Reviewerly/exHarmony huggingface dataset:
Remotely Accessed Smart CCTV System Using Machine Learning

This work aims to develop a ML based Smart CCTV system in real time framework to detect and recognize movement in closed-circuit television (CCTV) images. Traditionally CCTV requires 24/7 human support and supervision to detect mischief which is bound for errors and also costly. An automatic system on the other hand requires minimum supervision and also minimum error. This can also help and provide assistance to various law enforcement agencies to identify suspects and missing people and also see if anyone is entering a restricted area. This study aims to develop a face recognition system based on CCTV images using various methods of feature extraction and face recognition. The proposed system consists of image acquisition from CCTV cameras, image processing, face detection, localization and extraction of received images. We use Structural Similarity (SSIM) to find differences in frames, LBPH face recognizer algorithm. We used the waterfall model as a process model. The recognition is done using these technologies.
Role of Deep Learning in a Secure Telemedicine System with a Case Study of Heart Disease Prediction

In this modern world where everything is over Internet and everything is available at our fingertips. However, our health system is still dealing with the physical presence even when a person needs just a routine checkup. There physical presence or real time interaction is required with the doctor. These days everything can be done by the machines itself. It is possible through deep learning where we train our system with the help of huge amounts of data. There a person, i.e., a patient can check his/ her health-related issue directly. Deep learning is a method where we detect the results or symptoms according to the previously fetched data. In this paper, we discuss about the use of deep learning-based secure scheme for a telemedicine system. Here we also get the security of the healthcare data due to the deployment of authentication and key establishment process. It can be useful in emergency medical conditions, i.e., especially in rural areas and disaster affected areas.
Automatic Identification of Nepalese Tourism Related Tweets using Machine Learning Algorithms

Abstract available with the full text.
KNOWLEDGE BASED SYSTEM UNTUK REKOMENDASI DEWASA PENGABENAN PADA DESA ADAT MAMBAL

Applications for a good day to carry out the pengabenan ceremony of the mambal traditional village often encounter obstacles, with a process that is still manual, it certainly has a fairly high risk of error because it is caused by physical factors from a bendesa as an expert, including fatigue and forgetfulness. In this study, we will provide solutions to problems or obstacles experienced by the bendesa as an expert by designing and building a system that can represent a bendesa in the dewasa pengabenan application process by adopting the mindset of a bendesa in recommending dewasa pengabenan. The parameters used in this system consist of 54 conditions with 20 conditions that must be avoided. The parameters used are sasih, tri wara, panca wara, sapta wara, penanggal, panglong and wuku. The system that was built provides output in the form of recommendations for dewasa pengabenan, which are web-based, using the PHP framework Laravel programming language, and the database is managed with mysql. This system has been tested by the Bendesa as an expert and several users, the results obtained from the test can be concluded that the function of the features in the system is in accordance with what is expected
The Effectiveness of the Grammar Translation Method (GTM) in Improving English Language Skills at an Intermediate Level

In the past GTM has subjugated the other techniques was measured the significant technique of English language instruction technique. GTM, proper grammar, and syntax are given top priority. The ability to learn new words and phrases that can be used interchangeably (different terminology for different tenses) is what makes this method of teaching a second language to youngsters so much more effective than a verbal education. The students who participated in this study are from intermediate colleges, and their responses focus on how they improved their grammatical skills. In this quantitative study researcher have ten questions for the 200 students; the date was collected through a simple random sampling technique. The objective of this research is to find out how the GTM method is effective. The results of the survey found that most students strongly agreed that the GTM method is effective. The activist features are investigated; downbeat force and convict of GTM can be studied.
Big Data Processing over Cloud Computing Environment

The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.
Unsupervised learning of style-aware facial animation from real acting performances

This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a latent feature vector. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. In contrast to previous approaches, our animation model learns disentangling/synthesizing different acting-styles in an unsupervised manner, requiring only phonetic labels that describe the content of training sequences. For realistic real-time rendering, we train a U-Net that refines rasterization-based renderings by computing improved pixel colors and a foreground matte. We compare our framework qualitatively/quantitatively against recent methods for head modeling as well as facial animation and evaluate the perceived rendering/animation quality in a user-study, which indicates large improvements compared to state-of-the-art approaches.
Deep Dense Projection Estimator for Images With Multi-Geometric Structures

