Reviewerly/exHarmony huggingface dataset:
Incorporating Heisenberg’s Uncertainty Principle into Quantum Multiparameter Estimation

The quantum multiparameter estimation is very different from the classical multiparameter estimation due to Heisenberg's uncertainty principle in quantum mechanics. When the optimal measurements for different parameters are incompatible, they cannot be jointly performed. We find a correspondence relationship between the inaccuracy of a measurement for estimating the unknown parameter with the measurement error in the context of measurement uncertainty relations. Taking this correspondence relationship as a bridge, we incorporate Heisenberg's uncertainty principle into quantum multiparameter estimation by giving a tradeoff relation between the measurement inaccuracies for estimating different parameters. For pure quantum states, this tradeoff relation is tight, so it can reveal the true quantum limits on individual estimation errors in such cases. We apply our approach to derive the tradeoff between attainable errors of estimating the real and imaginary parts of a complex signal encoded in coherent states and obtain the joint measurements attaining the tradeoff relation. We also show that our approach can be readily used to derive the tradeoff between the errors of jointly estimating the phase shift and phase diffusion without explicitly parameterizing quantum measurements.
Efficient HEVC Integrity Verification Scheme for Multimedia Cybersecurity Applications

Multimedia cybersecurity is a prevalent research topic in the digital world due to the rapid progress of digital multimedia and Internet applications. Watermarking, encryption, and steganography schemes are employed to attain multimedia data confidentiality and robustness. However, these schemes are externally applied on trusted computers, and there has been a lack of similar schemes that can be effectively and efficiently enabled through an untrusted transmission medium. In this work, a self-embedding-based High-Efficiency Video Coding (HEVC) transmission and integrity verification framework is presented. This framework is robust and reliable for verifying the integrity of HEVC frames transmitted through insecure communication channels. Firstly, the transmitted HEVC frames are divided into a number of blocks with a certain block size. After that, a discrete transform is used for self-embedding of watermarks from each block into another one depending on a predefined mechanism. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) are tested for this task. The watermarked HEVC frames are transmitted through a wireless communication channel, and hence they become subject to different attacks and corruptions. At the receiver side, the secret watermarks in each block are sensed with a correlation-based method to discover dubious counterfeit operations. To verify the reliability of the suggested transmission framework and its ability to achieve high protection and robust content verification of the transmitted HEVC frames over insecure communication channels, different HEVC analyses and comparisons are performed. Simulation results demonstrate the suitability of the suggested transmission framework for different multimedia cybersecurity applications. Furthermore, the comparative analysis shows that the DFT is an efficient discrete transform that can be employed with the proposed transmission framework to guarantee a higher HEVC frame integrity. It allows higher sensitivity to simple modifications in the transmitted watermarked HEVC frames. This makes the suggested cybersecurity framework applicable, secure, and appropriate for multimedia integrity verification purposes.
Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

