Reviewerly/exHarmony huggingface dataset:
Lens law based optimization algorithm: a novel approach

The application of three different categories such as swarm-based, physics-based, and evolutionary-based techniques to various optimization problems related to different fields gained importance due to accuracy, speed and least chance to fall in local minima. Although there are number of physics based optimization techniques available to solve the optimization problems, however, they suffer from their complexity and may require multiple runs to achieve the optimum value. This paper focuses on a simple physics based problem which outperforms few of the existing algorithms. This work investigates the physics-based Lens formula integration using a population-based approach. The contender for a population-level solution to picture obstructions created by a convex, concave, or combination of the two lenses is the item with multiple dimensional locations. The object’s location is updated using the image position. The fundamental goals of population-based optimization algorithms are highlighted by the concave, convex, or combination of both lenses, which enhances the speed of exploration, exploitation, and local optimum avoidance of the search space. The focal length equation, controls how the object’s picture is produced, may be altered by specifying a number of random and adaptive factors to highlight the exploitation and exploration of the search space. The capacity of the proposed algorithm for exploitation, exploration, utilisation of the search space, avoidance of local minima, and attainment of global maxima is validated using unimodal, multimodal, and composite benchmark functions. In addition, a variety of real-world engineering issues taken into account to verify the performance of the proposed algorithm. The observations compared with few fundamental optimization techniques of the same category and found the proposed solution better performing in some context than the existing ones.
A Novel Neural Machine Translation Approach for low-resource Sanskrit-Hindi Language pair

Sanskrit is one of the earliest native languages and is correctly described as "the gods' language" because of its wide use in Indian religious literature from the past. However, it is becoming less popular in modern India. Due in significant part to the need for more materials for translation both in and out of Sanskrit, it is no longer commonly utilized. This study explores the feasibility of using machine translation (MT) to provide a link between Sanskrit and, one of the earliest native languages, and its contemporary descendant Hindi. A study was conducted between existing modelling methodologies, notably Statistical machine translation (SMT), and the proposed novel deep learning-based Machine translation strategy using a manually created parallel corpus for the Sanskrit-Hindi language pair. While SMT creates interpretations by mapping phrases from the languages of the source and destination, statistical models, and bilingual text corpora for learning parameters, neural machine translation (NMT) frequently models entire phrases in a single integrated model, using a convolutional neural network to calculate the probability of a word sequence. The proposed NMT model is implemented using an encoder-decoder with an attention mechanism paradigm and the inclusion of gated recurrent units. Our approach involved development of a novel model for Sanskrit-Hindi machine translation using deep learning and the creation of parallel corpora for the Sanskrit-Hindi language pair. The proposed model is evaluated on automated and human-based metrics, and results show that our proposed deep learning-based model outperforms statistical modelling techniques on Moses, surpassing them both with a BLEU score of 53.8% compared to 34.56%. This article examines the undiscovered area of machine translation from Sanskrit to Hindi and discusses the main benefits and drawbacks of statistical and neural machine translation while providing a fresh viewpoint on the subject.
Light field angular super-resolution based on intrinsic and geometric information

Light field imaging can encode abundant scene information, including the intensity and direction of light rays, into 4D light field images. However, the limited number of sensors in commercial light field cameras leads to a trade-off between spatial and angular resolutions. In this paper, an angular super-resolution framework is proposed to synthesize new views and overcome hardware restrictions. First, light field intrinsic feature convolution is proposed to extract intrinsic information, i.e., scene content, complete view correlations, and epipolar structures. Consequently, spatial, angular, and cross-domain information can be preserved in the extracted features. Second, the spatial–angular and depth streams are proposed based on the light field intrinsic feature convolution to synthesize high angular resolution light fields. The spatial–angular stream utilizes the light field intrinsic information to improve the angular resolution, whereas the depth stream disentangles the geometric information from the extracted light field intrinsic features, which is used to warp the given sub-aperture images to the new view positions. Both streams can synthesize high-quality intermediate results, where the intrinsic and geometric information are utilized separately. Finally, a confidence-based stream fusion module is proposed to fuse the outputs from the two streams, achieving the joint employment of light field intrinsic and geometric information and solving the problem of insufficient information exploration in the current methods. We conduct a series of experiments to validate the effectiveness of each component in the framework and demonstrate that our method can achieve state-of-the-art performance in various scenes.
The Lynchpin—A Novel Geometry for Modular, Tangential, Omnidirectional Flight

