Miracle-dz/UniFAR-FTUs huggingface dataset:
Hop-Constrained s-t Simple Path Enumeration on Large Dynamic Graphs

Hop-constrained s-t simple path (k-st path) enumeration is a fundamental problem in graph databases and plays an important role in many real-world applications. Given a dynamic graph G, a source-target pair s-t, and a hop constraint k, we aim to efficiently compute k-st paths: list all simple paths within length k from s to t, and then continuously maintain the results against edge updates. Although the k-st path enumeration has been well studied in static setting, the existing works on static graphs cannot be applied or adapted to handle dynamic graphs efficiently. To address the challenges on dynamic computation, we propose a partial path-based index structure and an efficient enumeration algorithm based on the index. We also propose several well-designed techniques to efficiently maintain the index and locate the affected results with graph updates. Comprehensive experiments verify that our proposed CPEupdate algorithm outperforms the state-of-the-art methods by up to 4 orders of magnitude on dynamic graphs. The experiment results also show that the time cost of our initialization step CPEstartup (including index construction) is similar to the state-of-the-art static method.
Classification Of Cancer Genome Atlas Glioblastoma Multiform (TCGA-GBM) Using Machine Learning Method

Glioblastoma multiforme (GBM) is a highly ma-lignant type of brain cancer with a bleak prognosis. This study aimed to apply machine learning methods to classify GBM samples from the Cancer Genome Atlas (TCGA) dataset. Several supervised learning algorithms, including Support Vector Machine, Ad boost, Neural Network, and Decision Tree, were employed in the analysis. Our findings indicate that the Decision Tree algorithm was the most effective for this classification task, achieving an impressive 99% accuracy. Our study provides evidence that machine learning can accurately classify GBM samples in large-scale genomic datasets, enabling a deeper understanding of the genomic characteristics of this cancer. This study emphasizes the potential of machine learning approaches for improved cancer diagnosis and treatment through the analysis of large-scale genomic datasets.
A Neuro-Symbolic Approach for Marine Vessels Power Prediction Under Distribution Shifts

This paper proposes a neuro-symbolic approach to predict the power of marine cargo vessels. The neuro-symbolic approach combines two parts. The first is a neural networks part, and the second is a symbolic part that relies on physics-based formulae. The Shifts-power dataset was used for evaluation. The experimental results showed that a combination of a physics-based module (symbolic part) with a neural networks model (namely ensemble Monte Carlo dropout) superseded the state-of-the-art results by 2.3% in terms of uncertainty estimation measured using R-AUC, and by 3.4% in terms of power prediction for out-of-distribution (OOD) examples measured using RMSE. It also superseded the symbolic approach by 6.3% in terms of uncertainty and 17.7% in terms of OOD power prediction.
Zero-shot Prompting for Code Complexity Prediction Using GitHub Copilot

Code generation models are gaining popularity because they can produce correct code from a prompt, speeding up the software development process. GitHub Copilot is currently one of the most commonly used tools for code generation. This tool is based on GPT3, a Large Language Model (LLM), and can perform zero-shot prompting tasks i.e., tasks for which the model is not specifically trained. In this paper, we describe a preliminary study that investigates whether GitHub Copilot can predict the runtime complexity of a given program using zero- shot prompting. In our study, we found that GitHub Copilot can correctly predict the runtime complexity 45.44% times in the first suggestion and 56.38 % times considering all suggestions. We also compared Copilot to other machine learning, neural network, and transformer-based approaches for code complexity prediction. We observed that Copilot outperformed other approaches for predicting code with linear complexity $\mathbf{O}(n)$.
Reinforcement Learning Based Routing For Deadline-Driven Wireless Communication

The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.
Inter-Satellite Link Prediction for Non-Terrestrial Networks Using Supervised Learning

Distributed Space Systems (DSS) are becoming increasingly popular in the space industry as they integrate advancements in 6G and Non-Terrestrial Networks concepts to offer innovative and efficient solutions for satellite communication and data transmission. In those DSS where communication be-tween heterogeneous satellites is required, achieving autonomous cooperation while minimizing energy consumption is crucial (especially in sparse constellations with nano-satellites). This work proposes an autonomous and scalable solution based on a Supervised Learning model that enables heterogeneous satellites in circular polar Low Earth Orbits to predict their encounters with other satellites given the orbital elements and assuming isotropic antenna patterns. The proposed solution obtains an accuracy of around 90 % when evaluated with realistic data from real Celestrak satellites. This work could be considered the first stage of a promising and alternative approach in the field of DSS.
ACapsule Q-Learning Based Reinforcement Model for Intrusion Detection System on Smart Grid

