Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight Delay Prediction for Indian Air Carriers with Explainable Artificial Intelligence

The aviation industry plays a crucial role in the world's transportation sector, and a lot of businesses rely on various airlines to connect them with other parts of the world. Flight delays are gradually increasing and bring more financial difficulties and customer dissatisfaction to airline companies. The present research takes into consideration the flight data for various Indian carriers, Airport scores, Airlines Ratings, and weather information to build a robust prediction system. This research is done in three phases. In the first phase, Classification models like Logistic Regression, Decision Tree, Decision Jungle, and Neural Network Classifier are used to predict if the flight is delayed more than 15 minutes. The most effective model is chosen, and the primary causes of the flight delay are identified using the model. These factors include both controllable and uncontrollable factors resulting in the flight delay. In the second phase, Regression models like Linear Regression, Linear Regression with Principal Component Analysis (PCA) technique, and Neural Network Regressor model are used to predict the time by which the flight is getting delayed. The third phase of the research focuses on the trips with significant delays in the chosen dataset, and actionable insights are identified and explained for each trip using the Explainable Artificial Intelligence (XAI) tool with the Local Interpretable Model-agnostic Explanations (LIME) package. The Neural Network Classifier is identified as the best non-linear model for the dataset under consideration, with an accuracy of 92.5% and a precision of 73.6% for predicting delayed flights. With the lowest RMSE of 3.52, MAE of 2.20, and maximum coefficient of determination(R square) of 93.44, the Linear Regression model with Principal Components stands out to be the best model for predicting delay in flight arrival time.
Mobile Robot Control Based on Virtual Impedance Force Feedback

In this study, a tactile feedback method is proposed for virtual repulsive force based on non-contact impedance and applied to collision avoidance for a biological signal-controlled mobile robot. Virtual walls based on mechanical impedance are placed around the robot, it can avoid obstacles using virtual repulsive forces when obstacles meet the virtual walls. The proposed method provides tactile feedback about the force to the operator, which enables the operator to recognize the environment around the robot. In the experiments, a blindfolded participant controlled a mobile robot using myoelectric signals. The results demonstrated that the robot could be operated stably.
Fitness App Design Based on “Internet +” and Big Data

With the proposal of the national strategy of national fitness for all and the arrival of the digital era, in order to meet the demand of people who can exercise at any time and anywhere, there is an urgent need to launch fitness apps based on “Internet +” and big data technology. This article designs the “Cloud Rhythm” online fitness app, uses SWOT method to analyze the advantages and disadvantages of this product, the opportunities and threats it faces, clarifies the strategic positioning, goals and business models, and uses data mining algorithm based on the association rule to make personalized recommendations for fitness projects. Through the product design process, it provides a certain idea for the digital transformation of the traditional fitness industry, and provides a good product choice for the national fitness.
Deep learning for predictive alerting and cyber-attack mitigation

The successful security management of ICT systems and services is essential for an effective cyber security posture. The main objective is to minimize and control the damage caused by cyber-attacks and incidents, to provide effective response and recovery, and to invest efforts in preventing future cyber incidents. To achieve this objective, cyber threat intelligence (CTI) is widely applied, as it is considered a crucial mechanism to proactively defend against modern and dynamically evolving cyber threats and attacks. However, there are multiple challenges in the field of CTI, as there is an enormous amount of unstructured threats data in cyberspace that needs to be collected, classified, analyzed, and shared between states, organizations, or companies. Facing this challenge, data mining techniques and machine learning algorithms are essential for providing meaningful CTI information due to their ability to extract indistinct and hidden patterns in the data. Based on data mining techniques and machine learning algorithms' potential for successfully implementing cyber threat intelligence services, this paper develops an efficient predictive alerting model in a threat intelligence engine using the Deep Residual Network (DRN) model. Further, the main goal is to compare the performance of the DRN model with other machine learning models such as Sequential Rule Mining, IntruDTree, ScaleNet, etc. According to our experimental results, the DRN outperformed other tested machine learning models by achieving better results on parameters such as precision, recall, and F-measure.
Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression

Summary. As wind energy penetration continues to grow, there is a critical need for probabilistic forecasts of wind resources. In addition, there are many other societally relevant uses for forecasts of wind speed, ranging from aviation to ship routing and recreational boating. Over the past two decades, ensembles of dynamical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic forecasts. However, even state of the art ensemble systems are uncalibrated and biased. Here we propose a novel way of statistically post‐processing dynamical ensembles for wind speed by using heteroscedastic censored (tobit) regression, where location and spread derive from the ensemble. The resulting ensemble model output statistics method is applied to 48‐h‐ahead forecasts of maximum wind speed over the North American Pacific Northwest by using the University of Washington mesoscale ensemble. The statistically post‐processed density forecasts turn out to be calibrated and sharp, and result in a substantial improvement over the unprocessed ensemble or climatological reference forecasts.
Predicting motifs and secondary structure of steroid aptamers using APTANI