In this letter, we propose a novel dense projection estimator called deep dense projection (Deep-DP) for 2-D image matching. Deep-DP addresses the challenge of nonsingle projection in ultralow-altitude remote sensing scenes caused by moving viewpoints and parallax of 3-D views. Deep-DP is based on affine transformation and consists of 23 cascaded layers, with specific design ideas to achieve reliable and accurate results. These design ideas include downscaled random sampling, random/semi-random sampling, abnormal matches filtering, and structural parallax suppression. To accommodate the cascade estimators, we have designed a GPU parallel computing framework that ensures high robustness, accuracy, real-time performance, and a low failure rate for Deep-DP. Our experiments demonstrate that Deep-DP outperforms the state-of-the-art single-model robust estimators by having fewer faults and higher fitting power. Additionally, Deep-DP exhibits superior accuracy compared to the state-of-the-art multistructure estimators.
Multi-Scale Transformer-Based Matching Network for Generalizable Person Re-Identification

Recently some researches have focused on the Domain-Generalization (DG) Re-ID problem that training and testing are not in the same domain distribution. To fit the unseen complex scenes, recently deep feature matching-based methods for DG Re-ID have been developed and achieved the state-of-the-arts. However, they ignored some cases in which the accuracy of key region matching is unstable at a single scale, and the bad impact of style variations for feature representations. To address the issues, we propose a novel deep image matching model named Multi-scale Transformer-based Matching Network (MTMN) for DG Re-ID problem. MTMN matches two images with multi-scale local respondence instead of fixed representations. Specifically, the Transformer is carefully modified to formulate efficient local interactions between query and gallery images in multiple scales. Moreover, the style normalization is introduced to filter out identity-irrelated features to promote the matching results. Comprehensive experiments on several DG Re-ID tasks demonstrate the superiority of the proposed method compared with the state-of-the-arts, e.g., 5.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 2.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> gains in Rank-1 and mAP on Market-1501 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> MSMT17(V1) task.
User Interaction Mining: Discovering the Gap Between the Conceptual Model of a Geospatial Search Engine and Its Corresponding User Mental Model

Designers often project into their creations usage models that attempt to idealise and anticipate how users will interact with the future system. However, these conceptual models do not always match the actual mental models of users. This mismatch may result in sub-optimal functioning that negatively impacts user experience. Discovering these deviations, especially in early design stages, could serve to align the system to the existing mental models or to train users appropriately for new paradigms. This paper reports on work in progress addressing the challenge of discovering models of user behaviour from usability tests on user interfaces. We present a case study performed at the Spanish National Geographic Institute, where a new geospatial search engine has been developed. Twenty-one participants, including novice and expert users, were recruited to perform a search task with the new geospatial search engine. The interactions mined were recorded as event logs and analysed with process mining techniques, descriptive and inferential statistics. The results indicate that the mental model of users is biased toward the archetype of a regular search engine, rather than taking advantage of the geographic functions provided by the platform, as intended by the designers. This case study illustrates the potential that interaction mining can add to the design and evaluation of new user interfaces.
Managing Variability of Large Public Administration Event Log Collections: Dealing with Concept Drift

The analysis of large event log collections aimed at variability management requires an intensive pre-processing phase. It is intuitive that obsolete behaviour that could be present in the logs must be removed in order to gain insight into the collection. Changes in the information system may indeed generate obsolete behaviour, more specifically, in the case of public administration, changes in the law may imply a change in the process, which must be updated in the information system. The logs containing the updated behaviour can then be used in variability management practices, such as the creation of configurable models. This type of analysis has numerous criticalities, one of which is the difficulty of obtaining an effective representation of the process, without running into excessive complexity of the model produced. Obsolete behavior results in an unnecessary increase in complexity and should therefore be removed. This paper introduces an event log analysis and visualisation technique based on the notion of complexity introduced by Lempel Ziv. The visualization enables process analysts to identify concept drift in the logs, thereby facilitating the removal of outdated behavior. Furthermore, when equilibrium is achieved, it indicates that the behavior is representative of the entire log. Consequently, during variability analysis, it becomes possible to prune the log, reducing computational complexity.
Research on Predicting the Impact of Venue Based on Academic Heterogeneous Network

