Reviewerly/exHarmony huggingface dataset:
Prediction of Claim Probability with Excess Zeros

Non-life insurance pricing is based on two components: claim severity and claim frequency. These components are used to estimate expected pure premium for the next policy period. Generalized linear models (GLM) are widely preferred for the estimation of claim frequency and claim severity due to the ease of interpretation and implementation. Since GLMs have some restrictions such as exponential family distribution assumption, more flexible Machine Learning (ML) methods are applied to insurance data in recent years. ML methods use learning algorithms to establish relationship between the response and the predictor variables as an intersection of computer science and statistics. Because of some insurance policy modifications such as deductible and no claim discount system, excess zeros are usually observed in claim frequency data. In the presence of excess zeros, prediction of claim probability can be a good alternative to the prediction of claim numbers since positive numbers are rarely observed in the portfolio. Excess zeros create imbalance problem in the data. When the data is highly imbalanced, predictions will be biased toward majority class due to the priors and predicted probabilities may be uncalibrated. In this study, we are interested in claim occurrence probability in the presence of excess zeros. A Turkish motor insurance dataset that is highly imbalanced is used for the case study. Ensemble methods that are popular ML approaches are used for the probability prediction as an alternative to logistic regression. Calibration methods are applied to predicted probabilities and results are compared.
Innovation of College English Teaching Based on the “Internet+” Era

In recent years, advanced teaching methods combining the Internet and the classroom, such as MOOC, micro-classes, video open classes, and flipped classrooms, have achieved impressive results. They have not only played a pioneering role in promoting the improvement of the quality of college English teaching, but also greatly It has promoted the innovation of teaching mode and made great contributions to the cultivation of comprehensive quality and practical English talents. The article studies the innovation of college English teaching based on the “Internet + “ era.
Improving Dialog System Grounded with Unstructured Knowledge by Domain Adaptive Pre-Training and Post-Ranking

Linguistic intelligence and the ability to converse with human are important and indispensable parts of humanoid robots. One of the most challenging tasks in knowledge-grounded task-oriented dialog systems (KTDS) is the knowledge selection task, which aims to find the proper knowledge snippets to respond to user dialog requests. In this paper, we first propose domain adapted-BERT (DA-BERT) which employs pre-trained bidirectional encoder representations from transformers (BERT) with domain adaptive training and dynamic masking probability for knowledge selection in KTDS. Domain adaptive training can minimize the domain gap between the general text data that BERT is pre-trained on and the dialog–knowledge joint data while dynamic masking probability enhances the training in an easy-to-hard manner. After knowledge selection, the next task in KTDS is knowledge-grounded generation. To improve the performance in knowledge-grounded generation, we propose GPT-PR to employ post-ranking on the generator’s outputs. Post-ranking eliminates the possibility of generating hallucination response by a large portion during the sampling-based decoding process and thus can improve the quality of the generated response. Experimental results on the benchmark dataset show that our proposed pre-training and post-ranking methods, DA-BERT and GPT-PR, respectively, outperform the state-of-the-art models with large margins across all the evaluation metrics. Moreover, in the experiments, we also analyze the bad cases of DA-BERT and GPT-PR and do visualizations to facilitate further research in this direction.
An efficient framework for sentence similarity inspired by quantum computing

Accurately extracting the semantic information and the syntactic structure of sentences is important in natural language processing. Existing methods mainly combine the dependency tree to deep learning with complex computation time to achieve enough semantic information. It is essential to obtain sufficient semantic information and syntactic structures without any prior knowledge excepting word2vec. This paper proposes a model on sentence representation inspired by quantum entanglement using the tensor product to entangle both two consecutive notional words and words with depen-dencies. Inspired by quantum entanglement coefficients, we construct two different entanglement coefficients to weight the different semantic contributions of words with different relations. Finally, the proposed model is applied to SICK_train to verify their performances. The experimental results show that the provided methods achieve perfect results.
Analyzing Decision Trees to Understand MNIST Misclassification

The MNIST dataset of handwritten digits is a classic tool for the study of machine learning. In this paper we use MNIST and a decision tree classifier to make a case study of misclassification for the digits three and five, analyzing the nodes of the decision tree plot to understand the process of classification. The paper concludes with a discussion of possibilities for our research and a look at some related, higher-level research that has been done.
A POI-Sequence Recommendation Method Based on an Exploitation-Exploration Strategy

