Reviewerly/exHarmony huggingface dataset:
3D reconstruction of organ from CT images and visualization in a virtual reality environment

Virtual reality (VR) presents objects or simulated scenes to reproduce situations in a way that is very similar to the real thing. In medicine, processing and 3D reconstruction of medical images is an important step in VR that has had significant breakthroughs. We propose a methodology for processing medical images (DICOM), to segment organs, reconstruct structures in 3D and represent structures in a VR environment, in order to provide the specialist with an alternative tool for the analysis of medical images. We present a DICOM image processing method based on area differentiation and 3D reconstruction by the `isodurface' and `alpha-shape' methods for the creation of a virtual environment in Blender. Different studies shows the benefits of VR applied to clinical practice, adding the uses they have as educational tool. Improved 3D segmentation and reconstruction methods are needed to obtain more detailed structures and better definition to be used in routine procedures.
Research on the performance evaluation of shantytown transformation funds based on logical model

In order to overcome the problems of low evaluation accuracy and efficiency existing in the traditional performance evaluation method of shantytown transformation funds, this paper proposes a new performance evaluation method of shantytown transformation funds based on logical model. Construct the framework of logical model and calculate the conditional probability. Improve the performance evaluation index, using analytic hierarchy process to calculate the index weight, according to the index weight to complete the shantytown transformation fund performance evaluation. The experimental results show that, compared with the traditional methods, the proposed method has higher evaluation accuracy and efficiency, and the practical application effect is good.
A Bayesian nonparametric model for inferring subclonal populations from structured DNA sequencing data

There are distinguishing features or “hallmarks” of cancer that are found across tumors, individuals and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement, and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma–Poisson hierarchy, and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals comutated loci across samples.
Software Bantu Pembuka Aplikasi Pada Dekstop Berbasis Voice Recognition

The human desire to make it easier to use a tool in order to increase the effectiveness of work production becomes a guide in the world of work. However, physical limitations or physical disabilities, especially the hands, make it difficult for someone to work using these tools. Therefore an assistive software was made to solve these problems, by utilizing the recognition of natural human characteristics using voice or voice recognition. Assistive software opening applications on a computer desktop with voice commands which are then converted into text using processing software with the help of the webscocket and speech-to-text library from Google and the results are processed to open the desired application by the user. In this paper, an application that has been made is able to convert voice to text and can open applications on the desktop so that it makes it easier for users to operate applications on a computer with a system success rate for speech recognition reaching 91.5%.
Sample complexity of hidden subgroup problem

The hidden subgroup problem (HSP) has been attracting much attention in quantum computing, since several well-known quantum algorithms including Shor's algorithm can be described in a uniform framework as quantum methods to address different instances of it. One of the central issues about HSP is to characterize its quantum/classical complexity. For example, from the viewpoint of learning theory, sample complexity is a crucial concept. However, while the quantum sample complexity of the problem has been studied, a full characterization of the classical sample complexity of HSP seems to be absent, which will thus be the topic in this paper. HSP over a finite group is defined as follows: For a finite group G and a finite set V, given a function f:G→V and the promise that for any x,y∈G,f(x)=f(xy) iff y∈H for a subgroup H∈H, where H is a set of candidate subgroups of G, the goal is to identify H. Our contributions are as follows: For HSP, we show that the number of uniform examples necessary to learn the hidden subgroup with bounded error is at least Ω(minH∈H′⁡max⁡{log⁡|H′|log⁡|G||H|,|G||H|log⁡|H′|log⁡|G||H|}), where H′=H∖{G}; on the other hand, O(maxH∈H⁡{r(H),|G||H|r(H)}) uniform examples are sufficient, where r(H) is the rank of H. By concretizing the parameters of HSP, we consider a class of restricted Abelian hidden subgroup problem (rAHSP) and obtain the upper and lower bounds for the sample complexity of rAHSP. We continue to discuss a special case of rAHSP, generalized Simon's problem (GSP), and show that the sample complexity of GSP is Θ(max⁡{k,k⋅pn−k}). Thus we obtain a complete characterization of the sample complexity of GSP.
Research and Application of Deformation Monitoring Algorithm for Single-Frequency GNSS Low-Cost Monitoring Equipment

