Reviewerly/exHarmony huggingface dataset:
Enhancing Parallel Coordinates Visualization Using Genetic Algorithm with Smart Mutation

Visualization techniques have received a lot of attention regarding their potential to interpret and analyze the data.One of the marked visualization methods is the Parallel Coordinates Plot (PCP) utilized to high-dimensional datasets (more than three dimensions). Due to that, in visualizing large-scale datasets, the method suffers from high clutters produced from numerous intersection lines between neigh-boring axes, numbers of researchers have conducted techniques to boost PCPs. For instance, reducing the number of crossing lines by utilizing the re-ordering the neighboring axes in the PCP technique is a useful procedure to reduce the clutter. Motivated by this goal, the acquisition of the optimal coordinate's order can be classified as a combinatorial optimization problem. However, in high-dimensional datasets, the optimization algorithm may face difficulty to deal with this issue. In this paper, we propose a smart mutation operator to enhance the performance of Genetic Algorithm (GA) in finding the optimal order of PCP based on diminishing the numerous intersection lines. However, any other user-desired metric can be utilized as an objective function. To assess the introduced method, we conducted a Monte Carlo simulation and several experiments to find an optimal coordinates' order in PCP to visualize the datasets with various numbers of samples and dimensions. In the experimental results, utilizing the smart mutation represents an improvement in PCP visualization in terms of reducing the intersection lines between the neighboring coordinates compared to the original GA.
Eligibility of English Hypernymy Resources for Extracting Knowledge from Natural-Language Texts

A common subtask of knowledge acquisition from natural-language texts is classifying words and recognizing entities and actions in the text. It is used in the analysis of both scientific and narrative texts. Thesauri and lexical databases containing hypernymy relationship between synsets may be a useful resource for entity and action recognition. In this study, we compared the performance of three major English thesauri containing hypernymy relationship in different forms - WordNet, Roget’s Thesaurus, and FrameNet - on 6 word-meaning categories that are used for the analysis of narrative and scientific natural-language texts. The results show that WordNet contains more words than FrameNet, and is more suitable for scientific texts, but FrameNet contains better-defined hypernyms and shows better precision for many narrative natural-language tasks, especially for verbs. Roget’s Thesaurus performance is average between WordNet and FrameNet in most word-meaning categories Enhancing FrameNet by adding more lexical units to existing frames would allow creating a powerful resource for entity and action recognition in text analysis. Fixing WordNet problems require revising its system of hypernyms.
Understanding Quantum Computation Concepts and Applications

It is extremely important to point out that through quantum computing, it is possible to obtain results previously unachievable until then by traditional computing. Based on this, this paper aims to give an overview and glimpse of the potential horizon of this area of study that has become increasingly robust in computing over the recent past. It is beyond our scope to address deep concepts of quantum mechanics on which quantum computing is based and reveals. It is rather to form a simple, comprehensive and consistent intellectual scope, not only for specialists but targeted common interest. Thus, this research was built on the latest studies on the subject recognized within the scientific and academic fields.
Binocular Human Body Attitude Distance Localization Recognition Algorithm Based on Dual Convolution

Objective: An algorithm based on real-time accurate target recognition and distance location for UAVs, the existing target location and discrimination methods often fail to meet practical requirements. Method: The image information collected under the common single camera can only obtain two-dimensional information, and the relative distance of the camera based on the target cannot be obtained. However, the commonly used dual camera-based distance acquisition algorithm is too complicated, not stable enough, and requires developers to have higher the level of knowledge, the high threshold for development, and the difficulty of application. Therefore, this paper proposes to train the feature extraction network based on the two-channel Darknet-53 basic structure through the dual camera under the human body posture recognition image dataset, and initialize the YOLOv2 network with its parameters, and to train the human body position in the human body posture image, relative distance, and category. Result: Experimental results verify that the human body position and category recognition of human posture using this method improves the recognition accuracy by 3.83% and 4.81% compared with the single-convolution chain, and the accuracy of the target-based relative distance is achieved 65.21%. Conclusion: The algorithm can be effectively applied to the UAV to quickly recognize the human body posture and obtain a better recognition effect to meet the real-time demand.
ANALISA DAN PERANCANGAN SISTEM PENGHITUNG ZAKAT

