Reviewerly/exHarmony huggingface dataset:
Current Status of Computational Intelligence Applications in Dermatological Clinical Practice

Background: The yeast infections are increasingly frequent and the correct diagnosis consists of the identification of the yeast fungus, which in our case we are going to refer to the different species of Candida. The prescription of a broad-spectrum antifungal without taking into account the etiological agent, leads to an increase in the resistance to these treatments. Objective: The objective of this work is to differentiate Candida albicans from other Candida species ( Candida spp.) By means of digital images obtained from the optical microscope. Material and Methods: It has reviewed about 100 photographs from patients in our consultations. In this study we will use the microscopic images of the Candida variety to be processed later with the Octave programming language and its image processing package (image-2.8.0). Results and Discussion: This system is able to differentiate Candida albicans from the other varieties of Candida such as C. parapsilosis, C. krusei, and C. kefyr with accuracy. The candida identifier application, which was designed and programmed in Octave, allows identification of candida species by locating certain geometric descriptors, such as the centroid and the surfaces of circular objects within the images. The program was highly effective for the diagnosis of Candida spp. So, we got a sensitivity and specificity above 90% with the images used. Conclusion: The results that we obtain from the Candida spp. identifier system that opens the way to be able to work with images obtained from the optical microscope.
Mathematical foundations of quaternion image matching

In recent years, the utility of multi-channel image processing techniques has increased. Applications to image processing include color image registration, edge detection, filtering, and color space correction, and multi-channel image matching. These techniques represent multiple image channels, such as three color channels, as image functions taking values in a finite-dimensional algebra, for example, the algebra of quaternions. Use of these algebras enables processing a multichannel image as a single entity, holistically. This holistic processing provides the opportunity to exploit cross-channel dependencies for improved performance, for example, improved matching performance in image matching or registration applications. In this paper, we make a critical analysis of the foundations of quaternion-valued image matching. We investigate standard alternatives to using quaternions for four-channel image matching including image tiling, image averaging, independent component processing, and coordinated component translation approaches. We examine the advantages that quaternion-based processing provides over these alternatives. We interpret the quaternion match metric as an inner product, which provides motivation for image matching using quaternion correlation. We generalize the quaternion match metric to use arbitrary combinations of component correlations to define new match metrics. We show that these new match metrics correspond to new product structures on four-dimensional algebras. We present numerical results demonstrating and validating aspects of the theory.
Simulasi Penggunaan Intrusion Detection System (IDS) Sebagai Keamanan Jaringan dan Komputer

In the current era of information technology, computer network security is part of a system that is very important to maintain the validity and integrity of data and to ensure the availability of users' morning services from anywhere and anytime. And on one hand humans are very dependent on information systems. That causes the statistics of network security incidents to increase sharply from year to year. So we need a solution to overcome this, one of which is by simulation. Simulations are carried out to simulate existing real network security systems with properties that are easier to observe than the original system, to determine system performance. In this research, a system that is able to cope with threats that may occur optimally in a fast time is needed, this will speed up the process of handling disruptions and system or service recovery. One way that can be used to overcome or overcome this is to use the Intrusion Detection System (IDS). One application that supports intrusion detection system (IDS) is Snort. Snort is able to do an analysis of the forms of intruder attacks that misuse network protocols
On the NP-Completeness of Satisfying Certain Path and Loop Puzzles

"Eye-Witless", "Haisu" and "Oriental House" are genres of logic puzzles invented by William Hu, and "Detour" is a genre of logic puzzle invented by online user Guowen Zhang. Each of these puzzles revolves around constructing a path or loop through the cells of a grid according to certain constraints given by clues in the grid. We prove that deciding whether a particular puzzle in each of these genres is solvable is NP-complete.
Modeling a multi-segment war game leveraging machine intelligence with EVE structures

The paper depicts a generic representation of a multi-segment war game leveraging machine intelligence with two opposing asymmetrical players. We show an innovative Event-Verb-Event (EVE) structure that is used to represent small pieces of knowledge, actions, and tactics. We show the war game paradigm and related machine intelligence techniques, including data mining, machine learning, and reasoning AI which have a natural linkage to causal learning, which can be applied for this game. We also show specifically a rule-based reinforcement learning algorithm, i.e., Soar-RL, which can modify, link, and combine a large collection EVE rules, which represent existing and new knowledge, to optimize the likelihood to win or lose a game in the end.
A conversation with Jesmin Jahan

In this series of interviews with innovation leaders, Ubiquity Associate Editor and software engineer, Dr. Bushra Anjum sits down with Dr. Jesmin Jahan Tithi, a research scientist in the Parallel Computing Labs at Intel, to discuss overcoming the scaling wall that is thwarting application efficiency, specifically within high-performance computing.
Connotation Imperative Mining with Regression for Optimization Methodology

