Reviewerly/exHarmony huggingface dataset:
Sylvester Equation Induced Collaborative Representation Learning for Recommendation

For an actual recommendation system, it generally involves a variety of heterogeneous interactive relationships, such as the typical user-user (U2U), item-item (I2I), and user-item (U2I) interaction relationships. With the application of graph neural networks (GNNs) in embedding various interactive relations, recommendation technology has made gratifying progress in recent years, which benefits lot from its powerful ability in relation modeling. However, most of the existing GNN-based methods fail to collaboratively explore the above heterogeneous multiple interactive relationships, including the internal correlations among multiple relationships and the intrinsic association behind different relationships. As a consequence, the user's personalized preference for the items to be recommended will not be well captured. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ylvester equation induced <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ollaborative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> epresentation <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning framework (S-CRL) for recommendation system by utilizing the heterogeneous multiple interactive relationships. In particular, we ingeniously define a novel Sylvester equation to associate tactfully the multiple heterogeneous relations together. From the perspective of rating propagation, such Sylvester equation is shown theoretically to be the optimal solution of a local structure sensitive rating propagation function. Additionally, to seek more expressive embeddings about user and item, a layer-wise attention is introduced to aggregate the multi-hop information from U2U and I2I graphs, respectively, so as to promote the aggregation with the corresponding embeddings from the U2I interaction graph. Extensive experiments on three real-world datasets verify that our model achieves more favorable performance over currently representative methods.
Facial expression translations preserving speaking content

This paper shows a method of translating a facial expression into other facial expressions using a DNN-based style embedding technique. Unlike existing work that translated a facial still image into other facial expressions, our algorithm can take into account the temporal movement of facial expressions. First, facial landmarks are obtained from the facial image sequence. Then, the relationship between the temporal facial landmark sequences, emotions and speaking context are learned by a GAN-based style transformer. Specifically, the transformer is trained to change the facial motions between different emotions while preserving the speaking content. The quality of the translation was evaluated through comprehensive experiments, including an objective evaluation and a subjective user study that evaluated the outputs using existing and GAN-based approaches combined with different subspace representations of facial landmark points. As a result, the proposed algorithm performed the best in facial emotion presentation and naturalness, and considerable good performance was achieved in the lip sync to the input original motion.
Image Super-Resolution Using Convolutional Neural Network

A deep learning technique for the super-resolution of a single image. With our approach, spatial dependencies are captured and end-to-end mapping between the low/high-resolution images is learned. A deep convolutional neural network (CNN) is used which accepts the low-resolution images as input and produces the high-resolution ones used to represent the mapping. This model demonstrates a lightweight construction, high restoration quality, and quick performance for practical online usage. This paper investigates multiple network architectures and parameter settings to accomplish trade-offs between performance and speed. Furthermore, our model is built to handle three color channels at the same time and demonstrate improved overall reconstruction quality.
Intellectual Property Protection using Blockchain and Digital Watermarking

Most of the digital systems completely ignore the generation of the watermark and how the watermark will be stored, while developing strong watermarking algorithms to avoid the unauthorized replication of the same asset. In this paper, a framework scheme based on digital watermarking using blockchain technology is proposed for protecting the digital assets of Intellectual Property. Blockchain is used to store the transaction details and the watermark information helps us track the asset's ownership transfer and provides authentication of the watermark. This work demonstrates an implementation of the light and efficient functions, i.e., image hashing using the Wavelet hash function, storing hashed information of the owner and the digital assets using a Quick Response Code, and enables watermarked image storage using the IPFS. We demonstrate that the blockchain can also be used to record the transfer of the asset from one owner to another. One of the takeaways from this proposal is that the elimination of the third-party results in fewer data and copyright leaks which in turn makes the system more reliable and secure. In fact, it speeds up the process of the ownership transfer of the assets.
A Comparative Empirical Evaluation of Neural Language Models for Thai Question-Answering

Despite engineers and researchers' significant and continuing efforts in developing natural language processing tools for the Thai language, the Thai language is, alongside many others, a de facto low-resource language. Can unsupervisedly trained neural language models come to the rescue? The remarkable success of transformer-based language models in most natural language processing tasks promises the advent of a much needed polyglot panacea. It seems, unfortunately, that powerful enough models are not yet available for most other-than-English languages. To assess the situation, we propose to empirically and comparatively evaluate the performance of existing neural language models for the task of extractive question-answering for the Thai language.
Evaluation of Smart Campus Educational Management System

With the development of artificial intelligence, big data, cloud computing, and other new technologies. The design and construction of smart campus has become a research hotspot. The smart campus, as a platform of educational management, provides a lot of services for the teaching and management of schools. Therefore, it is necessary to comprehensively evaluate the construction of educational management system. This paper constructs the comprehensive evaluation system of the educational management system. Firstly, it constructs the comprehensive evaluation index system of the educational management system. Secondly, the analytic hierarchy process (AHP) is used to determine the weight of indicators at all levels. Finally, the fuzzy comprehensive evaluation method (FCE) is used to calculate the quantitative score of the evaluation object. Through the comprehensive evaluation and analysis of the educational management system of Longdong University, Verify the effectiveness of the evaluation process and methods. At the same time, it provides guidance for the construction of an intelligent campus educational management system.
K-Means Clustering Approach to Determine Ore Type in Laterite Nickel Deposit

Nickel demand will increase as the electric vehicle production plan increases in the years ahead. Nickel matte is an important raw material in producing the producing electric cars. The precision and speed with which the kind of laterite nickel ore is determined based on the olivine concentration are critical because it affects energy requirements and nickel recovery. The present challenge is that it takes a long time to obtain the Loss on Ignition (LoI) number, which will be used to calculate the olivine concentration. As a result, an alternate approach for determining the type of ore without going through the phases of assessing the LoI value is required. The k-means clustering method examined data from one of the blocks in the PT Vale Indonesia Tbk mining region. The K-Means method is used to cluster data from 9 chemical elements. The olivine group that will be produced as a result of this analysis is characterized by three scenarios: the first one is the olivine group using two clusters according to the present split of olivine groups, namely high olivine and low olivine. The second scenario employs three clusters: high olivine, medium olivine, and low olivine. The third scenario uses four clusters based on the elbow method and silhouette guidelines. According to the findings, the best number of clusters was two clusters. The level of accuracy for 2 clusters, when compared with the conventional method, achieved 97.9% for cluster 1 and 85.0% for cluster 2.
Predicting Failures in HDDs with Deep NN and Irregularly-Sampled Data

As information systems became basic requirements for essential human services, safeguarding stored data became a requirement to maintaining these services. Predicting Hard Disk Drive (HDD) failure can bring efficiency gains in HDD maintenance and reduce the risk for data loss. Recurrent Neural Networks (RNN) are powerful tools for predicting HDD failure but require complete data entries sampled at regular intervals for efficient model training, testing, and deployment. Data imputation is a baseline method to preprocess data for RNN models. However, typical data imputation methods introduce noise into datasets and erase missing data patterns that would otherwise improve model predictions. This article surveys existing RNN models robust to the presence of substantial amounts of missing data and benchmarks the predictive capabilities of these methods on HDD failure prediction using Self-Monitoring, Analysis, and Reporting Technology (SMART) data. To evaluate different missing data conditions, we simulate binomial and exponential sampling schema with varying levels of missing data. The successful implementation and comparison of these methods demonstrated that the GRU-D, phased-LSTM, and CT-LSTM methods are well-rounded methods for multiple missing data conditions, having achieved better performance than basic LSTM networks.
A New Analysis of the Kipnis-Shamir Method Solving the MinRank Problem

The MinRank problem is investigated as a problem related to rank attacks in multivariate cryptography and the decoding of rank codes in coding theory. The Kipnis-Shamir method is one of the methods to solve the problem, and recently, significant progress has been made in its complexity estimation by Verbel et al. As this method reduces the problem to an MQ problem, which asks for a solution to a system of quadratic equations, its complexity depends on the solving degree of a quadratic system deduced from the method. A theoretical value introduced by Verbel et al. approximates the minimal solving degree of the quadratic systems in the method although their value is defined under a certain limit for the system considered. A quadratic system outside their limitation often has a larger solving degree, but the solving complexity is not always higher because it has a smaller number of variables and equations. Thus, in order to discuss the best complexity of the Kipnis-Shamir method, a theoretical value is needed to approximate the solving degree of each quadratic system deduced from the method. A quadratic system deduced from the Kipnis-Shamir method always has a multi-degree, and the solving complexity is influenced by this property. In this study, we introduce a theoretical value defined by such a multi-degree and show that it approximates the solving degree of each quadratic system. Thus, the systems deduced from the method are compared, and the best complexity is discussed. As an application, for the MinRank attack using the Kipnis-Shamir method against the multivariate signature scheme Rainbow, we show a case in which a deduced quadratic system outside Verbel et al.'s limitation is the best. In particular, the complexity estimation of the MinRank attack using the KS method against the Rainbow parameter sets I, III and V is reduced by about 172, 140 and 212 bits, respectively, from Verbel et al.'s estimation.
Leveraging Machine Learning and Model-Agnostic Explanations to Understand Automated Diagnosis of Cardiovascular Disease

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the need for artificial intelligence models to improve diagnosis accuracy. The study trains machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompts the creation of an automated open-source diagnosis tool powered by the MLP. Local Interpretable Model-Agnostic Explanations (LIME) are employed to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering segments patients into ten clusters, after which each example is passed through LIME. The resulting histograms depict a complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows nuances in the MLP's understanding of patients from different clusters. LIME analysis reveals that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and ST segment slope. Future experiments should replicate this study's LIME methodology on data sets containing more specialized features in order to gain practical medical insights about the different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models adaptable to other diseases as well.
Out of sequence measurement in multi sensor fusion

Autonomous vehicles perceive the surrounding environment by analyzing and correlating the data received from multiple sensors. The detection ability and accuracy are constrained by environmental conditions. These constraints can be overcome by fusing data from multiple sensors. But in multi-sensor fusion, Out of Sequence Measurement (OOSM) problem is increasing. In such a system, information is received at differed time at the fusion center due to different processing times of the sensors, cycle time, and communication delay which causes the later measurement from the higher rate sensor to overtake the newer measurement from the lower rate sensor. Out of sequence measurement problem can be avoided by introducing a delay in a higher rate sensor. A fixed lag algorithm is used which helps to synchronize the data at the fusion center by introducing a delay. Another approach for solving out of sequence is by predicting the data from the lower rate sensor when data from the higher rate sensor is available is done using regression. The algorithms are implemented on real-time data and the results of the fixed lag smoothing had delay hence the second approach is adaptable which will have no delay infusion.
A method for converting an image of a GO game record to training data

Supervised learning is an approach to training artificial intelligence using a labeled dataset. Since the interest in artificial intelligence is high, preparing training data for supervised learning has an important meaning. In this paper, we propose a feasible algorithm for converting a GO game dataset stored as an image into learnable labeled data. The training data need to have the location and color of GO stones. However, detecting a GO board is difficult because of the area covered by a GO stone. Furthermore, it is hard to know the reference point of the board, so recognizing the locations of the stone is also difficult. To detect a GO board, the proposed method is applied differently depending on whether or not there are GO stones on the board. Additionally, by finding the starting point of the GO board, we can convert the location of the GO stone in the image to training data.
Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot Learning

In a more realistic setting, the class distribution of a dataset is very imbalanced. Some set of classes have abundant samples while other classes are very limited. Standard deep learning approach is employed when the samples are abundant but when there are very few labelled data, few-shot learning approach are popularly used. Our work is concerned in between these two extremes. Specifically we tackle the problem of incremental few-shot (IFS) learning, which requires an adaptable model that can both perform well with classes with abundant samples, called base classes while also able to adapt and incrementally learn novel or unseen classes with very few samples. We modify the model-agnostic meta-learning (MAML) algorithm to create a much simpler and modular incremental-few shot learner that mitigates inherent problems encountered in this domain notably model overfitting and catastrophic forgetting. We propose DynMAML, which enables compartmentalization of the base and novel tasks into two independent heads while preventing gradient flow that causes feature drift. However due to two independent heads, each sample would have two outputs. We introduce the separator module placed in parallel to both classifier heads to select and predict the correct output, i.e. whether the final output is from the base or novel head. Experiments on a standard benchmark dataset shows our approach achieves similar performance with state-of-the-art methods while being much simpler and modular.
Integration and application of spatial information model for earthquake damage assessment based on cloud architecture in Yunnan

