Reviewerly/exHarmony huggingface dataset:
Research of Multi Sensor Fusion Positioning Algorithm Based on Kalman Algorithm

This paper proposes a multi-sensor fusion algorithm, combined with the traditional Kalman algorithm, to achieve UAV precise positioning. At the same time, consider more external constraints affecting the stability and safety of UAV flight, and build the extended Kalman multi-sensor filter model. The application of this model to the Intelligent forest inspection system will improve the positioning accuracy of UAV positioning, conducive to shorten the UAV cruise time and improve the reliability and stability of UAV.
Multi-robot Online Complete Coverage Based on Collaboration

Covering an unknown environment is a common task, and the multi-robot complete coverage path planning algorithm (MCPP) has received extensive attention based on its high efficiency. In the coverage task, the robot may fail or stop the task due to other reasons, and the lack of cooperative multi-robot strategy cannot deal with these situations flexibly, so this paper proposes an online multi-robot cooperative coverage method (MRCC). Considering the characteristics of path smoothness and speed constancy of the single-robot strategy Hex Decomposition Coverage Planning (HDCP), this paper extends it to multi-robot systems, and proposes a strategy that integrates communication, task sharing, and path planning based on information sharing, which can effectively deal with the problem that robots recognize each other as obstacles due to the lack of communication mechanism. When a single robot is trapped in a local area and cannot escape, the task can be shared with other team members, and the information sharing between robots can help robots plan valid path. Finally, experiments are carried out in the gazebo simulation environment, the results prove the effectiveness of our method, and the comparison experiments with the other two methods show that our method has higher efficiency.
Survey of Open-Source Tools/Industry Tools to Develop Quantum Software

Quantum computing is an advanced-level computing paradigm based on the fundamental principles on which nature operates, i.e. quantum mechanics and its ability to perform complex computations and high potential to set trends in the new era in computing technology. As quantum computing is slightly different from traditional computing, special types of software are needed for implementing Quantum Computing. There is a list of quantum software projects which are exclusively available on GitHub and/or maintained by the Quantum Open Source Foundation and there are proprietary tools offered by leading companies. With this in mind, we have compiled and curated detailed information & insights on some of the best quantum computing software tools which are widely used now-a-days. This chapter focuses on Microsoft Quantum Development Kit, IBM Quantum Tools, Amazon Braket, Google Quantum tools, and other popular open-source tools.
CNN-Based Model for Deepfake Video and Image Identification Using GAN

Deepfakes are the new age tools that automate the syntheses and detection of computer altered videos through GANs. Studies and researches are being done to detect and study the impact of deepfakes on social media and on human lives. In this paper, we will research about the DF technologies such as MTCNN and ResNext-v1 classification models to artificially automate the tasks of deepflakes detection by using datasets from varied sources and having different diversities of people. We also portray another deep learning-based technique that can successfully recognize AI-created counterfeit recordings from genuine recordings. It is inconceivably critical to foster innovation that can spot fakes, so the DF can be recognized and kept from spreading over the Web. Our strategy identifies by looking at the facial zones and their encompassing pixels by parting the video into outlines and separating the highlights with a ResNext-v1 CNN and utilizing the MTCNN catch the transient irregularities between frames presented by GANs during the remaking of the pixels. Our aim is to make an audio-less deepfakes detection system using ML and DL techniques to curb the spread of misinformation.
Specular Detection and Rendering for Immersive Multimedia

Immersive multimedia has received a lot of attention because of its huge impact on user experience. To realize high immersion in virtual environments, many virtual views should be generated at arbitrary viewpoints with advanced display devices. However, specular regions, which affect user experience, have not been fully investigated in an immersive multimedia field. In this article, we propose specular highlight detection and rendering methods to improve immersion. For specular detection, a high-performance variational attention U-network (VAUnet), which combines a variational autoencoder and a spatial attention mechanism, is proposed with a hybrid loss function. The specular regions detected from VAUnet are compressed with an immersive video coding standard (MPEG-I), and then the rendering is performed by considering the decompressed specular regions. Extensive experiments demonstrate that the proposed method improves specular detection performance and subjective rendering quality.
Knowledge Organization Systems to Support Cyber-Resilience in Medical Smart Home Environments

While real-time/near real-time healthcare solutions for the elderly are becoming increasingly popular in medical smart homes, the IT infrastructure and the sensitive information collected using sensor networks can be exposed to cyberattacks. The management of such systems requires 24/7/365 monitoring and partial automation. However, the wide variety of data formats used by the various vendors’ products in medical smart homes cannot be used directly by software agents for data aggregation and data fusion, let alone to support the overall cybersecurity posture. This can be enabled by capturing the semantics of the components of the sensor networks used in these settings. Network anomalies, sensor failure, and sensor data tampering can be detected automatically by performing automated reasoning. In this chapter, we present an overview of knowledge organization systems in a medical smart home environment. We discuss the cyberattack types that occurred in the medical smart home setting and the role of legislation and standards. Finally, we discuss information security countermeasures for cyber-resilience to improve overall the Medical IoT infrastructure.
pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">developer</i> convinces a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</i> of the truthful CNN performance over non-public data from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple testers</i> , while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a single multiplication gate</i> for expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly 13.9× faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and 17.6× faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.
Tracking of Artillery Shell using Optical Flow

