Reviewerly/exHarmony huggingface dataset:
Multipartite quantum entanglement creation for distant stationary systems

We present efficient protocols for creating multipartite Greenberger-Horne-Zeilinger (GHZ) and W states of distant stationary qubits. The system nonuniformity and/or the non-ideal single-photon scattering usually limit the performance of entanglement creation, and result in the decrease of the fidelity and the efficiency in practical quantum information processing. By using linear optical elements, errors caused by the system nonuniformity and non-ideal photon scattering can be converted into heralded loss in our protocols. Thus, the fidelity of generated multipartite entangled states keeps unchanged and only the efficiency decreases. The GHZ state of distant stationary qubits is created in a parallel way that its generation efficiency considerably increases. In the protocol for creating the W state of N distant stationary qubits, an input single photon is prepared in a superposition state and sent into N paths parallelly. We use the two-spatial-mode interferences to eliminate the “which path” single-photon scattering “knowledge”. As a result, the efficiency of creating the N -qubit W state is independent of the number of stationary qubits rather than exponentially decreases.
Optical correlator based algorithm for driver drowsiness detection

The human eye and its characteristics (texture, gaze, and movement) have shown to be reliable in expressing human emotional states. A non-invasive eye tracking and state (open/close) estimation system is, consequently, fundamental for developing human-machine interaction systems, hands-off process control, and fatigue detection. In the current work, a novel and fast method based on an optical correlator is proposed for eye detection first, followed by eye state estimation. The second aim of this work was to investigate the simulated optical correlator on a real world driving videos for driver drowsiness detection. For the first time, a numerical simulation of the Vander Lugt Correlator (VLC) was used for automatically detecting the eye center. Further, correlation filters tailored to all the eye states were proposed and their design, to recognize eyes in cluttering and noisy environments, was investigated. Besides, the correlation peak amplitude returned by the VLC based algorithm was investigated to detect the driver drowsiness. Results reported on four international databases reveal that the proposed method improves over the recently published methods in eye tracking. Evaluations of the simulated VLC on real world driving videos from the SHRP2 database demonstrate its good performance on the driver's eye tracking and closure detection.
Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems

This paper presents a neuro-fuzzy structure of a decoupling fuzzy neural PID controller with self-tuning parameters. This structure is appropriate for heating, ventilation and air conditioning HVAC nonlinear plants. The main advantage here is that the equation of classical PID control with decoupling coefficients are used as a Sugeno function into the consequent part of the fuzzy rules. Hence, the designed decoupling fuzzy PID controller could be viewed as a natural similarity to the conventional PID controller with decoupling elements. A benchmark HVAC system with temperature and humidity control is considered to illustrate the benefits of the design paradigm. The performance of this set up was studied for reference tracking and disturbance rejection cases. Simulation results confirm the effectiveness of the proposed control system.
Computational Modeling of Emotion-Motivated Decisions for Continuous Control of Mobile Robots

Immediate rewards are usually very sparse in the real world, which brings a great challenge to plain learning methods. Inspired by the fact that emotional reactions are incorporated into the computation of subjective value during decision-making in humans, an emotion-motivated decision-making framework is proposed in this article. Specifically, we first build a brain-inspired computational model of amygdala-hippocampus interaction to generate emotional reactions. The intrinsic emotion derives from the external reward and episodic memory and represents three psychological states: 1) valence; 2) novelty; and 3) motivational relevance. Then, a model-based (MB) decision-making approach with emotional intrinsic rewards is proposed to solve the continuous control problem of mobile robots. This method can execute online MB control with the constraint of the model-free policy and global value function, which is conducive to getting a better solution with a faster policy search. The simulation results demonstrate that the proposed approach has higher learning efficiency and maintains a higher level of exploration, especially, in some very sparse-reward environments.
Measuring eWhoring

