Reviewerly/exHarmony huggingface dataset:
Technology for Information Democracy: Case of GIS Enabled Entitlement Tracking System

Around eighty million households in the country suffer serious deprivation in India. The Union and State Governments of India have introduced various social security schemes and programmes intended for the well-being of these marginalized sections of the country. While most evaluations of the implementation of schemes blame their unsatisfactory performance on leakages in the system, lack of information concerning these schemes is also a major factor behind the same. Congruent with rapid developments and expanse of information and communication technologies (ICT), the world has witnessed a strong inclination towards informed societies. Governments across the world, including the Indian Government, started leveraging ICT to improve their public outreach. However, varying degrees of digital development, access to ICT, differentiated informational requirements and the digital divide have posed challenges to the same. Taking cognizance of these challenges as well as India’s rising Internet user base, many informational platforms like Haqdarshak, Schemopedia and India Iris have been developed in recent times. The GIS-Enabled Entitlement Tracking system is a similar effort undertaken by the Foundation For Ecological Security in association with the Ministry of Rural Development India and United Nations Development Programme to promote information democracy. The platform enables better first mile (usually termed as the last mile) delivery of social security schemes through a strategic mix of online and offline activities like a collation of information about benefits, eligibility criteria as well as concerned authorities related to various state and centrally sponsored schemes, capacity building, promotion of local leadership, etc.
Research on the Design and Path Planning of Child-mother UAV System for Search Task

Abstract UAVs are suitable for performing Dull, Dirty and Dangerous tasks due to their small size, strong flexibility and good concealment. The search task is one of the most typical tasks of drones. However, due to the limitation of technology and cost, the existing UAVs usually have one or more restrictions on flight time, flexibility and versatility. This creates certain restrictions for UAVs to perform different types of search tasks in different situations. In order to improve this situation, this paper proposes a search task-oriented unmanned aerial vehicle system, and designs the workflow to adapt to different types of search tasks. And according to the characteristics of the task, different types of path planning methods are designed for the mother UAV and sub-UAV respectively in order to improve the autonomy of the system. Finally, simulation experiments are carried out on MATLAB software to verify the effectiveness of these two methods, and the conclusion and the next work of the system are given.
Cadences in Grammar-Compressed Strings

Cadences are structurally maximal arithmetic progressions of indices corresponding to equal characters in an underlying string.This paper provides a detection algorithm for 3-cadences in binary strings which runs in linear time on uncompressed strings and in polynomial time on grammar-compressed strings.Furthermore, this paper proves that several variants of the cadence detection problem are \(\mathcal {NP}\)-complete on grammar-compressed strings and that the equidistant subsequence matching problem with patterns of length three is \(\mathcal {NP}\)-complete on grammar-compressed ternary strings.
From Coarse to Fine: Hierarchical Structure-Aware Video Summarization

Hierarchical structure is a common characteristic of some kinds of videos (e.g., sports videos, game videos): the videos are composed of several actions hierarchically and there exists temporal dependencies among segments of different scales, where action labels can be enumerated. Our ideas are based on two intuition: First, the actions are the fundamental units for people to understand these videos. Second, the process of summarization is naturally one of observation and refinement, i.e., observing segments in video and hierarchically refining the boundaries of an important action according to video hierarchical structure. Based on above insights, we generate action proposals to exploit the structure and formulate the summarization process as a hierarchical refining process. We also train a hierarchical summarization network with deep Q-learning (HQSN) to achieve the refining process and explore temporal dependency. Besides, we collect a new dataset that consists of structured game videos with fine-grain actions and importance annotations. The experimental results demonstrate the effectiveness of our framework.
A Disease Interaction Retrieval and Recommendation System based on NLP Technology

Abstract There is a great request for a Chatbot system that uses natural language processing for disease retrieval and recommendation in medical treatment with interactive functions. This paper uses cMedQA2 data for training, developing and testing. The Chinese word segmentation tool jieba to reassemble the text data of consecutive words into the word sequences, and uses Bag of Words to count the frequency of keywords appearing in sentences, and uses N-gram method to predict whether the evaluation and prediction of a sentence is reasonable. This paper proposes an improved TextCNN network framework structure, which uses overlap technology to ensure the contextual coherence of information. The final experimental results show that there are 96.2% on the training dataset and 87.5% on the testing dataset.
Method of Fuzzy Classification of Information with Limited Access