The effectiveness of the Artillery shell is measured by its precision of hitting the target. During hitting the target, the intended firing path can be affected by the inherited source as well as external factors like wind, temperature changes, etc. Thus tracking such a path becomes crucial. In this paper, we address the problem of artillery shell tracking with optical flow and adopted the solution for it with sparse and dense optical flow. The outcome of this work can be used in test firing to determine the accuracy of the artillery target. Experimental findings show that the proposed methodology is more effective than the current military artillery unguided shell’s impact point locating, even if the shell ended up being an unexplored or blind shell.
Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge

Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the other hand, low-latency requirements and available resources present challenges. Edge computing integrated with cloud computing minimizes communication delays but requires efficient use of resources due to its limited resources. For example, although deep learning-based object detection methods give very accurate and reliable results, they require high computational power. This overhead reveals a need to implement deep learning models with less complex architectures for edge deployment. In this paper, the performance of evolving deep learning models with their lightweight versions such as YOLOv5-Nano, YOLOX-Nano, YOLOX-Tiny, YOLOv6-Nano, YOLOv6-Tiny, and YOLOv7-Tiny are evaluated on a commercially available edge device. The results show that YOLOv5-Nano and YOLOv6-Nano with their TensorRT versions can provide real-time applicability in approximately 35 milliseconds of inference time. It is also observed that YOLOv6-Tiny gives the highest average precision while YOLOv5-Nano gives the lowest energy consumption when compared to other models.
Future Agriculture Farm Management Using Augmented Reality: A Study

Augmented reality is a live direct or indirect view of physical-word. ‘Augment’ by computer generated or extracted real word sensory. Augmented Reality supplement your word with digital object of any sort. It combines physical and virtual word. This is one of the immersive technologies that will change your lifestyle in the near future. AR technology has the positive opportunity and service to educate change. Agriculture has been an important source of food and has always been a very important aspect. Agriculture is highly labor-intensive and highly dependent on the knowledge of individual farmers, causing management problems. It can make a decisive input to the best possible operation management of AR. Improve farmers reality in insect research and pest control AR correction compared to orthodox techniques and teaching methods (circles, speeches, etc.). In the eyes of the overall public, agriculture has a tendency to be easier. This is just like the easy case of sowing and reaping seeds. Agriculture is absolutely the becoming a member of collectively of diverse sciences and is a completely complicated manufacturing system. The demanding situations dealing with in enterprise appear like exacerbated with the aid of using the extra of a well-skilled team of workers in at the upward thrust countries. It isn't unusual for uneducated humans to interact in agriculture the use of suspicious archaic techniques. Not surprisingly, they fail, which ends up in diverse demographic demanding situations for society.
Digital Degree Issuing and Verification Using Blockchain

Blockchain is a distributed, secure, and quick shared immutable ledger. They are transparent, non-modifiable, and technically impenetrable. Any asset may be tracked and recorded using blockchain, which lowers risk. More effectively than other existing systems, blockchain can address current problems. We may use it to secure the existing academic model and change it. As a result, there is a lot of potential for this technology to lead to numerous advancements. One of the most crucial documents for students to have is an academic certificate, which is granted by educational institutions, when applying for jobs, taking tests, applying for higher education, etc. There are several opportunities to produce false academic certifications because the current system for awarding certificates is not at all effectively auditable. The distributed ledger characteristic of the blockchain technology can be used to combat the forged and fake certificates in order to resolve this issue. Our decentralised blockchain application aims to correct this. Since it would be connected to our certificate, we will also use the Aadhar card to increase its authenticity. They will be kept on distributed file systems called IPFS servers.
Novel Approaches to Improve the Production of Cost-Effective Software Artifacts

For more than 5 decades, the software has made enormous computing facilities for mankind to optimize individual actions in almost all sectors of society, but design and implementation have become a challenging discipline. Many software problems include cost overruns, project cancelations, extending schedules, high maintenance costs, etc., and these problems are caused by several factors. Measuring the quality after the development activity does not reduce the cost of producing the software as the technology and requirements change from day-to-day life. So, quantitative analysis at the analysis and design level has the scope to reduce the overall cost of software development. So far, there is no optimum method(s) to predict the analysis and design quality of an object-oriented software system. Because of object-oriented and heterogeneous projects, the quality is depended on the producer of the software and the amount of object orientation involved in the software. Alternatively, the cost can be reduced after the software delivery by applying novel ideas/approaches/methodologies/techniques in the process of software development.
Institution Management System: Student Module

