Reviewerly/exHarmony huggingface dataset:
Mining conditional discriminative sequential patterns

Discriminative sequential pattern mining is one of the most important topics in pattern mining, which has a very wide range of applications. Discriminative sequential pattern mining is intended to extract sequential patterns with significant differences among different classes. In recent years, a variety of algorithms for mining discriminative sequential patterns have been proposed, but these algorithms still suffer from generating many redundant patterns. There are many factors that may lead to the redundancy of reported patterns, among which the subset-induced redundancy is the most critical one, i.e., some patterns are reported to be discriminative mainly because some of their sub-patterns are strongly discriminative. In order to solve the subset-induced redundancy issue, we propose the concept of conditional discriminative sequential pattern, and design a new algorithm called CDSPM (Conditional Discriminative Sequential Pattern Mining) for extracting such kinds of patterns. The experimental results on real data sets show that CDSPM can effectively remove discriminative sequential patterns that are redundant with respect to their sub-patterns.
Robust event-triggered state estimation: A risk-sensitive approach

In this work, we investigate a robust event-triggered remote state estimation problem for linear Gaussian systems with a stochastic event-triggering condition. The reference measure approach is used to obtain a robust event-triggered estimate that minimizes the so-called risk-sensitive criterion, which refers to the expectation of the exponential of the sum of the squared estimation error. We introduce the reference measure, under which, the measurements are identically independently distributed (i.i.d.) and independent of the states, and propose a map to link the "real-world" measure to the reference measure so that the recursions of the information states under the reference measure can be obtained. Based on these results, the risk-sensitive criteria are reformulated under the reference measure and closed-form expressions of the risk-sensitive event-triggered posterior and prior estimates are presented, which are shown to evolve in simple recursive Kalman-like structures. Moreover, two sufficient stability conditions for the proposed estimators are given, where the first requires the solution of a time-varying Riccati equation to be positive-definite and satisfy a specific inequality, which can be further extended to the scenario when the weighting matrix in the risk-sensitive criterion is time-variant; the second gives the range of values of the risk-sensitive parameter and covariance of the initial state for which the proposed estimators are stable. Comparative simulation results demonstrate that the proposed risk-sensitive event-triggered estimator is more robust to model uncertainties compared with a typical minimum mean squared error (MMSE) estimator with stochastic event-triggered sensor scheduling.
Faster Key Compression for Isogeny-Based Cryptosystems

Supersingular isogeny-based cryptography is one of the more recent families of post-quantum proposals. An interesting feature is the comparatively low bandwidth occupation in key agreement protocols, which stems from the possibility of key compression. However, compression and decompression introduce a significant overhead to the overall processing cost despite recent progress. In this paper we address the main processing bottlenecks involved in key compression and decompression, and suggest substantial improvements for each of them. Some of our techniques may have an independent interest for other, more conventional areas of elliptic curve cryptography as well.
Energy-Efficient Task Scheduling for CPU-Intensive Streaming Jobs on Hadoop

Hadoop, especially Hadoop 2.0, has been a dominant framework for real-time big data processing. However, Hadoop is not optimized for energy efficiency. Aiming to solve this problem, in this paper, we propose a new framework to improve the energy efficiency of Hadoop 2.0. We focus on the resource manager in Hadoop 2.0, namely YARN, and propose energy-efficient task scheduling mechanisms on YARN. Particularly, we focus on CPU-intensive streaming jobs and classify streaming jobs into two types, namely batch streaming jobs (i.e., a set of jobs are submitted simultaneously) and online streaming jobs (i.e., jobs are continuously submitted one by one). We devise different energy-efficient task scheduling algorithms for each kind of streaming jobs. Specially, we first propose to abstractly model performance and energy consumption by considering the characteristics of tasks as well as the computational resources in YARN. Based on this model, we study the energy efficiency of streaming tasks which consist of the performance model and energy consumption model of task. We propose two key principles for improving energy efficiency: 1) CPU usage aware task allocation, partitions tasks to NMs based on the task characteristic in term of CPU usage; and 2) resource efficient task allocation, reduce idle resource. Then, we propose a D-based binning algorithm for the batch task scheduling and K-based binning algorithm for the online task scheduling that can adapt to continuously arriving tasks. We conduct extensive experiments on a real Hadoop 2.0 cluster and use two kinds of workloads to evaluate the performance and energy efficiency of our proposal. Compared with Storm (the streaming data processing tool in Hadoop 2.0) and other approaches including TAPA and DVFS-MR, our proposal is more energy efficient. The batch task scheduling algorithm reduces up to 10 percent of energy consumption and keeps comparable performance. In addition, the online task scheduling algorithm reduces up to 7 percent over the existing algorithms.
Estimation of the complexity of a digital image from the viewpoint of fixed point theory

