Reviewerly/exHarmony huggingface dataset:
DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods. The code and data are available at: https://github.com/triplej0079/DeepGAR.
Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning

Biomedical interaction prediction is essential for the exploration of relationships between biomedical entities. Predicted biomedical interactions can help researchers with drug discovery, disease treatment, and more. In recent years, graph neural networks have taken advantage of their natural structure to achieve great progress in biomedical interaction prediction. However, most of them use node embedding instead of directly using edge embedding, resulting in information loss. Moreover, they predict links based on node similarity correlation assumptions, which have poor generalization. In addition, they do not consider the difference in topological information between negative and positive sample links, which limits their performance. Therefore, in this paper, we propose an adaptive line graph contrastive (ALGC) method to convert negative and positive sample links into two kinds of line graph nodes. By adjusting the number of intra-class line graph edges and inter-class line graph edges, an augmented line graph is generated and, finally, the information of the two views is balanced by contrastive learning. Through experiments on four public datasets, it is proved that the ALGC model outperforms the state-of-the-art methods.
When Chinese Martial Artists Meet Western Heroes: A Stylometric Comparison of Translated Wuxia Fiction and Western Heroic Literature

This study used stylometric analysesStylometric analyses to examine the ways that English translations of Chinese Wuxia fiction and Western heroic literature published in modern English are stylistically similar and different. We wish to contribute tostylometric studies and Wuxia translation research by introducing the stylistic panorama, a concept that describes the stylistic picture of a (translated) text in a relatively comprehensive and functional way through a set of stylistic indices. We also highlight stylistic similarities and differences between heroic literature in the East and that in the West, providing a potential connection that enhances our understanding of the current reception of translated Wuxia fiction. We examined six published English translations of Wuxia novels and 12 representative chivalric storiesChivalric stories and heroic fantasies in modern English and found that the stylistic panoramas of the Wuxia translations differed from those of the two Western subgenresSubgenres. We investigated possible translatorial and extra-translatorial factors, such as translators' motivations and the year of publication, to explain those findings. We hope that this research will broaden the understanding of the current reception of translated Chinese Wuxia stories in the English-speaking world and will encourage new applications for the concept of stylistic panorama in stylometric studies.
A Hybrid Deep Learning System to Detect Face-Mask and Monitor Social Distance

Coronavirus Disease 2019 (COVID-19) emerged towards the end of 2019, and it is still causing havoc on the lives and businesses of millions of people in 2022. As the globe recovers from the epidemic and intends to return to normalcy, there is a spike of anxiety among those who expect to resume their everyday routines in person.The biggest difficulty is that no effective therapeutics have yet been reported. According to the World Health Organization (WHO), wearing a face mask and keeping a social distance of at least 2 m can limit viral transmission from person to person. In this paper, a deep learning-based hybrid system for face mask identification and social distance monitoring is developed. In the OpenCV environment, MobileNetV2 is utilized to identify face masks, while YoLoV3 is used for social distance monitoring. The proposed system achieved an accuracy of 0.99.
Exploration of Emotions Developed in the lnteraction with Explainable AI

Innovations in the last decade have demonstrated Artificial Intelligence’s remarkable capabilities to augment humans in intensive cognitive tasks. Unfortunately, these recognized advancements do not correspondingly translate to increased trust or reliance, given the significant concern on its black-box structure. This paradox led to the recent rise of Explainable Al (XAI), which aims to provide human-level translation of Al’s complex inner workings, commonly via visual cues. Being relatively new, the focus has been on improving and developing new techniques to provide deeper explanations. However, its effectiveness may not entirely depend on the cognitive resource it provides. As social science and human-computer interaction research suggest, emotions developed from the interaction can affect decisions. To explore the viability of this theory in the context of XAI, preliminary research was done to determine whether a specific emotion set can be engendered from the interaction with it. Three hundred and twelve participants were randomly selected to partake in an online survey asking the emotions they might feel when exposed to XAI in an image classification scenario. Analysis shows that there are dominant emotions. Mainly, happy, confident, trusting, secure, surprised, pleased, amazed, and interested are the dominating emotions in a condition for a correct explanation. In contrast, concerned, sad, nervous, fearful, angry, dismayed, anxious, and suspicious are loaded under the wrong explanation scenario. Ultimately, the study confirms that a novel emotional set exists, potentially affecting XAI’s effectiveness.
Cold Start Problem in Recommendation System: A Solution Model Based on Clustering and Association Rule Techniques

Generally cold start problem refers to the new user who are coming in big data as well it also related to new items which are included in any data set. Problem arises when companies or policy makers don’t have the information about new user/item in recommendation of products. Recommendation system shows the user who is interested in particular product implementing contents based, collaborative based or hybrid approaches. Meanwhile recommender system faces the cold start issue which means the recommendation system is not recognizing the new product or new user. In another words the system doesn’t have information about preferences in order to make recommendation. To overcome the cold start problem, in this paper we are suggesting a solution by combining clustering techniques and association rule. The study is based on extracts of different approached studies. This paper also lets you understand the proposed models to overcome the problem of cold start.
Research on Evaluation System of the Judge Quality in Students’ Contest Based on Data Driven

