Reviewerly/exHarmony huggingface dataset:
Fuzzy spatio‐temporal ontologies and formal construction based on fuzzy Petri nets

Abstract Fuzzy spatio‐temporal knowledge is required in a wide range of application fields, including GIS, spatio‐temporal database, and artificial intelligence. Ontology, as a formal method of knowledge representation, plays a key role in the Semantic Web. Therefore, how to extend ontologies to represent fuzzy spatio‐temporal knowledge needs to be solved. In this paper, we propose an approach for representing fuzzy spatio‐temporal knowledge with ontologies and investigate a formal construction of fuzzy spatio‐temporal ontologies based on fuzzy Petri nets. First, we propose a formal definition of fuzzy spatio‐temporal ontologies. In addition, based on fuzzy description logic, a description logic named the fuzzy spatio‐temporal description logic, which provides a logical basis for the fuzzy spatio‐temporal ontologies, is presented. Furthermore, a fuzzy spatio‐temporal web ontology language, which is fuzzy and spatio‐temporal extension of standard ontology language OWL, is proposed to make the fuzzy spatio‐temporal ontologies easy to use and support efficient reasoning. On the basis of fuzzy spatio‐temporal ontologies, we investigate a formal approach for constructing fuzzy spatio‐temporal ontologies from fuzzy spatio‐temporal Petri nets, ie, transforming fuzzy spatio‐temporal Petri nets (including Petri net model and Petri net instance) into fuzzy spatio‐temporal ontologies at both structure and instance levels. Finally, we prove the correctness of the transformation and provide a detailed transformation example.
Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

With the development of science and technology, the accuracy requirements for solving engineering problems are getting stricter than before. Most structural design optimization problems in civil and mechanical engineering have proven to be the non-deterministic polynomial hard problems. The artificial bee colony (ABC) algorithm has been proven to be an effective method of design optimization problems. This paper proposes an improved ABC algorithm (DSM-ABC) combined with dual-search mechanism containing Lévy flight and differential self-perturbation and applies it to three classical structural design problems, including cantilever beam design, gear train design, and three-bar truss design. The experimental results of benchmark functions from CEC2005 reveal that the proposed DSM-ABC algorithm accelerates the convergence and improves the performance. Eventually, the obtained results of optimization structural design problems prove that the DSM-ABC algorithm has a strong superiority compared with the state-of-the-art algorithms in solving optimization engineering design problems.
Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes

Information gathering (IG) algorithms aim to intelligently select a mobile sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for IG that employ Gaussian processes (GPs) as underlying model of the process. However, most algorithms discretize the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they are not suited for online information gathering tasks as they assume prior knowledge about GP parameters. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: (i) a Rapidly-Exploring Random Tree (RRT) search that allows a robot to identify unvisited locations, and to learn the GP parameters, and (ii) an RRT*-based informative path planning that guides the robot towards those locations by maximizing the information gathered while minimizing path cost. The combination of the two steps allows an online realization of the algorithm, while eliminating the need for discretization. We demonstrate that our proposed algorithm outperforms state-of-the-art both in simulations, and in a lab experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles.
Pemanfaatan Augmented Reality Sebagai Media Pembelajaran Pengenalan Buah-Buahan

The concept of education for children is important. The aspects that must be considered are methods and learning media. In this research innovative and alternative learning media are made to understand fruits for children with Augmented Reality (AR). Augmented Reality (AR) in principle is a technology that is able to combine two-dimensional or three-dimensional virtual objects into a real environment and then project it. This learning media combines picture cards and virtual reality. Markers contained on picture cards will be captured by the mobile device camera, processed and will 3D animated pieces appear on the mobile screen in realtime. By using the concept of combining real world, real images on cards and virtual, applications can stimulate imagination and sense of desire in children and motivation to learn more and more. 3D fruit estimation created using the 3D Blender application and the Augmented Rea process lity is made using Unity and the Vuforia SDK library. The application of fruit recognition has been applied to several child respondents and has been tested on several types and brands of Android-based mobile phones. Based on research trials, 86% of 30 respondents stated that the application which was developed very effectively as a medium for the introduction of fruits.
Robust, atlas-free, automatic segmentation of brain MRI in health and disease

