Reviewerly/exHarmony huggingface dataset:
Pix2Map: Cross-Modal Retrieval for Inferring Street Maps from Images

Self-driving vehicles rely on urban street maps for autonomous navigation. In this paper, we introduce Pix2Map, a method for inferring urban street map topology directly from ego-view images, as needed to continually update and expand existing maps. This is a challenging task, as we need to infer a complex urban road topology directly from raw image data. The main insight of this paper is that this problem can be posed as cross-modal retrieval by learning a joint, cross-modal embedding space for images and existing maps, represented as discrete graphs that encode the topological layout of the visual surroundings. We conduct our experimental evaluation using the Argoverse dataset and show that it is indeed possible to accurately retrieve street maps corresponding to both seen and unseen roads solely from image data. Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs.
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks

In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+l model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for Vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://github.com/BrandonHanx/FAME-ViL.
Category Query Learning for Human-Object Interaction Classification

Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt via an auxiliary image-level classification task. This idea is motivated by an earlier multi-label image classification method, but is for the first time applied for the challenging human-object interaction classification task. Our method is simple, general and effective. It is validated on three representative HOI baselines and achieves new state-of-the-art results on two benchmarks. Code will be available at https://github.com/charles-xie/CQL.
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing can be overinterpreted if regarded as a primary criterion for selecting or discarding explanation methods. To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e.g. [20]). We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Firstly, we show that uninformative attribution maps created with zero pixel-wise covariance easily achieve high scores in this type of checks. Secondly, we show that top-down model randomization preserves scales of forward pass activations with high probability. That is, channels with large activations have a high probility to contribute strongly to the output, even after randomization of the network on top of them. Hence, explanations after randomization can only be expected to differ to a certain extent. This explains the observed experimental gap. In summary, these results demonstrate the inadequacy of model-randomization-based sanity checks as a criterion to rank attribution methods.
Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://github.com/rain305f/OSP.
Sanda training program based on multi‐sensor data fusion in the internet of things environment

Abstract Sanda, also known as Chinese kickboxing, is a Wushu that combines elements of traditional Kung Fu, kickboxing, and grappling. Considering that the use of action recognition can effectively improve the level of Sanda players, this study uses inertial sensors and visual sensors to collect the motion data of Sanda. A federated Kalman filter with time‐varying parameters is designed according to the error form of each sensor to complete the data fusion. The main idea is that the whole system is divided into several local filtering systems, and a primary filter is designed for these systems. The main structure of the filter is a cascaded decentralized filtering structure composed of several local filters and a primary filter. The experimental results show that the proposed Sanda training scheme based on multi‐sensor data fusion under the Internet of Things environment has high action recognition accuracy, and the joint inertial‐vision model produces significantly better results than the other two models.
Acrobats and Safety Nets: Problematizing Large-Scale Agile Software Development

Agile development methods have become a standard in the software industry, including in large-scale projects. These methods share a set of underlying assumptions that distinguish them from more traditional plan-driven approaches. In this article, we adopt Alvesson and Sandberg's problematization approach to challenge three key assumptions that are prevalent in the large-scale agile literature: (1) agile and plan-driven methods are mutually exclusive; (2) self-managing and hierarchically organized teams are mutually exclusive; and (3) agile methods can scale through simple linear composition. Using a longitudinal case study of large-scale agile development, we describe a series of trigger events and episodes whereby the agile approach was tailored to address the needs of the large-scale development context, which was very much at odds with these fundamental assumptions. We develop a set of new underlying assumptions which suggest that agile and plan-driven practices are mutually enabling and necessary for coordination and scaling in large-scale agile projects. We develop nine propositions for large-scale agile projects based on these new alternative underlying assumptions. Finally, we summarize our theoretical contribution in a generic process model of continuously adjusting agile and plan-driven practices in order to accommodate process challenges in large-scale agile projects.
Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications

The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolved concerns surrounding complexity, adaptability, and computational demands. Thus, we propose our scheme, GETS, utilizing a graph-based model to forge connections among words and sentences through statistical procedures. The structure encompasses a post-processing stage that includes graph-based sentence clustering. Employing the Apache Spark framework, the scheme is designed for parallel execution, making it adaptable to real-world applications. For evaluation, we selected 500 documents from the WikiHow and Opinosis datasets, categorized them into five classes, and applied the recall-oriented understudying gisting evaluation (ROUGE) parameters for comparison with measures ROUGE-1, 2, and L. The results include recall scores of 0.3942, 0.0952, and 0.3436 for ROUGE-1, 2, and L, respectively (when using the clustered approach). Through a juxtaposition with existing models such as BERTEXT (with 3-gram, 4-gram) and MATCHSUM, our scheme has demonstrated notable improvements, substantiating its applicability and effectiveness in real-world scenarios.
Snek5000: a new Python framework for Nek5000

