Reviewerly/exHarmony huggingface dataset:
Research for UAV Path Planning Method Based on Guided Sarsa Algorithm

As unmanned aerial vehicles (UA V) are widely used in unknown and complex environments, their path planning capabilities face higher requirements. In many cases, UA V cannot obtain the environmental information of the target area in advance, and the reinforcement learning (RL) algorithm to solve this problem faces the problem of slow convergence speed. To solve this problem, a UA V path planning method based on guided Sarsa algorithm is proposed, which defines the return function based on position information and improves the status update strategy. Simulation results show that the proposed method can realize fast path planning of UA V in static environment. Compared with Q learning and unimproved Sarsa algorithm, the obtained path length is shortened by 18 steps and 2 steps. At the same time, after 2000 iterations, only the results obtained by this method have obvious convergence. The convergence speed of guided Sarsa algorithm is relatively accelerated. At the same time, the trajectory tracking process of UA V is realized by MAT LAB platform, which proves the practicability and feasibility of the algorithm.
IoT Phantom-Delay Attacks: Demystifying and Exploiting IoT Timeout Behaviors

This paper unveils a set of new attacks against Internet of Things (IoT) automation systems. We first propose two novel IoT attack primitives: Event Message Delay and Command Message Delay (event messages are generated by IoT devices to report device states, and command messages are used to control IoT devices). Our insight is that timeout detection in the TCP layer is decoupled from data protection in the Transport Layer Security (TLS) layer. As a result, even when a session is protected by TLS, its IoT event and/or command messages can still be significantly delayed without triggering alerts. It is worth highlighting that, by compromising/controlling one WiFi device in a smart environment, the attacker can delay the IoT messages of other non-compromised IoT devices; we thus call the attacks IoT Phantom-Delay Attacks. Our study shows the attack primitives can be used to build rich attacks and some of them can induce persistent effects. The presented attacks are very different from jamming. 1) Unlike jamming, our attacks do not discard any packets and thus do not trigger re-transmission. 2) Our attacks do not cause disconnection or timeout alerts. 3) Unlike reactive jamming, which usually relies on special hardware, our attacks can be launched from an ordinary WiFi device. Our evaluation involves 50 popular IoT devices and demonstrates that they are all vulnerable to the phantom-delay attacks. Finally, we discuss the countermeasures. We have contacted multiple IoT platforms regarding the vulnerable IoT timeout behaviors, and Google, Ring and SimpliSafe have acknowledged the problem.
Exploiting Temporal Data Diversity for Detecting Safety-critical Faults in AV Compute Systems

Silent data corruption caused by random hardware faults in autonomous vehicle (AV) computational elements is a significant threat to vehicle safety. Previous research has explored design diversity, data diversity, and duplication techniques to detect such faults in other safety-critical domains. However, these are challenging to use for AVs in practice due to significant resource overhead and design complexity. We propose, DiverseAV, a low-cost data-diversity-based redundancy technique for detecting safety-critical random hardware faults in computational elements. DiverseAV introduces data-diversity between the redundant agents by exploiting the temporal semantic consistency available in the AV sensor data. DiverseAV is a black-box technique that offers a plug-and-play solution as it requires no knowledge of the internals of the AI agent responsible for executing driving decisions, requiring little to no modification to the agent itself for achieving high coverage of transient and permanent hardware faults. It is commercially viable because it avoids software modifications to agents that are costly in terms of development and testing time. Specifically, DiverseAV distributes the sensor data between the two software agents in a round-robin manner. As a result, the sensor data for two consecutive time steps are semantically similar in terms of their worldview but significantly different at the bit level, thus ensuring the state and data diversity between the two agents necessary for detecting faults. We demonstrate DiverseAV using an open-source self-driving AI agent which is controlling a car in an open-source world simulator.
Deep Learning in Fuzzing: A Literature Survey

Fuzzing is one of the most popular vulnerability discovery techniques today. Traditional fuzzing is faced with many drawbacks such as poor efficiencies and low code coverage, and expert experience largely determines the effect of fuzzing. Deep learning, which has made remarkable achievements in a variety of artificial intelligence(AI) research areas, provides promising ways for the development of fuzzing. This paper provides a comprehensive review of fuzzing techniques based on deep learning methods. First, we give an introduction to fuzzing and deep learning. Then, we introduce the application research of deep learning methods in fuzzing from the perspective of test cases and program analysis in the fuzzing process. Finally, we conclude the limitations and challenges faced in the current research of fuzzing techniques based on deep learning methods and discuss new trends and potential directions for the future development of this field.
An Inkjet-Printed Artificial Neuron for Physical Reservoir Computing

