Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation and Analysis of Cyber Attacks on Power and Energy Systems

The power grid has evolved over the course of many decades with the usage of cyber systems and communications such as Supervisory Control And Data Acquisition (SCADA); however, the cyber-power system can be infiltrated by malicious attackers due to this connectivity. Encryption is not a singular solution. Currently, there are several cyber security measures in development, including those based on artificial intelligence. However, there is a need for a varying but consistent attack algorithm to serve as a testbed for these AI tools to be trained and tested. This is important because in the event of a real attacker, it is not possible to know exactly where they will strike and in what order. Therefore, the proposed method in this paper is to use criminology concepts and fuzzy logic inference to create this algorithm and determine its effectiveness in making decisions on a cyber-physical system model. The method takes various characteristics of the attacker as an input, builds their ideal target node, and then compares the nodes to the high-impact target and chooses one as the goal. Based on that target and their knowledge, the attackers will attack nodes if they have resources. The results show that the proposed method can be used to model a variety of attacks with varying damaging effects, and one other set of tests shows the possibility for multiple attacks, such as denial of service and false data injection. The proposed method has been validated using an extended cyber-physical IEEE 13-node distribution system and sensitivity tests for the ruleset created.
Surrogate Gradient-Based Medical Image Classification Using Spike Neural Network

The human brain, an unparalleled paradigm for efficient signal processing, fuels the quest to design brain-like architectures in addressing real-world challenges. Among mimicked computational models, Spike Neural Networks (SNNs) have emerged notable in information processing due to their distinct resemblance to brain-inspired computing, with distinguishing features from conventional methodologies. In this paper, we introduce an innovative surrogate gradient-based technique tailored for medical image classification. Specifically, our approach propels a gradient-based SNN framework with the explicit objective of achieving proficient classification of breast cancer images. Employing the SNN architecture, our results reached an accuracy level of 92.52%. The voltage-driven spike behavior and spike patterns have also been apprehended to support the model's efficacy. This outcome highlights the latent potential of SNNs in achieving precise medical image classification. The framework we present signifies substantial advancements in comprehending the brain-inspired attributes of SNNs, with the way for streamlined and accurate diagnostic solutions in medical imaging.
Automated Table Detection Using R-CNN: A Cloud-Based Approach

In today's data-driven world, there is an ever-increasing requirement for effective data processing from document images that contain tables. These documents contain a wide variety of complexity, such as different layouts and visual distortions. This paper presents solution for table detection from document images by proposing deep learning models such as RCNN and MobileNetV2. The deep learning models were enhanced by intensive hyper-parameter tuning to optimize model parameters. In this research work experimentation is done on the Table bank dataset to demonstrate model performance. In addition to this, the paper emphasizes on practical deployment through Microsoft Azure. The work presented here represents a substantial improvement in the process of automating the detection of tables inside document pictures.
An Improved SFS Method for Achieving Fast, High-Precision, and Widely Adaptable 3-D Reconstruction

Different optical reconstruction methods have stringent requirements on the material or shape of the objects, and have poor generalization ability. To address this issue, this article proposes a stable and reliable target contour recognition algorithm based on omnidirectional illumination using shape from silhouette (SFS). It solves the problem of low signal-to-noise ratio of light signals reflected by materials with high reflectivity or transparency, and achieves performance that is comparable to the state-of-the-art deep learning networks. Moreover, this article solves the blind spot problem of the SFS algorithm in single-image fast reconstruction, and proposes a targeted growth visual hull (VH) method, which further enhances the reconstruction speed of the traditional SFS method while ensuring reconstruction accuracy. This article takes the reconstruction measurement of supermarket products as an example to demonstrate the value of the proposed method in practical applications.
Upper-Limb Position-Robust Motion Recognition With Unsupervised Domain Adaptation

Upper-limb position is one of the most critical factors that degrade sEMG-based motion recognition accuracy. Therefore, we propose an upper-limb position-robust motion recognition with unsupervised domain adaptation. The proposed method finds the feature representation which reduces the difference between the distributions of labeled and unlabeled data acquired from the specific upper-limb position and other conditions respectively. It is shown that the proposed method has enhanced the classification accuracy by up to 13.50% compared to machine learning-based classifiers, convolutional neural network (CNN), and domain adversarial neural network (DANN). Especially, the lowest classification accuracy among every pair of training and test upper-limb positions has been improved by up to 19.96%. The effectiveness of the proposed method is also verified with the visualization of feature representation and comparison of a learning curve. To see the feasibility and performance in the real-world applications, we designed the virtual interface to control the ball in accordance with the real-time network estimation and conducted the following experiment: With the current ball position given, the subject has to reach the goal position as soon as possible while the error between desired and real trajectory is minimized at the same time. The proposed method has shown significantly higher reaching speed and tracking accuracy.
Analysis of factors influencing the use of the Online Learning Management System after Covid-19 with the D&M Model

