Reviewerly/exHarmony huggingface dataset:
Telematic connections: sensing, feeling, being in space together

In the wake of the COVID-19 pandemic and current era of social distancing, video conferencing platforms have taken on a new importance as virtual gathering spaces in lieu of face-to-face interactions. Online interactivity has expanded the ways in which individuals experience connection, intimacy, and touch. Although digital media has traditionally been thought of as disembodied, I argue that it has the ability to elicit intense feelings of embodied touch. This essay will address the embodied nature of digital spaces and the urgent need for both physical distance and virtual closeness. Using Laura Marks's theory of haptic visuality, in which vision takes on a tactile quality, and the concept of virtual touch, a term I developed during my doctoral research in which an affective sensory response of touch is elicited through non-tactile senses, I analyze two performance works that I developed in the HopKins Black Box Performance Laboratory at Louisiana State University: Being Present (2016). Directed by Naomi Bennett, Performances by Jason Jedrusiak and Gabi Vigueira, HopKins Black Box, Louisiana State University. https://vimeo.com/450654368, and (dis)embodied in space: an interactive art installation (2019). Created by Naomi Bennett, Sound Design by Hal Lambert, Performances by Kalli Champagne, Emily Graves, Ethan Hunter, Greg Langner, Josiah Pearsall, and Montana Jean Smith, HopKins Black Box, Louisiana State University. https://vimeo.com/330611615. Using telepresence, which allows individuals to interact remotely in real-time using digital technology, or telematics, these two performances explore new possibilities for virtual intimacy, connection, and touch.
Involving IT professionals in Scrum student teams: An empirical study on the impact of students’ learning

This is a full paper under the Innovative Practice category. It presents an empirical study that describes our efforts, reflections and lessons learned from including Information Technology (IT) professionals in an undergraduate Computer Science and Software Engineering (CSSE) course, which introduces Scrum as an agile software development methodology. We discuss how students, who took such a course, perceive their interaction with IT professionals as an overall positive learning experience and an opportunity to interject real world lessons and scenarios into course content. Moreover, we report on some useful observations and the feedback we received from the professionals, who volunteered and committed to engage regularly with our student Scrum teams throughout this course. We discuss how we leveraged the feedback we received, from both students and IT professionals, to incrementally fine-tune our course while teaching it. Based on the empirical data gathered during our study, we explain how this win-win collaboration has been beneficial to everyone involved. More specifically, we have found that this approach helps students better understand and apply important principles, methods and tools for software development, become familiar with team dynamics, communicate effectively with an IT professional and gain a better appreciation of the inherent challenges involved in crafting larger and realistic software applications. In addition, we observed that such an engagement provides not only some gratification to IT professionals, who have the opportunity to mentor our students both inside and outside of the classroom, but also an opportunity for them to refresh their knowledge on certain methodologies, technologies and tools used during our course. We conclude by discussing some of the instructor’s challenges while teaching this course as well as our plans to continue improving it by strengthening the involvement of IT professionals in student teams.
A Fuzzy Reasoning System for Computer-Guided Laparoscopy Training

A system called the Computer-Assisted Surgical Trainer (CAST) that provides force, visual, and audio guidance with objective assessments for laparoscopic surgery skills training in a non-patient training setup was developed. To support the guidance and assessment of trainees, given an object transfer task, a fuzzy reasoning system is proposed to estimate states of objects as well as the completion of basic actions. A peg transfer task is implemented as a target application. Based on a modeling approach of this peg transfer task, fuzzy production rules are defined and a reasoning process is designed. The experimental test results show the effectiveness of the proposed fuzzy reasoning system.
Coral-Pie

