Reviewerly/exHarmony huggingface dataset:
A Combinatorial Recommendation System Framework Based on Deep Reinforcement Learning

In this paper, we propose a combinatorial recommendation system based on deep reinforcement learning (DRL). Specifically, we focus on the combinatorial product recommendation problem, design a consumer behavior simulator, and utilize deep reinforcement learning to find appropriate product combinations that can improve the sales of the platform. In order to replace real users and conduct massive real-time interactive training with recommendation system, user short- term characteristics are extracted from user-click-history by hierarchical recurrent neural network to train a user simulator. Through ingenious modeling, we transform the NP-hard combinatorial optimization problem into a multi-step sequential decision problem, and construct a framework of combinatorial recommendation system based on DRL. Relevant experiments show that the binary accuracy of the user simulator in predicting users’ consumption behavior reaches more than 80%, and the combinatorial DRL based recommendation system improves the platform sales and provides attractive combinations of products for customers.
Modeling Future Aviation and IT Specialists’ Professional Skills Development on Mathematical Practical Training with Application of Information Technologies

Specialist of any technical profile must have specific professional features and competencies. These characteristics of future professionals should preferably be formed in the process of studying both special and basic scientific disciplines. The result of the educational process is formation of both hard and soft skills of students. The purpose of this work is to study specifics of organization of educational process, contributing to the formation of students' intellectual skills, which are important for their future work. We try to link the theoretical constructions to the professional problems as much as possible so that students see the utility value of mathematics. Team work skills in a multinational team may be well developed in practical training specifically organized. Different models for organization of the teamwork on mathematical practical training in English-speaking multinational academic groups are considered.
The Increased Need for Cybersecurity in Developing Countries

With the coronavirus pandemic (SARS-CoV-2 or COVID-19) having such an immense impact on the world, a global reliance on communication through ICTs and digital formats has been forced. This means that more people are forced to use technologies for activities that they otherwise would not (i.e., banking), thusly increasing the traffic on cyberspace and advancing the risk of people being victims to cybercriminals. This is particularly problematic for developing countries due to their lack of institutional and legal frameworks to combat cybercrime as well as a lack of education and awareness of the dangers that are present in the cyberspace or how to combat them, a lack of training in governmental institutions to detect and deter such crimes and the lack of cooperation between the public and private sectors. The need for cybersecurity in developing countries, with the current pandemic particularly, is therefore at an all-time high.
On-Road Object Collision Point Estimation by Radar Sensor Data Fusion

This paper proposes an object collision point estimation scheme by developing a new data fusion method in a multi-radar network environment. In order to reduce radar’s estimation error due to measurement uncertainty, we first design radar accuracy models determined by the position of each object. Then, an interacting multiple model (IMM) filter based on occupancy zones is designed for accurate object estimation. For a multi-radar network’s object estimation, we also design a radar data fusion method using the estimated object information through the IMM instead of the object estimation information given by the radars. A collision point identification problem, where multiple sensors calculate the different vehicle surface points of the same object, is solved by developing the data fusion method to estimate the object surface’s collision point closest to the ego vehicle center. The utility of the proposed scheme was validated through a scenario-based object estimation experiment. We confirmed that the proposed data fusion method produced substantially improved error distributions over conventional methods.
Quantitative Research on the Differences of Indonesian News Texts

Indonesian, which is a relatively young language, is developed from the ancient Malay language. Studying differences of Indonesian news texts is meaningful for studying the Indonesian language development. This paper analyzes Indonesian news' vocabulary richness and content richness. We try to research the differences among the texts of different types, different categories and different years through the quantitative research on the titles and bodies of six categories of news of Indonesia's Antara News Agency from 2013 to 2021. The experimental results show that the difference in vocabulary richness between news titles and news bodies is small, while the difference in content richness is large. Secondly, no significant vocabulary richness difference is found between news texts of different categories and different years. Thirdly, with the development of time, the usage of Indonesian is also constantly developing and changing.
Threat Prediction of Abnormal Transaction Behavior Based on Graph Convolutional Network in Blockchain Digital Currency

