Reviewerly/exHarmony huggingface dataset:
Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned

The COVID-19 pandemic has shifted from traditional face-to-face teaching to technology-mediated distance learning, creating significant new challenges for teachers and students. The rapid integration of new technologies into the teaching and learning process can give rise to privacy issues. This study investigates how teachers and students of undergraduate and graduate courses in Information and Communication Technology (ICT) have been educated about protecting their privacy during online course activities. An exploratory qualitative-quantitative study was conducted, and 91 participants were interviewed to determine how they were guided to protect their privacy during online course activities. The study found that 75% of teachers consider teaching about privacy in ICT courses to be “very important”. However, these teachers still lack guidance, as 66% have not communicated privacy-related rights to their students. Furthermore, 80% have yet to communicate about websites or the location of privacy information from the educational institution. Despite some positive results identified in the study, such as 93% of teachers reporting that they are recording activities and 91% of students stating that they were informed about the recording of activities, challenges still need to be addressed regarding privacy issues during online teaching and learning activities. Therefore, the study proposes some good privacy practices that can be adopted by educational institutions, teachers, and students in the context of technology-mediated education.
Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures

Deep neural networks have been found vulnerable to adversarial attacks, thus raising potential concerns in security-sensitive contexts. To address this problem, recent research has investigated the adversarial robustness of deep neural networks from the architectural point of view. However, searching for architectures of deep neural networks is computationally expensive, particularly when coupled with an adversarial training process. To meet the above challenge, this paper proposes a bi-fidelity multiobjective neural architecture search approach. First, we formulate the neural architecture search (NAS) problem for enhancing the adversarial robustness of deep neural networks into a multiobjective optimization problem. Specifically, in addition to using low-fidelity estimations as the primary objectives, we leverage the output of a surrogate model trained with high-fidelity evaluations as an auxiliary objective. Secondly, we reduce the computational cost by combining three performance estimation methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based predictor. The effectiveness of the proposed approach is confirmed by extensive experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets.
Phase retrieval from single interferogram without carrier using Lissajous ellipse fitting technology

Phase extraction from single interferogram is of high significance and increasingly interest in optical metrology. In this contribute we propose an advanced Pixel-level Lissajous Ellipse Fitting (APLEF) method to extract the phase from single interferogram without carrier. At each pixel, a Lissajous figure is created by plotting N against D, where N and D are subtractions and additions of intensities of adjacent pixels in a small window. The so created Lissajous figure is already in phase quadrature because of the subtraction and addition process, and the Lissajous Figure is forced to be closed by taking the opposite values of N and D, i.e. -N and -D into account. The closed and in phase quadrature Lissajous Figure is the key point for APLEF to demodulate the single inteferogram without carrier in theoretically. The simulation shows its higher accuracy than existed SPT and Garbusi's method and the experiments finally corroborate its effectiveness.
ETHICAL CONCERNS ASSOCIATED WITH ARTIFICIAL INTELLIGENCE IN THE ACCOUNTING PROFESSION: A CURSE OR A BLESSING?

Due to the progress of digitization and the associated use of artificial intelligence in the economic and especially the accounting field, the cooperation between man and machine is becoming increasingly prominent in society. The objective of this research to address the ethics of using artificial intelligence in the accounting firms by looking at the novel challenges that it brings to the field. The research adopted a deductive approach, starting with the basic concepts and then conducting an empirical study based on an interview. The results of the interview were processed with the Nvivo12 application, through which a thematic analysis was carried out in order to present the results. The research results indicate that most of the accountants involved in the study have a basic knowledge of artificial intelligence but that few of them fully understand the phenomenon. However, they all believe that the ethics of artificial intelligence is vital and that the involvement of regulatory bodies in ethical legislation regarding artificial intelligence is indispensable. The results obtained can serve as an X-ray of the current situation and can be used to derive practical and managerial implications.
Semi-supervised t-SNE with multi-scale neighborhood preservation

