Reviewerly/exHarmony huggingface dataset:
Comparative analysis of properties for amplified coherent state and amplified squeezed vacuum

Two “amplified” quantum states, that is, amplified coherent state (ACS) and amplified squeezed vacuum (ASV), are considered in this paper by applying operator [Formula: see text] on coherent state (CS) and squeezed vacuum (SV), respectively. Here [Formula: see text] [Formula: see text] denotes a amplification factor and [Formula: see text]) denote the creation (annihilation) operator. Along these two lines, we make a comparative analysis of properties for ACS and ASV. The considered properties include density matrix elements, Wigner function, mean photon number, second-order autocorrelation function, and quadrature squeezing. We derive analytical expressions and make numerical simulations for all the properties. The noteworthy results include: (1) the ACS has antibunching and squeezing characters; (2) the ASV will have the bunching and antibunching effect in small initial squeezing.
Facial Expression Recognition Using Histogram of Oriented Gradients with SVM-RFE Selected Features

This study is an attempt towards improving the accuracy and execution time of a facial expression recognition (FER) system. The algorithmic pipeline consists of a face detector block, followed by a facial alignment and registration, feature extraction, feature selection, and classification blocks. The proposed method utilizes histograms of oriented gradients (HOG) descriptor to extract features from expressive facial images. Support vector machine recursive feature elimination (SVM-RFE), a powerful feature selection algorithm is applied to select the most discriminant features from high-dimensional feature space. Finally, the selected features were fed to a support vector machine (SVM) classifier to determine the underlying emotions from expressive facial images. Performance of the proposed approach is validated on three publicly available FER databases namely CK+, JAFFE, and RFD using different performance metrics like recognition accuracy, precision, recall, and F1-Score. The experimental results demonstrated the effectiveness of the proposed approach in terms of both recognition accuracy and execution time.
Edge Emergency Demand Response Control via Scheduling in Cloudlet Cluster

A cloudlet is a small-scale cloud datacenter deployed at the network edge to support mobile applications in proximity with low latency. While an individual cloudlet operates on moderate power, cloudlet clusters are well-suited candidates for emergency demand response (EDR) scenarios due to substantial electricity consumption and job elasticity: mobile workloads in the edge often exhibit elasticity in their execution. To efficiently carry out edge EDR via cloudlet cluster control, one fundamental problem needs to be addressed: how to schedule and allocate workloads in a cloudlet cluster to satisfy EDR requirements. We propose an online task scheduling algorithm for the chosen cluster to dispatch workloads to guarantee target EDR power reduction. By exploiting the primal-dual optimization theory, we prove that our control scheme runs in polynomial time and achieves near-optimal performance. Large-scale simulation studies based on real-world data also confirm the efficiency and superiority of our scheme over state-of-the-art algorithms.
Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks

Sum-Product Networks (SPN) are deep probabilistic models with demonstrated excellent performance in several machine learning tasks. As with many other probabilistic models, performing Maximum-A-Posteriori inference in SPNs is NP-hard. Selective SPNs are a subclass of SPNs that allow for efficient Maximum-A-Posteriori inference and closed-form parameter learning. Due to the high number of parameters, SPNs learned from data can produce unreliable and overconfident inferences, especially for instances with low statistical support. This issue can be partially mitigated by performing a robustness analysis of inferences with respect to small changes in the parameters. In this work, we address the problem of assessing the robustness of Maximum-A-Posteriori inferences produced with Selective SPNs to global perturbations of the parameters. We consider such an inference robust if it remains the single maximizer under small perturbations of the model parameters. We present efficient algorithms and an empirical analysis with realistic problems involving missing data completion and multilabel classification. The experiments show that our criteria are informative with respect to the inference accuracy, suggesting that it indeed discriminate robust and non-robust instances.
Analyzing the Effect of Block Size on the Quality of the Stego Audio

A rapid technological development of information technology has many advantages to the public. Transferring data which may be previously difficult to do, now can be done easier. Nevertheless, this convenience has brought many consequences, including the security aspect of the data being transmitted. It is possible that the transferred data are intercepted by an attacker. In order to prevent this issue, some methods have been introduced, for example the data hiding method. The existing approaches, however, still have problems in terms of the size of the protected data and the quality of the stego data. In this research, we provide an improvement in the performance and analyzing the effect of the block sizes which carry the protected data. The experimental results show that the proposed method is able to get better results, especially for the bigger block sizes.
Architecture of innovative educational spaces of a university campus. On the example of SYSTEMS Centre of Engineering Systems in Construction at KSUAE (Kazan, Russia)

Abstract University campuses in Russia require updating educational methods, modernizing buildings and territories, improving the material and technical base. Transformations should take place in accordance with the latest trends in the design of educational spaces. The article proposes an integrated approach in the design of innovative educational spaces, guided by the basic principles: openness and connectedness; multifunctionality and transformability; harmony. This method was used in the design and implementation of the modernization project of the Center for Engineering Systems in the construction of Kazan State University of Architecture and Engineering. The successful launch and operation of the center confirms the relevance of the method used.
Low light image denoising solution with contrast enhancement in curvelet domain using Gaussian mixture adaptation model

Images captured under low light are noisy and consist of unidentifiable features. Low light noise problem occurs in imaging devices because of smaller sensor size or insufficient exposure. Low light image denoising is an exacting task in many image processing applications. This paper proposes a patch-based image denoising method for low light images in the curvelet domain with contrast enhancement. Curvelet transform is a directional transform and it gives the best sparse representation for images with edges. Here the Expectation–Maximization (EM) algorithm, based on the Gaussian mixture adaptation method is performed in the curvelet domain for denoising. EM Algorithm helps in computing the Gaussian mixture model (GMM) parameters from the patches which are used in maximum a posteriori estimation to update them. GMM parameters and patches are updated periodically until a satisfactory result is achieved. Simulation is performed on standard test data set, and then extended to natural low light noisy images. The results of the proposed technique are then compared using quality metrics such as Peak Signal to Noise Ratio and Structural Similarity Index. It is observed that the use of curvelet transform in denoising process helps to restore the structural information satisfactorily.
The magnetic pendulum and weather

The magnetic pendulum represents an intriguing demonstration of chaotic motion and unpredictability. Because of this it provides an excellent tool to illustrate the challenges associated with weather prediction. Presented in this note are numerical simulations of various paths traced out by the magnetic pendulum which serve to highlight the well-known 'butterfly effect'. A mathematical model and computer program to solve the equations is provided to allow for further exploration. An analogy between this demonstration and weather prediction is also given.
Optimization on Purity K-Means Using Variant Distance Measure

The initial center point of the cluster (centroid) affects the results of the K-Means algorithm. In this study is discussing the results of K-Means clustering with initial centroid determination using purity and different distance measures performed on various datasets with varying amounts of attributes and amounts of data. So this paper presents the best distance in the process of clustering the K-Means algorithm. Based on the results of test conducted on the Iris dataset with 150 data amounts, the number of attributes 4 and 4 clusters, the results obtained using Euclidean Purity K-Means of 0.2935 is better than the Canberra Purity K-Means of 0,5949 and City Block Purity K-Means of 0.4771. The next comparison is the results of test conducted on the Birth and Death Rates dataset with 70 data, the number of attributes 2 and 4 clusters obtained the results are using the Canberra Purity K-Means of 0.4925 better than the Euclidean Purity K-Means of 0.5411 and City Block Purity K-Means of 0.4938. The last comparison of the results of test conducted on the Wholesale Customers dataset with a total of 440 data, the number of attributes 6 and 4 clusters are obtained using Canberra Purity K-Means of 0.9357 better than Euclidean Purity K-Means of 1.0357 and City Block Purity K-Means of 0.9619.
Internet communication: architecture and typology of digital folklore

The paper discusses the interconnection of network architecture and channels of information transmission and storage in the process of the internet communication. The concept of digital folklore as compared to traditional folk culture is explained. Genre classification of digital folklore is introduced. The mechanisms of digital folklore functioning within different patterns of modern network architecture are analyzed. The idea of the research is introduced on the base of the comparative study. The article argues that the internet forms a special communicative environment that gives rise to innovative cultural and linguistic phenomena. It reflects users' thoughts and ideas, expressed in the form of a text, synthesized with graphics, sound, and animation. The new communication medium contributes to the implementation of various language tools and stimulates users' creativity. This way language becomes a tool for their creative personal expression. Particular attention is drawn to the evolution of folklore and its traditions in typological coverage. The focus is made on the communicative function of digital folklore, conditioned by special aspects of the internet medium.
Eye-based interaction in graphical systems

The course starts with an overview of eye-tracking applications, distinguishing eye movement analysis from synthesis in virtual reality, games, and other venues including mobile eye tracking. The focus is on four forms of applications: diagnostic (off-line measurement), active (selection, look to shoot), passive (foveated rendering, a.k.a. gaze-contingent displays), and expressive (gaze synthesis). The course covers basic eye movement analytics, e.g., fixation count and dwell time within AOIs, as well as advanced analysis using ambient/focal attention modeling. The course concludes with an overview and a demo of how to build an interactive application using Python.
Mining Associations Rules Between Attribute Value Clusters

The approach presented in this paper clusters values of each attribute in a relational database and then finds associations between two clusters belonging to different attributes. The first experiment has shown that the approach proposed is accurate in discovering clusters and associations between them. The second experiment conducted on real dataset has discovered clusters of values of attribute ‘petiole thickness,’ wherein each cluster is uniquely associated with a particular species of a plant.
A Domain Adaptation Approach for Performance Estimation of Spatial Predictions

Spatial predictions, like other supervised learning tasks, require some criterion for a predictor's quality. Typical data-splitting schemes, such as holdouts and k-fold cross-validation, ignore the fact that the training data are usually not available where predictions are being made. The common data-splitting schemes are thus biased estimates of a predictor's performance, which in turn may lead to choosing suboptimal predictors. In this contribution, we borrow ideas from the domain adaptation machine-learning literature, to suggest the importance-weighted source risk (IWSR). IWSR is a principled approach for weighting the prediction risk, which allows the practitioner to explicitly state the target locations for prediction. IWSR essentially consists of down-weighting training locations and up-weighting target locations. We show that, unlike the usual (unweighted) empirical risk, IWSR is an unbiased estimator of the prediction error. Equipped with this risk estimator, we use it to learn a model in the empirical risk minimization framework and to evaluate the existing predictors. We show the superiority of this weighted risk, using both simulated data and an empirical control: air-temperature prediction in France.
A study on the decryption methods of telegram X and BBM-Enterprise databases in mobile and PC

Instant messenger (IM) apps, which store a variety of behavioral information about users, such as secret chats, group chats, and file sharing, are important tools for digital forensics investigation. Messenger apps on mobile devices store user-friendly data, but data collection can be difficult due to various constraints. PC messenger data, on the other hand, can be collected relatively easily, but tend to be less informative than data from mobile messengers. Most messengers are cross-platform, supporting both mobile devices and PCs, and providing synchronization services, a situation which can overcome the constraints of data extraction for evidence acquisition. This allows for complementary interaction when extracting data generated by the use of IMs. However, some IMs encrypt their data for protection against external threats. The use of encryption can effectively protect the user's data, but poses a significant challenge to digital forensics, in which data should be decrypted to be used as evidence. Such IMs normally use a combination of key derivation functions and cryptographic algorithms to encrypt data. It is therefore necessary to identify the relationships between the functions used for encryption, in order to decrypt IM data, so that it can be used as evidence, and to determine the secret values used for generating keys. In this paper, we propose methods for acquiring user data, including conversation history protected by encryption by analyzing the Telegram X and BBM-Enterprise apps that perform in various mobile and PC operating environments. Both applications encrypt their databases using an SQLite extension module called SQLCipher. In order to decrypt these databases, we identified the parameters of SQLCipher, and derived a Passphrase, the main secret. In addition, We validated our approach by conducting an experiment to decrypt the encrypted databases of Telegram X and BBM-Enterprise.
Three-dimensional path planning of manipulator based on improved RRT algorithm

This paper provides an improved algorithm which is proposed for the deviating optimal solution, non-target and path non-smooth problem of the original Rapidly-exploring Random Trees (RRT). The improved algorithm is based on the new parent node selection method to ensure that the random tree extends the asymptotic optimality of the new node. On this basis, the heuristic value function is introduced according to the Euclidean shortest distance thought, which improves the planning efficiency of the algorithm. In addition, the method of regression analysis is adopted to improve the search ability of the algorithm and solve the local minimum problem caused by the heuristic value function. The Bézier curve fitting method is then used to generate a smooth path, which further reduces the jitter during the 6-DOF manipulator motion. The simulation experiment of improved RRT algorithm is established in MATLAB platform, and the effectiveness and rationality of the algorithm are verified.
Generalized Net Model for Dynamic Decision Making and Prognoses

