Reviewerly/exHarmony huggingface dataset:
A Design of An Autoencoder-Based Audio Compression and Decompression System

Multimedia compression is a fundamental and significant research topic in the industrial field in the past several decades attempting to improve compression techniques. It is always a trade-off between size and quality where the growth rate of image, audio and video data is far beyond the improvement of the compression ratios achieved so far. Here, we are aiming to explore the potential of neural networks to achieve audio compression, making use of multilayer neural networks providing a more generic and efficient solution. In this paper, we present a lossy compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to replace the conventional transforms. Experimental results demonstrate that our method outperforms traditional coding algorithms, by achieving better compression ratios over both the related work. We refer to and traditional methods. The compression ratio for audio is 20x faster than related work. Moreover, the distortion is negligible.
Transformer based Online Continuous Multi-Target Tracking with State Regression

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking.
Multi-Objective Deep Reinforcement Learning with Priority-based Socially Aware Mobile Robot Navigation Frameworks

Socially aware robot navigation is a multi-objective decision-making problem. Nonetheless, the attempt to address this issue using single-objective reinforcement learning raises substantial challenges when it comes to extending navigation policies to accommodate the intricate demands of human social interactions. Our work presents a versatile framework for expanding existing robot navigation capabilities into the realm of multi-objective decision-making. This framework empowers users to easily specify their preferences without the burden of extensive engineering efforts. Experimental findings clearly demonstrate the effectiveness of our framework in guiding an existing reinforcement learning system toward the optimization of navigation policies in alignment with user-defined preferences.
T-S Fuzzy Control with Descriptor Model and High Gain Observer for a Serial Robot Manipulator

This article presents a control method for stabilizing a 2-DoF robot manipulator by combining Takagi-Sugeno (T-S) fuzzy control with a descriptor model and a high gain observer. The T-S fuzzy control framework is utilized to handle the system’s nonlinearities, employing a set of linguistic rules and fuzzy membership functions to capture complex relationships and uncertainties. Additionally, a high gain observer is integrated into the control system to estimate the robot’s arm velocities accurately. The control approach incorporates Linear Matrix Inequality (LMI) and Parallel Distributed Compensation (PDC) techniques to ensure stability and performance guarantees. The proposed control system’s effectiveness is demonstrated through simulation results in achieving stable control of the 2-DoF robot. The findings highlight the potential of the combined T- S fuzzy control and high gain observer for enhancing the system control performance, particularly in complex and uncertain environments.
Transformer Architecture Based Multi-frame TBD Algorithms for Maneuvering Targets

The multi-frame track-before-detect (MF-TBD) algorithm can effectively improve the tracking performance of the target in low signal-to-noise ratio (SNR) scenarios by considering all reasonable paths. However, when the target undergoes maneuvering motion, there will be a mismatch between the actual model and the assumed model. It leads to a sharp decline in algorithm performance. In this paper, a model-free trajectory sequence prediction network based on transformer architecture is studied for maneuvering targets. The network is trained using trajectory sequences from multiple types of target models to estimate the state of the target in different maneuvering characteristics. The trajectory sequence prediction network is integrated into MFTBD, replacing the target model, and the specific process of the improved MF-TBD algorithm is provided. The simulation results demonstrate the superior performance and flexible adaptability of the proposed algorithm.
Performance Evaluation of CNN-based Encoders for Image Captioning

Image captioning automatically generates syntactically and semantically correct descriptions for images. To construct an accurate and efficient system, the feature extraction step from the image plays a crucial role. This step involves identifying the main objects, their attributes, and possible relationships between objects in the image. Convolutional neural network (CNN)-based technologies have shown advantages in these tasks. Drawing inspiration from encoder-decoder architectures, this research proposes several modifications and updates to the encoder to extract the most important and concise features from the image. The decoder utilizes an attention module and Long short term memory (LSTM) models to generate accurate captions. The experiments are evaluated on two datasets as Flickr8k and Flickr30k with various pretrained models such as ResNet101, Swin-Transformer, ConvNeXt, EfficientNet, and ResNeXt. The results are assessed using BLEU-1, BLEU-2, BLEU-3, and BLEU-4 metrics, demonstrating the potential of this approach.
Emerging Technologies for Edge Intelligent Computing Systems

