Reviewerly/exHarmony huggingface dataset:
Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.
More-Than Human Centred Design: Considering Other Things

This paper responds to contemporary design contexts that frequently contain complex interdependencies of human and non-human actants. To adequately represent these perspectives requires a shift towards More-Than Human Centred Design. The Internet of Things (IoT) is one context that demonstrates this need. The ‘things’ within such networks transcend their physical forms and extend to include algorithms, humans, data, business models, etc. and each imports independent-but-interdependent motivations and perspectives. Therefore, we use the IoT to clarify our proposition and to convey our three contributions. First, we review the expanding corpus of contemporary Human–Computer Interaction research that seeks to expand the notion of Human Centred Design by moving beyond the dominant anthropocentric perspective. Second, we introduce a novel design metaphor, ‘constellations’, which allows both the interdependencies and independent perspectives to be considered. Third, we provide an account of a speculative design to demonstrate how it may be put into practice.
Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering

Nonlinear kernel-based subspace clustering methods that can reveal the multi-cluster nonlinear structure of samples are an emerging research topic. However, the existing kernel subspace clustering methods have the following three flaws: 1) their clustering performance is largely determined by the chosen kernel function; 2) they may lack robustness in the presence of non-Gaussian noise and impulsive noise; and 3) their learned affinity matrix can not hold the desired block diagonal property for clustering purpose, which possibly leads to incorrect clustering when using spectral clustering. In this paper, we propose a Joint Robust Multiple Kernel Subspace Clustering (JMKSC) method for data clustering, which has two primary innovations. First, our multiple kernel weighting strategy introduces the correntropy metric weighting instead of a fixed, or inappropriately assigned weighting, which is more robust to the non-Gaussian noise and contributes to learning the optimal consensus kernel. Second, our method encourages acquiring an affinity matrix with the optimal block diagonal property based on the block diagonal regularizer (BDR) and the self-expressiveness property. Experiments on several different types of datasets confirm that the proposed JMKSC significantly outperforms several state-of-the-art single kernel and multiple kernel subspace clustering methods in terms of accuracy, NMI and purity.
Towards Model-based Pricing for Machine Learning in a Data Marketplace

Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.
Democratizing Data Science through Interactive Curation of ML Pipelines

Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting a wider adoption of ML techniques in other fields. Existing libraries that claim to solve this problem, still require well-trained practitioners. Those frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems.
A High Capacity Multi-Level Approach for Reversible Data Hiding in Encrypted Images

This paper proposes a novel method of reversible data hiding in encrypted images (RDH-EI). We first provide an RDH-EI approach using single-level embedding, in which three parties are involved, including an image owner, a data hider, and a recipient. The image owner encrypts an original image into a ciphertext image. After dividing the original image into blocks, the owner pseudo-randomly permutes all blocks by a permutation key. With an encryption key, the image owner further encrypts the contents of all blocks using a stream cipher algorithm, during which pixels inside each block share the same stream bytes. Once the encrypted image is uploaded onto the server, a data hider embeds additional messages into the ciphertext. The data hider divides the encrypted image into blocks and selects peak pixels from each block using an embedding key. With the peak pixels, the data hider embeds an additional message using histogram shifting inside each block. On the receiver side, a recipient extracts the hidden message using the embedding key, and losslessly recover the original image with the permutation key and the encryption key. Based on the single-level algorithm, we further construct a multi-level approach. The embedding process is iteratively used to generate the marked encrypted images. Compared with state-of-the-art works, the proposed method achieves a better embedding efficiency and an error-free recovery.
Efficient Lattice-Based Zero-Knowledge Arguments with Standard Soundness: Construction and Applications

We provide new zero-knowledge argument of knowledge systems that work directly for a wide class of language, namely, ones involving the satisfiability of matrix-vector relations and integer relations commonly found in constructions of lattice-based cryptography. Prior to this work, practical arguments for lattice-based relations either have a constant soundness error $$(2/3)$$ , or consider a weaker form of soundness, namely, extraction only guarantees that the prover is in possession of a witness that “approximates” the actual witness. Our systems do not suffer from these limitations. The core of our new argument systems is an efficient zero-knowledge argument of knowledge of a solution to a system of linear equations, where variables of this solution satisfy a set of quadratic constraints. This argument enjoys standard soundness, a small soundness error $$(1/poly)$$ , and a complexity linear in the size of the solution. Using our core argument system, we construct highly efficient argument systems for a variety of statements relevant to lattices, including linear equations with short solutions and matrix-vector relations with hidden matrices. Based on our argument systems, we present several new constructions of common privacy-preserving primitives in the standard lattice setting, including a group signature, a ring signature, an electronic cash system, and a range proof protocol. Our new constructions are one to three orders of magnitude more efficient than the state of the art (in standard lattice). This illustrates the efficiency and expressiveness of our argument system.
Collaborative Welding System using BIM for Robotic Reprogramming and Spatial Augmented Reality

The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality.
Blockchain technology enabling the Physical Internet: A synergetic application framework