Technology is increasingly advancing and developing. There are a lot of internet services and a special bureaucracy where zakat is paid, but can everyone enjoy internet service? And do all people believe in bureaucracy? Sometimes in addition to the high cost of the internet, a sense of unbelief in this bureaucracy is an obstacle for those who want to check how much zakat they are required to pay, which is an obligation of Muslims to issue zakat for those who are already narrated as described in the Qur'an. and al-Hadith, to regulate expenditure and distribution of the property of the people and its distribution. The difficulty to calculate zakat independently or individually, because they do not yet understand the calculation of zakat, especially the calculation of zakat for animal products. In Scientific Writing, the author tries to make an analysis and design of Zakat Calculation System as one solution.The writing method used in the design uses a flowchart diagram that illustrates the flowchart outlining the stages in solving a problem as outlined in the form of an algorithm. This application program is still limited to the design of flowchart diagrams that do not use the application programming language. The results of this study can be concluded that the analysis and design of the flow diagram of the zakat calculation system can provide information about the steps of the zakat calculation system flowchart and help the calculation process that is accurate and precise as well as the prototype for the application to be applied so that it is easy to use by users with very interactive . Â  Keywords: Zakat, Flowcharti, Story Board
Regeneration of Gamma Oscillations in Large-scale Neural Network with Complicated Structure Based on CUDA

Gamma oscillations have been not only found in many biology experiments but also regenerated in many small neural network models. However, whether gamma oscillations can be regenerated in large-scale neural network with complicated structure is still an open problem. In order to deal with this problem, this paper constructs a large-scale neural network model with multi-layer columns. Based on the existing CUDA parallel algorithm and a synapse optimization algorithm, we design a novel parallel algorithm for simulation of the large-scale complicated neural network with multi-layer column structure. The simulation results verify that gamma oscillations can be regenerated in large-scale neural network with complicated structure.
Detecting Overlapping Data in System Logs Based on Ensemble Learning Method

Machine learning techniques are essential for system log anomaly detection. It is prone to the phenomenon of class overlap because of too many similar system log data. The occurrence of this phenomenon will have a serious impact on the anomaly detection of the system logs. To solve the problem of class overlap in system logs, this paper proposes an anomaly detection model for class overlap problem on system logs. We first calculate the relationship between the sample data and the membership of different classes, normal or anomaly, and use the fuzziness to separate the sample data of the overlapping parts of the classes from the data of the other parts. AdaBoost, an ensemble learning approach, is used to detect overlapping data. Compared with machine learning algorithms, ensemble learning can better classify the data of the overlapping parts, so as to achieve the purpose of detecting the anomalies of the system logs. We also discussed the possible impact of different voting methods on ensemble learning results. Experimental results show that our model can be effectively applied in a variety of basic algorithms, and the results of each measure have been improved.
Quantum radar with digital waveform generators

Quantum radars at X-Band suffer from two major issues today: cost and performance. The cost of quantum radar at X-Band is roughly ten orders of magnitude more than the corresponding classical radar today. Also, the detection performance of experimental quantum radars at X-Band is not any better than the optimal classical radar today. We discuss future research in X-Band quantum radars, including a theoretical idea to simulate quantum radar without cryogenic dilution refrigerators. This idea uses digital waveform generation rather than analog signal generation.
Convolutional Nonlinear Dictionary with Cascaded Structure Filter Banks

This study proposes a convolutional nonlinear dictionary (CNLD) for image restoration using cascaded filter banks. Generally, convolutional neural networks (CNN) demonstrate their practicality in image restoration applications; however, existing CNNs are constructed without considering the relationship among atomic images (convolution kernels). As a result, there remains room for discussing the role of design spaces. To provide a framework for constructing an effective and structured convolutional network, this study proposes the CNLD. The backpropagation learning procedure is derived from certain image restoration experiments, and thereby the significance of CNLD is verified. It is demonstrated that the number of parameters is reduced while preserving the restoration performance.
Heuristic Solutions for the $${(\alpha , \beta )}$$-k Feature Set Problem

Feature selection aims to choose a subset of features, out of a set of candidate features, such that the selected set best represents the whole in a particular aspect. The \((\alpha , \beta )\)-k feature set problem (FSP) is a combinatorial optimization-based approach for selecting features. On a dataset with two groups of data, the \((\alpha , \beta )\)-k FSP aims to select a set of features such that the set maximizes the similarities between entities of the same group and the differences between entities of different groups. This study develops a matheuristic algorithm for the \((\alpha , \beta )\)-k FSP. We test the algorithm on 11 real-world instances ranging from medium to large. The computational results demonstrate that the proposed matheuristic competes well with the standard solver CPLEX.
Property in Human Body Parts

