Reviewerly/exHarmony huggingface dataset:
Transparency Enhancing Technologies to Make Security Protocols Work for Humans

As computer systems are increasingly relied on to make decisions that will have significant consequences, it has also become important to provide not only standard security guarantees for the computer system but also ways of explaining the output of the system in case of possible errors and disputes. This translates to new security requirements in terms of human needs rather than technical properties. For some context, we look at prior disputes regarding banking security and the ongoing litigation concerning the Post Office’s Horizon system, discussing the difficulty in achieving meaningful transparency and how to better evaluate available evidence.
Decision Trees as a Tool for Data Analysis. Elections in Barcelona: A Case Study

Decision trees are inductive learning methods that construct a domain model easy to understand from domain experts. For this reason, we claim that the description of a given data set using decision trees is an easy way to both discover patterns and compare the classes that form the domain at hand. It is also an easy way to compare different models of the same domain. In the current paper, we have used decision trees to analyze the vote of the Barcelona citizens in several electoral convocations. Thus, the comparison of the models we have obtained has let us know that the percentage of people with a university degree is the most important aspect to separe the neighbourhoods of Barcelona according to the most voted party in a neighbourhood. We also show that in some neighbourhoods has always won the same party independently of the kind of convocation (local or general) .
On finding the optimal parameters for probability estimation with m-estimate

The estimation of probabilities from empirical data samples has been established as a crucial part of many machine learning and knowledge discovery research projects and applications. In addition to simple probability estimation with relative frequency, more elaborated probability estimation methods were proposed and applied in practice (e.g. Laplace's rule, m-estimate, Piegat's estimate). In this paper we analyze the role of parameter m in m-estimate. In most practical applications that used m-estimate, m was often set to 2 or, in more complex settings, determined with a cross-validation procedures. In this study we evaluate the impact of various values of m to the absolute error of m-estimate in the context of a carefully designed experimental framework. The results of our analysis suggest that the optimal value of parameter m does not depend only on the size of the observed sample, but also, to a much greater extent, on the difference between hypothetical pa used in m-estimate and the authentic p of the sample.
Auto Center Find Density Peaks Algorithm and its Application in Face Image Clustering

Face image clustering is one of the most important applications in digital signal process. Here, Find Density Peaks clustering algorithm with auto choosing centers is proposed and applied into the face image clustering problem. In the clustering process, the Complex Wavelet structural Similarity Index is introduced to quantize the difference between two face images. The deciding mechanism of two key parameters are modified. Avoiding the false centers, the true centers of each cluster are auto defined. And the face images dataset are properly clustered. The face images are dimensionality reduced and visualized via T-SNE as scatter points in 2D space. The results show a good partition of face images dataset. And the proposed algorithm shows practical values in most applications required with clustering.
Evolving Recurrent Neural Networks for Pattern Classification

While reaching outstanding prediction rates by means of black-box classifiers is relatively easy nowadays, reaching a proper trade-off between accuracy and interpretability might become a challenge. The most popular approaches reported in the literature to overcome this problem use post-hoc procedures to explain what the classifiers have learned. Less research is devoted to building classification models able to intrinsically explain their decision process. This paper presents a recurrent neural network—termed Evolving Long-term Cognitive Network—for pattern classification, which can be deemed interpretable to some extent. Moreover, a backpropagation learning algorithm to adjust the parameters attached to the model is presented. Numerical simulations using 35 datasets show that the proposed network performs well when compared with traditional black-box classifiers.
Eye Movement Patterns as a Cryptographic Lock

Eye tracking technologies play increasing role in advance vision systems, and multimedia applications. Such technologies can be also applied for security purposes, as a contactless devices and user interfaces. High precision of registered eye movements, allow also to use them in security cryptographic systems, in which individual and unique eye movement patterns, can be used as personal key or security lock. In this paper we define such solutions based on these technologies, which will be focused for creation of personalized security protocols for user authentication.
Spatial-Temporal Segmentation-based Tracking

Visual tracking, a fundamental task in computer vision, has been criticized less well-posed since reliable target information only given at first frame. In this case, most of the existing template-matching-based trackers fail to locate the target when non-rigid deformations or variations occur. To address these issues, we propose a principled way to take advantage of the superpixel labeling and discriminative tracking algorithms. For each frame, a correlation tracker is first adopted to provide the coarse target location. Afterwards, a collaborative segmentation approach is advocated to segment the surrounding region of the target into superpixels. Target appearance and motion trajectory are considered as spatial and temporal constrains and incorporated into superpixel labeling module. The fine-segmentation result, in turn, provides a more accurate target status for template updating. The effectiveness of the proposed algorithm is validated through experimental comparison on widely-used tracking benchmark datasets.
Convexity, distance, and connectivity

