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

Authors:
raw_author_name: Rafael Maio
raw_affiliation_string: IEETA, DETI, LASI, University of Aveiro, Aveiro, 3810-193, Portugal
raw_author_name: Tiago Araujo
raw_affiliation_string: IEETA, DETI, LASI, University of Aveiro, Aveiro, 3810-193, Portugal
raw_author_name: Bernardo Marques
raw_affiliation_string: IEETA, DETI, LASI, University of Aveiro, Aveiro, 3810-193, Portugal
raw_author_name: Andre Santos
raw_affiliation_string: Bosch Thermotechnology, Aveiro, 3800-533, Portugal
raw_author_name: Pedro Ramalho
raw_affiliation_string: Bosch Thermotechnology, Aveiro, 3800-533, Portugal
raw_author_name: Duarte Almeida
raw_affiliation_string: Bosch Thermotechnology, Aveiro, 3800-533, Portugal
raw_author_name: Paulo Dias
raw_affiliation_string: IEETA, DETI, LASI, University of Aveiro, Aveiro, 3810-193, Portugal
raw_author_name: Beatriz Sousa Santos
raw_affiliation_string: IEETA, DETI, LASI, University of Aveiro, Aveiro, 3810-193, Portugal

Abstract: 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

Authors:
raw_author_name: Liang Xue
raw_affiliation_string: Faculty of Data Science, City University of Macau, PR China; Guangdong University of Education, Guangzhou, PR China
raw_author_name: Tianqing Zhu
raw_affiliation_string: School of Computer Science, University of Technology Sydney, Australia

Abstract: 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.
Unscrambling Quantum Information with Clifford Decoders

Authors:
raw_author_name: Salvatore F. E. Oliviero
raw_affiliation_string: Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA; Physics Department, University of Massachusetts, Boston, Massachusetts 02125, USA; Theoretical Division (T-4), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
raw_author_name: Lorenzo Leone
raw_affiliation_string: Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA; Physics Department, University of Massachusetts, Boston, Massachusetts 02125, USA; Theoretical Division (T-4), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
raw_author_name: Seth Lloyd
raw_affiliation_string: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA; Turing Inc., Cambridge, Massachusetts 02139, USA
raw_author_name: Alioscia Hamma
raw_affiliation_string: Dipartimento di Fisica ‘Ettore Pancini’, Università degli Studi di Napoli Federico II, Via Cintia 80126, Napoli, Italy; INFN, Sezione di Napoli, 80126 Napoli, Italy

Abstract: 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

Authors:
raw_author_name: Syahdan Syahdan
raw_affiliation_string: Universitas Lancang Kuning, Indonesia.
raw_author_name: MHD Natsir
raw_affiliation_string: Universitas Lancang Kuning, Indonesia

Abstract: 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.
DistALANER: Distantly Supervised Active Learning Augmented Named Entity  Recognition in the Open Source Software Ecosystem

Authors:
raw_author_name: Banerjee, Somnath
raw_author_name: Dutta, Avik
raw_author_name: Agrawal, Aaditya
raw_author_name: Hazra, Rima
raw_author_name: Mukherjee, Animesh

Abstract: 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

Authors:
raw_author_name: Gao, Junqi
raw_author_name: Liu, Jianxing
raw_author_name: Wu, Ligang
raw_author_name: Zhou, Bowen

Abstract: 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

Authors:
raw_author_name: Shu Tian
raw_affiliation_string: School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract: 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

Authors:
raw_author_name: Zihao Yuan
raw_affiliation_string: Department of Information Management and Information Systems, Shanghai University, Shangda Road 99, Baoshan District, Shanghai, China

Abstract: 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

Authors:
raw_author_name: Jesus Arellano-Uson
raw_affiliation_string: Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, 31006 Pamplona, Spain
raw_author_name: Eduardo Magaña
raw_affiliation_string: Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, 31006 Pamplona, Spain; Institute of Smart Cities, Calle Tajonar 22, 31006 Pamplona, Spain
raw_author_name: Daniel Morato
raw_affiliation_string: Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, 31006 Pamplona, Spain; Institute of Smart Cities, Calle Tajonar 22, 31006 Pamplona, Spain
raw_author_name: Mikel Izal
raw_affiliation_string: Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, 31006 Pamplona, Spain; Institute of Smart Cities, Calle Tajonar 22, 31006 Pamplona, Spain