Student Information Report System provides an interface for maintaining student records, academic details, admission details, etc. The maj or goal of this system is to create a college portal that can assure an organization's seamless operation. Nowadays, an automated system is demanded in our daily lives, which expanded computers and applications. The educational infrastructures like colleges and other institutions need to operate their manual functions on a computer system. This Institution Management System (IMS) helps institutions like colleges and other small scale institutions to function the online management of their resources. This project deals with part of an institution of information of student details using placement training and events attended details, academic details and other non-academic details. Using this system, any institution can maintain computerized records without any redundancy of student information. That means no one needs to be distracted by the non-relevant information while reaching out for specific data. Its services customize the projects that align with a particular client's objectives, goals and requirements. Student module, one can manage all institution management works for student records like project works, publishing papers/journals moreover, student promotion, student record maintenance. Student information includes admission details, transfer details, promotion details, course details, internship details, project details, higher studies, etc. This system ultimately allows any institution for better management of student information.
IoT Based Smart Blind Stick Using GPS and GSM Module

It is unsafe for visually challenged people to move on a busy road. It may lead to some accidents, when they get some obstacles on their way and are not able to identify them, which may not be known to their parents or guardians. Without having some kind of navigator, it is challenging for such people to navigate their surroundings and commute. Hence, we propose an IOT based navigating solution, the smart blind stick equipped with an ultrasonic distance sensor to identify obstacles and also the GPS system to help their parents and guardians to know their location. GSM module is used to send SoS messages to particular people in case of emergency. The smart blind stick is a tool for defining their environment. Smart Stick, ensures the security and effective mobility of visually challenged people. This is useful to avoid abstractions and help them in easy commuting and to send emergency message alerts.
On the Transferability of Adversarial Examples between Encrypted Models

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, namely, AEs generated for a source model fool other (target) models. In this paper, we investigate the transferability of models encrypted for adversarially robust defense for the first time. To objectively verify the property of transferability, the robustness of models is evaluated by using a benchmark attack method, called AutoAttack. In an image-classification experiment, the use of encrypted models is confirmed not only to be robust against AEs but to also reduce the influence of AEs in terms of the transferability of models.
Connection Reduction of DenseNet for Image Recognition

Convolutional Neural Networks increase depth by stacking convolution layers, and deeper network models perform better in image recognition. Empirical research shows that simply stacking convolution layers does not make the network train better, and skip connection (residual learning) can improve network model performance. For the image classification tasks, models with global densely connected architectures perform well in large datasets like ImageNet, but they are not suitable for small datasets such as CIFAR-10 and SVHN. Different from dense connections, we propose two new algorithms to connect layers in this paper. Baseline is a densely connected network, and the networks connected by the two new algorithms are named ShortNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and ShortNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , respectively. The experimental results of image classification on CIFAR-10 and SVHN show that ShortNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> has a 5% lower test error rate and 25% faster inference time than Baseline. ShortNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> speeds up inference time by 40% with less loss in test accuracy. Code and pretrained models are available at https://github.com/RuiyangJu/Connection_Reduction/
Nuclei Segmentation in ER-IHC Stained Histopathology Images using Mask R-CNN

Breast cancer is the leading cause of mortality among women in both developing and underdeveloped countries. The nuclei segmentation in digital histopathology image analysis plays a crucial role in breast cancer in the early stages of its development and may allow patients to have proper treatment. Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation and feature extraction challenging. To mitigate the aforementioned problems, this paper employed a mask region-based convolution neural network (Mask R-CNN) to segment immunohistochemistry breast cancer images. The mask R-CNN algorithm introduces advanced Regional Proposal Network architecture that precisely addresses the object location to generate candidate regions. The Mask R-CNN used resnet50 as the backbone and applied Feature Pyramid Network (FPN) to fully explore multiscale feature maps. And then, Region Proposal Network (RPN) was used to propose candidate bounding boxes. The robustness of the Mask R-CNN model is enhanced by training the model with our collected dataset. The proposed architecture has the average of 72% precision, 84.2% recall, 77.62% F1-score, and Jaccard Index overall score of 0.59. The proposed model can be beneficial in assisting pathologist for a routine exam, as well as a second opinion for breast cancer segmentation from whole slide images. Since the process is fully automated, it can be done without supervision and only the final result will be attended by the pathologists.
Research of Federated Learning Communication Data Transmission Algorithm Based on User Grouping Policy

Since being proposed, Machine Learning (ML) has gained a great leap. However, the shortcoming of user privacy restricts its further application in communication. Therefore, Federated Learning (FL) has been proposed to overcome this shortcoming. FL allows users to compute gradient information and upload them to the server for global training. Nevertheless, it still has the problem of high communication overhead. In this work, we propose three user grouping policies, that is, parity, random, and orderly, to study the proper method to reduce communication overhead during the training process of federated learning. Simulation results prove that the parity and the random policy outperform the orderly policy and the no grouping one in test accuracy and channel loss.
An Intelligent IoT-based COVID-19 Contact Tracing System for Ashesi University

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology-based systems are designed to facilitate contact tracing. The aim of this research project is to develop a privacy focused IoT-based COVID-19 contact tracing system that leverages mobile devices and artificial intelligence for the Ashesi University community. To achieve this, we divided the project into two main parts: the software sub-system and the hardware subsystem. The software sub-system comprises of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the images to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them via its actuators and at the same time updates the admin portal. Through system testing, it was identified that 32% our system users considered privacy during the pandemic as critical even though 95% confirmed that the system assures very high level of privacy.