Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.
An Approach for Identifying Malicious Domain Names Generated by Dictionary-Based DGA Bots

SUMMARY Computer networks are facing serious threats from the emergence of sophisticated new DGA bots. These DGA bots have their own dictionary, from which they concatenate words to dynamically generate domain names that are di ﬃ cult to distinguish from human-generated domain names. In this letter, we propose an approach for identifying the callback communications of DGA bots based on relations among the words that constitute the character string of each domain name. Our evaluation indicates high performance, with a recall of 0.9977 and a precision of 0.9869.
An Algorithm for Topic Modeling of Researchers Taking Into Account Their Interests in Google Scholar Profiles

An algorithm for topic modeling of researchers based on their interests from Google Scholar’s profiles is proposed. As topics for modeling, we took research groups from research classification system ANZSRC – Australian and New Zealand Standard Research Classification. Researchers’ distribution to research groups is found based on their interests’ usage statistics in categorized publications from Dimensions. Topic modeling is conducted accordingly to principles of statistical support, multi-labeling, noise filtering, ignoring stop-words, solidarities, focusing, compactness and research groups’ interactions. We compare topic modeling based on data with low level of information from researchers’ profiles in Google Scholar with topic modeling based on a few dozen authored publications categorized by Dimensions. Comparison is made by modified Czekanowski metric that takes into account the interaction between research groups. By comparing the results of topic modeling based on different sources of initial information a good match was found. It allows to use the proposed algorithm as the intellectual core of information technology in regards to scientific staff, in particular, for the selection of candidates as opponents of a dissertation, as reviewers for research projects, for forming a team to collaborate on mutual research projects etc.
Parameters Identification for Fractional-fractal Model of Filtration-Consolidation Using GPU

The paper considers some computational problems arising in the important practical field of the determination of safe operation conditions of engineering facilities that pollute soils and groundwater. In the case of complex geological and hydrological conditions, such problems are widely considered using mathematical modeling of deformation and consolidation processes in water-saturated soils, particularly, in the foundations of hydraulic structures. To simulate the dynamics of such processes, we use a fractional-fractal approach that allows considering temporal non-locality of transfer processes in media of fractal structure. The used one-dimensional differential model contains a non-local Caputo derivative with respect to the time variable and a local fractal derivative with respect to the space variable. Some of model’s parameters, namely the orders of fractional derivatives, can only be determined fitting them to the measured data related to the state of a process. We propose to use particle swarm optimization algorithm to perform an identification of fractional derivatives’ orders and present the results of its testing on noised subsets of direct problem solutions. In this context, we have determined that the order of space-fractal derivative is restored with a relative error of not more than 1% while the order of time-fractional derivative is restored with higher errors of not more than 10%. The lowest number of observation points that ensures stable restoration of the orders was equal to 25. As high computational complexity is combined with highly independent computational blocks while applying evolutionary optimization algorithms to the problems of differential models’ parameters identification, we implemented the proposed algorithm on graphical processing units (GPU) using OpenCL framework and on multi-threaded systems using OpenMP. The results of performance testing showed up to 4-times lower GPU execution time compared to the case of multi-threaded execution on 6 cores of central processing unit (CPU).
Active Suppression of Piezoelectric Ringing in Pockels Cells for Laser Cavity Application

Pockels cells used as electro-optical modulators in high-power high-repetition lasers suffer from piezoelectric ringing phenomenon due to piezoelectric properties of the crystals. A new method for active suppression of the piezoelectric ringing in Pockels cells is proposed in this work. It is based on symmetric control of Pockels cell using burst of short positive and negative voltage pulses with the same amplitude instead of a single long pulse for light polarization modulation. Rising and falling edges of pulses of the burst induce symmetrical acoustic waves of the opposite phase and cancel the piezoelectric ringing of the crystal. A new high voltage driver capable of generating positive and negative pulses of tens of nanoseconds of 3 kV magnitude was developed for this purpose. The amplitude of laser beam intensity pulsations caused by the piezoelectric ringing can be reduced up to five times when active suppression method is used for the deuterated potassium dihydrogen phosphate (DKDP) Pockels cell. Such crystals like DKDP, LiNbO3, and LiTaO3 may benefit from the proposed method and find new use in lasers of high repetition rate where piezoelectric ringing is a major limiting factor.
Extending the Measurement Angle of a Gaze Estimation Method Using an Eye Model Expressed by a Revolution about the Optical Axis of the Eye

