Reviewerly/exHarmony huggingface dataset:
Interactive System Based on Leap Motion for 3D Medical Model

An interactive visualization of the patients’ 3D medical anatomical model as guide is often helpful for doctors during complex surgery. However, there are certain limitations according to the actual requirements of building sterile operating environment. Traditional human–computer interaction tools (mouse and keyboard) must be disinfected regularly and cannot be used in the process. A noncontact gesture control medical model based on Leap Motion is proposed in this study. The gesture is recognized and localized without using mouse and keyboards through a binocular camera assembled on Leap Motion. Hence, the model is directly controlled by the gesture to complete the operation of rotation, zoom, and other functions. In this study, a 3D heart model is combined with pseudo-color processing technology to enhance the observability of its 3D structure. Gesture recognition technology is then utilized to control the rendered model as rotation and zoom. Experimental results show that our system has an absolute accuracy in recognizing circle, swipe, and other actions. Thus, rotation is proposed as a new motion that can be identified steadily. Rotation plays an essential role in usability, intuition, and interactive efficiency of future system design. The system is applicable to sterile operating environments due to its stable recognition process and small space occupation.
Towards Layered Events and Schema Representations in Long Documents

In this thesis proposal, we explore event extraction and event representation on literary texts.Due to its variety of genres and varying document length, literature is a challenging domain, yet the representation of literary content has received relatively little attention.As most individual events contribute little to the overall semantics of literary documents, we model events at different granularities.On the conceptual level, we adapt the previous definition of schemas as sequences of events, all describing a single process connected through shared participants, and extend the notion to allow modeling a document's content using sequences of schemas.Technically, the segmentation of event sequences into schemas is approached by modeling such sequences, making use of the narrative cloze task, which is the prediction of masked events in event sequence contexts.We propose building on sequences of event embeddings to form schema representations, thereby summarizing sections of documents using a fixed-size representation.This approach will give rise to comparisons of sections such as chapters up to the comparison of entire literary works on the level of their schema structure, paving the way to a computational approach to quantitative literary research.
A Long Short Term Memory Model for Credit Card Fraud Detection

Credit card fraud detection over the years remains a major concern for all and this has made the field to receive huge attention from researchers. One way of addressing this concern is for financial sectors and government agencies to reliably detect fraud in any transaction so as to prevent financial losses incurred by these sectors and card owners. Exiting fraud detection system is plagued with misclassification and high false-positive rate. To prevent misclassification, this research therefore aims to deploy Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) for classifying financial transaction as fraudulent or not. The system extracted desired features from two different dataset gotten from kaggle repository using Principal Component Analysis and preprocessed using Arbitrary Assignment Method and Min–Max scalar algorithm for Normalization. The work was implemented using python programming language. The relevant features selected were then fed into the LSTM-RNN for classification. The results obtained were compared with past work and our fraud model recorded high classification accuracy as well as reduced false alarm rate. It has 99.58% Prediction Accuracy, 99.6% Precision, and Recall of 80%. The system will enable financial institutions and government agencies involved in financial transaction to detect the occurrence of fraud and be able to proffer the necessary solution as appropriate.
CIVIL SERVANTS AWARENESS GUIDELINE TOWARDS COMPUTER SECURITY POLICY: A CASE STUDY AT THE MANPOWER DEPARTMENT, MINISTRY OF HUMAN RESOURCES

ICT Security Policy includes information security-related policies, guidelines and best practices that are enforced in the Malaysian public sector. These policies are priority areas that contain guidelines for implementing ICT infrastructure in the public sector. However, there is a significant gap between these policies and awareness towards computer security policy among government servants in the public sector. Therefore a study involving government servants in the Manpower Department, Ministry of Human Resources was carried out to identify the critical success factor of these policies. The study was conducted through quantitative and qualitative methods. A survey was conducted to measure the level of awareness among government servants in agencies against computer security policies. Flaw factors in computer security policy implementation were discussed to obtain strategies to ensure the successful implementation of computer security policies in an agency. The significant factors leading to a successful implementation of computer security policy at the governmental agencies were validated by experts. As a result, a guideline has been prepared to be applied as an improvement proposal to increase the awareness of government servants on ICT security policy in the agencies.
Efficient Approach for Encryption of Lossless Compressed Grayscale Images