The present paper introduces and estimates the complexity of the fixed point property of a digital image (X, k) for any k−DC-self-map f of (X, k), where a k−DC-self-map f of (X, k) means a digitally k-continuous self-map of (X, k) with a digital version of the Banach contraction principle. To do this work, we need to study some properties of iterations of a k−DC-self-map f of (X, k) and to establish the notion of complexity of (X, k) denote by C♯(X, k) (see Definition 7 in the present paper). According to C♯(X, k), we can estimate complexity of the fixed point property of (X, k) for any k−DC-self-map f of (X, k). Based on this approach, the present paper investigates some relationships between the k-adjacency of (X, k) and C♯(X, k). Furthermore, we prove that C♯(X, k) is not a digital topological invariant. Besides, we develop the notions of uniform k-connectedness and strict k-connectivity to calculate C♯(X, k) for some digital images (X, k). In the paper each (X, k) is assumed to be a k-connected and non-empty set and 2 ≤ | X |≨∞, where | X | means the cardinal number of the given set X.
Sherlock N-overlap: Invasive Normalization and Overlap Coefficient for the Similarity Analysis Between Source Code

Some tools for detecting similarity, such as Sherlock, compare textual documents of any nature, but have limitations to compare source code files. The presence or absence of blank spaces between structure elements, variable names, among other actions interfere with the similarity index found. This paper evidences that the preprocessing of the source code improves Sherlock performance. The results are based on experiments conducted with 66 source code previously plagiarized, and a base formed by 2160 codes created by students of engineering courses in programming classes. In this last set, the situation of similarity was not previously known, so a method was created to calculate precision and recall, in a relative way, based on a set of reference tools, as a kind of oracle. Our approach, called Sherlock N-overlap obtained, in most of the cases tested, similarity indexes superior to other complex tools such as MOSS, JPlag and SIM.
Towards efficiently mining closed high utility itemsets from incremental databases

The set of closed high-utility itemsets (CHUIs) concisely represents the exact utility of all itemsets. Yet, it can be several orders of magnitude smaller than the set of all high-utility itemsets. Existing CHUI mining algorithms assume that databases are static, making them very expensive in the case of incremental data, since the whole dataset has to be processed for each batch of new transactions. To address this challenge, this paper presents the first approach, called IncCHUI, that mines CHUIs efficiently from incremental databases. In order to achieve this, we propose an incremental utility-list structure, which is built and updated with only one database scan. Further, we apply effective pruning strategies to fast construct incremental utility-lists and eliminate candidates that are not updated. Finally, we suggest an efficient hash-based approach to update or insert new closed sets that are found. Our extensive experimental evaluation on both real-life and synthetic databases shows the efficiency, as well as the feasibility of our approach. It significantly outperforms previously proposed methods that are mainly run in batch mode in terms of speed, and it is scalable with respect to the number of transactions.
Threat-Specific Security Risk Evaluation in the Cloud

