Reviewerly/exHarmony huggingface dataset:
Data Donations as Exercises of Sovereignty

We propose that the notion of individual sovereignty encompasses more than having the power to exclude others from one’s personal space. Instead, sovereignty is realized at least in part along outward-reaching, interactive and participatory dimensions. On the basis of reflections from gift theory, we argue that donations can generate social bonds, convey recognition and open up new options in social space. By virtue of these features, donations offer the potential to advance individual sovereignty. We go on to highlight distinctive benefits of data donations, before articulating several difficulties and puzzles: data donors are bound to have a limited grip on future uses of their data and the people affected by their decision to share. Further characteristic traits of data donations come from the invasive and comprehensive character of state-of-the-art data gathering and processing tools, and the fact that the relevant sense of data ownership is far from straightforward. In order to minimize tensions with negative, protective aspects of sovereignty, we argue that thoughtful mechanisms at the level of consent procedures, the representation of data subjects in governance structures, and organizational-level constraints are necessary. Along the way, we will devote particular attention to challenges and opportunities within big data contexts.
Adaptive Superpixel Generation for SAR Images With Linear Feature Clustering and Edge Constraint

Due to the speckle noise and complex geometric distortions within SAR images, it is still a challenge to develop a stable method that can produce superpixels with both high boundary adherence and visual compactness with low computational costs at the same time. In this paper, we propose an adaptive superpixel generation approach with linear feature clustering and edge constraint for synthetic aperture radar (SAR) images, which consists of three stages. First, the local gradient ratio pattern of each pixel in SAR imagery is extracted as features, which was previously proposed by us for SAR target recognition and has been proven to be insensitive to speckle noise. Second, we propose to use the feature-ratio-based edge detector with Gauss-shaped window instead of the traditional rectangle-shaped window to obtain the edge strength map and final edges for SAR images. Finally, a modified normalized cut (Ncut)-based superpixel generation strategy is adopted using a distance metric that simultaneously measures both the feature similarity and space proximity. In this strategy, we approximate the similarity measure through a positive semidefinite kernel function rather than directly using the traditional eigen-based algorithm. Therefore, the objective functions of weighted local K-means and Ncuts can achieve the same optimum point by appropriately weighting each point in this feature space, which greatly reduces the computation cost. During the linear feature clustering, the coefficient of variation is used to automatically determine the tradeoff factor between the feature similarity and space proximity, which helps change the superpixel shape and size adaptively according to the image homogeneity. Furthermore, the edge information is also introduced to constrain the clustering for the sake of high boundary adherence. By bridging the local K-means clustering and Ncuts, as well as the benefits of edge constraint, our method not only produces superpixels with good boundary adherence but also captures the global image structure information. Experimental results with simulated and real SAR images demonstrate the effectiveness of our proposed method, which performs better than other state-of-the-art algorithms.
Cluster-based spectral models for a robust assessment of soil properties

Soil spectroscopy has proven efficient for providing quantitative properties of soil chromophores and soil classification. However, building generic/global models might bring to light some problems which stem from the false premise that each chromophore affects all soil types equally. The objective of this paper is to offer a new cluster-based approach for the assessment of soil properties and compensating issues that might rise from using heterogeneous soil dataset. For that purpose, 1480 soil samples from varying climate zones in Israel were used. All samples were air-dried, ground, and sieved to 2 mm, then CaCO3, organic matter (OM), clay and sand contents together with the spectral signature were measured according to laboratory protocols. The spectral analysis was performed using the Spectral Angle Mapper (SAM) algorithm, spectral gradient and k-means clusters to split the dataset into distinct clusters. Subsequently, cluster-based models were performed and compared with generic models. Later, we focused on the OM parameter and applied the OM detection limit approach for a more robust and accurate assessment. Our results show that soil property prediction improved significantly when using the cluster-based models compared to the generic models and therefore should be considered when dealing with large and heterogeneous databases.
A Novel Dataset for English-Arabic Scene Text Recognition (EASTR)-42K and Its Evaluation Using Invariant Feature Extraction on Detected Extremal Regions

The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination. English as a secondary language is extensively used in Gulf countries along with Arabic script. Therefore, this paper introduces English-Arabic scene text recognition 42K scene text image dataset. The dataset includes text images appeared in English and Arabic scripts while maintaining the prime focus on Arabic script. The dataset can be employed for the evaluation of text segmentation and recognition task. To provide an insight to other researchers, experiments have been carried out on the segmentation and classification of Arabic as well as English text and report error rates like 5.99% and 2.48%, respectively. This paper presents a novel technique by using adapted maximally stable extremal region (MSER) technique and extracts scale-invariant features from MSER detected region. To select discriminant and comprehensive features, the size of invariant features is restricted and considered those specific features which exist in the extremal region. The adapted MDLSTM network is presented to tackle the complexities of cursive scene text. The research on Arabic scene text is in its infancy, thus this paper presents benchmark work in the field of text analysis.
Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters

In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.
Review and Analysis of Search, Extraction, Evacuation, and Medical Field Treatment Robots

One of the most impactful and exciting applications of robotic technology, especially autonomous and semi-autonomous systems, is in the field of search and rescue. Robots present an opportunity to go where rescuers cannot, keep responders out of danger, work indefatigably, and augment the capabilities of the humans who put their lives at risk while helping others. This paper examines the use of robotic systems in human rescue applications, with an emphasis on performing search, extraction, evacuation, and medical field treatment procedures. The work begins with a review of the various robotic systems designed to perform one or more of the above operations. The relative merits of each system are discussed along with their shortcomings. The paper also addresses the use of robotic competitions as a means of benchmarking field robotic systems. Based on the review of state of the art systems, a novel concept (Semi- Autonomous Victim Extraction Robot) designed to address the shortcomings of existing systems is described in the conclusion, along with detailed discussion on how it improves upon state of the art systems. The future research thrusts to be explored before realizing a fully integrated robotic rescue system are also detailed.
Modeling the usefulness of search results as measured by information use

The documents retrieved by a web search are useful if the information they contain contributes to some task or information need. To measure search result utility, studies have typically focused on perceived usefulness rather than on actual information use. We investigate the actual usefulness of search results—as indicated by their use as sources in an extensive writing task—and the factors that make a writer successful at retrieving useful sources. Our data comprise 150 essays written by 12 writers whose querying, clicking and writing activities were recorded. By tracking authors' text reuse behavior, we quantify the search results' contribution to the task more accurately than before. We model the overall utility of the search results retrieved throughout the writing process using path analysis, and compare a binary utility model (Reuse Events) to one that quantifies a degree of utility (Reuse Amount). The Reuse Events model has greater explanatory power (63% vs. 48%); in both models, the number of clicks is by far the strongest predictor of useful results—with β-coefficients up to 0.7—while dwell time has a negative effect (β between −0.14 and −0.21). As a conclusion, we propose a new measure of search result usefulness based on a source's contribution to an evolving text. Our findings are valid for tasks where text reuse is allowed, but also have implications on designing indicators of search result usefulness for general writing tasks.
Improving password memorability, while not inconveniencing the user

Passwords are the most frequently used authentication mechanism. However, due to increased password numbers, there has been an increase in insecure password behaviors (e.g., password reuse). Therefore, new and innovative ways are needed to increase password memorability and security. Typically, users are asked to input their passwords once in order to access the system, and twice to verify the password, when they create a new account. But what if users were asked to input their passwords three or four times when they create new accounts? In this study, three groups of participants were asked to verify their passwords once (control group), twice, and three times (two experimental groups). Psychological literature suggests that applying repetition in learning to the password process has significant effects on password memorability. However, previous password research has found a trade-off between password security and memorability, and more recently, user convenience. Our results suggest that verifying passwords three times can increase password memorability from 42% (verifying passwords just once as with current practices) to 70%. Even by increasing the verification to just two times can increase password memorability by 17%. However, we found that through increasing the number of verifications did not equate to a decrease in user convenience. What this means is that small changes to the password verification stage can have significant results on password memorability while not necessarily inconveniencing the user. The implications of these results could ultimately have a positive effect on password security, and the consequences of forgetting passwords.
Personalized Sketch-Based Image Retrieval by Convolutional Neural Network and Deep Transfer Learning

The sketch-based image retrieval (SBIR) finds the natural images according to the features and rules defined by human beings. The retrieval results are generally similar in contour; however, their complete semantic information of the image is missing. From the user's point of view, the same hand-drawn image may represent many different things, due to the semantic “one-to-many” category mapping relationship between the hand-drawn image and the natural image, that is the inherent ambiguity of hand-drawn image. In addition, the user's drawing has many different characteristics, so the retrieval results generally cannot fully match with his intent. For the above-mentioned challenges, a personalized SBIR architecture is proposed, including a deep full convolutional neural network as a general model and a personalized model using transfer learning to achieve fine-grained image semantic feature. On the basis of the pre-trained general model and the images selected by the user in history, we construct the personalized model training dataset. Moreover, the user history feedback with the current hand-drawn image is combined as the input of the transfer learning model, to fine-tune the distribution of features in vector space, so that the neural network can learn the personalized semantic information. The experiments show that the general model has strong generalization ability with the mean average precision as 0.64 on the Flickr15 K dataset. The migration model can realize fine-grained image semantic vector space division, which perfectly satisfies the personalized retrieval requirements by hand-drawn sketch-based image input.
Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring

Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient's health. To be successful, such system has to reason about the person's actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person's behaviour with probabilistic inference to reason about one's actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person's cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM.
Hierarchical Neural Variational Model for Personalized Sequential Recommendation

