Reviewerly/exHarmony huggingface dataset:
Single Frame Lidar and Stereo Camera Calibration Using Registration of 3D Planes

This work focuses on finding the extrinsic parameters (rotation and translation) between the lidar and the stereo camera setups. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both the sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The planes extracted from the sensor’s data are used as reference data sets to find the relative transformation between the two sensors. We use our proposed method Correntropy Similarity Matrix Iterative Closest Point (CoSM-ICP) algorithm to estimate the relative transformation. In this work, we use a single frame of the point cloud data acquired from the lidar sensor and a single frame from the calibrated Stereo camera point cloud to perform this operation. We evaluate our approach on a simulated dataset since it has the freedom to evaluate under multiple configurations. Through results, we verify our approach under various configurations.
Combining Word and Character Embeddings for Arabic Chatbots

The Arabic language has a rich morphology and structure with a diverse vocabulary and rarely used words. Consequently, most Arabic Natural Language Processing (NLP) tasks could benefit from embedding models that do not assign a distinct vector to each unique word in the used vocabulary but instead focus on the internal structure of words. The semantic meaning of a word is related to the meaning of its composing characters which contain rich internal information. In this paper, we propose a new embedding model using two levels of granularity; words and characters. Moreover, we describe the details of generating an Arabic word embeddings using Word2Vec and FastText models. Furthermore, a Deep Learning (DL) architecture will be applied to the top of the word-character embeddings. Experimental results show that the proposed scheme outperforms the state-of-the-art methods proposed for Arabic chatbots.
A training-free recursive multiresolution framework for diffeomorphic deformable image registration

Diffeomorphic deformable image registration is one of the crucial tasks in medical image analysis, which aims to find a unique transformation while preserving the topology and invertibility of the transformation. Deep convolutional neural networks (CNNs) have yielded well-suited approaches for image registration by learning the transformation priors from a large dataset. The improvement in the performance of these methods is related to their ability to learn information from several sample medical images that are difficult to obtain and bias the framework to the specific domain of data. In this paper, we propose a novel diffeomorphic training-free approach; this is built upon the principle of an ordinary differential equation. Our formulation yields an Euler integration type recursive scheme to estimate the changes of spatial transformations between the fixed and the moving image pyramids at different resolutions. The proposed architecture is simple in design. The moving image is warped successively at each resolution and finally aligned to the fixed image; this procedure is recursive in a way that at each resolution, a fully convolutional network (FCN) models a progressive change of deformation for the current warped image. The entire system is end-to-end and optimized for each pair of images from scratch. In comparison to learning-based methods, the proposed method neither requires a dedicated training set nor suffers from any training bias. We evaluate our method on three cardiac image datasets. The evaluation results demonstrate that the proposed method achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties.
Explainable Artificial Intelligence Methodology for Handwritten Applications

There has been explosive growth of practical AI in recent years. A major concern of current AI systems and compliance regulations is an inability to explain inferential decisions. This work explores an Explainable Artificial Intelligence (XAI) methodology that provides explanations for classification decisions. Experimental results using the MNIST handwritten digit database are provided with explainable conclusions.
An empirical study of cyclical learning rate on neural machine translation

Abstract In training deep learning networks, the optimizer and related learning rate are often used without much thought or with minimal tuning, even though it is crucial in ensuring a fast convergence to a good quality minimum of the loss function that can also generalize well on the test dataset. Drawing inspiration from the successful application of cyclical learning rate policy to computer vision tasks, we explore how cyclical learning rate can be applied to train transformer-based neural networks for neural machine translation. From our carefully designed experiments, we show that the choice of optimizers and the associated cyclical learning rate policy can have a significant impact on the performance. In addition, we establish guidelines when applying cyclical learning rates to neural machine translation tasks.
Integration Implementation and Application of Power Grid GIS Platform and Production Management System Based on ESB

