Reviewerly/exHarmony huggingface dataset:
Methodological Grounds for Philosophical-Anthropological Explication of the Phenomenon of Freedom

The main issue the author’s attention is concentrated on is the detection of the main methodological approaches to the analysis of the diversity of freedom types for the purpose of freedom’s philosophico-anthropological explication. A wide range of methodological and theoretical approaches of philosophical anthropology, cultural philosophy and social psychology is applied in the research. In the context of philosopho-anthropological research of the main methodological approaches to the analysis of the phenomenon “freedom” the author considers it to be necessary to differentiate the diversity of freedom types into three main types depending on the area it is applied: freedom to choose, freedom to wish, freedom to act. It is emphasized that in philosophico-anthropological explication the most adequate solution of the problem of freedom essence should cover all its types which are to be added with the investigation of peculiarities of a personality itself as its bearer. That is why the analysis of personalities typologization in accordance with coordination between external freedom and internal freedom. It is concluded that philosophico-anthropological explication is the phenomenon of freedom is its duality, antimony, irreconcilability and ambivalence impeding the possibility of full and exhaustive comprehension of its dialogic and polylogic character represented with the multitude of features. Real freedom displays are conditioned by sociocultural reality, at that real freedom exists when its existence is limited. True freedom discovery doesn’t come to solely its external display but mostly it is a consequence of the ego change at the expense of existence with the help of neantization, life contemplating giving a prospective of recognition and transformation of sense purposes. It performs as an anthropological and sociocultural phenomenon that enables a personality to realize transmission from potential state to realized one at the expense of work as the movement to a contemplated result that requires will and self-determination.
Feature Extraction of Dwarf Nova with Convolution Operation

Dwarf nova is a specific type of erupting cataclysmic viable star. Finding more Dwarf novae is significant in studying the theory about transferring matter to an accretion. To extract spectral features, convolution operation is an effective means which can improve the accuracy of spectral recognition. One dimensional convolutional neural network with four hidden layers is designed in this paper. Its feature detection layer implicitly learns the spectral features through training data. It reduces the complexity of the network through weight sharing so as to avoid the explicit extraction of spectral features. Convolution kernel with stable distribution is fitted in the form of discriminant learning from a mass of mixed spectra. The strategy can effectively reduce the complexity of data reconstruction in the process of feature extraction and classification. The experimental results indicate that the proposed technique achieves better accuracy and reliability.
Modelling last-act attempted crime in criminal law

In the court of law, a person can be punished for attempting to commit a crime. An open issue in the study of Artificial Intelligence and Law is whether the law of attempts could be formally modelled. There are distinct legal rules for determining attempted crime whereas the last-act rule (also called proximity rule) represents the strictest approach. In this paper, we provide a formal model of the last-act rule using structured argumentation.
Combination of Kolmogorov-Smirnov Statistic and Time-Frequency Representation for P-Wave Arrival Detection in Seismic Signal

The problem of determination of P-wave onset moment is elementary in seismic signals analysis. This problem is also widely discussed in the literature. In recent years many methods based on statistical properties of the signal have arisen. From the mathematical perspective the problem reduces to segmentation of the raw signal into parts with different features. Having the knowledge of the particular P-wave onset moment (for couple differently located sensors), the establishment of corresponding event’s location and energy is possible. The difference in signals’ frequency spectra for the registered event and its preceding noise allows for using time-frequency domain in designating the onset moment. In this paper an innovative method for searching of the P-wave arrival is proposed. The method incorporates using signal’s time-frequency representation (namely spectrogram) and Kolmogorov-Smirnov (KS) statistic analysis. We apply two-sample one-sided Kolmogorov-Smirnov statistic to spectra vectors obtained from the spectrogram. On the basis of KS map it is possible to find the regime switching point which indicates the P-wave onset moment. The method is tested on a real life signal originating from underground mine. Proposed methodology is compared with classical Short-Time Average over Long-Time Average (STA/LTA) algorithm and P-wave arrival moment indicated manually by the expert.
A Hybrid Tabu Genetic Metaheuristic for Selection of Security Controls

In the modern world, globalization has made way for easy access to information systems for running businesses effectively. The misuse of information systems may lead to loss of productivity, revenue, and sometimes to legal liabilities. Therefore, information security is a major concern for running the business successfully. It is the responsibility of the information security personnel of an organization to develop strategies, and identify the suitable controls to mitigate the risks to which the organization is exposed to. Selection of a suitable set of security controls depends upon, the risks to be addressed, the impact of the risks in terms of revenue, and the cost incurred in implementing the selected controls. To assist in the selection of optimal security controls a hybrid intelligent approach combining the tabu search technique and genetic algorithm has been proposed. The obtained results prove that the proposed approach provides better results in the selection of optimal counter measures.
Mover Position Detection for PMSLM Based on Sinusoidal Fringe Pattern and Fourier Phase Analysis

