Reviewerly/exHarmony huggingface dataset:
A Novel Deep Learning-Based Object Detector Using SPOTNET-SNIPER Network

Identification of objects based on drone is mostly utilized to monitor ground objects. The most common issue in detecting the ground objects is that the small objects in the ground are not monitored clearly. To enhance the detection capability of the small objects, a novel convolutional neural network (CNN) system based on SpotNet integrated with Scale Normalization for Image Pyramids with Efficient Resampling (SNIPER) is proposed. Initially, the feature pyramid SpotNet-SNIPER network is trained with the images from the VisDrone-DET2021 image dataset by varying the image size. Thus, the required features from the drone images are gathered by the proposed network approach. Then, from the extracted objects dense prediction is done with the help of the CNN ResNeXt-101. For the experimental purpose the VisDrone-DET2021 image dataset is utilized. Implementing the VisDrone-DET2021 image dataset with the proposed approach enhances the capability of detecting small objects with less computation. Finally, from the experimental outcomes it is evident that the suggested object detection approach produces better performance in detecting the small objects.
Security Audit of a University Information System: A Functional Model

In the context of transition to online education worldwide, it is of utmost importance to provide the protection of confidential data from information security threats. The present study intends to evaluate the level of security of the HSE University information system. The security audit of the system is carried out using the methodology of penetration testing which allows identifying vulnerabilities and assessing risks. The paper describes the stages of preliminary information gathering, preliminary vulnerability analysis. The exploration of methods and gathered information enabled developing a functional penetration testing model. Although some of the discussed (sub-)stages were not completed due to the legislative aspect, we were able to collect detailed information about the information system, determine providers and server locations, scan ports, determine file structures, describe the current state of the technologies used in the HSE University information system and compile a list of potential vulnerabilities. The proposed functional model and the completed phases will serve as a strong basis for conducting a full-fledged information security audit.
Towards a Correction Factors-Based Software Productivity Using Ensemble Approach for Early Software Development Effort Estimation

Accuracy of effort estimation is one of the necessary conditions for efficiently managing software development projects. Since the information available in the early stages of software development is insufficient, software sizing metrics are considered critical factors for effort estimation. However, there is no consistent method for converting software sizing into the corresponding effort. Previous estimation methods have not considered software productivity a critical factor in estimating effort based on software sizing. This paper proposes a software productivity model based on correction factors in the Optimizing Correction Factors method through an ensemble construction mechanism of three popular machine learning techniques. The results show that using the proposed software productivity minimizes the estimation error of the methods compared to using fixed productivity metrics.
Collaborative Blockchain Based Distributed Denial of Service Attack Mitigation Approach with IP Reputation System

Due to the high accessibility of the internet and unsecured internet-connected devices, Distributed Denial of Service attacks (DDoS) become devastating among all cyber-attacks. Attack complexity and sophistication have been increasing day by day. Many mitigation techniques and methods are available through which the risk of DDoS attacks will be reduced at a significant level. It is ideal to implement distributed solutions to mitigate DDoS attacks because of distributed nature of DDoS attacks. Many collaborators who share the same interest, participate in DDoS attack mitigation in a collaborative DDoS mitigation approach. But, creating a collaboration in a transparent, secured, and distributed way is still a challenge. Blockchain technology presents the ideology of decentralized, distributed, secured, and immutable ledger. Blockchain is an emerging technology to mitigate many cyber threats because of its fundamental characteristics like decentralization, immutability, authenticity, trust management, and integrity. In DDoS attacks mitigation, Blockchain technology is utilized in many different ways but, still in the initial phase. Due to all these characteristics of Blockchain, Blockchain become the ideal choice to create collaboration among peers. As well as it would be also useful to signalize the attack among various collaborators. Accuracy of shared IP addresses is equally important in the mitigation of DDoS Attacks and Botnet identification. IP Reputation systems are generally designed to get accurate information about IP addresses. In this paper, we present how blockchain is used collaboratively to mitigate DDoS attacks in various domains. This paper aim at providing a brief overview of DDoS mitigation strategies, the architecture of the proposed DDoS mitigation solution. This paper also discusses the integration of IP reputation scheme for Blacklisted IP addresses with Blockchain to build transparency and accuracy of data. Accurate IP addresses data would be helpful in not only DDoS Attack Mitigation but, in many attacks related to IP Addresses, identification of the Bots, Intrusion prevention system (IPS), and Intrusion detection system (IDS).
Determination of Phobic Disorders Using Virtual Reality Technologies

