Reviewerly/exHarmony huggingface dataset:
Evolving Image Enhancement Pipelines

Image enhancement is an image processing procedure in which the original information of the image is improved. It alters an image in several different ways, for instance, by highlighting a specific feature in order to ease post-processing analyses by a human or machine. In this work, we show our approach to image enhancement for digital real-estate-marketing. The aesthetic quality of the images for real-estate marketing is critical since it is the only input clients have once browsing for options. Thus, improving and ensuring the aesthetic quality of the images is crucial for marketing success. The problem is that each set of images, even for the same real-estate item, is often taken under diverse conditions making it hard to find one solution that fits all. State of the art image enhancement pipelines applies a set of filters that solve specific issues, so it is still hard to generalise that solves all types of issues encountered. With this in mind, we propose a Genetic Programming approach for the evolution of image enhancement pipelines, based on image filters from the literature. We report a set of experiments in image enhancement of real state images and analysed the results. The overall results suggest that it is possible to attain suitable pipelines that visually enhance the image and according to a set of image quality assessment metrics. The evolved pipelines show improvements across the validation metrics, showing that it is possible to create image enhancement pipelines automatically. Moreover, during the experiments, some of the created pipelines create non-photorealistic rendering effects in a moment of computational serendipity. Thus, we further analysed the different evolved non-photorealistic solutions, showing the potential of applying the evolved pipelines in other types of images.
Provably Secure Online/Offline Identity-Based Signature Scheme Based on SM9

Abstract SM9 is a Chinese cryptography standard, which includes a set of identity-based cryptographic schemes over pairings. SM9 identity-based signature scheme (SM9-IBS) was standardized by ISO/IEC and has been widely used in many real-world applications such as blockchain. Nevertheless, the signing algorithm of SM9-IBS suffers from several heavy calculations (e.g. pairings, scalar multiplications in groups), which might be a bottleneck for lightweight devices such as sensors. In this paper, we modify the SM9-IBS scheme slightly to support fast signing. In order to achieve this, we make the use of online/offline methodology and propose a new online/offline IBS scheme based on SM9. The proposed scheme is proved to be EUF-sID-CMA secure and is about 99% faster than SM9-IBS in terms of signature generation. Precisely, the time cost of online signing is &amp;lt;1 ms. Our scheme is appropriate for the Internet of Things. The theoretical analysis and demonstration show that the proposed scheme is comparable to existing efficient online/offline IBS schemes.
An exact algorithm for biobjective integer programming problems

We propose an exact algorithm for solving biobjective integer programming problems, which arise in various applications of operations research. The algorithm is based on solving Pascoletti-Serafini scalarizations to search specified regions (boxes) in the objective space and returns the set of nondominated points. We implement the algorithm with different strategies, where the choices of the scalarization model parameters and splitting rule differ. We then derive bounds on the number of scalarization models solved; and demonstrate the performances of the variants through computational experiments both as exact algorithms and as solution approaches under time restriction. The experiments demonstrate that different strategies have advantages in different aspects: while some are quicker in finding the whole set of nondominated solutions, others return good-quality solutions in terms of representativeness when run under time restriction. We also compare the proposed approach with existing algorithms. The results of our experiments show the satisfactory behaviour of our algorithm, especially when run under time limit, as it achieves better coverage of the whole frontier with a smaller number of solutions compared to the existing algorithms.
V-SOINN: A topology preserving visualization method for multidimensional data

Data visualization plays an important role in data analysis by displaying data to observers in an interpretable way. Visualizing multidimensional data requires projecting the data into a low-dimensional space that is visible to humans. In this paper, we propose a neural network model that can generate such projections while preserving the topology relationships within data points, which is named Visible Self Organizing Incremental Neural Network (V-SOINN). V-SOINN is able to construct a topology preserving visible network automatically and classify visible nodes to different classes in the low-dimensional space. The thought of topology preserving visualization stems from Self-Organizing Map (SOM). Compared to SOM, the main advantage of V-SOINN is that it does not need prior decision of network structure, including the number of nodes and grid in the output layer. V-SOINN can show the density distribution of datasets by using the activation counts of datasets. V-SOINN is able to depict the number of classes in the low-dimensional space as well. We perform experiments on artificial and real-world datasets, and V-SOINN outperforms PCA, MDS, t-SNE, Neural Gas and SOM on the datasets. Experiments show that V-SOINN can preserve the topology and V-SOINN can produce the correct classification result when the number of samples is small.
A New Family of Boolean Functions with Good Cryptographic Properties

