Reviewerly/exHarmony huggingface dataset:
Data Restoration by Linear Estimation of the Principal Components From Lossy Data

In this article, we propose a method based on principal component analysis (PCA) to restore data after the occurrence of data loss due to sensor defects or environmental factors. In the L2-PCA feature space, the feature vector, which consists of principal components of the data, converges to a point known as the “convergence point” as the extent of data loss increases. Using these characteristics, we approximately linearly estimated the principal components of the original data from the feature vectors of the lossy data. The estimated principal components are used as coefficients in the linear combination of the projection vectors of the PCA feature space for data restoration. The restoration performance of the proposed method is not only superior; the method is also computationally more efficient than other data restoration methods. Experimental results for gas measurement data and facial image data confirm the excellent data restoration performance of the proposed method.
Evaluation of coastal sediments

Abstract In the present study, beach sediments, cliff, and inland soil samples (within 1 km from the coastline) collected along the coast of Varkala in Trivandrum, the southernmost district of Kerala, India, were analyzed for examining the presence of major and trace elements. The samples were analyzed using advanced characterization techniques like energy dispersive X-ray fluorescence spectroscopy, X-ray powder diffraction (XRD), and Raman spectroscopy. XRD and Raman spectroscopy are used to identify the phase structure of minerals present in the samples. Further surface mapping of elements was successfully carried out using the ordinary kriging interpolation technique available in the GIS software environment. The presence of minor and trace elements, rare earth and radioactive elements, and their spatial variation along the beach and inland areas of Varkala were clearly delineated from the present study.
Educational Tool for Generation and Analysis of Multidimensional Modeling on Data Warehouse

The curricular inclusion of topics, study plans, and teaching programs related to the study of Data Science has been trending mostly in higher-level education for the last years.However, the previous knowledge requirements for students to adequately assimilate these lessons are more specialised than the ones they obtain during secondary education.On the one hand, the interaction with complexes techniques and materials is needed, and on the other, tools to practice on-demand are required in the current learning.So, this is an excellent opportunity for the creation of data analysis tools for educational purpose that could be considered as a starting point of a broad area of application.This paper presents a pedagogical support tool aimed to facilitate the student approach to the basic knowledge of data mining through the practice of the analysis of online analytical processing (OLAP).It is a prototype that allows the visualisation of the multidimensional cubes generated with all possible combinations of the dimensions of the data set, as well as their storage in databases, the recovery operations for views, and the implementation of an algorithm for the selection of the optimal view set for materialising the set of records resulting from a search of the database, and computing the materialisation costs and total records recovered.The prototype also carries out and present recurrent patterns and association rules while considering factors such as support variables and reliability.All of this is steps are done explicitly to aid the students to comprehend the generation process of data cubes in the data mining discipline.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion opera-tions for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recog-nition results, compared with other related deep models.
Understanding the stability of medical concept embeddings

Abstract Frequency is one of the major factors for training quality word embeddings. Several studies have recently discussed the stability of word embeddings in general domain and suggested factors influencing the stability. In this work, we conduct a detailed analysis on the stability of concept embeddings in medical domain, particularly in relations with concept frequency. The analysis reveals the surprising high stability of low‐frequency concepts: low‐frequency (&lt;100) concepts have the same high stability as high‐frequency (&gt;1,000) concepts. To develop a deeper understanding of this finding, we propose a new factor, the noisiness of context words , which influences the stability of medical concept embeddings regardless of high or low frequency. We evaluate the proposed factor by showing the linear correlation with the stability of medical concept embeddings. The correlations are clear and consistent with various groups of medical concepts. Based on the linear relations, we make suggestions on ways to adjust the noisiness of context words for the improvement of stability. Finally, we demonstrate that the linear relation of the proposed factor extends to the word embedding stability in general domain.
Continual Egocentric Object Recognition.

We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn. This setting has a set of unique characteristics:it assumes an egocentric point-of-view bound to the needs of a singleperson, which implies a relatively low diversity of data and a coldstart with no data; it requires to operate in an open world, where newobjects can be encounteredat any time; supervision is scarce and hasto be solicited to the user, and completelyunsupervised recognitionof new objects should be possible. Note that this setting differs fromthe one addressed in the open world recognition literature, where supervised feedback is always requested to be able to incorporate newobjects. We propose a first solution to this problem in the form ofa memory-based incremental framework that is capable of storinginformation of each and any object it encounters, while using the supervision of the user to learn to discriminate between known and unknown objects. Our approach is based on four main features: the useof time and space persistence (i.e., the appearance of objects changesrelatively slowly), the use of similarity as the main driving principlefor object recognition and novelty detection, the progressive introduction of new objects in a developmental fashion and the selectiveelicitation of user feedback in an online active learning fashion. Experimental results show the feasibility of open world, generic objectrecognition, the ability to recognize, memorize and re-identify newobjects even in complete absence of user supervision, and the utilityof persistence and incrementality in boosting performance.
Research on Semantic Analysis of Ocean Flow Field Based on Feature Similarity Collaborative Filtering