In order to extract geometric information from images, suitable operators must be constructed. After a discussion of convexity and geodesic distance, the important notion of metric dilation is introduced, followed by that of distance transforms. Sections are then devoted to geodesic and conditional operators, granulometries, connectivity and skeletons. A final section considers discrete metric spaces.
Decorrelation of Previously Communicated Information for an Interacting Multiple Model Filter

In a sensor network compensation of the correlated information caused by previous communication is of utmost interest for distributed estimation. In this article, we investigate an information decorrelation approach that can be applied when using interacting multiple model filters in the sensor nodes for a family of jump Markov linear systems. Implementation issues that might arise while applying the decorrelation approach are addressed in detail. The investigated approach is compared with alternatives on simple distributed single maneuvering target tracking examples.
Confidence-aware Practical Anime-style Colorization

We propose practical anime-style colorization of an input line-drawing. The key idea is the strategic withdrawal which reflects the prediction confidence that indicates the expected accuracy of the predicted color labels. Furthermore, we investigate the relation between the proposed confidence, prediction accuracy, and number of automatically colorized regions to maximize the efficiency of the colorization process including both automatic prediction and manual correction for practical use in production.
A Decision Support Method for Prehospital Emergency Care Based on Ranking the Importance of Physiological Variables

To the on-site nursing staff or field management in prehospital emergency care, it seems baffling to conduct more targeted checklist tests for a specific disease. To address this problem, we proposed a decision support method for prehospital emergency care based on ranking the importance of physiological variables. We used multiple logistic regression models to explore the effects of various physiological variables on diseases based on the area under the curve (AUC) value. We implemented the method on the intensive care database (i.e., the Medical Information Mart for Intensive Care (MIMIC-III) database) and explored the importance of 17 physiological variables for 24 diseases, both chronic and acute. We included 33,798 adult patients, using the full physiological dataset as experiment data. We ranked the importance of the physiological variables related to the diseases according to the experiments' AUC value. We discussed which physiological variables should be considered more important in adult intensive care units (ICUs) for prehospital emergency care conditions. We also discussed the relationships among the diseases based on ranking the importance of physiological variables. We used large-scale ICU patient data to obtain a cohort of physiological variables related to specific diseases. Ranking a cohort of physiological variables is a cost-effective means of reducing morbidity and mortality under prehospital emergency care conditions.
An Improved Stream Cipher Algorithm Based on NFLSR

Abstract To overcome defects of traditional stream cryptographic algorithms based on XOR operation, this paper proposes a new stream cipher algorithm with higher security and suitability for encrypting short information, which is based upon traversal operation and zipper-like random dynamic hash mapping, has the same time and space complexity as the traditional one. It generates the key stream through an application of Nonlinear Feedback Shift Register (NLFSR) and Bits Scrambling.
Kernel-Based Relocation Siamese Network for Real-Time Visual Object Tracking

Siamese networks have been paid more attention to video tracking due to its superiority in balance accuracy and speed. Based on the convolutional feature cross-correlation between the target template and the search region, trackers with Siamese network can search for the best result in the candidate box to get the tracking result. However, existing Siamese tracking algorithms are often affected by motion blurring, low resolution, distortion and other issues that blur search region in solving video object tracking problems. This paper presents a candidate box area generation method based on kernel density function to relocate the search region when track failed. Specifically, the tracker proposed in this paper fuses deep feature and color feature to generate candidate boxes from which more accurate tracking results can be obtained, moreover, the color feature is easily to calculate to reach real-time speed. Finally, by improving the candidate box generation algorithm, the problem of tracking missing due to fast motion, blurring and other factors is effectively solved with less time consuming.
Interaction Recognition Using Depth Information Based on 3D CNNs

