Reviewerly/exHarmony huggingface dataset:
Gaussian noise and the intersection problem in Human-Robot Systems - analytical and fuzzy approach

In this paper the intersection problem in human-robot systems with respect to noisy information is discussed. The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the intersections of trajectories. We discuss the intersection problem with respect to noisy information on the basis of an analytic geometrical model and its TS fuzzy version. The transmission of a 2-dimensional Gaussian noise signal, in particular information on human and robot orientations, through a non-linear static system and its fuzzy version, will be described. We discuss the problem: Given the parameters of the input distributions, find the parameters of the output distributions.
Human factors in risk communication

Following the 1977 Tenerife disaster, Billings and Cheaney (1981) concluded that pilots/controllers are not aware enough of the extensive problems involved in transferring information between each other which contribute to miscommunications. Since then, extensive advances have taken place regarding pilot-controller communications, including Crew Resource Management training, improving radiotelephony phraseology, and redesigning English language training/assessment. Despite these measures though, miscommunications still abound with increasing safety risks (e.g. Eurocontrol, 2006a; 2006b; Barshi and Farris, 2013; Bajaj and Majumdar, 2016). Bajaj and Majumdar have raised the question of why there are still so many problems considering the substantial improvements made and surmise that Billings and Cheaney’s observation of a lack of awareness being contributory could still be valid. Although occasionally mentioned, this notion has barely received attention in pilot-controller communication studies. Following the review of related work, voice recordings containing miscommunications from an accident flight are analysed to see whether communication awareness is detectable and to establish its impact. As there is no common method for analysing it, Nevile’s (2006) conversation analysis method for communication in interaction appears the most feasible one. The results show that communication awareness is identifiable by cues signalling it and contributed to the miscommunications.
Resampling methods combined with Rao-Blackwellized Monte Carlo Data Association algorithm

Multiple target tracking (MTT) is the process of computing the number of targets present in a surveillance area. MTT requires estimation of state variables and data association. New measurements are associated with existing tracks, clutter, or new tracks. MTT generally involves unknown number of targets. Mostly because of computational complexity faced by MTT algorithms, it is a difficult and challenging problem. Computational load, underlying assumptions of known number of targets, and high cluttered environment are the main reasons, which available methods cannot address properly. Rao-Blackwellized has been used for MTT. Our objective is to extend Rao-Blackwellized Monte Carlo Data Association (RBMCDA) that estimates number of targets and maintains track continuity enabling persistent tracking of targets. RBMCDA has been combined with seven different resampling methods to obtain the best resampling method. The modified RBMCDAs are applied to three-dimensional case study for its performance evaluation.
Understanding Dynamic Logic Using 2D Images

To understand the working of Dynamic Logic (DL) we discuss a simple example considering 2D images and finding a Gaussian pattern in the image if it exist. The technique is also noise resistant and works well on noisy data can be which can be simulated by adding randomness to the image. In implementing a pattern in the image, we do need to know the characteristics of the model for example in our case we are looking for circular patterns and simple equations of circle provides a basis for developing an analytic measure. We provide our implementation for understanding the basics of Dynamic Logic (DL). The understanding included a determination of when to terminate the iteration process and a dependence between model parameters.
Initial Value Filtering Optimizes Fast Global K-Means

K-means clustering algorithm is an important algorithm in unsupervised learning and plays an important role in big data processing, computer vision and other research fields.However, due to its sensitivity to initial partition, outliers, noise and other factors, the clustering results in data analysis, image segmentation and other fields are unstable and weak in robustness.Based on the fast global K-means clustering algorithm, this paper proposed an improved K-means clustering algorithm.Through the neighborhood filtering mechanism, the points in the neighborhood of the selected initial clustering center have not participated in the selection of the next initial clustering center, which can effectively reduce the randomness of initial partition and improve the efficiency of initial partition.Mahalanobis distance was used in the clustering process to better consider the global nature of data.Compared with the traditional clustering algorithm and other optimization algorithms, the results of real data set testing are significantly improved.
A Powerful and Efficient Method for Enhancing Images of Different Types

