Reviewerly/exHarmony huggingface dataset:
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation

In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples.Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration.To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model.Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other stateof-the-art sequence labeling domain adaptation methods. 1 1 Our code is available at https://github.com/yhy1117/FGKF-DA.Types Cases Strongly Ops Steve Jobs resigned as CEO of Apple.Share prices are rising soooo fast
CUNI Systems for the Unsupervised News Translation Task in WMT 2019

In this paper we describe the CUNI translation system used for the unsupervised news shared task of the ACL 2019 Fourth Conference on Machine Translation (WMT19).We follow the strategy of Artetxe et al. (2018b), creating a seed phrase-based system where the phrase table is initialized from cross-lingual embedding mappings trained on monolingual data, followed by a neural machine translation system trained on synthetic parallel data.The synthetic corpus was produced from a monolingual corpus by a tuned PBMT model refined through iterative back-translation.We further focus on the handling of named entities, i.e. the part of vocabulary where the cross-lingual embedding mapping suffers most.Our system reaches a BLEU score of 15.3 on the German-Czech WMT19 shared task. PBMT
Review Paper on Object Detection using Deep Learning- Understanding different Algorithms and Models to Design Effective Object Detection Network

This paper deals with the field of computer vision, mainly for the application of deep learning in object detection task. On the one hand, there is a simple summary of the datasets and deep learning algorithms commonly used in computer vision. On the other hand, a new dataset is built according to those commonly used datasets, and choose one of the networks called faster rcnn to work on this new dataset. Through the experiment to strengthen the understanding of these networks, and through the analysis of the results learn the importance of deep learning technology, and the importance of the dataset for deep learning.
CNN-Based Luminance And Color Correction For ILL-Exposed Images

Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor limitation or optical arrangement. They prevent the details of the scene from being adequately represented in the captured image. We proposed a deep neural network model due to the number of uncontrolled variables that impact the acquisition. The proposed network can converge in a representative model from the training data, loss, optimization and activation functions. The obtained results are evaluated using several image quality index which indicate that the proposed network is able to improve images damaged by heterogeneous exposure. Furthermore, our method offers a significant gain over the state-of-the-art methods both in simulated data and real data.
SVALA: Annotation of Second-Language Learner Text Based on Mostly Automatic Alignment of Parallel Corpora

Annotation of second-language learner text is a cumbersome manual task which in turn requires interpretation to postulate the intended meaning of the learner’s language. This paper describes SVALA, ...
Pengelompokan Data Puskesmas Banyuwangi Dalam Pemberian Imunisasi Menggunakan Metode K-Means Clustering

Improvement of immunization services and counselling services for mothers, infants, and toddlers in health care centres in achieving the target of Complete Basic Immunization (IDL).Banyuwangi Regency has 45 Puskesmas which will be grouped into 3 categories, namely: Puskesmas achieving IDL targets with sufficient status, Puskesmas achieving IDL targets with insufficient status, and Puskesmas achieving IDL targets with very good status.The purpose of this study was to determine the health centre in achieving the target of IDL coverage in the Banyuwangi Regency.The method used in this research is K-Means Clustering.This method will seek a maximal partition with optimum iteration procedure and has the best precision of the object measurement, so it is more scalable and efficient in processing a large data.The conclusion in this study is the first cluster has 19 health care centers data with sufficient immunization targets, the second cluster has 24 health care centers data with fewer immunization targets, and the third cluster has 2 health care centers data with very good immunization targets, so the Health Office can provide additional tasks for the Puskesmas group who have IDL targets with insufficient status to reduce the number of diseases that can be prevented by immunization (PD3I).
Mitigating security threats through the use of security tactics to design secure cyber-physical systems (CPS)

