Reviewerly/exHarmony huggingface dataset:
Developing better Civic Services through Crowdsourcing: The Twitter Case Study

&lt;p&gt;Civic technology is a fast-developing segment that holds huge potential for a new generation of startups. A recent survey report on civic technology noted that the sector saw $430 million in investment in just the last two years. It's not just a new market ripe with opportunity it's crucial to our democracy. Crowdsourcing has proven to be an effective supplementary mechanism for public engagement in city government in order to use mutual knowledge in online communities to address such issues as a means of engaging people in urban design. Government needs new alternatives -- alternatives of modern, superior tools and services that are offered at reasonable rates. An effective and easy-to-use civic technology platform enables wide participation. Response to, and a ‘conversation’ with, the users is very crucial for engagement, as is a feeling of being part of a society. These findings can contribute to the future design of civic technology platforms. In this research, we are trying to introduce a crowdsourcing platform, which will be helpful to people who are facing problems in their everyday practice because of the government services. This platform will gather the information from the trending twitter tweets for last month or so and try to identify which challenges public is confronting. Twitter for crowdsourcing as it is a simple social platform for questions and for the people who see the tweet to get an instant answer. These problems will be analyzed based on their significance which then will be made open to public for its solutions. The findings demonstrate how crowdsourcing tends to boost community engagement, enhances citizens ' views of their town and thus tends us find ways to enhance the city's competitiveness, which faces some serious problems. Using of topic modeling with Latent Dirichlet Allocation (LDA) algorithm helped get categorized civic technology topics which was then validated by simple classification algorithm. While working on this research, we encountered some issues regarding to the tools that were available which we have discussed in the ‘Counter arguments’ section. &lt;br&gt;&lt;/p&gt;
Design and Experiment of Silicon Racetrack-Loop Multi-Mode Waveguide Structure with Low-Loss and Adjustable Couplings for Compact Reservoir Computing Device

As a compact reservoir computing device, a silicon racetrack-loop multi-mode waveguide structure is studied from viewpoints of low-loss fan-in and adjustable fan-out coupling structures as well as memory effect. Structural design including novel triangular configurations are performed, and experimental characteristics with memory effect are demonstrated.
Real-Time Echo State Network Based on FPGA and Its Applications

In this chapter, a hardware processing architecture of real-time echo state network based on field-programmable gate array (FPGA) is proposed, which solves the problem that it is difficult to obtain the output weight of the network in real time. The design of this architecture strictly follows the reservoir calculation (RC) theory, and its five components are established in FPGA: input module, reservoir module, output module, training module, and system switch module. This paper implements the architecture in Altera FPGA chip and verifies it through the application of pattern recognition, waveform generation, and multiple-input multiple-output (MIMO) channel prediction. Experimental results show that the hardware-implemented real-time echo state network can identify the duty cycle of different input signals, generate floating-point waveforms, and predict the MIMO channel by training. In this paper, a real-time echo state network based on field programmable gate array is proposed, which has the advantages of fast computation speed, less resource consumption, and ideal simple task execution.
Leadership and Management in Science and Technology: Competency Model

The article deals with scientific and methodological substantiation and description of the competency model of a modern leader and manager in scientific and technological sphere. This model has been developed by the authors on the initiative of the Coordinating Council for Youth Affairs in Scientific and Educational Spheres of the Presidential Council for Science and Education in the framework of the managers’ contest “Leaders of Russia” (specialization “Science”, season 2019– 2020). The competency model is aimed at improving the efficiency of solving the problems of scientific and technological development of Russia by overcoming the lack of personnel which is ready not only to conduct research at the world level, but also to take responsibility for the formation of priorities for scientific and technological development that meet the interests of Russia, as well as for their implementation. The proposed model is the first of its kind developed and tested system of competencies for improving leadership and management potential of Russian science.
<i>Study in three phases</i>: An Adaptive Sound Installation

