Reviewerly/exHarmony huggingface dataset:
A high-level and adaptive metaheuristic selection algorithm for solving high dimensional bound-constrained continuous optimization problems

Metaheuristic algorithms are used to find sufficiently good solutions for the optimization problems that are not solvable in a polynomial time. Although metaheuristics offer a general problem-solving framework and can be applied to various types of optimization problems, their performances depend heavily on the problem to be solved. Thus, hybrid metaheuristics are used to combine strong parts of different algorithms. In this study, a novel adaptive metaheuristic selection algorithm is proposed for solving bound-constrained continuous optimization problems. The developed method hybridizes artificial bee colony, differential evolution, and particle swarm optimization at a high level where each algorithm works independently from each other. As a main contribution to the literature, adaptive selection at metaheuristic level among these three algorithms is achieved by using a rank-based credit assignment and UCB1 multiarmed bandit selection. The effectiveness of the developed algorithm has been evaluated on CEC'17 standard benchmark functions. The obtained numerical results indicate that the proposed algorithm outperforms the individual metaheuristics on which it is built and is more effective especially in high dimensional problems. It is also shown that the proposed algorithm is highly comparable with the related algorithms in the literature. Lastly, a case study that achieves adaptive selection of two good-performing algorithms (namely, covariance matrix adaptation evolution strategy and JADE) for the benchmark used in this study supports the effectiveness of the proposed method.
Knowledge Organization and Representation under the AI Lens

Abstract Purpose This paper compares the paradigmatic differences between knowledge organization (KO) in library and information science and knowledge representation (KR) in AI to show the convergence in KO and KR methods and applications. Methodology The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper. Findings A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations, while KR is problem-solving oriented. Differences between KO and KR are discussed based on the goal, methods, and functions. Research limitations This is only a preliminary research with a case study as proof of concept. Practical implications The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO. Originality/value: Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.
Chirp-Fourier transform for quadratic phase interference fringe analysis: Principles, method and application

Quadratic phase interference fringes are often encountered in optical measurement, its parameters estimation plays an important role in the interferometry field. This paper takes Newton's rings as a classical example of quadratic phase interference fringe, proposes a CFT-based method to estimate the parameters of Newton's rings. The phase derivative can be estimated directly and accurately without unwrapping, which are verified by the simulated and actual Newton's rings. Furthermore, the proposed CFT-based method is applied to measure the curvature radius of spherical surface, which makes the measurement method has the advantages of low computational complexity, high accuracy and fast speed. The experiments results confirm that the CFT-based method not only has better noise and obstacle robustness over traditional Newton's rings measurement, but also has higher efficiency over FRFT-based method.
Moving Vehicle Detection and Tracking Based on Video Sequences

Vehicle detection and tracking are key aspects of intelligent transportation.However, the accuracy of traditional methods for vehicle detection and tracking is severely suppressed by the complexity of road conditions, occlusion, and illumination changes.To solve the problem, this paper initializes the background model based on multiple frames with a fixed interval, and then establishes a moving vehicle detection algorithm based on trust interval.The established algorithm can easily evaluate the background complexity based on regional information.After that, the authors pointed out that the correlation filtering, a classical vehicle tracking algorithm, cannot adapt to the scale changes of vehicles, due to the weak dependence of background information.Hence, a tracking algorithm that adapts to vehicle scale was designed based on background information.Finally, the proposed algorithms were proved feasible for detection and tracking moving vehicles in complex environments.The research provides a good reference for the application of computer vision in moving target detection.
Analysis of Semi-supervised Text Clustering Algorithm on Marine Data

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised cluster analysis. Several semi-supervised clustering algorithms were chosen for comparison of learning performance: Constrained-K-Means, Seeded-K-Means, SAP (Semi-supervised Affinity Propagation), LSAP (Loose Seed AP) and CSAP (Compact Seed AP). In order to adapt the single label, this paper improves the above algorithms to SCKM (improved Constrained-K-Means), SSKM (improved Seeded-K-Means), and SSAP (improved Semi-supervised Affinity Propagationg) to perform semi-supervised clustering analysis on the data. A DSAP (Double Seed AP) semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect. The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.
Transfer Reinforcement Learning under Unobserved Contextual Information

