Reviewerly/exHarmony huggingface dataset:
A Systematic Approach for Enhancing Software Defect Prediction Using Machine Learning

In the modern world of software development, ensuring reliability and performance is of paramount importance. However, despite the best efforts from the developers, software defects can still emerge, causing frustration and wasted resources. Due to the numerous defects found during the software development process, researchers have developed numerous ways for defect prediction models. However, these models cut down the time and expense of development when problems in a concurrent software product are anticipated. Due to the increased amount of defects brought on by software complexity, manual defect detection can become an extremely time-consuming procedure. This encouraged researchers to create methods for the automatic detection of software defects. The study of this paper has shown that a combination of machine learning algorithms could be applied effectively for software defect prediction. Interestingly, the combination of Artificial Neural Network and Random Forest classifier has been performed with the mean accuracy of 91%, while the hyper-parameter optimization model classifier has been performed with the mean accuracy of 83%, 83%, 84%, 77% and 80% for Support Vector Machine, Random Forest, Logistic Regression, Naive Bayes Gaussian and Decision Tree, respectively. These findings have demonstrated the potential of Machine Learning in the area of software development.
Synthesizing Realistic Images from Textual Descriptions: A Transformer-Based GAN Approach

The ability to automatically generate realistic images from textual input is a challenging and important goal in artificial intelligence. In this research, a novel approach is represented that combines RoBERTa a transformer-based language model, with Generative Adversarial Networks (GANs) to synthesize high-quality images from textual description. The proposed architecture uses RoBERTa model to encode the text input and Generative Adversarial Network that relates it to each pixel to produce an image that closely represents the input. The quality of the synthesized images is measured using Fréchet Inception Distance (FID) and Inception Score (IS) metrics. Three variants of the architecture have been proposed and demonstrated that this approach can produce realistic images. The results indicate that transformer-based language models can effectively be used with GANs for image synthesis, thus paving the way for further research in this area.
A new ciphertext based OT protocol in cloud computing

Cloud computing is the on-demand availability of computer system resources, especially computing power. Cloud computing relies on sharing resources to achieve coherence, and it needs to communicate frequently with the user. In addition, the user's information must be kept secret from the cloud, and additional computing information in the cloud should also be kept private from the user. The problem can be solved with oblivious transfer. Oblivious transfer (OT) protocols are an essential part of secure multi-party computation in cryptography. However, the previous scheme of OT is inefficient. When messages need to be transmitted multiple times, it becomes challenging to use in protecting privacy data transfer for cloud computing. In this paper, we analyze the features of cloud computing and the former OT protocols based on public key encryption (PKE) and Diffie-Hellman (DH). We propose a new OT protocol strategy, which uses a function in the ciphertext. We map the obtained symmetric public key and reduce computation for the protocol based on DH. We provide an implementation on cloud computing and compare our protocols with well-known OTs. In cloud computing, it shows that the time for each OT is almost negligible. In addition, the experimental results show that our protocol is at least 24% to 40% more efficient than other protocols. On the premise of ensuring user privacy, our strategy can make cloud data transmission more efficient.
Area-Time Effective Modular Multiplication for Elliptic Curve Cryptography

Hardware implementation of elliptic curve cryptography (ECC) is a more competent operation in the field of cryptosecurity. Area, time, and power consumption are contradictory parameters for ECC processors. One of these parameters needs to be sacrificed to achieve effectiveness in terms of others. This research presents a 256-bit modified interleaved modular multiplication (IMM) over the prime field that uses fewer area slices and runs faster. Our main goal is to design a modular multiplier by minimizing the required area and keeping frequency maximum with less power consumption. The hardware architecture is developed with the help of the modified IMM algorithm. The hardware implementations are carried out using Xilinx software on Kintex-7 and Virtex-5 field programmable gate array (FPGA) technology. The suggested modular multiplier achieves a computation time of approximately 1.90108 μs at a maximum frequency of 134.660 MHz in Kintex-7 and 2.09176 μs at a maximum frequency of 77.9157 MHz in Virtex-5. We have preferred a 256-bit prime field among other prime fields for the Koblitz curve ECC. Almost no (0%) slice registers and just 3% of slice lookup tables (LUTs) are used in the proposed architecture. To obtain the simulation results, Modelsim software is utilized, and for verification purposes, Maple software is employed in this case. The modified algorithm suggests a more area and time-efficient hardware architecture that provides a higher throughput rate and less area-time product for elliptic curve cryptographic processors (ECP).
AI-based Affordable High-density Traffic Monitoring System

