Reviewerly/exHarmony huggingface dataset:
A Ranking based Language Model for Automatic Extractive Text Summarization

Increased availability of the Internet and social media has created another &#x2018;world of data&#x2019; comprised of text, audio and video files. It is very difficult for a user to get the accurate summary or to comprehend the relevant and important items from the available media. Additionally, readers or evaluators of these data files are interested only in the relevant content or summary to be retrieved in the less duration from the source files. Automatic text summarization (ATS) is the only way to summarize single or multiple documents to obtain relevant content from the source files. Available ATS systems generate bad summaries and take a lot of time and space for long documents due to inaccurate encoding. Therefore, in this work, we have introduced an approach for extractive text summarization using sentence ranking. Experiments have been performed over BBC and CNN news datasets and evaluated in terms of ROUGE using N-gram Language Model. The quantitative values of the metrics show the effectiveness of the proposed approach for news datasets.
Performance Evaluation of Interest Point Detectors for Heterologous Image Matching

In point-based heterologous image matching algorithms, high-quality interest point detection directly affects the final image matching quality. In this paper, starting from the detection mechanism of each interest point detector, optical images and SAR images with different resolutions and covering different areas are selected as experimental data. The five state-of-the-art SAR-Harris, UND-Harris, Har-DoG, Harris-Laplace and DoG interest point detectors are analyzed in terms of scale difference adaptability, nonlinear intensity difference adaptability, distribution uniformity, image registration alignment performance and detection efficiency. Then, we performed registration experiments on images from different sensors, at different times, and at different resolutions to further validate our evaluation results. Finally, the applicable image types of each detector are summarized. The experimental results show that SAR-Harris has the best performance in scale difference adaptability, and UND-Harris has the weakest performance. In terms of nonlinear intensity difference adaptability, SAR-Harris and UND-Harris are comparable, and DoG performance is the weakest. The distribution uniformity of UND-Harris is significantly better than other detectors. Although Har-DoG is weaker than Har-Lap and DoG in repeatability, it is better than both in final image alignment performance. DoG is superior in detection efficiency, followed by SAR-Harris. A comprehensive evaluation and a large amount of experimental data are used to evaluate and summarize each detector in detail. This paper provides a useful guide for the selection of interest point detectors during heterologous image matching.
SIP-UNet: Sequential Inputs Parallel UNet Architecture for Segmentation of Brain Tissues from Magnetic Resonance Images

Proper analysis of changes in brain structure can lead to a more accurate diagnosis of specific brain disorders. The accuracy of segmentation is crucial for quantifying changes in brain structure. In recent studies, UNet-based architectures have outperformed other deep learning architectures in biomedical image segmentation. However, improving segmentation accuracy is challenging due to the low resolution of medical images and insufficient data. In this study, we present a novel architecture that combines three parallel UNets using a residual network. This architecture improves upon the baseline methods in three ways. First, instead of using a single image as input, we use three consecutive images. This gives our model the freedom to learn from neighboring images as well. Additionally, the images are individually compressed and decompressed using three different UNets, which prevents the model from merging the features of the images. Finally, following the residual network architecture, the outputs of the UNets are combined in such a way that the features of the image corresponding to the output are enhanced by a skip connection. The proposed architecture performed better than using a single conventional UNet and other UNet variants.
Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Summary This paper presents a hybrid feature extraction and optimized deep learning model for the effective shot boundary detection (SBD). A cross‐guided bilateral filtering technique is used for pre‐processing. A hybrid fuzzy histogram with dual tree complex wavelet transform (FH‐DTCWT) is developed for the feature extraction to extract the visual features from the pre‐processed frames of the block. Candidate segment selection process is performed to identify the non‐boundary frames from the extracted features to improve the accuracy. Continuity matrix is created using the possible transition frames to verify that the frames are in sequential order without any gap. Classification of transition types are performed by the proposed optimized deep convolutional neural network. Optimized deep learning (DCNN‐RBESO) model is the combination of deep convolutional neural network (DCNN) and Rider bald eagle search optimization (RBESO) algorithm. To update the weight of DCNN model in learning, RBESO is utilized. MATLAB tool is used to develop the proposed SBD model. The experimental analysis is carried out for the proposed model using TREC Video Retrieval Evaluation (TRECVID) and VideoSeg datasets. The proposed model outperformed in the detection of shot boundaries in terms of precision, recall, and F1‐score.
A Decentralized Resource Management System Proposal For Disasters: NGO-RMSD (STK-AKYS)

