Reviewerly/exHarmony huggingface dataset:
Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.
Multi-factor Authentication Scheme Using Mobile App and Camera

Phishing attacks are one of the most serious threats faced by the users on the internet the attackers try to steal sensitive information such as login details, credit card details, etc. by deceiving the users to enter sensitive information on the phishing websites and thus leading to huge financial losses. Many schemes have been proposed to detect phishing attacks but the amount of such attacks has not decreased. New attacks like Active Man-In-The-Middle (MITM) phishing attacks have emerged which include Real-Time Man-In-The-Middle (RT MITM) and Controlled Relay Man-In-The-Middle (CR MITM) phishing attacks. These attacks allow the attackers to obtain the users’ account details and relay them in real-time. Similarly, the attacker can lure the user to enter details on a spoofed app and thus gain access to the user’s account. The existing popular authentication schemes fail to address these attacks. In this paper, we propose a novel user authentication scheme that enables the user to log into his/her account without memorizing any password or any other authentication token. In the proposed scheme, the user has to scan a dynamically generated QR-code using the smartphone app and then verify the image, captured by the webcam and sent it on the smartphone via push notification. Thus, the complete authentication procedure requires minimal user involvement and implements automatically. We have implemented and evaluated the proposed scheme in terms of usability, deployability, and security parameters and the results depict that the proposed authentication scheme performs well and can be used as a secure user authentication scheme.
Bed Exit Action Detection Based on Patient Posture with Long Short-Term Memory

It has been known that the fall of a patient in a hospital is a serious accident. In order to prevent such accidents, we have been studying the fall prevention using image processing technology. Our previous studies have detected the patient's end sitting position with high accuracy, but have problems responding to the sitting position of patients who are eating or responding to visitors. In order to solve these problems, this paper proposes a method to detect the patient's bed exit action by analyzing the posture of the patient extracted from the image of the monocular camera by long short-term memory (LSTM). Our proposed method introduces two strategies - abstraction of input information and use of relative position information for the input time-series human images, achieving a 99.2[%] detection rate of bed exit action with a 5.7[%] false detection rate. Detecting the bed exit action with high accuracy contributes to preventing the patient from falling down. The proposed solution handles only posture information that abstracts camera images for patient privacy purposes.
Developing Resources for Te Reo Māori Text To Speech Synthesis System

Te reo Māori (the Māori language of New Zealand) is an under-resourced language in terms of availability of speech corpora and resources needed to develop robust speech technology. Māori is an endangered indigenous language which has been subject to revitalisation efforts since the late 1970s, which are well known internationally. The Māori community recognises the need for developing speech technology tools for the language, which will improve its study and usage in wider and more digital contexts. This paper describes the development of speech resources in Māori to build one of the first Text To Speech synthesis system for the language. A speech corpus, extended dictionary and a parametric speech synthesiser are the main contributions of the study. To develop these resources, text processing, segmentation and alignment, letter to sound rules creation were also done with existing resources that were modified to be used for Māori. The acoustic similarity of synthesised speech vs natural speech was measured to evaluate the speech synthesis system statistically. Future work required is described.
Leveraging aspect-based sentiment prediction with textual features and document metadata

Aspect-based sentiment prediction is a specific area of sentiment analysis that models the sentiment of a text excerpt as a multi-dimensional quantity pertaining to various interpretations, rather than a scalar one, that admits a single explanation. Extending earlier work, the said task is examined as a part of a unified architecture that collects, analyzes and stores documents from various online sources, including blogs & social network posts. The obtained data are processed at various levels; initially, a hybrid, attention-based bi-directional long short-term memory network, coupled with convolutional layers, is used to extract the textual features of the document. Following, an additional number of document metadata are also examined, such as the number of repetitions, the existence, type and frequency of emoji ideograms and, especially, the presence of keywords, assigned either manually (e.g. in the form of hashtags) or automatically. All of the aforementioned features are subsequently provided as input to a fully-connected, multi-layered, feed-forward artificial neural network that performs the final prediction task. The overall approach is tested on a large corpus of documents, with encouraging results.
Electromagnetic scattered field time series from finite difference time domain trained time delay neural network