In 2005, Philippe Guillot presented a new construction of Boolean functions using linear codes as an extension of the Maiorana–McFarland’s (MM) construction of bent functions. In this paper, we study a new family of Boolean functions with cryptographically strong properties, such as non-linearity, propagation criterion, resiliency, and balance. The construction of cryptographically strong Boolean functions is a daunting task, and there is currently a wide range of algebraic techniques and heuristics for constructing such functions; however, these methods can be complex, computationally difficult to implement, and not always produce a sufficient variety of functions. We present in this paper a construction of Boolean functions using algebraic codes following Guillot’s work.
Recursive Prosody and the Prosodic Form of Compounds

This paper investigates the role recursive structures play in prosody. In current understanding, phonological phrasing is computed by a general syntax–prosody mapping algorithm. Here, we are interested in recursive structure that arises in response to morphosyntactic structure that needs to be mapped. We investigate the types of recursive structures found in prosody, specifically: For a prosodic category κ, besides the adjunctive type of recursion κ[κ x], κ[x κ], is there also the coordinative type κ[κ κ]? Focusing on the prosodic forms of compounds in two typologically rather different languages, Danish and Japanese, we encounter three types of recursive word structures: coordinative ω[ω ω], left-adjunctive ω[f ω], right-adjunctive ω[ω f] and the strictly layered compound structure ω[f f]. In addition, two kinds of coordinative φ-compounds are found in Japanese, one with a non-recursive (strictly layered) structure φ[ω ω], a mono-phrasal compound consisting of two words, and one with coordinative recursion φ[φ φ], a bi-phrasal compound. A cross-linguistically rare type of post-syntactic compound has this biphrasal structure, a fact to be explained by its sentential origin.
Narrative in VR journalism: research into practice

In 2015 The Tow Center for Journalism produced a live-motion virtual reality (VR) journalism story on the Ebola outbreak in West Africa, ‘Secret Location’. They stated that this new form appeared to change how journalists must construct their stories. They also challenged the industry to explore the journalistic application of VR beyond ‘highly produced documentaries’ (Owen et al. 2015 Owen, Taylor, Fergus Pitt, Raney Aronson-Rath, and James Milward. 2015. “Virtual Reality Journalism.” Tow Centre. CJR. https://www.cjr.org/tow_center_reports/virtual_reality_journalism.php. [Google Scholar]. “Virtual Reality Journalism.” Tow Centre. CJR. https://www.cjr.org/tow_center_reports/virtual_reality_journalism.php). We decided to make a short news VR film that explored the construction of this form of media and asked which narrative characteristic engaged the viewer to continue watching in a medium where the viewer was already immersed in the storyworld. Our findings suggest that even when the VR technology immerses the viewer into an illusion of presence, the traditional characteristics of narrative, character, plot, and subject remain of fundamental importance in the construction of a news story, and, it was the tension of what happens next, that is the plot, that was the strongest narrative driver in this short journalistic VR film. We also reflect on some issues raised in the production of the piece, such as the time taken, the limitations of creative editing and whether the expectations that VR raises might impact on the experience of viewing a VR news items.
A data independent approach to generate adversarial patches

Deep neural networks are vulnerable to adversarial examples, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently, adversarial patch, with perturbations confined to a small and localized patch, emerged for its easy accessibility in real-world attack. However, existing attack strategies require training data on which the deep neural networks were trained, which makes them unsuitable for practical attacks since it is unreasonable for an attacker to obtain the training data. In this paper, we propose a data independent approach to generate adversarial patches (DiAP). The goal is to craft adversarial patches that can fool the target model on most of the images without any knowledge about the training data distribution. In the absence of data, we carry out non-targeted attacks by fooling the features learned at multiple layers of the deep neural network, and then employ the potential information of non-targeted adversarial patches to craft targeted adversarial patches. Extensive experiments demonstrate impressive attack success rates for DiAP. Particularly in the blackbox setting, DiAP outperforms state-of-the-art adversarial patch attack methods. The patches generated by DiAP also function well in real physical scenarios, and could be created offline and then broadly shared.
A Succinct Review Of Intelligent Computational Techniques In Green Cloud Computing

