Reviewerly/exHarmony huggingface dataset:
Shannon and von Neumann entropies of multi-qubit Schrödinger's cat states

Using IBM's publicly accessible quantum computers, we have analyzed the entropies of Schrödinger's cat states, which have the form Ψ = (1/2)1/2 [|0 0 0⋯0〉 + |1 1 1⋯1〉]. We have obtained the average Shannon entropy SSo of the distribution over measurement outcomes from 75 runs of 8192 shots, for each of the numbers of entangled qubits, on each of the quantum computers tested. For the distribution over N fault-free measurements on pure cat states, SSo would approach one as N → ∞, independent of the number of qubits; but we have found that SSo varies nearly linearly with the number of qubits n. The slope of SSoversus the number of qubits differs among computers with the same quantum volumes. We have developed a two-parameter model that reproduces the near-linear dependence of the entropy on the number of qubits, based on the probabilities of observing the output 0 when a qubit is set to |0〉 and 1 when it is set to |1〉. The slope increases as the error rate increases. The slope provides a sensitive measure of the accuracy of a quantum computer, so it serves as a quickly determinable index of performance. We have used tomographic methods with error mitigation as described in the qiskit documentation to find the density matrix ρ and evaluate the von Neumann entropies of the cat states. From the reduced density matrices for individual qubits, we have calculated the entanglement entropies. The reduced density matrices represent mixed states with approximately 50/50 probabilities for states |0〉 and |1〉. The entanglement entropies are very close to one.
Interrupting Immersive Immediacy

Immersive experiences in theatre and digital games are often characterized by immediacy in space and time, focusing on the imminent action of the here and now. Live action roleplay (larp) practices, by contrast, can encourage reflexivity. Such reflexive play can support the agential capacities of players in participatory performance works to visualize and pursue alternative futures.
Fast and unbiased estimation of volume under the ordered multi-class ROC hyper-surface with continuous measurements

Receiver operating characteristic (ROC) analysis is a popular tool to deal with two-class problems in many science and engineering areas. However, in practice, multi-class problems are frequently encountered, such as in ordinal regression in the area of machine learning. The volume under the multi-class ROC hyper-surface (VUHS) has been proposed to evaluate the performance of multi-class classifiers. Unfortunately, however, the computational loads of current methods are rather heavy, making them impracticable in scenarios where the sample size is large. Moreover, the null distribution, which is mandatory for significance test, is also unknown to the best of our knowledge. To improve such unsatisfactory situations, in this article we develop an efficient algorithm for unbiased estimation of VUHS and the corresponding variance. Exploiting the technique of dynamic programming (DP) as well as Dyck paths, our proposed algorithm outperforms the state-of-the-art algorithm based on graph theoretic for large samples. In addition, we derive the analytical expressions of the mean and variance of VUHS under the null distribution. Theoretical analysis and Monte Carlo simulations verified both the unbiasedness and computational efficiency of our algorithm.
Basic modal congruent and monotonic multilattice logics

Abstract In the paper, we introduce multilattice versions of the basic congruent and monotonic modal logics. In the case of congruent and monotonic ones, we also study their extensions by Gödel’s rule. We formulate these logics in the form of sequent calculi and prove syntactic embedding theorems (as a consequence, we obtain cut admissibility and decidability). Then we present them algebraically and semantically: via modal multilattices and via general and descriptive neighbourhood frames. We show the dual equivalency of the categories of modal multilattices and descriptive neighbourhood frames. Using Lindenbaum–Tarski algebras, we prove that the sequent calculi under consideration are sound and complete with respect to modal multilattices.
<scp>SARS‐CoV2</scp> hospital surveillance and control system with contact tracing for patients and health care workers at a large reference hospital in Spain during the first wave: An observational descriptive study

During the first peak of the COVID-19 pandemic, the Preventive Medicine Department and the Occupational Health Department at Hospital Clinic de Barcelona (HCB), a large Spanish referral hospital, developed an innovative comprehensive SARS-CoV2 Surveillance and Control System (CoSy-19) in order to preserve patients' and health care workers' (HCWs) safety. We aim to describe the CoSy-19 and to assess the impact in the number of contacts that new cases generated along this time.Observational descriptive study of the findings of the activity of contact tracing of all cases received at the HCB during the first peak of COVID-19 in Spain (February 25th-May 3rd, 2020).A team of 204 professionals and volunteers performed 384 in-hospital contact-tracing studies which generated contacts, detecting 298 transmission chains which suggested preventive measures, generated around 22 000 follow-ups and more than 30 000 days of work leave. The number of contacts that new cases generated decreased during the study period.Coordination between Preventive Medicine and Occupational Health departments and agile information systems were necessary to preserve non-COVID activity and workers safety.
A Semisupervised Convolution Neural Network for Partial Unlabeled Remote-Sensing Image Segmentation