GNSS precision positioning technology has been widely used in earth surface subsidence monitoring, engineering building displacement monitoring and other directions. It has the advantages of global, all-weather, high precision, no intervisibility between stations and so on. In this paper, Aiming at the problems of low-cost GNSS equipment, such as single frequency, large observation noise and frequent cycle slips, a complete set of real-time and high-precision deformation monitoring algorithm is designed and verified in the actual projects such as power facilities deformation monitoring, tailings settlement monitoring and geological disasters monitoring. The results show that the algorithm and software can achieve the precision of millimeter/sub-millimeter level, and can realize the fast response to the actual deformation.
박물관 도슨트의 역량생애사 연구 - 역사박물관을 중심으로 -

This study understood the museum docents' competency biography. 'Competency Biography' is a life history study that looks at an individual’s experience in developing competency in relation to a particular activity or duty, unlike a typical life-history study. This study revealed the process in which the docents develops as a professional commentator and the various experiences in it. The data were gathered through narrative interviews and the collected data were analyzed through 'critical life events' and the 'process structure of biography'. As a result, eight events were affecting the docents' competency development experience. Also this study could be divided into the 'academic pursuit', 'field experience', 'sharing practice' docents and the detailed characteristics of each type were examined in depth through the process structure of biography. Based on these results, this study was concluded by providing suggestions for follow-up research along with policy-based and practical discussions for the competency development of the museum's docents.
Possibility of Reading Notes as Media to Enrich Communications Between Reader and Book

Reading gives readers intellectual stimuli, elicits their imagination and creativity, and expands their inner world. If reading has such importance, in Japan, many people don't understand the importance of reading. In this research, we introduce the concept to postulate that reading is communication between a reader and a book, even though a book is not reactive but non-reactive entity. An idea is to change a book into a reactive entity, and to achieve interactions between a reader and a book. Especially, we introduce a mechanism to feedback a reading note as response from a book to a reader's gaze information that the reader generates during reading as stimulus to the book. A reading note is a record that a reader makes about a book, based on the reader's intention and interest. Reader's gaze information generated during reading shows his/her mind about the book. Thus, we regard that a reading note is useful as response from a book, and employ reader's gaze information is useful stimuli to a book. The technological goal of this study is to build an automatic reading notes creation system that automatically generate reading notes based on gaze information that a reader naturally generates during reading. For that goal, we conducted experiments to measure a subject's gaze coordinate data, and to verify whether we could identify eye movements for specific four reading patterns through the analysis of the acquired data. As the result, we have confirmed it possible to identify a reader's reading patterns from the change of gaze information.
Socio-Technical Challenge in the Selection of the Remote E-Learning Platform

Abstract The current pandemic context represents, without any doubt, the most serious challenge for all working environments, implicitly and educationally. Students and teachers alike are unwittingly deprived of such a natural meeting characteristic of the learning process in an amphitheatre or laboratory and thus put in the challenging situation of continuing their efforts to achieve the goal, by making the efficient use of the online environment in all its complexity (hardware infrastructure, educational platforms, accessibility to the internet connection). The article deals with the selection of the distance education platform by using a mathematical model of multi-criterial analysis of the factors involved. It is essential that education does not interrupt its course and continues to evolve, thus adding value to society. Moreover, our future, of all, depends on our ability to cope with the highest standards, the challenge created by distance education. By linking the skills of teachers and instructors, the potential of students and the optimization of IT resources, this desire is possible provided that each of the elements involved contributes to the achievement of the ultimate goal: preparing generations of young people for the uncertain future. The resilience of the public and private education system is dependent on the use of tools appropriate to the purposes assumed.
Command Radiosignal Ensemble Synthesis for Automated Systems

The article describes the approach to synthesizing an ensemble of signals with good auto - and crosscorrelation properties that can be used for reliable control commands transmission. Such signals will not require the use of synchronizing signals. It can be used for different automated radio electronic systems. Using the signals based on polyphase sequences is proposed. Such signals occupy the whole baseband of the dedicated radio communication channel. A solution that allows to construct an ensemble of any size for any length of sequences is proposed. Solution is based on using spectrum of sequences. To synthesize such signals complex sequences of the corresponding length with the desired properties can be used. It is shown that periodic cross-correlation function of these sequences are good too. Authors found an approximate solution by numerical methods for any length N and any size of the ensemble M. Results are shown for sequences with length of 15 symbols but can be used for any numbers.
Modeling And Simulation for Digital Document Verification Scheme Using Blockchain in P2P Network

