Reviewerly/exHarmony huggingface dataset:
An Adaptive Filtering Algorithm for Non-Gaussian Signals in Alpha-Stable Distribution

Currently, many adaptive filtering algorithms are available for the non-Gaussian environment, namely, least mean square (LMS) algorithm, recursive least square (RLS) algorithm, least mean fourth (LMF) algorithm, and subspace minimum norm (SMN) algorithm.Most of them can converge to the steady-state, but face various constraints in the presence of alpha (α)-stable noises.To solve the problem, this paper aims to develop an adaptive filtering algorithm for non-Gaussian signals in α-stable distribution, drawing on the merits of existing adaptive filtering algorithms.Firstly, the authors introduced the theory of α-stable distribution, the central limit theorem and fractional lower-order statistics (FLOS).Next, two classic adaptive filtering algorithms, RLS and LMS, were summarized, and compared through tests.On this basis, the FLOS-SMN algorithm was designed in the light of the features of the LMS and the SMN, which applies to the filtering of non-Gaussian signals in αstable distribution.Finally, the proposed algorithm was proved as faster, more stable and more adaptable than the traditional method.
Blockchain Technology and Smart Universities

The objective of this paper is to conduct a conceptual assessment of blockchain technology applications to universities. The paper will first address two related questions namely, the concept of smart university, and the architecture of blockchain technology. This paper contributes towards the topical debate of whether the claimed blockchain technological transformation is a hype, reality, revolution, or just an ordinary computing upgrade. It follows that if blockchain is a significant technological advancement then it can only be ignored at own’s peril. This research should benefit innovation policy decisions in the academia for out-of-the box thinking regarding internal control systems and product offering for smart universities.
Novel quantum proxy signature scheme based on orthogonal quantum product states

With further research, the theory of local discrimination of orthogonal product states (OPSs) becomes more and more perfect. In this paper, we present a quantum proxy signature (QPS) scheme based on a set of OPSs that cannot be exactly discriminated by local operations and classical communation (LOCC). Our scheme possesses all the properties of QPS. More importantly, no one can obtain any meaningful information of a signature owing to the different particles of an OPS are separately transmitted. We show that our scheme is secure by a detailed security analysis.
An Instance-Specific Algorithm for Learning the Structure of Causal Bayesian Networks Containing Latent Variables

Almost all of the existing algorithms for learning a causal Bayesian network structure (CBN) from observational data recover a structure that models the causal relationships that are shared by the instances in a population.Although learning such population-wide CBNs accurately is useful, it is important to learn CBNs that are specific to each instance in domains in which different instances may have varying causal structures, such as in human biology.For example, a breast cancer tumor in a patient (instance) is often a composite of causal mechanisms, where each of these individual causal mechanisms may appear relatively frequently in breast-cancer tumors of other patients, but the particular combination of mechanisms is unique to the current tumor.Therefore, it is critical to discover the specific set of causal mechanisms that are operating in each patient to understand and treat that particular patient effectively.We previously introduced an instance-specific CBN structure learning method that builds a causal model for a given instance T from the features we know about T and from a training set of data on many other instances [12].However, that method assumes that there are no latent (hidden) confounders, that is, there are no latent variables that cause two or more of the measured variables.Unfortunately, this assumption rarely holds in practice.In the current paper, we introduce a novel instance-specific causal structure learning algorithm that uses partial ancestral graphs (PAGs) to model latent confounders.Simulations support that the proposed instance-specific method improves structure-discovery performance compared to an existing PAG-learning method called GFCI, which is not instance-specific.We also report results that provide support for instance-specific causal relationships existing in realworld datasets.
Model of Self-organizing Knowledge Representation and Organizational Knowledge Transformation

