Reviewerly/exHarmony huggingface dataset:
Content Authentication and Tampered Localization Using Ring Partition and CSLBP-Based Image Hashing

In the present world, innovation, and creative thinking were pivotal in bringing about the digital revolution. As a result of consistent development in multimedia processing algorithms, altering the contents of digital images has become more accessible. Image hashing has been found to be one of the most suitable approaches for content authentication applications. A ring partition and center-symmetric local binary patterns (CSLBP) approach has been introduced in the work for generating image hash. Our ring-based statistical characteristics remain unchanged when an image is rotated at any angle. As a result, it provides rotation invariant property. CS-LBP considers pairs of symmetrically opposite pixels around the central pixel. This symmetric encoding captures texture information that is invariant to certain rotations. The tampered detection and localization are done by computing the hash correlation between the original and tampered images. The experimental findings demonstrate that the suggested image hashing method can provide desirable robustness and discrimination in all content preserving operations (CPO). In comparison to specific current schemes, the proposed method offers shorter hash length, improved classification performance, and reduced hash generation time. Geometric rectification used in the suggested method can identify tampering even when geometric adjustments occur instantly. The receiver operating characteristics (ROC) curve demonstrates that the suggested model exhibits superior performance compared to other cutting-edge techniques.
Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

Natural Language Processing (NLP) is the most vital technology in currently utilized, specifically caused by the huge and growing count of online texts that requires that understood for its massive value that completely asserted. NLP is create a sense of unstructured data, which are created by social networks and other social data sources, and is supported to organize them into an additional structured model that assists several kinds of tasks and applications. Sentiment analysis (SA), a subfield of NLP contains determining the sentiment expressed or emotional tone from a piece of text. Deep learning (DL) approaches are significantly advanced the field of SA, permitting for more accurate and nuanced classification of sentiments from the text data. In this article, we present an Advanced Sentiment Analysis using a Red-Tailed Hawk Optimizer with Ensemble Learning (ASA-RTHEL) Strategy in NLP. The aim of ASA-RTHEL technique is to exploit the strategies of ensemble learning with a hyperparameter tuning process for SA. The ASA-RTHEL technique mainly follows an ensemble learning-based classification process, which combines prediction from three DL approaches convolutional neural network (CNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The ensemble process results in enhanced SA performance and decreases the risk of depending only on a single model bias or error. To boost the SA performance, the hyperparameter tuning strategy is performed by the use of the RTH algorithm. An extensive set of experiments were carried out for ensuring the superior SA results of ASA-RTHEL technique. The comprehensive comparison study highlighted the enhanced results of the MPONLP-TSA method on the recognition of various kinds of sentiments.
Biometric Recognition in 3D Medical Images: A Survey

This work explores the potential applications of biometric recognition in 3D medical imaging data. We investigate various 3D imaging techniques commonly used in the medical domain, including 3D ultrasound imaging, magnetic resonance imaging (MRI), computer tomography (CT) scans, and 3D near-infrared (NIR) imaging. For each technique, we provide an overview of its working principle and discuss the advantages of integrating biometrics into 3D medical imaging data. Major advantage of using biometrics in this context is motivated by the research that using biometrics could not only increase data security but, more importantly, decrease the mix-up errors in patient’s medical data and thus improving patient safety and patient care. Our analysis uncovers certain weaknesses in current algorithms and limitations in existing research. Possible reasons include insufficient data availability, the under-utilization of deep-learning-based approaches to enhance accuracy and performance, and the absence of standardized benchmarking databases to support research. Our survey frames existing works and lead to practical recommendations and motivates efforts to improve the current state of research. Beyond exploring the utilization of biometrics in 3D medical imaging data, our study touches on further potential interactions between them, such as extracting health information from biometric captures. This work is thus the first work to survey and present an overview on works proposing the use of medical images for biometric recognition.
Improving the Efficiency of Dysarthria Voice Conversion System Based on Data Augmentation