Computational fluid dynamics (CFD) simulations are essential tools in various scientific and engineering disciplines.Nek5000 is a CFD Fortran code based on spectral element methods with a proven track record in numerous applications.In this article, we present Snek5000, a Python package designed to streamline the management and visualization of fluid dynamics simulations based on Nek5000.The package builds upon the functionality of Nek5000 by providing a user-friendly interface for launching and restarting simulations, loading simulation data, and generating figures and movies.This paper introduces Snek5000, discusses its design principles, and highlights its impact on the scientific community.
Approaches to Improve the Quality of Person Re-Identification for Practical Use

The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal action prevention and searching for missing persons through a smart city’s video surveillance. In most of the papers devoted to the problem under consideration, the authors propose complex algorithms to achieve a better quality of person Re-ID. Some of these methods cannot be used in practice due to technical limitations. In this paper, we propose several approaches that can be used in almost all popular modern re-identification algorithms to improve the quality of the problem being solved and do not practically increase the computational complexity of algorithms. In real-world data, bad images can be fed into the input of the Re-ID algorithm; therefore, the new Filter Module is proposed in this paper, designed to pre-filter input data before feeding the data to the main re-identification algorithm. The Filter Module improves the quality of the baseline by 2.6% according to the Rank1 metric and 3.4% according to the mAP metric on the Market-1501 dataset. Furthermore, in this paper, a fully automated data collection strategy from surveillance cameras for self-supervised pre-training is proposed in order to increase the generality of neural networks on real-world data. The use of self-supervised pre-training on the data collected using the proposed strategy improves the quality of cross-domain upper-body Re-ID on the DukeMTMC-reID dataset by 1.0% according to the Rank1 and mAP metrics.
Continuous-variable quantum key distribution with time-division dual-quadrature homodyne detection

We propose a novel heterodyne detection scheme for continuous-variable quantum key distribution (CVQKD), which measures both quadrature components of a quantum signal encoded in optical phase space. The proposed method uses time division to achieve identical performance to conventional heterodyne detection with only a single homodyne detection system. Our method also uses a Faraday-Michelson interferometer to make it independent of polarization drift and eliminate the need for dynamic polarization control. Our method is experimentally demonstrated using the Gaussian-modulated coherent-states (GMCS) protocol over a 20.06 km optical fiber channel, achieving an expected secret key rate of up to 0.187 Mbps.
What Role Does AI Chatbot Perform in the F&amp;B Industry? Perspective from Loyalty and Value Co-Creation: Integrated PLS-SEM and ANN Techniques

Purpose: This study examines the process formation of customer loyalty and customer value co-creation towards AI chatbots by exploring the successive effects of perceived value aspects, perceived information quality, technological self-efficacy for online trust, aspects of loyalty, and value co-creation. Theorical framework: The increasingly strong reception of humans for a new wave of digitalization has promoted the need to learn about customer loyalty and customers' value co-creation formation for businesses applying AI chatbots to their operations business to attract and retain customers. The study utilized the perceived value dimension, as well as perceived information quality, technological self-efficacy, and online trust, to comprehend loyalty and value co-creation. Design/methodology/approach: The study was conducted using a self-administered questionnaire survey with 447 participants, who had used Pizza Hut's AI chatbot service in Vietnam. The data was analyzed by integrating two techniques: partial least square structural equation modeling (PLS-SEM) and artificial neural networks (ANN). Findings: The results show that aspects of perceived value, perceived information quality, and technological self-efficacy all have a significant impact on online trust except hedonic value, which in turn leads to the formation of aspects of loyalty and high ability to create value co-creation. The analysis results show that perceived information quality has a stronger impact on online trust than technological self-efficacy. In addition, the non-linear results from the ANN analysis show that attitudinal loyalty has relatively stronger importance for value co-creation than behavioral loyalty. Research, Practical &amp; Social Implication: This study contributes to the emerging literature on the use of AI chatbots by investigating the possibility of consumers and providers co-creating value. Second, in this study, the authors delved into the internal aspects of loyalty and separated it into two primary aspects, behavioral and attitudinal, in order to clarify their impact on the factors that influence AI chatbot and value co-creation. In conclusion, this research contributes to the existing body of knowledge by providing a more multidimensional perspective on theories. Originality/value: The integration of PLS-SEM and ANN techniques into the analysis to simultaneously explore both linear and non-linear mechanisms of this study explained the influence of aspects of perceived value, perceived information quality, and technological self-efficacy on aspects of loyalty and value co-creation via online trust in AI chatbots context. In addition, this study extends the perceived value to explore the impact of internal and external personal factors on AI chatbots.
Whom Do You Speak For? And How?