Abstract Study in three phases is an adaptive site-specific sound installation that includes 22 solenoids placed on metallic arches that surround visitors and react to environmental perturbations, creating a self-regulating soundscape of metallic hits that serves to renew the visitors’ acoustic perspective. Adaptivity is a crucial aspect of the work: Similar perturbations will not generally cause similar reactions from the installation based on past interactions, thus allowing evolution over time to play a key role artistically and technically. This article discusses the author's position on adaptivity in music interaction and composition and reports on the technical and artistic aspects of the installation.
Remote Sensing Applied to Regional-Scale Mapping of Solar Potential—Case Study on Florianopolis Island

Satellite imagery is used for many activities in different areas of the planet, including searching for alternative and sustainable sources to meet growing energy demand to reduce greenhouse gas (GHG) emissions.One way to minimize these effects and expand energy parks is to encourage local generation through the use of renewable sources, such as solar energy, which is free and affordable in many regions of the planet, but that in Brazil is not yet a reality.In order to make an assertive decision when installing a solar power system, one needs to use tools that involve remote sensing and geographic information systems (GIS), and compile information and variables that are relevant to the subject of solar power generation and take into account the inherent geographic space.In this context, the main objective of this work is to develop a GIS model to identify areas with solar potential on a regional scale using active remote sensor images and previously available solar models.To validate the model, this study used an area on the island part of the city of Florianópolis in Santa Catarina State-Brazil, which suffers from repeated climatic events which cause long power cuts, as its distribution occurs by air all over the island.Through the "solar analist" function of ArcGIS and the matrix bases derived from the Digital Model of the Space Shuttle Topography Mission (SRTM) with 30 m of spatial resolution and the supervised classification of panthromatic and multispectral images fused from LandSat 8 satellite, were generates indicative maps of the areas with solar potential.The results pointed to a high solar potential in the all year and that could be better explored by public managers and also by individual consumers.
Multi-modal Summarization for Video-containing Documents

Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth. Most existing multi-modal summarization works however have used visual complementary features extracted from images rather than videos, thereby losing abundant information. Hence, we propose a novel multi-modal summarization task to summarize from a document and its associated video. In this work, we also build a baseline general model with effective strategies, i.e., bi-hop attention and improved late fusion mechanisms to bridge the gap between different modalities, and a bi-stream summarization strategy to employ text and video summarization simultaneously. Comprehensive experiments show that the proposed model is beneficial for multi-modal summarization and superior to existing methods. Moreover, we collect a novel dataset and it provides a new resource for future study that results from documents and videos.
Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.
Transitioning from a First Generation to Second Generation Large-Scale Agile Development Method: Towards Understanding Implications for Coordination

Abstract This paper reports our initial findings from a longitudinal case study within a large development project in a public organization in Scandinavia. We focus on changes in coordination practices as the development project moved from a 1 st to a 2 nd generation large-scale agile development methodology. Building on four theories of coordination from different fields, we investigate how each theory illuminates our case and what insight they might provide. We find that two of the theories are well suited to characterizing each phase, providing answer to how coordination was done. While two other theories can provide answers to why these changes occurred.
Scientific catalog of the book collections of «Studion» as a «Byzantine» library: principles and features of formation

The article highlights the principles and sources of material selection for the Catalog; its structure and content are disclosed; attention is paid to the specifics of the processing and bibliography of Greek prints, a sample description of one of them is given. The already completed scientific catalog is the first attempt to identify, elaborate, describe and return to the scientific and public discourse the Byzantine materials of the Lviv «Studion» library, which Metropolitan Andrey has purposefully collected for over thirty years, to promote, first of all, theological research work, education of highly educated clergy, which would correspond to the level of knowledge of Western apologists of the time. The main source for the formation of the Catalog were the copies of the Byzantine-Christian content, which we found, first of all, in the retrospective fund of the Vasyl Stefanyk National Scientific Library of Ukraine in Lviv and other Lviv cultural and educational institutions. These sources were 279 searched, first of all, by studying the reference and bibliographic apparatus (DBA) of the Lviv academic institution; elaboration of manuscript materials, in particular, letters of the Italian theologian Father Aurelio Palmieri to Metropolitan Andrey Sheptytskyi in 1907–1914, stored in the Central State Historical Archive of Ukraine in Lviv; acquaintance with the reference and historical and literary literature etc. Thus, the Catalog includes the multi-genre, multi-sectoral and multilingual literature of the 15th – first half of the twentieth century, which determined the specifics and uniqueness of the Studion collection and which has not yet been investigated. Bibliographic descriptions of Greek and Latin prints are accompanied by Ukrainian translation of their titles. A special place is given to the problem of bibliography of Greek prints (transliteration and unification) as an important component of the national cultural heritage. On the example of one edition, an attempt was made to demonstrate the search algorithm and bibliographic stylistics, which is characteristic of the presented Catalog. The bibliographic reconstruction of the Studion Book Collection will show the depth of the idea of the Metropolitan of Galicia to create such a special library institution; will enlighten the participation of Fr. Klymentii Sheptytskyi in the organization and management of the library; will allow to define more fully its contents (thematic) and chronological features and multilingual character; will contribute to the study and discovery of not only valuable retrospective holdings of the academic library, but also a significant component of the national historical and cultural heritage. Keywords: Metropolitan Andrey Sheptytskyi, bookstore of «Studion» in Lviv, Byzantine materials (Byzantine literature), Greek books, printed catalog.
Reversible data hiding with segmented secrets and smoothed samples in various audio genres