&lt;div&gt;A novel geometry for a six degrees of freedom (6DOF) unmanned aerial vehicle (UAV) rotary wing aircraft is introduced and a flight mechanical analysis is conducted for an aircraft built in accordance to the thrust vectors of the proposed geometry. Furthermore, the necessary mathematical operations and control schemes are derived to fly an aircraft with the proposed geometry. A system identification of the used propulsion system with the necessary thrust reversal in the form of bidirectional motors and propellers was conducted at a whirl tower. The design of the first prototype aircraft is presented as well as the first flight test results. It could be demonstrated that an aircraft with the thrust vectors oriented according to the proposed geometry works sufficiently and offers unique maneuvering capabilities that cannot be reached with a conventional design. The biggest limiting factor could be identified to be the latency resulting from the time needed to reverse the direction of rotation of the bidirectional propellers. Special operational handling considerations for a manually flown 6DOF vehicle are summarized, and different approaches to control such a vehicle are described. Different mission scenarios and applications for a single aircraft in this design are concluded. Due to the symmetrical properties of the proposed geometry, several aircraft can also be connected at different orientations in flight. Consequently, the possibilities of docking in midair are illustrated, and possible mission scenarios for a group of aircraft, working together collaboratively, are presented. Especially when operated autonomously in a group of aircraft, new possibilities evolve from the capability of the proposed geometry to bond in different orientations.&lt;/div&gt;
Neural clustering based on implicit maximum likelihood

Abstract Clustering is one of the most fundamental unsupervised learning tasks with numerous applications in various fields. Clustering methods based on neural networks, called deep clustering methods, leverage the representational power of neural networks to enhance clustering performance. ClusterGan constitutes a generative deep clustering method that exploits generative adversarial networks (GANs) to perform clustering. However, it inherits some deficiencies of GANs, such as mode collapse, vanishing gradients and training instability. In order to tackle those deficiencies, the generative approach of implicit maximum likelihood estimation (IMLE) has been recently proposed. In this paper, we present a clustering method based on generative neural networks, called neural implicit maximum likelihood clustering, which adopts ideas from both ClusterGAN and IMLE. The proposed method has been compared with ClusterGAN and other neural clustering methods on both synthetic and real datasets, demonstrating promising results.
ADVANCED CHATBOT DEVELOPMENT TO IMPROVE STUDENT LITERACY AND NUMERACY SKILLS

The literacy and numeracy skills of prospective teacher students are still in the very low category. This is an indication that the opportunity to increase students' literacy and numeracy skills is still very high. The purpose of this study was to measure the increase in literacy and numeracy in science learning using the Advanced Chatbot media. This type of research is an experimental research with a pretest-posttest group design technique. The research was conducted at the students of Elementary School Teacher Education, Walisongo State Islamic University, Semarang and University of Sarjanawiyata Tamansiswa Yogyakarta, with a total of 57 students. The data acquisition technique used a test instrument. The literacy and numeracy indicators used are based on the achievements developed by PISA 2015 namely knowledge and skills to 1) use various numbers and symbols related to basic mathematical concepts to solve practical problems in various contexts of everyday life; and 2) analyze the information displayed in the form of graphs, tables, and charts. Then use the interpretation of these results to predict and make decisions. The test instrument was declared valid as seen from the level of difficulty and the suitability of the items. The results of the analysis show the mean value of n-Gain is 0.32 in the medium category. This means that there is an increase in student literacy and numeracy in science learning assisted by advanced chatbot media. In conclusion, advanced chatbots can be used as an alternative media for science learning which can effectively improve students' literacy and numeracy skills.
SENTIMENT ANALYSIS OF STUDENTS’ FEEDBACK ON INSTITUTIONAL FACILITIES USING TEXT-BASED CLASSIFICATION AND NATURAL LANGUAGE PROCESSING (NLP)

