Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning 6DoF Grasp-poses in Occluded Scenes Using RGB and 3D Point Cloud Modalities

Automating the process of harvesting fruits in an orchard-like environment can be more economical than traditional hand-picking methods. The number of people working in the agricultural sector is reducing daily, increasing the cost of manual labour. Localisation and pose estimation of the target fruit is central to any automation-based solution for fruit harvesting. These tasks can be challenging due to the occlusion caused by branches and leaves, as it limits the options to approach the target fruit. The efficacy of deep learning methods using RGB and 3D point cloud data has been demonstrated recently for grasp-pose estimation in dynamic situations. However, this process requires a large amount of data labelled using application-specific annotation tools. Data augmentation is a powerful and cost-effective solution for reducing the requirement for labelled data. In this paper, we propose an augmentation method, 3D-CopyPaste (3DCP), for augmenting point-cloud data for estimating the Six Degrees of Freedom (6DoF) grasp pose. A 6DoF apple grasp-pose dataset and a two-step deep learning-based baseline for apple grasp-pose estimation are also analysed. Additionally, we present a setup for our customised semi-automated annotation method for labelling 6DoF apple grasp-poses, developed in conjunction with the RViz widget. All the code and tools will be made publicly available for future research.
ITERATIVE AND RECURSIVE ESTIMATION OF TRANSFER FUNCTIONS

A unified treatment of non-linear estimation, pseudolinear regression and stochastic approximation for open-loop transfer function models is provided. Pseudolinear regression techniques are used to derive the recursive non-linear least-squares estimator, avoiding the methodological problems implicit in traditional derivations. Stochastic approximation analysis is used to investigate in a direct manner the conditions of convergence and consistency of both iterative and recursive algorithms. The various methods are compared using data for an industrial process.
Comparison of the Combined Deep Learning Methods for Load Forecasting

Accurate short-term electricity load forecasting is essential for reliability, stable system operation, planning, and network upgrade requirements. Atmospheric weather conditions play a crucial role in obtaining accurate electricity load forecasting. This study concentrates on multivariable combined deep-learning methods for hour-ahead load forecasting while considering weather conditions on ISO/New England (NE) datasets. Several combined models were compared and analyzed to determine which method is suitable for weather-related electricity load forecasting for the territory of the ISO/NE electricity load. Several models were examined, namely combined convolution neural network and recurrent neural network (CNN-RNN), combined CNN and long short-term memory (CNN-LSTM), and combined CNN and gated recurrent unit (CNN-GRU), and compared their performance evaluation metrics: RMSE, MAE, MAPE.
Improving fuzzing efficiency based on extracted constant values

Fuzzing is one of the most efficient methods for bug detection. Many companies use fuzzing for their products’ quality improvement. One of the downsides of fuzzing is the execution time. To provide good code coverage fuzzing should be performed long enough. It is a critical problem for regular testing of large code bases. This work aims to extract constant values used in branch conditions. Then develop a special mutation for fuzzing tool, which uses this information to perform better mutations with input buffer and cover program paths faster. Experimental evaluation of the developed method on Google’s OSS-Fuzz project proves the efficiency of the developed method.
Data-Driven Global Sensitivity Analysis Using the Arbitrary Polynomial Chaos Expansion Model

Considering the existence of stochastic uncertainty, quantifying the failure probability of structural systems is one of the important subjects of reliability research, and global sensitivity analysis (GSA) has been widely utilized to estimate the influence of the uncertainty on the system failure probability. Polynomial Chaos Expansion (PCE), a kind of surrogate model, is often utilized to replace the original model considering the heavy calculation cost of GSA. However, the traditional PCE and Sobol’ method are not suitable for the situation that input variables of systems do not have complete probabilistic distribution functions. To solve this problem, a data-driven sensitivity analysis method is proposed in this paper. The surrogate model of the original model is constructed by arbitrary Polynomial Chaos Expansion (aPCE), and different order sensitivity indices of the actual systems are calculated by Sobol’ combining with the Monte Carlo simulation. Compared with the traditional Sobol’ method, the developed algorithm has a wider scope of application in practice. The experiment results show that the proposed method is an effective GSA tool when the distribution types of input variables are not completely known.
Patch-Range Attention and Visual Transformer for Facial Expression Recognition

