Reviewerly/exHarmony huggingface dataset:
THERMOGRAPHIC MONITORING OF NUCLEAR POWER PLANT’S SPRAY PONDS USING INFRA-RED DATA PROCESSING METHODS

This paper outlines an approach to monitor nuclear power plant spray ponds using thermographic monitoring. The approach involves measuring temperatures during summer and winter periods to identify potentially dangerous or fault-prone areas and improve the reliability of technical water supply. The automated machine processing procedure for thermograms was developed based on digital image processing. The results demonstrate that the obtained database can effectively help determine losses resulting from critical situations at nuclear power plants.
Semantic Representation of Robot Manipulation with Knowledge Graph

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.
A Study on the Training Strategies of Spanish-English Application-Oriented Talents

With application-oriented talents of foreign languages being concerned and acquiring great attention, compound Spanish-English bilingual professionals should follow the trend. It is an important way for the foreign language colleges of universities to meet the requirements of the new external economy. This paper explores the problems of bi-foreign languages–majors, and probes into the bi-foreign languages application-oriented professionals cultivating strategies in terms of curriculum, teaching methods, teaching level and students’ extracurricular practice, and puts forward the specific suggestions on the discipline construction, reform and innovation according to their own conditions.
Contrast Enhanced Visual Cryptography

Contrast degradation is the problem in visual cryp-tography. This is because the white pixel is not perfectly recovered whereas the black pixel is perfectly recovered. So overall contrast of recovered secret image decreases. To overcome this, we have ‘‘Contrast enhanced visual cryptography Technique’’ which consist four pairs of basis matrices used for encrypting each pixel of secret image. Also, we have used an algorithm to resize the recovered secret image to the size of original secret image.
Sequential Recommendation Based on Multi-View Graph Neural Networks

The problem of sequential recommendation aims to use a user’s historical interaction sequence to predict and recommend the following item with which the user is most likely to interact. A range of outcomes has been attained in this field, from traditional methods to deep learning approaches. There are still two challenging problems with existing methods. First, users’ interaction sequences will always include noisy interest preferences. Additionally, most methods ignore the potential collaborative information among different features and cannot fully explore users’ true preferences. To solve these problems, we propose SR-MVG (Short for Sequential Recommendation based on Multi-View Graph Neural Networks) for sequential recommendation, which first transforms the user’s behavioral sequence into an item-item graph so that similar items are closely connected to clearly distinguish the core interests of users. Second, the user’s core preferences are retained adaptively by the designed multi-view attention network. In addition, we also designed a graph pooling strategy to reduce noise and extract more relevant user preferences. We carried out extensive tests on five public benchmarks, and the findings demonstrate that SR-MVG exhibits superior performance.
Real-Time Information Extraction for Phone Review in Car Loan Audit

Phone review is important in car loan audits, in which auditors contact applicants to make risk assessments by how applicants act to a sequence of questions. Due to the length of dialogues, auditors tend to miss important details, thus requiring an aiding system to record the dialogues in a compact form. Existing methods that utilize slot-value pairs to track the latest dialogue states fail to record the intermediate process which is critical for risk assessment. In this paper, we propose quadruples which consist of a dialogue act and a triple in a concept graph to represent the dialogue process, and model the dialogue recording task as a quadruple extraction problem for each utterance. To concisely construct quadruples, we convert slot-value pairs into a concept graph by disentangling domains from slots. In order to extract quadruples in real time, we design a model incorporating multi-head cross-attention mechanism and embedding sharing while considering parameter size and inference speed. Experiments on our real-world dialogue dataset show that our model achieves an accuracy of $$\sim $$ 82.7% which is similar to the best baseline with only $$\sim $$ 30 M parameters while performing real-time inference $$\sim $$ 3.6 times faster on an 8-core CPU with $$\sim $$ 90 ms per utterance.
DP-MHAN: A Disease Prediction Method Based on Metapath Aggregated Heterogeneous Graph Attention Networks

Disease prediction as an important component of medical assistant diagnostic systems has received much attention from researchers. Many studies attempt to extract disease-related features from electronic health records (EHRs). However, the heterogeneity of entities (e.g., diseases, symptoms, medications, and other treatment items) in EHRs is usually neglected in these studies. To tackle this issue, we propose a Disease Prediction method based on Metapath aggregated Heterogeneous graph Attention Networks (DP-MHAN) to improve the final prediction performance. First, we extract entities and the relationships between entities from the EHRs and construct a heterogeneous medical graph. Second, we define three special metapaths and aggregate the embedding of entities on the metapaths, in which the embedding of each entity can be calculated via its directly connected neighbors. Moreover, the attention mechanism is adopted to locate the most effective embedding, which is utilized for disease prediction. Experimental results show that our proposed method outperforms state-of-the-art methods.
INGRAMM: TOWARDS A DIAGRAMMING METHODOLOGY FOR EARLY-STAGE MISSION LIFE CYCLE DESIGN INFORMATION

