Reviewerly/exHarmony huggingface dataset:
Nonadiabatic transition probabilities for quantum systems in electromagnetic fields: Dephasing and population relaxation due to contact with a bath

We contrast Dirac's theory of transition probabilities and the theory of nonadiabatic transition probabilities, applied to a perturbed system that is coupled to a bath. In Dirac's analysis, the presence of an excited state |k0⟩ in the time-dependent wave function constitutes a transition. In the nonadiabatic theory, a transition occurs when the wave function develops a term that is not adiabatically connected to the initial state. Landau and Lifshitz separated Dirac's excited-state coefficients into a term that follows the adiabatic theorem of Born and Fock and a nonadiabatic term that represents excitation across an energy gap. If the system remains coherent, the two approaches are equivalent. However, differences between the two approaches arise when coupling to a bath causes dephasing, a situation that was not treated by Dirac. For two-level model systems in static electric fields, we add relaxation terms to the Liouville equation for the time derivative of the density matrix. We contrast the results obtained from the two theories. In the analysis based on Dirac's transition probabilities, the steady state of the system is not an equilibrium state; also, the steady-state population ρkk,s increases with increasing strength of the perturbation and its value depends on the dephasing time T2. In the nonadiabatic theory, the system evolves to the thermal equilibrium with the bath. The difference is not simply due to the choice of basis because the difference remains when the results are transformed to a common basis.
SM2-Based Offline/Online Efficient Data Integrity Verification Scheme for Multiple Application Scenarios

With the rapid development of cloud storage and cloud computing technology, users tend to store data in the cloud for more convenient services. In order to ensure the integrity of cloud data, scholars have proposed cloud data integrity verification schemes to protect users' data security. The storage environment of the Internet of Things, in terms of big data and medical big data, demonstrates a stronger demand for data integrity verification schemes, but at the same time, the comprehensive function of data integrity verification schemes is required to be higher. Existing data integrity verification schemes are mostly applied in the cloud storage environment but cannot successfully be applied to the environment of the Internet of Things in the context of big data storage and medical big data storage. To solve this problem when combined with the characteristics and requirements of Internet of Things data storage and medical data storage, we designed an SM2-based offline/online efficient data integrity verification scheme. The resulting scheme uses the SM4 block cryptography algorithm to protect the privacy of the data content and uses a dynamic hash table to realize the dynamic updating of data. Based on the SM2 signature algorithm, the scheme can also realize offline tag generation and batch audits, reducing the computational burden of users. In security proof and efficiency analysis, the scheme has proven to be safe and efficient and can be used in a variety of application scenarios.
Face Recognition: A Mini-Review

Biometrics involves face recognition for recognizing a human’s identity. It is exceedingly used in many fields, for example, computer vision, image analysis, and security applications. This paper contains five sections. The first section presents an overview of face recognition technology, and why this biometric modality is important. The second section describes the main stages of face recognition. The third section highlights the different methods used for face recognition including holistic, local, and hybrid. The next section summarizes some popular datasets available for face recognition. At the end, the paper concludes with a note on future research.
A distributed computing model for big data anonymization in the networks

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.
Repeated Cross-Scale Structure-Induced Feature Fusion Network for 2D Hand Pose Estimation

Recently, the use of convolutional neural networks for hand pose estimation from RGB images has dramatically improved. However, self-occluded keypoint inference in hand pose estimation is still a challenging task. We argue that these occluded keypoints cannot be readily recognized directly from traditional appearance features, and sufficient contextual information among the keypoints is especially needed to induce feature learning. Therefore, we propose a new repeated cross-scale structure-induced feature fusion network to learn about the representations of keypoints with rich information, 'informed' by the relationships between different abstraction levels of features. Our network consists of two modules: GlobalNet and RegionalNet. GlobalNet roughly locates hand joints based on a new feature pyramid structure by combining higher semantic information and more global spatial scale information. RegionalNet further refines keypoint representation learning via a four-stage cross-scale feature fusion network, which learns shallow appearance features induced by more implicit hand structure information, so that when identifying occluded keypoints, the network can use augmented features to better locate the positions. The experimental results show that our method outperforms the state-of-the-art methods for 2D hand pose estimation on two public datasets, STB and RHD.
Interaction With a Television Character Powered by Artificial Intelligence Promotes Children’s Science Learning

