Reviewerly/exHarmony huggingface dataset:
A Powerful Transferability Adversarial Examples Generation Method Based on Nesterov Momentum Optimization

Researchers have found that we artificially add perturbation to the input image to generate adversarial examples which can cause the deep learning model to misclassify.The existing method of generating adversarial examples can achieve high white-box attack success rate, but the one of black-box attack is low.In order to improve the transferability ability of adversarial examples and obtain higher attack success rate, we apply the Nesterov momentum optimization method to the gradient-based adversarial examples generation method.Combined with the momentum and decay factor, the iterative gradient is optimized during the optimization process.This effectively escapes the local minima during the optimization process, resulting in faster iterations and better adversarial examples generation.The experiment showed that the white-box attack achieves 100% attack success rate, and the powerful transferability of the examples make the black-box attack success rate significantly higher than the original methods.
Designing aesthetics for play(fulness)

Product design is developed and communicated through aesthetics (e.g.form, color, personality), enabling a link between design intention and resulting experience.More precisely, designers develop both functional affordances and the meaning that the product is intended to convey, in order to mediate certain experiences.Whilst aesthetics in a general sense has often been a subject for research, there is less research on how aesthetics more specifically relates to play and playfulness in experiences.In this paper I explore the connection between the aesthetics of a product and play(ful) experiences, focusing on the research question; "What is the link between aesthetic affordances and play experiences?"In the paper I describe the identification of six basic play forms and the development of aesthetic principles relating to the play forms, extracted through a study with design students from Design School Kolding.I exemplify how the principles can relate to different product categories, both in relation to toys and to objects that are not intended for playing, but can be considered 'playful'.My take on play experiences builds on the 'Play Blueprint' by Legaard (2018) describing four main activities within play, these being Construction, Storytelling, Investigation and Bodily activity.Grouping these categories two and two led to six distinct forms of play, which all relate to particular drivers of play activities also described in the Play Blueprint, these being autonomy, chance, pleasure and imagination.The contribution of the paper is the classification of the six play forms and establishing a link between the play forms and aesthetic principles, aiding designers in designing objects for particular play/playful experiences.The intention is to aid designers to incorporate design aesthetics that adds playfulness and 'joy' (Overbeeke et al, 2003) leading to more engaging product designs, or to design products eliciting meaningful play experiences, possibly directed at specific learning outcomes by nudging the user towards specific types of play/playful behaviors.
Cloud Centric Real Time Mobile Learning System for Computer Science

Education is an important component of life because it equips us with all that is needed to make our dreams come true.One of the most promising paradigms for education is e-learning.It is commonly referred to as the intentional use of networked information and communications technology (ICT) in teaching and learning.Some other terms are also used to describe this mode of teaching and learning including online learning, virtual learning, distributed learning, network, and web-based learning.Since the last decade, there is a growing interest in e-learning from several directions.Distance education programs see it as a logical extension of their distance education activities.The emerging concept of cloud computing and advancement in mobile devices enabled with Sensor technologies transforms many areas of modernday living.The main advantage of the cloud-centric environment is that this technology reduces the infrastructure and software cost and License for all.Mobile learning (M-learning) is considering a more effective system of providing study materials to learners anywhere anytime.It is necessary to develop the robust contents delivery mechanism for learning which can be accessed from internet-enabled mobile devices.In this paper, we discuss the impact of cloud computing and supporting technologies to acquire education in a broader dimension for the students over the country.We believe cloud-based mobile learning will surely enhance the current educational system and improve education quality at a low cost.
Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering

In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsidence, the geological and mineral characteristics are simplified according to the third similar theorem. The feature equation is obtained by using the equation analysis method and dimension analysis method. The original fuzzy clustering method is improved, and the IWFCM_CCS algorithm based on competitive merger strategy is obtained. The data of rock movement observation are analyzed by fuzzy clustering. The membership matrix and clustering center of observation station data are obtained, and the regression model based on the weight of membership degree is established. The accuracy and feasibility of the parameter prediction model are verified by analyzing and comparing the actual measurement data and the predicted results of the model. The method reduces the error of the predicted parameters caused by the observation data and provides a method for the future calculation of the predicted parameters.
Innovative Topologies and Algorithms for Neural Networks