In recent years, with the development of location-based services and widely-used social networks, people can easily share their activities and location with their friends on the social network. Meanwhile, large amounts of data generated by social networks provide an opportunity for mining user behaviors and realize accurate personalized service recommendations. Previous studies focus on a single Point of Interest (POI) recommendation while few consider recommending a POI sequence. This paper proposes a POI-sequence recommendation method based on an exploitation-exploration strategy. It utilizes the historical data from the social network, fully considers the public preference and user’s personalized preference. After obtaining the exploration score from the historical records, the POI-sequence with the highest overall preference score is recommended to the user. This method can ensure the diversity of recommended POIs. Besides, a breadth-first search method is adopted to improve the recommendation efficiency. Finally, we verify the effectiveness of the proposed method through experiments on real-world datasets.
Neural networks for medical image segmentation

This article focuses on the methods of segmentation of images of the human abdomen, the main emphasis is on the methods of segmentation based on neural networks. The structure of neural network architectures, which are now widely used for medical imaging, has been researched and compared based on experimental studies.
SiamPBN: Point-based Siamese Network for Rotating Objects Tracking

In recent years, the Siamese network based trackers have achieved outperforming results in the metrics of speed and accuracy in terms of visual tracking. However, it is difficult for the Siamese networks to achieve the accurate location of rotating objects. This paper proposes an efficient network architecture named SiamPBN to resolve rotation problem in tracking. We find that the predicted center of the object deviates from the center of the predicted box while the object rotates continuously. Therefore, we add a centerness head to the network to improve the performance of handling rotating objects. In addition, SiamPBN obtains the predicted outputs by point-based instead of anchor-based method, thus avoiding the complex computation associated with anchors. Our proposed method has gained the excellent performance on the datasets, including GOT-10k, LaSOT and VOT2019.
A Recommending System for Mobile Games Using Dynamic Nonparametric Model

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully Bayesian approach into the traditional dynamic collaborative filtering and hence captures the change of customer preferences in the mobile game industry with higher flexibility. We demonstrate that our DNP model closely predicts game usage patterns with higher accuracy than other widely used benchmarks. We also present various applications that reveal the managerial relevance of our model. Personalized recommendation lists of games generated by the DNP model outperform benchmarks in terms of adoption rate and repeated usage. Our model can also be used to improve the prediction performance of future in-app purchases.
Peculiarities of the organization and conduct of first aid trainings by rescue teams personnel

The purpose of the article is to familiarize the readers with the five-step methodology of training rescue workers in first aid. The proposed methodological techniques are based on a two-stage approach to providing first aid to victims in peacetime emergencies and military conflicts, including conditions of mass sanitary losses. A detailed description of the stages of the formation of knowledge, skills and abilities in the learning process will allow teachers to apply this technique when conducting first aid classes. The main attention in this article is paid to the fourth stage of training, at which the skills to provide first aid are formed in accordance with the order of elimination of life-threatening consequences of damage.
An Empirical Investigation of Authorial Writing Styles Based on a Vietnamese Corpus

A growing body of evidence suggests groups of people have their unique writing style, e.g., language use of females-males, the elderly-the youth, and so on.However, previous studies of Vietnamese texts have not dealt with language use associated with authors' occupation.This study aims to portray the different ways in which technical and non-technical groups of authors use personal pronouns and negative words based on VVC_JSEAL, a ten-thousandword Vietnamese news corpus.A combined qualitative and quantitative methodological approach was used to investigate authorial writing styles, including correspondence analysis along with sociolinguistic interpretation.Analysis of word use for occupational groups of authors revealed the most significant associations on writing style which were identified for either first or third personal pronouns.However, negative words demonstrate their insignificant associations, while a combination of these words and personal pronouns has more significant relationships with authorial writing style.This study implies that occupational group of authors has a unique linguistic style, and word-level features of that style will recur with a relative stable frequency.
Pupil changes in persons aged 8 to 79 during text reading – an eye-tracking study

Summary The Eye Tribe eye-tracker was used to capture pupil sizes and fixation times of 40 people aged 8 to 79 years during text reading. The dependence of the number of readable lines on the participants’ age was determined. A function describing the dependence of the eye surface area on age was also derived. Visual perception of the maximum number of consecutive lines with decreasing text font size is best for people aged 30–40. For the studied age group, the pupil area decreased with age by approximately 300%. An approximately two-fold increase in average fixation times was recorded.
NimbleLearn: A Scalable and Fast Batch-mode Active Learning Approach