The paper presents a rapid and accurate mover position detection method for permanent magnet synchronous linear motors (PMSLMs). Based on sinusoidal fringe pattern and machine visual method, a mover position detection system is established, and sequence of 2D fringe images with the information of mover position are recorded by a high-speed camera instantaneously. The 2D fringes images are converted into 1D curves with integer fringe periods to reduce the time delay and suppress the spectral leakage of Fourier transform. Fourier phase analysis is used to retrieve the phase evolution and the displacement can be calculated according to the fringe width and system calibration. Finally, simulations and experiments validate the superiority and reliability of the method.
A new method for constructing ensemble polynomial regression model in privacy preserving distributed environment

The idea of ensemble learning can be used to solve problems about privacy preserving distributed data mining conveniently. Owners of distributed datasets can get an integrated model securely just by sharing and combining their sub models which are built on their respective sample sets, and generally the integrated model is more powerful than any sub model. However, sharing the sub models may cause serious privacy problems in some cases. So in this paper, we present a new method, based on which the data holders can integrate their sub polynomial regression models securely and efficiently without sharing them, and get the optimal combination regression model. In addition to theoretical analysis, we also verify the availability of the new method through experiments.
Attribute-Based Encryption Without Abuse of Private Keys

In attribute-based encryption schemes, an honest authority are always used to distribute members’ private keys. But in reality applications, the authority is general dishonest. The authority can decrypt the encrypted message by using some member’s private key. So we should handle with authority. In this paper, a security ABE scheme is proposed to prevent the authority to abuse the members’ private keys. The proposed scheme relies on the interpolating polynomial. We prove that our scheme is secure for the protection of members’ private keys and can tolerate a curious, semi-honest and non-colluding authority.
Disaster Management Based on Geo-literacy

Abstract The large number of casualties and physical and material losses are opaque protests of the bad management of disasters in Indonesia. The impact of natural disasters in Indonesia should have been minimized and avoided considering that Indonesia is a disaster-prone country because it is located at the confluence of the three major plates of the world and is the ring of fire. The community is not familiar with the condition of the environment where they live is a major factor in the many casualties and losses due to disasters. By introducing people to geographic literacy, people can get to know the environment and manage disasters properly. This article will discuss the role of geographic literacy relating to understanding the community’s environment that can help manage disasters in pre-disaster, emergency response, and postdisaster actions in Indonesia.
Parallel Computation for n-ary Cartesian Product on Modern Microprocessors

As an efficient method of representing huge sets, n-ary Cartesian product is widely used in many applications nowadays. In such applications, each element in the set of Cartesian product needs to be generated and evaluated. Therefore, the performance of generating all elements in the set of Cartesian product becomes a key factor. In this paper, we propose a SIMD-friendly algorithm to accelerate the computation of n-ary Cartesian product on CPUs. Based on it, we further introduce a CPU-GPU collaborative method for n-ary Cartesian product on GPU. In addition, we discuss the software architecture and dynamic schedule algorithm for multiple GPUs in one server. Experiments show that our approaches for n-ary Cartesian product can achieve a throughput of 113M per second on a single Corei7 core and almost 80G per second on a server with eight GTX 1080 GPUs. These high-performance implementations can meet the requirements of many applications.
Obtaining Tractable and Interpretable Descriptions for Cases with Complications from a Colorectal Cancer Database

Colorectal cancer affects to a significant portion of the population and is one of the leading causes of cancer-related deaths in many countries. Professionals of the Reina Sofia University Hospital have fed a database about this pathology for more than 10 years. In this work, we apply classification and association rule learning tools, including a new methodology, to obtain tractable and interpretable descriptions of those cases where complications appeared, which is one of the attributes.
Ameliorated Domain Adaptation Using Adaptive Classification Algorithm

