Reviewerly/exHarmony huggingface dataset:
Predicting Marathi News Class Using Semantic Entity-Driven Clustering Approach

Document management is a need for an era and managing documents in the regional languages is a significant and untouched area. Marathi corpus consisting of news is processed to form Group Entity document matrix Marathi (GEDMM), Vector space model for Marathi (VSMM) and Hysynset Vector space model for Marathi (HSVSMM). GEDMM uses entity group extracted using Condition random field (CRF). The frequent terms are used to construct VSMM using TF-IDF. HSVSMM uses synsets using hypernyms-hyponyms and synonyms. GEDMM and HSVSMM use dimension reduction by selecting significant feature groups. Hierarchical agglomerative clustering (HAC) is used and a dendrogram is produced to visualize the clusters. The performance analysis is carried out using several parameters like entropy, purity, misclassification error and accuracy. The clusters produced using GEDMM shows the minimum entropy and the highest purity. A random forest classifier is applied and the results are evaluated using misclassification error and accuracy.
Credit Card Fraud Detection Using Machine Learning Approach

Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pops into the mind so far in the current scenario when the concept of fraud bursts into some conversation. Credit card fraud has escalated tremendously in recent times due to the incredible growth in credit card purchases. In order to assess, identify or prevent undesirable conduct, fraud detection requires tracking the purchase behavior of users/customers. The purpose of this project is to predict the genuine and fraud transactions with respect to the amount of the transaction utilizing various machine learning approaches like Logistic Regression, Decision Trees, Support Vector Machine, Naïve Bayes, Random Forest and K-Nearest Neighbor. The model built who has greater accuracy and precision is considered to be best fit for this system.
Making a science out of preanalytics: An analytical method to determine optimal tissue fixation in real-time

Modern histopathology is built on the cornerstone principle of tissue fixation, however there are currently no analytical methods of detecting fixation and as a result, in clinical practice fixation is highly variable and a persistent source of error. We have previously shown that immersion in cold formalin followed by heated formalin is beneficial for preservation of histomorphology and have combined two-temperature fixation with ultra-sensitive acoustic monitoring technology that can actively detect formalin diffusing into a tissue. Here we expand on our previous work by developing a predictive statistical model to determine when a tissue is properly diffused based on the real-time acoustic signal. We trained the model based on the morphology and characteristic diffusion curves of 30 tonsil cores. To test our model, a set of 87 different tonsil samples were fixed with four different protocols: dynamic fixation according to our predictive algorithm (C/H:Dynamic, N = 18), gold-standard 24 hour room temperature (RT:24hr, N = 24), 6 hours in cold formalin followed by 1 hour in heated formalin (C/H:6+1, N = 21), and 2 hours in cold formalin followed by 1 hour in heated formalin (C/H:2+1, N = 24). Digital pathology analysis revealed that the C/H:Dynamic samples had FOXP3 staining that was spatially uniform and statistically equivalent to RT:24hr and C/H:6+1 fixation protocols. For comparison, the intentionally underfixed C/H:2+1 samples had significantly suppressed FOXP3 staining ( p&lt;0 . 002 ). Furthermore, our dynamic fixation protocol produced bcl-2 staining concordant with standard fixation techniques. The dynamically fixed samples were on average only submerged in cold formalin for 4.2 hours, representing a significant workflow improvement. We have successfully demonstrated a first-of-its-kind analytical method to assess the quality of fixation in real-time and have confirmed its performance with quantitative analysis of downstream staining. This innovative technology could be used to ensure high-quality and standardized staining as part of an expedited and fully documented preanalytical workflow.
Internet Rumor Reporting System Based on the Blockchain Incentive Mechanism

This study examined the characteristics of internet rumors in the user-generated content (UGC) mode, analysed the advantages of using the blockchain technology to curb internet rumors, and proposed a framework of the internet rumor reporting system based on the blockchain incentive mechanism. With Truffle as a development framework, intelligent contracts such as create, read, update, and delete were created using Solidity. The end users were connected to the blockchain using the MetaMask plugin. Finally, rewards were obtained by the participants, and the decentralizing program Dapp was created. The results showed that the TRUES could support multiuser reporting and execute intelligent contracts automatically and efficiently in a complex internet rumor environment; for the range of 1–2896 participants, virtual currency rewards could be obtained in 0.5–2.5 d. The conclusion showed that the internet rumor reporting system under the blockchain incentive mechanism had the characteristics of multilevel management, and distributed ledger and digital signatures contributed to the retention and traceability of rumors. Intelligent contracts and consensus mechanisms have obvious advantages in dealing with the complex forms of internet rumors.
Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions

In this article, we establish a family of subspace-based learning methods for multiview learning using least squares as the fundamental basis. Specifically, we propose a novel unified multiview learning framework called multiview orthonormalized partial least squares (MvOPLSs) to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three types of regularizers on its basic ingredients, including model parameters, decision values, and latent projected points. With a set of regularizers derived from various priors, we not only recast most existing multiview learning methods into the proposed framework with properly chosen regularizers but also propose two novel models. To further improve the performance of the proposed framework, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted on multiview datasets in terms of both feature extraction and cross-modal retrieval. Results show that the subspace-based learning for a common latent space is effective and its nonlinear extension can further boost performance, and more importantly, one of two proposed methods with nonlinear extension can achieve better results than all compared methods.
EFEKTIFITAS PENGGUNAAN VIDEO LEARNING DALAM MENINGKATKAN HASIL BELAJAR LABSKILL MAHASISWA KEPERAWATAN

Video Learning is one of the learning media that can be used in the laboratory to improve the lab skills of nursing students.The importance of video learning being applied in the laboratory to answer the industrial revolution 4.0 in the form of the use of digital technology for the educational aspect in the laboratory and the urgency in the midst of the Covid-19 pandemic.The purpose of this study was to find out how effective video learning is in improving nursing students' lab skills learning outcomes in the laboratory.The research method used is a quasi-experimental type of research with a nonequivalent post-test only control group design experimental design.The population in this study were nursing students at the Faculty of Health, University of Fort De Kock.The research samples were students of the 2019 and 2020 batches which were taken with a sampling technique carried out by random sampling with a cluster sampling type of 160 students.Data analysis techniques hypothesis test (t test).The results of this study showed that the average of skill lab learning outcomes of nursing students in the laboratory using video learning was 83.71, while the average skill lab learning outcomes of nursing students using conventional learning methods (demonstrations) was 78.32.There is a significant difference in learning outcomes (p-value 0.001) between lab skills learning using video learning compared to lab skills learning in the conventional way.It is expected that educators can further increase creativity in making attractive learning lab skills videos so as to increase interest in learning and learning outcomes for nursing students, especially in learning in the laboratory.
ANALISIS CLUSTER DENGAN MENGGUNAKAN K-MEANS UNTUK PENGELOMPOKKAN ONLINE CUSTOMER REVIEWS (OCR) PADA ONLINE MARKETPLACE

Technological advances at this time are very influential on people's shopping culture, plus during the current pandemic, it has resulted in an increasing number of people shopping for daily necessities online. There are many conveniences offered in online shopping that make people switch to using these facilities. Besides the advantages of online shopping, there are also some disadvantages of online shopping, including the rise of online sales fraud such as goods not being shipped, damaged goods, items not as ordered, and much more. For this reason, in conducting online transactions, trust is needed between the seller and the buyer, and one of the factors that greatly affect the prospective buyer is to know the history of the seller, namely by looking at the reviews given by the buyer on the seller's homepage which is called Online Customers Reviews (OCR). OCR is considered to be very influential on customer buying interest. One of the indicators that are considered very important in influencing consumer buying interest and trust is OCR. This study aims to analyze OCR clustering in one of the marketplaces in Indonesia using the K-Means Clustering Method. K-Means is a clustering algorithm that is quite effective because it has the ability to group large amounts of data and with high speed, the K-Means algorithm partitions data into clusters so that they have the similarity of being in one cluster.
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

Abstract Natural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance? Our answers highlight major avenues for future research in transfer learning for the long tail. Lastly, using our meta-analysis framework, we perform a case study comparing the performance of various adaptation methods on clinical narratives, which provides interesting insights that may enable us to make progress along these future avenues.
Object detection in crowded scenes via joint prediction

Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.
The University Mission in the Humanitarian Dimension: Stating the Problem

The article focuses on the mission humanitarianism of a university as a social institution from the perspective of social ontology. The mission is viewed as a perfect benchmark (supreme goal and purpose) necessary for university existence as well as for maintenance and authorization of its institutional identity. It is shown that despite the changes in functionality of universities under the conditions of knowledge-intensive economy development (use of business models in interaction with the society), the humanitarian orientation has not lost its significance since it is necessary for the existence of a university as an institutionally organized specific educational activity, including knowledge generation, storage and transmission. Key institutional characteristics are considered that reveal the importance of humanitarianism for preserving the university as a unique social phenomenon. The authors are guided by the methodology of moderate constructivism – the study of value and meaning of human mentality, ideas and ideals in forming the institutional design of social reality. The role of the ideal and the intentionality of human actions in the construction and function of an educational social institution, which is expressed in the university corporation’s drive to be orientated at values, which give positive social significance to its activities and are aimed at achieving good, are explored. The university produces and conveys knowledge through establishing a knowledge subject, in other words, it forms the very intention to achieve a socially significant result not only in an objectified form of knowledge, but also in the form of evolution (development) of an individual who can produce and use knowledge for the good of society and for their personal advancement. In this context, the mission is understood to be a supreme goal and an ideal benchmark in the concrete historic practices of university education in forming a knowledge subject who must master the fundamental values necessary for society’s existence. The university mission is based on the concrete historic interpretation of the key socially significant goal of education: the development of a “human being” who acts for the good and benefit of society and its members via conveying the thesaurus of universal human values in their concrete historic theoretical and ideological formats.
Diversifying readership through open access: A usage analysis for OA books

