Miracle-dz/UniFAR-FTUs huggingface dataset:
Handle Dense Labeling in Human Activity Recognition Using Self Attention and BiLSTM

Dense labeling, which annotates an activity label for each data sample in the segment, is a common approach to handle the problem of multi-class windows in wearable sensor-based human activity recognition. Recent success in image-based semantic segmentation offers opportunities to solve this problem by using well-known fully convolutional neural networks. However, the long-term dependencies of human activity are often ignored in these works, thus, lead to unstable prediction results. In this study, we address this problem by proposing a hybrid deep learning system that can effectively extract the context information from the sequential sensor data and densely predict the activity for each data sample. The system is constructed from two main components: 1) a transformer encoder with multi-head self-attention modules that capture the relationship between data samples and extract the salient features from long sequential data, 2) a bidirectional long short-term memory (BiLSTM) maintains the long-term temporal information in human activity. Our experiments on the UCI HAPT public dataset indicate that the proposed hybrid model achieves an accuracy of 93.41%, which is 2% higher compared to other state-of-the-art image-based dense labeling HAR models.
The efficacy of combining Deep and Handcrafted Features for Breast Cancer Classification using Ultrasound Images

Breast cancer is a potentially fatal condition, and timely detection plays a vital role in enhancing survival rates. To address this issue and aid clinicians in early detection, Computer Aided systems have been explored. Many researchers have proposed solutions for breast cancer detection, and a popular approach involves using Convolutional Neural Networks (CNNs). CNN-based approaches have displayed encouraging outcomes owing to their capacity to autonomously capture advanced features from medical images. But relying solely on CNN-based global features might lead to suboptimal classification results, as local image details may be overlooked. To improve the classification performance, this paper introduces a breast cancer detection system called the Multi Featured Breast Cancer Detection System (MFBCDS). This system takes advantage of both CNN features and local handcrafted features. Histogram Oriented Gradient (HOG), and Local Binary Pattern (LBP) are utilized for extracting local handcrafted features, while global features are obtained using ResNet50. The MFBCDS model integrates both global and local features to create a comprehensive feature vector. This vector captures essential information from localized regions, complementing the global features extracted by the CNN. Therefore, this combination of features significantly enhances the performance of the model. The combined feature vector is classified using Support Vector Machine (SVM). The evaluation of the proposed MFBCDS model is carried out on the widely used BUSI dataset using a 5-fold cross-validation approach where the proposed model has achieved satisfactory performance on various evaluation matrices with an average accuracy of 88.87%.
Enhancing Temporal Information Retrieval through Contextual Query Reformulation

As the amount of data is rising exponentially on the World Wide Web, it becomes challenging to retrieve information that is relevant to a specific period. In this paper, a query reformulation architecture is proposed to extract and rank web pages based on the temporal context of the query. If a temporal query is given, the proposed architecture extracts the web pages as well as trending hashtags and tweets from X related to the query. After preprocessing of extracted web pages and trending hashtags as well as tweets, contextual terms are extracted from them and added to the original query to form a ‘Reformulated query’. The reformulated query is then used to re-rank the extracted web pages based on the similarity score between the reformulated query and extracted web pages. The proposed architecture is evaluated on the basis of MAP, MRR, and nDCG and it has been observed that the proposed architecture outperforms the baseline models in terms of relevance and trend scores.
Medical SAM: A Glioma Segmentation Fine-tuning Method for SAM Using Brain MR Images