The role of emotion is crucial to the learning process, as it is linked to motivation, interest, and attention. Affective states are expressed in the brain and in overall biological activity. Biosignals, like heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG) are physiological expressions affected by emotional state. Analyzing these biosignal recordings can point to a person's emotional state. Contemporary medical education has progressed extensively towards diverse learning resources using virtual reality (VR) and mixed reality (MR) applications.This paper aims to study the efficacy of wearable biosensors for affect detection in a learning process involving a serious game in the Microsoft HoloLens VR/MR platform.A wearable array of sensors recording HR, EDA, and EEG signals was deployed during 2 educational activities conducted by 11 participants of diverse educational level (undergraduate, postgraduate, and specialist neurosurgeon doctors). The first scenario was a conventional virtual patient case used for establishing the personal biosignal baselines for the participant. The second was a case in a VR/MR environment regarding neuroanatomy. The affective measures that we recorded were EEG (theta/beta ratio and alpha rhythm), HR, and EDA.Results were recorded and aggregated across all 3 groups. Average EEG ratios of the virtual patient (VP) versus the MR serious game cases were recorded at 3.49 (SD 0.82) versus 3.23 (SD 0.94) for students, 2.59 (SD 0.96) versus 2.90 (SD 1.78) for neurosurgeons, and 2.33 (SD 0.26) versus 2.56 (SD 0.62) for postgraduate medical students. Average alpha rhythm of the VP versus the MR serious game cases were recorded at 7.77 (SD 1.62) μV versus 8.42 (SD 2.56) μV for students, 7.03 (SD 2.19) μV versus 7.15 (SD 1.86) μV for neurosurgeons, and 11.84 (SD 6.15) μV versus 9.55 (SD 3.12) μV for postgraduate medical students. Average HR of the VP versus the MR serious game cases were recorded at 87 (SD 13) versus 86 (SD 12) bpm for students, 81 (SD 7) versus 83 (SD 7) bpm for neurosurgeons, and 81 (SD 7) versus 77 (SD 6) bpm for postgraduate medical students. Average EDA of the VP versus the MR serious game cases were recorded at 1.198 (SD 1.467) μS versus 4.097 (SD 2.79) μS for students, 1.890 (SD 2.269) μS versus 5.407 (SD 5.391) μS for neurosurgeons, and 0.739 (SD 0.509) μS versus 2.498 (SD 1.72) μS for postgraduate medical students. The variations of these metrics have been correlated with existing theoretical interpretations regarding educationally relevant affective analytics, such as engagement and educational focus.These results demonstrate that this novel sensor configuration can lead to credible affective state detection and can be used in platforms like intelligent tutoring systems for providing real-time, evidence-based, affective learning analytics using VR/MR-deployed medical education resources.
Requirements Engineering in Software Startups: A Systematic Mapping Study

Startups have high failure rates due to their inability to attain a sufficient product/market fit, i.e., delivering a solution that best matches the user needs in the market. Requirement engineering is the activity that could help startup teams identify the value proposition that provides high value to the users and continuously innovate it. The objective of the study is to analyse the state of art of the requirement engineering research in the context of startups, as available in the literature. The analysis of the research area highlights the research trends to achieve two things i.e., (a) predict how much support the startups can get from the literature for enhancing their success rates and (b) identify the research gaps to motivate researchers to conduct future research that could be adoptable in startup contexts. Systematic mapping is conducted on studies extracted from the four bibliographic databases (IEEExplore, ACM, Springerlink and ScienceDirect) and studies extracted by using a forward snowballing approach. Individual studies are coded to yield the classification scheme. Formulated schemes and those already available in literature, were populated with information extracted from the abstracts of the studies. The research is mostly focused on generic requirement engineering and product validation activities. The research is conducted mostly as evaluations (empirical studies) with the outcome of providing theory to the research community. Major underlying motivation of the research is to attain the product/market fit. However, research studies focusing on requirement documentation, prioritization and elicitation are losing focus from 2017, 2018 and 2019, respectively. The literature lacks the studies that reports research solutions which are validated in laboratory settings or in real contexts, experience reports, opinion papers and philosophical papers. The positive side of the finding is that the number of requirement engineering research studies in a startup context have increased in the past five years. At this instant, unfortunately the literature has limited ability to support startups by providing solutions (for instance, research solutions, evidence to support decision makings, best practices, experiences etc.) that are adoptable in their real context. Uniform focus of the researchers across all sub-activities of requirement engineering is required with effort distributed across different research types that supports startups, not only by providing validated solutions but experience reports, opinions, new conceptual frameworks and empirical evidence that can aid their decision making.
Measurement-device–independent quantum secure direct communication of multiple degrees of freedom of a single photon