Semantic segmentation methods for remote-sensing images based on the deep learning framework have achieved significant performance improvements. However, most of the existing work is based on fully supervised methods, which rely on a large number of manually annotated pixel-level labels. However, for remote-sensing images, labeling the ground-truth takes time and effort. To solve the problem in existing methods of overly relying on manual labeling, in this study, we propose a semisupervised convolution neural network based on contrastive loss for partial unlabeled remote-sensing image segmentation. In the design of the contrastive loss function, to capture the semantic relationship of pixels and improve the separability between different categories, we propose pixel-level and region-level contrastive loss. The pixel-level contrastive loss is designed to learn the correlation between different images, while region-level contrastive loss is designed to improve the quality of generated pseudo-labels. In addition, we designed a propagated self-training method that further guarantees the quality of the pseudo-labels and improves the richness of the labeled data. Experiments on POTSDAM and Vaihingen datasets demonstrate that the proposed method achieves the highest Mean Intersection over Union (mIOU) and significantly outperforms previous methods.
Shareability-Exclusivity Representation on Product Grassmann Manifolds for Multi-camera video clustering

With the rapid popularity of multi-camera networks, one human action is usually captured by multiple cameras located at different angles simultaneously. Multi-camera videos contain the distinct perspectives of one action, therefore multiple views can overcome the impacts of illumination and occlusion. In this paper, we propose a novel multi-camera video clustering model, named Shareability-Exclusivity Representation on Product Grassmann Manifolds (PGM-SER), to address two key issues in traditional multi-view clustering methods (MVC): (1) Most MVC methods directly construct a shared similarity matrix by fusing multi-view data or their corresponding similarity matrices, which ignores the exclusive information in each view; (2) Most MVC methods are designed for multi-view vectorial data, which cannot handle the nonlinear manifold structure hidden in multi-camera videos. The proposed PGM-SER firstly adopts Product Grassmann Manifolds to represent multi-camera videos, then simultaneously learn their shared and exclusive information in global structures to achieve multi-camera video clustering. We provide an effective optimization algorithm to solve PGM-SER and present the corresponding convergence analysis. Finally, PGM-SER is tested on three multi-camera human action video datasets and obtain satisfied experimental results.
Small screen-big information challenge for older adults: a study on visual momentum and gesture navigation

The explosion of information and the miniaturisation of mobile devices seems to be an oxymoron. A large amount of information means a wider information space, and a smaller screen means a more limited display capacity. ‘Small screen-big information’ can often be disorienting, especially for older adults. Treating it as navigation in information space, we explored solutions in terms of spatial cognition and gesture motor control. First, we introduce a concept from cognitive psychology, visual momentum (VM). In terms of gesture control, we propose the device-based method that differs from traditional touch-based methods. To test their roles, two experiments were conducted among 30 older adults and comparatively tested 30 younger adults. Experiment 1 examined the contribution of high VM and a device-based method. Experiment 2 further examined an important property of view translation, smoothness, in touch-based and device-based methods. Results show that increasing the VM by adding grids and using a device-based method can help users find targets faster and more accurately, which is particularly effective for the older group. Meanwhile, differences in navigation strategies between touch-based and device-based methods were proposed. Finally, the smoothness of a device-based method in visual information processing is also demonstrated.
Research and Implementation of Database Operation Recognition Based on YOLO v5 Algorithm

With the rapid development of information technology, traditional enterprises such as State Grid pay more and more attention to database security. In order to further strengthen the audit of database operations, a universal audit method is needed. This paper proposes a method for real-time database operation recognition based on the YOLO v5 algorithm. Through this method, real-time image recognition is performed on the database operation and maintenance interface, the instruction image is converted into a character sequence, and the database operation audit is completed according to the database audit rules. This article provides a new method for database auditing.
High Quality Disparity Remapping with Two-Stage Warping

A high quality disparity remapping method that preserves 2D shapes and 3D structures, and adjusts disparities of important objects in stereo image pairs is proposed. It is formulated as a constrained optimization problem, whose solution is challenging, since we need to meet multiple requirements of disparity remapping simultaneously. The one-stage optimization process either degrades the quality of important objects or introduces serious distortions in background regions. To address this challenge, we propose a two-stage warping process to solve it. In the first stage, we develop a warping model that finds the optimal warping grids for important objects to fulfill multiple requirements of disparity remapping. In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure. The superior performance of the proposed method is demonstrated by experimental results.
Spontaneous emission rate and phase diffusion in gain-switched laser diodes

