Reviewerly/exHarmony huggingface dataset:
D7.5 Summer/Winter School

The DESIR Winter School Shaping new approaches to data management in arts and took place in Lisbon on 10-13 December 2019. The event was hosted by NOVA FCSH University facilities and was organised in the framework of the Horizon 2020 funded project DESIR (DARIAH ERIC Sustainability Refined). It aimed at gathering PhD students, arts and humanities scholars from all career stages and research managers interested in the issues raised by sustainable practices to open up data in the arts and humanities. The event was a joint organisation between DARIAH and NOVA FCSH. As such, it brought together the collaboration of national and European experts in the data management field.Sharing knowledge, research outputs and other scholarly resources in ways that are tuned for long-term availability and maximal use and reuse are recognized as essential practices but also key challenges for arts and humanities research. In the last couple of years, this need has also been recognized in a strong political drive in the European Union giving rise to support structures but also policy imperatives for research data management. As a result, Research data management emerged to be a new field of expertise to explore and establish in all range of disciplines.The fact that the concept of data in the arts and humanities domain is far from being a straightforward one adds further complexities to the implementation of such policies in the arts and humanities domain. To meaningfully address the real data needs of the diverse communities of arts and humanities scholars in terms of skills, infrastructure and best practices, we need to keep a reflexive and open exchange about the function of data inspecific research questions and fields of enquiry.The DESIR Winter School provided a unique opportunity for arts and humanities scholars as well as for librarians and research managers to learn about how to maximize the potential of their scholarly resources and to take practical steps in opening up their research in ethically and legally responsible ways. To this end, the DESIR Winter School covered a wide selection of topics ranging from the optimal implementation of FAIR data in the arts and humanities, issues around ethics, Intellectual Property Rights and licensing, data and software citation practices, open research notebooks and innovative publishing practices in the arts and humanities.The main goals of the Winter School were to:• Introduce scientific and academic communities in the arts and humanities to the principles and practices of responsible research and Open Science;• Strengthen the skills of the arts and humanities communities in research data management, curation, sharing, preservation and reuse;• Enable RD• Foster national and international collaboration amongst the diverse research communities in the arts and humanities;• Introduce participants to innovative publishing practices in the arts and humanities, such as data journals, overlay journals etc.
Experimenting with Liveness in Cloud Infrastructure Management

Cloud computing has been playing a significant role in the provisioning of services over the Internet since its birth. However, developers still face several challenges limiting its full potential. The difficulties are mostly due to the large, ever-growing, and ever-changing catalog of services offered by cloud providers. As a consequence, developers must deal with different cloud services in their systems; each managed almost individually and continually growing in complexity. This heterogeneity may limit the view developers have over their system architectures and make the task of managing these resources more complex. This work explores the use of liveness as a way to shorten the feedback loop between developers and their systems in an interactive and immersive way, as they develop and integrate cloud-based systems. The designed approach allows real-time visualization of cloud infrastructures using a visual city metaphor. To assert the viability of this approach, the authors conceived a proof-of-concept and carried on experiments with developers to assess its feasibility.
An intelligent optimization method for information recommendation

Accurate information recommendation to users can significantly improve the service efficiency of recommendation platforms, and improve users' experience of the client. With the rapid development of the internet, information recommendation platforms such as e-commerce are facing problems of data sparsity or algorithms are difficult to deal with large-scale calculation, which cause the recommended information cannot meet the needs of users. In order to improve the accuracy of information recommendation, the need of users is regarded as the evaluation basis of information recommendation, and an intelligent optimization method for information recommendation is proposed in this paper. Firstly, Resource Evaluation, User Evaluation and Scenario Evaluation are taken as main factors of information recommendation, and each evaluation algorithm for every factor is designed. Then, the weight of each factor is optimally allocated by the adaptive genetic algorithm. Finally, the multiplication of the quantized value of each factor and its weight is accumulated to get the comprehensive evaluation result, which is compared with the threshold value to decide whether to recommend the information to the user. Taking the e-commerce platform as an example, simulation results show that the intelligent optimization method for information recommendation proposed in this paper is effective and can significantly improve both accuracy and precision of information recommendation.
Musical Mimicry to Learn Audio Processing

