Reviewerly/exHarmony huggingface dataset:
Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction.

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.
Binary Drone Squadron Optimization Approaches for Feature Selection

A great quantity of data is being created these days, which is kept in massive datasets with different irrelevant attributes that are unrelated to the goal notion.Feature selection deals with the selection of most pertinent features that also aid to increase the classification accuracy.The topic of feature selection is viewed as a multi-objective optimization problem with two goals: improving classification accuracy and reducing the number of features used.Drone Squadron Optimization (DSO) is one of the most recent artifactinspired optimization algorithm; having two key components: semi-autonomous drones that hover over a terrain and a command center that manages the drones.In this paper, two binary variants of the DSO are proposed to deal with the feature selection problem.The proposed binary algorithms are applied on 21 different benchmark datasets against five state-of-the-art algorithms, i.e., Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA) and Ant Lion Optimization (ALO).Different assessment indicators are used to assess the diversification and intensification of the optimization algorithms.When compared to current state-of-the-art wrapper-based algorithms, the suggested binary techniques are more efficient in scanning the dimension space and picking the most useful characteristics for categorization tasks, resulting in the lowest classification error rate.
Quantifying the Cost of Privately Storing Data in Distributed Storage Systems

Consider a user who wishes to store a file in multiple servers such that at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> servers are needed to reconstruct the file, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> colluding servers cannot learn any information about the file. Unlike traditional secret-sharing models, where perfectly secure channels are assumed to be available at no cost between the user and each server, we assume that the user can only send data to the servers via a public channel, and that the user and each server share an individual secret key with length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . For a given <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , we determine the maximal length of the file that the user can store, and thus quantify the necessary cost to store a file of a certain length, in terms of the length of the secret keys that the user needs to share with the servers. Additionally, for this maximal file length, we determine (i) the optimal amount of local randomness needed at the user, (ii) the optimal amount of public communication from the user to the servers, and (iii) the optimal amount of storage requirement at the servers.
Receiver-Device-Independent Quantum Key Distribution

We present protocols for quantum key distribution in a prepare-and-measure setup with an asymmetric level of trust. While the device of the sender (Alice) is partially characterized, the receiver&amp;apos;s (Bob&amp;apos;s) device is treated as a black-box. The security of the protocols is based on the assumption that Alice&amp;apos;s prepared states have limited overlaps, but no explicit bound on the Hilbert space dimension is required. The protocols are immune to attacks on the receiver&amp;apos;s device, such as blinding attacks. The users can establish a secret key while continuously monitoring the correct functioning of their devices through observed statistics. We report a proof-of-principle demonstration, involving mostly off-the-shelf equipment, as well as a high-efficiency superconducting nanowire detector. A positive key rate is demonstrated over a 4.8 km low-loss optical fiber with finite-key analysis. The prospects of implementing these protocols over longer distances is discussed.
Cryogenic Electronics and Quantum Information Processing

The goal of this International Roadmap for Devices and Systems (IRDS) chapter is to survey, catalog, and assess the status of technologies in the areas of cryogenic electronics and quantum information processing. Application drivers are identified for sufficiently developed technologies and application needs are mapped as a function of time against projected capabilities to identify challenges requiring research and development effort.
Using a holographic application in learning medical terminology for English as a foreign language students

The mastery of vocabulary is a fundamental step when learning a language but for many learners, it seems difficult and boring. English as Foreign Language (EFL) learners find some difficulties in learning new words. The aim of this study was to examine the effect of using the hologram application in learning medical English terminology for EFL learners. The study involved 109 EFL learners studying at a medical college to determine if a holographic tool affected their learning. They were divided into three groups: a control group using traditional methods to learn new medical terms (no technology was used), and two experimental groups. One experimental group was taught using PowerPoint without the holographic application, while the other experimental group was taught using the holographic application. The data were collected using the pre-, post-, and delayed post-test design. The findings of the study indicated that learners who were taught using the holographic application outscored other groups and remembered more medical English terms after three weeks. Moreover, the learners felt the hologram application had a positive impact on learning medical terms.
MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series

Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.
Virtual Screening of Drug Proteins Based on the Prediction Classification Model of Imbalanced Data Mining

We propose a virtual screening method based on imbalanced data mining in this paper, which combines virtual screening techniques with imbalanced data classification methods to improve the traditional virtual screening process. First, in the actual virtual screening process, we apply k-means and smote heuristic oversampling method to deal with imbalanced data. Meanwhile, to enhance the accuracy of the virtual screening process, a particle swarm optimization algorithm is introduced to optimize the parameters of the support vector machine classifier, and the concept of ensemble learning is brought in. The classification technique based on particle swarm optimization, support vector machine and adaptive boosting is used to screen the molecular docking conformation to improve the accuracy of the prediction. Finally, in the experimental construction and analysis section, the proposed method was validated using relevant data from the protein data bank database and PubChem database. The experimental results indicated that the proposed method can effectively improve the accuracy of virus screening and has practical guidance for new drug development. This research regards virtual screening as a problem of imbalanced data classification, which has obvious guiding significance and also provides a certain reference for the problems faced by virtual screening technology.
Region specific and subimage based neighbour gradient feature extraction for robust periocular recognition

