Reviewerly/exHarmony huggingface dataset:
Detecting two qubit both-way positive discord states

Quantum discord plays a pragmatic role in analyzing nonclassical feature of quantum correlations beyond entanglement. It is used in several information processing protocols which lacks sufficient amount of entanglement to be used as a resource. We have provided with an analytical method of detecting quantum discord of an arbitrary two qubit state. We have formulated a set of necessary and sufficient conditions for any two qubit state to be a both-way non-zero quantum discord state. As quantum discord is asymmetric in nature, we have framed the set of if and only if conditions for a two qubit state to be classical-quantum as well for it to be quantum-classical. Interestingly, not only correlation tensor but also local Bloch vector (corresponding to the classical party) plays a role for detecting the state to be a positive discord state.
Association Analysis of Serial Cases Based on Apriori Algorithm

In the past, people mainly analyzed serial cases by manual methods, which was time-consuming and labor-intensive and less efficient. In this paper, Apriori algorithm is used to analyze serial cases, to achieve intelligent analysis, and to study the implementation path of correlation analysis in serial cases. When the support is set to 0.4 and the degree of lift is set to 2.0, the best association rules can be mined. Apriori algorithm can realize intelligent association analysis effectively, and machine-learning methods in the future will further improve it, thus making it suitable for various types of serial cases.
Periodical key change for cloud mutable security protocol

Securing cloud databases is a challenge in the cloud computing paradigm. Several research studies have been conducted to improve the security level in cloud computing. Many security schemes do not preserve mathematical or logical operations over encrypted data. Order Preserving Encryption schemes (OPEs) preserve the ordering (logical) operations over encrypted data. However, many OPE schemes leak information. Mutable Order Preserving Encoding (MOPE) algorithm achieves ideal security when it uses Randomized Encryption (RND) rather than Deterministic Encryption (DET) Scheme. RND and DET can be implemented with any standard cipher such as AES. This research proposes to use DET for MOPE and achieves the same security level when it uses RND. This paper proposes to change the encryption key on a certain time or a certain number of transactions. MOPE keeps its search tree balanced on each transaction applied by the system to maintain the performance. To enhance the security level without degrading the performance, this paper is utilizing the time consumed by rebalancing part of the tree in MOPE algorithm. This paper finds an optimal window to change the encryption key with an acceptable loss of performance.
Modern Method and Software Tool for Guaranteed Data Deletion in Advanced Big Data Systems

In today’s digitally empowered world, confidential data protection is a bit difficult especially when the user lacks knowledge about the importance of data sanitization before discarding, selling, or donating devices with memory (storage). To protect data, generally, users encrypt files and folders, hard drives, password protect flash drives and encrypt SD card on their smartphone to avoid unauthorized access to their confidential data. But almost all of these devices are discarded, sold, or donated without proper data sanitization at the end of their life cycle. Deleted data can be easily recovered from any storage media whether it’s a USB thumb drive, HDD, SSD, SD card, etc. Interestingly, data from a formatted, encrypted, and broken or crushed storage media can also be recovered using data recovery software and services. Therefore, to protect and safeguard your confidential data from an unauthorized access, you must erase the files/folders and wipe the empty spaces (unused space) on your hard drive volume as the empty spaces might contain previously deleted sensitive and private data that can be easily recovered. This paper investigates international standards and methods of guaranteed data deletion that exist in different countries, a comparative characteristic of data deletion software. Also in this study, the method of guaranteed data deletion that has been improved due to the developed pseudorandom sequence generator develops algorithms and software for this method and conducts experimental researches of developed solutions to verify their effectiveness. The developed software module STM Shredder refers to information security particularly in Big Data concept. The results of experiments show its ability to expand the capabilities of information processing and storage systems for large files (faster by 30%).
Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Motion capture data are digital representations of human movements in form of 3D trajectories of multiple body joints. To understand the captured motions, similarity-based processing and deep learning have already proved to be effective, especially in classifying pre-segmented actions. However, in real-world scenarios motion data are typically captured as long continuous sequences, without explicit knowledge of semantic partitioning. To make such unsegmented data accessible and reusable as required by many applications, there is a strong requirement to analyze, search, annotate and mine them automatically. However, there is currently an absence of datasets and benchmarks to test and compare the capabilities of the developed techniques for continuous motion data processing. In this paper, we introduce a new large-scale LSMB19 dataset consisting of two 3D skeleton sequences of a total length of 54.5 hours. We also define a benchmark on two important multimedia retrieval operations: subsequence search and annotation. Additionally, we exemplify the usability of the benchmark by establishing baseline results for these operations.
Post-combustion of mazut with CO<sub>2</sub> capture using aspen hysys