This paper describes work done on the third phase of the InGraMM (Integrated Graphical Mission Modeler) Project - a five-phased effort targeted toward the development of an enhanced diagramming methodology and proof-of-concept tool to improve the capture, visualization, evaluation, and communication of early-stage mission life cycle design information within Integrated Product and Process Development (IPPD) team environments. Phase Three of the InGraMM Project concentrates on the development of a <i>semantic model</i> that provides the logical underpinnings for the development of InGraMM as a graphical language methodology. This semantic model development necessitates the creation of an <i>ontology</i> containing explicit definitions in a prescribed format that convey the exact meanings of all the logical concepts (entities, relationships, properties, and so on) that are to be represented as part of InGraMM. While the specifics of the completed ontology, as discussed herein, have been developed for and applied to a particular test-case domain involving government-sponsored scientific remote-sensing satellite missions, the InGraMM ontology is easily extensible and could be tailored for application in many design/manufacturing domains in both the public and private sectors.
Spatial-Temporal Graph Transformer for Skeleton-Based Sign Language Recognition

For continuous sign language recognition (CSLR), the skeleton sequence is insusceptible to environmental variances and achieves much attention. Previous studies mainly employ hand-craft features or the spatial-temporal graph convolution networks for skeleton modality and neglect the importance of capturing the information between distant nodes and the long-term context in CSLR. To learn more robust spatial-temporal features for CSLR, we propose a Spatial-Temporal Graph Transformer (STGT) model for skeleton-based CSLR. With the self-attention mechanism, the human skeleton graph is treated as a fully connected graph, and the relationship between distant nodes can be established directly in the spatial dimension. In the temporal dimension, the long-term context can be learned easily due to the characteristic of the transformer. Moreover, we propose graph positional embedding and graph multi-head self-attention to help the STGT distinguish the meanings of different nodes. We conduct the ablation study on the action recognition dataset to validate the effectiveness and analyze the advantages of our method. The experimental results on two CSLR datasets demonstrate the superiority of the STGT on skeleton-based CSLR.
Towards Hardware-Friendly and Robust Facial Landmark Detection Method

Facial Landmark Detection (FLD) plays an essential role in computer vision because it is the premise of many tasks such as face recognition and facial expression analysis. Although significant advancements have been achieved with the help of deep learning, the performance of FLD is still unsatisfactory due to the influence of occlusion, low illumination, and motion blur. Existing works are developed and implemented based on expensive computing GPUs, limiting their application. This paper proposes a hardware-friendly, fast, and high-performance FLD framework. We first utilize a lightweight CNN to extract its features given the face image. This procedure uses a multi-scale feature fusion strategy for better feature representation learning. We design a weighted model to guide the regression of other landmarks inspired by the spatial distribution of five key points on the face: the eyes, nose and mouth. Our proposed network can also be quantified and pruned for practical deployment running at 45 FPS on the ARM3288 chip. We collect and annotate a new dataset CTLM-100K, which contains 100K facial samples with various postures and lighting conditions. Extensive experiments on these three benchmark datasets all validated the effectiveness of our model.
Characteristics of Fish Fauna Data in jin Island, Korea

The fish community in the Jin Island (Jindo, Jisan, Munnae, Yulri, 1/25,000 scale) was investigated with the data collected through the 3rd National Ecosystem Survey from April to October in 2012. During the period, 25 species belonging to 7 families were collected. There were 11 biological resources subject to the approval of outbound transfer, including Squalidus gracilis majimae, Abbottina rivularis, Aphyocypris chinensis, Hemiculter eigenmanni, Rhodeus ocellatus, Iksookimia hugowolfeldi, Iksookimia koreensis, Trachidermus fasciatus, Monopterus albus, Macropodus ocellatus and Oryzias latipes. The dominant species was Pseudorasbora parva(26.3%), and the subdominant spaecies was Carassius auratus(16.0%).&lt;/br&gt;Background: In Korea, from 1989 National Ecosystem Survey (NES) was conducted by ministry of environment for every 10 years to cover whole south Korea in 1/25,000 scale. The scope of NES including 8 taxa – vegetation, plant, fish, macro invertebrate, insect, birds, amphibian &amp; reptile, mammal and geomorphology. With the results of ecosystem survey we build up species and ecosystem database based on GIS environment. Ecosystem database is becoming very necessary to understand origins and relationship between human and nature and also to reduce ecosystem disturbance.&lt;/br&gt;New information: An ecological information platform can play important roles as a computing system to collect knowledge, distributed research findings and separated data from researchers. We designed and developed ecological information portal service (EcoBank 1.0) for collecting and providing ecological information for various stake-holders. Then, we developed basic functions to analyze ecosystem for each stake-holders (researchers, general public and policy makers). We successfully designed a computing system named EcoBank supporting the functions of Digital Object Identifier (DOI) publish and data quality management.
Hawkes Process via Graph Contrastive Discriminant Representation Learning and Transformer Capturing Long-Term Dependencies