Abstract This chapter surveys the current legal position concerning property in bodies and bodily materials. Of especial relevance in the current age of advanced genetic and other bio technologies, it looks beyond property in bodies and their materials ‘as such’ to consider also (a) the availability of rights of personal and intellectual property in objects incorporating or derived from them, and (b) the reliance on quasi-property rights of possession and consent to regulate the storage and use of corpses and detached bodily materials, including so-called ‘bio-specimens’. Reasoning from first principles, it highlights the practical and conceptual, as well as the political and philosophical, difficulties in this area, along with certain differences in the regulatory approach of European and US authorities. By way of conclusion, it proposes the law of authors’ and inventors’ rights as simultaneously offering a cautionary tale to those who would extend the reach of property even further than it extends currently and ideas for exploiting the malleability of the ‘property’ concept to manage the risks of extending it.
A Corpus-Based Analysis of Text Complexity in NMET Reading and Its Implication on Reading Instruction

This study builds a small-scale corpus of 54 reading texts of the National Matriculation English Test from 2016 to 2019 and uses natural language processing tools, Python and Coh-Metrix, to investigate text complexity with 35 quantitative indices from five aspects, including length, readability, vocabulary, syntax and cohesion. Results reveal the major characteristics of these texts, showing they have appropriate length, similar difficulty as the texts in high school English textbooks and a higher level of lexical diversity and stem overlap among all sentences than those in textbooks. The findings offer support for more precise and effective reading instructional practice in high school from the perspectives of the WHAT and the HOW in classroom teaching.
From Sociology to ICTs

This chapter addresses the inclusion of women in the field of information technology from a perspective that promotes the creation of interdisciplinary spaces, making visible the knowledge inherent to the different professions capable of adding value to technological development. The model presented here was created to evaluate the level of technological development that will allow the determining of the components needed for the transformation towards Industry 4.0, from which you can determine the knowledge necessary for the development of products in their real context of use. Areas such as data science, virtual reality, or human-computer interaction design techniques, models, and/or tools for the construction of solutions that do not require strictly engineering-based knowledge. This chapter proposes a journey towards the development and adoption of technologies in the industry, which requires the inclusion of interdisciplinary knowledge, hence giving new meaning to the role of women in technological development.
Automatic Cross-City API Matching for Urban Service Collaboration Based on Semantics

In China, many government platforms begin to offer interfaces to each other for establishing urban service collaboration systems. However, it is expensive and tedious to migrate a successful collaboration procedure from one city to another as there are no uniform standards on API definitions. In this paper, we aim to develop a method that can match the cross-city APIs for service collaboration migration. We consider the matching task as a binary classification problem. A semantic feature engineering scheme is proposed and the matching is achieved via an XGBoost classifier. Experiments demonstrate the effectiveness of the proposed method.
WITHDRAWN: Historical text retrieval with spectral data and adaptive variants of bacterial foraging

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Recommendation-Based Meta-Search Engine for Suggesting Relevant Documents Links

The information available online is mostly present in an unstructured form and search engines are indispensable tools especially in higher education organizations for obtaining information from the Internet. Various search engines were developed to help learners to retrieve the information but unfortunately, most of the information retrieved is not relevant. The main objective of this research is to provide relevant document links to the learners using a three-layered meta-search architecture. The first layer retrieves information links from the web based on the learner query, which is then fed to the second layer where filtering and clustering of document links are done based on semantics. The third layer, with the help of a reasoner, categorizes information into relevant and irrelevant information links in the repository. The experimental study was conducted on a training data set using web queries related to the domain of sports, entertainment, and academics. The results indicate that the proposed meta-search engine performs well as compared to another stand-alone search engine with better recall.
Temporal-spatial-domanial features oriented modeling framework for Transboundary Service

Service model is an important form to describe service functions and non-functional attributes. Many scholars have given detailed model specifications and modeling languages for various aspects such as business processes, service value delivery, service ontology description, service decision making, and case management. The state of the art of the above models and their associations shows that temporal-spatial-domanial features have received little attention, however, which have great significance on service execution and evaluation, especially in the background of Transboundary Service. There-fore, this paper proposes a Transboundary Service modeling framework oriented to temporal-spatial-domanial features. It defines the representation of service domains, and analyzes the relationship between domain and service functional/non-functional model. In addition, a tool is developed to support visual modeling and annotation work based on this framework. Finally, an actual case of Freshhema from Alibaba is used to verify this framework. Compared with the existing modeling framework (e.g. BPMN, VDML), this framework pays special attention to the description and analysis of temporal-spatial-domanial features, and clarifies the dependency relationship between it and service function/non-function attributes, pro-vides the necessary model extensions to provide more detailed support for subsequent model application and optimization.
Implementing the RDA Research Data Policy Framework in Slovenian Scientific Journals