There is a wide range of resources for CO2 emissions. The net amount of CO2 emissions in the cement industry due to the consumption of fossil fuels and the chemical processes of cement production under heating raw materials is reported to be in the range of 15–25%; this industry, among all the industries and after the power plants and refineries, is the largest CO2 gas producer throughout the world. Using CO2 capture and storage (CCS), it can reduce greenhouse gas emissions in a short time. In this study, the technical feasibility study of recycling CO2 in Abyek Cement Company, with a cement production capacity of about 12,500 tons per day in two production lines, has been studied as one of the largest cement industries throughout the world. Fuel oil (Mazut) is used as the primary fuel for furnaces in this industry. Affected by combustion, the emissions emitted from the five-stage preheater contain 5/24% vol% of CO2, 7.6% H2O, 4.8% vol% of O2, and 63.1% N2.
Novel chromatic confocal differential interference contrast prototype

By combining classic differential interference contrast (DIC) with the chromatic confocal principle, we show that phaseshifting calibration can be avoided in DIC by using spectral information induced by the investigated sample. The created spectral fringe can be further used to unwrap the phase. This unwrapping is limited by the spectral resolution of the spectrometer. Therefore, the depth-difference around a single measurement point can be determined instantaneously. To reconstruct the depth profile, the integration of a depth-gradient is necessary. By combining the depth information of the chromatic confocal carrier signal with the differential depth information of the carried DIC signal, the accumulation of measurement uncertainty can be reduced. To our best knowledge, the proposed chromatic confocal differential interference contrast (CCDIC) is a novel profile reconstruction principle. To verify the feasibility of the CCDIC, a prototype probe with an adjustable shear and phase has been developed. Preliminary experiments achieve sub-micrometer depth resolution. A current challenge requiring further work is the stable unwrapping of the phase-difference by spectral frequencies.
Distillation of single-mode-squeezed entangled state in presence of strong squeezing

Recently, the strong single-mode squeezed vacuum state of light with $-15$−15 dB of classic noise has been created [Phys. Rev. Lett.117, 110801 (2016)10.1103/PhysRevLett.117.110801]. This is an important resource in continuous variable (CV) quantum communication and fault-tolerant quantum computing. Nevertheless, strong squeezing means a non-negligible population in high-photon-number subspace and may pose a strong challenge to photon-based technology, for example, entanglement distillation. Entanglement distillation is an efficient method for retrieving a higher quality of entanglement from a weakly entangled state. Up till now, almost all the schemes for entanglement distillation are restricted to the low-squeezing regime. The distillation of strong-squeezing induced entanglement is an interesting but open topic in the near future. Here, we take the single-mode squeezed entangled state (SMSE) as an example and show that conventional photon subtraction based distillation fails to generate a higher quality of entanglement with experimentally feasible optical beam splitters. To this point, we show that the superposition of photon annihilation and creation could be an effective method. A practical scheme for implementing such a superposition with on-line squeezing is suggested. Our method is verified numerically by the calculation of a 25 dB SMSE state with an ideal photon detector and for distillation of 20.5 dB SMSE state with practical dichotic on-off detectors. The analysis of the distillation of the strong squeezing effect could be extended straightforwardly to other squeezing-based quantum information processing, such as quantum metrology and quantum illumination.
Estimation of the Error in the Calculation of Mineral Reserves Taking into Account the Heterogeneity of the Geological Space

Abstract For effective application of the method of analogy in the geometrization and prediction of indicators, it is necessary to observe the principle of similarity of the field being studied to the field-analog developed by the development. Under the conditions of similarity, it can be assumed that the nature and intensity of deformation of rocks in the conduct of mining operations will be about the same. When choosing a mine-analogue should be based on the coincidence of the degree of metamorphism of coal and rock epigenesis, tectonic structure of the sites, the lithological and material composition of rocks and their physical and mechanical properties, power and depth of the coal seam, hydrogeological conditions. Homogeneous areas of the deposit can be distinguished by various methods of cluster analysis. The quality of the classification is assessed by comparing the average values of indicators in individual clusters (homogeneous areas) with their average values throughout the place.
On Development of Data Science and Machine Learning Applications in Databricks

Databricks is a unified analytics engine that allows rapid development of data science applications using machine learning techniques such as classification, linear and nonlinear regression, clustering, etc. Existence of myriad sophisticated computational options, however, can become overwhelming for designers as it may not always be clear what choices can produce the best predictive model given a specific data set. Further, the mere high dimensionality of big data sets is a challenge for data scientists to gain a deep understanding of the results obtained by a utilized model. This paper provides general guidelines for utilizing a variety of machine learning algorithms on the cloud computing platform, Databricks. Visualization is an important means for users to understand the significance of the underlying data. Therefore, it is also demonstrated how graphical tools such as Tableau can be used to efficiently examine results of classification or clustering. The dimensionality reduction techniques such as Principal Component Analysis (PCA), which help reduce the number of features in a learning experiment, are also discussed. To demonstrate the utility of Databricks tools, two big data sets are used for performing clustering and classification. A variety of machine learning algorithms are applied to both data sets, and it is shown how to obtain the most accurate learning models employing appropriate evaluation methods.
Training technologies for Industry 4.0 experts: updated andragogical model

