Reviewerly/exHarmony huggingface dataset:
Integrating Human Factors in the Visualisation of Usable Transparency for Dynamic Risk Assessment

Modern technology and the digitisation era accelerated the pace of data generation and collection for various purposes. The orchestration of such data is a daily challenge faced by even experienced professional users in the context of Internet of Things (IoT)-enabled environments, especially when it comes to cybersecurity and privacy risks. This article presents the application of a user-centric process for the visualisation of automated decision making security interventions. The user interface (UI) development was guided by iterative feedback collection from user studies on the visualisation of a dynamic risk assessment (DRA)-based security solution for regular lay users. The methodology we applied starts with the definition of the methodological process to map possible technical actions to related usable actions. The definition and refinement of the user interface (UI) was controlled by the survey feedback loop from end user studies on their general technological knowledge, experience with smart homes, cybersecurity awareness and privacy preservation needs. We continuously improved the visualisation interfaces for configuring a cybersecurity solution and adjusting usable transparency of the control and monitoring of the dynamic risk assessment (DRA). For this purpose, we have designed, developed and validated a decision tree workflow and showed the evolution of the interfaces through various stages of the real-life trials executed under European H2020 project GHOST.
Deep Active Contour-Based Capsule Network for Medical Image Segmentation

Medical Image Segmentation (MIS) has important ramifications for the whole of medical diagnostics. Despite the unparalleled success that various Deep Learning (DL) techniques had in image segmentation, there are setbacks with them. Generally, these setbacks arise from two of the most profound indicators of performance in DL, namely network architecture and loss function, both of which are being dealt with in the proposed method to improve the dice score on the task of MIS. To deal with the architecture part of our work, we have used Capsules that have proved to preserving more image details and indicating the spatial relationships found locally and globally, facilitating the detection of global features from the local features extracted, and hence we propose a modified capsules network for the proposed segmentation task. The loss function used in the proposed work draws inspiration from the active contours model that tends to incorporate external forces, regional information and other functions of choices in the working of segmentation extraction and treats this task as a continuous curve evolution and energy minimization problem. In our experimentation, we have used a brain tumor segmentation dataset for the performance comparison using Dice-score and mean-IoU as performance metrics and the results obtained are very optimistic.
Enhancing the Transferability of Adversarial Examples with Random Patch

Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples. This paper proposes the Random Patch Attack (RPA) to significantly improve the transferability of adversarial examples by the patch-wise random transformation that effectively highlights important intrinsic features of objects. Specifically, we introduce random patch transformations to original images to variate model-specific features. Important object-related features are preserved after aggregating the transformed images since they stay consistent in multiple transformations while model-specific elements are neutralized. The obtained essential features steer noises to perturb the object-related regions, generating the adversarial examples of superior transferability across different models. Extensive experimental results demonstrate the effectiveness of the proposed RPA. Compared to the state-of-the-art transferable attacks, our attacks improve the black-box attack success rate by 2.9\% against normally trained models, 4.7\% against defense models, and 4.6\% against vision transformers on average, reaching a maximum of 99.1\%, 93.2\%, and 87.8\%, respectively.
Dimension-aware attention for efficient mobile networks

Recently, attention mechanisms have shown great potential in improving the performance of mobile networks. Typically, they involve 2D symmetric convolution operations or generate 2D attention maps. However, such manners usually introduce high computational cost and large memory consumption, increasing the computational burden of mobile networks. To address this problem, we propose a novel lightweight attention mechanism, called Dimension-Aware Attention (DAA) block, by modeling the intra-dependencies of each dimension of the input feature map. Specifically, we factorize the channel and spatial attention by three parallel feature vector encoding branches, where stacked 1D asymmetric convolution operations can be naturally leveraged to capture large receptive fields. In this way, channel-aware, horizontal-aware, and vertical-aware attention vectors are extracted to effectively encode multi-dimensional information and greatly reduce the computational complexity of mobile networks. Experiments on multiple vision tasks demonstrate that our DAA block achieves better accuracy against state-of-the-art attention mechanisms with much lower computational operations. Our code is available at https://github.com/rymo96/DAANet.
Adaptive spatiotemporal graph convolutional network with intermediate aggregation of multi-stream skeleton features for action recognition

