Reviewerly/exHarmony huggingface dataset:
Multi-fuzzy-constrained graph pattern matching with big graph data

Graph pattern matching has been widespread used for protein structure analysis, expert finding and social group selection, ect. Recently, the study of graph pattern matching using the abundant attribute information of vertices and edges as constraint conditions has attracted the attention of schola rs, and multi-constrained simulation has been proposed to address the problem in contextual social networks. Actually, multi-constrained graph pattern matching is an NP-complete problem and the fuzziness of constraint variables may exist in many applications. In this paper, we introduce a multi-fuzzy-constrained graph pattern matching problem in big graph data, and propose an efficient first-k algorithm Fuzzy-ETOF-K for solving it. Specifically, exploration-based method based on edge topology is adopted to improve the efficiency of edge connection, and breadth-first bounded search is used for edge matching instead of shortest path query between two nodes to improve the efficiency of edge matching. The results of our experiments conducted on three datasets of real social networks illustrate that our proposed algorithm Fuzzy-ETOF-K significantly outperforms existing approaches in efficiency and the introduction of fuzzy constraints makes our proposed algorithm more efficient and effective.
Automatic LPI Radar Signal Sensing Method Using Visibility Graphs

The issue of the low probability of intercept (LPI) radar signal sensing has received considerable attention. Furthermore, the development of military technology further increased demand for it in future electronic warfare (EW). Meanwhile, so far, a systematic understanding of how radar signal detection and recognition technology contributes to EW is still lacking. Therefore, this study aims to contribute to this growing area of research by exploring an automatic method for detecting and identifying radar signals based on visibility graphs (VG), which can extract more network and feature information in the two-dimensional space of VG. In this article, the signal to be measured is subjected to wavelet noise reduction. Secondly, auto-correlation processing is performed on the signal, we subsequently convert the signal into a VG complex network. Then the average degree and weighted operation of the network are used for signal detection and recognition, respectively. Experiments in the last part indicate that the proposed method provides excellent performance, such as the robustness to noise and the probability of classification, over several state-of-the-art algorithms.
Local Stereo Matching: An Adaptive Weighted Guided Image Filtering-Based Approach

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.
Unsupervised domain adaptation with progressive adaptation of subspaces

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adaptation of Subspaces approach (PAS) in which we utilize such an intuition that appears much reasonable to gradually obtain reliable pseudo labels. Speci fically, we progressively and steadily refine the shared subspaces as bridge of knowledge transfer by adaptively anchoring/selecting and leveraging those target samples with reliable pseudo labels. Subsequently, the refined subspaces can in turn provide more reliable pseudo-labels of the target domain, making the mode collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not only effective for common UDA, but also outperforms the state-of-the arts for more challenging Partial Domain Adaptation (PDA) situation, where the source label set subsumes the target one.
Verifiable single-server private information retrieval from LWE with binary errors

Private Information Retrieval (PIR) allows a client to privately retrieve some data from a public database. There exist two types of PIR: (computational) Single-server PIR (SPIR) and (information-theoretic) multi-server PIR. In this paper, we focus on exploring SPIR. We first propose a simple and efficient additively-homomorphic encryption scheme of which privacy is based on the learning with binary errors assumption that is known as an interesting candidate for practical lattice-based cryptography. Then, according to our proposed homomorphic encryption scheme, we give a Verifiable (single/multi-bit) SPIR (VSPIR) scheme for the single-query case under the malicious server model. To the best of our knowledge, our proposal is the first practical non-interactive VSPIR scheme employing an efficient probabilistic proof that can discover the forged result with overwhelming probability. The corresponding communication complexity and computational complexity are comparable with those of some typical SPIR schemes. Moreover, we extend our single-query VSPIR scheme to construct a non-interactive multi-query solution. In particular, the corresponding communication complexity and computational complexity are the same as those of the single-query scheme. Finally, we provide detailed implementation results to confirm efficiency of our proposals.
Towards Analogy-Based Explanations in Machine Learning

Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogical reasoning is certainly useful for constructing new learning algorithms with high predictive accuracy, is arguably not less interesting from an interpretability and explainability point of view. More specifically, we take the view that an analogy-based approach is a viable alternative to existing approaches in the realm of explainable AI and interpretable machine learning, and that analogy-based explanations of the predictions produced by a machine learning algorithm can complement similarity-based explanations in a meaningful way. To corroborate these claims, we outline the basic idea of an analogy-based explanation and illustrate its potential usefulness by means of some examples.
EDLLIE-Net: Enhanced Deep Convolutional Networks for Low-Light Image Enhancement