Abstract In this age, information technology has grown significantly. Computer networks, which connect a device to others, have made it easier for people to transfer data than before. Moreover, smart devices have the capability of supporting this data transmission, including those in the cloud that may contain massive data. However, the security factor can be a severe issue if sensitive big data, such as military and medical data, do not have enough protection. Furthermore, an attacker may be able to disclose such data. Some algorithms have been introduced to solve that problem, one of which is the data hiding method. Nevertheless, some factors are still challenging, concerning the capacity of the secret data and the quality of the generated data, which are represented by bit and Peak Signal-to-Noise Ratio (PSNR), respectively. Besides, some techniques are not reversible, which means that they cannot reconstruct the carrier (cover). In this research, we investigate those problems by taking audio as the carrier. It is done by sampling the audio file before being interpolated to present spaces for accommodating the secret. Meanwhile, the secret is segmented before the embedding. Later, the embedded audio is smoothed according to the required level. The experimental result is obtained by using a public data set containing various audio genres and instruments, and 11 secret sizes, from 1 to 100 kb. It shows that the proposed method outperforms the others. This higher PSNR value means that the proposed method can generate more similar stego data; it also implies that at a certain quality level, the number of bits that can be hidden in the audio cover is higher than that of others.
Building a Low-Cost Weather Station

The study and construction of a low-cost weather station emerged from a scientific initiation project, "Modeling and Analysis of a Wind-Photovoltaic Hybrid System With the Application of a Permanent Magnet Synchronous Generator in Urban Areas," developed at the Universidade Federal Fluminense (UFF). During the initial stages of this project, there was a need to understand the climate data, which influence the development and proper functioning of wind and photovoltaic systems. However, buying or having access to such measuring equipment requires a high investment. As a result, we sought to collect data related to wind generation and solar photovoltaic energy, such as wind speed, irradiance, temperature, and air humidity, by constructing a low-cost weather station with an Arduino Uno board, electronic sensors, and data storage in a memory card.
On aesthetics for user-sketched layouts of vertex-weighted graphs

Recent empirical works on graph drawing have investigated visual properties of graph drawings created by users based on adjacency lists of graphs as well as drawing behaviors. This is mainly done by asking participants to sketch these graphs on a tablet computer so that they can freely express their interpretation. However, previous works did not consider weighted vertices, i.e., assigning a weight to a vertex to reflect its importance. Therefore, we conducted an empirical study on graphs with weighted vertices. More specifically, this work conducts an experiment and analyzes characteristics of the final graph layouts, participants’ drawing processes and strategies and their drawing preferences. Results indicated that minimizing the number of edge crossings was still the most important aesthetic for participants, and that participants preferred the aesthetic of creating grid-like drawings in the condition with weighted vertices. Hence, this work suggested that aesthetics of minimizing number of edge crossings and creating grid-like patterns should be the main consideration for designing a graph drawing software application.
Deep Learning Architectures: A Hierarchy in Convolution Neural Network Technologies

