Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting dairy herd resilience on farms with conventional milking systems

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.
System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.
Transient Electro-thermal and Bandgap Narrowing Analysis of the bipolar transistor

Given the bandgap narrowing (BGN) effect caused by heavy doping of the bipolar transistor and the self-heating effect (SHE) in the practical work environment, a transient electro-thermal and bandgap narrowing model is proposed in this paper, which is based on the Slotboom model and the modification of the drift-diffusion equation based on semiconductor equations and the heat conduction equation. The transient characteristics of the bipolar transistor in amplification mode are simulated using the differential method, and the results show that the terminal current of the bipolar transistor decreases after considering the SHE and the BGN effect. The model developed and the results obtained in this work can provide some reference significance for the optimization of the amplification characteristics of the bipolar transistor.
EM Simulation and Mechanism Analysis of Dielectric Coated Complex Target Based on MoM-PO Method

Based on the impedance boundary condition (IBC), the paper firstly proposes a MoM-PO hybrid algorithm for high-precision and rapid prediction of electromagnetic (EM) scattering of dielectric-coated complex targets. Aiming at the difficult problems of scattering sources and mechanism analysis of complex targets, a set of scattering source analysis methods based on component decomposition is proposed. The article takes the B-2 Ghost strategic bomber as an example. The simulation results are in good agreement with the exact solution. The in-depth analysis of the scattering mechanism of the target under the three conditions of ideal conductivity, overall dielectric coating and partial dielectric coating verifies the engineering application value of the research content of the article.
Identification Method for Household-Transformer Relationship Based on UMAP and BIRCH Clustering

The relationship between households and transformers in the low-voltage station areas is frequently changed due to the routine maintenance, renovation and upgrading of distribution network. Since it is difficult to maintain the household-transformer relationship in low-voltage station areas manually, an identification method based on voltage measurement data of smart meters is proposed. Firstly, the spatial distribution features of the original voltage data are extracted by the uniform manifold approximation and projection (UMAP) technology while dimensionality reduction is performed. Then, the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm is used to cluster the feature data, chieving recognition between nodes and their respective station areas. Finally, the results of an example are analyzed to prove the feasibility of the algorithm.
Scrutinizing the alignment limit in two-Higgs-doublet models

In the alignment limit of a multi-doublet Higgs sector, one of the Higgs mass eigenstates aligns with the direction of the vacuum expectation value, and its couplings approach those of the Standard Model (SM). Considering the CP-conserving Two-Higgs-Doublet Models of Type I and Type II near the alignment limit, we focus on the case in which the heavier of the two CP-even state, H, is the 125 GeV SM-like state observed at the LHC. A decoupling regime thus cannot be attained in these scenarios since the lighter CP-even state, h, has a mass below 125 GeV by definition. After a short review of the theoretical structure, the phenomenological consequences of this realization of the alignment regime are presented. Implications for the coming 13 TeV LHC run, including expectations regarding the observation of the extra scalar states, are discussed.
A Human-Computer Interaction Perspective on Clinical Decision Support Systems: A Systematic Review of Usability, Barriers, and Recommendations for Improvement

Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-Computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.
Exploring Bit-Level Sparsity for Partial Sum Quantization in Computing-In-Memory Accelerator

Computing-In-Memory (CIM) has demonstrated great potential in boosting the performance and energy efficiency of convolutional neural networks. However, due to the limited size and precision of its memory array, the input and weight matrices of convolution operations have to be split into sub-matrices or even binary sub-matrices, especially when using bit-slicing and single-level cells (SLCs). A large number of partial sums are generated as a result. To maintain high computing precision, high-resolution analog-to-digital converters (ADCs) are used to obtain partial sums at the cost of considerable area and substantial energy overhead. Partial sum quantization (PSQ), a technique that can greatly reduce the resolution of ADC, remains sparsely studied. This paper proposes a novel PSQ approach for CIM - based accelerators by exploring the bit-level sparsity of neural networks. Then, to find the optimal clipping threshold for ADCs, a reparametrized clipping function is also proposed. Finally, we develop a general post-training quantization framework for the PSQ-CIM. Experiments on a variety of neural networks and datasets show that, in typical case (ResNet18 for ImageNet), the required resolution of ADC can be reduced to 2 bits with little accuracy loss (~.92 %) and the hardware efficiency can be improved by 199.7%.
Electronic Healthcare Management System Using Blockchain Technology