Abstract The article concerns the experience in training useful academic competences to industry 4.0 experts. The developed English course being an example of the internet thing for adult experts is based both on the current technical resources and andragogical principles of selecting education technologies. The research materials emphasize the renewed forms of training content, and demonstrate interactive essence enlarging. We have created a formula to “calculate” the expertise achievability and highlighted the necessary conditions based on polyparadigm approach and the andragogical principles.
Deep Learn Helmets-Enhancing Security at ATMs

Automatic Teller Machine (ATM) plays a vital role in our modern economic society. Around 1,30,000 ATM centers are functioning across India. A real-time intelligent video analytics offers advanced monitoring capabilities that gives sophisticated video surveillance to recognize the abnormal activities. Person wearing helmet in ATM center is one of the anomalous activity. In such scenario, an automatic helmet detection algorithm is required to, alert when the person is wearing helmet in ATM. The detection of helmet is obtained using Deep Learning Convolutional Neural Network (CNN) architecture such as VGGNET (Visual Geometry Group) and ALEXNET. The region of helmet is detected using (Region Convolutional Neural Network) RCNN with 15 layers. The performance of this technique has been tested on 880 test images out of 1880 images in a database. The parameters are chosen to compare the different mini batch size and epoch in ALEXNET.
Editor’s introduction: Innovative dynamical approaches to cognitive systems

This Editor's Introduction to the Cognitive Systems Research special issue, "Innovative Dynamical Approaches to Cognitive Systems," has three aims: First, the background and motivation for the topic are stated. Second, overviews of the contributing papers are presented. Third, based on the papers, speculations on future directions in dynamical approaches to the investigation of cognitive systems are presented. Here, the focus is on concepts, data analysis methods, and computational modeling.
Fuzzy Theory Based Single Belief State Generation for Partially Observable Real-Time Strategy Games

As the basic problem of the real-time strategy (RTS) games, AI planning has attracted wide attention of researchers, but it still remains as a huge challenge due to its large searching space and real-time nature. The situation may get worse when the planning in RTS games is implemented under a partially observable environment considering the existence of the fog-of-war. Given the recorded past positions of an agent, it would be helpful if the targets' next position can be predicted based on the recorded data since this will increase the certainty of the target. Therefore, this paper proposes a fuzzy theory-based single belief state generation method named FTH to do what based on multi-layer information sets extracted from the history position information. Besides, we incorporate the FTH generation method into adversarial hierarchical task network repairing (AHTNR) planning algorithm, which can be used for the prediction of the unit's position and task planning. Finally, we carry out an empirical study based on the μRTS game and validate its effectiveness by comparing its performance with that of other state-of-the-art algorithms.
A Parallel Algorithm for Bayesian Text Classification Based on Noise Elimination and Dimension Reduction in Spark Computing Environment

The Naive Bayesian algorithm is one of the ten classical algorithms in data mining, which is widely used as the basic theory for text classification. With the high-speed development of the Internet and information systems, huge amount of data are being produced all the time. Some problems are certain to arise when the traditional Bayesian classification algorithm addresses massive amount of data, especially without the parallel computing framework. This paper proposes an improved Bayesian algorithm INBCS, for text classification in the Spark computing environment and improves the Naive Bayesian algorithm based on a polynomial model. For the data preprocessing, this paper first proposes a parallel noise elimination algorithm, and then proposes another parallel dimension reduction algorithm based on Information Gain and TextRank computation in the Spark environment. Based on these preprocessed data, an improved parallel method is proposed for calculating the conditional probability that comprehensively considers the effects of the feature items in each document, class and training set. Finally, through experiments on different widely used corpuses on the Spark computation platform, the results illustrate that INBCS can obtain higher accuracy and efficiency than some current improvements and implementations of the Naive Bayesian algorithms in Spark ML-library.
Gamma Enhanced Binarization - An Adaptive Nonlinear Enhancement of Degraded Word Images for Improved Recognition of Split Characters