Abstract: 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

Authors:
raw_author_name: Ren, Yiming
raw_author_name: Han, Xiao
raw_author_name: Zhao, Chengfeng
raw_author_name: Wang, Jingya
raw_author_name: Xu, Lan
raw_author_name: Yu, Jingyi
raw_author_name: Ma, Yuexin

Abstract: 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

Authors:
raw_author_name: Tobias Hecking
raw_affiliation_string: German Aerospace Center

Abstract: 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.
Innovation in security: a proposal for access control with dual authentication through facial recognition and radiofrequency

Authors:
raw_author_name: Giovany Frossard Teixeira
raw_affiliation_string: Federal Institute of Espírito Santo

Abstract: Property security represents a set of activities and preventive actions aimed at reducing potential losses in organisations. In this context, electric locks that support different authentication methods, such as passwords, digital keys with security tokens, and radio frequency identification (RFID) tags, offer improvements in access control; however, they represent a single barrier of protection. In this article, we propose FaceTag, a prototype with dual authentication via facial recognition and RFID, which has been integrated into an electronic lock to support future access control in educational institutions. To validate our proposal, we implemented a prototype and evaluated its operation in different scenarios. FaceTag proved to be effective and stable in testbed experiments, capable of providing a high degree of accuracy for access control in the desired environment.
PERANCANGAN MOCK UP SISTEM INFORMASI MANAJEMEN DESA BERBASIS WEBSITE (STUDI DI DESA MEKARMUKTI KECAMATAN CISAGA KABUPATEN CIAMIS JAWA BARAT)

Authors:
raw_author_name: Miftahul Huda
raw_affiliation_string: Universitas Putra Bangsa
raw_author_name: Rahmat Hidayat
raw_affiliation_string: Universitas Putra Bangsa

Abstract: Mekarmukti Village is located in Cisaga District, Ciamis Regency, West Java, Indonesia. The village is famous for its natural beauty and cultural heritage, as well as its agricultural practices. The village is surrounded by lush green forests and hills, making it a popular destination for tourists looking to explore the natural beauty of West Java. One issue that often arises in the Mekarmukti Village community that is the focus of attention is the lack of clarity of information conveyed to residents, causing them to repeatedly seek information. The village government has difficulty collecting family data due to various reasons and the busyness faced by residents, hampering the performance of the village government. From the above problems, the purpose of study is designing a Website-based Information System that is used to facilitate the Mekarmukti Village community in getting or obtaining the latest information from the Village Government. In addition to making it easier to get information, this information system also provides information related to tourist info and tourist maps, as well as financial transparency of Mekarmukti Village.
On the enumeration of signatures of XOR-CNF's

Authors:
raw_author_name: Creignou, Nadia
raw_author_name: Vilmin, Simon

Abstract: Given a CNF formula $\varphi$ with clauses $C_1, \dots, C_m$ over a set of variables $V$, a truth assignment $\mathbf{a} : V \to \{0, 1\}$ generates a binary sequence $\sigma_\varphi(\mathbf{a})=(C_1(\mathbf{a}), \ldots, C_m(\mathbf{a}))$, called a signature of $\varphi$, where $C_i(\mathbf{a})=1$ if clause $C_i$ evaluates to 1 under assignment $\mathbf{a}$, and $C_i(\mathbf{a})=0$ otherwise. Signatures and their associated generation problems have given rise to new yet promising research questions in algorithmic enumeration. In a recent paper, B\'erczi et al. interestingly proved that generating signatures of a CNF is tractable despite the fact that verifying a solution is hard. They also showed the hardness of finding maximal signatures of an arbitrary CNF due to the intractability of satisfiability in general. Their contribution leaves open the problem of efficiently generating maximal signatures for tractable classes of CNFs, i.e., those for which satisfiability can be solved in polynomial time. Stepping into that direction, we completely characterize the complexity of generating all, minimal, and maximal signatures for XOR-CNFs.