Reviewerly/exHarmony huggingface dataset:
An Application of Object Detection in Contract Analysis

In this paper, we discuss the application of visual object-detection analysis on an information governance business use case. Specifically, we discuss how handwriting detection can serve as the centerpiece of a workflow where signatures are detected in PDF documents, and the number of detected signatures is utilized as a method of singling out countersigned and fully executed contracts. We outline the business case and the recommended workflow involving final validation using an eDiscovery platform and provide statistics from a real-world project to illustrate the potential effectiveness of the solution.
An approach to constructing a graph data repository for course recommendation based on IT career goals in the context of big data

Graph data is widely regarded as the next frontier in big data modeling for a variety of domains. Graph-based data models have been used in big data to organize messy or complicated data points based on their relationships. Graph analytic technologies for big data provide a framework for absorbing both structured and unstructured data from a variety of sources, allowing analysts to see the connections between entities in graphs and draw new conclusions. A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. KG is also known as one of the graph-based data models, which is used to model entities and relationships in many domains. In this paper, we propose a solution for constructing a graph data repository in a big data environment for career goals-based course recommendation in the Information Technology (IT) fields. Our proposed KG framework is based on the Labeled Property Graph (LPG) to represent a graph data model and has defined three major concept layers: course, career, and competency. Important entities and the semantic interactions between those things are also found in each concept layer. We also designed and implemented a fully automated application system to extract a large number of courses from MOOC (Massive Open Online Courses) resources. Our application system specifically uses deep learning-based technology to extract entities, handle data duplication, and then store them in Neo4j, in which a graph data model is developed based on our suggested KG architecture. The collected data from our system includes 14 career titles, 999 online courses, and 3022 competencies. It is a valuable experimental dataset for evaluating career goal-based course recommendation algorithms in the IT fields.
Critic-over-Actor-Critic Modeling: Finding Optimal Strategy in ICU Environments

Reinforcement learning (RL) is mechanized to learn from experience. It solves the problem in sequential decisions by optimizing reward-punishment through experimentation of the distinct actions in an environment. Unlike supervised learning models, RL lacks static input-output mappings and the objective of minimization of a vector error. However, to find out an optimal strategy, it is crucial to learn both continuous feedback from training data and the offline rules of the experiences with no explicit dependence on online samples. In this paper, we present a study of a multi-agent RL framework which involves a Critic in semi-offline mode criticizing over an online Actor-Critic network, namely, Critic-over-Actor-Critic (CoAC) model, in finding optimal treatment plan of ICU patients as well as optimal strategy in a combative battle game. For further validation, we also examine the model in the adversarial assignment.
STCD: A Spatio-Temporal Causal Discovery Framework for Hydrological Systems

Causal learning has become an essential attribute in majority of the machine learning models. One of the widely studied fields in causal learning is causal discovery which aims to identify potential cause-effect relationships from observational data. Temporal causal discovery models are specifically curated to enforece the temporal constraints while discovering the causal relationships. However, in physical systems such as hydrological systems, there are additional constraints such as spatial constraints that play a crucial role in deciding whether a node is a causal parent for another node or not. Failing to enforce these additional constraints may mislead the model to classify an irrelevant relationship as a causal relationship. Furthermore, causal discovery models are evaluated against a ground truth causal graph. However, the hydrological systems contain a huge number of features making it challenging to obtain a ground-truth causal graph. To deal with the aforementioned problems, in this study we propose a new Spatio-Temporal Causal Discovery Framework named, STCD. By enforcing temporal and spatial constraints STCD aims at identifying meaningful causal relationships. Furthermore, to evaluate the causal relations inferred by STCD in the absence of the ground-truth causal graph, we utilize only the causal parents of a target variable for prediction across different years. We demonstrate that utilizing only the causal features identified by STCD to predict the flow-rate for a target location attains superior performance.
An Automatically Inferable Format for Creating Big Data of Morals, Ethics and ORDER OF VALUES Written Almost in Natural Language

If there is a format that is easy for both humans and AI (computers) to understand and infer, and also if the humans let the AI learn various morals, ethics and values in the above format, these data may be operated automatically by the AI in a way that the humans concerned are able to check for mistakes / lacks of thoughts in the automatic judgments by the AI, avoiding arbitrary thoughts / judgments by both the providers(humans) of such data and the AI.On the other hand, morals, etc. are diverse depending on the country, history, culture, social position, and/or individual. So in order to accurately express such data for reducing interpretation mistakes, lacks of required conditions of thought-rules and so on, we need to allow, as such data, expressions almost in (or from completely including all) the natural languages used in each cultural area.In this paper, we (1) propose a format based on natural language that we called as "situation-specific-value-order-formula" as a starting point for an expression format that satisfy the above needs, (2) explain its components: situations / viewpoints and items ( positive / 0 / negative ) ordered by the value all based on natural language, (3) explain related things including the specific l ogical i nference f eature o f t he n atural language based programming where variables are embedded in the natural language.We conclude that our proposed format is feasible and practical to some extent for the above Purpose and need.
DCPViz: A Visual Analytics Approach for Downscaled Climate Projections

This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.
Optimizing Parallel Proof of Vote Consensus Based on Mimic Security in Consortium Blockchains

Some Byzantine fault tolerant (BFT) consensus algorithm in consortium blockchains have lots of message broadcast to encroach bandwidth, which greatly affect the efficiency of consensus execution. Although reducing the number of global consensus nodes can help, it is also at the cost of reduced security. Based on the idea of Mimic Defense, this paper proposes a consensus framework for BFT consensus and takes the Parallel Proof of Vote (PPoV) algorithm as an example to design a randomized node sharding and role partitioning scheme to improve security of the consensus process without affecting efficiency much. In addition, the multimode adjudication group composed of the shard leaders can replace the single leader to complete voting statistics to improve the reliability of consensus results. Through theoretical analysis, it is known that the BFT consensus framework based on mimic security, taking PPoV as an example, has a strong defense effect against eclipse attacks and selfish mining. A simulation experiment is also designed to prove that through efficient iteration and dynamic threshold design, the adjudication group greatly improves the reliability of the results and the robustness of the algorithm.
Data-driven Approach using Unsupervised Learning for Detecting Anomalies in Facility Operations

As carbon emission reduction is being emphasized globally, the importance of efficient building operations to reduce energy use is emerging as an important factor. To optimize building operations (i.e., achieving better facility conditions with less energy use), this research proposes a methodology that leverages 1) the DBSCAN algorithm to not only cluster groups but identify anomalous behaviors and 2) the DBA algorithm to have a representative case of each cluster group. The methodology is demonstrated against real-world operational data (e.g., time-series temperature) of Building A operated by the Mastern Investment Group located in Seoul, South Korea. As a result, the anomalous cases are identified by the methodology, leading to interviews with the facility managers. After the cause of anomalies is defined and reasoned through discussion with the managers, a couple of strategies are suggested to the managers with the aim of managing the building in an efficient manner.
Cognitive Metric Monitoring - Characterizing spatial-temporal behavior for anomaly detection

Organizations across the globe require a reliable anomaly detection solution that allows for continuous quality control. Considering the scale and complexity of infrastructure, the most common methods include setting a blanket threshold by using knowledge of the experts or by applying simple statistical measures, which results in an alarm deluge. In this paper, we propose an approach to derive optimal thresholds by analyzing both the temporal and spatial properties of metrics related to entities. Additionally, our solution also self-tunes and self-learns to accommodate the tacit knowledge of experts and domains constraints. We demonstrate the effectiveness of our solution through a series of experiments and a real-world case study.
An Approximation Method for Querying Similar Large Graphs

Searching similar graphs in graph databases for a query graph has attracted extensive attention recently. However, existing works face many challenges in the context of big data. For example, the time and space complexity of completing queries in multiple large graphs is too high to be done on a single machine. This undoubtedly increases the hardware cost of large graph similarity calculation. Moreover, calculating accurate graph similarity is an NP-hard problem. To solve these problems, in this paper, we propose a novel approximation method for searching similar graphs among multiple large graphs for a query graph. Firstly, we propose a graph database tensor (GDT) to unify the representation of various graphs and reduce space complexity. Secondly, we propose an approximation method through several sampling operations on the GDT to obtain a limited set of graphs that are with a high probability similar to the query graph, which avoids a lot of futile similarity calculations and significantly reduces time complexity. Thirdly, we design a fast method to more precisely estimate similarities between the query graph with the ones in the similar graph set obtained. The results showed our method is efficient and feasible.
Efficient Multiple Objective Optimization for Fair Misinformation Detection

Multiple-objective optimization (MOO) aims to simultaneously optimize multiple conflicting o bjectives a nd has found important applications in machine learning, such as simultaneously minimizing classification a nd f airness l osses. At an optimum, further optimizing one objective will necessarily increase at least another objective, and decision-makers need to comprehensively explore multiple optima to pin-point one final solution. We address the efficiency of exploring the Pareto front that contains all optima. First, stochastic multi-gradient descent (SMGD) takes time to converge to the Pareto front with large neural networks and datasets. Instead, we explore the Pareto front as a manifold from a few initial optima, based on a predictor-corrector method. Second, for each exploration step, the predictor iteratively solves a large-scale linear system that scales quadratically in the number of model parameters, and requires one backpropagation to evaluate a second-order Hessian-vector product per iteration of the solver. We propose a Gauss-Newton approximation that scales linearly, and that requires only first-order i nner-product p er i teration. T hird, we explore different linear system solvers, including the MINRES and conjugate gradient methods for approximately solving the linear systems. The innovations make predictor-corrector efficient for large networks and datasets. Experiments on a fair misinformation detection task show that 1) the predictor-corrector method can find Pareto fronts better than or similar to SMGD with less time, and 2) the proposed first-order method does not harm the quality of the Pareto front identified b y t he second-order method, while further reducing running time.
Dark patterns in e-commerce: a dataset and its baseline evaluations

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.’s study in 2019 [1], which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.’s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.
Toward the Utilization of Artificial Intelligence in Life Sciences: A Voluntary Medical Data Provisioning Model

To extend people’s life expectancy while improving their health and quality of life, the promotion of artificial intelligence (AI) and big data technologies to life science fields is crucial. In the medical field, AI model training is quite difficult owing to its heterogeneity and the fact that the stakeholders and data owners have a variety of valid privacy and protection needs and concerns, which has resulted in heavily suppressed cooperation. To help remedy this problem, we examine the behaviors of life science researchers who use medical data for AI purposes from social and institutional perspectives. From this, we provide a mathematical model for medical data provisioning based on the public goods game theory. Numerical simulation of the proposed model shows that the "pump-priming effect" caused by the existence of voluntary data providers may result in cooperative behaviors instead of the non-cooperative equilibrium obtained in the conventional public goods game. We conclude that our approach provides an effective mathematical and quantitative mechanism design in response to extant qualitative medical data provisioning problems.
BioSentEval: An Evaluation Toolkit for Chinese Medical Sentence Representation

We introduce BioSentEval, a toolkit for evaluating the quality of medical sentence representations. BioSentEval contains a variety of tasks, medical term normalization, medical text classification a nd m edical s entences s emantic relationship determination. This set of tasks was selected based on CBLUE (Chinese Biomedical Language Understanding Evaluation), a Chinese biomedical information processing leaderboard led by CHIP (China Health Information Processing) Committee. Our toolkit provides scripts for pre-processing the datasets, as well as a simple interface for evaluating sentence representations. Given the popularity of SentEval, our aim is to generalize the idea from general domain to biomedical domain and to motivate further research works of biomedical sentence representations.
Making Karaoke Parties Lively by Reordering Songs Based on Pop Music Concert Program Data

This paper aims to sustain the liveliness of karaoke parties. To do this, the authors focused on programs for pop music concerts. Since they are carefully organized to make the concerts lively, they should also be useful to make karaoke parties lively. The authors’ principal idea is to reorder the requested songs at the karaoke party to follow the inclination of the order of songs in the concerts. As a preparation, the authors collected the programs of pop music concerts and added feature vectors to each song. In the parties, the score of each requested song is calculated with the feature vector. This score is higher when the song is expected to be performed at that time in the concerts. Based on the score and consideration of the participants’ waiting time, the songs in the request queue are reordered. The song orders like the music concert are expected to be reproduced at the karaoke parties. In the test drive, the prototype system worked as expected and at least didn’t disturb the karaoke experience. Development of the demonstrative system and tuning the algorithm are the major future works.
Development and Evaluation of Embedding Methods for Graphs with Multi Attributes

Graph embedding is the process of obtaining a vector representation of graph nodes. The representation obtained by graph embedding is highly versatile. It can be used for various tasks, such as recommendation and clustering tasks. However, there are only a few methods that incorporate attributes indicating node characteristics, such as user gender, age, or product category, into graph embedding. Therefore, we hypothesize that nodes with the same attribute are often connected to the same node. Consequently, we propose two methods for graph embedding, parallel and serial, that use metric learning to reflect attribute data in node features. The proposed method can be applied to any graph embedding and metric learning method, and thus can also be applied to many new methods yet to be developed. Numerical experimental results show that the proposed method using node attributes is superior to the existing methods in both AUROC and accuracy.
Hierarchical Multi-task Learning for Enterprise Risk Detection from Financial Documents

Enterprise risk detection from financial documents (ERD) is known to be a key decision-making tool for a company that relies on the mass media. It would help those companies to prepare for potential risks in advance or prevent further deterioration of risks. However, ERD is usually hindered by its natural complexity and ambiguity. For a typical ERD task, the risk factors are considered to be multi-labeled while the inputs are mostly redundant. To overcome these difficulties, we proposed a hierarchical multi-task learning module ERD-NET. A novel article encoder is introduced to combine company information with the document’s relative information well. A hierarchical multi-task framework is also involved so that the main risk detection task could utilize the information learned from two easier auxiliary tasks. Our evaluation on the collected dataset shows that our proposed method outperformed the current state-of-art models.
Knowledge Graphs of the QAnon Twitter Network

Using Knowledge Graphs to understand noisy naturalistic data has gained significant prominence in recent years. In this paper, we apply Knowledge Graphs to a new dataset of tweets of an ideologically far-right Twitter network by sourcing tweet histories of users who discussed QAnon in the summer of 2018 [1]. We further develop a new method that arms topic models with relational information from Knowledge Graphs and apply the new technique to study this dataset. Our analysis shows that users do not form a monolithic belief or social network, but rather comprise many smaller interlinking communities which discuss unique key political events (e.g., the January 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Capitol riots).
FVec2vec: A Fast Nonlinear Dimensionality Reduction Approach for General Data

Dimensionality reduction is a fundamental technique to address the curse of dimensionality problem in real-world big datasets. However, most existing methods either only target raw datasets that contain explicit relationships between data points, or construct the complete neighborhood graph of the dataset by calculating pairwise similarities, and then generate contexts of data points by random walking to measure the structure of the dataset, which are computationally expensive. In this paper, we propose a fast nonlinear locality-preserving dimensionality reduction approach called FVec2vec, which extends the Skip-gram model to embedding representation of general numerical matrices. Specifically, instead of constructing neighborhood graph by calculating pairwise similarities between data points, we approximate the k-nearest neighbors (kNN) of each data point in matrices by exploring its neighbors’ neighbors first. Then, we design a novel sampling algorithm to randomly sample on the kNN to depict the structure of the dataset. Experimental results show that FVec2vec is faster than most existing methods while achieving acceptable accuracy, and the accuracy is even higher than the state-of-the-art method under certain similarity metrics.
Label Denoising and Counterfactual Explanation with A Plug and Play Framework

Most supervised classification methods assume perfect training data, although this is not usually the case in the real–world. Meanwhile, counterfactual data generation approaches have emerged as a way to provide post–hoc explanation of decisions made by classification models. However, such approaches highly rely on the classification model output since different outputs lead to alternative, or even contradicting explanations. This work proposes a plug–and–play framework to learn a robust classification model in the presence of noisy labeled data and provide actionable suggestions for undesirable decisions (e.g., loan application rejection) made by a given classification model. The framework’s generalizability is demonstrated by considering alternative noisy label detection and counterfactual explanation methods, as well as diverse supervised classification models. The framework’s superiority against several baselines is demonstrated using three benchmark datasets.