Recognition performance of any OCR suffers because of the merged and split characters that occur in the scanned images of degraded printed documents. We propose an elegant method of non-linearly enhancing such degraded, gray-scale word images. This connects the broken strokes of the characters, so that binarization of the processed word images gives components with better connectivity for most characters or recognizable units. From an initial value of one, the value of gamma, the parameter determining the enhancement, is decreased in powers of 2 and the right value of gamma is chosen based on the recognition score of our character classifier. We have created a benchmark dataset of 1685 degraded word images obtained from scanned pages of several old Kannada books. The word images have been recognized before and after the proposed nonlinear enhancement. There is an absolute improvement of 14.8% in the Unicode level recognition accuracy of our SVM-based character classifier on the above dataset due to the proposed enhancement of the gray-scale word images. Even on the Google's Tesseract OCR for Kannada, our gamma enhanced binarization results in an improvement of 5.6% in the Unicode level accuracy.
A Comparative Objective Assessment on Mesh-Based and SVM-Based 3D Reconstruction of MRI Brain

Three-dimensional reconstruction is the process of acquiring the volumetric information from two dimensions, converting and representing it in three dimensions. The reconstructed images play a vital role in the disease diagnosis, treatment and surgery. Brain surgery is one of the main treatment options following the diagnosis of brain damage. The risk associated with brain surgery is high. Reconstructed brain images help the surgeons to visualize the exact location of tumor, plan and perform the surgical procedures from craniotomy to tumor resection with high precision. This survey provides an overview of the three-dimensional reconstruction techniques in MRI brain and brain tumors. The triangle generation methods and support vector machine methods are briefly described. The advantages and disadvantages of each method is discussed. The comparison reveals that Immune Sphere Shaped Support Vector Machine is the best choice when execution time is considered and triangle mesh generation algorithm is the best when visual quality is considered.
Constructing Meaning and Engaging Learners Through Digital Tools and Practices Within the Middle Level Science Classroom

With the continued growth of digital tools and practices, the manner in which instruction is planned and delivered within a middle-level classroom will need to evolve. Science educators have often been in the forefront of technology inclusion. With the current expectations of three-dimensional learning promoted within the Next Generation Science Standards, teachers have additional opportunities for students to utilize digital tools and practices during their instructional process. Through the utilization of carefully selected digital tools and practices, science teachers can engage learners and better assist them in constructing meaning through three-dimensional learning. The authors focus on the intersection of three areas: how middle school students make sense of content and develop understanding, the utilization of strategies for designing and creating a middle school classroom around digital practices, and the challenges and opportunities that this instructional shift encounters.
CFGVF: An improved correlation filters based visual tracking algorithm

Visual tracking is a challenging task in computer vision. Correlation filter (CF) based visual tracking algorithm has become an attractive tracking technique, while there are still some limitations. Existing CF-based tracking algorithms are vulnerable to the influence of the surrounding background, and the usage of fixed scale template may easily lead to tracking failure. To overcome the above limitations, we propose a CFs-based visual tracking algorithm, CFs with Gabor energy filter, variable-scale template and features fusion (CFGVF). In CFGVF, the Gabor energy filter is firstly adopted to preprocess every frame of the image sequence, which largely eliminates the influence of illumination variation. Then, the Gabor energy, Histogram of Oriented Gradient and color naming features are integrated to enhance the ability of dealing with significant appearance variations such as deformation and motion blur. Furthermore, we propose a variable-scale template method to estimate the scale of the target object. Finally, an online updating schema is adopted to reduce the interference of surrounding background and occlusion. Experimental results on the visual tracking benchmark dataset OOTB show that the performance and robustness of the CFGVF tracker outperform those of several state-of-the-art trackers.
New method of training two-layer sigmoid neural networks using regularization

Abstract We propose a complex learning algorithm for sigmoid Artificial Neural Networks (ANN). We introduce the concept of the working area of a neuron for sigmoid ANNs in the form of a band in the attribute space, its width and location associated with the center line of the band to a fixed point. We define of the centers and widths of the working areas of neurons by analogy to the radial ANNs. On this basis, an algorithm for selecting the initial approximation of network parameters, ensuring uniform coverage of the data area with neuron working areas was developed. Network learning is carried out using a non-smooth regularizer designed to smooth and remove non-informative neurons. The results of the computational experiment illustrate the efficiency of the proposed integrated approach.
The Architecture and Functionality of the Software to Identify the Actual Voltage Static Load Characteristics of Large Consumers

The correct voltage static load characteristics are required for efficient and reliable dispatching control in power systems and for stability simulation studies. Nowadays, one of the key problems is updating available static load characteristics due to the lack of special software for voltage and power measurement data processing. This article proposes the algorithm and architecture of special software and their implementation to computerize the process of correct voltage static load characteristics obtaining. The developed algorithm is based on data mining methods, and the results of calculations can be used in software packages that support the Common Information Model standard.