The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved the state of the art in many applications, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks is devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. The papers of this Special Issue make significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. Twelve papers are collected in the issue, addressing many relevant aspects of the topic.
Granulometry, heavy mineral and geochemical studies of stream sediments around Bula, Dass District, Northeast Nigeria

Active stream sediments generally consist of broken-down fragments of pre-existing rocks by the action of river (stream) flow. This makes them target materials for routine geochemical surveys and provenance analysis. Fifteen (15) stream sediment samples were collected in some parts of Bula and its environs, northeastern Nigeria, in order to determine their textural characteristics, heavy mineral and elemental composition. The sediments were subjected to granulometric, heavy mineral and elemental analyses. The result of granulometric analysis show that the streamsediments are poorly to moderately well sorted, very platykurtic to leptokurtic, fine to medium grained and positively skewed. Zircon, rutile and tourmaline are the dominant heavy mineral species occurring in the sediments. The computed Zircon-Tourmaline-Rutile (ZTR) index values for the samples range from 59.18 - 83.53, indicating mineralogical maturity. The geochemical data of the stream sediment samples show that the mean contents of the trace elements [Ti (0.73 ± 0.74%), Fe (0.39±0.19%), Cr (816±639ppm), Ni (258±108ppm), Pb (48±12.37ppm) and Zn (502±126ppm)] were higher than their respective average crustal values except for Fe. Computed threshold values indicate possible mineralization containing Fe and Ti. The elements have variable spatial distribution. The study shows that the trace elements composition of the stream sediments is majorly lithogenic. Because mineralization in rocks and sediments are often characterized by considerable variation in their trace elements contents, the metal concentrations in these sediments are large enough for Ilmenite and Rutile mineralization to be suspected within the study area.
Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning

Generative adversarial networks are currently used to solve various problems and are one of the most popular models. Generator and discriminator are characteristics of continuous game process in training. While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods. In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the original data distribution is constantly approximated. A new transfer learning training idea for the unsupervised generation model is proposed based on the generation network. The decoder of trained variational autoencoders is used as the network architecture and parameters to generative adversarial network generator. In addition, the standard normal distribution is obtained by sampling and then input into the model to control the degree of freedom of the model. Finally, we evaluated our method on using the MNIST, CIFAR10, and LSUN datasets. The experiment shows that our proposed method can make the loss function converge as quickly as possible and increase the model accuracy.
TYPES OF THINKING AS AN ACTION AT KNOWLEDGE FORMATION

The article actualizes the problem of individual differences in the ways of information perception and processing in order to find ways to improve the quality of students’ learning. Particular attention is focused on types of thinking (subject, figurative, signs and symbolic types), their individual relationships and creativity at first-year students. The article discloses the problem associated with comprehension by first-year students of educational material in psychology; this problem includes a lot of theories, discovered scientific facts, answers on the questions about what information processing methods are leading, how learning results depend on individual-typological peculiarities if the learning conditions are the same for all students. There are interesting facts among the obtained results about expressiveness of thinking types, as well as unexpected facts, which, perhaps, appeared because the examined students were fresh in university and met new conditions, content, methods, forms of educational organization. Since the majority of the respondents had more expressive sign thinking, based on objective and imaginative thinking, their thinking needed to be connected with the subject matter in the process of thinking and scientific text mastering. Unfortunately, the practice of students’ learning in psychology shows that mechanisms of the psychics, tendencies, patterns of it changes are not easy related with clear subjects. It is recommended to optimize symbolic thinking development with such means forming the method of general analysis as sign-symbolic modelling, which has much in common with the scientific modelling method. This is students’ activity that promotes text perception but also stipulates actions. Students analyze texts in order to find the central scientific concepts, essential scientific provisions, principles, logical connections between them. The sign-symbolic model materializes their reasoning, facilitates differentiation of essential content from the irrelevant one, self-control, the discovery of new knowledge. These data will allow lecturers to become more confident in the selection of educational texts for lectures, accompanying practical classes, and, in the long run, these texts can be transformed into manuals or textbooks. The article will be useful for lecturers, psychologists and pedagogues. &nbsp
Pixel Value Ordering with Prediction Error Expansion Based High Fidelity Reversible Data Hiding Scheme

