Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of RF Fingerprint Signals from UAV Controller Using Machine Learning Techniques

Unmanned aerial vehicles (UAVs) have gained widespread popularity in various industries with advent in technology due to their advantages and cost-effectiveness, but they also pose serious threats to security if misused. Detection and classification of radio frequency (RF) signals obtained through communication between a drone and its controller is one of the ways to look out for and prevent misapplication of UAVs. This paper presents a novel approach to classify RF signals from distinct drones using machine learning (ML) techniques. The study involves training and evaluating of multiple ML models on a limited batch of dataset consisting of 5 specimens of RF signals from various drone types. In addition, the paper also identifies the key features of signals that are needed to be extracted from RF signal data for appropriate modeling and classification. The key objective of this research was to achieve best accuracy while using limited dataset by identifying and including most valuable features of RF signal. Important signal features like Mean, Variance, Skewness, Kurtosis and Energy Spectral Entropy were extracted from the RF data. Despite the minimal data used, the kNN classification achieves the accuracy of 88.2% with inclusion of all 17 classes of drones and improved to 92.86% when trained on 14 classes. This shows promising potential for real-world application. The study demonstrates that even with a small training dataset, ML techniques can effectively classify RF signals from drones.
Permafrost of Northern Alaska

North of the Brooks Range, permafrost is continuous, ice-rich with wide occurrence of massive ground ice at different depths. The active layer is thin and saturated. In northern Alaska, ice wedges are the main type of massive ground ice and ice-wedge polygons occupy nearly the entire land surface except relatively small areas with exposed bedrocks and fresh deltaic and eolian deposits. With massive ice located close to the surface, increase in the active layer thickness often triggers development of thermokarst or thermal erosion along ice wedges. Most of the shoreline of the Beaufort Sea retreats due to coastal erosion with an average rate of about 1.2 m per year. Along several coastal segments, the rate of erosion reaches more than 15 m per year. Contemporary severe climate and cold permafrost allow for using frozen soils as foundations by simple means.
Exploring Pseudo-Haptics for Object Compliance in Virtual Reality

Virtual Reality (VR) has opened avenues for users to immerse themselves in virtual 3D environments, simulating reality across various domains like health, education, and entertainment. Haptic feedback plays a pivotal role in achieving lifelike experiences. However, the accessibility of haptic devices poses challenges, prompting the exploration of alternatives. In response, Pseudo-Haptic feedback has emerged, utilizing visual and auditory cues to create illusions or modify perceived haptic feedback. Given that many pseudo-haptic techniques are yet to be tailored for VR, our proposal involves combining and adapting multiple techniques to enhance compliance perception in virtual environments. By modifying the Mass-Spring-Damper model and incorporating hand-tracking software along with an inverse kinematics algorithm, our aim is to deliver compliance feedback through visual stimuli, thereby elevating the realism of the overall experience. The outcomes were encouraging, with numerous participants expressing their ability to easily discern various compliance levels with high confidence, all within a realistic and immersive environment. Additionally, we observed an impact of object scale on the perception of compliance in specific scenarios, as participants noted a tendency to perceive smaller objects as more compliant.
Dynamic concurrent van Emde Boas array

The growing popularity of shared-memory multiprocessor machines has caused significant changes in the design of concurrent software. In this approach, the concurrently running threads communicate and synchronize with each other through data structures in shared memory. Hence, the efficiency of these structures is essential for the performance of concurrent applications. The need to find new concurrent data structures prompted the author some time ago to propose the cvEB array modeled on the van Emde Boas Tree structure as a dynamic set alternative. This paper describes an improved version of that structure - the dcvEB array (Dynamic Concurrent van Emde Boas Array). One of the improvements involves memory usage optimization. This enhancement required the design of a tree which grows and shrinks at both: the top (root) and the bottom (leaves) level. Another enhancement concerns the successor (and predecessor) search strategy. The tests performed seem to confirm the high performance of the dcvEB array. They are especially visible when the range of keys is significantly larger than the number of elements in the collection.
Impediments to Sharing Knowledge Outside the School: Lessons Learnt from the Development of a Taxonomic E-Learning Portal

