Reviewerly/exHarmony huggingface dataset:
Biomedical Text Recognition Using Convolutional Neural Networks: Content Based Deep Learning

Named Entity Recognition (NER) targets to automatically detect the drug and disease mentions from biomedical texts and is fundamental step in the biomedical text mining. Although deep learning has been successfully implemented, the accuracy and processing time are still major issues preventing it from achieving NMR. This research aims to upgrade the accuracy of classification while decreasing the processing time, by paying more attention to significant areas of NMR. The novel proposed system consists of a Bi-Directional Long Short-Term Memory with Conditional Random Field (BiLSTM-CRF) using dropout strategy to effectively prevent overfitting and enhancing the generalization abilities. The system built includes the attention mechanism and attention fusion for redistributing the weight of samples belonging to each class in order to compensate the problem occurring from data imbalance and to focus only on the critical areas of the observed things and ignoring non-critical areas.
A Stage Relighting Method Based on Color Transfer

There are a variety of factors on stage environment such as dramatically changing lights, mutation and similar appearance of performers that make computer vision based performer detection task challenging. Relighting technique is adopted to handle the changing lights problem and can greatly relieve the negative effects caused by changing lights on detection results. Color transfer is one of the commonly used relighting techniques in performer detection task. However, existing color transfer methods will bring artifacts like structural defects, distortions and chromatic noises to processed images that make detection task fail. In this paper, we propose a novel color transfer network to improve the detection results. The proposed network is designed to switch the color distributions between two images. It can either transfers the color style of stage performance to public detection datasets to greatly expand the training datasets, or normalizes the style of captured stage images to some standard template to stable the data distribution. The proposed color transfer network deals with color transfer task in a one-shot framework, i.e. consistently training and predicting, taking advantage of the color name regularization. The effectiveness of the proposed method is validated by experiments on real-captured stage datasets.
Development of the quality assessment information system for infection control in penitentiary institutions

Abstract The article describes the quality assessment system for infection control in penitentiary institutions that was developed using a mathematical model based on analytic hierarchy process (AHP). The following parameters were chosen as indicators of infection control quality level: HIV, tuberculosis, incidence of other infectious diseases, compliance with sanitary regulations and the water quality. The reliability and adequacy of the research has been proved. Quality assessment of infection control is an important criterion for the successful development of effective measures aimed at the improvement of the epidemiological situation in penitentiary institutions.
Knowledge Discovery of News Text Based on Artificial Intelligence

The explosion of news text and the development of artificial intelligence provide a new opportunity and challenge to provide high-quality media monitoring service. In this article, we propose a semantic analysis approach based on the Latent Dirichlet Allocation (LDA) and Apriori algorithm, and we realize application to improve media monitoring reports by mining large-scale news text. First, we propose to use LDA model to mine news text topic words and reducing news dimensionality. Then, we propose to use Apriori algorithm to discovering the relationship of topic words. Finally, we discovery the relevance of news text topic words and show the intensity and dependency among topic words through drawing. This application can realize to extract the news topics and discover the correlation and dependency among news topics in mass news text. The results show that the method based on LDA and Apriori can help the media monitoring staff to better understand the hidden knowledge in the news text and improve the media analysis report.
Maturity Assessment of Cyber Security in The Workforce Management Domain: A Case Study in Bank Indonesia

In the growth of information technology that is very fast, it brings the era to be completely digital or so-called digital age. This phenomenon leads an institution or organization to involve an information technology that can help increase efficiency in carrying out activities and achieving business goals. The use of an information technology does not only have a positive impact. The higher utilization of information technology, the higher the gaps in security incidents and the potential for cybercrimes, so that cybersecurity becomes an important factor to minimize this issue. On the business processes of financial institutions that focus on economic stability and the role of IT as an enabler, however, workforce management that focuses on cybersecurity must be managed properly. This study attempts to assess cybersecurity maturity within the scope of Workforce Management in a case study in Bank Indonesia. The framework used in conducting this assessment is C2M2 Framework which focuses on the Workforce Management Domain. The results of this study indicate that cybersecurity in the workforce management domain at Bank Indonesia has not reached MIL 3. The results of the assessment will serve as a reference for improving cybersecurity in the field of workforce management at Bank Indonesia.
Performance comparison of CKF and UKF in passive state noise of UAV

