Reviewerly/exHarmony huggingface dataset:
A novel Security-by-Design methodology: Modeling and assessing security by SLAs with a quantitative approach

Recent software development methodologies, as DevOps or Agile, are very popular and widely used, especially for the development of cloud services and applications. They dramatically reduce the time-to-market of developed software but, at the same time, they can be hardly integrated with security design and risk management methodologies. These cannot be easily automated and require big economic investments, due to the necessity of security experts in the development team and to the lack of automatic tools to evaluate risk and to assess security in the design and operation phases. This paper presents a novel Security-by-Design methodology based on Security Service Level Agreements (SLAs), which can be integrated within modern development processes and that is able to support the risk management life-cycle in an almost-completely automated way. In particular, it relies upon a guided risk analysis process and a completely automated security assessment phase, which enable to assess the security properties granted by a cloud application and to report them in a Security SLA. We validated the proposed methodology with respect to a real case study, which showed its effectiveness in improving the awareness of designer and developer teams on security aspects and in reducing the secure design process time.
Lightweight Revocable Hierarchical Attribute-Based Encryption for Internet of Things

The Internet of Things (IoT) is an emerging technology that can benefit from cloud infrastructure. In a cloud-based IoT network, a variety of data is collected by smart devices and transmitted to a cloud server. However, since the data may contain sensitive information about individuals, providing confidentiality and access control is essential to protect the users' privacy. Attribute-based encryption (ABE) is a promising tool to provide these requirements. However, most of ABE schemes neither provide efficient encryption and decryption mechanisms nor offer flexible and efficient key delegation and user revocation approaches. In this paper, to address these issues, we propose a lightweight revocable hierarchical ABE (LW-RHABE) scheme. In our scheme, computation overhead on the user side is very efficient, and most of the computational operations are performed by the cloud server. Also, using the hierarchical model, our scheme offers flexible and scalable key delegation and user revocation mechanisms. Indeed, in our scheme, key delegation and user revocation associated with each attribute can be handled by several key authorities. We provide the security definition for LW-RHABE, and we prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.
Dynamic Receptive Field Generation for Full-Reference Image Quality Assessment

Most full-reference image quality assessment (FR-IQA) methods advanced to date have been holistically designed without regard to the type of distortion impairing the image. However, the perception of distortion depends nonlinearly on the distortion type. Here we propose a novel FR-IQA framework that dynamically generates receptive fields responsive to distortion type. Our proposed method-dynamic receptive field generation based image quality assessor (DRF-IQA)-separates the process of FR-IQA into two streams: 1) dynamic error representation and 2) visual sensitivity-based quality pooling. The first stream generates dynamic receptive fields on the input distorted image, implemented by a trained convolutional neural network (CNN), then the generated receptive field profiles are convolved with the distorted and reference images, and differenced to produce spatial error maps. In the second stream, a visual sensitivity map is generated. The visual sensitivity map is used to weight the spatial error map. The experimental results show that the proposed model achieves state-of-the-art prediction accuracy on various open IQA databases.
Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unknown. We extract useful univariate summary statistics and perform anomaly detection in a single-dimensional space. We model anomalies as persistent outliers and propose to detect them via a cumulative sum-like algorithm. In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold. Further, in the general case, we learn an acceptance region for nominal data via Geometric Entropy Minimization and evaluate whether the sequentially observed data persistently fall outside the acceptance region. We provide an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Moreover, we provide a sufficient condition to asymptotically guarantee that the decision statistic of the proposed algorithm does not diverge in the absence of anomalies. Experiments illustrate the effectiveness of the proposed schemes in quick and accurate anomaly detection in high-dimensional settings.
INFER: INtermediate representations for FuturE pRediction

In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (e.g. image sequences). As a result, such methods do not generalize to datasets they have not been trained on. In this paper, we propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information from images, we condition on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see Fig. above). We demonstrate that semantics provide a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize across different sensing modalities (stereo imagery, LiDAR, a combination of both), and also across completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferable representations and ensure reproducibility, we release all our code and data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">33</sup> More qualitative and quantitative results, along with code and data can be found at https://rebrand.ly/INFER-results.
Graph convolution machine for context-aware recommender system

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose Graph Convolution Machine (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.
Integrating adaptive moving window and just-in-time learning paradigms for soft-sensor design

