Reviewerly/exHarmony huggingface dataset:
A Reality Check of Positioning in Multiuser Mobile Augmented Reality

Multiuser Augmented Reality (MuAR) is essential to implementing the vision of Metaverse. With the pervasive mobile devices, MuAR enables multiple devices to share a common AR experience. In such experiences, the peer positions are critical to understand peers' intentions and actions so as to achieve the smooth interaction in AR. Such a spacial awareness requirement poses new challenges to MuAR. Traditionally, in AR experiences designed for the single user, the SLAM algorithm is adopted to compute self positions. However, the computed positions cannot be directly used to compute the relative positions of peer devices in MuAR, because they are computed with respect to independent coordinate systems associated with participating devices. To fill in the gap, the industry has recently proposed to implement peer tracking with the help of built-in Ultra Wideband (UWB) chip. In this work, we aim to perform a reality check on the proposed support, with the Nearby Interaction (NI) framework developed for iOS mobile devices as an example. The goal of our study is to gain an in-depth understanding about the reliability of the proposed support and identify potential issues. Through extensive measurements, we discover the peer tracking solution is not reliable sometimes, in terms of availability and accuracy. Furthermore, with regard to erroneous position reports, we present a quantitative analysis, summarizing the error types (e.g., transient errors and permanent errors) and revealing their underlying reasons. We believe the preliminary findings could help to improve the spacial awareness and enhance user experiences in MuAR.
Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Optimization of path planning refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOOs present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. The purpose of this paper is to present an overview of mobile robot navigation strategies employed to find the path that has the minimum number of criteria (shortest, smoothness, and safest) so far. Here, multi objective approaches are discussed in detail in order to identify research gaps. In addition, it is important to understand how path planning strategies are developed under various environmental circumstances.
Experimental Investigation: Vibration Measurement of a Rotating Blade with Digital Image Correlation and Blade Tip-Timing

High cycle fatigue has been known as an important form of aeroengine blade failure. This study aims to achieve a method of investigation for a rotating blade vibration measurement, combining the two non-contact optical techniques of digital image correlation (DIC) and blade tip-timing (BTT). Dynamic parameters of a thin-blade were obtained on a stationary vibration platform with stereo-DIC system. Meanwhile, the finite element analysis (FEA) of this thin-blade was performed within different rotating speeds. Then, the set of thin-blades was mounted in a simulated compressor test rig equipped with BTT and a wireless strain gauge (SG) system. A rotor speed sweep experiment was carried out and the blade synchronous resonance parameters were extracted. Results show that the displacement mode shapes match well between DIC and FEA, and that MAC values of the first six order modes are over than 0.88. The predicting strain from the FE model and SG agreed to within 32.41% in the worst case, and the predicting strain from the DIC model corresponds to 28.53% in the worst case. This is an effective non-contact, high-precision full-field deformation measurement method that is worth exploring for structural design and dynamic strain assessment of vibrating components.
Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.
Automatic knowledge exchange between ontologies and semantic graphs

This article presents an innovative knowledge management approach for the validation and transfer of knowledge between semantic networks or ontologies and a target ontology represented in web ontology language (OWL) format. This process has been designed for addressing the quality improvement of ontologies automatically created by learning techniques. The knowledge transfer process is a semi-automatic computer aided method to assist the domain expert to improve the target ontology. To validate our proposal, we have used an automatically generated target ontology. We used knowledge transfer from the well-known Babelnet semantic graph and a manually generated ontology to improve the quality of the target ontology. Finally, to show the suitability of our proposal in the ontology-fixing process, we compare the improved target ontology resulting from the application of the proposed validation and knowledge transfer techniques with its original version. We developed an example of our proposal in our OntologyFixer tool, which is available on a GitHub repository ( https://github.com/gabyluna/OntologyFixer/tree/ontofixer_v2 ).
Fidelity bounds for device-independent advantage distillation

Abstract It is known that advantage distillation (that is, information reconciliation using two-way communication) improves noise tolerances for quantum key distribution (QKD) setups. Two-way communication is hence also of interest in the device-independent case, where noise tolerance bounds for one-way error correction are currently too low to be experimentally feasible. Existing security proofs for the device-independent repetition-code protocol (the most prominent form of advantage distillation) rely on fidelity-related security conditions, but previous bounds on the fidelity were not tight. We improve on those results by developing an algorithm that returns arbitrarily tight lower bounds on the fidelity. Our results give insight on how strong the fidelity-related security conditions are, and could also be used to compute some lower bounds on one-way protocol keyrates. Finally, we conjecture a necessary security condition for the protocol studied in this work, that naturally complements the existing sufficient conditions.
Defining and Assessing Quality in IoT Environments: A Survey

