Reviewerly/exHarmony huggingface dataset:
Breast Cancer Detection using Machine Learning Approach

We have gathered the features of breast cancer and normal persons’ cells both. To classify malignant and benign tumors, we used a supervised machine learning classifier algorithm. This paper shows the last update in this machine learning field on breast cancer in Bangladesh. We have used many classifiers of ML in this review. Most of cases it is difficult to identify the malignant tumors. For this, we hoped that with the help of math and the computational power of ML we can resolve this issue at a significant scale. Yet, there were a few difficulties with the process. Starting with featuring the dataset and creating a data frame we proceed to apply different types of machine learning classifiers. This paper presents an overview of the opinion examination challenges applicable to their methodologies and strategies.
Theory and Simulation of Electromagnetic Systems Governed by Machine Learning Principles

This paper proposes the idea that electromagnetic systems can be formulated through probabilities once the system has been understood by the classical physics. With this, several physical observables are estimated. Also, with the diffusion equation, electrical circuits can be constructed. Thus, it is seen that classical electrodynamics dictated by action to distance forces, can be encompassed to the so-called Mitchell’s criteria. It has as consequence the validation of common problems in electromagnetism based at space-time probabilities more than the well-known methodologies based at vector algebra. Simulations of electric power contour plots are presented.
Joint Watermarking-Encryption Scheme Utilizing Transform Domain and Advanced Encryption Standard

In this research, a digital image joint watermarking-encryption method based on Lifting Wavelet Transform (LWT) for cover image decomposition and the Advanced Encryption Standard (AES) for embedded text protection ensuring in the Hue, Saturation, Intensity (HSI) color space is suggested. The method is about embedding a cipher-text into the intensity component of the cover image as it is in contrast with the hue and saturation, where this last was decomposed utilizing the LWT, and the Low-Low sub-band was exploited for text embedding, the intensity is the less perceptible component for the human visual system. The incorporated text was encrypted using the AES, which provides high robustness against tampering and spying using a key distinguished from the Singular Value Decomposition (SVD) decomposition. The experimental results extrapolate that the proposed method can provide a high imperceptibility.
Light Analysis for Optimized Object Detection with Cameras for Integrated Safety Systems

In order to achieve maximum vehicle safety, advanced driver assistance systems as well as integrated safety systems need to be established in the vehicles. Especially in the field of camera-based driver assistance systems, numerous systems have been introduced in order to detect the vehicle’s surroundings. However, the environmental sensors must be extremely reliable in detecting objects and tracking so that safety systems benefits. This paper presents an approach on how to analyze critical light conditions for cameras and extract disturbing parameters such as describing the glazing effect to improve object detection and tracking with cameras. In a model for detection of interfering and dazzling sources different methods of color and contrast processing were examined to determine critical regions. Subsequently, objects identified by a YOLO algorithm in the test images were compared with the critical areas and light parameters such as wavelength, intensity and color values were extracted for overlapping areas. In this way, on the one hand, the physical properties for reproducibility of the disruptive effect for test systems can be illustrated, and on the other hand, statements about correlations between objects and disruptions can be made.
Hybrid Photovoltaic Power Forecasting Algorithm for Managing Virtual Power Plants

This paper proposes a framework, based on the concept of virtual power plant, for promoting the effective participation of remote photovoltaic power generation installations in different energy markets. To this aim, the most significant challenge lies in providing accurate power forecasts for different lead times determined by the markets. To address this challenge, a hybrid forecast strategy based on day-ahead and intra-day prediction models is proposed. For each lead time, a point prediction is provided by choosing, from the outputs of prediction models, the value which most reduces the prediction uncertainty. The framework also requires the cloudiness forecast to improve the accuracy of the prediction, by classifying the days in three categories according to a cloud cover factor, namely, sunny, cloudy and overcast. The predictions are updated every 15 minutes by using real-time information of the day considered. Whenever the algorithm is executed, the generation forecast for the rest of the day is recalculated with a 15-minute resolution. The point prediction is provided with the corresponding confidence interval, which is modelled by a Laplacian distribution function. This interval is of particular importance in the context of energy markets as it allows the risk of penalties for any energy deviation to be modelled. The strategy is evaluated in a VPP working environment demonstrating the potential of the hybrid prediction algorithm.
The Double-slit experiment and Feynman Amplitudes to Interpret the Rise of Omicron at the Covid-19 Pandemic at 2022

