Reviewerly/exHarmony huggingface dataset:
Highly invariant recognition memory spaces for real-world objects revealed with signal-detection analysis

Recognition memory is an essential form of memory retrieval, allowing us to determine whether a thing or episode we experience in the present has been experienced in the past. Recognition typically involves decisions on whether stimuli look "old" or "new". These decisions can be difficult, especially when the new stimuli are similar to the old ones (e.g., as in police lineup identification). Although the role of similarity between old (targets) and new (foils) in recognition is obvious, it is often hard to predict how confusable particular targets and foils would be and how much of their confusability comes from their similarity as opposed to insufficient memory. Here, we show that, even when stimuli are various real-world objects whose subjective similarity depends on numerous non-defined factors and varies across people, they form highly consistent recognition memory spaces. That is, proportions of correct (hits) and incorrect recognitions (false alarms) produced by certain target-foil combinations are distributed similarly across different participants. Moreover, using the discriminability index d' from signal-detection theory as a distance metric, we found that the recognition spaces recovered from simple two-alternative choice tests (2-AFC) correctly predicted performance in more complex 4-AFC tests not only for hits but differentiated false alarms for each foil. We also found that the decreased encoding time reduced all absolute d' distances (presumably due to lower memory strength) but preserved the relative distances. Therefore, we found evidence for highly consistent signal-detection recognition memory spaces of real-world objects invariant to memory task complexity.
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.
Quantum Error Mitigated Classical Shadows

The rich toolbox of quantum error-mitigation strategies is synergized with powerful classical shadows for the purpose of estimating properties of an ideal quantum state, aiding in the development of near-term and early fault-tolerant quantum computers.
Transforming Healthcare Data Management: A Blockchain-Based Cloud EHR System for Enhanced Security and Interoperability

The adoption of cloud-based electronic health record (EHR) systems and blockchain technology in healthcare is gaining attention for enhancing data security and interoperability. This research focuses on designing and implementing a blockchain-based cloud EHR system. It explores selecting suitable blockchain technology, cloud infrastructure, and data management methods to ensure patient data confidentiality, integrity, and availability. The architecture and components of the system, including the blockchain network, cloud storage layer, and user interface, are thoroughly discussed. A pilot study evaluates the system’s feasibility and performance, showcasing improved data protection, sharing, and management compared to traditional EHR systems. The potential benefits, drawbacks, and barriers to adoption of a blockchain-based cloud EHR system are examined. This research provides valuable insights and recommendations for healthcare institutions considering the implementation of such systems, addressing the challenges, and offering guidance for successful adoption.
Dynamic Fit Index Cutoffs for Treating Likert Items as Continuous

Recent reviews report that about 80% of empirical factor analyses are applied to Likert-type responses and that it is exceedingly common to treat Likert-type item responses as continuous. However, traditional model fit index cutoffs like RMSEA ≤ .06 or CFI ≥ .95 were derived to have 90+% sensitivity to misspecification with continuous responses. A disconnect therefore emerges whereby traditional methodological guidelines assume continuous responses whereas empirical data often contain Likert-type responses. We provide an illustrative simulation study to show that this disconnect is not innocuous—sensitivity of traditional cutoffs to misspecification is close to 100% with continuous responses but can fall considerably if 5-point Likert responses are treated as continuous in some conditions. In other conditions, the reverse may occur, and traditional cutoffs may be too strict. Generally, applying traditional cutoffs to Likert-type responses can adversely impact conclusions about fit adequacy. This paper aims to address this prevalent issue by extending the dynamic fit index (DFI) framework to accommodate Likert-type responses. DFI is a simulation-based method that was initially intended to address changes in cutoff sensitivity to misspecification due to model characteristics (e.g., number of items, strength of loadings). Here, we propose extending DFI so that it also accounts for data characteristics (e.g., number of Likert scale points, response distribution) . Two simulations are included to demonstrate that – with 5-point Likert-type responses – the proposed method (a) improves upon traditional cutoffs, (b) improves upon DFI cutoffs based on multivariate normality, and (c) consistently maintains 90+% sensitivity to misspecification.
Automation