In this study, sentiment analysis, commonly referred to as opinion mining or emotional artificial intelligence (AI), is used to obtain student comments about institutional facilities. To ascertain whether an online text has a good, negative, or neutral emotional tone, it must first be analyzed. Natural Language Processing (NLP) includes the subfield of sentiment analysis, and NLP can be used to categorize and extract information with the aid of machine learning methods. Finding out if students are content with the amenities or services provided is important since, in an educational setting, they are consumers. This study of the resources and services that the libraries offer evaluated college students' perceptions of the books, audio CDs, and video CDs, the services provided by the library staff, and the personal computers that are made available to them as part of their facilities. In the current study, surveys were carried out to gather information and assess how well the needs of the students are addressed. On Kaggle.com, secondary data from a North Indian institution was used in the experiments. The research approach was sentiment analysis using a machine learning framework. The F1-score, a harmonic mean of precision and recall based on the attitudes evaluated by the algorithms, was calculated using a text-based classification method using Naive Bayes Multinomial and Support Vector machine learning algorithms. The findings offer views on how successfully library facilities are managed in the form of student sentiments.
DEVELOPMENT OF A MOBILE WEB APPLICATION FOR LEARNING THAI AS A FOREIGN LANGUAGE

One of the technological advances of the modern age is the mobile phone, as it enables free access to a wide variety of applications (apps) and language learning tools, which help students develop their language abilities in and out of the classroom. The purpose of this study was to develop a mobile web application (mobile web app) designed specifically for learning Thai as a foreign language and to examine whether the use of the mobile web app assists Thai language learning. The participants in the study consisted of 22 international undergraduates with multi-cultural backgrounds at an international college in the northeastern region of Thailand. The research instruments used in this study were a set of questionnaires and ‘Let’s Learn Thai’ web application designed by the researcher. Participants were assigned to use the ‘Let’s Learn Thai’ web app to study the same unit they had learned in class each week, and the experiment took place over the course of 6 weeks. Data was collected through online questionnaires in order to assess students’ perceptions of their use of the web app. Based on the statistics generated through SPSS, the results showed that participants had positive attitudes toward the web app, and it was convenient and helpful for them to learn and review Thai lessons at any time. Regarding additional comments and suggestions, the participants stated that more lessons and various learning activities and exercises should be provided so that the students can get more practice.
Trajectory Planning on Autonomous Truck with Dual-Modular Chassis Using Artificial Potential Field and Polynomial

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Autonomous truck with modular chassis has the characteristics of high driving flexibility and strong load capacity. It can be equipped with different numbers of modular chassis according to the task requirements. The application of autonomous truck can solve the problems of traffic accidents and shortage of drivers effectively, which is the development trend of trucks in the future. For the collision-free trajectory planning problem of dual-modular chassis autonomous truck, this paper designs a hierarchical local trajectory planner that combines the artificial potential field method with polynomial curve fitting method. This planner plans the center of mass trajectory firstly, and then generates the modular chassis trajectories according to the position relationship between the center of mass and the chassis. The center of mass local trajectory cluster satisfying the environment constraints and truck motion constraints is obtained by polynomial curve fitting method for sampling the road discrete points in the parameter space. In order to make the evasive maneuver of dual-modular chassis autonomous truck start earlier to make the obstacle avoidance process safer, a three-dimensional repulsive potential field is constructed based on artificial potential field method around the obstacle. The repulsive potential field of the obstacle is regarded as the constraint of the collision-free trajectory of the truck. The optimal collision-free trajectory is selected by constructing multi-object function. Simulations of obstacle avoidance of dual-modular chassis autonomous truck on different curvature roads with different speeds in Simulink/Simscape environment are conducted to demonstrate performance of the proposed collision-free trajectory planner. The simulation results show that the collision-free trajectory planner based on artificial potential field method and polynomial curve fitting method can make the autonomous truck avoid collision safely and steady on different curvature roads with different speeds.&lt;/div&gt;&lt;/div&gt;
A Semantic Segmentation Algorithm for Intelligent Sweeper Vehicle Garbage Recognition Based on Improved U-net