Based on the 2016 World Health Organization (WHO) Classification scheme for gliomas, accurate glioma segmentation serves as a fundamental basis for glioma diagnosis. In the realm of glioma diagnosis, brain MRI has emerged as an indispensable diagnostic tool due to its ability to provide comprehensive information. Over the past decade, there has been a notable surge in the utilization of machine learning techniques, particularly deep learning, for processing medical images. These deep learning methods, based on convolutional neural networks or transformers, proposed analogous architectures such as U-Net for precise segmentation of medical images, thereby significantly enhancing the accuracy of brain glioma segmentation. Thanks to the development of foundation models, models pre-trained with large-scale datasets have achieved better results on a variety of tasks. However, for medical images with small dataset sizes, deep learning methods struggle to achieve better results than on real-world image datasets. In this study, we proposed an adapter to effectively fine-tune the foundation model (SAM) for improved glioma segmentation using brain MR images. The effectiveness of the proposed method is validated via our private glioma data set from the First Affiliated Hospital of Zhengzhou University (FHZU) in Zhengzhou, China. The proposed method outperforms current state-of-the-art methods, achieving a Dice coefficient of 87.33% and a Hausdorff distance of 10.87 for accurate segmentation of the glioma region in glioma treatment, representing a significant 4% improvement in Dice coefficient.
On the Optimization of Virtual RIS Partitioning for Grant-Free Non-Orthogonal Multiple Access

The integration of reconfigurable intelligent surfaces (RISs) and grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising solution for enhancing spec-tral efficiency (SE) and massive connectivity in future wireless networks. This paper proposes a novel virtual RIS partitioning mechanism for GF-NOMA, where all user equipments (UEs) within a specific NOMA cluster have instantaneous access to resource blocks (RBs) without the need for grant acquisition and power control as in the traditional grant-based NOMA schemes. To achieve the required power disparity, RIS portions are allocated to the UEs in a manner that increases the reception power disparity. We derive closed-form equations for optimal RIS portions in two regimes of interest: 1) max-min fair regime and 2) maximum throughput regime. Simulation results demonstrate that the proposed RIS-assisted GF-NOMA yields a gain of 28% and 15% in terms of max-sum rate and max-min rate, respectively, outperforming existing grant-based approaches. The study highlights the potential of combining RIS with GF-NOMA as a powerful solution for future wireless communication systems.
A Brief Survey on Facial Expression Detection

Facial expression detection and Facial Recognition are important fields in computer vision. As per the statistics, a lot of people are going through anxiety and depression-related mental health problems, in such cases it is very important to build such a system where it can be able to identify the emotions of the people. To build systems for facial expression detection, every researcher requires three things, one is a good dataset for facial expression detection, better preprocessing methods, and a nontraditional-based AI architecture. In this paper, we explain various deep learning and machine learning methods used for facial expression detection and various dataset available for facial emotion recognition, which will help most people to explore datasets and methods which further will improve their research in the field of computer vision.
The ANN Algorithm-Optimized Deep Learning Model for Predicting Lung Cancer

Lung cancer is one of the leading causes of mortality globally, and it affects both men and women equally. Lung cancer patients have a dismal prognosis and therefore high fatality rates. Automating processes using image recognition and analytics software is happening in the IT industry and the medical area. This study evaluates three distinct approaches to early lung cancer detection, including a support vector machine (SVM), with the MultiScale Deep Convolutional Neural System to see which is most effective at saving lives. The lung cancer patient files kept at University of California at Irvine are the primary source for the data indices utilized in this investigation. In order to determine which classification algorithm works best in practice, this study uses the WEKA Tools to conduct empirical research. Multi-Scale DCNN achieves 95.56 percent accuracy, then followed by SVM (92.11) with KNN (88.40 percent) based on the data.
PHYSICAL AND CHEMICAL HETEROGENEITY IN THE SUBSURFACE: SPATIAL DISTRIBUTION OF TRANSPORT PARAMETERS AND THEIR RELATION TO DEPOSITIONAL PROCESSES

