Reviewerly/exHarmony huggingface dataset:
Using GAN to Generate Malicious Samples Suitable for Binary Classifier Training

Assigning data to one of two predefined classes is called a classification problem in machine learning (ML). This problem is very important and has become very popular, which has resulted in many methods designed to solve it. One of the problems we may encounter in the construction of this model is that the classes are not balanced – the classes have significantly different sizes. Of course, methods have been proposed to deal with this problem, one of which is to enlarge the minority class to the size of the larger class. In this paper, we present results obtained using the generative adversarial network (GAN) model to generate samples to increase the quality of binary classifier detecting malicious programs which was the goal of one of the competitions organized as part of the IEEE BigData 2022 conference. The GAN model we chose is the Wasserstein GAN (with gradient penalty), and the classifier is the LightGBM model. We reduced the RMSE on the public part of the test data from 0.19088 to 0.13497, and on the entire test set the RMSE difference was 0.11276. Due to some randomness in this solution, we showed that the presented method improves the quality of the classifier on average.
Clustering-based Unsupervised Generative Relation Extraction

Existing unsupervised relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an Encoder-Decoder architecture to train a relation extractor as the encoder. Given multiple sentences with the same entity pair as inputs, CURE is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled based on the words in the shortest paths corresponding to the entity pairs in each cluster. Experimental results demonstrate the effectiveness of CURE compared to state-of-the-art models across all benchmark datasets.
RaCAViz: Interactive Visualizations for Rapid Carbon Assessment

Soil organic carbon is an essential element of environmental quality assessment because carbon dioxide is the main greenhouse gas causing global warming and climate change. Trees and plants acquire a lot of carbon in their lifetimes, and soil helps keep this portion of carbon from releasing to the environment. Thus, understanding the current carbon storage in the soil and the relationship with tree richness is important. The soil organic carbon datasets and corresponding tree information are available. However, the current visualizations for these datasets are either static visualizations that report initial findings to the public or visualizations that are too complicated to be useful for a broad audience. Therefore, this work proposes an interactive visualization solution for analyzing soil organic carbon values distribution and their relationship with tree heights and diameters at breast heights, called RaCAViz. The design of this interactive visualization is based on the analytical evaluation of visual and interaction idioms to tackle typical analysis tasks on this type of dataset. However, this paper also provides a specific use case to illustrate its usefulness in bringing different perspectives on soil organic carbon datasets to a broad audience.
Comparative Reasoning for Knowledge Graph Fact Checking

Knowledge graph has been widely used in fact checking, owing to its capability to provide crucial background knowledge to help verify claims. Traditional fact checking works mainly focus on analyzing a single claim but have largely ignored analysis on the semantic consistency of pair-wise claims, despite its key importance in the real-world applications, e.g., multimodal fake news detection. This paper proposes a graph neural network based model INSPECTOR for pair-wise fact checking. Given a pair of claims, INSPECTOR aims to detect the potential semantic inconsistency of the input claims. The main idea of INSPECTOR is to use a graph attention neural network to learn a graph embedding for each claim in the pair, then use a tensor neural network to classify this pair of claims as consistent vs. inconsistent. The experiment results show that our algorithm outperforms state-of-the-art methods, with a higher accuracy and a lower variance.
An improved risk assessment method for substation power monitoring system

Aiming at the problems of incomplete system modeling, ambiguity of expert evaluation opinions and lack of consideration of the overall risk of the system in the risk assessment of substation power monitoring systems, a risk assessment method for substation power monitoring systems based on cloud model and improved evidence theory is proposed.. Firstly, according to the structure and security requirements of the substation power monitoring system, the equipment, security objectives and threats of the substation power monitoring system are analyzed, and the overall risk assessment model of the system is established; then combined with the FAHP and the modified entropy weight method, the optimal combination assignment is used. Finally, the cloud model and improved evidence theory are used to complete the comprehensive risk assessment of the substation power monitoring system, and the risk level of the system is obtained. The safety management of the monitoring system provides a new idea.
Learning Diversity Attributes in Multi-Session Recommendations

