Reviewerly/exHarmony huggingface dataset:
Caviar-Sunflower Optimization Algorithm-Based Deep Learning Classifier for Multi-Document Summarization

Abstract This paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870.
Detection of Malicious Webpages Using Deep Learning

Malicious Webpages have been a serious threat on Internet for the past few years. As per the latest Google Transparency reports, they continue to be top ranked amongst online threats. Various techniques have been used till date to identify malicious sites, to include, Static Heuristics, Honey Clients, Machine Learning, etc. Recently, with the rapid rise of Deep Learning, an interest has aroused to explore Deep Learning techniques for detecting Malicious Webpages. In this paper Deep Learning has been utilized for such classification. The model proposed in this research has used a Deep Neural Network (DNN) with two hidden layers to distinguish between Malicious and Benign Webpages. This DNN model gave high accuracy of 99.81% with very low False Positives (FP) and False Negatives (FN), and with near real-time response on test sample. The model outperformed earlier machine learning solutions in accuracy, precision, recall and time performance metrics.
Model Matching: Prediction of the Influence of UML Class Diagram Parameters During Similarity Assessment Using Artificial Neural Network

Model matching is an approach in which two or more software systems are compared. The comparison requires the computation of similarity between models in different systems. The idea is to find the most similar model and be re-used in another context especially during the development of a new software system. Models such as UML class diagrams consist of several parameters (such as class name); these parameters are used during the similarity assessment between class diagrams. This chapter investigates the influence of different parameters used during the computation of similarity between class diagrams using artificial neural network (ANN).
Knowledge of COVID-19, mental health status and prevention health behavior of university students' during COVID-19 pandemic

Purpose: This study examined the knowledge, mental health status, and infection prevention behavior of university students about COVID-19 epidemic. Methods: A survey was conducted on 180 students at the university in W city from 2020.10.19 to 2020.10.23. The research tools included a fear measurement tool(nine questions) developed by the KSTSS, and GAD-7 and PHQ-9, which measure anxiety and depression. The COVID-19 knowledge and infection prevention behaviors were developed and used by researchers referring to the Coronavirus Infectious Disease-19 Prevention National Code of Conduct and 2020 Living Prevention Guidelines(draft) of the KDCA. Results: Students knowledge about COVID-19 was high at 10.14±0.87 out of 11, fear at 17.12±0.58, anxiety at 3.28±4.19, and depression at 4.17± 4.96. Prevention health behavior was high with 4.04. The higher fear, the higher anxiety and depression. The better subjective health status, the lower anxiety and depression, and the higher prevention health behavior. Conclusion: Students fear, anxiety and depression about COVID-19, alcohol consumption, exercise, and subjective health was correlated. Mental health promotion intervention programs are need.
Indonesian speech recognition based on Deep Neural Network

The speech recognition system using deep neural network and large-scale datasets have had good performance. Indonesian is a language with hundreds of millions of people. Due to the late start of research and the lack of large enough speech data, the research of Indonesian speech recognition lags. In view of this situation, the deep neural network model is used to study Indonesian speech recognition. According to the transferability of deep neural network and the similarity between English and Indonesian, three transfer learning methods to improve the performance of Indonesian speech recognition system are proposed, and the effectiveness of different transfer methods is verified by experiments, and the acoustic model training scheme is further optimized. The experimental results show that the TDNN-Attention-HMM model based on hierarchical weight transfer has better performance than the other two transfer models. In this paper, this model is used as the optimal acoustic model of Indonesian speech recognition, and the word error rate is 6.79%. Compared with the DNN-HMM baseline system, the word error rate is relatively reduced by 26.52 %.
Simple Negative Sampling for Link Prediction in Knowledge Graphs