As perceived at mostly countries, after the 3th or 4th wave of Covid-19 pandemic, its potential end might be coming. In contrast to previous waves, the last one exhibits different characteristics such as a highly exponential behavior or also a sharped Gauss behavior as seen at the huge number of infections at America and Europe. In this paper is proposed the idea that the whole pandemic is clearly composed by a sinusoid and Gaussian functions. This claim is based at the world-wide data that exhbits a similar shape with the proposed scheme at this study. Thus, the pandemic can also be seen as a signal-noise being the Omicron variant the signal whereas the previous mutations can be seen as their background events or noise.
A hierarchical learning approach for generalized trajectory tracking validated on a magnetic bearing system

A hierarchical learning framework (HLF) was proposed recently, for enabling control systems to generalize a learned optimal tracking behaviour to new unseen trajectories. The idea is inspired by the generalization capacity of intelligent beings (humans in particular) in solving new tasks by extrapolating and combining skills from an existing knowledge base. The HLF uses the so-called primitive pairs which are in fact pairs of reference inputs-controlled outputs. While these pairs are learnable over a lower-level closed-loop control system (CLCS) with the goal of a desired trajectory following, this is not mandatory as long as the primitive output has sufficient approximation capability and the CLCS is almost linearized. This paper shows that the primitive pairs can be employed to infer the optimized CLCS reference input which forces the CLCS’s output to track a new targeted trajectory with better accuracy than in the case without exploiting the primitive-based prediction. The explanation resides in that the primitives indirectly encode the CLCS’s model within the trajectory data. All the HLF’s features ranging from the primitive’s database construction, to database enrichment with extended, delayed and padded pairs, up to the decomposition of the new desired trajectory into output primitives and to the recomposition of the optimal reference, are detailed in this work, on a multivariable real-time magnetic bearing system (MBS). The HLF shows intelligent behaviour features: learning, memorization and generalization of the learned behaviour.
Computationally-Efficient Secured IoT Networks: Devices Fingerprinting using Low Cost Machine Learning Techniques

The vulnerability of wireless devices to a well-known set of probable cyberattacks has made safeguarding the networks to which these devices connect a tremendous security issue, threatening the safety and security of thousands, if not millions, of private and public networks. Due to the rapid growth of embedded and wearable wireless devices on the market, wireless Internet of Things (IoT) devices are now one of the most vulnerable entry points because they don’t have advanced authentication procedures. This article provides a summary of our most recent findings in the development of a novel authentication and identification method for IoT ZigBee and Long Range (LoRa) devices based on the physical signals they emit. Our method relies on the extraction of a collection of unique features from the received modulated signal in order to construct a features vector for each device and then train a machine learning model using the acquired features. Following training, the trained model is evaluated by testing its ability to identify and recognize the authorized devices (i.e., those previously included in the training set) from the testing set, which contains an evenly distributed random mix of new and authorized devices. Our method employs differential constellation trace Figure (DCTF)-based features for the features vector and computationally-efficient machine learning methods, such as Quadratic Discriminant Analysis (QDA) and Gaussian Naive Bayes classifiers, which resulted in a recognition accuracy greater than 90 percent.
Proposed Architecture of Integrate Blockchain With VAT System in Oman

VAT system was implemented in April of the year 2021. The VAT in Oman provides for the payment of 5% of the value of the commodity as an added tax. Oman will face a few difficulty after establishing the VAT system such as tax evasion, manipulating the value of the tax owed and organization uses fake tax numbers. Moreover, gather all companies and institutions in one system consider a challenge in term of fraud management. This research propose an architecture of integrating Blockchain with the VAT system in Oman to ensure secure VAT system transaction. Analysis of the financial transaction. Blockchain is a technology that transfers information peer to peer connection. This information is encrypted and secure and no one in between can view it. Blockchain is used to transfer certain digital information in the form of blocks, and this block is linked in the form of a chain Moreover, to evaluate the current issues and challenges in integrating VAT system with blockchain. Thus, the best solution to monitor tax collection is to link the VAT system to blockchain technology. It ensures that the correct accounting information and invoice numbers are transferred.
Stochastic stabilization of Markovian jump cloud control systems based on max-plus algebra