In this paper, we study the problem of recommending personalized items to users given their sequential behaviors. Most sequential recommendation models only capture a user's short-term preference in a short session, and neglect his general (unchanged over time) and long-term preferences. Besides, they are all based on deterministic neural networks, and consider users' latent preferences as point vectors in a low-dimensional continuous space. However, in real world, the evolutions of users' preferences are full of uncertainties. We address this problem by proposing a hierarchical neural variational model (HNVM). HNVM models users' three preferences: general, long-term and short-term preferences through an unified hierarchical deep generative process. HNVM is a hierarchical recurrent neural network that enables it to capture both user's long-term and short-term preferences. Experiments on two public datasets demonstrate that HNVM outperforms state-of-the-art sequential recommendation methods.
Quantum Hoare Logic with Ghost Variables

Quantum Hoare logic allows us to reason about quantum programs. We present an extension of quantum Hoare logic that introduces “ghost variables” to extend the expressive power of pre-/postconditions. Ghost variables are variables that do not actually occur in the program and are allowed to have arbitrary quantum states (in a sense, they are existentially quantified), and be entangled with program variables. Ghost variables allow us to express properties such as the distribution of a program variable or the fact that a variable has classical content. And as a case study, we show how quantum Hoare logic with ghost variables can be used to prove the security of the quantum one-time pad.
Semi-supervised and active learning through Manifold Reciprocal kNN Graph for image retrieval

A massive and ever growing amount of data collections, including visual and multimedia content are available today. Such content usually possesses additional information, as text or other metadata, to form a rather sparse and noisy, yet rich and diverse source of annotation. Although the text-based retrieval models are well established, they ignore the rich source of information encoded in the visual data. In contrast, the promising content-based retrieval technologies, capable of considering the multimedia content, still face obstacles for mapping the low level features into high level semantic concepts. Supervised approaches based on relevance feedback techniques have been employed for mitigating such gap on visual retrieval tasks. Although often quite effective, such methods rely only on labeled data, which can severely impact the retrieval effectiveness when the number of user interventions is insufficient. In this scenario, the retrieval approaches are ideally suitable for the emerging weakly supervised and active learning technology to semi-autonomously explore data collections by taking into account the relationships among multimedia objects and saving the user’s efforts. In this paper, we discuss a novel semi-supervised learning algorithm for image retrieval tasks. While a manifold learning algorithm uses a reciprocal kNN graph to analyze the unlabeled data, the labeled information obtained through user interactions are represented using similarity sets. Both labeled and unlabeled information are modelled in terms of ranking information to allow a strict link between them. Experimental results obtained on various public datasets and several different visual features have demonstrated the effectiveness of the proposed approach.
g-RAT | A Novel Graphical Randomized Authentication Technique for Consumer Smart Devices

User authentication is the process that is exercised millions of times around the globe by using different techniques and methods. The most prominent way of authentication is alphanumerical password forms that have been used for decades. Authorized access is becoming a challenging issue because of the introduction of modern technologies. In addition, traditional alphanumerical passwords have significant security issues, for example, humans forget the combination of keys due to the selection of a difficult key combination. Moreover, when they choose an easy key combination, this helps hackers to crack their passwords easily. Traditional passwords are also vulnerable to several types of attacks, for example, dictionary attack, brute force attack, and malware. To provide an easy and more secure authentication technique, a graphical password has been introduced in this paper for consumer electronic devices, which uses an image or a set of images for authentication. We have categorized the existing graphical password methods into recognition-based, cued-recall-based, pure-recall-based, and hybrid techniques. Due to the limitations of the existing graphical passwords, we have introduced a new technique, named graphical random authentication technique (gRAT), which generates a randomized set of images every time a user tries to authenticate him/herself by maintaining the security and usability at the same time. The gRAT technique is also tested by user-centric evaluation in terms of security, usability, usefulness, and utility, and the experimental results show that the proposed technique is more secure and useful in the real-life authentication applications.
Fast and object-adaptive spatial regularization for correlation filters based tracking

Spatially-regularized correlation filters have achieved great successes in visual object tracking, with excellent tracking accuracy and robustness to various interferences. Performance improvement is mainly attributed to spatial regularization (SR), which is a powerful tool to alleviate the boundary effects of correlation filters (CF) based tracking. However, SR also causes high complexity, which cannot achieve real-time tracking. Furthermore, the fixed and handcrafted SR map, disregarding object’s appearance during the whole sequence, seems incapable of handling changeable scenes. In this paper, we propose a fast and object-adaptive spatial regularization (FOSR) model to alleviate those drawbacks. By introducing FOSR method, more discriminative filters can be efficiently obtained by jointly learning in spatial and frequency domain. Besides, an object-adaptive SR map that contains object information can be offline and online learned within a data-driven manner. Extensive experiments on two benchmarks, OTB-2015 and VOT-2016, validate the effectiveness and generality of our model in helping state-of-the-art SR based trackers to achieve more than 5 times of speedup and a relative gain of 3.7% and 3.3% in success and precision plots on OTB-2015, respectively. Additionally, FOSR can help pure CF based trackers to remarkably improve their accuracy with comparable speed.