In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.
Instagram Hashtag Trend Monitoring Using Web Scraping

In recent years, Instagram has become one of the fastest growing social media platforms. Searching images on Instagram can be done by using a particular keyword or often known as the hashtag. The hashtag is one of the parameters that can use to find out the topics that are being talked about on social media. There are many advantages for knowing a hot topic on social media to support decision making. This study aims to monitor trends of hashtags on the Instagram platform using web scraping techniques. This research has succeeded in extracting and analyzing post data on Instagram to provide trend information from a #MerryChrismas hashtag. The results of this study are the visible trend in the #MerryChrismas hashtag experienced an increase in the last two days, namely on 24 and 25 December 2019. In addition, this research also succeeded in displaying posts with the most number of likes and comments from a hashtag at a certain time period.
Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

Re-engineering (RE) of existing educational institutions (EI) with adoption of latest technology trends (LTT) in form of artificial intelligence (AI) can be great effective in term of quality systems. Increase in student’s strength in class and terrorist attacks on EI urged us to introduce such approach that can assure education quality. Class monitoring with heavy strength always remain major issue for teacher during lecture delivery. In this paper, we implemented reengineering using artificial intelligence based two theories of 1) Multi-face recognition (MFR) system 2) Facial expression recognition (FER) system. Both of these theories supported by intelligent techniques as principal component analysis (PCA), discrete wavelet transform (DWT) and k-nearest neighbor (KNN). After implementation of these intelligent techniques student’s attentiveness will increase. Our developed system can detect expressions like happiness, repulsion, fear, anger, and confusion. Student’s attentiveness score will be displayed on screen. Teacher can interpret on the basis of attentiveness %age. System decision making can be helpful for class continuity or short break. This system is also an application of an expert system (ES) and knowledge base system (KBS) for educational quality assurance. A similar monitoring system was imposed in china with Hikvision Digital Technology. Predations results proved monitoring can be best way for education quality.
Combined Detection and Segmentation of Cell Nuclei in Microscopy Images Using Deep Learning

We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells. Our model is trained end-to-end from scratch on a mouse parotid salivary gland stem cell nuclei dataset comprising 107 3D images from three independent cell preparations, each containing several hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets. The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to existing algorithms. Finally, we use a previously described test-time drop-out strategy to obtain uncertainty estimates on our predictions and validate these estimates by demonstrating that they are strongly correlated with accuracy.
copent: Estimating Copula Entropy in R.

Statistical independence and conditional independence are the fundemental concepts in statistics and machine learning. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and also closely related to conditional independence or transfer entropy. It has been applied to solve several statistical or machine learning problems, including association discovery, structure learning, variable selection, and causal discovery. Copula entropy was proposed to be estimated nonparametrically with rank statistic and the kNN method for estimating entropy. copent, is a R package which implements this proposed method for estimating copula entropy. The implementation detail of the package is presented in this paper. Two illustration examples with simulated data and real-world data on causal discovery are also presented. The copent package is available on the Comprehensive R Archive Network (CRAN) and also on GitHub at this https URL.
PELATIHAN IMPLEMENTASI SCIENCE AND TECHNOLOGY INDEX (SINTA)