It is critical for governments to immediately assess disaster loss in order to determine key rescue regions, send rescue teams, and other rescue materials following the earthquake. This study presents data resources, a geographical information model, and software system integration in a cloud computing environment to address unresolved difficulties in data, model, software, and service. On May 21,2021, we performed the fast assessment of a magnitude 6.4 earthquake in Yunnan Yangbi using the software system. The real application results reveal that the seismic quick assessment program based on cloud architecture has substantially improved in terms of system stability, model dependability, evaluation efficiency, emergency product diversification, and output result display. It can give basic information support and assurance for earthquake response decision-making.
Co-Attentive Graph Learning for Session-based Recommendation

The goal of session-based recommendation is to predict user's action at the next timestamp by modeling his recent limited behaviors. Existing methods mainly concentrate on the sequential signal or pairwise transition relation between items using recurrent neural networks (RNN) or graph neural networks (GNN). However, the sequential methods fail to consider the transition relation between items while the GNN-based models face a serious over-smoothing problem. Thus, we propose a Co-Attentive Graph Learning (CAGL) method for session-based recommendation, which introduces a co-attention network to prevent over-smoothing, thus learning accurate representations for items. Extensive experiments are conducted on two public datasets, i.e., Diginetica and Gowalla. The results show that CAGL can beat the state-of-the-art baselines in terms of Recall and MRR.
Near Failure Analysis Using Dynamic Behavioural Data

Automated testing is a safeguard against software regression and provides huge benefits. However, it is yet a challenging subject. Among others, there is a risk that the test cases are too specific, thus making them inefficient. There are many forms of undesirable behaviour that are compatible with a typical program’s specification, that however, harm users. An efficient test should provide most possible information in relation to the resources spent. This paper introduces near failure analysis which complements testing activities by analysing dynamic behavioural metrics (e.g., execution time) in addition to explicit output values. The approach employs machine learning (ML) for classifying the behaviour of a program as faulty or healthy based on dynamic data gathered throughout its executions over time. An ML-based model is designed and trained to predict whether or not an arbitrary version of a program is at risk of failure. The very preliminary evaluation demonstrates promising results for feasibility and effectiveness of near failure analysis.
The Discounted Reward-Based UCB in MCTS

In the classic Monte-Carlo Tree Search algorithm, Upper Confidence Bounds (UCB) is used as the tree policy to select child nodes. However, all nodes are updated in the same way in UCT, which does not reflect the role of some key nodes. Therefore, UCB cannot accurately assess the value of a node. So in this paper, we propose an improved UCB formula, named Discounted Reward-based UCB (DRUCB), to evaluate the value of each node more precisely by introducing discounted reward. We also introduce a hyperparameter to explore the best-performing formula. We evaluate the improved algorithm in the tic-tac-toe environment, and the experimental results demonstrate that our algorithm can win more games when plays against common UCT-based algorithm.
Agent Maze Path Planning Based on Simulated Annealing Q-Learning Algorithm

The problem of path exploration and planning of agents in unknown environments is a popular application problem in the field of reinforcement learning. In this paper, we propose an improved reinforcement learning algorithm called the Q-Learning algorithm for adaptive exploration based on simulated annealing (AE-SAQL). We apply the algorithm to the agent path planning problem, improve the setting of the reward function and add the feedback information of the environment. By simulating the Metropolis criterion in the annealing algorithm and adding an adaptive adjustment mechanism, the agent fully explores the environment and makes full use of the environmental information, solving the exploration-utilization dilemma during the algorithm and finally enabling the agent to reach the target location safely. Compared with the standard Q-Learning algorithm and SARSA algorithm, AE-SAQL achieves better.
Life Jacket Wearing Recognition Based on Semantic Segmentation

In order to automatically identify whether a person wear life jacket, we propose an automatic detection model based on semantic segmentation. To gain a better result, we made improvements and optimizations on the basis of U-net, which is the most classic network in semantic segmentation. We replaced the max-pooling layer with a special convolutional layer, such a way can extract more features from feature map and can achieve the same effect of max-pooling. We added an extra dropout layer to prevent the overfitting in our model. We also used reflection padding in it. Reflection padding is used to make the convolution kernel to extract more features. We used binary cross entropy as loss function and Leaky Relu as activation function. Compared with the traditional semantic segmentation networks such as U-net and FCN, the precision of our improved model is about 2% higher than U-net, and the F1-measure is 2.6% higher than it. Our precision is 9% higher than FCN-32S and F1-measure is about 10% higher. The final accuracy of our model can achieve 93% and the IoU value is 4% higher than the traditional U-net. The last result can reach the level of human eyes, such an accuracy is sufficient to practical applications.