Cyber-Physical Systems (CPS) attract growing interest from architects and attackers, given their potential effect on privacy and safety of ecosystems and users. Architectural tactics have been proposed as a design-time abstraction useful to guide and evaluate systems design decisions that address specific system qualities, but there is little published evidence of how Security Tactics help to mitigate security threats in the context of Cyber-Physical Systems. This article reports the principled derivation of architectural tactics for an actual SCADA-SAP bridge, where security was the key concern; the key inputs were (1) a well-known taxonomies of architectural tactics, and (2) a detailed record of trade-offs among these tactics. The project architects used client-specified quality attributes to identify relevant tactics in the taxonomy, and information on their trade-offs to guide top-level decisions on system global shape. We venture that all architectural tactics taxonomies should be enriched with explicit trade-offs, allowing architects to compare alternative solutions that seem equally good on principle but are not so in practice.
Reciprocal Leadership Influence on Organizational Change

The devastating effect of the crisis of 2011 on Syrian tourism industry was enormous; this led the industry's hoteliers to shift their attention to different strategies and organizational change to regain the business lost from such crisis. This research introduces the concept of reciprocal leadership as an effective tool in implementing organizational change in Dama Rose Hotel post-Syrian crisis of 2011. We presented the research problem in the following question: Do the dimensions of reciprocal leadership have any role in the success of managing the process of organizational change? The research used a qualitative approach; the primary data was composed of a questionnaire distributed to 65employeesworking at Dama Rose Hotel located in Damascus, Syria. The secondary data was composed of scientific journals, website, and textbooks. The outcome showed a positive relationship between reciprocal leadership dimensions on the process of managing the organizational change in the hotel, and there was a positive relationship between reciprocal leadership and organizational change.
Website Quality Analytics Using Metaheuristic Based Optimization

Background:: Studies are indicative of the fact that high-quality websites get better rankings on the search engines. A good website is the one which provides reliable content, has good design and user interface and can address global audience. But the end-users struggle with the predicament of selecting qualitative websites. Although “Quality” is fairly a subjective term, there is an obvious need for a useful and valid model which evaluates the quality attributes of a website. “A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness”. Objective:: The quality of a website must be assured in terms of technicality, the accuracy of information, response time, design of website, ease of use, and many more. The aim is to identify features of a website that determines its quality and build an automatic website quality prediction model. Method:: We conduct an empirical study on 700 websites and run 6 baseline classifiers to categorize websites into good, average and poor using quality attributes. Subsequently, metaheuristic-based algorithms (Particle Swarm Optimization, Elephant Search Algorithm and Wolf Search Algorithm) for optimal feature selection have been implemented to get an optimal subset of quality attributes that is able to predict the quality of websites more accurately. Result:: The study confirms that the proposed implementation of metaheuristics for feature selection in website quality classification improves the performance of the supervised learning algorithms. An average 12.74% improvement in accuracy was observed using the features selected by Particle Swarm Optimization, 5.56% average improvement in accuracy using Elephant Search Algorithm for feature selection while an average improvement of 5.77% was observed using Wolf Search Algorithm for feature selection. Conclusion:: The study validates that Particle Swarm Optimization for feature selection in website quality analytics task outperforms Wolf Search Algorithm and Elephant Search Algorithm.
A Fuzzy Adaptive Kalman Filter for Spacecraft Formation Navigation*

Over the past two decades, advances in spacecraft technologies have prompted the development of autonomous onboard navigation systems. This paper presents the design of a novel Fuzzy Adaptive Extended Kalman Filter (FAEKF) suitable for estimating the relative position and velocity between two spacecraft flying in formation. A fuzzy adaptation architecture is embedded within a standard Extended Kalman Filter (EKF), thereby allowing the filter to adapt internal noise characteristics that would otherwise remain constant after the initial filter design. Inaccurate tuning of the process and measurement noise covariance matrices within an EKF are commonly a limiting factor in the estimation performance, especially in situations where the behaviour of the noise processes are poorly defined or subject to change. In this context, the proposed approach provides a method to update the process and measurement noise covariances online based on a covariance-matching analysis of the filter residuals. A demonstration of the technique is given through numerical simulations of a spacecraft formation in low-Earth orbit, which are used to compare state estimates from the FAEKF with those from measurement-only and non-adaptive EKF solutions.
Could the Virtual Dinosaur See You? Understanding Children’s Perceptions of Presence and Reality Distinction in Virtual Reality Environments