Project-Based Learning (PBL) is widely used to promote deep learning, enhance critical thinking, help students create, build and critically evaluate design solutions to a variety of problems. e-Yantra is an initiative by IIT Bombay that conducts an annual e-Yantra Robotics Competition (eYRC) for college students. Teams who qualify selection test compete over a period of five months. 208 such teams comprising 832 students were alloted one of the themes. A "theme" may be regarded as a "hardware MOOC" where skills get taught through a single-player gaming paradigm. This paper describes the methodology implemented in the 7th edition (eYRC-2018), where we designed a "theme" based on Audio processing and Machine Learning. An important contribution has been to show how to design a "scalable" theme by using automatic evaluation that made it unique compared to other themes. This paper outlines the tasks designed for the competition and describes how technology can support students as they work on a theme so that motivation and thoughts are sustained. We then analyze the effectiveness of our approach based on the performance of teams.
Research on Non-collision Coordination Control Technology for Formation Autonomous Underwater Vehicle

In view of the risk of collision among intelligent bodies in autonomous underwater vehicles (AUVs) formation navigation, a non-collision and coordination control technology of autonomous underwater vehicle is proposed in this thesis, which includes the integration of various basic behaviors such as formation coordination control and non-collision strategy. First of all, the path tracking control technology based on cascade method and the coordination control method of parameter consistency are used to ensure the asymptotical stability of tracking error. And then, the collision avoidance strategy of HRVO algorithm is adopted so that AUVs can avoid the collision risk of common obstacles and other AUVs simultaneously. The simulation results show that the AUV formation with noncollision and coordination control technology can quickly converge to the respective reference paths and maintain the coordination of formation while avoiding collision risk.
Dynamic Clustering Recommendation Algorithm For Two-Layer Graph Attention Network

Collaborative filtering recommendation algorithm is one of the most widely used personalized recommendation algorithms in e-commerce websites. The traditional collaborative filtering recommendation algorithm has a high recommendation complexity and low accuracy with the increasing number of users and items in recent years. The previous differential clustering evolution process only recommended a single clustering results of users or items. Besides, the node state of network was only a scalar, which ignored the integration of user layer and item layer and could not better represent the attribute characteristics of users and items. This paper proposes an effective collaborative filtering recommendation algorithm for the above three problems. We fully explore the changes of interests of users and their attention to the items over time. Firstly, a time-weighted scoring matrix is constructed by combining the forgetting function. According to the new scoring matrix, the user-item attention matrix is obtained. Then, according to the differential equations, users and items with high relevance are gathered to obtain the user communities and item communities. Stabilizing the same user status values mean that they have similar interests and then they are assigned to the same community. Finally, the real time prediction results are obtained through improved prediction method and dynamic similarity measurement in each community. The effectiveness of the proposed algorithm is verified by comparison with several better algorithms.
Drug Abuse Prediction Model Based on Relevance Analysis

Based on the data sets of drug use and socioeconomic data from five states in the United States, this paper proposes a prediction model based on correlation analysis. Firstly, the indicators are screened according to the correlation degree, then the degree of drug abuse and the selected indicators are predicted by multiple regression. Secondly, the weighted polynomial regression algorithm is used to predict the degree of drug abuse separately. Finally, the Lagrange multiplier method is used to assign the optimal weight to the two prediction results. The experimental results show that the prediction effect of the model is much better than that of the single prediction result of the target index.
Variational Model for Depth Estimation from Images

We propose a variant of convex reformulation of the standard variational model with non-convex data terms. The proposed convex relaxation of multilabel problems is a continuous formulation of Ishikawa's method for extension of the graph min-cut to the multilabel problems. Our convex continuous reformulation is based upon functional lifting to a higher-dimensional space using superlevel functions. We solve the resulting convex variational problem by the augmented Lagrangian method. The most time consuming part of this method is the numerical solution of a boundary value problem for the Poisson equation in three-dimensional space, which is implemented by means of a fast Poisson solver. We illustrate the developed theory with several numerical examples for the standard correspondence problem for a rectified stereo image pair.
Providing Context Awareness in the Smart Car Environment: State of the Art