A lot of subordinating speech has moved online, which raises several questions for philosophers. Can current accounts of oppressive speech adequately capture digital hate? How does the anonymity of online harassers contribute to the force of their speech? This paper examines online abuse and argues that standard accounts of licensing and accommodation are not up to the task of explaining the authority of online hate speech, as speaker authority often depends on the community in more ways than these accounts suggests. Instead I argue that online abusive speech is best understood as collective subordinating speech acts, as their authority is drawn from an ad hoc collective. I argue that anonymity and shared language offer online abusers a path to a type of group-authority that explains the harm their speech is capable of. I close by suggesting that similar considerations are in play for IRL subordinating speech, and that online abuse helps to reveal key features of subordinating speech across mediums that are under-emphasized in the existing literature.
Global-and-local aware network for low-light image enhancement

Photos taken under nighttime or backlit conditions often suffer from complex and unpredictable degradation, such as low visibility, messy noise, and distorted color. Previous methods mainly focused on global brightness and contrast while ignoring structural and textural details, or they handled the fusion of features without adequately considering their intrinsic association, resulting in incomplete feature representations. To address this issue, we propose a global-and-local aware network (GLAN) by projecting the features into the frequency domain and incorporating them in a knowledge-sharing manner. This method effectively integrates the global modeling capability of the transformer and the local sensitivity of the convolutional neural network to represent structure and texture. First, the global branch, which is comprised of transformer blocks, performs feature extraction under the global receptive field, while the local branch constructs multi-scale features to provide local fine-grained details. Then, we design a novel adaptive multi-scale feature block (AMSFB) that deploys channel split operation to decrease the calculation amount. To better learn the channel and spatial correlations of intermediate features, we introduce a multi-scale channel attention module (MSCAM) and a pixel attention module (PAM) into the AMSFB. Finally, a frequency-aware interaction module (FAIM) is developed for bidirectional information supplementation, which builds feature descriptors simultaneously covering low-frequency and high-frequency information based on the discrete cosine transform (DCT). Through extensive quantitative and qualitative experiments, our method can achieve competitive results compared with over ten state-of-the-art image enhancement methods on eight benchmark datasets.
MetaCUX: Social Interaction and Collaboration in the Metaverse

In this poster, we present a Virtual Reality environment, named MetaCUX (Collaborative User eXperience in the Metaverse), where people may collaborate in a virtual workplace and interact with intelligent objects for simulating real tasks. Several multi-user workplace scenarios were reproduced in the MetaCUX, on which an experimental study has been carried out involving 15 participants. To analyze remote collaboration small group of participants performed a task consisting in putting out a fire. Self-reported surveys and qualitative measures are adopted to investigate the effectiveness of social interaction during the collaborative activity.
Enhancement of non-Gaussianity and nonclassicality of pair coherent states by superposition of photon addition and subtraction

Abstract Pair coherent states (PCSs) as a kind of two-mode non-Gaussian and nonclassical states were introduced and studied. Some nonclassical characteristics of them such as two-mode sum-squeezing, two-mode antibunching, and entanglement have been well investigated. In this paper, we focus on studying the superposition of photon addition and subtraction to the PCSs called superposition of photon-added and photon-subtracted pair coherent states (SPAPSPCSs). Our main purpose is to show that the non-Gaussian feature and all the above-mentioned nonclassical properties of the SPAPSPCSs can be enhanced compared with the photon-added-and-subtracted two modes pair coherent states (PAASTMPCSs) and the original PCSs. In general, we demonstrate that the SPAPSPCSs have more enhanced non-Gaussian character and nonclassical properties than the PCSs and the PAASTMPCSs by examining the negativity of the Wigner function. Specifically, we indicate that both the SPAPSPCSs and the PAASTMPCSs can exist in two-mode sum-squeezing while the original PCSs (without photon-addition and photon-subtraction) cannot, and the degree of squeezing is stronger in the SPAPSPCSs than in the PAASTMPCSs. The obtained results also indicate that the superposition of photon addition and subtraction of the SPAPSPCSs plays a vital role in enhancing the two-mode antibunching property and the entanglement degree compared with the PCSs and the PAASTMPCSs.
Development and performance test of a novel solar tracking sensor

A new solar tracking sensor based on image recognition is proposed and designed to solve the problem of low accuracy of photoelectric tracking in photovoltaic power generation.The sensor can directly output its angular deviation from the sun, and its mechanical structure and working principle are analysed in detail.We use a high-precision camera to collect the image of the two slots on the projector surface and use the Hough transform to identify the image of the light seam.After obtaining the linear equation for the two slots, the coordinate of the intersection point is found, and the calculation of the solar altitude and azimuth can be realized.We have improved the Hough transform scheme by using the skeleton image of the slots instead of the edge image.The improvement of the scheme has been proved to effectively improve the detection accuracy.A calibration test board is used to test the sensor and experimental results show that the scheme can achieve the measurement of azimuth and altitude with the accuracy of be 0.05°, which can meet the detection accuracy requirements of the solar tracking in photovoltaic power generation and many other photoelectric tracking implementations.
APPLICATION OF MIND MAPPING TECHNIQUE TO INCREASE STUDENTS’ READING COMPREHENSION AT THE SEVENTH GRADE OF SMP

The purpose of the study was to find out whether the application of Mind Mapping Technique can increase Students’ Reading Comprehension or not. This study was conducted at SMP N 3 Sangtombolang. The design of this study was pre-experimental with one group pre-test and post-test design. The subject in this study was class VII in academic year 2020/2021, which consisted of 15 students. The data of this study were collected through cloze test. The data were analysed by comparing the mean score of the tests. The mean score of the post-test (T2) is higher 83.7 than those of the pre-test (T1) 26.6. This heads to the conclusion that the application of mind mapping technique could increase students’ reading comprehensio