This study aims to determine the factors significantly influencing student satisfaction using the Online Learning Management System (LMS), especially after the covid 19 pandemic. This research focuses on several variables: Course Content (CC), Course Content Support (CCS), Course Assessment (CA), Course Design (CD), Device Compatibility (DC), Device Connectivity (DCO), Assurance (A), and System Reliability (R). The sample was 421 respondents from university students and academics from Indonesia and abroad. The research model used is the D&M model with purposive sampling technique and processed with SEM-Smart Partial Least Square (SmartPLS) version 4. From the results of the analysis, it is found that Course Content (4,719), Course Assessment (2,935), Course Design (2,560), Device Connectivity (7,632), and Assurance $(2,955)$ are factors that provide satisfaction to the intended application users. At the same time, Course Content Support $(1,879)$, Device Compatibility $(0,389)$, and Reliability $(1,036)$ do not influence the intended application. The results of these findings will more or less help improve the management of an institution regarding how to properly organize online learning so that the quality of the implementing institution is maintained.
Adult Autism Screening and Prediction using Ensemble Approach

Autism spectrum disorder is a neuro developmental disorder that affects communication and behavior. Although ASD is typically diagnosed in childhood, it can also be diagnosed in adulthood. However, adults with ASD are often underdiagnosed, which can lead to a number of challenges, such as social isolation, employment difficulties and mental health problems. Early diagnosis and intervention can make a significant difference in the lives of autistic people. With appropriate support, autistic people can thrive in school work and their personal lives. However, without a diagnosis, autistic people may not be able to access the services and support they need. Approximately half of adults with ASD (autism spectrum disorder) may remain undiagnosed due to reasons like limited awareness among healthcare providers, the stigma linked to ASD and the variation in how ASD demonstrates in adults compared to children. This research paper explores the crucial role of machine learning algorithms on screening of Autism in adults. The paper provides an introduction to the topic followed by a methodology section detailing the various algorithms used. This survey paper provides the comparative analysis of different machine learning algorithms for Autism screening through different models such as DecisionTreeClassifier, GaussianNB, RandomForestClassifier. This study assessed the effectiveness of machine learning algorithms: DecisionTreeClassifier, GaussianNB and RandomForestClassifier. GaussianNB demonstrated the best performance with an accuracy of 85.85%. Finally, the paper concludes by highlighting the significance of machine learning algorithms in improving disorder detection accuracy.
An Intelligent Software Tool for Audio Signal-based Hinglish to English Language Translation

Most of the current Indian generation speaks/writes using Hinglish on various social media platforms. It is difficult to understand and interpret Hinglish using existing computational linguistic tools mainly based on English. Hence, there is a need to provide automated Hinglish to English Language Translation tools to integrate with existing NLP-based applications like hate speech detection. Currently, such complex language translations are manually intensive and require domain experts. Also, there is a scarcity of standard datasets in this domain for carrying out helpful research. In this research, we propose an intelligent AI-based translator for translating audio data signals from Hinglish to English, which can be integrated into various NLP-based applications in the Indian Context. We also created a large-scale customized annotated Hinglish to English dataset containing 27135 entries of size 4.121MB. After extensive experimentation, we received the highest BLEU (Bi-Lingual Evaluation Understudy) score of 92.20 on our customized Hinglish to English dataset, while on the original PHINC dataset, we found a BLEU score of 70 approx. We also developed a Python TKinter-based user-friendly GUI application that takes audio signal/voice as input and provides English-translated text as output.
Theoretical and Practical Analysis of MIMO Simultaneous Wireless Information and Power Transfer Over Inductively Coupled Circuits