Diversifying readership through open access: A usage analysis for OA books is a new white paper by Springer Nature and COARD based on usage data for 3,934 books, including 281 open access books. This white paper presents the analysis of that data, exploring what effect, if any, publishing OA has on the geographic usage of books. The findings will be of interest to researchers, authors, librarians and publishers alike.The white paper PDF and a zip file of all figures within the white paper have been attached. These figures can be used in presentations. All are released under a CC BY 4.0 licence.The summary one page infographic can be downloaded here: https://figshare.com/articles/media/Infographic_summary_of_Diversifying_readership_through_open_access_A_usage_analysis_for_OA_books_white_paper/12933131 Data and code relating to the white paper can be found together with the technical paper prepared by COARD: Paper: https://doi.org/10.5281/zenodo.4014905Data: https://doi.org/10.5281/zenodo.4018842 Code: https://doi.org/10.5281/zenodo.4019215
Role of Some Integral Transforms in Cryptograph

There are so many methods for the process of cryptography in literature. In this paper we present encryption and decryption method by using Laplace transform &amp;Sumudu transform and their inverses. The purpose of using this method is for more security in communication as compared to other methods because cipher text obtained by this method could not be cracked by other persons easily. In the first part we apply Laplace transform to trigonometric cosine function for Sumudu transform for the same purpose.Fiinally we conclude by comparing these two methods.
Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data.Watermarking digital data for copyright protection is a current need of the community.For embedding watermarks, robust algorithms in die media will resolve copyright infringements.Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized.In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark.The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA).After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM).From the extraction process, the original image is obtained by this RNN-LSTM method.The experimental set up is carried out in the MATLAB platform.The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches.The results are achieved under various attacks to show the robustness of the proposed work.
Implementation of Privacy and Security for a Genomic Information System

Genomic information is key for the implementation of real personalized medicine. Nevertheless, access to this kind of information must be controlled because of its high privacy and security requirements. Several genomic information formats exist, although we have started from MPEG-G as it includes metadata and protection mechanisms since its inception and provides a hierarchical structure to organize the information contained. The proposed GIPAMS modular architecture provides a secure and controlled access to genomic information, which may help on improving personalized medicine as described in this paper.
A brain-inspired algorithm for training highly sparse neural networks

Abstract Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient. Pruning the dense neural networks is among the most widely used methods to obtain a sparse neural network. Driven by the high training cost of such methods that can be unaffordable for a low-resource device, training sparse neural networks sparsely from scratch has recently gained attention. However, existing sparse training algorithms suffer from various issues, including poor performance in high sparsity scenarios, computing dense gradient information during training, or pure random topology search. In this paper, inspired by the evolution of the biological brain and the Hebbian learning theory, we present a new sparse training approach that evolves sparse neural networks according to the behavior of neurons in the network. Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, “Cosine similarity-based and random topology exploration (CTRE)”, evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward. We carried out different experiments on eight datasets, including tabular, image, and text datasets, and demonstrate that our proposed method outperforms several state-of-the-art sparse training algorithms in extremely sparse neural networks by a large gap. The implementation code is available on Github.
Integrating Hexagonal Image Processing with Evidential Probabilistic Supervised Classification Technique to Improve Image Retrieval Systems

This paper presents a suggested approach to treat a major issue in images classification namely uncertainty. Uncertainty in image classification means some pixels within each cluster are more or less likely to actually belong to this cluster. So, techniques have been used in this paper to deal with the pixels that do not belong to specific regions, helping to raise image retrieval performance. This was done by merging one of the artificial intelligence techniques, which is image processing, with one of the statistical techniques for probability, which is evidential probabilistic. In such contexts, it may be advantageous to resort to two branches: hexagonal image processing based on partial down-sampling of the image resolution in both directions by half using weighted average performance then shifting the remaining pixels in alternate rows. The other is an evidential theory which is rich and flexible formalisms for representing and manipulating uncertain information. Both hexagonal image processing and evidential theory are used to obtain high accuracy in images classification. The hierarchical nature of the hexagonal image processing addressing scheme is exploited to extract features from the image efficiently.
Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model

Synchronization of dual servo motor control has become an important issue due to many applications in engineering fields, such as electric vehicle, robotics, electronics production machines, and others. This paper studies cerebellar model articulation controller (CMAC) neural network (NN) controller to synchronize two servo motors with dynamic LuGre friction model. CMAC is kind of NN method represented by associative memory with more powerful properties. Cross-coupling control structure is employed to synchronize two servo motors in this study. To investigates the performance, MATLAB Simulink is applied to simulate the control design of dual servo motor. The simulation results exhibit that CMAC controller has better output trajectory and works well for two servo motors with different parameters of LuGre friction model.
Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias

Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.&#x0D;