Disaster and emergency management are under the responsibility of many organizations and there are serious coordination problems in post-disaster crisis management. This paper proposes a decentralized non-governmental organization resource management system for disasters (NGO-RMSD / STK-AKYS). This system is based on blockchain technology and it will enable the non-governmental organizations (NGO) and public institutions to manage and coordinate the resources in a trusted environment in the case of disasters. A proof of concept implementation is developed by using the Quorum blockchain framework which is more energy-efficient than crypto currency-based blockchain solutions. Smart contracts are developed for the autonomous working of the system. These smart contacts are used for the verification of the needs of the one who is in need, delivering resources to the right people, and identifying the urgent needs. The system aims to reach more disaster victims in a more timely manner. NGO-RMSD is designed according to the needs of the NGOs in the field. The application is shared with the free software license and further development with the community is aimed.
Enlfade: Ensemble Learning Based Fake Account Detection on Ethereum Blockchain

Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users have risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of the blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology a safer place for investors. This study proposes a classification model to handle fake account problem over Ethereum blockchain, and its contribution is multi-faceted; firstly, available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Secondly, correlation-based feature selection technique has been applied to retain best discriminating features. Thirdly, an effective machine learning based model has been presented for the identification of fake accounts over the Ethereum system. A comparative study of ten machine learning techniques has been presented consisting of both individual and ensemble classifiers. Experimental results showed that ensemble classifiers appear to yield better performance measures over individual classifiers and among all, LightGbm-based classification model outperformed with 99.2% accuracy.
Effect of capital flight on domestic investment: Evidence from Africa

Capital flight is a major issue in developing economies; the problem is more severe in Africa, where domestic investment has been affected. Much attention has been given to the effect of legal and foreign capital flows in the international capital movement, disregarding illicit capital outflows (capital flight) from developing countries including Africa. This study examines the effects of capital flight and financial liberalization on domestic investment using the dynamic system generalized method of moments (GMM) for 30 African nations between 2000 and 2019. The econometric analysis revealed that capital flight is one of the conditions that severely constrains domestic investment financing in Africa. However, the impact of financial liberalization on domestic investment is shown to be insignificant. The empirical evidence is used to draw some policy implications aimed at reducing capital flight and enhancing domestic investment.
Applying Game-Learning Environments to Power Capping Scenarios via Reinforcement Learning

Research in deep learning for video game playing has received much attention and provided very relevant results in the last years. Frameworks and libraries have been developed to ease game playing research leveraging Reinforcement Learning techniques. In this paper, we propose to use two of them (RLlib and Gym) in a very different scenario, such as learning to apply resource management policies in a multi-core server, specifically, we leverage the facilities of both frameworks coupled to derive policies for power-capping. Using RLlib and Gym enables implementing different resource management policies in a simple and fast way and, as they are based on neural networks, guarantees the efficiency in the solution, and the use of hardware accelerators for both training and inference. The results demonstrate that game-learning environments provide an effective support to cast a completely different scenario, and open new research avenues in the field of resource management using reinforcement learning techniques with minimal development effort.
Multiple Histograms-Based Reversible Data Hiding Using Fast Performance Optimization and Adaptive Pixel Distribution

Abstract In prediction error-based reversible data hiding, multiple histograms modification (MHM) is well known for high image quality and thus has received wide attention in recent years. However, the computational cost for performance optimization in MHM is too high, which is particularly critical for real-time applications. This manuscript aims to reduce the computational complexity of MHM by presenting two techniques, including fast performance optimization and adaptive pixel distribution. Fast performance optimization provides a two-stage process for optimal bin selection by exploiting the concept of per-bit distortion of data embedding within a prediction error histogram (PEH). In fast performance optimization, the distribution characteristics of the per-bit distortion are investigated to significantly narrow down the solution space of optimal bin selection. The second technique is adaptive pixel distribution, which tries to nonuniformly allocate pixels into multiple PEHs to further reduce the time complexity. Extensive experiments show that the computational complexity of MHM is significantly reduced while well preserving the image quality.
Few-shot class-incremental learning based on representation enhancement