Green cloud computing (GCC) has various advantages and benefits, especially for power management and energy efficiency. Many techniques can reduce the consumption of energy to solve environmental problems caused by IT industries. An in-depth understanding of these techniques could have a positive impact on the development of GCC. This paper discusses the existing intelligent techniques applied to cloud computing and analyses them according to diversified parameters, such as resource utilization, response time, overhead, etc. A clear trend is that cloud computing is developing in a greener direction.
Image Anomaly Detection by Aggregating Deep Pyramidal Representations

Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset.
Skip-connected network with gram matrix for product image retrieval

Designers usually retrieve and review existing relevant product images to draw inspiration when designing a new proposal. Obtaining reasonable retrieval results of a given target is a challenging task due to the large-scale, low-correlation searching process in the product image field. In this paper, an end-to-end skip-connected network is introduced to improve the accuracy of retrieval by directly addressing the above problems. In more detail, skip-connections are employed to combine the multi-resolution feature maps produced by a pre-trained backbone network, which provides important improvements to address the presence of large scale variance problem. And the second-order information of the multi-resolution feature maps is obtained by using gram matrix operation, which allows the network to exploit more representative features and their correlations. Experimental results indicate that the proposed method achieves better retrieval performance when compared to current state-of-the-art techniques on there normal benchmark datasets as well as the product image data.
A study on detection of copy-move forgery in digital images

With the recent elevation in social networking services like Facebook and Instagram, there has been a rise in the abundance use of image data. Trending image processing software's have an access to create doctored images which is an utmost perturb for the society at large. Fake stories are emanated from these images and are habitually used in malicious ways. Hence, there is a need for a genuine authentication for images. Image forgery is carried out conveniently but analyzing image manipulation is a great concern. Copy-move forgery is a technique that copy's a content from an image and pastes it in the same image to make it unauthentic or to hide the meaningful information. In image manipulation, Copy-move forgery can be performed easily and effectively, especially when source and target regions both are from the same image and share the same properties such as color, illumination conditions and noise. This paper elaborates survey on various Copy-Move Forgery Detection (CMFD) approaches. It is categorized into block based and keypoint based methods. Copy-move forgery is easy to perform and can be relatively effective in image manipulation, particularly when both source and target regions are from the same image as properties such as color temperature, illumination conditions and noise will generally be well-matched between the tampered region and the image.
News Topic Classification Using Machine Learning Techniques

News topic classification is a method of classifying news articles available in text data into some predefined classes or labels. This is one of the applications of text classification. Text classification can be applied in the fields of spam filtering, language recognition, segmenting customer feedbacks, segregating technical documents, etc. This paper discusses news topic classification on AG's News Topic Classification Dataset using machine learning algorithms such as linear support vector machine, multinomial Naive Bayesian classifier, K-Nearest Neighbor, Rocchio, bagging, and boosting. This paper discusses three steps for classification, namely pre-processing of text, then applying feature extraction techniques, and finally implementing machine learning algorithms. These algorithms are compared using evaluation metrics like Accuracy, Recall, Precision, and F1 Score.
TrustGCN: Enabling Graph Convolutional Network for Robust Sybil Detection in OSNs

Detecting fake accounts (also called Sybils) is a fundamental security problem in online social networks (OSNs). Existing feature-based or social-graph-based approaches suffer from the key limitations: they can only leverage either node feature or graph structure properties such as fast-mixing and conductance, but not both. To overcome this shortcoming, we explore the introduction of recent advancements in deep neural networks for graph-structured data into Sybil detection field. These types of models enable integrating both user-level activities and graph-level structures for a new generation of feature-and-graph-based detection mechanisms. However, we find that although applying Graph Convolutional Networks (GCNs) are effective against naïve attacks, they are vulnerable to adversarial attacks in which fake accounts alter local edges and features with patterns to resemble real users. In this paper, we present TrustGCN, a Sybil-resilient defense algorithm that combines the idea of social-graph-based defense with GCN. TrustGCN first assigns trust scores to nodes based on the landing probability of short random walks that starts from known real accounts. As this short, supervised random walk is likely to stay within the subgraph consisting of real accounts, most real accounts receive higher trust scores than fakes. Then it introduces these trust scores as edge weights and adopts graph convolution operations to aggregate features of local graph neighborhoods over this weighted graph for classification. In this way, we prevent Sybil partners with low trust scores from contributing to the feature aggregation for a target node, thus is more robust against adverse manipulations of the attackers. Our experiment on real data demonstrates that TrustGCN significantly outperforms GCN in the robustness. To the best of our knowledge, this is the first attempt to combine social-graph-based defenses with graph neural networks into a unified model, paving the way for the robust feature-and-graph-based detection mechanisms.
Spoofprint: A New Paradigm for Spoofing Attacks Detection

