Reviewerly/exHarmony huggingface dataset:
A (t, n) threshold protocol of semi-quantum secret sharing based on single particles

Semi-quantum secret sharing is an important research issue in quantum cryptography. In this paper, we propose a ( t , n ) threshold semi-quantum secret sharing protocol, which combines the practicality of semi-quantum secret sharing protocols and the flexibility of ( t , n ) threshold quantum secret sharing protocols. Participants prepare and transmit single particles in a circular way, and then any t out of n participants can recover the secret according to Shamir’s secret sharing scheme. As quantum resources, single particles are easy to prepare. Furthermore, classical participants only need to possess the capability to prepare and insert particles. The security analysis shows our protocol has security against most attacks. Except decoy particles, all particles are useful to carry the secret message, so the efficiency of the proposed protocol can achieve 100%.
CCET: towards customized explanation of clustering

Classical clustering algorithms use all features to partition a dataset, making it difficult for users to understand the clustering results. Some scholars have proposed interpretable clustering algorithms that use a few understandable features to explain clustering results. However, the existing algorithms can only generate one interpretation and fail to satisfy the diverse needs of different users. To address this challenge, the Clustering Customized Explanation Tree (CCET), a visual analytics system, was constructed in this paper. The system helps users modify existing explanations to obtain customized explanations. Firstly, a variety of views are designed to visualize the explanations and help users judge whether the existing explanations meet the requirements. Then, an explanations modification strategy based on cluster centroids splitting is proposed making it easy for users to revise explanations according to the requirement. We demonstrate the CCET using a case study and a user study. The results show that the system can deepen users’ understanding of clustering results and make it easy for them to conduct further decision analysis.
Blockchain-Based E-Voting System

To elect the most qualified candidate, every democracy needs an open voting procedure that meets the needs of the people. The current voting procedures, however, have serious weaknesses and lack security and transparency. This survey research looks at the possible uses of BC technology in e-voting systems to improve the voting process and solve the issues of restlessness, privacy, and security. This study tries to evaluate several distributed electronic voting system implementations based on blockchain technology. While some have just existed as drafting papers, others have been implemented in the real world. An electronic voting system built on blockchain improves security and privacy while significantly lowering expenses. By outlining a case study involving the conduct of an election and the adoption of a blockchain-based application that increases security and decreases the cost of organizing a national election, we evaluate the potential of distributed ledger technology.
Differential privacy protection method for decision tree ensemble model

As a machine learning model widely used in data mining and data analysis, decision tree cannot balance properly data availability and security due to improper allocation of privacy budget in the process of applying differential privacy to protect data privacy. In order to solve the above problems, this paper proposes a decision tree ensemble model for allocating privacy budget based on leaf node depth. First, we design a strategy to allocate privacy budget based on leaf node depth. By assigning different privacy budgets to leaf nodes with different depths in the decision tree, each query consumes same privacy budget. This strategy enables the privacy budget to be more fully used, and better balances data availability and security. And we propose a decision tree ensemble model based on parallel composition theorem of differential privacy, which improves the privacy protection degree of the model when we use decision tree ensemble model to improve the performance. Simulation experiments show that the improved model is effective. Its privacy protection degree is improved compared with the general decision tree ensemble model, and the improvement is proportional to the number of decision tree. And its accuracy is close to the decision tree ensemble model without the differential privacy protection.
Using Sentiment Analysis and Machine Learning to Collect the Perception of Online Learning

The aim of this research is to collect the perception of the Education community towards the effectiveness of online learning taking place during the period of Covid-19, confinement and online learning. This has been achieved through sentiment analysis complemented by machine learning techniques. Public opinion has been mined predominantly from the famous social media site Twitter. The data collected was analysed through a web based application that has been developed, so that the behaviour and perception of people around the world is known. The opinion that has been mined was then classified into three categories positive, neutral and negative. An analysis has been performed on the classified data to see what percentage of the population sample falls into each category and this can be visualised geographically. Existing Sentiment Analysis Systems were critically analysed before developing the Web application. Features such as tokenization, lemmatisation, removal of stop words, feature extraction and confusion matrix were used to perform sentiment analysis. During this research, it was observed that features like removal of slang words, abbreviation, POS tagging and taking into consideration of negations are rarely present in existing Sentiment Analysis systems and these features have now been successfully implemented. These features greatly help to increase the accuracy of the supervised machine learning models. The results obtained were encouraging and it was observed that Random Forest classifier had better recall, precision and accuracy as compared to other techniques such as Bayes, Linear Support Vector Machine and Decision Tree.
Medical Reports Summarization Using Text-To-Text Transformer

Summarization of medical reports in order to make them accessible to the large public is an important task that can highly benefit from the recent emergence of the deep learning and large language models (LLM). In this work, we propose a fine-tuned Text-to-Text Transformer (T5) to summarize such reports. We train and test our model on the publicly available Indiana Dataset. We evaluate it using the ROUGE set of metrics. The obtained results are promising.
Biometric Authentication Based on Multi-Modal Facial Recognition Using Machine Learning

