Reviewerly/exHarmony huggingface dataset:
Joint Fourier Disparity Layers Unrolling With Learned View Synthesis for Light Field Reconstruction From Few-Shots Focal Stacks

This paper addresses the problem of capturing a light field using a single traditional camera, by solving the inverse problem of dense light field reconstruction from a focal stack containing only very few images captured at different focus distances. An end-to-end joint optimization framework is presented, where a novel unrolled optimization method is jointly optimized with a view synthesis deep neural network. The proposed unrolled optimization method constructs Fourier Disparity Layers (FDL), a compact representation of light fields which samples Lambertian non-occluded scenes in the depth dimension and from which all the light field viewpoints can be computed. Solving the optimization problem in the FDL domain allows us to derive a closed-form expression of the data-fit term of the inverse problem. Furthermore, unrolling the FDL optimization allows to learn a prior directly in the FDL domain. In order to widen the FDL representation to more complex scenes, a Deep Convolutional Neural Network (DCNN) is trained to synthesize novel views from the optimized FDL. We show that this joint optimization framework reduces occlusion issues of the FDL model, and outperforms recent state-of-the-art methods for light field reconstruction from focal stack measurements.
Importance Weighted Structure Learning for Scene Graph Generation

Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image. Currently, the message passing neural network based mean field variational Bayesian methodology is the ubiquitous solution for such a task, in which the variational inference objective is often assumed to be the classical evidence lower bound. However, the variational approximation inferred from such loose objective generally underestimates the underlying posterior, which often leads to inferior generation performance. In this paper, we propose a novel importance weighted structure learning method aiming to approximate the underlying log-partition function with a tighter importance weighted lower bound, which is computed from multiple samples drawn from a reparameterizable Gumbel-Softmax sampler. A generic entropic mirror descent algorithm is applied to solve the resulting constrained variational inference task. The proposed method achieves the state-of-the-art performance on various popular scene graph generation benchmarks
Decentralized Federated Learning on the Edge Over Wireless Mesh Networks

The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm.Federated learning enables model training at the edge, leveraging the processing capacity of edge devices while preserving privacy and mitigating data transfer bottlenecks.However, the conventional centralized federated learning architecture suffers from a single point of failure and susceptibility to malicious attacks.In this study, we delve into an alternative approach called decentralized federated learning (DFL) conducted over a wireless mesh network as the communication backbone.We perform a comprehensive network performance analysis using stochastic geometry theory and physical interference models, offering fresh insights into the convergence analysis of DFL.Additionally, we conduct system simulations to assess the proposed decentralized architecture under various network parameters and different aggregator methods such as FedAvg, Krum and Median methods.Our model is trained on the widely recognized EMNIST dataset for benchmarking handwritten digit classification.To minimize the model's size at the edge and reduce communication overhead, we employ a cutting-edge compression technique based on genetic algorithms.Our simulation results reveal that the compressed decentralized architecture achieves performance comparable to the baseline centralized architecture and traditional DFL in terms of accuracy and average loss for our classification task.Moreover, it significantly reduces the size of shared models over the wireless channel by compressing participants' local model sizes to nearly half of their original size compared to the baselines, effectively reducing complexity and communication overhead.
Enhanced Point Feature Network for Point Cloud Salient Object Detection

Point cloud salient object detection (SOD) aims to identify and segment the most prominent areas or targets in a 3D scene. Currently, research on point cloud SOD is still in its infancy, with most approaches neglecting the color information available in the point cloud. In this paper, we propose an enhanced point feature network (EPFNet) for point cloud SOD. Firstly, we extract RGB information from the point cloud with color and use it as input for the dual-stream network. Next, we introduce a multi-scale information enhancement (MIE) module to enhance common information and embed complementary information, acquiring RGB features at different scales and transforming them into point features. To gain access to global semantic information, we propose a point-image fusion (PIF) module, which aggregates the enhanced point features with the RGB global features and produces the final results. We conduct extensive experiments to validate the effectiveness of EPFNet and our approach outperforms 7 other state-of-the-art models on 4 metrics.
A Pragmatic Study of Machine Learning Models Used During Data Retrieval: An Empirical Perspective

