Reviewerly/exHarmony huggingface dataset:
Local-to-global structure prior guided high-precision point cloud registration framework based on FPP

As one of the popular point cloud reconstruction technologies, fringe projection profilometry (FPP) has great application prospects in point cloud registration from different viewing angles. While the existing point cloud registration algorithm that focuses on FPP is limited to high-speed fringe projection system. Besides, the deep learning methods based on the global feature retrieval mechanism can hardly mine the multi-modal data features of FPP, and are difficult to meet the accuracy requirements of FPP. To address the above issue, we design a local–global structure prior guided high-precision registration framework that focuses on the FPP data characteristics. Specifically, the consistent clustering matching (CCM) module is proposed firstly to obtain the structure priors of cluster correspondences in the overlap regions by analyzing the FPP multi-modal data. Then the local-cluster interaction network (LCINet) guided by the structure priors is introduced for feature extraction and interaction. Finally, the spatial alignment module based on cluster voting(SACV) is proposed to select cluster correspondences with high confidence to calculate the transformation matrix and align point clouds. Experiments show that our method achieves state-of-the-art performance on the dataset collected by FPP, and our framework can also greatly improve the training efficiency and performance of other deep learning models.
Blockchain as an Instrument for Improving Banking Processes

Abstract One of the best-known innovations of the recent past is blockchain. Due to its usability, it has generated considerable interest in several economic areas. A decentralised system’s approach can potentially reshape existing industries or make them redundant. Such technology can add a new dimension to processes, which is particularly relevant for the banking sector in the future. This demonstrates a great necessity to deal with blockchain with respect to market position and competition. This paper aims to provide insight into the current potential of blockchain applications in banking procedures. The benefits of process improvement in the financial sector are also outlined. This research shows that many experiences and adequate knowledge are already available. According to recent research, this technology will benefit banks’ business models, performance, and capabilities. However, blockchain is still in the phase of research and development, which is a crucial aspect of the banking industry.
Towards Real-Time Graph Neural Network-Based 3D Object Detection for Autonomous Vehicles

3D object detection is an essential task for applications such as autonomous vehicles. Its ability to percept the surrounding environment in 3D space makes it more practical to implement in a real-world application. Light Detection and Ranging (LiDAR)-based 3D object detection gains its popularity and many approaches have been proposed to achieve state-of-the-art performance. On the other hand, Graph Neural Network (GNN)-based approach for 3D object detection is still in early development as compared to other approaches. Point-GNN is one of the first architectures to implement GNN in the Lidar-based 3D object detection task. While Point-GNN achieved leading accuracy in the KITTI detection benchmark, it has low inference speed which makes it not sufficient to be implemented in real-time applications such as an autonomous vehicle. In this work, we propose a method to improve the inference speed of Point-GNN by observing the contribution of graph scale and graph radius in the graph construction part of Point-GNN. The proposed approach improves the inference speed up to 2.83 times faster than the original Point-GNN while maintaining the degradation in overall Average Precision (AP) not exceeding 3.5%. In addition, we also observed the contribution of different features in the GNN layer to improve the overall AP by adding distance and local neighbor state as new additional features in the GNN layer. The proposed model gains up to 0.22% improved performance in BEV AP and 0.35%in the 3DAP.
Critical Success Factors of Operational Excellence in Software Quality Assurance: Best Practices for Integrated Change Control Management

Integrated Change Control Management(ICCM) is a crucial phenomenon in the IT and Software Development industry. Through ICCM, project managers can avoid all unnecessary changes that disrupt the project execution and implementation; which further contribute to project delay, cost overrun, endless rework and facing the prolonged acceptance of User Acceptance Test (UAT). However, it is common that many project managers place much emphasis of time and effort in producing a detailed and comprehensive software quality assurance plan (SQAP) as part of ICCM adoption and implementation; neglecting and overlooking other important factors which indeed contribute to successful implementation of operation excellence. As a result, understanding the critical success factors (CRFs) allows the project managers to drive best practices to work routines; indirectly encouraging and enforcing project team members to understand and commit to the assigned task effectively. This paper aims to outline the critical success factors of operational excellence of ICCM in the context of the IT industry to exceed customer expectations through best practices in improving project mindset, processes and culture. In nutshell, every team member will understand the ‘‘flow of values’’ to the customers; fostering a competitive advantage within the industry in the long term.(Abstract)
Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest

Human activities that deviate from the norm are deemed abnormal, and such individuals are referred to as anomalous objects. Employing visual data to detect abnormal behaviour is a complex topic in video processing. This research proposes a novel method for detecting abnormal behaviour in complicated, crowded environments. In this article, we proposed a robust method for abnormal action recognition. We initially processed the data, applying fuzzy c mean and super pixel-based segmentation, extracting the features and tracking the object. The next step is to optimize the data. We used a deep data mining approach via t-distributed stochastic neighbor embedding procedure, and for classification, we applied random forest. We achieved 80.24% accuracy rate for human detection over UCSD dataset, and 79.19% for Shanghai tech dataset. We also got 84.00% accuracy of abnormal action recognition over UCSD dataset and 82.00% over Shanghai tech dataset.
A Machine Learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem

Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal human being needs to survive. In this regard, research studies have been proposed to predict the causes behind the heart failure of a patient using the 'Heart failure clinical records (HFCR's) dataset. Although, much research has been performed on this dataset, however, there is a lack of construction of a more reliable predictive model that helps to predict HF patients with better prediction results. We aimed to apply imbalance learning to handle the imbalance dataset as a very few researchers applied it. We trained the models using six ensemble and three non-ensemble classifiers with the help of multiple experiments. In the end, we performed an evaluation measure to compare our prediction results with the previous research work. Our proposed model gives a significant increase in accuracy value as well as in precision, recall, and f1-score.
Software Project Management - Gap between Theory and Practice

Proper management of software projects is crucial in preventing budget overruns, schedule slips, requirements creep, and other causes of project failure. The skills required for successfully managing software development projects, however, come with practical experience and rigorous training. Both the industry and the academia, therefore, need to work in unison. A differential in the skill set emphasized in the academia and the skill set required by the industry may lead to sub-optimal results. This research tries to identify the extent of the gap between the theory and practice of software project management by focusing on the landscape of universities and software development companies in Pakistan. The content of software project management-related courses offered by top universities in Pakistan is analysed to determine a set of subtopics emphasized by the academia. This set of subtopics is then used to design and, later, conduct an online survey of employees of various Pakistani software houses to gauge the differential in emphasis. Survey results indicate that a close mapping exists between what is being taught in the academia and what is required by the software development industry implying that this gap is not wide in the context of Pakistan.
CNN Model Compression by Merit-Based Distillation

Deep learning has advanced dramatically in recent years, and especially large convolutional neural networks (CNNs) have shown outstanding performance in a wide variety of tasks. However, such large-scale CNNs may not be able to be utilized in practice because they require a lot of computational resources. Therefore, there is a need to realize a CNN in a small model that has high performance similar to the large CNN. One method to solve these problems is Distillation, which uses a model of large scale and high performance as the teacher and a small model as the student model, and performs model compression by transferring the knowledge of the teacher model to the student model. However, in distillation, even when the teacher model incorrectly guesses the training input, the student model learns to mimic the teacher. This can prevent the student model from learning better. In this paper, we propose Meritbased Distillation, in which the accuracy of the teacher model’s output determines the degree to which the student model mimics the teacher model’s output, thereby preventing the student to learn undesired mappings. In the experiment, the effect of the Merit-based Distillation is evaluated. Consequently, the proposed method outperforms the improvements in the generalization by the student models trained by other known methods.
Open research knowledge graph for structuring scholarly contributions using transformers

More research papers are being published now than have ever been at any point in history. It is becoming increasingly difficult for the researchers to keep up with the papers that are being published in even a very narrow domain. This study proposes to build an open research knowledge graph (ORKG) that shows the scholarly contributions of the published papers. The paper makes use of natural language processing techniques and state-of-the-art deep learning models to achieve this task. The system generates a knowledge graph after performing four main steps including sentence classification, phrase extraction, triple formation (and classification) and finally, knowledge graph generation. Different state-of-the-art deep learning models such as RoBERTa have been used for classification and phrase extraction tasks whereas triple formation was performed using different heuristics. Finally, a knowledge graph is generated through which an end-user can identify the scholarly contributions in scholarly article. Experimental results are compared against other systems and show encouraging results.
Color-assisted Multi-input Convolutional Neural Network for Cancer Classification on Mammogram Images

