Reviewerly/exHarmony huggingface dataset:
Narrataa: Learning Tool for Generating Kid-Friendly Sinhala Names for Objects

Being fluent in the mother tongue benefits a child in many ways; it helps in the development of a child's personal, social, and cultural identity and also aids in learning other languages. Kids would one day want to be proficient readers or writers in their own tongue, and children need help with language development in order to accomplish this. Therefore, it is the responsibility of parents to aid in their children's vocabulary development. But the problem is that many parents today are employed and therefore unable to interact with and educate their children. As a result, past research suggests that the best way to solve this issue is to develop an application that makes use of cutting-edge tools like computer vision and machine learning. However, the issue is that those applications support many other languages but not Sinhala language. The current study's main gap is creating a mobile application using the Sinhala image caption. Therefore, the present study implemented the” Narrataa” mobile application, which provides a caption for the relevant taken image in English, Sinhala, and Tamil. To develop the current application, the researcher employed computer vision, machine learning technologies, the Android Studio IDE. and the Java programming language.
Real-Time Multiple Dyadic Interaction Detection in Surveillance Videos in the Wild

Social distancing measures are proposed as the primary strategy to curb the spread of pandemics caused by respiratory pathogens. Therefore, non-intrusive techniques to identify human-human interactions in public spaces play an important role in the curtailment of disease spread. This paper proposes a novel computer vision-based system that identifies multiple co-occurring dyadic (two-person) interactions in a crowded scenario and classifies them into six action classes. Human skeletons are extracted from the RGB videos to eliminate the background noise improving the overall detection accuracy. Two approaches have been used for the classifier: X3D-M and X3D-M+Attention. The latter achieves higher classification accuracy due to the ability of the attention layer to capture the long-distance interdependence of video frames. The efficacy of the proposed model was evaluated across two different datasets on more than 5000 frames, thus enabling a robust detection model in different environments. The proposed model is the first dyadic interaction detector in the wild, which enables it to be used in public spaces and thereby identify and mitigate transmission of respiratory diseases.
Dual Gated Fusion Network with Feature Calibration for Semantic Segmentation

Recently, deep learning-based feature fusion has attracted extensive interest due to its powerful representation and production capabilities. The goal of feature fusion is to extract more accurate and useful features by processing the relationships between features from a single or several sources, resulting in a more thorough knowledge of the scene. However, Traditional fusion approaches do not retain effective information well and consider the noise in the modalities. Therefore, this paper proposes Dual Gated Fusion Network with Feature Calibration (FC-DGFNet) for semantic segmentation. It mainly consists of two parts: Feature Calibration Module (FCM) and Gated Fusion Module (GFM). FCM is embedded to calibrate RGB and depth features with each other to generate more robust features. And GFM is used for the fusion of depth features and visible features, which improves the representativeness of features and better retains complementary information. Experimental results demonstrate that the proposed method outperforms the traditional methods, and mIoU and PA reach 51.6% and 77.5%, respectively.
Empirical Analysis for the Selection of Baseline Performances for Short Text Classification

Improvements of classification performance for short text have become increasingly popular due to explosive growth of social media and other online communication platforms. As a result, recent research works attempt to improve the classification performances over a selected baseline. Unfortunately, latest research works related to short text classification performance improvements with Neural networks [CNN, LSTM,RNN] and, Embeddings as feature representation haven't use common baseline to compare the performance improvements with their experiments. Therefore, it's hard to compare one work with the other. Hence this research work was carried out to identify the appropriate n-grams, feature representation, preprocessing technique and the machine learning algorithm which suits for the given short text type dataset which can be used as a baseline to compare latest experimented results. We used seven short text types datasets which are publicly available. We compared different n-grams [1-gram,2-gram,3-gram etc], feature representation techniques such as [TF] term frequency and [TF-IDF]term frequency with inverse document frequency techniques as well as the impact of using stop words. Moreover, we compared the classification performances among different traditional machine learning based algorithms. Our conclusions are, combining 1-gram with 2-gram word features gave high performances compared to other n-grams. [2] out of two feature representations techniques TF perform well compared to TF IDF. [3] further, removal of stop word support to improve the classification performances when feature representation type is TF.[4] Moreover, out of traditional machine learning algorithms, SVM gave good results. Finding of our research work can be used to compute baseline classification performances with future research work.
Characterisation of topological photons

