Miracle-dz/UniFAR-FTUs huggingface dataset:
Attack Detection in Wireless Sensor Networks using Novel Artificial Intelligence Algorithm

The Wireless Sensor Network (WSN) is a concept of an Internet of Things network (IoT) with limited energy resources. When many sensors are used despite their limitations, security becomes a significant concern in the network, necessitating the practical design of detection and mitigation techniques. Using Artificial Intelligence (AI) algorithms is a very effective way to develop cyber-attack detection systems in WSN. Squirrel Search Optimized Probabilistic Neural Network (SSOPNN), a unique classification approach for the detection of cyberattacks in WSN, is presented in this research. Gaussian Naïve Bayes (Gaussian NB), K-Nearest Neighbors (KNN), and Random Forest (R.F.) are three well-known methods against which their results are compared. The study utilizes Pearson's correlation and mutual information to choose features for the ensemble and to reduce the number of dimensions. T Pipeline optimization technique with a tree structure is used to fine-tune hyperparameters. Specifically, one-of-a-kind WSNDS dataset is used, which provides adequate material for four distinct attacks (Grayhole, Blackhole, Flooding, and TDMA scheduling). The study compares three approaches using a variety of metrics, including detection rate, false positive rate, false negative rate, average prediction time per sample, and memory and processing time requirements.
Development of Contactless Door Lock System Using Text-Dependent Voice Authentication

Electronic door locks offer advantages over traditional locks, but many require physical contact for password entry, raising virus transmission concerns. This study presents a voice recognition electronic door lock prototype, allowing users to unlock doors through smartphone voice commands, eliminating physical contact. Key findings include successful integration of voice recognition technology and the use of an Arduino Voice Control app as a user-friendly interface. Users can speak the door password, transmitted via Bluetooth to unlock the door securely. Rigorous testing affirms the prototype's functionality, assuring accurate voice recognition for door access. This innovative solution caters to modern security needs and addresses virus transmission concerns associated with traditional electronic locks.
CInC: Workload Characterization In Context of Resource Contention

Increased contention for hardware resources is a consequence of more, distinct workloads running on the same system; this is especially true for a shared cache. However, shared cache analysis is dominated by methods that center on how a workload behaves when run alone, or in isolation. Though evaluating a workload in isolation is important, it is increasingly irresponsible to not evaluate a workload under contention as a standard part of the analysis.Characterizing in Context, or CInC is both a position paper on modern performance analysis and a framework of contention-forward performance analysis. This work provides evidence that it is no longer responsible to make decisions using a workload’s isolation behavior. Additionally, we provide a way to talk about the contention that is formal and clear. Further, we provide two methods for cache sensitivity analysis built on top of the CInC framework: one simplifies multi-capacity-curve analysis via a Condensed Representation Model and the other distills capacity curves into Curve Analysis Features. We also expand curve analysis metrics by expressing the rich information available in a workload’s contention behavior through a novel measurement of stability under contention. The tools enable a contention-forward characterization of a subset of SPEC 2017 rate workloads run on a real system. Additionally, we present a case study exploring a CInC-based SVM classifier which we apply towards co-scheduling (accuracy 88% vs 78% with a clustering tool).
Enhanced Angular Resolution in Automotive Radar Imagery Using Burg-Aided MIMO-DBS Approach

To achieve higher levels of autonomous driving, imaging radar that is robust, regardless of lighting and weather conditions, is required in order to provide reliable, accurate and precise situational awareness. Spatial resolution is one of the important parameters enabling detection and differentiation of closely positioned targets. It has already been shown that the combination of multiple-input multiple-output (MIMO) radar with Doppler beam sharpening (DBS) can both suppress the high sidelobe levels inherent in MIMO and hence can help enhance cross-range resolution in the off-the-boresight directions. However, DBS cannot improve resolution in the forward-looking direction which is vital for path planning. In this paper, we propose the use of the Burg algorithm combined with a MIMO-DBS technique to improve cross-range resolution in both the forward looking and the lateral directions. The performance of the proposed method is evaluated through both simulation and experiment at 77 GHz.
Experimental Evaluation of Moving Target Compensation in High Time-Bandwidth Noise Radar

In this article, the effect a moving target has on the signal-to-interference-plus-noise-ratio (SINR) for high time-bandwidth noise radars is investigated. To compensate for cell migration we apply a computationally efficient stretch processing algorithm that is tailored for batched processing and suitable for implementation onto a real-time radar processor. The performance of the algorithm is studied using experimental data. In the experiment, pseudorandom noise, with a bandwidth of 100 MHz, is generated and transmitted in real-time. An unmanned aerial vehicle (UAV), flown at a speed of 11.5 m/s, is acting as a target. For an integration time of 1 s, the algorithm is shown to yield an increase in SINR of roughly 13 dB, compared to no compensation. It is also shown that coherent integration times of 2.5 s can be achieved.
Comparison and Validation of Arctic Precipitation Fields from Three Atmospheric Reanalyses: CFSR, MERRA, ERA-Interim