SUMMARY An eye model expressed by a revolution about the optical axis of the eye is one of the most accurate models for use in a 3D gaze estimation method. The measurement range of the previous gaze estimation method that uses two cameras based on the eye model is limited by the larger of the two angles between the gaze and the optical axes of two cameras. The previous method cannot calculate the gaze when exceeding a certain limit of the rotation angle of the eye. In this paper, we show the characteristics of reﬂections on the surface of the eye from two light sources, when the eye rotates. Then, we propose a method that extends the rotation angle of the eye for a 3D gaze estimation based on this model. The proposed method uses reﬂections that were not used in the previous method. We developed an experimental gaze tracking system for a wide projector screen and experimentally validated the proposed method with 20 participants. The result shows that the proposed method can measure the gaze of more number of people with increased accuracy compared with the previous method.
Handwritten Digits Recognition Using SVM, KNN, RF and Deep Learning Neural Networks

This article discusses several classification algorithms of recognizing numbers from photographic images or with manual input, namely: support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) and several variants of neural networks. The success rates of the algorithms in the field of handwriting recognition were compared. Six variants of recognition technology were analyzed and tested: using classifier from Scikit-learn package and using deep learning neural networks. To construct and train neural networks or train classifiers, a well-known and rather complete base of handwritten digits MNIST was chosen. Two types of neural networks were considered: sequential and convolutional. The training of neural networks was carried out using a variable number of steps (epochs). Recognition images were scaled to a size of 28x28 (784 cells in one-dimensional representation). Preliminary processing of images (filtering, scaling, etc.) was carried out using the OpenCV library. For recognition, each image of a digit was converted to a 28x28 size and fed to the input of a pre-trained neural network. A technique to select the area of interest in photographs containing hand-written digits for further recognition has been devised. For handwritten digit recognition, the best recognition accuracy is provided by a convolutional neural network, as 97.6% of car ladle digits were recognized correctly with it. To improve the recognition accuracy for handwritten digits, it is necessary to perform two additional stages of image preprocessing and dataset transformation. After building recognition models using all the algorithms mentioned above, the recognition accuracy of all handwritten digits on the test program turned out to be within 98-100%. For industrial images regardless of the used neural network version, the recognition accuracy was 96-98%.
Evaluation of Development Level and Technical Contribution of Recent Technologies Adopted to Meet the Challenges of 5G Wireless Cellular Networks

The evolution of the global wireless market is accompanied by an increased need in terms of speed and number of users, lower latency, better coverage, better spectral efficiency and quality of service, etc. To meet these needs, 5G has recently been introduced as an effective solution which targets, via the large scale deployment of symmetric antennas, a wide variety of sectors such as energy, health, media, industry, transport and especially wireless cellular networks which are among the most important pillars of modern societies. Multiple Input, Multiple Output (MIMO) systems, which have been extended to “Massive MIMO” mode and which consist of increasing the number of radiating elements involved in the transmission and reception of the radio link, are a very promising solution for improving the spectral efficiency of wireless communication systems (WCSs). Motivated by the aforementioned developments, the present paper investigates the increased capacity of MIMO systems to improve transmission in WCSs using 5G. It carefully focuses on the evaluation of the development level and technical contribution of MIMO systems and millimeter wave (mmWave) bands in 5G wireless cellular networks (WCNs) and gives important recommendations.
The Contour of Causality in Control Automata of Systems

Known models of control automata of systems describe only the necessary cause-and-effect relationships between sets of an automaton, except for the relationship between sets of outputs and inputs. This complicates the analysis of non-standard situations associated with the diagnosis and development of machines. A model of an automaton in the form of activity and control loops is proposed, which reflects the cause-and-effect relationships in the control automata of the system, including the relations between the elements of the set of outputs and the set of inputs of the automaton. These circuits interact through the machine states. The classification and models of the machine states are proposed. The structure of the knowledge base along the automaton contours and the predicates types describing causal relationships in the automaton are proposed. An example of constructing the contours of activity, control and Prolog program for diagnosing the technical state of the machine using the knowledge base is given
On (p, q)-Analogues of Laplace-Typed Integral Transforms and Applications