In this paper, stochastic stabilization is investigated by max-plus algebra for a Markovian jump cloud control system with a reference signal. For the Markovian jump cloud control system, there exists framework adjustment whose evolution is satisfied with a Markov chain. Using max-plus algebra, a max-plus stochastic system is used to describe the Markovian jump cloud control system. A causal feedback matrix is obtained by exponential stability analysis for a causal feedback controller of the Markovian jump cloud control system. A sufficient condition is given to ensure existence on the causal feedback matrix of the causal feedback controller. Based on the causal feedback controller, stochastic stabilization in probability is analyzed for the Markovian jump cloud control system with a reference signal. Simulation results are given to show effectiveness of the causal feedback controller for the Markovian jump cloud control system.
Heterogeneous Coupling Relationship Based Similarity Measure for Mixed Data

Mixed data is a typical heterogeneous structured feature of the complex system elements, which contains both categorical and continuous attributes. To effectively analyze the similarity of mixed data, taking into consideration the heterogeneous coupling relationship between the mixed attributes, this work proposes a heterogeneous coupling relationship-based similarity measure for mixed data (HMS). First, through automatic discretization based on K-means, continuous attributes are converted into categorical attributes and the categorical data view is extracted, for which the HGS method is used to measure the similarity. Besides, through similarity representation, the categorical attributes are converted into continuous attributes, then the continuous data view is constructed and its similarity measure is performed by Euclidean distance. Furthermore, the harmonic mean of the similarity between both views is calculated to obtain the integrated similarity. Finally, the effectiveness and feasibility of the HMS method are verified in clustering experiments. Compared with other common-used mixed data similarity measures, the HMS method can more fully capture the categorical and continuous views of mixed-type attributes of the complex system elements, as well as the complex heterogeneous relationships existing between views and inside the view, which as a result greatly improves the clustering performance.
GASN: A Graph Attention Stochastic Network against Adversarial Attack

In recent years, graph neural networks (GNNs) has shown its unique advantages in graph data research and analysis because it considers the attributes of graph nodes and topology information at the same time. However, graph neural networks is as vulnerable to counter attack as traditional deep learning, and the performance of the model can be seriously degraded by adding small disturbances. At present, researchers have proposed various methods to improve the robustness of graph neural network model for confrontation and defense, but the existing working methods are too complex and make great changes to the model, which can only be applied to specific graph neural network models. To solve the above problems, we propose a graph attention stochastic neural network defense method, which uses multivariate Gaussian distribution to replace the node vector representation in the hidden layer of graph neural network, uses the variance of Gaussian distribution to absorb the impact of attack, and adds a mean based attention mechanism, that is, when convolution aggregation is carried out, by measuring the difference of the mean value of multivariate Gaussian distribution, the weight is allocated to further reduce the impact of attack on the model. Experiments on three common datasets show that the model proposed in this paper shows excellent performance in resisting counter attacks.
A Study of Text Difficulty Assessment Based on Decision Trees

With the increase of information technology, stu-dents can receive more and more reading materials for learning, but how to choose from the huge amount of reading materials and find the right one for them efficiently and accurately has become a difficult problem for students. Therefore, quantitative assessment of text difficulty has become an important topic in the field of language research and education. In this paper, we first analyze the difficulty of English text, classify the text difficulty level into six difficulty levels: elementary school, junior high school, high school, university and subject specialization, and initially determine 27 difficulty quantification indexes in four aspects: vocabulary difficulty, sentence difficulty, difficulty of the whole corpus and speech chunks. The pre-processed text data were then quantified using the Range text quantization tool to generate the training dataset. The classification accuracy of the 27 difficulty indicators was then tested separately, and the different indicators were combined, and the classification accuracy of all the combined indicators was obtained using a genetic algorithm, and the ten combinations of indicators with the highest classification accuracy were finally determined. A decision tree based English text difficulty assessment model was built using the RapidMiner model building tool with the training dataset, and the model was analyzed by the validation results with good classification effects. Next, the model is extended to study the Chinese text difficulty assessment model. According to the characteristics of Chinese, we identified 21 indicators to assess the difficulty of Chinese text for comprehensive consideration, such as: the difficulty of Chinese characters, the difficulty of words, the difficulty of sentences, and the readability of the text as a whole. By collecting Chinese text corpus, a decision tree based Chinese text difficulty assessment model was established using the same method. Through model validation, it is shown that the model has good classification effect.
Towards Forecasting Internet Financial Frauds based on Advertising