The intent of thesis project was to investigate Arctic precipitation from three atmospheric reanalyses: Modern Era Retrospective Analysis for Research and Applications (MERRA), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts ReanalysisInterim (ERA-Interim). The first objective of the study was to determine if the three atmospheric reanalyses provide an accurate depiction of seasonal precipitation patterns. Spatial variability, recent trends, and anomalies in Arctic precipitation were investigated. The second objective was to conduct a statistical validation of each of the reanalysis products through a comparison with monthly GHCN station observations. Monthly precipitation fields were investigated for the period spanning January 1979 – December 2010. Overall, MERRA, CFSR, and ERA-Interim captured the known average monthly precipitation patterns across the Arctic. One notable discrepancy with ERA-Interim’s precipitation field exists across the northern North Atlantic and is further investigated using nearby coastal station data. Station observations were then used to validate the reanalyses across the entire Arctic. Bias, correlations, root mean-squared error were calculated. The largest inconsistency between the reanalyses and the station observations exists in summer; smaller biases and errors exist in winter. However, associated model errors and biases are found to be related to the number of observations utilized. The ability to pinpoint the best reanalysis for use in Arctic hydrologic studies lies in further investigation of the precipitation assimilation process in each reanalysis as well as an improvement in the accuracy of gauge measurements.
Enhancing Bankruptcy Prediction with Feature Selection in AdaBoost Algorithm

Since the 1960s, bankruptcy prediction research has been intensively examined, with the importance of accurate forecasting representing a nation's economic development. The high costs related to business failures have prompted efforts to enhance prediction techniques, which have been highlighted further as the effects of globalization have increased. There are two primary types of bankruptcy prediction models: statistical and artificial intelligence (AI) methods. The study found that several AI algorithms, namely C5.0, CART, and Support Vector Machines with evolutionary computation, demonstrated efficacy in forecasting impending bankruptcies, as well as short- and long-term predictions. Prior studies have proposed the utilization of Multilayer Perceptron (MLP), ensemble learning methodologies, and AdaBoost as potential approaches for the purpose of bankruptcy prediction. The significance of feature importance is frequently disregarded in such models, resulting in prejudiced outcomes. This study presents a proposed approach for feature selection in AdaBoost algorithm that incorporates the consideration of feature importance, with the aim of enhancing the aforementioned findings. The proposed methodology is validated through a comparative analysis with several other machine learning algorithms. The result showed that ADABoost with feature selection using feature importance generated better value with increased around 5-7% in accuracy score.
Design and Development of a Novel Haptic Device for Plucked Musical Instrument AR Simulation

Haptic devices have emerged as a promising technology for enhancing the experience of playing virtual musical instruments by providing tactile feedback to the user. This paper presents a novel haptic device that emulates plucked musical instruments as part of an augmented reality (AR) system. This wearable device features a pair of parallel 5-bar mechanism that provides haptic feedback. While initially developed to simulate harp playing, this technology could also be applied to emulate fingerstyle guitars, banjos, and other plucked instruments. The system can detect the position of a finger relative to a virtual string's projected position and provide haptic feedback by moving a string piece against the fingertip. Upon detecting a plucking motion, the device plays a musical note based on the virtual string's projection the finger is closest to and moves the string piece back to its rest position. The device has been designed, developed, and tested to ensure functionality and user comfort.
pRB family proteins are required for H3K27 trimethylation and Polycomb repression complexes binding to and silencing p16 INK4a tumor suppressor gene

Genetic studies have demonstrated that Bmi1 promotes cell proliferation and stem cell self-renewal with a cor-relative decrease of p16 INK4a expression. Here, we demonstrate that Polycomb genes EZH2 and BMI1 repress p16 expression in human and mouse primary cells, but not in cells deficient for pRB protein function. The p16 locus is H3K27-methylated and bound by BMI1, RING2, and SUZ12. Inactivation of pRB family proteins abol-ishes H3K27 methylation and disrupts BMI1, RING2, and SUZ12 binding to the p16 locus. These results suggest a model in which pRB proteins recruit PRC2 to tri-methylate p16 , priming the BMI1-containing PRC1L ubiquitin ligase complex to silence p16 . The mammalian pRB family proteins, pRB, p107, and p130 (also known as pocket proteins), play a key role in controlling the G1-to-S transition of the cell cycle and maintaining differentiated cells in a reversible quiescent or permanent senescent arrest state The pocket proteins are hypophosphory-lated in cells exiting mitosis as well as in quiescent cells, where they bind to and negatively regulate the function
Gratitude, Self-Regulation, And Academic Motivation During COVID-19 In University Students: Differential Associations For Earning And Non-Earning Students