With the proliferation of multimedia services, Quality of Experience (QoE) has gained a lot of attention. QoE ties together the users’ needs and expectations to multimedia application and network performance. However, in various Internet of Things (IoT) applications such as healthcare, surveillance systems, traffic monitoring, etc., human feedback can be limited or infeasible. Moreover, for immersive augmented and virtual reality, as well as other mulsemedia applications, the evaluation in terms of quality cannot only focus on the sight and hearing senses. Therefore, the traditional QoE definition and approaches for evaluating multimedia services might not be suitable for the IoT paradigm, and more quality metrics are required in order to evaluate the quality in IoT. In this paper, we review existing quality definitions, quality influence factors (IFs) and assessment approaches for IoT. This paper also introduces challenges in the area of quality assessment for the IoT paradigm.
Robot Voices in Daily Life: Vocal Human-Likeness and Application Context as Determinants of User Acceptance

The growing popularity of speech interfaces goes hand in hand with the creation of synthetic voices that sound ever more human. Previous research has been inconclusive about whether anthropomorphic design features of machines are more likely to be associated with positive user responses or, conversely, with uncanny experiences. To avoid detrimental effects of synthetic voice design, it is therefore crucial to explore what level of human realism human interactors prefer and whether their evaluations may vary across different domains of application. In a randomized laboratory experiment, 165 participants listened to one of five female-sounding robot voices, each with a different degree of human realism. We assessed how much participants anthropomorphized the voice (by subjective human-likeness ratings, a name-giving task and an imagination task), how pleasant and how eerie they found it, and to what extent they would accept its use in various domains. Additionally, participants completed Big Five personality measures and a tolerance of ambiguity scale. Our results indicate a positive relationship between human-likeness and user acceptance, with the most realistic sounding voice scoring highest in pleasantness and lowest in eeriness. Participants were also more likely to assign real human names to the voice (e.g., “Julia” instead of “T380”) if it sounded more realistic. In terms of application context, participants overall indicated lower acceptance of the use of speech interfaces in social domains (care, companionship) than in others (e.g., information &amp;amp; navigation), though the most human-like voice was rated significantly more acceptable in social applications than the remaining four. While most personality factors did not prove influential, openness to experience was found to moderate the relationship between voice type and user acceptance such that individuals with higher openness scores rated the most human-like voice even more positively. Study results are discussed in the light of the presented theory and in relation to open research questions in the field of synthetic voice design.
A Multi-Strategy Improved Arithmetic Optimization Algorithm

To improve the performance of the arithmetic optimization algorithm (AOA) and solve problems in the AOA, a novel improved AOA using a multi-strategy approach is proposed. Firstly, circle chaotic mapping is used to increase the diversity of the population. Secondly, a math optimizer accelerated (MOA) function optimized by means of a composite cycloid is proposed to improve the convergence speed of the algorithm. Meanwhile, the symmetry of the composite cycloid is used to balance the global search ability in the early and late iterations. Thirdly, an optimal mutation strategy combining the sparrow elite mutation approach and Cauchy disturbances is used to increase the ability of individuals to jump out of the local optimal. The Rastrigin function is selected as the reference test function to analyze the effectiveness of the improved strategy. Twenty benchmark test functions, algorithm time complexity, the Wilcoxon rank-sum test, and the CEC2019 test set are selected to test the overall performance of the improved algorithm, and the results are then compared with those of other algorithms. The test results show that the improved algorithm has obvious advantages in terms of both its global search ability and convergence speed. Finally, the improved algorithm is applied to an engineering example to further verify its practicability.
Foveal and Parafoveal Cues in Reading

Two experiments dealing with the use of parafoveal vision in reading and the integration of visual information over successive fixations in reading are presented. Eye position was monitored by a computer-based eye tracking system, and display changes on a cathode-ray tube were made when subjects initiated a saccade. A word was presented in parafoveal vision and subjects made an eye movement to the location of the word. As the eye was making the saccade, the computer replaced the word that was initially displayed with another word. In Experiment 1, subjects made a SAME-DIFFERENT response regarding the word that was present prior to the saccade and the word that was present after the eye movement. In Experiment 2, subjects were required to name the word that was presented after the saccade, and differences in naming times were compared as a function of the word that was present on the cathode-ray tube prior to the saccade. The results are discussed in terms of the types of visual information that readers could obtain at differing distances from the fovea, and how this information is integrated over two fixations.
Universal Conditional Masked Language Pre-training for Neural Machine Translation

Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT).Different from prior works where pre-trained models usually adopt an unidirectional decoder, this paper demonstrates that pre-training a sequenceto-sequence model but with a bidirectional decoder can produce notable performance gains for both Autoregressive and Nonautoregressive NMT.Specifically, we propose CeMAT, a conditional masked language model pre-trained on large-scale bilingual and monolingual corpora in many languages. 1 We also introduce two simple but effective methods to enhance the CeMAT, aligned code-switching & masking and dynamic dual-masking.We conduct extensive experiments and show that our CeMAT can achieve significant performance improvement for all scenarios from low-to extremely highresource languages, i.e., up to +14.4 BLEU on low-resource and +7.9 BLEU on average for Autoregressive NMT.For Non-autoregressive NMT, we demonstrate it can also produce consistent performance gains, i.e., up to +5.3 BLEU.To the best of our knowledge, this is the first work to pre-train a unified model for fine-tuning on both NMT tasks.
Possible Blockchain Solutions According to a Smart City Digitalization Strategy

With advances in Information and Communication Technologies (ICT) in convergence with blockchain technology, cities have been given the opportunity to improve their services, efficiently use resources, and, thus, become Smart Cities. The main properties of blockchain technology like decentralization, immutability, transparency, consensus, and robustness are qualities needed for Smart City. In this paper, we propose a digitalization strategy for the City of Osijek. Smart City digitalization strategy aims to solve problems of emerging urbanization, improve administration by reducing energy and water consumption, carbon emissions, pollution, and city waste management. To develop an information system based on blockchain technology, the administration structure and the current state of information systems are analyzed, and new solutions are presented.
CAP-YOLO: Channel Attention Based Pruning YOLO for Coal Mine Real-Time Intelligent Monitoring

Real-time coal mine intelligent monitoring for pedestrian identifying and positioning is an important means to ensure safety in production. Traditional object detection models based on neural networks require significant computational and storage resources, which results in difficulty of deploying models on edge devices for real-time intelligent monitoring. To address the above problems, CAP-YOLO (Channel Attention based Pruning YOLO) and AEPSM (adaptive image enhancement parameter selection module) are proposed in this paper to achieve real-time intelligent analysis for coal mine surveillance videos. Firstly, DCAM (Deep Channel Attention Module) is proposed to evaluate the importance level of channels in YOLOv3. Secondly, the filters corresponding to the low importance channels are pruned to generate CAP-YOLO, which recovers the accuracy through fine-tuning. Finally, considering the lighting environments are varied in different coal mine fields, AEPSM is proposed to select parameters for CLAHE (Contrast Limited Adaptive Histogram Equalization) under different fields. Experiment results show that the weight size of CAP-YOLO is 8.3× smaller than YOLOv3, but only 7% lower than mAP, and the inference speed of CAP-YOLO is three times faster than that of YOLOv3. On NVIDIA Jetson TX2, CAP-YOLO realizes 31 FPS inference speed.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks. Nevertheless, a unified causal analysis framework has not been established yet. Many causal-based prediction and debiasing studies rarely discuss the causal interpretation of various biases and the rationality of the corresponding causal assumptions. In this paper, we first provide a formal causal analysis framework to survey and unify the existing causal-inspired recommendation methods, which can accommodate different scenarios in RS. Then we propose a new taxonomy and give formal causal definitions of various biases in RS from the perspective of violating the assumptions adopted in causal analysis. Finally, we formalize many debiasing and prediction tasks in RS, and summarize the statistical and machine learning-based causal estimation methods, expecting to provide new research opportunities and perspectives to the causal RS community.
Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features.
Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy

When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway.The move to differential privacy introduced technical and procedural uncertainties, leaving stakeholders unable to evaluate the quality of the data.More importantly, this transformation exposed the statistical illusions and limitations of census data, weakening stakeholders' trust in the data and in the Census Bureau itself.This essay examines the epistemic currents of this controversy.Drawing on theories from Science and Technology Studies (STS) and ethnographic fieldwork, we analyze the current controversy over differential privacy as a battle over uncertainty, trust, and legitimacy of the census.We argue that rebuilding trust will require more than technical repairs or improved communication; it will require reconstructing what we identify as a 'statistical imaginary.'
Information System for Monitoring and Management of the Quality of Educational Programs: Development of Functioning Algorithms

The study develops a method for monitoring and management of the quality of educational programs using software instruments.The method describes each of the monitoring implementation stages in detail specifying the input and output data, as well as the controls that affect the time and quality of the implementation of the corresponding stage.Algorithm programs in the form of flowcharts reflecting all the content aspects are developed and formalized for the automatization of monitoring.The presented algorithm models allow implementing the program complex of the system for monitoring and management of the quality of educational programs using the modern object-oriented design and programming environments.The goal of the study is to develop a model of algorithms for the organization of monitoring and managing the quality of educational programs, which allow reducing the time costs of these processes while increasing the objectivity of quality assessment.The obtained results are intended for the audit of educational services, public control over the implementation of educational activities, accreditation of directions of training, or licensing of educational organizations.The developed algorithms provide for evaluating the content of each stage of the audit of an educational program, generate a list of notes ranked by importance, and ensure the rational allocation of resources for the implementation of each stage of the educational process.