Reviewerly/exHarmony huggingface dataset:
Exploring the Learning Efficacy of Digital Forensics Concepts and Bagging &amp; Tagging of Digital Devices in Immersive Virtual Reality

This work presents the first account of evaluating learning inside a VR experience created to teach Digital Forensics (DF) concepts, and a hands-on laboratory exercise in Bagging & Tagging a crime scene with digital devices. First, we designed and developed an immersive VR experience which included a lecture and a lab. Next, we tested it with (n = 57) participants in a controlled experiment where they were randomly assigned to a VR group or a physical group. Both groups were subjected to the same lecture and lab, but one was in VR and the other was in the real world. We collected pre- and post-test results to assess the participants' knowledge in DF concepts learned. Our experimental results indicated no significant differences in scores between the immersive VR group and the physical group. However, our results showed faster completion times in VR by the participants, which hints at VR being more time efficient, as virtual environments can be spun programmatically with little downtime.
Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin-Eosin slides, which partly mimics the pathologist's behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.
Square permutations are typically rectangular

We describe the limit (for two topologies) of large uniform random square permutations, i.e., permutations where every point is a record.The starting point for all our results is a sampling procedure for asymptotically uniform square permutations.Building on that, we first describe the global behavior by showing that these permutations have a permuton limit which can be described by a random rectangle.We also explore fluctuations about this random rectangle, which we can describe through coupled Brownian motions.Second, we consider the limiting behavior of the neighborhood of a point in the permutation through local limits.As a byproduct, we also determine the random limits of the proportion of occurrences (and consecutive occurrences) of any given pattern in a uniform random square permutation.
Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?

If the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood's (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying an unsupervised vector space model-based sentiment analysis tool called SentiArt (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (500 books). Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, both corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature.
Contact tracing apps: an ethical roadmap

Abstract This research statement presents a roadmap for the ethical evaluation of contact tracing apps. Assuming the possible development of an effective and secure contact tracing app, this roadmap explores three ethical concerns—privacy, data monopolists and coercion- based on three scenarios. The first scenario envisions and critically evaluates an app that is built on the conceptualization of privacy as anonymity and a mere individual right rather than a social value. The second scenario sketches and critically discusses an app that adequately addresses privacy concerns but is facilitated by data monopolists such as Google and Apple. The final scenario discusses the coerced installation and use of a privacy-friendly, independently developed contact tracing app. The main worry is coercion through societal exclusion and limited societal participation. The statement concludes with three suggestions for designing an ethical contact tracing app and a research agenda.
Requirements-Collector: Automating Requirements Specification from Elicitation Sessions and User Feedback

In the context of digital transformation, speeding up the time-to-market of high-quality software products is a big challenge. Software quality correlates with the success of requirements engineering (RE) sessions and the ability to collect feedback from end-users in an efficient, dynamic way. Thus, software analysts are tasked to collect all relevant material of RE sessions and user feedback, usually specified on written notes, flip charts, pictures, and user reviews. Afterward comprehensible requirements need to be specified for software implementation and testing. These activities are mostly performed manually, which causes process delays, with a negative effect on software quality attributes such as reliability, usability, comprehensibility. This paper presents Requirements-Collector, a tool for automating the tasks of requirements specification and user feedback analysis. Our tool involves machine learning (ML) and deep learning (DL) computational mechanisms enabling the automated classification of requirements discussed in RE meetings (stored in the form of audio recordings) and textual feedback in the form of user reviews. We use such techniques as they demonstrated to be quite effective in text classification problems. We argue that Requirements-Collector has the potential to renovate the role of software analysts, which can experience a substantial reduction of manual tasks, more efficient communication, dedication to more analytical tasks, and assurance of software quality from conception phases. The results of this work have shown that our tool is able to classify RE specifications and user review feedback with reliable accuracy.
Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

Background One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.
Biosensing and Actuation—Platforms Coupling Body Input-Output Modalities for Affective Technologies

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer’s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of “orchestration”. By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users’ body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.
Component‐aware orchestration of cloud‐based enterprise applications, from TOSCA to Docker and Kubernetes

Summary Enterprise IT is currently facing the challenge of coordinating the management of complex, multicomponent applications across heterogeneous cloud platforms. Containers and container orchestrators provide a valuable solution to deploy multicomponent applications over cloud platforms, by coupling the lifecycle of each application component to that of its hosting container. We hereby propose a solution for going beyond such a coupling, based on the OASIS standard TOSCA and on Docker. We indeed propose a novel approach for deploying multicomponent applications on top of existing container orchestrators, which enables managing each component independently from the container used to run it. We also present prototype tools that implement our approach, and we show how we effectively exploited them to carry out a concrete case study.
PNEN: Pyramid Non-Local Enhanced Networks

Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are adopted. However it is difficult and costly to take full advantage of long-range dependencies. We propose a novel non-local module, Pyramid Non-local Block, to build up connection between every pixel and all remain pixels. The proposed module is capable of efficiently exploiting pairwise dependencies between different scales of low-level structures. The target is fulfilled through first learning a query feature map with full resolution and a pyramid of reference feature maps with downscaled resolutions. Then correlations with multi-scale reference features are exploited for enhancing pixel-level feature representation. The calculation procedure is economical considering memory consumption and computational cost. Based on the proposed module, we devise a Pyramid Non-local Enhanced Networks for edge-preserving image smoothing which achieves state-of-the-art performance in imitating three classical image smoothing algorithms. Additionally, the pyramid non-local block can be directly incorporated into convolution neural networks for other image restoration tasks. We integrate it into two existing methods for image denoising and single image super-resolution, achieving consistently improved performance.
Quantum Control of Frequency-Tunable Transmon Superconducting Qubits

In this work we analyze the implementation of a control-phase gate through the resonance between the $\ensuremath{\mid}11⟩$ and $\ensuremath{\mid}20⟩$ states of two statically coupled transmons. We find that there are many different controls for the transmon frequency that implement the same gate with fidelities around $99.8\mathrm{%}$ (${T}_{1}={T}_{2}=17\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{s}$) and $99.99\mathrm{%}$ (${T}_{1}={T}_{2}=300\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{s}$) within a time that approaches the theoretical limit. All controls can be brought to this accuracy by calibrating the waiting time and the destination frequency near the $\ensuremath{\mid}11⟩\ensuremath{-}\ensuremath{\mid}20⟩$ resonance. However, some controls, such as those based on the theory of dynamical invariants, are particularly attractive due to reduced leakage, robustness against decoherence, and their limited bandwidth.
Quantum computation of silicon electronic band structure

We present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time.
Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform

Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.
Perfect Density Models Cannot Guarantee Anomaly Detection

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection.
PSANet: Pyramid Splitting and Aggregation Network for 3D Object Detection in Point Cloud

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird’s eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network.
Automatic segmentation of coronary lumen and external elastic membrane in intravascular ultrasound images using 8-layer U-Net

Abstract Background Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e., cross-sectional area (EEM-CSA). The database comprises single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images. Results The mean intersection of union (MIoU) of 0.937 and 0.804 for the lumen and EEM-CSA, respectively, were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D-IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.
User preference and embedding learning with implicit feedback for recommender systems

In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss. We show the minimization problem involves dependent random variables and provide a theoretical analysis by proving the consistency of the empirical risk minimization in the worst case where all users choose a minimal number of positive and negative items. We further derive a Neural-Network model that jointly learns a new representation of users and items in an embedded space as well as the preference relation of users over the pairs of items. The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering. The proposed model is by nature suitable for implicit feedback and involves the estimation of only very few parameters. Through extensive experiments on several real-world benchmarks on implicit data, we show the interest of learning the preference and the embedding simultaneously when compared to learning those separately. We also demonstrate that our approach is very competitive with the best state-of-the-art collaborative filtering techniques proposed for implicit feedback.
Detection-Based Object Tracking Applied to Remote Ship Inspection

We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.