This paper proposes a Periocular recognition algorithm that uses region-specific and sub-image-based neighbor gradient feature extraction to achieve better recognition results. This approach initially segments the periocular region into four sub-regions such as eyebrow, eye corner regions, upper and lower eye fold regions after detecting the left and right eye corner points. KAZE feature extraction algorithm is used to extract the features from the upper eye fold region, while the HOG feature extraction algorithm is used to extract the feature from the eyebrow and eye corner regions. This approach also estimates the shape of the eyebrow by estimating the distance from N points on the eyebrow region to the eye corner midpoint. The eyebrow shape feature also contains the width and height measures through N points on the eyebrow that give its shape. The proposed approach also proposes a sub-image-based neighbor gradient (SING) feature extraction that extracts the neighbor gradient features from a 3×3 sub-image. Finally, the extracted features are trained using the Naïve Bayes classifier. The experimental evaluation was done using the AR dataset, CASIA Iris distance dataset, and UBIPr dataset using the metrics such as rank-1, rank-5 recognition accuracies, the area under the ROC curve (AUC), and equal error rate (EER). The proposed scheme provides a rank-1 recognition rate of 92.32 %, 97.41 %, and 97.87 % for the dataset UBIPr, CASIA-Iris, and AR datasets respectively. The experimental results reveal that the proposed method outperforms the traditional periocular recognition algorithms.
Search for an efficient entanglement witness operator for bound entangled states in bipartite quantum systems

Entanglement detection problem is one of the important problem in quantum information theory. Gurvit showed that this problem is NP complete and thus this may be the possible reason that only one criterion is not sufficient to detect all entangled states. There are some powerful entanglement detection criterion such as partial transposition criterion, realignment criterion but it may not be possible to implement them successfully in the experiment. This situation can be avoided if the entanglement is detected through the construction of witness operator method. In this work, we take an analytical approach to construct a witness operator. To achieve this task, we first construct a linear map using partial transposition and realignment operation. Then we find some conditions on the parameters of the map for which the map represent a positive map. Further, we have constructed a Choi matrix corresponding to the map and have shown that it is not completely positive. We then construct an entanglement witness operator, which is based on the linear combination of the function of Choi matrix and the identity matrix and it can detect both NPTES and PPTES. Finally, we prove its efficiency by detecting several bipartite bound entangled states which were previously undetected by some well-known separability criteria. We also compared the detection power of our witness operator with three well-known powerful entanglement detection criteria, namely, dV criterion, CCNR criterion and the separability criteria based on correlation tensor (CT) proposed by Sarbicki et. al. and find that our witness operator detect more entangled states than these criterion.
Cyber Security Analysis for Quantum Computing

The next phase of the quantum revolution is the Quantum computer Network, a network that connects distant quantum devices using quantum links in conjunction with conventional ones. Innovation that has made way for radically improved communications and computing skills. Regular computers use and analyse data in bits (0 or 1), whereas quantum computers use qubits, or quantum bits, which can simultaneously represent other states in addition to ones and zeros. This is how quantum computers vary from traditional computers. The majority of these methods for processing information on computers rely on symmetric or asymmetric cryptography algorithms. These encryption techniques can be vulnerable to attack. We analyse the quantum key distribution (QKD) technique with in a noise-free channel. In addition, we evaluate the QKD protocol with noisy channel to simulate real scenarios on the future Internet. Therefore, it would be essential to explore using quantum cryptography, which cannot be cracked by quantum computing, to secure the standard communications infrastructure used in cyber physical systems (CPS).
On the Prediction of Chaotic Time Series using Neural Networks

Prediction techniques have the challenge of guaranteeing large horizons for chaotic time series. For instance, this paper shows that the majority of techniques can predict one step ahead with relatively low root-mean-square error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). However, some techniques based on neural networks can predict more steps with similar RMSE and SMAPE values. In this manner, this work provides a summary of prediction techniques, including the type of chaotic time series, predicted steps ahead, and the prediction error. Among those techniques, the echo state network (ESN), long short-term memory, artificial neural network and convolutional neural network are compared with similar conditions to predict up to ten steps ahead of Lorenz-chaotic time series. The comparison among these prediction techniques include RMSE and SMAPE values, training and testing times, and required memory in each case. Finally, considering RMSE and SMAPE, with relatively few neurons in the reservoir, the performance comparison shows that an ESN is a good technique to predict five to fifteen steps ahead using thirty neurons and taking the lowest time for the tracking and testing cases.
Development of a Virtual Object Weight Recognition Algorithm Based on Pseudo-Haptics and the Development of Immersion Evaluation Technology