There are some malicious traders in the current blockchain digital currency market, and their abnormal transaction behaviors have seriously threatened the security of a large number of users’ assets. Therefore, the research on the threat prediction method of abnormal transaction behavior of blockchain digital currency is beneficial to actively prevent the threat of abnormal transaction behavior, and is of great significance to maintain the ecological environment health of blockchain digital currency. The representative Bitcoin is a currency with the highest market value among many blockchain digital currencies, with a huge number of users and transactions. The Bitcoin trading system is dynamic, and different types of abnormal transaction behaviors have different structures, so it is extremely challenging to predict the threat of abnormal transaction behaviors. This paper designs a new threat prediction method for abnormal transaction behavior. Specifically, through the analysis of the transaction relation pattern of typical abnormal transaction behaviors of Bitcoin, the abnormal transaction behaviors are uniformly modeled as an object-relation pattern. Then, the research proposes TSRGL framework, which uses R-GCN to learn the topology structure of the historical object-relation snapshot graph, and realizes the threat prediction of abnormal transaction behavior. To evaluate the effectiveness of this approach, we verified the effectiveness of the TSRGL framework based on real Bitcoin abnormal transaction behavior dataset. The experimental results show that TSRGL framework has better performance than the baseline methods and is currently the most suitable framework for the threat prediction of abnormal transaction behavior.
MRSNet: A Spatial and Channel Attention Integration Network Considering Multi-Resolution Improves Image Denoising

Recently, convolution neural network (CNN) and attention mechanism are widely used in image denoising. A novel CNN feature extraction block is proposed in this paper, namely, MRSBlock, which processes multiple image resolutions and introduces sparse attention. Ablation experiments on MRSBlock show it improves the denoising effect. This paper presents MRSNet based on MRSBlock and channel attention mechanism to achieve an effective combination of sparse attention and channel attention in image denoising. Experiments results show that MRSNet gets competitive peak signal-to-noise ratio (PSNR) results with fewer parameters and computation cost.
Semi-Supervised Natural Language Processing Approach for Fine-Grained Classification of Medical Reports

Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning approaches burdens each development task as time and resource intensive. The vast majority of dense clinical information is stored in written reports, detailing pertinent patient information. The challenge with utilizing natural language data for standard model development is due to the complex and unstructured nature of the modality. In this research, a model pipeline was developed to utilize an unsupervised approach to train a language model, a bidirectional recurrent neural network, to generate document encodings; which then can be used as features passed into a classifier model that requires magnitudes less labeled data than previous approaches to differentiate between fine-grained disease classes accurately. The language model was trained on raw clinical reports from the Partner's Healthcare database <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=218,159)$</tex> and terminated with a loss of 1.62 and a word prediction accuracy of 62%. The classification models were trained on three labeled datasets, occlusion <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=1403)$</tex> , stroke <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=331)$</tex> , and hemorrhage <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=4350)$</tex> , to identify a variety of different occlusions, stroke, and hemorrhages, directly from the clinical report data; resulting in AUCs of 0.98, 0.95, and 0.99, respectively, for the occlusion, stroke, and hemorrhage datasets. The output encodings are able to be used in conjunction with images or waveform data, to create models that can process a multitude of different modalities. The ability to automatically extract relevant features from textual data allows for faster model development and integration of textual modality, overall, allowing clinical reports to become a more viable input for more encompassing and accurate deep learning models.
A Sentiment-aware Delightful Walking Route Recommendation System Considering the Scenery and Season

With the aggravation of the aging society, people choose exercise by walking increasingly for maintaining health or losing weight. However, even if we understand that exercising regularly has many benefits for our body and brain, it is not easy to keep on it. Therefore, the research of walking support system is of great significance. In order to make the walking route more interesting, in this paper, we propose a sentiment-aware delightful walking route recommendation system considering the scenery and season. Firstly, we analyze the content of tweets, calculate the emotional score by utilizing sentiment analysis tools, and extract the related words of season and sentiment features. Secondly, we calculate the cosine similarity of related words and geotagged tweets, and recommend the walking routes using the location of the tweets. Finally, to verify the validity of our walking route recommendation system, we ask 41 test subjects to evaluate our system by watching videos of walking routes, which are generated by tweet contents and Google Street View.
Emotion and Worry Measurement Comparison of United Kingdom and Thailand During The First COVID-19 Lockdown Situation