One of the most critical ways of data mining protocol is application of the association rule mining. Entrancing plans would apply to this protocol from the exchange of databases. The champion would be the Apriori count of all. The current article deals with the data structure that is based on different pre-fixed itemset. This will be used to create a filtered itemset. This enables in enhancing the viability of the regular Apriori computation. In order to discover various patterns and establish relation among various data sets, many data analysis tools are used. These tools can be further made use of to obtain valid predictions. One of the most popular techniques is the association rule for the pattern discovery in data mining, called KDD. Another important component in data mining is the rule mining. Association rule mining is the most effective rule in rule mining. This data mining rule is applied in many domains. The major focus of the article is to present an algorithm for efficient data mining and rule generation. Use of Apriori algorithm enhanced the precision and recall system, which is described in the article.
Predicting Plans and Actions in Two-Player Repeated Games

Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. Creating models of associates help to predict the modeled agents' actions, plans, and intentions. This work introduces algorithms that predict actions, plans and intentions in repeated play games, with providing an exploration of algorithms. We form a generative Bayesian approach to model S#. S# is designed as a robust algorithm that learns to cooperate with its associate in 2 by 2 matrix games. The actions, plans and intentions associated with each S# expert are identified from the literature, grouping the S# experts accordingly, and thus predicting actions, plans, and intentions based on their state probabilities. Two prediction methods are explored for Prisoners Dilemma: the Maximum A Posteriori (MAP) and an Aggregation approach. MAP (~89% accuracy) performed the best for action prediction. Both methods predicted plans of S# with ~88% accuracy. Paired T-test shows that MAP performs significantly better than Aggregation for predicting S#'s actions without cheap talk. Intention is explored based on the goals of the S# experts; results show that goals are predicted precisely when modeling S#. The obtained results show that the proposed Bayesian approach is well suited for modeling agents in two-player repeated games.
Correction to: Efficient and Secure Three-Factor User Authentication and Key Agreement Using Chaotic Maps

The original version of this chapter was revised. The affiliation of Sherali Zeadally was corrected to “College of Communication and Information, University of Kentucky, Lexington, USA.”
DIGITAL-BASED LITERATURE LEARNING AS AN OPTIMIZATION EFFORT TO INCREASE EDUCATIONAL VALUES OF MILLENNIAL CHILDREN

The learning process of millennial children cannot be separated from the influence of digital technology. Some various programs are offered through educational themed applications, both paid and free. One of them is literary learning media which is packaged in videos and interesting applications that attract millennial children in the learning process. In language learning, literary is considered as important learning because literary learning can help to shape the character of millennial children. Literature as a result of human artwork in the form of oral or written has a certain meaning and beauty. This writing aims to describe the success of digital-based literary learning that can motivate millennial children in optimizing the values of education in their life. This writing will broadly discuss two crucial issues, namely, 1) the strategy of developing digital-based literary learning materials, and 2) an effort to optimize the values of education through literary learning.
A Committee of Convolutional Neural Networks for Image Classication in the Concurrent Presence of Feature and Label Noise

Image classification has become a ubiquitous task. Models trained on good quality data achieve accuracy which in some application domains is already above human-level performance. Unfortunately, real-world data are quite often degenerated by the noise existing in features and/or labels. There are quite many papers that handle the problem of either feature or label noise, separately. However, to the best of our knowledge, this piece of research is the first attempt to address the problem of concurrent occurrence of both types of noise. Basing on the MNIST, CIFAR-10 and CIFAR-100 datasets, we experimentally proved that the difference by which committees beat single models increases along with noise level, no matter it is an attribute or label disruption. Thus, it makes ensembles legitimate to be applied to noisy images with noisy labels. The aforementioned committees' advantage over single models is positively correlated with dataset difficulty level as well. We propose three committee selection algorithms that outperform a strong baseline algorithm which relies on an ensemble of individual (nonassociated) best models.
LibGuides: Easy as ABC: Books

This guide focuses on alphabet picture books, and resources that can be used in conjunction with them.
Pengembangan Produk Fashion Menggunakan Kertas Bekas Kantong Semen Dengan Pendekatan Low-Impact Use

Garment material which is processed as a basic material for fashion products is very closely related to environmental problems that occur at this time. By encouraging the idea of ??sustainability through a low-impact use approach that focuses on the cleaning process, cement bags used paper are made into utilitarian materials that will produce products that cannot be washed. Cement bags used paper have good quality because they can withstand loads of up to 40 kg / sack and are generally used as wrappers or bags that function as covers. However, cement bags used paper tend not to be used or reprocessed into functional products. Therefore, this research aims to design and realize fashion products by recognizing the character and utilizing the potential of cement bags used paper to become products with high use values. The method used in this research is coloring experiments, testing materials, and exploring shapes and visuals. Material processing is applied using gyre and macrame techniques. The final result of this research is an alternative raw material from cement bags used paper for fashion products.&#x0D; Keywords:cement bags used paper, low-impact use, gyre technique, macramé technique.
Discrete Variational Attention Models for Language Generation

Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior collapse. The former arises from the fact that only the last hidden state from the encoder is transformed to the latent space, which is insufficient to summarize data. The latter comes as a result of the imbalanced scale between the reconstruction loss and the KL divergence in the objective function. To tackle these issues, in this paper we propose the discrete variational attention model with categorical distribution over the attention mechanism owing to the discrete nature in languages. Our approach is combined with an auto-regressive prior to capture the sequential dependency from observations, which can enhance the latent space for language generation. Moreover, thanks to the property of discreteness, the training of our proposed approach does not suffer from posterior collapse. Furthermore, we carefully analyze the superiority of discrete latent space over the continuous space with the common Gaussian distribution. Extensive experiments on language generation demonstrate superior advantages of our proposed approach in comparison with the state-of-the-art counterparts.
Hardware Feasibility Analysis of Spatial Propagation Algorithms

Although convolutional neural networks (CNNs) have revolutionized many computer vision tasks in recent years, spatial propagation algorithm still plays an important role in many applications such as image matting, segmentation, depth estimation, and colorization. However, most of these methods focus on qualitative performance and result in expensive computation, which is not conducive to be deployed on mobile or edge devices. In this paper, we take the spatial propagation network (SPN), convolutional spatial propagation network (CSPN), and guided convolution as our analysis objects. We evaluate these methods from the hardware accelerating aspect and use depth completion as an example. In addition to quality comparison, we propose a hardware feasibility metric that analyzes each method in terms of hardware resources. With this metric, developers can easily decide a proper algorithm on a specific hardware platform. We apply it to an example and get a consistent result, successfully building the connection between algorithm and hardware design.
Increase the Effectiveness of Education Based on the Study of Makam Works in the Lessons of Music Culture

Increasing the effectiveness of education in the lessons of culture, teaching the works of the National makam on the basis of a clear Organization of training is a period requirement. In many secondary schools, having studied the activities and experiences of many teachers in the process of passing the lessons of music culture and observing these lessons, we came to such a conclusion that in order to achieve positive results in the lesson, we found that it is necessary to use different stages. So, we can study these stages as three.
ОСОБЛИВОСТІ Я-КОНЦЕПЦІЇ У ОСІБ З РІЗНИМИ РІВНЯМИ АКТУАЛЬНОЇ ТА ОСОБИСТІСНОЇ ТРИВОЖНОСТІ

&#x0D; &#x0D; &#x0D; &#x0D; The article is devoted to the vital topic of mutual influence of state and trait anxiety and peculiarities of I-conception: self-attitude, self-monitoring and self-presentation.Persons with high level of state anxiety also have higher level of self-monitoring, self-confidence, self-management and intrinsic value. Persons with high level of trait anxiety have higher level of self-handicapping, ingratiation, supplication and self-attachment. State anxiety has positive correlation with self-confidence and intrinsic value. Trait anxiety has positive correlation with self-confidence, self- attachment and internal conflict. State anxiety is connected directly with positive characteristics of I-conception and trait anxiety has ambivalent connections with I-conception characteristics.Factor model shows connection between trait anxiety, internal conflict, self- handicapping and self-blaming and between state anxiety and self-confidence and makes clear adaptive role of medium state anxiety and neurotic character of trait anxiety.&#x0D; &#x0D; &#x0D; &#x0D;
The Making of MOBILLE a Year Later

Over a year has passed since the 2019 MOBILLE International Conference took place at the Lycée Français de New York [...]
Pengukuran Efektivitas Infografis Pada Portal Berita Online Kompas.com