Future next generation networks are expected to outline a wide class of ambitious challenges, mainly concerning the handling and fulfillment of requirements imposed by novel disruptive human-oriented services and applications, for example, the interactive/immersive gaming and the ultimate virtual or augmented reality, involving five senses solicitation. The more and more need of strict requirements in terms of intelligence capabilities, communication latency, and real-time response has witnessed the birth of the new Edge Intelligence (EI) era referring to a set of enabling technologies allowing computation to be performed at the edge of the network, close to the end users, according to the Edge Computing approach, in conjunction with Machine Learning (ML) capabilities. Nowadays, the novel EI paradigm is promoting the development of overarching cross-domains architectural ecosystems for the cost-effective integration of ML into new generation networks, and to contribute to the realization of hyperflexible architectures bringing human-like intelligence into every aspect of the novel EI systems with the aim at properly supporting massive data gathering and processing. Nevertheless, in this context, with the aim at empowering Edge Computing systems with ML modules, we have to take into account that most existing ML algorithms require a large number of data for learning and model training. Meanwhile, clients datasets are typically non-independent identically distributed, and users are often hesitant in sending personal raw data to a third-parties server, representing critical aspects for the practical realization of future intelligent environments. In this picture, the integration of Federated Learning (FL) in several applications scenarios (e.g., anomaly detection, recommendation system, next-word prediction, etc.) is becoming of paramount importance, since it favors users’ privacy preservation enabling a decentralized collaborative training process of a shared global model by using locally collected datasets. The FL approach acts as an iterative data exchange process between end-devices, named clients, and the central server unit, which updates the global model and merges the data processed by the clients side. In this way, personal users data sharing can be exploited, by keeping sensitive information protected.
Edge Intelligent Computing in Aqua Environments

Nowadays, an ambitious target of the next generation networks is to develop edge intelligence ecosystem able to efficiently operate in heterogeneous domains. Toward this end, the underwater environment requires a special attention, since they are recognized as the most challenging domain. Within this context, this chapter aims at illustrating a self-intelligent ground–aqua integrated system where the emerging semantic communication paradigm is envisaged to counteract the hostile behavior of underwater channels. In particular, we discuss in this chapter the use of deep convolution neural networks-based encoder–decoder architecture for this purpose. Numerical results are included here to show the better behavior of the proposed system in comparison with the conventional alternative that does not provide the use of the semantic communications approach. Finally, a specific performance evaluation analysis is devoted to the analysis of the convergence behavior of the proposed Federated Learning (FL) procedure in reference to the cross ground–aqua system considered to highlight its advantages with respect to a classical implementation.
Implementation of Leadership Strategy to Realize the Transformation Digital towards Smart Military University

The digitalization process towards a smart university requires adequate leadership support. The research objective is to analyze the implementation of leadership strategy to realize Digital transformation towards a smart military university (SmU). The mix method exploratory sequential design approach was chosen based on the uniqueness of the leadership style which is rich with a variety of approaches due to situational demands and cultural development. Qualitative data collection was carried out by in-depth interviews. Research participants consist of strategic decision makers, and academic staff who are involved in the digital transformation process. The collected data was analyzed using content analysis techniques to identify the main themes in relation to the smart military university. Quantitative analysis based on the results of path analysis to support qualitative findings involving 128 random respondents. The results of this study indicate that the implementation of an effective leadership strategy has an important role in realizing digital transformation towards a Smart Military University. Various situational challenges are key factors in the implementation of various leaderships to optimize their effectiveness. It can be concluded that the military leadership approach is combined with transformational to inspire and motivate change. A complexity leadership approach that focuses on recognizing that digital transformation processes tend to be unpredictable and complex, helping institutions and individuals to adapt, more flexibility, encouraging collaboration, and systemic thinking skills. The practical implication of this research is the importance of developing collaborative and transformational leadership among military leaders to face challenges and opportunities in the digital era.
A privacy preserving scheme for encrypted medical images using RDH