In machine learning, an algorithm usually acquits finer for a particular domain. The work proposes a novel idea of mapping features of source domain to the features of target domain. The model designed for one dataset is applied to another dataset of related domain. The idea throws light upon two subjects namely spam filtering and academic performance analysis. Spam filtering is one of the never-ending problems in the present Internet age. As the spammers guess the type of filter built, there exists a need to design an adaptive filter. The proposed idea is to collect data from a single user mailbox and adapt it to other users. The unseen tokens are estimated using Expectation-Maximization (EM) algorithm, which eliminates zero probability issue. There is an increase in recall value by a minimum of 6.6% from the existing approach. Secondly, the academic performance prediction for school and college students has similar features and hence, the model has been adapted as well. Adaptive mapping is used to map related features between the source and the target. Therefore, the constructed model from the source domain can predict the target data. The proposed classification model shows an increase in accuracy by 7.5% and evaluation accuracy by 75%. The comparison of words in proposed algorithm is in linear time O(n + m), where naïve algorithm is in exponential time O(n2). Overall, by adapting domains, the need of building multiple models for homologous data is avoided.
Time-Based QoS Prediction and Rank Aggregation of Web Services

Everyday activities are equipped with smart intellectual possessions in the modern Internet domain for which a wide range of web services are deployed in business, health-care systems, and environmental solutions. Entire services are accessed through web applications or hand-held computing devices. The recommender system is more prevalent in commercial applications. This research predicts the preference of consumers and lists the recommended services in order of ranking for consumers to choose services in a short time span. This proposed approach aims to offer the exact prediction of missing QoS (quality of service) value of web services at a specified time slice. The uncertainty of QoS value has been predicted using the cloud model theory. The focus is to give the global ranking using the aggregated ranking of the consumer's ranking list, which has been obtained through the Kemeny optimal aggregation algorithm. In this work, multidimensional QoS data of web services have experimented and given an accurate prediction and ranking in the web environment.
Design and Implement of HPC Simulation Cloud Platform for Automobile Industry

To provide users with on-demand using of HPC services, solve the users’ dependency problems of application deployment and improve the whole computing power of industry, this paper designs and realizes a kind of HPC simulation cloud platform for automobile industry based on the distributed architecture. It can provide the cloud of HPC rental services for users and solve the problems on high performance computing such as the resources construction cycle is long, the cost is high, resources at leisure are idled and so on.
Nonlinear Autoregressive Model Design and Optimization Based on ANN for the Prediction of Chaotic Patterns in EEG Time Series

This paper presents an Artificial Neural Network (ANN) based Nonlinear Auto-Regressive (NAR) model design to generate and predict time series outputs of Lorenz chaotic system. The ANN based chaotic time series generator can be used for the simulation, analysis and prediction of Electroencephalogram (EEG) signals, which demonstrate chaotic features. The Lorenz chaotic system outputs are used to simulate the chaotic dynamics demonstrated by EEG time series signals. The training performances are investigated for various ANN architectures with different numbers of hidden neurons and feedback delays in the NAR model. The training results show that better training performance can be achieved by increasing the number of hidden neurons, or the number of feedback delays, but the computational cost is also significantly increased. The former optimization method is more beneficial since fewer multiplications are required compared to the latter. This reduces the computational cost for both ANN training and hardware implementation of the trained ANN.
A progressive approach for single image super-resolution

Convolutional neural network has achieved excellent success in single image super-resolution. In this paper, we present a progressive approach which reconstructs a high resolution image and optimizes the network at each level. In addition, our method can generate multi-scale HR image by one feed-forward network. The proposed method also utilizes the relationships among different scales, which help our network perform well on large scaling factors. Experiments on benchmark dataset demonstrate that our method achieves competitive performance against most state-of-the-art methods, especially for large scaling factors (e.g. 8×).
Research on Relational Database Fusion Method for Data Mining

With the development of data mining technology, more and more problems can be solved by data mining technology. In the development of data mining, the scale of data is also growing, so we need to use the appropriate way to store and optimize the data. The relational database is the most commonly used database, however, how to integrate a large number of relational databases in data mining is an urgent problem to be solved. In this paper, we first analyze the existing relational database integration scheme. Then, based on this scheme, an Aprior fusion algorithm for data mining and its optimization algorithm are proposed. Finally, we give an example of data mining to prove the validity of the data fusion scheme proposed in this paper. It can greatly improve the speed of data reading in data mining and finally improve the performance of data mining system. The experiments demonstrate the effectiveness of the proposed relational database fusion method for data mining.
Safety Problems and Solutions of External Heating Network

With the continuous development of society and economy, people's material and cultural needs and quality of life continue to improve, the pace of urbanization is accelerating, and the safety of urban heating network is more and more worthy of our attention. Urban central heating is directly related to the comfort degree of residents' life and the harmonious development of society. Therefore, this paper focuses on the analysis of the security problems existing in the external network of central heating, and puts forward corresponding solutions.
Apriori Algorithm-Based Association Rules of Event Language Expression Discovery