By Dana Divine, M.S. Washington State University May 2002 This thesis consists of two parts. The goal of first section is to better understand the influence of heterogeneity on contaminant transport. This objective is approached by quantifying the heterogeneity that may have affected transport of the Stanford-Waterloo tetrachloroethene (PCE) plume. Specifically, I determine the statistical distribution ofthe PCE sorption distribution coefficient (Kd) at Canadian Forces Base Borden, identify the spatial distribution of Kd, and determine the cross-correlation structure between PCE Kd and permeability (k). The results show that Kd is not lognormally distributed, and that variance is insufficient to fully describe heterogeneity. Skewness may be an important aspect ofthe In Kd distribution because the samples constituting the high Kd tail are clustered spatially into predominantly one lens. The cross-correlation in this section of the aquifer is both very weak positive and negative, the sign depending on the spatial location of the samples. The primary goal of the second section is to determine if geologic processes separate sediment into distinct zones of chemical reactivity. If such a correlation exists, the sedimentology of site may be used to constrain the prediction of subsurface chemical
An LP-Based Online Dispatching Method with Privacy-Preserving in Online Ride-Hailing

While ride-hailing brings great convenience to our daily life, it also poses a threat to the passengers' location privacy. Although many perturbation-based methods have been proposed to protect the passengers' location privacy, imprecise can still result in poor assignments of the ride-hailing platform. In addition, the platform often has to balance multiple conflicting objectives when dispatching drivers. Thus, trading these objectives in an appropriate way is critical to the long-term development of the platform. In this paper, we focus on the assignment strategy design in ride-hailing, aiming to promote the performance of the dispatching system while protecting the passengers' location privacy. We first model the online order dispatching as a bipartite matching problem, where drivers are assumed to be offline available and orders arrive sequentially following a known distribution. Then, we construct several linear programs (LPs) to obtain the obfuscation matrix (for privacy-preserving) and the guiding solutions of assignments (for online dispatching). Finally, a parameterized LP-based online dispatching algorithm is proposed to flexibly trade the two assignment objectives, i.e., dispatch efficiency and fairness. Experimental results on real-world datasets demonstrate the effectiveness of our algorithms.
ZnO-Based Surface Acoustic Wave Droplet Sensor

In this work we designed and fabricated a ZnO-based SAW sensor with focused aluminum IDTs for DI water droplet sensing. We employed ZnO/SiO2/Si as the piezoelectric substrate in this study. A minimum measurable DI water droplet of 6 mgr was obtained in the presented SAW sensor.
Chipless RFID Tag by V-shaped Multi-resonator for Partial Discharge-affected Equipment Identification

Partial discharge (PD) in insulating materials is a threatening phenomenon for high voltage (HV) equipment. In PD diagnostics, chipless RFID (CRFID) tags have been rarely used despite their recent popularity in so many other sensing applications. So far, only a handful of works have been attempted where CRFID tags are applied to detect PD signals and identify the PD-affected equipment. However, due to the incompatible integration and complicated design of CRFID tags, those attempts have failed to solve the issue of early PD detection while identifying the HV equipment. In this paper, we propose a new CRFID tag that offers a compatible system integration and simple design for the application of early PD detection and HV equipment identification. The proposed tag is designed by using V-shaped multi-resonators on a one-eighth- wave transmission line that is reciprocally bent by a V-shape. With a compact area of 16 × 15 mm2, the tag is designed to separately exhibit a low-pass response in the ultra-high frequency (UHF) range for PD detection and a multi-resonating response within 2.86−5.27 GHz frequencies for HV equipment identification purposes. Our designed tag offers an encoding capacity of 12 bits, a high bit density of 5 bits/cm2, and a spectral density of 4.98 bits/GHz. We expect the proposed tag will bring a promising solution to overcome the integration complexity of CRFID tags in UHF sensor-based PD sensing applications.
The cloud-radiative forcing of the U.S. landfalling atmospheric rivers