The purpose of the paper is development of a conceptual model for the representation of knowledge as an active intellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individually and collectively in the practice of organizations.The first principle of knowledge engineering, as Edward Albert Feigenbaum noted, says that the power in solving problems that an intellectual subject (person or machine) manifests in the process of activity depends primarily on its knowledge base, and only secondly on the methods of inference used.Strength is hidden in knowledge.The process of producing knowledge is permanent and does not depend on whether an individual is going to use this knowledge or not.Knowledge constantly produces new knowledge regardless of the owner's desire.Besides that, knowledge can't arise from nothing, but always -from some knowledge obtained earlier.As well as the intelligence, knowledge is an emergent instance arising from the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta).Idiosyncrasy of this interaction is expressed precisely in the creation of new knowledge.Due to postulating the knowledge self-organizing, the hierarchical knowledge structures in memory and the process of thinking as a kind of syntax for the procedure of new knowledge generation are described.This is an effort towards understanding the memory mechanisms, the process of thinking, the sources of heuristic knowledge just through the inner nature of knowledge.Also, based on the knowledge self-organization principle, an archetype of the appropriate knowledge-based system architecture is presented too.As an implementation of the concept, the perceptual act model is described, and on its base, a possible scenario for the behavior of a robot meeting an obstacle in its path is considered.As the mutual transformation of tacit and explicit knowledge makes new knowledge, the impact of the self-organization of knowledge on the transformation process as well as conditions of self-organization of both individual knowledge and organizational knowledge are analyzed in detail.Finally, modification of the known model of knowledge dimensions by Nonaka and Takeuchi is proposed.Because of the native activity of knowledge, it is impossible to build a knowledge management system without considering the internal structure of knowledge and its emergent ability to self-organize.Ensuring the natural process of knowledge development at all ontological levels in an organization is an essential prerequisite for the evolution of values in this organization.
A Variable-Step RRT<sup>*</sup> Path Planning Algorithm for Quadrotors in Below-Canopy

Rapidly-exploring random tree (RRT * ) algorithm has been widely applied to path planning problem for quadrotor, which takes a great amount of static and dynamic constraints into account.However, conventional RRT * algorithm is suffering low convergence rate and efficiency in below-canopy environment, where is usually occupied with narrow aisles and uneven distributed obstacles.In order to enrich the forest information database and obtain information efficiently in blow-canopy, an improved variable-step RRT * algorithm has been proposed in this paper.In the proposed algorithm, the sampling nodes are determined by the target bias sampling strategy with variable probability.To further increase the efficiency of node generation and balance the searching time and security in areas with different obstacles densities, the eventtriggered step size extension has been proposed according to hyperbolic tangent function.After that, both Euclidean distance and angle constraints are adopted in the cost function of node connection optimization.Then, the path is further optimized by path trimming and Bezier curves smooth method.Finally, the demonstration of the proposed algorithm is presented to show its efficiency, and the comparison result with existing RRT-like algorithms indicates that the performance of our algorithm is better than RRT, RRT * and DDRRT in terms of convergence and accuracy.Comparing to other classical path planning methods such as A * , Artificial potential field and Fuzzy-logic, our method also shows its superiority.
Analysis of Indonesia’s Inflation Using ARIMA and Artificial Neural Network

This study aims to compare forecast performance of Neural Network (NN) to ARIMA in the case of Indonesia’s inflation and to find if there is any interesting trend in Indonesia’s inflation. We use year-on-year monthly Indonesia’s inflation data from 2006:12 to 2018:12 released by Bank Indonesia (BI) and the Indonesian Central Bureau of Statistics (CBS). We divide the series into 3 data series to capture the trend in the inflation (i.e DS1, DS2 and DS3). The data set 1 (DS1) covers data from 2006:12 to 2014:08, DS2 from 2006:12 to 2018:12, dan DS3 from 2010:12 to 2018:12. The series is then processed using the standard ARIMA method and NN model. We found that the NN model outperforms the ARIMA model in forecasting inflation for each respective series by analysing its Root Mean Squared Error (RMSE). We also found that short term lagged-inflation (backward-looking) variable has lesser effect on inflation compared to the more recent series.
Evaluation of clearness index and cloudiness index using measured global solar radiation data: A case study for a tropical climatic region of Nigeria