The depth image sequence has better illumination insensitivity and strong texture discrimination than the traditional RGB image sequence. Although many traditional action recognition methods have achieved good performance, these approaches don't have explicit model variations and interdependencies in time, which depend on well-designed feature. The convolutional neural networks (CNNs) are a typical model of deep learning, which can achieve superior performance on action recognition without relying on handcrafted features. In this paper, we study convolutional neural networks of deep learning-based action recognition using depth image sequences. Firstly, we develop a simple 3D Convolutional Neural Networks that learn directly spatiotemporal features from raw depth image sequences. With the help of parallel computing hardware and method based on Bayesian hyper-parameter optimization, experimental results show that the designed 3D CNNs architecture is effective on simple human action recognition even complex interactive recognition and yields competitive classification performances. Secondly, we analysis the impact of different temporal extents on the accuracy of action recognition, and demonstrate that increasing the temporal extents will improve the accuracy of action recognition. Finally, in order to verify the generalization ability of the training model, we conduct a study of transfer learning by transferring the learned features to other human action datasets, experimental results show that the designed 3D CNNs architecture generalizes well and yield considerable performance improvement.
Is There No Way to the Truth? Copyright Liability as a Model for Restricting Fake News

A proliferation of fake news has flooded U.S. websites and social media. From misinformation affecting the 2016 election to individuals making profits off of creating false stories, the United States desperately needs a legal response to the fake news crisis. However, U.S. law has effectively forestalled any attempts to bring fake news to heel. The First Amendment has been held to protect fake news. The Communications Decency Act, codified as Section 230, protects online platforms from the remaining potential sources of vicarious liability for fake news. Instead, gradually, self-regulation by websites has started to fill this void.Proposed solutions have tried to look forward, suggesting revisions to the First Amendment and Communications Decency Act doctrine, or analyzing self-regulation by websites. However, these proposals have ignored an already existing model for regulating unwanted conduct online: the Digital Millennium Copyright Act. The Digital Millennium Copyright Act imposes certain obligations on online service providers to remove copyright infringing content posted on their websites in exchange for a liability safe harbor. While copyright and fake news are different, ten distinct principles can be elucidated from the Digital Millennium Copyright Act. These principles provide insights into how online regulations could advance, whether through websites’ own self-regulation, or in codified law if the First Amendment and § 230 regime is relaxed in the future.
StateConsisIV: A Privacy-preserving Integrity Verification Method for Cloud Components Based on a Novel State Consistency Feature

Plain proofs (e.g., raw logs, report, etc.) are significant and effective for integrity verification. In our analysis and comparison of existing work, we found most of them did not employ any protection mechanisms on the proofs. However, these proofs contain sensitive information, which may cause privacy leakage risks when the third party verifier (TPV) is compromised. The situation is even worse when the verification objects are cloud components. Motivated by this, we present StateConsisIV, a privacy-preserving integrity verification method based on a novel state consistency feature to address the privacy leakage problem. The core idea of our work is to enable the integrity judgment through encrypted proofs, withholding plain proofs inside the cloud only to reduce attack surface and enhance privacy. In specific, we employ random transformation algorithm on cloud nodes to encrypt proofs on their birth places. Besides, we design a novel state consistency feature based on the deployment and operation pattern of structural cloud components and perform feature analysis on TPV to guarantee an accurate integrity judgment result. We evaluate our approach on one typical dataset. The experimental results show that our method is considered more worthy with a little bit of extra computation overhead.
Operators which are translation invariant

The notion of translation-invariant operators is described and explained with reference to binary images. Erosion and dilation, opening and closing are discussed at length. Grey-scale morphology is introduced and the use of the umbra is presented. T-operators and their representation are described.
Hit-or-miss topology and semi-continuity

The algebraic properties of the morphological operators are important but they are not alone in this. Topological properties can be equally important and are explored in depth in this chapter. The basic elements of topology are summarized, followed by those of metric spaces, notably the Hausdorff metric. Compact sets are then defined and this leads to the hit-or-miss topology and the myope topology. Semi-continuity is explained and a proposition concerning basis representation is advanced.
Flip paths between lattice triangulations

