Reviewerly/exHarmony huggingface dataset:
HIGHER EDUCATION AND EDUCATION OF “FALSE” INTERACTIONS: REFORMATION AND OPTIMIZATION OR IMITATION?

The modern higher education system undergoes a series of ever-evolving reforms aimed to optimize educational process, to raise the efficiency of universities’ activity and, in the long run, to raise the quality of education. The article examines the basic aspects of higher education reforms, provides a theoretical analysis of the basic results of current reforms, evaluates their influence on universities’ everyday life, teachers’ activity, identifies to what extent they promoted the spread of imitations and “false” interactions in everyday educational process.
Light Field Depth-of-Field Expansion and Enhancement Based on Multifocus Fusion

Light Field imaging systems allow capturing a larger amount of information of a scene with respect to conventional cameras. In fact, light field cameras record, in a single shot, multiple instances of the same scene taken from slightly different points of view. This enables new applications, like digital refocusing and free-viewpoint content creation. The main drawback of this hardware is in the trade-off between spatial and angular resolution, which has an impact on the depth of field of the refocused images. In this paper we propose a technique for increasing the depth of field by means of mul-tifocus fusion and, at the same time, for enhancing the quality of the content. The available views are processed in the Laguerre Gauss transform domain to perform a selective enhancement that sharpens the selected depth layer and blurs the others. The performed experiments prove the effectiveness of the proposed approach.
The Effect of Laziness on Agents for Large Scale Global Optimization

Practical optimization problems from engineering often suffer from non-convexity and rugged, multi-modal fitness or error landscapes and are thus hard to solve. Especially in the high-dimensional case, a lack of derivatives entails additional challenges to heuristics. High-dimensionality leads to an exponential increase in search space size and tightens the problem of premature convergence. Parallelization for acceleration often involves domain-specific knowledge for data domain partition or functional or algorithmic decomposition. On the other hand, fully decentralized agent-based procedures for global optimization based on coordinate descent and gossiping have no specific decomposition needs and can thus be applied to arbitrary optimization problems. Premature convergence can be mitigated by introducing laziness. We scrutinized the effectiveness of different levels of laziness on different types of optimization problems and for the first time applied the approach to a real-world optimization problem: to predictive scheduling in virtual power plant orchestration. The lazy agent approach turns out to be competitive and often superior to the non-lazy one and to standard heuristics in many cases including the real world problem.
An Efficient Attribute Based Encryption Scheme in Smart Grid

To protect the smart grid data sharing, a flexible access control strategy, the Ciphertext Policy Attribute-Based Encryption (CP-ABE) can be adopted, which often cost huge computational overheads. Hence, how to balance the efficiency and the level of security provided is of considerable importance. In this paper, we propose Co-CPABE, a cooperative CP-ABE scheme for smart grid. In our scheme, the heavy encryption computation tasks are dispersed to several data owners. Concretely, the access tree can be computed independently and simultaneously. As a result, the encryption is implemented by the cooperation of data owners and the Central Authority (CA), in which the CA only performs simple calculations such as multiplication and division. Moreover, an enhanced scheme is introduced to further improve the encryption efficiency. Finally, Co-CPABE is proved to be secure as well as efficient.
A Computationally Efficient Approach for Stochastic Reachability Set Analysis

The reachability set is defined as the collection of all states which can be traversed from arbitrary initial conditions due to the application of admissible control. Three different probabilistic approaches to compute the reachability sets for a class of discrete time nonlinear systems is discussed. The main idea of the probabilistic approach is to consider the bounded control variables as random variables and represent the reachability sets as the level sets of the state probability density function. In the first approach, the computation of the state density function due to variation in control input at each time is made tractable by computing the M- fold convolution of state density function at each time. To overcome the significant challenge of taking multi-dimension convolution of state density function, the second approach computes the probability density function using the Principle of Maximum Entropy (PME). The third approach utilizes the Conjugate Unscented Transform (CUT) method to curtail the combinatorial growth of samples. Finally, three numerical example problems are considered to show the efficacy and utility of the proposed ideas.
Entity Subword Encoding for Chinese Long Entity Recognition