This paper proposes to utilize combination of methods to obtain high quality images. Compared to the typical histogram the proposed method in this research work, the brightness of an original image is preserved during enhancing the contrast of a source image by providing a natural enhancement which can be utilized in many products of electronics. In this research work, various methods are combined to achieve efficient image quality. The proposed method used in this research work would provide subjectively and objectively good results for both true colour and gray scale images. Research work consists of an efficient algorithm for increasing the image contrast without disturbing the originality of an image. Reality of an image is checked and verified using ASP.NET program and proposed algorithm will be deployed in cloud server for service. In future this method will be deployed in cloud so that researchers can use this service to compare their results for quality of image. In future we have an idea to attempt to collaborate two emerging technologies of Information Technology like Digital Image Processing and Cloud Computing.
CONCEPT OF SIMPLE LIFE BY H. D.THOREAU

In this study, the author examines the concept of “simple life” in the work “Walden; or Life in the Woods” by H. D. Thoreau in the context of transcendental philosophy. The article gives a brief overview of R. Emerson’s works on transcendentalism (which were the foundations of Thoreau’s own philosophical and aesthetic views) and the work of S. Alexander on the philosophy of “simple life” by H. Thoreau. It is determined that one of the main concepts of transcendentalism is “self-reliance”. As a result of the study, it was determined that H. Thoreau’s “simple life” is a practical realization of the “self-reliance” concept and is based on the principles of the “economics of sufficiency”. These are the following principles: selfsufficiency, self-discipline (limiting our own material needs) and the abstinence from excessive (surplus) labor. According to H. Thoreau, people work overmuch because they do not recognize the true value of things. Their toil is unending as they strive for ever greater luxuries which they do not need. Thoreau thought that his fellow-citizen accept the consumerist style of life not because they like it, but because they think that they don’t have a choice. In his book “Walden; Or Life in the Woods” he tried to show them that the other way (in fact, as many ways as they would like) exists and it is possible for anyone to pursue it with success.
A Chinese-English Translation Model for Mobile Terminal System Based on Neural Network

This paper focuses on the improvement of translation results by neural network. Since different translation models and methods have their own advantages and disadvantages, sentence-level paraphrase is used to rewrite the translation, which is regarded as a translation task between the same language. In the absence of large-scale parallel paraphrase corpus, we approximate parallel paraphrase corpus using machine translation results and reference translations of source language. Then, the model is used to train a repetition system from machine translation results to reference translation, to produce sentence-level repetition results with consistent semantics. After that, the restatement results are introduced into the translation hypothesis candidates for system integration. Finally, based on translation and paraphrase, mobile application-oriented design and development are performed to achieve machine translation between Chinese and English.
An Experimental Study on Recovery of Energy from Biodegradable Waste with Eichhornia Crassipes in TKRCET, Hyderabad, Telangana

Objectives:The work is to recover the energy from food and vegetable waste with water hyacinth and cowdung.Methods: The present study focuses the lights to recover energy from organic waste and to adopt safe disposal and conserving natural resources.Laboratory scale batch digesters were set up to find the biogas production potential of Food waste and Vegetable waste with water hyacinth and cow dung.Food waste was collected from our TKR college mess similarly Vegetable waste was collected from the Hyderabad market and prepared for further analysis.Similarly water hyacinth was collected from a Meerpet lake in Hyderabad.Six digesters with varying Vegetable wastes and Food wastes were combined individually by increasing concentrations with fixed amount of water hyacinth and Cow dung by water displacement method.The effect of food waste and vegetable waste concentration on the biogas yield was investigated and the experiments were characterised before and after digestion.Findings: Modified Gompertz Equation (MGE) model was fitted to the experimental values and kinetic parameters such as maximum cumulative biogas potential B max (L/gVS), specific biogas production rate, R b (L gVS -1 d -1 ) and lag phase time, λ(d), were estimated.Experimental values were compared with MGE using MAT LAB.Applications/Improvements: From the experimental study observed that an aerobic degradation took place and the biogas produced was measured daily.Kinetic analysis of the biogas production was done and compared & conclusions were drawn.
Using Maps for Interlinking Geospatial Linked Data

The creation of interlinks between Linked Data datasets is key to the creation of a global database. One can create such interlinks in various ways: manually, semi-automatically, and automatically. While quite a few tools exist to facilitate this process in a (semi-)automatic manner, often with support for geospatial data. It is not uncommon that interlinks need to be created manually, e.g., when interlinks need to be authoritative. In this study, we focus on the manual interlinking of geospatial data using maps. The State-of-the-Art uses maps to facilitate the search and visualization of such data. Our contribution is to investigate whether maps are useful for the creation of interlinks. We designed and developed such a tool and set up an experiment in which 16 participants used the tool to create links between different Linked Data datasets. We not only describe the tool but also analyze the data we have gathered. The data suggests the creation of these interlinks from these maps is a viable approach. The data also indicate that people had a harder time dealing with Linked Data principles (e.g., content negotiation) than with the creation of interlinks.
Anonymous Asynchronous Payment Channel from k-Time Accountable Assertion