Most applications of soft sensors in process industries require learning from a stream of data, which may exhibit nonstationary dynamics, or concept drift. In this study, we develop a relevance vector machine (RVM) based novel adaptive learning algorithm called MWAdp-JITL, to meet the demands of continuous processes. The resulting algorithm combines active and passive learning: A moving window (MW) algorithm, which adapts the window size against virtual/real concept drifts, is coupled with a just-in-time learning (JITL) model, constructed using an appropriate region of historical data, and the ensemble weights of the MW and JITL models are adjusted for each query point. Tests on four real industrial datasets and a synthetic data, comprising various concept drift scenarios, show that MWAdp-JITL yields superior prediction accuracy and is generally more robust to changes in algorithm parameters compared to conventional adaptive learning methods and state-of-the-art algorithms from the literature. MWAdp-JITL complies with time limits of online prediction, and is applicable for high dimensional processes under various types of concept drifts. It is seen that MWAdp-JITL can successfully achieve a good balance in bias-variance tradeoff, justifying the use of only two exquisitely selected learners in ensemble learning.
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images

We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR to resolve the lack of information in some regions of stereo images due to textureless objects, shadows, ill-conditioned light environment and many more. Additionally, this fusion can overcome the difficulty of matching unstructured 3D points between LiDAR-only scans. Our LiDAR-Flow approach consists of three main steps; each of them exploits LiDAR measurements. First, we build strong seeds from LiDAR to enhance the robustness of matches between stereo images. The imagery part seeks the motion matches and increases the density of scene flow estimation. Then, a consistency check employs LiDAR seeds to remove the possible mismatches. Finally, LiDAR measurements constraint the edge-preserving interpolation method to fill the remaining gaps. In our evaluation we investigate the individual processing steps of our LiDAR-Flow approach and demonstrate the superior performance compared to image-only approach.
A Short Survey of Pre-trained Language Models for Conversational AI-A New Age in NLP

Building a dialogue system that can communicate naturally with humans is a challenging yet interesting problem of agent-based computing. The rapid growth in this area is usually hindered by the long-standing problem of data scarcity as these systems are expected to learn syntax, grammar, decision making, and reasoning from insufficient amounts of task-specific dataset. The recently introduced pre-trained language models have the potential to address the issue of data scarcity and bring considerable advantages by generating contextualized word embeddings. These models are considered counterpart of ImageNet in NLP and have demonstrated to capture different facets of language such as hierarchical relations, long-term dependency, and sentiment. In this short survey paper, we discuss the recent progress made in the field of pre-trained language models. We also deliberate that how the strengths of these language models can be leveraged in designing more engaging and more eloquent conversational agents. This paper, therefore, intends to establish whether these pre-trained models can overcome the challenges pertinent to dialogue systems, and how their architecture could be exploited in order to overcome these challenges. Open challenges in the field of dialogue systems have also been deliberated.
Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters

This paper formulates a new multi-objective virtual machine placement (VMP) problem, which is a challenging task in cloud datacenters (DCs). In cloud environment, there are two stakeholders, namely, users and providers. Both sides try to take more benefit whereas a trade-off between conflicting benefits is crucial. From providers’ perspective, power consumption and resource wastage are two objectives to be optimized whereas gaining high quality of service (QoS) is a critical point for users. The unpleasant issue that a user endures in the cloud environment is network delay; this is affected by common bandwidth linkage which is shared between different users’ applications; the reason for considering bandwidth usage optimization as the third objective function in users’ viewpoint. However, inefficient network bandwidth usage has drastic impact on overall performance even makes network links to get saturated and throttles communication-intensive applications. Therefore, VMs with high affinity and traffic dependency must be physically placed as close as possible so less traffic is sent on network layers. To figure out this combinatorial multi-objective problem, we extend a hybrid multi-objective genetic-based optimization solution. To evaluate this work, we conducted extensive scenarios with variable correlation coefficients between resources in requested VMs. The simulation results prove that our proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use. Also, the simulations in larger search space demonstrated proposed approach has high potential of scalability.
Designing With Ritual Interaction

Interactive public interfaces are opportunities for designers to affect how people relate to one another. We believe that traditional ritual can inspire a novel approach to the design of digital experience in public space. Ritual has been shown to support social cohesion and we argue that it can be used as a design strategy to encourage the cultivation of qualities like compassion in public space. We created our own interactive artwork, Wish Happiness that was inspired by methods of compassion cultivation from Tibetan Mahayana Buddhism. Through a concept-driven design research approach, we outline the set of design strategies we employed to translate key principles of Buddhist ritual and practice into the secular setting of a festival. We observed that compassion-like qualities, a positive state of mind and a sense of social harmony, were produced through interaction with the system, providing encouragement for future research into ritual interaction for compassion cultivation.
Fast Depth Estimation for Light Field Cameras