Named entity recognition (NER) is a fundamental and important task in natural language processing area, which jointly predicts entity boundaries and pre-defined categories. For Chinese NER task, recognition of long entities has not been well addressed yet. When character sequences of entities become longer, Chinese NER becomes more difficult with existing character-based and word-based neural methods. In this paper, we investigate Chinese NER methods that operate on subword units and propose to recognize Chinese long entities based on subword encoding. Firstly, our method generates subword units on known entities, which prevents noisy information brought by Chinese word segmentation and eases the determination of long entity boundaries. Then subword-character mixed sequences of sentences are served as input into character-based neural methods to perform Chinese NER. We apply our method on iterated dilated convolutional neural networks (ID-CNNs) and conditional random fields (CRF) for entity recognition. Experimental results on the benchmark People’s Daily and Weibo datasets show that our subword-based method achieves significant performance on long entity recognition.
Introducing Semantic Labels into the DeriNet Network

Abstract The paper describes a semi-automatic procedure introducing semantic labels into the DeriNet network, which is a large, freely available resource modeling derivational relations in the lexicon of Czech. The data were assigned labels corresponding to five semantic categories (diminutives, possessives, female nouns, iteratives, and aspectual meanings) by a machine learning model, which achieved excellent results in terms of both precision and recall.
Nonlinear Approximation of Images with Haar-Like Four-Point Orthogonal Transforms

We study simple linear orthogonal transforms for images, which are real-valued, non-overlapped, and fast transforms similar to the Haar wavelet transform. These transforms reveal the correlation of each block of four neighbor points (or pixels) of an image, and can thus be used for an efficient representation of the image. Since several parameters are necessary to determine the orthogonality of such transforms, we conducted numerical experiments to determine the optimal parameter for minimizing distortion errors in nonlinear image approximations.
Toward a Dynamic Threshold for Quality Score Distortion in Reference-Based Alignment

The intrinsic high-entropy sequence metadata, known as quality scores, is largely the cause of the substantial size of sequence data files. Yet, there is no consensus on a viable reduction of the resolution of the quality score scale, arguably because of collateral side effects. In this article, we leverage on the penalty functions of HISAT2 aligner to rebin the quality score scale in such a way as to avoid any impact on sequence alignment, identifying alongside a distortion threshold for "safe" quality score representation. We tested our findings on whole-genome and RNA-seq data, and contrasted the results with three methods for lossy compression of the quality scores.
A novel and dependable image steganography model for strengthening the security of cloud storage

Image steganography provides efficient techniques and methods for embedding secure data into an image. Researchers face many challenges in this field such as: ensuring the quality of the stego image is adequate, ensuring the hidden message is secure, increasing the hiding capacity, recovering the h idden message and the cover image losslessy, and overcoming the effects of lossy image compression on the hidden message. In this paper, we address the above challenges by proposing a new image steganography model to ensure the security of data in cloud storage. The fundamental processes within the base level of the proposed model are to preprocess both the cover image and the secret message. The cover image is transformed to the wavelet domain using integer-to-integer transform while the secret message is compressed using lossless entropy coding and then it is encrypted for additional security before embedding. The control level of the model drives the steganography process as follows. Firstly, it selects significant coefficients from the transformed cover image according to some threshold values. Then, it creates groups of 7-bits and 3-bits from non-lossy bits of selected significant coefficients and the encrypted bit stream of the secret message, respectively. Finally, the non-lossy bits in the selected significant coefficients are updated by injecting the secret bits. Through the process of steganography, the consistency between the payload of the secret message and the number of selected significant coefficients is checked. The model is validated and verified using extensive real experiments. Moreover, the performance of the proposed model is measured by comparison with other recent models.
Experiences Creating an Automated Information System for Gathering and Analyzing the Results of Activities of Researchers and Scientific Groups

In this paper, we discuss our experiences in creating a system for gathering and analyzing the information about the scientific achievements of employees as part of the knowledge management process in the scientific organization. We describe our research goals in doing the work, the software we have developed, and some of the unique aspects of the system.
Generalization of Quantum Strassen Theorem

In a recent paper, L.Zhou, S.Ying, N.Yu and M.Ying proved a quantum Strassen theorem for the quantum marginal problem. By using SDP method, a more general quantum Strassen theorem has been proved here, with which the quantum Strassen theorem can be generalized to infinite dimensional case more easily.
Universal and non-universal text statistics: Clustering coefficient for language identification