To transmit images over a communication medium at lower bandwidth and robustness against differential attacks, compression and encryption processes are used. In this paper, Huffman lossless compression technique is used to compress the grayscale images. In this, different prefix codes of variable lengths are assigned to each pixel. The length of the prefix codes is inversely proportional to frequency of occurrence of characters. Compressed image is encrypted by RSA asymmetrical encryption algorithm. In RSA encryption algorithm, encryption key is kept public and different from decryption key which is kept secret. The efficiency of proposed algorithms is expressed by analyzing the compression parameters as well as the correlation coefficient analysis and entropy analysis. The results are verified on five grayscale test images of 512 × 512 sizes.
Model Evaluation with Weighted Threshold Optimization (and the “mewto” R package)

mewto is an R package that allows users to experiment with different thresholds for classification of prediction results in the case of binary classification problems and to interactively visualize model evaluation metrics, confusion matrices, the ROC and PR curves. It can also calculate the optimal threshold based on a weighted evaluation criterion and displays related performance metrics. The package enhances model performance evaluation in two main ways: by providing an interactive interface through which the user can perform exploratory analysis of model result data and visualize key metrics, as well as by developing a tool that can be used to find the optimum threshold for binary classification problems using a weighted optimization formula.
Multi‐lingual text detection and identification using agile convolutional neural network

Abstract Multi‐lingual scene text detection and identification is a challenging task in today's world due to the prevalence of many digitized multi‐lingual documents, images, and videos. A valuable method for detecting multi‐lingual text from natural scene images is proposed which uses the convolutional neural network, namely, You Only Look Once (YOLOv3) as the backbone. The proposed system is more agile than YOLOv3 with the introduction of atrous separable convolution (ASC). The multi‐scale prediction in YOLOv3 emphasizes the integration of global features of multi‐scale convolutional layers while it overlooks the blend of the multi‐scale local region features on the same convolutional layer. To overcome this, ASC is applied to efficiently compute dense local region feature maps, thereby reducing computation complexity substantially. Complete IoU loss, which is an accumulation of overlap area, distance, and aspect ratio, is introduced for enhanced accuracy in bounding box regression, wherein IoU designates the measure of overlap between the predicted and the ground truth bounding boxes. The experimental results show that the proposed system is efficacious in detecting multi‐lingual as well as English text from natural scene images.
Study of The Soft Skills Development in The Framework Of The Activities Of Student Communities

The article presents the results of a study of the Soft Skills development in the activities of student societies. The purpose of the article is the theoretical and empirical study of the Soft Skills development in the activities of student societies. The research methodology and its algorithm were selected due to the purpose of the study. To solve the research questions posed, we compiled a list of student societies of the Mytischi Branch (MB) of the Bauman Moscow State Technical University. It included 87 student societies, which were classified according to the type of their activity. The hypothesis of the research is that a close connection between student societies stimulates members of one society to be interested in the activities of associate societies, thereby acquiring new skills within the framework of this activity, among which there are so-called Soft Skills. Student societies can form both general, characteristic of all student organizations, and specific skills, characteristic of certain types of activity. In the course of the research work, all possible student societies were identified and classified. Next, we built a network map of the interaction among student societies. Also, using a targeted sample, 9 student societies were selected, representing different fields of activity. The leaders of these societies were interviewed using a semi-formalized interview method. The statutory documents on the activities of the societies under consideration were analyzed using the traditional method of document analysis.
Emotion Recognition Using Facial Expressions

Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine learning can help automate this complicated task with the help of feature engineering. This work proposes some pipelines trained on the JAFFE dataset using feature extraction methods, namely principal component analysis (PCA) and local binary pattern (LBP) combined with Fisher discriminant ratio (FDR) as a feature selection method. In order to build a classification scheme capable of successfully identifying face images related to the six universal emotions and neutral expression, all possible combinations have been empirically analyzed. In the final model, PCA combined with FDR has been used on the support vector machine classifier with a linear kernel. The results obtained are encouraging and this work may also prove important for disciplines other than computer science such as for management purposes.
Next-Item Recommendation With Deep Adaptable Co-Embedding Neural Networks

The next-item recommendation has been in the central of interest in real-world applications such as e-commerce. However, it is challenging to infer what a user may purchase next due the complex interactions in the historical sessions and the changing semantics of an item over time. Most existing methods employ separate models to generate the general preference and the sequential patterns for the next-item recommendation without considering the interactions between the two factors or use a simple linear combination of the two factors. In this paper, we propose a deep adaptable co-embedding neural network (ACENet) to address these limitations. ACENet not only adaptably balances the combination of general preference and sequential patterns but also introduces dynamic attention for each factor in hybrid representations. Extensive experiments on two real-world datasets show the superiority of ACENet compared with other state-of-the-art methods.
Face Mask Detector using Deep Transfer Learning and Fine-Tuning

The world Health Organization declared a pandemic called the coronavirus. To avoid the spread of coronavirus disease (COVID-19), it released an advisory of some preventive measures to be taken care of while dealing at public places and health-care centers that include wearing masks, social distancing, monitoring health, and using disinfectants. In this paper, we have taken one of the measures used to prevent COVID-19 spread and aimed to develop a deep learning model to categorize people with or without a mask at public places such as schools, colleges, and corporates. In this paper we developed algorithm using concepts of deep transfer learning and fine-tuning. The system developed on the MobileNetv2 base model, the head of it was replaced by the custom face mask detection algorithm and enabled the training of face masked and non-face masked images. The testing results have shown an accuracy of 98% on both categories with mask and without mask.
Multistage reaction‐diffusion equation network for image super‐resolution

Deep learning-based models have progressed considerably in single-image super-resolution. A high-resolution pattern generation task is performed at the end of convolution neural networks (CNNs) with some convolution-based operations in these models. However, this process may be difficult because all the work is done through the remarkable learning ability of CNN without any specific learning target. Reaction-diffusion equation (RDE) is a mechanism involved in the pattern generation process that can serve as a guide for super-resolution. It is proposed to embed RDE into a super-resolution network by designing a reaction-diffusion process block (RDPB) in this study. The proposed RDPB uses Euler method for iteratively solving one particular RDE, which is determined by the parameter generated through CNN. Accordingly, this module guides and leads the CNN in generating patterns for image super-resolution. Moreover, a multistage framework is constructed to guide each network module further. On the basis of these two designs, the multistage reaction-diffusion equation network is proposed for image super-resolution. Experimental results demonstrated that the proposed model can obtain findings consistent with the conclusions of state-of-the-art methods with a relatively shallow structure and small model size.
NCUEE-NLP at MEDIQA 2021: Health Question Summarization Using PEGASUS Transformers

This study describes the model design of the NCUEE-NLP system for the MEDIQA challenge at the BioNLP 2021 workshop. We use the PEGASUS transformers and fine-tune the downstream summarization task using our collected and processed datasets. A total of 22 teams participated in the consumer health question summarization task of MEDIQA 2021. Each participating team was allowed to submit a maximum of ten runs. Our best submission, achieving a ROUGE2-F1 score of 0.1597, ranked third among all 128 submissions.
An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction

Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.
High-Dimensional Gaussian Process Inference with Derivatives

