Reviewerly/exHarmony huggingface dataset:
Phishing Web Page Detection using Web Scraping

In today’s Internet era, webpages act as major user interfaces for the applications hosted by the organizations. One of the most common security attacks on the web page applications is phishing attacks. Phishing is a social engineering attack in which an adversary tries to steal user credentials by tricking them to believe that they are on a legitimate web page. Adversaries are using sophisticated and new ways to forge the web page designs craftily to trick the users into visiting the malicious links. The phishing webpages are used as a medium to carry out the art of phishing attacks. Web scraping is a methodology to extract features of each webpage. In this paper, web scraping is employed to extract hybrid feature set to implement Machine Learning (ML) models. The machine learning models like XGBoost, Multilayer Perceptron, Logistic Regression, SVM, Auto Encoder, Random Forest, Decision Trees, K-means, and Naive Bayes are built. All the models are tested with and without applying principal component analysis (PCA), a feature reduction technique. Extracting hybrid features by employing web scraping to train the ML models and finding the key features contributing to phishing detection on Phishpedia, Kaggle, and PhishTank datasets is the key contribution of this paper. Results show that XGBoost algorithm outperformed the all other classifiers with 98% accuracy or higher on the web scraped features on all the datasets. The model achieved higher precision and recall when compared to other approaches like CANTINA+, URL based approaches, SenseInput, Knowing Thy Domain, and Phishpedia on the Phishpedia dataset.
BlockPlace: A Novel Blockchain-based Physical Marketplace System

The emergence of marketplaces, such as Airbnb, Uber, and Facebook-Marketplace, enables significant restructuring of economic activities by facilitating transactions between independent supply and demand-side parties. However, in a traditional marketplace, gaining customers’ trust remains a persistent obstacle as it does not provide any purchase protection for buyers or sellers, needs more potential product verification, and relies upon third parties for the secure transference of products. Therefore, blockchain marketplaces are a new paradigm of peer-to-peer systems that eliminate the need for intermediaries by leveraging the transparency and trustworthiness of distributed ledger technology. However, all the Blockchain-based existing marketplaces, such as Origami and OpenSea, only provide support for digital products. Hence, this paper presents BlockPlace, a blockchain-based marketplace framework that supports both digital and physical products. IoT devices that serve as gateways between buyers and sellers ensure the authenticity of the products and increase security in the transference of goods. In addition, lockers rely upon the security of blockchain technology. We provide a detailed operational model for token generation, data management, governance, and our reputation system to illustrate that designing such a physical marketplace is sound, safe, and viable.
Video Normalization in Identifying Fake Videos Using a Long Short-Term Memory Model

As the misinformation crisis continues, it creates a generation more politically divided than ever before. One of the most concerning types of misinformation are Deepfake videos, which use Generative Adversarial Networks to replace an existing video with another person’s face. Deepfake videos can interfere with diplomatic relations, reduce trust in journalism, and can tamper with court video evidence, which is why it is imperative to detect these videos accurately. Long Short-Term Memory models (LSTMs) are a type of Recurrent Neural Network, meaning that they are able to remember sequential information, which is helpful for processing the frames of a video. LSTMs are special in that they can account for lags between frames of a video, which makes them perfect for Deepfake video detection. One of the potential ways to increase the accuracy of a neural network is normalization, which ensures the input data are on the same scale, so the machine has to process a lower range of data. Because the effect of normalization varies for each dataset, in this study, image normalization was used- each pixel of the frames of Deepfake videos were converted to an RGB value of 0 to 255 to see if this can increase the accuracy of the LSTM model for Deepfake detection. First, a baseline LSTM model was created for Deepfake detection with the Pytorch library, and a classification accuracy of 88.191% was achieved. After that, the first ten frames of the Deepfake videos in the dataset were passed through an image normalization algorithm. This yielded an accuracy of 94.274%, illustrating that the addition of a video normalization algorithm to Deepfake videos increased the accuracy of the Deepfake detection LSTM model by 6.083%. This is a substantial improvement, and the study showed that video normalization can be very beneficial for Deepfake video detection.
Adaptive Visibility Graph Initialization on Edge Computing to Accelerate Hybrid Path Planning for Mobile Robots