Diversity in recommendation has been studied extensively. It has been shown that maximizing diversity subject to constrained relevance yields high user engagement over time. Existing work largely relies on setting some attributes that are used to craft an item similarity function and diversify results. In this paper, we examine the question of learning diversity attributes. That is particularly important when users receive recommendations over multiple sessions. We devise two main approaches to look for the best diversity attribute in each session: the first is a generalization of traditional diversity algorithms and the second is based on reinforcement learning. We implement both approaches and run extensive experiments on a semi-synthetic dataset. Our results demonstrate that learning diversity attributes yields a higher overall diversity than traditional diversity algorithms. We also find that training policies using reinforcement learning is more efficient in terms of response time, in particular for high dimensional data.
Tensor-based Approach to Big Data Processing and Machine Learning

We present an approach to tensor compression and decomposition, as well as to a design of data processing algorithms on the top of them. Our implementation uses the popular scalable data processing framework Apache Parquet to effectively store the data. This library does not directly store tensors as native data types, but we slightly changed its implementation for our purpose using its specific data storage format and extending it with additional compression. We summarize the performance of tensor storage, as well as the effectiveness of multiple machine learning methods and their hyperparameter tuning.
An Improved Vulnerability Detection System of Smart Contracts Based on Symbolic Execution

Smart contracts emerged as programs running on the blockchain. Security is one of the major concerns against smart contracts which also exist various vulnerabilities as for any other traditional programs. What was worse, security vulnerabilities in smart contracts may lead to irreversible economic losses. Hence, there is an apparent demand for security audits of contracts before deployment. In recent years, a large number of smart contract vulnerability detection tools have emerged. The methods used by these tools include formal verification, symbolic execution, machine learning, and fuzz testing. These methods can well analyze vulnerabilities, but there are still limitations. In this paper, we optimized and extended the Mythril symbolic execution tool. The optimized pruning algorithm improves the speed of symbolic execution, while the proposed detection algorithm for Transaction Order Dependence vulnerability expands the range of detecting vulnerability. In addition, a machine learning vulnerability detection model is introduced as an auxiliary detection method, which is used to build the complete smart contract vulnerability detection system. The experimental results show that the proposed system reduces the execution time, and improves the accuracy as well as the recall of vulnerability detection compared with the original Mythril tool.
Research on Remote Sensing Image Target Detection Algorithm Based on YOLOv5

In recent years, Remote-sensing image target detection is a hot research problem of the researchers at home and abroad in the field of remote-sensing image and the target recognition, but because of the large amount of remote-sensing image data and complex, target is obscured in the image and the area of target is so small, the traditional remote-sensing image target detection is not enough to solve the problems in the research stage. Therefore, single-stage target detection based on algorithms such as YOLO series and SSD omits the steps of two-stage detection model, and gains the favor of researchers by sacrificing accuracy for speed. In this paper, by introducing DIOU-NMS and Alpha-IoU, based on the YOLOv5 network structure, the non-maximum inhibition in the original YOLOv5 is replaced by the non-maximum inhibition based on DIOU_Loss, and the original IoU system is replaced by the α-IoU system. The experimental results show that under the same training conditions, compared with the original YOLOv5 network, the improved YOLOv5 network for remote-sensing image target detection mAP@0.5 is increased by 4.2% to 0.775, and the accuracy is increased by 1.0% to 0.946.
Don’t Blindly Use Data: Towards a Data Statement for Computational Financial Research

In recent years, there has been a growing focus on the veracity of datasets. This concern has raised important questions such as: is the data appropriate for answering the research questions or hypotheses, is the data biased or harmful in any way, is the data quality data, and is there a sufficient understanding of the data for it to be used appropriately? We reviewed 46 papers from Google Scholar, IEEE, and ACM, and found that the majority of authors provide only a basic discussion of the dataset used in the research and do not address important issues such as potential bias or data that requires special attention. Following the work of Bender and Friedman, we propose a data statement framework specifically targeted to computational financial research to provide critical information to users. We also provide a completed data statement for published work as an example. This tool will help researchers provide users and stakeholders a better understanding of what comprises the data and provide an overview of what considerations were made in its creation. This will also help address any potential bias, errors or problems, and data that could be considered misleading in the context of the research.
Research on Occlusion Face Detection Method in Complex Environment

In recent years, wearing masks has become a habit of some people. In this context, the requirements for masking face recognition technology are getting higher and higher, and people's requirements for identity recognition intelligence and security are getting higher and higher. In practical applications, the face images obtained by the face recognition system are easily blocked by other people or objects. These interference factors make the collected face data incomplete and affect the accuracy of the entire face recognition system. For example, Apple's FaceID is difficult to identify Faces with masks. Therefore, it is urgent to improve the effect of existing face recognition systems in occluding face recognition. At present, most of the mainstream face recognition methods are based on deep learning. This type of method uses large-scale face data sets for training and learning, and the deep learning method for occlusion face recognition is also based on this. However, this type of data set is very limited. Based on this paper It mainly uses a generated occlusion face data set, and adds the occlusion face data set in the real environment to train the Yolov5 deep learning model to improve the detection ability of identifying occluded faces. The experimental results show that in the real environment, the occluded face detection proposed in this paper has a high accuracy, close to 97%.
Comparison of Teenagers’ Writing Using Word Clouds and Analyzing Engines

Worldwide, there are writing competitions for teenagers. However, usually, it is only the winning articles that are presented and remarked off. In order to understand the gap between winning and unselected articles, we compare the selected-winning articles and unselected articles to find the writing quality difference. We do this by using word clouds and various analyzing engines. On Write the World (WtW), while selected-winning pieces are published on WtW Reviews, the users (teenagers between the age of 13-18) can also self-share their articles with the public on the WtW platform. Our finding shows that to get unbiased results, the analyzing engines require the raw text to be inputted, rather than using only the 50 most frequently appeared words from the word cloud. Additionally, there is a notable difference in writing quality between selected-winning articles and self-shared articles.
Data Leaves as Scenario-oriented Metadata for Data Federative Innovation on Trust

Communication regarding data use enhances and sustains trust in the data market. Showing this principle based on the literature on trust, this study proposes a novel method for representing the digest information of datasets to foster the thoughts of potential data users, who attempt to create valuable products, services, or business models using datasets and aid their communication with data providers. Compared with existing metadata, where variable labels in a dataset are listed, the presented metadata, called data leaf (DL), includes events, situations, and/or actions that, as a set, compose scenarios supposed to be active in the target real world of a dataset. This method considers the fitness of metadata corresponding to various datasets to a feature concept (FC), which provides an abstract illustration of the knowledge acquired or expected to be acquired from the data, and plays an essential role in data utilization. In experiments using metadata as elements to be combined in human thought for data federative innovation, DLs significantly outperformed cases using data jackets (DJs) where variable labels were used as attributes of the datasets.
Ripl: Document-Level Event Argument Extraction via Role-aware Interactive Pointer Labeling Network

Document-level event argument extraction (DEAE) aims to recognize event arguments spreading across multiple sentences and their corresponding roles. DEAE is a challenging but indispensable subtask in general document-level event extraction. Existing methods are not effective due to two challenges of this task: (a) traditional extraction methods cannot assign multiple labels to a token, so these methods are unable to identify two nested arguments belonging to different roles; (b) previous methods are weak in encoding the document-level sequence and they have never modeled the relevance between different roles. In this paper, we propose a Role-aware Interactive Pointer Labeling Network (Ripl) to solve these two challenges. For challenge (a), we construct specific document representations for each role and propose a novel interactive pointer labeling strategy to extract nested arguments. For challenge (b), we introduce role prior knowledge into the original document, make the representation of each word in document captures the semantic information of the role and model the semantic relevance among the roles. Experiments on the MUC-4 event extraction dataset show that Ripl outperforms the previous state-of-the-art model by 5.9 F1 on the head noun match metric. Then we demonstrate the effectiveness and interpretability of our model with extensive experiments.
Generating Interpretable Features for Context-Aware Document Clustering: A Cybersecurity Case Study

This paper extends the applications of ContextMiner, a framework that we proposed recently aiming to extract interpretable contextual features to conceptualize knowledge in security texts. We show that contextual features are suitable for building a document-level representation that can be used further in a downstream machine learning and natural language processing task: document clustering. Our results show that the intrinsic readability of contextual features is usable for analyzing the document clusters obtained in our experiments. Such analysis is performed through a density-based feature selection, cluster visualization, and statistical methods in order to unveil which contextual features better characterize each document cluster. Our findings suggest that statistical techniques alongside feature analysis can be utilized to discover meaningful commonalities among documents in a particular cluster without the need of querying each document manually.
Control System Design and Implementation Based on Big Data and Ontology

In this article, the decision mechanism of a control system has been created by using big data and by applying an ontology model. This type of control is important in order to minimize, or even eliminate human influence in systematic industrial processes. The development presented in the article in order to make the ontology model compatible with a relational database in the decision mechanism, is a step taken to eliminate the human effect. The method used in the article aims to carry out a decision mechanism under the guidance of big data by using relational databases integrated with the ontology model. In line with this goal, the ontology model associated with relational databases with high prevalence will be able to access the continuous data required for the decision process and enrich the decision mechanism. In this study, when the necessary parameters for the decision process are obtained, dynamic threshold determination is provided by a machine learning model with these parameters. This dynamic threshold varies over various time periods, with the combination of inputs provided to the machine learning model and differences in value. Our test results state that the Decision Tree model predictions’ accuracy is 100%.
Scene Text Recognition with Heuristic Local Attention

Scene text recognition is considered as a sequence labeling problem. For the text recognition task, the alignment between the scene text image and the output text is coincident, which means the latter characters corresponding to the image region will also be behind. However, the existing global attention-based method focuses too much irrelevant information which leads to alignment drift. Contrary, local attention selects the subset of feature representation most relevant to the current character. In this paper, we explore the local attention mechanism and attempt to replace the global attention to implement decoding. Therefore, we revise several variants of local attention methods and provide a comprehensive comparison, which is missing in the scene text recognition literature so far. Specially, we introduce two Heuristic approaches for Local Attention (HLA) and prove that monotonic alignment improves performance significantly. Evaluations on the benchmarks show that the local attention method outperforms the existing global attention methods.
Single-image Super-resolution Based on Generative Adversarial Network with Dual Attention Mechanism

Most of the single-image (SI) super-resolution (SR) methods based on convolutional neural network (CNN) focus on the broader or deeper model architecture design, ignoring the research on the correlation of features in the middle layer, which hinders the representative power of CNN. However, most of the classical SISR methods based on generative adversarial network (GAN) focus on improving generators and loss functions, which are difficult to balance visual quality and objective evaluation. In order to solve these problems, SRGAN-DAM-SISR is proposed in this paper, which is also a typical GAN structure. However, it is different from the classical SRGAN and ESRGAN. We have made many designs and innovations, including improving the traditional feature extraction module of the generator and the discriminator's discriminant target in the GAN, removing the batch normalization (BN) layer, using many loss functions for weighting and using some tricks to improve the performance of the network. Experimental results show the improvement and innovation to enhance the visual effect of the output image, the resulting image quality is also improved.
FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Classification

Mainstream deep learning algorithms are designed for typical images sizing from 224 × 224 to 600 × 600 pixels, which are relatively small compared to the whole-slide images (WSI) with billions of pixels, causing existing methods to be inefficient due to computational and memory constraints. Furthermore, most WSIs are annotated with slide-level labels, as patch-level annotations are time-consuming and have to be conducted by experienced pathologists. At the same time, the regions of interest (ROIs), e.g., where cancer, tumor, and bacteria reside, would occupy relatively small areas. Thus, we propose Flexible and Efficient Zoom-In (FEZ) as a method to precisely locate ROIs to accelerate ultra-large image classification. FEZ initially examines a low-resolution WSI with a policy network (PN) to select the potentially critical ROIs and zooms in the selected regions using medium-resolution images to select finer-grain ROIs with another PN. Eventually, FEZ loads the high-resolution patches and classifies them with a value network (VN) to make a prediction. Since FEZ only loads a few selected patches instead of the high-resolution WSI, it dramatically reduces the image loading time and the computation time needed for analyzing the patches. Experimental results show that FEZ is capable of delivering similar accuracy with higher speed than a recently proposed whole-slide training method (WST) and existing sampling methods such as Multiple Instance Learning (MIL) and CLAM. FEZ is 42× and 22× faster than MIL on the Camelyon16 and TCGA Lung Cancer datasets, respectively. With FEZ, we can reduce the classification time for ultra-large images from minutes to seconds to support interactive applications.
iFiG: Individually Fair Multi-view Graph Clustering

A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3] .