Abstract Interacting multiple model (IMM) is widely used in target tracking, and is recognized as the most effective method for maneuvering target tracking. In order to remove the motion state noise in UAV passive location, this paper uses the idea of target tracking algorithm for reference. By setting the observation station data points in advance, introducing detection factors into CV and CT models, and then constructing measurement equations through geometric kinematics and fusing IMM algorithm, the filtering performance of IMM-CKF-CV-CT and IMM-UKF-CV-CT under typical s-maneuver in two-dimensional plane is studied. The simulation results of MATLAB show that IMM-CKF-CV-CT has higher accuracy and stronger stability than IMM-UKF.
Load Forecast by Multi-Task Learning Models: Designed for a New Collaborative World

This paper proposes a forecasting model designed for lack of data problems based on Multi-Task Learning techniques (MTL). It is especially useful for evolutionary markets and systems, where new paradigms (like renewable penetration or prosumers) significantly impact behavior and dynamics, creating unforeseen responses that would be unpredictable from past (possibly obsolete) historical data. A case study targeting the recent Brazilian load changes illustrates the approach performance: it was possible to combine data from three different distribution companies, creating a learning network, yielding reliable results where all other models failed.
Design and Application on Complicated Power System Operation Cockpit Technologies

Power system operation cockpit(POC) is the integration of operation smart system(OS2). After 7 years of research and application, it has been popularized in CSG. At the same time, the original design concept of POC has been landed and become an important platform in power system operation. Both the intension and the extension of POC has been enriched by the test of actual application. This paper focuses the framework, function and practical benefits of POC after a profiles of its position in OS2. This paper is a system application summary of POC, which is aimed at supporting the development of power system automatization.
Edge Enhancement Method for Detection of Text From Handwritten Documents

Edge detection from handwritten text documents, particularly of Kannada language, is a challenging task. Kannada has a huge character set, amounting to 17,340 character combinations. Moreover, in handwritten Kannada, the character strokes are highly variable in size and shape due to varying handwriting styles. This chapter presents a solution for edge detection of Kannada handwritten documents. Sobel edge detection method, which efficiently enhances the image contrast and detects the character edges, is proposed. Experimentation of this edge detection approach yielded high F-measure and global contrast factor values.
Functional Data Clustering Analysis via the Learning of Gaussian Processes with Wasserstein Distance

Functional data clustering analysis becomes an urgent and challenging task in the new era of big data. In this paper, we propose a new framework for functional data clustering analysis, which adopts a similar structure as the k-means algorithm for the conventional clustering analysis. Under this framework, we clarify three issues: how to represent functions, how to measure distances between functions, and how to calculate centers of functions. We utilize Gaussian processes to represent the clusters of functions which are actually their sample curves or trajectories on a finite set of sample points. Moreover, we take the Wasserstein distance to measure the similarity between Gaussian distributions. With the choice of Wasserstein distance, the centers of Gaussian processes can be calculated analytically and efficiently. To demonstrate the effectiveness of the proposed method, we compare it with existing competitive clustering methods on synthetic datasets and the obtained results are encouraging. We finally apply the proposed method to three real-world datasets with satisfying results.
A Forgotten Strategy for Pooled Contextualized Embedding Learning

String (word) embeddings are keys to advanced neural natural language understanding (NLU) models. Recently, a new embedding called contextual string embedding (CtxEmb) reached a new state of the art on many NLU tasks. Specially, a pooling variant of CtxEmb demonstrated new best results on named entity recognition (NER) tasks. While the pooling variant is good at reaching new best performance in terms of Micro F1 score, it poses questions on what kind of information in the pool is helpful. In particular, the pooling variant maintains a (possibly long) list of previously seen occurrences of a word, and computes embedding for the word in a new context based on those occurrences. In this paper, we propose a strategy to forget some previous occurrences, and thus exploring which history are beneficial in constructing embeddings for a new context of a word. The proposed forgotten strategy is designed by accounting for a distance metric defined in this paper. Preliminary experiments on the WNUT-17 task show the effectiveness of our forgotten strategy, and uncover that embeddings that are diverse in terms of cosine similarity are helpful in forming an aggregated embedding.
Fine-Grained Recognition of Surface Targets with Limited Data

Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use transfer learning to improve the feature expression capabilities of the model under conditions of limited data. A function based on metric learning is adopted to increase the distance between different classes. Finally, a dataset with eight types of surface targets is established. Comparative experiments on our self-built dataset show that the proposed method focuses more on discriminative regions, avoiding problems like gradient disappearance, and achieves better classification results than B-CNN, RA-CNN, MAMC, and MA-CNN, DFL-CNN.
Automating Customer Claim Registration by Text Mining