It is known that we have entered in a challenging era when the educational systems are digitalized with a heavy reliance on digital technology. The challenge of keeping the students engaged, academically motivated, and self-regulated is common across grade levels, subject matters, and types of educational institutions around the globe. This challenge is particularly relevant to the students who work alongside education to meet their finances. This study was aimed at assessing whether earning and non-earning students would score differently on gratitude, self-regulation, and academic motivation during online education. A secondary objective was to assess whether gratitude and self-regulation would differently associate with specific academic motivation constructs across earning and non-earning students in online education. A cross-sectional research design was used and a sample of 247 participants (M age= 24.20 years, SD = 2.25 years;earning university students = 122 & non-earning = 125) was selected and approached through an online survey. The gratitude scale, self-regulation scale, and academic motivation scale were used to assess the levels of gratitude, self-regulation, and extrinsic and intrinsic academic motivation, in the sample. Results from t test analyses demonstrated that earning students scored significantly higher on four academic motivation outcomes including intrinsic motivation to know, intrinsic motivation towards accomplishment, extrinsic motivation of identified regulation, and extrinsic motivation of external regulation compared to non-earning students. Both the gratitude and self-regulation significantly and positively correlated with the 3 intrinsic motivation outcomes and 3 extrinsic motivation outcomes. Finally, analyzing the mediated moderation models, it was found that gratitude was strongly associated with all academic motivation constructs except extrinsic motivation of interjected regulation in earning students compared to non-earning students. Also, self-regulation significantly mediated the association of gratitude with all academic motivation constructs for all participants.
The 5 ‐ Ps of Fundraising: Lessons from Consumer Behavior to Non ‐ Profit Marketing

The Non ‐ Profit Sector contributes almost $1 trillion to the US economy, representing 5.4% of GDP and generating over 12 million jobs. Nonprofits have become widely recognized as playing a critical role in contemporary society. Fundraisers face increasing challenges, as more social causes compete for donors. “Marketization” seems to be an unstoppable trend, and the need for developing a comprehensive model of giving behavior has never been greater. Donations are shaped by income and information. Effective non ‐ profit organizations rely on a network of support, fiscal levers, and proper communication to obtain funds through well ‐ planned strategies and processes. The question is “what should NGOs say? ” Surveying 615 respondents, using their alma mater, the ASPCA, St. Jude’s Hospital for Children, a local homeless shelter, and their church as references, we tested which appeal works best to communicate an NGO’s message to obtain the expected results, considering pride, pity, PR, personal interest, and pleasure as driving motives. The model reflected a predictive ability of 49.7%; all criteria were statistically significant, but the pleasure of giving was the strongest driver, an underlying motivator in the donate decision. Different social causes respond differently to alternate fundraising appeals. Determining which appeal works best is key to success. Ignoring the key drivers in the decision to donate may lead to being both ineffective and inefficient.
FL-SERENADE: Federated Learning for SEmi-supeRvisEd Network Anomaly DEtection. A Case Study

The use of connected devices in the industry represents a necessity and, at the same time, a challenge. Building a network of interconnected industry assets can improve performance and scale but can lead to dangerous security risks and attacks. However, the amount of data shared, and the widespread distribution of devices make the protection of industrial resources cumbersome. One problem is to know the type of information flowing and check for anomalies, making the job of anomaly-based Intrusion Detection Systems (IDSs) arduous. In this direction, we explore a semi-supervised approach, “Deep-SAD,” to overcome the partial knowledge of the data. Due to the size of the data, and the need for privacy measures, we combine this model with a federated learning (FL) framework “Flower,” distributing the learning phase through five industrial areas. We evaluate our implementation over the WUSTL-IIoT-2021 dataset, a testbed simulation of an actual plant where threats have been injected. This work presents and evaluates a framework for semi-supervised anomaly detection, starting with feature engineering. The results reveal that the difference in the performance of the federated and centralized settings is minimal, denoting the promising application of the federated approach. Combined with the security and privacy-preserving characteristics of FL, this demonstrates the value of the federated approach to the semi-supervised anomaly-based IDS in the IIoT networks.
Experimental evaluation of the performances of multi-sensors ultrasonic deployment geometries

