Reviewerly/exHarmony huggingface dataset:
Research and Application of the Data Mining Technology in Economic Intelligence System

In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.
Exploiting Diverse Information in Pre-Trained Language Model for Multi-Choice Machine Reading Comprehension

Answering different multi-choice machine reading comprehension (MRC) questions generally requires different information due to the abundant diversity of the questions, options and passages. Recently, pre-trained language models which provide rich information have been widely used to address MRC tasks. Most of the existing work only focuses on the output representation at the top layer of the models; the subtle and beneficial information provided by the intermediate layers is ignored. This paper therefore proposes a multi-decision based transformer model that builds multiple decision modules by utilizing the outputs at different layers to confront the various questions and passages. To avoid the information diversity in different layers being damaged during fine-tuning, we also propose a learning rate decaying method to control the updating speed of the parameters in different blocks. Experimental results on multiple publicly available datasets show that our model can answer different questions by utilizing the representation in different layers and speed up the inference procedure with considerable accuracy.
MaWGAN: A Generative Adversarial Network to Create Synthetic Data from Datasets with Missing Data

The creation of synthetic data are important for a range of applications, for example, to anonymise sensitive datasets or to increase the volume of data in a dataset. When the target dataset has missing data, then it is common to just discard incomplete observations, even though this necessarily means some loss of information. However, when the proportion of missing data are large, discarding incomplete observations may not leave enough data to accurately estimate their joint distribution. Thus, there is a need for data synthesis methods capable of using datasets with missing data, to improve accuracy and, in more extreme cases, to make data synthesis possible. To achieve this, we propose a novel generative adversarial network (GAN) called MaWGAN (for masked Wasserstein GAN), which creates synthetic data directly from datasets with missing values. As with existing GAN approaches, the MaWGAN synthetic data generator generates samples from the full joint distribution. We introduce a novel methodology for comparing the generator output with the original data that does not require us to discard incomplete observations, based on a modification of the Wasserstein distance and easily implemented using masks generated from the pattern of missing data in the original dataset. Numerical experiments are used to demonstrate the superior performance of MaWGAN compared to (a) discarding incomplete observations before using a GAN, and (b) imputing missing values (using the GAIN algorithm) before using a GAN.
UTextNet: A UNet Based Arbitrary Shaped Scene Text Detector

The task of scene text detection has achieved notable success in recent years owing to its wide range of applications, such as automatic entry of information from the image to database, robot sensing, text translation, etc. Many works have already been proposed for horizontal text, and some works for multi-oriented scene text. However, currently, the works of text detection on arbitrarily shaped texts that commonly appear in a natural world environment are scarce. This paper proposed a segmentation-based arbitrary shaped scene text detector adopted from the UNet, called the UTextNet. It comprises of a ResNet-UNet encoder-decoder network where the residual blocks of the ResNet encoder perform features extraction, and UNet decoder module performs the segmentation of the text region. A shallow segmentation head called an approximate binarization (AB) is added for the post-processing task. It performs binarization by using the probability map and threshold map generated by the encoder-decoder framework. The performance of the UTextNet is validated on benchmark datasets, namely ICDAR 2015 and Total-Text, and has demonstrated competitive performance as with the existing state-of-the-art systems.
Accurate Recognition Method of Continuous Sports Action Based on Deep Learning Algorithm

In order to improve the recognition effect of continuous sports action, an accurate recognition method of continuous sports action based on deep learning is proposed. The convolution neural network is used to predict the key points and center vector of human body in grid mode, and the key points of human body are grouped according to the distance from the key points of the human body to the center of human body, so as to obtain the sequence diagram of sports posture, input the posture sequence diagram of sports action in the deep learning network, and apply the best strategy of continuous action recognition obtained in the deep learning training stage to the accurate recognition of continuous sports action. The position of the rectangular frame of the attitude instead of the target attitude is predicted online by the convolution neural network, and the recognition is completed when the attitude stops. The experimental results show that this method can effectively recognize continuous sports actions in real time. The expected average overlap rate of different video attributes is high, which has better recognition effect. At different position error thresholds, it has high recognition accuracy.
Attacking Bitcoin anonymity: generative adversarial networks for improving Bitcoin entity classification

