Reviewerly/exHarmony huggingface dataset:
Improving the Efficiency in Multiple Object Tracking by Tracker Switching According to Occlusion States

The objective of multiple object tracking (MOT) is to locate the positions of multiple objects in a video, maintain their identities, and obtain their individual trajectories. One of the most crucial challenges of MOT is how to handle object occlusion effectively. The existing methods try to treat different occlusion situations with the same architecture, which leads to limits on performance. More specifically some trackers can achieve fast speed (frame per second) but cannot handle occlusion effectively, while other trackers can handle occlusion properly but are expensive due to costly computational resources. The proposed method estimates the occlusion states of objects, applies different trackers according to the estimation results, and finally combines the results of different trackers. This method can reduce unnecessary computational costs, and consequently can improve the efficiency of the high accuracy trackers. We evaluate the effectiveness of our proposal for different scenarios.
SDCOR : Scalable density-based clustering for local outlier detection in massive-scale datasets[Formula presented]

This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume that all the data is memory-resident, our proposed method is scalable and processes the input data chunk-by-chunk within the confines of a limited memory buffer. A temporary clustering model is built at the first phase; then, it is gradually updated by analyzing consecutive memory loads of points. Subsequently, at the end of scalable clustering, the approximate structure of the original clusters is obtained. Finally, by another scan of the entire dataset and using a suitable criterion, an outlying score is assigned to each object called SDCOR (Scalable Density-based Clustering Outlierness Ratio). Evaluations on real-life and synthetic datasets demonstrate that the proposed method has a low linear time complexity and is more effective and efficient compared to best-known conventional density-based methods, which need to load all data into the memory; and also, to some fast distance-based methods, which can perform on data resident in the disk.
The impact of stimulating the development of Human Capital on Economic Development

Human capital is a significant ingredient of economic development. Today, the concepts of human capital have been defined as the knowledge, skill, creativity, and health of the individual. The long-run impact of human capital on the current innovation and economic development is still a question, therefore, the interest in research to explore the contribution of human capital in economic development is being increased. The production and processing of knowledge are directly related to educated and healthy manpower. Educated and healthy people is one of the most important factors in the models, namely the power of human capital, the development of the economy.In this study, I will use the qualitative methodology to examine the relationship between human capital and economic developmentThe most important difference between developed and underdeveloped countries is the level of economic development. The biggest obstacle to the development of underdeveloped countries is low education level, lack of health conditions and brain drain. These factors prevent the accumulation of human capital in underdeveloped countries and cause waste of resources in the country.Taking into account these factors, the main purpose of the study is to study the impact of human capital development on economic development in Azerbaijan
Modeling of Photovoltaic Power Station Considering the Geographic Factors

The impact that the photovoltaic (PV) power generation brings to the power system has become noticeable due to its increasing penetration. It is necessary to develop an accurate model of the PV power station for the related studies. However, in the existing electromechanical transient models, the geographic factors, that affect the solar irradiance and thus the output characteristics of the PV power station, have not been fully considered. Therefore, besides the basic factors, this paper takes into account the critical geographic factors to construct the electromechanical transient model of the PV power station. A calculation method of solar irradiance is designed by adding the longitude, latitude, and altitude factors. The PV power generation unit model, collector line and substation transformer models, and the plant-level control system are integrated to achieve the entire model. Finally, the proposed model is compared with the measured data and the existing model, through which the effectiveness is verified.
Healthcare challenge: protecting patient data privacy during a global pandemic

