Reviewerly/exHarmony huggingface dataset:
Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.
From Cracked Accounts to Fake IDs: User Profiling on German Telegram Black Market Channels

Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
Spatial Domain Method for Image Analysis: A Grey-Level Computation Approach

This paper discusses image enhancement, which involves adjusting captured images to improve their visual quality and suitability for display or analysis using techniques like filtering, histogram equalization, and contrast adjustment. The spatial domain method is used for manipulating image grey levels, and the study analyses the transformed images using BRIQUE, NIQE, and PIQE quality metric descriptors. The study shows that the type of image used for processing depends on the application, and binary images have the highest PIQE score. Power law transformation or Gamma-corrected images can reduce or enhance contrast, depending on the gamma’s value. The transformed images have different scores based on the quality metric used. The study also finds that the sum of the global threshold values of an input image and its inverse is 1, and the scores for binary images using both thresholds are similar. Finally, the study shows that BRISQUE and PIQE scores are higher for gamma and more significant than 1; NIQE scores are similar for both gamma values.
Deep Learning Based Speech Synthesis with Emotion Overlay

This paper proposes a Text-to-Speech System that synthesizes emotional speech. We have identified the lack of style diversity within audios produced for the same emotion, and have thus, come up with techniques of combining emotion embeddings with style embeddings in a novel weighted-controlled manner to produce speech with style varying based on target speakers and emotion depending on the emotion-category specified. We also provide different model variations which suggest different methods of injecting emotion embeddings into the TTS and various tactics of combining them with style embeddings. Tests conducted on our model variation which overlays emotion embeddings on the encoder outputs during inference and combines them with style embeddings in a 3:7 weight-ratio as per our novel approach gives a Mean Opinion Score of 3.612 which shows the more-than-satisfactory performance of our models in synthesizing style varying emotional speech.
A Novel Method for Near-Duplicate Image Detection Using Global Features

The rapid growth of digital multimedia content has led to an increase in near-duplicate images. Near-duplicate image detection is a critical task in the field of multimedia forensics, which aims to detect and identify illegally distributed copies of an original image. Gaussian Hermite Moments (GHM) have been proven to be an effective global feature for image representation and analysis in various computer vision tasks, including image forgery detection. In this paper, we propose a novel near-duplicate image detection method that utilizes GHM as the global feature descriptor. We conducted experiments on the CoMoFoD image dataset. The experimental results demonstrate that the proposed method outperforms existing methods for near-duplicate image detection under various post-processing operations and geometric transformations, particularly scaling and rotation. Moreover, the proposed method is significantly faster than existing methods for near-duplicate image detection and it can potentially make the proposed method more practical for real-world applications.
Machine Learning-Based Temperature Monitoring and Prediction

Critical monitoring of ambient temperature at all times is necessary for all areas of pharmaceutical storage inventory where a sudden temperature change can damage the medicine efficiency as sensitive medicines can only be stable when deviations from a required condition are minimal; otherwise, unexpected temperature changes might alter the physical-chemical and pharmacological properties of the medication, and potentially endangering the patient’s health. The temperature of a storage site is determined and data is produced from an LM35 temperature sensor, an analogue linear temperature sensor which has been interfaced to the Wi-Fi ESP8266 microcontroller. The generated data from the system is pushed to the cloud and can be fetched from any remote place where the internet is available, and by performing data analysis, the prediction of the temperature for the next instance of time is obtained. In this paper, the machine learning algorithms used for temperature prediction are linear regression and polynomial regression. To find the correct degree so it does not under-fit and over-fit with points and predict the temperature for the next instance of time.
Optimal Perfect Phylogeny Using ILP and Continuous Approximations

