Reviewerly/exHarmony huggingface dataset:
Machine versus corpus-based translation of multiword terms

Abstract Machine translation (MT) post-editing is an increasingly common practice in the translation industry which is also slowly being applied in the development of terminological resources. However, more studies have been devoted to analyze the practice in a translation scenario than in a terminographic context. Consequently, term-oriented post-editing guidelines are a current need if terminographers are also to become post-editors. With a view to enhancing the multilingual representation of environmental multiword terms (MWTs) in terminological resources, we analyze English–Spanish MWT translation in various generic MT systems. Our aims are: (1) to evaluate MT output in order to check whether it can be of any help to terminographers’ work; (2) to develop an error typology in order to raise terminographers’ awareness; and (3) to use the error typology to sketch a series of basic pre-editing and post-editing rules in a terminographic scenario. A comparison of MT output with the equivalents found in a comparable corpus is also presented. Even though MT often presents errors or unidiomatic choices, it can still serve as a basis for human post-editing, and provided that post-editors are familiarized with the potential errors. Comparable corpora, on the other hand, offer better results, but searches are more time-consuming and equivalents are not always available.
Inventorship in the Age of Artificial Intelligence

Artificial Intelligence is shaking up patent law. One of the most important questions in this regard is whether current patent laws adequately deal with increasing machine autonomy when it comes to inventorship. This paper uses two examples, namely Robot Scientists in drug development and antenna design in electrical engineering, to gauge the current level of machine automation in industrial production. A legal assessment carried out on this basis shows that the current use of the term inventorship is not future-proof and calls for policy adjustments: Patent law’s procedural provisions need to be amended and must overcome the requirement that only a human being can be an inventor. Moreover, droit moral considerations should be eliminated from substantive patent law altogether. Both measures would restore transparency in the patent register, reflecting the actual inventive activity.
認定された機械遺産の保存活用の現状（続報）

In order to know current situation of the preservation and utilization of the items recognized as The Mechanical Engineering Heritage by JSME, which has been done since 2007fy, seven recognized items arbitrarily selected in the other area than Fukuoka Prefecture are surveyed and reported. All the items are good condition and satisfied situation of the utilization except for two items. One item out of the two problematic items can be resolved by our proper actions. However, it is very difficult to resolve the problem on the other item.
国士舘大学における外国人留学生の日本文化教育の取り組み

In recent years, many Japanese companies that have set up overseas operations have occasionally experienced yield rate issues at their respective local production plants. When the company provides guidance for improvement, local workers sometimes resign from their jobs, which has become a problem. One of the reasons for this is that local workers do not understand the spirit of Japanese-style manufacturing (“monozukuri”). This is true not only for technical workers but also for clerical workers, as evidenced, for example, by conference interpreters who are unable to convey the correct message to the interested parties. Mutual cross-cultural understanding is a key to solving these problems.
Semi-Autonomous Search and Rescue System

A proposed semi-autonomous search and rescue system identifies and tracks a person using a piloted unmanned aerial vehicle with a downward-facing vision system. This vehicle communicates the person’s location to an autonomous unmanned ground vehicle to recover the target. An object detection neural network and image processing system can detect humans from an aerial perspective using RGB and thermal images. A Python simulation of the ground vehicle demonstrates that the heading and velocity controllers are robust to noise and disturbances.
Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark

Photographing in the under-illuminated scenes, the presence of complex light sources often leave strong flare artifacts in images, where the intensity, the spectrum, the reflection, and the aberration altogether contribute the deterioration. Besides the image quality, it also influence the performance of down-stream visual applications. Thus, removing the lens flare and ghosts is a challenge issue especially in low-light environment. However, existing methods for flare removal mainly restricted to the problems of inadequate simulation and real-world capture, where the categories of scattered flares are singular and the reflected ghosts are unavailable. Therefore, a comprehensive deterioration procedure is crucial for constructing the dataset of flare removal. Based on the theoretical analysis and real-world evaluation, we propose a well-developed methodology for generating the data-pairs with flare deterioration. The procedure is comprehensive, where the similarity of scattered flares and the symmetric effect of reflected ghosts are realized. Moreover, we also construct a real-shot pipeline that respectively processes the effects of scattering and reflective flares, aiming to directly generate the data for end-to-end methods. Experimental results show that the proposed methodology add diversity to the existing flare datasets and construct a comprehensive mapping procedure for flare data pairs. And our method facilities the data-driven model to realize better restoration in flare images and proposes a better evaluation system based on real shots, resulting promote progress in the area of real flare removal.
A Survey on Code-Mixed Sentiment Analysis Based on Hinglish Dataset

