Reviewerly/exHarmony huggingface dataset:
A Secure Blockchain-based Prescription Drug Supply in Health-care Systems

Due to existing issues (e.g., complexity/ invisibility on share data for multiple parties, patient's privacy's leak) in the pharmaceutical supply in health-care systems, we proposed the architecture for a prescription drug supply using blockchain. This architecture could provide an efficient authentication among un-trusted parties to prevent an illegal agent from knowing sensitive drug transactions. Besides, dynamic identity is employed to protect users' anonymity and privacy. What's more, secure drug transactions are designed to guarantee share data to be confidentiality, integrity, and non-repudiation by using blockchain. Security analysis shows that our protocol successfully mutually authenticates in addition to being resistant to user impersonation attacks, smart card loss attacks, denial of attacks, database compromised attacks. Furthermore, our protocol can provide reliable service as well as protecting patient's privacy. Performance analysis illustrates our scheme to be competitive in comparison to existing schemes relative to the added security benefits it provides.
Exploratory Data Analysis of Adverse Birth Outcomes and Exposure to Oxides of Nitrogen Using Interactive Parallel Coordinates Plot Technique

We propose that a parallel coordinates plot can be used to study multidimensional data particularly to explore discovery of patterns across the variables. This can assist researchers from the health sciences to visualize their cohort data with interactive data analysis. The study used data from Mother and Child in the Environment birth cohort in Durban, South Africa for the period 2013 to 2017 retrospectively registered. In this paper, we demonstrate that the exploration of multidimensional data with parallel coordinates plot and use of brushing using different colours assists with the identification of relationships and patterns. Parallel coordinates plot visualization facilitates the researcher's skills to find trends, identify outliers and perform quality checks in large multivariate data. We have identified trends in the data that provide directions for further research, and illustrated thereby the potential of parallel coordinates plot to explore patterns and relationships of prenatal oxides of nitrogen exposure with multidimensional birth outcomes. The study recognized the co-occurrence of adverse birth outcomes among infants and these infants had mothers with moderate to high level of NOx exposure during pregnancy. Brushing using different colours facilitated the detection of patterns of relationships to perform basic and advanced statistical model-based analysis.
Research challenges for the use of big data in policy-making

Purpose This paper aims to present pertinent research challenges in the field of (big) data-informed policy-making based on the research, undertaken within the course of the European Union-funded project Big Policy Canvas. Technological advancements, especially in the past decade, have revolutionised the way that both every day and complex activities are conducted. It is, thus, expected that a particularly important actor such as the public sector, should constitute a successful disruption paradigm through the adoption of novel approaches and state-of-the-art information and communication technologies. Design The research challenges stem from a need, trend and asset assessment based on qualitative and quantitative research, as well as from the identification of gaps and external framework factors that hinder the rapid and effective uptake of data-driven policy-making approaches. Findings The current paper presents a set of research challenges categorised in six main clusters, namely, public governance framework, privacy, transparency, trust, data acquisition, cleaning and representativeness, data clustering, integration and fusion, modelling and analysis with big data and data visualisation. Originality/value The paper provides a holistic overview of the interdisciplinary research challenges in the field of data-informed policy-making at a glance and shall serve as a foundation for the discussion of future research directions in a broader scientific community. It, furthermore, underlines the necessity to overcome isolated scientific views and treatments because of a high complex multi-layered environment.
Research on subway pedestrian detection algorithms based on SSD model

Accurate target recognition and location is one of the key technologies in the field of smart city application. In order to solve the problem of large pedestriain flow impact in crowded metro stations, a method of in-depth learning detection based on SSD (single shot multibox detector) is proposed. The algorithm extracts the feature information of the input image, then returns the boundary box of the location on the feature map and classifies the object categories. Using the method of local feature extraction, the features of different positions, different aspect ratios and sizes are obtained, and VGG16 is used as the base network to optimise and improve the network structure. The results of simulation experiments on VOC2007 and data_sub show that the maximum value of mAP is 77% and the highest accuracy is 96.31%. Compared with other mainstream deep learning target detection methods, SSD has higher accuracy, better real-time and robustness. It can solve the problem of different pedestrian target sizes and better realise pedestrians in subway station environment. Detection provides decision-making basis for flow statistics.
Stochastic Optimal Control as Approximate Input Inference

Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning. Given the intractability of the global control problem, state-of-the-art algorithms focus on approximate sequential optimization techniques, that heavily rely on heuristics for regularization in order to achieve stable convergence. By building upon the duality between inference and control, we develop the view of Optimal Control as Input Estimation, devising a probabilistic stochastic optimal control formulation that iteratively infers the optimal input distributions by minimizing an upper bound of the control cost. Inference is performed through Expectation Maximization and message passing on a probabilistic graphical model of the dynamical system, and time-varying linear Gaussian feedback controllers are extracted from the joint state-action distribution. This perspective incorporates uncertainty quantification, effective initialization through priors, and the principled regularization inherent to the Bayesian treatment. Moreover, it can be shown that for deterministic linearized systems, our framework derives the maximum entropy linear quadratic optimal control law. We provide a complete and detailed derivation of our probabilistic approach and highlight its advantages in comparison to other deterministic and probabilistic solvers.
Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy.
Augmented Reality for Outdoor Environmental Education