We present a distributed system architecture which is scalable by design for cross-camera vehicle tracking at video ingestion time dubbed Coral-Pie. To meet the latency bounds for timely processing of every frame at each camera, we associate dedicated low-cost computational resource for each camera, which consists of two Raspberry Pi 3B+'s and one Coral Accelerator (EdgeTpu). The end-to-end system generates and stores the tracks in a graph database for easy querying. We use the Cloud-Edge-Device continuum to appropriately place the components of the distributed system architecture. Using the timing profiles of the sub-tasks involved in the continuous processing that needs to happen on every frame in each camera, we map the elements of the processing onto the computational resource associated with each camera. Performance evaluation of the proof-of-concept system is conducted using live streams from five campus cameras. The evaluation includes microbenchmarks as well as application level studies. The controlled experiments using live cameras are augmented with a simulation-based study to show the self-healing property of the system and the system scalability.
Different Types of Photon Entanglement from a Constantly Driven Quantum Emitter Inside a Cavity

Bell states are the most prominent maximally entangled photon states. In a typical four-level emitter, like a semiconductor quantum dot, the photon states exhibit only one type of Bell state entanglement. By adding an external driving to the emitter system, also other types of Bell state entanglement are reachable without changing the polarization basis. In this paper, we show under which conditions the different types of entanglement occur and give analytical equations to explain these findings. We further identify special points, where the concurrence, being a measure for the degree of entanglement, drops to zero, while the coherences between the two-photon states stay strong. Results of this work pave the way to achieve a controlled manipulation of the entanglement type in practical devices.
Securing Access to Healthcare Data with Context-aware Policies

Data security in the healthcare domain is of paramount importance. There is a need for advanced access control mechanisms that can be used in the healthcare domain and raise security awareness. In this work, we report on the development of a web-based editor, that enables a user to edit concepts and properties for tailoring a context-aware security model for creating and enforcing access control policies for electronic health records (EHRs). These access control policies are to be enforced as part of two different sequential authorisation paradigms that will be employed for achieving high levels of security controls. These paradigms are the Attributebased Access Control (ABAC), which permits or denies access and/or grants or not editing rights to (encrypted) EHRs and the Attribute-based Encryption (ABE), which handles the way sensitive data should be decrypted, so as to edit functionalities for creating context-aware access policies (ABAC and ABE).
Hierarchical Physician Recommendation via Diversity-enhanced Matrix Factorization

Recent studies have shown that there exhibits significantly imbalanced medical resource allocation across public hospitals. Patients, regardless of their diseases, tend to choose hospitals and physicians with a better reputation, which often overloads major hospitals while leaving others underutilized. Guiding patients to hospitals that can serve their treatment needs both timely and with good quality can make the best use of precious medical resources. Unfortunately, it remains one of the major challenges both for research and in practice. In this article, we propose a novel diversity-enhanced hierarchical physician recommendation approach to address this issue. We adopt matrix factorization to estimate physician competency and exploit implicit similarity relationships to improve the competency estimation of physicians that we are of little information of. We then balance the patient preference and physician diversity using two novel heuristic algorithms. We evaluate our proposed approach and compare it with the state of the art. Experiments show that our approach significantly improves both accuracy and recommendation diversity over existing approaches.
Corner detection using the point‐to‐centroid distance technique

Corners, highly important local features of images and corner finding, play a crucial role in computer vision and image processing, such as object tracking and vehicle detection. Proposing effective and efficient corner detectors is the aim of corner detection. In this study, the authors first present a new measure of corner sharpness termed as the point-to-centroid distance (PCD) and then examine its behaviours, which display beneficial characteristics that help distinguish corners from non-corners. Based on PCD behaviours, the authors propose a novel corner detector. Extensive experimental results demonstrate that the PCD technique is effective and simultaneously efficient for corner detection compared with six other contour-based corner detectors in terms of two commonly used evaluation metrics – average repeatability and localisation error.
Robo : A Counselor Chatbot for Opioid Addicted Patients

Opioid as an addiction is a serious public health threat in the U.S., leads to massive deaths and other social problems. Medical treatment and mental supports are considering factors in rehabilitation process for opioid addicts. In this process families and friends play an important role in supporting and help the addict to stay clean. However, they may not know the best action to take due to lack of knowledge or certainty. Therefore, there are situations that addicts tend to use social media as a question/answering platform to seek answer for an inquiry. Unfortunately, It is often difficult to search over pages or different forums for a quick answer and it can be time-consuming, confusing and ultimately frustrating for the addicts. Hence, We propose a novel chatbot that is integrated with state-of-the-art deep learning techniques to retrieve an instant answer for a user's query from Reddit social media. Our experiment illustrates that the chatbot provides answers in scenarios that there is no exact matched question in the discussion forums but there are questions with semantic similarities to the user query. Consequently, we illustrate real use cases where our chatbot retrieves responses from Reddit social media forums.
Crowd Density Forecasting by Modeling Patch-Based Dynamics

Forecasting human activities observed in videos is a long-standing challenge in computer vision and robotics and is also beneficial for various real-world applications such as mobile robot navigation and drone landing. In this work, we present a new forecasting task called crowd density forecasting. Given a video of a crowd captured by a surveillance camera, our goal is to predict how the density of the crowd will change in unseen future frames. To address this task, we developed the patch-based density forecasting networks (PDFNs), which directly forecasts crowd density maps of future frames instead of trajectories of each moving person in the crowd. The PDFNs represent crowd density maps based on spatially or spatiotemporally overlapping patches and learn a simple density dynamics of fewer people in each patch. Doing so allows us to efficiently deal with diverse and complex crowd density dynamics observed when input videos involve a variable number of crowds moving independently. Experimental results with several public datasets of surveillance videos demonstrate the effectiveness of our approaches compared with state-of-the-art forecasting methods.
Measurement-Level Target Tracking Fusion for Over-the-Horizon Radar Network Using Message Passing

Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: Target detection, target tracking, multipath data association, and ionospheric heights identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this article, a unified message passing (MP) approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability density function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of target kinematic state and ionospheric heights, are approximated by MF due to its simple MP update rules for conjugate-exponential models. The posterior PDFs of target visibility state and multipath data association which contains one-to-one frame (hard) constraints (i.e., at each time, a measurement is either originated from one target via a particular path or it is clutter, and each target generates at most one measurement under a particular path) are approximated by BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among latent variables. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is beneficial to improving tracking fusion performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.
A Similarity Integration Method based Information Retrieval and Word Embedding in Bug Localization

To improve the performance of bug localization, there is necessity to solve the lexical mismatch between the natural language in the bug report and the programming language in the source file. A similarity integration method for bug localization is proposed, in which the similarity between bug report and source file is calculated by information retrieval (IR) and word embedding. More specifically, IR technique is used to collect the exact matches between bug report and source file. The terms in the bug report and the potential source files of different code tokens are connected by word embedding technique, which is used to complement with IR technique. Finally, deep neural network (DNN) is utilized to integrate extracted features to get the correlation between bug reports and source files. The experimental results show that the proposed approach outperforms several existing bug localization approaches in terms of Top N Rank, MAP, and MRR.
Secure fine-grained friend-making scheme based on hierarchical management in mobile social networks

With the rapid development of mobile social networks (MSNs) and smart mobile devices, mobile users can easily make new social interactions with others via their smartphones. Unfortunately, users enjoy these conveniences at the cost of revealing their personal data. The inevitable release of information conflicts with increasing privacy concerns. To address this problem, we propose a hierarchical management scheme for friend matching using attributes, which aims to facilitate secure friend discovery in MSNs. The scheme involves the establishment of several attribute centers, which perform fine-grained management based on various user attributes. The user attributes are used to generate attribute sub-keys. After meeting the conditions set by the friend-making initiator, the friend-making requester combines the sub-keys into a complete decryption key and decrypts the user data file in the friend-making center. By introducing hierarchical and sorting management of attribute sub-keys, we prevent the crypto key disclosure and single point failure, which usually happens because single-authority management centers are vulnerable to attack. The security and performance of this system were analyzed and evaluated via simulations, and the results indicate that the proposed scheme is CPA-safe.
Multi-Attention Network for Unsupervised Video Object Segmentation