Geographic information system technology is developed rapidly in the field of electric power production management, including huge data of power equipment and facilities, user data and planning data, showing a certain space and complexity from power generation, transmission, distribution to electricity consumption. Applying the potential advantages of geographic information system technology to the management of power system can organically combine the map, image and attribute data in the power grid management system, thus greatly improving the quality of power and reliability of power supply. This paper emphatically elaborates information integration technology based on the ESB. Applying the spatial data of the power grid GIS platform to the business planning of production management system can not only integrate or inherit the existing power system applications, but also facilitate the expansion of new business system functions, which has good dynamic scalability, realizes the automation and informatization of power grid production management, and provides strong support for the self-healing and reliability of power grid.
A Resource-Efficient Convolutional Neural Network Accelerator Using Fine-Grained Logarithmic Quantization

Convolutional Neural Network (ConNN) implementations on Field Programmable Gate Array (FPGA) are being studied since the computational capabilities of FPGA have been improved recently.Model compression is required to enable ConNN deployment on resource-constrained FPGA devices.Logarithmic quantization is one of the efficient compression methods that can compress a model to very low bit-width without significant deterioration in performance.It is also hardware-friendly by using bitwise operations for multiplication.However, the logarithmic suffers from low resolution at high inputs due to exponential properties.Therefore, we propose a modified logarithmic quantization method with a fine resolution to compress a neural network model.In experiments, quantized models achieve a negligible loss of accuracy without the need for retraining steps.Besides this, we propose a resource-efficient hardware accelerator for running ConNN inference.Our design completely eliminates multipliers with bit shifters and adders.Throughput is measured in Giga Operation Per Second (GOP/s).The hardware utilization efficiency is represented by GOP/s per block of Digital Signal Processing (DSP) and Look-up Tables (LUTs).The result shows that the accelerator achieves resource efficiency of 9.38 GOP/s/DSP and 3.33 GOP/s/kLUTs.
Malaria Parasite Classification using Deep Convolutional Neural Network

Malaria is one of the life-threatening diseases which spread by the Plasmodium parasites. Traditionally, microscopists analyze the microscopic blood smear images but it is time consuming and may leads to false negatives. Automated detection of malaria from the thin blood smear slide images is a challenging task. However, in the domain of medical and healthcare applications, classification accuracy plays a vital role. The higher level of false negatives in medical diagnosis systems may raise the risk of the patients by not employing the required treatment they exactly need. In this article, we have developed three Convolution Neural Network (CNN) models for the prediction of malaria from the red blood cell images into infected parasite red blood cells and uninfected parasite red blood cells. Finally, out of the three setups, proposed CNN setup-1 with kernel size 3 x 3 and pool size of 2 x 2 achieved an accuracy of 96%.
Enhanced Fuzzy Random Forest by Using Doubly Randomness and Copying From Dynamic Dictionary Attributes

While fuzzy random forest (FRF) as a fuzzy implementation of random forest has earned its strong ambiguity/uncertainty handling capability on a rich variety of considerably low dimensional datasets, this article revisits FRF and attempts to enhance its generalization capability and computational speed on high dimensional datasets. For the first issue, in addition to the original use of randomness in FRF, a doubly randomness is newly introduced into the generation of both the candidate attributes and the best splitting attributes in FRF. For the computational speed issue, while the proposed new fuzzy information gain (NFG) measure does not apply to all candidate attributes, the remaining NFG values can be quickly retrieved by copying from the dynamically generated dictionary. As a result, a new method called enhanced fuzzy random forest (E-FRF) is proposed and justified theoretically from the consistency perspective. Our extensive experimental results indicate that the proposed method E-FRF has at least comparable performance to the comparative methods, and is an advantageous alternative to FRF in terms of both testing accuracy and running speed in most of the adopted high dimensional datasets.
A Broker-Based Manufacturing Supply Chain Integration with Blockchain: Managing Odoo Workflows Using Hyperledger Fabric Smart Contracts