Proposes a method for fuzzy classification of information according to established criteria based on the theory of fuzzy sets and complex oriented information network "List of classified information" during the examination of material media for the presence of such information.
John Cage's Number Pieces, a Geometric Interpretation of “Time Brackets” Notation

Conceptual musical works that lead to a multitude of realizations are of special interest. One can’t talk about a performance without considering the rules that lead to the existence of that version. After dealing with similar works of open form by Iannis Xenakis, Pierre Boulez and Karlheinz Stockhausen, the interest in John Cage’s music is evident. His works are “so free” that one can play any part of the material; even a void set is welcomed. The freedom is maximal and still there are decisions to consider in order to make the piece playable. Our research was initially intended to develop a set of conceptual and software tools that generates a representation of the work as an assistance to performance. We deal here with the Number Pieces Cage composed in the last years of his life. Over time, we realized that the shape used to represent time brackets, brought important information for the interpretation and musical analysis. In the present text, we propose a general geometric study of these time brackets representations, while trying to make the link with their musical properties to improve the performance.
An Analysis on Scrum Methodology in Global Software Development – GSD

The development of applications in collaborative production environments poses problems relating to teamwork, control and communication. Global software development (GSD) has increasingly identified agile techniques that require regular contact and self-organization between remote sites to mitigate challenges. The main aim of this paper is to present an analysis of the challenges in GSD using Scrum practices. This study discusses the Scrum practices in GSD from existing database libraries from 2009 to 2020. It is found that there is relatively quite wide activity in the area of GSD and it may be difficult to apply Scrum in GSD but it provides solutions to the issues people face in this area.
Optimizing Ontology Alignments Through Evolutionary Algorithm with Adaptive Selection Strategy

Because of the subjectivity of different ontology engineers, one concept can be expressed in different ways, which leads to the so-called ontology heterogeneity problem. Ontology matching uses the similarity measures to distinguish the correspondence between entities in two ontologies, which is regarded as a feasible method to solve this problem. In order to get more accurate and complete alignment, different kinds of similarity measures need to be adequately combined, and how to determine the optimal aggregating weights is the so-called ontology meta-matching problem. Evolutionary algorithm (EA) can represent an effective methodology for addressing this problem, but due to the decline in population diversity during the evolving process, the conventional EA suffers from the premature convergence. To overcome this drawback, we introduce an adaptive selection strategy. By monitoring the variation of individual fitness in the population, the selection pressure can be dynamically adjusted to maintain the diversity of population thus to avoid the premature convergence of the population. In the experiment, we compare our approach with original EA-based ontology matching technique and the experimental results show the effectiveness of our proposal.
Discussion on the Application of Big Data in Tourism Management

The advent of the Internet age has made the travel industry more digital. Judging from the current actual situation in China, big data has penetrated into all aspects of tourism management, whether it is tourism services, tourism facilities or tourism products. Big data integrates information on people’s needs, government policies, and tourism resources to make tourism more systematic and comfortable. At the same time, it has further promoted the development of China’s tourism industry. This article first introduces big data in the tourism industry, and then analyzes the important role of big data in tourism management, so as to grasp the future industry development trend, and finally explains the use of big data, which is of great significance to the realization of tourism management information goals.
A Comprehensive Evaluation System for Coaches’ Evaluation Based on Hybrid Learning Algorithm

This article uses entropy value method to determine 5 important quotas'weights by questionnaire surveys. We use Linear Interpolation and Consecutive Scoring method, as well as Multi-index Comprehensive Evaluation, to calculate the comprehensive evaluation scores of coaches. Besides, we make a hypothesis test with null hypothesis and alternative hypothesis to verify that our model is available to coaches with different kinds of sports, different genders and different time line horizon. In addition, we propose an empirical analysis of 10 coaches of basketball, football and baseball respectively, for reference.
Research and market survey of a campus knowledge payment platform based on distributed ledger technology