In this work we analyze statistical properties of 91 relatively small texts in 7 different languages (Spanish, English, French, German, Turkish, Russian, Icelandic) as well as texts with randomly inserted spaces. Despite the size (around 11260 different words), the well known universal statistical laws -- namely Zipf and Herdan-Heap's laws -- are confirmed, and are in close agreement with results obtained elsewhere. We also construct a word co-occurrence network of each text. While the degree distribution is again universal, we note that the distribution of Clustering Coefficients, which depend strongly on the local structure of networks, can be used to differentiate between languages, as well as to distinguish natural languages from random texts.
Info-Retrieval with Relevance Feedback using Hybrid Learning Scheme for RS Image

Relevance feedback can be considered as a learning problem. It has been extensively used to improve the performance of retrieval multimedia information. In this paper, after the relevance feedback upon content-based image retrieval (CBIR) discussed, a hybrid learning scheme on multi-target retrieval (MTR) with relevance feedback was proposed. Suppose the symbolic image database (SID) of object-level with combined image metadata and feature model was constructed. During the interactive query for remote sensing image, we calculate the similarity metric so as to get the relevant image sets from the image library. For the purpose of further improvement of the precision of image retrieval, a hybrid learning scheme parameter also need to be chosen. As a result, the idea of our hybrid learning scheme contains an exception maximization algorithm (EMA) used for retrieving the most relevant images from SID and an algorithm called supported vector machine (SVM) with relevance feedback used for learning the feedback information substantially. Experimental results show that our hybrid learning scheme with relevance feedback on MTR can improve the performance and accuracy compared the basic algorithms.
Literature and Cinema: Ways of Interaction in the 21ST Century

The research is based on the analysis of interaction of different forms of art: cinema and literature.   Since the moment of emergence, cinema is in very close connection with literature. It is possible to speak about the variety of types of relationship existing today between cinema and literature. For a long time it was considered that cinema plays a secondary role in comparison with literature. The only possible algorithm of interaction between these types of arts is work-scenario-screen version. However, there is a strong tendency towards the impact of cinema on setting new trends in literature. Along with the traditional “literary – – screen version” algorithm we also consider – – screen version – literary work (when details which appeared in the screen version influence the following parts of a literary work) or scenario – movie – literary work (when a literary is created on the basis of the original or the movie). Today writers and directors look for new ways of transferring of one art form’s peculiarities into another. As a result we can speak about the existing interdependence between them.
Categorize the Power Grid Projects with SOM Method

Unsupervised learning is to find the potential features without any supervised information, which is much more extensive and robust in the real applications. In this paper, we proposed a self organized map(SOM) based method to categorize the power grid areas based on their features. Further, we analyze the areas in certain categories, which have the similar features that show the voltage transformers are overwhelming. Our SOM based method can significantly find the right categories, in comparison to the KMeans, the DBSCAN, and the Birch. Since the SOM based method is computing cost, we employed a static graphic to build the algorithm and then implement and run it with tensorflow. We evaluate our method with the Calinski-Harabaz and the Silhouette-Coefficient. The experimental studies demonstrate that our method has a better accuracy.
Swedish Undergraduate Information Systems Curricula: A Comparative Study

The authors do a comprehensive comparison of the Swedish Information Systems undergraduate programs in order to on the one hand get a better understanding of how the Swedish curriculum compares to the Australian and US counter parts and on the other hand also get an understanding of where the IS field has changed over time. This change is debated to get a clearer view of what courses should be core in a post 2020 curriculum. The study points to some significant overlaps where Foundations of Information Systems, Data and Information Management, and Systems Analysis and Design are important for both Swedish, Australian, and US undergraduate IS programs. The study also shows differences in focus in the different countries’ curricula, where the Swedish programs have a clear focus towards enterprise architecture and application development in comparison to both the Australian and US counterparts.
Inductive Reasoning with Multi-dimensional Concepts

Attribute spaces are a type of conceptual spaces which Carnap introduced in his late basic system of inductive logic. This article shows how to extend Carnap’s use of them into a full model of inductive reasoning with geometrically represented concepts, extending my earlier work (Sznajder [2017]). The proposed model draws on Bayesian non-parametric techniques in order to define a probability distribution over the attribute space and a way of updating it with data. The model is another example of conceptual and formal continuity of Carnapian inductive logic with subsequent developments in statistics.
Path Planning for Lunar Polar Exploration Mission Using Linear Temporal Logic