Knowledge graph (KG) embedding methods learn the low dimensional vector representations of entities and relations of a knowledge graph, facilitating the link prediction task in knowledge graphs. During learning of embeddings, sampling negative triples is important because KGs have only observed positive triples. To the best of our knowledge, uniform-random, generative adversarial network (GAN)-based, and NSCaching, structure aware negative sampling (SANS) are four negative sampling methods in the literature. Unfortunately, they suffer from computational and memory inefficiency problems. In addition, their prediction performance are affected by the ‘vanishing gradient’ problem because of poor quality of sampled negative triples. In this paper, we propose a simple negative sampling (SNS) method based on the assumption that the entities which are closer in the embedding space to the corrupted entity are able to provide high-quality negative triples. Furthermore SNS has a good exploitation potential as it uses sampled high-quality negatives for improving the quality of negative triples in next steps. We evaluate our sampling method through link prediction task on five well-known knowledge graph datasets, WN18, WN18RR, FB15K, FB15K-237, YAGO3-10. The method is also evaluated on a new biological KG dataset (FIGHT-HF-23R). Experimental results show that the SNS improves the prediction performance of KG embedding models, and outperforms the existing sampling methods.
Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection

Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.
Impact of Corporate Environmental Responsibility on Firm’s Financial Performance: Moderating Role of Organizational Slack and Industry Competition

Due to speedy trade and industry expansion in the emerging economies it is creating stern environmental corrosion. Effluence makers’ enterprises are mostly responsible for environmental deterioration. Therefore, it is the responsibility of those firms to take steps to control this corrosion in the environment. This research explains the effect of corporate environmental responsibility (CER) on corporate financial performance (CFP) with the moderating effect of organizational slack and industry competition. Data was collected from annual reports of 50 KSE 100 index companies from 2012-2019, containing total 450 observations. Dynamic penal model was used to test the study hypothesis by using Eviews, different pre and post estimations are applied to confirm the data validity. Empirical results indicate that corporate environmental responsibility has significant positive effect on corporate financial performance, while the moderating effect of organizational slack is negative. Industry competition has significant positive moderating effect on the relationship, i.e., if there is high competition in industry then firms will invest more in environment to attract more consumers and to create good will in market. The study reveal that those firms will lead where competition is high and who will focus on their responsibility towards environment.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding

Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications.The majority of these techniques are either supervised or unsupervised.Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results.However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios.To that end, we propose a generic and end-to-end approach -PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset.We experimentally show that PAUSE achieves, and sometimes surpasses, state-ofthe-art results using only a small fraction of labeled sentence pairs on various benchmark tasks.When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the burden of extensive manual annotation work.
Entropy Analysis for Clustering Based Lossless Compression of Remotely Sensed Images

In an Improved K-means clustering algorithm, initial clustering time can be saved by making initial division based on previous clustering results and maintaining the relationship among stable classes. Every pixel in superspace is required to calculate Euclidean distance for clustering, but only calculating and comparing distances with neighbor centers near to the pixel except those far away from it accelerates the clustering process with more and more classes becoming stable. Clustering lossless compression algorithm can efficiently eliminate the interspectral and intra-spectral redundancy at high convergent speed through enhancing intra-class redundancy. The comparison of the parameter analysis of the AVIRIS images with other lossless compression algorithms shows that this clustering lossless compression algorithm is more efficient.
Multi-modality Cardiac Segmentation via Mixing Domains for Unsupervised Adaptation

Unsupervised domain adaptation is very useful for medical image segmentation. Previous works mainly considered the situation with one source domain and one target domain. However in practice, multi-source and/or multi-target domains are generally available. Instead of implementing adaptation one by one, in this work we study how to achieve multiple domain alignment simultaneously to improve the segmentation performance of domain adaptation. We use the VAE framework to transform all domains into a common feature space, and estimate their corresponding distributions. By mixing domains and minimizing the distribution distance, the proposed framework extracts domain-invariant features. We verified the method on multi-sequence cardiac MR images for unsupervised segmentation. Results experimentally demonstrated that mixing target domains together could improve the segmentation accuracy, when the label distributions of mixed target domains are closer to that of the source domain than each unmixed target domain. Compared to state-of-the-art methods, the proposed framework obtained promising results.
L'intelligence artificielle au service des départements d'information médicale : construction et évaluation d'un outil d'aide à la décision pour cibler et prioriser les séjours à contrôler et fiabiliser les recettes hospitalières générées par la tarification à l'activité