Nowadays, in the e-healthcare environment, the transmission of medical issues and information via remote connection is very common. Since medical data or images contain sensitive information along with a patient's details, they are also more susceptible to attacks. Thus, it is very necessary to ensure the security of hidden data as well as the transmitted media in which secret data is embedded. Reversible data hiding contributes a noteworthy role in protecting the secrecy of medical data. This paper with an aim to ensure the safe and secure transmission of medical images, proposes a simple technique to preserve the privacy of medical data with the use of RDH along with encryption. In this paper, the space for data hiding is created before encrypting the medical image, such that highly correlated pixels can be utilised, leading to notable embedding capacity. The efficacy of the proposed work can be seen in the experimental results.
Age and Gender Identification through Advanced Deep Learning Techniques

The ability to automatically detect the gender and age of an individual based on their facial features or other attributes is an important task in computer vision and deep learning. The precision and dependability of gender and age detection models have significantly increased with the use of deep learning algorithms and massive datasets. The lack of diversity in the training data, which can result in prejudice and subpar performance on members of underrepresented groups, is only one of several issues that still need to be resolved. Deep learning for gender and age detection also poses ethical issues including the possibility of abuse and prejudice. The goal of this work is to overcome these difficulties and create models that are very accurate at identifying a person's gender and age. The study's objectives include incorporating ethical issues into the creation and use of the models as well as enhancing the models' effectiveness on members of underrepresented groups. The model will also be optimized for real-time uses in this study, including security and marketing.
Toward Hate Speech Analysis: Sentiment Analysis for Food Brands Review on Twitter

Social media has been a real-world sensor to observe the pulse of society. Although it provides unique communication opportunities, it also brings along vital challenges. One of them is hate speech, which attacks a single individual or targeted groups. Previous researchers claim that among social networks, the Twitter platform is mostly used to spread hate speech. However, data on a larger scale makes it hard to capture and understand the nature of hate speech on Twitter within specific food brands. In this study, sentiment analysis techniques were used to filter hate speech on Twitter and on three popular food brands in Malaysia. This study was conducted in five phases, namely raw data collection, pre-processing, sentiment analysis, visualization, and performance evaluation. This corpus consists of 28,898 data samples based on user tweet searches. A Twitter API was created and SQLite was used to store all the sample data. VADER sentiment analysis is used to classify tweet sentiment into positive, negative, and neutral. In the visualization phase, these three food brands are visualized using a histogram to gain sentiment analysis insights. Then, three machine learning methods were implemented to predict the best model for sentiment analysis. The Decision Tree classifier outperforms the average accuracy in Support Vector Machine and Logistic Regression with 99.99% for the training data set. This study provides insights to assist humans in making decisions. With the growth of opinions expressed in multimedia on social media, such as spoken feedback on Twitter, sentiment analysis has the potential to become a more news aggregation and low-cost endeavour.
Personalized dialogue systems: issues, methods and researches

Personalized dialogue systems have gained significant attention in the field of natural language processing (NLP) in recent years. Traditional dialogue systems often use a uniform answer mode and fail to meet the diverse needs of different users. On the other hand, personalized dialogue systems utilize the user&apos;s personal information and historical dialogue records to understand the user&apos;s preferences and characteristics. This is achieved by creating a user model or dialogue record model, which enables the generation of personalized responses that align with the user&apos;s expectations. This article provides a comprehensive review of the latest research in personalized dialogue systems. By conducting a thorough literature survey, we present an overview of the foundational aspects of open-domain dialogue systems, as well as the fundamental concepts and existing methods employed in personalized dialogue systems. We further categorize and compare various personalization methods based on user models and historical records to cater to different personalization needs. Additionally, we discuss the available datasets and evaluation metrics for personalized dialogue systems, and explore current research trends and future directions. This article serves as a valuable reference for researchers seeking a comprehensive understanding of personalized dialogue systems, offering guidance and inspiration for further research and applications.
Comparison analysis of Jetpack Compose and Flutter in Android-based application development using Technical Domain