Vehicle detection and traffic monitoring system play a significant role in many areas of transportation infrastructure. Autonomous vehicle detection and computations of the number of vehicles are becoming increasingly crucial in highway traffic management. In Bangladesh, especially in Dhaka, a substantial amount of time from daily life is wasted due to traffic congestion. However, the vehicle monitoring system is very challenging in highly dense traffic areas, and researchers are working relentlessly to improve the performance. In this paper, we have proposed an affordable AI-based vehicle detection and traffic monitoring system to fulfill this need. To validate our model, we have done experiments to detect five different vehicle types and implemented the model in Raspberry Pi. The experiment results and edge implementation indicate that our technique can lead to a simple, affordable solution for traffic monitoring and ultimately reduce traffic congestion. For still images, minimum accuracy 86%, and for traffic footage, minimum accuracy 80% is achieved by our experiment.
TSM-MobileNetV3: A Novel Lightweight Network Model for Video Action Recognition

The deployment of video action recognition models on mobile and embedded devices is challenging due to the limited computational resources and storage capacity. To address this issue, we propose a novel lightweight network architecture named TSM-MobileNetV3. Based on the Temporal Shift Module (TSM), we replace the backbone network with MobileNetV3, which is flexible and easy to implement. The proposed model is evaluated using the HMDB51 dataset, with detection accuracy, inference speed, and model size as the evaluation metrics. Experimental results demonstrate that TSM-MobileNetV3 achieves a detection accuracy of Top-1-0.70 and Top-5-0.89 with only a 0.02 decrease in accuracy, while achieving a 50.27% improvement in inference speed and a significant reduction in model size compared to other lightweight models. TSM-MobileNetV3 has been successfully deployed on NVIDIA-jetson devices, with reasonable agility and response speed. Our proposed model shows promising performance on mobile and embedded devices, with reduced training and deployment requirements, enabling deployment on edge devices. This study provides new insights and directions for designing and applying lightweight models. The proposed lightweight network model has broad prospects for application in various fields, such as smart homes, intelligent surveillance, and autonomous driving. Our team is currently investigating the deployment of this model on simulation platforms such as Unity for further testing.
A Control Method for Flying Robots

This paper proposes a flight robot control method by controlling the flight robot to fly to a reference hover point and activating the robotic arm for grasping operations to grasp airborne objects. During the grasping operation, the first kinematics information of the multi rotor flight platform and the second kinematics information of the manipulator are obtained. According to the first kinematics information and the second kinematics information, calculate the disturbance force and disturbance torque of the manipulator base. According to the disturbance force and disturbance torque, the compensation force of the Six degrees of freedom pose compensation device is calculated. The Six degrees of freedom pose compensation device is controlled to output compensation force to compensate the pose disturbance of the manipulator base. This can improve the accuracy of pose control at the end of the robotic arm and achieve reliable grasping of aerial objects.
Autonomous Following Algorithm for UAV Based on Multi-Scale KCF and KF

This paper presents an autonomous following approach for UAV, called AF, for the issue of target's motion state is unknown, complex and variable. This approach relies on the integration of Object Tracking algorithm and Flight Control algorithm. The proposed method aims to solve the problems of scale change, occlusion, speed and direction mutation during target's movement, so as to achieve accurate following flight of UA V. The method is divided into two main parts: firstly, the proposed mKCF-KF algorithm is used to track the target's position in the image sequence, which can solve the issues of target's scale change and occlusion. Secondly, based on the tracked target's position, a flight control algorithm for 3D following is designed, which can respond the issues of target's speed and direction mutation. The effectiveness of the proposed method is demonstrated by built semi-physical simulation platform based on ROS, Pixhawk, CopterSim and RflySim3D software. The results show that the proposed method achieves superior following performance.
YOLOv5s-Transformer: Improved YOLOv5 Network for Real-Time Detection of Cigarette Smoking Based on Image processing

Cigarette smoking is a significant cause of fires, resulting in increasingly devastating effects. However, the current detection methods fail to meet the real-time smoking detection requirements. The main contribution in this paper is to address the problems of low detection accuracy and inaccurate positioning of smoking detection. First, for the real-time detection speed, this paper proposes a lightweight smoking detection model based on YOLOv5s. Second, for better smoking detection accuracy, this paper uses image processing technology and combines C3TR block into the network architecture. Finally the proposed model, named YOLOv5s-Transformer, is deployed into edge computing platform NVIDIA Jeston Nano. The mean average precision of the proposed model is 18.3% higher than the baseline YOLOv5s, thereby achieving an improved balance between detection speed and accuracy.
Unsupervised Learning-Based Cross-Modal Transformation of Brain MR-CT Image