Electronic healthcare management using blockchain is an emerging area that has the potential to transform the healthcare industry.Blockchain technology's potential to transform the healthcare industry is due to its ability to provide a secure and transparent platform for managing electronic healthcare. Its use is rapidly growing and could revolutionize the field. This innovative approach offers a secure and transparent platform for managing patient records, drug supply chains, and clinical trials, while enhancing data privacy and security, reducing the risk of errors and fraud, and improving the efficiency of healthcare processes. Blockchain technology enables healthcare data to be stored on a decentralized ledger, ensuring the integrity and confidentiality of data and enabling secure sharing of data across different healthcare providers. Moreover, it facilitates the automation of various healthcare processes, including claims processing and drug supply chain management, through the use of smart contracts. This automation can reduce the administrative burden on healthcare providers, increase efficiency, and improve patient outcomes. Electronic Health Records (EHR) are becoming increasingly popular worldwide, but current systems have issues related to privacy and security. We suggest a solution that employs a permissioned Ethereum blockchain to link global hospitals and patients. Our mechanism uses symmetric and asymmetric key cryptography to guarantee secure record storage and selective access, empowering patients to control their health records and authorize or revoke a hospital's access. We adopt IPFS to store records, ensuring their immutability and distribution. Moreover, our model maintains disease statistics while preserving patient confidentiality.Implementing blockchain technology in healthcare can significantly improve the quality of care, patient outcomes, and cost-effectiveness of healthcare services. By enhancing data security, promoting interoperability and transparency, and automating healthcare processes, blockchain technology has the potential to revolutionize healthcare management. Future research should focus on developing more sophisticated applications of blockchain in healthcare and addressing the remaining challenges.
AI Enabled- Information Retrival Engine (AI-IRE) in Legal Services: An Expert-Annotated NLP for Legal Judgements

The relationships between the law and fact, and the use of the facts to support a legal conclusion requires practice. This study introduces a technique called the "legal analysis of data extraction" which helps students/law practitioners to quickly assess a law article on the basis of the extracted attributes, and then apply their understanding in order to support a legal conclusion. To ease the processing of the law text data and retrieval of important information (entities) like names of Judges, appellants, respondents, judgment dates, etc. by utilizing NLP processes and ML techniques. This study demonstrates a novel approach of advanced information retrieval in the law articles leveraging natural language processing and machine learning techniques which are productized and used on live data as well. In this custom NER, aggregate accuracy of 95% is achieved using text classification followed by a regex fallback and flagging mechanism. This study consists of the following sections- a) Introduction b) Related Work c) About Dataset d) Methodology and approach e) Fallback mechanisms f) Results and Evaluations g) Conclusions & Future Scope with h) References.
Adaptive-Bert Network for Advertising Text Generation

Our system automatically generates tailored ad text based on the travel magazine advertisements layout and multiple travel blogs. The user inputs the layout of the travel magazine advertisement, which is then processed by a prediction-evaluation model (PEM). This model predicts the required number of text words and font size in the layout based on the input layout. Next, feed the data generated by the PEM and multiple tourism blogs into the text generation model, and ultimately produce text that meets the requirements and fits the designated text area in tourism magazines' advertisements. The generated text is then placed in the text area of the layout. It includes our new self-attention layer to ensure that the output text contains essential information. We conducted extensive experiments on real datasets to demonstrate the effectiveness of our proposed model. Using our model(Adaptive-Bert), users can create travel magazine advertising text that meets their requirements. Our method ensures that the generated text is of high quality and meets layout requirements.
Detection of Whiteflies in Plants using Deep Learning

Whiteflies, One of the devastating pests seen in high-yielding crops that cause mass damage to agriculture due to their transmitting capability through a broad range of scopes. These pest species cause insufficient agricultural production on a large scale yields, so recognizing these insects in a larger area field is difficult and time-consuming for human observation. To overcome such a situation, in this paper, we used an object detection model known as Yolov5 with a nano variant, which helps detect thousands of small whiteflies rapidly with fewer parameters and power efficiency. Besides conventional pre-processing techniques, tilling images at inference helped to detect tiny whiteflies effectively and classified the abundance level based on counts. The model is fine-tuned with the pre-trained weight, and the result is evaluated using the Cassava Whitefly Dataset. Using the proposed methodology, the model achieved an average precision of 0.95@0.5 threshold with 0.92 F1-score. The experimental result demonstrates that the single-stage detector performs finer than the two-stage detector on speed and accuracy using tilling. The result of the proposed system increases the F1-score and average precision by 22% and 20% from the Raw data.
Advancement in Techniques towards Optimal Operation of UPQC for Power Quality Improvement

The advantages of RES for the environment have made them commercially viable and widely used today. The microgrid is also crucial to conventional power distribution systems due to its improved dependability and reduced overall energy losses. For more adaptability, renewable energy sources (RES) including solar, wind, batteries, fuel, tidal, etc., satisfy massive power demands based on dispersed generation and non-linear loads, which may result in power quality problems like voltage sag/swell and harmonic distortion. Many power-conditioning types of equipment are used to solve PQ issues. Among them, UPQC acts as the most significant choice for reducing PQ issues. As a result, a literature review was conducted in this research, using the 30 most significant research publications from IEEE, Science Direct, and Springer journals. These contirbutuons were all compiled between the years 2010 and 2021. This study has examined 30 academic research contributions in several ways, including from the perspectives of optimization algorithms and AI-based control strategies, to get an understanding of what are all about. Additionally, each work's performance under sag/swell conditions is demonstrated in terms of voltage, current, and THD. Furthermore, the present challenges in UPQC power quality enhancement methods for distributed generators connected to the grid will be described, which will be a significant milestone for researchers in the future.
A compact broadband amplifier for 5G FR2 in 65nm CMOS