Luo, Qianwen Ph.D., Purdue University, December 2016. The Cloud-Radiative Forcing of the U.S. landfalling Atmospheric Rivers. Major Professor: Wen-wen Tung. Atmospheric rivers (ARs) are narrow channels in the atmosphere that transport an enormous amount of moisture from the tropics to the higher latitudes. Streaks of highly reflective clouds are observed along with the ARs in satellite imagery. These clouds both influence the moisture transport of ARs, as well as modify the EarthAtmospheric energy budget through pathways such as cloud-radiative forcing (CRF). This dissertation studies the CRF of the U.S. Landfalling ARs in weather and climate scales. Three crucial questions are addressed. First, how do clouds produced by the ARs modulate the moisture and heat balance of the Earth-Atmospheric system? Even though studies of ARs date back to the 90s, past research has been primarily focused on their hydrological impacts. We addressed this research gap by comparing the dominant types of precipitating clouds and convection of two ARs. Through quantifying their e↵ects on the energy balance in the midlatitudes, we found that when deep convection was the dominant cloud types of an AR, impressive CRF cooling was produced. Second, what are the su cient climate conditions for the extensive CRF in the continental U.S.? We studied 60 ARs that reached the California coast (the Southwest ARs) and 60 ARs that reached Pacific Northwest during Nov – Mar, 2000 – 2008. It was found that when these West-Coast ARs were followed by the moisture surge from the Gulf of Mexico (the Gulf-Coast AR), it resulted in apparent statewide CRF. Such condition happened more frequently in the Southwest-AR scenario. Third, how does the subgrid-scale-convection-induced CRF influence the moisture transport of ARs? We ran two WRF ARW simulations for a Southwest-AR that was followed by
Mobility Performance of Scalable Cell-Free Massive MIMO Under Channel Aging and Handover

While cell-free massive MIMO (CF-mMIMO) provides superior throughput performance against traditional cellular networks in static scenarios, the impact of changing mobile channels and serving access point (AP) sets is not yet fully addressed. In this paper we propose an analysis framework for mobile scalable CF-mMIMO taking into account both the channel aging effect and handover cost, by deriving the through-put model of different AP selection methods as functions of UE mobility and AP serving set size. We then use this framework to compare the performance of different AP selection methods for scalable CF-mMIMO, and thereby provide guidelines for future practical mobile network design. Our results show that scalable CF-mMIMO with UE-centric AP selection methods can be superior to the network-centric method only at very low mobility.
TransUser's: A Transformer Based Salient Object Detection for Users Experience Generation in 360° Videos

A 360-degree video stream enables users to view their point of interest while giving them the sense of 'being there'. Performing head or hand manipulations to watch the salient objects and sceneries in such a video is a very tiresome task and the user may miss the interesting events. Compared to this, the automatic selection of a user's Point of Interest (PoI) in a 360° video is extremely challenging due to subjective viewpoints and varying degrees of satisfaction. To handle these challenges, we employed an attention-based transformer approach to detect salient objects inside the immersive contents. In the proposed framework, first, an input 360° video is converted into frames where each frame is passed to a CNNbased encoder. The CNN encoder generates feature maps of the input framers. Further, for an attention-based network, we used a stack of three transformers encoder with position embeddings to generate position-awareness embeddings of the encoded feature maps. Each transformer encoder is based on a multihead self-attention block and a multi-layer perceptron with various sets of attention blocks. Finally, encoded features and position embeddings from the transformer encoder are passed through a CNN decoder network to predict the salient object inside the 360° video frames. We evaluated our results on four immersive videos to find the effectiveness of the proposed framework. Further, we also compared our results with state-of-the-art methods where the proposed method outperformed the other existing models.
EVMNet: Classification of DDoS Attacks using ELM-Enhanced Deep Learning Model with Feature Fusion