This paper proposes a Blockchain based conceptual framework that offers a solution for fundamental barriers of the Physical Internet concerning the exchange of value and physical assets in logistics networks and decentralized leadership structures. In recent years, the Physical Internet has received high interest by researchers and practitioners alike, as it aims for a paradigm shift towards economically, environmentally, and socially sustainable logistics. However, the implementation requires transformations in infrastructure, technology, and business models, which cannot be met by centralized solutions proposed by the literature. Therefore, this paper provides a decentralized approach based on Blockchain technology describing in detail the configuration of the Blockchain and the integration into the distributed network structures of the Physical Internet. Grounded on an industry case study and literature on the Physical Internet and Blockchain, a four-layered framework is derived and subsequently presented in form of an application use case, guiding researchers for further analysis and practitioners for adoption opportunities.
The Product Backlog

Context: One of the most common artifacts in contemporary software projects is a product backlog comprising user stories, bugs, chores or other work items. However, little research has investigated how the backlog is generated or the precise role it plays in a project. Objective: The purpose of this paper is to determine what is a product backlog, what is its role, and how does it emerge? Method: Following Constructivist Grounded Theory, we conducted a two-year, five-month participant-observation study of eight software development projects at Pivotal, a large, international software company. We interviewed 56 software engineers, product designers, and product managers.We conducted a survey of 27 product designers. We alternated between analysis and theoretical sampling until achieving theoretical saturation. Results: We observed 13 practices and 6 obstacles related to product backlog generation. Limitations: Grounded Theory does not support statistical generalization. While the proposed theory of product backlogs appears widely applicable, organizations with different software development cultures may use different practices. Conclusion: The product backlog is simultaneously a model of work to be done and a boundary object that helps bridge the gap between the processes of generating user stories and realizing them in working code. It emerges from sensemaking (the team making sense of the project context) and coevolution (a cognitive process where the team simultaneously refines its understanding of the problematic context and fledgling solution concepts).
Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation

Discriminative region responses residing inside an object instance can be extracted from networks trained with image-level label supervision. However, learning the full extent of pixel-level instance response in a weakly supervised manner remains unexplored. In this work, we tackle this challenging problem by using a novel instance extent filling approach. We first design a process to selectively collect pseudo supervision from noisy segment proposals obtained with previously published techniques. The pseudo supervision is used to learn a differentiable filling module that predicts a class-agnostic activation map for each instance given the image and an incomplete region response. We refer to the above maps as Instance Activation Maps (IAMs), which provide a fine-grained instance-level representation and allow instance masks to be extracted by lightweight CRF. Extensive experiments on the PASCAL VOC12 dataset show that our approach beats the state-of-the-art weakly supervised instance segmentation methods by a significant margin and increases the inference speed by an order of magnitude. Our method also generalizes well across domains and to unseen object categories. Without fine-tuning for the specific tasks, our model trained on VOC12 dataset (20 classes) obtains top performance for weakly supervised object localization on the CUB dataset (200 classes) and achieves competitive results on three widely used salient object detection benchmarks.
Detecting Phishing Websites through Deep Reinforcement Learning

Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, orev encredentials and pass words. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A phisher usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.
Hyperspectral Images Denoising via Nonconvex Regularized Low-Rank and Sparse Matrix Decomposition

Hyperspectral images (HSIs) are often degraded by a mixture of various types of noise during the imaging process, including Gaussian noise, impulse noise, and stripes. Such complex noise could plague the subsequent HSIs processing. Generally, most HSI denoising methods formulate sparsity optimization problems with convex norm constraints, which over-penalize large entries of vectors, and may result in a biased solution. In this paper, a nonconvex regularized low-rank and sparse matrix decomposition (NonRLRS) method is proposed for HSI denoising, which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. The NonRLRS aims to decompose the degraded HSI, expressed in a matrix form, into low-rank and sparse components with a robust formulation. To enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions, a novel nonconvex regularizer named as normalized ε -penalty, is presented, which can adaptively shrink each entry. In addition, an effective algorithm based on the majorization minimization (MM) is developed to solve the resulting nonconvex optimization problem. Specifically, the MM algorithm first substitutes the nonconvex objective function with the surrogate upper-bound in each iteration, and then minimizes the constructed surrogate function, which enables the nonconvex problem to be solved in the framework of reweighted technique. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method.
Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach

Image recognition systems have demonstrated tremendous progress over the past few decades thanks, in part, to our ability of learning compact and robust representations of images. As we witness the wide spread adoption of these systems, it is imperative to consider the problem of unintended leakage of information from an image representation, which might compromise the privacy of the data owner. This paper investigates the problem of learning an image representation that minimizes such leakage of user information. We formulate the problem as an adversarial non-zero sum game of finding a good embedding function with two competing goals: to retain as much task dependent discriminative image information as possible, while simultaneously minimizing the amount of information, as measured by entropy, about other sensitive attributes of the user. We analyze the stability and convergence dynamics of the proposed formulation using tools from non-linear systems theory and compare to that of the corresponding adversarial zero-sum game formulation that optimizes likelihood as a measure of information content. Numerical experiments on UCI, Extended Yale B, CIFAR-10 and CIFAR-100 datasets indicate that our proposed approach is able to learn image representations that exhibit high task performance while mitigating leakage of predefined sensitive information.
Cloud Computing Assisted Blockchain-Enabled Internet of Things

Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In this paper, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this paper, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts, which generates high-quality pseudo-annotations in one forward path. The generated pseudo-annotations lead to the segmentation results that achieve the state of the art in weakly supervised semantic segmentation.