<h2>Abstract</h2><h3>Background</h3> Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. <h3>Methods</h3> Proton-density, T<sub>2</sub>-weighted, T<sub>1</sub>-weighted, brain-free water, 3D FLAIR, 3D T<sub>2</sub>-weighted, and 3D T<sub>2</sub>*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). <h3>Results</h3> C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. <h3>Conclusion</h3> C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.
Disentangled Network Alignment with Matching Explainability

Network alignment (NA) is a fundamental problem in many application domains - from social networks, through biology and communications, to neuroscience. The main objective is to identify common nodes and most similar connections across multiple networks (resp. graphs). Many of the existing efforts focus on efficient anchor node linkage by leveraging various features and optimizing network mapping functions with the pairwise similarity between anchor nodes. Despite the recent advances, there still exist two kinds of challenges: (1) entangled node embeddings, arising from the contradictory goals of NA: embedding proximal nodes in a closed form for representation in a single network vs. discriminating among them when mapping the nodes across networks; and (2) lack of interpretability about the node matching and alignment, essential for understanding prediction tasks. We propose d NAME (disentangled Network Alignment with Matching Explainability) - a novel solution for NA in heterogeneous networks settings, based on a matching technique that embeds nodes in a disentangled and faithful manner. The NA task is cast as an adversarial optimization problem which learns a proximity-preserving model locally around the anchor nodes, while still being discriminative. We also introduce a method to explain our semi-supervised model with the theory of robust statistics, by tracing the importance of each anchor node and its explanations on the NA performance. This is extensible to many other NA methods, as it provides model interpretability. Experiments conducted on several public datasets show that d NAME outperforms the state-of-the-art methods in terms of both network alignment precision and node matching ranking.
Hybrid Adaptive Video Steganography Scheme Under Game Model

The steganographic model based on game theory is improved. A hybrid steganography model that satisfies the Kerckhoffs principle by combining the strategy adaptation of the game theory model and the content adaptation in the cover model is proposed. This model is used to design a high-security steganography algorithm for an H.264 video. The distortion function in UNIWARD algorithm is improved according to the H.264 video coding characteristics. The improved distortion function is used to generate the bias function under the game theory model, and the embedding probability of each position in the cover is calculated. The new distortion function under the hybrid model is generated by combining the embedding probability of the cover position and the distortion cost function under the improved UNIWARD algorithm and using it to embed information in the H.264 residual DCT coefficient block. The experiment proves that the algorithm has higher security than the UNIWARD algorithm. With the same analysis algorithm, the detection error rate is significantly improved compared to the UNIWARD algorithm. The algorithm has a large embedding capacity and good invisibility. When fully embedded, the capacity reaches 1.9% or more, the visual effect does not change significantly, and the PSNR value decreases within 2 dB. After the secret message is embedded in the information, the video bit rate is increased by 9%.
Efficient Quantum Error Correction of Dephasing Induced by a Common Fluctuator

Quantum error correction is expected to be essential in large-scale quantum technologies. However, the substantial overhead of qubits it requires is thought to greatly limit its utility in smaller, near-term devices. Here we introduce a new family of special-purpose quantum error-correcting codes that offer an exponential reduction in overhead compared to the usual repetition code. They are tailored for a common and important source of decoherence in current experiments, whereby a register of qubits is subject to phase noise through coupling to a common fluctuator, such as a resonator or a spin defect. The smallest instance encodes one logical qubit into two physical qubits, and corrects decoherence to leading-order using a constant number of one- and two-qubit operations. More generally, while the repetition code on n qubits corrects errors to order t^{O(n)}, with t the time between recoveries, our codes correct to order t^{O(2^{n})}. Moreover, they are robust to model imperfections in small- and intermediate-scale devices, where they already provide substantial gains in error suppression. As a result, these hardware-efficient codes open a potential avenue for useful quantum error correction in near-term, pre-fault tolerant devices.
30.5 A 1.41pJ/b 56Gb/s PAM-4 Wireline Receiver Employing Enhanced Pattern Utilization CDR and Genetic Adaptation Algorithms in 7nm CMOS