Ding, L., 2020. Research on semantic analysis of ocean flow field based on feature similarity collaborative filtering. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 53–57. Coconut Creek (Florida), ISSN 0749-0208.In the field of ocean space-time data modeling research, fields and features are usually used to describe and organize ocean current phenomena, which is continuous and dynamic. However, during the process, the semantic analysis and expression of ocean flow field phenomena often produce heterogeneous problems. Because the feature theory can construct and define the conceptual model framework of domain knowledge system, the semantic analysis of the ocean flow field using the feature theory can be regarded as one of the ways to achieve the semantic sharing of the ocean flow field. Moreover, based on the collection and analysis of existing knowledge and concepts in the field of marine science, a quadruple-based organization in the paper is proposed for ocean flow field features, in which by expanding the concept features, attribute features, and relationship features, a basic structure-based approach is formed. The overall characteristic structure of the ocean flow field comprises concept, attribute, and relationship structure. Additionally, collaborative filtering is performed on the similarity of the structural features in the ocean flow field, and a formal grammatical expression system conforming to the characteristics of continuous flow field is also made so that a normative framework system is provided for the ocean flow field knowledge organization. The OWL language is used, which allows for the conduction of the formal expression and description of geostrophic flow, which provides semantic support for the integration and management of spatiotemporal data in the ocean flow fields.
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the best parameter values can change during the optimization process. Parameter control mechanisms are techniques developed to identify and to track these values. Recently, a series of rigorous theoretical works confirmed the superiority of several parameter control techniques over EAs with best possible static parameters. Among these results are examples for controlling the mutation rate of the $$(1+\lambda )$$ EA when optimizing the OneMax problem. However, it was shown in [Rodionova et al., GECCO’19] that the quality of these techniques strongly depends on the offspring population size $$\lambda $$ . We introduce in this work a new hybrid parameter control technique, which combines the well-known one-fifth success rule with Q-learning. We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO’19] and this – in contrast to previous parameter control methods – simultaneously for all offspring population sizes $$\lambda $$ . We also show that the promising performance of HQL is not restricted to OneMax, but extends to several other benchmark problems.
A direct product theorem for one-way quantum communication

We prove a direct product theorem for the one-way entanglement-assisted quantum communication complexity of a general relation f⊆X×Y×Z. For any 0 k ≥ 1, we show that [EQUATION] where Q1e(f) represents the one-way entanglement-assisted quantum communication complexity of f with worst-case error e and fk denotes k parallel instances of f. As far as we are aware, this is the first direct product theorem for the quantum communication complexity of a general relation - direct sum theorems were previously known for one-way quantum protocols for general relations, while direct product theorems were only known for special cases. Our techniques are inspired by the parallel repetition theorems for the entangled value of two-player non-local games, under product distributions due to Jain, Pereszlenyi and Yao [24], and under anchored distributions due to Bavarian, Vidick and Yuen [4], as well as message compression for quantum protocols due to Jain, Radhakrishnan and Sen [29]. In particular, we show that a direct product theorem holds for the distributional one-way quantum communication complexity of f under any distribution q on X × Y that is anchored on one side, i.e., there exists a y* such that q(y*) is constant and q(x\y*) = q(x) for all x. This allows us to show a direct product theorem for general distributions, since for any relation f and any distribution p on its inputs, we can define a modified relation [EQUATION] which has an anchored distribution q close to p, such that a protocol that fails with probability at most e for [EQUATION] under q can be used to give a protocol that fails with probability at most e + ζ for f under p. Our techniques also work for entangled non-local games which have input distributions anchored on any one side, i.e., either there exists a y* as previously specified, or there exists an x* such that q(x*) is constant and q(y\x*) = q(y) for all y. In particular, we show that for any game G = (q,X×Y, A×B, V) where q is a distribution on X × Y anchored on any one side with constant anchoring probability, then [EQUATION] where ω* (G) represents the entangled value of the game G. This is a generalization of the result of [4], who proved a parallel repetition theorem for games anchored on both sides, i.e., where both a special x* and a special y* exist, and potentially a simplification of their proof.
Separating Positive and Negative Data Examples by Concepts and Formulas: The Case of Restricted Signatures (Abstract).

We study the separation of positive and negative data examples in terms of description logic (DL) concepts and formulas of decidable FO fragments, in the presence of an ontology. In contrast to previous work, we add a signature that specifies a subset of the symbols from the data and ontology that can be used for separation. We consider weak and strong versions of the resulting problem that differ in how the negative examples are treated. Our main results are that (a projective form of) the weak version is decidable in $\mathcal{ALCI}$ while it is undecidable in the guarded fragment GF, the guarded negation fragment GNF, and the DL $\mathcal{ALCFIO}$, and that strong separability is decidable in $\mathcal{ALCI}$, GF, and GNF. We also provide (mostly tight) complexity bounds.
Leakless privacy-preserving multi-keyword ranked search over encrypted cloud data