We present a O(n32)-time algorithm for the shortest (diagonal) flip path problem for lattice triangulations with n points, improving over previous O(n2)-time algorithms. For a large, natural class of inputs, our bound is tight in the sense that our algorithm runs in time linear in the number of flips in the output flip path. Our results rely on an independently interesting structural elucidation of shortest flip paths as the linear orderings of a unique partially ordered set, called a minimum flip plan, constructed by a novel use of Farey sequences from elementary number theory. Flip paths between general (not necessarily lattice) triangulations have been studied in the combinatorial setting for nearly a century. In the Euclidean geometric setting, finding a shortest flip path between two triangulations is NP-complete. However, for lattice triangulations, which are studied as spin systems, there are known On2-time algorithms to find shortest flip paths. These algorithms, as well as ours, apply to constrained flip paths that ensure a set of constraint edges are present in every triangulation along the path. Implications for determining simultaneously flippable edges, i.e. finding optimal simultaneous flip paths between lattice triangulations, and for counting lattice triangulations are discussed.
Towards a Systematic Derivation of an Analysis Model From Business Process Models in the Context of Model-Driven Architecture

The necessity of aligning an enterprise's information system (IS) model to its business process (BP) model is incontestable to the consistent analysis of the business performance. However, the main difficulty of establishing/maintaining BP-IS model alignment stems from the dissimilarities in the knowledge of the information system developers and the business process experts. To overcome these limits, the authors propose a model-driven architecture compliant methodology that helps software analysts to build an IS analysis model aligned to a given BP model. The proposed methodology allows mastering transformation from computation independent model to platform independent model. The CIM level expresses the BP, which is modelled through the standard BPMN and, at the PIM level represents the aligned IS model, which is generated as use case diagram, system sequence diagrams, and class diagram. CIM to PIM transformation accounts for the BP structural and semantic perspectives to generate an aligned IS model that respects the best-practice granularity level and the quality of UML diagrams.
Interactive Verifiable Polynomial Evaluation

Cloud computing platforms have created the possibility for computationally limited users to delegate demanding tasks to strong but untrusted servers. Verifiable computing algorithms help build trust in such interactions by enabling the server to provide a proof of correctness of his results which the user can check very efficiently. In this paper, we present a doubly-efficient interactive algorithm for verifiable polynomial evaluation. Unlike the mainstream literature on verifiable computing, the soundness of our algorithm is information-theoretic and cannot be broken by a computationally unbounded server. By relying on basic properties of error correcting codes, our algorithm enforces a dishonest server to provide false results to problems which become progressively easier to verify. After roughly logd rounds, the user can verify the response of the server against a look-up table that has been pre-computed during an initialization phase. For a polynomial of degree d, we achieve a user complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> ), a server complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> ), a round complexity of O(logd) and an initialization complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> ).
Image Feature Recognition Algorithm for Rural Revitalization Product Design based on Visual Attention Model

Image feature recognition algorithm for the rural revitalization product design based on the visual attention model is designed in this research. The magnitude of its saliency is generally represented by an image. In recent year’s research, the visual attention model has been used in various scenarios. Hence, this paper integrates the image data mining, visual attention analysis and feature extraction technologies to construct the novel framework for the visualized design for the product. These noises not only reduce the image quality but also affect the subsequent image processing, so it is particularly important to denoise the image in preprocessing, we use the neural network based denoising model to help build the robust system for the image analysis. The experiment validates and tests the performance and robustness.
A Strategy of Cloud Resource Load Balancing Enhancement Based on Ant Colony Optimization

Cloud computing plays a core role in the era of big data and artificial intelligence. In cloud environment, the unbalanced resource allocation of server cluster greatly affects the performance of the system. To address this challenge, in this paper we proposed a load balancing strategy for cloud resource scheduling based on ant colony algorithm. By redefining pheromone and heuristic information used in classical ant colony algorithm, server load factor is introduced to provide dynamic changes of the server load to achieve the optimal balanced scheduling of the whole cluster. Three main parameters including CPU, memory, and network bandwidth utilization are considered in our strategy. Through a serials of experiments conducted on CloudSim simulation platform, our proposed strategy shows better performance compared to classical ant colony algorithm and load balancing ant colony algorithm in terms of task completion time, CPU utilization, and load balancing degree.
Fiscal classification using convolutional neural network

Currency classification is an important task in computer vision. Traditional models extract relevant features (brightness, shape, colour etc.) through complex mathematical calculations. A deep learning approach towards fiscal classification by automatically learning higher order feature representations of the dataset is presented. A family of Resnet models is trained to minimize the effect of distortions in currency dataset. The classifier achieves a peak test set performance of 98.09% and an ensembled accuracy of 98.3%. Finally, an optimization method is introduced to allow the models initialized with pretrained weights to converge faster and achieve better accuracy in certain cases.