Developing knowledge through research is one of academic staff tasks. This such productivity is now able to identified in SINTA, an indexation application used in Indonesia. All Indonesian researchers have to have account in SINTA, yet academic staff of St. Elisabeth School of Health had not had the account. Therefore, a training on SINTA implementation is critical to do. This activity aims to increase the knowledge of lecturers in registration at the SINTA portal, improve the skills of lecturers in registering at the SINTA portal and improve lecturer performance in publications. The method used in this activity is planning, action, evaluation and reflection. Planning is carried out for the preparation of activities and preparation of facilities. The action taken was SINTA implementation training. Evaluation Evaluation is done by monitoring the implementation of lecturer self-registration at the SINTA portal. Reflection to find out the weaknesses or strengths of the activities that have been carried out in order to establish recommendations for the continuation or development of the next activity. The results of the training are that lecturers have knowledge related to SINTA, lecturers can have a SINTA account, lecturers can update on a SINTA account based on performance that has been done, and lecturers increasingly have the motivation to make a publication.
AQA: An Adaptive Quality Assessment Framework for Online Review Systems

Computing robust and accurate quality scores for users and items in online review systems is critical, since scores directly reflect the community-wide belief about their quality. A broad range of methods have been proposed to compute rating scores, including simple aggregation, weighted aggregation, and iterative techniques, where the latter provides relatively accurate results. However, there are still serious challenges to address, especially in terms of time complexity, accuracy, and robustness against manipulation. In this article, we propose an adaptive quality assessment framework that computes dependable and accurate quality scores for users and items. The proposed method is a semi-iterative weighted aggregation technique in which, a novel approach is used to assign weights to received reviews. The weight depends on two parameters: similarity of reviews, and review prediction. In review prediction, we utilize a combination of online machine learning and collaborative filtering to predict the review expected from the user. The intuition behind using online learning is its ability to obtain lower time complexity in comparison with batch learning. We evaluate our proposed model using a real-word dataset, and compare it with two related approaches. Results show the superiority of our proposed approach, in terms of accuracy and robustness against manipulation.
The Triguarded Fragment with Transitivity

The triguarded fragment of first-order logic is an extension of the guarded fragment in which quantification for subformulas with at most two free variables need not be guarded. Thus, it unifies two prominent decidable logics: the guarded fragment and the two-variable fragment. Its satisfiability problem is known to be undecidable in the presence of equality, but becomes decidable when equality is forbidden. We consider an extension of the tri- guarded fragment without equality by transitive relations, allowing them to be used only as guards. We show that the satisfiability problem for the obtained formalism is decidable and 2-ExpTime-complete, that is, it is of the same complexity as for the analogous exten- sion of the classical guarded fragment. In fact, in our satisfiability test we use a decision procedure for the latter as a subroutine. We also show how our approach, consisting in exploiting some existing results on guarded logics, can be used to reprove some known facts, as well as to derive some other new results on triguarded logics.
Autoregressive Model for Multivariate Crime Prediction

Crime is a complex socio-demographic spectacle offering great challenge to public security across the. The analysis of existing crime dataset can reveal a meaning pattern among the past criminal activities to forecast the future crime happenings. The Autoregressive techniques are reliably tested techniques for time-series analysis and forecasting in the field of behavioural sciences, finance and other social events. The crime is complex socio-dynamic phenomena involving behavioural inquiry can easily be modeled over such mathematical tools and to yield better forecasting results. The desired research objectives were meatout by various researchers through the use of Autoregressive techniques with Univariate data analysis. The main issue with crime events is that it is highly dynamics and situation specific. Such complex dynamic system in real time are denoted by dichotomous variables of multivariate nature. This paper is an attempt to bridge this gap between theoretical analysis and real-time requirements. We have developed a hybrid model using vector autoregressive for multivariate crime scene and fuzzy membership function for analysis of crime based on the predicted values. Such model possesses dual advantage of using impending crime event based upon past multivariate trends and carryout future prediction in real environment. The results show that proposed model is capable of making accurate prediction by statistical methods in real-time situation.
Clifford 3-qubit states