This paper uses time delay neural network (TDNN) for predicting electromagnetic (EM) fields scattered from dielectric objects (cylinder, cylinder-hemisphere, and cylinder-cone) using: (a) FDTD generated initial field data for similar conducting objects and (b) Statistical information for the nature of fields. Statistical data indicated that the scattered field nature is close to deterministic. The TDNN structure determination uses statistical data for fixing the number of delays and tabular technique to obtain the number of hidden neurons. The TDNN training uses the Levenberg-Marquardt (LM) algorithm. The model outputs follow standard FDTD results closely.
Let’s Negotiate with Jennifer! Towards a Speech-Based Human-Robot Negotiation

Social robots are becoming prevalent in our society, and most of the time, they need to interact with humans in order to accomplish their tasks. Negotiation is one of the inevitable processes they need to be involved to make joint decisions with their human counterparts when there is a conflict of interest between them. This paper pursues a novel approach for a humanoid robot to negotiate with humans efficiently via speech. In this work, we propose a speech-based negotiation protocol in which agents make their offers in a turn-taking fashion via speech. We present a variant of time-based concession bidding strategy for the humanoid robot and evaluated the performance of the robot against human counterpart in human-robot negotiation experiments.
Methods of Didactic Design in E-Learning Sphere Based on Mind Maps

This article explains the mind maps usage according to e-didactics, solving different problems of e-didactics. Main advantages and disadvantages of mind maps according to advantages and disadvantages of computer tools of education, typical structure of the training program’s step, and also methods of construction and analysis of a mind map for didactic projection are given. The article also discusses how to control the process of learning outcomes’ writing using technologies of mind maps with evaluation of disciplines connected with associations of constructed mind map and evaluation of strength of interdisciplinary connections, how to describe the descriptions of competencies in accordance with educational standards, detail competencies and elementary learning outcomes, find different categories of competencies and evaluate their relationship. Algorithm for constructing and analyzing a mind map for the didactic design is presented. The article provides statistical data confirming the effectiveness of using the proposed method for constructing and analyzing mind maps during different stages of didactic design.
Measuring the Maturity of BizDevOps

DevOps has emerged as an effective approach to the interaction between development and operations. This approach has been extended to the interaction with business functions, generating the term BizDevOps. Although many proposals and tools support BizDevOps from a technical viewpoint, there has been no significant progress in management aspects, such as the evaluation of practices and processes involved in the area. In this article, we propose a maturity model for BizDevOps, based on relevant international standards, and apply this model to a software company, which demonstrates its applicability.
Developing and examining a virtual learning environment

Virtual reality (VR) provides several potentials to unlock in today’s world. VR is a computer interface that allows users to interact with each other or with the media in a computer-generated three-dimensional space using senses (Piovesan, Passerino, &amp; Pereira, 2012). One of the most important VR environment is Second Life. It provides both for educators and students the opportunity to deviate from the habits, to leave the traditional framework of teaching and learning (Chen, 2016). In our research, we created a virtual learning environment in the Second Life that is called Fornax. The devices placed in the area were intended to provide a thorough introduction to the different parts of the digestive system. In a virtual space, students can easily learn about human organs, as they also can see what they are learning, which can be a huge help in acquiring information (Huang, Liaw, &amp; Lai, 2016). Based on the results, we could state that test results of users who are more familiar with Second Life are significantly better respondents than the beginner users. However, one of the four types of questions that of multiple choices with short answers provided the same result.
Improving Accuracy and Efficiency of Object Detection Algorithms Using Multiscale Feature Aggregation Plugins