Dysarthria, a speech disorder often caused by neurological damage, compromises the control of vocal muscles in patients, making their speech unclear and communication troublesome. Recently, voice-driven methods have been proposed to improve the speech intelligibility of patients with dysarthria. However, most methods require a significant representation of both the patient's and target speaker's corpus, which is problematic. This study aims to propose a data augmentation-based voice conversion (VC) system to reduce the recording burden on the speaker. We propose dysarthria voice conversion 3.1 (DVC 3.1) based on a data augmentation approach, including text-to-speech and StarGAN-VC architecture, to synthesize a large target and patient-like corpus to lower the burden of recording. An objective evaluation metric of the Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC 3.1 under free-talk conditions. The DVC system without data augmentation (DVC 3.0) was used for comparison. Subjective and objective evaluation based on the experimental results indicated that the proposed DVC 3.1 system enhanced the Google ASR of two dysarthria patients by approximately [62.4%, 43.3%] and [55.9%, 57.3%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. Further, the proposed DVC 3.1 increased the speech intelligibility of two dysarthria patients by approximately [54.2%, 22.3%] and [63.4%, 70.1%] compared to unprocessed dysarthria speech and the DVC 3.0 system, respectively. The proposed DVC 3.1 system offers significant potential to improve the speech intelligibility performance of patients with dysarthria and enhance verbal communication quality.
General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds With One Stone

Facial age estimation has received a lot of attention for its diverse application scenarios. Most existing studies treat each sample equally and aim to reduce the average estimation error for the entire dataset, which can be summarized as General Age Estimation. However, due to the long-tailed distribution prevalent in the dataset, treating all samples equally will inevitably bias the model toward the head classes (usually the adult with a majority of samples). Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation. However, Long-tailed Age Estimation usually faces a performance trade-off, i.e., achieving improvement in tail classes by sacrificing the head classes. In this paper, our goal is to design a unified framework to perform well on both tasks, killing two birds with one stone. To this end, we propose a simple, effective, and flexible training paradigm named GLAE, which is two-fold. First, we propose Feature Rearrangement (FR) and Pixel-level Auxiliary learning (PA) for better feature utilization to improve the overall age estimation performance. Second, we propose Adaptive Routing (AR) for selecting the appropriate classifier to improve performance in the tail classes while maintaining the head classes. Moreover, we introduce a new metric, named Class-wise Mean Absolute Error (CMAE), to equally evaluate the performance of all classes. Our GLAE provides a surprising improvement on Morph II, reaching the lowest MAE and CMAE of 1.14 and 1.27 years, respectively. Compared to the previous best method, MAE dropped by up to 34%, which is an unprecedented improvement, and for the first time, MAE is close to 1 year old. Extensive experiments on other age benchmark datasets, including CACD, MIVIA, and Chalearn LAP 2015, also indicate that GLAE outperforms the state-of-the-art approaches significantly.
NCSiam: Reliable Matching via Neighborhood Consensus for Siamese-Based Object Tracking

An essential need for accurate visual object tracking is to capture better correlations between the tracking target and the search region. However, the dominant Siamese-based trackers are limited to producing dense similarity maps at once via a cross-correlations operation, ignoring to remedy the contamination caused by erroneous or ambiguous matches. In this paper, we propose a novel tracker, termed neighborhood consensus constraint-based siamese tracker (NCSiam), which takes the idea of neighborhood consensus constraint to refine the produced correlation maps. The intuition behind our approach is that we can support the nearby erroneous or ambiguous matches by analyzing a larger context of the scene that contains a unique match. Specifically, we devise a 4D convolution-based multi-level similarity refinement (MLSR) strategy. Taking the primary similarity maps obtained from a cross-correlation as input, MLSR acquires reliable matches by analyzing neighborhood consensus patterns in 4D space, thus enhancing the discriminability between the tracking target and the distractors. Besides, traditional Siamese-based trackers directly perform classification and regression on similarity response maps which discard appearance or semantic information. Therefore, an appearance affinity decoder (AAD) is developed to take full advantage of the semantic information of the search region. To further improve performance, we design a task-specific disentanglement (TSD) module to decouple the learned representations into classification-specific and regression-specific embeddings. Extensive experiments are conducted on six challenging benchmarks, including GOT-10k, TrackingNet, LaSOT, UAV123, OTB2015, and VOT2020. The results demonstrate the effectiveness of our method. The code will be available at https://github.com/laybebe/NCSiam.
&lt;i&gt;Philosophy of Data Science for Corpus Linguistics:&lt;/i&gt;

This paper demonstrates pragmatic constraints involved in corpus linguistic studies. Both philosophers and linguists have long recognised the difficulties in characterising meaning. Despite the widely acknowledged difficulty, quantitative semantic analysis has been attempted. In this paper, how corpus linguists make pragmatic decisions is explained by introducing the degrees of specificity (i.e., is-a relation, or class inheritance) and granularity (i.e., part-whole relation, or mereological relation) in identifying and describing linguistically expressed concepts. We show that pragmatic constraints are ubiquitous in many aspects of quantitative semantic analyses.
Smart Contracts as Private Governance Tools

Smart contracts are digital agreements partially or fully executed through blockchain-based code. This chapter examines smart contracts as tools for private governance. Smart contracts spur innovation in private governance because they are digital native, machine-agnostic, global, censorship-resistant and composable. We examine these characteristics of smart contracts before outlining their innovative applications in decentralized organizations, market infrastructure and dispute resolution. Smart contracts have the potential to lower the costs of private governance, expand the design space for private institutional experimentation, and enable greater institutional choice and competition. Ultimately this process expands the order of market-based coordination.
Local-global aware-transformer for occluded person re-identification

Recently, security protection is import in many scenarios. Occluded person re-identification (Re-ID) involves identifying obscured pedestrians from images captured by multiple cameras, even when the images are partially or fully occluded. Many state-of-the-art models for occluded Re-ID utilize auxiliary modules such as pose estimation, feature pyramid, and graph matching to address occlusion challenges. However, this approach results in complex models that struggle to generalize to diverse occlusions and may not effectively handle non-occluded pedestrians. Furthermore, real-world Re-ID applications frequently involve both occluded and non-occluded pedestrians, making it difficult to develop versatile models. To tackle these issues, we introduce a novel Re-ID model that learns discriminative features on both local and global scales for occluded pedestrian identification. Our proposed model, the Local-aware Transformer (LAT) for occluded person Re-ID, comprises three modules: a Discriminative Feature Extraction Module (DFEM), a Local Feature Extraction Module (LFEM), and a Global Feature Extraction Module (GFEM). Our experimental results on three occluded and two general Re-ID benchmarks demonstrate that our model surpasses existing state-of-the-art methods and achieves exceptional performance in both occluded and non-occluded Re-ID tasks.
Colour of Noise

This is a creative response to the topic of the special issue, “The Aesthetics of Tinnitus”, blending reflections on perception and imagination, race and sociality, sci-fi and sound studies via experimental poetics.
A Solution to Collinear Problem in Lyapunov-Based Control Scheme

Robots are widely used to carry out various tasks in different industries worldwide. The movement of a robot is necessary for any task accomplishment. While moving, a robot must prevent collisions with obstacles to reach its destination successfully. The motion control algorithm governs a robot’s movement. One such method is Lyapunov-based control scheme (LbCS). LbCS is a popular method for controlling a robot’s motion, but the technique suffers from a problem known as collinear. This problem occurs when a robot, an obstacle, and a target are in a linear position, which gets the method trapped into local minima. This paper tackles this problem using a heuristic-based method, ant colony optimization (ACO). The ACO will be activated when the LbCS gets trapped in local minima. This paper presents an algorithm, ACO-LbCS, that solves the collinear problem of LbCS. This hybrid algorithm has been strategically formulated using ACO and LbCS. The algorithm has been applied to multiple obstacle’s environment. The results show that the problem of local minima has been solved by the proposed algorithm.
A Real-Time Fall Detection System Using Sensor Fusion

There can be serious and harmful effects on people following a fall event due to its severity. This paper presents a real-time fall detection using sensor fusion to improve the overall accuracy of the system when subjected to continuous operation. The system combines data from accelerometer and an ultrasonic sensor to detect falls in real time. The developed mobile application accurately identifies fall scenarios and sends SMS notifications to emergency contacts. The proposed system's ultrasonic sensor module has wireless communication capabilities, while the accelerometer readings are acquired from the smartphone. Appropriate feature, threshold values, and program flow have been chosen, such that fall detection is accurate, and the system is operational even if one of the sensor malfunctions. The proposed system has been validated experimentally and the accuracy, sensitivity, and specificity are 87.5%, 89.47%, and 94.74%, respectively.
Speech Recognition and Its Application to Robotic Arms

This paper focuses on the development of a lightweight and easy-to-use robotic arm that can be controlled using speech recognition technology. The arm can be controlled using live voice instructions, and the system has been designed to work with both English and Kannada voice commands. The image model used for speech recognition predicts audio labels based on spectrograms, and the system transfers the prediction to the microcontroller using a serial port connection. The microcontroller then moves the arm based on the instructions received. The system is designed to be accessible to people of all backgrounds, regardless of their educational level or other factors. The project has the potential to make a significant impact by enabling people to use robotics to perform tasks that would otherwise be difficult or impossible. The work describes the use of four predefined English and Kannada commands to assess the success rate of speech recognition using machine learning models, specifically the ANN and the CNN. The accuracy of the models was evaluated and compared for both the English and Kannada datasets.
Drone Watch: A Novel Dataset for Violent Action Recognition from Aerial Videos

