Reviewerly/exHarmony huggingface dataset:
Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time.
Visual rhythms for qualitative evaluation of video stabilization

Abstract Recent technological advances have enabled the development of compact and portable cameras for the generation of large volumes of video content. Several applications have benefited from such significant growth of multimedia data, such as telemedicine, surveillance and security, entertainment, teaching, and robotics. However, videos captured by amateurs are subject to unwanted motion or vibration while handling the camera. Video stabilization techniques aim to detect and remove glitches or instabilities caused during the acquisition process to enhance visual quality. In this work, we introduce and analyze a novel representation based on visual rhythms for qualitative evaluation of video stabilization methods. Experiments conducted on different video sequences are performed to demonstrate the effectiveness of the visual representation as qualitative measure for evaluating video stability. In addition, we present a proposal to calculate an objective metric extracted from the visual rhythms.
Sliding mode control design for autonomous surface vehicle motion under the influence of environmental factor

Autonomous Surface Vehicle (ASV) is a vehicle that is operated in the water surface without any person in the vehicle. Since there is no person in the ASV, a motion controller is essentially needed. The control system is used to make sure that the water vehicle is moving at the desired speed. In this paper, we use a Touristant ASV with the following specifications: the length is 4 meters, the diameter is 1.625 meters, and the height is 1.027 meters. The main contribution of this paper is applying the Sliding Mode Control system to the Touristant ASV model under the influence of environmental factors. The environmental factors considered in this work are wind speed and wave height. The Touristant ASV model is nonlinear and uses three degree of freedom (DOF), namely surge, sway and yaw. The simulation results show that the performance of the closed-loop system by using the SMC method depends on the environmental factors. If environmental factors are higher, then the resulting error is also higher. The average error difference between those resulted from the simulation without environmental factors and those with the influence of environmental factors is 0.05% for surge, sway and yaw motions.
Towards Better Word Alignment in Transformer

While neural models based on the Transformer architecture achieve the State-of-the-Art translation performance, it is well known that the learned target-to-source attentions do not correlate well with word alignment. There is an increasing interest in inducing accurate word alignment in Transformer, due to its important role in practical applications such as dictionary-guided translation and interactive translation. In this article, we extend and improve the recent work on unsupervised learning of word alignment in Transformer on two dimensions: a) parameter initialization from a pre-trained cross-lingual language model to leverage large amounts of monolingual data for learning robust contextualized word representations, and b) regularization of the training objective to directly model characteristics of word alignments which results in favorable word alignments receiving more concentrated probabilities. Experiments on benchmark data sets of three language pairs show that the proposed methods can significantly reduce alignment error rate (AER) by at least 3.7 to 7.7 points on each language pair over two recent works on improving the Transformer's word alignment. Moreover, our methods can achieve better alignment results than GIZA++ on certain test sets.
Hybrid semantic service matchmaking method based on a random forest

Semantic Service Matchmaking (SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands. However, existing research on SSM mostly involves logical or non-logical matching, leading to unavoidable false-positive and false-negative problems. Combining different types of SSM methods is an effective way to improve this situation, but the adaptive combination of different service matching methods is still a difficult issue. To conquer this difficulty, a hybrid SSM method, which is based on a random forest and combines the advantages of existing SSM methods, is proposed in this paper. The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest, converting the service matching into a two classification problem. Therefore, our method avoids the flaws found in manual threshold setting. Experimental results show that the proposed method achieves an outstanding performance.
Virtual reality trajectory of modified PUMA 560 by hybrid intelligent controller