Analog mixed-signal (AMS) receivers for 50+Gb/s PAM-4 offer lower power than ADC-DSP receivers [1]-[3]. Those using DFEs [2]-[3] suffer from relatively high power consumption due to the large number of latches needed in PAM-4 speculative DFEs. Better power efficiency can be achieved using only a CTLE [1]. However, analog front-ends (AFEs) are sensitive to variations in process, supply voltage and temperature. To combat this while accommodating links with loss exceeding 20dB, an AFE with extensive programmability is combined with an efficient genetic adaptation algorithm to select a setting that minimizes BER thus equalizing a 22dB-loss channel. The lack of a DFE, combined with a novel PAM-4 clock recovery scheme greatly reduces the number of latches required compared to previous works, resulting in 1.41pJ/bit power consumption in 7nm CMOS technology.
Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.
Optimizing MSE for Clustering with Balanced Size Constraints

Clustering is to group data so that the observations in the same group are more similar to each other than to those in other groups. k-means is a popular clustering algorithm in data mining. Its objective is to optimize the mean squared error (MSE). The traditional k-means algorithm is not suitable for applications where the sizes of clusters need to be balanced. Given n observations, our objective is to optimize the MSE under the constraint that the observations need to be evenly divided into k clusters. In this paper, we propose an iterative method for the task of clustering with balanced size constraints. Each iteration can be split into two steps, namely an assignment step and an update step. In the assignment step, the data are evenly assigned to each cluster. The balanced assignment task here is formulated as an integer linear program (ILP), and we prove that the constraint matrix of this ILP is totally unimodular. Thus the ILP is relaxed as a linear program (LP) which can be efficiently solved with the simplex algorithm. In the update step, the new centers are updated as the centroids of the observations in the clusters. Assuming that there are n observations and the algorithm needs m iterations to converge, we show that the average time complexity of the proposed algorithm is O ( m n 1 . 65 ) – O ( m n 1 . 70 ) . Experimental results indicate that, comparing with state-of-the-art methods, the proposed algorithm is efficient in deriving more accurate clustering.
An Empirical Case Study on the Temporary File Smell in Dockerfiles

Docker is widely used in data centers to host services.The docker image adopts a hierarchical storage architecture, which means that the docker image is composed of a set of filesystem layers.In the image building process, only the top layer is read-write, while the bottom layers are all read-only.However, temporary files are often used in the image building process.Nevertheless, if a temporary file is imported and removed in different layers by a careless developer, it will lead a file redundancy.We termed this problem ''temporary file smell.''This smell leads to larger-size images, which seriously restricts the efficiency of image distribution and thus affects the scaling ability of services in facing of sudden high load.To address this problem, we make an empirical case study to the real-world Dockerfiles on DockerHub.Based on the case study, we summarize four different smell patterns and propose a state-depend static analysis method to detect this kind of smells.We also provide three feasible fixing methods as selective options to eliminate the temporary file smell.
On Comparing Software Quality Metrics of Traditional vs Blockchain-Oriented Software: An Empirical Study