Quantum random number generation (QRNG) has become a topic of growing interest in recent years due to important applications in cryptography and simulations. Interferometric detection of phase diffusion in gain-switched single-mode semiconductor lasers is one of the main generation techniques. In this paper, we study experimentally and theoretically the phase diffusion in gain-switched discrete mode laser diodes. We derive a stochastic rate equations model for the laser electric field that avoids numerical instabilities that appear when simulating amplitude and phase equations. Measurements are performed in order to extract the parameters of our semiconductor laser. Spontaneous emission rate coupled into the lasing mode is measured as a function of the carrier number for bias currents below threshold. A quadratic dependence is obtained that permits us to evaluate the validity of the linear approximations that have been used to describe laser phase diffusion in QRNG. The good agreement between experiments and theory permits us to give a realistic quantitative description of the dynamical evolution of the phase statistics in this type of QRNGs.
Measuring EOS.IO DApp Resource Allocation and Costs through a Benchmark Application

Decentralized Applications have become of paramount importance, especially thanks to the widespread adoption of blockchains, such as Ethereum and EOS.IO, which are two of the most known platforms where such applications can be executed. Even if the goal of Ethereum and EOS.IO is very similar, the two projects have distinct capabilities and properties. For example, they use different consensus algorithms, different languages to program smart contracts, and allocate and manage on-chain resources in different ways. In this paper, we perform in-depth analysis of the models used by EOS.IO blockchain to manage its resources (i.e., ram, cpu and network bandwidth). For this purpose, we instantiate an EOS.IO-based Decentralized Application (DApp) implementing a Decentralized Rating Framework and we measure its resource requirements. Finally, we evaluate and compare the cost in fees required for running the DApp under three different resource management models provided by EOS.IO, which are the staking, rex, and power up models.
Algorithms: Supervised Machine Learning Types and Their Application Domains

In today’s world, millions and trillions of data are available from anywhere and everywhere. However, these data have no use if not processed right away for obtaining unseen and meaningful information from it. Machine learning technique, a subfield under artificial intelligence, aims to train the computer systems to learn and apply knowledge from the given set of training data. Machine learning algorithms are used to extract unseen trends and patterns from the data for deriving meaningful insights and foresights to make future decisions in business, manufacturing, government and many more. This survey provides a complete view on supervised machine learning algorithms, their pros and cons along with their applications in specific areas under each machine learning class.
A Method for Accelerating YOLO by Hybrid Computing Based on ARM and FPGA

CNN has promoted the rapid development of target recognition and detection technology. By comparison with machine learning, it has faster detection speed and higher robustness. However, the deployment of the CNN network model often needs more computing resources, which hinders the application of artificial intelligence technology. In this paper, the authors use the hybrid architecture of ARM and FPGA to deploy a You Only Look Once (YOLO) model on the FPGA to improve the efficiency of target recognition and detection under condition of low resources consumption and low power consumption. YOLO is a one-stage real-time detection model and it has high detection speed and remarkable accuracy. High-level Synthesis (HLS) is a fast development and verification technology of FPGA based on C/C++. We use HLS to implement the pipeline mechanism and complete the parallel calculation of convolution, thereby constructing a forward reasoning model of YOLOv3-tiny. In order to accelerate the forward inference process of YOLO, we combine convolution with batch normalization. The FPGA we use in the paper is Xilinx Zynq-7035 containing system on chip (SoC). We build the software and hardware co-architecture of ARM and FPGA on Zynq-7035, which makes full use of the logic control advantages of ARM and the logic computing advantages of FPGA. In the end, we achieve 28.99 GOP/S speed with only 3.715W power consumption. Finally, compared with the Ryzen 5 3600, we achieve 41.3inference speed at a lower clock rate.
Website Development with Laravel and Scrum Method: A Study case of Stasiun Mebel Jepara Store Case

Stasiun Mebel Jepara is a furniture store located in Medan, Indonesia. At the Stasiun Mebel Jepara store, there are problems regarding recording the results of transaction services, where the shop owner calculates the transaction results relying on a calculator and does not use a computer, product data collection also depends on recording on books so that employees have difficulty seeing product prices because they have to communicate again with the shop owner. Shop owners also find it challenging to record and collect data on products to be repaired and forged, so shop owners often seek back information obtained through private chats from customers. The author builds the Stasiun Mebel Jepara website using the Scrum method and the Laravel framework. The author uses the Scrum method because it is iterative, incremental, and can apply a continuous learning culture. The author decided to use Laravel because it has complete documentation and can be used freely and free of charge—the work on this final project resulted in the Stasiun Mebel Jepara Website. After building the Stasiun Mebel Jepara website, the author tested the website’s usability using the System Usability Scale. The results of testing on 169 respondents in using the System Usability Scale website showed that the average SUS score of respondents was 64.41 with an interpretation of the C grade category. Respondents assessed that the website created was OK (adjectives) and marginal (acceptability), and passive in terms of NPS.
Vision-based Health Protocol Observance System for Small Rooms

COVID-19 has impacted the daily lives of millions. Businesses and educational institutions had to take preventive measures including social distancing to reduce the spread of COVID-19. This study aims to mitigate COVID-19 transmission in a small areas like classrooms, where occlusion and perspective issues are prevalent and highly challenging, using an innovative vision-based approach. Several human-head detection YOLOv4 models were trained on three different training datasets. Afterwards, they got evaluated to select the most reliable model for the social distancing solution. A 91.12% mAP was reached after improving the SCUT-HEAD dataset by generating face masks on the subjects. Using euclidean distance, triangle similarity and head size ratios a formula was developed to accurately calculate distances in small spaces using one camera; with no need of perspective annotation and highly reducing occlusion issues. Given most studies reviewed lacked ground-truth data, we created a real test scenario, with marks on the floor to readily provide ground-truth data during the experiment. The proposed human-head (triangle similarity) method managed to achieve an F1-Score of 85.66% compared to a reference state-of-the-art solution employing a whole body and perspective transform approach which achieved an F1-Score of 80.46%. Our solution achieved better results in small room scenarios, with high prospects of addressing challenges in a real-world environment.
A Data-Driven Based Energy Consumption Modeling for Heterogeneous Servers in Data Centers

The server is recognized as one of the main energy consumption equipment in data center, of which accurate energy consumption modeling is the key of improving the data center's energy efficiency. This paper proposes a data-driven based energy consumption modeling for the heterogeneous servers popularized in the modern data center, i.e. it constructs a polynomial based energy consumption model by using the real-time server performance data published in the SPECpower_ssj 2008. The results illustrate that the server can have different energy consumption characteristics even with the same CPU and it also shows the superiority of the proposed model. Lastly, this paper takes the data center with 1000 servers as an example, a comprehensive comparison analysis between the proposed model and the existing server energy consumption models is conduced, and the results show the accuracy of the proposed model and the effectiveness to track the actual energy consumption profile.
Anonymous Storage and Verification Model of IIoT Based on Blockchain

With the rapid development of smart industry, the data acquisition ability of edge devices in the perception layer of smart factory is gradually improved. The security of Industrial Internet of Things has increasingly become a common focus. Aiming at the data security problem in the security of industrial Internet of things, combined with the current situation of industrial Internet of things equipment, this paper proposed an anonymous storage and verification model of industrial production status which is based on blockchain. The purpose of this paper is to ensure the storage security and efficient use of industrial production data. We improve the existing Industrial Internet of Things architecture and add blockchain to participate in smart factory privacy protection and data automation processing. On this basis, anonymous storage and verification algorithm for production status, DCB (Double Color Ball) algorithm, is designed. Finally, the simulation industry platform is built, and the architecture is realized by taking the simulation industry platform as an example. The proposed structure can verify data anonymously on the premise of ensuring data security through theoretical analysis and experiment. Besides, the IIoT system still maintains a low overhead.
A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming

Abstract Multi‐criteria recommender systems (MCRSs) provide suggestions to users based on their preferences to various criteria. Incorporation of criteria ratings into recommendation framework can provide quality recommendations to users because these ratings can elicit users' preferences efficiently. However, elicitation of user's overall preference based on criteria ratings is a key issue in MCRS. Even though several aggregation methods for the elicitation of users' overall preference have been investigated in the literature, no method has been shown the superiority under all circumstances. Therefore, we propose a model based approach to user preference discovery in multi‐criteria RS using genetic programming (GP). In this work, we suggest three‐stage process to generate recommendations to users. First, we learn user preference transformation function to aggregate criteria ratings by using GP, and then we utilize the preference function, so derived, for computing similarities in MCRS. Finally, items are recommended to users. Experimental results on Yahoo! Movies dataset show the superiority of our proposed approach in comparison to other aggregation approaches.