Facial Expression Recognition (FER) task has attracted a lot of interests, particularly in human-computer interaction file. Many existing methods based on Convolutional Neural Network have been frequently used for the task of FER. However, convolutional filters in CNN rely heavily on the spatial locality, and are unable to learn the global features of facial expression at the beginning of the model. With the stacking of convolutional layers, the deep convolutional layer can extract global features, which however are incomplete in most instances. Therefore, the capacity of the model based on CNN is still insufficient for FER. In order to solve this problem, our paper proposes a different FER model that is made up of Patch-Range-Attention (PRA), Vision Transformer (ViT) and Squeeze-Excitation for the FER task. Our proposed method is accessed on four public FER datasets. The results on these four FER databases strongly prove our PVS model outperforms state-of-the-art models by a large margin.
Graph-Theoretic Partitioning for Differential Zone Protection in an Islanded Microgrid

The deployment of microgrids improves the reliability and resiliency of the electric power grid. Microgrids mitigate the impact of power outages. Microgrids, on the other hand, introduce new chal-lenges to the power system. Microgrid protection is a technological issue that arises in microgrid design and operation. These challenges arise due to the dis-tinction between microgrids and conventional electrical grids. Differential protection is fast, selective, and sensitive; it provides a potential solution to microgrid protection. The differential zone protection scheme is a cost-effective variation of differential protection that improves system reliability while decreasing the cost of the protection scheme. To deploy a differential zone protection scheme, it is necessary to divide the network into distinct protection zones. The number of protective zones results in a different level of mi-crogrid system reliability. This paper presents a graph partitioning approach for differential zone protection. Using the algorithm, the microgrid is partitioned into a number of protection zones. The proposed protection method is validated using the IEEE-13 node system as a microgrid.
On the Analysis of Information Resilience and the Spread of News

This paper presents TopicRes, a five-step methodology to analyze the impact of real-life events as a function of how topics spread in online news media. Combining concepts such as text analytics, network modeling, and systems resilience, TopicRes assists in exploring and analyzing online news media spread. Data analytics and statistical tools are used in every step: data collection, topic detection, topic influence network, and topic resilience. We present a case study in the Portuguese language to showcase how TopicRes functions, its usefulness, and its versatility. The results show that the system resilience model is convenient for identifying events and capturing their dynamic behavior over time. The network deepens this analysis with detailed static snapshots of the topics and their relationships. Results are then clustered by behavior, presenting a new way to fathom the system’s dynamics enduring a certain type of disruptive event. In the case study, it is possible to observe the power dynamics of the media outlets and how the local structure influences the news spread. TopicRes is a powerful analytic tool to sense important events in the media, aid in disaster response and crisis management, track the development of new technologies, and “fake news” propagation.
Real Time Attrition Prediction Mechanism Based on Deep Learning

Modern companies mostly have their own employee management systems, which retain a large amount of employee behavior data. These data can be used to mine valuable information, such as whether employees have attrition intentions. This paper proposes a mechanism of attrition prediction based on deep learning, which mainly includes three modules: (1) Using LSTM to build a performance-attrition pre-trained model; (2)Using Transformer to build a sentiment tendency model based on employee weekly reports; (3) Based on (1), (2), establishing Real Time Attrition Prediction Mechanism in the company's employee management system to predict employees' willingness to leave at any time. The purpose of this mechanism is to help companies manage better and reduce losses caused by employee attrition.
Soteria: An Approach for Detecting Multi-Institution Attacks

We present Soteria, a data processing pipeline for detecting multi-institution attacks. Soteria uses a set of Machine Learning techniques to detect future attacks, predict their future targets, and ranks attacks based on their predicted severity. Our evaluation with real data from Canada wide academic institution networks shows that Soteria can predict future attacks with 95% recall rate, predict the next targets of an attack with 97% recall rate, and detect attacks in the first 20% of their life span. Soteria is deployed in production and is in use by tens of Canadian academic institutions that are part of the CANARIE IDS project.
Machine learning classifier-based detection of cyber-attack on power system: Comparative analysis