This study uses a two-year dataset (January 2016-December 2017) on global solar radiation to model the clearness and cloudiness indices at Ile-Ife, Nigeria, a tropical location. Analysis of the daily variations showed that most days at the location are uniform in cloudiness, with scarcely any day either extremely cloudy or extremely clear. On a monthly basis, the clearness index ranged from 0.27 to 0.51, while cloudiness index ranged from 0.44 to 0.72, confirming that the variations of both ratios are influenced by changes in the position of the sun, turbidity and clouds. The seasonal variations of the two parameters were observed to be inverse, so that increases in the clearness index induced by cloudless sky and dry months correspond to decreases in the cloudiness index values and vice versa. The minimum values (0.10 and 0.28) of the clearness index at the location are observed to be higher than the minimum value of 0.05 stipulated for the clearness index in cloudy conditions. Two empirical equations for estimating the maximum clearness index in terms of the average clearness index and the cloudiness index in terms of the clearness index are developed and recommended for other locations with climatological conditions similar to those of the study site. It was furthermore observed from the evaluation of formulated empirical equations that the Hollands and Huget and the Saunier et al. models performed well and improved by about 23.62 and 3.66%, respectively, after calibration, to make the models suitable for their application at other tropical areas.
A Novel Modified Tree‐Seed Algorithm for High‐Dimensional Optimization Problems

To efficiently handle high-dimensional continuous optimization problems, a Modified tree-seed algorithm(MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient(SDSC) and Local reinforcement strategy(LRS) based on gradient information of adjacentgeneration best trees. SDSC is dynamically decreased with iterations to adjust the produced domain of offspring seeds, for achieving the tradeoff between the global exploration and local exploitation. LRS strategy is to execute local exploitation process by employing gradient information of two best trees, for enhancing convergence efficiency and local optima avoidance with probabilities. The compared experimental results show the different effects of differenttype SDSC on MTSA, the faster convergence efficiency and the stronger robustness of the proposed MTSA.
Dynamic software rejuvenation in web services: a whale optimizationalgorithm-based approach

In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services.
Combining Classifiers for Foreign Pattern Rejection

Abstract In this paper, we look closely at the issue of contaminated data sets, where apart from legitimate (proper) patterns we encounter erroneous patterns. In a typical scenario, the classification of a contaminated data set is always negatively influenced by garbage patterns (referred to as foreign patterns). Ideally, we would like to remove them from the data set entirely. The paper is devoted to comparison and analysis of three different models capable to perform classification of proper patterns with rejection of foreign patterns. It should be stressed that the studied models are constructed using proper patterns only, and no knowledge about the characteristics of foreign patterns is needed. The methods are illustrated with a case study of handwritten digits recognition, but the proposed approach itself is formulated in a general manner. Therefore, it can be applied to different problems. We have distinguished three structures: global, local, and embedded, all capable to eliminate foreign patterns while performing classification of proper patterns at the same time. A comparison of the proposed models shows that the embedded structure provides the best results but at the cost of a relatively high model complexity. The local architecture provides satisfying results and at the same time is relatively simple.
Enhanced Language Modeling with Proximity and Sentence Relatedness Information for Extractive Broadcast News Summarization

The primary task of extractive summarization is to automatically select a set of representative sentences from a text or spoken document that can concisely express the most important theme of the original document. Recently, language modeling (LM) has been proven to be a promising modeling framework for performing this task in an unsupervised manner. However, there still remain three fundamental challenges facing the existing LM-based methods, which we set out to tackle in this article. The first one is how to construct a more accurate sentence model in this framework without resorting to external sources of information. The second is how to take into account sentence-level structural relationships, in addition to word-level information within a document, for important sentence selection. The last one is how to exploit the proximity cues inherent in sentences to obtain a more accurate estimation of respective sentence models. Specifically, for the first and second challenges, we explore a novel, principled approach that generates overlapped clusters to extract sentence relatedness information from the document to be summarized, which can be used not only to enhance the estimation of various sentence models but also to render sentence-level structural relationships within the document, leading to better summarization effectiveness. For the third challenge, we investigate several formulations of proximity cues for use in sentence modeling involved in the LM-based summarization framework, free of the strict bag-of-words assumption. Furthermore, we also present various ensemble methods that seamlessly integrate proximity and sentence relatedness information into sentence modeling. Extensive experiments conducted on a Mandarin broadcast news summarization task show that such integration of proximity and sentence relatedness information is indeed beneficial for speech summarization. Our proposed summarization methods can significantly boost the performance of an LM-based strong baseline (e.g., with a maximum ROUGE-2 improvement of 26.7% relative) and also outperform several state-of-the-art unsupervised methods compared in the article.
Quantum illumination with simple detection