Aptamers are synthetic oligonucleotides that fold into flexible structures and bind to a variety of targets. Aptamers are selected using a biochemistry method called Systematic Evolution of Ligands by Exponential enrichment (SELEX), which can identify specific aptamers capable to bind to target from the pool of billions of random sequences. However, this iterative SELEX process is time consuming and requires specialized lab equipment. APTANI is an existing computational tool that can process the billions of random sequences generated from SELEX and generate secondary structure of the selected aptamer sequence. This can significantly reduce the complexity of SELEX and increase accuracy and throughput in lesser time and resources. Here, APTANI is used to analyze existing steroid aptamers (17ß-estradiol, deoxycortisol, estradiol, testosterone) and to identify sequence motifs/patterns and predict their secondary structure. Graphical Abstract Systematic Evolution of Ligands by Exponential enrichment: SELEX begins with an initial library of random sequences in the scale of 10e20 molecules. The library contains a variable region that is flanked by two fixed sequences. The oligonucleotide library is exposed to a desired target. A washing step will remove oligonucleotides that are weakly or unbound to the target. The remaining oligonucleotide-target complex will be separated with an elution step to recover the oligonucleotide sequence and amplified using PCR to regenerate a new pool for the next cycle. The goal of each new cycle is to enrich the high affinity sequences by modifying the binding conditions. The result is high affinity sequences for the target.
Ortho-CodeA: Orthogonal Codes Assisted Backscatter Multiple Access

The research on backscatter multiple access schemes has been an interesting topic. Existing schemes in the area of backscatter multiple access are not designed for specific applications, and leave out the contemplation of practical application requirements. In this paper, therefore, we present Ortho-CodeA, a backscatter multiple access scheme that targets indoor Internet of Things (IoT) applications (e.g., the smart home) and considers the practical application requirements. To implement Ortho-CodeA, we address several key challenges including imperfect time synchronization, high hardware complexity at receiver, and synchronizing multiple tags information in a low-power manner. We theoretically analyze the feasibility of our scheme and evaluate the performance of Ortho-CodeA through extensive simulations. The results show that Ortho-CodeA supports concurrent transmissions of up to 7 tags and achieves BER of 0 when SNR is greater or equal to 0 dB.
Techniques to Estimate the Status of Legal Proceedings Considering Sequential Text Data

The relationship between law and natural language processing is impressively expanding, and it has the potential to fundamentally alter how prosecutors go about their regular tasks. The volume of text produced by legal practitioners is enormous and has not yet been fully inves-tigated by data science. Recent developments in NLP and machine learning allow us the capability to develop forecasting analytics that can be used to identify trends influencing judicial judgments. This study's objective is to generate a classifier that can anticipate judicial decisions in Bangladeshi Supreme Court law. According to our exploratory research, the fundamental facts of a case are the most significant predictor. Then, some of their traits were ascertained from a small sample of judicial subjects. Then, taking these language qualities into consideration, a variety of classifiers were used to forecast the legal results.
Low-Distortion MMSE Estimator for Speech Enhancement Based on Hahn Moments

Discrete Hahn moments are considered efficient orthogonal moments applied in various scientific areas such as signal processing and computer vision. It has a high energy compaction, considered an advantage for speech enhancement algorithm (SEA). Most conventional SEA present undesirable distortion to the improved signal. Minimizing these issues demands a robust estimator. Therefore, this paper presents Hahn moments-based linear and non-linear estimators. Wiener filter and minimum mean squared error (MMSE) sense are used to form the estimators. These estimators with Hahn moments reduce the distortion in various underlying speech conditions. The presented SEA is evaluated in terms of different quality and intelligibility measurements. The experimental results show the advantage and effectiveness of the proposed system over other existing works.
MRM: Site Selection via Mutil-round Meanshift for Global Fairness

Elevator maintenance plays an important role in the safety and economic aspects of daily life, and the location of elevator maintenance sites is crucial in this scenario. A reasonable location can help reduce economic expenses and ensure the healthy operation of the system of maintenance sites. However, due to the lack of candidate location sets and unevenly distributed data layers, existing coverage siting methods have difficulties and challenges in solving this problem, and ignore the fairness of load among different sites. To address above problems, We propose a heuristic coverage siting method called MRM, which is based on multi-round MeanShift clustering with coverage radius variation. Experiments on a real elevator dataset with noise show that MRM not only achieves 99.21 % coverage with fewer sites but also achieves fairly good global fairness.
Performance analysis of self-learning forwarding algorithms for Vehicle-to-Vehicle networks on Named Data Networking (NDN)