Video-based action recognition is a challenging problem due to the rapid and uncertain changes in human actions. Recent studies show that incorporating video and human body skeleton helps improve action recognition performance. These methods generally use graph convolutional networks (GCNs) to extract structural features of the human body joints from skeleton data. Yet, most GCN-based methods have some limitations in skeleton-based action recognition. (1) The graph structure of the human body joints is time-invariant, making it difficult to represent the changing relationship between joints across actions. (2) Methods relying on single-stream models only utilize limited information of skeleton data, such as joints or bones, and fail to consider coherent features of movements. (3) Methods relying on multi-stream models have considerable parameters and are inefficient for real-life applications. To address these problems, we propose an adaptive spatiotemporal graph convolutional network with intermediate aggregation of multi-stream skeleton features for action recognition. First, our method learns an adaptive graph structure representing the changing relationship between joints. Secondly, we facilitate a multi-stream model to extract various features from the skeleton, including joint-stream, bone-stream, and motion-stream. Moreover, an intermediate aggregation strategy is employed to aggregate these features and to reduce the parameters of this model. The proposed method has been validated on various benchmarks and a real-world abnormal action dataset. Extensive experimental results show that our method achieves excellent performance in skeleton-based action recognition.
Agglomerative and divisive hierarchical Bayesian clustering

Cluster analysis methods are designed to discover groups of subjects or objects in datasets by uncovering latent patterns in data. Two model-based Bayesian hierarchical clustering algorithms are presented—divisive and agglomerative—that return nested clustering configurations and provide guidance on the plausible number of clusters in a principled way. These algorithms outperform many existing clustering methods on benchmark data. The methods are applied to identify subpopulations among Parkinson's disease subjects using only baseline data, and differing patterns of progression in the few years following diagnosis are demonstrated in the identified clusters.
Improving 2D object detection with binocular images for outdoor surveillance

Detecting objects and providing their 2D information (e.g., size and center) are crucial for outdoor visual surveillance. Because the cameras are static and their distances to background are fixed in the scenario of surveillance, we argue that, compared to 3D object detection, 2D object detection is usually enough in visual surveillance. To a great extent, determining the existence of objects is more important than determining their precise 3D shapes and locations. In the field of visual surveillance, almost all methods employ a single camera (or independently employing several cameras) to determine the existence and 2D information of objects. In this paper, we propose to improve the performance of the 2D object detection by binocular cameras (images) for the scenario of outdoor surveillance where RGB-D and depth cameras are not applicable due to their low resolutions and high expense. In the proposed binocular-image based 2D object detection framework, most of existing monocular-image based 2D object detection modules can be integrated in. Once integrated in the proposed framework, the object detection accuracy can significantly and consistently be improved. Experimental results on the challenging datasets of Cityscapes and KITTI demonstrate the superiority of the proposed method.
Face image-sketch synthesis via generative adversarial fusion

Face image-sketch synthesis is widely applied in law enforcement and digital entertainment fields. Despite the extensive progression in face image-sketch synthesis, there are few methods focusing on generating a color face image from a sketch. The existing methods pay less attention to learning the illumination or highlight distribution on the face region. However, the illumination is the key factor that makes the generated color face image looks vivid and realistic. Moreover, existing methods tend to employ some image preprocessing technologies and facial region patching approaches to generate high-quality face images, which results in the high complexity and memory consumption in practice. In this paper, we propose a novel end-to-end generative adversarial fusion model, called GAF, which fuses two U-Net generators and a discriminator by jointly learning the content and adversarial loss functions. In particular, we propose a parametric tanh activation function to learn and control illumination highlight distribution over faces, which is integrated between the two U-Net generators by an illumination distribution layer. Additionally, we fuse the attention mechanism into the second U-Net generator of GAF to keep the identity consistency and refine the generated facial details. The qualitative and quantitative experiments on the public benchmark datasets show that the proposed GAF has better performance than existing image-sketch synthesis methods in synthesized face image quality (FSIM) and face recognition accuracy (NLDA). Meanwhile, the good generalization ability of GAF has also been verified. To further demonstrate the reliability and authenticity of face images generated using GAF, we use the generated face image to attack the well-known face recognition system. The result shows that the face images generated by GAF can maintain identity consistency and well maintain everyone's unique facial characteristics, which can be further used in the benchmark of facial spoofing. Moreover, the experiments are implemented to verify the effectiveness and rationality of the proposed parametric tanh activation function and attention mechanism in GAF.
A new moving peaks benchmark with attractors for dynamic evolutionary algorithms

Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optimization Over Time. The benchmark problems in these studies inherently use randomly changing parameters and therefore such randomness may make it difficult to compare these algorithms with other EDO approaches. In this paper, we introduce a new benchmark, called Moving Peaks Benchmark with Attractors, which incorporates an attractor heuristic that attracts peaks to a certain location in the environment into the moving peaks problem. The proposed benchmark is fully flexible where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. By adjusting these characteristics, certain styles of movements can be achieved by a peak. We also introduce a new performance measure that focuses on the comparison of algorithms that use prediction. Seven EDO algorithms based on different working logics are chosen to give a wide representation of the state-of-the-art in this area. We argue that having predictable characteristics in the benchmark problem is more adequate for studying the performances and behaviours of those algorithms that embed prediction mechanisms. Experimental results obtained with the proposed benchmark show it’s suitability for the EDO domain as all algorithms featuring prediction capabilities display higher accuracy than their competitors.
The benefits of adversarial defense in generalization

Local) Vapnik-Chervonenkis theory (Local) Rademacher complexity theory
A unifying model for distributed data-intensive systems

Modern applications handle increasingly larger volumes of data, generated at an unprecedented and constantly growing rate. They introduce challenges that are radically transforming the research fields that gravitate around data management and processing, resulting in a blooming of distributed data-intensive systems. Each such system comes with its specific assumptions, data and processing model, design choices, implementation strategies, and guarantees. Yet, the problems data-intensive systems face and the solutions they propose are frequently overlapping.
Health Insurance Cost Prediction using Machine Learning

This paper represents a machine learning-based health insurance prediction system. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Features in the dataset that are used for the prediction of insurance cost include: Age, Gender, BMI, Smoking Habit, number of children etc. We used linear regression and also determined the relation between price and these features. We trained the system using a 70-30 split and achieved an accuracy of 81.3%.
Design of ANN Based Classifiers for Soil Fertility of Uttarakhand

The present article is based on the application of artificial neural network (ANN) in pattern recognition and classification. The ANN belongs to the family of AI (Artificial Intelligence). In general, an ANN is composed of single or multiple hidden neuron layers between input and output neuron layers. The way a human brain is trained, the training of an ANN is also performed exactly in the same way by showing it some input patterns. There are a variety of training algorithms for an ANN. In the present research two different training algorithms are used viz. competitive learning, and the scaled conjugate gradient (SCG) algorithm. Classification models are designed in MATLAB using these two algorithms for carrying out soil classification into two classes viz. class 1 (More Fertile) and class 2 (Less Fertile) based on the input pH value and available potassium content. The classification accuracy of both models is analyzed and compared.
An Empirical Study on Data Sampling Methods in Addressing Class Imbalance Problem in Software Defect Prediction

With the growing rate of software systems and their applications in diverse walks of life, developing a software system that has no defects is a subject that cannot be overemphasized. Detection of software defects is one of the most prominent difficulties in the area of software engineering (SE) or software development process. Defects are usually unconscious flaws that make the software system behave unexpectedly or contrary to the specified requirements. This has made the subject of software defect prediction (SDP) a very critical one. Due to their dynamism, SDP solutions based on machine learning (ML) methods are envisaged as a viable approach. However, the latent data quality problem is a significant challenge to developing effective SDP models. The class imbalance is a classic example of the data quality problem in which there is a huge differential in the number of class (majority and minority) labels. Findings from studies have shown that data sampling methods are capable of addressing the class imbalance problem. Hence, this study conducts an empirical comparative analysis on the effect of data sampling methods in addressing the class imbalance problem inherent in SDP. Specifically, the performance of five data sampling (oversampling techniques (SMOTE, ADASYN, and ROS) and undersampling techniques (RUS and NM) methods on four software defect datasets with varying granularities are investigated. As prediction models, decision tree (DT) and random forest (RF) classifiers are deployed as well. Predictive performances of developed models were evaluated using accuracy, the area under the curve (AUC), and Matthews correlation coefficient (MCC) values. Observations from the experimental results showed that the introduction of data sampling methods in SDP processes not only addresses the class imbalance problem but also improves the prediction performances of the experimented classifiers. In addition, models based on ROS resampled datasets had superior predictive performance compared with other studied data sampling-based datasets. In conclusion, it can therefore be recommended to deploy data sampling methods, particularly oversampling methods in SDP processes and other applicable machine learning tasks.
New school speech regulation as a regulatory strategy against hate speech on social media: The case of Germany's NetzDG

