Reviewerly/exHarmony huggingface dataset:
BSAT: A New Tool for Analyzing Cryptographic Strength of Boolean Function and S-Box of Symmetric Cryptosystem

The cryptographic primitives such as Boolean function and S-Box are used as a building block to design stream cipher and block cipher, respectively. However, it is important to evaluate the cryptographic strength to measure the security of such crytposystem resistance against important cryptanalytic attacks. We develop a new tool called Boolean function and S-Box analysis tool (BSAT) to evaluate the cryptographic strength of the Boolean function and S-Box. As the size of S-Box increases, it takes a lot of computational effort to evaluate all the required cryptographic properties. In this paper, we develop a tool to evaluate important cryptographic properties of both Boolean function and S-Box to minimize the computational time. The computational time of BSAT is lesser than the SET tool in terms of calculating the algebraic normal form and algebraic degree. Our BSAT takes only 0.404 s to evaluate the AES properties faster than the SET, which takes 650 millisecond (0.65 s).
Method for Automated Recognition of Frustration-Derived Aggression in Texts

The paper proposes a method for automatic recognition of reactions to frustration in texts. It also presents the results of a study that allows catching linguistic patterns for extrapunitive responses (E-reactions) aimed at protecting the “Self”; and shows the results of experiments with the Rosenzweig picture-frustration test protocols. We use a linguistic analyzer, “PCA Machine”, which considers the wide range of multi-level features aggregated by the relational-situational model to process the texts with E-reactions to frustration. Then we developed a set of templates, which can catch the revealed linguistic patterns and use them together with machine learning models. This approach turns out to be new for modeling the reasoning of expert cognitive research. Experimental results show that in most cases, the templates allow detecting E-reactions. Moreover, the share of unrecognized reactions is small; however, to increase the recognition reliability, it is necessary to consider additional features, including the context of the analyzed text fragments.
PARADIGMATIC NATURE OF COMPETENCE

The paradigmatic nature of competence is summarized by defining a disciplinary matrix based on an analysis of the history of the concept of competence at the international level Historical aspects of the development of science are analyzed T. Kuhn and describes the reasons for paradigmatic differences based on the thinking of Generation Z, which grows in the context of collaboration in social networks. Problems are identified in the implementation of competencies as a unit of content using traditional scientific procedures, and it is concluded that this competence is not an achievement of individual disciplines, but is the ability to adequately cope with the problem of the environment and life. It is also recommended to pay attention to the “paradigmatic competition” between communities in the development of educational content that increases the competence of Gen Z.
An Adaptive Weighted Image Denoising Method Based on Morphology

In its generation, transmission and record, image signal is often interfered by various noises, which have severally affected the visual effects of images; therefore, it is a very important pre-processing step to take proper approaches to reduce noises. Conventional denoising methods have also blurred image edge information while removing noises, which can be overcome by the method based on mathematical morphology. While eliminating different noises from images, it can not only keep clear object edges, but also preserve as many image details as possible and it also has excellent capacities in noise resistance and edge preservation. With image denoising and mathematical morphology as the research subject, this paper analyzes the generation and characteristics of common image noises, studies the basic theories of mathematical morphology and its applications in image processing, discusses the method to select structural elements in mathematical morphology and proposes a filtering algorithm which combines image denoising and mathematical morphology. This method conducts morphological filtering and denoising on noised image with filter cascade and its performance is verified with stimulation testing. The experiment results prove that the approach to build the morphological filter into cascaded filter through series and parallel connection can to a certain extent, affect the effect of common filter while being applied to different image processing.
Gradient Descent Learning for Hyperbolic Hopfield Associative Memory

This paper proposes a scheme for embedding patterns onto the Hyperbolic-valued Hopfield Neural Networks (HHNNs).This scheme is based on gradient descent learning (GDL), in which the connection weights among neurons are gradually modified by iterative applications of patterns to be embedded.The performances of the proposed scheme are evaluated though several types of numerical experiments, as compared to projection rule (PR) for HHNNs.Experimental results show that pattern embedding by the proposed GDL is still possible for large number of patterns, in which the embedding by PR often fails.It is also shown that the proposed GDL can be improved, in terms both of stability of embedded patterns and of computational costs, by configuring the initial connection weights by PR and then by modifying the connection weights by GDL.
Comparing Apples and Oranges: Human and Computer Clustered Affinity Diagrams Under the Microscope

