Reviewerly/exHarmony huggingface dataset:
Using Reinforcement Learning for Generating Polynomial Models to Explain Complex Data

Abstract Basic oxygen steel making is a complex chemical and physical industrial process that reduces a mix of pig iron and recycled scrap into low-carbon steel. Good understanding of the process and the ability to predict how it will evolve requires long operator experience, but this can be augmented with process target prediction systems. Such systems may use machine learning to learn a model of the process based on a long process history, and have an advantage in that they can make use of vastly more process parameters than operators can comprehend. While it has become less of a challenge to build such prediction systems using machine learning algorithms, actual production implementations are rare. The hidden reasoning of complex prediction model and lack of transparency prevents operator trust, even for models that show high accuracy predictions. To express model behaviour and thereby increasing transparency we develop a reinforcement learning (RL) based agent approach, which task is to generate short polynomials that can explain the model of the process from what it has learnt from process data. The RL agent is rewarded on how well it generates polynomials that can predict the process from a smaller subset of the process parameters. Agent training is done with the REINFORCE algorithm, which enables the sampling of multiple concurrently plausible polynomials. Having multiple polynomials, process developers can evaluate several alternative and plausible explanations, as observed in the historic process data. The presented approach gives both a trained generative model and a set of polynomials that can explain the process. The performance of the polynomials is as good as or better than more complex and less interpretable models. Further, the relative simplicity of the resulting polynomials allows good generalisation to fit new instances of data. The best of the resulting polynomials in our evaluation achieves a better $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> score on the test set in comparison to the other machine learning models evaluated.
Infobot: An Autonomous System for Offline Shopping

Abstract Transfiguring the customer expectation requires a ubiquitous range of precision and speed that conventional approaches can’t meet in real time. Recent technological progress in robotics has resulted in developing information bot which can interact with customers and made products feasible to commoditize. The source of information is the wealthiest thing in the world. As a human being everyone will tend to seek information from various sources. The source of information before a certain period of time were only books but now it has experienced a drastic growth. The proposed project facilitates the role of guide robot to locomote from unit to unit in the shopping mall. In most of the shopping malls and organizations, there is no provision to provide information about certain products that are available in the shopping mall itself. It becomes a tedious process to search for a product in the mall. To overcome the situation, people tend to move for online shopping. In online shopping, there will be lot of disappointment like product misplacement, product damage, product with low quality etc., while purchasing the products. To solve this problem a mini bot is introduced in this project that would provide information about the products. The same concept if introduced in organizations it will be quite easier to collect the information about any person or department. The motivation of the project is to design a movable bot which provides the information in all aspects based on user interaction. Thus, the project elicits a wireless operating bot with fast track product navigation in a continual zealous of incumbent based on direct customer choice.
The design of optimal reversible comparator circuit using new quantum gates

Abstract This paper presents a novel approach for designing reversible n-bit comparator based on new quantum gates. Their quantum equivalent implementations are also proposed. The design of comparator circuits is completed by using existing reversible gates and the above new reversible circuits. The comparative results are presented in terms of quantum cost, quantum delay and garbage outputs.
Cache Replacement Based on LSTM in the Second Cache in Virtualized Environment

Virtualized environments have become one of the most important platforms in current information systems. In the cases of a virtualized environment using both guest and host operating systems, two caches in the guest and host operating systems work concurrently and improvement of hit ratios of both caches are important. However, the second cache, which is the host operating system cache, has a negative locality of reference and the most popular cache replacement algorithm called LRU (least recently used) does not work well. For this issue, we propose to apply LSTM (Long short-term memory), which is a deep learning network for time series data, for cache replacement in the second cache. First, we explain a famous issue in the second cache. Second, we propose an LSTM-based cache replacement. Third, we evaluate this method with our prototype implementation and show that our method can improve I/O performance.
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

Abstract Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes. Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear. In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.
Comparison between Fisher’s Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition

Abstract The enthusiasm level of a person is an important measurement in real-world problems. This paper, therefore, presents face recognition classification for enthusiasm level, based on supervised machine learning, using Support Vector Machine (SVM) as a classifier, with a one-vs-one method because the data consists of more than two classes. In addition, Fisher’s Ratio and Information Gain are applied in the selection of contributive features, and the goals were to present an accuracy comparison between SVM and Fisher’s Ratio, as wells as with Information Gain, and the results showed the accuracy at 88,89%, and 80,95238%, respectively. This indicates the combination of SVM with Fisher Ratio to be better.
Image-Based Out-of-Distribution-Detector Principles on Graph-Based Input Data in Human Action Recognition

Living in a complex world like ours makes it unacceptable that a practical implementation of a machine learning system assumes a closed world. Therefore, it is necessary for such a learning-based system in a real world environment, to be aware of its own capabilities and limits and to be able to distinguish between confident and unconfident results of the inference, especially if the sample cannot be explained by the underlying distribution. This knowledge is particularly essential in safety-critical environments and tasks e.g. self-driving cars or medical applications. Towards this end, we transfer image-based Out-of-Distribution (OoD)-methods to graph-based data and show the applicability in action recognition.The contribution of this work is (i) the examination of the portability of recent image-based OoD-detectors for graph-based input data, (ii) a Metric Learning-based approach to detect OoD-samples, and (iii) the introduction of a novel semi-synthetic action recognition dataset.The evaluation shows that image-based OoD-methods can be applied to graph-based data. Additionally, there is a gap between the performance on intraclass and intradataset results. First methods as the examined baseline or ODIN provide reasonable results. More sophisticated network architectures – in contrast to their image-based application – were surpassed in the intradataset comparison and even lead to less classification accuracy.
Research on the Improvement of Data Mining Algorithm Based on “Internet +” System in Cloud Computing

In cloud computing environment, the research on data mining has very important practical significance. The traditional method of data mining has the problems of poor clustering effect and slow speed of data mining. In order to solve this problem, the “Internet +” system is used to improve the algorithm of data mining. Combined with the result of multiple sampling, the mining center of the data set is determined. On the basis of which, the multi-layer variable weighting concept is introduced, the non-leaf node data is distributed processing, the Euclidean distance algorithm is introduced to analyze the data linear relation, and the data convergence speed and the critical value in the data mining process are controlled, the optimal design of the data mining algorithm is realized. The simulation results show that, the improved algorithm improves the speed of data mining by 37.25%, and has higher distribution accuracy, better data clustering effect than the conventional algorithm.
Prediction of Hospital Readmission Combining Rule-based and Machine Learning Model

Hospital readmission shortly after discharge is threatening to plague the quality of inpatient care. In the United States, the Hospital Readmissions Reduction Program has been established to improve patient outcomes by reducing payments to hospitals with excess readmissions. This concern calls for an incentive for healthcare facilities to reduce their readmission rates by predicting patients at a high risk of readmission. Recently, scientists have developed prediction models by using machine learning algorithms. Extensive feature engineering, testing multiple algorithms, and algorithm tuning were conventionally performed to better identify potential high-risk readmission. To alleviate these challenges, we develop a readmission risk ensemble prediction model that combines the data- and knowledge-driven feature scores in risk computation. The proposed model showed better prediction performance, i.e improvements of 2-17% in sensitivity and 1% in the area-under-the-receiver operating characteristic curve, proving that the combination of the machine learning and rule-based score can detect patients that are not evident on the prediction of the standard model alone.
Enhancing Graph Neural Networks with Edge Features Through Sequential Representation

