Reviewerly/exHarmony huggingface dataset:
Optimal Clusters Generation for Maximizing Ensemble Classifier Performance

Clustering based ensemble classifiers have seen a lot of focus recently because of their ability to effectively classify real-world noisy datasets. One way of incorporating clustering in ensembles is to utilize clustering algorithms such as k-means to generate a pool of data clusters. This is done to generate a random subspace on which base classifiers are trained, as opposed to bagging. One key parameter to clustering algorithms is the number of clusters i.e. the number of groups the data should be partitioned into; this is commonly known as the variable K. Most of the existing approaches either determine the value of K through trial and error or use some derived formulae-based approach. The problem firstly is that using a static value of K for different datasets is not ideal and although a certain value may work well for one dataset it may not work well for others. Secondly, calculating the value of K using a formulae-based approach using the raw data is not effective either as an unbalanced data can have a negative effect on the derived value. Therefore, in this paper we first segregate the data based on the data classes and then on each data class we perform a Silhouette analysis to determine the optimal number of clusters each data class should be separated into. The generated clusters which are class pure and are balanced by adding samples from other classes that are closest to the cluster centroid. In this manner we generate a random subspace of an augmented data that is composed of class balanced data clusters. On all balanced data clusters, a diverse set of base classifiers is trained, and an ensemble is formed. The proposed ensemble approach is tested on 16 benchmark UCI datasets and results are compared with single classifiers, as well as state-of-the-art ensemble classifier approaches. A set of non-parametric tests are also adopted to further validate the efficacy of the results.
Sistem Pakar Diagnosis Penyakit Tanaman Padi Menggunakan Forward Chaining dan Dempster Shafer

Rice (Oryza sativa) is one of the main commodities in Indonesia. One of the inhibitors of rice crop production is disease. Rice plant diseases can be caused by pathogens, host plants or bad environment. The process of diagnosing rice diseases requires expertise, knowledge and experience. This study aims to build an expert system that can diagnose 13 types of rice plant diseases from 43 symptoms based on the knowledge of 3 experts with forward chaining reasoning methods and mobile-based dempster shafer calculation methods. The testing technique used is black box testing, theoretical calculation testing, system accuracy testing and MOS (Mean Opinion Score) testing. The black box test results state that the expert system has 100% compatibility in terms of functionality. The theoretical calculation test results state that the expert system calculations are in accordance with the results of manual calculations. System accuracy testing results from 30 test cases get an accuracy of 81.11%. The results of MOS testing conducted on 30 respondents produced MOS of 4.2 from a scale of 5 categorized into a good system.
An Embedded RISC-V Core with Fast Modular Multiplication

One of the biggest concerns in IoT is privacy and security. Encryption and authentication need big power budgets, which battery-operated IoT end-nodes do not have. Hardware accelerators designed for specific cryptographic operations provide little to no flexibility for future updates. Custom instruction solutions are smaller in area and provide more flexibility for new methods to be implemented. One drawback of custom instructions is that the processor has to wait for the operation to finish. Eventually, the response time of the device to real-time events gets longer. In this work, we propose a processor with an extended custom instruction for modular multiplication, which blocks the processor, typically, two cycles for any size of modular multiplication when used in Partial Execution mode. We adopted embedded and compressed extensions of RISC-V for our proof-of-concept CPU. Our design is benchmarked on recent cryptographic algorithms in the field of elliptic-curve cryptography. Our CPU with 128-bit modular multiplication operates at 136MHz on ASIC and 81MHz on FPGA. It achieves up to 13x speed up on software implementations while reducing overall power consumption by up to 95\% with 41\% average area overhead over our base architecture.
The Impact of Human Development on the Standard of Living in Alleviating Poverty: Evidence from Africa

The African Union 2030, 2063, and beyond strategic, focused on transformative economic growth and regional integration, empowering the populace and consolidating peace and society cannot be achieved without considering human development as synergies in elevating these policies. A large focus on empowering the populace through domestic credit can help this agenda be realized. For robustness sake, the researcher developed both subjective and objective measures to check the various relationships of the variable that helps to achieve the standard of living. Using data from the World Bank's development indicators and Human Development indicators from 1998 to 2017, the researcher examined human development reduces poverty. In this meta-analysis, the study empirically synthesizes a total of four African countries under study, fixed and random effects estimation, domestic credit and human development study find out these variables have a positive relationship of poverty which helps to improve the standard of living. The findings of this study have important implications for policy implementation and therefore, helps to improve the standard of living in the four countries under study.
Efficient Construction of Hierarchical Overlap Graphs