In recent years, the use of the Internet has been proliferating. Organizations use social media platforms to promote their products and find people’s opinions about their products. People share their experiences, views, and thoughts on social media platforms such as Twitter, Facebook, LinkedIn. Business organizations analyze the posts created by the users about their products through sentiment analysis. India is multilingual, so people use multiple languages to post their opinion, e.g., English and Hindi. Code-mixed language is a way in which people use words of different languages to express their thoughts. There is limited research done on code-mixed languages such as Hinglish, even though a great deal of effort has been devoted to assessing the sentiments of a single language. A comprehensive review of the Hinglish dataset is offered in this study. In this research, we first discuss several datasets used by the researchers to tackle this problem and then perform some extensive investigation to look more closely at them. Then, we examine several efficient approaches for categorizing sentiment for the Hinglish dataset.
Quantum Channel Certification with Incoherent Strategies

In the problem of quantum channel certification, we have black box access to a quantum process and would like to decide if this process matches some predefined specification or is $\varepsilon$-far from this specification. The objective is to achieve this task while minimizing the number of times the black box is used. Here, we focus on optimal incoherent strategies for two relevant extreme cases of channel certification. The first one is when the predefined specification is a unitary channel, e.g., a gate in a quantum circuit. In this case, we show that testing whether the black box is described by a fixed unitary operator in dimension $d$ or $\varepsilon$-far from it in the trace norm requires $\Theta(d/\varepsilon^2)$ uses of the black box. The second setting we consider is when the predefined specification is a completely depolarizing channel with input dimension $d_{\text{in}}$ and output dimension $d_{\text{out}}$. In this case, we prove that, in the non-adaptive setting, $\tilde{\Theta}(d_{\text{in}}^2d_{\text{out}}^{1.5}/\varepsilon^2)$ uses of the channel are necessary and sufficient to verify whether it is equal to the depolarizing channel or $\varepsilon$-far from it in the diamond norm. Finally, we prove a lower bound of $\Omega(d_{\text{in}}^2d_{\text{out}}/\varepsilon^2)$ for this problem in the adaptive setting. Note that the special case $d_{\text{in}} = 1$ corresponds to the well-studied quantum state certification problem.
AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition

Visual place recognition (VPR) is one of the research hotspots in robotics, which uses visual information to locate robots. Recently, the hierarchical two-stage VPR methods have become popular in this field due to the trade-off between accuracy and efficiency. These methods retrieve the top-k candidate images using the global features in the first stage, then re-rank the candidates by matching the local features in the second stage. However, they usually require additional al-gorithms (e.g. RANSAC) for geometric consistency verification in re-ranking, which is time-consuming. Here we propose a Dynamically Aligning Local Features (DALF) algorithm to align the local features under spatial constraints. It is significantly more efficient than the methods that need geometric consistency verification. We present a unified network capable of extracting global features for retrieving candidates via an aggregation module and aligning local features for re-ranking via the DALF alignment module. We call this network AANet. Meanwhile, many works use the simplest positive samples in triplet for weakly supervised training, which limits the ability of the network to recognize harder positive pairs. To address this issue, we propose a Semi-hard Positive Sample Mining (ShPSM) strategy to select appropriate hard positive images for training more robust VPR networks. Extensive experiments on four benchmark VPR datasets show that the proposed AANet can outperform several state-of-the-art methods with less time consumption. The code is released at https://github.com/Lu-Feng/AANet.
Multi-objective Task Scheduling Optimization Based on Improved Bat Algorithm in Cloud Computing Environment

In cloud computing environments, task completion time and virtual machine load balance are two critical issues that need to be addressed.To solve these problems, this paper proposes a Multi-objective Optimization Mutate Discrete Bat Algorithm (MOMDBA) that improves upon the traditional Bat algorithm (BA).The MOMDBA algorithm introduces a mutation factor and mutation inertia weight during the global optimization process to enhance the algorithm's global search ability and convergence speed.Additionally, the local optimization logic is optimized according to the characteristics of cloud computing task scenarios to improve the degree of load balancing of virtual machines.Simulation experiments were conducted using CloudSim to evaluate the algorithm's performance, and the results were compared with other scheduling algorithms.The results of our experiments indicate that when the cost difference between algorithms is within 4.47%, MOMDBA can significantly outperform other methods.Specifically, compared to PSO, GA, and LBACO, our algorithm reduces makespan by 56.26%, 59.87%, and 25.26%, respectively, while also increasing the degree of load balancing by 93.87%, 75.92%, and 39.13%, respectively.These findings demonstrate the superior performance of MOMDBA in optimizing task scheduling and load balancing.
Accelerated stochastic approximation with state-dependent noise

We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classical problem setting in which the variance of noise is assumed to be uniformly bounded, herein we assume that the variance of stochastic gradients is related to the "sub-optimality" of the approximate solutions delivered by the algorithm. Such problems naturally arise in a variety of applications, in particular, in the well-known generalized linear regression problem in statistics. However, to the best of our knowledge, none of the existing stochastic approximation algorithms for solving this class of problems attain optimality in terms of the dependence on accuracy, problem parameters, and mini-batch size. We discuss two non-Euclidean accelerated stochastic approximation routines--stochastic accelerated gradient descent (SAGD) and stochastic gradient extrapolation (SGE)--which carry a particular duality relationship. We show that both SAGD and SGE, under appropriate conditions, achieve the optimal convergence rate, attaining the optimal iteration and sample complexities simultaneously. However, corresponding assumptions for the SGE algorithm are more general; they allow, for instance, for efficient application of the SGE to statistical estimation problems under heavy tail noises and discontinuous score functions. We also discuss the application of the SGE to problems satisfying quadratic growth conditions, and show how it can be used to recover sparse solutions. Finally, we report on some simulation experiments to illustrate numerical performance of our proposed algorithms in high-dimensional settings.
Gender Stereotypes in Interaction Design. Render Me – Augmented Reality Masks to Inhabit the Metaverse

The purpose of this article is to understand how digital systems interact, reproduce societal power structures, and mimic the male canon in digital systems. Thus, it is necessary to understand how the algorithms of biometric technologies and Artificial Intelligence systems used in the categorization of images contribute to the inception of biases and stereotypes. In response to this question, the Render Me project was developed, which simulates how biometric technologies and design choices can marginalize individuals. Through a Feminist Speculative Design methodology, a fictional brand was developed that sells digital skins (augmented reality filters in Spark AR Studio) that protect the physical identity of its users against data capture and against surveillance of their real faces. This is meant as a satirical approach, as the female subject is placed as the main figure, abandoning the ideal of the man at the centre of the world that served its humanist desires. This female subject, however, is crossed by all the stereotypes attributed to her in the physical world. A skin is assigned to the user through the choice of keywords derived from the Myers-Brigg Type Indicator (MBTI) model. Additionally, only female masks can be purchased to navigate the entire metaverse, which vary their visual configuration according to female stereotypes. There are six skins in its current iteration, but it is intended that others can be developed in the future, representing other axes of identity such as race, class, and gender. In this way, the project aims to be an awareness tool for the perpetuation and amplification of gender stereotypes in digital systems. In short, by centering the female subject as the main figure, the Render Me project hopes to stimulate critical thinking about how technological objects reproduce ingrained prejudices and preestablished ideas and paradigms which might actually be causing harm.
Research About Usability Improvement in Cursor Operation of 3D Configurator

Automakers are accelerating their online sales strategies; augmented reality (AR) and three-dimensions (3D) configurators are being utilized to allow consumers to conveniently perceive information from multiple angles. However, since the user interface (UI) and posted content regarding, 3D configurators, on current e-commerce sites differ from manufacturer wise, many users who are not familiar with active information fail to use the said configurators. Therefore, examining the result of a past interview survey on the guide, cursor shape, and zoom function in the 3D configurator UI, we thought it necessary to clarify exactly how users are influenced by cursor manipulation in the 3D configurator, which is constantly manipulating the screen. For this purpose, we created a new questionnaire item with reference to the web usability scale (WUS) survey method, and conducted an experiment. Using the results of the experiment, we confirmed that the shape and rotation speed of the cursor influence the user when they are operating the cursor. In addition, we found that the optimal rotation speed differs depending on the method of rotation, and we are of the opinion that varying the speed will improve operability. Based on this research, we believe that ameliorating the shape and rotation speed of the cursor in 3D configurators will lead to a reduction in user stress and a positive user experience, thereby improving usability.
User Generated Inverted Item Evaluation (UGIIE)

Measuring short-term User Experience (UX) of interactive systems is often assessed by standardized questionnaire-based methods. As standardized methods are broadly applicable, they deploy a great variety of questions. Depending on the target system, certain questions may appear irrelevant and annoying, while others may even bias the participant. This paper proposes an alternative UX evaluation method, the User Generated Inverted Item Evaluation (UGIIE). It utilizes a think-aloud technique to elicit only relevant items for a system-tailored questionnaire and employs an item inversion to increase critical reflection. Utilizing four user studies, UGIIE is benchmarked through a comparative study with two common UX methods, meCUE and UEQ. We found UGIIE to decrease users’ frustration when filling out the questionnaire. UGIIE also indicates to provide a higher consistency in feedback when comparing the participants’ quantitative rating and qualitative comments. UGIIE’s drawback is the rather time-consuming preparation. Our study results suggest a preparation with a minimum of five participants to discover at least 80% of all relevant items for a UGIIE questionnaire. However, when testing much larger numbers of participants, UGIIE can be executed without the presence of an experimenter and thus becomes increasingly efficient.
Interaction Design Practice of Luohua Bird Embroidery Jacket Information in Mixed Reality Context

Current cultural relics are mostly exhibited in museums, exhibition halls and other display cases and shelves. Mostly in the form of physical presentation, the audience can not fully access the information of cultural relics, and the information of cultural relics can not be fully interpreted, resulting in the public’s recognition of the cultural value of cultural relics information is reduced. The design practice to Luohua bird embroidery Jacket as an example, in terms of product target positioning, refining user needs; clear content type. In terms of content and function design, screen key content; refine key content; explore transformed content. In terms of interaction design and implementation, we enhance interaction efficiency based on natural interaction principles; strengthen multimodal sensory cooperation to enhance interaction experience; mobilize subconscious behavior to enhance interaction participation. In terms of visual performance design, scenario-based design creates a sense of reality; narrative design builds a sense of presence; artistic design enhances expressiveness. We explore the issue of how to design cultural heritage information interaction in the context of mixed reality. From the perspective of heritage information interaction design as an entry point, combined with mixed reality context and features, to provide a basis and reference for heritage information interaction design, which is conducive to help improve user experience.
TCNN Architecture for Partial Occlusion Handling in Pedestrian Classification

Pedestrian classification is of increased interest to autonomous transportation systems due to the development of deep convolutional neural networks. Despite recent progress on pedestrian classification, it is still challenging to identify individuals who are partially occluded because of the diversity of the occluded parts, variation in pose, and appearance. This causes a significant performance reduction when pedestrians are covered by other objects, and feature information is lost due to the occluded parts. To solve this problem, we propose two network architectures using tree structure convolutional neural networks (T-CNN). They use the structural representation of multi-branch deep convolutional features, with the advantages of its end-to-end learning process. The high-level tree structure CNN (HT-CNN) architecture aims to concatenate the output of the classification layer from multi-segmented patches of pedestrians to handle partially occluded problems. The low-level tree structure CNN (LT-CNN) concatenates the discriminative features from each multi-segmented patch and global features. Our T-CNN architecture with a high-level tree structure performed with 94.64% accuracy on the INRIA dataset without occlusions, and with 70.78% accuracy on the Prince of Songkla University (PSU) dataset with occlusions, outperforming a baseline CNN architecture. This indicates that our proposed architecture can be used in a real-world environment to classify the occluded part of pedestrians with the visual information of multi-segmented patches using tree-structured multi-branched CNN.
Medical Test Results Management System Based on Blockchain, Smart Contracts, and NFT Technologies

The current diagnostic and treatment process is highly dependent on the results of medical tests (i.e., current and past addiction tests). This information directly affects the doctor’s decisions regarding the treatment regimen for each disease and the patient’s condition. However, current centralized storage methods pose a major barrier for patients (i.e., changing medical facilities). Medical information is extremely private and affects the patient directly if left unprotected, so it is constrained to retrieve this information from another treatment facility. Many previous approaches have proposed a decentralized storage model based on blockchain, smart contracts technologies to solve the above problems. However, the security of personal information is a potential risk for the above systems due to the openness of data stored on the chain. Based on the above limitations, we propose the NFT combination model to create document sets based on test results for easy sharing by patients in the medical environment.
A Reinforcement Learning-Based Approach for Continuous Knowledge Graph Construction

Currently, the knowledge graph construction from the text mainly relies on document-level relation extraction models. However, these models have lower accuracy due to the limitations in capturing relations across sentences and documents. To address this problem, this paper proposes a novel approach to continuously construct a knowledge graph based on a reinforcement learning framework, which leverages a question generation model, question answering model, and sentence-level relation extraction model to mine cross-document domain knowledge for effective knowledge updating. Three joint rewards are designed to optimize the question generation model, making it generate high-quality questions that facilitate the knowledge graph construction. Automatic evaluation combined with manual evaluation is conducted based on the SQuAD dataset, to assess the quality of the questions and generated knowledge graph respectively. The experiments and analyses demonstrate the effectiveness of our approach in improving the quality of the knowledge graph construction.