Graph neural networks have been paid a lot attentions in recent years since many real-word data can naturally be represented by graph structures. Graph neural networks such as GCNs and GATs mainly focus on node features while ignoring edge features in graphs. However, in many graph structure data such as knowledge graphs, social networks, edge features are also important as they contain vital information about relations between nodes which are commonly ignored or simplified into binary or scalar values by existing methods. In this work we build a novel learning method on graphs called GNN-EE, i.e. GNN with Edge Enhanced, which takes both the node features and edge features into account while updating representations of graph components and can be applied to most of the common graph neural networks such as GCNs and GATs. Our GNN-EE method fits in the message-passing framework and thus is easy to generalize. In addition, we extend the random-walk-based algorithms on graphs so that they can consider both node and edge features on graphs. We use those random-walk-based algorithms as a pre-training method on graph with few initial features. We demonstrate the effectiveness and flexibility of our GNN-EE method through entity classification tasks and graph classification tasks.
Topic and Sentiment Analysis Matrix Factorization on Rating Prediction for Recommendation

In today's online services, users' feedback such as numerical rating, textual review, time of purchase and so on for each product is often encouraged to provide. Many recommender systems predict the products that the users may like and purchase in the future using users' historical ratings. With the increase of user data in the systems, more detailed and interpretable information about product features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis on reviews using Latent Dirichlet Allocation (LDA) and a lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users on reviews to obtain a reliability measure to weight the ratings. Third, we integrate the obtained reliability measure and the results of the topic and sentiment analysis of reviews into the matrix factorization framework for prediction. Our experimental comparison using eight Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 20.82%.
Fault recognition method of smart grid data acquisition system based on FNN and sequential DS fusion

It is of significant practical importance to ensure the operational safety of the smart grid, which requires real-time fault diagnosis and identifying what causes it based on an enormous amount of data. This article further studies the intelligent fault-identification method based on the combination of multi-machine learning methods on the bases of researching on Fault Diagnosis of Smart Grid Data Acquisition System. Firstly, we should apply statistical analysis and feature extraction for fault data. Then, we can use fuzzy neural network (FNN) to calculate the probability of fault prediction of power distribution stations, manufacturers and operation businesses, and use the membership function to calculate the corresponding fault membership and uncertainty. Secondly, it makes use of Dempster/Shafer (DS) evidence sequential fusion method to realize fault membership fusion, and gives the corresponding decision criteria for failure causes. Thirdly, a fault-identification method of smart grid data-acquisition system is established based on FNN and DS Evidence Fusion. Finally, the experimental results based on the actual operation data of smart grid show that the new method has a very good application effect at fault cause identification.
A Belief Rule-Based Expert System to Assess Multiple Human Reaction in the Context of Facebook Posts under Uncertainty

Human may have multiple reactions at a time. Social media is a real-life example where people can express their reactions or opinions. For example, Facebook has become a widely used social media where users express their opinion on different posts such as status or photo post. Therefore, user opinions can easily be achieved through social media and then analyzed and applied in different practical fields. Reaction assessment of Social media can be an excellent source of information. Therefore, an accurate assessment of human reaction is a must. Facebook provides six emoticons for each of the posts of its users. The six emoticons define six types of reactions. The user, who wants to react to any post, have to choose only one of the six emoticons. There is no scope for a user to express multiple reactions at a time. Moreover, if a user selects an emoticon, then the system takes that input as 100% of that corresponding reaction, or 0% if not selected, thus the reaction assessment system becomes a Boolean system. Therefore, the assessment of multiple reactions of the user cannot be measured with 100% certainty due to the existence of various types of uncertainties such as vagueness, imprecision, randomness, ignorance, incompleteness, and ambiguity in the system. Therefore, to assess multiple human reactions, an expert system is needed to handle all these uncertainties. The system design, development process, and applications of an expert system to assess multiple human reactions are described in this paper. For the development of the expert system, the Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach has been used and the system is named as a Belief Rule-Based Expert System (BRBES). The developed BRBES can mitigate all the uncertainties mentioned above.
Sieve Diagram For Data Exploration of Instagram Usage Habit Obtained From Indonesia Questioner’s Sample