Data retrieval is a mandatory and universal process that is used by almost every digital deployment for their application-specific use cases. Researchers have developed various machine learning models to facilitate highly efficient, fast, low complexity, and multisource data retrieval techniques. But due to a wide variation in input data types there is a huge variation in deployment of these techniques. For instance, text retrieval techniques require estimation of distance metrics, while audio and image retrieval techniques require estimation of correlation metrics, and video retrieval techniques use recursive correlation for retrieval of video sequences. Furthermore, these strategies differ internally in terms of their qualitative and quantitative properties, making it challenging for researchers to find the best retrieval techniques for their application-specific use cases. For instance, audio and text retrieval models use 1D data representation and retrieval techniques, while image and video processing models use 2D, 3D & 4D based techniques, which causes ambiguity in model selection for real-time use cases. This study describes some of the recently presented data retrieval strategies and evaluates their deployment-specific nuances, application-specific advantages, functional lacunas, and perspective future research objectives to lessen model selection uncertainty. Researchers will have the ability to discover retrieving models with higher functional capabilities for application-specific use cases as a result of this debate. The effectiveness of these models was then studied by comparing performance indicators such as retrieval delay, retrieval accuracy, computational overhead, scalability, and installation cost parameters. This text also proposes the evaluation of a novel Data Retrieval Metric (DRM), which combines these evaluation parameters, and assists in identification of high-performance models suited for large-scale deployments.
Advanced Pointer-Generator Networks Based Text Generation

Meetings are an important way for people to exchange information at work, but they also consume a lot of time and resources. With the outbreak of Covid-19, many people have turned to virtual communication platforms like Zoom and Microsoft Teams to hold meetings. However, as these meetings get longer and more complex, it can be difficult to keep track of all the important information discussed. One solution to this problem is using meeting summarization models, which can automatically extract relevant information from video or audio recordings or meeting transcripts. This can save a lot of time for both attendees and non-attendees by partially replacing the need for manual note-taking. The goal of our work is to explore encoder–decoder models and pointer-generator networks with attention to comprehending the semantics of the text and producing an insightful summary that captures the essential gist of the text. The ROUGE score is used to measure the performance of the model.
CALTracker: Cross-Task Association Learning for Multiple Object Tracking

Multiple object tracking has recently achieved excellent performance based on the joint optimization of detection and re-identification tasks. However, joint optimization normally homogenizes the features in the detection and re-identification tasks, which weakens the representation of each task's inherent geometric and semantic information. Additionally, the stability of the tracking trajectory will be impacted by the feature misalignment of the associated information between different tasks. In this letter, we propose a Cross-task Association Learning Tracker (CALTracker) to trade off the inherent and associated information. We first design a Triplet Shrinkage Decoupling (TSD) module to ensure the independence of sub-task features in the optimization process, thereby minimizing the optimization conflicts caused by homogeneous features. Secondly, to improve the consistent representation of the associated information between subtasks, a Double Attention Cross-task Learning (DACL) strategy is designed to achieve cross-task feature alignment and mutual gain. Finally, extensive experimental results on MOT17 and MOT20 demonstrate the effectiveness of the proposed method over the state-of-the-art performance.
Deep Learning Based Smart Attendance System

The traditional attendance system is basically recording attendance of the students by calling their names or roll numbers which has many disadvantages especially if the students are in large number, such as, time spend in making roll or name call and marking attendance one by one resulting in unnecessary time wastage and proxy attendance, etc. The other methods like using biometric systems such as fingerprint scanning and iris scanning can only be used up to some extent because either of their portability issues or accessibility issue or majorly because of high implementation cost. In this paper, we propose a smart attendance system based on the concepts of face detection and face recognition. The attendance of the recognized faces is marked against their roll number in an excel sheet. This system works uses MTCNN to detect the faces and further ArcFace model is used to generate the relevant encodings. For classifying faces, the concept of Cosine Similarity is used. Thus, this system provides a better alternative to existing system of attendance.
Accelerating Split Federated Learning over Wireless Communication Networks

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.
Prediction of Children Age Range Based on Book Synopsis