In digital substations, organized cyber-attacks can severely impact the protection and security of the system and may cause generation and line disconnections, triggering cascading failures in the modern power system. In this study, the modeling of cyber-attack and the formulation of the detection problem as the classification problem for the machine learning classifier have been discussed. Two types of classification binary and multi-class have been taken up for analysis. Two publicly available datasets were used to train the classifier. Comparative analysis was performed in detail for different classifiers in different attack scenarios using Python, Matlab, and WEKA 3 software. The standard IEEE-24 bus system has been used for the dataset generation. It has been observed from the results that the Random forest Classifier works best in most of the cases followed by the Multi-Layer Perceptron Classifier.
Stochastic Peer-to-Peer Energy Trading with Price and Incentive Mechanism

With the emerging role of the Peer-to-peer (P2P) energy sharing concept in effectively utilising renewable energy resources, the need for a suitable incentivising/pricing method is also increasing. In this work, a cooperative game is formulated in a community of buildings where a community battery energy storage system (CBESS) acts as a player along with other buildings. With the help of generalised Nash bargaining, the problem is formulated and minimised in a decentralised way using ADMM. “Hong’s 2m point estimate method” is used for stochastic modelling renewable generation uncertainties. Suitable incentives are decided to motivate the players to engage in this game of P2P energy sharing. Along with the incentives, prices per unit of energy shared are also calculated. When compared, both methods give almost the same results.
A Cardiovascular Disease Detection System using Machine Learning

In recent times, the health sector has placed a greater emphasis on data comprehension through the utilization of data mining and collection technology. Multiple design standards indicate that medical practitioners make between 12 and 13% of incorrect prognostications. Therefore, an automated method for disease prediction is required in order to improve the accuracy of health diagnoses and to raise assessments reliability. Many academics have placed an emphasis on accuracy rate throughout the construction of automated techniques. The automated system that was created to forecast heart disease will reduce the burden of computational work and the factors that raise the amount of computational work. The accuracy of the prediction is improved through the employment of the classification models, which retrieves and selects data. The information about heart disease that is readily available to the public yields contradictory outcomes when it is investigated. Learning patterns involves both linear and non-linear examination of the underlying features. The prediction rates can be improved by Support vector regression that is linearly iterated (LlSVR) and classification using stacked monkey optimization (SMO). The computations were performed in MATLAB 2016b, and a number of evaluation techniques were used to assess the outcomes. The suggested model is 98.5 percent accurate, 98.8 percent precise, 99.5 percent recall, and 99.28 percent F-measure. The findings point to the possibility that the anticipated LISVR-SMO will function better than the approaches that are now being used and provide better trading.
Detailed analysis of Machine Learning and Deep Learning technologies for Web Security

As the Internet has expanded, web attacks have become more sophisticated, and web security is currently in a poor position. Websecurity technology is used to protect networks, programmes, and systems from webattacks. Webattacks are capable of getting access to delete or alter sensitive data, demand payment from users, and hinder company activities as usual. Currently, many technologies are becoming smarter than hackers and people, which make it challenging to put websecurity precautions into practise. Notwithstanding the primary writing surveys on ML and DL methods for network investigation and interruption recognition, this review report contains a concise instructional exercise portrayal of every ML/DL strategy. In light of the sequential or warm connections between's the works that addressed every strategy, they were listed, checked out, and summed up. We feature probably the most generally utilized network datasets; make sense of the troubles utilizing ML/DL for websecurity, and make ideas for future review since information are so essential to ML/DL strategies.
Efficient Neural Network Validation with Affine Forms

Affine forms, which are an extension of interval arithmetic, have been successfully used to assess the robustness of neural networks as well as to explain their decisions. They are well-suited to this application domain since they capture affine relations between variables and since neural networks mostly perform affine computations. However, a drawback is that affine forms are time and memory consuming, making the verification of industrial-size neural network difficult or impossible. In this article, we study the impact of noisy symbol merging on accuracy and time, in the context of neural network validation. Intuitively, we use less noisy symbols and therefore need less time and memory to analyze neural networks. This is confirmed by our experimental results carried out in a image recognition setting.
An Impact Study of Time of Use Pricing on Voltage Control Devices

Time of Use (TOU) pricing motivates customers to schedule flexible loads from peak to off-peak periods and minimizes electricity bills. TOU pricing minimizes peak demand and energy procurement costs. This paper investigates different TOU price levels in the Distribution System (DS) with varying load profiles and solar PV penetration levels. TOU pricing affects the system’s load profile, which in turn affects the performance of the voltage control devices. The demand response and its effect on voltage regulation have been researched in the literature. On the other hand, the impact of TOU pricing on voltage control devices has not been properly investigated. The effects of TOU pricing on Conservative Voltage Reduction (CVR), switching of On-Load Tap Changers (OLTC), Voltage Regulators (VR), and Capacitor Banks (CB), reactive power output, and active power curtailment by solar inverters are examined in 69-node DS and IEEE 123-node DS systems With varying levels of solar PV penetration.
Performance Analysis of Load Frequency Control for Power Plants Using Different Optimization Techniques