Affinity diagramming is a crucial yet time-consuming part of user research in human-centered design. In short, building affinity diagrams involves the hierarchical bottom-up clustering of user statements and observations, which later allow to derive insights and inspire design ideas. To support designers in this process, as a first contribution, we explored seven text-mining models for pre-clustering affinity notes and suggest fastText as most appropriate. Since affinity diagrams are not deterministic, there is no established measure to assess their quality. Our second contribution is, therefore, a thorough examination of the potential of fastText-clusters for design teams regarding technical, psychological and performance-related measures. Compared to reference ‘human built’ affinity diagrams, the fastText-clusters resulted in an overlap index of M = .694 (SD = .034). Surprisingly, a study with four design teams clustering small sets (112 notes) of pre-clustered or randomized affinity notes indicated an increased discussion overhead caused by algorithmic support that led to a decrease in both, efficiency and quality. As a third contribution, we report qualitative data from the instances, where algorithmic support failed designers’ expectations. We conclude that more research on the appropriate time and manner of pre-clustered data presentation is required to harness the full potential of algorithmic support while preserving the spirit of affinity diagramming.
Synchronization and calibration of a stereo vision system

In this paper, we design a stereo image and video acquisition system with adjustable baseline and relative angle. Stereo vision has been used both for field robotic navigation and 3D wave reconstruction. However, there has been inadequate documentation about the synchronization performance of such system. For a dynamically moving scene, such as the wave, any delay between the two cameras will affects the accuracy of the disparity map. The present study presents a physical process to measure the delay between the two cameras. Firstly, we obtain the maximum frame rate of camera when it is triggered by external signal. Then we adopt a free fall experiment to measure the delay between trigger intervals at maximum frame rate. The relationship between baseline distance, calibration checker box tile size and target distance is discussed. Two calibration tools, i.e. MATLAB and WASS are compared using a range of distances.
Analogical Embedding for Analogy-Based Learning to Rank

Learning to rank based on principles of analogical reasoning has recently been proposed as a novel approach to preference learning. The approach essentially builds on a regularity assumption of the following kind: Given objects A B, C, D, if A relates to B as C relates to D, and A is preferred to B, then C is presumably preferred to D. This assumption is formalized in terms of so-called analogical proportions, which operate on a feature representation of the objects. A suitable representation is therefore essential for the success of analogy-based learning to rank. Therefore, we propose a method for analogical embedding, i.e., for embedding the data in a target space such that, in this space, the aforementioned analogy assumption is as valid and strongly pronounced as possible. This is accomplished by means of a neural network with a quadruple Siamese structure, which is trained on a suitably designed set of examples in the form of quadruples of objects. By conducting experiments on several real-world data sets, we provide evidence for the usefulness of analogical embedding and its potential to improve the performance of analogy-based learning to rank.
Accurate Detection of Breast Cancer Using GLCM and LBP Features with ANN via Mammography

Mammography is a medical imaging technology that uses X-rays to depict the internal structure of breast reason. Mammograms generated through mammography process assist radiologists and doctors in the detection/diagnosis of breast diseases. A computer-aided diagnosis (CAD) system can be used for improving the diagnostic information using these mammograms. For the designing of an effective CAD system, texture features with machine learning play a vital role in the detection of breast cancer. This paper introduces a framework of hybrid texture features with an artificial neural network (ANN) for the accurate detection of breast cancer, where hybrid features are derived from combination of local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM) features. From the experimental analysis on the mammography image analysis society (MIAS) database, it has been found that our proposed approach is significantly encouraging than other methods and well suited for the accurate diagnosis of breast cancer.
The basic postulates of documentology – the general theory of documents

The author introduces the basic principles of documentology. Its subject is the document as an abstract concept considered at the methodological, theoretical, historical and practical levels. Structurally, it is legitimate to study the document a s a w hole, a s w ell a s i n r elation t o i ts i ndividual a spects a nd f eatures. The result of this study is the division of documentology into general, special and specific discipline. The pan-civilization significance of documents as the most important masterpiece of human genius is that it enables to freeze things in time or even to turn time back – which is otherwise impossible for the human beings. The documents give the material form to ethereal thoughts and knowledge of any kind, over the entire existence of the humankind. The appearance of the written language enables to draw the line between prehistorical and historical periods of human existence. No one social function is possible without documents; the documents has been the most powerful factors of the civilization progressing. The entity of document is compared to that of information; the former is characterized mainly with its components, namely nominative, generative, material, signative, semantic, syntaxive, temporal and pragmatic. The best definition of document is given by the International Organization for Standardization (ISO): Document is “recorded information or material object which can be used as a unit in the documentation process”; in particular, the document in library collections is the recorded information or a physical matter matching the collection profile. The basic postulates of documentology are generalized; they comprise the discipline structure and laws, the general principles of creating, disseminating, storing and using the documents; as well as the core document elements.
Introduction to the special section on deep learning-based intelligent systems (VSI-dlis)

