Reviewerly/exHarmony huggingface dataset:
Peer-to-peer Energy Trading Platform Using Consortium Blockchain

As the demand for energy grows, a large number of distributed energy resources with small capacity, intermittent and random output are incorporated into the operation of large power grid, and many prosumers participate in the electricity market transactions, which brings great influence to the current electricity market transaction mode. In view of the decentralized, tamper-proof, smart contract and other features of blockchain technology, which is naturally suitable for the transaction demand of distributed energy resources, it is necessary to develop peer-to-peer energy trading based on blockchain technology which can improve the efficiency of market participants and promote the construction of distributed energy resources. In this context, we propose a peer-to-peer energy trading platform using consortium blockchain named Hyperledger Fabric. Then we design market mechanism through smart contract. We show that our proposed platform meets the needs of peer-to-peer energy trading.
Assessment of the accuracy of the integration variable selection method and its practical application terahertz range

The paper presents an assessment of the accuracy of the method of choosing an integration variable for the numerical solution of the Cauchy problem in terahertz range. An example of using the method to determine ray paths in inhomogeneous media in the approximation of geometric optics is given. Unlike numerical methods of integration, where one pre-selected variable is used as an integration variable, in the considered method the integration variable is selected at each step. This approach reduces the risk of shifting to adjacent phase trajectories, which is especially important in the terahertz range. In addition, we note that using this approach allows you to effectively use computer resources. The integration method with the choice of the integration variable at each step is described. A feature of the method is that the variable with the highest rate of change is selected as the integration variable. The accuracy of the method is investigated by the example of a problem with a well-known analytical solution. The dependence of the relative error of the solution on the grid pitch is investigated. Extreme values of the grid pitch at which the relative error drops sharply are calculated. The dependence of the relative error of the solution on the direction of propagation of the rays is investigated. Shows the application of the numerical modeling algorithm for the example of constructing ray paths in inhomogeneous media in the approximation of geometric optics for geometry with three spatial coordinates. The ray paths in 3D space are presented.
Development of E-Learning Content Based on Kelase-Tat Twam Asi in Supporting Learning During the Covid-19 Pandemic

The main purpose of this paper was to provide information about the existence of e-learning content design based on Kelase-Tat Twam Asi.That design showed three main facilities on the Kelase platform needed to support the online learning process for 2 nd grade elementary school students in community service activities at Timpag Village during the Covid-19 pandemic.The model was used to develop the design was the 4D model with a focus on the define stage, design stage, develop stage, and disseminate stage.Six subjects were involved in testing the design quality using trial tools in the form of questionnaires.Data analysis of the design test results was carried out by comparing the percentage average of design quality with the range of percentages found in the design quality standards.The test result showed that the design quality was included in the category of very high quality with a percentage was 92.50%.
The Use of Blockchain in Financial Area: A Systematic Mapping Study

Blockchain is a technology for decentralized transactions that has been widely used with cryptocurrencies such as Bitcoin. Many studies have been conducted in the last decades, approaching cryptocurrencies, and blockchain technology, more strongly in the context of financial transactions. However, little has been done to provide a panoramic view of the current literature; as a consequence, a careful understanding of the state-of-the-art papers remains limited and inconclusive. This study, therefore, aims to classify and provide a thematic analysis of studies on the use of blockchain in the context of financial area, thus allowing to create a clear systematic map of the current literature, and identify challenges and research opportunities. To achieve these objectives a systematic mapping study (SMS) approach was performed to answer 6 research questions. In total, 1884 studies were reviewed from 6 data sources, being 23 studies selected after a careful filtering process. The main findings were: (1) Over 65% of the selected studies concentrated on adopting two blockchain platforms named Hyperledger Fabric and Ethereum; (2) Over 60% used blockchain technology to increase security; (3) Most studies did not reveal the consensus algorithm used; (4) Fintech (or Bank) and loan were the most explored application areas; (5) Over 60% focused on producing prototype rather than frameworks; and (6) most studies were published in conferences. This study can benefit research community by generating a map of the literature, serving as a starting point for future researches. Moreover, this study reports some challenges worth investigating.
Towards a Humorous Chat-Bot Companion for Senior Citizens

In the past decade, Singapore population has grown older with more elderly and fewer younger people. As such, government encourages research initiatives providing solutions to improve mental health and memory functions. In this paper, we discuss a within-subject experiment that we carried out to measure the impact of humour on information retention in senior citizens interacting with a chatbot. The recall and recognition rates of both humorous and non-humorous responses supported the hypothesis that humour aids recognition, but could not provide strong support for recall. We hope that studies going forward can assist the innovation of intelligent systems for other target groups and uses as well.
Decentralized IoT Data Authorization with Pebble Tracker

Decentralized identifiers (DIDs) and verifiable credentials (VCs) are emerging concepts for creating verifiable self-sovereign digital identities for people, organizations and Internet of things (IoT). In this demo, we share the implementation details and experiences for realizing a decentralized IoT data authorization framework [9] with a Pebble tracker, an in-house built trusted GPS tracking device with 4G connectivity and sensor technologies. Our implementation demonstrates great flexibility and tremendous advantages towards building decentralized and global-scale IoT applications. To the best of our knowledge, this is the first real-world demo for enabling decentralized IoT data authorization with DIDs and VCs.
Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions

Testing under what conditions a product satisfies the desired properties is a fundamental problem in manufacturing industry. If the condition and the property are respectively regarded as the input and the output of a black-box function, this task can be interpreted as the problem called level set estimation (LSE): the problem of identifying input regions such that the function value is above (or below) a threshold. Although various methods for LSE problems have been developed, many issues remain to be solved for their practical use. As one of such issues, we consider the case where the input conditions cannot be controlled precisely-LSE problems under input uncertainty. We introduce a basic framework for handling input uncertainty in LSE problems and then propose efficient methods with proper theoretical guarantees. The proposed methods and theories can be generally applied to a variety of challenges related to LSE under input uncertainty such as cost-dependent input uncertainties and unknown input uncertainties. We apply the proposed methods to artificial and real data to demonstrate their applicability and effectiveness.
XPySom: High-Performance Self-Organizing Maps

In this paper, we introduce XPySom, a new opensource Python implementation of the well-known Self-Organizing Maps (SOM) technique.It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs.We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives.Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error.
Efficient Cloud-based Secret Shuffling via Homomorphic Encryption

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers’ privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases.
A Two-Phase Cross-Modality Fusion Network for Robust 3D Object Detection

A two-phase cross-modality fusion detector is proposed in this study for robust and high-precision 3D object detection with RGB images and LiDAR point clouds. First, a two-stream fusion network is built into the framework of Faster RCNN to perform accurate and robust 2D detection. The visible stream takes the RGB images as inputs, while the intensity stream is fed with the intensity maps which are generated by projecting the reflection intensity of point clouds to the front view. A multi-layer feature-level fusion scheme is designed to merge multi-modal features across multiple layers in order to enhance the expressiveness and robustness of the produced features upon which region proposals are generated. Second, a decision-level fusion is implemented by projecting 2D proposals to the space of the point cloud to generate 3D frustums, on the basis of which the second-phase 3D detector is built to accomplish instance segmentation and 3D-box regression on the filtered point cloud. The results on the KITTI benchmark show that features extracted from RGB images and intensity maps complement each other, and our proposed detector achieves state-of-the-art performance on 3D object detection with a substantially lower running time as compared to available competitors.
WITHDRAWN: Experimental performance analysis of web recommendation model in web usage mining using KNN page ranking classification approach

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.
YAC2: An α-proximity based clustering algorithm

Clustering is the process of identifying objects with similar intrinsic properties and grouping them together into separate clusters. It also serves as a preliminary step in data classification by separating heterogeneous data into reasonably homogeneous groups, which can be further processed. Existing literature discusses a host of different clustering algorithms and their applications, albeit no single approach for all applications has yet emerged. In this paper, we introduce a novel clustering algorithm, YAC2, based on data binning and α-proximity/neighborhood. The binning process is a data transformation step that converts cardinal values of the attributes into their ordinal equivalence. The algorithm introduces a specialized centroid that is used with the α-proximity and a matching algorithm to partition data set into a sequentially generated clusters. We present the results of applying YAC2 to a set of established benchmark data sets, using a host of evaluation metrics. Our results show YAC2 to perform well besting a number of well-established algorithms. For several metrics, YAC2 has provided improvements averagely in the range of 6% (in the Iris data set)-180% (in the Wholesale Customer data set) over other algorithms.
A Mathematical Model for Optimum Error-Reject Trade-Off for Learning of Secure Classification Models in the Presence of Label Noise During Training

In the present contribution we investigate the mathematical model of the trade-off between optimum classification and reject option. The model provides a threshold value in dependence of classification, rejection and error costs. The model is extended to the case that the training data are affected by label noise. We consider the respective mathematical model and show that the optimum threshold value does not depend on the presence/absence of label noise. We explain how this knowledge could be used for probabilistic classifiers in machine learning.
Business Process Redesign Heuristics for Blockchain Solutions

Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring use cases in the hope of reaping benefits from this technology. However, to achieve the desired impact, it is not sufficient to merely replace existing technologies. Current business processes must be redesigned to realize benefits. This paper shows how a number of best practices for Business Process Redesign can be applied to leverage blockchain technology. Specifically, we adapt these best practices for redesigning processes intended to execute on blockchain. We further explore their applicability using a case study. The major elements of process redesign for blockchain are: (1) changing the view from intra- to inter-organizational processes by using the shared data ledger of blockchain, (2) using smart contracts performers of tasks and connectors of processes, (3) using blockchain as means for data communication, and (4) using tokens to manage digitally represented assets.
A New Approach to Detection of Abrupt Changes in Black-and-White Images

In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. There is a variety of data types: texts, ordinary numbers, images, audio or video files or streams, metadata descriptions, etc. All of them change in many ways. If the change happens then the interesting issue is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change in pictures. Many algorithms have been proposed to detect abnormalities and deviations in the data. We propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to one- and two-dimensional patterns to detect the abrupt changes.
Sign Language to Speech Translation