Students’ contest is a vital part of the education process. It can significantly exercise students' ability to innovate and teamwork. In the contest, the high-quality evaluation team is the guarantees fairness and efficiency. The supervision and evaluation of the quality of the judge is the basis of building a high-quality judge team. In this regard, the main research contents of this paper are as follows: Based on data driven, combined with psychology and statistical analysis, three dimensions of evaluation indexes are built by combining qualitative and quantitative methods, and the evaluation model for evaluating judges is built. The accuracy and efficiency of the model are verified by case analysis using the model and data visualization methods. The research results provide valuable help for improving the evaluation quality and efficiency of the judges.
Neural Machine Translation Systems and Chinese Wuxia Movies: Moving into Uncharted Territory

This chapter contributes to the discourse on the fansubbing of wuxiaWuxia movies movies using neural machine translationNeural machine translation (NMT) (NMT) systems. The chapter is informed by the current popularity of wuxia movies among global audienceGlobal audiences, increase in fansubs and user-generated translation agents, and ubiquity of free open-source MT systems. Wuxia movies are deeply rooted in the ancient culture and history of China, making their translation particularly challenging for MT systems. Therefore, this chapter attempts to answer the following question: How can fansubbersFansubbers make the best use of NMT systems in translating culture-bound elements of wuxia movies? To answer this question, we adopt a corpus-based approach consisting of MTs of culture-bound excerpts of the movie Crouching Tiger, Hidden Dragon (2002) by Ang Lee. Any MT errorsMT errors identified are extracted and categorized. Thereafter, we suggest competencies, translation strategies, and approaches likely to help fansubbers improve MT output quality. In addition to furthering the discourse on the use of MT and the intrusion of non-professionals in the translation profession, the chapter provides an opportunity for the industry, especially training institutions, to create targeted short-term training programs that offer fansubbers the basic skills and competencies they may need.
Demystifying visual design: a sequential analysis of design processes in infographic visual composition

New technologies have made the world highly visual, making visual literacy an important and relevant 21st-century skill. Educators and students are often required to produce visual communication products such as infographics, but they often lack the confidence and proficiency in visual design skills to create higher-quality infographics. We conducted a case study to examine how graphic and instructional designers perform the visual design process (i.e., a series of actions performed in composing an infographic, such as creating layout and alignment) when applying visual design principles. The goal was to investigate the visual design processes and identify differences in the strategies used to develop higher versus lower quality infographics rated across 18 design criteria. We identified the design actions and computed the transitional probability between the design actions to construct a process model for composing effective infographics. Results reveal that high-rated infographics were developed using a more systematic approach, starting by creating a well-planned structure (e.g., setting margins and columns) followed by setting spatial zones to map out a visual hierarchy prior to working on fonts, colors, and graphic elements, and using a consistent application of visual rules. These target processes were encapsulated into a five-stage Infographic Visual Design Model.
Construction method of hybrid quantum long-short term memory neural network for image classification

Long-short term memory (LSTM) neural network solves the problems of long-term dependence, gradient disappearance and gradient explosion by introducing memory units, and is widely used in time series analysis and prediction. Combining quantum computing with LSTM neural network will help to improve its computational efficiency and reduce the number of model parameters, thus significantly improving the performance of traditional LSTM neural network. This paper proposes a hybrid quantum LSTM (hybrid quantum long-short term memory, HQLSTM) network model that can be used to realize the image classification. It uses variable quantum circuits to replace the nerve cells in the classical LSTM network to realize the memory function of the quantum network. At the same time, it introduces Choquet integral operator to enhance the degree of aggregation between data. The memory cells in the HQLSTM network are composed of multiple variation quantum circuits (VQC) that can realize different functions. Each VQC consists of three parts: the coding layer, which uses angle coding to reduce the complexity of network model design; the variation layer, which is designed with quantum natural gradient optimization algorithm, so that the gradient descent direction does not target specific parameters, thereby optimizing the parameter update process and improving the generalization and convergence speed of the network model; the measurement layer, which uses the Pauli Z gate to measure, and the expected value of the measurement result is input to the next layer to extract useful information from the quantum circuit. The experimental results on the MNIST, FASHION-MNIST and CIFAR datasets show that the HQLSTM model achieves higher image classification accuracy and lower loss value than the classical LSTM model and quantum LSTM model. At the same time, the network space complexity of HQLSTM and quantum LSTM are significantly reduced compared with the classical LSTM network.
Post-Robustifying Deep Anomaly Detection Ensembles by Model Selection

Anomaly detection has been a major research area in machine learning with deep ensemble models showing exceptional performance. However, formal verification of robustness for anomaly detection in general, and ensemble models in particular, has been mostly neglected. Moreover, given an already trained, non-robust model, there is no way to adapt it for robustness as a post-processing step as of yet. By harnessing properties of ensemble methods - in particular of the DEAN model - we are the first to post-robustify a model via submodel selection. Beyond this new capability, our method significantly increases verification scalability by employing the inherent properties of ensemble methods. Our experiments show that the DEAN model is most suitable for our method: it proves to be the most robust from the start, allows for post-robustification and keeps a stable runtime across all datasets considered.
Parameters identification for a nonlinear partial differential equation in image denoising

