Reviewerly/exHarmony huggingface dataset:
Evaluation of Blood Cell Segmentation Using Level Set-Based Force in Normal Direction via Edge Detection of Cell Shape

Segmentation of blood cell considers a gold standard key in diagnosing blood disease and producing automatic reports to haematologists and doctors. Blood cell segmentation is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. In this paper, level set-based force in normal direction via edge detection of cell shape is proposed, for first time, to segment and split overlapped blood cell components, and deal with these challenges. The proposed method has used publicly CellaVision DM96 dataset. It is also compared with other existing segmentation methods. The result has shown that this method reduces the effects non-illumination and variation in cell densities. An extensive experiment demonstrates that the proposed method performance is superior against all other existing blood cell segmentation approaches except for one method in this paper which is slightly better than our proposed method. The result also shows that the proposed method can support pathology practice in the diagnosis of autoimmune diseases in the future.
AST-Based Source Code Migration Through Symbols Replacement

As software versions are developed iteratively, we are always faced with the necessity to migrate code that depends on one or more modules from an older version to a newer one. This article introduces ideas of automatic source code migration based on a theory of nodes operations to compiled AST using Typescript and the Jasmine Testing Framework as an example.
(Intelligent) Technical Systems in Elderly Care: The Caregivers Perspective

The market for (intelligent) support systems for nursing of an increasingly aging population has grown rapidly in recent years. Nevertheless, only a few of these systems have found their way into the care of the elderly. An important point, in addition to the lack of refinancing structures in the health care system, is the reservations of those in need of care and those caring for them. With our study, we tried to get an insight into the everyday care work and to better understand the attitudes of caregivers towards the use of technology in elderly care, especially with a focus on artificial intelligence. In our paper, we present the results from a contextual inquiry, interviews, and a workshop for this purpose.
Research on Intelligent networked new energy vehicles based on mining algorithm and big data analysis

With the continuous breakthrough of big data analysis technology, the market competitiveness of China's new energy vehicles and intelligent networked vehicles has been significantly enhanced. Major progress has been made in key technologies such as power battery, drive motor and vehicle operating system, and the safety level has been comprehensively improved, so as to plan a clearer path for the development of the industry in the next 15 years. The intelligent connected vehicl e technology and industrial system represented by big data analysis and calculation will inevitably make the connected highly autonomous vehicle have a wider application prospect. This paper mainly focuses on the collection, preprocessing, storage, analysis, mining and application of big data related t o new energy vehicles, so as to provide technical reference for the development of new energy vehicle industry.
Design of University Management System for Student and Faculty

This paper introduces a university management database for students and teachers. The database designs a logical structure by establishing a relational model and continuously standardizing it. According to this logical structure, SQL server and SQL statements are used to build tables. This table supports operations such as query and modification. These operations, together with the database itself, provide efficient and convenient management for information such as university students and teachers, and provide new ideas and ways for university data management. In this paper, the methods and results are designed and displayed in detail, and the advantages and disadvantages of the database are described.
Symmetric Cryptographic Algorithm Based on Permutation

With the development of information age, information security becomes more and more important. As a result, the research of encryption algorithms is necessary. Although the encryption algorithm has a lot of research results until now, from the performance point of view, the traditional encryption algorithms can not meet the needs of existing users. In this paper, we design a symmetric encryption algorithm based on the full permutation, combining the idea of full permutation with the traditional symmetric encryption algorithms. It greatly reduced the computational complexity of the encryption process without reducing the security. In the experiment, we compared the performance of many encryption algorithms. The results show that our algorithm has a great improvement in performance.
LitPorts: On-demand Illuminated Ports to Facilitate Inter-device Connections in Low-light Conditions

Plugging a cable or peripheral into a device is an integral aspect of daily use of computing devices for most users. Yet, in low-light or constrained-visibility conditions the simple act of finding the receptacle to connect a cable to can be challenging and lead to user frustration. To alleviate this frustration we present LitPorts, a technology that illuminates receptacles in low-light conditions when a compatible plug is in proximity, making plugging a cable in substantially easier and leading to a better all-around user experience. We also present user feedback from our prototype testing demonstrating the broad appeal and applicability of LitPorts.
A Platform of Containers Based on High Availability Kubernetes Cluster

