Reviewerly/exHarmony huggingface dataset:
Contrast Enhancement in Images by Homomorphic Filtering and Cluster-Chaotic Optimization

Homomorphic filtering (HF) is a methodology that separates an image into two components: illumination and reflectance. Through the processing of these components, it is possible to significantly improve the contrast of the low-frequency components while preserving the edges and sharp features of the image. The parameter values of the filter that produces the best possible contrast enhancement depends on the image conditions for each image. However, finding the optimal parameters for the filter can be challenging, often involves a trial-and-error process, and can be prone to errors due to human factors. In this paper, we consider the problem of identifying the filter parameters as an optimization problem. Under such conditions, the cluster chaotic optimization (CCO) method is used to efficiently explore the parameter space by evaluating an objective function that assesses the contrast quality of an enhanced image. The experimental results show that the proposed method produces competitive results in terms of quality, stability, and accuracy compared with other methods on various datasets. Different metrics were evaluated to demonstrate the quality of the results of our method compared with the other algorithms.
A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine Learning

In software engineering community, defect prediction is one the active domain. For the software’s success, it is essential to reduce the software engineering and data-mining gap. Software defects prediction forecasts the source code errors before the testing phase. Methods for predicting software defects, such as clustering, statistical methods, mixed algorithms, metrics based on neural networks, black box testing, white box testing and machine learning are frequently used to explore the effect area in software. The main contribution of this research is the use of feature selection for the first time to increase the accuracy of machine learning classifiers in defects pre-diction. The objective of this study is to improve the defects prediction accuracy in five data sets of NASA namely; CM1, JM1, KC2, KC1, and PC1. These NASA data sets are open to public. In this research, the feature selection technique is use with machine-learning techniques; Random Forest, Logistic Regression, Multilayer Perceptron, Bayesian Net, Rule ZeroR, J48, Lazy IBK, Support Vector Machine, Neural Networks, and Decision Stump to achieve high defect prediction accuracy as compared to without feature selection (WOFS). The research workbench, a machine-learning tool called WEKA (Waikato Environment for Knowledge Analysis), is used to refine da-ta, preprocess data, and apply the mentioned classifiers. To assess statistical analyses, a mini tab statistical tool is used. The results of this study reveals that accuracy of defects prediction with feature selection (WFS) is improve in contrast with the accuracy of WOFS.
Human Expectations and Perceptions of Learning in Machine Teaching

Interactive interfaces in tandem with Machine Learning (ML) models support user understanding of model uncertainty, build confidence, improve predictive accuracy and enable users to teach application-specific concepts that are difficult for the model to learn otherwise. These systems offer empirically proven benefits due to tightly coupled feedback loops and workflow scaffolding. However, deployment with ML non-experts who cannot manage the complex, expertise-heavy process remains challenging. Through deployment with non-expert users in a common classification task, we investigate the impact of human factors of machine teaching interfaces such as user expectations, their perceptions of the learning process and user engagement with respect to teaching process and outcomes. We measure how affective and performance attributes shape the success or failure of the process. Finally, we reflect on how intelligent user interfaces can be designed to accommodate these factors for successful deployment with a broad spectrum of human adjudicators.
A non-negative feedback self-distillation method for salient object detection

Self-distillation methods utilize Kullback-Leibler divergence (KL) loss to transfer the knowledge from the network itself, which can improve the model performance without increasing computational resources and complexity. However, when applied to salient object detection (SOD), it is difficult to effectively transfer knowledge using KL. In order to improve SOD model performance without increasing computational resources, a non-negative feedback self-distillation method is proposed. Firstly, a virtual teacher self-distillation method is proposed to enhance the model generalization, which achieves good results in pixel-wise classification task but has less improvement in SOD. Secondly, to understand the behavior of the self-distillation loss, the gradient directions of KL and Cross Entropy (CE) loss are analyzed. It is found that KL can create inconsistent gradients with the opposite direction to CE in SOD. Finally, a non-negative feedback loss is proposed for SOD, which uses different ways to calculate the distillation loss of the foreground and background respectively, to ensure that the teacher network transfers only positive knowledge to the student. The experiments on five datasets show that the proposed self-distillation methods can effectively improve the performance of SOD models, and the average Fβ is increased by about 2.7% compared with the baseline network.
Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs

Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification.
Revisit two memoryless state‐recovery cryptanalysis methods on A5/1

Abstract At ASIACRYPT 2019, Zhang proposed a near collision attack on A5/1 claiming to recover the 64‐bit A5/1 state with a time complexity around 2 32 cipher ticks with negligible memory requirements. Soon after its proposal, Zhang's near collision attack was severely challenged by Derbez et al. who claimed that Zhang's attack cannot have a time complexity lower than Golic's memoryless guess‐and‐determine attack dating back to EUROCRYPT 1997. In this article, both the guess‐and‐determine and the near collision attacks for recovering A5/1 states with negligible memory complexities are studied. Firstly, a new guessing technique called the move guessing technique that can construct linear equation filters in a more efficient manner is proposed. Such a technique can be applied to both guess‐and‐determine and collision attacks for efficiency improvements. Secondly, the filtering strength of the linear equation systems is taken into account for complexity analysis. Such filtering strength are evaluated with practical experiments making the complexities more convincing. Based on such new techniques, the authors are able to give 2 new guess‐and‐determine attacks on A5/1: the 1st attack recovers the internal state with time complexity 2 43.92 ; the 2nd one recovers a different state with complexity 2 43.25 . Golic's guess‐and‐determine attack and Zhang's near collision attacks are revisited. According to our detailed analysis, the complexity of Golic's recovery attack is no lower than 2 46.04 , higher than the previously believed 2 43 . On the other hand, Zhang's near collision attack recovers with the time complexity 2 53.19 : such a complexity can be further lowered to 2 50.78 with our move guessing technique.
Maritime Target Saliency Detection for UAV Based on the Stimulation Competition Selection Mechanism of Raptor Vision

A maritime target saliency detection method inspired by the stimulation competition and selection mechanism of raptor vision is presented for the airborne vision system of unmanned aerial vehicle (UAV) in an unknown maritime environment. The stimulation competition and selection mechanism in the visual pathway of raptor vision based on the phenomenon of raptor capturing prey in complex scenes are studied. Then, the mathematical model of the stimulation competition and selection mechanism of raptor vision is established and employed for the salient object detection. Popular image datasets and practical scene datasets are applied to verify the effectiveness of the presented method. Results show that the detection performance of the proposed method is better than that of other comparison methods. The proposed algorithm provides an idea for maritime target salient detection and cross-domain joint mission for UAV or other unmanned equipment.
A DATA DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASURING SKILLS DIVERSITY

Abstract Team composition in Project Based Learning is the first task for the class and has a great impact on the learning experience. Anyway, little space is dedicated in literature about team composition, considering their personal inclinations towards design tasks. For these reasons we propose a tool that aims to map the design skills of students to optimise team composition. The tool is based on a questionnaire grounded in the design theory and aims at measuring the willingness of students at performing certain design tasks. The results of the questionnaires are analysed using Principal Component Analysis to normalise each students’ answers to the whole class, and to show the distribution of students in the space of engineering design skills. We present the design process of the tool, and a first experimentation on two classes of master's degree students in Management Engineering and Data Science, testing the tool on a total of 72 students. The results are promising and demonstrate the robusteness of the questionnaire and of the analytical method. Also, we propose next steps for our research activity, calling for other researchers to test our method in different contexts.
TOWARDS A CONFIGURATION MANAGEMENT INTEGRATION TO FEATURE MODELS IN MODEL-BASED PRODUCT LINE ENGINEERING