Batch-mode active learning iteratively selects a batch of unlabeled samples for labelling to maximize model performance and reduce total runtime. To select the most informative and diverse batch, existing methods usually calculate the correlation between samples within a batch, leading to combinatorial optimization problems which are inefficient, complex, and limited to linear models for approximated solutions. In this paper, we propose NimbleLearn, a scalable deep imitation batch-mode active learning approach to address these drawbacks. NimbleLearn sequentially predicts an "ideal sample" by a deep policy network for each batch. Such ideal sample maximizes the model performance when combined with the labeled samples and the already-selected samples in the current batch. Unlike the existing batch-mode active learning methods which directly select one batch of samples from unlabeled ones, NimbleLearn reduces the dimension of the policy network output to the number of features (assuming the number of unlabeled samples is much greater than the number of features). In addition, NimbleLearn is a general framework and can be applied in both linear and nonlinear models. Experiments conducted on 4 public datasets show NimbleLearn can achieve similar or better performance as existing SOTA algorithms, while reducing the number of labeled samples and runtime by over 50%.
Exploiting Intra and Inter-field Feature Interaction with Self-Attentive Network for CTR Prediction

Click-Through Rate (CTR) prediction models have achieved huge success mainly due to the ability to model arbitrary-order feature interactions. Recently, Self-Attention Network (SAN) has achieved significant success in CTR prediction. However, most of the existing SAN-based methods directly perform feature interaction operations on raw features. We argue that such operations, which ignore the intra-field information and inter-field affinity, are not designed to model richer feature interactions. In this paper, we propose an Intra and Inter-field Self-Attentive Network (IISAN) model for CTR prediction. Specifically, we first design an effective embedding block named Gated Fusion Layer (GFL) to refine raw features. Then, we utilize self-attention to model the feature interactions for each item to form meaningful high-order features via a multi-head attention mechanism. Next, we use the attention mechanism to aggregate all interactive embeddings. Finally, we assign DNNs in the prediction layer to generate the final output. Extensive experiments on three real public datasets show that IISAN achieves better performance than existing state-of-the-art approaches for CTR prediction.
Named Entity Recognition of Thai Documents using CRF with a Simple Data Masking Technique

We examined the Named Entity Recognition (NER) of organizations in the Thai government’s project documents using a simple data masking technique with the help of an external dictionary. Our framework demonstrated its potential in the case that the external dictionary was incomplete and might not be used to label the training data exhaustively. A data masking technique on the administrative area part of the organization names was employed in an attempt to discover more organization entities outside the dictionary. The experimental results showed that our model gained higher recall while sacrificing a relatively small amount of precision. The proposed approach was also capable of recognizing entities which were never seen in the dictionary.
Geometric modeling of emotions of virtual characters

Methods and tools for modeling emotions of virtual computer characters are considered. Particular attention is paid to 3D modeling of the cranial vault, mandible and teeth, as well as facial muscles and tongue. Modeling was performed using the ZBrush program using information about the anatomical structure of the skull and facial muscles, such as the occipital and temporal muscles, the arrogant muscle, the depressor, the masseter muscle, the small zygomatic muscle, the zygomaticus major muscle and the muscle lifting the angle of the mouth, the muscle lowering the angle of the mouth, the muscle that lifts the upper lip and the wing of the nose, the chin muscle and the muscle that lowers the lower lip. The resulting high-poly model was then retopologized with a decrease in the number of polygons. An example of modeling the character's head is given. Animation is made in Autodesk Maya, which is adopted by many large film and animation studios and is used more often among professional 3D artists. The principles of rigging (adding a digital skeleton and its controls to the model) and skinning (attaching the vertices of the surface that simulate the skin to the corresponding areas of the digital skeleton) are described. The anatomical signs of the manifestation of basic emotions are given and the control of the obtained model for the manifestation of some emotions is described.
RRS: An Explainable Model Scale Search Strategy

Aiming at the problem that the existing CNN model cannot flexibly adapt to the model scale according to the needs of task data, we start from the correspondence between the data and the model, and propose a model scale search strategy based on Reinforced Real-Time Sequence (RRS). RRS first preprocesses the image data set based on an interpretable method, and then extracts the mapping rules between the data and the model through a real-time enhanced sorting algorithm. Through RRS, we can not only adapt the scale of a single model adaptively according to the scale of different data sets, but also provide a robust adaptation result for multiple models.
Monocularly Generated 3D High Level Semantic Model by Integrating Deep Learning Models and Traditional Vision Techniques

