Reviewerly/exHarmony huggingface dataset:
Automatic Sequential Pattern Mining in Data Streams

Given a large volume of multi-dimensional data streams, such as that produced by IoT applications, finance and online web-click logs, how can we discover typical patterns and compress them into compact models? In addition, how can we incrementally distinguish multiple patterns while considering the information obtained from a pattern found in a streaming setting? In this paper, we propose a streaming algorithm, namely StreamScope, that is designed to find intuitive patterns efficiently from event streams evolving over time. Our proposed method has the following properties: (a) it is effective: it operates on semi-infinite collections of co-evolving streams and summarizes all the streams into a set of multiple discrete segments grouped by their similarities. (b) it is automatic: it automatically and incrementally recognizes such patterns and generates models for each of them if necessary; (c) it is scalable: the complexity of our method does not depend on the length of the data streams. Our extensive experiments on real data streams demonstrate that StreamScope can find meaningful patterns and achieve great improvements in terms of computational time and memory space over its full batch method competitors.
A privacy‐preserving Blockchain with fine‐grained access control

Abstract This article proposes a simple, efficient, and easy‐to‐use mechanism to add privacy and fine‐grained access control features to a traditional Blockchain. It uses standard cryptographic algorithms and techniques, along with a novel key derivation algorithm and a fuzzy extractor component (that derives a cryptographic key from a biometric), to make access control functionality very simple for nonexpert users. Such a Blockchain would be suitable for the storage of participant data postings in long‐term–isolated environments.
Software trustworthiness evaluation model based on a behaviour trajectory matrix

Software trustworthiness is a highly important research topic. Trustworthiness evaluation based on factors that affect software behaviour is conducted mainly according to the influence degrees of these factors on the software behaviour to evaluate trustworthiness. As a result, minimization of the interference of human factors is considered. In this study, to ensure the objectivity of evaluating the trustworthiness of software behaviour, a software trustworthiness evaluation model based on a behaviour trajectory matrix, namely, BTBM-TM was proposed. Checkpoints were set up in the trajectory of the software behaviour, and binary code was introduced to express the software behaviour trajectory tree. The scenario information of the checkpoints was acquired, and used to construct behaviour trajectory matrices, which were used to represent the behaviour trajectory. The behaviour trajectory matrices were transformed into grayscale images, which were used to train the deep residual network (ResNet) to classify the software behaviour. The trained deep residual network was used to categorize the current software behaviour, and the cosine similarity algorithm was used to calculate the deviation degree of the software behaviour trajectory; to perform a dual evaluation of the trustworthiness of software behaviour. The behaviour trajectory information of the Model class of 300 cycles was used to evaluate the trustworthiness of the mine-sweeping game. The trustworthiness evaluation results of the software behaviour of the scheme proposed in this paper (BTBM-TM) were compared with those of the schemes from references [6] and [10]. The accuracies of the schemes from [6] and [10] are lower than that of the BTBM-TM scheme. The trajectory of software behaviour is represented by a matrix and converted into a grayscale image, whose processing method is used to evaluate the trustworthiness of software behaviour more objectively and accurately.
Multi-Feature Fusion for Enhancing Image Similarity Learning

Image similarity learning aims to exploit the correlation between different images by learning image appropriate common features. In recent years, the previous CNN-based methods have directly learned the similarity between image features, which effectively improves the learning efficiency of image similarity. However, it has the following limitations: (1) The extracted image features are too single to describe the content of the image accurately; and (2) the network training is limited by the amount of dataset size. Data augmentation and multi-feature fusion have been demonstrated to appropriately improve the model generalization ability for various vision tasks. This paper integrates these methods into the network structure to design three multi-feature fusion network. The input of network training adopts the same data from the multi-input method to realize data augmentation, and the diversity of extracted image features is greatly improved by fusing different features. Then, the trained network of different dataset size is utilized to verify the network training adaptability of multi-feature fusion network. Moreover, the influence of loss function and optimization algorithm on the learning efficiency of complicated networks have been studied. The experimental results show that our proposed method has excellent performance on the self-collected XPU and Totally-Looks-Like (TLL) dataset, the learning and model generalization ability of multi-feature fusion network are significantly improved through data augmentation and multi-feature fusion. The multi-feature fusion network proposed in this paper has strong adaptability to network training.
When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.
A neural network for semantic labelling of structured information

Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.
Proof of Game (PoG): A Game Theory Based Consensus Model

Now-a-days, Blockchain networks are widely accepted in various applications for its enhanced security levels. Blockchain characteristics like: peer-to-peer, decentralized and immutable distributed ledger makes this technology acceptable to academia, research and industry communities. This work proposes ‘proof-of-game (PoG)’ consensus algorithm suitable for resourceful and resource-constrained devices. Heavy computational challenge in block structure protects the blockchain network from selfish miners and majority attacks. PoG consensus algorithm is suitable for both single and multi-player challenges. It is observed that single and multi-bit challenges increases the resource consumption and makes it difficult for resource constrained device to confirm block in stipulated time period. However, a multi-round multi-bits challenge makes it feasible for resource constrained devices to provide high security within specified time period. In implementation, it is observed that mined blocks indicates the chances of attacks. Large number of blocks are mined if block miner is honest, computational challenge is high and number of participants associated with block is large. Similar scenario is possible with transactions. In results, it is observed that presence of large selfish miners decreases the blocks mined exponentially with increase in computational challenge.
On Energy Conservation in Data Centers

We formulate and study an optimization problem that arises in the energy management of data centers and, more generally, multiprocessor environments. Data centers host a large number of heterogeneous servers. Each server has an active state and several standby/sleep states with individual power consumption rates. The demand for computing capacity varies over time. Idle servers may be transitioned to low-power modes so as to rightsize the pool of active servers. The goal is to find a state transition schedule for the servers that minimizes the total energy consumed. On a small scale, the same problem arises in multicore architectures with heterogeneous processors on a chip. One has to determine active and idle periods for the cores so as to guarantee a certain service and minimize the consumed energy. For this power/capacity management problem, we develop two main results. We use the terminology of the data center setting. First, we investigate the scenario that each server has two states: an active state and a sleep state. We show that an optimal solution, minimizing energy consumption, can be computed in polynomial time by a combinatorial algorithm. The algorithm resorts to a single-commodity minimum-cost flow computation. Second, we study the general scenario that each server has an active state and multiple standby/sleep states. We devise a τ-approximation algorithm that relies on a two-commodity minimum-cost flow computation. Here, τ is the number of different server types. A data center has a large collection of machines but only a relatively small number of different server architectures. Moreover, in the optimization, one can assign servers with comparable energy consumption to the same class. Technically, both of our algorithms involve nontrivial flow modification procedures. In particular, given a fractional two-commodity flow, our algorithm executes advanced rounding and flow packing routines.
Remote Sensing Image Synthesis via Graphical Generative Adversarial Networks

We explore the use of graphical generative adversarial networks (Graphical-GAN) for synthesizing remote sensing images. The model is probabilistic graphical based generative adversarial networks (GAN). It pairs a generative network G with a recognition network R. Both of them are adversarially trained with a discriminative network D. Particularly, R is employed to infer the underlying causal relationships among both observed and latent variables from real remote sensing images. The advantages of the Graphical-GAN for synthesizing multiple categories of remote sensing images are two fold. Firstly, it considers the underlying causal relationships and captures the true data distribution of remote sensing images. Secondly, the adversarial learning generates synthetic sensing images that are similar to real ones with slight differences. Our remote sensing image synthesis scheme paves a promising way for remote sensing dataset augmentation, which is an effective means of improving the accuracy of learning models. Experimental results with high Inception Scores (IS) validate the effectiveness of the Graphical-GAN for remote sensing image synthesis.
On detecting and mitigating phishing attacks through featureless machine learning techniques