In this work, we propose a qualitative immersion evaluation technique based on a pseudo-haptic-based user-specific virtual object weight recognition algorithm and an immersive experience questionnaire (IEQ). The proposed weight recognition algorithm is developed by considering the moving speed of a natural hand tracking-based, user-customized virtual object using a camera in a VR headset and the realistic offset of the object’s weight when lifting it in real space. Customized speeds are defined to recognize customized weights. In addition, an experiment is conducted to measure the speed of lifting objects by weight in real space to obtain the natural object lifting speed weight according to the weight. In order to evaluate the weight and immersion of the developed simulation content, the participants’ qualitative immersion evaluation is conducted through three IEQ-based immersion evaluation surveys. Based on the analysis results of the experimental participants and the interview, this immersion evaluation technique shows whether it is possible to evaluate a realistic tactile experience in VR content. It is predicted that the proposed weight recognition algorithm and evaluation technology can be applied to various fields, such as content production and service support, in line with market demand in the rapidly growing VR, AR, and MR fields.
Cryptanalysis of RSA-Variant Cryptosystem Generated by Potential Rogue CA Methodology

Rogue certificate authorities (RCA) are third-party entities that intentionally produce key pairs that satisfy publicly known security requirements but contain weaknesses only known to the RCA. This work analyses the Murru–Saettone RSA variant scheme that obtains its key pair from a potential RCA methodology. The Murru–Saettone scheme is based on the cubic Pell equation x3+ry3+r2z3−3rxyz=1. The public, e, and private, d key generation process uses the secret parameter ψ=(p2+p+1)(q2+q+1) in place of the standard Euler–phi function ϕ(N)=(p−1)(q−1), where ed≡1(modψ). We prove that, upon obtaining an approximation of ψ, we are able to identify the provided key pair that was maliciously provided even if the private key d size is approximate to ψ. In fact, we are able to factor the modulus N=pq.
Semantic Embedding Guided Attention with Explicit Visual Feature Fusion for Video Captioning

Video captioning, which bridges vision and language, is a fundamental yet challenging task in computer vision. To generate accurate and comprehensive sentences, both visual and semantic information is quite important. However, most existing methods simply concatenate different types of features and ignore the interactions between them. In addition, there is a large semantic gap between visual feature space and semantic embedding space, making the task very challenging. To address these issues, we propose a framework named semantic embedding guided attention with Explicit visual Feature Fusion for vidEo CapTioning, EFFECT for short, in which we design an explicit visual-feature fusion (EVF) scheme to capture the pairwise interactions between multiple visual modalities and fuse multimodal visual features of videos in an explicit way. Furthermore, we propose a novel attention mechanism called semantic embedding guided attention (SEGA ), which cooperates with the temporal attention to generate a joint attention map. Specifically, in SEGA, the semantic word embedding information is leveraged to guide the model to pay more attention to the most correlated visual features at each decoding stage. In this way, the semantic gap between visual and semantic space is alleviated to some extent. To evaluate the proposed model, we conduct extensive experiments on two widely used datasets, i.e., MSVD and MSR-VTT. The experimental results demonstrate that our approach achieves state-of-the-art results in terms of four evaluation metrics.
Numerical Solution of Time Fractional Black–Scholes Model Based on Legendre Wavelet Neural Network with Extreme Learning Machine

In this paper, the Legendre wavelet neural network with extreme learning machine is proposed for the numerical solution of the time fractional Black–Scholes model. In this way, the operational matrix of the fractional derivative based on the two-dimensional Legendre wavelet is derived and employed to solve the European options pricing problem. This scheme converts this problem into the calculation of a set of algebraic equations. The Legendre wavelet neural network is constructed; meanwhile, the extreme learning machine algorithm is adopted to speed up the learning rate and avoid the over-fitting problem. In order to evaluate the performance of this scheme, a comparative study with the implicit differential method is constructed to validate its feasibility and effectiveness. Experimental results illustrate that this scheme offers a satisfactory numerical solution compared to the benchmark method.
Exploring the future building: representational effects on projecting oneself into the future office space

Abstract While virtual reality (VR) has been explored in the field of architecture, its implications on people who experience their future office space in such a way has not been extensively studied. In this explorative study, we are interested in how VR and other representation methods support users in projecting themselves into their future office space and how this might influence their willingness to relocate. In order to compare VR with other representations, we used (i) standard paper based floor plans and renders of the future building (as used by architects to present their creations to stakeholders), (ii) a highly-detailed virtual environment of the same building experienced on a computer monitor (desktop condition), and (iii) the same environment experienced on a head mounted display (VR condition). Participants were randomly assigned to conditions and were instructed to freely explore their representation method for up to 15 min without any restrictions or tasks given. The results show, that compared to other representation methods, VR significantly differed for the sense of presence, user experience and engagement, and that these measures are correlated for this condition only. In virtual environments, users were observed looking at the views through the windows, spent time on terraces between trees, explored the surroundings, and even “took a walk” to work. Nevertheless, the results show that representation method influences the exploration of the future building as users in VR spent significantly more time exploring the environment, and provided more positive comments about the building compared to users in either desktop or paper conditions. We show that VR representation used in our explorative study increased users’ capability to imagine future scenarios involving their future office spaces, better supported them in projecting themselves into these spaces, and positively affected their attitude towards relocating.