Germany's 2017 NetzDG law is an example of ‘new school speech regulation’ (Balkin, 2014), which restricts speech by coercing intermediaries into censoring users, rather than coercing speakers directly. It is the first such measure which specifically targets hate speech on social media, by requiring large platforms to operate complaints procedures which ensure illegal content is rapidly removed. Numerous other countries have since adopted similar regulations, indicating that states increasingly turn to new school speech regulation as a regulatory strategy to tackle hate speech on social media. This paper aims to evaluate the effectiveness of new school speech regulation in as a regulatory strategy to address online hate speech, taking NetzDG as a case study. A review of relevant empirical literature shows that many features of social media platforms actively promote or encourage hate speech. Key factors include algorithmic recommendations, which frequently promote hateful ideologies; social affordances which let users encourage or disseminate hate speech by others; anonymous, impersonal environments; and the absence of media ‘gatekeepers’. In mandating faster content deletion, NetzDG only addresses the last of these, ignoring other relevant factors. Moreover, reliance on individual user complaints to trigger platforms' obligations means hate speech will often escape deletion. Interviews with relevant civil society organisations confirm these flaws of the NetzDG model. From their perspectives, NetzDG has had little impact on the prevalence or visibility of online hate speech, and its reporting mechanisms fail to help affected communities. NetzDG represents an incremental, narrow approach to a complex sociotechnical problem which requires more fundamental regulatory reform. In this regard, it shows the limitations of censorship-based new school speech regulation. Rules which assert state authority by prescribing censorship of narrowly-defined content categories are ill-suited to large-scale, networked, algorithmically-curated social media, where other governance mechanisms influence user behaviour more than content deletion. The paper advocates a more systemic and preventive regulatory approach. Platforms should be required to take public interest considerations into account in all design and governance processes, aiming to shape platform environments to actively discourage users from posting or viewing hate speech, rather than simply deleting it afterwards.
RACE: RISC-V SoC for En/decryption Acceleration on the Edge for Homomorphic Computation

As more and more edge devices connect to the cloud to use its storage and compute capabilities, they bring in security and data privacy concerns. Homomorphic Encryption (HE) is a promising solution to maintain data privacy by enabling computations on the encrypted user data in the cloud. While there has been a lot of work on accelerating HE computation in the cloud, little attention has been paid to optimize the en/decryption on the edge. Therefore, in this paper, we present RACE, a custom-designed area- and energy-efficient SoC for en/decryption of data for HE. Owing to similar operations in en/decryption, RACE unifies the en/decryption datapath to save area. RACE efficiently exploits techniques like memory reuse and data reordering to utilize minimal amount of on-chip memory. We evaluate RACE using a complete RTL design containing a RISC-V processor and our unified accelerator. Our analysis shows that, for the end-to-end en/decryption, using RACE leads to, on average, 48 × to 39729 × (for a wide range of security parameters) more energy-efficient solution than purely using a processor.
Robust trimmed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-means

Clustering is a fundamental tool in unsupervised learning, used to group objects by distinguishing between similar and dissimilar features of a given data set. One of the most common clustering algorithms is k-means. Unfortunately, when dealing with real-world data many traditional clustering algorithms are compromised by lack of clear separation between groups, noisy observations, and/or outlying data points. Thus, robust statistical algorithms are required for successful data analytics. Current methods that robustify k-means clustering are specialized for either single or multi-membership data, but do not perform competitively in both cases. We propose an extension of the k-means algorithm, which we call Robust Trimmed k-means (RTKM) that simultaneously identifies outliers and clusters points and can be applied to either single- or multi-membership data. We test RTKM on various real-world datasets and show that RTKM performs competitively with other methods on single membership data with outliers and multi-membership data without outliers. We also show that RTKM leverages its relative advantages to outperform other methods on multi-membership data containing outliers.
Interaction of a Three-Level Atom (Λ, V, Ξ) with a Two-Mode Field Beyond Rotating Wave Approximation: Intermixed Intensity-Dependent Coupling

Interaction of a Three-Level Atom (Λ, V, Ξ) with a Two-Mode Field Beyond Rotating Wave Approximation: Intermixed Intensity-Dependent Coupling