In this paper, we present the use of text mining and machine learning in call centers to increase the efficiency of registering customer claims and improving customer satisfaction. Our proposed method makes the process of claim registration faster and more accurate compared to experienced call center agents. Use of text mining and machine learning techniques will increase the customer satisfaction and endows the call center staff with better ways to help the customer.
Least Square Adversarial Autoencoder

This research introduces least square adversarial autoencoder (LSAA)-an autoencoder that is able to reconstruct data and also generate data that has characteristics similar to data distribution from the prior distribution LSAA uses least square generative adversarial network loss function on its discriminator. LSAA minimizes Pearson χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> divergence between the latent variable distribution and the prior distribution. In this research, a Python program is developed to model LSAA by utilizing MNIST data set and FashionMNIST data set. The program is implemented using PyTarch. All of the programming activities are carried out in the cloud environment provided by the Tokopedia-Universitas Indonesia AI Center, using DGR-1 (GPU Tesla V100) as its computing resource. The experimental results show that the mean squared error of LSAA for MNIST data set and FasbionMNIST data set are 0.0080 and 0.0099, respectively. Furthermore, the Fréchet Inception Distance score of LSAA for MNIST data set and FashionMNIST data set are 11.1280 and 27.5737, respectively. These results indicate that the least square adversarial autoencoder is able to reconstruct the image properly and also able to generate images similar to the training samples.
A Local Feature Descriptor based on Improved Codebook Model

The commonly used local descriptors often suffer from the sensitivity to image rotation. To overcome such disadvantages, this paper proposes a new local feature descriptor based on an improved codebook of ascending permutation. The proposed descriptor is composed of four steps. Firstly, the original image is uniformly divided into numerous blocks. Then, the pixel values in each block are arranged into column vectors in clockwise. Subsequently, the pixel values in the column vector are sorted in ascending permutation. Finally, such pixel values that arranged in ascending order are used as the local feature descriptor for the given image block. The proposed local features can also be transferred to improve the classical local descriptors. To validate the effectiveness of the proposed descriptor, extensive experiments are conducted on Caltech 101 dataset, and the results demonstrate that the improved model of ascending permutation is more robust to image rotation than original ones.
The Importance of Where Central Bank Digital Currencies Are Custodied: Exploring the Need of a Universal Access Device

Issuance of digital currency is not new, but DLT enables to consider monetary policy and financialinclusion in a new way. CBDC is so disruptive, it can change money. As digital asset trading evolved into a market, developers released digital wallets for their storage. While these attempts meet the usersâ€™ needs, their technical level is open to improvements. The development of a secure wallet infrastructure can solve critical problems, like ensuring equal access to banking service, also offering a novel approach to digital identity. Whenever a financial transaction is carried out, we must face problems with fair competition, ownership, and management of our personal data. Infrastructure is needed to protect and support our digital rights. This technology is ready to develop a secure UAD, a single key tool for protecting and representing us and the organisation to keep it in a network. UAD would enable the full benefit of DLT and restore the separation of payment institutions and investment banks at the core of political agendas, store SSI related information, facilitate micropayments, and tackle using money for illicit purposes more effectively. UAD is a citizen-centric device, a symbol of the new digital currency and a tool for social inclusion.
CoiLED Display: Make Everything Displayable

We propose CoiLED Display, a flexible and scalable display that transforms ordinary objects in our environment into displays simply by coiling the device around them. CoiLED Display consists of a strip-shaped display unit with a single row of attached LEDs, and it can represent information, after a calibration process, as it is wrapped onto a target object. The calibration required for fitting each object to the system can be achieved by capturing the entire object from multiple angles with an RGB camera, which recognizes the relative positional relationship among the LEDs. The advantage of this approach is that the calibration is quite simple but robust, even if the coiled strips are misaligned or overlap each other. We demonstrated a proof-of-concept prototype using strips with a 5-mm width and containing LEDs mounted at 2-mm intervals. This paper discusses various example applications of the proposed system.
Flood Points Distribution Mapping for the Determination of Evacuation Routes and Disaster Posts in Gorontalo City

This research used Geographic Information System (GIS) approach with Round Graph method. Data used in this research are high resolution satellite imagery, Peta Rupa Bumi Indonesia (RBI) scale 1: 25.000 and Statistics data. High resolution satellite imagery was used to obtain data about the distribution of flood points, road map and locations of disaster posts. After obtaining those data, then the quickest and nearest evacuation routes can be determined by using round graph method so that the casualty of flood hazard can be significantly reduced and effectively helped.
Learning Spread Probability Based on User’s Influence and Flavour