This article proposes a platform of containers based on high availability Kubernetes cluster. The platform let containerized applications work continuously. The solution can be used by increasing master nodes or based on the original Kubernetes cluster in order to reduce cost. The article also providers installation scripts for quickly deployment.
The SMeFT Decks: A Card-Based Ideation Tool for Designing Hybrid Digital Boardgames for Distanced Play

Increasing numbers of people have turned to playing boardgames with physically distant friends and family via technological tools, particularly since the start of the COVID-19 pandemic. However, commercial hybrid digital boardgames (HDBs) are designed for co-located, rather than distanced, play and there is a need for more specific tools to support hybrid game design. This paper introduces the SMeFT Decks, a set of card decks to aid in the design of HDBs for distanced play, which support Story, Mechanism, Function and Technology. We describe the use of these cards for design ideation and demonstrate four game concepts for distanced play stemming from the use of these decks in participatory workshops. We report evaluative feedback from a pilot study and from 46 participants who used these cards across nine design workshops and reflect on what we learned from observing this process. Results suggest that the SMeFT Decks are a productive ideation tool for aiding in the design of HDBs for distanced play in collaborative workshop settings.
Data Protection for Backups and Recovery in Cloud Storage

Cloud provides its users with different services. Every day during cloud computing, a great deal of data is generated. The data should be saved on the cloud servers properly. If this data is missing from the server, a mechanism to retrieve this data should be created. This study states one such solution. The proposed scheme would allow the data to be stored simultaneously on the cloud server and the remote server. The data is returned from the remote server when the key file is lost. Secret key security is ensured so that the authentication is secure and authorized by the user based on the attributes of backup and recovery. A cloud is a distinct Information Technology infrastructure designed to provide its users with different facilities that can be accessed remotely. Cloud refers to the term network of networks that support access to decentralized Information Technology resources. Cloud uses the Internet as well as the remote central servers to manage consumers and businessmen's data and applications. This helps to save the consumer from costs and room problems. It is a technology that makes data collection, processing, and bandwidth much more centralized. A cloud has a certain limit, as it is a certain framework used to provide resources remotely. The Internet offers access to a vast number of clouds. Whereas the Internet offers free access to several web-based Information Technology services, a cloud is generally private and provides wireless data resources. Much of the Internet is accessible via the web service to Information Technology services. On the other hand, the Information Technology services supplied by cloud environments are intended to provide back-end computing capabilities and browser access.
Implementation of an Automated Result Management System in a Developing Country

Errors and omissions in the manual process of compiling student results in secondary schools in Pacific Countries were examined in this research. To do that, preliminary research on the existing manual record-keeping system in Fiji was examined in a few chosen secondary schools. A new software application was designed to automate the processing of such results. This solved the issues with manual result processing. C# programming language as well as SQL-Server were used to create a software package. The system's anticipated outcomes demonstrate that educators are not required to record student tests, examinations, and calculation results because the system takes care of almost everything, relieving teachers of the stress of having to prepare students' results. Similarly, it is less expensive to print individual student results on A4-sized paper or imprinted cardboard material that resembles a certificate and has a more attractive appearance than it would be to create result booklets. By reducing the time and energy needed for result preparation, this program will reduce the stress associated with the teacher's work and free up a lot of time for teaching as well as other instructional concerns. The record-keeping and evaluation process capabilities of such a new system can be adjusted to fit the needs of both higher secondary schools whether private or public.
Layer-One.X blockchain, enabling scalability and interoperability through mobile-enabled devices

Interoperability and scalability are currently the bottlenecks preventing mass adoption of blockchain technology. Development of an interoperable and scalable network that promotes a truly decentralised, permissionless and secure blockchain as well as one that enables micro validation is the main goal of this project. Layer-One.X, a truly decentralised ledger which utilises para-sharding, Directed Acyclic Graphs, Proof of Participation consensus mechanism, mobile computing, flash contracts and nucleus scripting is outlined in this paper. The conceptual framework that facilitates the growing need of transaction per second enabling micro-based payments is presented.
Classification of Quantum Graph State Codes using Decision Trees

Graph states are multipartite quantum states that exhibit rich entanglement because of their topology. We consider graph states for studying a family of quantum error correcting codes such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[n,\ 1,\ d]]$</tex> encoding one logical qubit into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> physical qubits of the graph state. We investigate the impact of edge connectivity on the minimum <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> of the resulting graph state codes (GSCs).
Novel Metrics for Evaluation and Validation of Regression-based Supervised Learning