Let us denote by [Formula: see text] the Clifford group (the circuit or operations generated by Hadamard, [Formula: see text] phase and the controlled-NOT gates) and by [Formula: see text] the set of qubit states that can be prepared by circuits from the Clifford group. In other words, a state [Formula: see text] if [Formula: see text] where [Formula: see text]. We will refer to states in [Formula: see text] as Clifford states. This paper studies the set of all three-qubit Clifford states. We prove that [Formula: see text] has 8640 states and if we define two states [Formula: see text] and [Formula: see text] in [Formula: see text] to be equivalent if [Formula: see text], with [Formula: see text] a local transformation in [Formula: see text], then the resulting quotient space has five orbits. More exactly, [Formula: see text] where the orbit [Formula: see text] is made up of states with entanglement entropy [Formula: see text]. For example, the first orbit [Formula: see text] contains the state [Formula: see text] and corresponds to the unentangled Clifford states. We say that [Formula: see text] is a real state if all its amplitudes [Formula: see text] are real numbers. We also say that an operator is real if all the entries of its matrix representation with respect to the computational basis are real numbers. In this paper, we also study the set of real Clifford 3 qubits and the way this set splits when we identify two real Clifford states [Formula: see text] and [Formula: see text] to be equivalent if [Formula: see text] where [Formula: see text] is a local real Clifford operator. An interesting aspect that follows from this study of Clifford states is the existence of two real Clifford states [Formula: see text] and [Formula: see text] that can be connected with a Clifford local transformation but they cannot be connected with a real Clifford local transformation. This is, the equation [Formula: see text] for [Formula: see text], does have a solution in the set of local transformations from [Formula: see text] but it does not have a solution among the local transformations from [Formula: see text] that are real. We go a little deeper and show that the equation [Formula: see text] does not have a solution for any local operation (not necessarily Clifford) whose entries are real numbers. Finally, we show how the CNOT gates act on the set of Clifford states and also in the set of real Clifford states.
CoBiLiRo: A Research Platform for Bimodal Corpora

This paper describes the on-going work carried out within the CoBiLiRo (Bimodal Corpus for Romanian Language) research project, part of ReTeRom (Resources and Technologies for Developing Human-Machine Interfaces in Romanian). Data annotation finds increasing use in speech recognition and synthesis with the goal to support learning processes. In this context, a variety of different annotation systems for application to Speech and Text Processing environments have been presented. Even if many designs for the data annotations workflow have emerged, the process of handling metadata, to manage complex user-defined annotations, is not covered enough. We propose a design of the format aimed to serve as an annotation standard for bimodal resources, which facilitates searching, editing and statistical analysis operations over it. The design and implementation of an infrastructure that houses the resources are also presented. The goal is widening the dissemination of bimodal corpora for research valorisation and use in applications. Also, this study reports on the main operations of the web Platform which hosts the corpus and the automatic conversion flows that brings the submitted files at the format accepted by the Platform.
Real-time Processing System for a Quantum Random Number Generator

Quantum random number generators (QRNG) provide quality random numbers, which are essential for cryptography by utilizing the unpredictable nature of quantum mechanics.Advancements in quantum optics made multiple different architectures for these possible.As part of a project aiming to realize a QRNG service, we developed a system capable of providing real-time monitoring and long term data collection while still fulfilling regular processing duties for these devices.In most cases, hardware validation is done by simply running a battery of statistical tests on the final output.Our goal, however, was to create a system allowing more flexible use of these tests, realizing a tool that can also prove useful during the construction of our entropy source for detecting and correcting unique imperfections.We tested this flexibility and the system's ability to adequately perform the required tasks with simulated sources while further examining the usability of available verification tools within this new custom framework.
On Dynamic Succinct Graph Representations

We address the problem of representing dynamic graphs using k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -trees. The k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree data structure is one of the succinct data structures proposed for representing static graphs, and binary relations in general. It relies on compact representations of bit vectors. Hence, by relying on compact representations of dynamic bit vectors, we can also represent dynamic graphs. In this paper we follow instead the ideas by Munro et al., and we present an alternative implementation for representing dynamic graphs using k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -trees. Our experimental results show that this new implementation is competitive in practice.