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Pervasive Augmented Reality to support real-time data monitoring in industrial scenarios: Shop floor visualization evaluation and user study

Augmented Reality (AR) is a key technology in the transition to Industry 4.0 and smart manufacturing, gaining reputation in a wide range of industrial fields One promising application is in another field of Industry 4.0, the data monitoring, where AR can be used to visualize and interact with complex and big data in real time, potentially improving the efficiency and accuracy of decision-making In this work, we propose a Pervasive AR tool for data monitoring, developed in collaboration with industry partners. An web application of the same data monitoring function is also created for comparison purposes A Human-Centered Design (HCD) methodology was used to identify the needs and requirements of industrial analysts, which led to the development of these systems Preliminary user studies, with 17 participants having distinct levels of expertise in industry, data analysis and computer science, were conducted to collect opinions and suggestions for improvements, as well as, quantitative data regarding the technologies considered A succeeding user study was then prepared, in which 12 participants used two conditions (C1 - Hands-free Pervasive AR tool for HMDs and C2 - Web tool for tablet devices) to fulfill a set of data monitoring tasks in an industry assembly line The result of these studies confirm the potential of Pervasive AR for data monitoring, as it engages users, promote environmental awareness, contextualizes data and allows fast self-localization.
Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data

In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is biased towards the majority class. Insufficient data makes it challenging for the classifier to learn the feature of the minority classes. Moreover, some existing techniques of binary classification cannot directly apply to multi-classification. Plain oversampling may generate redundant and irrelevant data for the minority classes while undersampling may eliminate too many features for the majority class. This paper proposes a combination of constrained undersampling, oversampling, noise cleaning, and a weighted majority voting classifier (WMVC) to detect multi-classification obfuscated malware via memory and network traffic anomalies. As a constraint on undersampling and oversampling, the proposed framework divides the total sample number by the class number to obtain the average class size. According to the constraint, one or more majority classes are down-sampled using Random Undersampling, while all the minority classes are up-sampled using Adaptive Synthetic Sampling Approach, which is followed by Tomek Link to remove the noisy data. Then a weighted majority voting classifier aggregated tree-based ensemble algorithms is designed and compared to the XGBoost and Convolutional Neural Network (CNN) classifiers and six state-of-the-art ensemble algorithms. The comparison results show that the performance of the classifiers on balanced data outperforms those on imbalanced data, and the WMVC outperforms XGBoost, CNN, and the six other ensemble algorithms. Our approach can alleviate the classifier's bias towards the majority class while improving its performance for the difficult minority class.
Application of Pythagorean Fuzzy Analytic Hierarchy Process for Assessing Driver Behavior Criteria Associated to Road Safety

The investigation of road safety issues has long posed a formidable challenge because of the intricate and unpredictable practice of human behavior. To address this complexity, experts in the field have turned to linguistic terms for evaluation, capitalizing on recent advancements in ordinary fuzzy sets. A promising way in Multi-criteria decision-making (MCDM) is the utilization of Pythagorean fuzzy sets (PFSs), which provide an extra flexible representation of membership tasks. This study introduces an innovative approach, the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP), to measure and rank essential driver behavior criteria in a hierarchical model tailored for diverse driver groups in Budapest city. Our method effectively ranks the model criteria and sub-criteria based on their weighted scores. Consequently, we determine that criteria 'lapses' and 'errors' are the most pivotal factors based on the aggregated weights as compared to all other considerations. In contrast, the criterion 'disobeying speed limits' emerges as the least critical one, followed by 'disobeying overtaking rules' as the second least criterion. Our research highlights that the proposed approach yields robust and useful outcomes, well accommodating the inherent ambiguity in decision-making processes. The resilience of our findings is further affirmed through one-at-a-time sensitivity analysis.
Unscrambling Quantum Information with Clifford Decoders

Quantum information scrambling is a unitary process that destroys local correlations and spreads information throughout the system, effectively hiding it in nonlocal degrees of freedom. In principle, unscrambling this information is possible with perfect knowledge of the unitary dynamics [B. Yoshida and A. Kitaev, arXiv:1710.03363.]. However, this Letter demonstrates that even without previous knowledge of the internal dynamics, information can be efficiently decoded from an unknown scrambler by monitoring the outgoing information of a local subsystem. We show that rapidly mixing but not fully chaotic scramblers can be decoded using Clifford decoders. The essential properties of a scrambling unitary can be efficiently recovered, even if the process is exponentially complex. Specifically, we establish that a unitary operator composed of $t$ non-Clifford gates admits a Clifford decoder up to $t\ensuremath{\le}n$.
Practicum anxiety and English language preservice teacher preparation in Indonesia: Experiences from the COVID-19 pandemic

Abstract The existing literature on practicum anxiety seldom scrutinises the preservice teachers’ experiences to uncover potential links between the sources of these anxieties and the conceptual frameworks they bring into their practicum journeys. This qualitative study interviewed five first-year preservice English language teachers in Indonesia amid COVID-19 lockdowns, exploring sources of their anxiety and coping strategies. The analysis revealed that, while their coping strategies align with those documented in prior international research, it also yielded novel insights. The preservice teachers in the study lacked conceptual frameworks to aid in translating the national ELT curriculum objectives into practice, did not mention utilising relevant professional literature for additional guidance, and appeared unfamiliar with the school culture at their practicum locations. Arguably, these factors are crucial for fostering a sense of ease during the practicum. Although the scope of this study is limited, its findings highlight the significance of this analysis and stress the necessity for more extensive investigations to gain a fuller understanding of the preservice practicum experience.
A Closer Look At Multi Word Verbs In Pakistani English E-Newspapers: A Wordsmith Concord Tool Approach

The study focuses on the particle or "multi-word" verbs (PVs) in online Pakistani English Newspapers’ feature articles corpus (NFAC) and news articles corpus (NAC). The study is confined to the period between January 1st and March 31st, 2022. To conduct a detailed analysis, the WordSmith Concord Tool was employed, utilizing a specific set of parameters: particles were defined as context words, and the context search horizons were set to 0L (zero left) and 5R (five words to the right of the search phrase).&#x0D; The present research starts with the assumption that in terms of frequency of use and structural behavior, particle verbs could acquire genre-specific characteristics. There were methodical variances among the focused sections of the newspapers. Daily Times NFAC, particularly, was discovered to have the highest particle verbs’ frequency, as well as the broadest range in terms of structural applications, formal and semantic innovations. In various genres for particle verbs, it was discovered that specific forms and connotations are predominant, even though the dataset’s small size and the resultant lower frequency of individual particle verbs make generalization relatively hard.
DistALANER: Distantly Supervised Active Learning Augmented Named Entity  Recognition in the Open Source Software Ecosystem

This paper proposes a novel named entity recognition (NER) technique specifically tailored for the open-source software systems. Our approach aims to address the scarcity of annotated software data by employing a comprehensive two-step distantly supervised annotation process. This process strategically leverages language heuristics, unique lookup tables, external knowledge sources, and an active learning approach. By harnessing these powerful techniques, we not only enhance model performance but also effectively mitigate the limitations associated with cost and the scarcity of expert annotators. It is noteworthy that our framework significantly outperforms the state-of-the-art LLMs by a substantial margin. We also show the effectiveness of NER in the downstream task of relation extraction.
Investigating Deep Watermark Security: An Adversarial Transferability  Perspective

The rise of generative neural networks has triggered an increased demand for intellectual property (IP) protection in generated content. Deep watermarking techniques, recognized for their flexibility in IP protection, have garnered significant attention. However, the surge in adversarial transferable attacks poses unprecedented challenges to the security of deep watermarking techniques-an area currently lacking systematic investigation. This study fills this gap by introducing two effective transferable attackers to assess the vulnerability of deep watermarks against erasure and tampering risks. Specifically, we initially define the concept of local sample density, utilizing it to deduce theorems on the consistency of model outputs. Upon discovering that perturbing samples towards high sample density regions (HSDR) of the target class enhances targeted adversarial transferability, we propose the Easy Sample Selection (ESS) mechanism and the Easy Sample Matching Attack (ESMA) method. Additionally, we propose the Bottleneck Enhanced Mixup (BEM) that integrates information bottleneck theory to reduce the generator's dependence on irrelevant noise. Experiments show a significant enhancement in the success rate of targeted transfer attacks for both ESMA and BEM-ESMA methods. We further conduct a comprehensive evaluation using ESMA and BEM-ESMA as measurements, considering model architecture and watermark encoding length, and achieve some impressive findings.
CenterInst: Center-Based Real-Time Instance Segmentation

Instance segmentation is a computer vision task that aims to give each pixel in an image an instance-specific label. Recently, researchers have shown growing interest in real-time instance segmentation. In this paper, we propose a novel center-based real-time instance segmentation method (CenterInst), which follows the FastInst meta-architecture. Key design aspects include a center-guided query selector, a center-guided sampling-based query decoder, and a lightweight dual-path decoder. The center-guided query selector selects queries via the per-pixel prediction of center point probabilities, avoiding excessive query proposals for single instances. The center-guided sampling-based query decoder adaptively generates local sampling points based on center positions, employing adaptive mixing to update queries without irrelevant sampling disturbances. The lightweight dual-path decoder enhances inference speed and maintains accuracy via pixel decoding on every layer during training but only utilizing the final layer’s decoder during inference. The experimental results show CenterInst achieves superior accuracy and speed compared to state-of-the-art real-time instance segmentation methods.
A virtual gym in your pocket: the influence of augmented reality exercise app characteristics on user’s continuance intention

Abstract In recent years, with the development of augmented reality (AR) technology and the prevalence of COVID-19, augmented reality exercise applications (AREAs) have entered people’s lives and changed people’s exercise ways. However, there is still little research on how AREAs affect users’ continuance intention, which limits our deeper understanding of the further use of AREAs by users for the potential benefits they provide. This study investigates the role of AREAs by proposing the original proposition of AREAs characteristic classification based on previous AR literature and dividing their characteristics into two categories: service and system characteristics. Through an online empirical study, 398 valid questionnaires were collected to test the hypotheses using the structural equation model. The results showed that hedonic and utilitarian value and presence are vital inner states which mediate the influence of service and system characteristics on user’s satisfaction and continuance intention. The results further show that only hedonic value is found to have a positive and significant relationship with continuance intention. This study contributes to the literature in the AR field by examining how different AR application characteristics affect user continuance intention in the context of sports and fitness. It also suggests that practitioners should identify the impact of different characteristics on user value and focus on the hedonic aspects of the application.
Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications

A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions.
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free  Environment

For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications. In this paper, we present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation without any limitation of light conditions and wearable devices. In particular, we design a distillation mechanism to mitigate the distribution-varying effect of LiDAR point clouds and exploit the temporal-spatial geometric and dynamic information existing in consecutive frames to solve the occlusion and noise disturbance. LiveHPS, with its efficient configuration and high-quality output, is well-suited for real-world applications. Moreover, we propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses, shapes and translations. It consists of multi-modal and multi-view acquisition data from calibrated and synchronized LiDARs, cameras, and IMUs. Extensive experiments on our new dataset and other public datasets demonstrate the SOTA performance and robustness of our approach. We will release our code and dataset soon.
Privacy-Preserving Vital node Identification in Complex Networks using Machine Learning

Abstract Identifying vital nodes in complex networks is critical in various research areas, including social network analysis, epidemiology, and physics. Centrality measures are commonly used and combined for this purpose. However, the impact of growing privacy concerns on the identification of vital nodes, particularly in networks containing sensitive data like Bluetooth-based contact networks, is not well understood. Our study evaluates the effectiveness of vital node identification algorithms under privacy-preserving network settings. Through simulations, we identify algorithms that are most effective for estimating node vitality when only limited network information is available. Furthermore, we demonstrate that machine learning models provided with aggregated output of the most promising approaches and trained with only 20 percent of the data can significantly outperform state-of-the-art approaches, especially when network information is strongly limited. This research advances the understanding of privacy-centric approaches in complex network analysis and shows how machine learning-based methods can empower advanced network analysis under privacy-preserving conditions.