Television and video programs are important educational resources for young children, especially those from under-resourced households. These programs’ potential can be amplified if children are given the opportunity to meaningfully interact with media characters during their video watching. In this project, we partnered with PBS KIDS to develop interactive science- focused videos in which the main character, powered by artificial intelligence, engaged in conversation with children by asking them questions and providing responsive feedback. The children who watched the interactive video performed better in a science posttest compared to their peers who watched the non-interactive or pseudo-interactive videos. This paper sheds light on the feasibility and effectiveness of using conversational technologies to support children’s active learning from video watching.
Informal Institutional Characteristics and Youth Involvement in Agribusiness Entrepreneurship in Fako Division, Cameroon: A Structural Equation Modeling Approach

This study aims at examining the role of informal institutional characteristics on youth involvement in agribusiness entrepreneurship activities in the Fako division of the South West Region of Cameroon. Data were collected with the use of a self-administered questionnaire. Questionnaires were administered using a stratified sampling technique. The target population was youths between the age of 18 to 35 years old who are involved in any agribusiness-related activity for commercial purposes. Questionnaires were administered to a sample of 500 youths but only 451 of the questionnaires were returned without any error. We adopted a partial least square structural equation modeling technique (PLS-SEM) using SamrtPLS4.0. The findings revealed that the custom of the Fako people has a positive relationship with youth involvement in agribusiness entrepreneurship activities in the Fako division (β = 0.230, p &lt; 0.000). However, the attitude of youths toward agricultural-related activities has a negative relationship with youths’ involvement in agribusiness entrepreneurship in the Fako division (β= –0.025, p &lt; 0.560). Meanwhile, land tenure norms/fragmentation have a positive relationship with youths’ involvement in agribusiness entrepreneurship (β = 0.301, p &lt; 0.,000), and traditional non-working days show a positive relationship with youths’ involvement in agribusiness entrepreneurship in Fako (β=0.274, p &lt; 0.000).Serious attention should be given to influencing the attitude of youths toward agribusiness-related activities through sensitization programs such as organizing special agropastoral shows only for young people and other radio and TV programs.
Hybridized Dimensionality Reduction Method for Machine Learning based Web Pages Classification

Feature space high dimensionality is a well-known problem in text classification and web mining domains, it is caused mainly by the large number of vocabularies contained within web documents. Several methods were applied to select the most useful and important features over the years; however, the performance of such methods is still improvable from different aspects such as the computational cost and accuracy. This research presents an enhanced cosine similarity-based hybridization of two efficient feature selection methods for higher classification performance. The reduced feature sets are generated using the Random Projection (RP) and the Principal Component Analysis (PCA) methods, individually, then hybridized based on the cosine similarity values between features’ vectors. The performance of the proposed method in terms of accuracy and F-measure was tested on a dataset of web pages based on several term weighting schemes. As compared to relevant methods, results of the proposed method show significantly higher accuracy and f-measure performance based on less feature set size. Index Terms— Cosine similarity, Dimensionality Reduction, Feature selection, PCA, Random Projection.
Single-flux-quantum signal processors monolithically integrated with a superconducting nanostrip single-photon detector array

We developed a monolithic integration process for a superconducting nanostrip single-photon detector (SNSPD) and a cryogenic signal processor based on a single-flux-quantum (SFQ) circuit. Sixteen-pixel SNSPDs consisting of a 10-nm-thick and 100-nm-wide NbTiN nanowire were fabricated on a 3-in. Si wafer with an SFQ merger fabricated by the National Institute of Advanced Industrial Science and Technology Nb 2.5 kA/cm2 standard process 2. Subsequently, we illuminated the photons on the 16-pixel SNSPD array through an optical fiber and obtained a similar bias-current dependence of the detection efficiency with a single-pixel SNSPD without the SFQ merger. This indicates that the SFQ merger works correctly, and that the power dissipation of the SFQ circuit, which is estimated to be 200 μW, does not deteriorate the photon detection of SNSPD fabricated on the same chip with a size of 5 × 5 mm2. Furthermore, we measured timing jitter using a pulsed laser as the photon source. Owing to the careful tuning of the wiring length between each pixel of the 16-pixel SNSPD and each input port of the SFQ merger, the obtained timing jitter was 41.4 ps, which is approximately equal to or less than that of the single-pixel SNSPD.
UNIVERSAL «SMART» SET-TOP BOX TO HOUSEHOLD APPLIANCES

A simple and cheap way to turn ordinary household appliances (refrigerators, microwave ovens, coffee makers, washing machines, coolers, etc.) into «smart» ones using a stand-alone wireless set-top box for implementation of a «smart house» within the «Internet of Things» concept is presented. The basic hardware architecture of the set-top box has been developed, which includes a microcontroller, a Wi-Fi module with a built-in antenna, a rechargeable battery or a 220 V power supply. Depending on the functions performed, the basic architecture of the set-top box can be supplemented with various modules. i. e. external sensors (detectors, scanners. etc.) receiving signals from household appliances, a memory module, modules for input or output of information, a clock module, etc. The software of the set-top box microcontroller provides receiving and processing information from various external sensors generating information about operation of the household appliance and transmitting it through a wireless access point based on Wi-Fi technology to Internet to notify the user through the corresponding mobile application. The set-top box can work in conjunction with conventional household appliances without interfering with their design. Potential consumers of the set-top box are, first of all, domestic consumers, as well as any organizations and firms that use various household appliances at their offices and industrial premises. Based on the proposed architecture of the set-top box, a solution was developed in the form of a «smart bottle», which allows you to control the water level in a 19-liter bottle and use a mobile application to automatically order bottles from a bottled water supplier. The set-top box can be used with any types of water coolers/dispensers and even with hand pump bottles.
Top concerns of user experiences in Metaverse games: A text-mining based approach

Studies on game experience, which are the most typical representatives of the Metaverse, are conceptual and in their infancy. Previous work on virtual worlds and user experience is context-specific, and user experience changes over time. Literature using text mining in game experience studies is highly fragmented and focused on extracting adjectives for user reviews, which prevents anticipating the context. Considering Metaverse's unique concepts and content, developments in supporting technologies, and the ever-increasing mobility trend, it is essential to explore how the user experience is affected, especially in mobile virtual world applications. For this reason, user comments with low scores of mobile applications related to three commonly used Metaverse games (Minecraft, Roblox or Play Together) were analyzed. To the best of our knowledge, this is the first study attempting to extract user concerns associated with mobile applications of Metaverse games by using text mining. As s result, we identified 12 topics, including social misconduct, controls, content lost, social interaction, price fairness, computing resources, support, playability, unstable connections, cheating, authorization, and bans. The analysis further indicated users were more frequently concerned about issues related to computing resources and price fairness. Besides, the comments that users liked the most belong to the controls, content lost and social interaction.
LWS: A framework for log-based workload simulation in session-based SUT

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets.
On the Comparability and Optimal Aggressiveness of the Adversarial Scenario-Based Safety Testing of Robots

This article studies the class of scenario-based safety testing algorithms in the black-box safety testing configuration. For algorithms sharing the same state–action set coverage with different sampling distributions, it is commonly believed that prioritizing the exploration of high-risk states and actions leads to a better sampling efficiency. Our proposal disputes the above intuition by introducing an impossibility theorem that provably shows that all the safety testing algorithms of the aforementioned difference perform equally well with the same expected sampling efficiency. Moreover, for testing algorithms covering different sets of states and actions, the sampling efficiency criterion is no longer applicable as different algorithms do not necessarily converge to the same termination condition. We then propose a testing aggressiveness definition based on the almost safe set concept along with an unbiased and efficient algorithm that compares the aggressiveness between testing algorithms. Empirical observations from the safety testing of bipedal locomotion controllers and vehicle decision-making modules are also presented to support the proposed theoretical implications and methodologies.
Features of academic mobility of Latin American and Caribbean students in modern circumstances

The authors identify the vectors of development and consider the features and main trends of academic mobility of Latin American students in the current conditions of the development of society and higher education. It is concluded that in recent years, academic mobility has been undergoing changes, including changing its vector in favor of obtaining education in the region. The key reasons for this process are identified: the traditional propensity of Latin Americans for good neighborliness, the active development of the idea of a “common market of knowledge” in the region, the increase in the level of education in a number of Latin American states, the adjustments made to the processes of academic mobility by the pandemic of the new coronavirus. Along with the regional vector, the role of Russian universities is determined and the place of Rossotrudnichestvo in the system of academic mobility is shown. The factors that can affect the dynamics of academic mobility of Latin American students to Russian universities are highlighted.
Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification

This article analyzes a kernel-based transfer learning method, under a k-class Gaussian mixture model for the input data. Following recent advances in random matrix theory, we propose new insights in transfer learning schemes for challenging cases, when the first-order statistics of all data classes coincide. The article proves the asymptotic normality of the LS-SVM decision function for any smooth kernel function. As a result, an optimization scheme is proposed to minimize the classification error rate. Our theoretical results are corroborated through simulations and then successfully applied to the context of transfer learning for PolSAR image classification.
Maskdul: Data Uncertainty Learning in Masked Face Recognition

Since mask occlusion causes plentiful loss of facial feature, Masked Face Recognition (MFR) is a challenging image processing task, and the recognition results are susceptible to noise. However, existing MFR methods are mostly deterministic point embedding models, which are limited in representing noise images. Moreover, Data Uncertainty Learning (DUL) fails to achieve reasonable performance in MFR. Therefore, we propose a novel two-stream convolutional network, masked face data uncertainty learning (MaskDUL), that solves the problems by sampling uncertainty and intra-class distribution learning in MFR. Specifically, a Hard Kullback-Leibler Divergence (H-KLD) method is proposed to serve as an adaptive variance regularizer and a magnitude-based module is adopted to adaptively adjust the angular margin of different samples. Finally, insightful evaluation demonstrates the effectiveness and robustness of our MaskDUL.
Fast and practical method for underwater stereo vision calibration based on ray-tracing

The perspective camera model has difficulty handling refracted light in the underwater environment. To achieve accurate and convenient calibration in large underwater scenes, we propose a method based on the underwater refractive camera model in this paper. First, the initial values of the refraction parameters are solved using refraction coplanarity constraints. Then the initial values are optimized nonlinearly using co-point constraints, which simplifies the optimization process of existing methods. In the field of view of 200mm×200mm, the experiment results show that the reconstruction accuracy of the proposed method can reach below 0.02 mm, and it is equally effective in the case of sparse calibration.
Time-Varying Signals Recovery Via Graph Neural Networks

The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series. Effectively capturing the spatiotemporal information in these signals is essential for the downstream tasks. Previous studies have used the smoothness of the temporal differences of such graph signals as an initial assumption. Nevertheless, this smoothness assumption could result in a degradation of performance in the corresponding application when the prior does not hold. In this work, we relax the requirement of this hypothesis by including a learning module. We propose a Time Graph Neural Network (TimeGNN) for the recovery of time-varying graph signals. Our algorithm uses an encoder-decoder architecture with a specialized loss composed of a mean squared error function and a Sobolev smoothness operator. TimeGNN shows competitive performance against previous methods in real datasets.