This paper studies the ability of advertising in forecasting Internet financial frauds. Online advertising reflects real-time operational decisions and is therefore more direct than features from financial reports. One of the most important tasks is identifying non-obvious Internet advertising in the form of general editorials so that we can use the implicit advocacy behavior to infer the true business decisions and motives. In order to describe the advertising behavior of enterprises, we propose a cascading classification method based on the linear Support Vector Machine model, and use the text data from multiple Internet channels, including Sina Weibo, Wechat official accounts, Internet financial forums, Baidu Post Bar and Zhihu, to identify advertising texts as well as the inflammatory degrees of advertisements. Dynamic logistic regression models are then applied to study the forecasting abilities of advertising for Internet financial frauds and the timeliness. The empirical results show that advertising characteristics have high prediction ability 2–3 months prior to the exposure of Internet financial fraud. It indicates that advertising behavior can effectively reflect corporate business decisions and thus potential operating risks. This study can provide useful references for the regulation of operating risks of unlisted companies and illegal fund raising practices of Internet financial platforms.
Synchronous Deep Reinforcement Learning (SDRL) Algorithm For Small Batch Image Recognition

In order to get a good image recognition model, a lot of high-quality data must be trained. However, some special fields (such as battlefield, rare medical images, etc.) have the characteristics of difficult to obtain data and difficult to label. In order to get a small batch image recognition model with good performance under the condition of insufficient data, a Synchronous Deep Reinforcement Learning algorithm for small-batch image recognition is proposed, and the algorithm is used to recognize image data such as combat elements. The algorithm is based on reinforcement learning and convolutional neural network and so on, it is obtained by optimizing it. Through experimental analysis, the algorithm can quickly realize small batch image classification, and the accuracy rate is higher than other similar algorithms, which proves the practicability and feasibility of this algorithm.
Construction and Completion of Operational Entity Relationship Knowledge Graph

A construction and completion method of operational entity relationship knowledge graph (ERKG) is proposed to support tactical decision aids. The operational ERKG can be built by using a syntax indicator extraction method and an entity relationship extraction model combining syntax indicator with sentence-level context. And this ERKG can be complemented by employing a graph neural network inference model based on structured attention mechanism, and a relationship inference model combining operational entity knowledge with attention mechanism. While the entity relationship reasoning method combining the operational entity knowledge with attention mechanism is adopted to evaluate the trained relationship inference model. On the basis of structured ERKG, entity knowledge graph (EKG) and event knowledge graph (EventKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.
Depth Completion Using Consistent Regions

Depth or disparity acquired by current structured-light depth sensors often in poor quality, such as missing regions and noisy map. Current guiding depth enhancement algorithms usually are noise sensitive or limited to small region missing region completion. In this paper, we propose a novel robust high order guiding model for depth enhancement which simultaneous complete missing areas arbitrary size and denoising depth. The key to our model is the incorporation of region constrains of pixels cliques and the joint bilateral filter scheme for guiding information calculation, information is integrate in a high order Conditional Markov Random Field model, the depth enhancement is solved as an onptimal estimation problem. We run experiments on two challenging RGB-D datasets and show significant improvement for maintaining details and structures over state-of-the-art models.
Towards Method of Horizontal Federated Learning: A Survey

With the rapid evolution of artificial intelligence and deep learning, people pay increasing attention to the value of data privacy, which inspires the development of federated learning technologies. As an innovative machine learning application for decentralized data, federated learning unites different clients to cooperatively train on the premise of strictly protecting the data privacy. Among all categories of federated learning frameworks, horizontal federated learning is the most common and popular type. Under the setting of horizontal federated learning, a series of optimization methods based on Fedavg greatly improve the availability of federated learning, but they still face the Non-IID problem seriously. Therefore federated personalization technology is proposed to adapt federated model to local data, which has achieved impressive success in dealing with data heterogeneity and further expanding the application scenarios of federated learning. In this paper, characteristics of different categories of federated learning are described in detail, which enable horizontal federated learning to be distinguished clearly. In addition, we summarize the classical optimization methods of horizontal federated model and the problems it faces. To comprehensively sum up the research for these problems and challenges, we also introduce personalized horizontal federated learning and strategies to implement it.