The article considers the possibility of using virtual reality technologies to diagnose phobic disorders by modeling stressful situations. This makes it possible to determine a phobia in a virtual environment in conditions that are safe for humans, and to predict in advance the occurrence of a panic state that is dangerous both for the person himself and for his environment. The study considers possible approaches to fixing the state of a phobic disorder, as well as the developed software for virtual scenes to determine acrophobia and claustrophobia. The presented virtual scenes simulate the main scenarios for the emergence of these phobias, confronting a person with their manifestations in virtual space. As a metric in this work, we used an estimate of the heart rate. As a result, it was found that virtual reality technologies make it possible to induce signs of a phobia in a person suffering from a corresponding disease, which can be recorded by an expert. Experiments have also shown that the heart rate metric is not effective enough. In further studies, it is necessary to expand the list of metrics for assessing the human condition for a comprehensive, automated and more accurate diagnosis.
Profile Consistency Discrimination

Consistency discrimination between attribute information and generated response is vital to personality-based dialogue. On existing work, few works specially investigates the consistency. In this paper, we propose a feasible method to solve this problem. We combine the typical natural language inference model (ESIM) and natural language understanding model (Bert) to discriminate consistency. But ESIM will fail when the input is structured attribute information. To solve this, We introduce external knowledge to expand the attribute information. Additionally, we observed the characteristics in the dialogue and found that adding a keyword matching label to the generated response is effective. We experimented on KvPI dataset and analyze the impact of different data sizes on the model. Compared with traditional methods, our method overall achieved better results.
Enhancing Transferability of Adversarial Examples by Successively Attacking Multiple Models

Deep neural networks (DNNs) are very vulnerable to malicious attacks by adversarial examples, which is the process of deceiving the network by adding small perturbations to the original input. Moreover, the adversarial examples exhibit transferability that is more threatening to deep learning models: Adversarial examples generated by a specific network can mislead other black-box models. However, the adversarial examples tend to overfit the parameters of a particular network, which leads to their limited transferability. To boost the transferability of adversarial examples, we propose a successively attacking multiple high-accuracy model to obtain adversarial examples toward the common vulnerable directions of the models. Our approach differs from previous methods in that it adds modest adversarial perturbations to the image in a sequential and progressive manner over multiple models. Our strategy can be well integrated into several state-of-the-art approaches to improve their transferability. Numerous experiments have demonstrated that our approach greatly improves the ability of adversarial examples to transfer to unknown black-box models. The experiments also show that our attack strategy is superior to the traditional ensemble-based approach in terms of transferability improvement of adversarial examples.
A Detection of a Sequence of Jump-Like Pulses Against Random Distortions

In this paper, a wide class of effects defined as a sequence of jump-like pulses with a harmonic filling and discontinuous points of the first order at the moments of appearance and disappearance is considered. This kind of pulses is often characterized by the unknown amplitudes and initial phases observed against the Gaussian random distortions. To detect such pulses, the algorithm synthesized based on the maximum likelihood method is proposed. The structure and the main statistical properties of the introduced detection algorithm are studied. The technique for the calculation of the detection characteristics is presented. The dependence of the type I error probability upon the threshold as well as the dependence of the type II error probability upon the signal-to-noise ratio are demonstrated. A statistical simulation for the obtained algorithms is carried out. It is established that the proposed detector is operable while the theoretical formulas describing its characteristics are in good agreement with the corresponding experimental data in a wide range of values for the parameters of the analyzed pulse sequence. #CSOC1120.
Blockchain-Based E-Pharmacy to Combat Counterfeit Drug Transactions

A World Health Organization (WHO) study in 2017 found that 1 in 10 medical products circulating in low-income and middle-income countries are either substandard or falsified. This cynical trade is becoming increasingly difficult to track owing to the advent and growth of technology that enables illicit organizations all over the world to carry out their transactions via the Internet. The pharmaceutical industry is in dire need of a transparent, reliable, and tamper-proof strategy through which medical products can be tracked from supplier to consumer, in order to identify counterfeit products both at the point of sale and at the point of purchase. In order to achieve this and combat this rampant issue, this project proposes the establishment of a decentralized e-pharmacy, powered by blockchain. The overarching aim is to devise a mechanism through which every transaction of a product through the e-pharmacy is recorded in an immutable and irrefutable manner, thereby reducing fraudulent activity. This would effectively enable legitimate tracking of pharmaceutical transactions by employing immutable smart contract transactions using cryptocurrency, on an Ethereum framework. This system also provides the pharmaceutical vendors as well as the end users with a unique hexadecimal identification sequence, which serves as their “address” on the blockchain. This address would definitively certify the authenticity of both transacting parties when a product is purchased. Leveraging the transparency and security of the transactions that transpire through the blockchain network, the proposed solution could fill in the noticeable gaps in the current system, potentially saving the lives of countless people who are negatively impacted by the circulation and consumption of counterfeit drug circulation.
Localized Metric Learning for Large Multi-class Extremely Imbalanced Face Database