Unsupervised dimensionality reduction (DR) aims to preserve input data structure in a low-dimensional (LD) space based on neighborhood information. In contrast, supervised DR intends to improve the learning performance, i.e., classification and regression, in an LD representation. Unfortunately, obtaining the complete label outputs of a data set for real-world applications is hard. Here, we introduce a novel DR framework coupling both available class labels and input feature similarities to extend the well-known t-distributed Stochastic Neighbor Embedding (SNE) for semi-supervised scenarios. Our proposal, termed Semi-Supervised t-SNE (SS.t-SNE), properly fixes the widths of Gaussian neighborhoods to reveal the salient local and global data structures in an LD space. Indeed, our approach is presented as a generalization of unsupervised and supervised versions of t-SNE. SS.t-SNE outperforms other semi-supervised DR methods in data visualization and classification tasks in LD embeddings.
Research on Multimodal Fusion Recognition Method of Upper Limb Motion Patterns

In order to solve the problems of single movement pattern recognition information and low recognition accuracy of multi-joint upper limb exoskeleton rehabilitation training, a multimodal information fusion method with human surface electromyography(sEMG) and electrocardiogram(ECG) was proposed, and an Inception-Sim model for upper limb motion pattern recognition was designed. Integrating the advantages of multimodal information, inspired by the convolutional neural network processing image classification problem, the original signal was converted into a Gramian Angular Summation/Difference Fields-Histogram of Oriented Gradient (GASF/GADF-HOG) image based on the principle of Grameen angle superposition/difference field, and the directional gradient histogram feature of the GASF/GADF image was extracted. The Inception-Sim model was constructed based on the Inception V3 model, and the human motion pattern recognition was completed on the basis of the transfer learning network. VGG16, ResNet-50, and other backbone networks were selected as comparison models. The recognition accuracy of each motion pattern for all participants reaches up to 90%, which is better than that of the control model. The average iteration speed of the proposed Inception-Sim model improved by about 21% compared to the control model. The experimental results show that the proposed multimodal information fusion recognition method can improve the accuracy and iteration speed of upper limb motion recognition mode and then improve the effect of upper limb rehabilitation training.
Managing the plot structure of character-based interactive narratives in games

The use of narrative generation methods in games is a complex challenge that involves multiple problems of plot-based processes integrated with character-based methods. Examples of these problems are the high computational complexity of many story generation algorithms, the difficulties associated with the generation of interactive narratives that are compelling and emotionally impactful, the complex interactions among characters, and the need for tools and methods to support story writers in the process of creating and managing the narrative structure of interactive stories. In this work, we present and evaluate a new approach to generate and manage the plot structure of character-based interactive narratives in games, which combines multi-agent planning with a drama management strategy based on narrative structures. The proposed method is supported by an authoring tool that allows authors to create and test interactive narratives using graphical interfaces and intuitive diagrams. The results of our study suggest the effectiveness of our approach in generating interactive narratives for highly interactive game environments. In addition, a user study of the proposed authoring tool indicates that it can successfully support the development of character-based interactive narratives without requiring programming knowledge.
Two Wrongs Don’t Make a Right: Combating Confirmation Bias in Learning with Label Noise

Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of noisy labels could result in a loss of information, especially when the corruption has a dependency on data, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: pseudo-labels fail to correct a considerable amount of noisy labels, and consequently, the errors accumulate. To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR)—a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art performance across datasets and noise types, namely CIFAR under different levels of synthetic noise and mini-WebVision and ANIMAL-10N with real-world noise.
Improved YOLOv5 Network for Real-Time Object Detection in Vehicle-Mounted Camera Capture Scenarios