Medical Information Departments help to optimize the hospital revenues generated by activity-based pricing. A review of medical files, selected after the targeting of coding summaries, is organized. The aim is to make any corrections to the diagnoses or coded procedures with a potential impact on the pricing of the stay. Targeting is of major importance as a means of concentrating resources on the files for which coding can be effectively improved. The tools available for targeting can be optimized. We have developed a decision-making support tool to make targeting more efficient. The objective of our study was to evaluate the performance of this tool.The tool combines an artificial intelligence module with a rule-based expert module. A predictive score is assigned to each coding summary that reflects the probability of a revalued stay. Evaluation of the performance of this tool was based on a sample of 400 stays of at least 3 nights of patients hospitalized at the Paris Saint-Joseph Hospital from 1st November to 31st December 2019. Each stay was reviewed by a coding expert, without knowledge of the score assigned and without help from expert queries. Two main assessment criteria were used: area under the ROC curve and positive predictive value (PPV).The area under the ROC curve was 0.70 (CI 95% [0.64-0.76]). With a revalued coding rate of 32%, PPV was 41% for scores above 5, 65% for scores above 8, 88% for scores above 9.The study made it possible to validate the performance of the tool. The implementation of new variables could further increase its performance. This is an area of development to be considered, particularly with in view of generalizing individual invoicing in hospitals.
Using topic modeling to restructure the archive system of the German Waterways and Shipping Administration

The German Federal Waterways Engineering and Research Institute (BAW) is responsible for a large number of technical documents in its archive system. These include the design process in accordance with VV-WSV 2107, which covers the entire planning cycle from basic evaluation to implementation planning. In the process of planning, construction and operation of objects of the hydraulic engineering infrastructure, a large and varied number of documents is being accumulated at the responsible authorities. Hierarchical filing systems provided with metadata are often not sufficient to search the documents in a targeted manner. The object of research is therefore machine learning methods that generate new classification systems on the basis of the given document stock and can integrate the existing documents into them. The filing is object-related and the clerk specifies various descriptive attributes. Of interest are now procedures that automatically generate topic models on the basis of the specified texts in the metadata documents in order to assign the documents to them. For this study, the words in the metadata attributes were combined into so-called bag of words and latent Dirichlet allocation (LDA) was applied to automatically find word groups that belong together. With the topic models generated in this way, documents can be searched according to topic composition or, in the case of a keyword search, documents can be displayed which do not contain the keyword but which match the topic. Due to the high number of topics that overlapped within the planning data and the few words per document, the algorithm found it difficult to generate unambiguous topics that could be easily interpreted by humans. In order to generate such topics, so-called Seeded LDA was used. Here the generation of topics can be influenced by setting seed words per topic. With Seeded LDA it is possible to fix certain topics while the algorithm decides others freely and finds new topics.
ActAnyware - Blockchain-Based Software Licensing Scheme

Software validation is a long battle between software developers and software pirates. License validation is proposed as the protection of both developer’s revenue and intellectual properties. However, as the license model becomes more complex alongside the constant changing of technology and the economic environment, a trusted and auditable software licensing validation method is required. In this paper, we briefly discuss the desirable characteristics of licensing validation method and how Blockchain can provide these desirable characteristics.
Upcoming meetings

Artificial OrgansVolume 43, Issue 9 p. 932-932 UPCOMING MEETINGS Upcoming meetings First published: 08 September 2019 https://doi.org/10.1111/aor.13528Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume43, Issue9September 2019Pages 932-932 RelatedInformation
Virtual Gaming Using Gesture Recognition Model

