Reviewerly/exHarmony huggingface dataset:
BA-IKG: BiLSTM Embedded ALBERT for Industrial Knowledge Graph Generation and Reuse

As the industrial production mode is shifting towards digitalization and intelligence in the new era. Enterprises put forward higher requirements for efficient processing and utilization of accumulated unstructured data. At present, the knowledge and data contained in a large number of unstructured documents are scattered. The types of entities and relationships are diverse. And the constraints of production rules are complicated, which increases the difficulty of knowledge management and utilization. Therefore, this paper studies the semantic knowledge graph generation and reuse method for industrial documents, which can form standardized production resources, the knowledge related to the industry, and question and answer strategies for industrial processing. The challenge of the research is to explore a feasible process knowledge model and efficient industrial information extraction method to effectively provide structured knowledge of process documents. We build process knowledge representation models and information extraction models and algorithms based on process knowledge representation model and natural language processing. The entities and relations of the main production factors are extracted. The knowledge representation model associates the extracted entities and relations to form an industrial knowledge graph, which provides information support for processing knowledge retrieval and question answering methods. Finally, the approach is evaluated by employing the aerospace machining documents. And the proposed method can obtain valuable information in the document and improve utilization of industrial unstructured data.
A Blockchain-Based Vehicle Condition Recording System for Second-Hand Vehicle Market

Due to the cheap price and decent performance of used cars, the second-hand vehicle market is quite active in Taiwan. However, the vehicle information in the market of second-hand vehicles is centralized at present. In other words, the vehicle information is still provided by the second-hand vehicle dealers, which are not transparent and have no tampering cost. As the result, the authenticity, accuracy, and fairness of these second-hand vehicle data are worth discussing. To solve the above problems, this paper proposed a trusted vehicle data source system based on blockchain technology. The system is built on the Ethereum platform with the private chain structure. With the help of our platform, a trusted third party (e.g., maintenance plant and government branch) can record vehicle information into Blockchain so the integrity of vehicle information can be maintained. Customers can easily query the relevant vehicle information through our system interface and avoid receiving fake vehicle information. This system is a Proof of Concept (PoC), and the feasibility is examined by evaluating transactions per second (TPS) of the proposed system.
Knowledge is Power

This paper deals with the challenging problem of building visual reasoning models for answering questions related to artworks in artistic domains. The nature of abstract styles and cultural contexts within an artistic image makes the corresponding learning tasks extremely difficult. We propose a novel framework termed as Hierarchical-Knowledge Embedded Meta-Learning to address the critical issues of visual reasoning in artistic domains. In particular, we firstly present a deep relational model to capture and memorize the relations among different samples. Then, we provide the hierarchical-knowledge embedding that mines the implicit relationship between question-answer pairs for knowledge representation as the guidance of our meta-learner. This is a case of "knowledge is power" in the sense that the hierarchical knowledge representation is incorporated into our meta-learning based model. The final classification is derived from our model by learning to compare the features of samples. Experimental results show that our approach achieves significantly higher performance compared with other state-of-the-arts.
Visually Maintained Image Disturbance Against Deepfake Face Swapping

As a deep learning-based application, DeepFake can generate malicious images or videos through replacing the face of a source image with the target face, which poses a significant threat to social media. In this paper, we propose a scheme to prevent such tampering by exploring adversarial examples against DeepFake. Specifically, adversarial examples are produced by adding tailored distortion to source images. The added distortion is imperceptible to human vision but can mislead the generation of face-swapped images effectively. We present three novel adversarial attacks against DeepFake autoencoders from perspectives of adversarial transferability and latent representation. Our first method synthesizes universal perturbation, which is image-agnostic. By contrast, the latter two methods directly perform the preciser perturbation specific to a source image. Extensive experiments demonstrate the effectiveness of our adversarial examples against DeepFake in terms of both reference and non-reference image quality assessment.
KeywordMap: Attention-based Visual Exploration for Keyword Analysis

With the high growth rate of text data, extracting meaningful information from a large corpus becomes increasingly difficult. Keyword extraction and analysis is a common approach to tackle the problem, but it is non-trivial to identify important words in the text and represent the multifaceted properties of those words effectively. Traditional topic modeling based keyword analysis algorithms require hyper-parameters which are often difficult to tune without enough prior knowledge. In addition, the relationships among the keywords are often difficult to obtain. In this paper, we utilize the attention scores extracted from Transformer-based language models to capture word relationships. We propose a domain-driven attention tuning method, guiding the attention to learn domain-specific word relationships. From the attention, we build a keyword network and propose a novel algorithm, Attention-based Word Influence (AWI), to compute how influential each word is in the network. An interactive visual analytics system, KeywordMap, is developed to support multi-level analysis of keywords and keyword relationships through coordinated views. We measure the quality of keywords captured by our AWI algorithm quantitatively. We also evaluate the usefulness and effectiveness of KeywordMap through case studies.
Identification of the fractures of carbonate reservoirs and determination of their dips from FMI image logs using Hough transform algorithm

Carbonate reservoirs are of great importance due to having many fractures and the effectiveness of these fractures in oil production. The most effective tools for studying fractures are image logs that capture high resolution images from the well. An example of these images is the FMI tool, which provide important information on the orientation, depth, and type of fracture. Today, the detection of fractures on these logs is done manually, which in the absence of sufficient experience, will encounter errors. The purpose of this study is to identify the reservoir fractures and the dips of the fractures using Canny edge detection algorithm and Hough transform algorithm and image processing operators, so that in the first stage, fractures are identified in Geolog Software and in the second stage, using MATLAB Software , fractures and their dip are interpreted.
Are Multilingual Models Effective in Code-Switching?

Multilingual language models have shown decent performance in multilingual and crosslingual natural language understanding tasks.However, the power of these multilingual models in code-switching tasks has not been fully explored.In this paper, we study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting by considering the inference speed, performance, and number of parameters to measure their practicality.We conduct experiments in three language pairs on named entity recognition and part-of-speech tagging and compare them with existing methods, such as using bilingual embeddings and multilingual meta-embeddings.Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching, while using meta-embeddings achieves similar results with significantly fewer parameters.
Southern California Earthquake Data Now Available in the AWS Cloud

Abstract The Southern California Earthquake Data Center is hosting its earthquake catalog and seismic waveform archive in the Amazon Web Services (AWS) Open Dataset Program (s3://scedc-pds; us-west-2 region). The cloud dataset’s high data availability and scalability facilitate research that uses large volumes of data and computationally intensive processing. We describe the data archive and our rationale for the formats and data organization. We provide two simple examples to show how storing the data in AWS Simple Storage Service can benefit the analysis of large datasets. We share usage statistics of our data during the first year in the AWS Open Dataset Program. We also discuss the challenges and opportunities of a cloud-hosted archive.
GBKM: A New Genetic Based K-Means Clustering Algorithm

Clustering is an unsupervised classification method that focused on grouping data into clusters. The objects in each cluster are very similar but different from the objects in the other clusters. As clustering methods deal with the massive amount of information, many intelligent software agents have been widely utilized clustering techniques to filter, retrieve, and categorize documents that exist on the World Wide Web. Web mining is generally classified under data mining. In data mining, one of the significant clustering centroid-based partitioning methods is the K-Means algorithm. One of the K-Means algorithm's challenges is its extreme sensitivity to initial cluster centers' choice, which may yield get stuck in the local optimum if the initial centers are selected randomly. A variant of the K-Means method is the K-Means++ algorithm, which improves the algorithm's performance by smart choices of initialization of the cluster centroids. Evolutionary techniques, widely utilized for optimizing clustering methods by providing their prerequisite parameters. The Genetic Algorithm is stochastic and population-based, that applied in optimization problem-solving. This paper proposed a Genetic-based K-Means (GBKM) clustering algorithm where the clusters' centroids are encoded by chromosomes rather than random initial cluster centroids. The best cluster centers gave by the Genetic algorithm that maximizes the fitness function, as initial points of the K-Means algorithm. The results show this model helps increase the K-Means algorithm's performance by appropriate choice of initialization of the cluster centroids, compared to four other clustering algorithms.
Dynamic binary neural networks with time-variant parameters and switching of desired periodic orbits

This paper studies 3-layer dynamic binary neural networks characterized by binary connection parameters and the signum activation function. The connection parameters and the number of hidden neurons are time-variant. The dynamics is described by a non-autonomous difference equation of binary state variables and the network can generate various periodic orbits of binary vectors. First, we present a simple synthesis method that guarantees storage of desired multiple periodic orbits and switching of the periodic orbits through desired entrances. Second, as a basic step to engineering applications, we present an FPGA based hardware prototype. The hardware can realize switching of various periodic orbits experimentally and typical examples are demonstrated. The switching of periodic orbits is applicable to switching of walking patterns in robotics and variable output of switching power converters.
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence

Text corpora are widely used resources for measuring societal biases and stereotypes. The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as genders). In this study, we show that, depending on what one aims to quantify as bias, this commonly-used approach can introduce non-relevant concepts into bias measurement. We propose an alternative approach to bias measurement utilizing the smoothed first-order co-occurrence relations between the word and the representative concept words, which we derive by reconstructing the co-occurrence estimates inherent in word embedding models. We compare these approaches by conducting several experiments on the scenario of measuring gender bias of occupational words, according to an English Wikipedia corpus. Our experiments show higher correlations of the measured gender bias with the actual gender bias statistics of the U.S. job market – on two collections and with a variety of word embedding models – using the first-order approach in comparison with the vector similarity-based approaches. The first-order approach also suggests a more severe bias towards female in a few specific occupations than the other approaches.
Fast Similarity Computation for t-SNE

Data visualization has become a fundamental process of data engineering. t-SNE is one of the most popular data visualization approaches. However, its computation cost is quadratic to the number of data points because it needs to compute similarities for all pairs of data points. One practical way of using t-SNE is random walk-based t-SNE. This approach visualizes user-specified landmark points from the similarities between them based on random walks in a neighborhood graph of data points. It offers two approaches to computing similarities: the direct and analytical approaches. The direct approach approximately computes similarities by explicitly computing random walks in the graph. Unfortunately, it needs to perform numerous random walks for adequate computation accuracy. The analytical approach performs Cholesky factorization on the graph Laplacian and computes exact similarities using the decomposed graph Laplacian. This, however, incurs high computation cost in performing Cholesky factorization. Our proposal, F-tSNE, reduces the computation cost of random walk-based t-SNE by computing the LDL decomposition for the graph Laplacian based on two ideas: (1) reducing non-zero elements in the LDL decomposition by using a reordering matrix and (2) exploiting the sparse structure of the graph when computing the similarities. Theoretically, our approach is guaranteed to yield exact similarities. Experiments show that it is up to 88.4 times faster than the existing alternatives.
Rainbow Signature Scheme to Secure GOOSE Communications From Quantum Computer Attacks

The International Electrotechnical Commission (IEC) 62351-6 standard prescribes provisions to secure IEC 61850-8-1 generic object-oriented substation events (GOOSE) communications from cyber threats. The GOOSE authentication schemes recommended by IEC 62351-6 have shortcomings, such as long authentication-value computation times, large signature lengths, the usage of symmetric keys, and susceptibility to quantum computer attacks. To overcome these, this paper proposes the usage of the rainbow signature scheme (RSS), a quantum cryptographic method to secure GOOSE messages. The RSS offers fast signature generation and verification using asymmetric keys, making it very attractive to meet the time-latency and security requirements of the GOOSE protocol. The adaptation of the RSS cryptography method for its application to the GOOSE protocol is presented in this paper. The algorithms for key generation, signature computation and message verification are discussed in detail. A first-of-its-kind C library that incorporates security features within the GOOSE protocol in compliance with the IEC 62351-6-stipulated format, is developed. The superior features of the RSS in comparison with the other IEC 62351-6 security schemes are demonstrated.
FoodChattAR: Exploring the Design Space of Edible Virtual Agents for Human-Food Interaction

There has been recent criticism from researchers towards simple replication of traditional role models in the design of virtual agents and robots, and a call for new forms of interaction and communication with technology. By exploring the field of Human-Food interaction (HFI) – a sub-area of Human-Computer Interaction (HCI) which aims to investigate the diversity of ways people interact with food – we therefore specifically examine the design space of edible anthropomorphic virtual agents (EAVAs). To understand human-to-food interactive communication, we conducted an interview study with 19 participants, followed by a co-design workshop on the design of conversational agents for personified food. Based on the results, we implemented a prototype called FoodChattAR that employs augmented reality and chatbots to interact and communicate with food. Our evaluation with 21 participants shows that FoodChattAR turns eating into fun, while at the same time the food conveys relevant societal facts about itself. We contribute to the field of HCI by introducing EAVAs as a novel human-to-food interaction.
Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when complicated models are used. Furthermore, from both rate-making and rate-regulation perspectives, it is critical to investigate the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors. ANN models are applied to meet this goal, and variable importance is measured to improve the model’s explainability due to the models’ complex nature. The results obtained from different variable importance measurements are compared, and dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for applications in auto insurance rate regulation. This study focuses on analyzing major risks only, but the proposed method can be applied to more general insurance pricing problems when additional risk factors are being considered. In addition, the proposed methodology is useful for other business applications where statistical machine learning techniques are used.
Reactive Planning for Mobile Manipulation Tasks in Unexplored Semantic Environments

Complex manipulation tasks, such as rearrangement planning of numerous objects, are combinatorially hard problems. Existing algorithms either do not scale well or assume a great deal of prior knowledge about the environment, and few offer any rigorous guarantees. In this paper, we propose a novel hybrid control architecture for achieving such tasks with mobile manipulators. On the discrete side, we enrich a temporal logic specification with mobile manipulation primitives such as moving to a point, and grasping or moving an object. Such specifications are translated to an automaton representation, which orchestrates the physical grounding of the task to mobility or manipulation controllers. The grounding from the discrete to the continuous reactive controller is online and can respond to the discovery of unknown obstacles or decide to push out of the way movable objects that prohibit task accomplishment. Despite the problem complexity, we prove that, under specific conditions, our architecture enjoys provable completeness on the discrete side, provable termination on the continuous side, and avoids all obstacles in the environment. Simulations illustrate the efficiency of our architecture that can handle tasks of increased complexity while also responding to unknown obstacles or unanticipated adverse configurations.
Random Interval Attendance Management System (RIAMS): A Novel Multimodal Approach for Post-COVID Virtual Learning

The exceptional circumstance caused by the COVID-19 pandemic demands substantial modifications in the teaching-learning processes across the globe. Teachers and students are making use of online learning in virtual classrooms as an alternative for face-to-face learning in physical classrooms. However, students' attendance management during virtual learning is a challenging problem. It is quite difficult to identify students' disengagement and even to know whether they are in front of their smart devices or not. In this paper, we introduce the `Random Interval Attendance Management System' (RIAMS), which is an innovative solution for attendance monitoring issues, students' disengagement, and attendance faking during virtual learning. In RIAMS, we employed a face recognition module built using the Dlib open-source software library. In order to improve the efficiency of the system, we introduced two ancillary modalities - verifying students' responses to CAPTCHAs and UIN (Unique Identification Number) queries. Both the face recognition and ancillary modalities operate at random intervals of time. This distinctive feature of randomness in our design ensures that students' attention and engagement in virtual learning are enhanced. Furthermore, the RIAMS' multimodal architecture and its sub-modalities' adaptive weight system enable teachers to customize their attendance strategy for every course. The output analysis of each of the RIAMS modalities and the combined results emphasize the effectiveness and reliability of our system in the attendance management for virtual learning. The novel RIAMS model has the potential to be extensively deployed for virtual learning in post-COVID settings.
Exploiting Active Learning for Microcontroller Performance Prediction

Speed monitors provide on-chip measurements of the the performance of integrated circuits. In recent years, they have been extensively used to predict F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> of microcontrollers for speed binning and performance screening during production test. However, while the use of machine learning is getting increasingly popular, the models may become significantly inaccurate if not trained on the appropriate devices. Previous research has demonstrated how to predict performance from speed-monitor data using corner-lot wafers. We show how to extend this approach to select the best corner-lot wafers to label when preparing the training set, thus significantly reducing the time and cost required for the process.