This study demonstrates the stability of three-dimensional optical topologies, making them ideal for use in secure communication channels that are prone to noise. The research investigates the stability of link and knot phase singularities when exposed to various types of aberration, providing valuable insights into their performance under perturbation. In addition, the study proposes a highly precise and efficient method for characterising complex bi-photon quantum states, which outperforms traditional quantum state tomography schemes. This novel approach allows for rapid reconstruction of structured entangled photon pairs, enabling researchers to more effectively engineer and utilise these quantum states for a wide range of applications.
Geospatial Blockchain Based Data Management of Decentralized Energy Trading System

The paper proposes a geospatial based blockchain method to efficiently manage and transmit the critical smart meter data to cloud server. The method divides the grid into several edges based on geospatial classification. Each edge comprises a local storage unit to store the smart meter data temporarily for processing and calculation of other functionality in the grid. These edges comprise a unique blockchain to manage the smart meter data. These actual data are divided into shard of data and each data has its unique hashed ID. Based on these IDs the data is transmitted and verified under cloud server. With the identification of data ambiguity, the shard is reinitialized. This eliminates latency and inefficiency. Further the verified data are removed from a local edge storage device and the verified data block is freed from the geospatial blockchain ledger to resolve the data storage issue.
Kernel Extreme Learning Machine Based Prediction for Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) are a class of special multi-objective problems (MOPs) in which constraints and parameters may change over time. Most existing approaches prefer to employ simple linear models to depict the relationships between consecutive environments, although the non-linear model seems more in line with reality. In this paper, the kernel extreme learning machine (KELM) is used as a predictor to learn the potential non-linear correlation between the constructed time series and then estimate the new positions of Pareto solutions. KELM enables efficient and accurate mapping of non-linear time series from low to high dimensional space and kernel mapping in high dimensional space. Specifically, Specifically, based on the reference point approach, the historical solutions are correlated to form multiple time series, which can maintain the diversity and distribution of the predicted population. Then, the constructed time series are fed into KELM as training samples to extract the moving trends of the time series solutions. When the environment changes, the trained KELM quickly predicts the positions of the solutions in the new environment and generates the corresponding initial population, enabling the algorithm to adapt to the dynamic environment better and faster. Comprehensive experiments are conducted on 14 benchmark problems of DF by comparing the proposed method with six popular algorithms. The experimental results demonstrate the superiority of the proposed method.
Spacecraft Attitude Maneuver Planning Based on Deep Reinforcement Learning under Complex Constraints

During the execution process of the spacecraft mission, a large number of attitude maneuvers are required. When the external environment and the spacecraft's dynamic constraints are coupled, it will cause certain difficulties in solving the spacecraft's attitude maneuver path planning. Therefore, the spacecraft attitude maneuver planning must be improved. However, traditional methods often have the problems of low solution efficiency and strong model dependence. We propose the Deep Q-network (DQN) method to solve these problems and directly perform end-to-end inference. Finally, by comparing with the Rapid-exploration Random Tree (RRT) algorithm, the results show that the planning results meet all constraints and energy consumption of the DQN method is reduced by about 62% compared to the RRT method, and the planning time is also reduced by about 81 %.
Mobile Robots with Dynamic Obstacle Avoidance

Robots with autonomous navigation capabilities have become increasingly popular after the advances in robotic automation, particularly in the area of pathfinding algorithms.These algorithms enable robots to safely traverse through complex environments with both stationary and moving obstacles.Applications of this field range from data acquisition to surveys of hazardous situations and transportation by industrial robots.The most commonly utilized approach for two-dimensional obstacle avoidance is grid-based pathfinding algorithms.These methods function by initially generating a grid consisting of nodes and edges based on the environment.In this paper, we explore an implementation of a variation of the A* pathfinding algorithm on a 15x15 grid.The A* algorithm was chosen because it guarantees finding the optimal route between starting and ending points.A* is a grid-based algorithm that falls under the category of search-based algorithms.The Maximum Velocity Obstacle (MVO) algorithm undergoes rigorous testing to evaluate its performance, and we examine how the simulation input parameters influence the algorithm's effectiveness.The experimental results indicate that the MVO algorithm is an efficient and reliable solution for dynamic obstacle avoidance in a grid-based setting.Moreover, this study demonstrates that the algorithm can be further optimized by using more advanced techniques such as combining it with existing pathfinding algorithms, like artificial neural networks.This would enable the robot to adapt to unpredictable environments in future research.
Blockchain-based Secure EV Charging System