Although a certain fraction of the world suffers from speech and hearing disabilities, sign language is not a widespread language across the world. In today's oral world, a gesture-based language is not particularly popular with the general masses. However, sign language itself is a fully developed language with multiple regional dialects across the globe. Therefore, to aid with a smoother communication between the speaking and non-speaking world, technical developments can be introduced. A considerable amount of work has been done in this direction. The basic need of the hour is for an application that can function in real-time and can facilitate real-time conversations between a person who can sign in sign language and one that cannot. This paper proposes an application that works on this problem statement. In order to construct such an application designed to respond in real-time and aid a live conversation between a speaking and nonspeaking individual, it is necessary to allow for live video inputs to be made to the app which would then be translated to speech. This paper proposes the use of convolutional neural networks (CNN) alongside the use of Text-to-Speech translator. By use of the CNN algorithm, the gestures can be identified by the proposed application and converted to text, which can then be converted into speech.
An Improved C4.5 Algorthm in Bagging Integration Model

The C4.5 algorithm has three shortcomings: the wide range of candidate segmentation threshold sequences for continuous attributes, the comprehensive influence of different attributes and local subsets under the same attribute, and the inter-attribute redundancy. When dealing with continuous attributes, sampling and threshold supplement processing near the transition boundary of the attribute interval corresponding to the adjacent different categories are performed for narrowing the range of candate segmentation threshold sequences. By adding standardizing Euclidean distance of the attribute global and local factors to represent attribute weight, the calculation of C4.5 information gain is otpimized. And averaging Gini index of other attributes and adding correction factor, the influence of redundancy between attributes is greatly decreased. The overall average improvement range of the base classifier and the bagging integration classifier is 0.6%~2.1% and 0.7% ~ 2.7%, respectively, which shows that this integration model can improve the classification accuracy and also validate its feasibility and reliability.
3-D geochemical interpolation guided by geophysical inversion models

3-D geochemical subsurface models, as constructed by spatial interpolation of drill-core assays, are valuable assets across multiple stages of the mineral industry's workflow. However, the accuracy of such models is limited by the spatial sparsity of the underlying drill-core, which samples only a small fraction of the subsurface. This limitation can be alleviated by integrating collocated 3-D models into the interpolation process, such as the 3-D rock property models produced by modern geophysical inversion procedures, provided that they are sufficiently resolved and correlated with the interpolation target. While standard machine learning algorithms are capable of predicting the target property given these data, incorporating spatial autocorrelation and anisotropy in these models is often not possible. We propose a Gaussian process regression model for 3-D geochemical interpolation, where custom kernels are introduced to integrate collocated 3-D rock property models while addressing the trade-off between the spatial proximity of drill-cores and the similarities in their collocated rock properties, as well as the relative degree to which each supporting 3-D model contributes to interpolation. The proposed model was evaluated for 3-D modelling of Mg content in the Kevitsa Ni-Cu-PGE deposit based on drill-core analyses and four 3-D geophysical inversion models. Incorporating the inversion models improved the regression model's likelihood (relative to a purely spatial Gaussian process regression model) when evaluated at held-out test holes, but only for moderate spatial scales (100 m).
InCorr: Interactive Data-Driven Correlation Panels for Digital Outcrop Analysis

Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedimentary logs and combine them in `correlation panels' to map the extents of key geological horizons, and build a stratigraphic model to understand their position in the ancient landscape. Currently, the creation of correlation panels is completely manual and therefore time-consuming, and inflexible. With InCorr we present a visualization solution that encompasses a 3D logging tool and an interactive data-driven correlation panel that evolves with the stratigraphic analysis. For the creation of InCorr we closely cooperated with leading planetary geologists in the form of a design study. We verify our results by recreating an existing correlation analysis with InCorr and validate our correlation panel against a manually created illustration. Further, we conducted a user-study with a wider circle of geologists. Our evaluation shows that InCorr efficiently supports the domain experts in tackling their research questions and that it has the potential to significantly impact how geologists work with digital outcrop representations in general.
Efficiently factoring polynomials modulo p4

Polynomial factoring has famous practical algorithms over fields– finite, rational and p-adic. However, modulo prime powers, factoring gets harder because there is non-unique factorization and a combinatorial blowup ensues. For example, x2+pmodp2 is irreducible, but x2+pxmodp2 has exponentially many factors in the input size (which here is logarithmic in p)! We present the first randomized poly(deg⁡f,log⁡p) time algorithm to factor a given univariate integral polynomial f modulo pk, for a prime p and k≤4.1 Thus, we solve the open question of factoring modulo p3 posed in (Sircana, ISSAC'17). Our method reduces the general problem of factoring fmodpk to that of root finding of a related polynomial E(y)mod〈pk,φ(x)ℓ〉 for some irreducible φmodp. We can efficiently solve the latter for k≤4, by incrementally transforming E. Moreover, we discover an efficient refinement of Hensel lifting to lift factors of fmodp to those modp4 (if possible). This was previously unknown, as the case of repeated factors of fmodp forbids classical Hensel lifting.