The epidemic situation of the Coronavirus Disease 2019 (COVID-19) and the measures to control the spread of the disease that the government enforced have greatly affected the daily lives of Thai people because they must suffer various pressures, stress, and anxiety. For this research, the researchers compared the anxiety caused by the novel coronavirus disease 2019 (COVID-19) between the UK and Thailand to see if they are similar or different. In sentiment analysis, the collection of data comes from the discussion on the web forums. In this research, the researcher used information from pantip.com to collect topics related to coronavirus disease 2019 and disease control measures from the government from March 2020 to May 2020. It was found that 10,746 available thread titles were extracted from all topics, and the first collected threads were then re-checked to see if their content could be used for further analysis. The chosen topics were analyzed by counting words that expressed various emotions. Thailand's information will use the AI for THAI platform, which provides artificial intelligence (AI) services under the concept of "Thai AI" by extracting emojis to express emotions that will be converted into words. The results found that Thai people responded most emotionally to the matter in a sad mood. The results are compared with the UK data, including a sample of 2,500 people living in the UK, by having respondents write a short text and a long text of 5,000 examples of how they felt about the epidemic situation and government measures at that time. Emotions were extracted through the LIWC technique, and the results showed that most of them had more anxiety than other feelings, which were different from those in Thailand. However, most of the sorrows and worries influence jobs and incomes in both countries, but in Thailand, debt is another factor that causes more sadness than just worry. According to this research, researchers consider it essential to study and analyze people's emotions to know the impact on people's emotions, which may affect their lifestyles when faced with other serious disease outbreaks in the future. The agency or organization can use this information to plan and adapt the measurements to have the least impact on people's mental health.
Designing a Deceptive Comment Detection Platform with a Rule-based Artificial Intelligent Architecture

One of the most important factors in a purchasing decision nowadays is the evaluation of comments online. Businesses or individuals use deceptive comments to mislead people for the sake of economic gain and thus hurt the benefit of the customers and the welfare of society. In this research, we propose a rule-based artificial intelligence (AI) machine learning (ML) architecture for online review fraud detection. The proposed methodology features an AI and ML hybrid architecture, where ML refers to the common well-developed machine learning models, and the AI part features a rules-based controller that prioritizes and customizes the fraud detection rules based on human intelligence to improve the accuracy of the result and computational efficiency.
Case Study in Fall Prevention in Indoor Environments

Falls are one of the most common and problematic issues for the elderly, especially for the elderly living alone or going out independently. Falling can cause severe injuries to the elderly such as disability, hospitalisations and premature death. Aligning with the government policy, i.e. "Aging in place", this chapter aims to design an IoT-based intelligent health system with smart devices (IIHS) to prevent falls and maintain the autonomy of the elderly in an indoor environment. In IIHS, it integrates IoT, fog computing and artificial intelligence for (i) enhancing the balancing ability of the elderly and (ii) achieving better health monitoring and planning and forecasting.
A Comparative Analysis with the Hybrid Algorithm Approach for Sentimental Analysis through Machine Learning

This research work focuses on the latest studies that have used Machine learning to find a solution to sentiment analysis problems related to sentiment polarisation. In preprocessing steps, the Models applied to stop words and Bag of words to collect datasets. Even with the widespread usage and acceptance of some approaches, a superior technique for categorising the polarisation of text documents is tough to make out. Machine learning has lately evoked attention as a method for sentiment investigation. The present work proposes a machine learning-based hybrid algorithm that incorporates N-gram technique as feature extraction. It combines a Decision tree classifier and Random forest Classifier techniques as a classification for sentiment analysis. Naïve bayse, linear classifier and support vector machine approaches are perform in the perspective of sentiment classification. Finally, a comparative study with the different supervised algorithms implemented on the product reviews dataset. The performance of the model evaluated on the confusion matrix. In the comparative analysis of classification techniques, the combined technique has shown better results than previously used supervised techniques of naïve bayse, linear classifier and support vector machine.
Multi-Attribute Decision-Making Model for Ranking of Web Development Frameworks

The modern dynamic of digital technologies is a prerequisite for the emergence of new tools to address new business challenges. One of the fastest-growing technologies is related to web development frameworks. In this regard, the current article deals with the problem of ranking web development frameworks considering different objective and subjective points of view. For the goal, a mathematical decision-making model is proposed. Distinguish feature of the model for web frameworks ranking is formulated modified utility function able to aggregate not only parameters for objective metrics but also considers the individual preferences. As a result of applying the proposed model, it is possible to select the most preferred web development framework taking into account different evaluation criteria. Once the selection of framework is done it is planned to be used in developing a web application to study the impacts of excessive use of smart technologies. The applicability of the proposed approach is numerically tested for ranking a set with 20 web frameworks. The obtained results show the usefulness of the modified utility function in the process of identifying the most preferred framework, taking into account various individual or business needs.
Content-based Remote Sensing Image Retrieval Based on Ant Colony Optimization and Convolutional Neural Networks