Scene reconstruction using Monodepth2 (Monocular Depth Inference) which provides depth maps from a single RGB camera, the outputs are filled with noise and inconsistencies. Instance segmentation using a Mask R-CNN (Region Based Convolution Neural Networks) deep model can provide object segmentation results in 2D but lacks 3D information. In this paper we propose to integrate the results of Instance segmentation via Mask R-CNN&#x2019;s, CAD model Car Shape Alignment, and depth from Monodepth2 together with classical dynamic vision techniques to create a High-level Semantic Model with separability, robustness, consistency and saliency. The model is useful for both virtualized rendering, semantic augmented reality and automatic driving. Experimental results are provided to validate the approach.
Dy-Drl2Op: Learning Heuristics for TSP on the Dynamic Graph via Deep Reinforcement Learning

In recent years, learning effective algorithms for combination optimization problems based on reinforcement learning has become a popular topic in artificial intelligence. In this paper, we propose a model Dy-Drl2Op that combines the multi-head attention mechanism with hierarchical reinforcement learning to solve the traveling salesman problem on a dynamic graph. In the Dy-Drl2Op model, we design a policy network to process the feature vector of each node, with the help of distributed reinforcement learning algorithm to quickly predict the probobility of each node being selected at the next step. Dy-Drl2Op also utilizes the beam search algorithm to explore the optimal solution in the whole solution space. The trained model will generate the action decision sequence that meets the specific target reward function in real time. Dy-Drl2Op is evaluated on a series of dynamic traveling salesman problems. The experimental results show that Dy-Drl2Op is better than several baseline models in terms of solution quality and efficiency.
Building Intelligent Navigation System for Mobile Robots Based on the Actor – Critic Algorithm

This article presents the construction of an intelligent automatic navigation system for mobile robots in a flat environment with defined and unknown obstacles. Programming tools used in the studies are the operating system for mobile robots (Robot Operating System – ROS). From the updated information on maps, operating environment, robot control position and obstacles (Simultaneous Localization and Mapping (SLAM)), we can calculate the motion trajectory of the mobile robot. The navigation system calculates the global and local trajectory for the robot based on the application of Actor-Critic (AC) algorithm. The results of simulation studies in the Gazebo environment and the experimental run on the real Turtlebot mobile robot showed the practical efficiency of automatic navigation for this mobile robot.
The Role of IoT in Digitalizing Mining Sector of Bangladesh

Mining is expected to play an important part in Bangladesh’s economic development, regardless of the country’s economy, with potential outcomes in terms of business, manufacturing, and employment. Recent developments in technologies such as the Internet of things (IoT) and Artificial Intelligence (AI) have paved the way for digitalizing the mining sector. When these technologies are deployed in the mining industry, the results acquired may be used immediately to support process optimization, machine health, worker safety, and asset management. Similarly, mine digitization is expected to provide significant possibilities and benefit Bangladesh’s Industrial growth. However, digital mining is difficult to implement in reality owing to constraints in communication, data management, and storage infrastructure. Furthermore, the mining companies’ inclination to remain with outdated methods rather than depending on experimental novel technologies hinders development. This paper briefly describes the architecture of mine IoT, different applications, and potential challenges to enable mine digitalization from the perspective of Bangladesh.
Label Preserved Heterogeneous Network Embedding

Recently, the heterogeneous network embedding (HNE for short) methods have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, the rich node label information is not considered by these HNE methods, which leads to suboptimal node embeddings. In this paper, we propose a novel Label Preserved Heterogeneous Network Embedding (LPHNE) method to tackle this problem. Briefly, for each type of the nodes, LPHNE projects these nodes and their labels into a same low-dimensional hidden space by modeling the interactive relationship between the labels and the contexts of the nodes. Thus, the discriminability of node embedding is improved by utilizing the label information. The extensive experimental results demonstrate that our semi-supervised method outperforms the various competitive baselines on two widely used network datasets significantly.
An Improved Approach for Removal of Salt and Pepper Noise in MR Images

Jyotiyana, Monika Kesswani, Nishtha Agarwal, AnupamRemoval of the noise entirely is the most intricate process, as it sometimes ruins the visual excellence of image along with its details. So, the exclusion of noise is imperative for the better quality of images. Noisy pixels also reserves the memory; that space is unnecessary reserved for senseless data. Noise added in an image during image acquisition mostly. In this manuscript, we present an efficient method for removing salt and pepper noise using the modified weighted mean filter. It includes noise identification and removal processes from the Magnetic Resonance Images. And reproduce an enhanced de-noised image. In the proposed method, we use the cosine rule for detecting the noisy pixel and replaced by their mean value within a window size 3 $$\times $$ 3. Similarly, we applied the proposed methods on various filters like alpha trimmed filter, mean, mode and median filter and all the filters give promising results. Experiment results indicate efficiency over other approaches in removing salt-and-pepper noise.