Despite knowledge sharing tools, lack of motivation is one of the primary reasons why organizational knowledge -sharing initiatives fail. In this interpretive case study, we look at knowledge sharing not within an organization, but in the context of schoolteachers and students in Singapore secondary schools. Through interviews with teachers and students, we investigate the reasons behind the reluctance of teachers/students to share educational content outside the school through an e -learning digital repository, and ways to address these. It was found that very real human reasons of fear and trust stand in the way of knowledge sharing. An important contribution of this study is a set of impedimen ts concerning schoolteachers (and students), and potential solutions to these impediments. Despite apparent difference in motivation /time -constraints between organizational employees and schoolteachers/students, the findings support Husted and Michailova’s model of organizational knowledge sharing hostility, and should be useful for application in similar e -learning knowledge -sharing initiatives.
Acceleration and Implementation of Database Aggregation Query Based on FPGA

Database is an important carrier to meet information overall management. With the continuous development of global information construction in recent years, the types and quantities of information stored in databases are increasing. Implementing high-speed query response to massive database storage resources through hardware-level optimization design has become an important research direction in related fields. Aiming at the key problems such as low efficiency and high delay existing in the process of performing aggregation operation in the current mainstream database, this paper thorough analysis the principles and key steps of database execution of aggregation queries, and a new FPGA-based database aggregation query accelerator is designed using heterogeneous thinking. The accelerator is designed according to the actual application scenario of relational database aggregation query, and implements the aggregation grouping strategy based on hash algorithm and five commonly used aggregation functions through FPGA; In order to maximize processing efficiency and fully utilize the excellent parallel processing capabilities of FPGA, this study also optimizes the existing aggregation query architecture and proposes a decentralized multi-core collaborative aggregation query architecture to meet the needs of single time period and multithreaded queries. The aggregation query experiments on ZNBase, an open source database platform, show that the proposed database aggregation query accelerator based on FPGA is 21.71 times more efficient than the traditional software algorithm to realize the aggregation query scheme.
Cohesive Group Emotion Recognition using Deep Learning

Emotions are pivotal in shaping social dynamics and interactions. An in-depth grasp of the emotions within a group setting can offer valuable insights into group cohesion and the overall emotional atmosphere, thereby bolstering effective communication, teamwork, and collaboration. This study introduces an innovative methodology designed to predict the collective emotions of a group exclusively from an image. Leveraging advanced deep learning techniques, our approach transcends previous methods that treat groups as homogeneous entities. Instead, it acknowledges the diverse spectrum of emotions within a group, capturing the distinct emotional expressions of each individual. To facilitate this, we harnessed cutting-edge deep learning techniques encompassing face detection and pre-trained emotion recognition models. A custom dataset was meticulously curated through web-scraping, manual photography, and Google’s FEC (Facial Emotion Comparison) dataset. Our research entailed the utilization of models such as YOLOv3, HaarCascade, and SSD for precise face detection in images. Subsequently, we employed pre-trained DeepFace and FER models to predict seven distinct emotions for each individual featured in the image. Comprehensive evaluations encompassed metrics such as cross-entropy losses among emotions and sample analyses, culminating in an impressive top-3 accuracy rate of approximately 90%. These findings underscore the effectiveness of deep learning in accurately predicting emotions from group images, thereby enhancing communication and collaboration across diverse contexts. This research signifies a substantial breakthrough by pioneering an approach that deconstructs group images to individuate emotional recognition, yielding a more nuanced comprehension of group dynamics and promoting improved social interactions.
AI-Assisted Markerless Activity Tracking System for Supporting Aging and Wellness

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters.
An Introduction to Gossip Protocol Based Learning in Peer-to-Peer Federated Learning

Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL.
Nanoindentation characterization of sintered porous Cu nanoparticles used in power electronics packaging – A molecular dynamic simulation study