Despite the growing interest and use of virtual reality (VR) in American homes, there is a notable gap in empirical studies that examine VR and children. This study identifies two important research concepts in children's research that have been studied across many types of media 1) reality distinction and 2) presence, and applies them to studying VR experiences. Taking a qualitative approach, 6 to 8-year-old children (N=29) participated in a VR experience as an extension of the children’s television show called Dino Dana. During the child’s VR experience where they swam in a pool with dinosaurs, we recorded a computer capture of what the child sees within the VR experience; and a video recording of the child in the VR headset and their behaviors during the VR experience. In addition, children responded to questions before and after their VR experience. We observed several behaviors of how children attempted to test and assess the reality of VR (e.g. holding their breath). Through interviews, we also found that children had certain presence experiences within VR that challenged their understanding of reality, where the dinosaurs were treated as real and evoked social presence. This study builds on our understanding of how VR might impact on young children and their perception of VR experiences, which have important implications for VR researchers, designers, and consumers.
SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.
EVENTS RECOGNITION FOR A SEMI-AUTOMATIC ANNOTATION OF SOCCER VIDEOS: A STUDY BASED DEEP LEARNING

Abstract. In this work, we propose an efficient way of web video annotation in soccer domain. To achieve this, it is necessary to enjoy different architectures of deep learning. We aim at realising a system of annotation able to recognise several events from information of the object that is the ball in our case, in order to fuse them as a part of actions in video. We propose to use Deep Neural Network (DNN) to detect ball and actions. However, Mask R-CNN can play a very important role for features extracted as an output using a training network on ImageNet dataset. The Mask R-CNN is chosen as a method using different CNN as backbone (convolutional Neural Network) ResNet50, ResNet101 and ResNet152, VGG16, VGG 19. We experimentally verify the effectiveness of the proposed method in the test phase.
PRINCIPLES OF PHASED REORGANIZATION OF THE ARCHITECTURAL AND SPATIAL STRUCTURE OF THE URBAN ENVIRONMENT BASED ON INNOVATIVE TECHNOLOGIES

The article discusses the main criteria for selection of the Russian Federation regions as a pilot sites in the federal program «Smart City. Successful region». The concept of participation in the Samara region has been developed. Indicators of the expected eff ectiveness of this innovative project are disclosed. Proposed new approaches to the development of Samara as a smart city. The socio-spatial basis of these approaches is the “triad of development agents”: a smart university, a smart city, a smart park factory. The scientifi c and design potential of Samara State Technical University is considered as a “smart” university. “Smart” park-factory is an industrial park of this University and its partners on the site of one of the former industrial enterprises. “Smart” city relies on the administrative and public organizations of urban development, activists of urban social movements. The goal is formulated and the objectives of a pilot project for the reorganization of the urban environment of the Samara city historical zone are stated. The pilot project goal and objectives for the urban environment reorganization of the Samara city historical zone are formulated. The selection and implementation of innovative technologies in the historic center of the city is determined by a number of proposed model projects - “Smart Quarter”, “Smart Street”, “Smart Square”. The expediency of introducing into the urban environment about 30 innovative technologies in the areas of resource conservation, public services, preservation of the architectural heritage, media technologies, transport and pedestrian infrastructure and fi scal taxation systems has been substantiated. Set out in detail six key criteria that determine а compliance of the historical sett lement to the “smart city” concept: technology, enЕ. vironmental friendliness, management, mobility, heritage, people. For Samara as a historic city, the necessity of creating a competence regional center in the fi eld of architectural heritage engineering restoration has been justifi ed. It is concluded that the future implementation of pilot projects will contribute to the formation a sustainable urban environment of the largest city historical center, focused on the digital economy.
A Hybrid Model of Experiential Learning within the Social Virtual World of Second Life