Event is the basic unit of knowledge representation, when human beings communicate with each other. Although event is objective in the real world, when it is described by voice, characters and other means, event has some language features. We treated event as the basic unit of knowledge representation for Chinese news texts. We pre-processed event data in the CEC2.0 corpus from positional feature, Chinese POS tagging feature and other aspects. And then, we proposed minding core word association rules for event language expression from CEC2.0 corpus with Apriori algorithm and optimized them. we detected event elements from Chinese news texts with the mined association rules, including action, object and time. The value of F1 is 60.7%.
Fine-Grained Fusion With Distractor Suppression for Video-Based Person Re-Identification

Video based person re-identification aims to associate video clips with the same identity by designing discriminative and representative features. Existing approaches simply compute representations for video clips via frame-level or region-level feature aggregation, where fine-grained local information is inaccessible. To address this issue, we propose a novel module called fine-grained fusion with distractor suppression (short as FFDS) to fully exploit the local features towards better representation of a specific video clip. Concretely, in the proposed FFDS module, the importance of each local feature of an anchor image is calculated by pixel-wise correlation mining with other intra-sequence frames. In this way, 'good' local features co-exist across the video frames are enhanced in the attention map, while sparse 'distractors' can be suppressed. Moreover, to maintain the high-level semantic information of deep CNN features as well as enjoying the fine-grained local information, we adopt the feature mimicking scheme during the training process. Extensive experiments on two challenging large-scale datasets demonstrate effectiveness of the proposed method.
A Fast End-to-End Method with Style Transfer for Room Layout Estimation

In this paper, we focus on the problem of estimating the spatial layout of indoor scenes from a single RGB image. Earlier solutions to this task largely rely on hand-crafted features and vanishing point detection, and recent practices usually utilize a fully convolution network (FCN) to achieve better results. However, existing works suffer from time consumption and sufficient usage of semantic information in each pixel. To tackle these issues, based on the FCN architecture, we propose a fast end-to-end method for room layout estimation. For the first time, style transfer is introduced for data enhancement, which not only extends the amount of training dataset, but also makes the pixels in images more discriminative, especially in highly cluttered rooms. Besides, we further improve the accuracy by fusing the output from multiple networks. Extensive evaluations on the public large-scale scene understanding challenge (LSUN) dataset demonstrate that our proposed method builds a new state-of-the-art result in terms of speed and accuracy.
Designing High Reliable Ensemble Classifiers Using Heuristic Algorithms

The measure of reliability in decision of a classifier is sometimes more important than its recognition rate. Military and security applications are clear examples to show the importance of this factor. In this paper, a method is described for designing ensemble classifiers with high reliability. The proposed method is established based on multi-objective optimization techniques. After analyzing the measure of reliability in a classifier, the multi-objective optimization algorithms are utilized to optimize reliability, error rate, and ensemble size simultaneously. Multi-Objective Inclined Planes Optimization algorithm (as a novel multi-objective technique) and Multi-Objective Particle Swarm Optimization algorithm (as a conventional one) are the multi-objective heuristic methods applied in the present research. Using the proposed method, it's possible to create various and user-defined conditions, due to the ability of multi-objective optimization algorithm in the presentation of the Pareto front; conditions in which the importance of each factor can be strengthened and weakened. The experimental results confirm the requirement of considering the measure of reliability in ensemble classifiers; in this case, the reliability for each class is higher than reliability values obtained in the other case (without considering reliability as an objective function). It's worth noting that adding this measure will not destroy two other measures. Also, superiority of Multi-Objective Inclined Planes Optimization is seen in obtained results for all datasets; this algorithm leads to improvements in the average values of reliability, error rate, and ensemble size by 21.5%, 76.9% and 53.8% for test dataset in the best condition.
Employing Augmented Reality for Cybersecurity Operations in High Performance Computing Environments

With the advent of myriad different technologies in the modern computing era, the future of high performance computing (HPC) data centers comprising heterogeneous compute servers and networks involves a significantly greater degree of cyber-threats than we are observing currently. As a result, conducting cybersecurity operations at the HPC data centers encompasses a highly cognitive workload environment, where information is rapidly generated, is exceedingly dynamic and is vastly unpredictable. An important challenge faced by cybersecurity specialists is managing the extreme levels of stress and utilizing emerging technologies for monitoring, assessing and defending multiple cyber-attacks in real-time. In this paper, we present an augmented reality based framework for storing, analyzing, visualizing and augmenting data at the HPC centers. This framework is proposed with the goal of improving the data presentation, forecasting, analysis and decision making, thus reducing the time to insight and learning the HPC security operations to combat increasing cyber physical threats.