The paper aims to present the implementation of the RDA research data policy framework in Slovenian scientific journals within the project RDA Node Slovenia. The activity aimed to implement the practice of data sharing and data citation in Slovenian scientific journals and was based on internationally renowned practices and policies, particularly the Research Data Policy Framework of the RDA Data Policy Standardization and Implementation Interest Group. Following this, the RDA Node Slovenia coordination prepared a guidance document that allowed the four pilot participating journals (from fields of archaeology, history, linguistics and social sciences) to adjust their journal policies regarding data sharing, data citation, adapted the definitions of research data and suggested appropriate data repositories that suit their disciplinary specifics. The comparison of results underlines how discipline-specific the aspects of data-sharing are. The pilot proved that a grass-root approach in advancing open science can be successful and well-received in the research community, however, it also pointed out several issues in scientific publishing that would benefit from a planned action on a national level. The context of an underdeveloped data sharing culture, slow implementation of open data strategy by the national research funder and sparse national data service infrastructure creates a unique environment for this study, the result of which can be used in similar contexts worldwide.
Motorcycle Secondary Authentication System Using Arduino-Based HC-05 and SIM8001 Module

Motorcycles are still the dominant means of transportation in Indonesia, marked by the number of motorcycle sales in 2018 reaching 6.3 million units. One problem that motorcycle users do not want is the loss of keys. Therefore in this study will create a motorcycle secondary authentication system with the Product Development Lifecycle method, by using the SA-RT (Situation Awareness Rating Technique) method to the analysis of functional system requirements, this research stated the authentication system is operated from an android application that is connected to an Arduino device on a motorcycle via Bluetooth and SMS. Passwords are sent through the Android application to the Arduino microcontroller to turn on or turn off the electricity on the motorcycle. The maximum distance of a Bluetooth connection on this system is 5 m, the average Bluetooth authentication response time is 344 ms, the average SMS authentication process time is 249 ms, and the suitability of the function is 100%. The results obtained show that the proposed method can be an alternative solution for motorcycle security and motorcycle secondary authentication system.
Noisy polynomial interpolation modulo prime powers

We consider the noisy polynomial interpolation problem of recovering an unknown s-sparse polynomial f(X) over the ring Zpk of residues modulo pk, where p is a small prime and k is a large integer parameter, from approximate values of the residues of f(t)∈Zpk. Similar results are known for residues modulo a large prime p, however the case of prime power modulus pk, with small p and large k, is new and requires different techniques. We give a deterministic polynomial time algorithm, which for almost given more than a half bits of f(t) for sufficiently many randomly chosen points t∈Zpk∗, recovers f(X).
The Method “cut cylinder” for Approximation Round and Cylindrical Shape Objects and Its Comparison with Other Methods

A method has been developed to approximate the volumes of cylindrical objects by slices. The method is based on an algebraic formula of circle. It allows to determine the thickness by width of the cylindrical object measured on the current and subsequent slices, which enables the morphometry of histological sections made with unequal pitch. The ‘‘cut cylinder’’ method is compared with other popular approximation methods: unilateral rectangles, trapezoids and reconstructions in the Amira software. A theoretical comparison of approximation methods revealed that an error of less than 5% can be achieved for the unilateral rectangle method with more than 11, and for the trapezoids’ method more than 6 slices. The ‘‘cut cylinder’’ algorithm allows to reproduce the contour of the cylindrical object in the most accurate way with a smaller number of slices - less than 6. In practical application of methods for round objects reconstructed by 6 histological sections with the equal pitch, the method of approximation by trapezoids gives a more accurate results and is easy to apply. In order to determine the thickness of the slice, the trapezoids’ method can be supplemented by a “cut cylinder” method, which contains a formula for determining the thickness of the slice by the width. The developed “cut cylinder” method allows to obtain data comparable to 3D-reconstruction in the Amira software (FEI, USA), however with a large dispersion for sections with equal pitch. #CSOC1120