The proliferation of high-speed Internet Service Providers and cloud-based applications has increased network vulnerabilities. Distributed denial of service attacks (DDoS) are considered one of the most hazardous threats due to their ability to disrupt services and applications dependent on the Internet instantaneously. As interconnected devices grow, DDoS attacks become more prevalent and destructive. Intrusion Detection Systems (IDS) are required to identify and prevent these threats. As the Internet becomes more intelligent, it becomes more challenging to employ standard security measures. Therefore, it is crucial to develop effective measures for cybercrime. This paper introduces the EVMNet deep learning architecture for classifying DDoS attacks. It is based on cutting-edge Convolutional neural network (CNN) models, such as VGG 16 and MobileNet V2, with Extreme Learning Machine (ELM) feature fusion. The lightweight MobileNet V2 model can be integrated into devices to identify and prevent DDoS attacks. The objective is to differentiate between Benign traffic and DDoS attacks. The model’s effectiveness was assessed using several metrics, such as testing accuracy (TA), precision, sensitivity, false positive rate (FPR), negative prediction value (NVP), and false negative rate (FNR). The results of the proposed study indicate that the suggested model accomplished an impressive testing accuracy of 93.00%, a precision of 92.66%, a sensitivity of 92.44%, a false positive rate of 8.84%, a negative predictive value of 93.30%, and a false negative rate of 8.00% which surpassed the existing state-of-the-art methods.
Convolutional Neural Network Application for Detection & Classification of Brain Tumour

Brain tumours may be either benign or malignant. The highest grade of brain tumours is associated with a very poor survival rate. So, plan your treatments ahead of time. Improve the patients' living conditions on stage. Cancers of the brain, lung, liver, breast, and prostate are often evaluated using imaging modalities such computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images. In this research, MRI images are employed specifically for the diagnosis of brain cancer. Unfortunately, it is currently difficult to manually categorise a tumour from a non-tumour MRI scan due to the sheer volume of data generated by such scans. There is a limitation, too, in that only a limited number of images can reliably get quantitative information. Thus, reducing the death rate among humans depends critically on a trustworthy and automated categorization system. Brain tumours are notoriously difficult to automatically classify due to the wide variety of tumour locations and surrounding tissues. In this research, the authors propose using CNN classification to quickly and easily identify brain cancers. The underlying architecture is developed using small kernels. There has been a great deal of research towards improving the efficiency with which different kinds of brain tumours may be identified. Segmenting, identifying, and extracting the contaminated tumour region from magnetic resonance (MR) images is a time-consuming and labour-intensive process that relies heavily on the expertise of the clinician doing the procedure. Because of this limitation, it is crucial to use computer-aided technologies. We evaluate the size of the tumour in the brain using the Convolutional Neural Network method, which consistently yields accurate results.
Recent Advancements and Application of Signal Processing in Financial Data Analysis

Large and complex datasets produced by stock exchanges, trading platforms, economic indicators, and other sources are what define the financial industry. Making sense of this abundance of data and forecasting the future is a very difficult task. Techniques for signal processing, which have historically been connected to fields like engineering and telecommunications, have become useful instruments in addressing these issues in recent years.Financial data analysis has become a major application for signal processing techniques in recent years. An overview of the most current developments and uses of signal processing in the financial industry is given in this study. It looks at how techniques for signal processing, which were first created for industries like telecommunications and engineering, have been modified to deal with the particular difficulties presented by financial data. The benefits of using signal processing in financial research are covered in the study, including enhanced data extraction, pattern detection, and predictive modeling. Additionally, it outlines a few particular uses for these methods in fields including fraud detection, risk management, and algorithmic trading. Through the utilization of signal processing, scholars and financial experts can acquire more profound understanding of the workings of financial markets and to make decisions.
Finite Blocklength Decoding Error Probability Oriented Resource Allocation for Uplink URLLC Systems

We study the resource allocation for an uplink ultra reliable and low-latency communication (URLLC) system. The receive beamformer at the base station (BS) and the transmit power of users are jointly optimized to minimize the finite block-length (FBL) decoding error probability. To solve the difficult nonconvex problem, we first acquire a tractable reformulation by approximating the Gaussian Q-function. Although the resultant problem is still nonconvex, we propose an efficient algorithm where the optimal receive beamformer is obtained in closed form and the transmit powers of users are optimized by employing the majorization-minimization (MM) technique. Simulation results and complexity analysis verify the superiority of the proposed algorithm over existing benchmark schemes.