Abstract In this work and in the context of PDE constrained optimization problems, we are interested in identification of a parameter in the diffusion equation proposed in [1]. We propose to identify this parameter automatically by a gradient descent algorithm to improve the restoration of a noisy image. Finally, we give numerical results to illustrate the performance of the automatic selection of this parameter and compare our numerical results with other image denoising approaches or algorithms.
Which Companies are Likely to Invest: Knowledge-graph-based Recommendation for Investment Promotion

Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.
An FPGA-Based Hardware Accelerator for the k-Nearest Neighbor Algorithm Implementation in Wearable Embedded Systems

The k-Nearest Neighbor (k-NN) is one of the most used Machine-Learning based algorithm performing the classification task. The latter is very used in many application fields, especially in the context of Body Sensor Networks (BSNs) where features of interest have to be extracted from the data collected by wearable sensors. In the last few years, the ever increasing size of the collected data, as well as low-power and high-speed constraints, have brought out the need for hardware-based implementations of the k-NN algorithm. This paper describes an effective approach to design k-NN FPGA-based hardware accelerators. Low-power and high-speed are achieved by exploiting the dedicated Digital Signal Processing (DSP) slices and their fast interconnections typically available in commercial FPGA devices. Moreover, an effective hardware-friendly strategy is proposed in order to avoid the computational bottleneck shown by the sorting step of the algorithm when implemented in software. The new design has been implemented within a complete embedded system on the FPGA platform hosted on the Xilinx Zynq XC7Z020-1CLG400C heterogeneous System on Chip (SoC), and it has been tested for a real wearable application. Compared to the pure software implementation, the new design has shown an execution speed-up of several orders of magnitude, with a total power dissipation of only 198 mW.
Towards Scalable and Fast Distributionally Robust Optimization for Data-Driven Deep Learning

We introduce a scalable and fast method for solving distributionally robust optimization (DRO). Previous works have demonstrated that DRO outperforms empirical risk on a collection of inconsistent distribution of test data (the property of “uncertainty set”). However, DRO is hard to be applied for large-scale datasets and large parameterized model, due to the datapoint-level and non-differentiable objective function. In this paper, we formalize the DRO problem with the supremum of a family of subgroup-level loss functions. Subgroup loss is the cost function of partitioned uncertainty set. Then we implement the maximum of subgroup loss as the objective function and update model parameters by reweighting the descent direction, calculated from a differentiable objective function. Experimental results unveil that large parameterized models with the proposed method successfully adapt to uncertainty set whether the distribution contains out-of-domain or imbalanced property. Remarkably, with the explored reweighting strategy, the proposed algorithm effectively achieves competitive performance and robustness.
Orbit design of satellite quantum key distribution constellations in different ground stations networks

In the field of Cryptography, Quantum Key Distribution (QKD) is an application of Quantum Information theory that obtained a great deal of attention in recent years. It allows to establish secret keys between two or more parties, in a much safer way than that implemented by classic cryptography (based on discrete logarithms and factorization of prime numbers). The most promising way of realizing a QKD network (especially over great distances) in the near future is by a constellation of satellites. This paper considers the problem of optimizing the orbits of the satellites in order to maximize the minimum key length shared in a network of ground stations over a fixed amount of time. Different networks of stations are considered and the influence of their geographical disposition on the design and the performance index is highlighted. The networks considered are: a global constellation, a regional European constellation, and two in which there are groups of stations in two different narrow bands of latitude. The effect of Inter-satellite links is then taken into account and how, in some cases, they can improve the performances. Finally the daily performance of the considered constellations are analyzed.
The Effects of Type and Form of Collaborative Robots in Manufacturing on Trustworthiness, Risk Perceived, and Acceptance

As one of the latest applications in the field of artificial intelligence, collaborative robots (cobots) are playing an increasingly important role in manufacturing. This study is aimed to ascertain the effects of the type and form of a collaborative robot in manufacturing on trustworthiness, risk perceived, and acceptance. A 2 (single-arm cobot/dual-arm cobot) * 2 (cobot with a screen/cobot without a screen) full factorial within-subject experiment was conducted by using eye-tracking metrics and subjective questionnaires. A total of 40 participants were recruited and all stimuli were played randomly. Results suggest both features are essential attributes with interactive effects in the process of viewing tasks. For single-arm cobots, cobots without a screen showed higher trustworthiness than those with a screen. Dual-arm cobots with a screen showed higher perceived risk than those without a screen. Furthermore, compared to the cobots without a screen, cobots with a screen evidenced more fixation counts and longer fixation duration, which means more attention resources and greater cognitive effort were required. The same results were also found in dual-arm cobots. This study contributes to the extant literature on cobot design and training by emphasizing the effect of cobot form and type in particular.