In this paper, we propose a new topology free model of social network information dissemination, IPM for short, based on the representation learning method. We construct two latent spaces: user influence space and user interest space. Each user and each propagation item are embedded to feature vectors in latent space. When the model predicts the probability of a user receiving a propagation item, it considers not only the influence from other users but also the user's flavour for the propagation item. We calculate propagation probability according to the distance between vectors. Experimental results on real data show that the model can simulate diffusion and predict more accurately. It is superior to the state-of-the-art model in many metrics.
Penalized estimation in finite mixture of ultra-high dimensional regression models

In this paper, we propose a penalized estimation method for finite mixture of ultra-high dimensional regression models. A two-step procedure is explored. Firstly, we conduct order selection with the number of components unknown. Then variable selection is applied to ultra-high dimensional regression models. A specific EM algorithm is designed to maximize penalized log-likelihood function. We demonstrate our method by numerical simulations which performs well. Further, an empirical study of return on equity (ROE) prediction is shown to consolidate our methodology.
Tips and tools to automate OMNeT++ simulations and to facilitate post data management tasks

Report with tips and tools for PhD students who need to start working with tools like OMNeT++, SUMO; OpenStreetMaps. We collect here a list of tips to help them to speed up the learning process. Also, we introduce the researcher to machine learning techniques focusing on neural networks. We give tools, references and useful examples to start with. Finally, we also comment how to use latex editors easily.
Word Alignment Model Based on Maximum Entropy in Foreign Language Translation

Automatic mining of bilingual knowledge from large-scale real bilingual corpus has become a very important way to acquire translation knowledge. Therefore, this paper proposes a word alignment algorithm to acquire translation knowledge manually. First, a hybrid heuristic word alignment algorithm is implemented on the Chinese and English parallel corpus of clause alignment. Then, through a lot of research and practice of existing methods, a maximum word alignment model with noise training is proposed. The English and Chinese word entities are extracted from English and Chinese parallel corpora to form multiple candidate entity equivalent pairs, and the eigenvalues of multiple features between each candidate equivalent pair are calculated. Finally, using the entity equivalent pair alignment module, the candidate word entity equivalent pairs are aligned using the maximum entropy model, to obtain the final set of named entity equivalent pairs. The experimental results show that the maximum entropy alignment model achieves better alignment accuracy in the noise training environment.
Coordinate Descent Method for Log-linear Model on Posets

In this study, we address a learning problem of probabilistic models that represent high-order interactions among discrete attributes. To include the second-order interaction of discrete attributes, probabilistic models such as Ising models and Boltzmann machines are widely used. Both are regarded as special cases of the log-linear model on partially ordered sets (posets), which can represent not only second-order but also higher-order interactions between discrete attributes. Such a model is also known by its preferable information-geometric structure. We propose a coordinate descent method for efficient learning of the log-linear model on posets and present an information-geometric understanding of its functionality. The proposed method has no hyperparameter, whereas the standard gradient descent method requires the stepsize to be set appropriately. We theoretically and empirically show that our proposed method is faster than the gradient descent method in learning distributions by the log-linear model on posets.
A Cost-Effective Method for Improving and Re-purposing Large, Pre-trained GANs by Fine-Tuning Their Class-Embeddings

Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities. Specifically, BigGANs—a class-conditional Generative Adversarial Networks trained on ImageNet—achieved excellent, state-of-the-art capability in generating realistic photos. However, fine-tuning or training BigGANs from scratch is practically impossible for most researchers and engineers because (1) GAN training is often unstable and suffering from mode-collapse; and (2) the training requires a significant amount of computation, 256 Google TPUs for 2 days or 8 \(\times \) V100 GPUs for 15 days. Importantly, many pre-trained generative models both in NLP and image domains were found to contain biases that are harmful to the society. Thus, we need computationally-feasible methods for modifying and re-purposing these huge, pre-trained models for downstream tasks. In this paper, we propose a cost-effective optimization method for improving and re-purposing BigGANs by fine-tuning only the class-embedding layer. We show the effectiveness of our model-editing approach in three tasks: (1) significantly improving the realism and diversity of samples of complete mode-collapse classes; (2) re-purposing ImageNet BigGANs for generating images for Places365; and (3) de-biasing or improving the sample diversity for selected ImageNet classes.