Existing security risk evaluation approaches (e.g., asset-based) do not consider specific security requirements of individual cloud computing clients in the security risk evaluation. In this paper, we propose a threat-specific risk evaluation approach that uses various security attributes of the cloud (e.g., vulnerability information, the probability of an attack, and the impact of each attack associated with the identified threat(s)) as well as the client-specific security requirements in the cloud. Our approach allows a security administrator of the cloud provider to make fine-grained decisions for selecting mitigation strategies in order to protect the outsourced computing assets of individual clients based on their specific security needs against specific threats. This is different from the existing asset-based approaches where they do not have the functionalities to provide the security evaluation of the cloud with respect to specific threats. On the other hand, the proposed approach enables security administrators to compute a range of more effective client-specific countermeasures with respect to the importance of security requirements and threats. The experimental evaluation results demonstrate that effective security solutions vary due to specific threats prioritized by different clients for an application in the cloud. Further, the proposed approach is not limited to only the cloud-based systems, but can easily be adopted to other networked systems. We have also developed a software tool to support the proposed approach.
Effectively training neural machine translation models with monolingual data

Improving neural machine translation models (NMT) with monolingual data has aroused more and more interests in this area and back-translation for monolingual data augmentation Sennrich et al. (2016) has been taken as a promising development recently. While the naive back-translation approach improves the translation performance substantially, we notice that its usage for monolingual data is not so effective because traditional NMT models make no distinction between the true parallel corpus and the back translated synthetic parallel corpus. This paper proposes a gate-enhanced NMT model which makes use of monolingual data more effectively. The central idea is to separate the data flow of monolingual data and parallel data into different channels by the elegant designed gate, which enables the model to perform different transformations according to the type of the input sequence, i.e., monolingual data and parallel data. Experiments on Chinese-English and English-German translation tasks show that our approach achieves substantial improvements over strong baselines and the gate-enhanced NMT model can utilize the source-side and target-side monolingual data at the same time.
Real-Time Detection of Power Analysis Attacks by Machine Learning of Power Supply Variations On-Chip

Reliably and power efficiently securing integrated systems against advanced power analysis attacks (PAAs) is a significant design challenge in modern integrated circuits. Power masking and hiding are typical countermeasures for increasing system resilience for power attacks at the expense of the overall system performance and power efficiency. These method are, however, not able to alert the user or trigger additional protective actions in case of the attack. In this paper, a method for detecting power attacks in real-time is proposed. The proposed approach exploits statistical methods to analyze the on-chip voltage variations across an on-chip power grid and detect the attacker probe connected to the system. The problem of the full security coverage of the power grid is formulated and solved in this paper. Adjusting the density of the on-chip sensors and exploiting sparse analysis techniques is considered to simultaneously enhance the accuracy and power efficiency of the proposed solution. The proposed attack detection system is designed, simulated, and evaluated in Simulink based on IBM microprocessor benchmark data. Machine learning (ML) models are trained in Python and with scikit-learn ML library. The proposed system has been demonstrated to efficiently detect PAA within a period of time that is orders of magnitude shorter than a typical attack duration length. The system is expected to exhibit high detection accuracy and power efficiency across a wide spectrum of integrated systems.
Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA

Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.
Weight enumerators of reducible cyclic codes and their dual codes

Let F q be a finite field and n a positive integer. In this paper, we find a new combinatorial method to determine weight enumerators of reducible cyclic codes and their dual codes of length n over F q , which just generalize results of Zhu et al. (2015); especially, we also give the weight enumerator of a cyclic code, which is viewed as a partial Melas code. Furthermore, weight enumerators obtained in this paper are all in the form of power of a polynomial.
Variability of environmental factors and the effects on vegetation diversity with different restoration years in a large open-pit phosphorite mine

Open-pit mining activities can cause serious disturbance to the local land and even regional vegetation communities. Vegetation restoration is a crucial approach for maintaining ecological stability in a post-mining area. In this study, the role of the environment factors on vegetation diversity under different restoration years (2a, 5a, 10a and control area) in the largest phosphate mining areas in Southwestern China was evaluated by a multi-model inference (MMI) based on the Akaike information criterion (AIC). Three competing models of environmental factors included vegetation nutrient content (M1), soil heavy metal content (M2) and human disturbance (M3) were constructed to analyze how they impact vegetation diversity. The results revealed the changing effects of different environmental factors under four restoration stages. Heavy metal content in the soil (M2) had the strongest effect on vegetation diversity in the restoration areas at 2 and 10 years, whereas human disturbance (M3) had the strongest effect in the restoration area at 5 years. In the control area, the vegetation nutrient content (M1) had the strongest effect on vegetation diversity. According to the different models, a comprehensive index of environmental factors (CIEF) was established to build the fitting formula with vegetation diversity. The index could be used for assessing the efficiency of vegetation restoration by comparing the theoretical value and the actual value of Shannon's diversity index in the study area and other phosphate mining areas.
Software feature refinement prioritization based on online user review mining