Inkjet printing circuits onto thin, flexible substrates is a newly explored field with respect to the transistor; a critical element needed to form logic gates and high-level active circuitry. The traditional approach is to achieve comparable performance to MOSFETs or BJTs. However, the introduction of neuromorphics, spintronics, memristors, chaotic materials, and limitations of transistor sizes have incited a shift toward alternative computing schemes that do not behave as standard transistors. This work explores a minimal fabrication, low-cost, nonlinear, current-controlled graphene inkjet printed artificial neuron (AN) that performs the computing of a recurrent neural network when multiple units are coupled. An activation function (inverse hyperbolic sine) is fit to the electrical curve of the current-controlled graphene element with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> -squared of 0.997. The standard neuron is replaced with the modeled one in the echo state network (ESN) for training on the MNIST benchmark dataset for handwritten digit classification. Testing performance of the simulated inkjet printed neuron reached 88.1% classification and was marginally better than the sigmoid and hyperbolic tangent functions. This work demonstrates a minimal-fabrication alternative computing element functioning as a simulated AN in the ESN. Benefits include low-cost fabrication, high power-efficiency, physically flexible edge computing, and development into many applications within telehealth, infrastructure, military, sports, etc. This work supports efforts toward a printable, cost-effective, flexible, and scalable physical machine learning system.
Using Instagram to raise the effectiveness of distance learning in English: The experience of Kazakhstani students

Despite the fact that social media are commonly considered entertaining means of communication, they have educational potential, which grants the teacher an opportunity to influence the learning process carried out in a distance format by means of a targeted system of pedagogical interventions. The problem of the study lies in the fact that in practice, the educational potential of social media is hardly ever used by teachers in Kazakhstan in spite of great opportunities to influence the process of students’ distance learning in the English language through a targeted system of measures, filling gaps in educational practice. The purpose of the study is to analyze the effectiveness of distance learning in the English language through the development and approbation of a teaching methodology using the Instagram social network. The main data collection methods used in the study are the survey method and a pedagogical experiment. Surveys of students and experts are employed in establishing the general appropriateness of using social media in educational activities in English lessons, as well as to assess the level of formation of students’ general and foreign language competencies. The pedagogical experiment method is used to analyze the effectiveness of social networks in the process of learning English. A method of organizing a thematic online marathon on Instagram is developed and experimentally tested by the authors. The study establishes that social media platforms can become a powerful tool for teaching contemporary students in the distance learning format if the teacher knows how to use their potential in educational work.
EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Abstract The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.
Performance Loss Rates of a 1 MWp PV Plant with Various Tilt Angle, Orientation and Installed Environment in the Capital of Cyprus

Evolution from fossil fuel sources to environmentally friendly and sustainable energy sources is one of the key aspects of the energy transition goal. On the other hand, Solar Photovoltaic systems are the most promising and improving technologies in the energy market However, it is well known that the performance of PV panels decreases in the process of time. This paper focuses on analyzing the performance loss rates (PLR) of mid-scale crystalline silicon (c-Si) PV systems of the same manufacturer with different orientations and tilt angles in the same region for the duration of four years of outdoor exposure. Three commonly used statistical methods are deployed to calculate PLRs; seasonal and trend decomposition using locally weighted scatterplot smoothing (STL), classical seasonal decomposition (CSD) and year on year (YoY) methods coupled with various performance metrics, namely Performance ratio (PR), temperature corrected performance ratio TCPR) and weather corrected performance ratio suggested by National Renewable Energy Laboratory (NRELPR). It is found that PLRs of analyzed venues, Arazi, Arena and Stonite, with the YOY approach are −1.2%/year, −0.73%/year and −2.65%/year, respectivelty., respectively. Furthermore, STL method demonstrated PLRs of −0.69%/year, −0.65%/year and −2.91%/year, respectively. On the other hand, the CSD method generated positive PLRs for some inverters while aforementioned statistical approaches proposed PLRs close to −2%/year, making the CSD method inappropriate for use under a heavy soiling environment. Within two km distance, variations up-to four-fold in PLRs are recorded between the venues during analyzed period. Furthermore, variations in PLRs are more dependent on the statistical approach rather than the performance metric.
A Cascade Defense Method for Multidomain Adversarial Attacks under Remote Sensing Detection