In this paper, we study the use of plugins that perform multiscale feature aggregation for improving the accuracy of object detection algorithms. These plugins improve the input feature representation, and also remove the semantic ambiguity and background noise arising from feature fusion of low and high layers representation. Further, these plugins improve focus on the contextual information that comes from the shallow layers. We carefully choose the plugins to strike a delicate balance between accuracy and model size. These plugins are generic and can be easily merged with the baseline models, which avoids the need for retraining the model. We perform experiments using the PASCAL-VOC2007 dataset. While the baseline SSD has 22M parameters and an mAP score of 77.20, the use of the SFCM (one of the plugins we used) increases the mAP score to 78.82 and the number of parameters to 25M.
Automatic Pulmonary Vein and Left Atrium Segmentation for TAPVC Preoperative Evaluation Using V-Net with Grouped Attention

Accurate segmentation of pulmonary vein (PV) and left atrium (LA) is essential for the preoperative evaluation and planning of total anomalous pulmonary venous connection (TAPVC), which is a rare but mortal congenital heart disease of children. However, manual segmentation is time-consuming and insipid. To free radiologists from the repetitive work, we propose an automatic deep learning method to segment PV and LA from Low-Dose CT images. In the method, attention mechanism is incorporated into the widely used V-Net and a novel grouped attention module is applied to enforce the segmentation performance of the V-Net. We evaluate our method on 68 3D Low-Dose CT images scanned from patients with TAPVC. The experiment result shows that our method outperforms the popular 3D-UNet and V-Net, with mean dice similarity coefficient (DSC) of 0.795 and 0.834 for the PV and LA respectively.Clinical relevance-We proposed a CNNs-based method for the automatic segmentation of PV and LA with good accuracy, which can be used for the preoperative evaluation and planning of TAPVC. Our method can improve the efficiency and reduce the workloads of radiologists (400 milliseconds vs. 2-3 hours per-case).
An Automated Hybrid Approach for Generating Requirements Trace Links

Trace links between requirements and software artifacts provide available traceability information and in-depth insights for different stakeholders. Unfortunately, establishing requirements trace links is a tedious, labor-intensive and fallible task. To alleviate this problem, Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI), and their variants, have been widely used to establish trace links automatically. But with the widespread use of agile development methodology, artifacts that can be used to generate automatic tracing links are getting shorter and shorter, which decreases the effects of traditional IR-based trace link generation methods. In this paper, Biterm Topic Model–Genetic Algorithm (BTM–GA), which is effective in managing short-text artifacts and configuring initial parameters, is introduced. A hybrid method VSM[Formula: see text]BTM–GA is proposed to generate requirements trace links. Empirical experiments conducted on five real and frequently-used datasets indicate that (1) the hybrid method VSM+BTM[Formula: see text]GA outperforms the others, and its results can achieve the “Good” level, where recall and precision are no less than 70% and 30%, respectively; (2) the performance of the hybrid method is stable and (3) BTM–GA can provide a number of “hard-to-find” trace links that complement the candidate trace links of VSM.
BitCom: A Commerce Model on Blockchain

The supply chain is a complex framework of facilities working together to gain profits. Rapid changes in technology have made the commerce a place of high competition and hugely affected by price fluctuation. BitCom is a proposed architecture bolstered by the emerging concept of blockchain. It aims to provide a clean and efficient supply chain to decrease system-wide costs. It is a framework deriving its roots form both- permissioned and permissionless blockchains to achieve a feasible solution to the problem of automating commerce. The model stays true to the roots of the supply chain while maintaining security. It uses flexible smart contracts for negotiations and transactions while permissionless entry is provided to the users. The concept of a credibility score has been introduced to help introduce trust in a decentralised network.
A Study of Blockchain Technology in Farmer's Portal

Blockchain is a method in which a confirmation of a transaction is kept by means of a crypto-currency. The record is maintained transversely, linking several computers in a peer to peer network. Contracts, transactions, and the records of them define the economic system of a country. They set boundaries and provide security to the assets. Considering the features of blockchain such as immutability and maintaining the footage of transaction details, this paper highlights the usage of blockchain technology with farmer's portal that keep the footage of selling and buying information of crops. The proposed solution uses the python as a programming language in integration with the blockchain system that will benefit the farmers or vendors and individuals by preserving the contract of trade. An interface for the farmers is designed using a python programming language in addition with blockchain technology, which is used to store the information related to seller, buyer, selling and buying an item and total value transacted.
A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.
Several classes of complete permutation polynomials over finite fields of even characteristic