In this paper, a sensor spacing estimation model and a new deployment geometry are proposed to evaluate the performance of an ultrasonic sensor array according to the deployment geometry. The proposed spacing estimation model is based on the probability of false detection alarms, aperture angle and sensor range. We have proposed a design technique for the elliptical deployment geometry by giving expressions for its parameters. A practical method of calibrating the ultrasonic sensors was designed to reduce the measurement errors. An experiment was carried out, and the data collected was then analyzed statistically. An evaluation of the performance of an ultrasonic sensor array with linear and circular deployment geometry with and without our method was done and then compared to our proposed deployment geometry which was combined with the proposed method. The results show that the proposed geometry combined with the proposed method performs better than the circular and linear geometry with a correlation of 0.9997, RMSE of 2.8056, MAPE of 1.3966 and standard deviation of 0.2891. These results show the importance of the choice of deployment geometries for an application.
The paradox of popularity in mammography screening and quaternary prevention

Introduction: Although overdiagnoses (diagnoses of diseases that would not manifest clinically) in mammographic screening have made the balance between benefits and harms doubtful, its positive recommendations to women (>50 years) and professionals persists, which demands quaternary prevention — avoidance of iatrogenic injury and overmedication. Fictitious expectations, preventive moralistic culture, surveillance medicine, and biocapitalism (economic interests) are involved in this persistence. We argue that the “paradox of popularity” — paradoxical expansion of the popularity of screening fueled by the production of its iatrogenic injury — has more importance in this context than it has been taken into account. Objective: To describe and discuss some possible modes of operation of this paradox in healthcare professionals. Methods: Essay based on intentionally selected literature. Results: In addition to the synthesis of this paradox in populations, its operational feasibility in healthcare professionals involves cognitive factors (invisibility of overdiagnosed cases, dilution of severe cases among overdiagnosed ones, and only positive cognitive feedback in clinical experience), political factors (powerful corporate and commercial interests) and psychological factors (significant subjective reward of treating more people with excellent results and less emotional exhaustion derived from caring for overdiagnosed cases, in addition to other common psychocognitive biases). Conclusions: The discussed processes may be relevant for quaternary prevention and better clinical and institutional management of this screening, which should involve Primary Health Care professionals and several other social actors.
Outsourcing Strategies and Organizational Survival: a Study of Selected Hospitals in Port Harcourt, Rivers State

his study X-rayed the relationship between outsourcing strategies and organizational T survival in selected hospitals in Port Harcourt, Rivers state. The study adopted a quasi-experimental design in course of the investigation. A survey method was adopted in the research process. Three research questions were developed and a concomitant trio research hypotheses were proposed to guide the study. The population of the study is 170 respondents. The sample size of 118 was obtained using the Krejice and Morgan Table of sample determination . Structured questionnaires were administered to elicit responses from the respondents. The responses of the respondents were analysed using the Statistical package for social sciences (SPSS VERSION 20). The analysis of the data indicated that there are significant relationships between outsourcing strategies and the three dimensions of organizational survival. The policy thrust of this paper is that hospital management in Rivers State should be meticulous and strategic in their choices of outsourcing strategies to be employed in their organizations. This is to galvanize the inflow of positive possible outcomes in the survival bid of their organizations.
Connections between freshwater carbon and nutrient cycles revealed through reconstructed population genomes

Metabolic processes at the microbial scale influence ecosystem functions because microbes are responsible for much of the carbon and nutrient cycling in freshwater. One approach to predict the metabolic capabilities of microbial communities is to search for functional marker genes in metagenomes. However, this approach does not provide context about co-occurrence with other metabolic traits within an organism or detailed taxonomy about those organisms. Here, we combine a functional marker gene analysis with metabolic pathway prediction of microbial population genomes (MAGs) assembled from metagenomic time series in eutrophic Lake Mendota and humic Trout Bog to identify how carbon and nutrient cycles are connected in freshwater. We found that phototrophy, carbon fixation, and nitrogen fixation pathways co-occurred in Cyanobacteria MAGs in Lake Mendota and in Chlorobiales MAGs in Trout Bog. Cyanobacteria MAGs also had strong temporal correlations to functional marker genes for nitrogen fixation in several years. Genes encoding steps in the nitrogen and sulfur cycles varied in abundance and taxonomy by lake, potentially reflecting the availability and composition of inorganic nutrients in these systems. We were also able to identify which populations contained the greatest density and diversity of genes encoding glycoside hydrolases. Populations with many glycoside hydrolases also encoded pathways for sugar degradation. By using both MAGs and marker genes, we were better able to link functions to specific taxonomic groups in our metagenomic time series, enabling a more detailed understanding of freshwater microbial carbon and nutrient cycling.