Driven by the surge of interest generated around blockchain technologies over the last years, a new category of systems, called Blockchain-Oriented Software (BOS), which are strictly tied to Blockchain distributed environment, has become increasingly popular. Yet, there is not a thorough understanding of their structure and behaviour and if and to which extent they differ from traditional software systems. The present work provide a first statistical characterisation of BOS. We analysed and compared 5 C++ open source Blockchain-Oriented and 5 Traditional Java software systems, aiming at detecting potential differences between the two categories of projects, and specifically in the statistical distribution of 10 software metrics. Although, in general, the statistical distributions for Traditional software and Blockchain software show similarities, the distribution of Average Cyclomatic and Ration Comment To Code metrics reveal significant differences in their queue, whereas the Number of Statements metric shows meaningful differences on the double Pareto distribution.
Probabilistic Termination by Monadic Affine Sized Typing

We introduce a system of monadic affine sized types, which substantially generalizes usual sized types and allows in this way to capture probabilistic higher-order programs that terminate almost surely. Going beyond plain, strong normalization without losing soundness turns out to be a hard task, which cannot be accomplished without a richer, quantitative notion of types, but also without imposing some affinity constraints. The proposed type system is powerful enough to type classic examples of probabilistically terminating programs such as random walks. The way typable programs are proved to be almost surely terminating is based on reducibility but requires a substantial adaptation of the technique.
Blind Image Deblurring using the l0 Gradient Prior

Many blind image deblurring methods rely on unnatural image priors that are explicitly designed to restore salient image structures, necessary to estimate the blur kernel. In this article, we analyze the blur kernel estimation method introduced by Pan and Su in 2013 that uses an l0 prior on the gradient image. We present deconvolution results using the estimated blur kernels. Our experiments show the effectiveness of the method as well as some of its shortcomings.
The Effect of Operational Flexibility on Performance: A Field Study on Small and Medium-sized Industrial Companies in Jordan

In the context of an unstable business environment, companies should be looking for a mechanism to deal with uncertainty.Flexibility maybe one of those mechanisms which can help company to cope with instability in the best way possible.As one of the basic types of flexibility, operational flexibility has become an essential capability which an organization seeks to acquire because it enables companies to respond quickly and effectively to dynamic environments, and as a result, improve firm performance.The aim of this study is to investigate the relationship between operational flexibility and firm performance.The sample consists of 90 industrial companies in Jordan, and the questionnaire has been developed for and distributed to senior managers and managers in operations, product development, financial and marketing departments.Descriptive analysis was also correlated and regression techniques used to obtain the results.Our field study showed that operational flexibility positively affects both the operational and the financial performance of small and medium industrial companies in Jordan.More specifically, volume flexibility as a type of operational flexibility does not affect the performance of these companies in Jordan, but both mix flexibility and new product flexibility positively affect the operational and financial performance of these companies.
A Temporal Distributed Group Decision Support System Based on Multi-Criteria Analysis

-However, the group of decision-makers will be modeled by a set of entities called computer agents.These agents represent each decisionmaker in a multi-agent system (MAS) [11] [27].The problem addressed in the current study concerns the proposition of a system that models the different decision makers who have their own information, constraints, decision strategies, preferences, and objectives generally not shared or communicated.Hence, the need for a negotiation process integrated into a group decision support system (GDSS) allows finding a common agreement for this group, in the face of a conflict.Several reasons can be mentioned besides the group and
Effect of spatial enhancement technology on input through the keyboard in virtual reality environment

Scientific developments have enabled the application of virtual reality (VR) technology in various fields. However, this technology is disadvantaged by low recognition, existence of bias, lack of precision, and fatigue of text input in VR environments. To address these problems, this study proposed a spatial enhancement technique. This study investigated the effectiveness of spatial enhancement keys of a virtual keyboard from various angles and explored the impact of enhanced response time and enhanced protrusion distance on the spatial enhancement technology. Finally, the following conclusions are obtained: (1) The average text input performance of the keyboard using the spatial enhancement technique is significantly better than that of the ordinary virtual keyboard without using the spatial enhancement technique. (2) The recommended time interval for enhanced response time and the protrusion distance are 0–100 ms and 1.85 diopter, respectively. The keyboard angle insignificantly affects the input through the keyboard performance.