Content-based Remote sensing image retrieval (CBRSIR) methods usually only use the distance between a query image and retrieved images to retrieve and neglect the semantic similarity among images on the retrieval dataset during the retrieval process. The semantic similarity of these retrieved images helps improve the performance of CBRSIR. Hence, we propose a new retrieval method based on ant colony optimization (ACO) to use the semantic similarity between these retrieved images. The pheromone concentration is used to measure the semantic similarity between these retrieved images for improving retrieval performance. We first present how to initial the pheromone concentration of images. Next, we construct retrieval paths by mimicking the foraging process of ants and use a CNN regression model to accurately evaluate the length of these paths. Then, we update the pheromone concentration of images according to the length of the paths. Finally, we integrate the pheromone of images into the process of CBRSIR for improving retrieval performance. Experiments are conducted on a publicly available remote sensing image (UCMD) dataset. Compared with the state-of-the-art methods, the proposed method can improve retrieval precision.
In-the-Wild Facial Highlight Removal via Generative Adversarial Networks

Facial highlight removal techniques aim to remove the specular highlight from facial images, which could improve image quality and facilitate tasks, e.g., face recognition and reconstruction. However, previous learning-based techniques often fail on the in-the-wild images, as their models are often trained on paired synthetic or laboratory images due to the requirement on paired training data (images with and without highlight). In contrast to these methods, we propose a highlight removal network, which is pre-trained on a synthetic dataset but finetuned on the unpaired in-the-wild images. To achieve this, we propose a highlight mask guidance training technique, which enables Generative Adversarial Networks (GANs) to utilize in-the-wild images in training a highlight removal network. We have an observation that although almost all in-the-wild images contain some highlights on some regions, small patches without highlight can still provide useful information to guide the highlight removal procedure. This motivates us to train a region-based discriminator to distinguish highlight and non-highlight for a facial image and use it to finetune the generator. From the experiments, our technique achieves high-quality results compared with the state-of-the-art highlight removal techniques, especially on the in-the-wild images.
A Vietnamese Answer Extraction Model Based on PhoBERT

PhoBERT pre-trained models have shown its outperformance in many natural language processing tasks. Fine-tuning PhoBERT models is possibly the efficient way to build Vietnamese deep models for answer extraction. For building a Vietnamese answer extraction model using PhoBERT pre-trained model, we need a large SQuAD style annotated dataset. However, there are existing English annotated datasets for answer extraction task and multilingual BERT models which are possibly fine-tuned on English dataset and used in other languages. Therefore, we would find a pre-trained model and a way of fine-tuning this pretrained model for Vietnamese answer extraction task with a low cost of building Vietnamese annotated dataset. We have conducted the experiments with multilingual BERT pre-trained model and PhoBERT pre-trained model to show the performance of these pre-trained models. In the experiments, we have used Vietnamese translated version of SQuAD dataset and Vietnamese manually annotated dataset to show whether the Vietnamese translated dataset is useful in building an answer extraction model. Our experiment results showed that a PhoBERT pre-trained model is a good choice for building a Vietnamese answer extraction model.
A Method for Searching Documents using Knowledge Bases

We propose a document search method using knowledge bases (KBs) to explore documents through mentioned entities and their relationship. Documents are one of the most popular media to accumulate and transmit information. People often want to search for necessary documents or explore necessary information across multiple documents. However, it is generally difficult to find the target documents (or information) in particular when the search conditions are complicated, or the conditions are implicitly represented. Thus, users cannot represent their information needs in terms of a query, such as keywords. To address this problem, we exploit knowledge bases (KBs). More precisely, we extract entities mentioned in the documents and link them with the KBs, allowing users to explore the documents through the entities and their relationships. Furthermore, we propose a user-friendly interface to query documents and the relevant information. We experimentally show the proposed system performs better than the search system with a popular full-text search engine in some search tasks.