Aim: During the last decade, various type of cloud services have encouraged individuals and enterprises to store personal data in the cloud.Despite its flexibility, cost efficiency, and convenient service, protecting security and privacy of the outsourced data has always been a primary challenge.Although data encryption retains the outsourced data's security and privacy to some extent, it does not permit traditional plaintext keyword search mechanisms, and it comes at the cost of efficiency.Hence, proposing an efficient encrypted cloud data search service would be an important step forward.Related work focuses on single keyword search and even those which support multi-keyword search suffer from private information leakage.Methods: Our proposed method, employs the secure inner product similarity and our chaining encryption notion.The former helps to provide sufficient search accuracy and the latter yields the privacy requirements. Results:In this paper, we address the problem of leakless privacy-preserving multi-keyword ranked search over encrypted cloud data (LRSE), and our new contributions address challenging problems of search pattern, and cooccurrence information leakage in the cloud. Conclusion:Our security and performance analysis shows that the proposed scheme guarantees a high level of privacy/security and efficiency.
Data4Good

We are witnessing unprecedented datafication of the society we live in, alongside rapid advances in the fields of Artificial Intelligence and Machine Learning. However, emergent data-driven applications are systematically discriminating against many diverse populations. A major driver of the bias are the data, which typically align with predominantly Western definitions and lack representation from multilingually diverse and resource-constrained regions across the world. Therefore, data-driven approaches can benefit from integration of a more human-centred orientation before being used to inform the design, deployment, and evaluation of technologies in various contexts. This workshop seeks to advance these and similar conversations, by inviting researchers and practitioners in interdisciplinary domains to engage in conversation around how appropriate human-centred design can contribute to addressing data-related challenges among marginalised and under-represented/underserved groups.
DIGITALIZATION: THE USE OF BLOCKCHAIN IN PUBLIC SECTOR

In a world of nonstop developing technology, Blockchain has become a trusted tool to apply transparency in the public sector. The consensus mechanism provides trusted data that can support clear and adjusted as well as well-structured procedures. Nowadays, the public sector can increase trust by adapting Blockchain applications in the services offered to be e-government portals. In this paper, the researchers review the literature to identify the potential use cases and application of Blockchain in e-government services. This new technology along with its related applications will be discussed and defined. Moreover, the possibilities of using Blockchain in the public sector and its impact on organizational transformation, financial management, and performance are increasing. The study concluded that the use of blockchain technologies and applications is still limited in the public sector. Obstacles and barriers are related mostly to (security and privacy) and law and by-laws support. This paper will provide a useful reference for researchers in blockchain applications and their impact on e-government and propose future research questions that need to be addressed to inform how the public sector should approach the blockchain technology adoption.
Reliable and Privacy Preserving Blockchain Based Medical Data Sharing Digital Ledger

Secure sharing of health-care details along electronic media are challenging and yet an inevitable thing in the digital era. As valetudinarians pass between contributors, patients medical documents become disseminated across different organizations, resulting from drastic ethical issues to reliability and security. The decentralized and distributed mechanism of the blockchain gives a new stance to the digital frugality. Blockchain permits neither the entrant needs to know each other, and nor does it require third-party certification bodies to engage. It ensures data reliability and security through cryptographic algorithms and consensus used by the blockchain technology medical data sharing digital ledger using blockchain. In this paper, we use a private blockchain which makes use of ethereum and hyperledger fabric smart contract. Medical data services can be accessed by only those who have permission to ingress. The System uses a secure hash algorithm, proof of stake (PoS) consensus algorithm used for data provenance and thus it makes the block with tamper proof medical records than existing systems. Here the access granted for valid users only. Blockchain enhances security, anonymity, and privacy for medical assets.
A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification.
Modeling a Virtual Bare-Hand Interface System Using a Robust Hand Detection Approach for HCI

A practically deployable gesture recognition system is developed using a robust hand detection method implemented using a motion-based image segmentation process and a two-level bare hand classification model, which is integrated with a gesture classification system of 58 gestures using new robust features. Since detection of bare hand is affected by nonideal conditions, multiple color-texture features are analyzed in this study. In the second stage of the system, 18 new ASCII characters are introduced and analyzed along with the existing 40 characters (alphabets, numbers, and arithmetic operators). New 15 dimensional features are introduced along with the existing features to enhance the classification accuracy of the gestures. Significance of features statistically tested using one-way analysis of variance (ANOVA), Kruskal–Wallis and Friedman test, which are sequentially ranked and evaluated using incremental feature selection (IFS) method. Performance of the proposed hand detection system is observed to be 12.5% higher than the existing hand detection system under clean conditions, while 46.4% higher under the nonideal conditions. Performance of 58 gestures classification model has improved by 12.08% (Naïve Bayes), 8.86% (ELM), 10.83% (SVM), 8.02% ([Formula: see text]NN), and 6.61% (ANN) after using the new features. Majority voting-based classifier fusion method further improves the performance of the gesture recognition system by 3.88%, which is validated by Turkey’s HSD test.