This paper presents a new initialization method for hybrid path planning that integrates adaptive visibility graphs (AVG), Dijkstra’s algorithm, and genetic algorithm (GA) on an edge computing platform. Several algorithmic innovations are proposed to improve its accuracy and computing efficiency. First, an adaptive approach is developed to stochastically eliminate segments/links of the full visibility graphs during each iteration, generating diverse AVGs. Then, many shortest paths corresponding to various AVGs are found using Dijkstra’s algorithm. Next, multiple different paths with low fitness values are selected to initialize the GA populations for enhanced exploration, which is distinctly different from the existing method. Its performance is evaluated on an edge computing device (Jetson AGX Xavier), and a strategy to properly utilize CPU/GPU resources is also elucidated. Parametric numerical experiments are carried out to configure desirable GA hyperparameters. Given various practical constraints of mobile robots, the present method yields different optimal paths accordingly. It is then compared with other benchmark techniques in terms of the fitness value, computing speed, and number of waypoints, and exhibits superior performance.
Bipedal Navigation Planning over Rough Terrain using Traversability Models

We present a planning framework designed for humanoid navigation over challenging terrain. This framework is designed to plan a traversable, smooth, and collision-free path using a 2.5D height map. The planner is comprised of two stages. The first stage consists of an A* planner which reasons about traversability using terrain features. A novel cost function is presented which encodes the bipedal gait directly into the graph structure, enabling natural paths that are robust to small gaps in traversability. The second stage is an optimization framework which smooths the path while further improving traversability. The planner is tested on a variety of terrains in simulation and is combined with a footstep planner and balance controller to create an integrated navigation framework, which is demonstrated on a DRC Boston Dynamics Atlas robot.
Baby Physical Safety Monitoring in Smart Home Using Action Recognition System

Humans are able to intuitively deduce actions that took place between two states in observations via deductive reasoning. This is because the brain operates on a bidirectional communication model, which has radically improved the accuracy of recognition and prediction based on features connected to previous experiences. During the past decade, deep learning models for action recognition have significantly improved. However, deep neural networks struggle with these tasks on a smaller dataset for specific Action Recognition (AR) tasks. As with most action recognition tasks, the ambiguity of accurately describing activities in spatial-temporal data is a drawback that can be overcome by curating suitable datasets, including careful annotations and preprocessing of video data for analyzing various recognition tasks. In this study, we present a novel lightweight framework combining transfer learning techniques with a Conv2D LSTM layer to extract features from the pre-trained I3D model on the Kinetics dataset for a new AR task (Smart Baby Care) that requires a smaller dataset and less computational resources. Furthermore, we developed a benchmark dataset and an automated model that uses LSTM convolution with I3D (ConvLSTM-I3D) for recognizing and predicting baby activities in a smart baby room. Finally, we implemented video augmentation to improve model performance on the smart baby care task. Compared to other benchmark models, our experimental framework achieved better performance with less computational resources.
Algorithms for Calculating the Position of the Pupil in Tasks of the Optical-Oculographic Interface

This paper demonstrates the implementation of an algorithm for processing data from the hardware part of the infrared oculography interface. The generated regression model is based on a linear regression model and uses the received information about the resistance of infrared resistors directed at the human eye and registering the power of reflected infrared radiation to predict the horizontal and vertical deflection angles of the user's eye.
A Study of the Automatic Object Extraction Algorithm on the Image Sequence Under Scaling Ttransformations