In this paper, we find three classes of complete permutation polynomials over finite fields of even characteristic. The first class of quadrinomials is complete in the sense of addition. The second and third classes of binomials and trinomials are complete in multiplication. Moreover, a result related to the complete property in multiplication of a special class of polynomials is also given.
Finding Missing Child in Shopping Mall Using Deep Learning

Recent reports show that there is a multifarious number of children missing each year. This is higher mostly in the case of India. Most of the cases involve missing children from the shopping mall. This paper takes a deep learning perspective in solving this serious issue with the help of face recognition due to its accuracy when a huge amount of data is under consideration. The main focus is to design an efficient system that can be used in shopping malls to find the missing child using deep learning. The system makes use of real-time analysis of video captured by the surveillance camera to detect the most recent presence of missing child. When the child goes missing the parent approaches the security who accesses the already uploaded photo by the parent of the missing child using the parent’s phone number. The facial features are extracted from the photo and compared with the video being captured by the camera for face recognition. When a match is found an alert is sent to the security within the area in which the child was found.
Multi-target hybrid CTC-Attentional Decoder for joint phoneme-grapheme recognition

In traditional Automatic Speech Recognition (ASR) systems, such as HMM-based architectures, words are predicted using either phonemes or graphemes as sub-word units. In this paper, we explore such joint phoneme-grapheme decoding using an Encoder-Decoder network with hybrid Connectionist Temporal Classification (CTC) and Attention mechanism. The Encoder network is shared between two Attentional Decoders which individually learn to predict phonemes and graphemes from a unique Encoder representation. This Encoder and multi-decoder network is trained in a multi-task setting to minimize the prediction error for both phoneme and grapheme sequences. We also implement the phoneme decoder at an intermediate layer of Encoder and demonstrate performance benefits to such an architecture. By carrying out various experiments on different architectural choices, we demonstrate, using the TIMIT and Librispeech 100 hours datasets, that with this approach, an improvement in performance than the baseline independent phoneme and grapheme recognition systems can be achieved.
Empirical study of fuzzy quantification models for linguistic descriptions of meteorological data

In this work we present an experimental comparison of six widely used quantification methods (Zadeh's scalar and fuzzy cardinality, Yager's OWA, Delgado's GD, Sugeno integral and Vila's VQ) when evaluating Type-1 and Type-2 linguistic descriptions of data generated from meteorological data provided by the Galician Meteorological Agency MeteoGalicia. The objective of this study is to evaluate if there are significant differences among these models for the data considered. We ranked the generated descriptions based on their degree of truth for each quantification model and we analyzed those results calculating the Pearson correlation coefficient. Results show that there are not significant differences in the models when evaluating Type-1 descriptions. However, in Type-2 evaluation the methods can be grouped in three clusters with a significantly different behavior among them: i) Zadeh's scalar cardinality, Delgado's GD and Zadeh's fuzzy cardinality, ii) Yager's method and iii) Vila's VQ.
Markov Decision Processes with Discounted Costs over a Finite Horizon: Action Elimination

Semmouri, Abdellatif Jourhmane, MostafaMarkov Decision Processes (MDPs) are a popular decision model for stochastic systems. Since the computational complexity is an open problem, all researchers are interesting to find methods and technical tools in order to solve the proposed problem. In this paper, we develop the backward induction algorithm to calculate optimal policies and value functions for solving finite horizon discrete-time MDPs in the discounted case. Firstly, we present the backward induction algorithm for solving Markov decision problem employing the total discounted expected cost criterion over a finite planning horizon. Second, we propose a new test to identify non-optimal decisions in the same context. Combining the preceding presented results, we give an efficient algorithm by linking the recursive approach and the action elimination procedures. It is a powerful analytical tool used for sequential decision making under uncertainty that have been widely used in many industrial manufacturing, financial fields and artificial intelligence.