This paper introduced a reversible data hiding method based on pixel value ordering with the prediction-error expansion technique and the average value of end pixels'.A host image is first segmented into non-overlapping sub-blocks of three pixels and ordered them as ascending order.For each subblock maximum pixel value and the minimum pixel value is predicted by the middle pixel value and also the middle pixel value is predicted by the average of the maximum and minimum pixel values.Then by using prediction-error expansions, we can embed secret bits into maximum pixel and minimum pixel and also by using the average value of these two pixels we can embed secret bit into the middle pixel of every sub-block.All secret bits can be recovered and restored the cover image completely from watermarked image.Experimental result of this scheme demonstrates that the embedding capacity and average PSNR value is larger than another pixel value ordering and prediction error expansion based approach for relatively smooth images.Also, the visual quality of the obtained marked image is better than other Pixel Value Ordering and Prediction Error Expansion based method.
The Impact of Fuzzy Discretization’s Output on Classification Accuracy of Random Forest Classifier

Random Forest is known as among the widely used classification algorithms by researchers and machine learning enthusiast in solving classification problems.Recently, fuzzy discretization has been paired with Random Forest (RF) classifier to enhance the classification accuracy of Random Forest classifier when dealing with continuous variables.However, there are many different opinions on whether there is a need to perform discretization in data pre-processing for tree-based classifiers such as J48, Decision Tree and Random Forest.On top of that, it is known that different classification algorithms produce different classification accuracies depending on the type of data used.In other words, the output of data discretization process.Thus, to unravel this mentioned hypothesis, this study intends to shed some lights on the impact of different fuzzy discretization's output on the classification accuracy of Random Forest classifier.In this study, three version of simulations were done with different fuzzy discretization output.Those fuzzy discretization's outputs are 1) without fuzzy discretization 2) with fully fuzzy discretization and 3) with partial fuzzy discretization.Then, classification phase is done through Random Forest classifier and the classification accuracy for all the simulation versions were observed, recorded, and analyzed.
Student Behavior Analysis Affecting Learning Achievement of Information Technology and Computer Science Students

The analysis of student behavior affecting the learning results with that data mining is research.To find behavioral factors that affects the student's academic performance.Learn more by the research team leading the reference information from collecting data from questionnaires of students of the Faculty of Science.To analyze the model of decision making to separate groups with similar behavior together and use grouping techniques (Clustering).Making decision to find out answers to behavioral factors that affect learning results.In small groups based on various behavioral factors such as Use of free time in everyday life Learning anxiety Concentrate on learning Motivation for learning and using K -Means Cluster and Random Tree to find a model that has Correctly Classified Instances when tested with 380 datasets of training data, with an average of 98% which is the average at a good level can Bring the model that has been Continued to develop the system.
A Novel approach for Retrieval of Historical Monuments Images using Visual Contents and Unsupervised Machine Learning

In Recent Era monuments image identification and retrieval task is too complex than other dataset images due to its distinct features variation and complex architecture.Structure orientation also plays a vital role for identification and recognition of monuments images.Using content based image retrieval our proposed approach provides a solution of this problem.The aim of proposed work is to make an efficient model to retrieve correct monuments images using machine learning algorithm.Proposed approach extract the handcrafted features by using the RGB Histogram (fast) color features and Histogram of Oriented Gradient texture feature.To make a perfect cluster with minimum outliers of images we used unsupervised machine learning i.e. k-means clustering.This algorithm has been made the retrieval process fast by avoid the whole search for matching similarity in the complete dataset.The experiments show the performance of proposed approach which gives 97% accuracy for retrieval of monument images.
SINE: Second-Order Information Network Embedding