The use of Convolutional Neural Network (CNN) in the computer-aided diagnostic (CAD) system in the radiological medicine field involving breast cancer is gaining wider attention due to the ability of this system to use mammogram images directly to obtain a robust model. However, large amounts of data are required to produce the best model. Moreover, their effectiveness may be hindered since grayscale mammogram images show atypical color distributions compared to those observed in three-channel RGB images by the network during training. This study proposes a multi-input CNN method to classify benign and malignant breast masses in mammograms. The converted images served as additional data from the original image and are trained parallelly. Using threechannel scaled-color images provides additional features that can be learned to construct more distinct learning weights for each class. We use an established digital mammogram dataset, the INbreast, to test the proposed method. The best model shows an increase in accuracy performance at 92.54% and an AUROC score of 0.9820 when using multi-input CNN. The fusion of features from the additional images that have been processed to three-channel as an addition to the original image shows performance improvement without the need for other external images by exploiting existing data.
Machine Learning and Speech Analysis Framework for Protecting Children against Harmful Online Content

Nowadays, it becomes significant to protect children from the detrimental influences of offensive online things like hateful messages, violence content, bullying and pornography that are now broadly spreading on Internet. Thus, Machine Learning (ML) algorithms can diagnose such content and then remove them. This paper depicts a unique ML model to detect the abusive content via speech recognition technique, especially for children. ML provides efficient tools for the assessment of web content and filtering of offensive material. The performance outcomes of developed model validate its accuracy and effectiveness. With the advancement of several methodologies, machine-learning approach proves its dependency on the quality of information that involves diversity and representativeness. ML can only handle such dataset efficiently if the developed algorithm has the potential to deal with poor-quality training datasets. The human moderation experts needed to interpret the contextually hard content as per the current scenario or even in foreseeable future in spite of the progress in ML technique. The key research activities under online content domain using ML approaches because of the massive volume of information for researching/training models. This developed ML algorithm also shows its effectiveness to deliver accurate outputs for the cases of limited training data or experiencing issues in understanding them.
Investigation of Learning Rate for Directed Acyclic Graph Network Performance on Dysgraphia Handwriting Classification

Intelligence-based detection techniques are significant for dysgraphia diagnosis in school children at an early age. The method involves analyzing the symptoms through handwriting images. Directed Acyclic Graph (DAG) network is one of the intelligence-based technique in handwriting identification. The layer of DAG construction differs from the standard sequential convolution network that exhibits successful image classification. Recently, limited research has been conducted to investigate the learning rate that could affect the DAG network performance in classifying handwriting images. The issue of inconsistent handwriting patterns among dysgraphia children solved by the standard sequential convolution network layer remains different from the DAG operation. Therefore, this study aims to investigate the DAG operations with four acyclic blocks, along with varying the learning rates that could impact the performance of dysgraphia handwriting classification based on synthetic letter images. Experimentally, an improved DAG network model was investigated on four variant values of learning rate; 0.1, 0.01, 0.001, and 0.0001. The performance was measured using a confusion matrix for predicting dysgraphia or non-dysgraphia handwriting. The results obtained the best training accuracy of 99.01% produced by the DAG model at 0. 01learning rate. The testing performance for dysgraphia handwriting classification dropped at the lowest learning rate.
Handwritten Character Recognition Based on Adabelief Optimized Convolutional Neural Network

Neural Network plays an important role in image classification. It uses Convolutional neural networks (CNN) which is successful on image classification. The reason for using CNN is performs better on features than any other. Alphanumeric character recognition is a very useful and powerful system to translate human written characters, extract text from documents, detect traffic signals, etc. The recognition technologies which are already in use are not very reliable and it needs to be improved further in the accuracy and efficiency. Here both alphabets as well as numbers are recognized from the EMNIST dataset (Extended Modified Institute of Standards and Technology database) which consists of alphanumeric characters. First the required dataset is imported, and it is preprocessed. Then they are trained, and models are created. Those models are run over the test set. Then the input image is fed. Binarization is done followed by feature extraction. The analyzation is done with two optimizers – Adam and Adabelief. This article discussed about few models by analyzing the validation and training accuracy and losses. Further, different EPOCH values are set for the models and the validation accuracy obtained for Adam is 97% and Adabelief is 98.5 and the losses are 8% and 4% respectively. Hence, Adabelief performs better than Adam in terms different performance metrics such as training duration, accuracy and loss.
Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth

Nearly 70% of the population in India depends on agriculture. The agricultural field is used for a variety of tasks like ploughing and seeding. The equipment required for the aforementioned operations is pricy and cumbersome to operate. Therefore, by creating a system that will save time and labor, the agricultural system in India should be supported. Developing a robot that can sow seeds, plough, and spray water while being fully powered by solar energy is the objective. The built robot is run by a solar panel and is signaled by an Android or Bluetooth app to operate the essential mechanisms and movement. This boosts increases the effectiveness of seed sowing and lessens the difficulty of manual planting. The goal is to create a robot that can carry out tasks like mechanized picking, spraying, dispensing seeds, and ploughing. Additionally, it offers manual control as necessary and uses humidity sensors to monitor the humidity. The AVR at the mega microcontroller, which controls everything here, is the key element. The robot starts by tilling the entire field and then moves on to ploughing while distributing seeds. An ultrasonic sensor that transmits data to the microcontroller continually is the navigational device. The robot only uses human operation outside of the field; it runs in automated mode inside. The robot may be manually controlled using a Bluetooth pairing app, which also aids in navigating the robot outside of the field.
Copy Move Counterfeiting Identification based on CNN using Scalar Invariant Feature Technique

Since there are lot of efficient techniques for picture manipulation, their credibility is questioned, especially when they are used to support insurance claims, news stories, or judicial processes. With the quick advancements in science and technology, it is getting easier and easier to access a wealth of knowledge through various multimedia platforms. However, the authenticity and authenticity of multimedia are in danger because it is simple to alter the contents with various editing programmes. To address this issue, forensics technology is created. This study primarily focuses on examining copy move forgery blind image forensics technologies. Copy move forgeries, which often involve concealing items by flattening areas of the picture or adding elements to an existing image, are one of the most widely used methods for photo manipulation. Picture forensic procedures determine the integrity of images by utilising a variety of high-tech processes that have been found in the literature. Through this study, the photos are checked for a particular type of forgery in which a part of one picture is pasted onto another image to duplicate or conceal it.
Prediction of Cervical Cancer using Multilayer Perceptron Algorithm

The fourth most frequent illness-related cause of death in women is cervical malignant growth. Cervical cancer is associated with the presence of the human papillomavirus (HPV). Early detection has made cervical cancer preventable, which has decreased overall impact of the disease. Due to the high expense of routine exams, a lack of awareness, and limited access to medical facilities, women do not participate in enough screening programs in underdeveloped countries. This way, each patient is expected to be at extremely high risk. There are numerous threat factors that can lead to the growth of cervical cancer. As a result, the datasets will be checked for cervical cancer by using a variety of data analytics tools, including machine learning and deep learning algorithms. The classification of normal and abnormal cervical data is done by performing a quick overview of how cervical cancer works and is detected.
Detection of Intoxication in Automobile Drivers

The advancement of innovative technology that can accurately aid in the timely detection of intoxication in humans, particularly in scenarios requiring intoxication detection while driving, requires more research attention. Despite the fact that there are conceptual ideas for using facial recognition to perform this task, the results still need improvement. While the existing research works have demonstrated the capabilities of specific architectures, this article intends to develop an improved system to predict whether a driver is intoxicated or not by utilizing an ocular approach and CNNs. It also provides a general overview of face recognition and its applications. It can be assumed that if an improved system is developed and implemented in vehicles, the severe effects of drunk driving will be reduced. This study has tested five methods, within which four methods are composed of CNN architectures: VGG19, VGG16, MobileNet V2, and ResNet 50. The performance of an LSTM+ Attention Mechanism approach is also tested in this scenario. Finally, this article demonstrates that the VGG16 architecture provides the best validation accuracy for the given classification problem while also considering the results of other approaches to assess its applicability in alcoholic detection systems.
Deep Analysis on Patient Health Records using Augmented Reality

Surgeons are constantly looking for better technology for operating rooms. They typically make use of innovations that speed up the delivery of the greatest surgical and patient occurrence in the area. As a result of continuous improvement in surgical procedures, there are many developments in the environment of the digital age. Identified as a potentially disruptive technology surgical environment. As augmented reality technology becomes more universally accessible, it's affordable and can also be used in healthcare improving the medical use of data is inevitable. Applications for the operating room are already being considered, including those that deal with human anatomy, surgical procedures, or post-operative rehabilitation. AR is the process of using artificial information to improve one or more senses.