Object detection in the process of driving is a convenient and efficient task. However, due to the complex transformation of the road environment and vehicle speed, the scale of the target will not only change significantly but also be accompanied by the phenomenon of motion blur, which will have a significant impact on the detection accuracy. In practical application scenarios, it is difficult for traditional methods to simultaneously take into account the need for real-time detection and high accuracy. To address the above problems, this study proposes an improved network based on YOLOv5, taking traffic signs and road cracks as detection objects and conducting separate research. This paper proposes a GS-FPN structure to replace the original feature fusion structure for road cracks. This structure integrates the convolutional block attention model (CBAM) based on bidirectional feature pyramid networks (Bi-FPN) and introduces a new lightweight convolution module (GSConv) to reduce the information loss of the feature map, enhance the expressive ability of the network, and ultimately achieve improved recognition performance. For traffic signs, a four-scale feature detection structure is used to increase the detection scale of shallow layers and improve the recognition accuracy for small targets. In addition, this study has combined various data augmentation methods to improve the robustness of the network. Through experiments using 2164 road crack datasets and 8146 traffic sign datasets made by LabelImg, compared to the baseline model (YOLOv5s), the modified YOLOv5 network improves the mean average precision (mAP) result of the road crack dataset and small targets in the traffic sign dataset by 3% and 12.2%, respectively.
Needs Matter: A Detailed Typology of Fundamental Needs for Human-Centered Design

The relevance of human needs is generally recognized in human-centered design research and practice. However, a challenge of working with needs is that the word “need” can be interpreted in various ways, which can hinder effective communication and collaboration. Need typologies, in such cases, can serve as a shared language by providing an overview of distinct human needs. Building on a design-focused typology of thirteen fundamental needs, this paper introduces a detailed version in which two complementary but distinct facets explain each need. We envision this fine-grained vocabulary of basic human needs can support initiatives in human-centered design research and practice, particularly facilitating and enriching conversations among people from different backgrounds. Furthermore, we propose three directions for future research on this topic.
Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models

In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed’s neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out.
Learning Context-Aware Classifier for Semantic Segmentation

Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing. Different from the mainstream literature where the efficacy of strong backbones and effective decoder heads has been well studied, in this paper, additional contextual hints are instead exploited via learning a context-aware classifier whose content is data-conditioned, decently adapting to different latent distributions. Since only the classifier is dynamically altered, our method is model-agnostic and can be easily applied to generic segmentation models. Notably, with only negligible additional parameters and +2\% inference time, decent performance gain has been achieved on both small and large models with challenging benchmarks, manifesting substantial practical merits brought by our simple yet effective method. The implementation is available at https://github.com/tianzhuotao/CAC.
Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

The Dwarf Mongoose Optimization Algorithm (DMO) is a popular metaheuristic algorithm utilized to solve real-world problems. However, DMO has limitations such as slow convergence rate, susceptibility to local optima, and poor performance in high-dimensional problems. Aiming at the defects of the DMO, we propose an improved version called IDMO. This paper introduces the best leader and proposes a novel nonlinear control strategy based on the sine function, significantly enhancing the convergence rate while ensuring accuracy. Moreover, we propose a novel exploration strategy to solve the global optimization and high-dimensional problem. To demonstrate the performance of the proposed IDMO, we test on 65 test functions, including CEC2017, CEC2020, CEC2022, and CEC2013 large-scale global optimization. IDMO is compared with three classes of widely recognized algorithms: (1) highly-cited algorithms, namely, GSA, GWO, WOA, and HHO, (2) advanced algorithms, such as CPSOGSA, CSA, AVOA, and SO, (3) high-performance optimizers including L-SHADE, AL-SHADE, LSHADE-SPACMA, and LSHADE-cnEpSin. Moreover, we also compare with the original DMO and variants such as GDNNMOA, CO-DWO, and BDMOSAO. Meanwhile, we apply IDMO to solve 19 engineering design problems from the CEC2020 real-world optimization suite. Experimental results demonstrate that IDMO outperforms other algorithms, exhibiting excellent convergence rate, stability, and accuracy. The effectiveness of IDMO is confirmed by the Friedman mean ranking, showcasing its potential for metaheuristic optimization and real-world engineering design problems.
Dose blockchain-based agri-food supply chain guarantee the initial information authenticity? An evolutionary game perspective