Error consistency is a validation metric for evaluating the sample-based error variability across machine learning models trained as part of in-lab validation. Many machine learning (ML) based regression algorithms are likely to be inconsistent with each other when trained repeatedly on the same task as part of standard cross validation, in part due to sampling, but also, potentially associated with the inclusion of randomness in their training paradigms, which is common in many learning techniques. In this work, we propose a novel approach to validation and evaluation of regression-based learning algorithms, called regression ‘error consistency’ (EC), to assist in assessing sample-wise consistency of errors as part of in-lab validation. We have applied novel EC metrics on six real-world datasets with six different regressors, evaluated the model performance with well-known metrics and compared the results with previously developed classification EC. The results demonstrate that, out of six models, the random forest achieved high accuracy but exhibited less consistency in its error profiles. This finding matches with classification based EC results. In addition, we applied the EC metrics on the MNIST digits dataset using a convolutional neural network (CNN) as part of a preliminary deep learning experiment. Though MNIST is typically treated as a classification dataset, we considered this dataset as a regression problem and the CNN model developed demonstrated good performance. We believe that the proposed EC metrics will be useful in evaluating and validating regression algorithm error consistency, including in deep learning, and will hopefully guide the machine learning research community to develop more reproducible and predictable (in terms of the errors they will make) regression algorithms. Public domain code is provided.
Light weight Real Time Indian Sign Language Symbol Recognition with Captioning and Speech Output

Sign language is the principal mode of communication for the vision and hearing impaired. It has allowed us to communicate with differently-abled individuals in a manner that is as expressive as a spoken language. Sign language has satisfied all the objectives of a traditional language system, like expressing emotions while also fulfilling a greater humanitarian role. Just like there are different dialects of languages spoken around the world, there also exist dialects of sign language that differ from country to country. Each of these dialects use a different symbol or method of representing the same lexicon. This work proposes a method of dynamic Indian sign language translation. that uses neural networks and image processing. The parameter utilized is minimum with good degree of predicted accuracy is the major contribution of this paper. The trained weights for the model are small enough to be efficiently ported over to a mobile phone or web browser and can be used without an active internet connection. The model is tested on the dataset with 99% accuracy.
Efficient Digital Assistant Workflow for Ticket Monitoring and Dispatching

In a larger cloud operation team, there are groups working on Monitoring and Dispatching whose primary work is monitoring the ticket queue, understanding the ticket, and assigning it to a respective workgroup. In a shift, at least one colleague must be working in the Monitoring and Dispatching role, and in most scenarios, it's an if-else work. In S/4HANA, we hire people specifically for the Monitoring and Dispatching role, contributing to the total cost of ownership. It's not quality work for the people working on the Monitoring and Dispatching role. To solve this problem, we can develop a solution that can understand the sentiment of the tickets, know about the workgroups in a team, and assign them automatically. If we can achieve this, we can reduce the total cost of ownership, provide quality work to our colleagues, avoid manual misses, and have intelligent and optimized dispatching. The vision is to build a Digital assistant with continuous ticket monitoring, intelligent ticket dispatching based on the user workload, and understanding the context of the ticket to give more insights about the ticket like any similar issues solved, what the solution provided last time, and who solved the similar issue last time.
Securing Cloud from Data Misconfiguration using Cryptographic Techniques

The advent of cloud computing has made data accessible anytime, anywhere. The past few years have seen a boom in organisations migrating to cloud computing. How far are the companies successful in preserving data security and privacy? Cloud security vulnerabilities are on the rise making the cloud environment susceptible to malicious attacks. Among the many threats, cloud misconfiguration can prove very costly as it leads to data leakage and malicious attacks. The purpose of this study is to examine the various methods available to prevent cloud misconfiguration breaches with emphasis on security mitigation techniques for multi-cloud storage environments. Cloud computing is advancing and so is the need for sophisticated security measures to ensure data privacy and integrity. Therefore, traditional security strategies are insufficient to meet the security requirements and ensure safe cloud computing. The paper focuses on the various encryption techniques related to single and multi-cloud security. Research papers on encryption practices in different cloud deployment models have been surveyed. Homomorphic encryption techniques are found to ensure strong data security. In this paper, a hybrid model is proposed after conducting a comparative study of RSA, Paillier and their hybrid. The implementation of the combined approach using two different partial homomorphic encryptions aims at accomplishing a new encryption system that emulates an improvised fully homomorphic encryption. The proposed encryption can beat the large key size and computational complexity of fully homomorphic encryption. The comparative results show that the RSA-Paillier hybrid model ensures higher data security than RSA and Paillier algorithms.
Generating Robust Convolutional Networks by Injecting Partial Noise in the Training Data