In this paper, a compact broadband amplifier is realized based on 65nm CMOS technology. The three differential CMOS pairs adopt the common source (CS) structure, rely on the capacitance neutralization technology and use the transformer for impedance transformation to achieve ultra-wideband matching. The simulation results show that in FR2 frequency band (24.26 - 52.6GHz), the output gain of the amplifier is 17±2.5dB, the DC power consumption is 45mW, the supply voltage is 1V, and the core circuit area is 0.11mm2.
Machine Learning Approaches for the Classification of Knee Osteoarthritis

Machine learning systems have gained tremendous attention in solving complex tasks. For instance, image classification using machine learning has multiple applications in the medical field, where different imaging modalities are used for diagnosing, detecting, and classifying diseases. This study aims to analyze different machine-learning approaches for detecting knee osteoarthritis using X-rays. We used different machine learning algorithms to classify X-ray images of the knee to various levels of osteoarthritis severity. We have solved the problem as a binary classification task, multi-class classification, and ordinal regression task. The Histogram of Oriented Gradients and Haralick Features are used for feature extraction in the binary classification task. For Binary Classification, six experiments are performed using different machine learning classifiers. Logistic regression has a maximum accuracy of 84.50%, F measure of 81%, AUC of 0.84, Mathew’s Correlation Coefficient of 0.69, and Cohen’s Kappa 0.66. 10-Fold cross-validation also reveals that the models’ performance is consistent across all folds. Furthermore, texture and shape features are extracted for ordinal classification using the histogram of oriented gradients and dense DAISY descriptors. Finally, separate experiments are performed, and results for both descriptors are compared. Our implemented approaches have shown state-of-the-art results.
Application of Collaborative Filtering Algorithm in Computer Network Security Technology Evaluation

After entering the 21st century, with the rapid development of the economy, people's living standards are also constantly improving, especially the use and popularization of computers. Computers have penetrated into every aspect of people's lives and work, and the use of networks is also becoming increasingly widespread. This is a new challenge to computer networks. For example, virus intrusion, malicious code, massive sending of malicious information, data theft, data leakage, and other issues are becoming increasingly serious. At present, billions of computer viruses have been invaded globally, causing great impact on people's daily lives and posing great harm to computer systems. The prevention and control of computer virus has become a hot issue in many countries and regions in the world. The security protection of computer networks is the key to ensuring their normal operation and normal operation. Currently, the security of computer networks is a very important issue. The article mainly discussed the security of computer networks in the following aspects.
A Wideband Dual Circularly Polarized Metasurface Antenna with Directional Coupler

This paper proposes a wideband dual circularly polarized metasurface antenna. Through the combination of directional coupler and two-port microstrip metasurface antenna, the dual circularly polarized antenna can be realized. After simulation, the antenna has a VSWR ≤ 3 bandwidth from 3.17 GHz to 4 GHz, and has the 3-dB axial ratio bandwidth of 12.4 % (3.17 ~ 3.59GHz) for two circularly polarized modes. And the antenna has the boresight radiation gains of more than 7.63 dBic.
A Switchable Scatterer/Reflector Metasurface for Antenna RCS Reduction

In this paper, we propose a scatterer and reflector switchable metasurface based on p-i-n diodes for an integrated dipole antenna. The proposed scatter metasurface is constructed based on Pancharatnam-Berry (PB) phase by ordered layout, which can achieve monostatic RCS reduction of the antenna from 3 to 10 GHz. In addition, when the metasurface is switched to the reflector, the integrated dipole antenna can effectively improve the realized gain of the antenna and reduce its profile.
Dual-band Dual-polarized Two-layer Microstrip Patch Antenna with Dual-port Feeding

In this paper, a dual-band dual-polarized microstrip patch antenna is proposed. The antenna is composed of two-layer printed-circuit boards. Two mutually perpendicular microstrip lines are used for feeding on the top of the lower-bottom printed-circuit board, and four symmetrical equal square patches are placed on the top of the upper printed-circuit board. The proposed antenna has two frequency bands. The first center frequency is 4.32 GHz and the second center frequency is 5.86 GHz. The port isolation of the antenna is larger than 15dB in both working bands. The suggested antenna has linear polarization as its polarization characteristic. It shows perfect radiation performance in two frequency bands, where the difference between co-polarization and cross-polarization in the principal radiation direction is larger than 20dB.
Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in Software-Defined Networks (SDN)

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate users.This paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data processing.The main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard.