Reviewerly/exHarmony huggingface dataset:
Intelligent federated learning on lattice‐based efficient heterogeneous signcryption

Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. The ordinary signcryption technology cannot realize communication between two different cryptographic systems. Therefore, to implement efficient communication between different cryptosystems and resist quantum attacks, this paper proposes a lattice-based efficient heterogeneous signcryption scheme. The heterogeneous signcryption scheme is proved to be secure assuming the hardness of small integer solution and learning with errors problems. Then this paper applies the lattice-based efficient heterogeneous signcryption scheme to the federated learning system to achieve the transmission of confidential information, and designs an intelligent federated learning system on lattice-based efficient heterogeneous signcryption. This system realizes federated learning and the quantum security of data transmission while preserving private data.
Spatial state-action features for general games

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. This approach is defined for a highly general and abstract description of the problem—phrased as optimising the order in which propositions of formulas in disjunctive normal form are evaluated—and may therefore also be of interest to other types of problems than board games. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees, and demonstrates that the additional efficiency significantly improves the playing strength of agents using the features to guide search.
Sentence-Level Sentiment Analysis Using a CNN Model on Contextualized Word Representations

The progress in Internet-based applications such as websites and social networks has resulted in the generation of significant amounts of reviews and opinions associated with policies, influencers, services, and products. The emotion in these opinions becomes a significant source for politicians, celebrities, servicers, and producers who intend to improve their activities and make better decisions. Sentiment analysis (SA) is an essential tool for extracting and analyzing these emotions. Various state-of-the-art SA methods with good performance have been developed. This paper presents a model, called context-based convolutional neural network (CNN), for sentence-level SA. Unlike previous methods, the proposed model focuses on the role of context information expressed in a sentence for SA. The proposed method comprises the following procedures: (i) Sentences are converted into vectors based on the BERT model; (ii) contextualized word representations are extracted using the BiLSTM model over word embeddings; (iii) sentence-level sentiment is analyzed using the CNN model over the contextualized word representations. Experiments on benchmark datasets prove that the proposed method can improve the performances of previous methods.
Mathematical Expression Detection in Camera Captured Document Images

The detection of mathematical expressions (MEs) in document images has attracted the attention of many researchers in recent years. Most conventional methods focus on detecting MEs in scanned images. In recent years, mobile devices with integrated digital cameras have been popularly used. Camera captured images that are curved and skewed cause many difficulties in the detection of MEs. Therefore, the detection of MEs in camera captured images needs to be considered. This paper presents a system of two stages to detect MEs in camera captured images. At the first stage, the pre-processing technique is applied to reduce the geometric distortions of camera captured images. The dewarping process is performed using the property of both text-lines and non-text regions in camera captured images. At the second stage, the YOLOv5 network is fine-tuned to detect MEs efficiently. The proposed system has been evaluated on a large dataset of camera captured images. The obtained detection accuracy of $$94\%$$ of MEs in camera captured images and the performance comparison with various state-of-the-art methods demonstrate the effectiveness of the proposed method.
Naturalness Improvement of Vietnamese Text-to-Speech System Using Diffusion Probabilistic Modelling and Unsupervised Data Enrichment

Speech synthesis, which aims to generate natural and comprehensible speech from input text, is a popular research topic with a wide range of industrial applications. However, it appears to be a difficult problem due to its strong dependency on data, particularly for accent-sensitive and multi-dialect languages, e.g. Vietnamese. Perhaps the most common model applied in this area is Tacotron 2, using Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) architectures. Still, Tacotron 2 has not yet achieved the expected naturalness, possibly because it was not sophisticated enough to capture the natural expression of human voice. Moreover, with a low-resource language like Vietnamese, to collect a sufficient training dataset for this task is also a non-trivial problem. Hence, in this paper we propose an end-to-end framework with Grad-TTS, a denoising diffusion probabilistic model, as an acoustic model in the Text-to-speech (TTS) system instead of the traditional approach employed by Tacotron 2. The proposed approach helps us achieved a more natural synthesized speech, as depicted in the experiments. Furthermore, we also introduce an unsupervised approach to collect Vietnamese data from the Internet resource as well as to pre-process the data before putting it into training. This helps solve the problem of lacking Vietnamese data, and enhance our outcome. We released the dataset for further development of TTS system for Vietnamese at: https://bit.ly/3rnNsFi .
A Study of Filter-Based Feature Selection in Software Fault Prediction

Software fault prediction (SFP) assists developers in diagnosing the potential defects in the early stage. In SFP, software metrics have strong influence on the performance of a predictive model. However, high dimensional data impacts negatively on the predictive accuracy. As a solution, feature selection provides a process of selecting the optimal features that combine with machine learning techniques to build SFP models. For feature selection, filter selection is a way of addressing the high dimensionality, reducing computation time and improving prediction performance. In this research, we investigate a comparative analysis to review how different of nine filter feature selection methods on both datasets in PROMISE repository, namely CM1 and KC1. The experimental results show that the performances of classifiers are varying on different datasets, especially, in the CM1 dataset, Gain Ratio and Relief based on XGBoost (XGB) and Extra Trees (ET) achieved the highest accuracy and AUC values. In KC1, Gain Ratio and Mutual Information presented the greatest performance among nine methods.
A Framework to Verify the ABAC Policies in Web Applications

Security attacks are increasingly diverse and complex so ensuring security properties has become an important criterion in evaluating the quality of software systems. Besides, attribute-based access control (ABAC) is widely applied to ensure the security properties of software systems, especially large-scale and complex systems. However, deploying this security policy is always likely to contain potential security flaws that are the causes of the failures to guarantee customer security requirements, especially at the programming stage. In this paper, we introduce an approach to check the compliance of implemented ABAC policy with the Spring Security framework of a web application and its specification automatically. The proposal helps developers in detecting security breach situations that lead to disrupting the confidentiality, integrity, and availability of application systems.
Analysis of the Effects of Stop-word Removal in Hate Speech Detection Problem for Vietnamese Social Network Data

The usefulness of removing the stop-word in detecting Vietnamese hate speech on social media has yet to be determined. In this study, statistical hypothesis testing is used to evaluate the effect of stop-word removal in hate speech detection using two datasets, the ViHSD dataset and the HSD dataset of VLSP-HSD, six models, including traditional machine learning models, deep neural learning models, transformer models, and two methods of stop-word selection, word frequency and word form. The results indicate stop-word removal is bad effective in hate speech detection problem on Vietnamese.
Vietnamese Sentence Paraphrase Identification Using Sentence-BERT and PhoBERT

In 2019, Reimers et al. proposed SBERT to derive sentence embedding for many purposes. It highly reduced the time complexity of finding the most similar pair of sentences from traditional BERT/RoBERTa to SBERT, while the accuracy is maintained. There are many English SBERT models, but lacking the other languages ones. In this publication, we develop our Vietnamese SBERT model for Vietnamese sentence embeddings, using PhoBERT as our main transformer for Vietnamese token embeddings. For the training processes, we use the Vietnamese NLI and STSb datasets, and for the evaluation of sentence paraphrase identification task to compare with other models, we use the VnPara dataset in. Our model has achieved an accuracy of 95.33% and F1 of 95.42%, slightly outperforming many recent methods in Vietnamese.
An Approach to Monitoring Solar Farms in Vietnam Using GEE and Satellite Imagery

Solar energy has been developed rapidly in Vietnam for three recent years. It becomes a promising energy sources when Vietnamese government shows the important factors of clean energy and commit to reduce thermal power stations as they cause side effects for environment. As the result, there is a need to manage solar stations that spread out the whole country. Satellite images are the data sources to observe the earth surface that can be used in many monitoring applications. For this purpose, the paper proposes a machine learning approach to automatically detect and calculate the solar farm area using Google Earth Engine which is a cloud-based platform for processing large scale spatial data. The method has been employed to solar stations in Dak Lak province and showed the potential solution for solar electricity management in Vietnam.
Alpha-divergence minimization for deep Gaussian processes