Nowadays, Blockchains are considered the go-to solution when it comes to secure and trust-ensuring platforms for critical data exchange. However, integrating this technology with existing systems is quite a challenging task as there is an unclear path to achieve the integration. Usually, the main motivation behind integrating blockchain with information systems is the fact that it ensures trustworthiness, security, and traceability by design. In this paper, we investigate the integration of this technology from an information system perspective in the context of manufacturing supply chains. We propose a proof-of-concept of a manufacturing supply chain scenario integration between Odoo (Supply Chain processes) and Hyperledger Fabric (blockchain development platform). We demonstrate how Odoo workflows can be optimized, secured, and made trustworthy using smart contracts and highlight the impact of this solution on current and future applications especially in the Industry 4.0 context where data is an abundant and critical resource.
Parallel Faces Recognition Attendance System with Anti-Spoofing Using Convolutional Neural Network

Face recognition which is a sub-discipline of computer vision is gaining a lot of attraction from large audience around the world. Some application areas include forensics, cyber security and intelligent monitoring. Face recognition attendance system serves as a perfect substitute for the conventional attendance system in organizations and classrooms. The challenges associated with most face recognition techniques is inability to detect faces in situations such as noise, pose, facial expression, illumination, obstruction and low performance accuracy. This necessitated the development of more robust and efficient face recognition systems that will overcome the drawbacks associated with conventional techniques. This paper proposed a parallel faces recognition attendance system based on Convolutional Neural Network a branch of artificial intelligence and OpenCV. Experimental results proved the effectiveness of the proposed technique having shown good performance with recognition accuracy of about 98%, precision of 96% and a recall of 0.96. This demonstrates that the proposed method is a promising facial recognition technology.
Security Techniques in Communications Networks Applied to the Custody of Digital Evidence

As new technologies spread in organizations with a growing influence of digital transformation, security challenges began to identify new threats and increasing attacks. Threat and vulnerability detection have encouraged cybersecurity professionals’ new methods and techniques accompanied by tools that guarantee digital evidence’s admissibility in the short and long term. This project establishes a set of security techniques applied in communication networks that ensure adequate digital custody. To answer the research question, it is planned to carry out a process of four stages: state of the art, description of the security methodologies and techniques applied in communication networks, diagnosis of the methodologies or techniques used by the justice operators, and the synthesis of acceptable practices applied with good techniques on the networks of the custody of digital evidence. The essential part of this project consists of the diagnosis made to the main operators of the Judicial Council of Chimborazo Province, which determines that these operators do not have the knowledge, methodologies, techniques, and resources for the preservation and admissibility of the digital evidence, resulting in an extensive vulnerability for Cybercriminals to access the information being it altered, modified, or destroyed.
Research Perspective on Digital Forensic Tools and Investigation Process

The digital forensic tool plays a crucial role in protecting from share trading fraud, financial fraud, identity theft, and money laundering. Investigators in Digital Forensic analysis have the right to use various forensic tools for investigation. Regretfully, several contemporary digital forensics technologies are lacking in a number of areas. The research aims to study different phases of digital forensic methods and various issues encountered during the investigation process. In addition, the study also focuses on the mindful analysis of different kinds of digital forensic tools. The methodology includes building, providing defense to any scene, proper review, effective communication, and identification. It also involves the identification of the digital investigation opportunities and recognizing all the policies for controlling proof. The tools and procedures that can be utilized to investigate digital crime are discussed in this article. Desktop forensic tool, Live Forensic tool, Operating System Forensic tool, and Email Forensic tool are some of the covered categories. AI-based network logs are evaluated using a variety of machine learning techniques and compared with various metrics such as accuracy, precision, recall, and F1-Score. A comparative analysis of popular tools in each category is tabulated to understand better, making it easy for the user to choose according to their needs. Furthermore, the paper presents how machine learning, deep learning, and natural language processing, a subset of artificial intelligence, can be effectively used in digital forensic investigation. Finally, the future direction of the challenges and study scope in digital forensics and artificial intelligence is also mentioned for potential researches.
Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification

In recent years, the need to monitor health using sensors integrated on popular smart devices is receiving attention. The development of the human activity classification (HAR) system allowed the monitoring and assessing human health status. Most research in this area has been done on smartphones with the limitation of a fixed position on the body to collect raw data and combine it with other machine learning algorithms to improve activity classification performance. However, the phone’s location on the body in many studies was not the same, leading to different data collection. Smartwatches solved this problem because they were worn on the human hand and had stability and sensitivity to the body’s activities. This research would evaluate the accuracy using data from accelerometers on smartphones and smartwatches, combining with some machine learning algorithms to classify four activities: sitting, standing, walking, and jogging. The classification performance was evaluated through accuracy, sensitivity, and specificity. The overall results showed that the data from the smartwatches accelerometer had higher accuracy than data from smartwatches.
Arabic text classification using principal component analysis with different supervised classifiers

Text classification (TC) is the process of automatically labeling a text based on its topic. Generally, TC suffers from data high dimensionality of the feature space. In this paper, we use the principal components analysis (PCA) as a feature extraction method for reducing the space features size with an application to Arabic texts. To evaluate the effect of this technique on the classification process, we use five popular classifiers: logistic regression, k-nearest neighbors, decision trees, random forest and support vector machines. The results show that the proposed method yields a substantial improvement of classification accuracy of most of the classifiers with reduction in execution time. To the best of our knowledge, it is the first time PCA is used for Arabic TC. In addition, the gain in training time is comprised between 5 and 800. Accuracy is improved in 60&#x0025; of the cases.
Twitter Bot Account Detection Using Supervised Machine Learning

Twitter is a primary social media platform gaining popularity among social networking websites at an alarming rate. Twitter's popularity and relatively open nature make it an excellent target for automated programs known as bots, which are computer programs that run automatically. In addition to spamming, bots can be used for various purposes, such as inducing conversations to change the topic of discussion, modifying user popularity, contaminating materials to spread misinformation, and conducting propaganda. This study's goal was to provide a fresh perspective on estimating the possibility of an account being identified as a bot by applying Machine Learning algorithms to a variety of scenarios. Both Random Forest and XGBoost algorithms are used in this application. The inquiry began with exploratory data analysis to determine the current status of the dataset. Next comes the process of model engineering, which involves the steps of requirement gathering and specification, feature selection and optimization, hyperparameter tweaking, and algorithm benchmarking. The findings of this investigation suggest that the XGBoost algorithm outperforms Random Forest, with an accuracy of 0.8908 for XGBoost and 0.8762 for Random Forest.
Global Level Smart Vaccination Tracking System using Blockchain and IoT

The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment.Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19.Most of these vaccination supply chain models are cloud-centric and depend on humans.Due to this, the transparency in the supply chain and vaccination process is questionable.Moreover, we con't trace where the vaccination programs are facing issues in real-time.Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities.This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things.Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity.We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities.The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases.From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure.Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation.
An Empirical Investigation on Software Cost Estimation Techniques and Barriers on Agile Software Development in Software Industry of Pakistan

Software cost estimation is very crucial for the success of the software. For this reason, many studies have been conducted to identify various cost estimation techniques and barriers affecting the software estimation process in traditional development. However, these days, most software development organizations are working following agile methodologies. Therefore, there is a need to identify the critical success factors and barriers of cost estimation for agile methodology followers. In this study, a systematic literature review and survey is conducted to determine the crucial success factors and barriers of cost estimation. Therefore, this study might help an agile project manager plan the cost and effort of the software project by getting an idea from the list we have provided after validating the techniques used from the state of the art and industry so that we can come up with practical guidelines.
HSUM-HC: Integrating Bert-based hidden aggregation to hierarchical classifier for Vietnamese aspect-based sentiment analysis

Based Sentiment Analysis (ABSA), which aims to identify sentiment polarity towards specific aspects in customers’ comments or reviews, has been an attractive topic of research in social listening. In this paper, we construct a specialized model utilizing PhoBert’s top-level hidden layers integrated into a hierarchical classifier, taking advantage of these components to propose an effective classification method for ABSA task. We evaluated our model’s performance on two public datasets in Vietnamese and the results show that our implementation outperforms previous models on both datasets.