As a critical part of speeding up industrial electrification, power electronics, and its packaging technology are undergoing rapid development. Cu nanoparticle sintering technology has therefore received extensive attention for its excellent performance in the die-attachment layer, where the mechanical properties are essential to be known for design for reliability. Both sintering and subsequent nanoindentation were studied by simulation. The effect of porosity on the nanoindentation response was investigated by setting different initial packing densities. In addition, the impact of indenter size and indentation positions on the nanoindentation response were discussed. The nanoindentation behaviors were studied by extracting loading-displacement (P-h) curves and calculating elastic modulus and hardness. In addition, the microstructure evolution was adopted using atomic configuration to study the nanoindentation mechanism. This work offers valuable insights into the Cu sinter paste preparation phase for sintering technology.
Automated Fact Checking Using A Knowledge Graph-based Model

Misinformation is a growing threat to the economy, social stability, public health, democracy, and national security. One of the most effective methods to combat misinformation is fact checking. Fact checking is the process of verifying the factual accuracy of a statement or claim. Fact checkers employ rigorous methodologies to scrutinize claims, verify sources, and expose falsehoods. However, the huge volume of content circulating online makes it challenging for humans to identify misinformation manually. Automated tools can analyze large datasets to detect patterns in misinformation content, scaling up fact checking efforts. This paper proposes a knowledge graph-based fact checking model that uses two separate knowledge graphs, one containing true claims and the other, false claims. The model uses knowledge graph embeddings which are based on convolutional neural networks. The deep learning model is trained on the above two knowledge graphs to learn distinguishing patterns between true and false claims. Additionally, we employ explainable artificial intelligence (XAI) techniques to provide explanations for the model's classification, reducing cost of errors and increasing transparency and user trust in the system.
Performance Analysis of Two-Stage Charging System for Light Electric Vehicles

In this paper, a single-phase two stage on-board charging scheme for light electric vehicles (LEV s) is presented. The first phase is an AC-DC power factor correction (PFC) stage, which transforms the AC input from the utility grid into DC supply while parallel setting up the input current, and the second stage has bidirectional DC-DC converter, which enables the charging of battery during grid-to-vehicle mode of operation with desired voltage and current values. The present system is simulated in MATLAB/Simulink and real-time validation is also performed on Opal-RT platform. The results obtain through both the simulation and real-time controller indicate the effectiveness of the developed LEVs battery charger.
Using semantic treemaps to categorize and visualize bookmark files

As more and more information is available on the Internet, search engines and bookmark tools become very popular. However, most search tools are based on character-level matching without any semantic analysis, and users have to manually organize their bookmarks or favorite collections without any convenient tool to help them identify the subjects of the Web pages. In this paper, we introduce an interactive tool that automatically analyzes, categorizes, and visualizes the semantic relationships of web pages in personal bookmark or favorites collections based on their semantic similarity. Sophisticated data analysis methods are applied to retrieve and analyze the full text of the Web pages. The Web pages are clustered hierarchically based on their semantic similarities. A utility measure is recursively applied to determine the best partitions that are visualized by what we call the Semantic Treemap. Various interaction methods such as scrolling, zooming, expanding, selecting, searching, filtering etc. are provided to facilitate viewing and querying for information. Furthermore, the hierarchical organization as well as the semantic similarities among Web pages can be exported and visualized in a collaborative 3D environment, allowing a group of people to compare and share each other's bookmarks.
Scenario Digitalization Autonomous Driving Based on Digital Twin Maps

Constrained by the complexity of mixed traffic environments, autonomous driving is currently operational only within certain enclosed scenarios and is not yet equipped to cater to the daily commuting needs of the populace. In response to this challenge, we propose a novel approach to autonomous driving, referred to as scenario digitalization autonomous driving. Scenario digitalization autonomous driving commences with the utilization of high definition map to render a static digital repre-sentation of the driving scene. This is then coupled with vehicle-to-cloud communication to construct a real-time digital twin scene of both the vehicle and its environment in the cloud. The allocation of road rights for autonomous vehicles is subsequently managed through a cloud control dispatch platform. This process creates a structured, efficient digital driving environment that fundamentally circumvents the endless predicament of game-theoretical scenarios potentially encountered by autonomous vehicles, thereby ensuring the smooth operation of the traffic network. In conclusion, the feasibility of scenario digitalization autonomous driving is corroborated through two experiments. These tests confirm the efficacy of the cloud control dispatch platform in terms of its robust scheduling capabilities and its precise interventions for safety, thereby endorsing the viability of this novel approach to autonomous driving,
Error Prediction of Springback in Bending Process Based on GA-BPNN for Accurate Forming Angle