In recent developments, a lot has been done for computer vision applied to human action recognition and violence detection. Although various datasets are available for action and violence recognition, there is a clear lack of datasets that include non-violent and violent activities simultaneously from an aerial view. A new aerial video dataset for concurrent human action recognition, including violence detection, is presented in this study. It consists of 60 min of fully annotated data with two action classes, namely violent and normal (non-violent). The current dataset addresses various factors that are not considered in the existing datasets, like changes in the altitude of the drone, changes in the angle at which the video is being captured, video captured during motion, changes in frame rates, videos from different cameras with different configurations, multiple labels for every subject, and labels for violent activities. The resulting dataset is a multifaceted representation of the real-world scenarios, which addresses various shortfalls in the existing datasets. The current dataset will push forward computer vision applications for action recognition, particularly automated violence detection in real-time video streams from an aerial view. Furthermore, the curated dataset is validated for violence detection using machine and deep learning algorithms, namely Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bi-Directional LSTM (Bi-LSTM) and Adaptive Boosting (AdaBoost).
A Comparative Analysis of Real-Time Sign Language Recognition Methods for Training Surgical Robots

This project proposes a real-time robot that can interact with humans based on the gestures fed to it as input. The proposed proposal aims to develop a constructive design of a robot that has computer vision and is trained to read human gestures. There is a need for intelligent robots in the healthcare industry. The impact of this project will be on sophisticated healthcare systems, especially the surgical system. The implementation is achieved by training the robot using deep CNN and making the robot perform certain functions like moving the arm upwards and downwards as well as opening and closing the robot grippers. It is also important to mention that a comparative analysis has been made with the existing system and advanced technology called MediaPipe framework for the acquisition of input signals. The comparative analysis will give us a clear picture of the usage of different types of classifiers for training robotic models. With the impact of this project, it would be easy for the physicians to pick and place the medical equipment in a correct manner and provide assistance to the surgeon during surgery. This device can also be very useful in robot-assisted surgeries as it can be further developed to perform actions like drilling and making incisions.
Does economic freedom foster education in Sub‐Saharan Africa?

Abstract This study investigates the effect of economic freedom on education in Sub‐Saharan Africa. We used a panel of 41 countries covering the period 2000–2019 and the two‐stage least squares (IV‐2SLS) estimates. We find that economic freedom fosters secondary education. Furthermore, we find that the five components of economic freedom have varying effects on education. Indeed, size of government increases secondary education. Legal system increases secondary and tertiary education, while freedom to trade fosters primary and secondary education. In contrast, sound money has a negative impact on primary and tertiary education. These results are robust when we consider education by gender.
Detailed Performance Study of Data Balancing Techniques for Skew Dataset Classification

Many real-world classification problems involve changing events where one class has comparatively fewer samples called minority class which is more important to detect. Consequently, the dataset is often unbalanced and shows significantly skewed data. Since the majority class dominates the learning process and tends to sketch all predictions, the conventional classification model leads to biased results where it may easily display excellent performance in the dominant class and bad performance in the minority class. Additionally, the traditional accuracy score is inaccurate since it assigns equal weight to actual positives and actual negatives. This study is aimed to present an empirical analysis of the data imbalance effect on classification algorithms. Six popular and effective data balancing techniques are applied to eight benchmark skewed datasets from the KEEL and Kaggle repositories to analyze and compare the performance.
Performance Analysis of Various Machine Learning Classifiers on Diverse Datasets

Machine learning is used to analyze data from different perspectives, summarize it into useful information, and use that information to predict the likelihood of future events. Classification is one of the main problems in the field of machine learning. The aim here is to study various classification algorithms in machine learning applied on different kinds of datasets. The algorithms used for this analysis are J48, Naive Bayes, multilayer perceptron, and ZeroR. The performance is analyzed using various metrics such as true positive rate, false positive rate, and error rates such as root mean squared error and mean absolute error. The performance of J48 algorithm is better than other algorithms for large datasets. The proposed algorithm still increases the performance in terms of error rates for large datasets. The contemplated algorithm is eventuated by mutating the splitting paradigm in the tree-based algorithms. The experimental analysis demonstrates that the proposed algorithm has reduced error rate as compared with the traditional J48 algorithm.