The expansion of the Internet has grown the possibilities for fraudulent actions. Among these possibilities, we highlight the phishing activity, created with the objective of capturing user's credentials through a false page similar to the original one. This work proposes PhishKiller, a tool capable of detecting and mitigating phishing attacks by means a proxy approach employed to intercept user‐accessed addresses, and featureless machine learning techniques to classify URLs. The proof‐of‐concept evaluation results revealed that PhishKiller has a more cost‐effective compared to state of the art, with an accuracy of 98.30% and taking only 81.68 ms to predict and block malicious websites.
ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion

Latent variable models cover a broad range of statistical and machine learning models, such as Bayesian models, linear mixed models, and Gaussian mixture models. Existing methods often suffer from two major challenges in practice: (a) a proper latent variable distribution is difficult to be specified; (b) making an exact likelihood inference is formidable due to the intractable computation. We propose a novel framework for the inference of latent variable models that overcomes these two limitations. This new framework allows for a fully data-driven latent variable distribution via deep neural networks, and the proposed stochastic gradient method, combined with the Langevin algorithm, is efficient and suitable for complex models and big data. We provide theoretical results for the Langevin algorithm, and establish the convergence analysis of the optimization method. This framework has demonstrated superior practical performance through simulation studies and a real data analysis. Supplementary materials for this article are available online.
A CNN-Based Method for Sar Image Despeckling

In this paper, to remove the speckle noise of SAR images, we propose a modified method for SAR image despeckling based on Convolutional Neural Networks (CNNs). The network uses dilated convolutions for feature extraction, which can extend the receptive field and prevent too many layers that may result in computational burden and low efficiency. The network also uses residual learning to accelerate training procedure and improve performance for SAR image despeckling. Experimental results show that the proposed method achieve good performance for SAR image despeckling both on simulated and real data. And compared with the traditional despeckling methods, the proposed method has better performance and higher efficiency.
Efficient Distributed Graph Analytics using Triply Compressed Sparse Format

This paper presents Triply Compressed Sparse Column (TCSC), a novel compression technique designed specifically for matrix-vector operations where the matrix as well as the input and output vectors are sparse. We refer to these operations as SpMSpV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . TCSC compresses the nonzero columns and rows of a highly sparse matrix representing a large real-world graph. During this compression, it encodes the sparsity patterns of the input and output vectors within the compressed representation of the sparse matrix itself. Consequently, it aligns the compressed indices of the input and output vectors with those of the compressed matrix columns and rows, thus eliminating the need for extra indirections when SpMSpV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> operations access the vectors. This results in fewer cache misses, greater space efficiency and faster execution times. We evaluate TCSC's performance and show that it is more space and time efficient compared to CSC and DCSC, with up to 11× speedup. We integrate TCSC into GraphTap, our suggested linear algebra-based distributed graph analytics system. We compare GraphTap against GraphPad and LA3, two state-of-the-art linear algebra-based distributed graph analytics systems, using different dataset scales and numbers of processes. GraphTap is up to 7× faster than these systems due to TCSC and the resulting communication efficiency.
A Type-Based HFL Model Checking Algorithm

Higher-order modal fixpoint logic (HFL) is a higher-order extension of the modal $$\mu $$-calculus, and strictly more expressive than the modal $$\mu $$-calculus. It has recently been shown that various program verification problems can naturally be reduced to HFL model checking: the problem of whether a given finite state system satisfies a given HFL formula. In this paper, we propose a novel algorithm for HFL model checking: it is the first practical algorithm in that it runs fast for typical inputs, despite the hyper-exponential worst-case complexity of the HFL model checking problem. Our algorithm is based on Kobayashi et al.'s type-based characterization of HFL model checking, and was inspired by a saturation-based algorithm for HORS model checking, another higher-order extension of model checking. We prove the correctness of the algorithm and report on an implementation and experimental results.
The Alternating BWT: An algorithmic perspective