Quantum secure direct communication (QSDC) can realize the direct and secure transmission of information instead of sharing secure keys between two parties. Similar to quantum key distribution (QKD), in practical applications, QSDC may suffer from attacks from imperfect detector. Measurement-device–independent QSDC (MDI-QSDC) can remove all attacks at detector's side and close all loopholes in the detection system. However, existing MDI-QSDC protocols have relatively low capacity. In this paper, we propose a high-capacity MDI-QSDC protocol of multiple degrees of freedom of a single photon. Compared with original MDI-QSDC protocols, our protocol has larger channel capacity which can use a single photon to transmit multi-bit of secure information. This protocol may have potential application in future quantum secure communication.
Simulation of English teaching quality evaluation model based on gaussian process machine learning

The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system.
Screening Algorithms in Dense Breasts: <i>AJR</i> Expert Panel Narrative Review

Screening Algorithms in Dense Breasts: AJR Expert Panel Narrative ReviewWendie A. Berg, MD, PhD1, Elizabeth A. Rafferty, MD2, Sarah M. Friedewald, MD3, Carrie B. Hruska, PhD4 and Habib Rahbar, MD5Audio Available | Share Claim CREDIT
Quality Assessment in Systematic Literature Reviews: A Software Engineering Perspective

Context: Quality Assessment (QA) of reviewed literature is paramount to a Systematic Literature Review (SLR) as the quality of conclusions completely depends on the quality of selected literature. A number of researchers in Software Engineering (SE) have developed a variety of QA instruments and also reported their challenges. We previously conducted a tertiary study on SLRs with QA from 2004 to 2013, and reported the findings in 2015. Objective: With the widespread use of SLRs in SE and the increasing adoption of QA in these SLRs in recent years, it is necessary to empirically investigate whether the previous conclusions are still valid and whether there are new insights to the subject in question using a larger and a more up-to-date SLR set. More importantly, we aim to depict a clear picture of QA used in SLRs in SE by aggregating and distilling good practices, including the commonly used QA instruments as well as the major roles and aspects of QA in research. Method: An extended tertiary study was conducted with the newly collected SLRs from 2014 to 2018 and the original SLRs from 2004 to 2013 to systematically review the QA used by SLRs in SE during the 15-year period from 2004 to 2018. In addition, this extended study also compared and contrasted the findings of the previous study conducted in 2015. Results: A total of 241 SLRs between 2004 and 2018 were included, from which we identified a number of QA instruments. These instruments are generally designed to focus on the rationality of study design, the rigor of study execution and analysis, and the credibility and contribution of study findings and conclusions, with the emphasis largely placed on its rigor. The quality data is mainly used for literature selection or as evidence to support conclusions. Conclusions: QA has received much attention in SE in more recent years and the improvement is evident since the last study in 2015. New findings show that the aims are more concise, the instruments are more diverse and rigorous, and the criteria are more thoughtful.
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone

We present a real-time monocular 3D reconstruction system on a mobile phone, called Mobile3DRecon. Using an embedded monocular camera, our system provides an online mesh generation capability on back end together with real-time 6DoF pose tracking on front end for users to achieve realistic AR effects and interactions on mobile phones. Unlike most existing state-of-the-art systems which produce only point cloud based 3D models online or surface mesh offline, we propose a novel online incremental mesh generation approach to achieve fast online dense surface mesh reconstruction to satisfy the demand of real-time AR applications. For each keyframe of 6DoF tracking, we perform a robust monocular depth estimation, with a multi-view semi-global matching method followed by a depth refinement post-processing. The proposed mesh generation module incrementally fuses each estimated keyframe depth map to an online dense surface mesh, which is useful for achieving realistic AR effects such as occlusions and collisions. We verify our real-time reconstruction results on two mid-range mobile platforms. The experiments with quantitative and qualitative evaluation demonstrate the effectiveness of the proposed monocular 3D reconstruction system, which can handle the occlusions and collisions between virtual objects and real scenes to achieve realistic AR effects.
Proprioception but not cardiac interoception is related to the rubber hand illusion