Fast depth estimation for light field images is an important task for multiple applications such as image-based rendering and refocusing. Most previous approaches to light field depth estimation involve high computational costs. Therefore, in this study, we propose a fast depth estimation method based on multi-view stereo matching for light field images. Similar to other conventional methods, our method consists of initial depth estimation and refinement. For the initial estimation, we use a one-bit feature for each pixel and calculate matching costs by summing all combinations of viewpoints with a fast algorithm. To reduce computational time, we introduce an offline viewpoint selection strategy and cost volume interpolation. Our refinement process solves the minimization problem in which the objective function consists of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> data and smoothness terms. Although this problem can be solved via a graph cuts algorithm, it is computationally expensive; therefore, we propose an approximate solver based on a fast-weighted median filter. Experiments on synthetic and real-world data show that our method achieves competitive accuracy with the shortest computational time of all methods.
Introduction to the special issue on spatial modelling and analysis of ore-forming processes in mineral exploration targeting

Ore deposits are diverse with much of their diversity attributable to the complex interplay of ore-forming processes with a variety of geological environments, over a range of scales and both in space and time. This diversity makes it difficult for geoscientists to accurately predict the location of undiscovered ore deposits. Improving our understanding of the processes that are critical to ore deposit formation would help us to hone our predictive capabilities. However, this task is difficult to achieve as we cannot observe these genetic processes first-hand and different parameters and ingredients are important at different scales. Modelling offers a means of simulating and analysing ore-forming processes and their mappable expressions. This knowledge can then be used to build a predictive model by translating key process components into spatial proxies that can be mapped or recognized in mineral exploration data. Modelling and analysis of ore-forming processes are therefore critical for the future success of mineral exploration. Currently underutilized in exploration targeting, the application of statistical and mathematical concepts can help steer geoscientists towards a better understanding of the complex geological processes critical in the formation of mineral deposits and, ultimately, improved exploration success rates. This editorial article presents a brief introduction to the main concepts that support a collection of articles published in a virtual special issue (VSI) of Ore Geology Reviews entitled “Spatial modelling and analysis of ore-forming processes in mineral exploration targeting”. The articles examine three critical themes: (1) Translating the expressions of ore-forming processes and critical parameters of mineral systems into mappable spatial proxies; (2) identifying mineral deposit footprints through geochemical and geophysical data analysis; and (3) targeting and improving the discovery chance of mineral deposits by way of spatial data analysis.
Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.
Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse, mainly related to the complexity in manual annotation. Deep convolutional neural networks (CNNs), with transferable knowledge, have been employed as a solution to limited annotated data through: 1) fine-tuning generic knowledge with a relatively smaller amount of labelled medical imaging data, and 2) learning image representation that is invariant to different domains. These approaches, however, are still reliant on labelled medical image data. Our aim is to use a new hierarchical unsupervised feature extractor to reduce reliance on annotated training data. Our unsupervised approach uses a multi-layer zero-bias convolutional auto-encoder that constrains the transformation of generic features from a pre-trained CNN (for natural images) to non-redundant and locally relevant features for the medical image data. We also propose a context-based feature augmentation scheme to improve the discriminative power of the feature representation. We evaluated our approach on 3 public medical image datasets and compared it to other state-of-the-art supervised CNNs. Our unsupervised approach achieved better accuracy when compared to other conventional unsupervised methods and baseline fine-tuned CNNs.
A multi-objective genetic algorithm for text feature selection using the relative discriminative criterion

With exponentially increasing the number of digital documents, text classification has become a major task in data science applications. Selecting discriminative features highly relevant to class labels while having low levels of redundancy is essential to improve the performance of text classification methods. In this paper, we propose a novel multi-objective algorithm for text feature selection, called Multi-Objective Relative Discriminative Criterion (MORDC), which balances minimal redundant features against those maximally relevant to the target class. The proposed method employs a multi-objective evolutionary framework to search through the solution space. The first objective function measures the relevance of the text features to the target class, whereas the second one evaluates the correlation between the features. None of these objectives use learning to evaluate the goodness of the selected features; thus, the proposed method can be classified as a multivariate filter method. In order to assess the effectiveness of the proposed method, several experiments are performed on three real-world datasets. Comparisons with state-of-the-art feature selection methods show that in most cases MORDC results in better classification performance.