As an important data type, the demand of network analysis and learning is increasingly prominent. A key problem of network analysis is to study how to reasonably represent the feature information in the network, that is network embedding. However, in the study of network embedding, only the first-order proximity relationship of nodes is characterized, while the second-order proximity relationship hidden in the network nodes is ignored. Therefore, we propose an algorithm, called SINE, to realize the representation learning of nodes in the network which fuses the first-order and second-order proximity of nodes in the original network. Through applying SINE to three real networks, we obtain the feature representation of the nodes in the networks and cluster the nodes based on these features. Comparing with the existing network embedding learning methods-Node2vec, Large-Scale Information Network Embedding (LINE) and Structural Deep Network Embedding (SDNE), the SINE algorithm showed better performance in clustering tasks.
Contextualising Hate Speech: A Study of India And Malaysia

Hate speech is a verbal expression, which is designed to promote hatred based on race, religion, ethnicity and violence between different communities . Hate speech are systematically used to lash hatred towards minorities and for inflaming regional, religious and ethnic passion. It has been repeatedly observed that inflaming and deleterious words have been used by people of influence to further their vested interests and incite violence. Indian laws as well as Malaysian laws forbid anyone from making hate speeches that disturb the communal harmony and creates discord among the people. When the issue of suppression of hate speech comes to purview, the notion of reasonable restriction comes into picture. Consequent debate narrows down to two sharply contrasting opinions ie: restraints v. freedom of speech and expression. It is difficult to balance both divergent opinions. Article 19(1) of the Indian Constitution, provides for the freedom of speech and expression but also places reasonable restrictions under Article 19(2) on the grounds of public order, decency and morality. Similarly, Article 10 of the Malaysian Constitution empowers the Malaysian citizens with the freedom of speech and expression subject to restrictions imposed by the Parliament. Whenever these contradictory opinions are presented in front of the court, even the Apex court of the respective countries has shown its reluctance towards curbing such communally charged statements. Even if the judgment restraining hate speech is pronounced, it often comes too late, sometimes after the damage has already been inflicted, thus failing to exert any deterrent effect. By using doctrinal research methods,this paper a) discusses the terminology of hate speech; b) analyzes different legal provisions that deals with hate speech in India and Malaysia; and c) discusses non-legal solutions to the problem. This paper argues that although several legal provisions are available to curb hate speech, Alternative Dispute Resolution and counter speech may be used as alternative ways to address hate speech.
Blockchain Based Data Transmission Control for Tactical Data Link

Tactical Data Link (TDL) is a communication system that utilizes a certain message format and a protocol to transmit data via wireless channels in an instant, automatic, and secure way. So far, TDL has shown special importance in military applications. Current TDL adopts a distributed architecture to enhance anti-destruction capacity. However, TDL faces the problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains. To tackle this problem, we propose to leverage blockchain to build an automatic and adaptive data transmission control mechanism for TDL. It achieves automatic data transmission and realizes information consensus among TDL consensus nodes. Besides, smart contracts based on blockchain further enable adjusting data transmission policies automatically. The experimental results illustrate effectiveness and efficiency of our proposed blockchain based data transmission control for TDL.
Vector Image Retrieval Methods Based on Fuzzy Patterns

In this work we present two methods of vector graphic objects retrieval based on a fuzzy description of their shapes.Both methods enable the retrieval of vector images resembling to a given fuzzy pattern.The basic method offers a geometrical interpretation of a fuzziness measure as a radius of a circle with the center in each vertex of a given candidate object.It enables the representation of uncertain information about a pattern object defined by its "fuzzy" vertices.The advanced method generalizes this approach by considering an ellipse instead of a circle.The basic method can be used for the comparison of polygons and other primitives in vector images.The advanced method can be used for complex shapes retrieval.To enable saving a "fuzzy" image as a file, the modification of the SVG format with a new attribute "fuzziness" is proposed for both methods.The advanced method practical implementation is illustrated by the retrieval of medical images, namely, heart computer tomography images.