Abstract Based on the distributed ledger technology, this paper designs a kind of campus knowledge payment platform, and fully investigates the market of campus knowledge payment. First, this article analyzes the existence environment of the campus knowledge payment platform, and does a certain survey of the consumption potential of the college student consumer group, then elaborates the basic principles and technical advantages of the distributed ledger technology, and then conducts the operation process of the knowledge payment platform It is expounded and finally concluded that the campus knowledge payment platform project is worthy of promotion, and has prospects and research value.
Can an amateur station support the professionals? A comparison between two <scp>Dutch</scp> stations

Location of the weather station sites to the northeast of Utrecht.
Domain Specific Conversational Intelligence: Voice Based E-Channeling System

In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).
Fuzzy Recognition of Color and Its Application in Image Retrieval

Content based image retrieval (CBIR) has become a research hotspot in the field of image processing because of its high theoretical and application value. Extracting and matching image features is the main method of CBIR. However, it is extremely difficult to extract the effective features of images. Based on HSV color space characteristics and human perception of color, a color recognition method is proposed. And the structure of this class of pixels is extracted by the method of classification. Image feature matching will be carried out among the same pixel sets, which reduces the complexity of image feature extraction and matching. Experimental results show that the proposed image retrieval method has a good effect.
The Digital Potential of the University: Notion, Structure and Methodological Principles of Assessment

The purpose of the article is to substantiate the “digital potential of the university”, as well as the principal methodology for its assessment. The authors understand this term as the ability of the university to use advanced digital technologies in the production and transfer of new knowledge, as well as to make educational and scientific achievements in the digital economy and artificial intelligence. The author’s methodology for investigating this phenomenon lies in the system-dynamic approach to the consistent description of internal and external factors of developing the university’s digital potential, forms of unlocking it and predicting fundamental prospects of further development. The key finding of the article is the author’s presentation of the structure of the modern university’s digital potential, as well as the principal method of its assessment. This technique can be used in the monitoring of university performance conducted by the Ministry of Science of Russia, as well as in independent assessment operations by civil society institutions.
Acquisition Parameter-conditioned Magnetic Resonance Image-to-image Translation

A Magnetic Resonance Imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through a multitude of acquisition parameters that influence image contrast, signal-to-noise ratio, scan time and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As the acquisition of MR sequences is time consuming, and acquired images may be corrupted due to motion, a method to synthesize MR images with fine-tuned contrast settings is required. We therefore trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition and echo time. Our approach is able to synthesize missing MR images with adjustable MR image contrast and yields a mean absolute error of 0.05, a peak signal-to-noise ratio of 23.23 dB and structural similarity of 0.78.
Hybrid POS Tagger for Gujarati Text

POS tagger is one of the most essential steps in Natural Language Processing applications to analyze the text. We perform a comparative study of POS taggers available for Gujarati language. Comparative study is based on evaluation criteria namely Precision, Recall, and F1 score for each tag and overall accuracy of the POS tagger. A hybrid approach for improving the accuracy of POS tagger has been proposed for Gujarati Language which comprises the LSTM based POS tagging and Computational Linguistic Rules. We found that accuracy of the statistical taggers is improved by applying language specific rules.
Implementing Block-Chain 2.0 Technology in Agribusiness

This paper address and propose the solution for the problem in supply chain cycle of Agribusiness and also gives new solution to bring trust in customer about products.
Intelligent Agent to Negotiate on Goal Oriented Conversations

In this study we propose a model to develop intelligent agents which are capable of negotiating on goal oriented conversations. These agents have the ability to learn negotiation using past experience and form strategies such as persuasion to negotiate successfully. In order to train these agents, they were made to interact with Simulated Users (SU). A corpus was generated and then annotated with speech acts to be used by a n-gram model. The SUs use this model to generate responses. In this study, we focused on single issue negotiations. A Markov Decision Process (MDP) was used to model the problem by defining the states and actions. The agent was made to interact with the SU to learn an optimal policy. For this we used the SARSA algorithm which is a temporal difference (TD) method. Once the agent is trained it was evaluated by a set of SUs built on different cultural norms. The number of dialogue turns and the policy score obtained when negotiating with these SUs were recorded and evaluated. It was observed that the agent was able to successfully negotiate to make a deal and also persuade them to a profitable offer. It can be concluded that the proposed model is successful and it can be used to train intelligent agents which can negotiate.
Results of the Research in the Comparison and Analysis of Historical Artifacts' Photographic Images Catalogued in Online Databases