Vehicle Ad-Hoc Network (VANET) is a special network that provides wireless communication for moving vehicles and other infrastructure to reduce accident rates and collect various information related to the vehicle environment that is useful as a support for driver navigation to improve the safety of drivers and vehicle passengers. The network architecture currently being developed, namely Named Data Networking (NDN) implemented in VANET is an innovative technology to meet future information needs. The self-learning forwarding algorithm is one of the forwarding strategies whose working mechanism avoids running routing protocols, the network can learn routes by itself after broadcasting packets of Interest Base on successful data packet retrieval routes. Self-learning forwarding algorithm based on broadcast is used in VANET networks on NDN which can enable networks to adapt to changing environments and enable mobility of vehicle nodes without using a routing mechanism on NDN networks, thereby reducing computation time and speeding up the information exchange process.
The Metaverse and Conversational AI as a Threat Vector for Targeted Influence

Over the last 18 months, two human-computer interaction (HCI) technologies have rapidly come to mainstream markets, funded by massive investments from major corporations. The first area of advancement has been virtual and augmented worlds, now commonly called “The Metaverse. ” The second area of advancement has been the foundational AI models that allow users to freely interact with computers through natural dialog. Commonly referred to as “Conversational AI,” this technology has advanced rapidly with the deployment of Large Language Models (LLMs). When combined, these two disciplines will enable users to hold conversations with realistic virtual agents. While this will unleash many positive applications, there is significant danger of abuse. Most significant is the potential deployment of real-time interactive experiences that are designed to persuade, coerce, or manipulate users as a form of AI-powered targeted influence. This issue has largely been overlooked by policymakers who have focused instead on traditional privacy, bias and surveillance risks. It is increasingly important for policymakers to appreciate that interactive influence campaigns can be deployed through AI-powered Virtual Spokespeople (VSPs) that look, speak, and act like authentic users but are designed to push the interests of third parties. Because this “AI Manipulation Problem” is unique to real-time interactive environments, it is presented in this paper in the context of Control Theory to help policymakers appreciate that regulations are likely needed to protect against closed-loop forms of influence, especially when Conversational AI is deployed.
A Hybrid Data Sensing in Intra cluster Communication by using Mac Protocols in Wireless Communication

Wireless Sensor Networks (WSNs) have become increasingly popular due to their numerous beneficial features, such as low power consumption, adaptability, fast response time, reliability, dynamic nature, affordability, and ease of installation, making them applicable in various fields for different purposes. However, effective data sensing in WSNs requires the use of proper Medium Access Control (MAC) protocols. In order to conserve energy and minimize redundant sensing, cluster members transmit their data to the leader node, which is facilitated by appropriate clustering methods. In this study, we propose the Enhanced Energy-efficient Real-time MAC (EERMAC) protocol. The EERMAC protocol uses a combination of two different access methods: CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) for real-time data and vTDMA (variable Time Division Multiple Access) for periodic data. The proposed EERMAC protocol outperforms existing protocols in terms of packet delivery ratio (PDR), throughput, jitter, and packet drop rates. The queuing model implemented by EERMAC effectively prevents congestion, leading to reduced packet delay. This study presents an innovative solution for addressing the challenges of data sensing in WSNs and showcases the potential of EERMAC to enhance the performance of WSNs for various applications.
Emerging Coordinated Cyber-Physical-Systems Attacks and Adaptive Restoration Strategies

A cyber-physical-system (CPS) framework serves as the foundation for the modern architecture of critical infrastructures, including electrical power grids, oil and natural gas distribution, transportation systems, and many more. Although the CPS design has assisted in the reliable, efficient, and robust service of physical systems, concurrently it has raised tremendous concern about secure and resilient operation due to the coupling and the presence of several hardware and software products among different CPS layers. These vulnerabilities have opened up a new avenue for adversaries to deploy coordinated cyber-physical attacks, with difficult detection, isolation, and recovery of the CPS. In this paper, we use smart grids and telecommunication networks as an example to elaborate on emerging challenges and analyze new types of coordinated cyber-physical attacks, such as multistage and multiwave attacks. A general model is presented for multistage and multiwave attacks along with adaptive restoration strategies for resilient recovery. The proposed framework is applicable to other CPS as well.
Proposal of Client-Server Based Vertical Handover Scheme Using Virtual Routers for Edge Computing in Local 5G Networks and WLANs