Although it is widely known that Gaussian processes can be conditioned on observations of the gradient, this functionality is of limited use due to the prohibitive computational cost of $\mathcal{O}(N^3 D^3)$ in data points $N$ and dimension $D$. The dilemma of gradient observations is that a single one of them comes at the same cost as $D$ independent function evaluations, so the latter are often preferred. Careful scrutiny reveals, however, that derivative observations give rise to highly structured kernel Gram matrices for very general classes of kernels (inter alia, stationary kernels). We show that in the low-data regime $N<D$, the Gram matrix can be decomposed in a manner that reduces the cost of inference to $\mathcal{O}(N^2D + (N^2)^3)$ (i.e., linear in the number of dimensions) and, in special cases, to $\mathcal{O}(N^2D + N^3)$. This reduction in complexity opens up new use-cases for inference with gradients especially in the high-dimensional regime, where the information-to-cost ratio of gradient observations significantly increases. We demonstrate this potential in a variety of tasks relevant for machine learning, such as optimization and Hamiltonian Monte Carlo with predictive gradients.
Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?

Most work in NLP makes the assumption that it is desirable to develop solutions in the native language in question. There is consequently a strong trend towards building native language models even for low-resource languages. This paper questions this development, and explores the idea of simply translating the data into English, thereby enabling the use of pretrained, and large-scale, English language models. We demonstrate empirically that a large English language model coupled with modern machine translation outperforms native language models in most Scandinavian languages. The exception to this is Finnish, which we assume is due to inferior translation quality. Our results suggest that machine translation is a mature technology, which raises a serious counter-argument for training native language models for low-resource languages. This paper therefore strives to make a provocative but important point. As English language models are improving at an unprecedented pace, which in turn improves machine translation, it is from an empirical and environmental stand-point more effective to translate data from low-resource languages into English, than to build language models for such languages.
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning

Single-table text-to-SQL aims to transform a natural language question into a SQL query according to one single table. Recent work has made promising progress on this task by pre-trained language models and a multi-submodule framework. However, zero-shot table, that is, the invisible table in the training set, is currently the most critical bottleneck restricting the application of existing approaches to real-world scenarios. Although some work has utilized auxiliary tasks to help handle zero-shot tables, expensive extra manual annotation limits their practicality. In this paper, we propose a new approach for the zero-shot text-to-SQL task which does not rely on any additional manual annotations. Our approach consists of two parts. First, we propose a new model that leverages the abundant information of table content to help establish the mapping between questions and zero-shot tables. Further, we propose a simple but efficient meta-learning strategy to train our model. The strategy utilizes the two-step gradient update to force the model to learn a generalization ability towards zero-shot tables. We conduct extensive experiments on a public open-domain text-to-SQL dataset WikiSQL and a domain-specific dataset ESQL. Compared to existing approaches using the same pre-trained model, our approach achieves significant improvements on both datasets. Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive. More importantly, on the zero-shot subsets of both the datasets, our approach further increases the improvements.
Frivolous Units: Wider Networks Are Not Really That Wide.

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity? If so, how do these depend on the architecture, dataset, and hyperparameters? We identify two distinct types of “frivolous” units that proliferate when the network’s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network computes could be expressed without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.
Consistent-Separable Feature Representation for Semantic Segmentation

Cross-entropy loss combined with softmax is one of the most commonly used supervision components in most existing segmentation methods. The softmax loss is typically good at optimizing the inter-class difference, but not good at reducing the intra-class variation, which can be suboptimal for semantic segmentation task. In this paper, we propose a Consistent-Separable Feature Representation Network to model the Consistent-Separable (C-S) features, which are intra-class consistent and inter-class separable, improving the discriminative power of the deep features. Specifically, we develop a Consistent-Separable Feature Learning Module to obtain C-S features through a new loss, called Class-Aware Consistency loss. This loss function is proposed to force the deep features to be consistent among the same class and apart between different classes. Moreover, we design an Adaptive feature Aggregation Module to fuse the C-S features and original features from backbone for the better semantic prediction. We show that compared with various baselines, the proposed method brings consistent performance improvement. Our proposed approach achieves state-of-the-art performance on Cityscapes (82.6% mIoU in test set), ADE20K (46.65% mIoU in validation set), COCO Stuff (41.3% mIoU in validation set) and PASCAL Context (55.9% mIoU in test set).