Accountable assertion enables a payer to make off-chain payments to a payee, and at the same time, the payer's secret credentials can be revealed if she equivocates (i.e., makes conflicting statements to others). In this paper, we introduce a new construction of accountable assertion that allows an assertion to be accountable for k times. We also present a new construction of anonymous payment channels for the cryptocurrency Bitcoin that allows a payer with k-time accountable assertions to anonymously make off-chain payments to the payee. In particular, we define formal security models for the new constructions, we also prove that the k-time assertion can achieve strong secrecy, and the asynchronous payment channel can achieve anonymity and untraceability. The proposed anonymous payment channel with k-time accountable assertions ensures that: (1) the payee can anonymously receive funds at asynchronous points of sale, and (2) the payee can trace the real identities of payers when they equivocate, and penalize them afterward.
Can One Drink Informal Water Alternatives Directly? Using Mobile Phone to Establish Trust in Quality

The informal water market in cities within the Global South is expanding, thanks to drought associated water shortage challenges and other socio-hydrological factors.A midst the growth is the inherent information signal asymmetry driven mainly by the vendors' unwillingness to share the actual quality data of their source water with their customers.As a result a big mistrust environment has been created as currently; the customers have no mechanism to verify the water quality in real time.This paper aimed at developing an android application software system to fill the gap.The system is to operate the water vending business landscape as a trusted social network site (SNS) using handheld mobile phone devices.An Agile-Scrum methodology was utilized as it allows for quick changes to the system as necessary.An android platform was chosen as the initial Operating System (OS) to run the software system due to its faster global outreach capability.Specifically, Android Studio 3.4.2IDE running on Windows 10 was deployed.And the primary languages used within the IDE were; Kotlin for the functionality and XML for the user interface (UI).Additionally, the Firebase SDK tools were used for cloud-based database functionality.The results of the prototype include; user side access and feedback exchanges, backend side supports and other added functionalities.The paper is of the strong view that since the system works on anytime-anywhere modality, then it is possible that one can drink the informal water directly.The system is recommended for full scale trial in the affected cities.
Koncept udžbenika ruskog jezika struke - stanje i moguća rešenja

The paper discusses the possibility of designing an optimal model of the Russian language textbook for students of Social Sciences and the Humanities. The research starts from the analysis of the current situation - a critical overview of available textbooks, then points to certain shortcomings of textbooks in terms of their structure and content, and suggests certain corrections and modifications for improving the quality of textbooks and designing the most optimal model of the Russian language textbook for the above-mentioned educational profile. The textbook is examined from theoretical, conceptual and practical aspects. The theoretical-conceptual plan analyzes various structural components of textbooks at the macroand microlevels, examines the elements of its content and points to the functional specifics of the language for specific purposes textbooks in relation to the general foreign language textbooks. At practical level, the paper analyzes and evaluates the quality of the textbook contents and its functional compliance with the real needs of the profession and science. The results of the research should show whether the current textbooks of the Russian language for specific purposes are sufficiently functional, to what extent they are realistically involved in building professional communicative competencies of future experts in the sphere of Social Sciences and the Humanities, and whether they can offer a concept that would serve potential authors - methodologists and practitioners - as a guideline in creating the most optimal model of the Russian language for specific purposes textbook.
A Personal Activity Recognition System Based on Smart Devices

With the continuous evolution of technology, mobile devices are becoming more and more important in people’s lives. In the same way, new needs related to the information provided by their users arise, making evident the need to develop systems that take advantage of their daily use. The recognition of personal activity based on the information provided by the last generation mobile devices can easily be considered as an useful tool for many purposes and future applications. This paper presents the use of information provided from two smart devices in different acquisition schemes, assessing conventional supervised classifiers to recognize personal activity by an identification of seven classes. The classifiers were trained with a generated database from eight users and were evaluated in offline mode with other two generated databases. The prediction experiments were qualified by using F1-score indicator and were compared with the native prediction from the cellphone. The obtained results presented a maximum F1-score of 100% for the first validation test and 80.7% for the second validation test.
Quantum Circuits Specifications based on Multiformity of Flexible Design Flows