In this paper, we establish (p,q)-analogues of Laplace-type integral transforms by using the concept of (p,q)-calculus. Moreover, we study some properties of (p,q)-analogues of Laplace-type integral transforms and apply them to solve some (p,q)-differential equations.
Examination and Comparison of Theta Band Connectivity in Left- and Right-Hand Dominant Individuals throughout a Motor Skill Acquisition

The majority of the population identifies as right-hand dominant, with a minority 10.6% identifying as left-hand dominant. Social factors may partially skew the distribution, but it remains that left-hand dominant individuals make up approximately 40 million people in the United States alone and yet, remain underrepresented in the motor control literature. Recent research has revealed behavioral and neurological differences between populations, therein overturning assumptions of a simple hemispheric flip in motor-related activations. The present work showed differentially adaptable motor programs between populations and found fundamental differences in methods of skill acquisition highlighting underlying neural strategies unique to each population. Difference maps and descriptive metrics of coherent activation patterns showed differences in how theta oscillations were utilized. The right-hand group relied on occipital parietal lobe connectivity for visual information integration necessary to inform the motor task, while the left-hand group relied on a more frontal lobe localized cognitive based approach. The findings provide insight into potential alternative methods of information integration and emphasize the importance for inclusion of the left-hand dominant population in the growing conceptualization of the brain promoting the generation of a more complete, stable, and accurate understanding of our complex biology.
Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time

The Topics over Time (TOT) model allows users to be aware of changes in certain topics over time. The proposed method inputs the divided dataset of security blog posts based on a fixed period using an overlap period to the TOT. The results suggest the extraction of topics that include malware and attack campaign names that are appropriate for the multi-labeling of cyber threat intelligence reports. key words: topic model, cyber threat intelligence, text mining, multilabeling, security blog posts
L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production

SUMMARY Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary ﬂux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic ﬂows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to com- pare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1- FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The de- veloped software, minL1-FMDL, implemented in MATLAB, is available on https: // sunﬂower. kuicr.kyoto-u.ac. jp / ˜tamura / software, and can be used for genome-scale metabolic network design for metabolite production.
Application of binary dynamical systems in the problem of classification of Boolean vectors

The article proposes a method based on using binary dynamical systems in the classification problem for Boolean vectors (binary feature vectors). This problem has practical application in various fields of science and industry, for example, bioinformatics, remote sensing of natural objects, smart devices of the Internet of things, etc. Binary synchronous autonomous nonlinear dynamic models with an unknown characteristic matrix are considered. Matrix elements are chosen in such a way that the Boolean reference vectors are equilibrium states of the binary dynamic model. The attraction regions of equilibrium states act as classes (one reference vector corresponds to each class). The classified vector is the initial state of the model. Simple and aggregated classifiers are considered. The proposed method is demonstrated using an illustrative example.
Single cell and spatial transcriptomics in human tendon disease indicate dysregulated immune homeostasis

Nina Paulshus Sundlisater , AnnaBirgitte Aga, Inge Christoffer Olsen, Hilde Berner Hammer , Till Uhlig , Désirée van der Heijde , Tore K Kvien, espen A Haavardsholm, Siri Lillegraven Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway Clinical Trial Unit, Oslo University Hospital, Oslo, Norway Faculty of Medicine, University of Oslo, Oslo, Norway Rheumatology, Leiden University Medical Center, Leiden, ZuidHolland, The Netherlands
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network