Nowadays, heterogeneous event sequence data is an inseparable and extremely important part for our daily life. The extraordinary nature of event sequence data is characterized by its existing complex long-term and short-term temporal dependencies. Most point process models based on a recurrent neural network fail to capture these dependencies and make accurate predictions. The Transformer Hawkes Process(THP) model, utilizes the self-attention mechanism to capture long-term dependencies, which is suitable and effective for the prediction of event sequence data. Graph contrastive learning (GCL) with adaptive reinforcement can enhance data by making the intra-class hidden features of the instances close while keeping the inter-class hidden features scattered away. Inspired by these, we propose the idea of combining the THP with adaptive enhanced GCL. The proposed Hawkes Process via Graph Contrastive Discriminant representation Learning and Transformer capturing long-term dependencies(GCDRLT) is the two-stage pipeline to enhance the capacity of hidden representation both on long-term dependencies and discriminant feature extraction. Experimental results on multiple datasets validate that the graph contrastive learning method can improve the accuracies of the Transformer-Hawkes process model for predicting heterogeneous event sequences.
Optimizing Knowledge Distillation via Shallow Texture Knowledge Transfer

Knowledge distillation (KD) is a widely used model compression technology to train a superior small network named student network. KD promotes a student network to mimic the knowledge from the middle or deep layers of a large network named teacher network. In general, existing knowledge distillation methods neglect to explore the shallow features of neural networks that contain informative texture knowledge. In this paper, we propose Shallow Texture Knowledge Distillation (SeKD) for distilling these informative shallow features. Moreover, we investigate the traditional machine learning method and adopt Gradient Local Binary Pattern (GLBP) for shallow features extraction. However, we have found that using GLBP to process shallow features will introduce an additional computational burden. To reduce computation, we design a texture attention module to optimize shallow feature extraction for distilling. We have conducted extensive experiments to evaluate the effectiveness of our proposed method. When training on the CIFAR-10 and CIFAR-100 datasets, the student network WideResNet16-2 trained by SeKD achieves 94.35% and 75.90% accuracies, respectively.
ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets

This paper presents an Attention-based Data Augmentation (ADA) approach that extracts keywords from minority class data points using a vector similarity-based mechanism, uses the extracted keywords to extract significant contextual words from minority class documents using an attention mechanism, and uses the significant contextual words to enrich the minority class dataset. By creating new documents based on significant contextual words and adding them to the minority class dataset, we oversample the dataset for the minority class. On the classification job, we compare the original and oversampled versions of the datasets. We also compare ADA over the augmented datasets with two popular state-of-the-art text data augmentation methods. According to the experimental findings, classification algorithms perform better when used to augmented datasets produced by any data augmentation technique than when applied to the datasets’ original versions. Additionally, the classifiers trained over the augmented datasets generated by ADA are more effective than those generated by state-of-the-art data augmentation techniques.
DETERMINATION OF MAXIMUM DROP SIZES IN ANNULAR GAS/LIQUID FLOW

The problem of identifying the size of the largest drop in a population from a sample of finite size is addressed. Using the statistics of extremes, the dependence of the observed maximum with sample size is explained and a methodology for determining the maximum in the population is proposed.
Non-adaptive Universal One-Way Hash Functions from Arbitrary One-Way Functions

In this work we give the first non-adaptive construction of universal one-way hash functions (UOWHFs) from arbitrary one-way functions. Our construction uses $$O(n^9)$$ calls to the one-way function, has a key of length $$O(n^{10})$$ , and can be implemented in NC1 assuming the underlying one-way function is in NC1. Prior to this work, the best UOWHF construction used $$O(n^{13})$$ adaptive calls and a key of size $$O(n^5)$$ (Haitner, Holenstein, Reingold, Vadhan and Wee [Eurocrypt ’10]). By the result of Applebaum, Ishai and Kushilevitz [FOCS ’04], the above implies the existence of UOWHFs in NC0, given the existence of one-way functions in NC1. We also show that the PRG construction of Haitner, Reingold and Vadhan (HRV, [STOC ’10]), with small modifications, yields a relaxed notion of UOWHFs, which is a function family which can be (inefficiently) converted to UOWHF by changing the functions on a negligible fraction of the inputs. In order to analyze this construction, we introduce the notion of next-bit unreachable entropy, which replaces the next-bit pseudoentropy notion used by HRV.
The Application of Blockchain in Supply Chain Management： Knowledge Mapping Analysis Based on Citespace

This study examines the present scholarly research on blockchain technology in supply chain management. The primary purpose of this working paper is to visualize and perform a scientometric review of 468 publications and research published between 2007 and 2021 from the Web of Science database. Co-author analysis, co-word analysis, and co-citation analysis highlight main concepts and research hotspots and provide light on important specializations and developing trends. This study primarily uses Citespace5.8. R1 to conduct a systematic analysis of author collaboration networks, keyword co-occurrence networks, keyword clustering, keyword burstiness, and literature co-citation graph spectrum networks in the domain of blockchain in supply chain management to identify the research state, development trend, hotspots, and frontiers. Research shows that :(1) since 2017, the literature on the application of blockchain in supply chain management has expanded rapidly. China and the United States are particularly advanced in this area compared to other nations. (2) The "blockchain in supply chain management" research mainly focuses on technical framework theory, which uses distributed ledger technology and smart contracts to control supply chain operations. Simultaneously, technical innovation should be combined with artificial intelligence, the Internet of Things, and other technologies to enhance the trust, transparency, traceability, performance index, and security of the supply chain. (3) The horizon of study in this area is the integration of RFID and blockchain technology for technical innovation in Industry 4.0, intending to improve and optimize each supply chain node.
Compass: Supporting Large Group Mentorship in a Chat-Based UI

While mentorship offers many benefits, student access to mentorship is often limited. In this paper, we introduce Compass, a chat platform where industry professionals mentor large cohorts of 30+ students with the support of novel features that enable full engagement without the typical chaos of group chat. Specifically, we conceptualize conversations as composed of not only individual messages, but also multi-person conversational units that collapse large numbers of small but related conversational exchanges into single conceptual units in the main dialogue. Doing so makes it possible to preserve a coherent linear flow of conversation while also supporting non-linear conversational exchanges that can be concisely summarized computationally and built on in the main conversation. We report on design lessons learned over a year of small real-world studies culminating in a final deployment in which 2 industry professionals successfully mentored 30+ students over a 10-week period. We find that both mentors and mentees find the chat UI effective and sometimes preferable, and discuss broader implications for the design of chat UI for large group conversations.
CAN LEARNINGAPPS.ORG ENHANCE STUDENTS' TOEFL STRUCTURE SCORES?: FOCUSING ON SELF ASSESSMENT

TOEFL (Test of English as Foreign Language) is still taking place as a demanding English proficiency test in many countries, especially in Indonesia. This initial test is extensively used for various purposes, such as scholarship admission or job application. Among the three sections in the TOEFL test, the structure has been challenging for the test takers regarding examining the grammatical rules. Due to the need for study numbers on TOEFL structure score betterment by employing the learning media, this study aims to outline the improvement of TOEFL score, particularly in structure and written expression, after using learningapps.org as self-evaluation. The 26 high school students participated in this study. The data were gathered through documentation and test-in structure pretest and post-test scores. Data were statistically analyzed by compare means, using paired sample test. The result of the study reveals that learningapps.org did not positively affect students' enhancement of structure and written expression on the TOEFL test. The study implies that more precise learningapps must be chosen and tried to know the correlation.
Wizundry: A Cooperative Wizard of Oz Platform for Simulating Future Speech-based Interfaces with Multiple Wizards

Wizard of Oz (WoZ) as a prototyping method has been used to simulate intelligent user interfaces, particularly for speech-based systems. However, as our societies' expectations on artificial intelligence (AI) grows, the question remains whether a single Wizard is sufficient for it to simulate smarter systems and more complex interactions. Optimistic visions of 'what artificial intelligence (AI) can do' places demands on WoZ platforms to simulate smarter systems and more complex interactions. This raises the question of whether the typical approach of employing a single Wizard is sufficient. Moreover, while existing work has employed multiple Wizards in WoZ studies, a multi-Wizard approach has not been systematically studied in terms of feasibility, effectiveness, and challenges. We offer Wizundry, a real-time, web-based WoZ platform that allows multiple Wizards to collaboratively operate a speech-to-text based system remotely. We outline the design and technical specifications of our open-source platform, which we iterated over two design phases. We report on two studies in which participant-Wizards were tasked with negotiating how to cooperatively simulate an interface that can handle natural speech for dictation and text editing as well as other intelligent text processing tasks. We offer qualitative findings on the Multi-Wizard experience for Dyads and Triads of Wizards. Our findings reveal the promises and challenges of the multi-Wizard approach and open up new research questions.