Medical imaging technology serves as a crucial instrument for disease screening and medical diagnosis in clinical medicine, with computed tomography (CT) and magnetic resonance imaging (MRI) standing as prevalent and significant diagnostic imaging techniques. However, the limitations of medical imaging acquisition and patient-specific factors can hinder medical images acquisition, resulting in certain challenges for clinical diagnoses. Furthermore, MR image-guided radiotherapy necessitates the synthesis of pseudo-CT images from MR images for radiotherapy dose estimation. Consequently, there has led to a growing interest among researchers in the research of a cross-modal transformation between MR and CT images. In clinical medicine, the limited volume of aligned medical images data and the systematic errors in the alignment process, while non-aligned medical images data are more in line with the actual clinical environment, make unsupervised learning-based cross-modal conversion of medical images gradually become a research focus. In light of this, we develop a novel cycle-consistent adversarial networks (CycleGAN) model for the unsupervised learning-based cross-modality transformation task of brain MR-CT images. Within this model structure, we propose an Att-UCTransformer generator and an HDD-PatchGAN discriminator. The proposed model undergoes quantitative analysis and demonstrates more promising results than the original CycleGAN model in three evaluation metrics, namely MAE, PSNR, and SSIM, effectively enhancing the accuracy of unsupervised learning-based cross-modality conversion tasks for medical images.
Homomorphic Encryption on Deep Learning in accurate prediction of Brain Tumour

Data breach and data theft has become a repulsive problem in the digital world and medical data is the most vulnerable in this case as it contains numerous information of patients which can hinder a patient's personal life. Moreover, due to patients' rights and confidentiality, hospital's are unwilling to share the medical data to third parties. Also, Brain tumour is one of the most occurring disease as everyday, around the globe vast amount of MRI image are taken. Many existing models have been introduced throughout the decades to detect brain tumors, mostly based on Neural Network models. To secure the MRI images and protect patient rights, we have worked on a technique based on Partial Homomorphic Encryption (PHE) which produces encrypted images while keeping most of the features intact. As a result, existing NN models can produce a higher accuracy by extracting features from the encrypted images and also reduces the time complexity of established Paillier cryptosystem. We were able to achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\max$</tex> 82% accuracy using a VGG-19 model. Using our proposed model we can safely and securely read encrypted medical image data via our PHE method while being efficient enough to be used on a mass scale in the medical industry.
EF<sup>2</sup>Net: Better Extracting, Fusing and Focusing Text Features for Scene Text Detection

Text detection in natural scene images is a chal-lenging task that requires localization and fitting of text regions. Currently, existing natural scene methods use fixed-size convolutional kernels to extract text instance features and have achieved good results. However, due to the extremely large aspect ratio of text regions in natural scenes, extracting features using fixed-size convolutional kernels introduces background noise, which affects the accuracy of text detection. In addition, complex backgrounds in natural scenes may cause text features in existing methods to be incorrectly detected as text, while small and ambiguous text may be missed in the detection. To address these challenges, first, we use a new backbone with multi-branch depth band convolution to better capture text features in large aspect ratios and multi-scale backgrounds. Then, we propose a novel FPN that can obtain detailed information and scale sequence features to enhance the feature information of small texts. Finally, we design a dynamic text detection head that combines a text detection head with three attention mechanisms. We perceive from three dimensions: scale, space, and channel, enhance multi-scale text region features, focus on foreground targets, and accurately locate text regions, finally achieving the effect of reducing false and missed detections. In conclusion, the method proposed in this paper achieves good performance in text detection tasks in natural scenes and solves some problems in existing methods. Experimental results show that our proposed model achieves a comprehensive surpass compared with the text detection baseline.
Blockchain Based Healthcare System to Secure Health Records

With the advancement in technology, blockchain is playing a major role all around the world. The blockchain applications shows it's significance in many applications such as financial services, aircraft industry, e-voting mechanisms etc. However, the healthcare sector is focusing more attention towards the technology to use benefits of security, decentralization and trust. Motivated from the aforementioned facts, this paper proposes MyEasyHeathcare, an efficient healthcare model to help provide the patients a secure facility from the hospital. The model helps doctor and patient to register and then authorizes a particular doctor to a particular patient. The physician or pathologist checks the patients accordingly and gives prescription as treatment. This paper also discusses research challenges faced by traditional systems and their possible blockchain solutions.
An End-to-end Learning Based Covolutional Neural Network for Single Image Defogging Algorithm