The work is devoted to the problem of moving objects extraction in a video sequence. The main feature of the problem considered is the presence of geometric transformations. The novelty of the proposed approach is considering the impact of scaling transformations to the image observation model. Geometric transformations are caused due to the spatial movement of the image sensor. To evaluate transformation parameters, a multi-template approach is used. Then object extraction is performed using spatio-temporal background subtraction algorithm. The results of experimental studies on an extended video sequence dataset are presented. For test purposes, we use natural video sequences taken with a camera in the visible wavelength range. In the process of research, the influence of the standard deviation of the scale distortion estimation on the quality of object localization is considered. The research shows high efficiency of the proposed algorithm.
GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel highperformance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird’s Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.
Fast Intrusion Detection in High Voltage Zone of Electric Power Operations Based on YOLO and Homography Transformation Algorithm

The high voltage zone of electric power is a typical high-risk work area. It is harm to the safety of operators and maintainers especially in the case of leakage and arcing. It is especially important for the electrical power safety to track the worker’s inspection, monitor the operation behavior and follow the current position. The complicated environment limited the application of monitoring instruments. This paper proposes an operator positioning method in high voltage zone based on the CNN method. Firstly, a homography transformation algorithm is used to transfer the 2D and 3D scenes to enhance detection accuracy. Secondly, a graph sequence detecting method based on YOLO is used on the purpose of fast detection. Finally, a trajectory prediction and tracking method is proposed for the early warning. The onsite testing results show the precision and speed of proposed method compared with existing method.
Development of the automatic item generation system for the diagnosis of misconceptions about force and laws of motion

The understanding of force and laws of motion is a fundamental foundation for learning mechanics and understanding other complex physics-related subjects. Automatic item generation (AIG) is also suitable for generating items and able to reduce the chance of item exposure. We, thus, developed an AIG system for the diagnosis of misconceptions about force and laws of motion in order to create a large number of quality items that would be used to diagnose students’ misconceptions. AIG system that has been developed contains 18 item models; it can generate 320-3,200 test items. The system contains six menus, i.e., (1) users’ data, (2) item models, (3) item generation, (4) test generation, (5) the users’ guide, and (6) the system’s developer. Based on the examination of AIG system’s quality by experts on educational assessment and experts on information technology, AIG’s quality in terms of utility, feasibility, propriety, and accuracy is at the highest level. The system was improved using the two dimensions of users’ experiences with physics instructors, i.e., (1) pragmatic dimension and (2) hedonic dimension. This research offers an approach to developing AIG system that responds to users’ needs.
A modular approach for multilingual timex detection and normalization using deep learning and grammar-based methods

Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.
AeroNet: An efficient relative localization and object detection network for cooperative aerial-ground unmanned vehicles

This paper proposes an efficient relative localization and object detection network (AeroNet) based on incremental learning for minimalistic high-speed cooperative navigation of aerial-ground unmanned vehicles in cluttered environments. Due to highly limited computation capability and memory resources in micro-UAVs, YOLO series are applied as the baseline of object detection network, and a lightweight backbone is built based on the depthwise separable convolution. To improve the real-time performance, the detection head is formulated with broad learning system. Besides, 6D relative pose estimation is achieved via equation fitting of an elliptical cooperative mark. To verify the effectiveness of AeroNet, experiments are conducted on Intel NUC and NVIDIA TX2 with our self-collected dataset. Results show that AeroNet can progressively increase the accuracy of object detection to 89%, and the computational time is only 76ms on Intel NUC and 28ms on Nvidia TX2, respectively, which meet the need of real-time requirement of on-board calculation in micro-UAV avionics systems.
Stateful detection of adversarial reprogramming

Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model trained to recognize images of animals can be reprogrammed to recognize medical images by embedding an adversarial program in the images provided as inputs. This attack can be perpetrated even if the target model is a black box, supposed that the machine-learning model is provided as a service and the attacker can query the model and collect its outputs. So far, no defense has been demonstrated effective in this scenario. We show for the first time that this attack is detectable using stateful defenses, which store the queries made to the classifier and detect the abnormal cases in which they are similar. Once a malicious query is detected, the account of the user who made it can be blocked. Thus, the attacker must create many accounts to perpetrate the attack. To decrease this number, the attacker could create the adversarial program against a surrogate classifier and then fine-tune it by making a few queries to the target model. In this scenario, the effectiveness of the stateful defense is reduced, but we show that it is still effective.
Multiple single-photon generations in three-level atoms coupled to a cavity with non-Markovian effects