Few-shot class-incremental learning (FSCIL) is crucial and practical for artificial intelligence in the real world, which learns novel classes incrementally from few samples without forgetting the previously learned classes. However, FSCIL confronts two significant challenges: “catastrophic forgetting” and “overfitting new.” We focus on convolutional neural network (CNN)-based FSCIL and propose a human cognition-inspired FSCIL method, in which the knowledge of novel classes is learned under the guidance of the previously learned knowledge. Specifically, we learn a discriminative and generalized CNN feature extractor from the base classes in the first task. We generate the representations of base and novel classes in unified feature space without training on novel classes, thus avoiding “forgetting old.” For the novel classes in long sequential tasks, beyond the representation generation, we enhance the representation by exploring the correlations with the previously learned classes to alleviate overfitting new and ensure that the novel classes adapt to the feature space. Experimental results show that our proposed method achieves very competitive results on MiniImageNet and CIFAR-100 datasets.
A Gaze into the Internal Logic of Graph Neural Networks, with Logic

Graph Neural Networks share with Logic Programming several key relational inference mechanisms. The datasets on which they are trained and evaluated can be seen as database facts containing ground terms. This makes possible modeling their inference mechanisms with equivalent logic programs, to better understand not just how they propagate information between the entities involved in the machine learning process but also to infer limits on what can be learned from a given dataset and how well that might generalize to unseen test data. This leads us to the key idea of this paper: modeling with the help of a logic program the information flows involved in learning to infer from the link structure of a graph and the information content of its nodes properties of new nodes, given their known connections to nodes with possibly similar properties. The problem is known as graph node property prediction and our approach will consist in emulating with help of a Prolog program the key information propagation steps of a Graph Neural Network's training and inference stages. We test our a approach on the ogbn-arxiv node property inference benchmark. To infer class labels for nodes representing papers in a citation network, we distill the dependency trees of the text associated to each node into directed acyclic graphs that we encode as ground Prolog terms. Together with the set of their references to other papers, they become facts in a database on which we reason with help of a Prolog program that mimics the information propagation in graph neural networks predicting node properties. In the process, we invent ground term similarity relations that help infer labels in the test set by propagating node properties from similar nodes in the training set and we evaluate their effectiveness in comparison with that of the graph's link structure. Finally, we implement explanation generators that unveil performance upper bounds inherent to the dataset. As a practical outcome, we obtain a logic program, that, when seen as machine learning algorithm, performs close to the state of the art on the node property prediction benchmark.
Pose- and Attribute-consistent Person Image Synthesis

Person Image Synthesis aims at transferring the appearance of the source person image into a target pose. Existing methods cannot handle large pose variations and therefore suffer from two critical problems: (1) synthesis distortion due to the entanglement of pose and appearance information among different body components and (2) failure in preserving original semantics (e.g., the same outfit). In this article, we explicitly address these two problems by proposing a Pose- and Attribute-consistent Person Image Synthesis Network (PAC-GAN). To reduce pose and appearance matching ambiguity, we propose a component-wise transferring model consisting of two stages. The former stage focuses only on synthesizing target poses, while the latter renders target appearances by explicitly transferring the appearance information from the source image to the target image in a component-wise manner. In this way, source-target matching ambiguity is eliminated due to the component-wise disentanglement of pose and appearance synthesis. Second, to maintain attribute consistency, we represent the input image as an attribute vector and impose a high-level semantic constraint using this vector to regularize the target synthesis. Extensive experimental results on the DeepFashion dataset demonstrate the superiority of our method over the state of the art, especially for maintaining pose and attribute consistencies under large pose variations.
Translation of AADL model to security attack tree (TAMSAT) to SMART evaluation of monetary security risk

Designing secure architectures for IT infrastructure is a difficult process that needs mechanisms to provide security risk metrics that can help guide the system design process. It is through this evaluation process that a designer can ensure that implementations of a model meet the necessary security-based requirements. This work presents a scheme called TAMSAT for translating early-stage system architecture design models into security-based attack trees, which are evaluated for security risk. These attack trees can be evaluated around a set of assets of importance, whose security risk is classified by a monetary value. This security risk value can inform the system designer and provide input into an iterative design process, as well as illuminate unexpected sources of potential future security issues.
Graph filter design by ring-decomposition for 2-connected graphs