The rubber hand illusion (RHI) is a widely used tool in the study of multisensory integration. It develops as the interaction of temporally consistent visual and tactile input, which can overwrite proprioceptive information. Theoretically, the accuracy of proprioception may influence the proneness to the RHI but this has received little research attention to date. Concerning the role of cardioceptive information, the available empirical evidence is equivocal. The current study aimed to test the impact of proprioceptive and cardioceptive input on the RHI. 60 undergraduate students (32 females) completed sensory tasks assessing proprioceptive accuracy with respect to the angle of the elbow joint, a heartbeat tracking task assessing cardioceptive accuracy (the Schandry-task) and the RHI. We found that those with more consistent joint position judgements (i.e., less variable error) in the proprioceptive task were less prone to the illusion, particularly with respect to disembodiment ratings in the asynchronous condition. Systematic error, indicating a systematic distortion in position judgements influenced the illusion in the synchronous condition. Participants with more proprioceptive bias toward the direction of the rubber hand in the proprioceptive test reported a stronger felt embodiment. The results are in accordance with Bayesian causal inference models of multisensory integration. Cardioceptive accuracy, however, was not associated with the strength of the illusion. We concluded that individual differences in proprioceptive processing impact the RHI, while cardioceptive accuracy is unrelated to it. Theoretical and practical relevance of the findings are discussed.
Entanglement-Assisted Absorption Spectroscopy

Spectroscopy is an important tool for probing the properties of materials, chemicals, and biological samples. We design a practical transmitter-receiver system that exploits entanglement to achieve a provable quantum advantage over all spectroscopic schemes based on classical sources. To probe the absorption spectra, modeled as a pattern of transmissivities among different frequency modes, we employ broadband signal-idler pairs in two-mode squeezed vacuum states. At the receiver side, we apply photodetection after optical parametric amplification. Finally, we perform a maximum likelihood decision test on the measurement results, achieving an error probability orders of magnitude lower than the optimum classical systems in various examples, including ``wine tasting'' and ``drug testing'' where real molecules are considered. In detecting the presence of an absorption line, our quantum scheme achieves the optimum performance allowed by quantum mechanics. The quantum advantage in our system is robust against noise and loss, which makes near-term experimental demonstration possible.
An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability

Human-centered approaches are of particular importance when analyzing urban spaces in technology-driven fields, because understanding how people perceive and react to their environments depends on several dynamic and static factors, such as traffic volume, noise, safety, urban configuration, and greenness. Analyzing and interpreting emotions against the background of environmental information can provide insights into the spatial and temporal properties of urban spaces and their influence on citizens, such as urban walkability and bikeability. In this study, we present a comprehensive mixed-methods approach to geospatial analysis that utilizes wearable sensor technology for emotion detection and combines information from sources that correct or complement each other. This includes objective data from wearable physiological sensors combined with an eDiary app, first-person perspective videos from a chest-mounted camera, and georeferenced interviews, and post-hoc surveys. Across two studies, we identified and geolocated pedestrians' and cyclists' moments of stress and relaxation in the city centers of Salzburg and Cologne. Despite open methodological questions, we conclude that mapping wearable sensor data, complemented with other sources of information-all of which are indispensable for evidence-based urban planning-offering tremendous potential for gaining useful insights into urban spaces and their impact on citizens.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention

Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterwards, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-word card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncover fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.
BAE-Net: A Band Attention Aware Ensemble Network for Hyperspectral Object Tracking

Hyperspectral videos contain images with a large number of light wavelength indexed bands that can facilitate material identification for object tracking. Most hyperspectral trackers use hand-crafted features rather than deep learning generated features for image representation due to limited training samples. To fill this gap, this paper introduces a band attention aware ensemble network (BAE-Net) for deep hyperspectral object tracking, which takes advantages of deep models trained on color videos for feature representation. Specifically, an autoencoder-like band attention block is introduced to learn the dependencies among bands and generate band-wise weights. Guided by these weights, hyperspectral images are then divided into a number of three-channel images. These three-channel images are fed into a deep color tracking network, producing several weak trackers. Finally, weak trackers are fused using ensemble learning for target location. Experimental results on hyperspectral datasets show the effectiveness and advantages of the proposed deep hyperspectral tracker.