Reviewerly/exHarmony huggingface dataset:
Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes

For applications in chemistry and physics, machine learning is generally used to solve one of three problems: interpolation, classification or clustering. These problems use information about physical systems in a certain range of parameters or variables in order to make predictions at unknown values of these variables within the same range. The present work illustrates the application of machine learning to prediction of physical properties outside the range of the training parameters. We define 'physical extrapolation' to refer to accurate predictions y(x ∗) of a given physical property at a point $$\boldsymbol x^\ast = \left [ x^\ast _1, \ldots , x^\ast _{\mathcal {D}} \right ]$$ in the $$\mathcal {D}$$ -dimensional space, if, at least, one of the variables $$x^\ast _i \in \left [ x^\ast _1, \ldots , x^\ast _{\mathcal {D}} \right ]$$ is outside of the range covering the training data. We show that Gaussian processes can be used to build machine learning models capable of physical extrapolation of quantum properties of complex systems across quantum phase transitions. The approach is based on training Gaussian process models of variable complexity by the evolution of the physical functions. We show that, as the complexity of the models increases, they become capable of predicting new transitions. We also show that, where the evolution of the physical functions is analytic and relatively simple (one example considered here is a + b∕x + c∕x 3), Gaussian process models with simple kernels already yield accurate generalization results, allowing for accurate predictions of quantum properties in a different quantum phase. For more complex problems, it is necessary to build models with complex kernels. The complexity of the kernels is increased using the Bayesian Information Criterion (BIC). We illustrate the importance of the BIC by comparing the results with random kernels of various complexity. We discuss strategies to minimize overfitting and illustrate a method to obtain meaningful extrapolation results without direct validation in the extrapolated region.
2nd Data Literacy Workshop

The 2nd Data Literacy Workshop took place within the ACM Web Science Conference in 2021. Data literacy is an increasingly relevant concept within the area of Web Science, where transactions with data have a prominent role in the interplay of the web and society. This interdisciplinary workshop combines presentations of the papers selected after a peer review process, round tables and an interactive tutorial. The workshop promotes the cross-pollination of knowledge and experiences around Data Literacy, accrued by different communities of practice.
The Practice of Political Online Teaching of Wireless Sensor Network Course based on ZIGBEE

ZIGBEE technology is based on mature communication standards, and its technology application field is more and more extensive, such as intelligent home appliances, medical health, military reconnaissance, environmental monitoring and other aspects have great development space. With the development of science and technology, information technology, represented by multimedia and network technology, has been widely used in education field. The use of information technology has attracted more and more attention of teachers and students. But the current situation is not ideal for the deep integration of information technology and political teaching. The purpose of this paper is to study the application of wireless sensor network in the political online classroom, to increase the integration of information technology and political education, and to improve the interest of students in political learning. This paper, through the relevant theoretical research, considers the application of WLAN in political classroom, analyzes its advantages and makes teaching process. The shortcomings of traditional wireless sensor network are analyzed and the optimization algorithm is proposed to save energy. In the environment of wireless sensor network deployment, an online teaching platform is built. This paper uses SSH framework technology to realize the functions of the system, and gives the specific introduction of the realization of each function module in the system. Through the system debugging, deployment and implementation, the system successfully completed the course deployment and implementation, which strongly supports the smooth progress of the flipped classroom teaching reform. Finally, through questionnaire and interview, teachers and students have a good attitude towards online teaching platform, the highest satisfaction of the system stability is 100%, 86.67% of the teachers are satisfied with the ease of use of the system operation.
Explicit and implicit join point designation in aspect-oriented business modeling

The aspect-oriented paradigm can be applied at different stages of the software development lifecycle. Its use in the business modeling phase does not have a large number of proposals, and existing ones only cover the part of requirements engineering known as “requirements development”, regardless of “requirements management” practices. Additionally, most of these proposals do not use standard notations. For this reason, in this article we propose a modeling technique that allows the implicit designation of join points to support business requirements management, in addition to adapting the explicit form in a way that is strictly based on BPMN 2.0, the OMG's standard notation.
Hybrid Model For Abstractive Text Summarization

Text Summarization is a method to reduce a huge text into a meaningful condensed form. Today’s era is technological era. The people are always in the need of instant information in the concise form. As well as World Wide Web has expanded now-a-days so there is tremendous amount of data is created and available online. The today’s life is a very fast likes a machine. People do not have time to read a large document and conclude whether the document contain information which they want, So here text summary generator is a solution for this problem. Summary can help by providing the condensed form which can be read in minimum time.The paper presents a rigorous literature survey to study the different methods used to obtain the summary. As the issue of data over-burden has developed, what's more, as the amount of information has expanded, so has enthusiasm for programmed synopsis. It is hard for individuals to physically sum up enormous records of text documents.Text Summarization techniques can be divided into extractive text summarization and abstractive text summarization method. An extractive text summarization technique involves selection of significant words, sentences or passages and after that which is most significant sentences or passages form the summary by taking as it is words, sentences or passages. On the other hand Abstractive technique also involves selection of significant words, sentences or passages and after that which is most significant sentences or passages form the summary by forming it own words.
Volunteer’s Abode - Improving Community Service with a Graph-based Recommender System

In the recent years, awareness for community service events is created via popular social media applications, such as Instagram, Facebook and LinkedIn, that are frequently used by most individuals. This has aided NGOs over the years in carrying out their mission of serving the community. However, these platforms also cater to entrepreneurs, businesses and the like to promote their presence on social media and attract users to their products and services. Consequently, the use of interest based recommendation systems for end users in social media applications are common. With the use of smart strategies, one can easily reach out to multiple users across platforms and countries. However, it is likely that non-profit organizations may not have access to such resources and that their social media presence is not acknowledged as much, leading to such organizations losing out on quality volunteers and individuals.Our proposed solution aims to reduce this gap between NGOs and individuals by introducing a separate platform called ”Volunteer’s Abode” that makes use of a graph-based recommendation system mapping quality volunteers to NGOs. It maps relationships between volunteer’s interests with an NGO’s area of work to provide recommendations that encourage volunteers to actively participate.
LLP Settlement Scheme, 2020:AN Analysis From an Indian Law Perspective

The concept of Limited Liability Partnership ("LLP") in India was introduced with the introduction of the Limited Liability Partnership Act, 2008 (the "Act") in the year 2009. Ever since, LLP’s have been a popular way to run operations in India, as they are viewed as an alternative corporate business vehicle which provides benefits of limited liability of a company and the flexibility of a partnership.Further, as the liability of the partners is limited to their agreed contribution in the LLP and it offers various advantages with regard to the flexibility of the operations, LLP seems to be a favorite with both promoters within India and from outside India. Additionally, unlike a traditional partnership setup, no partner is liable on account of the independent or un-authorized actions of other partners, thus individual partners are shielded from joint liability, created by another partner’s wrongful business decisions or misconduct.Keeping in view the growing popularity of LLP’s in India and as part of the Indian governments, efforts to promote the ease of doing business, the Ministry of Corporate Affairs, Government of India (the "MCA") through a general circular number 6/2020, dated March 4, 20204, has introduced the LLP Settlement Scheme, 2020 (the "Scheme")2 and subsequently through a modification through a general circular number 13/2020, dated March 30, 20205("Modification Circular").Upon a review of the wordings of the Scheme and the Modification Circular the intention of the MCA appears to be to enable the defaulting LLPs in India, a one-time relaxation by way of allowing a one-time condonation of delay in filing of certain statutorily required documents under the provisions of the Act and the allied rules made thereunder with the relevant governmental authority- i.e. the office of the relevant registrar, enabling it to serve as a compliant LLP in the future.This article analyses the applicability of the Scheme and the Modification Circular summarizes the procedural requirements provided therein and also provides a broad summary of the relaxations provided.
Memory Efficient Invertible Neural Networks for Class-Incremental Learning

Recent advances in specialized hardware accelerators for Deep Neural Networks (DNN) training are opening the way for an increasing use of DNN models in embedded systems. At the same time, as new data is continuously acquired, it is becoming a major challenge to build models that are able to deal with a continuous stream of data. The issue lies in how we can update a model without storing all the previous data. In this article, we are interested in the case of learning new classes in a sequential fashion. We propose to adapt the learning procedure of one-versus-all invertible neural networks, a state-of-the-art method in class-incremental learning, to reduce its memory impact. We conduct our experiments on the CIFAR-100 dataset for which we learn each class one after the other. Our results show that the proposed approach is able to perform with a similar accuracy whilst reducing the memory cost by a factor up to five compared to the original implementation.
Label hierarchy constraint network for fine-grained classification

Recognize fine grained categories is challenging task, which has attracted more and more attention in recent years. Different from traditional image recognition, fine-grained image recognition is to recognize different sub-classes under the same general category. Due to posture, illumination and other reasons, fine grained recognition has large intra-class diversities and subtle inter-class similarities, Most of the works focused on how to localize discriminative regions and fine-grained feature learning. But they negative the structure of the fine grained labels. In this paper, we propose label hierarchy constraint network (LHC) for fine-grained classification. The network include two branchs, coarse-level branch and fine level branch. In the middle layers of the neural networks, we use the coarse branch to predict the coarse labels, which can be regarded as the guidance of fine-grained labels. In the upper layers, we predict fine-grained labels. Then we map the result of coarse-grained branch prediction probability distribution to the fine-grained branch. Experiments show the effectiveness of our method.
Breast Cancer Detection Based on Decision Fusion of Machine Learning Algorithms

A lot of new methods have been invented in Machine Learning since 1959 when Arthur Samuel first coined the term. The ability to learn and pattern checking in data persuaded many researchers in this field. With so many algorithms and their hybrid combinations, the task to solve a problem includes which combination of methods can produce better and efficient results. In this paper, we have used MIAS dataset for our experiment. First, we have improved the contrast of the mammograms using Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Second, Region of Interest (ROI) is selected from the images and cropped, then a CNN model used for the extraction of features. Finally, SVM and Decision tree classifier are used for the classification and voting classifier is used for the final decision. After using decision fusion based on a voting classifier, we were able to achieve 93.4% accuracy.
Approximating Probability Distributions by ReLU Networks

How many neurons are needed to approximate a target probability distribution using a neural network with a given input distribution and approximation error? This paper examines this question for the case when the input distribution is uniform, and the target distribution belongs to the class of histogram distributions. We obtain a new upper bound on the number of required neurons, which is strictly better than previously existing upper bounds. The key ingredient in this improvement is an efficient construction of the neural nets representing piecewise linear functions. We also obtain a lower bound on the minimum number of neurons needed to approximate the histogram distributions.
Research on Logistics Transportation of Detection and Segmentation Based on Deep Learning

Nowadays, logistics transportation plays an increasingly important role in our life, and higher transportation efficiency has always been the goal of people. People can perform more efficient vehicle scheduling according to the quantity of goods. Therefore, obtaining the quantity of goods that needs to be transported is the key to improve the efficiency of logistics transportation. At present, the amount of goods can be counted by monitoring the change of objects on the pallet through artificial intelligence. Therefore, this paper uses the YOLACT deep learning method in artificial intelligence to study the detection and segmentation of the pallet in the carriage. Since human movement will affect the detection of goods and in order to exclude the influence of pallets outside the carriage, this paper also studies the detection and segmentation of the human body and the ground inside the carriage. Firstly, a data set for the detection and segmentation of the pallet, human body and ground inside the carriage is established, and then the training set of random segmentation is used for effective training to a certain extent, and the corresponding verification set is verified. After the training, the model is tested accordingly. The detection results show that the reasoning speed of YOLACT with resnet50 as the backbone is 14.6FPS and mAP is 27.2 under the condition of GTX 1660Ti.
Knowledge-Driven Conversation for Social Robots: Exploring Crowdsourcing Mechanisms for Improving the System Capabilities

Social robots and artificial agents should be able to interact with the user in the most natural way possible. This work describes the basic principles of a conversation system designed for social robots and artificial agents, which relies on knowledge encoded in the form of an Ontology. Given the knowledge-driven approach, the possibility of expanding the Ontology in run-time, during the verbal interaction with the users is of the utmost importance: this paper also deals with the implementation of a system for the run-time expansion of the knowledge base, thanks to a crowdsourcing approach.
Evaluation of GPU Virtualisation Approaches for Machine Learning Enhanced Debugging of Cloud Orchestration