Smart grid is an innovative technology that offers efficiency, low carbon emissions, and high energy storage. However, this promising technology has several shortcomings, including limited security. In this network, Intrusion Detection System (IDS) is one of the likely targeted systems that has limited security and is prone to several cyber vulnerabilities. To address a such challenge, several studies have been proposed to detect, classify, and mitigate these attacks using Artificial Intelligence (AI) techniques, although the proposed techniques in the literature suffer from high misdetection and false alarm rates. Additionally, limited data availability motivated the researchers to use another type of AI method, namely reinforcement learning to detect and classify attacks. In this paper, we propose a deep reinforcement learning-based technique, namely Q learning and capsule network as a deep learning model to detect attacks for IDS on smart grid networks. The benchmark of CICDDOs 2019 is selected to evaluate the model in terms of accuracy, detection, misdetection, false alarm rates, training time, and prediction time. We also investigate the performance of the proposed model based on discount values of 0.001 and 0.9. The experiments demonstrate that the proposed model has acceptable results, and the model with the lower discount values provides better results.
Laboratory Set-up for Testing of Vector Controlled Induction Motor Drives

In this paper an educational approach for development of induction motor control algorithms on dSPACE hardware platform and Matlab/Simulink environment is described. The control algorithms, Rotor Field Oriented Control (RFO) and Direct Torque Control (DTC), are successfully integrated into Electric Drives Laboratory and represent the user friendly learning tool for understanding of theoretical concepts.
Analyzing the Effectiveness of Machine Learning Models in Nifty50 Next Day Prediction: A Comparative Analysis

The stock market is a prediction is a complicated task and is difficult to predict accurately using traditional methods. It also relates to the economy of the country. Advance knowledge and education can make a person a good investor. Machine learning algorithms have shown great efficiency in predicting the short term and long-term stock prices by analyzing a large amount of historical data. In this research, we compare the performance of five popular machine learning algorithms in predicting stock prices: Random Forest, SVR, Ridge, Lasso Regression and KNN model. The study is conducted using a dataset of historical stock prices of the NIFTY50 index, covering the period from March 2022 to March 2023. The performance of the models is evaluated using various metrics, including Mean Absolute Error (MAE) and R squared (R2). The results show that the (SVR) Support Vector Regression model outperforms the other models in terms of accuracy followed by Lasso and Random Forest with same accuracy and then followed by KNN and at last Ridge model.
Motor Side-View Recognition System Based on Wavelet Entropy and Naïve Bayesian Classifier

As the traffic accident becomes a serious problem, we need some more efficient methods to identify the car. Luckily, we can use artificial intelligence to recognize the motors by using side-view images. It will help a lot to solve the traffic accident. We used the wavelet entropy to extract the feature of the images. Then we employed the naïve Bayesian theory as the classifier. And we used 10-fold cross validation in our experiment. We used a three-level decomposition for WE. It got an overall accuracy of 75% in recognizing motors. In the future we will try to improve the accuracy of this method and try to identify cars form different brand. Keywords—artificial intelligence; wavelet entropy; naïve Bayesian classifier; cross validation
Sustain the Smartness: From Smart Things to Sustainable Smart Things

Retraining is an essential process for the sustainability of any production Machine Learning (ML) model and/or any software with ML based components, as retraining addresses the problem of data shift. However, uncertainty from different sources makes the successful retraining a challenging task. Here, we provide an outline of a multi-armed bandit based decision framework, which can address the uncertainty related to a retraining framework of any production ML model.
Inconsistency Detection with Temporal Graph Functional Dependencies

Data dependencies have been extended to graphs to characterize topological and value constraints. Existing data dependencies are defined to capture inconsistencies in static graphs. Nevertheless, inconsistencies may occur over evolving graphs and only for certain time periods. The need for capturing such inconsistencies in temporal graphs is evident in anomaly detection and predictive dynamic network analysis. This paper introduces a class of data dependencies called Temporal Graph Functional Dependencies (TGFDs). TGFDs generalize functional dependencies to temporal graphs as a sequence of graph snapshots that are induced by time intervals, and enforce both topological constraints and attribute value dependencies that must be satisfied by these snapshots. (1) We establish the complexity results for the satisfiability and implication problems of TGFDs. (2) We propose a sound and complete axiomatization system for TGFDs. (3) We also present efficient parallel algorithms to detect inconsistencies in temporal graphs as violations of TGFDs. The algorithm exploits data and temporal locality induced by time intervals, and uses incremental pattern matching and load balancing strategies to enable feasible error detection in large temporal graphs. Using real datasets, we experimentally verify that our algorithms achieve lower runtimes compared to existing baselines, while improving the accuracy over error detection using existing graph data constraints, e.g., GFDs and GTARs with 55% and 74% gain in F1-score, respectively.
Optimized Resource and Deep Learning Model Allocation in O-RAN Architecture