Over the last decade, mobile learning has become one of the most important trends in education. Leveraging on the technological capabilities of mobile devices, researchers and teachers have investigated the use of mobile devices in a variety of disciplines, including environmental education, in order to design innovative educational material such as augmented reality (AR) apps. Designing technologically enhanced learning activities for environmental education is particularly challenging because they often take place in informal settings and outdoors, for example through field trips or visits to parks. In this chapter, we discuss the potential of augmented reality for outdoor environmental education. More specially, we: (i) give a brief overview of learning theories that promote learning in context; (ii) describe a number of illustrative examples of augmented reality mobile apps for environmental education; (iii) and discuss the purpose and forms of digital augmentations in the context of environmental education.
Nonorthogonal-qubit-state expansion for the asymmetric quantum Rabi model

We present a physically motivated variational wave function for the ground state of the asymmetric quantum Rabi model (AQRM). The wave function is a weighted superposition of squeezed coherent states entangled with non-orthogonal qubit states, and relies only on three variational parameters in the regimes of interest where the squeezing effect becomes negligible. The variational expansion describes the ground state remarkably well in almost all parameter regimes, especially with arbitrary bias. We use the variational result to calculate various relevant physical observables of the ground state, and make a comparison with existing approximations and the exact solution. The results show that the variational expansion is a significant improvement over the existing approximations for the AQRM.
PRISM: a strategic decision framework for cybersecurity risk assessment

Purpose This study aims to develop a framework for cybersecurity risk assessment in an organization. Existing cybersecurity frameworks are complex and implementation oriented. The framework can be systematically used to assess the strategic orientation of a firm with respect to its cybersecurity posture. The goal is to assist top-management-team with tailoring their decision-making about security investments while managing cyber risk at their organization. Design/methodology/approach A thematic analysis of existing publications using content analysis techniques generates the initial set of keywords of significance. Additional factor analysis using the keywords provides us with a framework comprising of five pillars comprising prioritize, resource, implement, standardize and monitor (PRISM) for assessing a firm’s strategic cybersecurity orientation. Findings The primary contribution is the development of a novel PRISM framework, which enables cyber decision-makers to identify and operationalize a tailored approach to address risk management and cybersecurity problems. PRISM framework evaluation will help organizations identify and implement the most tailored risk management and cybersecurity approach applicable to their problem(s). Originality/value The new norm is for companies to realize that data stratification in cyberspace extends throughout their organizations, intertwining their need for cybersecurity within business operations. This paper fulfills an identified need improve the ability of company leaders, as CIOs and others, to address the growing problem of how organizations can better handle cyber threats by using an approach that is a methodology for cross-organization cybersecurity risk management.
An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data

Sepsis is a life-threatening disease with high mortality and expensive cost of treatment.In order to improve the outcomes of patients, it is important to detect atrisk patients with sepsis at an early stage.The Phys-ioNet/Computing in Cardiology Challenge 2019 focused on improving predicting sepsis six hours before the clinical diagnosis by using the latest definition of Sepsis-3.A total of 40,336 ICU patients were provided as public training data, A hidden test dataset was used to evaluate.An ensemble model, which combined boosting and bagging tree models (lightgbm, xgboost and random forest ) were designed to predict sepsis based on the records of the patient's hourly data.We compared the ensemble model and each single model of evaluation metrics results on selected inner test data Offline, the best performance was achieved AUC of 0.792, ACC of 0.727.Finally, the proposed model was evaluated on the full test sets received an official utility score, defined by the organizers, was 0.087, ranked 75/105 (our team name: cinc sepsis pass).While the single model of lightgbm only received a utility score of -0.036.The ensemble model utilized the preprocessing data and achieved better performance than a single tree-based model.
Loss-tolerant quantum key distribution with mixed signal states

The security of measurement device-independent quantum key distribution (MDI QKD) relies on a thorough characterization of one's optical source output, especially any noise in the state preparation process. Here, we provide an extension of the loss-tolerant protocol [Phys. Rev. A 90, 052314 (2014)], a leading proof technique for analyzing the security of QKD, to MDI QKD protocols that employ mixed signal states. We first reframe the core of the proof technique, noting its generalization to treat $d$-dimensional signal encodings. Concentrating on the qubit signal state case, we find that the mixed states can be interpreted as providing Alice and Bob with a virtual shield system they can employ to reduce Eve's knowledge of the secret key. We then introduce a simple semidefinite programming method for optimizing the virtual operations they can perform on the shield system to yield a higher key rate, along with an example calculation of fundamentally achievable key rates in the case of random polarization modulation error.
Standing on shoulders or feet? An extended study on the usage of the MSR data papers

Context: The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Method: Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. Results: We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. [...]
Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