Convolutional Neural Networks (CNN) have emerged as a highly efficient deep learning algorithm for processing, classifying, and analyzing images. Currently, there is a high demand for networks that are robust to noise in the training and testing data. However, the presence of noise in images has been an ongoing problem when it comes to deploying CNN for real-world applications. In this paper, we propose a solution to this problem by incorporating noisy and noise-free images in the training set of a CNN network. Using the Modified National Institute of Standards and Technology database (MNIST), we demonstrate that marked improvements to classification accuracy only require a small proportion of noisy images in the training set. This result generalized across test sets with different amounts of noise and types of noise. In sum, our novel training method increases the generalization of a CNN model when processing noisy data.
Decentralized Federated Learning and Deep Learning Leveraging XAI-Based Approach to Classify Colorectal Cancer

Convolutional Neural Networks based automated approaches are vastly utilised to anticipate and diagnose cancer, saving time and reducing mistakes. Deep Learning CNN methods use a variety of probabilistic and statistical methodologies to make computers understand and identify patterns in datasets based on previous experiences. We proposed federated learning(FL) based model to classify histopathological images for detecting colorectal cancer efficiently while providing high pre-diction accuracy. FL solves the problem of retaining privacy while utilizing vast and heterogeneous private datasets collected from numerous healthcare facilities. As the amount of patient data obtained is significantly responsible for the success of enhancing the accuracy of the system, the experiment was performed on a large dataset including cancerous and non-cancerous colorectal tissue images. FL is also capable of mitigating costs resulting from traditional ML approaches. Moreover, we have applied XAI method, a model-agnostic approach to acquire an explicit demonstration of the applied machine learning models. With XAI, we can visualize the super pixels of our colorectal tissue images through accepting and rejecting features. Applying vari-ous CNN models such as VGG, InceptionV3, ResNet, ResNeXt, and comparing their precision, we ascertained that ResNeXt50 bears the highest accuracy of 99.53%. Hence, we have applied ResNeXt50 on FL that brings forth the accuracy of 96.045% and F1 Score of 0.96.
A Comparative Analysis of ResNet Architectures

Neural networks today are becoming increasingly complex, from a few layers to more than 100 layers. The principal advantage of a totally deep neural network is that it may represent very complicated functions. Functions can be learned at different levels of abstraction, such as low-level boundary functions and high-level complex functions. However, the use of deep networks is not always efficient. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent prohibitively slow. Deep residual networks are nearly like networks in which convolution, pooling, activation, and fully connected layers are superimposed. The only construct of a simple network that can be created as a residual network is the identifying link between the layers. Different types of ResNet can be developed depending on the depth of the network, such as ResNet-50 or ResNet-152. The number at the end of ResNet suggests the variety of layers in the community or the depth of the network. ResNet can be designed to any depth using ResNet's basic building blocks. In this article, we demonstrated a residual network with depths between 34 and 152 and tried to differentiate the architectures by training them on the same dataset.
Performance Improvement for Speaker Detection from Mixed Speech of Multiple Speakers Using Probabilistic Prediction and Combining LPCSE, MFCC and pLPCPD

This paper presents a method for performance improvement using the combination of feature vectors for speaker detection from the mixed speech of multiple speakers. Recently, we have shown that the combination of the linear predictive coding spectral envelope (LPCSE), the Mel-frequency cepstral coefficients (MFCC) and the piecewise linear predictive coding pole distribution (pLPCPD) improves the performance of the speaker verification, where pLPCPD is the feature vector that we have introduced and developed for speaker verification. On the other hand, we have examined a method of speaker verification from the mixed speech of multiple speakers using pLPCPD feature vectors, where we have shown that the recall, or the performance measure for the verification, decreases suddenly with the change of the speech from unmixed to mixed, while the precision does not decrease so much. This paper applies the above method of combining features and the findings on speaker verification to the speaker detection from the mixed speech of multiple speakers using the probabilistic prediction which we are also developing. Through the experiments, we show the performance improvement by the combination of three features and the effectiveness of the pesent prediction method.