Academic venues play an important role in the exchange and dissemination of knowledge. The effective prediction of venues' impact plays a positive role in guiding authors to correctly submit manuscripts and evaluating the impact of new papers. Most recent research has utilized relevant features of the author or paper to predict the impact of venues and made some success. In contrast, only a little attempt has been made to simultaneously mine information about papers, authors, and venues based on academic heterogeneous networks for predicting the future impact of venues. The current mainstream methods are based on academic heterogeneous networks. But these don't fully utilize the local network information of nodes, which lead to the neglect of information from strongly correlated neighbor nodes. In addition, the existing studies didn't integrate network topology, textual information, and attribute features effectively. To solve the above problems, we propose a hybrid model of academic heterogeneous network representation learning combined with multivariate random walk, termed as AHRV. The specific content is to mine the heterogeneous local network information of nodes in the academic heterogeneous network, and embed local network structure, text information and attribute features into a unified embedding representation. Then use the graph neural network to aggregate the features and neighbors to get the embedding of nodes. Finally, the embedded vector of the node is combined with the multivariate random walk model to rank the future impact of venues. The experimental results on two real datasets show that the method improved performance.
Geochemical and Geospatial Distribution of Organic Contaminants in the Flood Plain of Ekpetiama, Niger Delta Region of Nigeria

This study investigated the geochemical and geospatial distribution of organic contaminants in the floodplain water and sediments of Ekpetiama in the Niger Delta Region of Nigeria. This study is necessary because there are limited data on the level of organic contamination in this section of the Niger Delta Region of Nigeria. The extinction of planktons in Ekpetiama became a source of concern to the residents. This concern is because this section of the coastal plain provides fisher folks with livelihood. So, there was a need to track the source of contamination in this part of the Niger Delta region. Previous studies have suggested a high level of Total Petroleum Hydrocarbon and Total Hydrocarbon Content as possible sources of reduced dissolved oxygen in similar deltaic terrain. A total of 10 water and 10 sediment samples were collected and analyzed in triplicate at an interval of 100 m in the flood plain. A particle size analyzer was used to perform particle size analyses of air-dried sediments. The American Public Health Association method (APHA) was used to do the chemical analysis of the water samples. Here, a liquid-liquid extraction procedure was performed on sediment samples using 30 mL of Dichloromethane (DCM) as the extracting agent. The results were subjected to statistical validation. The mean grain size ranged from 2.37-4.83, kurtosis (1.94-0.49), and skewness (-0.8-0.71). The contaminant indicators (pH, biochemical oxygen demand, chemical oxygen demand, dissolved oxygen and Total Organic Carbon) point to the presence of organic contamination of the flood plain. The results indicated a total petroleum hydrocarbon range of 0.47-0.87 ppm in water and 0.69-0.96 ppm in sediments and a total hydrocarbon content range of 1.10-2.80 ppm in water and 2.56-3.90 ppm in sediment# samples. The results were above the permitted limits of the World Health Organisation. The source of ecological damage is the abnormal concentrations of organic contaminants in the flood plain. These results significantly caused ecosystem damage and human health effects in the food chain. This study provides information to the National Oil Spill Detection and Response Agency for a cleanup process.
An Approach for Knowledge Graphs-Based User Stories in Agile Methodologies

In this paper, we present AOAME4UserStories, a modelling and ontology-based approach that enables the creation of visual user stories grounded in a knowledge graph. The approach includes an ontology-based domain-specific modelling language - User Story Modelling & Notation (USMN) - and resolves the problem of creating inconsistent user stories in agile software development methodologies such as Scrum. The Design Science Research methodology was followed for the creation of USMN and its implementation in the modelling tool AOAME. The evaluation was conducted by first creating a visual user story reflecting a real-world use case and then by proving the consistent production of knowledge graphs for the given visual story.
Validating Enterprise Architecture Principles Using Derivation Rules and Domain Knowledge