In this paper, several optimization techniques including the Particle Swarm Optimization (PSO) technique, the Genetic Algorithm (GA), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are applied to determine the most efficient output for load frequency control. These optimization techniques analyze the optimal level of system performance. The goal of this paper is to identify the most effective optimization technique for this sophisticated LFC system. In this research, three strategies (PSO, GA, ANFIS) are used in the LFC system to analyze frequency fluctuation and compare the load change rate. The model consists of the transfer function of the governor, turbine, rotating mass, and load. In this analysis, the ideal performance is examined across three separate case scenarios. The MATLAB/SIMULINK software simulates the performance analysis, which offers more realistic data and is generally preferred in this sort of optimization strategy work.
A Novel Framework for Secure Cloud Computing Based IDS Using Machine Learning Techniques

Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. But cloud security remains a serious concern for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. To address this gap machine learning approaches are being explored. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning system. Proposed Method identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes' one previous decisions are coupled with the machine learning algorithm's current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilized to derive our findings. Our data demonstrate that it enhances anomaly detection to 97.68 percent.
Field Test Evaluation of an AI Model for Recycle Waste Separation running on Embedded Systems

Developing an automatic waste sorting AI model for embedded systems requires measuring performance in real-world contexts to determine if the model is acceptable. From our previous research, we selected a pre-trained model named ssd_mobilenet_v2_coco to construct and train an AI model for automatic waste sorting system running on Jetson Nano. The results of our laboratory evaluation are satisfactory. However, in real world, many environmental factors affect the accuracy of the AI model. In this research, we explored the factors that affect the accuracy of the AI model in real-word settings. The targeting factors include the weather's conditions, electricity stability, light intensity inside of the detection chamber, and the user behavior of waste littering. In the real-world setting, the AI model has an accuracy rate of 80.70%, which is decreased by 18.13% comparing to laboratory conditions.
PCANT: Programmable Capture and Analysis of Network Traffic

The rapid proliferation of heterogeneous devices, complex network architectures and distributed services leads to a growing demand for monitoring solutions. Focusing on solutions that analyze traffic data to monitor devices and applications, we observe that measurements and analytics are rarely tailored to the specifics of the service they monitor. The difficulty of creating ad-hoc solutions is not only due to the diversity of devices and applications, but also to the difficulty of deploying such features: acquiring the right data for a new monitoring need requires manually configuring the different network equipment to extract this data. Without this, monitoring is constrained to a fixed set of input features. In this paper, we present and demonstrate a novel platform for network traffic monitoring that removes this limitation: the Programmable Capture and Analysis of Network Traffic (PCANT) platform. It allows network administrators to dynamically deploy new monitoring functions with no manual intervention inside the network. On demand, it automatically installs arbitrary data acquisition methods in network equipment, instantiates analysis functions in a dedicated compute node, and manages the data transmission between the two. Monitoring functions can thus be implemented by data analysts with limited networking knowledge, and deployed at no cost.
Cross Subsidy in Loss Allocation in Power Distribution Network Based on Game Theory Approach

The loss occurring in a power distribution network is allocated to the users to recover it. The allocation of losses should be fair, transparent and consider users’ contributions to losses. It should also be free from cross subsidy, i.e., any user should not be subsidized over others. It necessitates a mechanism to identify and quantity cross subsidy in the results of loss allocation. This paper, therefore, investigates the cross subsidy by modeling the distribution network as a cooperative game. The coalition of loads and distributed generators is separately modeled as a surplus game and cost game. In surplus and cost games, a subsidy-free allocation lies within the core, and the deviation from the subsidy-free solution is utilized to determine the level of cross-subsidization. The proposed method is applied to the results of various loss allocation methods such as prorata, branch current decomposition method, Shapley value-based method, and $\tau-$value based method for cross subsidy evaluation. The results are tested in three different test systems, and the findings are analyzed and discussed in this paper.