Abstract Exploration data using a scatter plot made it more accessible when the datasets correlated. However, the case of Instagram Usage Habit in the previous study was hard to follow. The problem was that many datasets were not specific. Therefore, it difficult to classification for a Decision Tree. The other option of exploration data used the sieve diagram. The sieve diagram summarizes the relationship between the categorical variables using frequencies—the purpose of this study to understand the data and found out what wrong in the datasets. Based on the result of the sieve diagram in the study, the main problem found out in the age of the respondent on attributes. We deduce that several attributes had not characteristic unique for the habit of using Instagram because the attributes based on age have a similar pattern. We suggest that the questions for respondents need to be improved, such as Tiered questions. Therefore, The classification of decision trees would become more precise in the subsequent studies.
Implement a Model for Describing and Maximising Security Knowledge Sharing

Employees play a crucial role in improving information security in their enterprise, and this requires everyone having the requisite security knowledge. To maximise knowledge, organisations should facilitate and encourage Security Knowledge Sharing (SKS) between employees. This paper reports on the design and implementation of a mobile game to enhance the delivery of information security training to help employees to protect themselves against security attacks. The collaborative Transactive Memory System (TMS) theory was used to model organisational knowledge sharing. We then satisfy the self-determination needs of employees to maximise intrinsic motivation to share knowledge at the individual level, via an Educational Security Game. An empirical study evaluated the intervention, an application that facilitates and encourages Information Security Knowledge Sharing. The results are still in progress.
Mutual Q-learning

Mutual learning is an emerging technique for improving performance in machine learning models by allowing functions to learn from each other. In this paper, we present an updated version of Q-learning, improved with the application of mutual learning techniques. We apply this algorithm to a traditional reinforcement learning control problem and compare our performance with that of a standard Q-learning approach. We show that these approaches lead to a significant improvement in performance.
Compressed Video Action Recognition Using Motion Vector Representation

Action recognition is an important task for video understanding. Due to expensive time consumption, the conventional approaches employing the optical flow are difficult to be used for real-time purpose. Recently, the Motion Vector (MV), which can be directly extracted from the compressed video, has been introduced for action recognition. In this paper, we propose a novel approach by utilizing motion vector representation for action recognition. On the one hand, we use the motion vector information to select key information sequences for recognition. On the other hand, we further use the motion vector to formulate the representation of the selected sequences. We evaluate the proposed approach on UCF101 and HMDB51 datasets. The experimental results demonstrate that the proposed approach is able to achieve competitive recognition performance, and is able to maintain a 461.5 fps end-to-end processing rate at the same time.
DenseNet Matting Algorithm Based on Embedded Improved SKNet

This paper proposes a DenseNet model based on embedding improved SKNet. The advantage of our method is that it can't only identify different local image structures, but also effectively enhance the spread of useful features in the network by introducing a channel attention mechanism into the network. In addition, we have augmented our DataSet on the existing public DataSet ;and perform qualitative and quantitative evaluation on our new augmented DataSet. In terms of qualitative comparison, the matting obtained by our network is very consistent with the benchmark matting in processing texture, hair and other details. In terms of quantitative comparison. We use the Sum of Absolute Difference and Mean Squared Error to analyze the results, compared with other methods, there is a big improvement. Experimental results show that the algorithm in this paper is superior to other algorithms in subjective vision and objective quantification, and is more robust.
Chinese Live-streaming Barrage Chatbot Combined with User Vectors

The barrage is a communication method in the webcast environment. It has the characteristics of freedom and openness, fun and vividness. However, the live barrage often mixes content that deviates from mainstream values. In order to better manage the content of the barrage and guide the barrage to a good direction, this research proposed a barrage generation robot algorithm that are allowed to automatically generate high quality responses, called Chinese live barrage chatbot (CLBC). First, according to the characteristics of barrage data, the paper proposed a Barrage Value Function to build a high-quality barrage datasets. Next, CLBC constructed a CLBC model combined with user vectors and transformer. Finally, CLBC designed a barrage text classifier to detect the quality of the barrage generated by itself. The training and testing of a 78,370 pairs of the Barrage Q&A dataset show that CLBC is able to generate more interesting responses and it perform well as the leading seq2seq and transformer question-and-answer system on many indicators such as perplexity, BLEU and barrage text classifier F1 score.