This paper proposes the minimization of α-divergences for approximate inference in the context of deep Gaussian processes (DGPs). The proposed method can be considered as a generalization of variational inference (VI) and expectation propagation (EP), two previously used methods for approximate inference in DGPs. Both VI and EP are based on the minimization of the Kullback-Leibler divergence. The proposed method is based on a scalable version of power expectation propagation, a method that introduces an extra parameter α that specifies the targeted α-divergence to be optimized. In particular, such a method can recover the VI solution when α→0 and the EP solution when α→1. An exhaustive experimental evaluation shows that the minimization of α-divergences via the proposed method is feasible in DGPs and that choosing intermediate values of the α parameter between 0 and 1 can give better results in some problems. This means that one can improve the results of VI and EP when training DGPs. Importantly, the proposed method allows for stochastic optimization techniques, making it able to address datasets with several millions of instances.
I Feel You

Digital twins refer to a digital replica of potential actual physical assets, people, or systems, which are relevant for the future of digital health. These virtual replicas can be used to perform simulations that help assess possible risks, test performance, and optimize processes before applying them in the real world. Applied to the healthcare sector, a digital twin can refer to a replica of a patient or certain aspects of a human, like body parts, body organs or body systems. As a digital twin would age with the owner the question arises as to how we should visualize our digital twin (i.e., how to represent ourselves in a digital way with data). We do not yet know how people want their data (quantitative or qualitative) to be represented as digital twins. We addressed this question using generative design research methods, and more particularly co-design sessions that explored users’ perspectives and design preferences on digital twins. Our findings suggest a preference for qualitative representation unless there are emergency alerts, in which quantitative representations were preferred. People were reluctant towards health forecasting through a digital twin and saw it more as a reflection tool to improve quality of life.
On the Potentials of Realtime Sentiment Analysis on Text-Based Communication in Software Projects

Sentiment analysis is an established possibility to gain an overview of the team mood in software projects. A software analyzes text-based communication with regards to the used wording, i.e., whether a statement is likely to be perceived positive, negative, or neutral by the receiver of said message. However, despite several years of research on sentiment analysis in software engineering, the tools still have several weaknesses including misclassifications, the impossibility to detect negotiations, irony, or sarcasm. Another huge issue is the retrospective analysis of the communication: The team receives the results of the analysis at best at the end of the day, but not in realtime. This way, it is impossible to react and to improve the communication by adjusting a message before sending it. To reduce this issue, in this paper, we present a concept for realtime sentiment analysis in software projects and evaluate it in a user study with twelve practitioners. We were in particular interested in how realtime sentiment analysis can be integrated in the developers' daily lives and whether it appears to be helpful. Despite the still missing long-term case study in practice, the results of our study point to the usefulness of such kind of analysis.
Clustering Faster and Better with Projected Data

The K-means clustering algorithm can take a lot of time to converge, especially for large datasets in high dimension and a large number of clusters. By applying several enhancements it is possible to improve the performance without significantly changing the quality of the clustering. In this paper we first find a good clustering in a reduced-dimension version of the dataset, before fine-tuning the clustering in the original dimension. This saves time because accelerated K-means algorithms are fastest in low dimension, and the initial low-dimensional clustering bring us close to a good solution for the original data. We use random projection to reduce the dimension, as it is fast and maintains the cluster properties we want to preserve. In our experiments, we see that this approach significantly reduces the time needed for clustering a dataset and in most cases produces better results.
Little Stakeholder Communication in Distributed Scrum Projects During the Covid-19 Period

Agile methods have recently been popular by software development teams, with Scrum being the prominent process over the last decade. In Scrum, the Scrum Master serves the developers, the Product Owner, and the organization by coaching, leading, mentoring, planning, removing impediments, etc. Additionally, the Scrum Master is accountable for establishing the developer’s effectiveness. The global pandemic of Covid-19 changed the world unexpectedly in March 2020. Work has mostly shifted to a remote setting, and meetings have mostly transferred to being remote. People have had to adapt to these new circumstances worldwide. This research aimed to explore the changes that Scrum Masters and their teams have had to face during the first year of the Covid-19 period by increasing distributed working and the changes in the Scrum Master’s responsibilities. The results indicate that the most significant change has been decreasing communication with stakeholders and users after working more distributed. Additionally, not meeting socially, as the teams did before, has been a big challenge for the Scrum Teams.
Link prediction with Simple Graph Convolution and regularized Simple Graph Convolution