Online media in greater demand information because the community in looking for quick and easy to access, one of the pioneer is Kompas.com which become a reference to journalism amid all the uncredible information. Nowadays, the Public has limited time to read and understand the news that contains much texting, that is why kompas.com presenting news in infographics with that look attractive but can convey that complex information being easy and fast to understand by the Society. This study focused on the right standard infographics and submission of a simple understanding of infographics that rises for five months of every month taken samples three randomly infographics. The purpose of this research is to evaluate infographic design in the online news website Kompas.com and to see whether the purpose in Kompas.com infographics suitable for the purpose with four important uses categories: usability, readable, design, and aesthetics. The methodology used quantitative methods. The data to the questionnaire online to FLP communities with four variables that usability, readability, design, and aesthetics. The validity test is using moment Pearson; and readability test using Alfa Cronbach. Data analysis using descriptive statistics. Based on data analysis and discussion in this research can be concluded that infographic Kompas.com filled four variables assessment that usability is 80,2 %, Readable is 80,3 %, the design is 82,6 %, and aesthetics is 79,4 %. This shows that the making of Kompas.com infographics is in accordance with generation purposes infographic, which is understandable, easy to read, good in visual, and straight information.
Pengaruh Modul Pembelajaran dan Kecerdsasan Intrapersonal terhadap Hasil Belajar kimia Siswa di SMPN 90 Jakarta

This study aims to find out the impact of instructional media and intrapersonal intelligence on the learning outcome of Chemistry course of students of SMP Negeri 90 Jakarta. This study applies experimental method with Treatment by level 2x2 research design. For data collection purposes, this study uses multiple choice test to collect data of the students’ learning outcome and questionnaire to collect data of the students’ intrapersonal intelligence. The result of this study shows that : (1) learning outcome of Chemistry course of the students who were given internet-based instructional media is higher than those who were given module-based instructional media, (2) there is interactional influence between instructional media and intrapersonal intelligence on the learning outcome of Chemistry course of the students, (3) for students who have high intrapersonal intelligence, learning outcome of Chemistry course of the students who were given internet-based instructional media is higher than those who were given module-based instructional media, and (4) there is no significant difference of learning outcome of Chemistry course of students with low intrapersonal intelligence between those who were given internet-based instructional media and those who were given module-based instructional media
3D Zonation model of primary haloes and geochemical prospecting pattern of Aliabad Cu-Mo deposit, Yazd, Central Iran

In order to explore probable blind mineralized zones of the Aliabad deposit, it is necessary to evaluate the element concentrations towards depth or margins of the deposit. Modelling primary geochemical haloes could be useful in this stage. Ore bodies at the Aliabad deposit are primarily controlled by structural features, which provide an opportunity to investigate the zonality in primary halos in this copper-molybdenum porphyry deposit. The primary geochemical characteristics of the mineral deposit were studied based on geochemical analysis of 1559 core samples from 24 drill holes. The formation of the primary geochemical haloes, which joins the ore body up to the surface, can be associated with hydrothermal fluid diffusion through fracture (fissures) zone developed in the rocks of the folding axis in the mining area. Along the vertical direction, the concentrations of Cu, Ag and Fe shows an increasing trend from surface to the ore body, at all boreholes; while the concentration of Pb, Mn and Bi are decreased with depth at the same environment. A detailed zonality sequence of indicator elements is obtained using the variability index of these elements: Pb→(Bi,Mn,Mo) →Cr→Ni→(Sb,V,Zn)→(Ag,Co,Cu,Fe,S)→P. According to this zonality, indexes such as Vz4=Pb×Mn/Cu×Ag and Vz5=Pb×Mn/Cu×Ag×Co can be constructed and considered as a significant criterion for predicting the Cu potential at a particular depth. Studying the distribution of the zoning indexes at different levels revealed high values of proposed indexes in the northwest and south of the area. It can be concluded that copper mineralization will continue to deeper and unexplored parts of the deposit at northwest of the study area. Consequently, it is suggested that further investigations concentrate on geophysical operations and it is highly recommended to drill additional boreholes at these areas. It is noteworthy that new drillings of the north western part must continue deeper than current boreholes (>150 m); because geochemical zonality indexes are extended to deeper parts. This extension is not observed for the southern part, so, additional drillings at the southern part can be shallower than 150 m.
THE EFFECTIVENESS OF USING VIDEO YOUTUBE TOWARD STUDENTS’ SPEAKING ABILITY

One of the media in globalization era in teaching speaking is video YouTube. Learning English by using YouTube will get a lot of benefits, learn the proper pronunciation of a word in a foreign language and make the material more quickly understandable. The research finding in this research is the mean score of speaking before being taught by using video YouTube is enough because the mean score of 16 students is 58.4375. After getting treatment, the mean score of speaking ability is 67.8125 based on the criteria of the students' score is good score it was improved, with the t-test analysis that used by researcher, the result of t Count is by comparing the t Table that she has got in SPSS 16.0, T Count 6.895 and value of t Table on the score table t 0,01 1.753 , its mean that t Count is much bigger than t Table ( t Count > t Table) = ( 6.895 > 1.753). It was concluded that video YouTube is effective media to improve the students’ speaking ability.