This study examined how multi-user virtual worlds can enhance learning, by extending and refining a prior VR-based model to include two new constructs: virtual identity, and social constructivism. The fit of the model was analyzed using structural equation modeling (SEM), and the results supported both the extension and the hypothesized refined model. Findings: VR features were found to indirectly impact on the learning outcomes, mediated by the perception of usability and the learning experience. The learning experience was measured by seven individual psychological factors: presence, virtual identity, motivation, cognitive benefits, agentic learning, social constructivism, and reflective thinking. These factors mediated the learning outcomes, measured by the perception of learning effectiveness and satisfaction, and may have a range of implications for the instructional design of learning activities using the virtual world. This research blends a technology acceptance model with the technology-mediated learning perspective to advance the development of a hybrid theoretical framework as a basis for future research into enhanced learning within a social virtual world.
Testing Techniques Selection

[Context] Software projects must consider the selection of testing techniques and criteria during their life cycles. This practice increases the chances of testing activity to be appropriately performed. In a previous work, an infrastructure to support the selection of testing techniques was proposed for the context of concurrent software. This infrastructure considers information (attributes) of the project to make the selection closer to the testers need. [Objective] This paper extends the previous work by identifying new studies concerning testing techniques selection, project attributes that can be used for this selection and which approaches can be employed to support the combined selection. [Method] A mapping study was conducted and a total of 15 primary studies, published in the last 20 years were selected. Information about approaches to testing techniques selection was analyzed and classified. [Results] The following results were obtained: (i) existing approaches for selection of testing techniques; (ii) proposition of a taxonomy of selection approaches; (iii) characterization of attributes to offer the support for the selection of a testing technique; and (iv) identification of approaches that perform combined selection of testing techniques. [Conclusion] Combining testing techniques is essential to improve the testing activity quality by finding different failure categories and supplementing other techniques limitations. This paper describes an initiative to offer support for the construction of new or combined strategies for the selection of testing techniques capable of being used in practice.
A New Approach to Unequal Dimension States Mixing for IMM Estimator

The interacting multiple model (IMM) estimator has been proven to be of excellent performance and low complexity in tracking agile targets. The success of IMM attributes to mode mixing, where model outputs are mixed for model-conditional reinitialization. Here, a new mode mixing approach is proposed for state estimators with unequal dimensions. The proposed approach adds switching state into target state and uses mode probability and innovation to make the optimal choice online among suggested methods. Compared with the existing methods, the new approach performs better in simulation.
Introduction to Matplotlib

Matplotlib is a Python graphing and plotting library that can generate a variety of different types of graph or chart in a variety of different formats. It can be used to generate line charts, scatter graphs, heat maps, bar charts, pie charts and 3D plots. It can even support animations and interactive displays. In this chapter we will introduce the Matplotlib library, its architecture, the components that comprise a chart and the pyplot API.
Improving Writing for Romanian Language

Writing is an essential skill for all users of a language, regardless of their age and level of expertise. Each language has a complex set of rules addressing correct writing which oftentimes contradicts the current spoken parlance. Thus, our aim is to introduce a method that automatically highlights and suggests corrections to common mistakes that people make when writing in Romanian language. Our approach relies on a multi-layered rule-based system that enforces the formal rules of writing in Romanian language by considering as reference a collection of more than 7,000 mistakes encountered in media. The provided suggestions can be easily integrated in smart learning environments centered on writing activities and can prove highly useful for all Romanian speakers, from students to professionals. Although the model is tailored for a specific language, our approach is extensible, and we strive to adjust the provided rules to several other languages.
Path Planning for Multiple UAVs Covering Disjoint Non-convex Areas

Rescue and security applications call for fast mapping of complex environments. For this purpose we propose a planner that generates paths for multiple unmanned aerial vehicles (UAVs) that need to cover multiple disjoint non-convex polygonal areas with nadir pointing cameras of varying field of view. These fulfill a user specified overlap to allow for taken images being used in different map generation procedures. The proposed planner first decomposes the given areas into suitably sized convex subareas. These are allocated to the available UAVs by a metaheuristic solver while minimizing the longest path of all involved UAVs. Given the allocation, subareas are adjusted by a developed heuristic to satisfy the overlap constraints at the inner borders of the split areas. Finally paths allowing for scanning the areas are generated. Experiments are conducted to demonstrate and validate the results of the planner.