In the era of 5G and beyond, telecommunication networks tend to move Radio Access Network (RAN) from centralized architecture to a more distributed architecture for greater interoperability and flexibility. Open RAN (O-RAN) architecture is a paradigm shift that is proposed to enable disaggregation, virtualization, and cloudification of RAN components, possibly offered from multiple vendors, to be connected through open interfaces. Leveraging this O-RAN architecture, Deep Learning (DL) models may be running as a service close to the end users, rather than on the core network, to benefit from reduced latency and bandwidth consumption. If multiple DL models learn on the virtual edge, they will compete for the available communication and computation resources. In this paper, we introduce Optimized Resource and Model Allocation (ORMA), a framework that provides optimized resource allocation for multiple DL models learning at the edge, that aims to maximize the aggregate accuracy while respecting the limited physical resources. Distinguished from related works, ORMA optimizes the learning-related parameters, such as dataset size and number of epochs, as well as the amount of communication and computation resources allocated to each DL model to maximize the aggregate accuracy. Our results show that ORMA consistently outperforms a baseline approach that adopts a fixed, fair resource allocation (FRA) among different DL models, at different total bandwidths and CPU combinations.
The production and reception of underwater sound in Hawaiian monk seals (Neomonachus schauinslandi)

The endangered Hawaiian monk seal is a primitive phocid (true) seal endemic to the tropical Hawaiian Islands. At present, there is a lack of substantive bioacoustic information available for this species, with no formal descriptions of underwater vocalizations and limited data concerning underwater hearing. To address these knowledge gaps, we are working to better understand species-typical auditory capabilities and sound production by thoroughly evaluating a single individual living in human care. A mature male monk seal was trained to perform an auditory go/no-go signal detection task in water. Detection thresholds were measured for narrowband tones across the frequency range of hearing to generate a full underwater audiogram. Additionally, an acoustic recorder was placed in this monk seal’s living enclosure for a full year, enabling characterization of his underwater repertoire and seasonal trends in vocal behavior. This study presents the first examination of underwater vocalizations in Hawaiian monk seals, provides insight into the perceptual abilities of this species and the evolution of underwater hearing within the phocid lineage, and enables improved assessments of noise effects on these vulnerable seals. [Work supported by Navy’s Living Marine Resources Program. ]The endangered Hawaiian monk seal is a primitive phocid (true) seal endemic to the tropical Hawaiian Islands. At present, there is a lack of substantive bioacoustic information available for this species, with no formal descriptions of underwater vocalizations and limited data concerning underwater hearing. To address these knowledge gaps, we are working to better understand species-typical auditory capabilities and sound production by thoroughly evaluating a single individual living in human care. A mature male monk seal was trained to perform an auditory go/no-go signal detection task in water. Detection thresholds were measured for narrowband tones across the frequency range of hearing to generate a full underwater audiogram. Additionally, an acoustic recorder was placed in this monk seal’s living enclosure for a full year, enabling characterization of his underwater repertoire and seasonal trends in vocal behavior. This study presents the first examination of underwater vocalizations in Hawaiian monk .
A CMOS 183 GHz Millimeter-Wave Spectrometer for Exploring the Origins of Water and Evolution of the Solar System

This paper discusses advanced CMOS-based remote sensing emission spectrometers towards measuring the D/H (Deuterium/Hydrogen) ratio throughout the solar system. These isotopic measurements are critical to gain a clearer understanding of the water on Earth and its origins. We briefly review the MIRO instrument aboard the ESA/Rosetta mission, the isotopic measurements performed at a Jovian comet 67P and the challenges they pose to the cometary hypothesis for the origin of water on Earth. We then present the next generation low-cost CMOS-based water sensing spectrometer and describe how it can help address these critical science questions. We discuss design and development of the CMOS spectrometer and its first space test flight aboard the NASA ReckTangLE sub-orbital mission in 2019.
A mm-Wave Wideband/Reconfigurable LNA Using a 3-Winding Transformer Load in 22-nm CMOS FDSOI