The demand for electronic vehicles (e-vehicle) is drastically increasing in the recent times. However, the customer details for the charging of e-vehicles should be securely stored to prevent malicious and unintended access or manipulations, which may otherwise have an impact on the overall e-vehicle charging system. In traditional charging system, manual charging by the eligible and valid customers is in existence in because of majority of the chargings hailing from rural areas and unavailability of efficient technological access. However, these systems may lead to repudiation and fraudulent access by intruders due to anonymity of users. To overcome all these difficulties, a secure blockchain based system for efficient charging mechanism is proposed in this work, where transparency is ensured across all the participating entities by getting them all connected across the blockchain network. The work is developed to allow the deployment of a e-vehicle charging application that will help to keep track of the person who made a charging into the smart contract. The charging details are encrypted before storing in the blockchain and the time taken to carry out the encryption process for a varying set of customers is recorded. Experimental results are obtained by creation of an e-vehicle smart contract across both permissionless, and permissioned environments and the time taken to store and retrieve the eligible customers documents is analyzed. From the results, it is identified that the permissionless network such as ethereum poses a threat to security and non-repudiation aspects which can be overcome in the permissioned networks like hyperledger fabric as used to develop this work. It is also observed that as the number of eligible customers increases, the time taken to encrypt is also increased in a linear manner. The work is developed on a hyperledger fabric environment that serves to be a permissioned one, thereby strengthening the overall security of the e-vehicle chargings made across the network.
A Novel Bi-Center Intuitionistic Fuzzy Large Margin Classifier

Utilizing the marginal mean and variance, the large margin distribution machine (LDM) is verified to obtain better generalization performance than the support vector machine (SVM). However, it still doesn't achieve satisfactory performance when handling noise points and outliers. While the single-center intuitionistic fuzzy (IF) strategy has been widely used in SVM and LDM models, the bi-center (BC) strategy takes into account the overall distribution and obtains a more accurate confidence level for each sample, which enhances the noise resistance. Therefore, we introduce the IF set theory into LDM and propose BC-IFLMC based on the BC strategy, which effectively enhances the robustness of classification tasks. Specifically, the membership function is related to the sample's distance to homogeneous class center, while the non-membership function is associated with the distance to both class centers. Based on the membership and non-membership degrees, the score value for each sample can be calculated to reflect its confidence level, thus mitigating the effects of noise. To the best of our knowledge, this is the first work to embed the IF theory into the conventional LDM. The experiments on both the original and noise-added UCI datasets demonstrate the effectiveness of the proposed model in improving the anti-noise performance.
An Novel Predictive Control Method Based on Regularized Echo State Network and Improved Sparrow Search Optimization Algorithm

In this paper, combining the advantages of regularized echo state network and improved sparrow search optimization, a novel prediction control method is proposed. Compared with the traditional recurrent neural network, the prediction model based the regularized echo state network has the advantages of simple training and less computational burden. Through using the reverse learning strategy and the Levy flight strategy, the sparrow optimization algorithm is improved for optimizing the reservoir parameters of the regularized echo state network. Finally, for the predictive control of the buck converter, the simulation experiments demonstrate the effectiveness of the proposed predictive control method.
Anti-Swing Strategy of a Quadrotor with Suspended Payload Based on Model Predictive Control

The proposed approach in this paper uses a nonlinear model predictive control (NMPC) algorithm to control the swing angle and position of suspended loads using quadrotor UAVs. The system model is based on the Euler-Lagrange method, which considers the two-degree-of-freedom swing angle of the load dependent on the UAV's acceleration. The NMPC controller is implemented through an inner-outer loop control strategy, where the outer loop regulates the position and swing angle of the load, while the inner loop controls the UAV's attitude and altitude. The proposed approach ensures accurate trajectory tracking by suppressing the load swing while maintaining the position of the UAV. Simulation results confirm the effectiveness of the proposed approach. The anti-swing strategy provides a promising solution to the control of suspended loads using quadrotor UAVs, and can have numerous applications in cargo transportation.
Utilizing Non Linear Functions Based Quantum Kernels to Categorize Small Datasets

Quantum computing is the application of Quantum mechanical resources to solve computational problems more precisely, quickly, and effectively than classical computers. Machine learning is all about discovering patterns and relationships in data. Quantum Machine Learning (QML) blends Machine Learning (ML) and Quantum Computing (QC) technologies. QML approaches may be utilized efficiently for data classification problems. Quantum Feature Maps (QFMs) are used for transferring non-linear classical data from lower input space to higher dimensional Hilbert space. Calculating the inner product between data points for classification purposes is exceedingly costly when Hilbert Space is vast. Quantum Kernel Techniques, which are immediate results of QFMs, are used to overcome this problem. In general, we cannot claim that QFMs are superior than Classical Feature Maps (CFMs); yet, there are datasets for which QFMs perform better while CFMs are less effective. It gives us the motivation behind using QFM for classification. In this paper, we proposed two new non-linear encoding functions based quantum kernels for data classification in the range [0, 2π]. The make_moons and make_circles datasets are utilized in this article to perform binary classifications using Qiskit and Sklearn. Construction of quantum kernels is performed, and Support Vector Machines (SVM) are then applied for classification. We have compared the results of our two newly proposed non-linear encoding functions to those mentioned in the literature. Our classifier's performance is approximately comparable or in some cases better to that of existing classifiers in literature.
Towards Efficient Anomaly Detection Using Memory Broad Learning System