Abstract Classification of Bitcoin entities is an important task to help Law Enforcement Agencies reduce anonymity in the Bitcoin blockchain network and to detect classes more tied to illegal activities. However, this task is strongly conditioned by a severe class imbalance in Bitcoin datasets. Existing approaches for addressing the class imbalance problem can be improved considering generative adversarial networks (GANs) that can boost data diversity. However, GANs are mainly applied in computer vision and natural language processing tasks, but not in Bitcoin entity behaviour classification where they may be useful for learning and generating synthetic behaviours. Therefore, in this work, we present a novel approach to address the class imbalance in Bitcoin entity classification by applying GANs. In particular, three GAN architectures were implemented and compared in order to find the most suitable architecture for generating Bitcoin entity behaviours. More specifically, GANs were used to address the Bitcoin imbalance problem by generating synthetic data of the less represented classes before training the final entity classifier. The results were used to evaluate the capabilities of the different GAN architectures in terms of training time, performance, repeatability, and computational costs. Finally, the results achieved by the proposed GAN-based resampling were compared with those obtained using five well-known data-level preprocessing techniques. Models trained with data resampled with our GAN-based approach achieved the highest accuracy improvements and were among the best in terms of precision, recall and f1-score. Together with Random Oversampling (ROS), GANs proved to be strong contenders in addressing Bitcoin class imbalance and consequently in reducing Bitcoin entity anonymity (overall and per-class classification performance). To the best of our knowledge, this is the first work to explore the advantages and limitations of GANs in generating specific Bitcoin data and “attacking” Bitcoin anonymity. The proposed methods ultimately demonstrate that in Bitcoin applications, GANs are indeed able to learn the data distribution and generate new samples starting from a very limited class representation, which leads to better detection of classes related to illegal activities.
Frequentist statistical inference without repeated sampling

Abstract Frequentist inference typically is described in terms of hypothetical repeated sampling but there are advantages to an interpretation that uses a single random sample. Contemporary examples are given that indicate probabilities for random phenomena are interpreted as classical probabilities, and this interpretation of equally likely chance outcomes is applied to statistical inference using urn models. These are used to address Bayesian criticisms of frequentist methods. Recent descriptions of p -values, confidence intervals, and power are viewed through the lens of classical probability based on a single random sample from the population.
DIFCS: A Secure Cloud Data Sharing Approach Based on Decentralized Information Flow Control

Summary In the field of cloud computing, collaboration and data sharing among clouds have dramatically increased. How to protect the security of shared cloud data is being an urgent problem. Based on decentralized information flow control (DIFC) model, this paper presents an approach, DIFC-based data sharing approach (DIFCS), to protect both confidentiality and integrity of shared cloud data. Additionally, this paper designs a privilege protection policy for DIFCS, resulting in its capability of preventing malicious users from modifying privilege. The correctness and security properties of DIFCS are proved by formal analysis and verification, where firstly, DIFCS is formally interpreted into high-level petri net (HLPN) representation and analyzed using Z language, then is automatically verified with SMT-Lib and Z3 solver. The formal analysis and verification results reveal that DIFCS holds the security properties of confidentiality, integrity, authenticity, and privilege tamper-proof. The experimental results further demonstrate the high efficiency of DIFCS.
SchemaWalk: Schema Aware Random Walks for Heterogeneous Graph Embedding

Heterogeneous Information Network (HIN) embedding has been a prevalent approach to learn representations off semantically-rich heterogeneous networks. Most HIN embedding methods exploit meta-paths to retain high-order structures, yet, their performance is conditioned on the quality of the (generated/manually-defined) meta-paths and their suitability for the specific label set. Whereas other methods adjust random walks to harness or skip certain heterogeneous structures (e.g. node type(s)), in doing so, the adjusted random walker may casually omit other node/edge types. Our key insight is with no domain knowledge, the random walker should hold no assumptions about heterogeneous structure (i.e. edge types). Thus, aiming for a flexible and general method, we utilize network schema as a unique blueprint of HIN, and propose SchemaWalk, a random walk to uniformly sample all edge types within the network schema. Moreover, we identify the starvation phenomenon which induces random walkers on HINs to under- or over-sample certain edge types. Accordingly, we design SchemaWalkHO to skip local deficient connectivity to preserve uniform sampling distribution. Finally, we carry out node classification experiments on four real-world HINs, and provide in-depth qualitative analysis. The results highlight the robustness of our method regardless to the graph structure in contrast with the state-of-the-art baselines.
Cryptographic Accumulator and Its Application: A Survey

Since the concept of cryptographic accumulators was first proposed in 1993, it has received continuous attention from researchers. The application of the cryptographic accumulator is also more extensive. This paper makes a systematic summary of the cryptographic accumulator. Firstly, descriptions and characteristics of cryptographic accumulators are given, and the one-way accumulator, collision-free accumulator, dynamic accumulator, and universal accumulator are introduced, respectively. Cryptographic accumulator can be divided into two types: symmetric accumulator and asymmetric accumulator. In the asymmetric accumulator, three different cryptographic accumulator schemes were classified based on three security assumptions. Finally, this paper summarized the applications of cryptographic accumulators in ring signature, group signature, encrypted data search, anonymous credentials, and cryptographic promise.
Exploiting high-order local and global user–item interactions for effective recommendation

Recommender systems aim to suggest items of interest from historical interactions between users and items. Advanced methods such as path-based recommendations attempt to capture high-order user–item interactions for better recommendation performance. However, these methods focus more on the local view of user–item interactions, ignoring the global view, which limits performance. Two more drawbacks further restrict the performance of existing methods. First, most do not consider the relations (or relation directions) between successive nodes when constructing a multi-hop path (i.e., high-order local interactions) from a user to a target item. Second, the influential factors of path length and number of relation types are ignored when computing path importance to aggregate paths for better local representation. To resolve these issues, we propose a recommendation model that can well capture high-order local and global (HOLG) user–item interactions. A path-embedding module learns a local representation of user–item interactions via a long short-term memory network, taking (directed) relations of successive nodes as input. Multiple local representations are aggregated with an attention network, using both path length and number of relation types as important factors. A graph-embedding module learns a global representation of user–item interactions by constructing a subgraph from sampled user–item paths. Experiments on the LinkedIn, MovieLens-1M, and Yelp datasets validate our approach, which performs best in comparison with eight baselines.
Permissioned Blockchain-based Trustworthy Edge Storage Scheme for Smart Grid

Background: With the development of technologies such as the Internet of Things (IoT) and cloud/edge computing, a large number of edge devices in the smart grid accomplish information interconnection and generate massive amounts of data. On the one hand, smart grids need to obtain a large amount of grid data from edge terminal devices. On the other hand, edge computing also brings some security threats. Therefore, edge data security faces major challenges. Objective: In order to ensure the security of data in the edge computing environment of the smart grid, we propose an edge data storage scheme (BlockSE) for the smart grid edge computing environment. Method: BlockSE combines permissioned blockchain and smart contract technology to achieve secure storage of data on edge. We adapted ring signature technology to achieve privacy preserving of participating nodes. To allocate the storage resources of edge servers fairly and transparently, we designed a storage resource allocation algorithm based on the smart contract, whereby edge devices can obtain storage resources fairly according to demand. Results: Simulation experiments were performed; the results show the effectiveness of the proposed BlockSE scheme. Conclusion: This paper focused on data security in the smart grid's edge computing environment and proposed a secure storage scheme for edge data based on permissioned blockchain. The evaluation shows the safety, effectiveness, and practicability of the proposed BlockSE scheme.
Ranking of Basin-Scale Factors Affecting Metal Concentrations in River Sediment

River sediments often contain potentially harmful pollutants such as metals. Much research has been conducted to identify factors involved in sediment concentrations of metals. While most metal pollution studies focus on smaller scales, it has been shown that basin-scale parameters are powerful predictors of river water quality. The present study focused on basin-scale factors of metal concentrations in river sediments. The study was performed on the contiguous USA using Random Forest (R.F.) to analyze the importance of different factors of the metal pollution potential of river sediments and evaluate the possibility of assessing this potential from basin characteristics. Results indicated that the most important factors belonged to the groups Geology, Dams, and Land cover. Rock characteristics (contents of K2O, CaO, and SiO2) and reservoir drainage area were strong factors. Vegetation indices were more important than land cover types. The response of different metals to basin-scale factors varied greatly. The R.F. models performed well with prediction errors of 16.5% to 28.1%, showing that basin-scale parameters hold sufficient information for predicting potential metal concentrations. The results contribute to research and policymaking dependent on understanding large-scale factors of metal pollution.
Hash-Based Signature for Flexibility Authentication of IoT Devices

5G provides a unified authentication architecture and access management for IoT (Internet of Things) devices. But existing authentication services cannot cover massive IoT devices with various computing capabilities. In addition, with the development of quantum computing, authentication schemes based on traditional digital signature technology may not be as secure as we expected. This paper studies the authentication mechanism from the user equipment to the external data network in 5G and proposed an authentication protocol prototype that conforms to the Third Generation Partnership Program (3GPP) standard. This prototype can accommodate various Hash-based signature technologies, applying their advantages in resource consumption to meet the authentication requirements of multiple types of IoT devices. The operation of the proposed authentication scheme is mainly based on the Hash function, which is more efficient than the traditional authentication scheme. It provides flexible and high-quality authentication services for IoT devices cluster in the 5G environment combining the advantages of Hash-based signature technology and 5G architecture.
The argumentative style of the opening speech of a debate in the European Parliament

Abstract In this contribution a characterization is given of the argumentative style utilised in an opening speech of a plenary debate in the European Parliament. This debate is initiated through an opening speech in which the rapporteur of a parliamentary committee presents the European Commission’s proposal, as well as the amendments and the arguments to support them. As it is the rapporteur’s aim to gain broad parliamentary agreement with the proposal, the opening speech is vital to the debate, because all contributions of the MEPs relate directly to the argumentation put forward by the rapporteur. Based on the analytic overview the strategic design and the three dimensions of argumentative style the argumentative style that is used by the rapporteur is characterized.
Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.
Efficient Set Membership Proofs using MPC-in-the-Head

Abstract Set membership proofs are an invaluable part of privacy preserving systems. These proofs allow a prover to demonstrate knowledge of a witness w corresponding to a secret element x of a public set, such that they jointly satisfy a given NP relation, i.e. ℛ( w, x ) = 1 and x is a member of a public set { x 1 , . . . , x 𝓁 }. This allows the identity of the prover to remain hidden, eg. ring signatures and confidential transactions in cryptocurrencies. In this work, we develop a new technique for efficiently adding logarithmic-sized set membership proofs to any MPC-in-the-head based zero-knowledge protocol (Ishai et al. [STOC’07]). We integrate our technique into an open source implementation of the state-of-the-art, post quantum secure zero-knowledge protocol of Katz et al. [CCS’18].We find that using our techniques to construct ring signatures results in signatures (based only on symmetric key primitives) that are between 5 and 10 times smaller than state-of-the-art techniques based on the same assumptions. We also show that our techniques can be used to efficiently construct post-quantum secure RingCT from only symmetric key primitives.
The Effects of Resampling on Classifying Imbalanced Datasets

The majority of data classifiers work well when the distribution of different classes in the dataset is well balanced. Issues start to occur due to the cases of dataset imbalances. This paper applies solutions to the dataset imbalance problem by utilizing Oversampling, Undersampling and hybrid approaches in order to tackle the imbalanced datasets. A dataset which contains 400 observations, and 4 variables was used. The data distribution was imbalanced where 70% of the data belonged to one class and the remaining 30% belonged to the other class. The number of instances that were used for training the dataset was 188 and 97 while the number of instances that were used for testing the dataset was 92 and 23. Three models were used for classification: Random Forest, Support Vector Machine, and Naïve Bayes. Resampling was applied on the training instances. Results show that the classifiers’ accuracy increases after treating the problem of imbalance.