Blockchain is very emerging trend in recent years; it is basically decentralized approach which provides transparency to transactional data. Various researchers already introduces blockchain and its state of art, it is too much effective in a large data processing as well as global transactional systems. In this paper we introduce blockchain base E-certificate generation using cloud environment. Basically in real time environment it is hard to carry educational or other important documentaries. Some confidential information should be leak from various centralized systems when any resource has compromised with attackers. In this system we provide drastic supervision base blockchain technique to generate e certificate according to submitted documentary by respective student as well as user. The system illustrates into four different sections. First we defines user can upload his documentary or educational certificates, similarly the middleware authority known as Third Party Auditor (TPA) will verify such documents from authenticated organization. If the entire documents have authorized then it dynamically generate e-certificate with QR code and unique serial identification number. Once this process has done whole information has stored into various data nodes and it returns QR code as well as UID to student. Moreover when any organization wants to verification documentary of respect to student he can submit the QR code or are given Unique Identification number to respective organization. When such organizations will verify the student’s data the blockchain will provide consistent information after the secure authentication. In entire execution we have written open Smart contract, had generation approach using SHA family algorithm, mining algorithm to generator valid hash, in consensus algorithm to evaluate the proof of work.
Public-Key Projective Arithmetic Functional Encryption

Ananth and Sahai proposed the projective arithmetic functional encryption (PAFE) and showed that PAFE derives a single-key selective secure functional encryption with the help of the randomizing polynomials scheme (RP), namely PAFE with RP achieves the indistinguishability obfuscation (iO). Their PAFE considers a secret-key type functional encryption only and a public-key counterpart is not known. We propose the public-key version: pkPAFE, and show that pkPAFE with RP derives a public-key functional encryption which is single-key selective secure. This means that our pkPAFE achieves iO as well as the original PAFE by Ananth and Sahai.
Halftone Image Steganalysis by Reconstructing Grayscale Image

Utilizing some special pixels patterns constructing the steganalytic features is popular in binary image steganalysis, which could be also used in halftone image. There is almost no specific image steganalysis for halftone image. In this paper, a halftone image steganalysis scheme is proposed and achieves a satisfactory performance, which is totally different from the previous works focussing on some special pixels patterns. Inspired by the fact that halftoning techniques based on the low-pass characteristic of human visual system model (HVS model), the grayscale image is considered reconstructing with a Gaussian filter. And the distortions caused by embedding secret messages will still exist. After that, some common grayscale image steganalysis can be used for extracting the steganalytic features. Furthermore, a series of experiments are conducted and the experimental results show that the proposed scheme is effective on halftone image steganalysis.
Implementation of and experimental software for active selection of classification features

In some machine learning applications, obtaining data on the most predictive features is costly, but other features are readily available. Recently, first active learning approaches for this Actively Selecting Classification Features problem (ASCF) have been proposed. In this paper, we introduce a Python package that provides a framework for ASCF, including implementations of a supervised and an unsupervised selection approach, as well as a framework for performing experimental evaluations. This framework has been used in recent publications in the context of neuroimaging research on mental disorders, where its usefulness has been demonstrated in a simulated study design with MRI data.
Non-well-founded Deduction for Induction and Coinduction

Abstract Induction and coinduction are both used extensively within mathematics and computer science. Algebraic formulations of these principles make the duality between them apparent, but do not account well for the way they are commonly used in deduction. Generally, the formalization of these reasoning methods employs inference rules that express a general explicit (co)induction scheme. Non-well-founded proof theory provides an alternative, more robust approach for formalizing implicit (co)inductive reasoning. This approach has been extremely successful in recent years in supporting implicit inductive reasoning, but is not as well-developed in the context of coinductive reasoning. This paper reviews the general method of non-well-founded proofs, and puts forward a concrete natural framework for (co)inductive reasoning, based on (co)closure operators, that offers a concise framework in which inductive and coinductive reasoning are captured as we intuitively understand and use them. Through this framework we demonstrate the enormous potential of non-well-founded deduction, both in the foundational theoretical exploration of (co)inductive reasoning and in the provision of proof support for (co)inductive reasoning within (semi-)automated proof tools.
EDUCATION AS A DETERMINANT OF SOCIOECONOMIC DEVELOPMENT IN INDIA: AN EMPIRICAL INVESTIGATION

