. They have been explored and exploited recently and have been used to address several problems in image processing and computer vision. Notwithstanding notable advancements, implementing them to real-world problems such as talking human face generation remains challenging. The generation of deepfakes created by the abovementioned methods would greatly promote many fascinating applications, including augmented reality, virtual reality, computer games, teleconferencing, virtual try-on, special movie effects, and avatars. This research reviews and discusses DL related methods, including CNN, GANs, NeRF, and their implementation in talking human face generation. We aim to analyze existing approaches regarding their implementation to talking face generation, investigate the related general problems, and highlight the open study issues. We also provide quantitative and qualitative evaluations of the existing research approaches in the related field.
Deep Inception Based Convolutional Neural Network Model for Facial Key-Points Detection

The identification of facial key-points is a difficult challenge in computer vision. Each person’s face has incredibly distinctive facial characteristics. The centres and borders of the eyes, the brows, the nose, and the lips are some of the key elements of the face. Thus, finding the facial key features in a particular face is the major aim of the facial key point detection process. This is a very challenging task. In this work, the initial step of the proposed methodology for identifying facial key-points includes a range of training data augmentations to improve the number of training instances and the generalizability of the proposed model. In the second phase, an Inception-based deep neural network model is proposed that accomplishes this task quicker by reducing the training time. While testing, we combined the images from different data augmentation approaches to enable averaged predictions. We evaluate the model design with and without data augmentation techniques based on their Mean Squared Error (MSE) scores on the test set. The research demonstrates that the MSE of the proposed Inception-based model is very low as compared to the other state-of-the-art methods.
The Internet Of Behaviour(IOB) and Its Significant Impact on Digital Marketing

The Internet of Behaviour is the next phase of the IoT (Internet of Things). It is said to be a technology that focuses on merging behavioural analysis and psychological sciences. The IOB aims at understanding the data gathered from users’ online behaviours from the perspective of behavioural psychology. Its mission is to provide a solution to the problem of understanding data and how to use that information for the development and promotion of new products from the aspect of human thinking. The advancement of behaviour based on vast data analytics is its best feature. As marketing and psychology merge in advertising to impact mass customers’ behaviour and industrial buyer behaviour, the IOB (Internet of Behaviour) is indisputably becoming a potent new advertising instrument for businesses and organisations all over the world. With IOB, organisations and businesses may discover and target specific persons or groups of people who could profit from their products and services. This paper presents the notion of the Internet of Behaviour (IOB) and its significant impact on digital marketing. The paper also explores the Internet of Behaviour applications for improved customer service, the risk of the Internet of Behaviour and how security or data privacy can be improved.
Deep Learning based Methods for Cyberbullying Detection on Social Media

The rise of social media sparked a revolution in the fields of communication and digitalization. These platforms were developed with the goal of bringing together people from all over the world while enabling them to express themselves and absorb the thoughts of others. Additionally, given the variety of origins, beliefs, races, and cultures represented among site users, many of them have a tendency to use derogatory language and bully people. Automated cyberbullying detection is not only challenging, but it is also a pressing requirement, considering how essential social networks have become in people’s life and the serious consequences of cyberbullying, particularly among youth. To address this problem, we are identifying cyberbullying in the twitter dataset based on age, religion, gender, and ethnicity in this work. We have employed machine learning models Naive Bayes, Logistic Regression, SVM and ensemble machine learning models Random Forest and XGBoost while we have leveraged LSTM and GRU deep learning models as the dataset contains approximately 48k tweets. GRU outperformed all the other models with F-1 score 0.92.
Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis

Recent years have experienced phenomenal growth in computer-aided diagnosis systems based on machine learning algorithms for anomaly detection tasks in the medical image domain. However, the performance of these algorithms greatly depends on the quality of labels since the subjectivity of a single annotator might decline the certainty of medical image datasets. In order to alleviate this problem, aggregating labels from multiple radiologists with different levels of expertise has been established. In particular, under the reliance on their own biases and proficiency levels, different qualified experts provide their estimations of the "true" bounding boxes representing the anomaly observations. Learning from these nonstandard labels exerts negative effects on the performance of machine learning networks. In this paper, we propose a simple yet effective approach for the enhancement of neural networks’ efficiency in abnormal detection tasks by estimating the actually hidden labels from multiple ones. A re-weighted loss function is also used to improve the detection capacity of the networks. We conduct an extensive experimental evaluation of our proposed approach on both simulated and real-world medical imaging datasets, MED-MNIST and VinDr-CXR. The experimental results show that our approach is able to capture the reliability of different annotators and outperform relevant baselines that do not consider the disagreements among annotators. Our code is available at https://github.com/huyhieupham/learning-from-multiple-annotators.
An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System

Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the need of manual feature engineering. Researchers use normal and hybrid deep learning schemes for training and comparing the models. This paper proposes an efficient CNN-LSTM model for recognising daily human activities using smartphone sensor data. A contemporary CNN-LSTM model is created using time distributed feature extraction layers as it can efficiently handle hierarchical features and can selects the relevant features easily using LSTM memorization scheme. The proposed CNN-LSTM model is compared with two other models - DNN and LSTM in terms of accuracy, precision, recall, F1- score, training loss and computational times. The proposed model managed to outperform other models optimally in all the evaluation metrics. Using holdout training and testing split, the model managed to achieve an average accuracy of 97.609% and 98.69% with relu activation function and 100 training iteration. On validating the different models, the hybrid models takes less computational time and managed to achieve an computational efficiency of (76.23 ± 140.76)% from other models.
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy

Pre-trained Large Language Models (LLMs) are an integral part of modern AI that have led to breakthrough performances in complex AI tasks. Major AI companies with expensive infrastructures are able to develop and train these large models with billions and millions of parameters from scratch. Third parties, researchers, and practitioners are increasingly adopting these pre-trained models and fine-tuning them on their private data to accomplish their downstream AI tasks. However, it has been shown that an adversary can extract/reconstruct the exact training samples from these LLMs, which can lead to revealing personally identifiable information. The issue has raised deep concerns about the privacy of LLMs. Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i.e., with a cryptographically small success probability). While the theoretical privacy guarantees offered in most extant studies assume learning models from scratch through many training iterations in an asymptotic setting, this assumption does not hold in fine-tuning scenarios in which the number of training iterations is significantly smaller. To address the gap, we present \ewtune, a DP framework for fine-tuning LLMs based on Edgeworth accountant with finite-sample privacy guarantees. Our results across four well-established natural language understanding (NLU) tasks show that while \ewtune~adds privacy guarantees to LLM fine-tuning process, it directly contributes to decreasing the induced noise to up to 5.6\% and improves the state-of-the-art LLMs performance by up to 1.1\% across all NLU tasks. We have open-sourced our implementations for wide adoption and public testing purposes.
Internet of Things Connected Wireless Sensor Networks for Smart Cities

The Smart City is the most complete and covered framework that meets the need of different project facets related to the smart city. It allows the cities to use the urban network and raise their economic power, unique solutions of technology, and build the most efficient systems. Smart City is the advanced developmental product of the smart economy and information technology. It relies upon wireless networking, broadcast networking, internet mesh networking, telecommunication network, and the end-to-end sensor network in which the Internet of Things (IoT) is the core. The IoT serves as the core for integrating the wide variety of sensors in each day's objects and interconnects the sensors through the internet using specific protocols for exchanging the communications and information that lead to location tracking, management, monitoring, and intelligent achievement recognition. It not only supports one city but also interconnects it with the other smart cities. This paper aims to explore the use of IoT-based machine learning approaches that help develop a smart city.
The Impact of Brand Loyalty Determinants on the Tourists’ Choice of Five Stars Hotels in Jordan

This Study aims to investigate the determinants of brand loyalty (Customer satisfaction, Price sensitivity and word of mouth) affecting tourists’ choice of five stars hotels on Jordan. The population of the study consisted of people who frequent select these hotels to stay. The researcher study data were collected using the non-random sampling method, where (400) questionnaires were distributed to people who visited five-star hotels in Amman, Aqaba and the Dead Sea. To achieve the objective of the study, and to test hypotheses, the researcher used statistical program of social Sciences (SPSS) and (AMOS). The study results revealed that there is a statistical significant impact of brand loyalty determinants on tourists’ choice in five stars hotel in Jordan, based on the study results the researchers recommends managers and decision makers should provide a superior service quality also training the staff and every person who would contact the tourists, finally they should undertake promotional and informational marketing activities. Thus, the guest has wonderful experience which will ultimately lead customer to be loyalty to the brand and repeat their visits to the same hotel.
Compression Methods for Transformers in Multidomain Sentiment Analysis

Transformer models like BERT have significantly improved performance on many NLP tasks, e.g., sentiment analysis. However, their large number of parameters makes real-world applications difficult because of computational costs and latency. Many compression methods have been proposed to solve this problem using quantization, weight pruning, and knowledge distillation. In this work, we explore some of these task-specific and task-agnostic methods by comparing their effectiveness and quality on the MultiEmo sentiment analysis dataset. Additionally, we analyze their ability to generalize and capture sentiment features by conducting domain-sentiment experiments. The results show that the compression methods reduce the model size by 8.6 times and the inference time by 6.9 times compared to the original model while maintaining unimpaired quality. Smaller models perform better on tasks with fewer data and retain more remarkable generalization ability after fine-tuning because they are less prone to overfitting. The best trade-off is obtained using the task-agnostic XtremeDistil model.
Neural network model for imprecise regression with interval dependent variables

This paper presents a computationally feasible method to compute rigorous bounds on the interval-generalization of regression analysis to account for epistemic uncertainty in the output variables. The new iterative method uses machine learning algorithms to fit an imprecise regression model to data that consist of intervals rather than point values. The method is based on a single-layer interval neural network which can be trained to produce an interval prediction. It seeks parameters for the optimal model that minimizes the mean squared error between the actual and predicted interval values of the dependent variable using a first-order gradient-based optimization and interval analysis computations to model the measurement imprecision of the data. An additional extension to a multi-layer neural network is also presented. We consider the explanatory variables to be precise point values, but the measured dependent values are characterized by interval bounds without any probabilistic information. The proposed iterative method estimates the lower and upper bounds of the expectation region, which is an envelope of all possible precise regression lines obtained by ordinary regression analysis based on any configuration of real-valued points from the respective y-intervals and their x-values.