Guarantee the initial information of the agri-food supply chain (AFSC) authenticity based on the blockchain is a complex problem. This paper develops an evolutionary game model of AFSC participants based on the blockchain and discusses the impacts of the key parameters on the dynamic evolution process of participants. To verify the theoretical results, simulation experiments and sensitivity analysis were conducted through Matlab 2022b . The study results show that: (1) Guaranteeing the initial information authenticity could become the common belief of all AFSC participants, with the scientific design of parameters; (2) Higher reward and synergistic effect, lower information cost and risk contribute to improving the probability of initial true information sharing. (3) when the default penalty is too severe, the enterprise will evolve into not sharing the initial true information. Finally, this study could provide some suggestions and countermeasures for the leading enterprise in the agricultural supply chain and local governments to guarantee initial information authenticity in China. That is the way to realize the sustainability of AFSC in the long run.
Adaptive and Resilient Model-Distributed Inference in Edge Computing Systems

The traditional approach to distributed deep neural network (DNN) inference in edge computing systems is data-distributed inference. In this paradigm, each worker has a pre-trained DNN model. Using the DNN model, the worker processes the data that is offloaded to itself. The data-distributed inference approach (i) has high communication cost especially when the size of data is large, and (ii) is not efficient in terms of memory as the whole model should be stored and computed in each worker. Model-distributed inference is emerging as a promising solution, where a DNN model is distributed across workers. Although there is a huge amount of work on model-distributed training, the benefit of model distribution for inference is not understood well. In this paper, we analyze the potential of model-distributed inference in edge computing systems. Then, we develop an Adaptive and Resilient Model-Distributed Inference (AR-MDI) algorithm based on our optimal model allocation formulation. AR-MDI performs model allocation in a lightweight and decentralized way and it is resilient against delayed workers and failures. We implement AR-MDI in a real testbed consisting of NVIDIA Jetson TX2s and show that AR-MDI improves the inference time significantly as compared to baselines when the size of data is large, such as ImageNet.
A Study of Mycobacterium tuberculosis Detection Using Different Neural Networks in Autopsy Specimens

Tuberculosis (TB) presents a substantial health risk to autopsy staff, given its three to five times higher incidence of TB compared to clinical staff. This risk is notably accentuated in South Korea, which reported the highest TB incidence rate and the third highest TB mortality rate among OECD member countries in 2020. The standard TB diagnostic method, histopathological examination of sputum or tissue for acid-fast bacilli (AFB) using Ziehl–Neelsen staining, demands microscopic examination of slides at 1000× magnification, which is labor-intensive and time-consuming. This article proposes a computer-aided diagnosis (CAD) system designed to enhance the efficiency of TB diagnosis at magnification less than 1000×. By training nine neural networks with images taken from 30 training slides and 10 evaluation slides at 400× magnification, we evaluated their ability to detect M. tuberculosis. The N model achieved the highest accuracy, with 99.77% per patch and 90% per slide. We discovered that the model could aid pathologists in preliminary TB screening, thereby reducing diagnostic time. We anticipate that this research will contribute to minimizing autopsy staff’s infection risk and rapidly determining the cause of death.
A Requirement Quality Assessment Method Based on User Stories

Agile development processes based on user stories often face issues such as incomplete, inconsistent, and inaccurate user requirements, which increase the workload of agile development teams and reduce the efficiency of product function development, ultimately resulting in the inability to respond quickly to user requirements. This paper proposes a user requirement quality assessment method based on user stories to address these problems. This method relies on the agile development process, constructs a user requirement quality assessment framework, defines a user story model and a user requirement quality model, develops seven user requirement quality assessment criteria, and designs a user requirement quality assessment process. A data experiment exploring the development of smartphone requirements is used to validate the feasibility and effectiveness of the method. The experimental results demonstrate that the method improves user requirement quality to some extent, providing an automated solution for agile development teams to enhance user requirement quality.