The Burrows-Wheeler Transform (BWT) is a word transformation introduced in 1994 for Data Compression. It has become a fundamental tool for designing self-indexing data structures, with important applications in several areas in science and engineering. The Alternating Burrows-Wheeler Transform (ABWT) is another transformation recently introduced in Gessel et al. (2012) [21] and studied in the field of Combinatorics on Words. It is analogous to the BWT, except that it uses an alternating lexicographical order instead of the usual one. Building on results in Giancarlo et al. (2018) [23], where we have shown that BWT and ABWT are part of a larger class of reversible transformations, here we provide a combinatorial and algorithmic study of the novel transform ABWT. We establish a deep analogy between BWT and ABWT by proving they are the only ones in the above mentioned class to be rank-invertible, a novel notion guaranteeing efficient invertibility. In addition, we show that the backward-search procedure can be efficiently generalized to the ABWT; this result implies that also the ABWT can be used as a basis for efficient compressed full text indices. Finally, we prove that the ABWT can be efficiently computed by using a combination of the Difference Cover suffix sorting algorithm (Kärkkäinen et al., 2006 [28]) with a linear time algorithm for finding the minimal cyclic rotation of a word with respect to the alternating lexicographical order.
Correlation classifiers based on data perturbation: New formulations and algorithms

This paper develops a novel framework for a family of correlation classifiers that are reconstructed from uncertain convex programs under data perturbation. Under this framework, correlation classifiers are exploited from the pessimistic viewpoint under possible perturbation of data, and the max-min optimization problem is formulated by simplifying the original model in terms of adaptive uncertainty regions. The proposed model can be formulated as a minimization problem under proper conditions. The proximal majorization-minimization optimization (PMMO) based on Bregman divergences is devised to solve the proposed model that may be nonconvex or nonsmooth. It is found that using PMMO to solve the proposed model can exploit the convergence rate of the solution sequence in the nonconvex case. In the case of specific functions we can use the accelerated versions of first-order methods to solve the proposed model with convexity in order to make them have fast convergence rates in terms of the objective function. Extensive experiments on some data sets are conducted to demonstrate the feasibility and validity of the proposed model.
Performance enhancement of two-camera robotic system using adaptive gain approach

Purpose This paper aims to improve the performance of a two-camera robotic feedback system designed for automatic pick and place application by modifying its velocity profile during switching of control. Design/methodology/approach Cooperation of global and local vision sensors ensures visibility of the target for a two-camera robotic system. The master camera, monitoring the workspace, guides the robot such that image-based visual servoing (IBVS) by the eye-in-hand camera transcends its inherent shortcomings. A hybrid control law steers the robot until the system switches to IBVS in a region proven for its asymptotic stability and convergence through a qualitative overview of the scheme. Complementary gain factors can ensure a smooth transition in velocity during switching considering the versatility and range of the workspace. Findings The proposed strategy is verified through simulation studies and implemented on a 6-DOF industrial robot ABB IRB 1200 to validate the practicality of adaptive gain approach while switching in a hybrid visual feedback system. This approach can be extended to any control problem with uneven switching surfaces or coarse/fine controllers which are subjected to discrete time events. Practical implications In complex workspace where robots operate in parallel with other robots/humans and share workspaces, the supervisory control scheme ensures convergence. This study proves that hybrid control laws are more effective than conventional approaches in unstructured environments and visibility constraints can be overcome by the integration of multiple vision sensors. Originality/value The supervisory control is designed to combine the visual feedback data from eye-in-hand and eye-to-hand sensors. A gain adaptive approach smoothens the velocity characteristics of the end-effector while switching the control from master camera to the end-effector camera.
Aircraft Detection from Remote Sensing Image Based on A Weakly Supervised Attention Model

Aircraft detection from high resolution remote sensing image is a challenging task due to the lack of annotation information, large-scale image size, and sparse distribution of aircraft. Recently, some convolutional neural network(CNN) based methods explore the attention based weakly supervised way to localize the aircraft without manual annotation information. However, the detection results are not satisfied with high false detection ratio. In this paper, a method of utilizing weakly supervised attention model to localize the multi-scale aircrafts is presented, in which the attention model is carried out in a weakly supervised way. Compared with other CNN based method, the proposed attention model can obtain more accurate attention map and localize the aircrafts more precisely. The experimental results on two challenging datasets demonstrate that the proposed method achieves higher detection accuracy and lower false detection ratio than other methods.