$A$ mm-Wave wideband/reconfigurable LNA using a 3-winding transformer load has been implemented in 22-nm CMOS FDSOI technology for 5G applications. The proposed LNA has three stages with the first two stages utilizing a novel 3-winding transformer as a load to provide three parallel paths, one main and two auxiliary paths. The load expands the frequency band of interest in the wideband mode when the main path is active and reconfigures the LNA bandwidth in the reconfigurable mode when either of the auxiliary paths are active. The third stage acts as a buffer. The LNA shows a measured S21 by the peak gain of 32 at 30 GHz and 3-dB bandwidth of 12.6 GHz for the wideband mode and peak gains of 32.4 dB and 33.4 dB at 22 and 29 GHz for 3-dB bandwidths of 5 and 5.3 GHz for two auxiliary paths in the reconfigurable mode, respectively. The measured NF is lower than 4.5 dB for the entire frequency band and measured OP1dB and OIP3 are better than -6 dBm and 2 dBm for the entire frequency band of interest, respectively. The total power consumption of the proposed LNA is 35 mW in each mode of working, and the chip area is 1155 μ $m$ × 642 μ $m$ excluding pads.
A detailed and significant analysis of The Effects of Big-Data over The Revolution of Internet Marketing

The current trend of Big Data places an emphasis on analytics that are oriented for the general public. Because of advances in technology, it is now possible to get in real time data that is both rich and abundant on consumer phenomena. As a direct result of this, customers now provide primary data in the form of Big Data, which is unrivalled in terms of its volume, velocity, and variety. To have a better understanding of how the influence of Big Data manifests itself in various forms of advertising,the authors present a conceptual framework that extends on resource-based theory in order to activities, which in turn enables companies to better enjoy the benefits of this theory. The subsequent is governed by three assets: organizational capital, human resources, and physical resources. Enhancing adaptive capabilities requires three processes, which are as follows: first, collecting and keeping evidence of client behavior as Big Data; second, deriving customer insights from Big Data; and third, using consumer insight.In addition, an investigation into the particular assets that companies need in order to realize the advantages of Big Data is conducted.
Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model training.To address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.
Efficient and Accurate Splitting Methods for Flow Problems

This dissertation studies two classes of splitting methods, Yosida methods and projection methods, for simulating incompressible fluid flows. The former ones are algebraic splitting methods that split the system after discretization, while the latter ones split before. Both methods are more efficient than the exact solver as they decouple the system. Here we propose some variational forms of these methods that improve their accuracy and preserve the efficiency. Chapter 3 studies a new alteration of Yosida algebraic splitting methods for the NavierStokes equations. By applying the usual or pressure-corrected Yosida splitting techniques to discretizations written in terms of velocity and pressure updates (u − u, p − p), we show that the accuracy is increased by one full order in ∆t without any additional cost in the respective methods. Proofs of the convergence results are given both in linear algebraic and finite element frameworks. Several numerical tests are given which reveal the (sometimes dramatic) improvement in accuracy offered by the proposed fix. Chapter 4 analyzes the accuracy of the ‘discretize-then-split’ Yosida solver for incompressible flow problems, when divergence-free elements are used together with grad-div stabilization (with parameter γ ). The Yosida method uses an inexact block LU factorization to create linear algebraic systems that are easier to solve, but at the expense of accuracy. We prove the difference between solutions of the exact and approximated linear algebraic systems is O(γ−2) in the natural norms of the associated finite element problems, and thus that full accuracy can be obtained by the Yosida method if large γ is used (γ ≥ 10 is sufficient in our numerical examples). The proof is based on transforming the Yosida inexact linear algebraic system into finite element problems, and analyzing these problems with finite element techniques based on pointwise divergence-free subspaces and their orthogonal complements. Chapter 5 studies and compares fully discrete numerical approximations for the CahnHilliardNavier-Stokes (CHNS) system of equations that enforce the divergence constraint in different ways, one method via penalization in a projection-type splitting scheme, and the other via strongly divergence-free elements in a fully coupled scheme. We prove a connection between these two approaches, and test the methods against standard ones with several numerical experiments. The tests reveal that CHNS system solutions can be efficiently and accurately computed with penaltyprojection methods.