The number of wireless devices continues to increase, and this growth will be further fueled by the Internet of Things (IoT). There is an urgent need to develop new techniques to both wirelessly transfer power to and communicate with these devices. Recently, inductive power transfer has gained much attention as a potentially efficient wireless powering solution for short- and medium-distance charging scenarios. In this article, we consider multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) using inductive coupling. While an analysis of SWIPT requires a model that correctly captures transmit power, harvested power, and information capacity, the majority of previous studies have overlooked the relationship between the mathematical signal representations and the real-world physical quantities. To overcome this challenge, we propose a framework in which signals and channels are represented by traveling waves and scattering parameters. The optimal covariance matrices of the transmit traveling waves are derived for three different working scenarios. We propose a simple estimation procedure to determine unknown scattering parameters and noise distribution. The rate–energy (R-E) regions and the magnetic field strengths are computed for two inductively coupled MIMO models based on the finite element method (FEM).
Non Invasive Approach for Dental Caries Detection

In developing nation like India, dental health is highly ignored health domain. This is due to the lack of awareness, lack of education, socio-economic background and poverty. Hence an automated system of dental caries detection is necessary which will reduce the burden on dental practitioners. The presented system focuses on dental caries detection using convolutional neural network with four layers of convolution. Web based database of 74 single tooth digital RGB images are used for the study. 60 images were used for training and 14 were used for testing. Data augmentation is done since the database is low. System with three convolution layers have been implemented initially. The results are compared. With three convolution and pooling layers 78.57% accuracy is obtained whereas with four layers of convolution and pooling layers 85.71% accuracy is obtained. This model gives the platform for future research in the dental disease's detection using deep learning approaches using multiclass classification.
Young and mature oil palm tree detection and counting using convolutional neural network deep learning method

ABSTRACT Detection and counting of oil palm are important in oil palm plantation management. In this article, we use a deep learning approach to predict and count oil palms in satellite imagery. Previous oil palm detections commonly focus on detecting oil palm trees that do not have overlapping crowns. Besides this, there is a lack of research that builds separate detection system for young and mature oil palm, utilizing deep learning approach for oil palm detection and combining geographic information system (GIS) with deep learning approach. This research attempts to fill this gap by utilizing two different convolution neural networks (CNNs) to detect young and mature oil palm separately and uses GIS during data processing and result storage process. The initial architecture developed is based on a CNN called LeNet. The training process reduces loss using adaptive gradient algorithm with a mini batch of size 20 for all the training sets used. Then, we exported prediction results to GIS software and created oil palm prediction map for mature and young oil palm. Based on the proposed method, the overall accuracies for young and mature oil palm are 95.11% and 92.96%, respectively. Overall, the classifier performs well on previously unseen datasets, and is able to accurately detect oil palm from background, including plant shadows and other plants.
Exploring the Horizons of Speaker Recognition: Contemporary Advancements and Prospective Trajectories in the Age of Deep Learning

Speaker recognition is a prominent area of study within speech technology. In recent times, there has been a notable transition towards embedding-based end-to-end speaker recognition approaches. These methods enable speaker recognition without the need for retraining when encountering new speakers in real-world scenarios. Earlier models were trained on limited monotonous hand-crafted datasets, which worked on pattern matching, but failed to be robust because of architectural limitations and insufficient data. Research shows that training deep neural networks, which rely on attention mechanisms, on extensive datasets containing weakly labeled and diverse samples can significantly improve their resilience when applied to various classification tasks downstream. Drawing upon knowledge acquired from extensive datasets like VoxCelebl and VoxCeleb2, along with specially crafted proprietary data tailored to handle various modalities, we initiated an investigation into the capabilities of these versatile techniques for building speaker recognition models. Our attention was directed towards two reputable models, ECAPA-TDNN [1](Emphasized Channel Attention, Propagation, and Aggregation) and Titanet[2], as we aimed to comprehend the architectural enhancements and assess their impact on the model's ability to generalize effectively. Also, we propose a faster and scalable inference pipeline using Elasticsearch.
Development and Control of a Four-Wheel Drive Holonomic Mobile Robot

In this piece of work, we present both the development and control of a four Mecanum wheeled mobile robotic system ensuring precise maneuverability, and satisfactory tracking performance. Particularly, the development involves the design of actuators for each wheel with appropriate encoders and selection of embedded system for multi-actuator control. Likewise, the closed loop path-tracking control strategy is designed by utilizing the identified model of the drive wheel unit as the inner loop and closed-loop inverse kinematic control as the outer loop, involving system sensory feedback. Both simulations and experimentations are performed to ensure zero turning radius and lateral movements (with constant yaw angles) during the system maneuverability in various pre-defined paths. From these results, it is inferred that both the zero turning radius and lateral movement tasks execute a fine performance with a maximum error of 0.025 m and 0.01 m, respectively. Thus, it is confirmed from the test results that our developed four-Mecanum wheeled robotic system will execute satisfactory performance towards the application of mobile robots.
Recommendation System for Clinical Concept Mapping