In the era of big data, there are more and more outdoor camera acquisition equipment. Due to the influence of extreme weather, such as fog, camera acquisition equipment is easy to lead to the decline of image quality and destroy the value of image application. Therefore, this paper will propose an advanced dehazing algorithm to make the foggy image clearer. Based on the principle of residual neural network, combined with attention mechanism and feature pyramid idea, this paper proposes an end to-end learning single image dehazing algorithm. Let the network learn the relationship between channels and pixels, and use the feature pyramid multi-scale fusion feature to restore the foggy image to a clear image. The SSIM score was 0.9687 and the PSNR score was 29.16. Very good results have been achieved on the RESIDE outdoor dataset. This paper finds the scores obtained by testing DCP, AOD-NET, DeHazeNet, and GFN methods on the same dataset. Compared with these four methods, there is a significant improvement. In particular, it is 15.39% higher than the DCP method on SSIM and 10.03% higher on PSNP.
Blockchain Applications in Healthcare- Recent Advancements

In recent years, new inventions are taking place to adapt blockchain technology in various domains. Different researchers are proposing different frameworks for many applications. From the research done so far, it was seen that there was the need to focus on in-depth blockchain application analysis in healthcare sector. Motivated from the aforementioned facts, this research paper delivers blockchain's effectiveness and usefulness in the emerging domain of healthcare. The authors have focused on the researches done in last three years and identified a number of applications of blockchain technology in healthcare, a few of them discussed are – biomedical, prediction of disease, electronic health records, genomics and pharmaceuticals. Additionally, various challenges faced by the traditional healthcare sector along with the blockchain possible solutions are also discussed. Hence, this paper helps researchers to provide insights about works done in blockchain and its application in healthcare sector.
Inclusive Co-research with Experts with Disabilities

Existing co-research methods often limit participation and perpetuate exclusion of highly diverse populations. This chapter introduces an approach to co-design research that is inclusive and supports full participation of individuals with unique or diverse needs. Co-research is presented as having moved from the “design for” perspective to the “design with” perspective, and more recently to the “design by” perspective. Design by means that the population most impacted by the design is supported in creating the design. Inclusive co-design is a mindset that is applied in three ways: appreciating, scaffolding, and keeping (the ASK approach). Appreciating involves recognition of co-researchers as experts in their lived experience. Through scaffolding, co-researchers are supported and given the means to participate fully in the research process. Keeping gives ownership and builds capacity within the co-researchers and their communities in the research process and outcomes. Each of these three aspects are described in detail, building on our own experience from over one hundred inclusive co-design research activities, to provide guidance and approaches to other researchers who want to engage in inclusive co-design research.
The Dynamic Path Planning of Indoor Robot Fusing B-Spline and Improved Anytime Repairing A* Algorithm

A path planning method fused B-spline curves, and an improved anytime repairing A* algorithm path planning method (BS-IARA*) is proposed to address the needs for timeliness, feasibility, and optimality in robot path planning in indoor dynamic environments. In this method, firstly, adaptive expansion factors and dynamic search step strategies are introduced in the initial stage and iterative process of the algorithm, respectively, to improve the low expansion factor preference defect and low search efficiency in the anytime repairing A* algorithm; Secondly, the resulting path is optimized using B-spline curves to enhance the smoothness and feasibility of the track. Simulation experiments in a static environment show that the introduced improved strategies can combine to enhance path quality and planning efficiency; simulation experiments in a dynamic environment show that the method can generate feasible paths quickly, with better path cost and smoothing than the standard anytime repairing A* and Randomized Weighted A* algorithms, and continuously improve the path optimality in a specified time to meet the needs of dynamic path planning.
CrossDomain Recommendation Based on MetaData Using Graph Convolution Networks

Recent advancements in the domain of recommender systems have stemmed from the inspiration of representing the user-item interaction into graphs [11]. These heterogeneous graphs comprehensively capture the non-linear relationships between users and items alongwith features and emneddings. GCNs (Graph Convolution Networks) are state-of-the-art graph-based learning models that learn and represent the graph structures by recursively stacking layers of convolution and non-linear activation operations [3] [4]. GCNs are augmented with the strength of deep learning paradigms resulting in achieving better performance as compared to traditional CF (Collaborative Filtering) methods [7]. Despite modern improvements in the domain of recommender systems, cold-start users are a daunting challenge in the design of recommender systems since the conventional recommendation services are based on solely one data source [16]. During the recent years, cross-domain recommendation methods have gained popularity because of the availability of information in multiple domains for cold start users [34]. We supplement this information by utilizing the data contained in the metadata of users alongwith the strength of modelling graphs using GCNs [21]. Our proposed algorithm seams the strength of GCNs with cross domain paradigm utilizing the richness in metadata in user’s feedback to overcome the sparsity in user-item rating matrix. The combined advantages of GCNs and cross-domain approaches alleviated the issues of cold-start users by transferring user preferences from an source domain to a target domain [17].