Currently, the development of Android applications generally used native-based programming languages, such as Java or Kotlin. However, other programming languages have frameworks that can be applied across platforms, such as Dart. Unlike Dart which has a Flutter framework to develop Android applications, Kotlin requires integration with XML, making it complex enough to create just one view. That’s why Jetpack Compose was created. Despite belonging to the newly released technology, Jetpack Compose has been widely used by many large companies such as Twitter to Pinterest, which means that in terms of quality, it is already quite impeccable. Therefore, it is necessary to compare the two frameworks, which use technical domains (comparative domains) as a tool that helps in comparison, where the domains used are performance and testability. In the performance domain, the comparable parameters are application file size, CPU consumption and memory consumption. As for the domain testability, the comparison is the implementation of unit testing. In this study, the case studies used are the calculator application and the film list application. After conducting research, the results showed that there were differences in the results of both types of applications. In the performance domain, Flutter is superior in CPU consumption parameters, both in calculator and film list applications. While Jetpack Compose is better in memory consumption parameters, both in calculator applications and film listing applications. Then in application size parameters, Jetpack Compose is superior to the film listing app, while Flutter is superior to the calculator app. While in the domain of testability, Jetpack Compose is superior in the duration of the test unit on the calculator application although based on the higher deviation standard but the maximum duration is not more than the length of the testing unit owned by Flutter and the difference in duration between each test is not too wide. As far as Flutter is concerned, it is superior in the duration of the test unit on the film list application even though it is based on a higher deviation standard because the difference in duration between each test is quite wide. Based on the analysis, the difference in results in both types of applications can be due to several factors, such as the used rendering machine and compilation technology. But since both have their respective advantages and disadvantages, it is impossible to say that one is superior to the other. The choice between Jetpack Compose and Flutter depends on the needs and preferences of the developer. If developers are more familiar with the Kotlin language and want to use new technologies, then Jetpack Compose might be a better choice. However, if developers want to focus on performance and development speed as well as cost-effectiveness, then Flutter could be a better choice for development platform.
Knowledge-based economic development of Iran: Mitigating sanctions and enhancing national competitiveness

The knowledge economy is an economic system in which the production of goods and services is based principally on knowledge-intensive activities that contribute to advancement in technical and scientific innovation. A knowledge-based economy is a kind of economy without limitations. This study studies the prospects of Knowledge Economy in Iran. In recent years, the scope of innovative activity and the necessity of a knowledge economy in Iran has grown. However, this study shows that knowledge development alone is not efficient: it is useful only when knowledge is being generated and applied in a real business environment. Iran would probably benefit as well in the case the expertise of national academia and business community is used more intensively; otherwise, it would grow vulnerable if the foreign innovative technologies were only used. Under the current circumstances, Iran, dealing with ever-expanding sanctions, should be seeking opportunities to commercialize its domestic researches and developments.
Breast cancer diagnosis based on detecting lymph node metastases using deep learning

Introduction: Automated detection of metastatic breast cancer from whole slide images of lymph nodes utilizing a deep convolutional neural network was proposed in this study. Methods: The dataset is taken from the PatchCamelyon subset, which contains 220,025 images divided into training, validation, and testing sets at a ratio of 60:20:20. The pretrained ResNet50 model was utilized, and transfer learning was subsequently applied to adjust the weights of the model. To elevate the model performance, the evaluation metrics were assessed by the accuracy score, confusion matrix, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. Results: As a result, the proposed algorithm obtained high performance, with scores over 95% in all the evaluation methods, especially the AUC score, which achieved 0.989. Moreover, the model is validated in a testing set with the test-time augmentation (TTA) technique to enhance prediction quality and reduce generalization error. Conclusion: Overall, the proposed model achieves high accuracy when applying transfer learning. The results prove that the trained Resnet50 model can extract useful information from small cells in histopathologic images for breast cancer detection.
Performance analysis of various machine learning models for membership inference attack

In order to function correctly during the training phase, many ML models require enormous amounts of labelled data. There is a possibility that the data will contain private information, which must be protected regarding privacy. Membership inference attacks (MIA) are attacks that try to identify if a target data point was utilised for training a particular ML method. These attacks have the potential to compromise users' privacy and security. The degree to which an algorithm for ML divulges user membership information varies from implementation to implementation. Hence, a performance analysis was performed based on different ML algorithms under MIA inference attacks. This study proposed for comparing different ML approaches against MIAs and analyses which ML algorithm is better performing to such privacy attacks. Based on the performance analysis observation, the GAN and DNN models are considered as the best ML models to defend against MIA attacks with better performances.