Text data form an important and predominant component of data processing pipelines in a variety of domains and have applications in health care, e-commerce, etc. Statistical machine learning approaches have been employed in recent times to obtain informative representations for text documents. These rich representations have enabled many useful text-driven prediction tasks. In this paper, the problem of predicting the recommended age range of an English book based on its text synopsis has been investigated. Data-driven machine learning-based approach is adopted for this task. Popular machine learning classification models such as KNN, SVM, XGBoost, LGBM, and MLP are considered. In addition, feature representations of text such as word2Vec, Doc2Vec, BERT, and handcrafted features are used. The input data are preprocessed, cleaned, and grouped into three distinct age range classes. Extensive experimentation and comparative evaluation reveal that SVM classifier with word2Vec features provides the best performance for the task of predicting age range.
Smart Knowledge Management for IT Services

Knowledge Management is a critical area for the success of an IT Service Provider, especially for Managed Services providers of infrastructure platforms like iSeries systems on IBM Power Servers. In this niche skilled area, developing the knowledge base (KB), updating, reviewing on regular intervals and ease of access to the support personnel are essential. The support ecosystem contains two different units based on their skill levels- Level 1 (L1) and Level 2 (L2). These teams refer to the Knowledge base (KB) articles that includes info on the Standard Operating Procedures (SOP), troubleshooting guides, etc. to perform these duties. One of the main challenges of iSeries Support is the dearth of skilled and talented resources in the open market for hiring. The service providers create a talent pool by providing training to the early professional hires from the campus. The pandemic and the work-from-home followed by that further impacted the technical education progress of talent pools and upskilling of resources. The customer’s core business runs on iSeries, so prompt responses are required for any issues reported and incorrect actions can cause a huge impact. A smart KB with ease of access to the team is identified as a solution to address this challenge. This can be achieved by incorporating Azure AI Services into the KB documents and providing a communication channel interface for the end users to interact with KB and receive the proposed resolution. This can help the team to improve the key performance indicator (KPI) of the mean time to resolve (MTTR) the incidents.
Prediction of Age, Gender, and Ethnicity Using Haar Cascade Algorithm in Convolutional Neural Networks

Recently, the cases of missing children, intruders, refugees from different countries, accidents are more observed in media and newspaper. It was mandatory to identify the basic details of unknown for security purposes which helps the organizations to provide services to customers. Entities such as age, gender, and ethnicity are fundamental information which can be used for these purposes. Ethnicity refers to classifying individuals based on demographic grounds using specific facial features. Likewise, age and gender shall be predicted using facial features extracted from unprocessed real-time images. This paper is proposed to predict the age group, gender, and ethnicity of people by extracting their facial features from real-time images using the Haar Cascade algorithm and classifying them into distinct categories using Convolutional Neural Networks (CNN). The results obtained from the proposed customized CNN model are compared with VGG face model, a pre-trained network for facial image analysis using Transfer Learning. It was found that the customized CNN model performs on par with the pre-trained network, with the advantage of consuming fewer resources and a shorter training time by using grayscale images. The proposed model was also tested on real-time images proving its robustness and generalization to real world images. This custom CNN model may be used for surveillance, genealogy research, and search investigations.
Mining Optimal Patterns from Transactional Data Using Jaya Algorithm

As the demand for strategic information grows rapidly throughout the world, it has become difficult to manage the expanding data and effectively extract valuable knowledge from the raw data. In association with rule analysis, pattern mining is the primary area of study. Increased revenue, cost savings, and greater competitive edges are just a few of the many advantages that insights from this kind of pattern analysis offer. However, as data volumes grow over time, mining patterns become slower and more labor-intensive. Furthermore, high memory consumption is required in mining patterns due to the algorithm’s extensive calculations. As a result, an effective method is needed to mine the hidden patterns as the volume of the data grows over time while using less memory. There are only a few control parameters needed such as the number of candidates, the number of iterations, the size of the elite, etc., because all population-based optimization strategies are probabilistic. A number of algorithms also require control parameters that are specific to them in addition to these. If the algorithm-specific parameters aren’t tuned properly, the computation will take longer or the local optimal solution will be found. So, in this paper, the algorithm known as the Jaya Algorithm (JA), which operates without any algorithm-specific parameters, is applied.
Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm

Credit card security is one of the needful criteria nowadays, which can cause billions of economic frauds worldwide. Some security firewalls provided by the banks are not up to the criteria in the daily life of the common user. The present work focuses on the fraud detection areas in data links and the scope of finding the accuracy to eliminate fraud while using. Data security with machine learning objectives needs more promising algorithms to improve accuracy; the hybridization of algorithms is a new era where two methods can evolve to find solutions for e-commerce applications. Integration of random forest with honey bee algorithms in machine learning for fraud detection is presented in this work. Our findings indicated that the hybrid model of random forest algorithm and honey bee algorithm is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.
Classification Models in Education Domain Using PSO, ABC, and A2BC Metaheuristic Algorithm-Based Feature Selection and Optimization

During the process of creating a machine learning model for real-life data, we often encounter numerous features in the dataset, but not all of them are essential. Incorporating irrelevant features during model training can lead to a decrease in overall accuracy, an increase in complexity, a decrease in generalization capability, and bias in the model. Therefore, selection of features is a crucial initialization step when building a machine learning model. The primary objective of feature selection is to identify the optimal set of features by reducing the dimensionality of the data to improve the model’s accuracy. Recently, swarm intelligence-based optimization techniques have gained popularity due to their effectiveness in solving dimensionality optimization problems. Techniques like PSO, ABC, and A2BC (automated ABC) have been explored, and feature selection methods based on these algorithms have been implemented and evaluated using various classifiers like SVM, Random Forest, XGBoost, and Voting (SVM + RF + DT) classifiers. The education science domain is used to evaluate these algorithms, which tackled a classification problem which is binary in nature related to freshman student’s success. The aim is to compare the accuracies and the performances of the three machine learning algorithms, namely, PSO, ABC, and automated ABC (A2BC), and conclude the best algorithm.
A Retrospective Study on English Teachers' Strategies to Improve Students' Reading Comprehension

Reading is a complex cognitive process from deciphering symbols to obtaining meaning. Although there is a lot of research on reading comprehension by teachers in Indonesia, there is still very little research on teacher strategies in reading comprehension to improve students' reading skills. This study aims to understand how teachers practice reading instruction, how they are assessed, and the strategies teachers use when teaching students to read. The research data were analyzed using qualitative data analysis techniques, namely: data collection, data reduction, analysis and data extraction. closing. This study uses qualitative methods and survey design. From the results of the research we have done, observations of teachers by giving questionnaires via a google form, so from these results we can conclude that the average of the 10 teachers we studied emphasized more strategies for giving practice and also reading practice, from both of these things the teacher mentioned that it is the most effective in improving the ability of students in reading skills, namely the teacher familiarizes his students with reading as a habit of problems that arise from the method used by the teacher when his students have difficulty in reading. The results of the second study with the formulation of the problem of what the students should do to improve their reading ability, which depends on the teacher, and what strategy the teacher will use to improve the student's abilities. practice to students as the main goal to facilitate better students' reading skills. The results of data collection from all the collected questionnaires can be concluded that about 85% of teachers prefer the type of practice or exam using the multiple choice method in reading because reading is more effective and efficient if the multiple choice technique is used. This test is a learning ability test that requires a good understanding because the test is a summary of the definitions of reading so this test is very good to use to improve student's reading comprehension.
Students Perspective on the Use of Rock Song in Learning to Improve Students Vocabulary

Mastering English vocabulary is important thing to be able to master English. The use of interesting learning media in vocabulary learning has a positive role in the learning process. The purpose of this study was to determine the students perspective on the use of rock song to increase students vocabulary. This research is a descriptive study using a mixed method. The subjects of this study were 70 8th grade students of Shalahuddin Junior High School. This study uses 2 data collection techniques, namely questionnaires and interviews. The results of this study indicate that students' perceptions of the use of rock songs in learning get a positive response from students. The use of rock songs in learning can increase students' learning motivation because the media used is interesting and new for students. In addition, students are easier to remember vocabulary and students are easier to get a lot of new vocabulary contained in the lyrics of the rock song used.