Reviewerly/exHarmony huggingface dataset:
Nonlinear distributed state estimation on the Stiefel manifold using diffusion particle filters

Many relevant problems in engineering demand the estimation of dynamic system states that are constrained to non-Euclidean spaces. Furthermore, it is generally advantageous to implement estimation methods in a network of cooperating nodes instead of relying on a single data fusion center. With this in mind, we propose in this paper two new distributed particle filtering methods to track the states of a dynamic system that evolves on the Stiefel manifold, which arises naturally when the states are subject to nonlinear orthogonal constraints. The proposed algorithms are based on the Random Exchange and Adapt-then-Combine diffusion techniques, and perform parametric approximations using the matrix von Mises-Fisher distribution to compress information exchanged between network nodes. Estimates of the state are then determined via an empirical averaging method that approximates the centroid (Karcher mean) of the particle set. As we verify via numerical simulations, the proposed methods show improved performance compared to previous Particle and Extended Kalman filters designed for Euclidean state variables, and compared to a non-cooperative particle filtering algorithm.
Efficient Traffic Signs Recognition Based on CNN Model for Self-Driving Cars

Self-Driving Cars or Autonomous Cars provide many benefits for humanity, such as reduction of deaths and injuries in road accidents, reduction of air pollution, increasing the quality of car control. For this purpose, some cameras or sensors are placed on the car, and an efficient control system must be set up, this system allows to receive images from different cameras and/or sensors in real-time especially those representing traffic signs, and process them to allows high autonomous control and driving of the car. Among the most promising algorithms used in this field, we find convolutional neural networks CNN. In the present work, we have proposed a CNN model composed of many convolutional layers, max-pooling layers, and fully connected layers. As programming tools, we have used python, Tensorflow, and Keras which are currently the most used in the field.
PPPNE: Personalized proximity preserved network embedding

After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personalized informativeness of each vertex. In this work, we propose a method named Personalized Proximity Preserved Network Embedding (PPPNE) to adaptively capture the personalization of vertices based on the personalized ranking loss. Our theoretical analysis shows that PPPNE generalizes prior work based on the matrix factorization or the neural network with a single layer, and we argue that preserving personalized proximity is the key to learning more informative representations. Moreover, to better capture the network structure in multiple scales, we exploit the distance ordering of each vertex. Our method can be efficiently optimized with a vertex-anchored sampling strategy. The results of extensive experiments on five real-world networks demonstrate that our approach outperforms state-of-the-art network embedding methods with a considerable improvement on several common tasks including link prediction and vertex classification. Additionally, PPPNE is efficient and can be easily accelerated by parallel computing, which enables PPPNE to work on large-scale networks.
Developing Machine Learning and Deep Learning Models for Host Overload Detection in Cloud Data Center

Cloud service providers need to deal with many challenging problems to provide services such as guaranteeing Service Level Agreement (SLA) with clients and minimizing energy consumption of the data centers. Cloud resource management tackles these problems using different techniques such as consolidation. In consolidation, the migration of VMs depends on host overloading which evaluates the state of a host (overloaded or under loaded) before migrating a VM. Forecasting the state of a host accurately and in time is crucial to guarantee SLA with clients and reduce energy consumption in a datacenter. CPU utilization is used in the present work to determine the state of the host. In this paper, we propose a methodology to evaluate how eight different algorithms including machine and deep learning, which forecast the servers' CPU utilization, impact the migration of VMs affecting SLA and energy consumption in datacenters. The outcomes enable decision-makers to take better decisions which result in the reduction of energy consumption and improvement in SLA with clients. The performances of the models are evaluated with random and real users' workloads. We propose a methodology to develop, evaluate and implement host overload detection models in cloud data center based on machine learning (ML) and deep learning (DL) techniques. We validate the methodology and demonstrate that Long Short-Term Memory (LSTM) and Simple Moving Average (SMA) provide excellent results with on average over 2.5 times reduction of Energy and SLA Violation (ESV) metric when compared to other algorithms. Also, LSTM is found to be the most robust solution to predict CPU utilization using unseen data.
Sport action mining: Dribbling recognition in soccer

Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players’ positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players’ locations. The proposed method takes advantage of an association rule mining algorithm (e.g., FPGrowth) to generate displacement sequences for modelling actions in soccer. In this context, transactions are sequences of traces representing player displacements, while itemsets are players’ coordinates on the pitch. The experimental results pointed out the Random Forest classifier achieved a balanced accuracy value of 93.3% for detecting dribbling actions, which are considered complex events in soccer. Additionally, the proposed framework provides insights on players’ skills and player’s roles based on a small amount of positional data.
A privacy preservation method for multiple-source unstructured data in online social networks