Outlier detection is an important task in many domains and is intensively studied in the past decade. Further, how to explain outliers, i.e., outlier interpretation, is more significant, which can provide valuable insights for analysts to better understand, solve, and prevent these detected outliers. However, only limited studies consider this problem. Most of the existing methods are based on the score-and-search manner. They select a feature subspace as interpretation per queried outlier by estimating outlying scores of the outlier in searched subspaces. Due to the tremendous searching space, they have to utilize pruning strategies and set a maximum subspace length, often resulting in suboptimal interpretation results. Accordingly, this paper proposes a novel Attention-guided Triplet deviation network for Outlier interpretatioN (ATON). Instead of searching a subspace, ATON directly learns an embedding space and learns how to attach attention to each embedding dimension (i.e., capturing the contribution of each dimension to the outlierness of the queried outlier). Specifically, ATON consists of a feature embedding module and a customized self-attention learning module, which are optimized by a triplet deviation-based loss function. We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded. ATON finally distills a subspace of original features from the embedding module and the attention coefficient. With the good generality, ATON can be employed as an additional step of any black-box outlier detector. A comprehensive suite of experiments is conducted to evaluate the effectiveness and efficiency of ATON. The proposed ATON significantly outperforms state-of-the-art competitors on 12 real-world datasets and obtains good scalability w.r. t. both data dimensionality and data size.
Stock Prediction by using NLP and Deep Learning Approach

Abstract: People have a tendency to analyze existing strategies and so planned new strategies for inventory prediction. We have used Sentiment evaluation and Technical evaluation through NLP and Deep mastering approach. In order to exploit benefits of sentiment analysis on enterprise associated inventory, we have proposed a model that will use the sentiment analysis on twits associated with special sectors that are Information Technology sector, Banking sector, Pharmaceutical sector, Automobile sector, Infrastructure sector which are extracted from twitter. These twits are extracted from twitter for calculating polarity. The rating of sentiment analysis is calculated here by using Natural Language Processing’s method. According to sector we've taken five groups. Top four performer businesses of every sector. Using polarity score we got finalized pinnacle ten groups with great sentiment rating. We then downloaded the CSV facts of historical share charge of top ten organizations that we've selected. Then downloaded CSV records are used to build a CNN version to predict in addition stock movement of these pinnacle ten companies.
New historical data for long-term swordfish ecological studies in the Mediterranean Sea

Abstract. Management of marine fisheries and ecosystems is constrained by knowledge based on datasets with limited temporal coverage. Many populations and ecosystems have been perturbed long before scientific investigations have begun. This situation is particularly acute for the largest and commercially most valuable species. We hypothesized that historical trap fishery records for bluefin tuna, Thunnus thynnus, could contain catch data and information for other, bycatch species, such as swordfish, Xiphias gladius. This species has a long history of exploitation and is presently overexploited, yet indicators of its status (biomass) used in fishery management only start in 1950. Here we examine historical fishery records and logbooks from some of these traps and recovered ca. 110 years of bycatch data (1896–2010). These previously-neglected, but now recovered, data include catch dates and amounts in numbers and/or weights (including individual weights) for the time period before and after major expansion of swordfish fisheries in the Mediterranean Sea. New historical datasets such as these could help understand how human activities and natural variability interact to affect long-term dynamics of this species.
Proceedings in Management, Social Sciences and Humanities, 9th International Research Conference-KDU, Sri Lanka

Investigations were done over a long period to see whether or not there is a correlation between Corporate Social Responsibility (CSR) and organization profitability. The major reason for disparity is due to the complexity in measuring CSR. Scholars later found out that Correlation co-efficient can be used to measure this. Main objective is to find out whether there is a correlation between the two variables or not within the hospitality industry. The data is taken from the financial statements of John Keells PLC (JKH), the selected firm for this study that comprises of two city hotels and nine holiday resorts. Due to the qualitative nature of CSR, it is taken as a percentage on other operating expenses, as CSR is a part of it under JKH. Measuring profitability is done by four ratios; Net profit margin, Operating profit margin, Return on Assets (ROA) and Return on Capital Employed (ROCE). The calculation showed a negative correlation between three of the indicators and CSR and a positive correlation between ROA and CSR, marking an overall negative correlation between CSR and profitability. Note that the even though most of the researches done relevant to this topic have given out a positive result, it could have a negative result too as CSR is a concept that generates results in the long run thereby reaching sustainability.