The high performance is a goal for all designers to get better, faster, or more efficient than others. This paper proposes a design for virtual reality (VR) of modified PUMA 560 by hybrid controller between adaptive neuro fuzzy inference system (ANFIS) controller and fractional order proportional, integral, derivative (FOPID) controller. The main purpose is to obtain the optimal trajectory by get the best value of controller’s parameters that regulate the manipulator movements smoothly to the desired target. The procedure of design start by obtains the optimal values of the traditional PID controller parameters normally. The next step is applied the FOPID controller with high accuracy. It is high performance to control the perplexing physics system than, the classical integer order of PID controller. The final step to get high performance of the control system under considers is achieved by hybrid between FOPID with ANFIS controller which used the pervious output as predictive point. The whole proposed hybrid controller model was simulated and reproduction by MATLAB Version 2019b and Robotic system Toolbox 9. The optimal design of this controller is applied with 3D model of modified PUMA 560 which design by using VR technique under MATLAB/Simulink.
Fair and trustworthy: Lock-free enhanced tendermint blockchain algorithm

Blockchain Technology is exclusively used to make online transactions secure by maintaining a distributed and decentralized ledger of records across multiple computers. Tendermint is a general-purpose blockchain engine that is composed of two parts; Tendermint Core and the blockchain application interface. The application interface makes Tendermint suitable for a wide range of applications. In this paper, we analyze and improve Practical Byzantine Fault Tolerant (PBFT), a consensus-based Tendermint blockchain algorithm. In order to avoid negative issues of locks, we first propose a lock-free algorithm for blockchain in which the proposal and voting phases are concurrent whereas the commit phase is sequential. This consideration in the algorithm allows parallelism. Secondly, a new methodology is used to decide the size of the voter set which is a subset of blockchain nodes, further investigating the block sensitivity and trustworthiness of nodes. Thirdly, to fairly select the voter set nodes, we employ the random walk algorithm. Fourthly, we imply the wait-freedom property by using a timeout due to which all blocks are eventually committed or aborted. In addition, we have discussed voting conflicts and consensuses issues that are used as a correctness property, and provide some supportive techniques.
SPARQL Rewriting: Towards Desired Results

Recent years witnessed the emergence of various applications on knowledge graphs, which are often represented as RDF graphs. However, due to the lack of data schema and the complexity of SPARQL language, there is usually a gap between the user's real desire and the actual meaning of a SPARQL query, especially when the query itself is complicated. In this paper, we try to narrow this gap by modifying a given query with a set of modifiers, so that its result approaches a user-provided example set. Specifically, we model this problem as two individual sub-problems, query-restricting, and query-relaxing, both of which are shown to be NP-hard. We further prove that unless P=NP, query-restricting has no polynomial-time approximation scheme (PTAS), and query-relaxing has no polynomial-time constant-factor approximation algorithm. Despite their hardness, we propose a (1-1/ε)-approximation method for query-restricting and 2 heuristics for query-relaxing. Extensive experiments have been conducted on real-world knowledge graphs to evaluate the effectiveness and efficiency of our proposed solutions.
HowSim: A General and Effective Similarity Measure on Heterogeneous Information Networks

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. Measuring the similarity among objects is an important task in data mining applications. Currently, several similarity measures are defined for HIN. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of the similarity scores. This makes the existing similarity measures difficult to use in real applications. To address this problem, we extend SimRank, a well-known similarity measure for homogeneous graphs, to HINs, by introducing the concept of decay graph. The newly proposed relevance measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, are demonstrated by extensive experiments.
On the Reusability of Sentiment Analysis Datasets in Applications with Dissimilar Contexts

The main goal of this paper is to evaluate the usability of several algorithms on various sentiment-labeled datasets. The process of creating good semantic vector representations for textual data is considered a very demanding task for the research community. The first and most important step of a Natural Language Processing (NLP) system, is text preprocessing, which greatly affects the overall accuracy of the classification algorithms. In this work, two vector space models are created, and a study consisting of a variety of algorithms, is performed on them. The work is based on the IMDb dataset which contains movie reviews along with their associated labels (positive or negative). The goal is to obtain the model with the highest accuracy and the best generalization. To measure how well these models generalize in other domains, several datasets, which are further analyzed later, are used.
Distributed Broad Learning System