With the development of voice spoofing techniques, voice spoofing attacks have become one of the main threats to automatic speaker verification (ASV) systems. Traditionally, researchers tend to treat this problem as a binary classification task. A binary classifier is typically trained using machine learning (including deep learning) algorithms to determine whether a given audio clip is bonafide or spoofed. This approach is effective on detecting spoofing attacks that are generated by known voice spoofing techniques. However, in practical scenarios, new types of spoofing technologies are emerging rapidly. It is impossible to include all types of spoofing technologies into the training dataset, and thus it is desired that the detection system can generalize to unseen spoofing techniques. In this paper, we propose a new paradigm for spoofing attacks detection called Spoofprint. Instead of using a binary classifier to detect spoofed audio, Spoofprint uses a paradigm similar to ASV systems and involves an enrollment phase and a verification phase. We evaluate the performance on the original and noisy versions of ASVspoof 2019 logical access (LA) dataset. The results show that the proposed Spoofprint paradigm is effective on detecting unknown type of attacks and is often superior to the latest state-of-the-art.
Using Lego<sup>®</sup> Serious Play<sup>®</sup> in Higher Education with Law Students: Encouraging Playfulness and Creativity within Library Workshops

Abstract The following article began life as a ten-minute presentation titled ‘Building student engagement brick by brick; using Lego ® Serious Play ® to explore subject engagement in HE’, presented at BIALL's 2020 virtual conference on 12 June 2020. Since 2016 I have been incorporating Lego ® Serious Play ® (LSP) within my practice as an academic librarian at Middlesex University. This article will explore how I have embedded LSP into workshops with students and staff, the tactics employed to gain acceptance for using LSP within academic settings and offering some tentative predictions on whether playful approaches will be successful in the current Higher Education (HE) landscape, dominated as we currently are by virtual learning and screen technologies.
Linear capabilities for fully abstract compilation of separation-logic-verified code

Abstract Separation logic is a powerful program logic for the static modular verification of imperative programs. However, dynamic checking of separation logic contracts on the boundaries between verified and untrusted modules is hard because it requires one to enforce (among other things) that outcalls from a verified to an untrusted module do not access memory resources currently owned by the verified module. This paper proposes an approach to dynamic contract checking by relying on support for capabilities, a well-studied form of unforgeable memory pointers that enables fine-grained, efficient memory access control. More specifically, we rely on a form of capabilities called linear capabilities for which the hardware enforces that they cannot be copied. We formalize our approach as a fully abstract compiler from a statically verified source language to an unverified target language with support for linear capabilities. The key insight behind our compiler is that memory resources described by spatial separation logic predicates can be represented at run time by linear capabilities. The compiler is separation-logic-proof-directed : it uses the separation logic proof of the source program to determine how memory accesses in the source program should be compiled to linear capability accesses in the target program. The full abstraction property of the compiler essentially guarantees that compiled verified modules can interact with untrusted target language modules as if they were compiled from verified code as well. This article is an extended version of one that was presented at ICFP 2019 (Van Strydonck et al., 2019).
Nonlocal adaptive direction-guided structure tensor total variation for image recovery

A common strategy in variational image recovery is utilizing the nonlocal self-similarity property, when designing energy functionals. One such contribution is nonlocal structure tensor total variation (NLSTV), which lies at the core of this study. This paper is concerned with boosting the NLSTV regularization term through the use of directional priors. More specifically, NLSTV is leveraged so that, at each image point, it gains more sensitivity in the direction that is presumed to have the minimum local variation. The actual difficulty here is capturing this directional information from the corrupted image. In this regard, we propose a method that employs anisotropic Gaussian kernels to estimate directional features to be later used by our proposed model. The experiments validate that our entire two-stage framework achieves better results than the NLSTV model and two other competing local models, in terms of visual and quantitative evaluation.