We show that by using the non-classical two-mode squeezed vacuum (TMSV) to illuminate an object, quantum correlations contribute to a detectable enhancement even under regimes of high signal loss and background thermal noise. We also consider a realistic measurement scenario with click detectors, along with sequential Bayesian inference; a single click on one mode of the TMSV produces a vacuum removed thermal state which enhances the probability of subsequent click detection.
Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Cloud computing technology success comes from its manner of delivering information ‎technology services, how they are designed, propagated, maintained and scaled. Job Scheduling ‎on cloud computing is a crucial ‎research area and is known to be an NP-complete problem. Scheduling refers to assigning user requests to underlying resources effectively. ‎This paper proposes a new Job Scheduling mechanism for cloud computing ‎environment. The proposed mechanism is based on the Ions Motion Optimization (IMO) algorithm. IMO has two phases, liquid, and crystal. These two phases balance the algorithm behavior ‎between convergence and local optima avoidance. To evaluate the proposed mechanism, a ‎simulation with different scenarios using the CloudSim simulator is conducted. The performance of ‎the proposed algorithm is compared with two metaheuristic algorithms known as Cat Swarm ‎Optimization (CSO) and Glowworm Swarm Optimization (GSO). Furthermore, the proposed IMO ‎mechanism is compared with First Come First Served and random solution. The experimental ‎results demonstrated that the proposed mechanism outperformed both CSO ‎and GSO and produced the shortest execution time in all experimental scenarios.
A Parallel Grid Optimization of SVM Hyperparameter for Big Data Classification using Spark Radoop

The big data phenomenon is currently a challenge to the process of relevant knowledge extraction using classical machine learning technique. This is due to the need for efficient data reduction and new fast-distributed machine learning algorithms for such process on big data. The extensive application of SVM demands efficient methods of constructing the classifier to be suitable for big data and high classification capability. In reality, the efficiency of SVM relies on the efficient derivation of the optimal feature subset and the algorithmic parameters. The grid search optimization method usually presents global optima and high learning accuracy compared to PSO and GA, but its larger computation takes much time. The grid search is more attractive because it can simultaneously take part in the learning of every SVM since they do not rely on each other. A novel parallel implementation of grid optimization using Spark Radoop is proposed in this paper to minimize the great computation load and make it suitable for big data processing issues. A major contribution of this study is a significant reduction in the algorithmic computational time when compared to the serial version of gridSVM, as well as the high classification accuracy compared to the other parallel optimization techniques.
Bisimulations for verifying strategic abilities with an application to the ThreeBallot voting protocol

We propose a notion of alternating bisimulation for strategic abilities under imperfect information. The bisimulation preserves formulas of ATL⁎ for both the objective and subjective variants of the state-based semantics with imperfect information, which are commonly used in the modeling and verification of multi-agent systems. Furthermore, we apply the theoretical result to the verification of coercion-resistance in the ThreeBallot voting system, a voting protocol that does not use cryptography. In particular, we show that natural simplifications of an initial model of the protocol are in fact bisimulations of the original model, and therefore satisfy the same ATL⁎ properties, including coercion-resistance. These simplifications allow the model-checking tool MCMAS to terminate on models with a larger number of voters and candidates, compared with the initial model.
Creating classification rules using grammatical evolution

A genetic programming based method is introduced for data classification. The fundamental element of the method is the well-known technique of Grammatical Evolution. The method constructs classification programs in a C - like programming language in order to classify the input data, producing simple if-else rules. The paper introduces the method as well as the conducted experiments on a series of datasets against other well known classification methods.
Uniformity and isotropy of speckle pattern cause the doubled random error phenomenon in digital image correlation

For subset-based digital image correlation, the random error of second-order shape function roughly doubles that of first-order shape function, termed doubled random error phenomenon. In this work, we suggest that the fundamental cause of this phenomenon is the uniformity and isotropy of speckle pattern. Specially, we prove that the ratio of random error of second-order shape function to that of first-order shape function, from a view of statistic, is expected to be 7/2 ( ≈ 1.87), which is quite close to the empirical value 2. Numerical experiments demonstrate the correctness of propose theoretical analysis.