The fundamental challenge of data analytics scheduling is the heterogeneity of both data analytics jobs and resources. Although many scheduling solutions have been developed to improve the efficiency of data analytics frameworks (e.g., Spark), they either (1) focus on the scheduling of a single type of resource, without considering the coordination between different resources; or (2) schedule multiple resources by factoring in limited information about analytics jobs without considering the heterogeneity of resources. This paper presents Stargazer, a novel, efficient system that tackles diversity data analytics jobs on heterogeneous cluster by inferring the completion times of their decomposed tasks. Specifically, Stargazer adopts a deep learning model, which takes into considerations multiple key factors of diversity data analytics jobs and heterogeneous resources, to accurately infer the completion time of different tasks. A prototype of Stargazer is fully implemented in the Spark framework. Extensive experiments show that Stargazer can reduce the average job completion time by 21% and improve average performance by 20%, while incurring little overhead.
A Lightweight Convolutional Neural Network for White Blood Cells Classification

Our immune system is a complex network that consists of cells, tissues, and organs that operates concurrently to shield our body from millions of diseases, causing bacteria, parasites, and viruses. For the identification of different kinds of hematological disorders, the accurate identification of various White blood cells (WBC) is necessary for classification purposes. Most of the diseases can be diagnosed by the numbers and sizes of white blood cells found in a blood smear. A drastic change in a particular WBC count relative to the standard range provides us a hint about being attacked by distinct enzyme. As the incorrect segmentation of cells leads to inaccurate disease detection, it demands utmost significance that this process is performed in the best effective way. Still now, in many medical centers the detection and categorization of WBCs is performed manually by experts. As there remains a great probability of error due to manual classification, automatic systems should be designed in such a way that there will be a very minimal error rate as compared to the manual. With this aim, in this paper, a renowned methodology named Deep learning is proposed to conduct the whole classification process automatically applying an improved lightweight convolutional neural network which has been implemented for both multiclass and binary classification with an accuracy rate of 98.63% and 91.95% respectively.
Privacy preserved spatio-temporal trajectory publication of Covid-19 patients

To control the community spread of Covid-19 by health authorities, citizens' use of contact tracing mobile applications is vital. Publication of these traces for prospective probes, the collected spatio-temporal traces of Covid-19 positive cases through LBS contributes to personal privacy violation. So, the data collector must anonymise the essential attributes in the trajectories before initiating the release of trajectory data. We propose an approach that provides sufficient personal protection to the individuals while publishing their trajectory data by anonymising very sensitive stay locations like home, work-locations, etc. Anonymisation of more locations in trajectories upsets the data utility of Covid-19 traces in future studies. This work creates Haversine distance measured Minimum Bounding Rectangular (MBR) stay zones, over the most sensitive stay locations with similar Places of Interest to provide anonymity and prevents the adversary from getting known the exact information about the sensitive stay locations. Since the published versions of GPS traces of Covid-19 patients were unavailable, we created sample dummy datasets by altering the available spatio-temporal datasets. The result proves that the data utility is a little high, and the information loss is low, but comparable to the other similar methods.
Recent Developments and Methods of Cloud Data Security in Post-Quantum Perspective

Cloud computing has changed the paradigm of using computing resources. It has shifted from traditional storage and computing to Internet based computing leveraging economy of scale, cost saving, elimination of data redundancy, scalability, availability and regulatory compliance. With these, cloud also brings plenty of security issues. As security is not a one-time solution, there have been efforts to investigate and provide countermeasures. In the wake of emerging quantum computers, the aim of post-quantum cryptography is to develop cryptography schemes that are secure against both classical computers and quantum computers. Since cloud is widely used across the globe for outsourcing data, it is essential to strive at providing betterment of security schemes from time to time. This paper reviews recent development, methods of cloud data security in post-quantum perspectives. It provides useful insights pertaining to the security schemes used to safeguard data dynamics associated with cloud computing. The findings of this paper gives directions for further research in pursuit of more secure cloud data storage and retrieval.
Diffusion representation for asymmetric kernels