This work covers the implementation of a Machine Learning-based multi-modal biometric authentication system for Automatic Teller Machines (ATMs). The system will be trained to identify and recognize the user's facial biometric signature, even if the user is wearing feature-altering add-ons. The work addresses specifically add-ons affecting the appearance of the hair and facial features. The work takes practicality and convenience into consideration while attempting to address security-related concerns regarding granting access to an ATM.
ccfDetector: Utilizing GAN and Deep Learning for Credit Card Fraud Detection

In recent years, the widespread adoption of e-commerce has led to a corresponding growth in credit card fraud. To combat this issue, organizations have begun implementing fraud detection systems using machine learning techniques. However, most credit card fraud detection datasets suffer from imbalanced data, which can impact the performance of these systems. In this study, we propose using Generative Adversarial Networks (GANs) to address this problem. By training the GAN on the unbalanced dataset of credit card fraud detection, we can generate additional synthetic records belonging to the underrepresented class (e.g. fraudulent transactions) to balance the dataset. Moreover, we propose a deep learning model based on Artificial Neural Networks to classify the dataset. Also, we examine the impact of feature selection on the performance of the model and demonstrate that our approach results in improved sensitivity. We combined these three components in a system called ccffDetector. Our results show that using generated records merged with the training dataset results in the highest sensitivity of 0.9024%. This indicates that our approach has the potential to improve the credit card fraud detection system's effectiveness.
Design and Implementation of a Solar Tracker with Live Data Feed Utilizing IoT

A solar tracking system with the internet of things (IoT) live feed data for a windows platform was investigated. This paper presented a step-by-step design methodology of a dual-axis tracking system. Further, this study presented the design, implementation scheme, and suitable set of software that works with an Arduino Mega 2560 microcontroller. The operation of the whole system solely depended on the effectiveness of the designed solar tracking structure, which in turn supported the photovoltaic module(s) that were constantly maintained perpendicular to the incoming sun rays. For proper tracking and coordination of the sun's azimuth and elevation angles, two servo motors -MG995- were introduced in conjunction with the 2560-Microcontroller to synchronize the movements efficiently. The system exhibited the ability to execute and monitor the real-time operations of the photovoltaic (PV) module and showed the potential to optimize the performance of the solar PV system. Results showed that the tracking system is 23.30% more efficient than a fixed system, which is equivalent to removing 0.31 metric tons of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{CO_{2}}$</tex> from the environment, thus creating the possibility of harnessing more solar energy. Also, results revealed that the dual-axis system was able to follow the movement of the sun instantly.
Evaluation of Deep Convolutional Neural Networks for Detecting Nonpalpable Breast Abnormalities in Mammography

Advances in deep learning (DL) have renewed the motivation to build a computer-aided diagnosis (CAD) system for mammography as a feasible tool to diagnose breast cancer in its infancy. Current research reported the potential of the D L- based approach for characterizing malignancy of breast abnormalities. However, a preceding and desired feature of the complete mammography CAD, using DL, is the detection, as a first task, of breast abnormalities. This work focuses on developing multiclass DL-based algorithms to detect the most relevant and critical breast abnormalities (masses, clustered microcalcifications, architectural distortion), and the normal breast structure. For this purpose, several deep convolutional neural networks are trained and validated on a dataset of 1157 mammographic regions extracted from the Digital Database for Screening Mammography (DDSM), a public and common-use database. Preliminary results, in this work, have shown the impact of model selection but with insufficient performance in detecting mass and architectural distortion with most mispredicted representing incurred and so low precision and recall rates. To build a high-performance CADe, this work suggests adopting data-centric CAD in which data selection is an iterative and continuous process.
Comprehensive Study of The Cloud Computing Service Providers

Whether it's cellphones, personal computers, or gaming consoles, technology is a part of everyone's everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services-AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services' shared features and unique aspects that they provide to consumers.
Deep CNN approach for Unbalanced Credit Card Fraud Detection Data

Recent developments in electronic payment technologies have significantly increased the volume of daily online transactions and payments via credit cards. As internet use grows exponentially, it naturally follows that there is also a rise in credit card fraud, which is having a big effect on many organizations, including those in the financial industry. To detect risks such as fraudulent transactions and nonuniform attacks, the development of advanced financial detection mechanisms proactively completes the required task. However, these problems have been addressed by machine learning techniques on a large scale over the past years. Hence, these techniques need some improvement in terms of identifying unfamiliar attack patterns, velocity calculations, and big data analysis. In this paper, we propose a convolutional neural network approach (CNN) along with two machine learning algorithms to tackle the issue of credit card fraud detection. Our proposed models were evaluated and compared when dealing with large amounts of data using a highly imbalanced real-world credit card fraud detection dataset. Python programming languages were used to preprocess the data and test the model's measurements and performance. As observed in the results, an accuracy of 99.7% using the Random Forest classifier was obtained, which achieved a superior result in comparison to other models.
Low-Cost Three-Dimensional Surface Recovery Scanner for Fine-Detailed Objects

