Reviewerly/exHarmony huggingface dataset:
Key feature analysis: a simple, yet powerful method for comparing text varieties

To date, corpus-based methods for comparing language varieties have fallen into one of two camps: ( 1) md analysis – a complicated multi-variate approach based on analysis of functionally motivated linguistic features in each text of a corpus, or ( 2) keyword/key pos analysis – simple, univariate techniques to identify any feature with a statistically skewed distribution in a corpus. In this paper, we introduce a complementary technique – key feature analysis – which is a simple quantitative approach to compare the texts in two varieties with respect to a set of functionally motivated lexico-grammatical features. We introduce the methods of key feature analysis, contrast them with other approaches for comparing text varieties, and present case studies from the domains of online registers and US presidential debates.
Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System

Fashion recommendation (FR) has received increasing attention in the research of new types of recommender systems. Existing fashion recommender systems (FRSs) typically focus on clothing item suggestions for users in three scenarios: 1) how to best recommend fashion items preferred by users; 2) how to best compose a complete outfit, and 3) how to best complete a clothing ensemble. However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. More specifically, our SAFRS addresses this problem by encoding scene and outfit information in separation attention encoders and then fusing the resulting feature embeddings via a novel scene-aware compatibility score function. Extensive qualitative and quantitative experiments are conducted to show that our SAFRS model outperforms all baselines for every evaluated metric.
Stratification-based semi-supervised clustering algorithm for arbitrary shaped datasets

Semi-supervised clustering is not only an important branch of semi-supervised learning but also an improvement direction for clustering. Semi-supervised clustering algorithms designed based on Kmeans, such as the classical Seeded-Kmeans and Constrained-Kmeans, where supervision information is used to guide clustering iterations, have the same disadvantages as the original Kmeans algorithm: they are confined to the assumption of isotropic spherical clusters, leading to the narrow adaptability in handling data of various characteristics. To solve the problem, we propose the scattered centroids initialization clustering algorithm based on Stratification (SCICS). First, based on the concept of influence space, a method for modeling the cluster-level location of any object is presented, according to which we can obtain well-defined cluster decision boundaries through stratification. On this basis, by extending the seed thought, we propose a semi-supervised subclustering algorithm that can break through the limitations of partitional clustering methods that rely on strict assumptions on particular cluster distributions. Experiments on artificial and real-world datasets show that the proposed algorithm gains the ability of clustering arbitrary shaped data and surpasses the competitors in terms of performance and adaptability.
MAGIC: Manipulating Avatars and Gestures to Improve Remote Collaboration

Remote collaborative work has become pervasive in many settings, ranging from engineering to medical professions. Users are im-mersed in virtual environments and communicate through life-sized avatars that enable face-to-face collaboration. Within this context, users often collaboratively view and interact with virtual 3D models, for example to assist in the design of new devices such as cus-tomized prosthetics, vehicles or buildings. Discussing such shared 3D content face-to-face, however, has a variety of challenges such as ambiguities, occlusions, and different viewpoints that all decrease mutual awareness, which in turn leads to decreased task performance and increased errors. To address this challenge, we introduce MAGIC, a novel approach for understanding pointing gestures in a face-to-face shared 3D space, improving mutual understanding and awareness. Our approach distorts the remote user's gestures to correctly reflect them in the local user's reference space when face-to-face. To measure what two users perceive in common when using pointing gestures in a shared 3D space, we introduce a novel metric called pointing agreement. Results from a user study suggest that MAGIC significantly improves pointing agreement in face-to-face collaboration settings, improving co-presence and awareness of interactions performed in the shared space. We believe that MAGIC improves remote collaboration by enabling simpler communication mechanisms and better mutual awareness.
Quantum Machine Learning Implementations: Proposals and Experiments

Abstract This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews specific high‐impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their experimental realizations in the platforms of quantum photonics and superconducting circuits. The field of quantum machine learning can be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society. Therefore, it is necessary to push forward initial quantum implementations of this technology, in noisy intermediate‐scale quantum computers, aiming for achieving fruitful calculations in machine learning that are better than with any other current or future computing paradigm.
EXPLORING THE PSYCHOLOGICAL IMPLICATIONS OF CHATGPT:A QUALITATIVE STUDY