Attributed graphs are used to model real-life systems in many domains such as social science, biology, etc. Link prediction is an important task on attributed graph with a wide range of useful applications. Simple link prediction approaches have limitation in their capability to capture network topology and node attributes. Graph Neural Networks (GNNs) provide an efficient framework incorporating node attributes and connectivity to produce informative embeddings for many downstream tasks including link prediction. In this work, we study two variants of GNNs, namely Simple Graph Convolution (SGC) and its extension for link prediction on three citation datasets. While it is fast and efficient, our model is insufficient to capture the complex node connectivities. On the other hand, imposing regularization reduces overfitting and improves model performance.
Considering Users’ Personal Values in User-Centered Design Processes for Media and Entertainment Services

The way users consume media is defined and influenced by users’ values. Methods in User-Centered Design (UCD) processes typically do not address users’ values adequately to enable designers to understand the impact values have when it comes to the design of long-term user experience. This work presents two contributions (1) the relationship between users’ values and entertainment behavior based on a web survey study, and (2) a proposed set of long-term oriented value-based persona that can be used as guidance for the design of future TV and entertainment services and systems.
A Practical and Emotional Response Technique: Context-Based Sticker Suggestion Model on the Line App

Stickers on LINE app are designed as colorful visual characters with body language and facial expressions. Replying instant messages with these vivid stickers that can express user's emotions and behaviors via characteristic feature makes communication more enjoyable and naturally. However, the method by typing keywords to choose stickers on most apps can't help users efficiently and easily find out appropriate ones for current messages. In this study, we focus on instant messaging in Japanese and propose a context-based sticker suggestion model for Line app. The proposed model automatically suggests stickers in 3 steps: analyze the latest message of the chatter with 3 modules (replying-keyword selection, emotion classification and sentiment analysis), examine the consistency between the results of sentiment analysis and current messages, and pick up the stickers from a pre-prepared database that matches the analytical results generated by the above two steps. Finally, we have conducted two experiments to examine the validity and usefulness of our model.
Traffic Sign Recognition Based on Deep Learning Technique

Traffic sign recognition plays a significant role in intelligent transportation system. Therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). The dataset collected to train and test in experiments is the “German Traffic Sign Recognition Benchmark” (GTSRB). In addition, the CNN model is evaluated by comparing with a Deep Neural Network (DNN) model based on the accuracy rate and loss rate. Finally, the result shows the proposed CNN model yields high accuracy rate on both training and test images.
A Framework for Exploring Computational Models of Novelty in Unstructured Text

Novelty modeling in unstructured text data is a research topic within the Natural Language Processing (NLP) Community. Effective novelty models can play a key role in providing relevant and interesting content to the users which is the central goal in many applications including education and recommender systems. This paper presents a framework for comparing different approaches and applications of computational models of novelty in unstructured text data. We focus on computational models that apply methods such as natural language processing and information theory. The framework provides an ontology for computational novelty with respect to the source of text data, methods for representing the data, and models for measuring novelty. We explore the value of the framework by applying it to research on computational novelty in news articles, research publications, books, and recipes. This framework is independent of the type of data in the items and can be used as a tool for researchers to study, compare, and extend existing computational novelty models and applications.
Access Control using Blockchain: A Taxonomy and Review

After the introduction of blockchain as a cryptocurrency platform, researchers and industry leaders have come up with novel ways of utilizing the technology. One emerging use case for blockchain is access control, since it solves the problem of trust deficit while being distributed, auditable and private. This paper lists various access control models that have been proposed or implemented on blockchain platforms. It also provides an analysis of the performance of some of these models. Analysis shows that access control models are progressing from the traditional identity-based systems to role/attribute-based systems with observed structural shifts from centralized to decentralized. Key benefits of blockchain-based access control systems noted are improved transparency over access control and logging; improved, more complex policy management; and the possibility to implement access controls in trust-less systems.
Towards Simplifying and Formalizing UML Class Diagram Generalization/Specialization Relationship with Mathematical Set Theory

The Unified Modeling Language (UML) is considered the de facto standard for object-oriented software model development. This makes it appropriate to be used in academia courses at both the graduate and undergraduate levels of education. Some challenges to using the UML is academia are its large number of model concepts and the imprecise semantic of some of these concepts. These challenges are daunting for students who are being introduced to the UML. One approach that can be taken in teaching UML towards addressing these concerns is to limit the number of UML concepts taught and recognize that students may not be able to develop correct UML system models.