In the field of archaeology, when a discovery is made, the comparison of images is often used to catalogue a find and give it an interpretation. The image on an exhibit is always subject to analysis, comparisons, graphic reconstructions, which can define it, classify it, and most of all, understand it as a whole. The problem arises when the discovered find proposes a completely new and unpublished image. It therefore requires an in-depth study in all its elements. Photographic images, online databases, and archive collections of museums provide some valid help for solutions or interpretations; and the theories that come out of this comparison can then shed light on the meaning of an image present in a find, when there is no direct confirmation.
UrTra2D – Urban Traffic 2D Object Detection Dataset

With progress being made in the field of artificial intelligence and especially machine learning, tech and vehicle companies acquired a powerful tool and made a large step towards realisation of a fully autonomous vehicle. Along with the exploding development of more and more powerful hardware, deep learning has become one of the most dominant fields of research in the automotive domain, succeeding the classical computer vision methods. However, to be able to apply deep learning methods to solve a problem, large and appropriate datasets are required in developing a solution, as there is never enough data for deep learning. In this paper, Urban Traffic 2D Object Detection (UrTra2D) dataset is presented, which is intended for training 2D detectors of specific objects common for urban traffic scenes. The data was recorded with an affordable camera mounted inside the vehicle. The dataset contains video sequences and labelled frames of the traffic in the city of Osijek in different weather conditions during both day and night. There are 5 770 labelled frames, totalling in 22 764 labelled objects throughout 11 categories. The UrTra2D dataset is freely available to the research community upon request.
The Linguistic Landscape Approach as a Strategy for Reflection and Intervention in Higher Education: Mediations, Practices and Voices to Overcome Borders

Speaking of linguistic landscapes supposes positioning oneself before a multidisciplinary object to address the existing relations between language and public space, recovering the way how it materializes from posters, graffiti and other discursive manifestations. Addressing the linguistic landscape means transcending the limits of the classroom with proposals that force us to look at our environment again, rethink the relationships between subjects and objects of study, our research contexts and the methodological strategies we use. This work recovers experiences developed from a hybrid methodology in the context of a university subject, oriented to the critical analysis of linguistic landscapes of exterior and interior, with special attention to the location of violent discourses and resistance. We assume here that the tensions observed in public space can only be understood from a situated knowledge that encourages social transformation. In this context, we propose the revision of the didactic sequence that supported the proposal, technological tools and materials used, mediations carried out and experiences and learning promoted, focusing on the analysis of didactic documents, photographs obtained by the students, conclusions of their reports and narratives of the experience that allow us to recover evaluations of the proposal and its own learning processes.
Chinese named entity recognition in the field of Architecture

This paper presents an IDCNN-BILSTM-CRF model for named entity recognition in Chinese architecture field. This model uses the text data in the field of architecture constructed in this paper. First, the word vectors are put into the IDCNN model for feature extraction, then the accuracy of feature extraction is further improved in the input BILSTM model, and finally the CRF is used to find the optimal solution of annotation. And in order to greatly reduce the training speed, this paper also puts forward feasible solutions.
Generating Supportive Utterances for Open-Domain Argumentative Dialogue Systems

Towards creating an open-domain argumentative dialogue system, preparing a database of structured argumentative knowledge for the system as reported in previous work is difficult because diverse propositions exist in the open-domain setting. In this paper, instead of structured knowledge, we use a simple seq2seq-based model to generate supportive utterances to user utterances in an open-domain discussion. We manually collected 45,000 utterance pairs consisting of a user utterance and supportive utterance and proposed a method to augment the manually collected pairs to cover various discussion topics. The generated supportive utterances were then manually evaluated and the results showed that the proposed model could generate supportive utterances with an accuracy of 0.70, significantly outperforming baselines.
Attempt to Extract Lacking Information on University Campus by Augmented Reality Content Production Activities

Augmented Reality (AR) makes it easy to link certain information to contents on the Internet and present it to others. This study conducts workshop as specific course that asked first-year university students who were not familiar with the campus to link them with information using the items in the college as AR markers. Participants proposed a campus map contents as a marker for the trees and objects. Participants showed us the information that freshly students wanted. In further works, we would indicate to find out what problems to be promote student's learning activities. This work-in-progress paper describes a workshop.