Education is regarded as one of the major tools in the development process. Education can go a long way in the growth of productivity, promote employment avenues, better health etc. Education can improve the socio-economic development of a state or a region by providing skilled workforce, higher employment and also helps in reduction of poverty in the society. These in turn help in increasing per capita income, improve people’s status, reduce population growth, improve living standards, improve environmental quality and also help in the decision making of the people and thus help in attaining the broader objective of socio-economic development. This paper makes an attempt to examine the relationship between education and socio-economic development in India over the period 2008 to 2018. Education Development Index and Socio-Economic Development Index were developed on the basis of indicators taken under consideration to find out whether there exists any relationship between education and socio-economic development in India. A simple regression was conducted among them and the results shows that there exists a significant association between education and socio-economic developmen
Educating creative activity as a factor in the civilization of an individual

The article reveals the problem of educating creative activity as a factor in the civil socialization of the individual comprehends the phenomenon of creative activity and civil socialization, interprets their real representation and sociocultural potential for the development of civil society in modern Russia. The authors analyze the positions of scientists on the essence of the key terms of this problem and the results of a survey of students on the individual activity, factors that contribute to and inhibit the successful socialization of youth, as well as effective forms, methods, means of educating creative activity, on the importance of project, research, volunteer, analytical, evaluative and reflective activities in the educational process of the university. The main problems and prospects for the creative activity in the sociocultural space are summarized.
Metric Learning Based Similarity Measure For Attribute Description Identification Of Energy Data

Combining the yearbooks of different cities in China is important for investigating and planning the usage of energy. However, since the yearbooks of cities may be prepared according to different habits and regulations, the same attributes may be described differently. As a result, identifying the same attribute from different yearbook is an important problem. Manual processing is not preferable since it is inefficient and inaccurate. A machine learning model based automatic approach is proposed in this study. Our model applies a metric learning method to quantify the similarity between the attribute descriptions for energy-related data. The attribute descriptions are first converted from texts to a Boolean vector by a bag of words method. The embedding layer method is applied to deal with the sparsity problem of the Boolean vector. A metric learning model is then trained to construct a metric for the similarity of the descriptions. The experimental results indicate that our proposed method outperforms the one without using metric learning.
Sparkle: A Detachable and Versatile Wearable Sensing Platform in a Sustainable Casing

An easy to wear versatile and a compact sensing platform, Sparkle, which consists of a customizable array of sensors: 3-axis accelerometer, temperature sensor, capacitive touch sensor, RFID chip, and light emitting diodes (LED) is presented. The sensing system is cased inside naturally occurring materials such as a seashell and a seed of an oak tree; thereby showing a possibility of usage of ecofriendly materials in smart wearable gadgets. Sparkle uses a Velcro patch that enables easy attachment and removal from any already worn ornament or a gadget. Prototypes of Sparkle attached to a silicone ring, a beaded necklace and to a watch were evaluated for their functionality. The results from an exploratory user study are presented.
Image Classification with A-MnasNet and R-MnasNet on NXP Bluebox 2.0

Bluebox 2.0 by NXP Semiconductors, which has goal of enabling autonomy in vehicles for ADAS applications, is used to enhance car capabilities to perform sensor fusion and run AI algorithms. It focuses on sensor data coming from radars, lidars, and cameras. This research focuses on enabling computer vision application, Image Classification, by implementation of Convolutional Neural Networks in Bluebox 2.0. In this paper, two CNN architectures namely A-MnasNet and R-MnasNet have implemented on Bluebox 2.0. These models have been derived by Design Space Exploration of MnasNet, a CNN architecture, proposed by Google Brain team in 2019. These models have been trained and tested on CIFAR-10 dataset. The model size and accuracy of A-MnasNet are 11.6 MB and 96.89% and that of R-MnasNet are 3 MB and 91.13% respectively. They outperform the MnasNet architecture which has an accuracy of 80.8% and a model size of 12.7 MB. These neural networks can also be used to perform other computer vision applications.