The hierarchical overlap graph (HOG for short) is an overlap encoding graph that efficiently represents overlaps from a given set P of n strings. A previously known algorithm constructs the HOG in \(O(\vert \vert P \vert \vert + n^2)\) time and \(O(\vert \vert P \vert \vert +n\times \min (n,\max \{|s|:s\in P\}))\) space, where \(\vert \vert P \vert \vert \) is the sum of lengths of the n strings in P. We present a new algorithm of \(O(\vert \vert P \vert \vert \log n)\) time and \(O(\vert \vert P \vert \vert )\) space to compute the HOG, which exploits the segment tree data structure. We also propose an alternative algorithm using \(O(\vert \vert P \vert \vert \frac{\log n}{\log \log n})\) time and \(O(\vert \vert P \vert \vert )\) space in the word RAM model of computation.
Complementary properties of multiphoton quantum states in linear optics networks

Abstract We have developed a theory for accessing quantum coherences in mutually unbiased bases associated with generalized Pauli operators in multiphoton multimode linear optics networks (LONs). We show a way to construct complementary Pauli measurements in multiphoton LONs and establish a theory for evaluation of their photonic measurement statistics without dealing with the computational complexity of Boson samplings. This theory extends characterization of complementary properties in single-photon LONs to multiphoton LONs employing convex-roof extension. It allows us to detect quantum properties such as entanglement using complementary Pauli measurements, which reveals the physical significance of entanglement between modes in bipartite multiphoton LONs.
Theoretical Research on Integrated Development of Student Affairs Management and Multimedia Technology

In the field of higher education student affairs management, the key solution to the problem of innovation development in current social conditions is the effective implementation and high-level integration of multimedia technology. Therefore, a theoretical system built for the great combination and integration of student affairs management and multimedia technology is inevitable towards this practical topic. To be specific, the objectives, as the prerequisite, of this research should be put into consideration at first, which constitutes the fundamental background and driving force. Additionally, in the light of objectives, the basic principles stipulated are the standards of real-world practice, which should be efficiently followed in the process of development. Finally, as for the realization of setting objectives and practical implementation of principles, it is of the essence to make creative exploitation and utilization of the condition factors for student affairs management and multimedia technology.
Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.
Comparative analysis of face recognition systems built using standard architecture blocks

Research subject. The article reviews ways of constructing face recognition systems based on standard modules. Method. The study is based on comparison of performance and recognition quality of various pipelines. Core results. Values of reached recognition quality and dependencies from a type of original data are presented. Practical relevance. The results could be used while implementing various face recognition system pipelines.
End-to-End Deep Kernel Map Design for Image Annotation

Deep kernel map networks have shown excellent performances in various classification problems including image annotation. Their general recipe consists in aggregating several layers of singular value decompositions (SVDs) - that map data from input spaces into high dimensional spaces - while preserving the similarity of the underlying kernels. However, the potential of these deep map networks has not been fully explored as the original setting of these networks focuses mainly on the approximation quality of their kernels and ignores their discrimination power.In this paper, we introduce a novel “end-to-end” design for deep kernel map learning that balances the approximation quality of kernels and their discrimination power. Our method proceeds in two steps; first, layerwise SVD is applied in order to build initial deep kernel map approximations and then an “end-to-end” supervised learning is employed to further enhance their discrimination power while maintaining their efficiency. Extensive experiments, conducted on the challenging ImageCLEF annotation benchmark, show the high efficiency and the out-performance of this two-step process with respect to different related methods.
Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study

Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. Since mBERT is not pre-trained with explicit cross-lingual supervision, transfer performance can further be improved by aligning mBERT with cross-lingual signal. Prior work proposes several approaches to align contextualised embeddings. In this paper we analyse how different forms of cross-lingual supervision and various alignment methods influence the transfer capability of mBERT in zero-shot setting. Specifically, we compare parallel corpora vs. dictionary-based supervision and rotational vs. fine-tuning based alignment methods. We evaluate the performance of different alignment methodologies across eight languages on two tasks: Name Entity Recognition and Semantic Slot Filling. In addition, we propose a novel normalisation method which consistently improves the performance of rotation-based alignment including a notable 3% F1 improvement for distant and typologically dissimilar languages. Importantly we identify the biases of the alignment methods to the type of task and proximity to the transfer language. We also find that supervision from parallel corpus is generally superior to dictionary alignments.
Playing with your project data in scrum retrospectives

Modern, agile software development methods rely on iterative work and improvement cycles to deliver their claimed benefits. In Scrum, the most popular agile method, process improvement is implemented through regular Retrospective meetings. In these meetings, team members reflect on the latest development iteration and decide on improvement actions. To identify potential issues, data on the completed iteration needs to be gathered. The Scrum method itself does not prescribe these steps in detail. However, Retrospective games, i.e. interactive group activities, have been proposed to encourage the sharing of experiences and problems. These activities mostly rely on the collected perceptions of team members. However, modern software development practices produce a large variety of digital project artifacts, e.g. commits in version control systems or test run results, which contain detailed information on performed teamwork. We propose taking advantage of this information in new, data-driven Retrospective activities, allowing teams to gain additional insights based on their own team-specific data.
Novel method of data compression for the online detection signal of coal mine wire rope

Coal mine wire rope detection is related to personnel and production safety. With the Chinese coal mining trend tending towards deep mining, a considerable amount of data is critical for the online detection of deep well lifting wire rope. To improve the sampling rate, decrease the analysis processing time and realise real-time online detection, this paper proposes an online detection data compression processing method. The study focuses on the distortion compression method for the online detection signal of deep well hoisting wire rope. The set partitioning in hierarchical trees (SPIHT) algorithm is one of the most advanced methods in the field of image transformation coding. Compared with other coding algorithms, the SPIHT algorithm demonstrates desired characteristics such as a high signal-to-noise ratio, lower complexity and decreased computational load, among others. This paper discusses how, in combination with the image processing method, a compression coding method for the one-dimensional signal of the magnetic leakage detection of the mining wire rope is developed. Furthermore, the set partitioning sorting algorithm is investigated and analysed, the temporal orientation tree structure of the one-dimensional signal of the wavelet coefficient is defined for wire rope magnetic leakage detection and the SPIHT algorithm is presented, in addition to an example of the one-dimensional signal from the magnetic leakage detection of the wire rope. The results reveal that under the condition of the normalised mean square error (NMSE; NMSE &lt; 0.01) of distortion, the compression ratio improved by 30%. The online detection signal lossy compression method proposed in this study has a considerable influence on the recovery of the original signal, in addition to a higher compression ratio and a reduced computation time, compared to the existing method.
[Paper] Object Manipulation for Perceiving a Sense of Material using User-Perspective Mobile Augmented Reality

In this study, we conducted an experiment using a mobile augmented reality (AR) system that allows direct manipulation of a virtual object with the user's hand and that presents material appearance of the object. The system presents user-perspective images on the mobile display, which enables natural interaction with virtual objects. We compared the methods with user-perspective and camera-perspective presentation using an object manipulation task for material perception. The result showed that the answer time in the task became significantly shorter with user-perspective presentation, which indicates that user-perspective presentation makes manipulation of virtual objects in mobile AR easier.
Application of SOMs and k-means clustering to geophysical mapping: Lessons learned

PreviousNext You have accessSEG Technical Program Expanded Abstracts 2020Application of SOMs and k-means clustering to geophysical mapping: Lessons learnedAuthors: Angela Carter-McAuslanColin FarquharsonAngela Carter-McAuslanMemorial University of NewfoundlandSearch for more papers by this author and Colin FarquharsonMemorial University of NewfoundlandSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-w10-01.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractMachine learning techniques are of growing interest to the geosciences. We discuss the use of self-organizing maps and k-means clustering as techniques for the analysis of potential field and radiometric data in the production of predictive maps. By looking at examples of predictive maps produced for surface geology on the Baie Verte Peninsula, Newfoundland, Canada as well as basement geology of the mid-continent rift in Decorah, Iowa, USA we show the benefits of a combined SOM – K-means clustering method over using SOMs or K-means clustering as stand-alone methods.Keywords: self-organizing maps, geological mapping, airborne geophysics, k-means clustering, machine learningPermalink: https://doi.org/10.1190/segam2020-w10-01.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Angela Carter-McAuslan and Colin Farquharson, (2020), "Application of SOMs and k-means clustering to geophysical mapping: Lessons learned," SEG Technical Program Expanded Abstracts : 3843-3846. https://doi.org/10.1190/segam2020-w10-01.1 Plain-Language Summary Keywordsself-organizing mapsgeological mappingairborne geophysicsk-means clusteringmachine learningPDF DownloadLoading ...
Pembuatan Komik Literasi Informasi Untuk Meningkatkan Literasi Siswa di Perpustakaan SMA Negeri 1 Padang