In this work, we propose a way to construct Gaussian processes indexed by multidimensional distributions.More precisely, we tackle the problem of defining positive definite kernels between multivariate distributions via notions of optimal transport and appealing to Hilbert space embeddings.Besides presenting a characterization of radial positive definite and strictly positive definite kernels on general Hilbert spaces, we investigate the statistical properties of our theoretical and empirical kernels, focusing in particular on consistency as well as the special case of Gaussian distributions.A wide set of applications is presented, both using simulations and implementation with real data.
Entanglement breaking rank and the existence of SIC POVMs

We introduce and study the entanglement breaking rank of an entanglement breaking channel. We show that the entanglement breaking rank of the channel Z:Md→Md defined by Z(X)=1d+1(X+Tr(X)Id) is d2 if and only if there exists a symmetric informationally complete positive operator-valued measure in dimension d.
Discrimination of Ohmic thermal baths by quantum dephasing probes

We address the discrimination of structured baths at different temperatures by dephasing quantum probes. We derive the exact reduced dynamics and evaluate the minimum error probability achievable by three different kinds of quantum probes, namely, a qubit, a qutrit, and a quantum register made of two qubits. Our results indicate that dephasing quantum probes are useful in discriminating low values of temperature and that lower probabilities of error are achieved for intermediate values of the interaction time, where the minimum probability of error scales as $1/2N$, with $N$ the number of energy levels of the probe. A qutrit probe outperforms a qubit one in the discrimination task, whereas entangled probes show smaller optimal times.
Squeezing as a resource to counteract phase diffusion in optical phase estimation

We address a phase estimation scheme using Gaussian states in the presence of non-Gaussian phase noise. At variance with previous analysis, we analyze situations in which the noise occurs before encoding phase information. In particular, we study how squeezing may be profitably used before or after phase diffusion. Our results show that squeezing the probe after the noise greatly enhances the sensitivity of the estimation scheme, as witnessed by the increase of the quantum Fisher information. We then consider a realistic setup where homodyne detection is employed at the measurement stage, and address its optimality as well as its performance in the two different scenarios.
Lower Bounds for Encrypted Multi-Maps and Searchable Encryption in the Leakage Cell Probe Model

Encrypted multi-maps (EMMs) enable clients to outsource the storage of a multi-map to a potentially untrusted server while maintaining the ability to perform operations in a privacy-preserving manner. EMMs are an important primitive as they are an integral building block for many practical applications such as searchable encryption and encrypted databases. In this work, we formally examine the tradeoffs between privacy and efficiency for EMMs. Currently, all known dynamic EMMs with constant overhead reveal if two operations are performed on the same key or not that we denote as the $$ global\ key\text {-}equality\ pattern $$ . In our main result, we present strong evidence that the leakage of the global key-equality pattern is inherent for any dynamic EMM construction with O(1) efficiency. In particular, we consider the slightly smaller leakage of $$ decoupled\ key\text {-}equality\ pattern $$ where leakage of key-equality between update and query operations is decoupled and the adversary only learns whether two operations of the $$ same\ type $$ are performed on the same key or not. We show that any EMM with at most decoupled key-equality pattern leakage incurs $$\varOmega (\lg n)$$ overhead in the $$ leakage\ cell\ probe\ model $$ . This is tight as there exist ORAM-based constructions of EMMs with logarithmic slowdown that leak no more than the decoupled key-equality pattern (and actually, much less). Furthermore, we present stronger lower bounds that encrypted multi-maps leaking at most the decoupled key-equality pattern but are able to perform one of either the update or query operations in the plaintext still require $$\varOmega (\lg n)$$ overhead. Finally, we extend our lower bounds to show that dynamic, $$ response\text {-}hiding $$ searchable encryption schemes must also incur $$\varOmega (\lg n)$$ overhead even when one of either the document updates or searches may be performed in the plaintext.
Design of a high-tech vending machine

Imagine, paying and placing an order in a food outlet only to wait for an extra twenty to thirty minutes to get the ordered products. How tiring. This is the current situation in different food outlets and shops. What if we could save time by 75% to 83% and get an order in five minutes or less? This can be achieved by the design and development of a fully fletched high–tech vending machine. Current vending machine owners are facing challenges from hacking and vandalism. The aim of this paper is to design a high-tech vending machine that can dispense a variety of products at the same time. The machine uses fingerprint sensors and other high technological features for security and user-friendliness. The security system which uses biometrics for opening and closing doors was interrogated and used. An advertising platform was developed using HTML, JavaScript and CSS to cater for different products on offer. The alarm system is interfaced with a servo motor and a buzzer for providing added security. Sublime Text was used for creating a website and XAMPP was used for port forwarding. The result is a fully fletched vending machine that can safely vend about fifteen products or more all housed in a robust cabin.
Narupa iMD: A VR-Enabled Multiplayer Framework for Streaming Interactive Molecular Simulations

Here we present Narupa iMD, an open-source software package which enables multiple users to cohabit the same virtual reality space and interact with real-time molecular simulations. The framework utilizes a client-server architecture which links a flexible Python server to a VR client which handles the rendering and user interface. This design helps ensure utility for research communities, by enabling easy access to a wide range of molecular simulation engines for visualization and manipulation of simulated nanoscale dynamics at interactive speeds.