Significant amounts of data are generated in different formats (e.g., text, audio, and video) in online social networks and from other online sources, which can be mined to facilitate decision-making in different applications. However, it is nontrivial to preserve user and/or data privacy when we are analyzing data from multiple sources, with different characteristics. In this paper, we propose a method to preserve privacy when dealing with data obtained from multiple/disparate sources. Taking images and corresponding text descriptions as examples, we set up different methods and protection scenarios to protect the private information in the data. Our proposed method is also designed to maintain the availability of existing information and links between the two forms to some extent. We then quantitatively analyze the performance of the proposed approach.
Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

Abstract Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.
Trajectory Selection for Power-over-Tether Atmospheric Sensing UAS

Power-over-tether aircraft is an effective tool for persistent spatiotemporal monitoring of environmental phenomena. This paper presents the design and evaluation of flight trajectories for the tethered aircraft unmanned system (TAUS) sensing a dynamic temperature field. TAUS is a novel power-over-tether-based unmanned aerial system (UAS) configured for long-term, high throughput atmospheric monitoring. It is unique in that it provides position control while measuring atmospheric properties on-board the aircraft and with sensors along the tether. We validated the robotic system by conducting outdoor experiments to characterize the sensor performance against a meteorological tower. We found minimal sensing error at the corresponding altitude relative to the ground truth installation. We then used the experimental data to simulate four trajectories (Lawn-mower, Spiral, Star, and Flower) on power-tethered and untethered system models to evaluate performance factors related to trajectory selection. The analysis of the simulated data indicated that the power-tethered Star trajectory performed well concerning key performance factors when measuring changing atmospheric fields.
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing

Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users’ behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run.
Classification accuracy improvement of the optical diffractive deep neural network by employing a knowledge distillation and stochastic gradient descent β-Lasso joint training framework

Optical diffractive deep neural network (OD 2 NN) is an optical machine learning framework that utilizes diffraction on the cascaded diffractive surfaces to perform an arbitrary function. Compared with the deep neural networks (DNNs) implemented in electronic domain, proof-of-principle demonstrations of OD 2 NNs show promising advantages in terms of speed and power efficiency. However, the classification accuracy of the demonstrated OD 2 NN has been limited by the absence of optical nonlinear operations, even in the hybrid OD 2 NNs which are integrated with electronic neural networks. Here, we propose a novel training framework to improve the classification accuracy of the OD 2 NNs without employing any nonlinear physical elements. In this framework, the hybrid OD 2 NN with a fully connected electronic layer integrated (hybrid) are preferred and knowledge distillation (KD) and stochastic gradient descent β -Lasso (SGD- β -Lasso) joint-training are used. A blind testing classification accuracy of 70.19% and 85.17% have been obtained for Cifar-10 and Cats vs. Dogs dataset, respectively, which is the state-of-the-art accuracy achieved by the hybrid OD 2 NN so far. In addition, the proposed framework can significantly reduce the complexity of hardware fabrication and layers alignment since the hybrid OD 2 NN only consists of 5 diffractive layers. This work take a big step forward the application of the OD 2 NN in realistic scenarios.
Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

With the extensive application of unmanned aerial vehicles (UAVs), there is an increasing demand for fast processing of coloured UAV images. The coloured UAV image pixels are usually represented by quaternion vectors with three bands of visible light corresponding to the three imaginary parts of the pure imaginary quaternion. Accordingly, the colour image edge points can be determined based on the quaternion polar coordinating the rotation principle. Here, a quaternion-based improved cuckoo algorithm is proposed to perform fast processing for UAVs images. In particular, a novel guiding equation is used to optimize the positions of the improved cuckoo algorithm before the Levi flight. Furthermore, a novel disturbance equation is used to obtain a varied location for the next location after the Levi flight. Comprehensive experiments are conducted to evaluate the performance of the proposed solution. The experimental results showed that the proposed method significantly reduces the image processing time and remarkably improves the quality.
Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells

This paper introduces a distributed multi-robot collision avoidance algorithm based on the concept of Buffered Voronoi Cells (BVC). We propose a novel algorithm for avoiding deadlocks consisting of three stages: deadlock prediction, deadlock recovery, and deadlock recovery success prediction. Simple heuristics (such as the right-hand rule) are often used to avoid deadlocks. Such heuristics might reduce deadlock in simple configurations and sparsely populated environments, but they begin to fail in complex configurations and more densely populated environments. We evaluate the performance of our algorithm using an open-source web-based multi-robot simulation. The results show that while the proposed algorithm does not eliminate the occurrence of deadlocks, it drastically reduces their occurrence, and leads to a considerable improvement in performance, especially in high-density environments. We also validate the real-world performance of the proposed algorithm in live experiments.
Detection of Phising Websites using Machine Learning Approaches