Smart Car is an automobile with an advanced driver assistance system that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a state of the art of the research published in the last three years on context-based driver assistance systems. Simply put, the context-aware plays a critical role and the inclusion of contextual factors influence the intelligence level of the Smart Car. The proposed comparison which falls within the scope of the research addresses the context of the system (driver, vehicle, physical environment), the algorithms used (machine learning algorithm ML, deep-learning algorithm DL, HMM etc.), the technology used (internet of thing sensor, Smartphone sensor, Radar, Lidar…), and the performance provided. The first analysis indicates that machine learning algorithms are widely used in the suggested solutions in general and in the support vector machine (SVM) and the artificial neural network (ANN) in particular. Moreover, the given analysis shows that the monitoring of a complex driving context becomes possible with the emergence of IoT technology.
Costs and Benefits of Atmospheric Correction in the "Clouds"

The evolution of software applications from single desktops to sophisticated cloud-based systems is challenging. In particular, applications that involve massive data sets, such as geospatial applications and data science applications are challenging for domain experts who are suddenly constructing these sophisticated code bases. This paper provides a study of moving an atmospheric correction algorithm to two different cloud based systems. The costs and benefits are evaluated through design and development on two different platforms-a batch-based cloud system, and a general purpose pay-for-use cloud environment.
Express Internet of Things: Concept, Architecture and Frontier Outlook

The Internet of Things, as an important component of the new generation of information and communication technology, has been deeply integrated with the express industry, constantly leading the industrial transformation and promoting the rapid development of the express industry. Starting from the application of the Express Internet of Things, this paper reviews the development of the Internet of Things in the express industry, puts forward the concept of the Express Internet of Things based on the characteristics of the whole-network cooperation of the express industry. On this basis, the concept and system architecture of the Express Internet of Things have been studied. Finally, the prospect of the Express Internet of Things has been looked forward.
A study of analogical grids extracted using feature vectors on varying vocabulary sizes in Indonesian

Indonesian as an agglutinating language is known for its derivative morphological richness. Word forms are constructed by combining stem and affixes. In this paper, we study the influence of surface form and morphological information in analogical grids extracted from a set of word forms with varying sizes. Each word form is represented as a feature vector. In the experiment setting, we consider three features: characters, affixes, and morphosyntactic definition. The sizes and saturation are then observed to characterize the extracted grids.
Identifying Trends in Data Science Articles using Text Mining

The research conducted in this paper presents a detailed analysis of the latest research publications related to Data Science using information retrieval and text mining approach. The database used in this study was created by collecting the latest research papers from well-reputed Journals and Conference proceedings published by IEEE and Springer. This comprehensive study shows the significance of information retrieval and text mining in the identification of key insight from textual documents.
Use Case Points based software effort prediction using regression analysis

Software development effort prediction was an important stages in project planning. Poor prediction would lead to project failure, losing tenders and reduced profits. Several studies have improved Use Case Points as the effort prediction model using regression analysis. However, evaluation performance on the prediction models were biased and produce an asymmetric error distribution. Moreover, the dataset used were primarily from industrial, and less from universities. This study aims to investigate the performance of the regression model in terms of software development effort prediction based on Use Case Points using standardized accuracy (SA) and effect size (Δ) as the evaluation measurement. From the experiment results, regression model yielded 92%-0.64, 96%-1.86, and 69%-0.53 in term of SA and (Δ) over dataset DS1, DS3, and DS4, respectively. Experiment results shows that regression model yielded the best accuracy compared with the Karner model over three dataset. In the future, our results maybe used in development of effort prediction framework for calculating software project costs.
Building Block Sorting Method Based on Binocular Vision

Aiming at the problems of low manual sorting efficiency and tedious work in the production and sorting process of building blocks, a building blocks sorting method based on binocular vision is proposed. First of all, this paper introduces how to transform 2D image coordinates into 3D world coordinate so as to achieve building block positioning through images of building blocks. Secondly, it introduces how to pretreat images of building blocks, apply median filtering algorithm to process original images and achieve classification of building block colors through transformation of RGB space into HSV space. Finally, the positioning experiment and sorting experiment of building blocks indicate this method improves building block sorting efficiency.
Improvement of Light Field Feature