In this paper we show how to generate multiple single-photon wave packets of arbitrary temporal shape from an optical cavity coupled with $N$ three-level atoms driven by a driving field in the non-Markovian regime. We derive an exact analytical expression of the optimal driving field to generate such wave packets, which depends on two detunings of the cavity and driving field with respect to the three-level atoms. The cavity we use consists of two mirrors facing each other, where one is perfect and the other includes dissipation (a one-sided cavity), which couples with the corresponding non-Markovian input-output fields. If the first single-photon wave packet generated by the Markovian system is the same as the non-Markovian case, the Markovian system cannot generate the same multiple single-photon wave packets as the non-Markovian system when the spectral widths of the other environments take values different from the spectral width of the first environment, while setting the equal spectral widths for the different environments can generate this. The generated multiple different single-photon wave packets are not independent of each other, which satisfies certain relations with non-Markovian spectral parameters. We analyze the transition from Markovian to non-Markovian regimes and compare the differences between them, where the cavity interacts simultaneously with the multiple non-Markovian environments. Finally, we extend the above results to a general non-Markovian quantum network involving many cavities coupled with driven three-level atoms.
Probabilistic quaternion collaborative representation and its application to robust color face identification

Quaternion representation (QR) has been shown to be an effective tool for image processing by modelling color images as quaternion matrices. However, previous QR based classification (QRC) methods overlook the labels of training data and may lead to sub-optimal results. In this article, we propose a Probabilistic Quaternion Collaborative Representation (ProQCR) method based on maximum likelihood estimation with application to color face identification. The proposed ProQCR method has the following merits: (1) Full supervision: In the representation and classification processes, the ProQCR method exploits the label information of training data, in contrast to prior methods using such supervision information only in the classification step. (2) Multi-channel integrality: ProQCR represents multiple color channels of each test color image as a quaternion linear combination of training data in a holistic manner. (3) Robustness: Equipped with the quaternion Huber loss function, ProQCR is robust against gross corruption in the test data. Experiments on benchmark databases demonstrate the effectiveness and robustness of ProQCR for color face identification.
Teacher–student network for 3D point cloud anomaly detection with few normal samples

Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The current mainstream methods focus on anomaly detection for images, whereas little attention has been paid to 3D point cloud. In this paper, drawing inspiration from the knowledge transfer ability of teacher–student architecture and the impressive feature extraction capability of recent neural networks, we design a teacher–student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud and then minimize a multi-scale loss between the feature vectors produced by the teacher and the student networks. Moreover, our method only requires very few normal samples to train the student network due to the teacher–student distillation mechanism. Once trained, the teacher–student network pair can be leveraged jointly to fulfill 3D point cloud anomaly detection via the calculated anomaly score. To the best of our knowledge, our method is the first attempt to realize anomaly detection for point cloud with few samples. Extensive experimental results and ablation studies quantitatively and qualitatively confirm that in the case of training with very few samples, our model can achieve higher performance compared with the state of the arts in 3D anomaly detection.
FDT: A python toolkit for fake image and video detection

Twitter a b s t r a c tWith the advent of readily and widely available applications based on deepfake technology, several cybersecurity threats are on the rise.It is challenging to curtail these threats as deepfakes are realistic and very difficult to detect.The present work proposes a Fake Detection Tool (FDT) that streamlines the procedure of fake detection by incorporating various manipulation techniques and aids users in detecting and visualizing the same.The tool is also integrated with Twitter for streaming facial image posts based on hashtags.It provides an output dataframe and presents statistics of virality, sentiments, etc, using pie charts for better visualization.The proposed tool uses a wide variety of large-scale datasets for training to deal with the fakes in the wild and deploys models that are at par with the cutting-edge models.It is an efficient, user-friendly, and freely available software for fake detection.