As chatbots become increasingly prevalent in our daily lives, it is important to consider their psychological implications. In this qualitative study, we conducted a focus group with 10 psychology students who had experience interacting with chatbots. Participants discussed their emotional responses, attitudes, and behaviors related to chatbots, as well as ethical concerns. Thematic analysis revealed four main themes: (1) convenience and efficiency, (2) frustration and dissatisfaction, (3) social and emotional connections, and (4) ethical considerations. The findings suggest that chatbots can provide convenience and efficiency, but may also lead to frustration and dissatisfaction when they are unable to meet users' needs. Additionally, participants reported feeling a sense of social and emotional connection with chatbots, and expressed concerns about ethical issues such as data privacy and the potential for chatbots to replace human interaction. These findings have implications for the design and implementation of chatbots, as well as for ethical considerations in their use
Improving Massively Multilingual ASR with Auxiliary CTC Objectives

Multilingual Automatic Speech Recognition (ASR) models have extended the usability of speech technologies to a wide variety of languages. With how many languages these models have to handle, however, a key to understanding their imbalanced performance across different languages is to examine if the model actually knows which language it should transcribe. In this paper, we introduce our work on improving performance on FLEURS, a 102-language open ASR benchmark, by conditioning the entire model on language identity (LID). We investigate techniques inspired from recent Connectionist Temporal Classification (CTC) studies to help the model handle the large number of languages, conditioning on the LID predictions of auxiliary tasks. Our experimental results demonstrate the effectiveness of our technique over standard CTC/Attention-based hybrid models. Furthermore, our state-of-the-art systems using self-supervised models with the Conformer architecture improve over the results of prior work on FLEURS by a relative 28.4% CER. Trained models are reproducible recipes are available at https://github.com/espnet/espnet/tree/master/egs2/fleurs/asr1.
Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.
Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status.We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset.The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall.Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications.We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.
Augmented reality in education for industry 4.0: What are the barriers to adoption?

One of the enabling technologies of Industry 4.0 is augmented reality. Augmented reality is currently supported on mobile devices and often does not require costly equipment, allowing it to expand quickly across industries such as entertainment and healthcare. Studies in education demonstrate that augmented reality can improve conceptual comprehension and learning in STEM fields and that educational organizations and teachers support its use. However, augmented reality is still used in only a few educational establishments. There have been few studies on the topic, raising concerns such as: what are the barriers to broader adoption of this technology? Understanding the issues better would allow educational organizations and instructors to take action to mitigate them. This study aims to identify and categorize the barriers, difficulties, and challenges for the adoption of augmented reality in education. The investigation was conducted in two stages: first, the barriers mentioned in 28 papers published in the Science Direct, Scielo, and Scopus databases were preliminary categorized, and then this categorization was validated by an interdisciplinary focus group of experts in education and industry 4.0. We identified seven categories as barriers to the adoption of augmented reality in education: technical, adoption, usability and user interaction, learning, pedagogical, financial, and technological nature. Our results contribute to mitigate these barriers and encourage actions to increase the potential of augmented reality in education.
Privacy-Preserving Graph Matching Query Supporting Quick Subgraph Extraction

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, it is an attractive alternative to make use of the cloud to store massive data graphs and conduct the complex graph matching. To protect the privacy, data graphs are usually encrypted before being outsourced to the cloud. A few schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. The time complexity of judging subgraph isomorphism is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n!n^{2})$</tex-math></inline-formula> at most, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of vertices in the data graph. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. The first cloud server can prune some impossible matched vertices from the data graph, and get a possible matched matrix to represent which vertices in the data graph might match with the vertices in the query graph. The second cloud server performs the related matrix operations based on its stored information and the information sent by the first cloud server. The two cloud servers jointly verify the correctness of the matched matrix. The graph user can directly and quickly extract the matrix of the subgraph isomorphic to the query graph from the data graph matrix based on the matched matrix, and further recover this subgraph. No subgraph isomorphism operation is involved in the procedure of subgraph extraction for the graph user. The time complexity of subgraph extraction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2})$</tex-math></inline-formula> in our scheme, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of vertices in the query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.
On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles

Abstract It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56–64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA international conference on machine learning and applications, IEEE Computer Society, pp 147–153, 2009. https://doi.org/10.1109/ICMLA.2009.16 ). In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. In contrast to previous comparison studies, where the models (algorithms, parameters) are selected by using examples from both classes (outlier and inlier), here we also study and compare different approaches for model selection in the absence of examples from the outlier class, which is more realistic for practical applications since labeled outliers are rarely available. Our results showed that, overall, SVDD and GMM are top-performers, regardless of whether the ground truth is used for parameter selection or not. However, in specific application scenarios, other methods exhibited better performance. Combining one-class classifiers into ensembles showed better performance than individual methods in terms of accuracy, as long as the ensemble members are properly selected.
Focused Decoding Enables 3D Anatomical Detection by Transformers

Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with or even superior to their highly optimized CNN-based counterparts operating on 2D natural images, their success is closely coupled to access to a vast amount of training data. This, however, restricts the feasibility of employing Detection Transformers in the medical domain, as access to annotated data is typically limited. To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder. Focused Decoder leverages information from an anatomical region atlas to simultaneously deploy query anchors and restrict the crossattention’s field of view to regions of interest, which allows for a precise focus on relevant anatomical structures. We evaluate our proposed approach on two publicly available CT datasets and demonstrate that Focused Decoder not only provides strong detection results and thus alleviates the need for a vast amount of annotated data but also exhibits exceptional and highly intuitive explainability of results via attention weights. Our code is available at &lt;a href='https://github.com/bwittmann/transoar'&gt;https://github.com/bwittmann/transoar&lt;/a&gt;
Path Planning Algorithm for a Wheel-Legged Robot Based on the Theta* and Timed Elastic Band Algorithms

Aimed at the difficulty of path planning resulting from the variable configuration of the wheel-legged robot for future deep space explorations, this paper proposes a path planning algorithm based on the Theta* algorithm and Timed Elastic Band (TEB) algorithm. Firstly, the structure of the wheel-legged robot is briefly introduced, and the workspace of a single leg is analyzed. Secondly, a method to judge complete obstacles and incomplete obstacles according to the height of the obstacles is proposed alongside a method to search for virtual obstacles, to generate a grid map of the wheel and a grid map of the body, respectively. By dividing obstacles into complete obstacles and incomplete obstacles, the path planning of the wheel-legged robot is split into the planning of the body path and the planning of the wheel path. The body can be still simplified as a point by searching for the virtual obstacle, which avoids the difficulty of a planning path of a variable shape. Then, we proposed hierarchical planning and multiple optimization algorithms for the body path and wheel path based on the Theta* algorithm and TEB algorithm. The path can be optimized and smoothed effectively to obtain a shorter length and higher safety. On that basis, the proposed algorithm is simulated by Matlab. The results of simulations show that the algorithm proposed in this paper can effectively plan the path of the wheel-legged robot by using variable configurations for different types of obstacles. The path-planning algorithm of the wheel-legged robot proposed in this paper has a broad prospect for deep space exploration.
UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection

Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.
Asymmetric efficiency of cryptocurrencies during the 2020 and 2022 events

&lt;p&gt;&lt;big&gt;In this study, we examined the efficiency of cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), DASH, EOS, and MONERO from March 1, 2018, to March 1, 2023. We separated the sample into four subperiods for this purpose: a Tranquil period that includes the period from March 1, 2018, to December 31, 2019; a First Wave that includes the year 2020; a Second Wave that includes the year 2021; and a fourth subperiod that includes Russia&amp;#39;s invasion of Ukraine in 2022-2023. The results are mixed, with some cryptocurrencies exhibiting equilibrium and others exhibiting autocorrelation and predictability in their pricing. When the sample is divided into subperiods, most digital currencies have long memories in their returns during the Tranquil period, BTC, LTC, and XRP exhibit efficiency during the First Wave of the pandemic, while BTC, ETH, and MONERO indicate efficiency during the Second Wave. Most assessed digital currencies showed equilibrium by 2022, with the exception of ETH and MONERO, which exhibit long memories, and LTC, which demonstrates anti-persistence. These results hold significance for investors in these alternative markets, as they suggest that some cryptocurrencies may be more predictable and therefore potentially profitable, whereas others may require greater caution and risk management strategies.&lt;/big&gt;&lt;/p&gt;