Abstract In parallel with the industry 4.0 revolution, customers’ demand and number of product requirements are increasing, inducing increased product variability. System complexity is growing with the new technologies and system architectures. Hence, maintaining a system consistent and in the desired state becomes crucial. This paper tackles two problems: a product's variability and its temporal evolution. Regarding variability, Model Based Systems Engineering (MBSE) methods and Product Line Engineering (PLE) techniques become essential to configuring products by selecting and arranging features in a combination. In parallel, versioning and temporal evolution are managed with the configuration management (CM) principles. A versioned feature model (FM) is proposed. In PLE methodology, the first step is to model variability through traditional FM. This research presents an extension of the FM by integrating CM into it. The versioned FM includes not only features’ variants but also their versions, making it evolve in time. This work is presented as a base for a work that will study the application of CM for PLE. This study is done in an automotive industry context at Renault Group, and the model proposed is applied to Renault's systems.
Self-supervised contrastive representation learning for large-scale trajectories

Trajectory representation learning aims to embed trajectory sequences into fixed-length vector representations while preserving their original spatio-temporal feature proximity. Existing works either learn trajectory representations for specific mining tasks or fail to utilize large amounts of unlabeled trajectory data for representation learning. In this work, we propose a self-supervised Trajectory representation learning based on Reconstruction Contrastive Learning called TrajRCL. To be specific, TrajRCL first obtains low-distortion and high-fidelity views of trajectories through trajectory augmentation. Then, TrajRCL leverages a Transformer based encoder–decoder network to reconstruct low-distortion view trajectories to approximate high-fidelity trajectories. Self-supervised contrastive learning is finally used to enhance the consistency of the two view's trajectory representations. Extensive experiments on two real-world demonstrate the superiority of our model over state-of-the-art baselines and significant efficiency on similarity trajectory search and k-NN query.
The role of entanglement for enhancing the efficiency of quantum kernels towards classification

Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.
A survey on motion estimation and de-hazing algorithms and architectures

Motion Estimation is used many video processing algorithms for video compression, moving object detection, 3D reconstruction, video segmentation, etc. Video standards use motion estimation in the encoder and decoder to compress the video. It becomes difficult to determine motion vectors when the video is prone to weather conditions like haze. So, there is a necessity to remove haze for video analysis. In this paper, hardware and software implementation of Motion Estimation (ME) and Haze removal techniques are reviewed. Architectures like low power and parallelism Motion Estimation techniques for Block Matching algorithms (BMA) in video coding standards and Deep Learning based methods Pixel-based Methods are studied and discussed. The atmospheric model for hazy images and videos is discussed. Techniques such as single-image dehazing are analyzed. Performance metrics carried out for video analysis are also mentioned. Based on the analysis and observation the advantages and drawbacks of these techniques are addressed.
Physics-based Penalization for Hyperparameter Estimation in Gaussian Process Regression

In Gaussian Process Regression (GPR), hyperparameters are often estimated by maximizing the marginal likelihood function. However, this data-dominant hyperparameter estimation process can lead to poor extrapolation performance and often violates known physics, especially in sparse data scenarios. In this paper, we embed physics-based knowledge through penalization of the marginal likelihood objective function and study the effect of this new objective on consistency of optimal hyperparameters and quality of GPR fit. Three case studies are presented, where physics-based knowledge is available in the form of linear Partial Differential Equations (PDEs), while initial or boundary conditions are not known so direct forward simulation of the model is challenging. The results reveal that the new hyperparameter set obtained from the augmented marginal likelihood function can improve the prediction performance of GPR, reduce the violation of the underlying physics, and mitigate overfitting problems.
Accurate video saliency prediction via hierarchical fusion and temporal recurrence