The recent development of light field feature detectors has brought light field imaging into many 3D reconstruction applications. Compared with the traditional 2D, higher dimensional feature descriptors reveal more angular information, but with significantly decreased efficiency in structure from motion (SfM) applications. To improve the efficiency of feature extraction, a new 4D light field feature detector and descriptor (LiFF) was invented by Dansereau et al. [1]. LiFF is scale and rotation invariant with refocusing ability, which makes the features more robust to partial occlusions. Moreover, structure from motion software, such as Colmap, can work with this a prior knowledge to benefit the incremental reconstruction. LiFF uses joint search technology for scale and slope space, and feature extraction speed is increased by more than ten times. However, the efficiency and accuracy of LiFF can be further improved by optimizing the scale and slope space. In this work, we add a rough slope estimation into the feature extraction pipeline, which can further improve the speed of LiFF.
Knowledge-oriented Sentiment-level Embedding for Sentiment Classification

Sentiment classification in document-level is an important task in Sentiment Analysis (SA). The existing methods learn mainly information from data for identifying the sentiment polarity of a document. We reveal that the sentiment information such as polarity can be an important external knowledge resource for classification. Our proposals are based on this idea and construct the models by employing hierarchical attention network and creating sentiment-level embedding. Furthermore, we also develop the attention mechanism by allowing it to receive both lexicon-level and sentiment-level information to improve the effectiveness of the model. The experiments on the IMDB movie reviews and Review Polarity 2.0 show good performance on document-level sentiment classification.
Emerging Laws for Cyber-crime and Cyber security in India: Information Technology Act, 2000

This paper basically deals with the concept, background and the emerging laws enacted in the field of cyber-crime and cyber security in India. The paper specifically focuses on the key feature of the laws, reason for the cyber-crime and also the institution that made under the Act to handle with this type of crime. With the improvement of the technology, cyber-crime also increasing day by day, and to control this new type of crime all the government institution should have to be more equipped than the criminal. In spite of this, people should also have to be attentive and careful in this matter.Now adays everybody is at risk of becoming the victim of the cyber-crime. Cyber space offerschances for cyber criminals to cause injuries to innocent people.To defend this form of crime, India give due consideration as countrywho enact IT Act, 2000. This Act is generally protected commercial and economic crimes which are precisely mentioned in the preamble of the Act. However,for the proper implementation of this Act it is necessary to strictly follow the procedure as per law and punish the criminal accordingly.
Improved User Adaptable Human Fall Detection and Verification Using Statistical Analysis

Human fall detection systems are an important part of human monitoring systems especially for elderlies. Different studies were conducted using varieties of sensor to develop systems to accurately classify unintentional human falls from other activities of daily life. The major issues with the current studies using depth maps were the use of single threshold based algorithms and highly complex machine learning to detect falls. Therefore, the available systems cannot cater for the detection of fall events from people with different physical capabilities and differing environments they are living. This study proposes a user adaptable fall detection system with a statistical analysis based fall verification to overcome the issues of related works.
Synonym-based Attack to Confuse Machine Learning Classifiers Using Black-box Setting

Twitter being the most popular content sharing platform is giving rise to automated accounts called “bots”. Majority of the users on Twitter are bots. Various machine learning (ML) algorithms are designed to detect bots avoiding the vulnerability constraints of ML-based models. This paper contributes to exploit vulnerabilities of machine learning (ML) algorithms through black-box attack. An adversarial text sequence misclassifies the results of deep learning (DL) classifiers for bot detection. Literature shows that ML models are vulnerable to attacks. The aim of this paper is to compromise the accuracy of ML-based bot detection algorithms by replacing original words in tweets with their synonyms. Our results show 7.2% decrease in the accuracy for bot tweets, therefore classifying bot tweets as legitimate tweets.
Consensus Clustering by Weight Optimization of Input Partitions

This paper proposes a weighted consensus approach for data clustering, where each input basic clustering method is weighted. The weights are automatically determined by solving an optimization problem. Experiments are carried out on three datasets: NSL-KDD, Forest Cover Type, and Phone Accelerometer datasets. The results show the effectiveness of the proposed approach to Big data clustering compared to single clustering methods.