eWhoring is the term used by offenders to refer to a type of online fraud in which cybersexual encounters are simulated for financial gain. Perpetrators use social engineering techniques to impersonate young women in online communities, e.g., chat or social networking sites. They engage potential customers in conversation with the aim of selling misleading sexual material -- mostly photographs and interactive video shows -- illicitly compiled from third-party sites. eWhoring is a popular topic in underground communities, with forums acting as a gateway into offending. Users not only share knowledge and tutorials, but also trade in goods and services, such as packs of images and videos. In this paper, we present a processing pipeline to quantitatively analyse various aspects of eWhoring. Our pipeline integrates multiple tools to crawl, annotate, and classify material in a semi-automatic way. It builds in precautions to safeguard against significant ethical issues, such as avoiding the researchers' exposure to pornographic material, and legal concerns, which were justified as some of the images were classified as child exploitation material. We use it to perform a longitudinal measurement of eWhoring activities in 10 specialised underground forums from 2008 to 2019. Our study focuses on three of the main eWhoring components: (i) the acquisition and provenance of images; (ii) the financial profits and monetisation techniques; and (iii) a social network analysis of the offenders, including their relationships, interests, and pathways before and after engaging in this fraudulent activity. We provide recommendations, including potential intervention approaches.
Culturally-Embedded Visual Literacy

Social media is often analyzed as a "front stage" where social media users perform their identities and manage impressions through various forms of user-generated content: text, picture, video, as well as visual expression such as emoticon, emoji, sticker, and meme. Previous scholarship has suggested various functions of visual expression in supporting computer-mediated communication. In this study, we move beyond the utility perspective to ask what constitutes the literacy to utilize visual expression for impression management. Towards this goal, we conducted an interview study with 30 social media users in China. We found that visual literacy intersects with its cultural context. Our interviewees used text and visual expressions in sophisticated, skillful ways and tailored for different audiences in order to construct desired images. They also interacted with audience memory in order to perform their uniqueness and creativity. Lastly, we discuss culturally-embedded visual literacy and provide implications for design.
Changes in academic libraries in the era of Open Science

In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data – from changes in libraries’ administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects.
Hydrogeological controls of arsenic and uranium dissolution into groundwater of the Pine Ridge Reservation, South Dakota

This study integrates geochemical modeling, spatial analysis and several statistical methods including principal component analysis, multivariate regression and cluster analysis to investigate hydrogeologic controls of arsenic and uranium contamination within groundwater of the Arikaree aquifer on the Pine Ridge Reservation (PRR). Located in southwestern South Dakota, the PRR is largely rural and many people rely on domestic supply wells completed in the Arikaree aquifer as their primary drinking water source. Locally, the White River Group, which unconformably underlies the Arikaree Group, is enriched in arsenic and uranium related to volcanic ash deposits and acts as a geogenic metal source. Geochemical data from over 250 groundwater samples were obtained through collaboration with the Oglala Sioux Tribe. Cluster spatial statistics analyses delineated four regions of statistically significant variations in groundwater chemistry that represent upgradient, intermediate, and downgradient portions of the Arikaree aquifer. Groundwater evolves as it flows through the Arikaree aquifer with increasing alkalinity, sodium, and pH along flow paths. These chemical changes are likely due to dissolution of carbonate minerals and volcanic ash. Thermodynamic calculations suggest increasing supersaturation of the groundwater with respect to calcite; thus, volcanic ash dissolution may be an important secondary source of alkalinity. Elevated alkalinity and pH levels were found to be the driving factors of arsenic and uranium mobility, and downgradient sections of the aquifer in the northern portions of the PRR are most likely to be impacted by metal(loid) contamination, with 73% of the wells in this grouping failing a USEPA maximum contaminant level for arsenic, uranium, and/or gross alpha.
Q-Graphplan: QoS-Aware Automatic Service Composition With the Extended Planning Graph

With the progress of web technologies, web services with abundant functionalities, such as video transmission, location, navigation, etc., are becoming more and more pervasive. Automatic web service composition aims to automatically combine selected elementary web services from a finite service set by matching the input and output parameters given an initial state and a goal state. Considering the end-to-end Quality-of-Service(QoS) of each web service, the service composition problem becomes an optimization problem to find the optimal solution. This paper maps this problem to an automatic planning problem and proposes Q-Graphplan based on the classical graphplan, an efficient planner for solving classical planning problems. First, we construct a planning graph based on the dependency relationships of the web services and extract essential heuristics according to the reachability analysis. Second, we convert this planning graph to a directed path generation graph. Finally, we extract the optimal solution from the path generation graph using a backward A* algorithm with the heuristics of the planning graph. Furthermore, our approach avoids redundancies when constructing the planning graph and improves the searching effectiveness in extracting solution. We conduct experiments on the WSC-2009 dataset to compare performance against present approaches, and the results show the efficiency and effectiveness of our proposed approach.
User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering

The collaborative filtering algorithm based on the singular value decomposition plus plus (SVD++) model employs the linear interactions between the latent features of users and items to predict the rating in the recommendation systems. Aiming to further enrich the user model with explicit feedback, this paper proposes a user embedding model for rating prediction in SVD++-based collaborative filtering, named UE-SVD++. We exploit the user potential explicit feedback from the rating data and construct the user embedding matrix by the proposed user-wise mutual information values. In addition, the user embedding matrix is added to the existing user bias and implicit parameters in the SVD++ to increase the accuracy of the user modeling. Through extensive studies on four different datasets, we found that the rating prediction performance of the UE-SVD++ model is improved compared with other models, and the proposed model’s evaluation indicators root-mean-square error (RMSE) and mean absolute error (MAE) are decreased by 1.002–2.110% and 1.182–1.742%, respectively.
Scalable revocable identity-based signature over lattices in the standard model

Revocable identity based signature (RIBS) is a useful cryptographic primitive, which provides a revocation mechanism to revoke misbehaving or malicious users over ID-based public key settings. In the past, many RIBS schemes have been previously proposed, but the security of all these existing schemes is based on traditional complexity assumptions, which are not secure against attacks in the quantum era. Lattice-based cryptography has many attractive features and it is all believed to be secure against attacks of quantum computing. Recently, Hung et al. proposed a RIBS with short size over lattices. However, in their scheme, it requires the private key generator (PKG) to perform linear work in the number of users and does not scale well. Moreover, their scheme is secure in the random oracle model. In this paper, we adopt the binary tree structure to present a scalable lattice-based RIBS scheme which greatly reduces the PKG'S workload associated with users from linear to logarithm. We prove that our proposed scheme is existentially unforgeable against chosen message attacks (EUF-CMA) under standard short integer solutions (SIS) assumption, in the standard model. Compared with the existing RIBS schemes over lattices, our proposed RIBS construction is secure in the standard model with scalability and meanwhile has efficient revocation mechanism with public channels.
Truthmaker Semantics for Relevant Logic

Abstract I develop and defend a truthmaker semantics for the relevant logic R . The approach begins with a simple philosophical idea and develops it in various directions, so as to build a technically adequate relevant semantics. The central philosophical idea is that truths are true in virtue of specific states. Developing the idea formally results in a semantics on which truthmakers are relevant to what they make true. A very natural notion of conditionality is added, giving us relevant implication. I then investigate ways to add conjunction, disjunction, and negation; and I discuss how to justify contraposition and excluded middle within a truthmaker semantics.
Wearable RemoteFusion: A Mixed Reality Remote Collaboration System with Local Eye Gaze and Remote Hand Gesture Sharing

We present a wearable Mixed Reality (MR) remote collaboration system called Wearable RemoteFusion. The system supports spatial annotation and view frustum sharing, and enables natural non-verbal communication cues (eye gaze and hand gesture) for visual assistance in a stitched live dense scene. We describe the design and implementation details of the prototype system, and report on a pilot user study investigating how sharing natural gaze and gesture cues affects collaborative performance and the user experience. We found that by sharing augmented natural cues like the local eye gaze and remote hand gesture, participants had a stronger feeling of Co-presence, and the remote user could guide the local user to complete tasks with less physical workload. We discuss implications for collaborative interface design and directions for future research.
An improved analytical method for calculating stiffness of 3D needled composites with different needle-punched processes

The mechanical properties of 3D needled composites are greatly associated with the complex needle-punched process. Needle-punched process is described quantitatively to relate with the internal features of 3D needled composites combining with experiment observations. The relationship between pinhole spacing and fiber breakage is established by Bayesian inference method and described by a probability function. Four kinds of internal needle-punched features whose local stiffness properties are calculated using an analytical method are distinguished. The stiffness properties of 3D needled composites are predicted analytically by using the volume fraction of each internal structural features based on the needle-punched process. The predicted results for 3D needled composites with different needle-punched processes are validated by the experimental results. This study can be used to construct the material design database for 3D needled composites with different needle-punched processes.
Cybersecurity: Nurses on the Front Line of Prevention and Education