AbstractThe writing of this paper aims to explain the making of information literacy comics to increase student literacy in the Public Library of Padang 1 High School. The method used in this paper is descriptive method, the data is taken through field observations and interviews with librarians and librarians, based on facts that occur in the field of Public Library 1 Padang. Based on these results it can be concluded that the making of information literacy comics can be concluded the following steps. Determine the topic or theme of the Comic, Thinking of characters or extras, Determine the character to be played, Determine the setting of the place, Determine the setting of the atmosphere, Determine the setting, Determine the title, Make a title, Make a script, Provide tools and materials, Make a comic panel, Make a sketch of a picture comic characters, thickening comic character drawings, sketching conversation balloons, thickening panel lines, making storyline text columns according to narration, coloring sketch pictures on comics, thickening story conversation balloon lines, filling text in conversation bubbles and storyline columns in comics, Thicken the character image using a ballpoint pen. Keywords: comics, information literacy comics, student literacy
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversarial machine learning (AML) in the computer vision domain have raised interests in the security of state-of-the-art model-based recommenders. Recently, worrying deterioration of recommendation accuracy has been acknowledged on several state-of-the-art model-based recommenders (e.g., BPR-MF) when machine-learned adversarial perturbations contaminate model parameters. However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks. In this work, inspired by the basic iterative method (BIM) and the projected gradient descent (PGD) strategies proposed in the CV domain, we adapt the multi-step strategies for the item recommendation task to study the possible weaknesses of embedding-based recommender models under minimal adversarial perturbations. Letting the magnitude of the perturbation be fixed, we illustrate the highest efficacy of the multi-step perturbation compared to the single-step one with extensive empirical evaluation on two widely adopted recommender datasets. Furthermore, we study the impact of structural dataset characteristics, i.e., sparsity, density, and size, on the performance degradation issued by presented perturbations to support RS designer in interpreting recommendation performance variation due to minimal variations of model parameters. Our implementation and datasets are available at https://anonymous.4open.science/r/9f27f909-93d5-4016-b01c-8976b8c14bc5/.
Updating Ontology Alignment on the Instance Level Based on Ontology Evolution

This paper is devoted to investigating if it is possible to update stale alignments, based solely on the analysis of changes introduced to ontologies participating in the alignment. A change-significance function will be presented, which can be used to indicate whether or not the alignment revalidation is potentially required. Next, an algorithm for revalidation of existing mappings will be proposed. Due to the limited space of this paper, only the instance level will be considered.
MEDIA REALIA DALAM MENGENALKAN KOSAKATA ANAK KELOMPOK A DI TK KEMBANG TERATAI KELURAHAN LEKOBALO KECAMATAN KOTA BARAT KOTA GORONTALO

This study The purpose of this study was to describe the use of realia media conducted by teachers in learning. The method used in this research is descriptive qualitative. Data collection techniques used in the form of observation, interviews and documentation. Based on the results of the study, the application of Realia Media in introducing the Group A Children's Vocabulary in TK Kembang Teratai Kelurahan Lekobalo, Kecamatan Kota Barat, Kota Gorontalo, illustrates that the teacher has applied realia media in the learning process. It can be seen from the teacher preparing the real media of chickens and cats to be used as learning material and Such real media can increase vocabulary in children but there is still one child who is not fluent in speaking, there is still a long pause when children tell stories, and the child's voice when speaking is still slow. for that as a teacher must work even harder in equipping children with words related to a particular field and introducing vocabulary needs to be done continuously and must go through in various ways.