Metric learning serves to mitigate, to a great extent, the class-imbalance problem associated with large multi-class image databases. However, the computational complexity associated with metric learning increases when the number of classes is very large. In this paper, a novel localized metric learning scheme is proposed for a large multi-class extremely imbalanced face database with an imbalance ratio as high as 265:1. The Histogram of Gradient (HOG) features are extracted from each facial image and these are given as input for metric learning. The proposed scheme involves confining the metric learning process to local subspaces that have similar class populations. The training dataset is divided into smaller subsets based on the class populations such that the class imbalance ratio within a local group does not exceed 2:1. The locally learnt distance metrics are then, one by one, used to transform the entire input space. The nearest neighbor of the test sample, in the training set, is noted for each transformation. A comparison amongst all transformations for the closest nearest neighbor in the training set establishes the class of the test sample. Experiments are conducted on the highly imbalanced benchmark Labeled Faces in the Wild (LFW) dataset containing 1680 classes of celebrity faces. All classes are retained for the experimentation including those minority classes having just two samples. The proposed localized metric learning scheme outperforms the state of the art for face classification from large multi-class extremely imbalanced face databases.
Attention-Cooperated Reinforcement Learning for Multi-agent Path Planning

Multi-agent path finding (MAPF), in multi-agent systems, is a challenging and meaningful problem, in which all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding the obstacles. Effective extraction from the agent's observation, effective utilization of historical information, and efficient communication with neighbor agents are the challenges to completing the cooperative task. To tackle these issues, in this paper, we propose a well-designed model, which utilizes the local states of nearby agents and obstacles and outputs an optimal action for each agent to execute. Our approach has three major components: 1) observation encoder which uses CNN to extract local partial observation and GRU to make full use of historical information, 2) communication block which uses attention mechanism to combine the agent's partial observation with its neighbors, and 3) decision block with the purpose to output the final action policy. Based on the three major components, all agents formulate their own decentralized policies to apply. Finally, we use success rate and extra time rate to measure our approach and other well-known algorithms. The results show that our method outperforms the baselines, demonstrating the efficiency and effectiveness of our approach, especially in the case of large scale in the world.
Multi-level Thresholding Partitioning Algorithm for Graph Processing in Cloud Computing

Graphs are an effective way for data representation, and generally, the graph data size is enormous. Graphs are linked with various revenue-generating applications such as social media, online retail, drug discovery, clinical trials, and businesses. Partitioning is a mechanism used for processing the vast graph data effectively. Graph partitioning efficiently minimizes the energy consumption and computational complexity caused while processing the extensive interlinked data. In this work, node priority and threshold-based graph partitioning algorithms are proposed for achieving energy-efficient graph processing. Most graph partition algorithms promptly minimize energy consumption, but the node priority-based graph partitioning algorithm also delivers enhanced results in the aspect of execution time. In this research work, the power consumption is measured through online power estimation tools. To calculate the energy consumption, in this work, five popularly known graph applications are deployed. The power consumption is categorized into static and dynamic power consumption, estimated using four benchmarked graph datasets. The result analysis includes the energy and performance cost according to the processor. The obtained results show that the proposed runtime is enhanced effectively than the existing works, thus achieving the motto of this research work in the aspect of minimizing the overall energy percentage.
A Novel Cs 2g-Starlet Denoising Method for High Noise Astronomical Imagei

It is well-known that astronomical image denoising has always been an important research topic in deep space. However, for the denoising of high-noise astronomical images, obtaining good reconstruction results from the current denoising methods is difficult. This paper proposes a novel compressed sensing (CS) 2G-starlet denoising (NCSSD) method to effectively address the denoising problem of high-noise astronomical images. Firstly, the proposed 2G-starlet Wiener filtering operator is used to select these starlet coefficients in the process of sparse image transformation. Then, a novel iterative stepsize operator is proposed to enhance the convergence ability of the algorithm. At the same time, an optimized cycle spinning method is proposed to adjust the reconstructed image. Through extensive simulation experiments and deep theoretical analysis and verification, the effectiveness of this method in high-noise astronomical image denoising is fully proven, and the image reconstructed by the proposed method is significantly improved. The proposed method can effectively solve the denoising problem of high-noise astronomical images, which has been successfully interconnected with the telescope and industrial camera, and has realized the rapid acquisition of high-quality astronomical images. The research results of this paper have significance for promoting the effective acquisition and analysis
Meta-Contrast Self-Supervised Video Representation Learning Based on Multi-Task Spatio-Temporal Representation Fusion and Scale Decoupling

In recent years, self-supervised learning has achieved remarkable success in video representation. How to effectively adapt the pre-training domain to downstream dense tasks and capture key motion information is a very challenging problem. Most of the existing methods use contrast loss and video-specific excuse tasks to learn instance-level differences and capture motion information. However, the lack of category information can lead to hard positivity problems, thereby limiting the performance of such methods. We have found that the performance of downstream tasks can be significantly improved through well-designed excuse tasks. We prove through experiments that spatio-temporal domain augmentation can be used to guide the network to learn the semantic features needed in comparative learning through regularization operations, and we propose a method of using intermediate supervised spatio-temporal domain features. We propose a representation fusion module; for the first time, the representations between different excuse tasks are fused and compared to represent learning. Experimental results show that our proposed fusion contrast representation learning significantly improves the performance of this type of method in downstream tasks. We also explored that the multi-tasking process of meta-learning can significantly improve the generalization performance of such methods. In this project, we propose a new contrast self-supervised representation learning technology: (1) We have carefully designed two joint optimization excuse tasks, namely the context matching task based on the residual frame and the motion feature prediction task. (2) We discussed in depth the effectiveness of space-time domain augmentation, and for the first time independently carried out time-domain augmentation and spatial domain augmentation on an excuse mission. (3) We performed multi-scale intermediate supervision and comparison representation learning on the features of the backbone model on multiple excuse tasks. (4) For the first time, we carried out fusion contrast representation learning for the representations between joint tasks, and proposed the representation fusion contrast representation learning module (RFCRL). (5) We combined contrast learning with model agnostic meta-learning (MAML). Make the existing self-supervised methods have stronger characterization learning ability. We have conducted extensive experimental evaluations on the UCF101 and HMDB51 datasets. A large number of experimental results show that our method has obtained the most advanced performance in video action recognition and retrieval.
Human Activity Recognition Based on Hybrid Learning Algorithm for Wearable Sensor Data

Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessitates the constant usage of smartphones, smartwatches, and wearable devices to capture patients’ daily activities. To predict multiple human activities, deep learning (DL)-based methods have been successfully applied to time-series data that are generated by smartphones and wearable sensors. Although DL-based approaches were deployed in activity recognition, they still have encountered a few issues when working with time-series data. Those issues could be managed with the proposed methodology. This work proposed a couple of Hybrid Learning Algorithms (HLA) to build comprehensive classification methods for HAR using wearable sensor data. The aim of this work is to make use of the Convolution Memory Fusion Algorithm(CMFA) and Convolution Gated Fusion Algorithm(CGFA) that model learns both local features and long-term and gated-term dependencies in sequential data. Feature extraction has been enhanced with the deployment of various filter sizes. They are used to capture different local temporal dependencies, and thus the enhancement is implemented. This Amalgam Learning Model has been deployed on the WISDM dataset, and the proposed models have achieved 97.76%, 94.98% for smartwatch and smartphone of CMFA, 96.91%, 84.35% for smartwatch and smartphone of CGFA. Experimental results show that these models demonstrated greater accuracy than other existing deep neural network frameworks.
Cross-Domain Multi-Style Merge for Image Captioning

Multi-style image captioning has attracted wide attention recently. Existing approaches mainly rely on style synthetics within a single domain. They cannot deal with multiple styles combination since various styles naturally cannot be included in a uniform dataset.This paper is the first one to investigate the cross-domain multi-style merge for image captioning. Specifically, we propose a novel image caption model with a multi-style gated transformer block to fit the cross-domain caption generation task. Conventional generative adversarial learning for language methods may suffer from the distribution distortion problem, since real datasets do not contain captions with style combinations. Therefore, we devise a multi-stage self-learning framework for the proposed image caption model to exploit real corpus with pseudo styles gradually. Comprehensive experiments and ablation studies demonstrate the effectiveness of our proposed method on the multi-style merge for image captioning.