Reviewerly/exHarmony huggingface dataset:
FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation

Federated Graph Neural Networks have recently attracted considerable attention for its application of graph-structured data distributed in different organizations in joint training of GNNs. Current FedGNN methods mainly focus on aggregating or sharing node features and local subgraphs held by each client under privacy protection. However, the critical premise that the graph structure held by the client is reliable is commonly not met. This is because, under partial semantics, an isolated client may only witness an insufficient amount of structural interactions, which is clearly suboptimal. The performance of FedGNN can be enhanced if the observed structure can gather additional structural data from the features or interactions of additional clients. The optimized structure will approximate the optimal graph structure. Inspired by this, we propose Federated Graph Structure Learning (FedGSL). FedGSL acquires multiple encrypted views and pseudo-views for each client while hiding features and the true graph, which we anonymously propagate and integrate into the learned graph structure. We concurrently aggregate the interactions of multi-hop structures to supplement the segmented topology for each individual semantic. Each client updates parameters in parallel after training local data, server-distributed desensitized and shared structures, and individualized embeddings. Extensive experimental findings reveal that FedGSL outperforms selected baselines on numerous widely used raw graph datasets and greatly optimizes the client’s local subgraph.
Learning Robust Food Ontology Alignment

In today’s knowledge society, large number of information systems use many different individual schemes to represent data. Ontologies are a promising approach for formal knowledge representation and their number is growing rapidly. The semantic linking of these ontologies is a necessary prerequisite for establishing interoperability between the large number of services that structure the data with these ontologies. Consequently, the alignment of ontologies becomes a central issue when building a worldwide Semantic Web. There is a need to develop automatic or at least semi-automatic techniques to reduce the burden of manually creating and maintaining alignments. Ontologies are seen as a solution to data heterogeneity on the Web. However, the available ontologies are themselves a source of heterogeneity. On the Web, there are multiple ontologies that refer to the same domain, and with that comes the challenge of a given graph-based system using multiple ontologies whose taxonomy is different, but the semantics are the same. This can be overcome by aligning the ontologies or by finding the correspondence between their components.In this paper, we propose a method for indexing ontologies as a support to a solution for ontology alignment based on a neural network. In this process, for each semantic resource we combine the graph based representations from the RDF2vec model, together with the text representation from the BERT model in order to capture the semantic and structural features. This methodology is evaluated using the FoodOn and OntoFood ontologies, based on the Food Onto Map alignment dataset, which contains 155 unique and validly aligned resources. Using these limited resources, we managed to obtain accuracy of 74% and F1 score of 75% on the test set, which is a promising result that can be further improved in future. Furthermore, the methodology presented in this paper is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain.
Learning on Health Fairness and Environmental Justice via Interactive Visualization

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations.
Academic Graph: A Literature Review System

As the number of academic publications increase, preparing a literature review becomes more challenging. This paper introduces an automated literature review support system to ease the literature review process for academia with reference graphs, abstract and full document summaries, paper clusters by keywords, abstracts, and abstract summaries combined. The output of the proposed system may ease exploring the state-of-the-art research.
Appley: Approximate Shapley Value for Model Explainability in Linear Time

We have seen complex deep learning models out-performing human benchmarks in many areas (e.g. computer vision, natural language processing). Clever architectures and higher model complexity are two of the major drivers of such outstanding performances. Higher model complexity generally makes the decision-making process of a model opaque to human perception. But understanding the decision-making process is very important for many reasons including enhancing trust in the model’s prediction, improving model robustness, gaining actionable insight from why a model made a particular prediction and discovering new knowledge about a problem. Model explainability has been an active area of research for some time now, but the problem is still far from being solved. An established way of model explanation is to assign a score to each variable (also known as variable attribution), which represents the importance of the variable in a particular prediction of a model. In a lot of techniques, the scoring process involves distributing the output to each variable. This is challenging when the model is complex and consists of a high degree of interaction terms. A coalition game theoretic approach called Shapley Value provides a fair way to tackle the challenge. However, the growth of computation time of the exact Shapley Values is exponential in the number of variables. Hence, many attribution techniques use approximations instead of the exact Shapley Value as attribution. In this manuscript, we propose a novel variable attribution technique called Appley (short for Approximate Shapley) by approximating the Shapley Values in linear time. Moreover, we show that the “Appley” attributions are generally closer to the exact Shapley Values than an existing well-known and comparable attribution technique.
Bitcoin Evolution Analytics: Twitter Sentiments to Predict Price Change as Bearish or Bullish

In the financial market, Bitcoin analytics has gained lots of attention due to its high-risk high-reward nature. It is interesting to find better techniques to analyze and predict the Bitcoin price change. In this paper, we propose Bitcoin Evolution Analytics, which aims to predict the Bitcoin price change after one hour as Bearish or Bullish. For the prediction, the approach combines the Sentiment analysis and the Technical indicators. For Sentiment analysis of tweets related to Bitcoin, the approach uses three Natural Language Processing (NLP) libraries, namely VADER, FinBERT, and TextBlob, which generated eight different sentiment scores. For Technical indicators, the approach used three features of Bitcoin: User Sentiment Score, Aroon Indicators, and Accumulation/Distribution Line Indicators. We represented all these features of Bitcoin Data over time, which created a novel Bitcoin State Series. To predict the price change of the next hour as Bearish or Bullish, we built the state series for each hour of continuous 13 months (March 2021 - March 2022). To find the most reliable set of features, we have trained 27 ML models. For each feature set, we compared the average and maximum of the accuracies and f-measures. The results of our experiment show that considering the followers of the user as the "weight" of the sentiment gives a more accurate prediction. We found that a combination of Sentiment Analysis and Technical Indicators performs better than using only Sentiment Analysis.
Trustworthy Distributed Deep Neural Network Training in an Edge Device Network

With the increased usage of edge devices having local computation capabilities, deep neural network (DNN) training in a network of edge devices becomes promising. Several recent works have proposed fully edge-based distributed training systems for situations when the communication to cloud is unstable or intermittent. However, such distributed systems become vulnerable when there are untrusted devices that launch data and model poisoning attacks during training, deteriorating the accuracy of the DNN model. To handle this challenge, we propose a Trustworthy distributed system for Machine learning training in an edge device network (TrustMe). TrustMe realizes both data and model parallelisms. It detects the untrusted devices producing illegitimate outputs. Next, it reassigns the training tasks of the untrusted devices to other trusted devices in such a way that the reassignment and the training that is restarted after the reassignment require minimal time. Our container-based emulation and real device experiments demonstrate that TrustMe achieves up to 12% higher accuracy and 45% less training time compared to existing methods in the presence of untrusted devices.
Collaborative Filtering Guided Deep Reinforcement Learning for Sequential Recommendations

Earlier recommendation techniques, such as Collaborative Filtering (CF), assume the users’ preferences do not change over time and strive to maximize the immediate reward. In recent studies, Reinforcement Learning (RL) has been used to make interactive recommendation systems that capture users’ preferences over time and maximize the long-term reward. However, these methods have two limitations. First, they assume that items are independently distributed and do not consider the relations between items. This assumption ignores the power of the relations between items for recommendation systems, as demonstrated by CF. RL-based methods rely primarily on users’ positive feedback to understand their preferences, and sampling is used to incorporate their negative feedback. In a practical setting, users’ negative feedback is just as crucial as their positive feedback for understanding their preferences. We present a novel Deep Reinforcement Learning (DRL) recommendation framework to address the limitations above. We specifically utilize the actor-critic paradigm, which considers the recommendation problem a sequential decision-making process to adapt to users’ behaviors and maximize the long-term reward. Motivated by the intuition that similar users like similar items, we extract the relations between items using CF and integrate it into our framework to boost overall performance. Instead of negative sampling, our proposed framework relies on all users’ positive and negative feedback to understand users’ preferences more accurately. Extensive experiments with our dataset and two public datasets demonstrate the effectiveness of our proposed framework.
Workflow for Domain- and Task-Sensitive Curation of Knowledge Graphs, with Use Case of DRKG

Recently, knowledge graphs have seen a significant increase in popularity in a wide variety of domains, as they provide a basis for many data-analytics and knowledge-discovery approaches. At the same time, many knowledge graphs are not immediately usable due to their format, unreadable or missing data, and inaccessibility. These issues present barriers to the exploration and use of knowledge graphs for big data analytics and knowledge discovery. In this paper we present a workflow for domain- and task-sensitive curation of large-scale knowledge graphs, and detail our experience with implementing this workflow with the biomedical knowledge graph called Drug Repurposing Knowledge Graph (DRKG). The workflow aims to address usability-related issues of real-life knowledge graphs, by performing data setup and curation that align with the needs of specific tasks and domains. Recognizing that domain experts and anticipated users of a knowledge graph provide invaluable expertise regarding the desired graph format, the proposed workflow involves them as humans-in-the-loop. We present the processes required to execute the workflow, detail our experience in the biomedical domain with the use case of DRKG, and discuss the challenges and lessons learned throughout the experience. We anticipate that the proposed workflow and experiences will be applicable to other domains, and that our workflow will enable and encourage exploration and wider use of large-scale knowledge graphs, thereby improving big data analytics.
Preserving Cluster Features in Imputing High Dimensional Data with Extensive Missing Rate

It is a daunting task to impute a large dataset that has majority of data missing from its tens of thousands of predictors, but still to preserve a known cluster structure in the imputed results. In this study, we propose a novel two-step approach for this task. First, we use simple linear classification models to derive the global and local features of cluster structures from a template ground truth (i.e. known gold data). Second, we integrate the cluster features extracted from each cluster into a neural network architecture and its training responses for guiding the imputation process. Since our neural network utilizes the global and local features of gold data in training the imputation network, we refer our neural network as GLIN (Global-Local Imputation Network). We test our imputation method on two high-dimensional datasets: a single cell dataset and a movie rating dataset, that have up to of 95% missing rates. Finally, we use four evaluation metrics: distance, correlation, data distribution, and predictability difference, to evaluate how well the cluster structures of the gold data are preserved in the imputed results.
Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud

Outlier detection is a challenging activity. Several machine learning techniques are proposed in the literature for outlier detection. In this article, we propose a new training approach for bidirectional GAN (BiGAN) to detect outliers. To validate the proposed approach, we train a BiGAN with the proposed training approach to detect taxpayers, who are manipulating their tax returns. For each taxpayer, we derive six correlation parameters and three ratio parameters from tax returns submitted by him/her. We train a BiGAN with the proposed training approach on this nine-dimensional derived ground-truth data set. Next, we generate the latent representation of this data set using the encoder (encode this data set using the encoder) and regenerate this data set using the generator (decode back using the generator) by giving this latent representation as the input. For each taxpayer, compute the cosine similarity between his/her ground-truth data and regenerated data. Taxpayers with lower cosine similarity measures are potential return manipulators. We applied our method to analyze the iron and steel taxpayer’s data set provided by the Commercial Taxes Department, Government of Telangana, India.
Fast Illegal Webpage Detection Algorithm Based on Massive Domain Name Resolution Records

Domain names are an essential entry point for Internet access. Blocking the domain names of illegal web pages can effectively prevent the spread of illegal online content such as gambling and pornography. Due to the low efficiency and accuracy of the current illegal web page detection algorithms, on the one hand, it is not conducive to security prevention if the detection is not timely. On the other hand, the misjudgment and omission of domain name blocking will lead to adverse consequences for Internet access. Therefore, this paper focuses on domain name resolution records to improve the detection accuracy and efficiency of illegal web pages, so that domain name servers can timely block access to lousy domain names and improve the security of Internet infrastructure. Firstly, a data pre-processing process is established based on an empirical analysis of the world’s largest passive DNS database. Secondly, we propose a hybrid temporary random domain name filtering algorithm based on LSTM-ParallelCNN. Finally, an illegal web-page detection algorithm based on combined features is proposed to balance the speed and the accuracy of the detection. In the end, experiments show that our algorithms can improve the detection accuracy to 99.35% while reducing the overall detection time by about 80%.
Research on Digital Modeling Method of the Control Protection System of DC Converter Stations based on Digital Twin Technology

The digital twin of the control protection system of the ±800kV DC converter station is based on the three-dimensional digital modeling of the control protection system and its secondary loop. The digital modeling platforms in this thesis include aspects such as three-dimensional modeling of physical objects, digital modeling of physical loops, digital modeling of signal loops, and the interaction and coupling of them. The three-dimensional modeling construction of the whole station is implemented based on the board library, equipment library, and cabinet library. For the first time, loop relationship ID tags are used to implement the in-cabinet and inter-cabinet physical loop digital modeling management and configuration, and convert-er-station-oriented control protection device information is entered to define the process, format and attribute of signal loop digital modeling, and transmission information and physical loop are coupled based on the loop relationship, to conduct a complete digital configuration platform and process, implementing the entire mod-eling and digitalization handover of the control protection system and its loop of the DC converter station, setting a basis for researches on the digital twin of the control protection system of the DC converter station and related advance application of operation and maintenance.
TAED: Topic-Aware Event Detection

Identifying event trigger words and classifying event types known as the event detection task is a fundamental step for extracting event-related knowledge from textual sources. Examples of the topics within documents include "military conflict," "earthquake," "concert tour," "wrestling," and others. Topical information embedded within documents where the events are extracted from is rarely explored. Rich topic information could be a helpful indicator of the event’s type. Semantically similar topics share similar event types, while event types are quite different between distinguishable document topics. In this paper, we explored a novel method of integrating document topic information to complete the event detection task. We summarized our contribution as the following: we used the topic information of the documents to generate topic comprehensive sentence representations. We adopted a multi-task deep neural network, trained with event detection and topic classification t asks. We evaluated our method with two datasets that are designed for more diverse and general event types event detection MAVEN [1] and RAMS [2]. We demonstrated that the topic-aware model outperformed the baseline model F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score on both MAVEN and RAMS datasets. An analysis in the few-shot event types scenario showed that topic-aware model can beat the baseline by up to 13.34% on the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score for the rare event types.
Learning from Disagreement for Event Detection

Using a newly developed model to upgrade a legacy model is a common practice in machine learning applications. After the upgrade, it is expected that the new model should outperform the legacy model in the regions of interest. However, it is observed that the new model often makes incorrect decisions on some instances where the legacy model still performs well. For a binary classification model (e.g., click-through-rate/CTR prediction model), such undesirable behavior could even occur in the low false positive region of the receiver operating characteristic (ROC) curve. Finding the reasons behind this phenomenon can help business partners in an organization gain confidence in adopting the new model and help modelers to improve the new model in future releases. In this paper, we present the "Learning from Disagreement" framework to understand and improve the performance of a predictive model. Under the setting of a binary classification task, this proposed approach focuses on instances that lead to contradictory decisions between a pair of models at a given operating point. We perform feature importance analysis exclusively on these instances, gain insights into the pair of models without even knowing their inner operations, and offer actionable feedback for model improvement. We demonstrate the usefulness of this framework on two real-world event detection datasets.
Research On Deep Learning Based End-To-End Wireless Communication System Technology

As an important part of AI, deep learning is widely used in image classification and speech recognition. In particular, the application of deep neural networks with powerful digital signal processing capabilities in communication systems has become a research hotspot in recent years. As a common unsupervised learning model in deep learning, the autoencoder is similar to the traditional wireless communication system and can be used as a design scheme for the physical layer of the wireless communication system. From the perspective of “global optimization” of the wireless communication system, this paper is based on deep learning autoencoder and uses the MATLAB deep learning toolbox to design an end-to-end wireless communication system and conducts simulation under AWGN. The results show that the proposed system is similar to the traditional wireless communication system in terms of performance and has certain “generalization” capability for coding rate, EbNo and other parameters.
Data Fusion and Graph Analysis in Fraud Transaction Detection: walkthrough of a case study

The use of data acquisition and fusion techniques allow to generate event graphs in support of criminal investigations. In this paper, an anonymized real case study will be presented to identify undue transactions through graph analysis. All the steps of an investigation protocol are illustrated by describing how the tools adopted in this paper allow to semi-automatically analyze huge amounts of data coming from different nature, identifying suspicious transactions with high precision.
An Overview of PAI: Distributed Machine Learning Platform

Due to the size, complexity, and rapid change of big data, the need for faster training and data processing, and the growing inability of conventional single-machine training methods to match the demands. Recently, some distributed AI platforms have appeared for machine learning. In this article, we describe the OpenPAI distributed machine learning platform's architecture and examine its capabilities and design. We present a summary of the current issues in distributed machine learning and discuss potential future trends.