Cybercrime has become an increasing concern for consumers in the United States and internationally. In recent years, cybercrimes in the healthcare industry have drastically increased in type, impact, and frequency. These attacks have negatively impacted patient privacy, the ability of providers to deliver care, and the security of healthcare organizations. Nurses are uniquely positioned to help protect against and report cybercrimes because they are one of the largest employed populations in the healthcare industry and they are on the front line of patient care and healthcare technology use. This article discusses the main concerns of cybersecurity in healthcare, the nurse’s role in preventing and managing cyber security, and recommendations for nurses, educators, and regulators. Cybercrime has become an increasing concern for consumers in the United States and internationally. In recent years, cybercrimes in the healthcare industry have drastically increased in type, impact, and frequency. These attacks have negatively impacted patient privacy, the ability of providers to deliver care, and the security of healthcare organizations. Nurses are uniquely positioned to help protect against and report cybercrimes because they are one of the largest employed populations in the healthcare industry and they are on the front line of patient care and healthcare technology use. This article discusses the main concerns of cybersecurity in healthcare, the nurse’s role in preventing and managing cyber security, and recommendations for nurses, educators, and regulators. Jessica L. Kamerer, EdD, MSN, RNC-NIC, is Associate Professor, Department Head of Nursing at Robert Morris University School of Nursing and Health Studies, Moon Township, Pennsylvania. Donna McDermott, PhD, RN, CHSE, is Associate Professor of Clinical, Assistant Dean, Simulation Programs at University of Miami School of Nursing and Health Studies, Miami Florida. Conflict of Interest Compliance Article 5Journal of Nursing Regulation Vol. 12Issue 2PreviewAs is standard in scholarly publishing, NCSBN’s Journal of Nursing Regulation (JNR) requires its authors to disclose any potential conflicts of interest (COI). Although COI information has always been collected by our staff in order to support editors’ review of the paper, it was not our standard practice to publish COI statements in each article. In this issue, JNR is retrospectively publishing the COI statements, which were collected with the below papers at submission, in order to make potential COI’s transparent to readers, as well as editors. Full-Text PDF
A novel incremental Kernel Nonparametric SVM model (iKN-SVM) for data classification: An application to face detection

In this paper, we propose a novel incremental classifier to overcome problems associated with batch techniques, along with issues related to data spread that Kernel Support Vector Machines (KSVM) may encounter. Basically, we present a Kernel SVM-based model that learns incrementally, as new data is available over time, in order to handle dynamic and large data effectively and reduce the computational time. The proposed model deals with the data spread issues by introducing near-global variations, from the scatter matrices of the Kernel Nonparametric Discriminant Analysis (KNDA), into the optimization problem of incremental KSVM, while considering local characteristics of the data provided by KSVM. Besides, our model has a quadratic convex optimization problem with one global solution. Furthermore, an extensive comparison of the model with other state-of-the-art incremental and batch algorithms on various datasets, has been carried out, in order to show its advantages and effectiveness for classification tasks. Moreover, an evaluation of the proposed method on face detection is provided.
Efficient location privacy-preserving range query scheme for vehicle sensing systems

Vehicle sensing systems (VSS) has received widespread concern in recent years. However, implementing data query efficiently while protecting data privacy in VSS is a challenge. Our work introduces an efficient location privacy-preserving range query (ELPRQ) scheme for secure VSS communications. Specifically, ELPRQ combines privacy-preserving scalar product protocol to blur the location information and sensed data, so that no one but data requester and data uploading vehicle know their own sensitive information. Detailed security analysis indicates that ELPRQ can resist all kinds of security threats and protect location privacy. Meanwhile, the performance evaluations demonstrate that ELPRQ is more efficient than the reported scheme with respect to computation complexity and communication overhead.
Efficient hindsight reinforcement learning using demonstrations for robotic tasks with sparse rewards

The goal of reinforcement learning is to enable an agent to learn by using rewards. However, some robotic tasks naturally specify with sparse rewards, and manually shaping reward functions is a difficult project. In this article, we propose a general and model-free approach for reinforcement learning to learn robotic tasks with sparse rewards. First, a variant of Hindsight Experience Replay, Curious and Aggressive Hindsight Experience Replay, is proposed to improve the sample efficiency of reinforcement learning methods and avoid the need for complicated reward engineering. Second, based on Twin Delayed Deep Deterministic policy gradient algorithm, demonstrations are leveraged to overcome the exploration problem and speed up the policy training process. Finally, the action loss is added into the loss function in order to minimize the vibration of output action while maximizing the value of the action. The experiments on simulated robotic tasks are performed with different hyperparameters to verify the effectiveness of our method. Results show that our method can effectively solve the sparse reward problem and obtain a high learning speed.