The article deals with the interrelated set of design flows for quantum circuit, providing an integrative view on the basic procedures of their specifications design. The proposed use of flexible design flows should provide an analysis of model representations of quantum circuits from different sides (levels of perception and technological maintenance), their integration into modern means of development and simulation of quantum computing. The results of our approach application are demonstrated. Multistage procedures for designing of quantum circuits specifications with necessary characteristics are considered by means of the example of fixed polarity Reed-Muller expressions synthesis. The results of templates using for minimization of SWAP gates groups are presented for the purpose of quantum circuits obtaining in the linearly nearest neighborhood architecture. The analysis of automatically generated specifications of quantum circuits is based on a set of criteria.
Low-Rank Representation Based Domain Adaptation for Classification of Remote Sensing Images

A low-rank representation (LRR) based domain adaptation method is proposed for classification of remote sensing images. LRR achieves domain adaptation by constraining one domain can be well reconstructed by the other domain. In this paper, source data are transformed to target domain so that the transformed source domain data can be linearly reconstructed by the data of target domain. The domain distribution difference can be reduced by constraining the reconstruction matrix to be low rank. Further, we introduced a per-class maximum mean discrepancy (MMD) strategy to obtain an improved cross-domain alignment performance. The experimental results using hyperspectral remote sensing images demonstrated the effectiveness of the proposed method.
Similarity Steered Generative Adversarial Network and Adaptive Transfer Learning for Malignancy Characterization of Hepatocellualr Carcinoma

Deep learning with Convolutional Neural Network (CNN) has exhibited high diagnostic performance for lesion characterization. However, it is still challenging to train powerful deep learning systems for lesion characterization, because there are often limited samples in different malignancy types and there exist considerable variabilities across images from multiple scanners in clinical practice. In this work, we propose a similarity steered generative adversarial network (SSGAN) coupled with pre-train and adaptive fine-turning of data from multiple scanners for lesion characterization. Specifically, SSGAN is based on adding a similarity discriminative measure in the conventional generative adversarial network to effectively generate more discrepant samples, while the adaptive fine-tune strategy is adopted to optimally make decisions on whether to use the pre-train layers or the fine-tune layers. Experimental results of pathologically confirmed malignancy of clinical hepatocellular carcinoma (HCCs) with MR images acquired by different scanners (GE, Philips and Siemens) demonstrate several intriguing characteristics of the proposed end-to-end framework for malignancy characterization of HCC as follows: (1) The proposed SSGAN remarkably improves the performance of lesion characterization and outperforms several recently proposed methods. (2) The adaptive fine-tuning combined with the proposed SSGAN can further improve the performance of lesion characterization in the context of limited data. (3) Clinical images acquired by one MR scanner for pre-train can be used to improve the characterization performance of images acquired by another MR scanner, outperforming the pre-train with ImageNet.
Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Membership Functions

Weighted Pattern Tree (WPT) algorithm is as an extension of the Pattern Tree (PT) algorithm, which could be used for fuzzy modelling. This algorithm utilizes the similarity between two fuzzy sets in order to quantify how much a particular tree model is confident to predict a given class. The Membership Functions (MFs) play an important role in model induction and thus on the model’s performance. Therefore, this paper aims to investigate the influence of different MFs, not only by analyzing different mathematical distributions, but also to investigate the influence of the number of MFs per attribute used for fuzzification of the datasets, as well as the different settings of the algorithm in the area of diatom ecological modelling. The experimental results show that WPTs with depth 10 using polygonal MFs with high number of MFs per attribute are excellent for describing the training data, while the models that are built with low number of MFs are excellent for making predictions for unseen data. The results from this research can be used for ecological modelling of diatoms, to classify a given diatom into a particular water quality class.
Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills

There has been a growing interest in measuring students in a learning context. Cognitive diagnosis models (CDMs) are traditionally used to measure students’ skill mastery at a static time point, but recently, they have been combined with longitudinal models to track students’ changes in skill acquisition over time. In this chapter, we propose a longitudinal learning model with CDMs. We consider different kinds of measurement models, including the reduced-reparameterized unified model (r-RUM) and the noisy input, deterministic-“and”-gate (NIDA) model. We also consider the incorporation of theories on skill hierarchies. Different models are fitted to a data set collected from a computer-based spatial rotation learning program (Wang S, Yang Y, Culpepper SA, Douglas JA, J Educ Behav Stat, 2016. https://doi.org/10.3102.1076998617719727 ) and we evaluate and compare these models using several goodness-of-fit indices.