Deep neural networks have been widely used in detection tasks based on optical remote sensing images. However, in recent studies, deep neural networks have been shown to be vulnerable to adversarial examples. Adversarial examples are threatening in both the digital and physical domains. Specifically, they make it possible for adversarial examples to attack aerial remote sensing detection. To defend against adversarial attacks on aerial remote sensing detection, we propose a cascaded adversarial defense framework, which locates the adversarial patch according to its high frequency and saliency information in the gradient domain and removes it directly. The original image semantic and texture information is then restored by the image inpainting method. When combined with the random erasing algorithm, the robustness of detection is further improved. Our method is the first attempt to defend against adversarial examples in remote sensing detection. The experimental results show that our method is very effective in defending against real-world adversarial attacks. In particular, when using the YOLOv3 and YOLOv4 algorithms for robust detection of single-class targets, the AP60 of YOLOv3 and YOLOv4 only drop by 2.11% and 2.17%, respectively, under the adversarial example.
Data augmentation based estimation for the censored composite quantile regression neural network model

Composite quantile regression neural network (CQRNN) model has been widely applied to explore complex patterns among variables, but few researchers consider its possible applications in censoring problems (left censoring, right censoring, and interval censoring might occur in the responses y ). In this paper, we propose an iterative estimation method based on the data augmentation algorithm for censored CQRNN model. Firstly the censored data are imputed through a data augmentation process, then we update the CQRNN model with the imputed data, finally the updated CQRNN model is employed to make predictions. Simulation studies and real data application illustrate that the proposed method outperforms the existing censored methods in terms of mean absolute error and root mean squared error, meanwhile producing very close results to those of the uncensoring case. The proposed method can be easily adapted to deal with different censoring types including left censoring, right censoring, and interval censoring, remedying the defect that the available censored methods are only suitable for right censoring type. • An iterative estimation method for censored CQRNN model is proposed. • The proposed method works for any censoring type. • The proposed method can be generally prompted to other analogous models.
Active-MTSAD: Multivariate Time Series Anomaly Detection With Active Learning

Time series anomaly detection is an important research topic in the field of intelligent operation and maintenance. When software systems are frequently updated with continuous integration and deployment, the distribution of KPI data will also change, and the accuracy of anomaly detection models will inevitably decrease. To tackle this problem, we propose an active anomaly detection framework named Active-MTSAD suitable for multi-dimensional time series, combining unsupervised anomaly detection and active learning. The active learning module introduces three feedback strategies, namely denominator penalty, negative penalty, and metric learning, to learn new anomalous patterns under new data distribution. In metric learning, we consider the difference between normal and abnormal samples in reconstruction error and latent space. We conduct extensive experiments on a large-scale public dataset and a real-world dataset coming from Tencent. The experimental results show that Active-MTSAD can still achieve excellent performance in real scenarios where the distribution changes with only 0.2% of labels.
Runway Image Recognition Technology based on Line Feature

Aiming at the accuracy of identifying enemy airports and attacking their key runways in military, the runway image recognition technology is studied. Firstly, the runway features in the image are analyzed in an all-round way, and an airport image recognition method based on Hough algorithm is proposed. Secondly, the runway model is established and the runway features are extracted according to the runway gray, spacing, width, length and crossing degree. According to the runway features, the runway edge is recognized, and then the runway is recognized. Simulation results show that this method can effectively identify the target image with runway characteristics.
Take a close look at mode collapse and vanishing gradient in GAN

The Generative Adversarial Nets (GAN) has made great advances during these years. Many research branches of the GAN have appeared. However, the researchers have to face the difficulties of the several problems of the GAN. This paper presents a literature review of two problems in GAN over the years, i.e., mode collapse and vanishing gradient. We start with a comprehensive tutorial of GAN. Then, we review the solution for these two problems from the perspective of architecture and loss. Furthermore, some conclusions about these two main challenges are given. Finally, we look into the GAN's future by summarizing the various GAN applications.
Asymmetric Encryption Algorithms: Primitives and Applications