Low-light image enhancement technology has been developed in recent years. However, most existing related methods need to adjust too many arguments or performs unstably when the environment differs greatly. In our paper, we propose a novel low-light image enhancement method named enhanced deep convolutional low-light image enhancement network (EDLLIE-Net) to address these problems. Firstly, our proposed method extracts multi-scale feature map, which can improve the utilization of context information. Subsequently, our proposed method rescales the feature map by attention mechanism to perceive the most useful information and characteristics. Finally, our proposed method uses encode-decode and residual-learning architecture to obtain the normal image from low-light image. To prove the effectiveness of our proposed model, we evaluate it from two aspects. On one hand, we show EDLLIE-Net can not only handle different dark scenes effectively but also achieve better performance than other representative methods by common metric judgement. On the other hand, a novel evaluation method by combining enhanced result and high-level vision task is proposed, we show our proposed method can gain the higher improvement degree for high-level vision tasks.
A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Explainable Semantic Segmentation of Trees in Satellite Images using HSV Color Space

In recent years, new sensor technologies have increased the accessibility of high-resolution satellite images. The information in these images can help to improve activities like urban planning and growth analysis of cities. Additionally, information extracted from these images can be used for taking decisions related to infrastructure planning, e.g. identifying objects that might interfere with network assets like underground cables. To be able to justify the cost of network planning decisions a high degree of interpretability is required. Convolutional Neural Networks (CNNs) are the state of the art for segmenting these images, but like any black box model they do not offer any explanation for their output. In this paper, we present an approach on how to use a Fuzzy Logic System (FLS) for performing explainable semantic segmentation of trees in satellite images. The FLS uses the HSV (hue, saturation, value) of the pixels as inputs and was optimized by using an evolutionary algorithm called Big Bang Big Crunch. The best configuration for the Interval Type-2 FLS has an Intersection over Union metric measure of 60.6%, which is close to the results obtained from neural network, however the proposed FLS provides interpretable outputs which is highly needed for the real-world operation especially in the telecommunication domain.
Security, Privacy, and Dependability Evaluation in Verification and Validation Life Cycles for Military IoT Systems

The Internet of Things (IoT) is a disruptive technology that complements the usage of modern day information and communications technology (ICT) systems. IoT systems, with their small form and cost factor coupled with their increasing reliability, have made huge inroads in all markets. Connected to some form of interconnected networks, either to the mainstream Internet or private networks, they enable ubiquitous and to a large extent autonomous operation. This has taken away the idea that sensing, actuation, and computing needs to be dependent on expensive and complicated legacy systems. Although complete discontinuation of legacy systems is still quite a long journey and would not be completely possible, IoT systems show the waypoints as to how the ICT industry will evolve. The military domain is not far away from adopting IoT technologies in its operational construct. The NATO IST-147 and 176 research task groups have closely examined applicability of IoT for federated and ubiquitous military applications. Introducing IoT devices in the military domain requires verification and validation (V&V) of ICT systems as per operational guidelines. This article proposes and applies a concept for security, privacy, and eependability evaluation of IoT systems that could be used for V&V processes. This would enable more streamlined and standardized evaluations of IoT systems before they can be deemed usable for military contexts.
Strategic leadership in cyber security, case Finland

Cyber security has become one of the biggest priorities for businesses and governments. Streamlining and strengthening strategic leadership are key aspects in making sure the cyber security vision is achieved. The strategic leadership of cyber security implies identifying and setting goals based on the protection of the digital operating environment. Furthermore, it implies coordinating actions and preparedness as well as managing extensive disruptions. The aim of this article is to define what is strategic leadership of cyber security and how it is implemented as part of the comprehensive security model in Finland. In terms of effective strategic leadership of cyber security, it is vital to identify structures that can respond to the operative requirements set by the environment. As a basis for national security development and preparedness, it is necessary to have a clear strategy level leadership model for crises management in disturbances in normal and in emergency conditions. In order to ensure cyber security and achieve the set strategic goals, society must be able to engage different parties and reconcile resources and courses of action as efficiently as possible. Cyber capability must be developed in the entire society, which calls for strategic coordination, management and executive capability.
Multiview Consensus Structure Discovery

Multiview subspace learning has attracted much attention due to the efficacy of exploring the information on multiview features. Most existing methods perform data reconstruction on the original feature space and thus are vulnerable to noisy data. In this article, we propose a novel multiview subspace learning method, called multiview consensus structure discovery (MvCSD). Specifically, we learn the low-dimensional subspaces corresponding to different views and simultaneously pursue the structure consensus over subspace clustering for multiple views. In such a way, latent subspaces from different views regularize each other toward a common consensus that reveals the underlying cluster structure. Compared to existing methods, MvCSD leverages the consensus structure derived from the subspaces of diverse views to better exploit the intrinsic complementary information that well reflects the essence of data. Accordingly, the proposed MvCSD is capable of producing a more robust and accurate representation structure which is crucial for multiview subspace learning. The proposed method can be optimized effectively, with theoretical convergence guarantee, by alternatively iterating the argument Lagrangian multiplier algorithm and the eigendecomposition. Extensive experiments on diverse datasets demonstrate the advantages of our method over the state-of-the-art methods.
Highly scalable parallel genetic algorithm on Sunway many-core processors