The concept of perfect phylogeny is observed to be non-universal, however, in some studies, it is shown that it provides great insights from a biological standpoint. Most natural phylogenies are imperfect but this is debated by the presence of noise in data acting as a disguise, the solution to uncover the underlying perfect phylogeny in the simplest approach is known as Minimum Character Removal (MCR) problem. Another variant of the solution achieved by a change in perspective and computational methodology is known as Maximal Character Compatibility (MCC) problem. Both MCR and MCC problems are solved optimally with reasonable efficiency by using Integer Linear Programming (ILP) technique. A central part of MCC solution involves solving the Max-clique problem of the generated representation allowing numerous clique-solving algorithms to generate various solutions. The comparison between the solution’s optimality and the run-time of these methods substantiates the goal of the different algorithms being applied. The above methods are formulated and implemented using modern programming languages like Python and Matlab. This exploration introduces the problem of perfect phylogeny, its ILP formulations, and its solution along with comparisons between different methods in consideration.
Development of a Stroke Detection System for Racket Sports

Racket sports, such as tennis, badminton and squash, require accurate technique and detailed performance analysis to improve player performance. However, the lack of accurate and portable tools for monitoring and analysing movement in these sports presents a problem for players and coaches. To address this problem, a new wearable device has been developed that is worn on the wrist of the racket sports player and is able to detect and record every type of stroke the player makes. This device allows the analysis and improvement of the player’s technique, strategy and performance in the game.
Depth-Gyro Sensor-Based Extended Face Orientation Estimation Using Deep Learning

Conventional face direction estimation techniques detect the characteristic parts of the face, such as the nose, eyes, and mouth, and estimate the face orientation based on the movements of these features. However, these methods cannot accurately estimate the face direction when the characteristic parts of the face are hidden; for example, when the face is turned sideways or a mask is worn. Face detection using point cloud data has been investigated as a solution to these issues. Previous studies applied five classes of face direction estimation for the head using 3D point cloud data. However, considering the practical use of driver assistance systems that verify the driver’s status, these five classes are not sufficient for accurately detecting the face direction, and a more precise horizontal wide-range angle detection approach is necessary. In this study, we acquired 3D point cloud data in k (where k > 5) classes while accurately measuring the horizontal angle of the face during the acquisition of the training data using gyroscopic sensors. The training data captured by this depth-gyro sensor integration generates accurate depth data for each direction. As a result, a low number of point cloud data samples for each face direction were sufficient for generating the directional classification model. Therefore, this depth-gyro sensor integrated data capturing significantly reduces the amount of required training data. Furthermore, we applied a weight reduction process for the point cloud data to reduce the training time and performed deep learning to estimate the face direction. The proposed method achieved high performance in face direction detection using deep learning, even with a comparatively small dataset.
An Adaptive Conversion Speed Q-Learning Algorithm for Search and Rescue UAV Path Planning in Unknown Environments

With the wide application of unmanned aerial vehicles (UAVs), performing search and rescue missions autonomously in unknown environment has become an increasingly concerning issue. In this paper, we propose an adaptive conversion speed Q-Learning algorithm (ACSQL). Performing UAV missions autonomously is divided into two stages: rescue mission search stage and optimal path search stage. In the first stage, a UAV can find task points as soon as possible, and the efficiency of exploration is increased by adaptively adjusting the speed of the UAV. In the second stage, to get a secure and short path, we propose a subdomain search algorithm. Based on the above two stages, we improve state space and action space in reinforcement learning, and design a composite reward function, finally obtain the path of UAV to perform multiple search and rescue missions through this algorithm. In order to solve the problems of slow training convergence and high uncertainty, we initialize the Q-table by combining detection information of UAV sensors in first stage. Simulation results show that ACSQL algorithm can realize autonomous navigation and path planning of UAV in an unknown environment. Compared with traditional action space, the learning process of UAV converges faster and more stable, and it can converge in about 30 episodes. Compared with DDPG algorithm and IDWA algorithm in different scenarios, ACSQL algorithm has the shortest path length. Finally, ACSQL algorithm is verified by UAV simulator Airsim.
Transferability Properties of Graph Neural Networks

Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. However, they are difficult to learn on large-scale graphs. In this paper, we study the problem of training GNNs on graphs of moderate size and transferring them to large-scale graphs. We use graph limits called graphons to define limit objects for graph filters and GNNs—graphon filters and graphon neural networks (WNNs)—which we interpret as generative models for graph filters and GNNs. We then show that graphon filters and WNNs can be approximated by graph filters and GNNs sampled from them on weighted and stochastic graphs. Because the error of these approximations can be upper bounded, by a triangle inequality argument we can further bound the error of transferring a graph filter or a GNN across graphs. Our results show that (i) the transference error decreases with the graph size, and (ii) that graph filters have a transferability-discriminability tradeoff that in GNNs is alleviated by the scattering behavior of the nonlinearity. These findings are demonstrated empirically in a recommendation problem and in a decentralized control task.
Modelling Inter-Rater Uncertainty in Spoken Language Assessment

In a subjective task, such as Spoken Language Assessment (SLA), the reference scores provided by different human raters may vary. A collection of annotated scores from multiple raters can be interpreted as an expression of data uncertainty. Previous studies often treat SLA as classification or regression tasks, and train and evaluate models against scalar reference scores that were computed from the multiple rater scores, for example by majority voting. However, a scalar representation may not adequately capture information about uncertainty that is expressed by the multiple rater scores. This paper proposes to reformulate this subjective task as a distribution fitting problem, where the model should aim to emulate the uncertainty expressed by the multiple raters. Toward this aim, the model is trained and evaluated by computing a distance between the model's output posterior and the distribution of reference scores from the multiple raters. Different methods to infer a scalar score from the model's output posterior are also considered. This paper also proposes to improve the match between the model and the SLA task, by interpreting the model's outputs as parameters of a beta density function, to capture both uncertainty and score monotonicity. Finally, ensemble combination is investigated and a novel combination method is proposed, to marginalise out model uncertainty from the combined output distribution. These approaches are evaluated on the speechocean762 dataset and an in-house Tamil dataset.
The Mirage of Breaking MIRAGE: Analyzing the Modeling Pitfalls in Emerging “Attacks” on MIRAGE

This letter studies common modeling pitfalls in security analyses of hardware defenses to highlight the importance of accurate reproduction of defenses. We provide a case study of MIRAGE (Saileshwar and Qureshi 2021), a defense against cache side channel attacks, and analyze its incorrect modeling in a recent work (Chakraborty et al., 2023) that claimed to break its security. We highlight several modeling pitfalls that can invalidate the security properties of any defense including a) incomplete modeling of components critical for security, b) usage of random number generators that are insufficiently random, and c) initialization of system to improbable states, leading to an incorrect conclusion of a vulnerability, and show how these modeling bugs incorrectly cause set conflicts to be observed in a recent work’s (Chakraborty et al., 2023) model of MIRAGE. We also provide an implementation addressing these bugs that does not incur set-conflicts, highlighting that MIRAGE is still unbroken.
A Suboptimal Fusion Estimation Algorithm Weighted by Matrices Based on LMI and Machine Learning

This article is concerned with suboptimal fusion estimation weighted by matrices for multisensors linear systems with unknown cross-covariance. First, for reasonable correlation coefficients, the simplest constraints are derived by Shure complement theorem. The constraints can ensure the positive definiteness of the extended estimation error covariance matrix and the fusion estimation error covariance matrix weighted by matrices, and the consistency of the proposed fusion estimation. Then, based on the linear matrix inequality (LMI) algorithm combined with AutoML, a suboptimal fusion estimation weighted by matrices is proposed. The proposed suboptimal fusion algorithm provides an effective way for fusion estimation of linear distributed systems with unknown cross-covariance. Simulations analyses verify the effectiveness and correctness of the conclusion.
A Comprehensive Study on Feature Extraction Techniques for Indian Sign Language Recognition System

Indian Sign language (ISL) recognition system is an effective communication tool for hearing and speech-impaired sections of society. It is an emerging field of research to facilitate the interaction of the deaf and mute communities with the people of mainstream society. There are broadly four phases in an automated ISL recognition system, i.e. data capturing, preprocessing, feature extraction, and recognition. The phase of feature extraction is a significant phase that decides the efficiency and effectiveness of the recognition system. The quality of the output generated by the feature extraction phase depends upon the feature extraction techniques that are employed. This paper is a comprehensive study of the feature extraction techniques used for the ISL recognition system. A detailed classification of the currently employed techniques for ISL are introduced. The study also presents the advantages and limitations of each feature extraction technique used in the vision-based ISL recognition system.
Gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination

Online advertising models are vulnerable to click fraud, which occurs when an individual or a group repeatedly clicks on an online advertisement with the intent to generate illegitimate clicks and make money from the advertiser. In machine learning-based approaches for detecting click fraud, the performance of the models can be affected by the presence of collinear, redundant, and least significant features in the dataset. These types of features can lead to overfitting, where the model becomes too complex and fails to generalize well to new data. Therefore, a Manifold Criterion Variable Elimination method is proposed in this work to select significant features utilizing the potential of six filter-based feature selection techniques for the discrimination of fraud and genuine publishers. Experimentations are conducted on the online advertisement user click dataset in two modes, first considering all extracted features and second considering only selected features. An extraction of 103 statistical features from the user-click dataset is performed for each class instance labelled with OK, Fraud and Observation. The Manifold Criterion Variable Elimination method selects the top 15 most relevant features. Individual and ensemble learning models are trained with selected feature-set and tuned parameter values. The performances of learners are evaluated using standard evaluation measures. The results demonstrated that, in general, the performance of all learners improved with the selected feature set. Particularly, the Gradient Tree Boosting (GTB) ensemble model performed superiorly by improving the weak learners by minimizing the model's loss via merging the weak learners into a strong one iteratively.
Demystifying the Cloud-Native Computing Paradigm

Undoubtedly, the cloud journey is on the fast track. There are many powerful technologies and tools to simplify and speed up cloud processes and operations. Pioneering algorithms are being unearthed to advance the cloud movement. Enabling frameworks, integrated platforms, knowledge guides, design patterns, evaluation metrics, best practices, a growing array of tools and other enablers from product, platform, and tool vendors and service organizations are collectively making the cloud paradigm mesmerizing. It all started with server virtualization. Then came a number of automated tools to accelerate and augment IT server operations. Thereby every physical server machine gets segmented into a few logical/virtual machines (VMs). With such segmentation, the IT resource utilization efficiency has gone up significantly. Further on, provisioning and using coarse-grained IT resources such as virtual machines became easier and faster. That is, the on-demand computing method emerged and evolved quietly. In other words, the computing requirements of individuals and industries are getting fulfilled rapidly through VMs. Typically provisioning and bringing VMs into real action consumes a few minutes. But doing so with physical machines takes a few hours. The IT costs came down sharply with the virtualization concept. In a nutshell, the much-needed IT agility, affordability and adaptivity requirements are being neatly guaranteed through the virtualization. There are Virtual Machine Monitors (VMMs) (alternatively referred to as hypervisors) to realize the distinct benefits of virtualization.
A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card fraud detection

&lt;abstract&gt; &lt;p&gt;Credit cards have become an integral part of the modern financial landscape, and their use is essential for individuals and businesses. This has resulted in a significant increase in their usage in recent years, especially with the growing popularity of online payments. Unfortunately, this increase in credit card use has also led to a corresponding rise in credit card fraud, posing a serious threat to financial security and privacy. Therefore, this research introduces a novel deep learning-based hybrid Harris hawks with sine cosine method for credit card fraud detection system (HASC-DLCCFD). The aim of the presented HASC-DLCCFD approach is to identify fraudulent credit card transactions. The suggested HASC-DLCCFD scheme introduces a HASC technique for feature selection, by combining Harris hawks optimization (HHO) with the sine cosine algorithm (SCA). For the purpose of identifying credit card fraud, an architecture of a convolutional neural network combined with long short-term memory (CNN–LSTM) is utilized in this study. Finally, the adaptive moment estimation (Adam) algorithm is utilized as a hyperparameter optimizer of the CNN-LSTM model. The performance of the suggested HASC-DLCCFD approach was experimentally evaluated using a publicly available database. The results demonstrate that the suggested HASC-DLCCFD approach outperforms other current techniques and achieved the highest accuracy of 99.5%.&lt;/p&gt; &lt;/abstract&gt;