Recently, broad learning system (BLS) has been proposed and widely applied to the fields of machine learning and time series analysis. Compared with the well-known deep learning models, BLS does not need the deep architecture, and the learning process is time efficient. However, the existing BLS belongs to centralized processing, which is not applicable to the cases when data are distributed over multiple nodes. To solve this problem, in this paper, we propose a consensus-based distributed implementation of BLS (dBLS), in which each node cooperates with its one-hop neighbors to train the weights of the dBLS. Besides, a distributed extreme learning machine auto-encoder (dELM-AE) is also developed to refine the features extracted from the input data. Some simulations are performed and results show that the proposed dBLS is effective in solving distributed classification problems.
A Generalized Information Security Model SOCMD for CMD Systems

As a new security defense theory, Cyberspace mimic defense (CMD) provides an architecture named Dynamic heterogeneous redundancy (DHR) to enhance the defense level of system security. Due to the new dynamic defense mechanism DHR introduced in CMD systems, traditional security modelling and analysis methods can hardly be used for them. In this paper, we propose a Security ontology-based modelling method for CMD systems (SOCMD), which uses ontology to represent DHR components and to define their inner relationships. SOCMD also connects information components including DHRs with security vulnerabilities, threats and attackers in cyberspace. Next, attacking rules, multi-mode arbitration mechanism and combination rules are designed with SOCMD for CMD systems and a new logical-checking method is proposed to make judgement about the security state of SOCMD. Finally, different use cases and performance tests are developed to demonstrate the application process for the model and to verify the validity of our method.
Structural and Geochemical Characterization of Gold Mineralized Quartz Veins in Belikombone Gold Prospect, Betare-oya Gold District, East Cameroon

Au-bearing quartz veins in the Belikombone area are confined to major NW-SE orientations in schists and N-S and ENE -WSW in other host rocks.The objectives of this work were to study field, mineralogy and geochemical characteristics of gold mineralised quartz veins.Schists (chlorite rich) and gneisses (moderately magnetic) are the dominant rock types in the area with granite occurring as intrusions within the gneisses.Gold-bearing quartz veins occur in association with metamorphosed rocks ranging in composition from quartz ± gold to those that contain quartz ± gold ± sulphides ± ironoxides ± sericite and mafic minerals.The mineralized veins contain Au from 0.50 to 25 ppm, while the quartz veins considered barren contain 0.20 ppm Au.The concentration of As in the quartz veins ranges from 0.30ppm to 10.70 ppm and it show positive correlation with Au in samples with high Au concentration.The ΣREE varies from 5.79 to 173.64 in the quartz veins.The investigated veins show evidence of multiphase deformation and it is associated with D 2 deformation in the area.This structural setting suggest that the emplacement of gold mineralization occurred during the late Pan African orogeny.The main alteration types are silification, sericitization and sulphidation processes.
Constrained Unscented Kalman Filtering for Bearings‐Only Maneuvering Target Tracking

To track the bearings-only maneuvering target tracking accurately online, the soft measurement constraints are implemented into the Unscented Kalman filtering (UKF). To deal with the soft measurement constraints, the Lasso regularization is added as the obstacle function. In doing this, the sampled sigma points can be restricted into the feasible region. To enhance the sampling efficiency, the global optimal solution is acquired by a heuristic optimizer. To smooth the outliers, the posterior distribution is approximated by a Gaussian mixture consists of the original and the modified priors with the fuzzy weighted factor. Simulated results indicate the accuracy and the computational efficiency of the proposed method.
A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient’s characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients.
Investigating Cultural Dimensions in EFL Textbook by Using Byram Checklist