Albeit deep learning has chronicled roots and has been applied to computer vision task since 2000 but a decade ago, neither the expression “Deep learning” nor the methodology was well known. This dormant field regains consciousness when a highly influential paper “Image Net Classification with Deep Convolutional Neural Networks by Krizhevsky, Sutskever and Hinton’in 2012” was published. Now, availability of abundance of data, computational power, and improved algorithms has contributed altogether and brought this technology to forefront in the field of machine learning. In this paper, we focus on growth of various convolution neural network architectures (deep learning architectures), from their predecessors up to recent state-of-the-art deep learning systems. The paper has three sections: (1) Introduction about neural networks along with necessary back ground information. (2) Hierarchy of classical and modern architectures; In this section, the existing methods are explained and their contribution and significance in field of machine learning are highlighted. At last, we point out a set of promising future works and draw our own conclusions.
Convolution Neural Network Based Visual Speech Recognition System for Syllable Identification

Introduction: This paper introduces novel and reliable approach for speech impaired people to assist them to communicate effectively in real time. A deep learning technique named as convolution neural network is used as its classifier. With the help of this algorithm, words are recognized from an input which is a visual speech, disregards with its audible or acoustic property. Methods: This network extracts the features from mouth stances and different images respectively. With the help of a source, non-audible mouth stances are taken as an input and then segregated as subsets to get desired output. The Complete Datum is then arranged to recognize the word as an affricate. Results: Convolution neural network is one of the most effective algorithms that extracts features, performs classification and provides the desired output from the input images for speech recognition system. Conclusion: Recognizing the syllables at real time from visual mouth stances input is the main objective of the proposed method. When tested, datum accuracy and quantity of training sets is giving satisfactory output. A small set of datum is taken as first step of learning. In future, large set of datum can be considered for analyzing the data. Discussion: On the basis of type of Datum, network proposed in this paper is tested to obtain its precision level. A network is maintained to identify the syllables but it fails when syllables are of same set. Requirement of Higher end graphics pro-cessing units is there to bring down the time consumption and increases the efficiency of network.
Implementation of DCGAN to Generate Gamocha Design Patterns

Generative adversarial network (GAN) [] can learn to mimic any kind of distribution of data. The main motive of GAN is to generate images of a particular class on its own. One of the most popular and successful network of GAN is DCGAN [], termed as deep convolutional generative adversarial network. It consists of deconvolutional and convolutional layer in the generator and discriminator network instead of dense layers. The generation and discrimination of DCGAN is more precise compared to the other GANs, as it features the minute details of the image. In this paper, we implement the DCGAN network to generate new Gamocha (traditional towel of Assamese tribe) design patterns. The design patterns made by DCGAN are as indistinguishable and unique as the human-made Gamocha. This GAN can follow the pattern structure of art to create new designs.
Person Fall Detection System Using Arduino UNO, SIM900A, and NEO6

In the world, most of the peoples living their life separately at their home. Some activity in our daily life is likely to have some coincidences, such as falls. Falls can make people in deadly conditions, even death. This project is focused on those peoples who are physically handicap. For that, we have used MPU6050 Accelerometer and gyroscope sensors. This sensor is connected with the Arduino UNO R3 device to get the result of fall detection more accurately. We have also used SIM900A module and NEO6 module for sending notification and for the live location of that person. Automatic SMS alert will be sent to family members of the person who is using smartphones. The result showed that the proposed system could successfully record the level of accuracy of the fall detection system till 96% in activity daily living and error detected of fall was 4%.
Bayesian Approach for Estimating the Distribution of Magnitudes, Interevent Times and Distances of Earthquake Sequences

A Bayesian approach has been applied to estimate the distribution of magnitudes, interevent distances and times of earthquakes occurred in 2017 in central Italy by using a small amount of random samples drawn from the distribution of the same seismic parameters for the earthquakes occurred in 2014-2016. We applied the method to the whole and aftershock-depleted seismicity by using the exponential and the normal model to fit the distributions of the seismic parameters. Our findings indicate that the exponential model fits the distributions of the seismic parameters much better than the normal model. Furthermore, in the whole seismicity case, the method requires at least 2100 to 2300 random samples to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01; while in the aftershock-depleted case, a minimum number of random samples varying between 360 and 1470 occurred in 2014-2017 is required to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01.