Online software reviews have provided a wealth of user feedback on software applications. User reviews along with ratings have been influential in a series of software engineering tasks e.g. software maintenance and release planning. Our research aims to assist managers in prioritizing features to be refined in next release from the perspective of enhancing user ratings via mining online reviews. We first extract software features from user reviews and determine their probability distribution in each review with LDA. Then the ground truth rating of each feature is estimated by linear regression under the assumption that the software functionality rating is a convex combination of all feature ratings weighted by their distribution probabilities over the review. Finally, we formalize feature refinement prioritization as an optimization problem which maximizes user group's rating on the software functionality under the constraint of development budget. The proposed approach can use topic model to jointly extract features from user reviews semi-supervisedly and determine each feature's weight in each user's rating on the software functionality. The estimated ground truth ratings of all features reveal how reviewer group evaluate those features. Finally, we provide an illustrative example to demonstrate the key idea of our framework. Our proposed framework is general to various software products with mass user reviews and semi-automatic without much human efforts and intervention. The framework's interpretability helps managers better understand user feedback on the software functionality and make feature refinement plan for the upcoming releases.
Video Big Data Retrieval Over Media Cloud: A Context-Aware Online Learning Approach

Online video sharing (e.g., via YouTube or YouKu) has emerged as one of the most important services in the current Internet, where billions of videos on the cloud are awaiting exploration. Hence, a personalized video retrieval system is needed to help users find interesting videos from big data content. Two of the main challenges are to process the increasing amount of video big data and resolve the accompanying “cold start” issue efficiently. Another challenge is to satisfy the users’ need for personalized retrieval results, of which the accuracy is unknown. In this paper, we formulate the personalized video big data retrieval problem as an interaction between the user and the system via a stochastic process, not just a similarity matching, accuracy (feedback) model of the retrieval; introduce users’ real-time context into the retrieval system; and propose a general framework for this problem. By using a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contextual</i> multiarmed bandit-based algorithm to balance the accuracy and efficiency, we propose a context-based online big-data-oriented personalized video retrieval system. This system can support datasets that are dynamically increasing in size and has the property of cross-modal retrieval. Our approach provides accurate retrieval results with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sublinear</i> regret and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linear</i> storage complexity and significantly improves the learning speed. Furthermore, by learning for a cluster of similar contexts simultaneously, we can realize sublinear storage complexity with the same regret but slightly poorer performance on the “cold start” issue compared to the previous approach. We validate our theoretical results experimentally on a tremendously large dataset; the results demonstrate that the proposed algorithms outperform existing bandit-based online learning methods in terms of accuracy and efficiency and the adaptation from the bandit framework offers additional benefits.
Enhanced speed control of a DC servo system using PI + DF controller tuned by stochastic fractal search technique

Since Proportional + Integral + Derivative (PID) controller is still the workhorse in taking over the workload of process control systems, this article introduces a new design methodology toward improving the performance of such controller. After a PI control law with windup protection is given, it is combined with a derivative path employing a first-order low pass filter in an innovative way to develop a performant controller called PI + DF controller. In attempting to attain a high level of control performance, gains of this controller including proportional, integral, derivative and filter gains are tuned choosing the recently introduced Stochastic Fractal Search (SFS) algorithm owing to its superiority to many state-of-the-art algorithms considering convergence, accuracy and robustness. To evaluate the efficacy of SFS, Particle Swarm Optimization (PSO) is also applied to the case study. Furthermore, the presented SFS optimized PI + DF controller is compared to a recently reported control scheme through simulation and experimental tests on an identical DC servo system. After providing the stability proof, SFS tuned PI + DF controller is found to be the pioneer in exhibiting the most accurate speed response profile under complicated scenarios, which is followed by PSO tuned PI + DF controller and the existing control approach, respectively.