The present study is a content analysis that aims to investigate cultural dimension in an English textbook prescribed for senior high school in Indonesia which is officially published by the Ministry of Education and Culture in 2017. The data include 11 conversations, 17 reading texts, and 83 visual elements that are analysed qualitatively by employing Byram’s checklist. The findings show that stereotypes and national identity dominate the cultural dimensions with 28.05% and followed by social interaction and socialisation and life cycle with 15.9%. The occurrence of national geography is significant with 12.2%. However, social identity and social group, belief and behaviour, national history and social and political institutions are prescribed with less than 10%. The inclusion of various cultural dimensions has certain purposes. The inclusion of national identity, for instance, is pivotal for learners since the textbook is prescribed for senior high school students who are tremendously curious to determine their characters. This idea is aligned with the aim of the curriculum to facilitate the development of students with good character, good behaviour and strong nationality. It is concluded that the English textbook in Indonesia is on the way to its perfection in terms of proportionally represented cultural dimensions. However, the presence of various cultural aspects of the target culture and international target culture is also important to help students develop cultural competence and a certain level of respect as well as tolerance for others. Keywords: Textbook, cultural dimensions, cultural competence, national identity
Ontology Construction and Evaluation of UAV FCMS Software Requirement Elicitation Considering Geographic Environment Factors

The quality of unmanned aerial vehicle flight control and management system (UAV FCMS) software is crucial to guaranteeing the quality of UAVs.Software requirement elicitation (SRE) is an important part of the UAV FCMS software development process.However, this activity suffers from ambiguity, heterogeneity and incompleteness; furthermore, because the use of UAVs is closely related to their geographic environment, geographic environment factors must be fully considered when conducting UAV FCMS SRE activity.In the knowledge engineering community, an ontology is an explicit specification of a conceptualization.Introducing the ontology method into the SRE process is an effective way to solve the above problems.This paper creates a UAV FCMS SRE ontology (SREO) based on domain knowledge and empirical data, as well as a geo-ontology based on geographic information metadata.Then, the paper integrates the above two ontologies into a new ontology.The goal of ontology integration is to analyze ontology concepts by adopting a hybrid ontology mapping method.The specific process analyzes the semantic similarities between the concepts of two ontologies and then decides whether to use a description logic (DL) strategy based on the analysis results.When the corresponding conditions are satisfied, the DL strategy is used to perform both direct and transitive reasoning for the relationships to achieve the ontology mapping, and the ontology integration is eventually implemented.Finally, this paper uses a criteria-based ontology evaluation approach to evaluate the quality of the newly integrated ontology.The evaluation results show that the UAV FCMS SREO considering geographic environment factors exhibits high quality.Further engineering practices also show that the SRE activities and the generated software requirement specifications (SRSs) exhibit a large increase in quality.Through the above activities, improvements to both the quality and reliability of UAV FCMS software can be achieved.
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction

Traditional monocular visual simultaneous localization and mapping (SLAM) algorithms have been extensively studied and proven to reliably recover a sparse structure and camera motion. Nevertheless, the sparse structure is still insufficient for scene interaction, e.g., visual navigation and augmented reality applications. To densify the scene reconstruction, the use of single-image absolute depth prediction from convolutional neural networks (CNNs) for filling in the missing structure has been proposed. However, the prediction accuracy tends to not generalize well on scenes that are different from the training datasets.In this paper, we propose a dense monocular SLAM system, named DeepRelativeFusion, that is capable to recover a globally consistent 3D structure. To this end, we use a visual SLAM algorithm to reliably recover the camera poses and semi-dense depth maps of the keyframes, and then use relative depth prediction to densify the semi-dense depth maps and refine the keyframe pose-graph. To improve the semi-dense depth maps, we propose an adaptive filtering scheme, which is a structure- preserving weighted average smoothing filter that takes into account the pixel intensity and depth of the neighbouring pixels, yielding substantial reconstruction accuracy gain in densification. To perform densification, we introduce two incremental improvements upon the energy minimization framework proposed by DeepFusion: (1) an improved cost function, and(2) the use of single-image relative depth prediction. After densification, we update the keyframes with two-view consistent optimized semi-dense and dense depth maps to improve pose- graph optimization, providing a feedback loop to refine the keyframe poses for accurate scene reconstruction. Our system outperforms the state-of-the-art dense SLAM systems quantitatively in dense reconstruction accuracy by a large margin.For more information, see the demo video and supplementary material.