British Journal of Healthcare AssistantsVol. 14, No. 9 Health MattersHealthcare challenge: protecting patient data privacy during a global pandemicNitin AgaleNitin AgaleVice President for Product and Strategy, SecuronixSearch for more papers by this authorNitin AgalePublished Online:12 Oct 2020https://doi.org/10.12968/bjha.2020.14.9.434AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References BBC News. NHS data breach affects 150,000 patients in England. 2 July 2018. https://www.bbc.co.uk/news/technology-44682369 (accessed 24 September 2020) Google ScholarIBM. How much would a data breach cost your business? 2020. https://www.ibm.com/security/data-breach (accessed 24 September 2020) Google ScholarLumension. PHI (personal health information) is more valuable than credit cards: time to get serious about data security. 2020. https://www.healthcareinfosecurity.com/webinars/phi-more-valuable-than-credit-cards-time-to-get-serious-about-data-w-577 (accessed 24 September 2020) Google ScholarNHS. When to self-isolate and what to do. 2020. https://www.nhs.uk/conditions/coronavirus-covid-19/self-isolation-and-treatment/when-to-self-isolate-and-what-to-do/ (accessed 24 September 2020) Google ScholarSmith A. Cyber attacks hit more than half of healthcare orgs in last year. 2020. http://www.pharmatimes.com/news/cyber_attacks_hit_more_than_half_of_healthcare_orgs_in_last_year_1322396 (accessed 24 September 2020) Google Scholar FiguresReferencesRelatedDetails 2 October 2020Volume 14Issue 9ISSN (print): 1753-1586ISSN (online): 2052-4420 Metrics History Published online 12 October 2020 Published in print 2 October 2020 Information© MA Healthcare LimitedPDF download
Comparing Grammatical Theories of Code-Mixing.

Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue. A paradigm of these generation systems relies on well-defined grammatical theories of code-mixing, and there is a lack of comparison of these theories. We present a large-scale human evaluation of two popular grammatical theories, Matrix-Embedded Language (ML) and Equivalence Constraint (EC). We compare them against three heuristic-based models and quantitatively demonstrate the effectiveness of the two grammatical theories.
Evaluation of Solar radiation in humid subtropical climatic belt in north India

In this study, one year data measurements for the solar radiation (January 2016 to December 2016) was performed in India for the city of Sonipat of SRRA station, Deenbandhu Chhotu Ram University of Science & Technology(DCRUST), Murthal, Haryana. During this study consist global radiations, direct radiations, diffuse radiations and other parameters like relative humidity and ambient temperature is investigated. Data investigation shows Study area received good solar radiations in the months of February to October. In the month of June 2016 maximum global radiations is 250.64 W/m2 associated ambient temperature is 30.08°C and minimum values found global radiations in month of December 2016 is 88.96 W/m2 with ambient temp is 14.86°C. Relative humidity is found low in the early months of year (February to June) and during this time study area receives good amount of direct radiations. Moreover, the ambient temperature found to be on lower side in winter time and relative humidity is high. So relative humidity and direct radiations shows good correlation as direct radiations increases when relative humidity decreases.
Topology-Imbalance Learning for Semi-Supervised Node Classification

The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existing studies roots from the unequal quantity of labeled examples in different classes (quantity imbalance), we argue that graph data expose a unique source of imbalance from the asymmetric topological properties of the labeled nodes, i.e., labeled nodes are not equal in terms of their structural role in the graph (topology imbalance). In this work, we first probe the previously unknown topology-imbalance issue, including its characteristics, causes, and threats to semi-supervised node classification learning. We then provide a unified view to jointly analyzing the quantity- and topology- imbalance issues by considering the node influence shift phenomenon with the Label Propagation algorithm. In light of our analysis, we devise an influence conflict detection -- based metric Totoro to measure the degree of graph topology imbalance and propose a model-agnostic method ReNode to address the topology-imbalance issue by re-weighting the influence of labeled nodes adaptively based on their relative positions to class boundaries. Systematic experiments demonstrate the effectiveness and generalizability of our method in relieving topology-imbalance issue and promoting semi-supervised node classification. The further analysis unveils varied sensitivity of different graph neural networks (GNNs) to topology imbalance, which may serve as a new perspective in evaluating GNN architectures.
Interactive Bot Using Python

The goal of computer science (AI) is that the identification of natural dialogue between machines and humans. In recent days, the dialogues, additionally known as interactive informal square measures are the quickest growing space in computer science. Dialogue system technique has been utilized by several firms to determine varied forms of Virtual Personal Assistants (VPAs) supported their areas and their applications, like Cortana, Siri, Alexa, Google Assistant. However, we've used the multi-model dialogue systems that method 2 or additional combined user knowledge inputs, like speech recognition, image, video. The new model of VPAs that square measure accustomed increase the interactions between machines and humans by victimization varied technologies, like speech recognition, text to speech the huge dialogue and informal content, and also the cognition base. The VPAs are often utilized in totally different applications that embrace medical, robotics, automation, security access.
Implicit SVD for Graph Representation Learning

Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e.g., for calculating gradients via backprop over many data epochs. Meanwhile, Singular Value Decomposition (SVD) can find closed-form solutions to convex problems, using merely a handful of epochs. In this paper, we make GRL more computationally tractable for those with modest hardware. We design a framework that computes SVD of \textit{implicitly} defined matrices, and apply this framework to several GRL tasks. For each task, we derive linear approximation of a SOTA model, where we design (expensive-to-store) matrix $\mathbf{M}$ and train the model, in closed-form, via SVD of $\mathbf{M}$, without calculating entries of $\mathbf{M}$. By converging to a unique point in one step, and without calculating gradients, our models show competitive empirical test performance over various graphs such as article citation and biological interaction networks. More importantly, SVD can initialize a deeper model, that is architected to be non-linear almost everywhere, though behaves linearly when its parameters reside on a hyperplane, onto which SVD initializes. The deeper model can then be fine-tuned within only a few epochs. Overall, our procedure trains hundreds of times faster than state-of-the-art methods, while competing on empirical test performance. We open-source our implementation at: this https URL
Manipuri-English Machine Translation using Comparable Corpus

Unsupervised Machine Translation (MT) model, which has the ability to perform MT without parallel sentences using comparable corpora, is becoming a promising approach for developing MT in low-resource languages. However, majority of the studies in unsupervised MT have considered resource-rich language pairs with similar linguistic characteristics. In this paper, we investigate the effectiveness of unsupervised MT models over a Manipuri-English comparable corpus. Manipuri is a low-resource language having different linguistic characteristics from that of English. This paper focuses on identifying challenges in building unsupervised MT models over the comparable corpus. From various experimental observations, it is evident that the development of MT over comparable corpus using unsupervised methods is feasible. Further, the paper also identifies future directions of developing effective MT for Manipuri-English language pair under unsupervised scenarios.
СРАВНИТЕЛЬНОЕ ИССЛЕДОВАНИЕ СОВРЕМЕННЫХ НЕЙРОСЕТЕВЫХ АРХИТЕКТУР ДЛЯ ЗАДАЧ СЕГМЕНТИРОВАНИЯ МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ

Computer Vision is the area of Machine Learning that is responsible for machine perception of visual information. Image segmentation is a subfield of Computer Vision that solves the task of dividing a digital image into segments by their class label. One of the main problems in the subfield is the scarcity of data and the restoration of spatial information for the classified image. This article is a brief survey of current Biomedical Image Segmentation approaches, specifically Convolutional Neural Networks architectures and the morphological transformation for data augmentation.
Network Security Situation Prediction Based on Dempster-Shafer Evidence Theory and Recurrent Neural Network

Due to characteristics of evaluation conflicts and subjective biases in conventional fuzzy evaluation system, and problems of low accuracy and robustness performance for the network security situation prediction caused by such characteristics, a novel network security evaluation algorithm based on Dempster-Shafer(D-S) evidence theory and recurrent neural network was proposed. The roles of network security based on the evaluation data of different experts were constructed, and then the triangle fuzzy function was applied to obtain the experts’ evaluation index. The weight D-S evidence theory was used to screen infer, and correct the evaluation index, and the loss and possibility matrixes were constructed. Lastly, the loss and possibility matrixes were imported in RNN model to finish network security evaluation. The results of simulation experiments show that the D-S evidence theory is proved to be able to deal with experts’ collision problems, and the RNN model can improve the precision and robustness of network security situation prediction results.
Joint Multimedia Event Extraction from Video and Article

Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, while text describes more high-level and abstract concepts. However, existing event extraction methods either do not handle video or solely target video while ignoring other modalities. In contrast, we propose the first approach to jointly extract events from both video and text articles. We introduce the new task of Video MultiMedia Event Extraction and propose two novel components to build the first system towards this task. First, we propose the first self-supervised cross-modal event coreference model that can determine coreference between video events and text events without any manually annotated pairs. Second, we introduce the first cross-modal transformer architecture, which extracts structured event information from both videos and text documents. We also construct and will publicly release a new benchmark of video-article pairs, consisting of 860 video-article pairs with extensive annotations for evaluating methods on this task. Our experimental results demonstrate the effectiveness of our proposed method on our new benchmark dataset. We achieve 6.0% and 5.8% absolute F-score gain on multimodal event coreference resolution and multimedia event extraction.
Incorporating Circumstances into Narrative Event Prediction