In Enterprise Architecture Management (EAM), rules, constraints, and principles guide and govern the Enterprise Architecture (EA). These can be formulated and verified in ontology-based enterprise architecture models. The automatic validation of EA principles relies on the knowledge available in the EA models. However, there is knowledge implicit in models that humans may understand but machines cannot. For example, relationships between model elements may be derived using derivation rules and domain knowledge. Formalizing derivation rules in an enterprise ontology, we can infer this implicit knowledge and make it available to the machine for reasoning. This research demonstrates the feasibility of using derivation rules to extract implicit knowledge from enterprise models allowing EA principles validation and supporting EAM. The research contribution is presented using a concrete real-world use case and implementing the derivation rules for the EA modeling standard ArchiMate.
Impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

Abstract Amassing the available solar energy over the Sahara desert, through the installation of a large-scale solar farm, would satisfy the world’s current electricity needs. However, such land use changes may affect the global carbon cycle, possibly offsetting mitigation efforts. Here a fully coupled Earth System model EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of photovoltaic solar panels over the Sahara desert. The resulting changes to the carbon cycle were an enhancement of the carbon sink across Northern Africa, particularly around the Sahel but a simultaneous weakening of the carbon sink in the Amazon basin. This is observed through spatial pattern changes to the values of net biome production (NBP), more evident during Northern Hemisphere summer season. NBP changes are contributed by competing responses in the net primary production and heterotrophic respiration rates. These changes to carbon exchange correspond to a wetter and warmer climate occurring in Northern Africa and a drier and warmer climate in the Amazon, with stronger driving effects of precipitation. Due to these coupled responses and complex teleconnections, thorough investigation of remote impacts of solar farms are needed to avoid unintended consequences on the terrestrial carbon cycle.
Chinese Nested Named Entity Recognition Based on Boundary Prompt

The paper proposes a novel method called BPCNNER that enhances boundary information for Chinese nested named entity recognition using prompt learning. The method first involves populating the nested entities into a boundary prompt template based on predefined rules and combining it with the original text. The pre-trained BERT model is then used to obtain semantic features of the text, while the BiLSTM-CRF framework captures contextual information and calculates label dependencies. The experimental results demonstrate that the proposed method achieves an F1 score of 93.02% on the People's Daily dataset, outperforming other models. Therefore, the method is effective in improving Chinese nested named entity recognition, which is a challenging task due to the lack of effective boundary supervision.
A Multi-label Imbalanced Data Classification Method Based on Label Partition Integration

The problem of multi-label classification is widespread in real life, and its imbalanced characteristics seriously affect classification performance. Currently, resampling methods can be used to solve the problem of imbalanced classification of multi-label data. However, resampling methods ignore the correlation between labels, which may pull in new imbalance while changing the distribution of the original dataset, resulting in a decrease in classification performance instead of an increase. In addition, the resampling ratio needs to be manually set, resulting in significant fluctuations in classification performance. To address this issue, a multi-label imbalanced data classification method ESP based on label partition integration is proposed. ESP divides the dataset into single label datasets and label pair datasets without changing its original distribution, and then learns each dataset to construct multiple binary classification models. Finally, all binary classification models are integrated into a multi-label classification model. The experimental results show that ESP outperforms the five commonly used resampling methods in two common measures: F-Measure and Accuracy.
Rule-Enhanced Evolutional Dual Graph Convolutional Network for Temporal Knowledge Graph Link Prediction

The aim of link prediction over temporal knowledge graphs is to discover new facts by capturing the interdependencies between historical facts. Embedding-based methods learn the interdependencies into low-dimentional vector space while rule-based methods mine logic rules that can precisely infer missing facts. However, most of the embedding-based methods divide the temporal knowledge graph into a snapshot sequence with timestamps which increases the inherent lack of information, and the structural dependency of relations is often overlooked, which is crucial for learning high-quality relation embeddings. To address these challenges, we introduce a novel Rule-enhanced Evolutional Dual Graph Convolutional Network, called RED-GCN, which leverages rule learning to enhance the density of information via inferring and injecting new facts into every snapshot, and an evolutional dual graph convolutional network is employed to capture the structural dependency of relations and the temporal dependency across adjacent snapshots. We conduct experiments on four real-world datasets. The results demonstrate that our model outperforms the baselines, and enhancing information in snapshots is beneficial to learn high-quality embeddings.