With the ability to extract spatiotemporal features, 3D convolutional networks have become the mainstream method for Video Saliency Prediction (VSP). However, these methods cannot make full use of hierarchical spatiotemporal features and also lack focus on past salient features, which hinders further improvements in accuracy. To address these issues, we propose a 3D convolutional Network based on Hierarchical Fusion and Temporal Recurrence (HFTR-Net) for VSP. Specifically, we propose a Bi-directional Temporal–Spatial Feature Pyramid (BiTSFP), which adds the flow of shallow location information based on the previous flow of deep semantic information. Then, different from simple addition and concatenation, we design a Hierarchical Adaptive Fusion (HAF) mechanism that can adaptively learn the fusion weights of adjacent features to integrate them appropriately. Moreover, to utilize previous salient information, a Recall 3D convGRU (R3D GRU) module is integrated into the 3D convolution-based method for the first time. It subtly combines the local feature extraction of the 3D backbone with the long-term relationship modeling of the temporal recurrence mechanism. Experimental results on the three common datasets demonstrate that the HFTR-Net outperforms existing state-of-the-art methods in accuracy.
Rethinking Data Integrity in Federated Learning: Are we ready?

Federated learning is a machine learning technique that allows for the training of high-level models using data from users without the data leaving the edge device. This provides more personalized results for the user’s community. However, as federated learning is relatively new, it is vulnerable to data leakage or manipulation by attackers. The architecture of federated learning is an iterative process, so even a small amount of data manipulation can lead to significant errors. The goal of federated learning is to keep user data on the edge device. This is accomplished by sending a base machine learning model to the edge device and training it on the user’s data. The weights and biases of the trained model are then sent to the "access layer" at the server end, which collects these parameters from all the devices on the federated learning network. This means that the data is not actually sent to the server, but the model learns the weights and biases. Given the high risk of data manipulation through cyber-attacks, this paper aims to explore data integrity in transit in federated learning, including various types of cyber-attacks and activity attack modeling on the federated learning architecture. The paper concludes with a summary of the different characteristics and behavioral properties of various cyber-attacks.
Fooling State-of-the-art Deepfake Detection with High-quality Deepfakes

Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we show that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world scenarios with well-crafted fakes. First, we propose a novel autoencoder for face swapping alongside an advanced face blending technique, which we utilize to generate 90 high-quality deepfakes. Second, we feed those fakes to a state-of-the-art detector, causing its performance to decrease drastically. Moreover, we fine-tune the detector on our fakes and demonstrate that they contain useful clues for the detection of manipulations. Overall, our results provide insights into the generalization of deepfake detectors and suggest that their training datasets should be complemented by high-quality fakes since training on mere research data is insufficient.
Fake Profile Identification Using Machine Learning

Online social networks have permeated our social lives in the current generation. These sites have allowed us to see our social lives differently than they did in the past. Nowadays we can connect with new friends and maintain relationships with them via social and personal activities become quite easy. Online Social Networks (OSN) are contributed in all areas such as Research in all domains, Job-related areas, Technology oriented areas, Health care, and business-oriented areas, Information gathering and data collection, and so on. One of the biggest problems on these social media platforms is fake profiles. Impersonating to be someone else and causing harm and defamation to the real person or advertising or popularizing removed propaganda on someone’s name to get more benefit is the motto of such profile creators. There have been many studies regarding these fake accounts and how can they be mitigated. Many approaches such as graph-level activities or feature analysis have been taken into consideration to identify fake profiles. These methods are outdated when compared to arising issues of these days. In this paper, we proposed a technique using machine learning for fake profile detection which is efficient. . The benchmark data set is collected and mixed with manual data first furthermore; a data cleaning technique is used to present the data more feasibly. Then the preprocessed data is used for model building with sufficient information such as profile name, profile ID name, number of followers, and so on. We added Cross validation process where many training algorithms are implemented on the given data and are then tested on the same data. Based on the experiments the RF classifier performed better than the other classification methods. The Random Forest classifier is used to forecast the profile whether is fake or genuine in an efficient way.
Coin concentration of Proof-of-Stake blockchains

This paper studies the concentration of block production in selected Proof-of-Stake (PoS) blockchains and finds evidence consistent with participants entering and leaving the consensus process, thereby changing the concentration level, but not with disproportionate compounding of wealth for large stakes.