As human–computer interaction continues to evolve, gesture-based applications are becoming more and more popular. Traditionally, computer interfaces were limited only to keyboard and mouse functions where the input depended on the click of the button. With the advancement in technology, vision-based human–computer interaction provides a more extensive range of taking input by using computer vision in order to process data from multiple cameras. Computer Vision alludes to a field of study that tries to create systems that allow computers “to see” and comprehend the content of digital images such as photographs and videos. The gesture-based interaction interface being proposed in this paper can have several applications, although the main focus is on the gaming aspect. This interface consists of the detection, tracking, and recognition module. This paper explores the use of Computer Vision to create a gesture-based game, controlled by hand using the Haar Cascade classifier to classify and detect the object. With the help of simple hand gestures, the model has been trained to enable users to play the game of “Flying Wing Mario”. In this proof-of-concept game, the users are able to play the game through gesture recognition as it provides them with an enhanced interactive experience integrating the virtual and the real-world object. The concept, however, can be applied to any other game as well.
A Vision Based Fractional Order TV-Model for Underwater Motion Estimation

As we are aware that underwater motion estimation is an active and challenging area of vision system which belongs to the category of robot navigation. In particular, motion detection and tracking in underwater image sequences is carried out based on optical flow. In this paper, a vision based underwater navigation system is developed based on the fractional order total variation (TV) model. The objective of this work is to design a variational model by using a quadratic data and total variation terms to provide the optimal performance against radiometric characteristics such as turbidity, non-uniform illumination and marine snow, etc. The presented ameliorated model is more robust against outliers and reduces the problem of local minima. The fractional derivative discretization of non-differentiable terms is performed using Grünwald-Letnikov derivative scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed model are tested on a variety of datasets.
Document Retrieval and Cluster Based Indexing using Rider Spider Monkey Optimization Algorithm

Document retrieval process is more significant in the field of research community for retrieving the highly-relevant documents that fit for the user query. Even though various document retrieval methods are introduced, retrieving the exact document based on the indexing is a quite challenging task in the document retrieval framework. Thus, an effective document retrieval algorithm named Rider Spider Monkey Optimization Algorithm (RSOA) is proposed in this research. Initially, the documents are pre-processed by the stop word elimination and the stemming process, and the features are extracted to find the key words of the documents by applying the Term Frequency-Inverse Document Frequency (TF-IDF). The selected keywords are passed into the cluster-based indexing phase, where the cluster centroids are identified by using the proposed Rider Spider Monkey Optimization Algorithm. Moreover the query matching is carried out at two levels, at first, the query is forwarded and is matched to the entire cluster centroid to find the appropriate centroid. At the second level; the user query is matched based on the records present inside the matched centroid. Moreover, the query matching is progressed using the distance measure by the Bhattacharya distance to retrieve the documents. The performance is analyzed using the metrics, namely precision, F-measure, and recall and accuracy with the values of 90.141%, 91.876%, 91.178%, and 91.202%, respectively using 20 news group dataset .
L3BOU: Low Latency, Low Bandwidth, Optimized Super-Resolution Backhaul for 360-Degree Video Streaming

In recent years, streamed 360° videos have gained popularity within Virtual Reality (VR) and Augmented Reality (AR) applications. However, they are of much higher resolutions than 2D videos, causing greater bandwidth consumption when streamed. This increased bandwidth utilization puts tremendous strain on the network capacity of the cloud providers streaming these videos. In this paper, we introduce L3BOU, a novel, three-tier distributed software framework that reduces cloud-edge bandwidth in the backhaul network and lowers average end-to-end latency for 360° video streaming applications. The L3BOU framework achieves low bandwidth and low latency by leveraging edge-based, optimized upscaling techniques. L3BOU accomplishes this by utilizing down-scaled MPEG-DASH-encoded 360° video data, known as Ultra Low Resolution (ULR) data, that the L3BOU edge applies distributed super-resolution (SR) techniques on, providing a high quality video to the client. L3BOU is able to reduce the cloud-edge backhaul bandwidth by up to a factor of 24, and the optimized super-resolution multi-processing of ULR data provides a 10-fold latency decrease in super resolution upscaling at the edge.