The issue of springback in sheet metal bending has always been of great concern in industrial applications. To reduce its influence on forming angle in bending process. , a prediction model based on GA-BPNN (Genetic Algorithm-Backpropagation Neural Network) was proposed and optimized. To train the network. , the finite element method was introduced to construct the dataset. Then the parameters in the neural network were determined. , and a 5-dimensional input improved model considering material classification was adopted to further improve the prediction accuracy. The experimental results showed that the maximum errors of forming angle reduced from $2.45^{\circ}$ to $0.4^{\circ}$, and $0.15^{\circ}$ by applying different models.
Acoustic Analysis of Cutting Tool Vibrations of Machines for Anomaly Detection and Predictive Maintenance

This work focuses on developing a sound-based anomaly detection model for predictive maintenance in vertical milling machines. The curated dataset includes a wide range of normal and anomalous sound patterns encountered during machine turning operations, with a specific focus on cutting tool wear during the milling process. An autoencoder-based unsupervised machine learning technique is employed to detect anomalies by comparing reconstructed outputs with original inputs. The model is seen to perform better with a longer duration of audio training samples. The results demonstrate the feasibility and efficacy of the system in reducing downtime, improving productivity, and optimizing maintenance practices.
Latent Fault Detection in Cloud Services

Large scale internet cloud services comprising of thousands of computers are ubiquitous. With so many machines, it is not reasonable to assume that all of them are working properly and are well configured [13]. If faults are left unnoticed they might accumulate to the point where redundancy and fail-over mechanisms break. Therefore, detecting latent faults is essential for preventing failures and increasing the reliability of cloud services. This work provides evidence that latent faults are common. We show that these faults can be detected using domain independent techniques, and with high precision. Monitoring for faults is usually done by collecting and analyzing performance counters [1, 4, 10]. Hundreds of counters per machine are reported by the various service layers, from service-specific information (such as the number of queries for a database) to general information (such as CPU usage). Manual monitoring of large datacenters is impractical due to the large number of machines to track and the large number of counters to follow. Therefore, some level of automation is necessary. Existing automated techniques for detecting failures are mostly rule-based. A set of watchdogs [10] is defined. A watchdog typically monitors a single counter on a single machine or service. For example, a watchdog may monitor the temperature of the CPU or the amount of free disk space. Whenever a predefined threshold is crossed a certain action is trigged. These actions range from notifying the system operator to automatic recovery attempts. Rule-based failure detection suffers from several key problems. For example, thresholds need to be made low enough such that faults will not be left unnoticed. At the same time, they should not be set too low to avoid spurious detections. However, since the workload changes over time, no fixed threshold is adequate. Moreover, different services, or even different versions of the same service may have different operating points. Therefore, maintaining the rules requires constant, manual adjustments. Often,
The DIPART Project: A Status Report

Current-generation AI planning technology lacks e ective, exible techniques for managing the planning process in the presence of changing information, and for coordinating multiple, distributed planning processes. However, these issues have been the focus of theoretical work in AI over roughly the past ve years, and similar issues have been studied by researchers developing distributed and real-time operating systems. The DIPART project is aimed at adapting relevant techniques developed in operating systems research, and combining them with techniques from real-time and distributed AI, to support plan generation in dynamic, multi-agent environments. Towards this end, we are building DIPART|the Distributed, Interactive Planner's Assistant for Real-time Transportation planning|a prototype simulation system that includes a network of agents, each of which assists a human planner, and a simulated dynamic environment, which implements Reece and Tate's Paci ca NEO scenario [34]. In this paper, we describe the main objectives of DIPART and the approach we are using in its design and implementation.