Aiming at discovering the event evolution, the narrative event prediction is essential to modeling sophisticated real-world events.Existing studies focus on mining the inter-events relationships while ignoring how the events happened, which we called circumstances.However, we observe that the circumstances indicate the event evolution implicitly, and are significant for the narrative event prediction.To incorporate circumstances into the narrative event prediction, we propose the CircEvent, which adopts the multi-head attention to retrieve circumstances at the local and global levels.We also introduce a regularization of attention weights to leverage the alignment between events and local circumstances.The experimental results demonstrate that CircEvent outperforms existing baselines by 12.2%.Further analysis demonstrates the effectiveness of our multi-head attention modules and regularization.
A novel classification system and database for faults and tectonic features in the Netherlands

Summary Here we present the first results of fault collection and -classification for all Dutch faults that have been mapped over the last 3 decades. This work acts as one of the prototypes for a final European FDB that is being set up by the GeoERA HIKE project and which is intended to incorporate all types of faults and aims for a broad range of end-user applications. In the work presented, the focus will be on applying the classification of the faults using a tested semantic concept which places faults in a common/hierarchical structural framework that allows for pan-European correlation. The results are stored in a semantic vocabulary that serves to provide the end-user with valuable information on regional importance and meaning of the mapped faults.
Text Simplification for Comprehension-based Question-Answering

Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and understand while preserving the content and approximating the original meaning. Text simplification has been exploited in NLP applications like machine translation, summarization, semantic role labeling, and information extraction, opening a broad avenue for its exploitation in comprehension-based question-answering downstream tasks. In this work, we investigate the effect of text simplification in the task of question-answering using a comprehension context. We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset. Firstly, we outline each step in the dataset creation pipeline, including style transfer, thresholding of sentences showing correct transfer, and offset finding for each answer. Secondly, we verify the quality of the transferred sentences through various methodologies involving both automated and human evaluation. Thirdly, we benchmark the newly created corpus and perform an ablation study for examining the effect of the simplification process in the SQuAD-based question answering task. Our experiments show that simplification leads to up to 2.04% and 1.74% increase in Exact Match and F1, respectively. Finally, we conclude with an analysis of the transfer process, investigating the types of edits made by the model, and the effect of sentence length on the transfer model.
The Key to Success in Implementing College E-Learning Systems During the Covid 19 Pandemic

During the Covid-19 Pandemic, changing the learning system to be online-based, learning using e-learning, zoom or google meet. The purpose of this study was to determine student satisfaction with the implementation of e-learning in terms of Information Quality (IQ), System Quality (SQ), Service Quality (SEQ), User (U), User Satisfaction (US) and the net benefits (NB) received by students. The population in this study are students at private universities who use eLearning in their learning system. A sample of 353 respondents using certain criteria. Questionnaire as a research instrument by using an online application. Data analysis using SEM PLS. The results showed that Information Quality (IQ), System Quality (SQ), Service Quality (SEQ)  had an effect on User (U) and User Satisfaction (US). The findings of this study also show that User (U) and User Satisfaction (US) is capable of moderating between Information Quality (IQ), System Quality (SQ), Service Quality (SEQ) and net benefits (NB) received. The learning system using eLearning is able to transfer the knowledge of lecturers to students.
Aplikasi Sistem Pakar Dengan Metode Forward Chaining Dan Certainty Factor Untuk Mendeteksi Penyakit Ayam

The development of computer technology now is really fast. The computer is not only used as a tool to complete the work of humans but can also run applications designed to access information quickly. Application of expert system application is moved to a computer expert knowledge. So the computer could also resolve the problem as usual is done by specialists. Expert system has been widely developed in various fields, including the field of animal husbandry. Chicken breeders usually get the chicken disease information from the livestock extension officers. But sometimes often constrained because the number of extension officers of the cattle that are not evenly distributed in each area. Therefore, the breeder can access information about diseases of chicken with the help of the method of Forward Chaining and Certainty Factor into the making of the application. The resulting application is an expert system for detecting diseases of chickens that could be used to assist farmers in obtaining information about the disease of chickens and handling solutions.
Quantum Computing Today

Presents the approaches to building a quantum computer, the different substrates being used to build a scalable quantum computer, the profound challenges in doing so, and finally, an outlook on how the scientific challenges and economic incentives will shape quantum computing projects.
Negative Statements Considered Useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.