For path planning of planetary exploration rover travelling toward several points of interest, it is necessary to optimize the order of the exploratory points, and consider various safety features, such as terrain hazards, illumination condition, and communication visibility to the earth. This paper presents a method to obtain optimal path in consideration of the remaining charge of the rover, according to the prospected mission in an environment with time-varying safety features, such as in lunar polar region. We demonstrated the effectiveness of the proposed method in numerical simulations.
Research On 3D Reconstruction of Face Based on Binocualr Stereo Vision

People usually perceive things from a three-dimensional real world, requiring the computer to automatically reconstruct the corresponding three-dimensional shape model using two-dimensional human face images. The three-dimensional face reconstruction based on binocular vision is to use SIFT algorithm for feature point matching and disparity calculation to obtain three-dimensional coordinates. The three-dimensional coordinates are applied directly to the face deformation model, which overcomes the traditional method of finding two-dimensional and its coordinates, convert the relationship, the disadvantage of low precision, thus reconstruct a three-dimensional face model with a strong sense of reality.The experimental results show that the algorithm can be stably matched under illumination, blur, noise, near and far, and the experimental error of Zhang zheng you's calibration method is controlled within 0.4 pixel, and accuracy to meet the general application requirements.The three-dimensional reconstruction of the indoor scene can also learn from this method.
Belnap and Nāgārjuna on How Computers and Sentient Beings Should Think: Truth, Trust and the Catuṣkoṭi

The Meyer-Dunn semantics for First Degree Entailment and the Belnap four-valued data base logic are strikingly similar to the Buddhist catuṣkoṭi, or four-cornered logic deployed by Nāgārjuna. I show that we can exploit this similarity to better understand the nature of truth.
An Intelligent E-Learning System for Autistic Children: Multi-Agent Architecture

E-learning systems are the result of the new era of digitalization, which has affected many areas including education. These systems offer many benefits like ease of access and time management. Therefore, the educational paradigm has been changed. Otherwise, since E-learning systems consider only the cognitive state of the learner regardless of his emotional state, they cannot instruct students effectively. Moreover, if the target learner has non-ordinary cognitive and socio-emotional abilities, such as a child with ASD (Autism Spectrum Disorder). So, the e-learning system must be able to satisfy those needs and propose a suitable content according to his learning rhythm. This paper proposes a new multi-agent architecture of an emotional intelligent e-learning system, it aims to help ASD children overcome learning impairments. The proposed architecture is based on several agents, endowing to address emotional, cognitive and pedagogical issues intelligently. They are working in a collaborative and cooperative fashion to provide the appropriate content.
Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach

In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases.
Data Streams Mining for Anomaly and Change Detection in Continuous Plant Operation

Data streams are collected from the real time operation of complex machines, plants and other technological systems. The collected information could be further used for different purposes, such as performance evaluation, anomaly detection, change detection or fault diagnosis of the operating systems. The analysis of the data streams is done by a calculation tool for estimating the similarity level between pairs of given chunks of data from the data stream, considered as data clouds. One of these data cloud represents a prerecorded (previously known) abnormal operation of the system, while the other data cloud represents a current (still unknown) behavior of the system. Then the similarity analysis will show how close the two data clouds are. In this paper we propose a novel method for similarity analysis that uses two types of models called Data Cloud Model (DCM) and Window Cloud Model (WCM). The DCM is obtained from the data cloud that represents a previously known operation of the system, while the WCM is obtained from the newly collected data cloud from the data stream, called window data cloud. Both data clouds have an equal length (number of data). The algorithms for creating the DCM and WCM are explained in the paper. The DCM consists of Active Grid cells that represent approximately the data density within the known data cloud. The WCM estimates the data density at the same active grid cells, but based on the data from the new window data cloud. Both densities are represented as two Histograms that are compared to each other in order to calculate the similarity level as a value between 0.0 and 1.0. Another problem discussed in the paper is finding a plausible method for detection of significant changes in the data streams. Here the moving window technique is used for collecting series of subsequent data clouds. Then two procedures of moving windows are run in parallel, each of them with different window lengths, in order to calculate the center-of-gravity of the respective data in a real time. The difference between the results of the two moving windows is used as an estimate of the change in the process operation. Both technologies developed in this paper are explain in details in the paper and illustrated on the example of real data stream from a petrochemical plant.