We extend the diffusion-map formalism to data sets that are induced by asymmetric kernels. Analytical convergence results of the resulting expansion are proved, and an algorithm is proposed to perform the dimensional reduction. A coordinate system connected to the tensor product of Fourier basis is used to represent the underlying geometric structure obtained by the diffusion-map, thus reducing the dimensionality of the data set and making use of the speedup provided by the two-dimensional Fast Fourier Transform algorithm (2-D FFT). We compare our results with those obtained by other eigenvalue expansions, and verify the efficiency of the algorithms with synthetic data, as well as with real data from applications including climate change studies.
Spatial Estimation of Solar Radiation Using Geostatistics and Machine Learning Techniques

In large solar fields, where the control system is distributed, it is important to know the values of solar radiation in the complete area. Local solar radiation can be obtained by means of static sensors, using e.g. a wireless sensor network or movable sensors with drones for the general obtainment of variables. In this paper, solar radiation estimation is accomplished using Ordinary Kriging and distance weighting, and an alternative method is presented, which is based on a non-supervised competitive artificial neural network called Self-Organizing Map. This neural network generates a map with the most representative nodes and their weights, which are used to obtain the spatial variability of solar radiation in the area.
A Toxic Comment Classification Model Based on Ensemble

Abstract Accurate classification of toxic comments in different languages is an important task in today’s international social networking platforms. In order to improve the classification of toxic comments in different languages, this paper combines the advantages of high accuracy of monolingual model and strong generalization ability of multi-language model, and adopts the ensemble of multilingual model and monolingual model to classify toxic comments. For the monolingual model, the monolingual pre-training model is fine-tuned with labeled task data; for the multilingual model, before fine-tuning the model with labeled task data, a further pre-training is applied to train model on unlabeled data, which aims to make full use of the large amount of unlabeled data and reduce the dependency of amount of labeled comment data while improving the classification effect. Comparative experiments on Conversasion AI’s multilingual toxic comment dataset show that the model in this paper has improved results on different evaluation metrics compared to the XLM-RoBERTa multilingual fine-tuning model, illustrating the validity of the model.
Analytical expressions for estimating sky diffuse irradiance and illuminance on tilted planes for the CIE Standard General Skies

This work proposes a set of analytical expressions for the calculation of the relationship between sky diffuse illuminance on a tilted plane and horizontal plane corresponding to the 15 ISO/CIE standard sky types. Given the parallelism in the angular distribution of sky diffuse radiance and luminance, these expressions can also be used to calculate the diffuse sky irradiance on an inclined plane. The proposed analytical expressions are obtained by adjusting the results of a numerical calculation whose methodology is described in detail in the paper. The use of these analytical expressions significantly reduces the calculation time. It is verified that the relative root-mean-square errors obtained with the analytical expressions are small compared with the use of numerical calculation, ranging from 0.03% to 4.09%, and with a tendency to increase with clear skies and high solar zenith angles.
Learnable Heterogeneous Convolution: Learning both topology and strength

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5 × on CIFAR10 and 2 × on ImageNet without harming the performance, where the weights are compressed by 10 × and 4 × respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution .
Bacteriological air quality at transfer station area (case study: Gegerkalong Transfer Station, Bandung City)

Abstract This study aims to measure air bacteriological concentration, to determine the effect of temperature, humidity, and wind speed on bacteriological concentration, and to identify the genus of bacteria found around Gegerkalong waste transfer station. Sampling of air bacteria at stations was based on a radius of 0, 10, 30 and 60 meters East, West, South and North of the transfer station at 09:00, 12:00 and 18:30 with 3 replicates. The method used to measure the concentration of air bacteria was the settling plate method. The results showed that maximum concentration of air bacteria found around the transfer station area was 67626 cfu/m 3 and the minimum was 1153 cfu/m 3 for agar nutrient media. Referring to the quality standards of the UK Environmental Agency, bacterial concentration in Gegerkalong waste transfer station exceeds the bacterial concentration standard of 1000 cfu/m 3 , whereas Coliform bacteria were below the standard. Bacteria found in the air around the transfer station are Pseudomonas, Klebsiella, Shigella, Alcaligenes, Salmonella, Escherichia, Bacillus, Staphyloococcus and Coliform . These can be concluded that a transfer station can be a source of airborne bacteria dispersion, which can have an impact on the surrounding community.