As a heuristic method, the genetic algorithm provides promising solutions with impressive performance benefits for large-scale problems. In this study, we propose a highly scalable hybrid parallel genetic algorithm (HPGA) based on Sunway TaihuLight Supercomputer. First, the Cellular model is presented on a thread level, so that each individual can be processed by a single computing unit which is in charge of the parallel fitness calculation, crossover, and mutation operations. The information exchange between individuals is realized by register communication. Second, the Island model is assigned to a process level, so that each process accounts for a single sub-population, and the migration among sub-populations is implemented using MPI communication. The proposed approach can fully exploit the individual diversity of the genetic algorithm and reasonably maintain the communication overhead. Based on the widely used CEC2013 benchmark, the experimental results show that the algorithm presents a sound performance in terms of both accuracy and convergence speed.
Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of ≈0.84 (i.e. an increase of ≈27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of ≈0.78 (i.e. an increase of ≈6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant’s profit in ≈0.44 M€/year, as compared with the original forecasts. Operational strategies are proposed accordingly.
Random projection-based auxiliary information can improve tree-based nearest neighbor search

Nearest neighbor search using random projection trees has recently been shown to achieve superior performance, in terms of better accuracy while retrieving less number of data points, compared to locality sensitive hashing based methods. However, to achieve acceptable nearest neighbor search accuracy for large scale applications, where number of data points and/or number of features can be very large, it requires users to maintain, store and search through large number of such independent random projection trees, which may be undesirable for many practical applications. To address this issue, in this paper we present different search strategies to improve nearest neighbor search performance of a single random projection tree. Our approach exploits properties of single and multiple random projections, which allows us to store meaningful auxiliary information at internal nodes of a random projection tree as well as to design priority functions to guide the search process that results in improved nearest neighbor search performance. Empirical results on multiple real world datasets show that our proposed method significantly improves nearest neighbor search accuracy of a single tree compared to baseline methods.
Simulation-Extractable zk-SNARK With a Single Verification

Among the zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK), the simulation-extractable zk-SNARK (SE-SNARK) introduces a security notion of non-malleability. The existing pairing-based zk-SNARKs designed from linear encoding are known to be vulnerable to algebraic manipulation of the proof. The latest SE-SNARKs check the proof consistency by increasing the proof size and the verification cost. In particular, the number of pairings increases almost doubles due to further verification. In this article, we propose two novel SE-SNARK constructions with a single verification. The consistency check is subsumed in a single verification through employing a hash function. The proof size and verification time of the proposed SE-SNARK schemes are minimal in that it is the same as the state-of-the-art zk-SNARK without non-malleability. The proof in our SE-SNARK constructions comprises only three group elements (type III) in the QAP-based scheme and two group elements (type I) in the SAP-based scheme. The verification time in both requires only 3 pairings. The soundness of the proposed schemes is proven under the hash-algebraic knowledge (HAK) assumption and the (linear) collision-resistant hash assumption.
Tractable Bayesian density regression via logit stick-breaking priors

There is a growing interest in learning how the distribution of a response variable changes with a set of observed predictors. Bayesian nonparametric dependent mixture models provide a flexible approach to address this goal. However, several formulations require computationally demanding algorithms for posterior inference. Motivated by this issue, we study a class of predictor-dependent infinite mixture models, which relies on a simple representation of the stick-breaking prior via sequential logistic regressions. This formulation maintains the same desirable properties of popular predictor-dependent stick-breaking priors, and leverages a recent Pólya-gamma data augmentation to facilitate the implementation of several computational methods for posterior inference. These routines include Markov chain Monte Carlo via Gibbs sampling, expectation–maximization algorithms, and mean-field variational Bayes for scalable inference, thereby stimulating a wider implementation of Bayesian density regression by practitioners. The algorithms associated with these methods are presented in detail and tested in a toxicology study.
A Situation-aware Learning System based on Fuzzy Cognitive Maps to increase Learner Motivation and Engagement

The lack of motivation and engagement is recognized as one of the main causes of learners dropping out of e-learning systems. In this paper, an Adaptive Learning System, based on the principles of situation awareness, is proposed to tackle such an issue. The work proposes a situation model based on motivation and engagement. A technique based on Fuzzy Cognitive Map (FCM) has been defined to identify the current situation by tracking the behavior and the interactions of the learner with the system. The FCM drives the process of feedback generation to improve the situation awareness of the learner, and therefore their motivation and engagement. The system has been evaluated using the Situation Awareness Global Assessment Technique, involving students and teachers. The experimental results demonstrate that the system is able to significantly improve the situation awareness of both learners and teachers, reducing the risk of learner dropout.
Dashed strings for string constraint solving

String processing is ubiquitous across computer science, and arguably more so in web programming — where it is also a critical part of security issues such as injection attacks. In recent years, a number of string solvers have been developed to solve combinatorial problems involving string variables and constraints. We examine the dashed string approach to string constraint solving, which represents an unknown string as a sequence of blocks of characters with bounds on their cardinalities. The solving approach relies on propagation of information about the blocks of characters that arise from reasoning about the constraints in which they occur. This approach shows promising performance on many benchmarks involving constraints like string length, equality, concatenation, and regular expression membership. In this paper, we formally review the definition, the properties and the use of dashed strings for string constraint solving, and we provide an empirical validation that confirms the effectiveness of this approach.