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model. Next, we select the Softmax as the activation function to adjust the classification probability of each category. The cross-entropy function, selected as the loss function, is used to evaluate the fitting ability of the model. We adjust the stride and the size of the feature map in the convolution process to reduce the amount of calculation. The k-fold cross-validation makes full use of the dataset to better optimize the model. Compared with Unet algorithm, the data shows that the improved algorithm can extract more effective features for classification prediction, and simplify the network for parameter selection and optimization, and improve the training speed by 10%. The recognition accuracy is increased by 18% while ensuring the speed, and it has higher MPA and MIoU. This algorithm has good generalization ability to deal with different test sets, which improves the efficiency of the intelligent sweeper vehicle and provides a reference for the design of the semantic segmentation model of garbage classification at the same time.&lt;/div&gt;&lt;/div&gt;
Cascading (3D) reconstruction procedure of composite structures from microtomography data

Reconstruction of three-dimensional (3D) structure from experimental image acquisition (e.g., from micro computed tomography data) is very useful in composite material science. Composite considered are characterized by a dispersion of particles in a continuous phase. Many properties of the composite (e.g., mass transfer properties) depend on its structural assembly. A reliable prediction of these properties requires to well represent this structure and especially, the region at the vicinity of the dispersed phase. (3D) structure generation must thus permit to (1) simplify the real composite structure observed to make it compatible with further modelling tasks (e.g., meshing constraints in finite elements methods, computation time) and (2) keep enough representativeness of the structure of the specimen to produce reliable numerical predictions. This article describes an innovative, cascading (3D) reconstruction procedure of composite material from microtomography data.•First step of this pipeline is the extraction of relevant structural markers from microtomography images using image analysis.•Second step is the modelling of the distribution of the structural markers selected (statistical laws).•Third and final step is the reconstruction of the (3D) structures based on the pre-determined distribution laws in a RVE (representative volume element) of the composite.
A Best Approach to Load Balancing in Cloud Computing Security Using SSL in Iaas Infrastructure

Now a days there is low performance of cloud computing service provider cloud computing service provider allow users to hire a service on rent basis to use high cost software and hardware resources so now a days this feature are usable without suffering discontinue services and performance of the system the secure socket layer and associated security algorithm come with internet age and first introduce by Netscape but some problem found with it which is found by some researcher. Now a days cloud computing facility are face problem of performance degradation but we have to avoid such problem.in this paper we have to avoid such problem so we propose a solution on that so in existing system the commercial offering from the F5 Inc a new network equipment manufacturer introduced a product BIG-IP With in simultaneous approach high availability load balancing and secure data channel Key Words: Key words: Secure Socket Layer (SSL), Local TrafficManager (LTM), Access Policy Manager (APM), Infrastructure – as – a - Service (IaaS), Throughput
Composite likelihood methods for parsimonious model-based clustering of mixed-type data

Abstract In this paper, we propose twelve parsimonious models for clustering mixed-type (ordinal and continuous) data. The dependence among the different types of variables is modeled by assuming that ordinal and continuous data follow a multivariate finite mixture of Gaussians, where the ordinal variables are a discretization of some continuous variates of the mixture. The general class of parsimonious models is based on a factor decomposition of the component-specific covariance matrices. Parameter estimation is carried out using a EM-type algorithm based on composite likelihood. The proposal is evaluated through a simulation study and an application to real data.
Interior Décor using Augmented Reality

Augmented reality helps us to visualize computer-generated content in our physical space. Often, when you buy furniture or any other decorative item, you regret that purchase as it did not suit your home space. The inability to place that object in your home space before buying it, probably due to the size of the furniture or some other reasons renders this problem. Augmented reality can help to solve this problem. Using AR one can view 3d models of furniture in their home or office environments and purchase them accordingly. This can help businesses to reduce the return rate, and increase order and cart conversion rates of the products thereby improving customer experience and benefiting the retailers
Resume Parser and Summarizer

Resume parsing is a highly important process for HR departments and recruiters looking to streamline their hiring process. By converting a resume into structured data, it allows for easy organization and quick searchability for specific qualifications and skills. However, there are still limitations to the technology, as language can be complex and ambiguous. It is important for companies to stay up to date with advancements in Natural Language Processing and Artificial Intelligence to improve the accuracy of resume parsing and avoid overlooking qualified candidates. Overall, resume parsing is a crucial tool for modern recruitment, but it still requires human oversight to ensure the best candidates are not overlooked