Generalized nets (GN) are invaluable tools in the effort to universally depict complex systems' adaptive, flexible, structured, and well-established models. Here, GN are used to develop an adaptable decision-making model of dynamic student assessment. The main purpose of the study is to propose a student-based decision-making model that is adaptable to the various newly introduced training courses. Furthermore, the combination of the so far accumulated data from documented processes, along with the usefulness of the developed model, may aid in identifying trends applicable to online education. The data presented in this paper is based on studies, experiments and analytics taken from the following courses: "Databases"- led by Prof. Orozova at the Burgas Free University, Burgas, Bulgaria, and "College Mathematics"-Prof. Hristova at Valencia College, Orlando, Florida, United States of America.
Edge and Node Graph Convolutional Neural Network for Human Action Recognition

The skeleton-based method in the task of human action recognition has been a research hotspot in recent years. Graph Convolutional Neural Networks (GCNs) are often utilized in extracting the spatial features of skeleton joints data. However, Skeleton joints, the product of pose estimation algorithms, cover only part of spatial information. Skeleton edges, which are in the representation of orientations and center coordinates of bones, also contain critical information. Our work explores how skeleton edges and skeleton nodes work with each other. Furthermore, we design a two-stream network with a specially designed fusion mechanism, named Edge and Node Graph Convolutional Neural Network (EN-GCN). Experimental results on the NTU-RGB+D large-scale dataset confirm the superiority of our model.
Color-based microscopic image steganography for telemedicine applications using pixel value differencing algorithm

There are some ethics in the medical world to keep patient information confidential on every result of medical observations and analysis. Therefore, steganography technique is an alternative to conceal sensitive text information on a medical image in the telemedicine system process. Research on steganography using Simple LSB-Substitution and other algorithm has been available but in this study, the implementation of steganography was designed to hide text in color medical images before being sent through the telemedicine system. Sensitive text is hidden in medical images using the Pixel Value Differencing (PVD) algorithm. The main purpose of using this algorithm is to maintain the peak signal to noise ratio (PSNR) and mean square error (MSE) of the steganography image result. As a result, the PSNR value can be maintained at the 57,98dB for 10KB hidden text, and lower MSE at 0.05 for high-density object microscopic image.
Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising

In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor.Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0 < p < 1) norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank.To avoid over-shrink the tensor tubal rank components, a flexible weighted t-Schatten-p norm model is proposed with weights assigned to different elements of tensor singular tubes.We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently.Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively.
Marker-less Motion Capture Based on Openpose Model Using Triangulation

Previous works in openpose are only applicable in 2D human pose estimation. 3D human pose estimation can be done using two types of inputs, single and multi view. 3D pose estimation using multi view are more robust than single view, due to multi view allowing better depth estimation. 3D pose estimation are obtained from 3D data set poses or 2D joint location poses. However, with the limitations inherent in the 3D data set, such as lack of sufficient data and usage difficulties, this research applies 2D joint location. From the cases outlined before, we choose to develop multi view camera and 2D joint location as input to obtain 3D human motion capture. The inputs are two images with different views, where each image is processed using an inference openpose model to get the 2D joint location. Camera calibration is needed to precisely obtain the intrinsic and extrinsic features of the camera. With these features and the 2D joint location results, we obtain the 3D motion capture using the triangulation method. This system can be carried out on any combination of genders, apparels and poses. The best distance between cameras is 66 cm. The error depth range is 16.3-18.7 cm. Error in depth can be minimized by improving 2D joint location from openpose for obtain better 3D motion capture.
A Survey of Signal Processing Filters, Calibration, and Interactive Applications based on Smooth Pursuit Eye Movement

Eye tracking has been considered as an important modality of interaction between human and computer. Three types of eye movement commonly used in interactive applications are fixation, saccadic, and smooth pursuit. Fixational eye movement has been considered as the most common technique in gaze-based interactive applications. Nevertheless, fixation-based interaction requires conventional eye tracking calibration, which limits its practicality when the interactive applications have to be used in a public space. In the last decade, smooth pursuit-based interaction has emerged as a new interaction technique. This method enables spontaneous interaction with minimum calibration targets, allowing the users to be calibrated faster or even interacting with the applications without prior calibration. Despite of its emerging popularity, there is no a review study that observes various techniques behind this interaction technology. To fill this gap, this paper presents a review on signal processing filters, calibration methods, and different interactive applications based on smooth pursuit eye movement. This review may be used as a guideline to start a new research project on interactive application based on smooth pursuit eye movement.