This paper aims to develop a Low-cost three-dimensional (3D) scanning device to reconstruct surfaces of objects with fine details or small in size. In this work, a prototype model is examined that mimics a larger scanner that can be utilized for bulky objects. The model consists of two stepper motors, one forces vertical displacement, while the second forces object rotation. A distance infrared (IR) sensor will collect the depth at each rotation angle and vertical displacement. After collecting the distances, an image restoration algorithm is applied to enhance fine details. The algorithm applies an averaging filter in the x and y-axis coordinates that reduce noising effects. The surface image was generated using MeshLab software. Different 3D objects have been examined using the proposed scanner that demonstrates promising exterior shape reconstruction. s
The Extended Innovation Kalman-Sliding Filter for Nonlinear Estimation

Predicting and planning a path and extracting the current location are important aspects in the fields of navigation, localization, and autonomous vehicles. This brief paper belongs to these applications with measurement signals that are obtained from linear sensors. The kinematic states of a vehicle, and the maneuvering angle, are extracted by a filter from a noisy environment. Filters are considered to be either accurate or robust, and typically not both (a trade-off exists). In this paper, we introduce a method that combines accuracy with robustness. The well-known extended Kalman filter (EKF) is combined with the relatively new sliding innovation filter (SIF). The proposed algorithm makes use of the EKF gain and structure while utilizing the robustness of the SIF switching-based gain in an effort to provide a good estimate of the states. The result is a suboptimal nonlinear estimation strategy that resists uncertainties and disturbances. The proposed filter is demonstrated on a vehicle in the Cartesian coordinate while maneuvering and performing turns. The results are compared to the classical EKF and SIF.
Estimating the Optimum Tilt Angle of Solar Panels in Fujairah

A shift is taking place toward using renewable energy sources, with solar energy being one of the most prominent. The challenge with solar energy is that the power output is low or in other words, low efficiency. therefore, the sunbeam must be directed toward photovoltaics (PVs) to catch the maximum irradiance. The proposed solution is to use a MATLAB program to calculate the optimum tilt angle in Fujairah and the find the irradiance too, taking into account the location, day, declaration angle, and hour angle. also, by comparing the results found by our mathematical model with a real application called (PVWatts) which is developed by the NREL (US National Renewable Energy Laboratory) and by previous studies done before on the same topic. The results have been verified and the values are very similar.
New Data Steganography Technique Exploiting Redundancies in Language Structure: Arabic Language Case Study

Nowadays, there is a huge reliance on digital communication. Due to the lack of security, attackers can steal or tamper with valuable and sensitive data like personal information, medical records, or financial data. Steganography refers to the art of hiding data inside other digital content, including text, image, video or audio content in an inconspicuous way. Good steganography algorithm obscures the fact that there is sensitive data hidden in the cover medium. Steganography is one of the effective ways of securing data. This paper proposes a new technique for hiding secret data in ordinary Arabic text. The proposed technique exploits the redundancies present in natural language structures. For example, one can exploit the commutative property of conjunctive words such as “and” and “or” to hide one bit. Furthermore, one can say “The boy likes blue and yellow colors” or “The boy likes yellow and blue colors”. Whether saying “blue and yellow” or “yellow and blue”, the meaning remains the same. The desirable features in steganography are the high capacity and imperceptibility. The capacity is the amount of data bits that can be hidden in the cover medium. Imperceptibility is the inability of an eavesdropper to notice or figure out the hidden data. A study on random Arabic passages was conducted to measure the capacity. Moreover, an empirical study on the imperceptibility of the proposed technique was conducted as well. The results showed that the proposed technique can effectively conceal data within Arabic text.
Intelligent Adaptive RRT<sup>*</sup> Path Planning Algorithm for Mobile Robots

In this paper a new variant of the widely used Rapidly exploring Random Tree (RRT*) algorithm is proposed. The main goal of this variant is to improve the efficiency of the generated path, in both computation time and the quality of the path. In a previous work by the authors, an improved version RRT*N was proposed, where a normal probability distribution was used to control the generation of the random nodes. The proposed variant utilizes an intelligent fuzzy logic system (FLS) to control the generation of the random nodes and commands the robot to the required target introducing an intelligent fuzzy adaptive RRT*N path planning approach (FA-RRT*N). The proposed approach not only reduced the time required about 29.5 % of the time required by the RRT*, but also generally resulted in shorter path about 63% of the path generated by the traditional RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> .
Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute.We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size.We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records.The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist.Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.
Research on the Strategy of Cultivating University Creative Talents Based on Industrial College

In this paper to letter and the present situation of talent shortage as a starting point, analyzes the present situation of college letter and talent training, put forward the construction of modern industry college, including building letter and talent training demonstration platform, build the letter and industry science and technology innovation platform, make teaching fusion practice employment platform, deepen the connotation of the fusion, the construction of education fusion curriculum system, analysis of school, enterprise, political cooperation advantages and development plan, realize the reform of software technology personnel training mode.