As the world responded to the Coronavirus Disease 2019 (COVID-19) pandemic in 2020, digital operations became more important, and people started to depend on new initiatives such as the cloud and mobile infrastructure. Consequently, the number of cyberattacks such as phishing has increased. Phishing websites can be detected using machine learning by classifying the websites into legitimate or illegitimate websites. The purpose of the study is to conduct a mini-review of the existing techniques and implement experiments to detect whether a website is malicious or not. The dataset consists of 11,055 observations and 32 variables. Three supervised learning models are implemented in this study: Decision Tree, K-Nearest Neighbour (KNN), and Random Forest. The three algorithms are chosen because it provides a better understanding and more suitable for the dataset. Based on the experiments undertaken, the result shows Decision Tree has an accuracy of 91.16% which is the lowest compared to the other models, 97.6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for the KNN model which is the highest among all the models and 94.44% accuracy for the Random Forest model. Through comparisons between the three models, KNN was the prime candidate for the best model considering that it has the highest accuracy. However, Random Forest is deemed more suitable for the dataset even though the accuracy is lesser because of the lowest false-negative value than the other models. The experiments can be further investigated with different datasets and models for comparative analysis.
IADD: An integrated Arabic dialect identification dataset

Arabic language has different variants that can be roughly categorized into three main categories: Classical Arabic (CA), Modern Standard Arabic (MSA) and Dialectal Arabic (DA). There are subtle differences between MSA and CA in terms of syntax, terminology and pronunciation. However, Dialectal Arabic (DA) significantly differs from CA and MSA in that it reflects geographic location of the speaker, or at least the country of origin, if mobility factors are taken into account. This paper presents IADD, an Integrated dataset for Arabic dialect identification, that contains 135,804 texts representing Arabic dialects from 5 regions and 9 countries. IADD dataset is created, from the combination of subsets of five corpora, to support the task of automatic Arabic dialects detection.
ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer's self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.
Understanding the Threats of Trojaned Quantized Neural Network in Model Supply Chains

Deep learning with edge computing arises as a popular paradigm for powering edge devices with intelligence. As the size of deep neural networks (DNN) continually increases, model quantization, which converts the full-precision model into lower-bit representation while mostly preserving the accuracy, becomes a prerequisite for deploying a well-trained DNN on resource-limited edge devices. However, to properly quantize a DNN requires an essential amount of expert knowledge, or otherwise the model accuracy would be devastatingly affected. Alternatively, recent years witness the birth of third-party model supply chains which provide pretrained quantized neural networks (QNN) for free downloading.
DGG: A Novel Framework for Crowd Gathering Detection

Crowd gathering detection plays an important role in security supervision of public areas. Existing image-processing-based methods are not robust for complex scenes, and deep-learning-based methods for gathering detection mainly focus on the design of the network, which ignores the inner feature of the crowd gathering action. To alleviate such problems, this work proposes a novel framework Detection of Group Gathering (DGG) based on the crowd counting method using deep learning approaches and statistics to detect crowd gathering. The DGG mainly contains three parts, i.e., Detecting Candidate Frame of Gathering (DCFG), Gathering Area Detection (GAD), and Gathering Judgement (GJ). The DCFG is proposed to find the frame index in a video that has the maximum people number based on the crowd counting method. This frame means that the crowd has gathered and the specific gathering area will be detected next. The GAD detects the local area that has the maximum crowd density in a frame with a slide search box. The local area contains the inner feature of the gathering action and represents that the crowd gathering in this local area, which is denoted by grid coordinates in a video frame. Based on the detected results of the DCFG and the GAD, the GJ is proposed to analyze the statistical relationship between the local area and the global area to find the stable pattern for the crowd gathering action. Experiments based on benchmarks show that the proposed DGG has a robust representation of the gathering feature and a high detection accuracy. There is the potential that the DGG can be used in social security and smart city domains.
Confidence level auto-weighting robust multi-view subspace clustering

For the subspace clustering task of multi-view data, it is an important research content to mine the complementary feature across all views. However, when learning the consensus representation of all views, each view may have different confidence levels. In addition, owing to the non-linearity and noise pollution of the data, different samples in the same view may have different confidence levels. Unfortunately, most of the existing methods only assign a uniform weight to each view, and may only obtain a suboptimal solution. In this work, we put forward a confidence level auto-weighting robust multi-view subspace clustering (CLWRMSC) model. Specifically, we designed an adaptive sample weighting strategy that enables our model to focus on the confidence of both views and samples while learning the consensus representation of all views. At the same time, using the self-expression property of subspace, an adaptive low-rank multi-kernel learning (MKL) strategy is designed. Further, the Weighted Truncated Schatten p-Norm (WTSN) is proposed to better solve the optimization problem of low-rank constraints. A lot of experiments prove that the proposed model is a superior clustering algorithm.