This paper proposes a client-server based vertical handover scheme for edge computing services that selects shorter RTT (Round Trip Time) routes between local 5G networks and WLANs (Wireless Local Area Networks). WLAN access points are used to cover 5G poor reception areas caused by high diffraction losses at 4.7 GHz. The key technology of the proposed scheme is dynamic routing performed by server-side and client-side virtual routers. Client IP (Internet Protocol) address is fixed to one so that the client can use TCP (Transmission Control Protocol) during vertical handover. IP addresses assigned by the local 5G networks and WLANs are used as relay node addresses. QUIC and MP (Multi-Path) TCP, standardized by IETF (Internet Engineering Task Force), are known to have client-server based vertical handover capabilities; however, these protocols have the following problems. (1) Since application programs intended for TCP cannot be used for QUIC as they are, a huge number of client-side programs must be modified to replace TCP with QUIC. (2) MPTCP is OS (Operating System) dependent and is currently not implemented in Windows. On the other hand, the proposed scheme can use widely spread TCP and TCP application programs as they are. Moreover, the proposed scheme is OS-independent since the virtual routers are implemented as application programs. We constructed a test network using 5G-SA (Stand Alone) and WLAN on a virtual machine and evaluated the performance of the proposed scheme. The evaluation results show that the handover outage time the proposed scheme is less than 20 ms.
Technical Notes and Correspondence Positive Filtering for Positive Linear Discrete-Time Systems: An Augmentation Approach

In this note, we address the reduced-order positive filtering problem of positive discrete-time systems under the performance. Commonly employed approaches, such as linear transformation and elimination technique, may not be applicable in general due to the posi- tivity constraint of the filter. To cope with the difficulty, we first represent the filtering error system as a singular system by means of the system augmentation approach, which will facilitate the consideration of the positivity constraint. Two necessary and sufficient conditions are obtained in terms of matrix inequalities under which the filtering error system has a prescribed performance. Then, a necessary and sufficient condition is proposed for the existence of the desired positive filters, and an iterative linear matrix inequality (LMI) algorithm is presented to compute the filtering matrices, which can be easily checked by standard software. Finally, a numerical example to illustrate the effectiveness of the proposed design procedures is presented.
Classification of Road Objects Using Convolutional Neural Networks

Driving is the primary means of transportation for many people around the world. Whether the use is to assist human drivers or create autonomous driving, the use of machine learning can create safer road conditions. Drivers must consider other objects on the road, most commonly other vehicles, and pedestrians. These three components, road signs, pedestrians, and vehicles make up a large majority of objects that a driver will encounter when on the road. This research applies machine learning algorithms, specifically Convolutional Neural Networks (CNN), to classify these road objects. The goal is to create a classification model that can reliably classify road objects and classify the different road signs into individual classes. The results showed high accuracy in classifying the objects, even at lower resolutions and poor conditions. An accuracy of 99.13% was achieved on the test set of all classes and an accuracy of 97.45% on the German Traffic Sign Recognition Benchmark competition dataset from the 2011 International Joint Conference on Neural Networks (IJCNN).
The Smart Workflow Analysis Framework For Channel Allocation In Ultra Dense Cloud Computing

Cloud computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. In cloud computing, resources such as networks, servers, services, and applications are all provided over the Internet. In this paper, a smart workflow analysis framework has proposed for channel allocation in ultra dense cloud computing. The Smart Workflow Analysis Framework for Channel Allocation in Ultra Dense Cloud Computing is a highly advanced system to optimize wireless resource allocation. It employs a combination of cognitive radio networks, graph convolutional networks, reinforcement learning approaches and neural networks to allocate channels in an optimal way. By using deep reinforcement learning, the framework is able to make decisions based on real-time edge network data.
Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks

Thanks to a relatively new technology known as the Internet of Things, devices can now easily and wirelessly share data with one another over the internet or other networked systems (IoT). Despite these benefits, IoT systems are now more vulnerable to hacker attacks, which could have neg-ative consequences. This is due to the ongoing expansion of the IoT ecosystem. These incursions have the potential to cause fi-nancial and physical harm. The IoT is a network that config-ures itself automatically. This network is susceptible to a varie-ty of attacks, all of which can be started by rogue nodes. For instance, during a denial of service attack, a malicious node bombards a targeted node with a large number of packets. For the purpose of locating these malicious nodes in a network, a threshold-based procedure utilising cutting-edge machine learning techniques is launched. By checking the path latency and alerting on it if it exceeds a set threshold value, the sug-gested method can help identify an attacker node. The NS2 programme will be used to mimic the suggested method. We evaluate the suggested methodology and demonstrate that our system performs well in terms of a number of measures, such as throughput, latency, and packet loss.