This paper mainly focuses on reviewing the preliminaries of asymmetric encryption techniques and systems. Starting from the trapdoor function, we discussed the math theories behind three representative asymmetric encryption algorithms and systems, including the RSA algorithm, ElGamal cryptosystem, and the elliptical curve cryptographies. In RSA algorithm, we mathematically analyze its security from the process of encryption and decryption. Since there are some disadvantages in RSA algorithm, based on Diffie Hellman key exchange, we studied the basic principle for the operation of ElGamal cryptosystem, deeply analyzing the mathematical meaning of ElGamal cryptosystem in DLP (Discrete Logarithm Problem). Then, from the definitions and properties of elliptic curves in fields, we deduced an elliptic curve version of DLP (Discrete Logarithm Problem), proving how ECDLP (Elliptic Curve Discrete Logarithm Problem) can be utilized as a trapdoor function in cryptography.
Sentiment Analysis for YouTube Educational Videos Using Machine and Deep Learning Approaches

YouTube is one of the most popular video-sharing platforms. Recently, the use of YouTube as an educational tool has increased due to the covid-19 pandemic. The quality of the educational videos is crucial in the learning process. Users' comments on educational videos can help to determine the quality of the videos. Such comments can be utilized using a natural language processing technique called Sentiment Analysis. This research proposes a model to perform sentiment analysis on YouTube Arabic educational videos using classical machine learning classifiers and deep learning models. There are six experiments were conducted on two types of datasets: imbalance and balanced datasets. In addition, three balancing techniques were utilized which are: oversampling, under-sampling, and SMOTE. The results show that the best accuracy obtained from all the experiments was 96% by the SVC, RF, and DL model using the oversampling and SOMTE techniques. The worst accuracy achieved by the ML classifiers was 74% by the KNN classifier using the under-sampling technique. Meanwhile, the worst accuracy achieved by the DL model was 89% for the manually balanced dataset using the bigram, trigram, and five-gram. Additionally, this research introduced a novel dataset based on Arabic YouTube educational videos. This model can be beneficial to many researches in data mining research area or related research areas.
Path Tracking Control Algorithm for Unmanned Vehicles based on Improved RRT Algorithm

Unmanned vehicles are a very popular research direction now. Based on the improved RRT algorithm, the application of the path tracking control algorithm of unmanned vehicles in real life is discussed in depth, which can be widely used in specific experiments. This paper improves the traditional RRT algorithm and uses the target point as a random point to expand the random tree, which can realize the tracking control technology for the path of the unmanned vehicle, and has certain feasibility for the steering constraint of the unmanned vehicle. This experiment is based on the path tracking control algorithm of unmanned vehicles, and an improved RRT algorithm is proposed for this technology. For the research on path tracking control trajectories of unmanned vehicles, the actual unmanned vehicles can be built according to this algorithm. A model of path deviation. The experimental results show that the trajectory planning of the driverless vehicle path tracking control algorithm is different under the improved RRT algorithm. Compared with the traditional RRT algorithm, the lateral position deviation coefficient increases from the initial fixed gain of 80 to 90 after the experiment. Left and right, and then adjusted between 80 and 100, there was a significant improvement in the quality and speed of path planning for self-driving cars.
Cooperative Path Planning of UAV Formation Based on Improved Artificial Potential Field

Aiming at the problem of sacrificing formation maintenance in the process of obstacle avoidance in solving UAV formation path planning by artificial potential field method, a route planning method based on improved artificial potential field method is proposed. Firstly, the UAV formation maintenance controller is designed by “virtual leader-follower” formation. Then, a UAV formation cooperative obstacle avoidance controller is designed by combining the communication topology of UAV formation with the artificial potential field method improved by introducing the attraction field of the goal point and the auxiliary potential field. In addition, a strategy of transforming linear formation is proposed to adapt to the narrow environment. Finally, the experimental simulation shows that the UAV formation can not only maintain the formation while avoiding obstacles, but also transform the linear formation through the narrow area.
Face Recognition based Artificial Intelligence With AttendX Technology for Student Attendance

AttendX is a platform that uses a facial recognition system to perform attendance. AttendX is a solution for attendance systems that exist in the scope of work or agency. AttendX can make attendance activities more efficient because of its features, making it easier for users to attend. To register AttendX, only use the face (left, right, front side, wearing a mask) and a brief bio of the person who registers. AttendX can verify the face of the user who has registered and is stored in the database. This application is designed with high-speed facial recognition technology. It can be used to solve the current problem, preventing the spread of the covid-19 virus, because AttendX can also recognize the user&#x0027;s face when wearing a mask. This study provides a real-world view of technological developments, significantly facilitating the use of facial recognition technology to complement those presented on the Internet, the latest face recognition and verification, and propose possible use cases for various applications. AttendXN et facial recognition function, the method used is ResNet and a hierarchical feedforward network to achieve the planned results.