In recently years, some useful unsupervised video object segmentation methods that emphasize the common information in videos have been proposed. Despite the effectiveness of these methods, they ignore the information from the shallow layers of the network and thus fail to segment the details of the objects. To address this problem, we propose a multi-attention network for unsupervised video object segmentation (MANet). Recent studies show that the deep layers of networks are sensitive to high-level semantic information but messy details, while it is opposite for shallow layers. From this insight, a multi-attention module is designed by taking into account the information from the shallow layers in addition to that from the deep layers. This module can distinguish the primary object and segment the details of the object effectively by enhancing the common information between video frames while combing the features from the shallow layers and the deep layers. Experimental results on the DAVIS-2016 and SegTrack v2 datasets show that our network outperforms the state-of-the-art methods.
Secure key generation and distribution scheme based on two independent local polarization scramblers

We propose a practical secure key generation and distribution (SKGD) scheme for actual application. Based on the experiment results of the polarization properties of the actual optical-fiber link, we propose a SKGD method by the active modulation of the state of polarization of the optical-fiber by two polarization scramblers placed at the local end of the legal users in the point-to-point communication system. Experiment results prove that the signals received by legal users share high correlation using 20 km standard single-mode fiber and 1 KHz polarization scrambling rate, and the bit generation rate can reach to 1216 bit/s with a bit error rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼<!-- ∼ --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.33</mml:mn> </mml:mrow> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> . Security of the system is also analyzed in terms of the polarization properties of the system; the theoretical and experimental results indicate that the proposed scheme possesses high security due to the invisibility of the key signal and the technical difficulty of the recovery of the key sequence.
Class Imbalance and Cost-Sensitive Decision Trees

Class imbalance treatment methods and cost-sensitive classification algorithms are typically treated as two independent research areas. However, many of these techniques have properties in common. After providing a background to the two fields of research, this article identifies the fundamental mechanism which is common to both. Using this mechanism, a taxonomy is created which encompasses approaches to both class imbalance treatment and cost-sensitive classification. Through this survey, we aim to bridge the gap between the two fields such that lessons from one field may be applied to the other. Many data mining tasks are naturally both class imbalanced and cost-sensitive. This survey is useful for researchers and practitioners approaching these tasks as it provides a detailed overview of approaches in both fields. Many of the surveyed techniques are classifier independent. However, we chose to focus on techniques which were either decision tree-based or compatible with decision trees. This choice was based on the popularity and novelty of their application to both fields.
Classification Algorithm for Person Identification and Gesture Recognition Based on Hand Gestures with Small Training Sets

Classification algorithms require training data initially labelled by classes to build a model and then to be able to classify the new data. The amount and diversity of training data affect the classification quality and usually the larger the training set, the better the accuracy of classification. In many applications only small amounts of training data are available. This article presents a new time series classification algorithm for problems with small training sets. The algorithm was tested on hand gesture recordings in tasks of person identification and gesture recognition. The algorithm provides significantly better classification accuracy than other machine learning algorithms. For 22 different hand gestures performed by 10 people and the training set size equal to 5 gesture execution records per class, the error rate for the newly proposed algorithm is from 37% to 75% lower than for the other compared algorithms. When the training set consists of only one sample per class the new algorithm reaches from 45% to 95% lower error rate. Conducted experiments indicate that the algorithm outperforms state-of-the-art methods in terms of classification accuracy in the problem of person identification and gesture recognition.
Accurate interpolation methods for the annual simulation of solar central receiver systems using celestial coordinate system

Heliostat field layout optimization bases on simulations of the annual energy production. To reduce the computation time of the optimization process, one can try to reduce the number of simulation points of the annual domain, while keeping similar accuracy. For the temporal domain, there exist already different approaches as aggregation of days. To further reduce the number of needed simulation points, in this paper we decouple the power computation from the irradiation, such that we just regard the computation of the power plant efficiency. This time-dependent parameter is transformed into a celestial coordinate system where the solar angle-dependent efficiency will be approximated using suitable multi-dimensional interpolation methods. We distinguish between an accurate approximation of the received annual optical energy and the electrical energy of each moment of a year. These methods are demonstrated for the existing heliostat field layouts PS10 and Gemasolar in Seville, while using realistic weather conditions. With this new approach just around 40 simulation points suffice to reach an accuracy of 99.9% for the received power for smaller power plants as PS10, and for larger plants as Gemasolar. Compared to a state-of-the-art method, this investigation helps to accelerate the simulation by factor three.