In the past decade, the healthcare industry has shifted from paper-based document storage to Electronic Health Records (EHR), enabling quick, safe access to patient data. A key role is played by Semantic Interoperability (SI) which enables seamless data exchange between diverse care settings and clinical software. SI necessitates the linking of bio-medical data (aka. clinical events) with shared, standardized, and controlled vocabularies like SNOMED, LOINC, etc. However, the healthcare data across various client domains are filled with ambiguous textual representations of clinical events that may be present in the form of synonyms, acronyms, and abbreviations. To make interoperability work, Healthcare IT service providers must map related clinical events with the appropriate standard concepts, which requires additional time and resources. Natural Language Processing (NLP) plays a vital role in addressing the challenges of SI by learning effective representations of text words in the bio-medical domain thereby capturing their semantic meaning. Our method utilizes various pre-trained word embeddings trained on the bio-medical corpus like BioWordVec fastText and SapBERT that captures fine-grained semantic relationships. In this study, we have developed a recommendation system that provides recommendations of Top ‘N’ clinical events for mapping to a standard concept. The recommendation system showed good performance with a sensitivity of above 99 % using both the pre-trained word embedding. Further, this product can be integrated into the mapping workflow to help make accurate automated suggestions that minimize manual effort.
Fake News Detection

The aim of this master’s thesis is to evaluate the unsupervised representation learning of the five large pretrained NLP models BERT, RoBERTa, DistilBERT, ALBERT and XLNet. In choosing these specific models, the performance of encoder-based models can be evaluated in contrast to an autoregression-based model. The models are examined with respect to two datasets that both consist of instances with a respective headline, associated news article and the stance of the article towards the respective headline. The headline states a short claim and the article bodies either Agree or Disagree with the stated claim, Discuss it or are simply Unrelated. The datasets are introduced in the scope of the Fake News Challenge Stage 1 that took place in 2017. Specifically, the thesis aims at understanding how much hyperparameter tuning is necessary when finetuning the five models, how well transfer learning works in the specific context of stance detection of Fake News and how sensitive the models are to changing the hyperparameters batch size, learning rate, learning rate schedule, sequence length as well as the freezing technique. Furthermore, the finetuning for large pretrained NLP models is evaluated by opposing the encodingto the autoregression-based approach. The results indicate that the much more expensive autoregression approach of XLNet is outperformed by BERT-based models, notably RoBERTa. The encoding approach thus yields better results. The hyperparameter learning rate is most important, while the learning rate schedule is relatively robust to changes. Experimenting with different freezing techniques indicates that all models learn powerful language representations that pose a good starting point for finetuning. Using the larger FNC-1 ARC dataset that is more evenly distributed boosts prediction performance especially for the sparse category of Disagree instances for most models.
Fake News Detection: A Comparative Study of Machine Learning Techniques

This paper represents a comprehensive study to detect fake news using the 2016 U.S. Elections dataset. Due to the advancement of technology and the abundance of online content, it has become complicated to distinguish fake content from real one. Our research aims to explore different text preprocessing techniques and methodologies for accurately classifying news articles as genuine or fabricated. We have performed a range of text preprocessing and cleaning techniques to improve the quality of the dataset. We have also conducted topic modelling using latent semantic analysis on both real and fake news separately to get further insights. Exploratory data analysis such as identifying the most frequent words, and co-occurring words with varying window size have undoubtedly guided us to understand the distinct characteristics of genuine and fabricated news. Interpretable machine learning models like Random Forest, SVM, Logistic Regression, KNN, Decision Tree and different deep learning models such as Bidirectional LSTM and Transformer-based networks have been used to build a classification model for detecting fake news. We found the transformer-based model performed better in terms of accuracy as well as computation time compared to the other models. We have also used different hyperparameter tuning and regularization methods to make the model simple enough to work on text data and reduce the overfitting vulnerability. Finally, the overall performance of these models has been compared to choosing the best model that could be used in other text classification tasks. Firstly, this paper highlights the importance of text preprocessing techniques and EDA in building accurate classifiers for fake news detection. Secondly, the proposed models provide an effective solution to detect fake news, which significantly impacts the credibility of online news sources.