The graph filter is an essential part of graph signal processing. And the design of filters is booming in various applications. However, most previous works on graph filter design consider the entire graph as their object and concentrate on global processing. That causes less efficiency and little attention to some detailed information hidden in graph signals. This paper proposes a framework to design graph filters, named Ring-Decomposition-based Filter (RDF), on 2-connected graphs constructed from a ring by adding paths. The approach designs ring filters to capture small-scale features after decomposing the given graph into a set of rings. Experimental results show that the proposed graph filters outperform the baselines on synthetic and real-world graph signals for denoising and outlier detection tasks. In addition, a computational cost analysis is given to illustrate the efficiency of the proposed graph filters.
An effective parameter tuned deep belief network for detecting anomalous behavior in sensor-based cyber-physical systems

Cyber-physical system (CPS) integrates cyber systems and the corporal world to perform critical processes that are begun from the developments in digital electronics. The sensor placed in CPS is used to monitor and control processes that are vulnerable to anomalies (reliability failures and cyber-attacks). For the detection of anomalies and prevention of destructive issues, Abnormality Detection System (ADS) is introduced. But ADS suffered from a false alarm rate and missed detection rate, which has resulted in the degraded performance in CPS applications. A novel deep learning approach for detecting Anomalous Behavior in sensor-based CPS is presented in this study. Preprocessing is used in the conceptual scheme to remove noisy data. CPS irregularity is then detected using a DL-based deep belief network (DBN) framework. Furthermore, the number of hidden layers, number of iterations, period frequency, and learning rate of DBN are all tuned using a squirrel search algorithm (SSA). Several tests were conducted with the help of data from device CPS to validate the performance of the SSA-DBN model. To assure the SSA-DBN framework's appropriate detection accuracy, a complete comparable study is conducted, and the produced extensive experiments confirmed the framework's outstanding quality on the applicable information over the comparable approaches.
Courses as Code: The Aquinas Learning System

Aquinas aims to maximally apply an everything-as-code approach to teaching the practice of programming and exploit development using hands-on exercises. Teachers define exercises using a machine-readable format, and Aquinas processes these definitions to setup artifacts such as instructions, grading scripts, and network targets. Students submit solutions using Git and benefit from immediate grading. This paper describes the design and implementation of Aquinas, and it evaluates Aquinas's effectiveness as a teaching tool in undergraduate and graduate courses. Aquinas is open source software.
A Lightweight and User-centric Two-factor Authentication Mechanism for IoT Based on Blockchain and Smart Contract

Two-factor authentication (2FA) is commonly used in Internet of Things (IoT) authentication to provide multi-layer protection. Tokens, often known as One-Time Passwords (OTP), are used to offer additional information. While this technique provides flexible verification and an additional layer of security, it still has a number of security issues. This is because it relies on third-party services to produce tokens or OTPs, which leads to serious information leakage issues. Additionally, relying on a third party to provide authentication tokens significantly increases the risk of exposure and attacks, as tokens can be stolen via Man-In-The-Middle (MITM) attacks. In trying to rectify this issue, in this paper, we propose and develop a blockchain-based two-factor authentication method for web-based access to sensor data. The proposed method provides a lightweight and user-centric authentication that makes use of Ethereum blockchain and smart contracts technologies. Then we provided performance and security analysis of our system. Based on the evaluation results, our method has proven to be effective and has the ability to facilitate reliable authentication.
Evasion Generative Adversarial Network for Low Data Regimes

A myriad of recent literary works have leveraged generative adversarial networks (GANs) to generate unseen evasion samples. The purpose is to annex the generated data with the original train set for adversarial training to improve the detection performance of machine learning (ML) classifiers. The quality of generated adversarial samples relies on the adequacy of training data samples. However, in low data regimes like medical diagnostic imaging and cybersecurity, the anomaly samples are scarce in number. This paper proposes a novel GAN design called evasion generative adversarial network (EVAGAN) that is more suitable for low data regime problems that use oversampling for detection improvement of ML classifiers. EVAGAN not only can generate evasion samples but its discriminator can act as an evasion-aware classifier. We have considered auxiliary classifier GAN (ACGAN) as a benchmark to evaluate the performance of EVAGAN on cybersecurity (ISCX-2014, CIC-2017, and CIC2018) botnet and computer vision (MNIST) datasets. We demonstrate that EVAGAN outperforms ACGAN for unbalanced datasets with respect to detection performance, training stability, and time complexity. EVAGAN's generator quickly learns to generate the low sample class and hardens its discriminator simultaneously. In contrast to ML classifiers that require security hardening after being adversarially trained by GAN-generated data, EVAGAN renders it needless. The experimental analysis proves that EVAGAN is an efficient evasion hardened model for low data regimes for the selected cybersecurity and computer vision datasets.