Nowadays, computing demand on General-Purpose Graphics Processing Units (GPGPUs) is steadily increasing due to the great interest in machine learning. The computational time of embarrassingly parallel tasks can be reduced with such GPUs by orders of magnitude compared to CPUs. In this paper, we briefly overview a wide range of GPU virtualisation strategies (including API remoting, para/full virtualisation and hardware based virtualisation) and their related methods. The fundamental details are also discussed to understand the differences between the presented solutions. Finally, the key features are described and are evaluated to help choose an effective baseline framework for a challenging graph-based machine learning method to be applied in the field of debugging of cloud orchestration.
Differentially Private Node Similarity Matrix Release for Large-Scale Social Networks

Node similarity, which is usually released in the form of a matrix, provides the basis for network analysis. However, directly releasing node similarity matrix may entail privacy risk. More concretely, the sensitive network structure implied in node similarity potentially poses threats to individual privacy in the original network. For large-scale social networks, the problem with network analysis is to extract effective node similarity matrix on the premise of preserving privacy. Privacy-preserving matrix factorization (MF) is a valid means to address this problem. Existing methods using differentially private MF, however, have two limitations. First, existing methods incur a complex analytic calculation when reaping the gradient of the objective function of MF, which results in poor scalability. Second, existing methods typically use private gradient descent as a solver of MF yet require adding plenty of noise to each gradient, swamping the signal provided by the gradient and rendering poor overall result quality. Motivated by this, we propose GPMF, a generic differential privacy matrix factorization method for node similarity matrix release. Specifically, to uniformly calculate the gradient, we design a provable gradient estimation method, which theoretically reveals the intrinsic relationship between actual and estimated gradients whose error can be preset for optimization, liberating the form of function. Further, to achieve optimal gradient error without compromising privacy, we devise a private Langevin Monte Carlowe (LMC), which uses the Gaussian noise that satisfies differential privacy instead of the noise term in LMC, eliminating the damage of extra noise on the gradient while preserving the inner property of LMC. Theoretical analysis is provided to ensure that GPMF yields processed node similarity matrix with high network structure utility while obeying (&#x03B5;, &#x03B4;)-differential privacy. Simulations confirm the effectiveness and efficiency of GPMF.
Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.
ELMx-based Channel Estimation for Massive MIMO Systems

The channel estimation of massive MIMO systems is one of the recent developments in wireless communication technology. The more accurate channel is estimated, the higher performance is achieved. In this paper, the use of machine learning applied to channel estimation is studied. Also, the proposed channel estimations based on Extreme Learning Machine (ELM) family are implemented for massive MIMO systems. These estimations include ELM, Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, the comparison between two legacy channel estimations using Least Squares (LS) and Minimum Mean Squares Error (MMSE) is presented. The simulation results reveal that the proposed methods significantly overcome LS and MMSE.
Product Recommendation System Using Tunicate Swarm Magnetic Optimization Algorithm-Based Black Hole Renyi Entropy Fuzzy Clustering and K-Nearest Neighbour

The recommendation system takes the information related to the user profile or interest to suggest the user with convenient materials that the user is interested in. Most of the existing implicit methods find the user preferences and automatically recommend the desired products in the interface, but failed to generate user-oriented results. Hence, an effective product recommendation method is developed in this research using the proposed Tunicate Swarm Magnetic Optimisation Algorithm-based Black Hole Renyi Entropy Fuzzy Clustering+K-Nearest Neighbour (TSMOA-based BHrEFC+KNN) to generate more user convenient result by grouping relevant products and recommends the similar products to users with great interest. The proposed TSMOA is designed by integrating the Tunicate Swarm Algorithm (TSA) and Magnetic Optimisation Algorithm (MOA), respectively. With the entropy measure and Jaro–Winkler distance, the process of group matching and the matching sequence of visitor and query are performed more effectively that enable to achieve the sentiment classification based on the binary visitor sequence. The performance obtained by the proposed TSMOA-based BHrEFC+KNN is evaluated in terms of accuracy, True Positive Rate (TPR), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the proposed system is compared with the existing methods, such as Deep learning+Naïve–Bayes, K-medoids clustering+Long Short-Term Memory (LSTM), BHEFC+Support Vector Machine (SVM) and TSMOA-Black Hole Entropy Fuzzy Clustering (BHEFC)[Formula: see text]Neural Network (NN), in which the TSMOA-BHEFC+NN obtained better results. The proposed TSMOA-based BHrEFC+KNN is 0.32%, 0.29%, 50%, and 6.44% is better than the existing TSMOA-BHEFC+NN in terms of accuracy, TPR, MAPE, and RMSE, respectively.