Anomaly detection aims to identify data points in real-world data that do not conform to expected patterns. The lack of labels, large sample size, and numerous features make it challenging to develop an effective and straightforward algorithm. This paper proposes a new approach called Memory Broad Learning System for Anomaly Detection (MemBLSAD) that can be trained end-to-end. MemBLSAD seamlessly integrates a memory module and a Double BLS-decoder module to achieve hybrid anomaly detection scores, thereby enhancing its capability. Despite the added complexity, MemBLSAD maintains the fast training ability of classic BLS and ensures an end-to-end training process. Our experiments on MNIST and CIFAR10 datasets demonstrate that MemBLSAD outperforms the comparison algorithms on the AUC measure. We also provide reliable visualizations to analyze MemBLSAD's outstanding performance and explain why it works better.
Weak-Consciousness Echo State Network for Time Series Prediction

After training, the parameters and weights of the standard echo state network are fixed, which will result in poor prediction accuracy for different types of input data. Therefore, a weak-consciousness echo state network (W-ESN) for time series prediction is proposed in this article. By introducing the concept of “label” in the network, the network can select the optimal network weights and parameters for different tasks based on the defined labels. Moreover, the network structure is improved by adding multiple reservoirs in series. Finally, the effectiveness of the W-ESN is proved by two examples.
The Visual Assistant - Image-to-Speech Generator

In this paper, we presented an image-to-voice conversion model designed to assist drivers. The model can be integrated into vehicle navigation systems to analyze images of roads and signs captured by onboard cameras. It can then convert into voice instructions, providing real-time guidance to the driver. This can be particularly useful in unfamiliar areas or when visibility is limited. Image caption Generator is a well-known research field within the field of artificial intelligence that focuses on image comprehension in addition to providing a linguistic description of the image. To be able to produce sentences that are well formed requires a syntactic as well as a semantic understanding of the language. Additionally, our model can contribute to the development of intelligent transportation systems by providing an accessible means for visually impaired individuals to navigate public spaces. By converting visuals into voice instructions, the model ensures that individuals with visual impairments can independently and confidently travel through urban environments, enhancing their mobility and inclusivity.
An accurate novel circular hole inspection method for sheet metal parts using edge-guided robust multi-view stereo

Abstract Sheet metal parts are frequently used in the industrial field, and the circular holes on these parts are commonly employed as assembly references. Therefore, precise measurement of these circular holes is essential. Because of the scratches, rust, and complex reflection around the edges, the measurement of circular holes on sheet metal parts is a challenging task. In this research, an accurate circular hole inspection method for sheet metal parts is proposed to realize online inspection using a multi-camera system. After capturing images in a single shot, edge detection is adopted to obtain sub-pixel circular hole contours. A new adaptive matching cost based on the descriptor of binary robust independent elementary features and bilinear normalized cross-correlation is introduced to the PatchMatch-based multi-view stereo method to obtain dense and accurate point clouds. To improve the accuracy and integrity of circular hole measurement, a new edge-guided multi-scale geometric consistency matching strategy is proposed accompanied by depth detail restoration. Finally, the points around the circular hole in a spherical range are sampled using the trinocular constraint principle and fitted with the circular equation. The experimental results show that the proposed algorithm performs well, with an error of approximately 0.04 mm, and improves the measurement accuracy compared with the state-of-the-art algorithms.
Heterogeneous group path planning algorithm based on data and mechanism model

Abstract It is a challenging task to find a feasible path from the start to the end for heterogeneous group and avoid collision between dynamic agents and static obstacles. The existing methods are usually applicable to static simple scenes or the type of scenes including a single kind of agents, and it is difficult to meet the high dynamic heterogeneous group movements. To address the above issues, we propose a hybrid driven heterogeneous group path planning method based on data and mechanism model. Data and mechanism model are combined to drive movements of heterogeneous groups. The experimental results show that our method can describe movements of heterogeneous groups more realistically and solve the collision avoidance of heterogeneous groups well. We quantitatively evaluate our method using metrics such as the number of inflection points and the average turning angle. Average turning angle has decreased by 59.50% on average over prior methods. Number of inflection points has decreased by 69.19% on average over prior methods.